DAY/NHUE RIVER BASIN POLLUTION SOURCES STUDY

Final Report Acknowledgements 1 ICEM - The International Centre for Environmental Management Department of Water Resources Management, Ministry of Natural Resources and Environment December 2007 DAY/NHUE RIVER BASIN POLLUTION SOURCES STUDY Improving Water Quality in the Day/Nhue River Basin, Vietnam: Capacity Building and Pollution Sources Inventory

Acknowledgements 2 ACKNOWLEDGEMENTS This Study for Improving Water Quality in the Day River Basin - Capacity Building and Pollution Sources Inventory was undertaken through the DWRM Red River Basin Sector Project: Water Resources Management, ADB/MARD/MONRE Project 3892 VIE. The study was overseen and guided by Nguyen Thai Lai - Director of the Department of Water Resources Management, Ministry of Natural Resources and Environment, and National Project Director. Dave Hebblethwaite, Senior Technical Advisor to the Project and Trinh Thi Thanh, Senior Project Expert played significant roles in initiating the study and throughout its implementation with technical contributions and facilitation. The DWRM study team members were Phan Mai Linh, Trinh Xuan Quang, Nguyen Viet Hong and Vu Hoai Thu of the Water Quality Division, Phan Que Nga Water Resources Investigation Division, Bui Duy Tung Centre of Water Resources Assessment Technology, Truong Mai Hoa Surface Water Division, Do Thi Bich Ngoc Water Law Division and Giang Thanh Binh of the Water Law Division. This study report was prepared by ICEM - the International Centre for Environmental Management. The ICEM technical team consisted of Craig Meisner - Team Leader, Jeremy Carew-Reid, Do Thi Nham, Tran Quang Lam, Ben Cole, Le Thi Hong Anh, Jeffery Spickett, Dean Bertolatti, Bruce Dunn, Nguyen Thu Ha, Nguyen Thi Kim Dung and Ho Sy Hiep.

Contents 3 CONTENTS ACKNOWLEDGEMENTS... 2 CONTENTS... 3 ACRONYMS AND ABBREVIATIONS... 6 SUMMARY... 8 1 INTRODUCTION... 10 1.1 BACKGROUND TO OVERALL PROJECT... 10 1.2 AMBIENT WATER QUALITY STUDY CONTEXT... 10 2 GOALS AND OBJECTIVES OF THE STUDY... 10 3 STRUCTURE AND APPROACH OF THE STUDY... 11 3.1 THE OVERALL DESIGN OF THE STUDY... 11 3.2 THE TEAM APPROACH... 11 3.3 THE CAPACITY BUILDING APPROACH... 11 3.4 AN AREA-BASED POLLUTION IDENTIFICATION TOOL... 12 4 DESCRIPTION OF THE DAY/NHUE RIVER SUB-BASIN... 13 4.1 CLIMATIC AND HYDROLOGICAL CHARACTERISTICS... 13 4.2 SOCIO-ECONOMIC AND INSTITUTIONAL CHARACTERISTICS... 15 4.2.1 Demographic and settlement patterns... 15 4.2.2 Economic sectors in the sub-basin... 16 4.2.3 Administrative arrangements for water quality management... 16 4.3 MANAGEMENT ARRANGEMENTS FOR WATER SUPPLY AND USE... 22 4.3.1 Drainage and irrigation system... 22 4.4 WATER QUALITY AND POLLUTION MANAGEMENT... 23 4.4.1 Water quality monitoring... 23 4.4.2 Water pollution sources... 23 4.4.3 State of surface water pollution... 23 4.5 SUMMARY... 25 ANNEX 4.1: SIZE OF MAIN DRAINAGE WORKS IN THE DAU-NHUE RIVER BASIN... 27 ANNEX 4.2: WATER QUALITY MONITORING POINTS IN THE DAY/NHUE RIVER BASIN... 28 ANNEX 4.3: DATA FROM AMBIENT WATER QUALITY SAMPLING IN THE DAY/NHUE RIVER BASIN 2005-2006... 29 ANNEX 4.4 WASTEWATER QUALITY IN SELECTED CRAFT VILLAGES AND INDUSTRIAL ENTERPRISES... 36 5 METHODS, MODELS AND ASSUMPTIONS... 41 5.1 POLLUTION LOAD MODELS... 41 5.1.1 The Industrial Pollution Projection System (IPPS)... 41 5.1.2 Methods for assessing industrial pollution hazard... 42 5.1.3 The Agricultural Pollution Projection System (APPS)... 44 5.1.4 The Domestic Pollution Projection System (DPPS)... 45 5.1.5 The Craft-village Pollution Projection System (CVPPS)... 46 5.1.6 Summary of the pollution models... 47 5.2 DISPERSION MODELS AND ASSUMPTIONS... 48 5.2.1 Equation for general dispersion for pollutant Classes 1 & 2... 48 5.2.2 Equation for river BOD... 49 5.2.3 River coliform count... 50 5.2.4 Summary of the dispersion models... 51 5.3 WATERBORNE POLLUTION AND HEALTH IMPACTS... 51 5.3.1 Health impacts and pollution in the Day/Nhue River Basin... 51

Contents 4 5.3.2 Waterborne diseases... 52 5.3.3 Sensitive sub-populations... 53 5.4 ENVIRONMENTAL HEALTH RISK ASSESSMENT... 53 5.5 ISSUE IDENTIFICATION... 54 5.5.1 Risk matrix... 54 5.5.2 Qualitative measures of likelihood of exposure to waterborne pollutants... 55 5.5.3 Qualitative measures of health consequences of waterborne pollutants... 56 5.5.4 Risk matrix Identifying significant pollutants... 56 ANNEX 5.1: METHODOLOGY FOR IPPS... 58 ANNEX 5.2: METHODOLOGICAL REVIEW OF DATA COLLECTION AND ACTIVITIES ON THE GSO ANALYSIS OF CROP SURVEYS.. 67 6 POLLUTION LOAD IN THE DAY/NHUE RIVER BASIN... 72 6.1 CONTRIBUTIONS TO DAY/NHUE RIVER BASIN... 72 6.2 INDUSTRY... 74 6.3 RANKING BY HAZARD CLASS... 78 6.4 AGRICULTURE... 81 6.5 DOMESTIC POLLUTION... 87 6.6 CRAFT VILLAGES... 88 6.7 OVERALL AREAS OF SIGNIFICANCE... 92 ANNEX 6.1: POLLUTION LOADS BY SECTOR AND INDUSTRIAL COMPOSITION... 94 ANNEX 6.2: MAPS OF POLLUTION ESTIMATES...100 7 DISPERSION OF POLLUTANTS IN THE DAY/NHUE RIVER BASIN... 107 7.1 OPPORTUNITIES AND LIMITATIONS...107 ANNEX 7.1: DISPERSION OF BOD5 AND SS BY RIVER SEGMENT AND FUTURE DISPERSION MODELING...110 7.2 AMBIENT BOD5 AND SS FOR SELECTED RIVER SEGMENTS...110 7.3 PROSPECTS FOR FUTURE POLLUTION DISPERSION MODELING IN THE RIVER BASIN...111 8 WATER POLLUTION AND PUBLIC HEALTH... 112 8.1 HAZARD ASSESSMENT...112 8.1.1 Biological pollutants...112 8.1.2 Chemical pollutants...113 8.1.3 Emerging issues in chemical exposure and health effects...115 8.2 EXPOSURE ASSESSMENT...118 8.2.1 Drinking water...118 8.2.2 Consumption of vegetables and fruit...121 8.2.3 Consumption of fish and shellfish...121 8.2.4 Soil intake...122 8.2.5 Occupational exposure...122 8.2.6 Recreational exposure...122 8.2.7 Inhalation...123 8.3 RISK CHARACTERISATION...123 8.3.1 Biological pollutants...123 8.3.2 Chemical pollutants...124 8.3.3 Uncertainties and limitations...126 8.4 THE HEALTH STUDY...127 8.4.1 Introduction...127 8.4.2 Results and discussion...128 8.4.3 Uncertainties and limitations...129 8.4.4 Conclusions...130 ANNEX 8.1 RISK MATRIX OF TOP 30 CHEMICAL POLLUTANTS BY HAZARD RANKING IN THE DAY-NHUE RIVER BASIN...132 ANNEX 8.2 - INTERNATIONAL TREATIES ON HAZARDOUS CHEMICALS...141 ANNEX 8.3 BENEFICIAL USERS AND EXPOSURE PATHWAYS IN THE DAY/NHUE RIVER BASIN...143

Acronyms and Abbreviations 5 9 FINAL PRIORITIES & NEXT STEPS... 147 9.1 SUMMARY OF MAIN FINDINGS AND CONCLUSIONS...147 9.2 RECOMMENDATIONS...147 9.2.1 Areas of interest in terms of estimated pollution load...147 9.3 FUTURE EFFORTS FOR DISPERSION MODELING...148 9.4 AREAS OF INTEREST RELATING TO POLLUTION AND PUBLIC HEALTH...148 9.5 RECOMMENDED NEXT STEPS IN THE BASIN...149 9.6 RECOMMENDED NEXT STEPS IN TAKING THE MODELING TO OTHER BASINS...150 REFERENCES... 152

Acronyms and Abbreviations 6 ACRONYMS AND ABBREVIATIONS ACC Aquaculture Certification Council ADB Asian Development Bank APPS Agricultural Pollution Projection System ALEP Amended Law on Environmental Protection (2005) ASEAN Association of South East Asian Nations BAP Best Aquaculture Practices CEA Country Environmental Analysis CEFR Central Economic Focal Region CIDA Canada International Development Agency CIEM Central Institute for Economic Management CP Cleaner Production CPRGS Comprehensive Poverty Reduction and Growth Strategy DANIDA Danish International Development Agency DOE Department of Environment DPPS Domestic Pollution Projection System DONRE Department of Natural Resource and Environment DOST Department of Science and Technology DWRM Department of Water Resources Management EIA Environmental Impact Assessment EFR Economic Focal Region EMS Environmental Management System EPA Environmental Protection Activities EPZ Export Processing Zones GIS Geographic Information System GSO General Statistics Office IEMB Industrial Estate Management Board ICEM International Centre for Environmental Management ICZM Integrated Coastal Zone Management IPM Industrial Pollution Management IPPS Industrial Pollution Projections System LEP Law on Environmental Protection (1993) LL Law on Land (2003) LMR Law on Mineral Resources (2005) LWRM Law on Water Resources Management (1998) MARD Ministry of Agriculture and Rural Development MOC Ministry of Construction MOF Ministry of Finance MOFI Ministry of Fisheries MOI Ministry of Industry MONRE Ministry of Natural Resources and Environment MOT Ministry of Transport MPI Ministry of Planning and Investment NEFR Northern Economic Focal Region NGO Non-Governmental Organization NRE 5YP Five Year Plan for Natural Resources and Environment, 2006-2010 NRM Natural Resource Management NSEP National Strategy for Environmental Protection, 2001-2010 ODA Overseas Development Assistance

Acronyms and Abbreviations 7 PAC Pollution Abatement and Control Politburo Political Bureau of the Communist Party of Vietnam PPS Pollution Projection System NGO Non-Government Organisation PPC Provincial People's Committee SEA Strategic Environmental Assessment SEDP Five-Year Socio-Economic Development Plan, 2006-2010 SOE State Owned Enterprise SOER State of Environment Report TOR Terms of Reference VEPA Vietnam Environment Protection Agency VNCPC Vietnam Cleaner Production Center VND Vietnamese Dong VNEPF Vietnam Environmental Protection Fund VSIC Vietnam Standard Industrial Classification WB The World Bank

Summary 8 SUMMARY Findings on pollution load The estimates derived from each of the pollution models suggest several areas in which to focus attention: At the basin level domestic sources of water pollution are the most significant contributor to estimated BOD5 pollution loads. On the other hand, industrial sources are the largest in terms of Suspended Solid (SS) water pollution loads. At the provincial and district level estimated industrial BOD5, SS and hazardous substance water pollution loads are largely confined to selected districts in Hanoi and Ha Tay (e.g. Hai ba Trung, Phu Xuyen, Dong Da, Tu Liem, Hoan Kiem and Hoang Mai). Among manufacturing sectors the most significant in terms of estimated water pollution load include: Pulp, paper and paperboard (VSIC-4 2101) (BOD5, SS) Distilling (VSIC-4 1551) (BOD5, SS) Dairy products (VSIC-4 1520) (BOD5, SS) Basic iron and steel sector (VSIC-4 2710) (SS) Basic chemicals, except fertilizers (VSIC-4 2411) (BOD5, SS) Preparation and spinning of textile fibres (VSIC-4 1711) (HCS) 1 Fertilizers and nitrogen compounds (VSIC-4 2412) (HCS) Sawmilling and planing of wood (VSIC-4 2010) (HCS) Agricultural pesticide and fertilizer-related pollution are most intensive in the districts of Ha Tay (Ba Vi, Chuong My, Phu Xuyen, Ung Hoa, Thuong Tin, H. Thanh Oai, My Duc and Phuc Tho) and selected districts in Nam Dinh (Y Yen, Nghia Hung and Hai Hau). Domestic BOD5, SS and solid waste loads are primarily concentrated in the highly populated districts of Hanoi (Hai ba Trung, Dong Da, Ba Dinh, Tu Liem, Hoan Kiem, Thanh Xuan, Thanh Tri, Cau Giay and Tay Ho), with a few exceptions for rural solid waste in Nam Dinh (Hai Hau and Y Yen) and Ha Tay (Chuong My). The water pollution situation in craft-villages is very clear. The results show that focusing resources in the province of Ha Tay would serve to target well over 75% of estimated craftvillage wastewater pollution for each of the selected indicators covered in this study (BOD5, COD, SS, Total N, Total P, Total Fe, Oil and Coliform). Summarizing, estimates at the basin level suggest that domestic sources of BOD5 are the most significant contributor compared to industry and craft village sources and industrial sources are the most significant contributor with respect to suspended solids. At the provincial and district level, very clear synergies exist in giving the provinces of Hanoi and Ha Tay special attention, as districts in these provinces rank consistently in the first or second quartile of estimated water pollution load. Findings relating to public health risk 1 Hazardous Chemicals and Substances listed in Table A5.4.

Summary 9 A basin-wide collation of the top 30 chemicals by hazard ranking derived from the PPS model were classified using a risk matrix to classify the chemicals into low, moderate, high and extreme risk to public health. Chemicals identified as posing an extreme risk to human health were ammonium nitrate (solution), formaldehyde, phenol, chloroform, lead, ethylene oxide and mercury Cryptosporidium oocysts are resistant to chlorination and therefore may be capable of passing through the water supply systems that source their intake water from the Day and Nhue River. The risk of exposure to Cryptosporidium is insignificant in households that boil their water prior to consumption Collated health data did not support the hypothesis that proximity to the Day and Nhue River was associated with higher incidence rates of waterborne illnesses in the surrounding populations Further research is required to determine the health impact of exposure to biological and chemical pollutants in the Day and Nhue River upon sensitive sub-populations including young children, the elderly and people with impaired immune systems

Introduction 10 1 INTRODUCTION 1.1 BACKGROUND TO OVERALL PROJECT The water resources management studies carried out by Project TA 3892-VIE for the Cau and Day/Nhue River Sub-basins found pollution loads from many sources, some of which were well above national standards and negatively impacting local economies and the health of affected communities. 2 The studies of both rivers called for a comprehensive inventory of pollution sources so that priorities can be set for taking quick action to manage the most important sources while adopting a more systematic and long term approach to controlling others. This study of pollution load, disbursement and links with public health in the Day/Nhue River Subbasin is to help in identifying the most important sources and areas requiring urgent management attention. Government policy requires all wastewater discharges greater than 10m 3 /day to be licensed. Yet, there has been no formal determination of the timing, process and priorities to meet this requirement. Limited resources and capacity in the DARDs, DoNREs and central Departments involved in managing and licensing discharges requires a realistic and staged approach that maximises efficiencies and strengthens capacity. Effective licensing (e.g. licensing that leads to cost-effective environmental improvement) will be possible only if licensing efforts are prioritised across the thousands of diverse discharges from industrial, agricultural and domestic sectors. This prioritisation should identify the most important discharges requiring immediate attention, and allow effective licensing to be applied to all significant discharges in a systematic and step-by-step way. 1.2 AMBIENT WATER QUALITY STUDY CONTEXT Under the Second Red River Basin Sector Project (SRRBSP), Part A, Subcomponent 3: Ambient Water Quality (AWQ) Management in the Day River Sub-basin, a team was mobilised under Phase 3 to develop and improve the capacity of MoNRE s Department of Water Resources Management (DWRM) and provincial departments to monitor and manage water quality in the Day/Nhue River Sub-basin. The Day River has many serious pollution problems but Project TA resources are limited and Phase 2 found the available AWQ data to be deficient to set priorities for action in a logical, transparent and systematic way. Rapid and replicable methods for assessment and estimation of pollution are needed to support management while more systematic and reliable information gathering is set in place. This AWQ Study undertaken in Phase 3 develops tools and a process for setting priorities for high-risk pollutants and polluting activities on a comprehensive and systematic basis for targeting management responses such as licensing. 2 GOALS AND OBJECTIVES OF THE STUDY The AWQ study aims to assess likely pollution loads and AWQ from secondary data, such as the number, size and type of industry, as well as from existing primary AWQ data which is available on an anecdotal and case basis. This is the first step in AWQ management. It is an input to the process of setting priorities for pollutants, and for selecting enterprises and polluting activities for pilot licensing with local stakeholders. The AWQ Study provides the information to enable the TA to prioritise efforts to meet its key outcomes. Its purpose is to systematically identify the most serious sources of pollution in the 2 (i) Second Red River Basin Sector Project (TA 3892 VIE), February 2006, Component 1: IWRM in the Cau Basin, Final Report Phase 2, ADB, MARD, MONRE. (ii) Second Red River Basin Sector Project (TA 3892 VIE), October 2005, Component 4: IWRM in the Day/Nhue Sub-Basin, Draft Final Report Phase 2, ADB, MARD, MONRE.

Structure and approach of the study 11 Day/Nhue River Sub-basin and link these to associated risks to public health in basin communities. The Study will enable the DWRM to target and tailor its management responses to the most critical water quality issues in the Day/Nhue River Sub-basin and to apply these methods and skills to other seriously polluted river systems in the future. 3 STRUCTURE AND APPROACH OF THE STUDY 3.1 THE OVERALL DESIGN OF THE STUDY The study is designed in three broad components: (i) Estimating Pollution Releases: a) Provide a comprehensive and accurate description of each pollution source (industry, agriculture, domestic and craft-village) and their water pollution loads, using the best practically available data; b) Provide a comprehensive and accurate description of the basin s demographic context, including the distribution of population, households and activities, as well as the various uses and sources of water. (ii) Modelling the Dispersion of Pollutants and Estimating Ambient Water Quality: a) Develop pollutant dispersal models of the major pollutants from each pollution source, considering seasonal river flow rates and estimating the relative contribution of pollution from each sector to overall ambient conditions. (iii) Assessing Environmental Health Risks associated with industrial, agricultural and domestic pollutants: a) Identify the pollutants of most concern to the health of specific communities in the Day/Nhue River Basin, based on the above modelling work and on assessments of hazard, dose response and exposure; b) Construct human health indicators in priority areas of the basin, and assess the association between priority pollutants and available health data in the basin. 3.2 THE TEAM APPROACH The AWQ Team from ICEM the International Centre for Environmental Management comprised of both international and local expertise in environmental modeling, water hydrology and engineering and environmental health and toxicology. This ICEM team worked with experts from DWRM on all stages and activities of the study. The ICEM and DWRM experts were divided into three sub-component groups: (i) socio-economic, (ii) water quality and (iii) health. Each sub-team was responsible for the data collection and ground-truthing of the preliminary results in the field with respect to their sub-component. Cross-cutting issues were discussed and shared where necessary. 3.3 THE CAPACITY BUILDING APPROACH The capacity building approach was used to strengthen the capacity of the DWRM to prioritise water quality management responses based on the most serious risks to human health. The aim was to enhance the ability of the DWRM to provide accurate and practical advice to the regional DoNREs to assist them in prioritizing and targeting their own water quality management responses.

Structure and approach of the study 12 The formal and on-the-job training was a critical step towards ensuring that the DWRM possesses the skills to input effectively to the development and completion of TA outputs, including: the preparation of a Framework Plan for AWQ and its management; the establishment of a monitoring framework for AWQ; and the establishment of a wastewater discharge licensing framework targeted at high-risk enterprises and locations. 3.4 AN AREA-BASED POLLUTION IDENTIFICATION TOOL The resulting pollution estimates and models are part of an overall picture of the pollution situation in a specific area. This is called an area-based approach since pollution from various sources can be analyzed in a specific geographic area defined by the investigator. The area can be delimited by physical, economic, or administrative boundaries. In this study the area is physically-defined as the Day/Nhue River Sub-basin and pollution sources include industry, agriculture, domestic and craft-villages. Developing an objective area-based tool such as the one used in this study is useful for policymakers in the decision making process. When combined with other economic and political considerations, the results from this study can act as a key input when defining which pollution sources are relatively more important in terms of their overall contribution to the pollution situation in the River Sub-basin. The study helps focus on the sources and areas that warrant attention for further research or pilot programs such as discharge licensing.

Description of the Day/Nhue River Sub-basin 13 4 DESCRIPTION OF THE DAY/NHUE RIVER SUB-BASIN 4.1 CLIMATIC AND HYDROLOGICAL CHARACTERISTICS The Day/Nhue sub-basin is located to the south-west of the Red River Delta (Figure 4.1). The sub-basin has a wet-hot monsoon-tropical climate with dry-cold winter and rainy-hot summer. Annual average temperatures range from 24-27 0 C. Annual average rainfall is 1500-2200 mm, with peak rainfall occurring at Ba Vi Mountain in the upper catchment of the Tich River. Figure 4.1: Location of the Day/Nhue Sub-basin within the Red River Basin The Day/Nhue Sub-basin has eight main tributaries and four main distributaries and has a rather complicated hydrology due to the many diversions and flow alterations (Table 4.1). Over many hundreds of years but especially during the past 80 years the natural systems have been fundamentally altered and controlled by engineering interventions and management regimes. It is a system with an annual flow of approximately 28.8 billion m 3-89.5% or 25.7 billion m 3 coming from the Nam Dinh River, 0.68 billion m 3 (2.4%) from the Hoang Long River at Hung Thi and 1.35 billion m 3 (4.7%) from the Tich and Day Rivers at Ba Tha (Table 4.2). The flood season (from June to October) contributes 80% of the total annual flow, while the dry season (from November to May) contributes only 20% of the annual water volume. The drought season s water source for the Day River is mainly from the Dao River of Nam Dinh Province, which feeds water from the Red River, with an average of 250 300 m 3 /s during the dry season. The Nhue River also takes water from Red River through the Lien Mac sluice. Table 4.1: River characteristics in the Day/Nhue Sub-basin. River Name Length / Catchment Area Location & Further Details Tributaries

Description of the Day/Nhue River Sub-basin 14 Tich River: 91 km / 1,330 km 2 Originates from Tan Vien mountain of Ba Vi mountain range, flows to Northwest- Southeast direction and runs in Day River at Ba Tha. Thanh Ha River 40 km / 271 km 2 Originates from limestone range nearby Kim Boi district of Hoa Binh province, runs in to the plain from Dong Chiem T-junction to Duc Khe, separated by the plain and connected by My Ha canal, runs to Day River. Hoang Long River 12.5 km / 1,550 km 2 Originates from mountain range of South of Hoa Binh town. From downstream of joining of Boi River and Dap and Lang River, runs in to Day River at Gian Khau, Ninh Binh. Nhue River 74 km, 1,070km 2 Takes water from the Red River through Lien Mac sluice, supplies water for Dan Hoai irrigation system, drains waste water for Hanoi city, Ha Dong town, and joins with Day River at Phu Ly town. To Lich river discharges regularly in to Nhue river with average flow 11-17 m 3 /s, maximum flow 30 m 3 /s. Four tributaries drained from Hanoi: To Lich river: 14.6 km Start from Buoi sewer, run through area of Tu Liem, Thanh Tri districts, Thanh Liet Dam and discharge in to Nhue river. Downstream of To Lich river is received water from Lu, Kim Nguu rivers, collected whole waste water of Hanoi City. Set river: 5.9 km Starts from Ba Trieu sewer at Bay Mau Lake, then discharges to Kim Nguu at Giap Nhi. Kim Nguu river: 11.8 km Start from outlet of Lo Duc sewer, receives Set river water at Giap Nhi and joins with To Lich at Thanh Liet. Lu river: 5.6 km Receives water from Trinh Hoai Duc, Trang sewers (Kham Thien), flow through Trung Tu, Truong Chinh roads and discharges to To Lich river. Distributaries Chau River: 27 / 368km 2 Formerly was a distributary of the Red River at Hung Yen, joined with Day River at Phu Ly town. Nowadays, the river mouth is silted by water taken from the Red River, so the Chau River has now become a drainage river for pumping stations from six zones in Ha Nam, Nam Dinh provinces. Dao River 32 km A distributary of the Red River at Phu Long in the North of Nam Dinh city, joins into the Day River at Doc Bo. Dao River takes water from Red River to Day River and has an average annual volume of about 25.7 billion m 3 ). The discharge is about 250 300m 3 /s in dry season. This is the main fresh water source for downstream areas of the Day River. Ninh Co River A distributary of Red River, takes water from Red River at Mom Ro, flows and discharges water to the sea at Lach Giang Estuary. The Ninh Co river is connected to Day River by Quan Lieu Canal and water from Day River is diverted to Ninh Co River through Quan Lieu canal all year round and is strongly affected by tide. Day River 240 km A distributary of the Red River, starts from Hat Mon and runs in a Southwest Northeast direction and discharges to the sea at Day Estuary. The river is narrow and shallow due to siltation. Since 1937 when the Day Dam was completed, Red River water didn t flow to the Day River, unless flood waters were diverted. Thus, the upstream course (from km 0 to Ba Tha about 71 km), the Day River is considered as a dead river. River bed siltation and transgression for cultivation obstructed flood drainage. Day River is feed by tributaries, mainly the Tich, Boi, and Dao tributaries. Table 4.2: Flow characteristics in Day/Nhue River Basin River Name Catchment area Annual Average Annual Average Dry Months Dry flow/ total flow Diverted from

Description of the Day/Nhue River Sub-basin 15 Water volume flow rate Water volume Km 2 10 9 m 3 m 3 /s 10 9 m 3 % Total Day River 3 6965.42 28.8 5.76 20 (without Ninh Co) Tich+Day at Ba Tha 1 1330 1.35 42.8 0.27 20 Right tributary Thanh Ha 1 390 0.26 8.25 0.52 20 Right tributary Hoang Long at Hung Thi 1 664 0.68 21.5 0.136 20 Dao River 1 25.7 5.14 20 Red river Ninh Co River 1 6.94 1.39 20 Red river Hoang Long 4 1550 1.02 32.6 2.04 20 Right tributary Nhue River 5 0.47 15 20 Red River through Lien Mac Intake Chau River 6 20 Red River 4.2 SOCIO-ECONOMIC AND INSTITUTIONAL CHARACTERISTICS 4.2.1 DEMOGRAPHIC AND SETTLEMENT PATTERNS The Day/Nhue sub-basin has a total area of 6,965.45 km 2 and covers six provinces - in whole or in part - in northern Vietnam. Its total area is (Figure 4.2): 1. Ha Tay, 2. Ha Nam, 3. Nam Dinh, 4. Ninh Binh, 5. Hanoi (four districts of Tu Liem, Thanh Xuan, Thanh tri, Thanh Xuan) and 6. Hoa Binh (four districts of Luong Son, Kim Boi, Yen Son, Lac Thuy), There are 996 precincts and communes in the sub-basin. In 2006, the total population in the subbasin was 7,915,304 persons (2,268,500 in Hanoi, 2,266,771 in Ha Tay, 824,335 in Ha Nam, 1,295,559 in Nam Dinh, 922,582 in Ninh Binh and 337,557 in Hoa Binh). Population density is 1,136 persons/km 2 - four times higher than the average national population density (Figure 4.3). Urban population in the sub-basin is relatively high at 35% of the total population, with 2.8 million urban dwellers in 2006. The urban network consists of Hanoi city with 2.1 million persons, Nam Dinh City with 243,000, Ha Dong City and Son Tay Town with 138,000 and 119,000 respectively, Phu Ly Town with 76,000, and Ninh Binh and Tam Diep Towns with 102,000 and 52,500 persons respectively (Figure 4.4). 3 Source: Day/Nhue Environmental Protection-Hanoi, DONRE, 2007 and Day/Nhue River Sub-basin s Integrated Water Resources Planning, 2003, Institute of Water Resource Planning. 4 Estimated from Hung Thi Hydrology Station and annual rainfall. 5 Through Day/Nhue Environmental Protection-Hanoi DONRE, 2007 & Day/Nhue: To Lich River discharges regularly in to Nhue river with average flow 11-17 m3/s. 6 Chau River is the drainage river for drainage pumping stations six zones in Ha Nam.

Description of the Day/Nhue River Sub-basin 16 4.2.2 ECONOMIC SECTORS IN THE SUB-BASIN Economic structures vary considerably among the provinces in the river basin. While agriculture still plays an important role in all provinces except Hanoi (Table 4.3), the contribution of the sector in terms of GDP has been gradually reducing with industry and services growing in importance. Even so, about 80% of the population is employed in the agriculture sector and it remains the most significant source of income for most families. Agricultural crops cover around 87% of the sub-basin, with rice making up 70% of agricultural area. Table 4.3: Sector contributions to GDP per province in 2005 Province Sector (% of GDP) Agriculture Industry Service Hanoi 2 40 58 Ha Tay 31 38 31 Ha Nam 28 40 32 Nam Dinh 32 32 36 Ninh Binh 28 39 33 Hoa Binh 41 24 35 Within the sub-basin, there are a total of 156,269 industry, commerce and service establishments, with 13,837 (9%) in Hanoi, 60,656 or 39% in Ha Tay, 22,699 or 14% in Ha Nam, 36,007 or 23% in Nam Dinh, 21,466 or 14% in Ninh Binh and 1,604 or 1%in Hoa Binh. The main industries in Hanoi are electronics-informatics, mechanics, textile, shoes, food processing and new materials. Cement, construction materials, food processing and textiles are main industries in Ha Nam. In Nam Dinh there are two main industries: textile and food processing. Cement and construction materials are major industrial products in Ninh Binh. Industrial products in Hoa Binh include tea, textile, sugar and cement, however technology development involved in product manufacturing is limited. The number of craft villages in the sub-basin is increasing in all provinces. This is likely due to their promotion by government as an alternative livelihood activity and the relatively high income returns for the sector in comparison. The largest number of craft villages in the sub-basin is in Ha Tay Province. The main products are textile-dyeing, food processing, metal recycling, wood products, construction materials, bamboo and lacquer ware. In general, production technologies used in craft villages are simple and labour inputs seasonal. In recent years, the tourism sector has been developing rapidly and this trend is expected to continue. The tourism sector is particularly important for Ha Tay and Ninh Binh provinces, with principle tourism locations including Hoa Lu, Cuc Phuong National Park and Van Long. 4.2.3 ADMINISTRATIVE ARRANGEMENTS FOR WATER QUALITY MANAGEMENT The administration of the river sub-basin is undertaken by national ministries and the administrative units of the five provinces and Hanoi City. There is an overlapping division of responsibilities between these administrative authorities for water resource management and environmental protection in the basin. This makes coordination between provinces difficult, leading to ineffective basin wide management and regulation of water use and waste water discharges and licensing.

Description of the Day/Nhue River Sub-basin 17 Within each province, management is complicated by a lack of coordination and cooperative action between government departments, each of which has overlapping planning and management responsibilities that influence water resources (Table 4.4). Two important innovations to address the difficulties of coordination and integration of management within the sub-basin are the preparation of a Day/Nhue environmental plan and the drafting by the MONRE DWRM of a Decree for integrated river basin planning and management, a responsibility which now rests definitively with MONRE. Table 4.4. Administrative responsibilities of provincial departments Department Roles related to river basin management. Department of Natural Resources and Overall responsibility for water resource management. Environment (DONRE) Land use planning, environmental assessment and monitoring. Planning for mining activities. Department of Agriculture and Rural Development (DARD) Department of Industry (DOI) Department of Construction (DOC) Department of Transport (DOT) Department of Fisheries (DOFi) Department of Health (DOH) Department of Finance (DOF) Department of Planning & Investment (DPI) Development and promotion of agricultural programs and rural development, planning and management of irrigation, rural water supply and sanitation. Management of hydraulic structures. Flood and typhoon protection and management. Industry and trade promotion and development. Planning and management of hydropower. Urban planning, infrastructure planning and construction, management of solid waste. Road planning and development. Planning and construction of water way transport systems. Planning and management of fisheries including aquaculture. Management of drinking water quality. Development of policies for taxes and fees for water resources. Overall responsibility for socio-economic planning coordination A comprehensive plan for environmental protection in the Day/Nhue River Sub-basin is being jointly developed by the provincial people s committee s of the six provinces in the subbasin. The Hanoi DONRE is the main coordinating body for the plan. A standing committee for the Day/Nhue river management will be established to manage and coordinate all activities relating to water resource and environment management in the sub-basin. The Minister of MONRE is proposed as the committee s chair, with the vice-chair being a provincial people s committee chairman, appointed on a two year rotational basis. Committee members will include the chairman of the other five provinces and leaders of Ministry of Planning and Investment, Ministry of Finance, Ministry of Agriculture and Rural Development, Ministry of Industry and Trade, Ministry of Science and Technology, Ministry of Construction, Ministry of Health, Ministry of Transportation and the Government Office. Members of the committees will work on a parttime basis and will meet every six months. A permanent office in MONRE will be established to coordinate the committee s activities. It is expected that the plan will be approved by the Prime Minister in 2008. Once formally established, the committee will be to promote and coordinate activities to help the people s committees in the river basin to develop policies and regulations for effective use and protection of water resources and the environment. This will include the development and implementation of inter-provincial projects and programs. The legal mandate of the committee will include powers to:

Description of the Day/Nhue River Sub-basin 18 1. Request the provinces to provide information on water resource and the environmental situation as well as activities/programs relating to exploitation and environmental protection. 2. Request the provinces to provide the necessary manpower and resources to carry out projects/programs for overall benefits of all the provinces. 3. Establish a scientific consultation committee to consult, consider and assess projects/programs that exploit water resource and possibly cause pollution to river water. 4. Make formal recommendations on effective exploitation and environmental protection concerning projects which may cause water pollution.

Description of the Day/Nhue River Sub-basin 19 Figure 4.2: Administrative boundaries in the Day/Nhue River Sub-basin

Description of the Day/Nhue River Sub-basin 20 Figure 4.3: Population Density in the Day/Nhue River Sub-basin (2003)

Description of the Day/Nhue River Sub-basin 21 Figure 4.3: Land use patterns in the Day Nhue-River Sub-basin.

Description of the Day/Nhue River Sub-basin 22 4.3 MANAGEMENT ARRANGEMENTS FOR WATER SUPPLY AND USE 4.3.1 DRAINAGE AND IRRIGATION SYSTEM The Day/Nhue River Sub-basin is heavily regulated and water is used intensively to supply irrigated agriculture. There are five main intakes from the Red River including the Lien Mac intake and regulation sluice, and the Dan Hoai, Red Van and Phu Sa pumping stations. From the Day River there are a further two pumping stations the Trung Ha and Son Da (Table 4.5). These intakes and pumping stations have a total discharge capacity of 107.5 m 3 /s. Along the Nhue River, there are a further eight gravity fed sluices, which were constructed between 1937 and 1941 (Table 4.6) Table 4.5: Irrigation works sourced from the Red and Day Rivers No Name of works Size Design Discharge (m 3 /s) A Source from Red River 1 Lien Mac Intake 4 gates x 3m+1gate x 6m 36.25 2 Lien Mac 2 Regulation Sluice 3 gates x 6m 36.25 3 Dan Hoai Pumping Station 6 x 8.000 m3/h 13.00 4 Red Van Pumping Station 5 x 8.000 m3/h 11.00 5 Phu Sa Pumping Station 4 x 11.000 m3/h 11.00 B Source from Da River 1 Trung Ha Pumping Station 26 x 1.000 m3/h 7.00 2 Son Da Pumping Station 10 x 1.000 m3/h 2.70 Total 80.95 Table 4.6: Main irrigation works by gravity along the Nhue River Name Location Size of gate Design Factors Construction of Number Bxh (m) Water Discharge time Sluce of gate level (m) (m3/s) Lien Km 0+304-Nhue River 1 6.0 x 6.0 Max 3.90 47.13 1937-1940 Mac Km 53+700- Red river 4 3.0 x 3.3 Avg 3.30 36.25 dyke Ha Dong Dong Quan Nhat Tuu Km 16+182 Nhue river Km 43+750 Nhue river Km 63+405 Nhue river 1 2 6.0 3.5 Max 3.79 Avg 3.19 1 6.0 Max 3.55 5 2.5 Avg 2.90 8 2.5 x 6.8 Max 3.40 Avg 2.84 26.20 20.15 11.92 9.17 1938-1939 1939-1941 1939-1941 Luong Km 72+506 Co Nhue river La Khe Km 6+322- La Khe river Km 38.0 Day river Dyke Van Km 11+929 Dinh Van Dinh river Km 72 Day river Dyke Diep Son Km 21 Duy Tien river 1 6.0 1936-1940 5 6.0 x 4.25 2 4.5 x 3.55 1938-1940 2 4.5 x 3.95 Max 3.53 Avg 2.50 1 6.0 3 2.50 Max 3.40 Avg 2.84 1938-1940 1941-1942

Description of the Day/Nhue River Sub-basin 23 In addition to the main intakes, pumping stations and sluice gates, there are 14 main drainage and discharge works in the sub-basin, with a discharge capacity of 728.3 (m 3 /s) as indicated in Annex 4.1. 4.4 WATER QUALITY AND POLLUTION MANAGEMENT 4.4.1 WATER QUALITY MONITORING Under the Law on Environmental Protection 2005, PPCs are responsible for organizing regular environmental monitoring. Environmental monitoring is usually undertaken by the DONREs or Centres for Environmental Monitoring in each province. In addition, every five years, the PPCs are responsible for developing state of environment reports which must be submitted to the Provincial People's Council and Ministry of Natural Resources and Environment (MONRE). Reports on the provincial status of water quality are also prepared by DONRE yearly. For the Day/Nhue Sub-basin, water quality monitoring has also been undertaken on an ad-hoc basis by water resources planning institutes, usually for project specific needs. Across the Day/Nhue River Sub-basin, there is a network of 36 water quality monitoring points (Annex 4.2). Data collected at each site varies between provinces and years and may include: DO, ph, TSS, BOD 5, COD, coliform, heavy metals and sampling for pesticides analysis. While this data does provide point source information on water quality, the usefulness of the data is limited because of uncertainties relating to monitoring program design and seasonal coverage and variability in analytical results. At best, the data provides an indication of ambient water quality at the time and point location at which sample were collected. Even so, through a combination of site visits, community complaints, point source monitoring and anecdotal evidence, the provincial DONREs have developed an overall picture of the main pollution sources in the Sub-basin and of the extent and seriousness of the problem. 4.4.2 WATER POLLUTION SOURCES According to annual reports from DONREs, the main pollutants in the sub-basin are generated by eight main categories of development: (i) eight industrial zones and complexes with 157 projects; (ii) 358 handicraft villages; (iii) waste water from urban and residential areas; (iv) tourist activities; (v) hospitals; (vi) small industrial enterprises; (vii) agricultural activities; and (viii) inland waterway transport. Wastes from these sources are generally not treated at all or not treated to national TCVN standards. 4.4.3 STATE OF SURFACE WATER POLLUTION The results of surface water quality monitoring in the basin from 2003 to 2006 appear as Annex 4.3, with a summary provided below

Description of the Day/Nhue River Sub-basin 24 1 Organic Matter Pollution by organic matter is reflected through decreases in the concentration of Dissolved Oxygen (DO) in the water, increases in Chemical Oxygen Demand (COD) and increases in Bio- Chemical Oxygen Demand (BOD). Most of monitoring sites in the Nhue River, To Lich River, Kim Nguu River, Lu River and Set River have DO values ranging from 1.6 to 5 mg/l. DO values measured in Lu, Kim Nguu and To Lich Rivers are well below the TCVN standard of 2 mg/l. Long periods of DO below 5 mg/l can harm larval life stages for many fish and other aquatic organisms. In many countries, for example in Australia and USA, if the DO exceeds the chronic protective value for growth (4.8 mg/l), the site meets objectives for protection. If the DO is below the limit for juvenile and adult survival (2.3 mg/l), the site does not meet objectives for protection. When the DO is between these values, the site requires evaluation of duration and intensity of hypoxia to determine suitability of habitat for larval recruitment. COD values range from 6.1 to 384 mg/l from 18 surveys, 585 water samples at 50 different sites in the basin. 99% of COD values exceeded the standard of 10 mg/l for domestic use (Category A) and 22% of COD values exceeded the TCVN standard of 35 mg/l for use in agriculture (Category B). Normal COD in river water should be less than 10 mg/l. A COD of 60 mg/l in a natural system is in emergency need of treatment. BOD values have a range of 2 to 297 mg/l, including 150 samples with values exceeding the TCVN standard of 25 mg/l for agricultural purposes and 582 samples with values that exceed the standard of 4 mg/l for domestic use. Typical natural water has a BOD from 0.8 to 5 mg/l. Anything above 6 mg/l needs to be treated as it will rob the water of needed oxygen for the fish. Municipal sewage that is efficiently treated by a three stage process would have a value of about 20 mg/l. In summary, over 99% of monitoring sites have organic matter pollution concentrations exceeding the national standards for domestic water supply, especially in Ha Nam Province. 2 Nutrients Significant causes of poor water quality and aquatic habitat loss are elevated levels of nutrients, such as nitrogen and phosphorous. In addition to natural sources, untreated sewage, industries and runoff from agricultural, residential and urban areas all contribute nutrients from point and non-point sources to the river systems in the sub-basin. Monitoring results show for ammonium (NH4+) range from 0.01 to 60.76 mg/l, with 564 samples having ammonium concentration exceeding TCVN standard of 0.05 mg/l for domestic use and 184 samples exceeding the standard 1 mg/l for agricultural and industrial use. The content of nitrite (NO2-) ranges from 0.001 to 11.71 mg/l compared to the standard 0.01 for domestic use and less than 0.05 mg/l for agricultural and industrial use. With such high concentrations of ammonium and nitrite, the surface water in the basin tends to be eutrophic and prone to algae growth and blooms typified by the green and turquoise water, observed in Ha Noi lakes. 3 Suspended solids (SS) 223 samples with concentrations of suspended solids that exceed the standards of 20 mg/l and less than 80 mg/l for domestic use (category A) and for agriculture (category B) respectively. The presence of high levels of solids in the waterways increases the turbidity of water, which affects lights penetration and in turn the biological life within it. High concentrations such as these can also make the water unsuitable for domestic use and industrial applications and irrigation. 4 Coliform and metals

Description of the Day/Nhue River Sub-basin 25 Coliform were present at most monitoring sites in the river basin. In the Lu river, total coliform levels were 160,000 MPN/100 ml, which exceeds the standard of 5,000 MPN/100 ml for domestic use by 32 times and by 16 times the standard of 10,000 MPN/100 ml for agricultural and industrial use. The presence of these organisms in the water indicates a high degree of organic pollution, which would result primarily from human and animal wastes. This is particularly significant for human health and sanitation. Many areas of the basin are also affected by oil, grease, and unpleasant odors from various vehicle servicing and industrial locations. High heavy metal concentrations such as Cr, Cu, Pb, Cd, Zn and Fe, have been found to be above national standards at some monitoring sites. 5 Domestic Waste Water Domestic waste water makes up 56% of the total volume of waste water in the entire sub-basin. Throughout the basin waste water is not treated and contains many pollutants such as organic matter, nutrients, suspended solids, and pathogens in high concentrations. The contribution of the domestic waste water to total waste water volume by localities is as follows: Ha Noi (54%), Ha Tay (17%), Nam Dinh (13%), Ha Nam (7%), Ninh Binh (5%) and Hoa Binh (4%). 6 Handicraft Village Waste Water Results of environmental assessment studies at 19 handicraft villages in Hanoi, Ha Tay, Ha Nam and Nam Dinh (Annex 4.4), all show that waste water samples at drainage points were seriously polluted with organic matter - BOD5, COD, suspended solids and coliform - all above standards for either domestic, agricultural or industrial use. In addition, heavy metal concentrations, including chromium associated with dying and other cottage industries have been found to be above national standards. 7 Industrial zones and complexes DONREs have conducted inspections of 141 facilities within industrial zones in the River Subbasin. These inspections led to an estimate for the total volume of waste water generated from industrial zones in the basin of 28,507 m 3 /day. Close to 85% of facilities discharge untreated or partially treated waste water directly into river systems. While almost 65% of zones have waste water treatment facilities, only 10% treat their wastes to national standards. For industrial zones and complexes that have been constructed based on approved EIA reports, many do not comply with their environmental commitments. With regard to regulation and enforcement, only 60% of inspected facilities have paid fees for treatment of waste water and only 31% conducted environmental monitoring programs to regulation standards. Consequently, 40 facilities have been fined for serious breaches of the Law on Environmental Protection (2005), with fines totaling 249 million VND. These facilities have been fined pursuant to Decree No. 81/2006/ND-CP and Article No. 49 of the Law of Environmental Protection. 4.5 SUMMARY The Day/Nhue Sub-basin is administered by six provinces - Ha Tay, Ha Nam, Nam Dinh, Ninh Binh, Hoa Binh and Hanoi City and has a total population of 7.915 million people. Economic performance and sectoral structure in the basin varies considerably between the provinces, with agriculture still playing an important role in all except Hanoi and the industrial sector making up a growing proportion of regional GDP.

Description of the Day/Nhue River Sub-basin 26 Within the Sub-basin, water pollution is a major issue for social well being, environmental quality and economic sustainability. Environmental monitoring by DONRE shows that all parameters of pollution in surface waters throughout the Sub-basin are well above national standards for domestic use. A high proportion of samples are also unsuitable for agriculture and industrial use. Water pollutants are thought to come from a combination of domestic, agricultural and industrial sources including craft villages. To date, there have been no comprehensive studies undertaken to assess the relative contributions of these sources to total pollution loads in the basin. A more systematic understanding of pollution sources, types and loads is critical in supporting government prioritise pollution management action and investment. Management of pollution in the basin is complicated by a number of factors. First, information on pollution is anecdotal and point source which means that trends and ecosystem wide effects are difficult to assess. Second, dispersion of pollutants throughout the river system is hard to evaluate because Sub-basin hydrology is so intensively managed and altered by the many engineering structures, diversions and flow regimes. Third, a lack of coordination and cooperation among government departments inhibits appropriate management interventions. Fourth, overlapping planning and management responsibilities prevents concerted action on water quality based on clear definition of authorities and responsibilities. Recent developments are addressing these difficulties through the establishment of a cross sector sub-basin environment committee and the preparation of an integrated river basin management decree.

Description of the Day/Nhue River Sub-basin 27 ANNEX 4.1: SIZE OF MAIN DRAINAGE WORKS IN THE DAU-NHUE RIVER BASIN No Name of works River is received by Location Design Discharge (m 3 /s) 1 La khe Sluice Day Ha Dong 60 Ha Tay 2 Van Dinh Sluice Day Ung Hoa 20 Ha Tay 3 Luong Co Sluice Nhue Phu Ly 286 Ha Nam 4 Diep Son Sluice Chau Duy Tien 123 Ha Nam 5 Yen So Pumping Station Red Thanh Tri 45 Ha Noi 6 Bo Dau Pumping Station Red Thuong Tin 7.5 Ha Tay 7 Khai Thai Pumping Station Red Phu Xuyen 21.0 Ha Tay 8 Yen Lenh Pumping Station Red Duy Tien 21.0 Ha Nam 9 Song Phuong Pumping Day Dan Phuong 17.3 Station Ha Tay 10 Van Dinh Pumping Station Day Ung Hoa 50.5 Ha Tay 11 Ngo Xa Day Ung Hoa 16.0 Pumping Station Ha Tay 12 Ngoai Do Pumping Station Day Ung Hoa 33.3 Ha Tay 13 Que Day Kim bang 27.7 Pumping Station Ha Nam 14 Lac Trang Duy Duy Tien 30.5 Pumping Station Tien/Chau Ha Nam Total 728.3

Description of the Day/Nhue River Sub-basin 28 ANNEX 4.2: WATER QUALITY MONITORING POINTS IN THE DAY/NHUE RIVER BASIN Province No Location/ River Observed point Hanoi 1 Nghia Do To Lich 2 Phuong Liet Lu 3 Mai Dong Kim Nguu 4 Cau Set Set 5 Thanh Liet Dam To Lich 6 Lien Mac Nhue 7 Cau Dien Nhue Ha Tay 8 Thuy Phuong-Co Nhue Nhue 9 Ha Dong Bridge Nhue 10 Cau To Nhue Van Coc Sluice Day Mai Linh Day 11 Ba Tha- Chuong My Day 12 My Duc Day Ha Nam 13 Nhat Tuu Nhue 14 T-Junction (Day, Chau, Nhue)/ Red Phu Bridge Day 15 Hoa Mac Town Chau 16 Bong Lang Bridge Day 17 Sat Bridge-Binh My Sat (small river) Ninh Binh 18 Ninh Binh thermal power Day 19 Ninh Phuc Post Day 20 Van Lock Van Ben De Hoang Long 21 Gia Tan Hoang Long 22 Khanh Phu Day 23 Doc Bo Day 24 Kim Tan Day Nam Dinh 25 Yen Phuong-Y Yen Day 26 Nghia Hung Day 27 My Tam/My Loc (up stream) Dao 28 20 m to upstream of waste water Gia canal (middle stream) Dao 29 Nghia Hung (downstream) Dao 30 80 m to Mom Ro (upstream) Ninh Co) 31 200 m to Lac Quan Bridge (middle stream) Ninh Co 32 Nghia Lac-Nghia Hung (downstream) Ninh Co 33 Huu bi (up stream) Red 34 T-Junction of Red &Ninh Co (middle stream) Red 35 Ngo Dong-Giao Thuy Sluice (downstream) Red Hoa Binh 36 Lac Thuy Boi

Description of the Day/Nhue River Sub-basin 29 ANNEX 4.3: DATA FROM AMBIENT WATER QUALITY SAMPLING IN THE DAY/NHUE RIVER BASIN 2005-2006 ph SS Turb E.Cond TDS DO BOD5 COD NO3 - NO2 - - mg/l NTU ms/cm mg/l mg/l mg/l mg/l mg/l mg/l Segment No River Location Commune / ward District Province latitude longitude 1. To Lich Nghia Do Nghia Do Cau Giay Hanoi 21 02 19 105 48 36 2. Lu Phuong Liet Dong Da Hanoi 20 59 53 105 50 24 6.5 69 61 2.1 48 70 3. Kim Nguu Mai Dong Hai Ba Trung Hanoi 21 00 03 105 51 58 7 47 4.1 65 90 4. Set Cau Set Hai Ba Trung Hanoi 7 25 3.2 36 54 5. To Lich Thanh Liet Dam Thanh Tri Hanoi 20 57 45 105 48 61 6. Nhue Lien Mac Tu Liem Hanoi 6.5 5 14 7. Nhue Cau Dien Tu Liem Hanoi 7.45 169 380 5.6 5 18.6 7.4 0.08 8. Nhue Co Nhue Tu Liem Ha Tay 6.9 173 5.4 18.1 60.3 3.87 0.03 9. Nhue Ha Dong Bridge Ha Dong Town Ha Tay 7.25 1.5 3 22 32 4.42 10. Nhue 50 m fr upstream of Thanh Liet Khanh Ha Thuong Tin Ha Tay 11. Nhue Cau To Khanh Ha Thuong Tin Ha Tay 20 57 17 105 50 24 7.6 51 66 0.2 29 69 5.18 0.049 12. Nhue Cong Than Phu Xuyen Ha Tay 20 41 31 105 53 42 13. Nhue Nhat Tuu/Do Kieu/Phu Van Bridge Ha Nam 20 35 02 105 55 52 7.55 32.78 5.01 33.54 1.84 0.08 14. Day Van Coc Sluice Dan Phuong Ha Tay 15. Day Mai Linh 16. Day Ba Tha-ChMy Ung Hoa Ha Tay 20 41 31 105 53 42 17. Day My Duc- My Duc Ha Tay 18. Day Phu Ly WTS-intake Phu Ly Town Ha Nam 20 32 47 105 54 64 19. Day Red Phu Bridge Phu Ly Town Ha Nam 20 32 24 105 54 54 7.52 62.5 5.4 20.25 2.05 0.079 20. Day Bong Lang Thanh Liem Ha Nam 8.26 21.45 6.9 23.18 3.37 0.09 21. Day Ninh Binh Thermal power Ninh Binh Town Ninh Binh 20 14 50 106 01 16 22. Day Ninh Phuc Post Ninh Phuc Hoa Lu Ninh Binh 23. Day Yen Phuong-Y Yen Yen Phuong Y Yen Nam Dinh 6.9 172 7.8 7 12 24. Day Yen Nhan-Doc Bo Yen Nhan Y Yen Nam Dinh 20 15 07 106 06 04 7 101 7.9 25. Day Yen Khanh- Khanh Phu Khanh Phu Yen Khanh Ninh Binh 20 14 51 106 51 52 26. Day Kim Tan-Kim Son Ninh Binh 27. Day Day Estuary Ninh Binh 28.

Description of the Day/Nhue River Sub-basin 30 29. Dao My Tam-My Loc Nam Dinh 7 108 0.038 7.8 12 18 30. Middle stream of Dao Nam Dinh 7.1 144 0.041 7.8 15 20 31. Nghia Hung Nam Dinh 7 103 0.025 8 10 17 32. Bui Lam Son Lam Son Luong Son Hoa Binh 7.24 80 76 245 4.3 9.2 20 0.05 33. Boi Kim Boi Kim Boi Town Kim Boi Hoa Binh 34. Lac Thuy/ Thung Tram Thung Tram Lac Thuy Hoa Binh 35. Hoang Long Ben De Ninh Binh 20 21 11 105 48 14 36. Gia Tan Ninh Binh 20 19 13 105 55 26 37. Van Van Lock Ninh Binh 7.5 95 45.7 358 277 3.24 35.5 42.5 14.18 0.095 Data from Ambient Water Quality Sampling in the Day/Nhue River Basin 2005-2006 (Continued) No Location PO4 3- Salinity Fe 3+ Oil Coliform Cr Zn Hg As Cu Total pest mg/l mg/l mg/l MNP/100 ml µg/l µg/l µg/l µg/l µg/l 1. Nghia Do 2. Phuong Liet 0.73 100000 3. Mai Dong 1.01 24000 4. Cau Set 0.19 20000 5. Thanh Liet Dam 6. Lien Mac 7. Cau Dien 3.65 30000 0.16 8. Co Nhue 0.5 0.51 15000 9. Ha Dong Bridge 0.86 0.6 9200 0.07 not detected 30 not detected 10. 50 m fr upstream of Thanh Liet 11. Cau To 1.158 5.2 24000000 0.176 12. Cong Than 13. Nhat Tuu/Do Kieu 1.85 14. Van Coc Sluice 15. 16. Ba Tha-ChMy 17. My Duc- 18. Phu Ly WTS-intake 19. Red Phuc Bridge 0.69

Description of the Day/Nhue River Sub-basin 31 20. Bong Lang 0.71 21. Ninh Binh Thermal power 22. Ninh Phuc Post 23. Yen Phuong-Y Yen 0.06 6300 24. Nghia Hung-Doc Bo 0 25. Yen Khanh-Phu Khanh 26. Kim Tan-Kim Son 27. Day Estuary 28. 29. My Tam-My Loc not detected 11000 0 30. Middle stream of Dao 0 5000 0.014 31. Nghia Hung 0 7000 0 32. 0.03 33. Kim Boi 34. Lac Thuy 35. Ben De 0.1 36. Gia Tan 37. Van Lock 0.8 10500 0.58 Source: DONRE s Note: Hanoi: 2000-2003 Ha Tay: Ha Dong Bridge, 2005 Nam Dinh: Dry season, 2004 Ninh Binh: July,2006 Hoa Binh: Oct, 2006 River segment characteristics in the Day/Nhue River Basin Segment No River Location Commune/ ward District Province Lat Long River Depth (m) River Width (m) Flow Rate (m3/s) Flow Velocity (m/s) Max flow rate (m3/s) 1 To Lich Nghia Do Nghia Do Cau Giay Hanoi 21 02 19 105 48 36 2.5 25 1.74 2 Lu Phuong Liet Dong Da Hanoi 20 59 53 105 50 24 2.5 30 0.64 6 3 Kim Nguu Mai Dong Hai Ba Trung Hanoi 21 00 03 105 51 58 3 25 1.45 15 4 Set Cau Set Hai Ba Trung Hanoi 3 20 0.75 8

Description of the Day/Nhue River Sub-basin 32 5 To Lich Thanh Liet Dam Thanh Tri Hanoi 20 57 45 105 48 61 1.9 25 5.5 30 6 Nhue Lien Mac Tu Liem Hanoi 2.54 30 36.2 0.47 7 Nhue Co Nhue Tu Liem Ha Tay 35 8 Nhue Cau Dien Tu Liem Hanoi 35 9 Nhue Ha Dong Bridge Ha Dong Town Ha Tay 3.6 35 24 0.2 10 Nhue 50 m fr upstream of Thanh Liet Khanh Ha Thuong Tin Ha Tay 1.7 32 28 0.532 11 Nhue Cau To Khanh Ha Thuong Tin Ha Tay 20 57 17 105 50 24 2.3 44 43.8 1.03 12 Nhue Cong Than Phu Xuyen Ha Tay 20 41 31 105 53 42 4.1 70 63.4 0.451 13 Nhue Nhat Tuu/Do Kieu/Phu Van Bridge Ha Nam 20 35 02 105 55 52 4.2 82 64.4 0.347 14 Day Van Coc Sluice/before Day Dam Dan Phuong Ha Tay 15 Day Mai Linh 0.3 30 2.7 0.3 16 Day Ba Tha-ChMy Ung Hoa Ha Tay 20 41 31 105 53 42 0.45 105 14 0.3 17 Day My Duc- My Duc Ha Tay 18 Day Phu Ly WTS-intake Phu Ly Town Ha Nam 20 32 47 105 54 64 6.4 77 76.8 0.318 19 Day Hong Phu Bridge Phu Ly Town Ha Nam 20 32 24 105 54 54 8.8 104 96 0.214 20 Day Bong Lang Thanh Liem Ha Nam 21 Day Ninh Binh Thermal power Ninh Binh Town Ninh Binh 20 14 50 106 01 16 4.2 230 113 0.21 22 Day Ninh Phuc Post Ninh Phuc Hoa Lu Ninh Binh 23 Day Yen Phuong-Y Yen Yen Phuong Y Yen Nam Dinh 24 Day Yen Nhan-Doc Bo Yen Nhan Y Yen Nam Dinh 20 15 07 106 06 04 25 Day Yen Khanh- Khanh Phu Khanh Phu Yen Khanh Ninh Binh 20 14 51 106 51 52 4.5 400 395 0.45 26 Day Kim Tan-Kim Son Ninh Binh 27 Day Day Estuary Ninh Binh 28 Dao My Tam-My Loc Nam Dinh 6.8 240 280 0.35 29 Middle stream of Dao Nam Dinh 7 250 282 0.33 30 Nghia Hung Nam Dinh 31 Bui Lam Son 0.3 8 0.4 0.4 32 Boi Kim Boi Kim Boi Town Kim Boi Hoa Binh 33 Boi Lac Thuy/ Thung Tram Thung Tram Lac Thuy Hoa Binh 1 15 4.4 0.4 34 Hoang Long Ben De Ninh Binh 20 21 11 105 48 14 35 Hoang Long Gia Tan Ninh Binh 20 19 13 105 55 26 36 Van Van Lock Ninh Binh 37 Ninh Co Estuary of Ninh Co/to the sea 38 Hong Giao Thuy Data from Ambient Water Quality Sampling in the Day/Nhue River Basin 2003

Description of the Day/Nhue River Sub-basin 33 Segment No River Location ph SS (mg/l) Turbidity (NTU) E. Cond (ms/cm) TDS (mg/l) DO (mg/l) BOD5 (mg/l) COD (mg/l) NO3- (mg/l) NO2- (mg/l) PO4 3- (mg/l) Salinity (0%) Cl- (mg/l) 1 To Lich Nghia Do 7.9 83.6 873 35 70.4 <0.01 <0.001 2.35 79.9 2 Lu Phuong Liet 7.1 22.8 71 0.9 580 0.66 158 268 3.35 0.118 6.71 0.4 56.8 3 Kim Nguu Mai Dong 7.5 0.37 958 75 94.8 <0.01 0.104 5.51 68.7 4 Set Cau Set 7.3 83.5 852 105 129 <0.01 0.106 5.71 47.9 5 To Lich Thanh Liet Dam 7.71 93 1000 500 0.31 48.6 77.6 3.85 0.03 12.4 72.4 6 Nhue Lien Mac 7.89 46 44.2 192 95.9 5.11 6.7 13.4 3.24 1.17 8.52 7 Nhue Co Nhue 6.9 175 5.4 14.6 49.7 1.52 0.026 0.14 8 Nhue Cau Dien 7.45 169 17.5 22.8 1.98 0.858 0.96 8.88 9 Nhue Ha Dong Bridge 7.25 38.11 872 26 28.5 5.55 0.01 0.23 12.78 10 Nhue 50 m fr upstream of Thanh Liet 7.84 31 214 107 4.11 4.9 9.4 1.02 0.26 0.61 9.23 11 Nhue Cau To 7.75 48 473 236 2.65 19.7 28.6 2.26 0.02 3.82 27 12 Nhue Cong Than 7.61 32 395 198 4.78 11.8 17.7 0.29 0.03 2.73 22 13 Nhue Nhat Tuu/Do Kieu/Phu Van Bridge 14 Day Van Coc Sluice/before Day Dam 15 Day Mai Linh 7.69 30 302 151 4.55 9.3 13.8 7.18 0.42 17.8 16 Day Ba Tha-ChMy 7.15 47.6 310 18 29.4 2.3 0.1 0.07 11.36 17 Day My Duc- 7.8 4.98 297 15 25.5 3.65 0.001 <0.01 12.78 18 Day Phu Ly WTS-intake 7.68 28 2200 153 4.59 5.9 8.9 9.48 0.43 0.56 18.1 19 Day Hong Phu Bridge 7.68 25 20 Day Bong Lang 7.16 55 6.47 17.7 0.26 0.41 0.27 21 Day Ninh Binh Thermal power 8.15 52.3 319 5 13 2.83 0.105 0.02 23.07 22 Day Ninh Phuc Post 7.25 44 7.9 5.5 9.5 23 7.25 0.042 5.1 23 Day Yen Phuong-Y Yen 6.9 172 7.8 7 12 24 Day Yen Nhan-Doc Bo 7 101 7.9 8 13 0.29 0.129 0.033 8.87 25 Day Yen Khanh- Khanh Phu 8.05 25.1 318 7.9 6 12.2 2.25 0.103 0.05 21.3 26 Day Kim Tan-Kim Son 7.5 14.7 14.5 3.82 244 5.65 13 24.4 3.875 0.158 1.64 1.6 158 27 Day Day Estuary 8.5 185 14.2 32.5 0.0015 0.007 28 Dao My Tam-My Loc 7 108 7.8 12 18 29 Middle stream of Dao 7.1 144 7.8 15 20 30 Nghia Hung 7 103 8 10 17 31 Bui Lam Son 32 Boi Kim Boi 7.48 87 62 0.2 94 6.62 14.3 26.6 33 Boi Lac Thuy/ Thung Tram 7.48 67 49 0.2 95 6.62 14.1 26.5

Description of the Day/Nhue River Sub-basin 34 34 Hoang Long Ben De 7.7 9.78 5 0.24 153 5.1 14 26.2 3.999 0.036 2.508 0.1 10.65 35 Hoang Long Gia Tan 8.3 8.3 298 4 15.15 0.65 <0.001 12.78 36 Van Van Lock 7.2 46 82 3.2 31.1 39 13.45 0.28 5.8 37 Ninh Co Estuary of Ninh Co/to the sea 38 Hong Giao Thuy Data from Ambient Water Quality Sampling in the Day/Nhue River Basin 2003 (continued) Segment No River Location Fe 3+ (mg/l) Oil (mg/l) Coliform (MPN/100ml) Cr (µg/l) Cd (µg/l) Pb (µg/l) Zn (µg/l) Hg (µg/l) As (µg/l) Cu (µg/l) DDT (µg/l) Lindane (µg/l) Total pesticides 1 To Lich Nghia Do 0.78 2.2 500 <50 <10 <50 30 0.1 1 10 2 Lu Phuong Liet 1.29 >16000 0.33 0.44 3 Kim Nguu Mai Dong 3.2 1.04 5000 <50 <10 <50 30 0.1 6 10 4 Set Cau Set 3.1 1.28 22000 <50 <10 <50 30 0.1 10 10 5 To Lich Thanh Liet Dam trace 590000 6 Nhue Lien Mac 0 5500 7 Nhue Co Nhue 0.47 14000 8 Nhue Cau Dien 0.72 340 2 31 9 Nhue Ha Dong Bridge 0.17 1.26 270 <50 <10 <50 20 0.1 4 <10 <0.0002 10 Nhue 50 m fr upstream of Thanh Liet 0 3300 11 Nhue Cau To 0 410000 12 Nhue Cong Than 0 2900 13 Nhue Nhat Tuu/Do Kieu/Phu Van Bridge 14 Day Van Coc Sluice/before Day Dam 15 Day Mai Linh 0 2600 16 Day Ba Tha-ChMy <0.01 500 <50 <10 <50 20 0.1 <0.5 10 17 Day My Duc- <0.01 2.24 <50 <10 <50 30 0.2 1 10 <0.0002 18 Day Phu Ly WTS-intake 0 19 Day Hong Phu Bridge 20 Day Bong Lang 21 Day Ninh Binh Thermal power 0.11 <0.1 500 <50 <10 <50 20 0.1 <1 <10 <0.0002 22 Day Ninh Phuc Post 1.32 4000 80 23 Day Yen Phuong-Y Yen 0.01 6300 24 Day Yen Nhan-Doc Bo <0.01 0 6000 <50 <10 <50 10 0.1 1 10

Description of the Day/Nhue River Sub-basin 35 25 Day Yen Khanh- Khanh Phu 0.11 Not detected 26 Day Kim Tan-Kim Son 2 1600 0.99 1.62 220 <0.05 <0.01 0.05 0.02 0.0001 0.001 <0.01 27 Day Day Estuary 0.35 3.4 8.1 47.6 0.5 21 37.8 28 Dao My Tam-My Loc 0 11000 29 Middle stream of Dao 0 5000 30 Nghia Hung 0 7000 31 Bui Lam Son 32 Boi Kim Boi 0.17 0 33 Boi Lac Thuy/ Thung Tram 0.11 0 34 Hoang Long Ben De 1.756 200 0.41 11.52 35 Hoang Long Gia Tan <0.01 17 <50 <10 <50 20 0.1 3 <10 36 Van Van Lock 0.97 17000 70 37 Ninh Co Estuary of Ninh Co/to the sea 38 Hong Giao Thuy

Description of the Day/Nhue River Sub-basin 36 ANNEX 4.4 WASTEWATER QUALITY IN SELECTED CRAFT VILLAGES AND INDUSTRIAL ENTERPRISES Typical wastewater quality of dye-knit establishments No Parameter Unit Duong Noi Commune Phung Xa Commune Van Phuc N1 N2 N3 N4 N5 N6 1 ph - 7.19 7.87 11.08 10.54 11.03-2 Temp o C - - 31.5 30.4 34.8-3 COD mg/l 207 774 756 272 430 27.39 4 BOD 5 mg/l 80 55 10 80 10 8.491 5 TS mg/l 674 838 1870 1122 1684-6 SS mg/l 24 74 45 32 41 92 7 Color Pt-Co 313 1777 77.1 91.9 139.8 15086 8 Free mg/l - - 255 125 160 - Alkalinity (NaOH) 9 Cl - mg/l - - 680 238 240-10 S 2- mg/l - - - - - 0.123 Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 2003 Note: N1: Main sewer of Phuc Hung Co.,Ltd - Duong Noi N2: Wastewater of Tin Thanh Dye-knit - Duong Noi N3: Wastewater of Toan Thang Dye-knit complex - Duong Noi N4: Wastewater of Thien Hoang Co.,Ltd - Phung Xa N5: Wastewater of Truong Thinh Co.,Ltd - Phung Xa N6: Wastewater of Mrs Minh's company - Van Phuc Wastewater quality of zinc-plated steel manufacture in Khai Hung Commune No Parameter Unit Result N1 N2 N3 N4 1 ph - 7.8 7.5 7.5 6.4 2 Temp oc 26.9 27 57.6 35.4 3 DO mg/l 6.3 4.7-2.4 4 COD mg/l 33 38-109 5 E.cond mg/l 20 24 122 122 6 Turbidity mg/l 324 355-114 7 TS mg/l 1082 728 394 864 8 SS mg/l 474 543 20 103 9 Cl - mg/l 2 17 44 213 10 NH 4 + mg/l 1.46 2.67 14.55 28.1 11 Oil mg/l 1 1.2 0.2 2.1 12 Cr(III) mg/l 0.239 0.525 1.111 2.242 13 Total Fe mg/l 0.98 1.5 23.3 6.2 14 Pb 2+ mg/l 0.027 0.048 0.106 0.55 15 Zn 2+ mg/l 0.42 0.571 0.896 0.25 Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 2003 Note:

Description of the Day/Nhue River Sub-basin 37 N1: Water of Nhue river before factory sewer N2: Water of Nhue river after factory sewer N3: Wastewater of cooling process N4: Wastewater of factory Wastewater quality of mechanical production establishments No. Parameter Unit Thanh Thuy Da Sy TCVN 5945- N1 N2 N3 1995 (column B) 1 ph - 7.2 6.1-5.5-9 2 COD mg/l 88 181 719 100 3 SS mg/l 756 139 131 100 4 Ni2+ mg/l 21.23 2.291-1 5 Cr(VI) mg/l 42.4 - - 0.1 6 Cr(III) mg/l 116.2 18.7-1 7 Pb 2+ mg/l 12.5 3.2-0.5 8 Zn 2+ mg/l 12.535 64.64-2 9 Oil mg/l 2.2 1.8 525 1 10 Fe 2+ mg/l 1.927 1.361-5 Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 2003 Note N1: Plating wastewater - Mr. Tran Van Hung 's Company - Rua Ha hamlet, Thanh Thuy Commune N2: Plating wastewater - Mr. Tran Van Red 's Company - Rua Thuong hamlet, Thanh Thuy Commune N3: Forging wastewater Dasy Water quality at wastewater discharge points of mechanical establishments No Parameter Unit Thanh Thuy Da Sy TCVN 5942-1995 (column N1 N2 N3 B) 1 ph - 6.1 6-5.5-9 2 COD mg/l 44 318 42 35 3 BOD 5 mg/l 11 110 27 35 4 SS mg/l 53 13.252 19 80 5 Ni2+ mg/l 0.029 0.236-1 6 Cr(VI) mg/l 0.02 - - 0.05 7 Cr(III) mg/l 0.041 3.54-1 8 Pb 2+ mg/l 0.35 0.47-0.1 9 Zn 2+ mg/l 0.598 7.79-2 10 Oil mg/l - - 0.67 0.3 11 Fe 2+ mg/l 0.042 58.96 0.105 2 12 Coliform MNP/100ml - - 54000 10000 13 CN - mg/l - - 6.5 0.05 Note: N1: Wastewater receiving pond - Rua Ha Hamlet N2: Main sewer of Rua Ha Hamlet N3: Plating wastewater receiving pond

Description of the Day/Nhue River Sub-basin 38 Underground water and wastewater quality at selected arts and handicraft production processing No Parameter Unit Thanh Thuy Phung Xa TCVN 5944-1995 (column N1 N2 N3 B) TCVN 5945-1995 (column B) 1 ph - 6.9 6.4 6.6 6.5-8.5 5.5-9 2 COD mg/l - - 345-100 3 BOD 5 mg/l - - 125-50 4 TDS mg/l 194 30-750 - 1500-5 SS mg/l - - 183-100 6 Color Pt-Co - - 432 - - 7 Total Nitrogen mg/l - 3.21 8.78-60 8 Total Phosphorous mg/l - 0.91 0.4-6 9 Hardness mgcaco 3/l 125 45-300 - 500 10 NO 2 - mg/l - 0.045 - - 11 NH4 + mg/l 0.37 1.22 1.47 1 12 Total Fe mg/l 1.53 0.15 1-5 13 Mn 2+ mg/l - 0.1-0.5 14 Cu 2+ mg/l 0.47 1 1 15 Pb 2+ mg/l 0.7 0.05 0.5 16 Total Cr mg/l 0.103 17 Zn 2+ mg/l 0.26 5 2 18 Oil mg/l 1.1 1 19 Coli form MNP/100ml 9200 3 10000 Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 2003 Note N1: Groundwater - Luu Thuong village N2: Ground water - My Thai Company N3: Centralized wastewater of My Thai Company Wastewater quality at discharge points in selected arts and handicraft production villages No Parameter Unit HaThai Village's Pond Luu Thuong village's pond TCVN 5942-1995 (column B) 1 ph - 8.7 7.6 5.5 9 2 COD mg/l 61 117 35 3 BOD 5 mg/l 22 45 25 4 SS mg/l 33 27 80 5 Color Pt-Co 69 493-6 Total Nitrogen mg/l 4.45 6.3-7 Total Phosphorous mg/l 0.61 0.28-8 NH 4 + mg/l 3.02 4.28 1 9 Total Fe mg/l - 1.53 2

Description of the Day/Nhue River Sub-basin 39 10 Cu 2+ mg/l 0.035-1 11 Pb 2+ mg/l 0.087-0.1 12 Oil mg/l 0.2-0.3 13 Coli form MNP/100ml 45 35000 10000 Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 2003 Surface water quality at selected mechanical production villages - Thanh Thuy Commune No Parameter Unit Result TCVN 5942-1995 (column B) N1 N2 1 ph - 6.1 6 5.5-9 2 COD mg/l 44 318 35 3 BOD 5 mg/l 11 110 25 4 SS mg/l 53 13256 80 5 Ni2+ mg/l 0.029 0.236 1 6 Cr(VI) mg/l 0.02-0.05 7 Cr(III) mg/l 0.041 3.54 1 8 Pb 2+ mg/l 0.35 0.47 0.1 9 Zn 2+ mg/l 0.598 7.97 2 10 Fe 2+ mg/l 0.042 58.96 2 Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 2003 Note N1: Wastewater receiving pond in Rua Ha Hamlet N2: Central sewer in Rua Thuong Hamlet Wastewater quality in selected dye-knit workshops - Phung Xa Commune No Parameter Unit Result TCVN 5945-1995 (column N1 N2 N3 B) 6948-2001* 1 ph - 11.08 10.54 11.03 5.5-9 2 T o C 31.5 30.4 34.8 40 3 BOD mg/l 765 272 430 100 4 BOD 5 mg/l 10 80 10 50 5 TS mg/l 1870 1122 1648 6 SS mg/l 45 32 41 100 7 Color mg/l 77.1 91.9 139.8 50* 8 Free Alkalinity (NaOH) mg/l 255 125 160 9 Cl - mg/l 680 238 240 750* 10 S 2- mg/l - - - Source: Institute of Environmental Science and Technology - Hanoi University of Technology, 07-2003 Note

Description of the Day/Nhue River Sub-basin 40 N1: Wastewater of Toan Thang Dye-knit Complex - Phung Xa Commune N2: Wastewater of Thien Hoang Co.,Ltd - Phung Xa Commune N3: Wastewater of Truong Thinh Co.,Ltd - Phung Xa Commune Water quality in selected traditional villages in Ha Nam Province No Parameter Unit 1 2 3 4 5 6 1 ph - 7.53 7.4 7.9 7.24 7.74 7.82 2 BOD mg/l 222.5 112.5 190 575 617.5 247.5 3 COD mg/l 448.7 271.25 431.25 2280.7 2523 533 4 Pb mg/l 0.024 0.18 0.078 0.019 0.0255 0.01 5 Hg mg/l 0.0002 0.0002 0.0013 0.0002 0.0012 0.0002 6 As mg/l 0.0134 0.0145 0.007 0.026 0.05 0.0004 7 Cd mg/l 0.026 0.033 0.026 0.01 0.005 0.013 Source: Center of Monitoring and Analysis resources and environment of Ha Nam Note: 1: Water in pond of Mr. Le Duc Toan's house, Nha Xa, Duy Tien (Dye-knit) 2: Water in pond of Mr. Le Nhu Tho's house, Nha Xa, Duy Tien (Dye-knit) 3: Water in pond of Mr. Nguyen Red Tien's house, Nha Xa, Duy Tien (Dye-knit) 4: Water in pond of Mr. Tran Ba Yen's house, hamlet No.15, Hoa Hau, Ly Nhan (Dye-knit) 5: Water in pond of Mr. Tran Van Nhan's house, hamlet No.15, Hoa Hau, Ly Nhan (Dye-knit) 6. Wastewater of Branch of Duc Tin Food production Company, Da hamlet, Boi Cau, Binh Luc (agricultural production)

Methods, models and assumptions 41 5 METHODS, MODELS AND ASSUMPTIONS Environmental regulators in developing countries often lack the necessary resources, staffing and capacity to undertake systematic monitoring of pollution releases to water. Such information tends to be available on an occasional basis, for a limited number of pollutants, and for a limited number of areas. Despite its very best efforts and recent increases in the budget and staffing allocated by local authorities for pollution control, Vietnam works under similar resource constraints to many of its neighbors. In addition, what information exists in Vietnam is largely anecdotal or is collected in an ad hoc manner which does not provide a comprehensive view for setting priorities. What is needed is a systematic method that can serve as a first-order approximation of the overall pollution situation and can aid in priority setting and monitoring efforts. Recognising these constraints and the urgent need for effective pollution management based on identifying the most important areas for action, several models have been developed that can aid in the targeting of pollutants, sectors and areas of greatest concern. The overall model presented in this report combines information from manufacturing industry (Industrial Pollution Projection System, IPPS), agricultural production (Agricultural Pollution Projection System, APPS), domestic waste (Domestic Pollution Projection System, DPPS), and craft-village activities (Craft-village Pollution Projection System, CVPPS). Each model assumes that pollution is affected by the scale of economic activity, and where applicable, its sector composition. Taken together, the Area-Based Pollution Projection System (ABPPS) operates through estimates of pollution intensity (usually defined as pollution per unit of economic activity, e.g. industrial sector composition, cropping patterns or wastewater discharges). The ABPPS is an innovative system as it brings together many different sources of pollution under one analytical framework for pollution control. The interest lies in being able to use the system either as one unit of prioritization or to follow the results separately from each one of the models for industry, agriculture, domestic sources and craft villages. Taken together, the models can be used to set priorities for more focused field study and management action. 5.1 POLLUTION LOAD MODELS 5.1.1 THE INDUSTRIAL POLLUTION PROJECTION SYSTEM (IPPS) IPPS is a modeling system which combines data from industrial activity (such as production and employment) with data on pollution emissions to calculate pollution intensity factors (or coefficients), i.e. the level of pollution emissions per unit of industrial activity. 7 The IPPS has been used in many developing countries and is especially applicable to those countries which have not embarked on a systematic method of pollution inventorying. 8 Initially for this model, pollution intensities/coefficients have been calculated with data available in the United States from the U.S. Manufacturing Census and the U.S. Environmental Protection Agency (EPA). The details of each of how these data sets were used and the calculations made are contained in Annex 5.1. The basic calculation took manufacturing information on output value, value-added and employment and matched this with the US EPA s comprehensive database on pollution releases, on a plant-by-plant basis. Pollution intensities were then calculated as the total amount of releases divided by the manufacturing indicator (i.e. output value, value-added or employment). 7 Further information may be obtained in Hettige et al. (1995). 8 For example, IPPS has been used to estimate the contribution of various industrial sectors to total pollution intensity in Brazil (Dasgupta et al., 1998), China, Latvia (Laplante and Smits, 1998), Mexico, and Thailand (Laplante and Meisner, 2001).

Methods, models and assumptions 42 In the case of the employment-based indicator, the figure is the number of kg of pollutant per unit of employment. IPPS has pollution intensities using several different manufacturing measures, however the one retained for this study is the employment-based pollution intensity since it has been shown to be much more stable across different technologies and across both developed and developing countries (Dasgupta et al. 2002). The US EPA contains emissions information for close to 250 pollutants and chemical substances known to be harmful to both human health and the environment. IPPS coefficients are available for the following list of pollutants: Air pollutants: sulfur dioxide (SO2); nitrogen dioxide (NO2); volatile organic compounds (VOC); and particulate matter: both total suspended particulates (TSP) and particulate matter of size less than 10 microns (PM10). Water pollutants: Biological oxygen demand (BOD); and Suspended solids (SS). IPPS also includes pollution intensities for over 240 priority chemicals and metals released to air, water, and land. 9 Listed below are some of the chemicals known to be toxic to human health and metals known to be bio-accumulative: 10 Chemicals: Benzene, chloroethane, chloromethane, toluene and zylene; Metals: Antimony, arsenic, asbestos, beryllium, cadmium, chromium, copper, cyanide, lead, mercury, nickel, thallium and zinc. For each of the air and water pollutants and the 240 toxic chemicals and metals, IPPS provides a lower bound value for the pollution intensity coefficients, an upper bound value, and an interquartile mean value. For the purpose of this study, only the lower bound intensities of water pollution load are used. It was decided to adopt the more conservative measure of pollution intensity as the basis for identifying sectors and enterprises for more detailed analysis. Pollution release estimates presented in this report are to be interpreted as under-estimates of the actual pollution releases. In the context of this study, and for priority setting in general, what matters most are the relative rankings of one sector or area over another. 5.1.2 METHODS FOR ASSESSING INDUSTRIAL POLLUTION HAZARD Hazardous substance is a term usually relating to the effect of industrial chemicals on human health. It is a substance which has the potential to harm the health or safety of persons in or outside of the workplace. Hazardous substances are: harmful/toxic - causing transient or permanent damage to body functions corrosive - causing damage to living tissue irritant - causing local irritation to living tissue sensitising - causing an allergic reaction 9 The TRI requires firms to report their releases of chemicals known to be toxic and metals known to be bio-accumulative in each of the following categories: (1) Fugitive or non-point air emissions; (2) stack or point air emissions; (3) discharges to streams or receiving water bodies; (4) underground injection on-site; (5) release to land on-site; (6) wastewater discharges to publicly-owned treatment works; and (7) transfer to off-site facilities for treatment, storage or disposal. These seven categories are grouped into 3: air, water, and land. 10 For a complete list of substances refer to Annex 5.1.

Methods, models and assumptions 43 carcinogenic - causing cancer mutagenic - causing genetic damage substances toxic to human reproduction. To calculate the relative hazard risk, chemical loads derived above are also categorized into three ranges of acute exposure hazard using lethal concentration, LC 50, and lethal dose, LD 50, values (Horvath et al., 1995; Swanson et al., 1995). Details of their description and derivation are provided in Annex 5.1. These measures form the basis of epidemiological impact studies in evaluating the potential risk to human life. Each chemical in the database was classified as a (1) high hazard, (2) moderate hazard, or (3) low hazard according to its LC 50 and LD 50 value and then aggregated up to the plant level and summarized at the provincial, district and sector level. 5.1.2.1 THE INDUSTRY CENSUS DATABASE The key piece of information required to calculate pollution load in Vietnam is the information on the scale of manufacturing activity. The data source used in this exercise is the 2004 Enterprise Census from the General Statistical Office (GSO) of the Ministry of Planning and Investment (MPI). Details on the history and sampling methods are contained in Annex 5. Enterprise information is collected according to the Vietnam Standard Industrial Classification (VSIC) system 11 and is based on the International Standard Industrial Classification (ISIC) system (Revision 3.1). 12 In the 2004 Census used as a basis for this report, there were over 90,000 recorded enterprises spanning 14 industrial sectors. 13 Since the interest here is to estimate industrial pollution from manufacturing activities in the Day/Nhue River Basin, the sample is restricted to the manufacturing sector data within the Census. This includes 121 sub-sectors at the VSIC-4 unit of analysis. For the Day/Nhue River Basin, the GSO Census of 2004 covers 3,290 firms in the manufacturing sector, and 275,109 workers. 5.1.2.2 VALIDATION OF GSO SURVEY One noted limitation of the GSO Enterprise data set is that some of the observations are the headquarters of a company that has operations in other locations. To test the extent to which this was true, a field study was conducted by selecting the top two communes in each of the top ten provinces in terms of the estimated pollution load by IPPS. Then the top 2-3 most polluting factories and their commune location were identified. Field operators then placed a phone call to the factory to substantiate its location. In the case that a factory could not be contacted, a call to the relevant provincial DONRE was placed. Since the factories were serious polluters, the DONRE s were aware of the factories and could pin point their location. However this was not always the case as one went down the list of highest polluters. During informal discussions, it was noted that some communes did not contain such factories, and that these were only the headquarters for administration purposes. This anomaly was found for only a few enterprises. 11 Data are collected at the 6-digit VSIC level. 12 The United Nations classification of industry ie the International Standard for Industry Classification (ISIC) is adopted by all countries. The Vietnam system of industrial classification follows the ISIC Revision 3.1. The detailed descriptions can be found on the UN Statistics Office Website: http://unstats.un.org/unsd/cr/registry/regcst.asp?cl=17 13 Agriculture, forestry and fishing; mining and quarrying; manufacturing; electricity, gas and water supply; construction; wholesale and retail trade; hotels and restaurants; transport, storage and communications; financial intermediation; real estate, renting and business activities; public administration and defense; education; health and social work; and other community, social and personal service activities.

Methods, models and assumptions 44 The precise bias this phenomena introduces for the Day-Nhue Sub-basin industries is not known. What ever the case, the magnitude of this bias diminishes with greater aggregation of analysis. At the provincial level this may not be a concern since most operations, including the headquarters may co-locate in the same province. However at levels of aggregation smaller than the commune level results will need to be checked. Field observations will play a critical role in the follow-up to the study and in future monitoring activities. Discussions should be conducted with the GSO on improving the coverage of the survey, and the method of collection so that factory locations are fully accounted for in survey responses. 5.1.2.3 METHOD OF VALIDATION FOR ESTIMATION One common question that arises when IPPS is used in a developing country context such as Vietnam is how realistic is it to use coefficients derived from data in the United States? The answer is two fold. In the absence of detailed information on pollution emissions, IPPS serves as a first-order approximation until such time when locally monitored information can be substituted into the model to make it country-specific. Another reason for using IPPS lies in understanding whether Vietnamese industrial technology is well represented by the underlying technology reflected in IPPS. Coefficients derived for IPPS were based on 1987 emissions profiles for over 20,000 plants in the US. Many Vietnamese enterprises continue to use that vintage of technology (ie 20 or more years old) although of course many old plants are now modernizing and new plants are adopting more recent technologies. This is why this study uses the lower bound coefficients, rather than averages or the higher threshold coefficients. It will be essential to follow up on the priority listings from this study with plant specific assessments to confirm or otherwise the estimated pollution releases and the underlying assumptions concerning the levels of pollution control. In the case of Vietnam, some monitored information on pollution is available however it is not collected on a systematic basis and does not cover as many pollutants or sectors as in the IPPS. The purpose of this section is to compare the relative ranking of pollution load for provinces and sectors using Vietnamese observed coefficients with those estimated by IPPS. This validation process provides insights on the key question - does the underlying Vietnamese technology concord well with the technology inherent in the US-based IPPS coefficients? To test this proposition, pollution coefficients for BOD and SS, were obtained from the CTC Vietnam (http://www.ctcgroupe.com/en/locations/ctc_vietnam.php). Since the CTC database of coefficients included coefficients for 54 sectors measured in terms of manufacturing employment, this facilitated a comparison with the IPPS coefficients used for this exercise (employment-based coefficients). Rank correlation coefficients were calculated between estimated BOD and SS loads using both CTC and IPPS coefficients. The results suggest that at the provincial level the correspondence between Vietnamese and the IPPS coefficients is quite high, while among sectors it is lower. However, the differences between sector estimates are not statistically significant. In addition, since only 54 sectors were comparable this perhaps may explain the lower correlation. Details of the comparison are provided in Annex 5.1. 5.1.3 THE AGRICULTURAL POLLUTION PROJECTION SYSTEM (APPS) The Agricultural Pollution Projection System (APPS) is very similar to the Industrial Pollution Projection System (IPPS) where the level of economic activity is multiplied by a coefficient which reflects the pollution intensity of that activity. However instead of sector-specific pollution intensities, for agriculture the study adopted crop-specific pollution intensities, which are multiplied by the amount of crop production. Pollution in this case is assumed to be the amount

Methods, models and assumptions 45 of pesticides or fertilizers applied to a particular crop. The variety of pesticides used in the Subbasin is diverse. Yet, without access to comprehensive farm-level survey data on pesticide and fertilizer use in the region, the study depended on secondary level information at a higher level of aggregation. In this study we were able to obtain overall pesticide and crop-specific fertilizer use data at the district level. For crop production data the study utilized recent information compiled from the 2006 Agricultural Census initiated by the GSO. 14 The database contained information on crop production at the commune level for a selected number of crops. 15 In terms of a calculation for APPS, figures on production were combined with use rate of pesticides and fertilizers to derive values of pesticide and fertilizer use per hectare of land under cultivation. Application rates were obtained to district level (where information was available) from the Departments of Agriculture and Rural Development in each province. The procedure multiplied production numbers and the pesticide and fertilizer use rates to arrive at a pesticide and fertilizer load amount for that particular area. This was then summed to the province, and district level for analysis. A number of assumptions were made during the analysis. Since farm-level data were not available, pesticide and fertilizer use rates were assumed to be the same across all farmers within the same district. As the decision to apply pesticides and fertilizers is an individual decision, the study recognizes this limitation. However, to the extent that farmers remain consistent in their application rates from pest attacks at the district level, these estimates are most likely congruent with actual practice and use rates. To make the point clear, the calculation is as follows: Agricultural production per crop (kg) x pesticide (fertilizer) coefficient (kg/ha) = potential pesticide (fertilizer) volume (kg) 5.1.4 THE DOMESTIC POLLUTION PROJECTION SYSTEM (DPPS) Localized information on the pollution content of domestic sewage was not available in a consistent format for each province or district in Day/Nhue River Basin. In the absence of this information, the study adopted the assumptions used in the Vietnam National Environmental Performance Assessment (EPA) Report (2006), where the BOD5 and SS content of domestic wastewater was assumed to be 200 mg/l and 150 mg/l, respectively. Despite this limitation, the study was able to obtain information on domestic wastewater discharges and solid waste volume per person per day. The information was collected from provincial reports and included totals for the province with some dis-aggregation at the district level. When combined with actual population, it was possible to derive domestic wastewater and solid waste estimates per person per day. If information was only available at the provincial level, all districts were assumed to have the same discharge rate. Combining the BOD5 and SS coefficient with information on population, annual wastewater and solid waste per capita the calculations are: Domestic BOD5 and SS load: Population number x Domestic wastewater coefficient x (m3 wastewater per person/ day) 365 x (annual figure) 1000 x (convert m3 to liters) 14 The methods by which the GSO samples crops for their Census is reviewed in Annex 5.2. 15 Crops included spring and autumn paddy rice, maize, potato, cassava, sugar cane, ground nuts, soya beans, tobacco, vegetables, tea, long an and litchi.

Methods, models and assumptions 46 200 (or 150) / (BOD5 or SS in mg/l) 1,000,000 (convert mg to kg) = BOD5 or SS pollution load per annum (kg) Domestic solid waste: Population number x Domestic coefficient x (kg solid waste per capita/ day) 365 (annual figure) = Solid waste volume per annum (kg) 5.1.5 THE CRAFT-VILLAGE POLLUTION PROJECTION SYSTEM (CVPPS) The Agricultural Census conducted by the GSO in 2006 also contained an information module on craft villages. The survey specifically had information on the number of households, location and employment figures of these villages. It also described which type of craft activity was undertaken in that location. In certain instances some villages contained several different activities. To collate the many different activities in a craft-village, broad categories were constructed as follows: Food and agricultural products processing Textile and dyeing Metal recycling Wood products Bamboo, lacquer, statue painting Others (making hat, salt, incense, ball sewing, etc) These broad groups encompassed many hundreds of separate activities and allocated them into a logical framework for analysis. However, one key piece of information was not recorded in the survey namely the amount of goods or production associated with that particular craft-village activity. This precluded an analysis based on the output of products in the craft-village. Therefore, in this study we used the number of full-time employs recorded as a proxy of the scale of economic activity. In terms of the types and quantities of pollution emanating from craft villages the study was able to obtain the amount of wastewater produced per worker. Although these numbers were only available at the district level, they did differentiate across the type (or sector) of activity, allowing one to observe more pollution intensity of one craft activity over another. In addition, the study obtained information on the ambient concentration (mg/liter) of several pollutants measured from wastewater discharges in the village: BOD5 COD SS Total nitrogen (N) Total phosphorus (P) Iron (Fe) Oil Total coliform

Methods, models and assumptions 47 Taken together, load estimates of the above pollutants were produced by multiplying the number of CV households times the wastewater discharge per person per day and the ambient concentration of the pollutant in the wastewater as: Number of CV households x Wastewater discharge x (m3/person/day) Ambient pollutant concentration x (mg/l) 1000 / (convert m3 to liters) 1,000,000 x (convert to kg) 365 (annual figure) = kg of pollutant 16 Although it would have been preferable to estimate pollution load based on production, for the purposes of estimation, employment is considered an adequate surrogate measure of the productive capacity of the craft-village. Yet, this approach assumes that productive capacity is homogeneous across all craft villages (or that worker productivity is the same everywhere), which may or may not be true. As a first-order estimate of the pollution intensity of craft villages, and in the absence of a comprehensive database on craft-village pollution coefficients, these estimates should be interpreted as conservative measure of the true extent of pollution. 5.1.6 SUMMARY OF THE POLLUTION MODELS This chapter of the study report briefly describes several methodologies which can help provide a broad picture of selected pollution sources in the Day/Nhue River Basin. By using these methods, in conjunction with other tools and knowledge, policymakers can analyze the relative pollution situation at varying degrees of aggregation, from the specific pollutant to the river basin level. This allows for a great deal of flexibility in applying the results to the policy and regulatory context. The models are also capable of producing results that are useful for occupational health and safety concerns, where high risk pollutants can be identified and policies set to minimize potential exposure to hazardous substances. This methodology also makes use of the extensive information collected by the General Statistics Office of the Ministry of Planning and Investment. While there remain issues and questions pertaining to these data sets, 17 they are considered the most systematic in terms of methods and area-based coverage. Systematic access and use of these data sets by environmental authorities could guide and greatly facilitate the identification of areas for the collection of environmental data (monitoring). In particular increased coverage would be most useful in areas of environmental concern, industrial zones and craft villages. This would serve to focus resources in areas of higher pollution intensity and where calibrating the models to the local context would be of greatest benefit. One innovation of this study was in the initial identification of hotspot sectors and geographical areas and the subsequent field visits as a method of results validation. This represented an opportunity to gauge the accuracy of the data and allowed the team to collect more localized information on the river basin. This interactive method of cross-validation and data collection proved to be invaluable for model calibration. Activities such as these can be used to calibrate the models in a more systematic and timely fashion through regular field visits, spot checks and sampling of emissions. As the frequency of monitored data become more available, this will allow authorities to being substituting locally observed/monitored data for the US-based intensities currently used by the IPPS for industry. Once hotspot sectors and areas are identified, 16 Multiplied by 1000 to convert mg/l to mg/m3 and then divided by 1,000,000 to get pollution load in kg. 17 For example, in the Enterprise Census, insofar as enterprises of less than 10 employees are concerned, the GSO surveys only 20% of these enterprises.

Methods, models and assumptions 48 increased frequency of monitoring would allow trends to be analyzed over time. This will enable regulators to track progress in improving pollution performance and help identify those areas where greater efforts are most needed. 5.2 DISPERSION MODELS AND ASSUMPTIONS 18 The goal of the dispersion models is to estimate the impact of a particular potential pollution load on ambient water quality. The dispersion models used in this study were selected because of their relatively modest input requirements. These equations are not comprehensive in describing river health. The equations do serve as convenient screening tools for the identification of areas of concern. The models calculate ambient concentrations of water pollutants by using simple (screening) dispersion models with minimum hydrological data. A key input into the model is pollution load, which was estimated using the IPPS, APPS, DPPS and CVPPS models described in the previous section. One implication of using a set of simplified models is that the results can help to decide whether or where more sophisticated water quality models should be used, or which kind of wastewater treatment options at specific sources should be given further consideration and a detailed analysis. There are three basic equations that we present in this study: Equation for general dispersion for pollutant Classes 1 & 2 19 Equations for river BOD (Dissolved Oxygen deficit) Equation for total coliform count The first equation is used as a general mass balance dispersion model for pollutants which have a higher retention time in water. The study also included an equation for BOD as this parameter is one of the most commonly monitored and has a substantial effect on the oxygen content of the water. Proper levels of dissolved oxygen are necessary to support different levels of aquatic life. If this balance is upset by high BOD concentrations, DO will tend to fall. The specific BOD equation measures the dissolved oxygen deficit as a result of the BOD load. The third equation is included as it is highly related to the biological nature of the river system and its links to health. Below we describe each equation, its input requirements and output. 5.2.1 EQUATION FOR GENERAL DISPERSION FOR POLLUTANT CLASSES 1 & 2 For each pollutant j with stability class 1 or 2, the model first calculates the discharge load B j (in grams per second) from the calculated substance load L j (in tons per year): B L 10 6 j = 365 24 3600 and then calculates the concentration of conservative pollutant j after discharge C j (in milligrams per liter): 18 The River BOD model is from Water, contamination and self-purification, Prof. Huisman, Technical University of Delft, The Netherlands, 1973. All the other models are taken from Management and Control of the Environment, WHO/PEP/89.1, World Health Organization, 1989. 19 Water pollutants are classified into three stability classes: Class 1: stable pollutants (retention time > 5 days); Class 2: not stable pollutants (0.5 5 days); Class 3: unstable pollutants (< 0.5 day). j B j C j = C j 0 + Q (1) (2)

Methods, models and assumptions 49 where C j0 is initial concentration of the pollutant j (in milligrams per liter) Q is river average flow rate (in cubic meters per second). Input requirements: River flow rate (m3/second) Initial concentration for each pollutant in the water body (mg/liter) Output: Concentration of the substance (pollutant j) 5.2.2 EQUATION FOR RIVER BOD Model calculates D c - critical DO deficit after the discharge point - by the formula: In this equation, D c = K K r 2 L a e K V r X m (3) K r is BOD removal rate constant (per day) at temperature T (in degrees Celsius): K r = 0.23 e 0.046( T 20) (4) K 2 is re-aeration constant (per day) at temperature T ( o C): K 2 = 5 V H 0.67 1.85 e 0.024( T 20) (5) H is river depth (in meters); V is water velocity in m/sec determined as: V = Q B H (6) where B is river width (in meters), Q is river average flow rate (in m3/sec). Value L a, used in the equation (3), is the concentration of BOD u right after the discharge point. It is calculated as: L a ( T t e o 20) 0.23 ( 1 e ) + ( To ) o 1 0.02 20 = 1 ( ) L o Lq + Q (7)

Methods, models and assumptions 50 where L o is river background BOD concentration in milligrams per liter (concentration just before the discharge point, determined by BOD analysis); T o is temperature for background BOD analysis (in degrees Celsius); t o is number of days for background BOD analysis; and L q is river BOD load (in g/sec) Finally, X m is distance from discharge point where critical oxygen deficit occurs: V K 2 K2 ( K 2 Kr X m = ln 2 K2 Kr Kr Kr ) D L a a (8) D a is initial DO deficit (D a = C s - C a ); C a is river initial oxygen concentration (in mg/l), and Cs is oxygen saturation concentration at temperature T (in mg/l), given as: C s = 9.1 e 0.023 ( T 20) (9) Input requirements: River depth (average in meters) River width (average in meters) River volume / average flow rate (m3/sec) River initial oxygen concentration (mg/l) Water temperature (in degrees Celsius) Number of days (for BOD analysis) 20 Temperature (for BOD analysis) River BOD background concentration (mg/l) Constants: Oxygen saturation concentration value based on temperature (mg/l) Output: The critical DO deficit (Dc) 5.2.3 RIVER COLIFORM COUNT Using number of days since discharge, water temperature, coliform die-off rate and initial number of coliforms per 100ml, model calculates the coliform count N (per 100 ml): N = N i e T 20 ( Kb20 1. 075 )t where N i is initial number of coliforms per 100 ml K b20 is coliform die-off rate constant per day at 20 degrees Celsius 21 T is actual temperature in degrees Celsius (10) 20 Assumed to be 5-day BOD in this study. 21 Assumed to be 0.7 in this study.

Methods, models and assumptions 51 t is time since discharge in days Input requirements: River coliform die-off rate constant at 20 degrees Celsius Time since last discharge (in days) Water temperature (in degrees Celsius) Initial number of coliforms per 100ml Output: Total coliform count 5.2.4 SUMMARY OF THE DISPERSION MODELS The dispersion models presented here capture a few of the basic relationships between the dispersal of pollution in the river, its impact on ambient water quality and consequently the river s general health. A logical follow-on question given estimates of changes in ambient water quality is the potential implications for human health. In the following sections, a description of the relationship between waterborne pollution and the impacts on health is given along with the methodology for the study s environmental health risk assessment (results detailed in Section 8). 5.3 WATERBORNE POLLUTION AND HEALTH IMPACTS Rapid industrialization of countries has been accompanied by dramatic increases in waterborne pollution. Exposure to waterborne pollutants has been shown to initiate, promote, sustain and stimulate a number of diseases (Moeller, 2004). The study of the relationship between pollution and human health began in the early 1800s. One of the most notable early studies was conducted by John Snow. Snow identified the effluent from London s sewerage system was entering the public drinking water supply resulting in localized cholera epidemics (Snow, 2002). Since Snow s investigation the scientific fields of epidemiology and environmental health have matured into critical components in the assessment of pollution and the protection of public health (Moeller, 2004). Understanding the linkages between public health and pollution is an important component in developing a management program for polluting activities. Identifying chemical and biological pollutants that pose the greatest risk to human health informs regulators to make good decisions about where to allocate scarce resources available for licensing (Ritter et al., 2002). Also it informs water resource managers in their interactions with other government departments (such as the Department of Health) on water quality monitoring and management strategies. International media attention has been drawn to the degradation of rivers and the effects on human health. The benzene spill into China s Songhua River attracted worldwide media attention and led to public uncertainty and fear (Environmental News Service, 2006). In Vietnam, the last five years have seen a dramatic increase in the level of media and community concern over river pollution and its impacts on health (Dien, 2007; O Rourke, in press). Understanding the relationship between pollution and public health provides political leaders and government departments with vital information to respond to community concern and to set priorities for making cost-effective regulatory decisions. 5.3.1 HEALTH IMPACTS AND POLLUTION IN THE DAY/NHUE RIVER BASIN Previous studies of the Day/Nhue River Basin have found adverse health effects related to pollution in communities living in close proximity to the Day and Nhue Rivers. The State of the

Methods, models and assumptions 52 Environment Report for the Day/Nhue River Basin showed provinces, districts and communes through which the Nhue River flows had higher rates of people infected by amoeba and diarrhea than other districts not located along the river (MONRE, 2006). A report filed by the Hanoi-based University of Medicine in April, 2007 identified that water from the seriously contaminated Nhue River caused digestive, gynecological, and skin diseases among residents in Ha Nam province (Dien, 2007). These studies provide the impetus to search for further linkages that exist between available health data and the outputs of the ICEM Pollution Projection System. 5.3.2 WATERBORNE DISEASES Biological pollutants are the most common cause of waterborne-related disease (Ritter et al., 2002). Microbial diseases range from low-level illnesses such as diarrhea and worm infestations, through to cholera, that if untreated can be lethal. Diseases caused by chemical pollutants are more commonly related to long-term, low-level exposure however nitrate is a notable exception to this rule (WHO, 2006). Table 5.3.1 presents common waterborne pollutants and the associated diseases. Table 5.3.1 - Diseases and symptoms associated with exposure to biological and chemical waterborne pollutants Group Pollutant Disease and symptoms Source Bacteria Escherichia coli Watery diarrhea, stomach pain CDC, 2007 Vibrio cholerae Cholera watery diarrhea lethal if not treated Shigella Shigellosis dysentery (bloody diarhhea) vomiting, cramps, fever, Reiters syndrome Parasitic protozoan Giardiasis Stomach pain, watery Cryptosporodium parvum diarrhea Cryptosporidiosis - Watery diarrhea, abdominal pain, low-level fever. Potentially lethal for persons living with HIV/AIDS Virus Hepatitis A Hepatitis relapsing diarrhea, fatigue, fever and abdominal pain Helminths Hookworm Skin rash, itch, cough and protein deficiency Chemicals Nitrate Methemoglobinemia lethal to young children Chlorination byproducts Evidence of association with cancer Arsenic Skin diseases, skin cancer, cancers of the bladder, kidney and lung, and diseases of the blood vessels of the legs and feet Schonning C. and Stenstrom T.A., 2004 Schonning C. and Stenstrom T.A., 2004 CDC, 2007 CDC, 2007 CDC, 2007; WHO, 2007 Schonning C. and Stenstrom T.A., 2004 WHO, 2007 Cantor, 1997 WHO, 2007

Methods, models and assumptions 53 5.3.3 SENSITIVE SUB-POPULATIONS Traditional risk assessment and toxicological studies have focused on the impact of exposure to healthy adults (WHO, 2007a). Research has shown that some groups of the community are at greater risk of health effects due to exposure to biological and chemical pollutants. Three important sensitive sub-populations are children, the elderly and people with compromised immune systems, particularly people living with HIV/AIDS. Children living in the Day/Nhue River Basin may have a higher likelihood of exposure to waterborne pollutants due to their behaviour and activities. Children and toddlers have a higher likelihood of placing contaminated sediment from their hands into their mouths (WHO, 2007a). Adolescents were observed during the field trips to engage in swimming and games within and around the river. The higher likelihood of exposure to waterborne pollutants is matched with an increased risk of health effects during different life stages owing to their dynamic growth and developmental processes as well as physiological, metabolic, and behavioural differences (WHO, 2007a). The elderly and people with comprised immune systems have also been recognised as sensitive sub-populations. People living with HIV/AIDS have been identified as having higher risk of death from exposure to biological contaminants (such as Cryptosporidium) in drinking water (Nel, Markotter and Weyer 2004). The process of aging is associated with a reduction in the ability of the immune system to fight new infections. The elderly are often susceptible to illnesses (such as influenza) that do not present a significant risk to the health of the general population. 5.4 ENVIRONMENTAL HEALTH RISK ASSESSMENT An environmental health risk assessment (EHRA) is an internationally standardized approach used to determine the consequences and likelihood of exposure to water, air and soil-borne pollutants (EnHealth, 2002). Conducting a preliminary assessment of the impacts of pollutants upon human health must include determining those pollutants at high enough concentration to cause concern, the consequences of exposure to these pollutants and the likelihood that the human population will be exposed to the pollutants (US EPA, 1992). The methodology consists of four stages: 1) Issue identification, 2) Hazard assessment, 3) Exposure assessment and 4) Risk characterisation (Table 5.4.1 and Figure 5.41). Table 5.4.1 - Environmental Health Risk Assessment paradigm for human health effects (adapted from Ashbolt, 2004) Step Aim Issue identification Hazard assessment Exposure assessment Risk characterization To outline the scope and objectives of the EHRA To describe acute and chronic human health effects associated with any particular hazard, including toxicity, carcinogenicity, mutagenicity, developmental toxicity, reproductive toxicity, and neurotoxicity To determine the size and nature of the population exposed and the route, amount, and duration of the exposure To integrate the information from exposure, doseresponse, and health steps in order to estimate the magnitude of the public health problem and to evaluate variability and uncertainty

Methods, models and assumptions 54 Figure 5.4.1 - Schematic diagram of environmental health risk assessment (En Health, 2002). Issue identification Hazard Assessment Exposure Assessment Hazard Identification Dose-response Assessment Review and reality check Risk characterisation Review and reality check Risk management 5.5 ISSUE IDENTIFICATION Issue identification outlines the scope and objectives of the EHRA (EnHealth, 2002). The scope for this assessment was to assess the human health effects of waterborne pollutants in the Day/Nhue River Basin. The objectives are: Prioritise biological and chemical pollutants of concern in the Day/Nhue River Basin Provide policymakers and regulators with in-depth information on the potential health effects of pollutants to communities within the Day/Nhue River Basin Establish a basis for testing causal relationships between pollutants and health effects in the Day/Nhue River Basin 5.5.1 RISK MATRIX In this study, a qualitative EHRA was performed due to the limitations of available data and the time constraint on the assessment. To identify a manageable number of pollutants a qualitative risk matrix was developed under the guidance of Australian Standard: 4360 Risk management. A qualitative analysis of risk uses a scale of words or descriptions to examine the consequences of each event arising and its likelihood (Australian Standard: 4360). The standardised methodology of the AS/NZ: 4360 was used to assess the risk of waterborne pollutants identified by the Pollution Projection System. The consequences and likelihood of exposure to waterborne pollutants was combined in the risk matrix. The risk matrix identified extreme, high, moderate and low priority pollutants.

Methods, models and assumptions 55 5.5.2 QUALITATIVE MEASURES OF LIKELIHOOD OF EXPOSURE TO WATERBORNE POLLUTANTS Justification of descriptions for likelihood Hazard ranking: Calculates the pollution load and multiplies this volume amount by 1/LC50. Since LC50 is small for more lethal chemicals, multiplying by 1/LC50 will place greater weight on more toxic substances and on the calculated load. The ranking of 1-15 and 16-30 were arbitrary and selected due to the time available to conduct the assessment. Table 5.5.1: Ranking of likelihood of exposure to pollutants Level Descriptor Description A Almost certain Top 1-15 by hazard ranking for Day/Nhue River Basin. Highly persistent in water. Resistant to flocculation and chlorination in water treatment plants. Highly soluble. Evidence of bioaccumulation in vegetables or fish. Evidence of exposure in Vietnam in epidemiological studies. B Likely Top 16-30 by hazard ranking for Day/Nhue River Basin. Highly persistent in water. Resistant to flocculation and chlorination in water treatment plants. Evidence of bioaccumulation in vegetables or fish. Highly soluble. Documented evidence of exposure in Vietnam in epidemiological studies. Biodegradable or highly adsorptive to sediment. C Possible Top 1-15 by hazard ranking for Day/Nhue River Basin. Moderate to low persistence in water. No evidence of resistance to flocculation and chlorination in water treatment plants. Medium to low solubility. No evidence of bioaccumulation in vegetables or fish. Biodegradable or highly adsorptive to sediment. D Unlikely Top 16-30 ranking by hazard ranking. Moderately persistent in water. No evidence of resistance to flocculation and chlorination in water treatment plants. Medium to low solubility. Evidence of bioaccumulation in vegetables or fish. Documented evidence of exposure in Vietnam in epidemiological studies E Rare Not listed in top 30 by hazard ranking for Day/Nhue River Basin. Unable to pass through flocculation and chlorination in water treatment plants. Low solubility. No evidence of bioaccumulation in vegetables or fish. No evidence of exposure in Vietnam in epidemiological studies. Biodegradable or highly adsorptive to sediment. Persistence in water: Pollutants that are persistent have a higher likelihood of remaining in the water body and therefore being present when humans are exposed to the water eg. heavy metals, POPs, atrazine. Chemical pollutants: High > weeks, Medium days to weeks, Low < 1 week Biological pollutants: High: resistant oocysts, Medium: Amplification Low: Degrade under normal environmental conditions 22 Solubility: Chemicals with high solubility have a higher likelihood of remaining in the water column and not settling into the sediment. Evidence of bioaccumulation, bioconcentration or biomagnification in vegetables or fish: Water from the Day and Nhue rivers are used to irrigate vegetable crops and to supply aquaculture facilities. Pollutants that are bioaccumulated, bioconcentrated or biomagnified by vegetables and/or fish have a higher likelihood of being ingested by the human population. Documented evidence of exposure in Vietnam in epidemiological studies: Biomarker or medical evidence of exposure in Vietnam provides supporting evidence of the likelihood of exposure to a chemical or biological pollutant. 22 Committee on Drinking Water Contaminants, Water Science and Technology Board, Board on Environmental Studies and Toxicology, National Research Council Classifying Drinking Water Contaminants for Regulatory Consideration (2001) The National Academies Press.

Methods, models and assumptions 56 5.5.3 QUALITATIVE MEASURES OF HEALTH CONSEQUENCES OF WATERBORNE POLLUTANTS Table 5.5.2: Qualitative measures of human health consequences of waterborne pollutant Level Descriptor Description 1 Catastrophic High toxicity at predicted ambient water concentrations. Strong evidence of causing acute or chronic disease states and epidemiological and laboratory evidence of causing DNA damage, reproductive damage or developmental toxicant. Epidemiological evidence of lethal health impacts in Vietnam. IARC I classification. 2 Major High - moderate toxicity at predicted ambient water concentrations. Good evidence of causing acute or chronic disease states and epidemiological and laboratory evidence of causing DNA damage, reproductive damage or developmental toxicant. Epidemiological evidence of non-lethal health impacts in Vietnam. IARC IIa or IIb classification. 3 Moderate Moderate toxicity at predicted ambient water concentrations. Some evidence of causing acute or chronic disease states and epidemiological and laboratory evidence of causing DNA damage, reproductive damage or developmental toxicant. IARC III classification. 4 Minor Low toxicity at predicted ambient concentrations. Limited evidence of causing acute or chronic disease states and epidemiological and laboratory evidence of causing DNA damage, reproductive damage or developmental toxicant. Not listed on IARC monographs. 5 Insignificant Low toxicity at predicted ambient concentrations. No evidence of causing acute or chronic disease states and epidemiological and laboratory evidence of causing DNA damage, reproductive damage or developmental toxicant. Not listed on IARC monographs. Justification of descriptions for consequences Toxic at predicted ambient water concentrations: Short-term exposure to toxic pollutants can result in a range of adverse health impacts. Common acutely toxic pollutants in drinking water supplies are pathogenic micro-organisms and nitrate. Epidemiological and laboratory evidence of causing DNA damage, reproductive damage or developmental toxicant: International epidemiological and toxicological studies are a primary source of information to determine the hazard of pollutants. Epidemiological evidence of chronic adverse health impacts in Vietnam: Epidemiological studies have investigated the health impacts of a range of waterborne pollutants. Evidence from these studies will provide information on the ranking of the consequence of exposure. IARC classification: International Agency for Research on Cancer (IARC) monographs compile all available toxicity and epidemiological data to classify agents as known- (Group1), probable (Group 2A), possible- (Group 2B), not classifiable- (Group 3) or non-carcinogens (Group 4). 5.5.4 RISK MATRIX IDENTIFYING SIGNIFICANT POLLUTANTS The risk matrix combines the likelihood and consequence categorisation to prioritise pollutants into extreme, high, moderate and low risk to human health. Consequence Likelihood Catastrophic Major Moderate Minor Insignificant Almost certain E E E H H Likely E E H H M Possible E E H M L Unlikely E H M L L Rare H H M L L

Methods, models and assumptions 57 E = extreme risk to human health H = high risk to human health M = moderate risk to human health L = low risk to human health This methodology and framework of assessment of the links between public health and pollution from various sources in the Day/Nhue River system was applied in the study with results described in Section 8 of this report.

Methods, models and assumptions 58 ANNEX 5.1: METHODOLOGY FOR IPPS Further details on the Industrial Pollution Projection System (IPPS) The EPA maintains a number of databases on pollution emissions. These include the Toxics Release Inventory (TRI), the Aerometric Information Retrieval System (AIRS), the National Pollutant Discharge Elimination System (NPDES), and the Human Health and Ecotoxicity Database (HHED). All of these data sets have been used in the calculation of pollution intensities in IPPS. 23 When the datasets from the manufacturing census are combined with the various EPA databases, it is possible to calculate pollution intensity factors for approximately 20,000 plants. Figure A5.1: Industrial Pollution Projection System - Pollution Intensity US Manufacturing Census (200,000 plants) US EPA EMISSIONS (20,000 plants) Economic Data Toxics Air W ater IPPS Data A common issue with the calculation of industrial pollution intensity concerns the choice of the variable to capture the extent or size of the manufacturing activity. While physical volume of output would be the ideal unit of measurement, facilities across industrial sectors and sub-sectors often use different units to report the volume of their production. 24 This does not allow for comparison across industrial sectors. Perhaps more importantly, international experience clearly indicates that pollution released per unit of output vary greatly across technology vintages even within an industrial sector. Generally, recent vintages of technology produce less pollution per unit of output than older technologies. Hence, using pollution per unit of output as obtained from developed countries to estimate pollution releases in developing countries would lead to significant underestimation of pollution releases. However, the value of output and plant-level employment do offer common units of measurement. Perhaps more important, pollution per unit of labor has proven to be relatively similar across vintages of technology within specific industrial sectors and sub-sectors. The reason for this is explained by the fact that as an industrial sector moves up the technology ladder, it tends to be less and less labor intensive and more and more capital intensive. Hence, while pollution release tends to fall as technology improves, the number of workers also tends to fall (to the benefit of capital). Pollution per unit of employment proves to be relatively constant across different 23 The TRI contains information on annual emissions for more than 300 toxic chemicals to the environment. Manufacturing establishments that (1) employ 10 full-time employees or more and (2) produce, import or process 25,000 pounds or more of any listed chemical must report the nature and quantity of the chemical produced, imported, or processed. In 1987, approximately 20,000 enterprises reported their releases of such chemicals. The AIRS is the US national database for ambient air quality, air emissions and compliance data with the U.S. Clean Air Act. The NPDES contains the self-reported data of plants facing standards for water emissions. Finally, the HHED contains various indices of toxicological potency. 24 For example, some facilities may report their production using a weight measure (kilograms or tons) while others may use a volumetric measure (for example, cubic meters), or an area measure (for example, square meter).

Methods, models and assumptions 59 vintages of technology within industrial sectors and sub-sectors and is similar in developed and developing countries (Dasgupta et al. 2002). Hence, pollution coefficients obtained in developed countries where pollution intensity is measured as pollution per unit of labor are used to estimate pollution load in developing countries by assuming that similar pollution intensities prevail. This is the core assumption supporting the use of the IPPS model in Viet Nam. Further details on the methods for assessing industrial pollution hazard The purpose of this section is to briefly describe the methods used to index hazard utilizing the extended list of over 240 substances listed in Table A5.4 in the Annex at the end of this section. Acute exposure hazard To gain further insights into the relative hazard risk of chemical loads not only to humans, but to the environment in general, we separated the estimated un-weighted load above, Q ix, into three categories of acute exposure hazard - more specifically, we constructed three risk thresholds using LC 50 and LD 50 values (lethal concentration and lethal dose). These measures form the basis of epidemiological impact studies in evaluating the potential risk to human life. An LC 50 value is the concentration of a material in air that will kill 50% of the test subjects (typically mice or rats) when administered as a single exposure (typically 1 or 4 hours). Also called the median lethal concentration and lethal concentration 50, LC 50 provides a measure of the relative acute toxicity of an inhalable material. LC values usually refer to the concentration of a chemical in air but in environmental studies it can also mean the concentration of a chemical in water. 25 The LD 50 is similarly interpreted, but is administered orally. To the extent that a substances fate is landbased, we adopt the LD 50 value for measuring the relative risk of land-based pollution loads. Each chemical in the database was classified as a (1) high hazard, (2) moderate hazard, or (3) low hazard according to its LC 50 and LD 50 value. For example, if a chemical s LC 50 value fell into the high hazard range for air, then the load of that specific chemical was attributed to that hazard level (Table A5.1). Each chemical and metal load was similarly categorized and then aggregated up to the plant level and summarized at the provincial, commune and sector level (see Box A5.1 for details). Table A5.1: Categories of chemical hazard Category Air (mg/liter) Land (mg/kg) Water (mg/liter) High hazard if LC 50 0.1 LD 50 10 LC 50 0.1 Moderate hazard if 0.1 < LC 50 0.5 10 < LD 50 25 0.1 < LC 50 0.5 Low hazard if LC 50 > 0.5 LD 50 > 25 LC 50 > 0.5 Source: ILO (1990). Box A5.1: Calculation of the percentage of load per category of chemical hazard Consider the 246 substances included in the analysis. Let L(1) equal the pollution load of substance #1, L(2) the pollution load of substance #2, and similarly up to L(246) for the pollution load of substance #246. These loads are computed for every province, and for every sector. Now let L(T) equal the sum of the load of all substances, i.e. L(T) = L(1) + (2) + + L(246) for any given province, or from any given sector. Further details on the GSO industry census database Suppose that substances #1 to #4 fall into the category of High hazard (those substances with LC 50 The equal Census or less was than initiated 0.1 mg/liter in 1995 for and air). since Then the 2001 share has (percentage) been conducted of substances on annual falling basis into the with the category following of High variations hazard in is coverage: simply computed as: [L(1) + L(2) + L(3) + L(4)] / L(T). 25 The largest single collection of LC 50 values is in the database Registry of Toxic Effects of Chemical Substances (RTECS) available by subscription on the internet (http://www.ccohs.ca/products/rtecs/).

Methods, models and assumptions 60 The first economic census in 1995 covered all business enterprises and administrative offices. The Industrial census in 1998 covered all industrial enterprises, including enterprises established by law and small industrial households. From 2001 forward, the Census covers 100% of all enterprises employing 10 or more employees. It also covers 20% of non-state owned enterprises employing less than 10 employees. Among these 20%, target enterprises include those classified by law as stateowned enterprises, cooperatives (excluding ones in agriculture, forestry and fishing sectors), private enterprises, joint-venture companies, limited companies, shareholding companies, foreign direct investment enterprises (FDI), and partnership companies. Information is collected by the National Statistics Office (GSO), the Provincial Statistical Offices (63 PSOs) as well as the District Statistics Bureaus (630 DSOs). Monthly and annual data are combined from the district to province levels, and are then sent to the GSO. The sampling methods for the Census are based on similar methods used for the monthly collection of industrial indicators depending on the population and area sampled (see Box A5.2 for details). Box A5.2: GSO survey sampling methods The two basic designs are a one-stage sample design, and two-stage sample design. One-stage sample design: Each district is a sample frame: A one-stage sample design is used where enterprises are selected from a list of enterprises from the previous years Census within the district. Enterprises are sorted into VSIC-2 digit sectors and ordered in descending order according to turnover. Two strata are formed, the stratum boundary being the average turnover. The number of sampled enterprises for each 2-digit sector is divided equally for each of the two strata. Two-stage sample design: By this method, a two-stage sample design is constructed where in the first stage three communes are selected based on the PPS (Proportion Probability to the Size), where the size is the number of enterprises in the commune. In the second stage enterprises are selected using the same sampling technique as in the one-stage design. The two-stage design is reserved for use in sampling the 20% of non-state owned enterprises that employ less than 10 employees. This two-stage sample design is taking place only in the following provinces and cities: Ha Noi, Hai Phong, Ha Tay, Quang Ninh, Thanh Hoa, Nghe An, Thu Thien Hue, Da Nang, Binh Dinh, Khanh Hoa, Ho Chi Minh city, Binh Duong, Dong Nai, Ba Ria-Vung Tau, Long An, Dong Thap, An Giang, Tien Giang, Ben Tre, Kien Giang, Can Tho and Ca Mau. For state, foreign and privately owned enterprises, the one-stage design is used for all enterprises. Further details on the method of validation for estimation Initially BOD and SS pollution loads were calculated for each VSIC-4 coded enterprise using CTC estimated coefficients as well as with IPPS coefficients. These results were then summed to the VSIC-4 sector and province level, and ranked from lowest to highest in terms of BOD and SS load. Correlation coefficients were calculated between the CTC and IPPS results. The purpose was to see if high values of pollution load from the CTC data corresponded well with high values of pollution load estimated with IPPS coefficients. Table A5.2 presents the results at the province level, where there is a high positive correlation for both BOD and SS (0.69 and 0.71) implying a close match in terms of priority ranking. Table A5.3

Methods, models and assumptions 61 contains the sector-level results for which both the CTC and IPPS had coefficients (54/132 total sectors). Unfortunately, the correlation coefficients are not as high in this case (BOD: 0.33, SS: 0.24). To see whether the correlation coefficients were systematically different (independent) or simply due to randomness, a statistical test was performed and could not be rejected at the 1% and 8% level of significance for BOD and SS, respectively. The test implies that although the relationship between CTC and IPPS coefficients at the sector level are not that strong, the positive correlation is not due to random noise or chance. Thus one may be tempted to conclude that that IPPS does not reflect priority sectors at highly disaggregated levels of sector accounting. However, owing to the limited sector coverage of the CTC coefficients (54/132) and the different methods which they may have been derived, this result is perhaps not that surprising. Table A5.2 Spearman rank correlation between estimated BOD and SS load at the provincial level IPPS BOD CTC BOD IPPS SS CTC SS IPPS BOD 1 CTC BOD 0.69 1 IPPS SS 0.77 0.66 1 CTC SS 0.71 0.96 0.71 1 Table A5.3 Spearman rank correlation between estimated BOD and SS load at the sector level IPPS BOD CTC BOD IPPS SS CTC SS IPPS BOD 1 CTC BOD 0.33 1 IPPS SS 0.74 0.20 1 CTC SS 0.37 0.95 0.24 1 Note: Rank correlations were calculated for only those sectors in which CTC coefficients were available (n=54). Table A5.4 List of chemical substances and hazard class The below list of chemical substances were extracted from the US EPA TRI list of toxic chemicals as of 1987. These substances were used to calculate the chemical intensities in the Industrial Pollution Projection System (IPPS). Hazards are defined according to their current LD 50 and LC 50 values. CAS Substance LD50 LC50 LD50 hazard 56382 PARATHION 2.00 0.08 High High 74908 HYDROGEN CYANIDE 4.00 0.18 High High 7440280 THALLIUM 5.71 High 534521 4,6-DINITRO-O-CRESOL 7.00 0.00 High High 151564 ETHYLENEIMINE 15.00 0.10 High High 62737 DICHLORVOS 17.00 0.02 High High 75558 PROPYLENEIMINE (2-METHYLAZIRIDINE) 19.00 1.17 High Low 463581 CARBONYL SULFIDE 23.00 2.63 High Low 107028 ACROLEIN 26.00 0.02 High High LC50 hazard 87865 PENTACHLOROPHENOL 27.00 0.36 High Moderate 7440473 CHROMIUM 27.50 High 51285 2,4-DINITROPHENOL 30.00 High 79107 ACRYLIC ACID 33.50 11.78 High Low 132649 DIBENZOFURAN 36.00 High Low 76448 HEPTACHLOR 40.00 0.15 High High 7439976 MERCURY 43.00 0.04 High High

Methods, models and assumptions 62 CAS Substance LD50 LC50 LD50 hazard 120832 2,4-DICHLOROPHENOL 47.00 High 7440417 BERYLLIUM 51.00 Moderate 624839 METHYL ISOCYANATE 51.50 0.01 Moderate High 79118 CHLOROACETIC ACID 55.00 0.18 Moderate High 7440622 VANADIUM (FUME OR DUST) 59.00 Moderate Low LC50 hazard 302012 HYDRAZINE 60.00 0.75 Moderate Moderate 75218 ETHYLENE OXIDE 72.00 1.44 Moderate Low 111444 BIS(2-CHLOROETHYL) ETHER (DICHLOROETHYL ETHER; 2,2'-DICHLORODIETHYL ETHER) 75.00 0.33 Moderate Moderate 58899 LINDANE (HEXACHLOROCYCLOHEXANE-gamma) 76.00 0.12 Moderate High 107131 ACRYLONITRILE (VINYL CYANIDE) 78.00 0.72 Moderate Moderate 106503 PHENYLENEDIAMINE (P-ISOMER) 80.00 0.92 Moderate Moderate 569642 C.I. BASIC GREEN 4 80.00 Moderate 87683 HEXACHLORO-1,3-BUTADIENE 82.00 0.37 Moderate Moderate 106898 EPICHLOROHYDRIN (1-CHLORO-2,3- EPOXYPROPANE) 90.00 0.95 Moderate Moderate 50000 FORMALDEHYDE 100.00 0.20 Moderate High 91087 TOLUENE-2,6-DIISOCYANATE 100.00 0.00 Moderate High 90948 MICHLER'S KETONE 100.00 Moderate 55630 NITROGLYCERIN (NG) 105.00 Moderate 74953 METHYLENE BROMIDE 108.00 40.00 Moderate Low 106934 1,2-DIBROMOETHANE (EDB) (ETHYLENE DIBROMIDE) 108.00 14.30 Moderate Low 95487 CRESOL (O-ISOMER) 121.00 1.22 Moderate Low 57147 1,1-DIMETHYL HYDRAZINE 122.00 0.62 Moderate Moderate 79061 ACRYLAMIDE 124.00 Moderate 62566 THIOUREA 125.00 Moderate 106514 QUINONE (P-BENZOQUINONE) 130.00 Moderate 7664417 AMMONIA 132.00 1.39 Moderate Low 156627 CALCIUM CYANAMIDE 158.00 0.15 Moderate High 7439921 LEAD 160.00 0.00 Moderate High 1344281 ALUMINUM OXIDE (FIBROUS FORM) 164.00 0.62 Moderate Moderate 7429905 ALUMINUM (FUME OR DUST) 164.00 0.62 Moderate Moderate 606202 2,6-DINITROTOLUENE 177.00 0.24 Moderate High 156105 NITROSODIPHENYLAMINE (P-ISOMER) 178.00 Moderate 7440393 BARIUM 198.00 Moderate 135206 CUPFERRON 199.00 Moderate 79345 1,1,2,2-TETRACHLOROETHANE 200.00 4.50 Moderate Low 57749 CHLORDANE 200.00 0.10 Moderate High 88891 PICRIC ACID (2,4,6-TRINITROPHENOL) 200.00 Moderate 60093 4-AMINOAZOBENZENE 200.00 Moderate 100027 4-NITROPHENOL 202.00 Moderate 77781 DIMETHYL SULFATE 205.00 0.05 Moderate High 106445 CRESOL (P-ISOMER) 207.00 0.71 Moderate Moderate 79210 PERACETIC ACID 210.00 0.45 Moderate Moderate 542881 BIS(CHLOROMETHYL) ETHER (DICHLOROMETHYL ETHER) (BCME) 210.00 0.03 Moderate High 74839 METHYL BROMIDE (BROMOMETHANE) 214.00 1.17 Moderate Low 63252 CARBARYL (SEVIN) 230.00 0.39 Moderate Moderate 108394 CRESOL (M-ISOMER) 242.00 0.71 Moderate Moderate 62533 ANILINE 250.00 0.95 Moderate Moderate 120809 CATECHOL (PYROCATECHOL) 260.00 Moderate 121142 2,4-DINITROTOLUENE 268.00 Moderate 25376458 DIAMINOTOLUENE (MIXED ISOMERS) 270.00 Moderate

Methods, models and assumptions 63 CAS Substance LD50 LC50 LD50 hazard LC50 hazard 541413 ETHYL CHLOROFORMATE 270.00 0.84 Moderate Moderate 96333 METHYL ACRYLATE 277.00 4.75 Moderate Low 10049044 CHLORINE DIOXIDE 292.00 0.72 Moderate Moderate 123319 HYDROQUINONE (DIHYDROXYBENZENE) 302.00 Moderate 77474 HEXACHLOROCYCLOPENTADIENE 315.00 0.02 Moderate High 108952 PHENOL 317.00 0.32 Moderate Moderate 95807 2,4-DIAMINOTOLUENE 325.00 Moderate 91225 QUINOLINE 331.00 Moderate 88755 2-NITROPHENOL 334.00 Moderate 98953 NITROBENZENE 349.00 2.80 Moderate Low 94757 2,4-D (DICHLOROPHENOXYACETIC ACID) 375.00 Moderate 75569 PROPYLENE OXIDE (1,2-EPOXYPROPANE) 380.00 9.50 Moderate Low 80159 CUMENE HYDROPEROXIDE 382.00 1.37 Moderate Low 108316 MALEIC ANHYDRIDE 400.00 Moderate 7697372 NITRIC ACID 430.00 0.17 Moderate High 75274 DICHLOROBROMOMETHANE (BROMOCHLORO.) 430.00 Moderate CHLOROPRENE (BETA-CHLOROPRENE; 126998 NEOPRENE) 450.00 11.80 Moderate Low 52686 TRICHLORFON 450.00 1.30 Moderate Low 107051 ALLYL CHLORIDE 460.00 11.00 Moderate Low 615054 2,4-DIAMINOSANISOLE 460.00 Moderate 542756 1,3-DICHLOROPROPYLENE 470.00 4.54 Moderate Low 621647 N-NITROSODI-N-PROPYLAMINE (NDPA) 480.00 Moderate 91203 NAPHTHALENE 490.00 0.34 Moderate Moderate 1310732 SODIUM HYDROXIDE (SOLUTION) 500.00 Moderate 75014 VINYL CHLORIDE 500.00 459.84 Moderate Low 1163195 DECABROMODIPHENYL OXIDE 500.00 Moderate 95501 DICHLOROBENZENE 1,2-(O-ISOMER) 500.00 4.93 Moderate Low 106467 DICHLOROBENZENE 1,4-(P-ISOMER) 500.00 72.11 Moderate Low 25321226 DICHLOROBENZENE (MIXED ISOMERS) 500.00 72.11 Moderate Low 593602 VINYL BROMIDE (BROMOETHENE) 500.00 218.58 Moderate Low 106887 1,2-BUTYLENE OXIDE (1,2-EPOXYBUTANE) 500.00 1.17 Moderate Low 92671 4-AMINODIPHENYL (P-isomer) 500.00 Moderate 101779 4,4'-METHYLENE DIANILINE (4,4'- DIAMINODIPHENYLMETHANE) 517.00 Low 7723140 PHOSPHORUS (YELLOW OR WHITE) 550.00 0.58 Low Moderate 115322 DICOFOL 575.00 5.00 Low Low 10034932 HYDRAZINE SULFATE 601.00 0.01 Low High 111422 DIETHANOLAMINE 620.00 Low 108883 TOLUENE (TOLUOL) 636.00 49.00 Low Low 90437 2-PHENYLPHENOL (SODIUM SALT) 656.00 Low 75070 ACETALDEHYDE 661.00 23.95 Low Low 107062 1,2-DICHLOROETHANE (ETHYLENE DICHLORIDE) 670.00 4.05 Low Low 95534 TOLUIDINE (O-ISOMER) 670.00 3.78 Low Low 67663 CHLOROFORM 695.00 47.70 Low Low 79469 2-NITROPROPANE 720.00 1.46 Low Low 4,4'-DIAMINODIPHENYL ETHER (4,4'- 101804 OXYDIANILINE) 725.00 Low 134327 NAPHTHYLAMINE (ALPHA or 2-NAPHTHYLAMINE) 727.00 Low 120821 1,2,4-TRICHLOROBENZENE 756.00 Low 7440382 ARSENIC 763.00 Low 540590 1,2-DICHLOROETHYLENE 770.00 0.12 Low High 71363 N-BUTANOL (N-BUTYL ALCOHOL) 790.00 24.24 Low Low 140885 ETHYL ACRYLATE (ACRYLIC ACID & ETHYL 800.00 5.79 Low Low

Methods, models and assumptions 64 CAS Substance LD50 LC50 ESTER) LD50 hazard 79005 1,1,2-TRICHLOROETHANE 836.00 2.73 Low Low 64675 DIETHYL SULFATE 880.00 1.58 Low Low 110861 PYRIDINE 891.00 28.50 Low Low 7647010 HYDROCHLORIC ACID (HYDROGEN CHLORIDE) 900.00 4.66 Low Low 141322 BUTYL ACRYLATE (ACRYLIC ACID & N-BUYTL ESTER) 900.00 14.30 Low Low 71432 BENZENE 930.00 31.93 Low Low 121697 DIMETHYLANILINE (N,N-DIMETHYLANILINE) 951.00 0.25 Low High 78842 ISOBUTYRALDEHYDE 960.00 23.58 Low Low 842079 C.I. SOLVENT YELLOW 14 1000.00 Low 541731 1,3-DICHLOROBENZENE (M-ISOMER) 1062.00 Low 108907 CHLOROBENZENE (CHLORINATED BENZENE) 1100.00 13.64 Low Low 139139 NITRILOTRIACETIC ACID 1100.00 Low 82688 QUINTOZENE (PENTACHLORONITROBENZENE) 1100.00 1.40 Low Low 1314201 THORIUM DIOXIDE 1140.00 Low 101144 4,4'-METHYLENE BIS(2-CHLOROANILINE) (MBOCA) 1140.00 Low 75150 CARBON DISULFIDE 1200.00 25.00 Low Low LC50 hazard 100447 BENZYL CHLORIDE 1231.00 0.78 Low Moderate 98828 CUMENE 1400.00 39.30 Low Low 123386 PROPIONALDEHYDE 1410.00 18.99 Low Low 120718 CRESIDINE (P-ISOMER) 1450.00 Low 2164172 FLUOMETURON 1450.00 Low 1319773 CRESOL (ALL ISOMERS) 1454.00 Low 97563 AMINOAZOTOLUENE, O-ISOMER (C.I. SOLVENT YELLOW 3) 1500.00 Low 7664382 PHOSPHORIC ACID 1530.00 0.85 Low Moderate 85449 PHTHALIC ANHYDRIDE 1530.00 0.21 Low High 75092 DICHLOROMETHANE (METHYLENE CHLORIDE) 1600.00 52.00 Low Low 74873 METHYL CHLORIDE 1800.00 5.30 Low Low 51796 URETHANE (CARBAMIC ACID, ETHYL ESTER) 1809.00 Low 86306 N-NITROSODIPHENYLAMINE 1825.00 Low 96457 ETHYLENE THIOUREA (2-IMIDAZOLIDINETHIONE) 1832.00 Low 12122677 ZINEB 1850.00 0.80 Low Moderate 72435 METHOXYCHLOR 1855.00 Low 1336363 POLYCHLORINATED BIPHENYLS (CHLORODIPHENYLS, 54% CHLORINE) 1900.00 Low 98884 BENZOYL CHLORIDE 1900.00 1.87 Low Low 1582098 TRIFLURALIN (2,6-DINITRO-N,N-DIPROPYL-4- (TRIFLUOROMETHYL) BENZENAMINE) 1930.00 2.80 Low Low 78875 1,2-DICHLOROPROPANE (PROPYLENE DICHLORIDE) 1947.00 14.00 Low Low 90040 ANISIDINE (O-ISOMER) 2000.00 Low 96093 STYRENE OXIDE 2000.00 2.46 Low Low 104949 ANISIDINE (P-ISOMER) 2000.00 Low 108101 METHYL ISOBUTYL KETONE (HEXONE) 2080.00 100.00 Low Low 110805 2-ETHOXYETHANOL (ETHYLENE GLYCOL MONOETHYL ETHER; CELLOSOLVE) 2125.00 7.37 Low Low 7664939 SULFURIC ACID 2140.00 0.51 Low Moderate 92524 BIPHENYL (DIPHENYL) 2140.00 0.20 Low High 78922 BUTYL ALCOHOL (SEC-BUTANOL) 2193.00 48.48 Low Low 6484522 AMMONIUM NITRATE (SOLUTION) 2217.00 Low 85687 BUTYL BENZYL PHTHALATE 2330.00 Low 7440439 CADMIUM 2330.00 0.03 Low High CARBON TETRACHLORIDE 56235 (TETRACHLOROMETHANE) 2350.00 50.30 Low Low

Methods, models and assumptions 65 LD50 hazard CAS Substance LD50 LC50 2-METHOXYETHANOL (ETHYLENE GLYCOL 109864 MONOMETHYL ETHER; METHYL CELLOSOLVE) 2370.00 4.67 Low Low 75058 ACETONITRILE 2460.00 12.67 Low Low 123728 BUTYRALDEHYDE 2490.00 23.58 Low Low 127184 TETRACHLOROETHYLENE (PERCHLOROETHYLENE) 2629.00 34.20 Low Low 100425 STYRENE (PHENYLETHYLENE; VINYL BENZENE) 2650.00 12.00 Low Low 1313275 MOLYBDENUM TRIOXIDE 2689.00 5.84 Low Low 78933 METHYL ETHYL KETONE (MEK; 2-BUTANONE) 2737.00 23.50 Low Low 75650 BUTYL ALCOHOL (TERT-BUTANOL) 2743.00 30.30 Low Low 7783202 AMMONIUM SULFATE (SOLUTION) 2840.00 Low 108054 VINYL ACETATE 2900.00 11.40 Low Low 12427382 MANEB 3000.00 Low 4,4'-METHYLENE BIS(N,N-DIMETHYL) 101611 BENZELAMINE 3160.00 Low 108781 MELAMINE 3161.00 3.25 Low Low 105679 2,4-DIMETHYLPHENOL 3200.00 0.03 Low High LC50 hazard 98873 BENZAL CHLORIDE 3249.00 0.40 Low Moderate 80057 4,4'-ISOPROPYLIDENEDIPHENOL (BISPHENOL A) 3250.00 1.70 Low Low 100414 ETHYL BENZENE 3500.00 17.36 Low Low 133904 CHLORAMBEN (3-AMINO-2,5-DICHLOROBENZOIC ACID) 3500.00 Low 91941 3,3'-DICHLOROBENZIDINE (AZO DYE) 3820.00 Low 1634044 METHYL-TERT-BUTYL ETHER 4000.00 84.95 Low Low 961115 TETRACHLORVINPHOS (STIROFOS) 4000.00 1.50 Low Low 39156417 2,4-DIAMINO ANISOLE SULFATE 4000.00 Low 123911 1,4-DIOXANE (1,4-DIETHYLENE DIOXIDE) 4200.00 165.66 Low Low 1330207 XYLENE (MIXED ISOMERS) 4300.00 21.70 Low Low 67721 HEXACHLOROETHANE 4460.00 57.09 Low Low 3844459 C.I. ACID BLUE 9, DISODIUM SALT 4600.00 Low 2650182 C.I. ACID BLUE 9, DIAMMONIUM SALT 4600.00 Low 107211 ETHYLENE GLYCOL 4700.00 0.20 Low High 79016 TRICHLOROETHYLENE 4920.00 25.78 Low Low 7440020 NICKEL 5000.00 Low 95636 1,2,4-TRIMETHYLBENZENE (PSEUDOCUMENE) 5000.00 18.00 Low Low 108383 XYLENE (M-ISOMER) 5000.00 34.72 Low Low 106423 XYLENE (P-ISOMER) 5000.00 19.75 Low Low 95476 XYLENE (O-ISOMER) 5000.00 26.58 Low Low 67630 ISOPROPYL ALCOHOL (MANUFACTURING, STRONG-ACID PROCESS ONLY, NO PROCESS) 5045.00 31.44 Low Low 106990 1,3-BUTADIENE 5480.00 285.00 Low Low 67561 METHANOL (METHYL ALCOHOL) 5628.00 83.82 Low Low 67641 ACETONE 5800.00 50.10 Low Low 584849 TOLUENE-2,4-DIISOCYANATE (TDI) 5800.00 0.10 Low High 7757826 SODIUM SULFATE (SOLUTION) 5989.00 Low 98077 BENZOIC TRICHLORIDE (BENZYL TRICHLORIDE; TRICHLOROMETHYLBENZENE) 6000.00 0.15 Low High 7440484 COBALT 6171.00 Low 100210 TEREPHTHALIC ACID 6400.00 Low 7782492 SELENIUM 6700.00 0.03 Low High 131113 DIMETHYL PHTHALATE 6800.00 9.30 Low Low 7440360 ANTIMONY 7000.00 Low 60355 ACETAMIDE 7000.00 Low 84742 DIBUTYL PHTHALATE 7499.00 4.25 Low Low 94360 BENZOYL PEROXIDE 7710.00 Low

Methods, models and assumptions 66 LD50 hazard CAS Substance LD50 LC50 METHYL METHACRYLATE (METHACRYLIC ACID 80626 METHYL ESTER) 7872.00 78.00 Low Low LC50 hazard 84662 DIETHYL PHTHALATE 8600.00 1.00 Low Moderate 7439965 MANGANESE 9000.00 0.00 Low High 133062 CAPTAN 9000.00 5.70 Low Low 103231 BIS(2-ETHYLHEXYL) ADIPATE 9100.00 Low 101688 METHYLENE BISPHENYL ISOCYANATE (DIPHENYLMETHANE-4,4'-DIISOCYANATE; MDI) 9200.00 0.18 Low High 71556 1,1,1-TRICHLOROETHANE (METHYL CHLOROFORM) 9600.00 98.15 Low Low 7440508 COPPER (FUME OR DUST) 9930.00 0.82 Low Moderate 1897456 CHLOROTHALONIL 10000.00 0.31 Low Moderate 118741 HEXACHLOROBENZENE 10000.00 3.60 Low Low 7440224 SILVER 10000.00 Low 110827 CYCLOHEXANE 12705.00 89.60 Low Low 120127 ANTHRACENE 17000.00 Low 81072 SACCHARIN (MANUFACTURING ONLY, NO PROCESSOR REPORTING) 17000.00 Low 117817 DI (2-ETHYLHEXYL) OR (SEC-OCTYL) PHTHALATE (DEHP) 30000.00 Low 76131 1,1,2-TRICHLORO-1,2,2-TRIFLUOROETHANE (FREON 113) 43000.00 294.87 Low Low 117840 DI-N-OCTYL PHTHALATE 47000.00 Low 7440666 ZINC (FUME OR DUST) 0.12 High 1332214 ASBESTOS (FRIABLE) 0.00 High 7782505 CHLORINE 0.85 Moderate 75003 ETHYL CHLORIDE (CHLOROETHANE) 152.00 Low 74851 ETHYLENE 1089.3 8 Low 115071 PROPYLENE 86.00 Low 7664393 HYDROGEN FLUORIDE (HYDROFLUORIC ACID) 1.04 Low 7550450 TITANIUM TETRACHLORIDE 0.40 Moderate 75445 PHOSGENE (CARBONYL CHLORIDE) 0.36 Moderate 107302 CHLOROMETHYL METHYL ETHER (CMME) 0.18 High

Methods, models and assumptions 67 ANNEX 5.2: METHODOLOGICAL REVIEW OF DATA COLLECTION AND ACTIVITIES ON THE GSO ANALYSIS OF CROP SURVEYS Area, yield, and production (AYP) are the important indicators of crop statistics and each of them regularly vary from season to season. In Vietnam, the household is considered as the basis-producing unit with more than 90% of annual agricultural products being produced by farm households. In regards to tracking all crop production the GSO adopts a methodology of sampling with surveys on a periodic basis per season. In addition, Vietnam produces many crops which are successively grown and harvested yearround. Therefore, the most important accounting stance is the agricultural calendar for each main crop to avoid double-counting in data collection and to make results comparable amongst localities and regions. With paddy rice, there are 3 main seasons per year, namely: Spring paddy Autumn paddy Winter paddy In the Mekong Delta provinces, paddy is grown and harvested upwards of 5 times a year. However, it is essentially reduce to 3 seasons according to the rule regulated by the Central Committee. For other crops, there are 2 main seasons: Spring season. Referring to crops grown between January and harvested by June. Winter season. Referring to crops grown between July and harvested by December. On the basis of the above agricultural calendar of each crop group, the GSO carry out surveys to collect and assess the results of AYP by crop and by season. There are two basic methods of surveying farmer fields one based on area and one based on yield/production. For the areabased survey a comprehensive survey is elicited, whereas in the case of yield/production surveys a sampling method is used at the district, province and national level. I. Area survey In this type of sampling the commune is an enumeration unit. Communes record statistics of area of all crops planted twice a year (spring and winter season). The statistical approach using area is first executed based on the following information: - Reports made by the head of villages (thorps) on planted area by crops during the given period; - Reports using the implementation plan of crops varying from village to village; reports from the Commune Farmer Association, and; from Commune extenuation teams. - Reports from last season which document land use; provided by land officers. II. Survey on yield and production of crop (SYP) 1- With Paddy

Methods, models and assumptions 68 During 1996-2000 GSO has conducted a sampling survey on yield and production of crop across Viet Nam. Data collection is performed through farmer interviews. This survey is also known as the survey on real - storable output of household or an economic yield survey. The sample domain of the survey is to cover all districts producing paddy. Process of SYP consists of 2 staged activities 1.1- Forecast: Commune level: The early forecasting of paddy yield is made by all communes twice per season - The first when majority of paddy rice are in complete booting - The second when paddy rice are in dough ripe These data are also forwarded to the DSO. At District level: When the majority of paddy rice is in complete booting, an inter-office delegate (including statistics, finance, agriculture, and planning) visits a number of leading communes to validate and review the Commune s forecast result. At the end, the forecasted yield of these communes as well as that of the whole district is recalculated to make it more reliable. The forecasted yield not only meets the timely requirements of district leader s economic management and planning but also becomes a basic reference when the actual survey of households is undertaken. 1.2- Actual survey: All districts are applied one method of sampling that is approved and issued by the GSO. Based on the pilot test result conducted several times based on land size, number of communes in the district, yield variance, survey budget, district statistician, whether the GSO made a determination of sampling as follows: The domain of survey is district with one sample of 3 stages only. The sample unit is the household. 1.2.1 Sample size and allocation: All districts are divided by the number of communes they have and the area planted to paddy according to the below criteria: Number of commune in district Number of sample communes 30 or more 10 20 to 29 8 10 to 19 5 9 or less 3 Number of sample households will be selected from each district: Planted area of district Number of sample households 10 000 ha or more 300 7 000 to 9 999 ha 250 4 000 to 6 999 ha 200 1 000 to 3 999 ha 150 999 or less 100 Throughout this procedure the number of communes and households to be monitored is decided (for instance, in a district with 15 communes and total planted area of 8000 ha will have 250 households in a commune slated for surveillance).

Methods, models and assumptions 69 1.2.2 Selection of sample communes (first stage) a Listing of communes: In each district, communes are sorted according to their location. According to the administrative map of the district, all communes in a district are sorted from North to South, and from East to West. A table of the communes sorted on the above rule is outlined below, with a column showing its planted area, and the other column showing the cumulative area: Commune Area Cumulative Area 1 X1 X1 2 X2 X1+X2 3 X3 X1+X2+X3 4 X4 X1+X2+X3+X4 5 X5 X1+X2+X3+X4+X5 6 X6 X1+X2+X3+X4+X5+X6 7 X7 X1+X2+X3+X4+X5+X6+X7......... N XN X1+X2+X3+X4+X5+X6+X7+Xn b Selection of sample communes: Sample interval (I): Total planted area of the district is divided by the number of sample communes to arrive at I, or the interval: Sample interval ( I ) = Total planted area of district --------------------------------- Total sample communes (n) Planted area per one commune to recognize the commune is drawn first or locator (L) through below formula: Total planted area of the district is divided by total communes to arrive at L, or the locator. Planted area per communes (L) = Total planted area of district --------------------------------- Total communes (N) The commune with the area closest to L is chosen first: L, L+ I, L+2I, L+3I..., and, L- I, L-2I, L- 3I..., are also calculated to find the next sample communes. Communes where the cumulative area is closest to these figures will be incorporated into sample population of n. 1.2.3 Selection of sample villages (second stage) In each sample commune, all villages are also sorted according to their location (very similar to the procedure applied to communes). Then the number of villages is divided by 3 to get village interval (I2): Villages interval ( I2 ) = Total villages of sample commune ---------------------------------------- 3 Villages with the serial number (n+1)/2 are selected if n is odd, and if n is even, choose the villages with serial number n/2. This is the median village (m), selected villages m-i2 and m+i2, along with m as the sample villages.

Methods, models and assumptions 70 1.2.4 Selection of sample households (third stage) 1.2.4.1 Selection of enumeration area (EA). All enumeration areas are from the Population Census in 1999 and are considered as the identifying clusters of households for sample selection. Only one EA, in which most of the households are engaged in paddy production are selected. A list of these households is built according to the rule of their dwelling location door to door. 1.2.4.2 - Household interval (h) In each EA, the total number of households is divided by the total sample households to get household interval (h) Household interval ( h ) = Total household 26 in EA ---------------------------------------- Total sample households need to be selected from EA The household with serial number (n+1)/2 is selected if n is odd. And if n is even, choose the households with serial number n/2. This is the median household (s), select villages s-h, s-2h..., and s+h, s+2h..., along with s as the sample household. 1.2.5 Estimation: Estimates are calculated at three levels: district, province and nation. 1.2.5.1- Estimation at district: An estimate of yield rate is calculated as the following formula: Y d n h= 1 = n h= 1 P A h h where: - Y d : An estimate of yield rate for district - P h : Paddy production of sample household - A h : Paddy planted area of sample household - n : Number of sample households in district Estimation of production is based on the following formula: Pd = Yd x Ad Where: Pd: Paddy production of district Ad: Paddy planted area of district 1.2.5.2 - Estimation for provinces The following formula is used for estimating yield rate of province: Y m d = 1 p = m d = 1 P A d d 26 Refers to households in paddy production only.

Methods, models and assumptions 71 where: Yp: Yield estimate of province m: Number of districts in province. The estimation of production for a province is as follows: Pp = Yp x Ap where: Pp: Ap: Paddy production of province Paddy planted area of province. 2 - Surveys with other crops: 2.1 Survey on yield, production of other crops (besides paddy) is conducted only in the 3 leading communes (communes where most of the surveyed crop is planned). 2.2 With a specific crop, all communes in a district are grouped into 3 strata according to yield of crop, high yield, middle yield and low yield. One commune with the largest crop is selected as a sample commune. 2.3 In the sample communes, 3 villages are randomly chosen and 10 sample households are selected to arrive at a total of 3 x 3 x 10 = 90 sample households for the district. 2.4 Sample selection procedures are the same as are applied in the paddy survey. 2.5 Yield is calculated from the 90 sample households and used to represent the yield of the district, on average. Crop production is the product of this yield and total area planted by this crop in the whole district. 2.6 A specific sample is also designed to correspond for other specific crops. However the sample size could be expanded due to its relative importance in the local area; but does not exceed the number of sample households determined in the paddy survey. 2.7- With tree crops (perennial crop) - namely Coffee, Tea, Rubber, and Logan, special attention is given to: - Area planted by crop - Area under productivity It is clear that the yield of tree crops is the corresponding area under productivity of that crop. Therefore in the survey, yield and production of tree crops are restricted to the area under productivity and harvesting to arrive at the following production numbers: production harvested from area under productivity = Yield from area under production x Total area under productivity However, production from new planted areas is added to production harvested from the area under current production to equal total production. Thus the above 3 types of production are reported in the same survey results.

Pollution load in the Day/Nhue River Basin 72 6 POLLUTION LOAD IN THE DAY/NHUE RIVER BASIN 6.1 CONTRIBUTIONS TO DAY/NHUE RIVER BASIN Each source of pollution in the Day/Nhue River Basin has a set of indicators that can be used to evaluate its relative contribution to the state of ambient water quality. In this study four major groups of pollution sources have been included industry, agriculture, domestic and craft villages. While not exhaustive, those four are the main sources of pollution load in the Sub-basin and it is important to assess them together to obtain a sense of total load and potential areas of significance. In each of the following sections pollution estimates are derived for each source with a particular focus on the most pollution intensive areas and sectors. Before exploring these sources in detail, it is useful to analyse each source relative to the others. For example, it would be of interest to know the contribution of each source to total load within each district. We first explore this question then turn to the detailed assessment of each pollution source in turn. Since each pollution source may include a wide variety of different variables, the first step in the study was to find a common set of pollution parameters that could be compared. Two such parameters were identified Biological Oxygen Demand (5-day) and Suspended Solids (SS). In this sub-section information on BOD5 and SS is used to compare estimated pollution loads between industry, domestic and craft village (CV) sources at the basin, provincial and district levels of aggregation. 27 In Figure 6.11, estimates of potential basin-wide BOD5 and SS show that domestic is the by far largest source of BOD5 and industry for SS pollution in the overall river basin. This trend also holds true when the results are dis-aggregated to the provincial level. Figure 6.11 Contribution of industry, domestic and craft village sources to total Day/Nhue River Basin BOD5 and SS pollution load CV SS (kg) 3.5% Domestic BOD5 (kg) 89.5% CV BOD5 (kg) 3.0% Industrial BOD5 (kg) 7.5% Domestic SS (kg) 34.9% Industrial SS (kg) 61.6% In Figure 6.12 the estimated share of domestic in provincial BOD5 is in excess of 81% for each of the six provinces in the river basin and the industrial share is over 50% in the case of estimated SS pollution; with the exceptions of Ha Nam (16%) and Nam Dinh (32%). Thus when setting priorities at high levels of aggregation, the message seems to be fairly clear domestic would be the main target for controlling BOD5 and industry for SS pollution parameters. Domestic sources would be the focus of SS pollution in Ha Nam and Nam Dinh and industrial sources in Ha Tay, 27 A BOD5 or SS pollution coefficient was not obtainable for agriculture.

Pollution load in the Day/Nhue River Basin 73 Hanoi, Hoa Binh and Nam Binh. But does this relationship hold true at more refined units of analysis? Figure 6.12 Contribution of industry, domestic and craft village sources to province-level BOD5 and SS pollution load 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% Ha Nam Ha Tay Hanoi Hoa Binh Nam Dinh Ninh Binh 0% Ha Nam Ha Tay Hanoi Hoa Binh Nam Dinh Ninh Binh Industrial BOD5 (kg) Domestic BOD5 (kg) CV BOD5 (kg) Industrial SS (kg) Domestic SS (kg) CV SS (kg) In the provinces of Ha Nam, Ha Tay, Hanoi, Hoa Binh, Nam Dinh selected districts have industrial sources of estimated BOD5 pollution representing greater than 25% of the total projected district load (Table 6.11). Thus at a more refined unit of analysis, industrial sources play a more significant role in defining the impact of BOD5 on water quality. The pattern for SS loads is much more pronounced where industry is significant in a number of hot spot districts in Hanoi, Ha Tay and Ninh Binh (see shaded in Table A6.1, Annex 6.1). Another finding relates to the relative contribution of CVs to the BOD5 and SS water pollution situation. Note that at the basin and provincial levels of aggregation, CVs account for less than 4% and 11% of total estimated BOD5 or SS load, respectively. However, when we drill down to the district level CVs account for larger shares in the districts of Ba Vi, Dan Phuong, Hoai Duc and Phuc Tho for estimated BOD5 load (Table 6.11) and even larger shares in the case of estimated SS load (Table A6.1). Thus the role of CVs in defining ambient water quality conditions with respect to BOD5 and SS appears to be significant. Table 6.11 Contribution of industry, domestic and craft village sources to district-level BOD5 pollution load Province District Ind BOD5 (kg) Dom BOD5 (kg) CV BOD5 (kg) BOD5 total % Basin BOD5 % Ind BOD5 % Dom BOD5 % CV BOD5 Ha Nam Binh Luc 1433 719519 13896.4 734848.4 1.4 0.2 97.9 1.9 Ha Nam Duy Tien 6863 674045 17369.1 698276.9 1.3 1.0 96.5 2.5 Ha Nam Kim Bang 484 607891 131.2 608505.8 1.1 0.1 99.9 0.0 Ha Nam Ly Nhan 1837 971927 118424.8 1092188.6 2.1 0.2 89.0 10.8 Ha Nam Phu Ly 91724 252209 0.0 343933.3 0.7 26.7 73.3 0.0 Ha Nam Thanh Liem 1057 624871 8793.3 634721.6 1.2 0.2 98.5 1.4 Ha Tay Ba Vi 37 913921 220711.5 1134669.3 2.1 0.0 80.6 19.5 Ha Tay Chuong My 73856 980123 33456.3 1087435.8 2.0 6.8 90.1 3.1 Ha Tay Dan Phuong 7451 514545 103786.5 625783.1 1.2 1.2 82.2 16.6 Ha Tay H. Thanh Oai 6661 685632 80268.7 772561.5 1.5 0.9 88.8 10.4 Ha Tay Ha Dong 49159 379965 6986.9 436110.8 0.8 11.3 87.1 1.6 Ha Tay Hoai Duc 13468 746914 329881.5 1090263.9 2.1 1.2 68.5 30.3 Ha Tay My Duc 223 637350 8854.3 646427.8 1.2 0.0 98.6 1.4 Ha Tay Phu Xuyen 487094 695515 66705.8 1249314.1 2.4 39.0 55.7 5.3 Ha Tay Phuc Tho 177305 600922 151750.3 929977.6 1.8 19.1 64.6 16.3 Ha Tay Quoc Oai 730 570534 34235.7 605499.4 1.1 0.1 94.2 5.7 Ha Tay Son Tay 45442 372964 0.0 418406.1 0.8 10.9 89.1 0.0

Pollution load in the Day/Nhue River Basin 74 Province District Ind BOD5 (kg) Dom BOD5 (kg) CV BOD5 (kg) BOD5 total % Basin BOD5 % Ind BOD5 % Dom BOD5 % CV BOD5 Ha Tay Thach That 2190 602148 106672.3 711010.4 1.3 0.3 84.7 15.0 Ha Tay Thuong Tin 2847 780559 69879.3 853285.3 1.6 0.3 91.5 8.2 Ha Tay Ung Hoa 63532 673641 40223.5 777396.1 1.5 8.2 86.7 5.2 Hanoi Ba Dinh 65565 2497719 0.0 2563283.8 4.8 2.6 97.4 0.0 Hanoi Cau Giay 21332 1593764 0.0 1615096.0 3.0 1.3 98.7 0.0 Hanoi Dong Da 476271 4142530 0.0 4618801.0 8.7 10.3 89.7 0.0 Hanoi Hai ba Trung 1359392 4423929 0.0 5783321.0 10.9 23.5 76.5 0.0 Hanoi Hoan Kiem 172288 2130409 0.0 2302697.3 4.3 7.5 92.5 0.0 Hanoi Hoang Mai 137800 410575 0.0 548374.6 1.0 25.1 74.9 0.0 Hanoi Tay Ho 46019 1143030 0.0 1189049.1 2.2 3.9 96.1 0.0 Hanoi Thanh Tri 44993 1616217 16366.7 1677577.3 3.2 2.7 96.3 1.0 Hanoi Thanh Xuan 53392 1880137 0.0 1933528.9 3.6 2.8 97.2 0.0 Hanoi Tu Liem 179777 2340605 63888.1 2584269.8 4.9 7.0 90.6 2.5 Hoa Binh Kim Boi 862 488471 0.0 489333.6 0.9 0.2 99.8 0.0 Hoa Binh Ky Son 215722 121138 0.0 336860.5 0.6 64.0 36.0 0.0 Hoa Binh Lac Thuy 60 168599 0.0 168659.5 0.3 0.0 100.0 0.0 Hoa Binh Luong Son 245 257373 0.0 257617.6 0.5 0.1 99.9 0.0 Hoa Binh Yen Thuy 105 212835 0.0 212940.0 0.4 0.1 100.0 0.0 Nam Dinh Giao Thuy 9 879759 20029.2 899797.4 1.7 0.0 97.8 2.2 Nam Dinh Hai Hau 3154 1158249 0.0 1161402.8 2.2 0.3 99.7 0.0 Nam Dinh My Loc 282 302436 372.8 303091.5 0.6 0.1 99.8 0.1 Nam Dinh Nam Dinh 83795 227920 0.0 311715.6 0.6 26.9 73.1 0.0 Nam Dinh Nam Truc 39051 835273 6052.8 880377.1 1.7 4.4 94.9 0.7 Nam Dinh Nghia Hung 502 844470 0.0 844972.6 1.6 0.1 99.9 0.0 Nam Dinh Truc Ninh 97 777847 28691.3 806635.2 1.5 0.0 96.4 3.6 Nam Dinh Vu ban 8 569724 9863.1 579594.8 1.1 0.0 98.3 1.7 Nam Dinh Xuan Truong 3093 774439 0.0 777532.5 1.5 0.4 99.6 0.0 Nam Dinh Y Yen 78 992421 21611.3 1014109.9 1.9 0.0 97.9 2.1 Ninh Binh Gia Vien 380 526858 1308.2 528546.3 1.0 0.1 99.7 0.3 Ninh Binh H. Nho Quan 84 621320 11724.4 633128.7 1.2 0.0 98.1 1.9 Ninh Binh Hoa Lu 270 297245 0.0 297515.2 0.6 0.1 99.9 0.0 Ninh Binh Kim Son 215 779278 0.0 779492.4 1.5 0.0 100.0 0.0 Ninh Binh Ninh Binh 56011 318477 0.0 374487.8 0.7 15.0 85.0 0.0 Ninh Binh Tam Diep 25065 178225 0.0 203289.5 0.4 12.3 87.7 0.0 Ninh Binh Yen Khanh 50 602311 0.0 602361.4 1.1 0.0 100.0 0.0 Ninh Binh Yen Mo 1412 536105 556.0 538072.8 1.0 0.3 99.6 0.1 With this brief overview of the pollution profile of the Day/Nhue River Basin from different sources of BOD5 and SS, we now turn to more detailed source decompositions and add agriculture to the analysis. We also broaden the pollution indicator list to a wider array of pollutants that are of significance for ambient water quality. 6.2 INDUSTRY Owing to is relative proximity to Hanoi and its surrounding areas, the Day/Nhue River Basin contains a wide variety of manufacturing activities which employ a significant share of the population. According to the GSO Enterprise Census, a total of 3,290 manufacturing firms are operating in the Day/Nhue River Basin, employing 275,109 full-time employees, and across 23

Pollution load in the Day/Nhue River Basin 75 VSIC-2 classified sectors (Table 6.21; see Annex 6.1 for further details). 28 Briefly looking at the structure of manufacturing, the sectors with the largest number of firms are in the Fabricated metal products (VSIC-2 28), Food products and beverages (VSIC-2 15) and Publishing, printing and media (VSIC-2 22). From an employment perspective, the largest are in Wearing apparel (VSIC-2 18), Textiles (VSIC-2 17) and Other non-metallic mineral products (VSIC-2 26). 29 Table 6.21 Structure of manufacturing firms and employment VSIC-2 VSIC-2 Description # firms Employment % firms % employment 15 Food products and beverages 354 18969 10.8 6.9 16 Tobacco products 1 1222 0.0 0.4 17 Textiles 195 36532 5.9 13.3 18 Wearing apparel; dressing and dyeing of fur 214 49825 6.5 18.1 19 Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness 50 15261 1.5 5.5 20 Wood products of wood and cork, except furniture; articles of straw and plaiting material 303 15023 9.2 5.5 21 Paper and paper products 127 7581 3.9 2.8 22 Publishing, printing and reproduction of recorded media 337 10661 10.2 3.9 23 Coke, refined petroleum products and nuclear fuel 4 86 0.1 0.0 24 Chemicals and chemical products 156 12728 4.7 4.6 25 Rubber and plastics products 201 12450 6.1 4.5 26 Other non-metallic mineral products 193 26740 5.9 9.7 27 Basic metals 59 3411 1.8 1.2 28 Fabricated metal products, except machinery and equipment 436 17180 13.3 6.2 29 Machinery and equipment n.e.c. 127 11826 3.9 4.3 30 Office, accounting and computing machinery 2 20 0.1 0.0 31 Electrical machinery and apparatus n.e.c. 98 8771 3.0 3.2 32 Radio, television and communication equipment and apparatus 44 2295 1.3 0.8 33 Medical, precision and optical instruments, watches and clocks 23 1475 0.7 0.5 34 Motor vehicles, trailers and semi-trailers 37 2778 1.1 1.0 35 Other transport equipment 95 10511 2.9 3.8 36 Furniture; manufacturing n.e.c. 231 9736 7.0 3.5 37 Recycling 3 28 0.1 0.0 The wide variety of manufacturing sectors in the Day/Nhue River Basin also results in a multitude of pollutants that are generated by the industrial processes. For the purposes of this study we capitalize on the detailed pollution coefficient information contained in the Industrial Pollution Projection System (IPPS) and calculated pollution loads for each sector in the river basin. These are then aggregated up to the district level for presentation. First the sector composition of industry in the overall river basin is analysed followed by discussion of disaggregated results. In Table 6.22 each VSIC-4 sector is ranked by its estimated pollution load of BOD5 and SS. The table is sorted in descending order from the largest to the least BOD5 load. The top two sectors are responsible for over 62% of the total BOD5 load in the river basin. This is generated by 17 28 VSIC stands for the Vietnam Standard Industrial Classification. It is modeled on the International Standard Industrial Classification (ISIC). It groups enterprises into sectors of activities from a high level of aggregation (2-digit) to a finer and more disaggregated level (3-digit, and 4-digit). For example, ISIC code 15 stands for Manufacture of food products and beverages ; ISIC code 155 stands for Manufacture of beverages ; and ISIC code 1554 stands for Manufacture of soft drinks; production of mineral waters. At the 2-digit level, the manufacturing sector is grouped into 23 sectors coded from 15 to 37, as they appear in Table 6.21. 29 This division groups different areas that are all related to a single substance of mineral origin. This division includes glass and glass products (e.g. flat glass, hollow glass, fibres, technical glassware etc.); and ceramic products, tiles and baked clay products, and cement and plaster, from raw materials to finished articles. Shaped and finished stone and other mineral products complete the division.

Pollution load in the Day/Nhue River Basin 76 Pulp, paper and paperboard (VSIC-4 2101) and 9 Distilling (VSIC-4 1551) plants. Although not shown in the table, the largest in terms of estimated SS is the Basic iron and steel sector (VSIC-4 2710) with only 35 plants and representing over 55% of the total load (see Table A6.2 Annex 6.1 for full details). Table 6.22: Top 20 sectors of manufacturing pollution significance in the Day/Nhue River Basin * VSIC-4 VSIC-4 Description # firms Employment BOD5 (kg) % SS (kg) % 2101 Pulp, paper and paperboard 17 1127 1688048.1 42.0 5733254.0 9.1 1551 Distilling, rectifying and blending of spirits; ethyl alcohol production from fermented materials 9 856 821518.3 20.4 1476540.2 2.3 1520 Dairy products 4 423 490497.5 12.2 70649.7 0.1 2520 Plastics products 171 8975 222955.2 5.5 4818.0 0.0 2411 Basic chemicals, except fertilizers and nitrogen compounds 9 371 211270.4 5.3 326557.6 0.5 1711 Preparation and spinning of textile fibres; weaving of textiles 57 21886 91320.4 2.3 141814.8 0.2 2732 Casting of non-ferrous metals 9 342 89212.2 2.2 1289569.2 2.0 2109 Other articles of paper and paperboard 71 4070 77481.5 1.9 76427.2 0.1 1542 Sugar 3 176 48093.7 1.2 68955.2 0.1 1920 Footwear 35 13994 37021.6 0.9 36301.1 0.1 2720 Basic precious and non-ferrous metals 4 120 31302.5 0.8 452480.4 0.7 2423 Pharmaceuticals, medicinal chemicals and botanical products 43 4799 30873.8 0.8 7740401.3 12.2 1553 Malt liquors and malt 40 5009 28145.4 0.7 65053.4 0.1 1513 Processing and preserving of fruit and vegetables 19 651 16051.2 0.4 25320.8 0.0 1512 Processing and preserving of fish and fish products 15 411 15834.8 0.4 26995.2 0.0 2102 Corrugated paper and paperboard, containers of paper and paperboard 39 2384 12316.1 0.3 21146.5 0.0 2695 Articles of concrete, cement and plaster 41 7082 9188.3 0.2 13479.3 0.0 1712 Finishing of textiles 33 2179 9092.0 0.2 14119.3 0.0 2899 Other fabricated metal products n.e.c. 236 6576 9079.4 0.2 261343.6 0.4 2424 Soap and detergents, cleaning and polishing preparations, perfumes and toilet preparations 18 525 7944.3 0.2 11220.7 0.0 * - Listed in descending order of BOD5 load. Summarizing sector pollution at the district level, Table 6.23 shows that BOD5 and SS pollution loads are highest in Hai Ba Trung (Hanoi Province) and Nihn Binh (Ninh Binh Province), respectively. The first two districts Hai Ba Trung and Phu Xuyen (Ha Tay Province) are responsible for over 55% of all BOD5 load in the river basin whereas together, Ninh Binh, Hai Ba Trung and Tam Diep represent over 40% of estimated SS releases. It is also useful to examine sector composition of these pollution releases within the district. Table 6.24 presents the top 5 sectors by load in each of the top five districts. Table 6.23: Ranking of districts by BOD5 and SS pollution load in the Day/Nhue River Basin * District Province # firms Employment BOD5 (kg) % SS (kg) % Hai ba Trung Hanoi 352 29841 1359392.2 33.8 6439513.9 10.2 Phu Xuyen Ha Tay 22 1163 487093.7 12.1 1501070.7 2.4 Dong Da Hanoi 420 23042 476271.1 11.8 3291321.3 5.2 Ky Son Hoa Binh 7 545 215722.2 5.4 732814.1 1.2 Tu Liem Hanoi 169 12215 179777.1 4.5 1913045.2 3.0 Phuc Tho Ha Tay 5 946 177304.8 4.4 601560.1 1.0 Hoan Kiem Hanoi 235 18585 172288.4 4.3 1886693.6 3.0 Hoang Mai Hanoi 259 24966 137799.6 3.4 4124500.4 6.5 Phu Ly Ha Nam 48 4693 91724.1 2.3 150103.4 0.2

Pollution load in the Day/Nhue River Basin 77 Nam Dinh Nam Dinh 157 28028 83795.3 2.1 1621742.8 2.6 Chuong My Ha Tay 54 4455 73856.3 1.8 48043.9 0.1 Ba Dinh Hanoi 204 15101 65564.7 1.6 2522536.9 4.0 Ung Hoa Ha Tay 13 2134 63532.1 1.6 1374793.9 2.2 Ninh Binh Ninh Binh 37 3223 56011.3 1.4 14154950.6 22.4 Thanh Xuan Hanoi 220 27451 53391.6 1.3 2120057.1 3.4 Ha Dong Ha Tay 86 11057 49158.5 1.2 1159863.1 1.8 Tay Ho Hanoi 96 4617 46019.3 1.1 1071976.1 1.7 Son Tay Ha Tay 22 2670 45441.7 1.1 165602.3 0.3 Thanh Tri Hanoi 133 10105 44993.3 1.1 4650317.0 7.4 Nam Truc Nam Dinh 38 1877 39051.0 1.0 648988.3 1.0 Tam Diep Ninh Binh 9 1488 25064.7 0.6 5216262.2 8.3 Cau Giay Hanoi 108 5795 21332.1 0.5 239678.5 0.4 Hoai Duc Ha Tay 63 3943 13468.0 0.3 501208.7 0.8 Dan Phuong Ha Tay 69 2966 7451.4 0.2 28748.1 0.0 Duy Tien Ha Nam 29 1708 6863.1 0.2 14293.9 0.0 H. Thanh Oai Ha Tay 28 3287 6660.7 0.2 107481.6 0.2 Hai Hau Nam Dinh 15 668 3154.0 0.1 7344.2 0.0 Xuan Truong Nam Dinh 23 1985 3093.2 0.1 11524.9 0.0 Thuong Tin Ha Tay 18 1310 2846.7 0.1 58186.3 0.1 Thach That Ha Tay 60 2514 2189.7 0.1 4332189.2 6.9 Ly Nhan Ha Nam 7 592 1836.8 0.0 3631.9 0.0 Binh Luc Ha Nam 6 573 1433.3 0.0 3767.2 0.0 Yen Mo Ninh Binh 9 227 1412.1 0.0 2542.1 0.0 Thanh Liem Ha Nam 22 1268 1057.1 0.0 129286.9 0.2 Kim Boi Hoa Binh 4 165 862.1 0.0 1341.5 0.0 Quoc Oai Ha Tay 15 1686 730.2 0.0 162455.6 0.3 Nghia Hung Nam Dinh 6 292 502.4 0.0 2263.6 0.0 Kim Bang Ha Nam 8 1766 483.8 0.0 330090.9 0.5 Gia Vien Ninh Binh 5 636 380.0 0.0 27745.6 0.0 My Loc Nam Dinh 3 20 282.3 0.0 57.4 0.0 Hoa Lu Ninh Binh 18 1894 270.2 0.0 1237116.4 2.0 Luong Son Hoa Binh 8 674 244.5 0.0 121728.7 0.2 My Duc Ha Tay 10 200 223.1 0.0 8374.9 0.0 Kim Son Ninh Binh 22 4500 214.8 0.0 337.6 0.0 Yen Thuy Hoa Binh 2 354 104.8 0.0 74458.4 0.1 Truc Ninh Nam Dinh 15 645 96.5 0.0 394190.0 0.6 H. Nho Quan Ninh Binh 4 293 84.1 0.0 221.0 0.0 Y Yen Nam Dinh 104 4739 78.1 0.0 130.2 0.0 Lac Thuy Hoa Binh 2 49 60.1 0.0 84.1 0.0 Yen Khanh Ninh Binh 6 934 50.3 0.0 1209.3 0.0 Ba Vi Ha Tay 5 246 36.6 0.0 76.5 0.0 Giao Thuy Nam Dinh 4 196 8.9 0.0 56.8 0.0 Vu ban Nam Dinh 6 782 7.8 0.0 25.9 0.0 * - Listed in descending order of BOD5 load. The top 5 sectors in each of the top five districts show several interesting patterns. First, the Pulp and paper (VSIC-4 2101) and Distilling (VSIC-4 1551) sectors are estimated to be the highest in terms of BOD5 and SS load in Hai ba Trung and Phu Xuyen. The Pulp and paper sector is also estimated to be the highest for BOD5 load in Ninh Binh. The district of Dong Da in Hanoi has a different pattern of sectors where it is estimated that over 80% of BOD5 is attributable to Dairy products (VSIC-4 1520) and over 78% of SS to the Pharmaceuticals (VSIC-4 2423) sector. The Basic chemicals, except fertilizers (VSIC-4 2411) sector is estimated to be the top contributor to industrial BOD5 in Tam Diep. Three firms in the Basic iron and steel (VSIC-4 2710) sector play an important role in terms of their contribution to district-level industrial SS releases. It is

Pollution load in the Day/Nhue River Basin 78 estimated to be the largest in the districts of Tam Diep and Ninh Binh, with over 95% and 63% of the total load. Perhaps the most striking finding is how the results are influenced by a relatively few sectors and firms. This is a common finding where manufacturing pollution loads are often highly concentrated in relatively few sectors, and in many cases, relatively few plants. 6.3 RANKING BY HAZARD CLASS The second part of the analysis with respect to the manufacturing data was to calculate the relative hazard of water pollutants using the hazard classification techniques detailed in Section 5. Substances and chemicals used in industrial processes are categorized into three ranges of acute exposure hazard using acute lethal concentration values, LC 50 (Horvath et al., 1995; Swanson et al., 1995). These measures form the basis of epidemiological impact studies in evaluating the potential risk to human life. In this study, each chemical in the database was classified as a (1) high hazard, (2) moderate hazard, or (3) low hazard according to its LC 50 value and then aggregated up to the plant level and summarized at the sector and district level. A hazard index is then constructed by adding a weighted combination of the three classes of chemicals, giving greater weight to more toxic materials. 30 Table 6.31 summarizes the industrial chemical loads for each district. The table is ranked in descending order of the hazard index. Several interesting patterns emerge from the estimation exercise. First, the top seven districts with the highest index value are also the largest with respect to total releases of all substances, and with the exception of Dan Phuong (Ha Tay province) all are in the top quartile across all classes of hazards. It is also interesting to note that the top districts also represent the largest in terms of the number of firms and employment. In the next table the sectors associated with the high hazard index ranking are identified and then the top 5 chemicals associated with the production process of the top sector are examined. Table 6.32 lists the top 5 sectors in each of the top 5 districts. A consistent pattern is presented by the frequency of the Preparation and spinning of textile fibres sector for Hoang Mai, Nam Dinh and Hai Ba Trung, whereas for Thanh Tri and Dan Phoung it is Fertilizers and nitrogen compounds and Sawmilling and planing of wood sectors, respectively. These sectors involve processes that utilize several high or moderately hazardous substances, giving them their top ranking. In addition there are several large employers that are contributing a significant proportion to overall industrial chemical releases; especially moderate and low hazard chemicals. The next step in analyzing industrial chemical load estimates is to break down the top sector within each of these top districts and look at the chemical composition of that load. Table 6.33 lists the top 5 chemicals in descending order of the hazard index calculated at the district level. 30 The weighting procedure is outlined in Section 5. The weights are 0.1 for low, 0.3 for moderate, and 1 for highly toxic substances.

Pollution load in the Day/Nhue River Basin 79 Table 6.24: Ranking of top 5 sectors by BOD5 load in selected districts * VSIC-4 Description VSIC-4 Province District # firms Employment BOD5 % SS % 1. Distilling, rectifying and blending of spirits; ethyl alcohol production from fermented materials 1551 Hanoi Hai ba Trung 1 602 577750.0 42.5 1038407.9 16.1 2. Pulp, paper and paperboard 2101 Hanoi Hai ba Trung 6 374 560186.3 41.2 1902606.0 29.5 3. Casting of non-ferrous metals 2732 Hanoi Hai ba Trung 5 295 76952.0 5.7 1112347.7 17.3 4. Basic chemicals, except fertilizers and nitrogen compounds 2411 Hanoi Hai ba Trung 1 125 71182.8 5.2 110026.1 1.7 5. Plastics products 2520 Hanoi Hai ba Trung 20 1137 28245.1 2.1 610.4 0.0 Other 319 27308 45076.0 3.3 2275516 35.3 1. Pulp, paper and paperboard 2101 Ha Tay Phu Xuyen 1 257 384940.9 79.0 1307405.8 87.1 2. Distilling, rectifying and blending of spirits; ethyl alcohol production from fermented materials 1551 Ha Tay Phu Xuyen 1 105 100770.3 20.7 181117.7 12.1 3. Other articles of paper and paperboard 2109 Ha Tay Phu Xuyen 1 30 571.1 0.1 563.3 0.0 4. Tanks, reservoirs and containers of metal 2812 Ha Tay Phu Xuyen 1 286 394.9 0.1 11366.2 0.8 5. Plastics products 2520 Ha Tay Phu Xuyen 1 8 198.7 0.0 4.3 0.0 Other 17 477 217.7 0.0 613.4 0.0 1. Dairy products 1520 Hanoi Dong Da 1 331 383817.2 80.6 55283.8 1.7 2. Plastics products 2520 Hanoi Dong Da 33 1496 37163.3 7.8 803.1 0.0 3. Basic precious and non-ferrous metals 2720 Hanoi Dong Da 1 54 14086.1 3.0 203616.2 6.2 4. Pulp, paper and paperboard 2101 Hanoi Dong Da 1 9 13480.4 2.8 45784.6 1.4 5. Pharmaceuticals, medicinal chemicals and botanical products 2423 Hanoi Dong Da 16 1594 10254.8 2.2 2570993.9 78.1 Other 368 19558 17469.2 3.7 414839.7 12.6 1. Pulp, paper and paperboard 2101 Ninh Binh Ninh Binh 1 34 50926.0 90.9 172964.2 1.2 2. Pharmaceuticals, medicinal chemicals and botanical products 2423 Ninh Binh Ninh Binh 1 325 2090.9 3.7 524198.9 3.7 3. Processing and preserving of fruit and vegetables 1513 Ninh Binh Ninh Binh 1 30 739.7 1.3 1166.9 0.0 4. Basic iron and steel 2710 Ninh Binh Ninh Binh 1 635 606.4 1.1 8934224.0 63.1 5. Malt liquors and malt 1553 Ninh Binh Ninh Binh 1 103 578.8 1.0 1337.7 0.0 Other 32 2096 1069.6 1.9 4521059.0 31.9 Basic chemicals, except fertilizers and nitrogen compounds 2411 Ninh Binh Tam Diep 1 43 24486.9 97.7 37849.0 0.7 Basic iron and steel 2710 Ninh Binh Tam Diep 2 355 339.0 1.4 4994723.5 95.8 Builders' carpentry and joinery 2022 Ninh Binh Tam Diep 2 29 146.2 0.6 689.4 0.0 Cement, lime and plaster 2694 Ninh Binh Tam Diep 1 763 83.4 0.3 182897.3 3.5 Structural non-refractory clay and ceramic products 2693 Ninh Binh Tam Diep 1 256 5.5 0.0 97.6 0.0 Other 2 42 3.7 0.0 5.4 0.0 * - Listed in descending order of BOD5 load.

Pollution load in the Day/Nhue River Basin 80 Table 6.31: Chemical pollution load per district in the Day/Nhue River Basin * Hazard Index Province District # firms Employment LC50 hazhigh (kg) LC50 hazmedium (kg) LC50 hazlow (kg) Total (kg) 988562 Hanoi Hoang Mai 259 24966 271.2 589284.2 8115050.6 8704606.0 723251 Nam Dinh Nam Dinh 157 28028 154.2 770120.7 4920610.5 5690885.4 609284 Hanoi Hai ba Trung 352 29841 303.7 413541.9 4849174.7 5263020.2 568031 Hanoi Thanh Tri 133 10105 172.6 54254.1 5515821.5 5570248.2 375303 Ha Tay Dan Phuong 69 2966 63.2 15882.5 3704751.0 3720696.7 331230 Hanoi Dong Da 420 23042 286.2 69612.2 3100597.0 3170495.3 294695 Hanoi Tu Liem 169 12215 151.7 27160.5 2863953.7 2891265.9 276508 Hanoi Thanh Xuan 220 27451 193.2 170106.7 2252831.0 2423130.9 261266 Hanoi Hoan Kiem 235 18585 101.8 59888.0 2431976.1 2491965.8 156831 Ha Tay Thach That 60 2514 47.5 4578.8 1554098.0 1558724.3 148205 Hanoi Ba Dinh 204 15101 84.3 65297.3 1285319.2 1350700.7 135548 Ha Tay Hoai Duc 63 3943 37.2 17395.3 1302924.8 1320357.3 133000 Ha Tay Phu Xuyen 22 1163 109.0 6743.6 1308682.3 1315534.9 120870 Ha Tay Ha Dong 86 11057 118.5 60715.1 1025372.3 1086205.9 98514 Ha Nam Phu Ly 48 4693 49.4 83888.0 732985.6 816923.0 78442 Nam Dinh Hai Hau 15 668 17.1 3586.9 773491.6 777095.6 73972 Hoa Binh Ky Son 7 545 38.8 2552.5 731675.4 734266.8 69715 Ha Tay Son Tay 22 2670 15.9 1583.6 692239.4 693838.9 60712 Hanoi Cau Giay 108 5795 68.3 9783.9 577085.3 586937.6 60081 Ninh Binh Ninh Binh 37 3223 53.4 12065.9 564074.0 576193.2 58651 Ha Tay Phuc Tho 5 946 31.5 2717.6 578038.8 580787.9 54378 Ha Tay Chuong My 54 4455 28.1 5645.9 526560.8 532234.8 49677 Ha Tay Thuong Tin 18 1310 44.4 3712.5 485188.2 488945.0 33925 Ninh Binh Gia Vien 5 636 2.0 671.8 337214.8 337888.6 30044 Nam Dinh Nghia Hung 6 292 6.0 364.0 299290.5 299660.6 29778 Ninh Binh Tam Diep 9 1488 16.0 4997.7 282626.2 287639.9 28805 Ha Tay Quoc Oai 15 1686 19.6 2191.6 281280.7 283492.0 27688 Ha Tay H. Thanh Oai 28 3287 28.0 2173.7 270079.8 272281.5 22006 Ninh Binh Hoa Lu 18 1894 3.5 944.3 217191.5 218139.2 21233 Hanoi Tay Ho 96 4617 197.7 13129.9 170964.9 184292.5 21134 Ha Tay Ung Hoa 13 2134 10.3 18944.9 154399.1 173354.3 21065 Ha Nam Binh Luc 6 573 5.3 27910.8 126864.4 154780.5 20999 Nam Dinh Nam Truc 38 1877 9.1 12071.4 173687.5 185768.0 14531 Ha Nam Thanh Liem 22 1268 8.3 17410.4 92994.1 110412.8 12432 Ha Tay My Duc 10 200 0.0 4015.4 112273.2 116288.6 12430 Ninh Binh Yen Khanh 6 934 3.7 379.4 123129.4 123512.4 12267 Ha Nam Ly Nhan 7 592 1.4 16164.3 74164.6 90330.2 8254 Ha Nam Duy Tien 29 1708 15.7 4550.4 68730.4 73296.5 5373 Nam Dinh Y Yen 104 4739 0.0 7778.0 30395.9 38173.9 4690 Nam Dinh Xuan Truong 23 1985 20.5 685.7 44633.1 45339.3 3728 Ha Nam Kim Bang 8 1766 0.8 76.1 37038.6 37115.5 2800 Ninh Binh Yen Mo 9 227 0.4 322.3 27028.7 27351.5 1434 Hoa Binh Luong Son 8 674 0.0 140.0 13919.9 14059.9 937 Hoa Binh Yen Thuy 2 354 6.3 207.0 8689.6 8902.9 582 Nam Dinh My Loc 3 20 0.0 768.4 3516.6 4285.0 217 Nam Dinh Truc Ninh 15 645 0.9 288.2 1300.9 1590.0 123 Ha Tay Ba Vi 5 246 0.1 389.5 58.4 448.0 118 Hoa Binh Kim Boi 4 165 0.5 348.4 133.4 482.3 111 Ninh Binh Kim Son 22 4500 0.0 354.6 42.0 396.6 83 Ninh Binh H. Nho Quan 4 293 0.1 261.2 47.0 308.3 39 Nam Dinh Giao Thuy 4 196 0.2 108.4 62.4 171.0

Pollution load in the Day/Nhue River Basin 81 9 Nam Dinh Vu ban 6 782 1.3 14.9 33.9 50.0 4 Hoa Binh Lac Thuy 2 49 0.0 5.3 25.9 31.2 * - Listed in descending order of the Hazard Index. For the top three districts the pattern is clear. Processes involving the Preparation and spinning of textile fibres include significant use of sodium sulfate, sodium hydroxide, sulfuric acid ammonia and chlorine. In Hanoi s district Thanh Tri, the Fertilizers and nitrogen compounds sector is associated with significant releases of moderately hazardous substances ammonia and formaldehyde. On the other hand, the Sawmilling and planing of wood sector in Dan Phuong is associated with significant amounts of methanol and hydrochloric acid. 6.4 AGRICULTURE The GSO Agricultural Census of 2006 contained the most recent statistics available at the district level for a wide variety of crops. Among them, rice (spring and autumn paddy) was the most dominant in terms of area and production with over 76% of all land dedicated to permanent crop production and over 71% of all production in tons. In Table 6.41, the production amounts are recorded for each district in the Day/Nhue River Basin. Note that not all areas are under annual crop production, while others are diversified across many different crops. In terms of overall crop production across districts, the largest producers are H. Nho Quan (Ninh Binh province), Kim Boi (Hoa Binh), Y Yen (Nam Dinh), Nghia Hung (Nam Dinh), and Chuong My (Ha Tay). Using information on crop-specific pesticide and fertilizer use rates per hectare of land under cultivation, pesticide and fertilizer pollution loads were calculated for each district in the river basin. A note of caution is warranted about interpreting the results from these estimates. Although the dynamics of pesticide and fertilizer runoff into the river basin is a complex process, we take a simplified approach and simply delineate areas that are more pesticide- and fertilizerintensive in terms of their actual use-load. In other words, we use pesticide and fertilizer application rates by district to calculate the pollution load, and then use this load as a proxy of what is converted into runoff into the river basin. While pesticide and fertilizer use is an individual decision made by the farmer, this is recognized as a limitation of the study. However this does not preclude the study from making some broad predictions of where pesticide- or fertilizer-use hotspots may lie. For example, if specific cropping areas are more pesticide or fertilizer intensive, this is captured in the estimates and the relative ranking of one district over another. Thus what is important is the relative ranking of one area over another in this case. Further research into pesticide and fertilizer runoff coefficients would be a step forward in creating more detailed estimates. Estimates of district level pesticide and fertilizer load are reported in Table 6.42. Districts are ranked in descending order of the estimated potential pesticide load. Among the top districts, other than Kim Boi (Hoa Binh) and Y Yen (Nam Dinh), the vast majority are in Ha Tay province which may be indicative of specific environmental conditions which result in higher application rates of pesticides and fertilizers. While the estimates provided here show a clear pattern of high application rates for districts in Ha Tay province, further agricultural extension research is obviously required to validate this result.

Pollution load in the Day/Nhue River Basin 82 Table 6.32: Top 5 polluting sectors in selected districts in the Day/Nhue River Basin * # firms Employment VSIC-4 description VSIC-4 Province District 1. Preparation and spinning of textile fibres; weaving of textiles 1711 Hanoi Hoang Mai 5 6380 411365.0 5.5 543983.0 2481650.0 3025640.0 Hazard Index LC50 hazhigh (kg) LC50 hazmedium (kg) 2. Builders' carpentry and joinery 2022 Hanoi Hoang Mai 4 685 366763.0 58.6 3552.9 3656390.0 3660000.0 3. Corrugated paper and paperboard, containers of paper and paperboard 2102 Hanoi Hoang Mai 4 333 67239.3 0.0 1620.7 667531.0 669152.0 4. Fertilizers and nitrogen compounds 2412 Hanoi Hoang Mai 5 55 52566.1 8.9 244.1 524840.0 525093.0 5. Knitted and crocheted fabrics and articles 1730 Hanoi Hoang Mai 1 300 27900.7 0.0 539.3 277389.0 277928.0 1. Preparation and spinning of textile fibres; weaving of textiles 1711 Nam Dinh Nam Dinh 5 7908 509887.0 6.8 674265.0 3076000.0 3750280.0 2. Builders' carpentry and joinery 2022 Nam Dinh Nam Dinh 6 148 79242.3 12.7 767.6 789994.0 790774.0 3. Finishing of textiles 1712 Nam Dinh Nam Dinh 6 846 54547.8 0.7 72133.1 329072.0 401206.0 4. Knitted and crocheted fabrics and articles 1730 Nam Dinh Nam Dinh 3 381 35433.9 0.0 684.9 352284.0 352969.0 5. Pharmaceuticals, medicinal chemicals and botanical products 2423 Nam Dinh Nam Dinh 1 719 13895.3 0.0 5327.8 122970.0 128297.0 1. Preparation and spinning of textile fibres; weaving of textiles 1711 Hanoi Hai ba Trung 6 3833 247142.0 3.3 326816.0 1490940.0 1817760.0 2. Pulp, paper and paperboard 2101 Hanoi Hai ba Trung 6 374 184057.0 99.7 6360.8 1820490.0 1826950.0 3. Corrugated paper and paperboard, containers of paper and paperboard 2102 Hanoi Hai ba Trung 6 436 88037.1 0.0 2121.9 874005.0 876127.0 4. Basic chemicals, except fertilizers and nitrogen compounds 2411 Hanoi Hai ba Trung 1 125 29839.3 6.4 6430.5 279038.0 285475.0 5. Pharmaceuticals, medicinal chemicals and botanical products 2423 Hanoi Hai ba Trung 4 679 13122.3 0.0 5031.4 116128.0 121160.0 1. Fertilizers and nitrogen compounds 2412 Hanoi Thanh Tri 2 442 422440.0 71.4 1961.5 4217800.0 4219840.0 2. Veneer sheets; plywood, laminboard, particle board, other panels and boards 2021 Hanoi Thanh Tri 3 116 62108.8 9.9 601.7 619184.0 619796.0 3. Corrugated paper and paperboard, containers of paper and paperboard 2102 Hanoi Thanh Tri 4 89 17970.9 0.0 433.1 178409.0 178842.0 4. Preparation and spinning of textile fibres; weaving of textiles 1711 Hanoi Thanh Tri 7 253 16312.8 0.2 21571.7 98410.3 119982.0 5. Builders' carpentry and joinery 2022 Hanoi Thanh Tri 3 28 14991.8 2.4 145.2 149458.0 149606.0 1. Sawmilling and planing of wood 2010 Ha Tay Dan Phuong 45 664 355520.0 56.8 3444.0 3544300.0 3547800.0 2. Corrugated paper and paperboard, containers of paper and paperboard 2102 Ha Tay Dan Phuong 1 52 10499.8 0.0 253.1 104239.0 104492.0 3. Preparation and spinning of textile fibres; weaving of textiles 1711 Ha Tay Dan Phuong 1 135 8704.4 0.1 11510.6 52511.4 64022.2 4. Prepared animal feeds 1533 Ha Tay Dan Phuong 1 10 297.2 0.0 29.2 2884.4 2913.6 5. Other fabricated metal products n.e.c. 2899 Ha Tay Dan Phuong 3 50 121.1 6.1 199.0 552.6 757.7 * - Listed in descending order of the Hazard Index. LC50 hazlow (kg) Total (kg)

Pollution load in the Day/Nhue River Basin 83 Table 6.33: Top 5 chemical loads in the top sector in selected districts * # firms Employment Substance CAS VSIC-4 description VSIC-4 Province District 1. SODIUM SULFATE Preparation and spinning of textile 7757826 (SOLUTION) fibres; weaving of textiles 1711 Hanoi Hoang Mai 5 6380 228196.0 0.0 0.0 2281960.0 2. SODIUM HYDROXIDE (SOLUTION) 1310732 1711 Hanoi Hoang Mai 5 6380 159542.0 0.0 531806.0 0.0 3. SULFURIC ACID 7664939 1711 Hanoi Hoang Mai 5 6380 17475.7 0.0 0.0 174757.0 Hazard Index LC50 hazhigh (kg) LC50 hazmedium (kg) 4. AMMONIA 7664417 1711 Hanoi Hoang Mai 5 6380 3596.1 0.0 11986.9 0.0 5. CHLORINE 7782505 1711 Hanoi Hoang Mai 5 6380 1945.1 0.0 0.0 19451.1 1. SODIUM SULFATE Preparation and spinning of textile 7757826 (SOLUTION) fibres; weaving of textiles 1711 Nam Dinh Nam Dinh 5 7908 282849.0 0.0 0.0 2828490.0 2. SODIUM HYDROXIDE (SOLUTION) 1310732 1711 Nam Dinh Nam Dinh 5 7908 197752.0 0.0 659173.0 0.0 3. SULFURIC ACID 7664939 1711 Nam Dinh Nam Dinh 5 7908 21661.1 0.0 0.0 216611.0 4. AMMONIA 7664417 1711 Nam Dinh Nam Dinh 5 7908 4457.3 0.0 14857.8 0.0 5. CHLORINE 7782505 1711 Nam Dinh Nam Dinh 5 7908 2411.0 0.0 0.0 24109.6 1. SODIUM SULFATE Preparation and spinning of textile 7757826 (SOLUTION) fibres; weaving of textiles 1711 Hanoi Hai ba Trung 6 3833 137096.0 0.0 0.0 1370960.0 2. SODIUM HYDROXIDE (SOLUTION) 1310732 1711 Hanoi Hai ba Trung 6 3833 95850.1 0.0 319500.0 0.0 3. SULFURIC ACID 7664939 1711 Hanoi Hai ba Trung 6 3833 10499.1 0.0 0.0 104991.0 4. AMMONIA 7664417 1711 Hanoi Hai ba Trung 6 3833 2160.5 0.0 7201.6 0.0 5. CHLORINE 7782505 1711 Hanoi Hai ba Trung 6 3833 1168.6 0.0 0.0 11685.9 1. SODIUM SULFATE (SOLUTION) 7757826 Fertilizers and nitrogen compounds 2412 Hanoi Thanh Tri 2 442 421283.0 0.0 0.0 4212830.0 2. AMMONIA 7664417 2412 Hanoi Thanh Tri 2 442 444.1 0.0 1480.2 0.0 3. METHANOL (METHYL ALCOHOL) 67561 2412 Hanoi Thanh Tri 2 442 276.9 0.0 0.0 2768.6 4. AMMONIUM NITRATE (SOLUTION) 6484522 2412 Hanoi Thanh Tri 2 442 123.3 0.0 0.0 1233.2 5. FORMALDEHYDE 50000 2412 Hanoi Thanh Tri 2 442 104.3 0.0 347.6 0.0 1. SODIUM SULFATE (SOLUTION) 7757826 Sawmilling and planing of wood 2010 Ha Tay Dan Phuong 45 664 350764.0 0.0 0.0 3507640.0 2. METHANOL (METHYL ALCOHOL) 67561 2010 Ha Tay Dan Phuong 45 664 1992.6 0.0 0.0 19925.7 3. CHLORINE 7782505 2010 Ha Tay Dan Phuong 45 664 1201.5 0.0 0.0 12014.6 4. AMMONIA 7664417 2010 Ha Tay Dan Phuong 45 664 922.5 0.0 3074.9 0.0 5. HYDROCHLORIC ACID (HYDROGEN CHLORIDE) 7647010 2010 Ha Tay Dan Phuong 45 664 255.6 0.0 0.0 2555.7 * - Listed in descending order of the Hazard Index. LC50 hazlow (kg)

Pollution load in the Day/Nhue River Basin 84 Table 6.41: Ranking of district level agricultural crop production in the Day/Nhue River Basin * District Province Spring Paddy (tons) Autumn Paddy (tons) Maize (tons) Potato (tons) Cassava (tons) Sugar Cane (tons) Groundnuts (tons) Soya Beans (tons) Vegetables (tons) H. Nho Quan Ninh Binh 38269 27463 1735 2206 10322 72681 229 14 9960 179 503 182 365 164106 Kim Boi Hoa Binh 18747 26561 22355 0 11832 54120 938 829 24789 144 295 0 0 160610 Y Yen Nam Dinh 75293 67705 3952 0 0 177 10940 337 0 0 0 0 0 158404 Nghia Hung Nam Dinh 84036 61402 4318 0 0 2626 495 238 0 0 0 3344 0 156459 Chuong My Ha Tay 54511 52332 7448 0 3135 340 1860 4377 27737 1371 274 501 468 154354 Ung Hoa Ha Tay 67496 62708 3508 0 11 0 626 4558 10925 0 173 347 29 150381 Hai Hau Nam Dinh 84176 56973 2139 0 0 2525 1105 719 0 0 0 546 0 148183 Binh Luc Ha Nam 55654 46532 1489 4130 0 29 61 684 33158 0 450 685 185 143057 Ba Vi Ha Tay 37675 30780 16264 0 12975 0 2067 2972 25419 8198 1488 2060 2710 142608 Phu Xuyen Ha Tay 56476 51642 3696 0 0 0 790 11627 12385 0 212 294 5 137127 H. Thanh Oai Ha Tay 45346 42324 1360 0 24 180 202 1243 37122 0 524 211 272 128809 Ly Nhan Ha Nam 38236 36129 4246 2571 117 175 403 3740 36198 0 1085 1151 264 124315 My Duc Ha Tay 46852 41330 1346 0 3300 1224 296 7290 14437 168 192 1475 435 118345 Nam Truc Nam Dinh 59030 46219 1788 0 0 88 4573 1155 0 0 0 3600 0 116453 Yen Khanh Ninh Binh 46989 41437 26 3172 128 206 0 798 21121 0 17 94 9 113997 Giao Thuy Nam Dinh 61514 46928 1009 0 0 460 852 568 0 0 0 785 0 112116 Thuong Tin Ha Tay 38691 36714 3175 0 0 259 233 3407 29005 0 210 273 24 111991 Kim Son Ninh Binh 57031 42941 105 487 65 0 0 2 7768 0 15 17 29 108461 Truc Ninh Nam Dinh 61447 41895 657 0 0 494 440 341 0 0 0 1500 0 106774 Vu ban Nam Dinh 47606 41034 2139 0 0 24 4171 916 0 0 0 0 0 95890 Kim Bang Ha Nam 34046 30497 4718 2426 4350 0 1073 1800 11089 245 430 1452 465 92591 Thanh Liem Ha Nam 44994 34836 981 1463 950 54 338 524 5991 0 385 1327 445 92288 Duy Tien Ha Nam 37994 34903 2162 540 0 0 606 3029 11837 8 208 565 95 91946 Phuc Tho Ha Tay 26486 24196 7357 0 486 1650 304 6066 22471 0 325 360 119 89820 Yen Mo Ninh Binh 40278 35923 160 2021 111 72 0 425 8013 46 57 44 29 87179 Xuan Truong Nam Dinh 47197 34519 1571 0 0 40 46 149 0 0 0 3168 0 86690 Gia Vien Ninh Binh 39917 28429 551 1680 880 2255 23 4 9533 0 92 308 196 83869 Quoc Oai Ha Tay 30887 24502 4228 0 313 750 327 589 13003 1600 165 557 241 77162 Hoai Duc Ha Tay 21665 15023 4507 0 104 515 223 186 33310 0 274 152 135 76093 Yen Thuy Hoa Binh 2126 13489 6236 0 2887 32678 3457 590 10485 658 73 0 134 72813 Thach That Ha Tay 26301 22427 494 0 0 0 1126 1378 9156 1232 129 800 610 63653 Luong Son Hoa Binh 11556 15054 5482 0 6758 2145 543 430 9562 2230 170 0 1168 55098 Thanh Tri Hanoi 8652 6008 1526 0 0 0 0 66 30429 0 0 131 0 46812 Dan Phuong Ha Tay 13843 10749 6032 0 12 0 6 2762 12467 0 288 220 112 46491 Ky Son Hoa Binh 5486 5356 2446 0 4658 18120 30 42 3653 54 195 0 185 40225 Hoa Lu Ninh Binh 21760 15369 0 684 0 0 0 23 1640 0 113 114 42 39744 My Loc Nam Dinh 21871 16509 1080 0 0 60 62 46 0 0 0 0 0 39628 Lac Thuy Hoa Binh 6125 7459 8235 0 3114 2408 998 258 9461 1265 215 0 57 39595 Son Tay Ha Tay 9036 8305 918 0 4324 803 1040 587 8621 210 65 1404 1423 36735 Tu Liem Hanoi 6071 3359 65 0 0 0 0 0 20307 0 0 383 0 30185 Ninh Binh Ninh Binh 10347 8440 112 1090 0 0 0 102 3147 0 40 66 21 23365 Tea (tons) Orange (tons) Longan (tons) Litchi (tons) Overall (tons)

Pollution load in the Day/Nhue River Basin 85 Spring Paddy (tons) Autumn Paddy (tons) Maize (tons) Potato (tons) Cassava (tons) Sugar Cane (tons) Groundnuts (tons) District Province Ha Dong Ha Tay 7858 7124 52 0 36 0 4 8 6327 0 11 7 0 21428 Phu Ly Ha Nam 5631 5239 448 1277 0 0 381 512 6675 0 305 287 437 21192 Tam Diep Ninh Binh 5057 2543 488 0 895 5800 54 1 1777 689 11 561 556 18432 Nam Dinh Nam Dinh 5405 3496 19 0 0 0 38 0 0 0 0 0 0 8958 Hoang Mai Hanoi 801 99 0 0 0 0 0 0 5534 0 0 0 0 6434 Ba Dinh Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hoan Kiem Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Tay Ho Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cau Giay Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dong Da Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hai ba Trung Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Soya Beans (tons) Vegetables (tons) Thanh Xuan Hanoi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 * - Listed in descending order of overall agricultural production. Tea (tons) Orange (tons) Longan (tons) Litchi (tons) Overall (tons)

Pollution load in the Day/Nhue River Basin 86 Table 6.42: Ranking of districts by pesticide and fertilizer use in the Day/Nhue River Basin * District Province Pesticide total (kg) Fertilizer total (kg) Kim Boi Hoa Binh 49854 15739780 Ba Vi Ha Tay 47166 22974557 Chuong My Ha Tay 41461 24049401 Phu Xuyen Ha Tay 37976 22402397 Ung Hoa Ha Tay 33386 24644258 Y Yen Nam Dinh 31574 23187830 Thuong Tin Ha Tay 29610 16615769 H. Thanh Oai Ha Tay 29289 18280984 My Duc Ha Tay 28760 18296830 Phuc Tho Ha Tay 25831 12774855 Nghia Hung Nam Dinh 24953 21351221 Hai Hau Nam Dinh 22605 20381233 Ly Nhan Ha Nam 20993 16969697 Nam Truc Nam Dinh 20393 17160498 Binh Luc Ha Nam 19954 18849085 Kim Bang Ha Nam 19759 13323083 Yen Thuy Hoa Binh 19604 6524540 H. Nho Quan Ninh Binh 19377 14999032 Hoai Duc Ha Tay 19168 9916977 Vu ban Nam Dinh 19086 14194920 Quoc Oai Ha Tay 18820 12089306 Duy Tien Ha Nam 17359 13509716 Giao Thuy Nam Dinh 16835 15222328 Thanh Liem Ha Nam 16055 14097314 Luong Son Hoa Binh 15906 5776060 Truc Ninh Nam Dinh 15544 14901563 Lac Thuy Hoa Binh 15069 4855540 Dan Phuong Ha Tay 15005 6605169 Yen Khanh Ninh Binh 14864 14170797 Kim Son Ninh Binh 14317 14721293 Thach That Ha Tay 13611 10274116 Yen Mo Ninh Binh 13285 12107811 Gia Vien Ninh Binh 13048 12388945 Xuan Truong Nam Dinh 12538 11369161 Thanh Tri Hanoi 10662 4560877 Son Tay Ha Tay 9324 5247491 My Loc Nam Dinh 7733 5965900 Ky Son Hoa Binh 7681 2971450 Tu Liem Hanoi 7046 3292558 Hoa Lu Ninh Binh 5828 5568822 Ha Dong Ha Tay 4179 2951321 Tam Diep Ninh Binh 3949 2352673 Phu Ly Ha Nam 3863 2977324 Ninh Binh Ninh Binh 3590 3345735 Nam Dinh Nam Dinh 1805 1580573 Hoang Mai Hanoi 1467 479104 Ba Dinh Hanoi 0 0 Hoan Kiem Hanoi 0 0 Tay Ho Hanoi 0 0 Cau Giay Hanoi 0 0 Dong Da Hanoi 0 0

Pollution load in the Day/Nhue River Basin 87 Hai ba Trung Hanoi 0 0 Thanh Xuan Hanoi 0 0 * - Listed in descending order of total pesticide use (kg). 6.5 DOMESTIC POLLUTION Domestic pollution consists of many different types of waste products generated by households. This study focuses on domestic BOD5, SS and solid waste. Information on household wastewater was available at the province and, in several instances, at the district level. An assumption about the BOD5 and SS content of wastewater was drawn from the Vietnam National Environmental Performance Assessment (EPA) Report (2006). Domestic solid waste was available from reports at the province and district level, with a further differentiation between rural and urban areas. However since the pollution coefficients used for domestic solid waste were only available at the district level, the study was constrained to classifying the entire district as only rural or urban, and could not differentiate within the district. Despite this minor limitation, the estimates remain indicative of the significant areas of domestic pollution. Table 6.51 summarizes the estimates of domestic pollution at the district level and is listed in descending order of domestic BOD5 discharges. Among the top 10 districts in the river basin nine out of ten are in the province of Hanoi, owning to the high populations figures in these areas. Taken together Hanoi districts constitute nearly 46% of all estimated domestic BOD5 and SS in the river basin. For Hai ba Trung and Dong Da the BOD5 estimates are nearly double the estimates of those ranked lower in the list. In addition, eight out of the top ten districts are associated with significant amounts of solid waste production. Table 6.51: Ranking of top domestic pollution releases by district the Day/Nhue River Basin * District Province Dom BOD5 (kg) Dom SS (kg) Dom SW (kg: urban) Dom SW (kg: rural) Hai ba Trung Hanoi 4423929 3317947 97299304 0 Dong Da Hanoi 4142530 3106897 91110240 0 Ba Dinh Hanoi 2497719 1873289 54934496 0 Tu Liem Hanoi 2340605 1755454 0 33744915 Hoan Kiem Hanoi 2130409 1597807 46855924 0 Thanh Xuan Hanoi 1880137 1410103 41351484 0 Thanh Tri Hanoi 1616217 1212163 0 23301294 Cau Giay Hanoi 1593764 1195323 35053028 0 Hai Hau Nam Dinh 1158249 868687 0 55110218 Tay Ho Hanoi 1143030 857272 25139644 0 Y Yen Nam Dinh 992421 744315 0 47220010 Chuong My Ha Tay 980123 735092 0 15972377 Ly Nhan Ha Nam 971927 728945 0 12565629 Ba Vi Ha Tay 913921 685441 0 14893530 Giao Thuy Nam Dinh 879759 659820 0 41859518 Nghia Hung Nam Dinh 844470 633353 0 40180434 Nam Truc Nam Dinh 835273 626455 0 39742842 Thuong Tin Ha Tay 780559 585420 0 12720227 Kim Son Ninh Binh 779278 584458 0 7415706 Truc Ninh Nam Dinh 777847 583386 0 37010482 Xuan Truong Nam Dinh 774439 580830 0 36848323 Hoai Duc Ha Tay 746914 560186 2254580 11151871 Binh Luc Ha Nam 719519 539639 0 9302348 Phu Xuyen Ha Tay 695515 521636 0 11334313

Pollution load in the Day/Nhue River Basin 88 H. Thanh Oai Ha Tay 685632 514224 2616784 9989320 Duy Tien Ha Nam 674045 505534 0 8714436 Ung Hoa Ha Tay 673640 505230 0 10977845 My Duc Ha Tay 637350 478013 0 10386452 Thanh Liem Ha Nam 624871 468653 0 8078693 H. Nho Quan Ninh Binh 621320 465990 0 5912563 Kim Bang Ha Nam 607891 455918 0 7859158 Yen Khanh Ninh Binh 602311 451733 0 5731669 Thach That Ha Tay 602148 451611 0 9812788 Phuc Tho Ha Tay 600922 450692 0 9792810 Quoc Oai Ha Tay 570534 427900 0 9297584 Vu ban Nam Dinh 569724 427293 0 27107827 Yen Mo Ninh Binh 536105 402079 0 5101641 Gia Vien Ninh Binh 526858 395144 0 5013649 Dan Phuong Ha Tay 514545 385909 0 8385183 Kim Boi Hoa Binh 488471 366354 0 4468994 Hoang Mai Hanoi 410575 307931 9030131 0 Ha Dong Ha Tay 379965 284974 13685792 0 Son Tay Ha Tay 372964 279723 13433627 0 Ninh Binh Ninh Binh 318477 238857 5059667 0 My Loc Nam Dinh 302436 226827 0 14390118 Hoa Lu Ninh Binh 297245 222934 0 2828622 Luong Son Hoa Binh 257373 193030 0 2354689 Phu Ly Ha Nam 252209 189157 4629840 0 Nam Dinh Nam Dinh 227920 170940 8638915 0 Yen Thuy Hoa Binh 212835 159626 0 1947216 Tam Diep Ninh Binh 178225 133669 4584977 0 Lac Thuy Hoa Binh 168599 126450 0 1542505 Ky Son Hoa Binh 121138 90854 0 1108287 * - Listed in descending order of domestic BOD5 (kg). 6.6 CRAFT VILLAGES Craft villages (CVs) are an important contributor to local and rural economies in terms of income generation. However, rapid expansion in this form of economic activity has lead to increases in both the quantity and quality of pollution being emitted into waste streams such as the Day/Nhue River Sub-basin. As a whole, the Red River Delta is home to over 60% of all craft-village activity in Vietnam (GSO, 2006). As the Day/Nhue is a sub-basin of the Red River, the status of craftvillage activities plays an important role in defining the general health of the river systems. In this study information on craft-villages was obtained from the GSO Craft-Village Survey of 2006. 31 The survey contained information for over 300 craft-villages employing over 220,000 fulltime workers in 90,000 households in the Day/Nhue River Sub-basin. The information was organized such that a number of different CV activities could be recorded at the village level. This information was then aggregated up to the commune level and made available to the study team. To make this data set congruent with the other information, the study aggregates it to the district level. The survey also tabulated hundreds of different activities of which people are engaged. For the purposes of this analysis, sector activities were constructed by broadly categorizing the hundreds of activities into the following set of CV sectors: 31 This sub-questionnaire was included along with the Agricultural Census 2006.

Pollution load in the Day/Nhue River Basin 89 Food and agricultural products processing Textile and dyeing Metal recycling Wood products Bamboo, lacquer, statue painting Others (making hat, salt, incense, ball sewing, etc) Despite good coverage in terms of CV economic activity, pollution coefficients associated with the level of activity in terms of either employment or production was difficult to obtain. Only relatively recently have craft-villages been perceived as a serious source of pollution and subject to pilot monitoring programs and assessments. During this study data for the following pollutant concentrations in craft-village wastewater were collected: BOD5 COD SS Total nitrogen (N) Total phosphorus (P) Iron (Fe) Oil Total coliform This set of pollution concentrations were used as proxies for pollution coefficients, which were often specific to a particular sector of activity. Pollution load estimates were calculated for each of the above sectors within a CV and then aggregated up to the district level. Table 6.61 summarizes the estimates derived for this study. Perhaps the most obvious result is the concentration of CV activity in the province of Ha Tay, and as a result its districts are ranked in the top quartile across many of the pollutants covered in this exercise. Ha Tay is well-known to be home to most of the craft-village activity in the Red River Delta and thus should be subject to further CV monitoring efforts in the future. Table 6.61: Ranking of top craft-village districts in the Day/Nhue River Basin * District Province # CV HH CV employment BOD5 (kg) COD (kg) SS (kg) Total N (kg) Total P (kg) Total Fe (kg) Oil (kg) Coliform (billion MPN) Hoai Duc Ha Tay 5545 14337 329881.5 1042101.6 851560.7 32025.1 7765.7 101.5 5533.4 18507827.6 Ba Vi Ha Tay 3960 7220 220711.5 684298.0 477900.3 22625.3 5457.8 0.0 3924.8 12469739.7 Phuc Tho Ha Tay 2816 6597 151750.2 501862.6 318758.2 14880.0 3619.3 72.3 2570.3 8134452.4 Ly Nhan Ha Nam 3516 6940 118424.6 456863.6 228338.5 10207.2 2548.1 206.2 1739.6 5435280.7 Thach That Ha Tay 6242 17575 106672.3 292247.8 178237.5 8284.4 1758.2 493.5 1484.1 2656227.0 Dan Phuong Ha Tay 2087 4042 103786.3 317570.4 215282.5 10352.5 2407.9 133.6 41465.5 5266199.7 H. Thanh Oai Ha Tay 9264 18045 80268.6 233830.0 265948.5 21665.3 1721.0 296.0 10725.5 3695777.2 Thuong Tin Ha Tay 9174 25261 69879.3 219070.8 145773.0 2950.7 635.1 319.7 50892.0 938427.5 Phu Xuyen Ha Tay 5028 11450 66705.9 220670.2 83447.0 21319.3 579.6 163.2 39745.6 794863.2 Tu Liem Hanoi 1759 6877 63888.1 639761.4 266166.0 10934.3 2582.4 155.5 34192.5 5634208.2 Ung Hoa Ha Tay 3705 12295 40223.4 167650.7 102636.9 2862.3 647.9 185.8 19931.2 1275722.4 Quoc Oai Ha Tay 2918 7832 34235.5 94228.4 57155.0 1261.4 325.2 152.6 193.8 573874.4 Chuong My Ha Tay 6033 14410 33456.3 54406.9 40496.8 557.4 66.1 89.4 4446.8 270876.3 Truc Ninh Nam Dinh 4380 8619 28691.2 155180.1 27962.3 470.5 196.4 200.0 55.8 73007.5 Y Yen Nam Dinh 2540 5885 21611.3 70887.7 36662.8 2664.9 271.6 369.7 2132.7 274779.2 Giao Thuy Nam Dinh 3106 6828 20029.0 62098.9 43368.6 2053.1 495.3 0.0 356.2 1131605.5 Duy Tien Ha Nam 2164 6757 17369.3 90068.5 17469.6 294.2 96.0 120.8 31.8 62321.5 Thanh Tri Hanoi 437 1197 16366.6 88073.0 40530.3 2112.3 513.2 16.4 368.7 1150515.0 Binh Luc Ha Nam 612 1223 13896.3 41714.8 51638.7 1335.5 299.7 19.3 1088.4 687119.9

Pollution load in the Day/Nhue River Basin 90 H. Nho Quan Ninh Binh 120 250 11724.5 36350.4 47584.3 1201.9 289.8 0.0 208.4 662403.2 Vu ban Nam Dinh 1767 5755 9863.0 38791.1 30154.5 143.8 36.5 39.1 16934.9 69027.6 My Duc Ha Tay 1483 3817 8854.2 107557.5 10419.7 266.1 111.0 129.9 26.3 29993.3 Thanh Liem Ha Nam 940 2055 8793.2 27262.9 35688.2 901.6 217.5 0.0 156.2 496802.4 Ha Dong Ha Tay 565 1108 6986.8 83034.6 15128.5 1358.2 99.3 189.8 60630.2 138697.0 Nam Truc Nam Dinh 965 2032 6052.8 40371.9 6605.0 247.1 53.3 69.7 35.0 21894.9 Gia Vien Ninh Binh 847 2150 1308.2 1929.4 1520.6 21.2 1.1 3.7 2.9 11068.9 Yen Mo Ninh Binh 793 2006 555.9 820.2 646.4 9.1 0.4 1.5 1.1 4704.3 My Loc Nam Dinh 282 761 372.7 550.1 433.3 6.2 0.4 1.1 638.0 3154.6 Kim Bang Ha Nam 10 15 131.4 926.0 121.5 2.2 1.1 1.1 0.4 148.5 Ba Dinh Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hoan Kiem Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Tay Ho Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Cau Giay Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Dong Da Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hai ba Trung Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hoang Mai Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Thanh Xuan Hanoi 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Ky Son Hoa Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Luong Son Hoa Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Kim Boi Hoa Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Yen Thuy Hoa Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Lac Thuy Hoa Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Son Tay Ha Tay 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Phu Ly Ha Nam 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Nam Dinh Nam Dinh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Nghia Hung Nam Dinh 1742 4568 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Xuan Truong Nam Dinh 284 568 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hai Hau Nam Dinh 3858 11995 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Ninh Binh Ninh Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Tam Diep Ninh Binh 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hoa Lu Ninh Binh 480 750 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Yen Khanh Ninh Binh 300 1200 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Kim Son Ninh Binh 815 1317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 * - Listed in descending order of estimated BOD5 load (kg). One further step was possible in analyzing the craft village data by classifying the activities into six sub-sectors. Estimating pollution loads by each of these sectors within the district, Table 6.62 summarizes the top sectors associated with the top 5 districts from Table 6.61. The table is ordered in descending order of the estimated BOD5 load. The results indicate that in these districts, activities related to Food and agricultural products processing contribute the majority to estimated BOD5 pollution load. Textiles and dying of materials is the second most important activity in terms of BOD5 load and Wood products is significant for Thach That (Ha Tay). Across other pollutants a similar result is found where the food processing sector is likely to be associated with significant pollution loads. Given these results, one note on interpretation should be highlighted. Although the study used several broad categories to aggregate CV activities, it is not uncommon for a particular CV to specialize in certain crafts owing to the economies of scale in location or supply-chain accessibility. The study findings are consistent with this character of CVs, even at the district level. The results show that pollution is concentrated in relatively few categories of CV activities (ie. Food processing).

Pollution load in the Day/Nhue River Basin 91 Table 6.62: List of craft-village product sectors in top districts in the Day/Nhue River Basin * CV employment Product sector Province District # HH 1. Food and agricultural products processing Ha Tay Hoai Duc 3164 6553 309133.1 958441.5 830522.1 31689.3 7644.2 0.0 5497.3 17465363.4 BOD5 (kg) COD (kg) SS (kg) Total N (kg) Total P (kg) Total Fe (kg) Oil (kg) Coliform (billion MPN) 2. Textile and dyeing Ha Tay Hoai Duc 1832 6697 13949.2 69599.3 16230.1 181.0 83.2 95.3 17.2 11838.7 3. Wood products Ha Tay Hoai Duc 549 1087 6799.6 14060.9 4808.5 154.8 38.3 6.2 19.0 1030625.5 1. Food and agricultural products processing Ha Tay Ba Vi 2259 4554 220711.5 684298.0 477900.3 22625.3 5457.8 0.0 3924.8 12469739.7 2. Others (making hat, salt, incense, ball sewing, ) Ha Tay Ba Vi 1701 2666 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1. Food and agricultural products processing Ha Tay Phuc Tho 1646 3067 143819.1 445899.3 311407.4 14743.1 3556.2 0.0 2557.6 8125479.2 2. Textile and dyeing Ha Tay Phuc Tho 1170 3530 7931.1 55963.3 7350.7 137.2 63.1 72.3 13.1 8973.3 1. Food and agricultural products processing Ha Nam Ly Nhan 980 2015 95749.0 296862.5 207322.9 9815.2 2367.8 0.0 1702.7 5409625.8 2. Textile and dyeing Ha Nam Ly Nhan 1728 3360 22675.6 160001.0 21015.6 392.0 180.3 206.2 37.2 25654.9 3. Others (making hat, salt, incense, ball sewing, ) Ha Nam Ly Nhan 808 1565 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1. Food and agricultural products processing Ha Tay Thach That 465 1273 45431.9 140858.2 98372.6 4657.4 1123.5 0.0 807.7 2566812.1 2. Wood products Ha Tay Thach That 3859 10577 41092.8 78523.2 40260.6 425.2 402.2 44.2 134.0 41466.1 3. Metal recycling Ha Tay Thach That 1305 3183 16137.7 66952.3 34943.3 3136.8 229.2 438.4 533.6 14022.6 4. Bamboo, lacquer, statue painting Ha Tay Thach That 613 2542 4009.5 5914.1 4661.1 65.3 2.9 11.0 8.8 33926.1 * - Listed in descending order of estimated BOD5 load (kg).

Pollution load in the Day/Nhue River Basin 92 6.7 OVERALL AREAS OF SIGNIFICANCE The estimates derived from each of the pollution models suggest several areas of focus in terms of identifying pollution significance. To bring together each of the results, Table 6.71 presents the estimates for industry, hazards, agriculture, domestic and craft-villages. For industry and hazardous substances, the areas of common interest are largely confined to similar (and selected) districts in Hanoi and Ha Tay. Agricultural pesticide and fertilizer-related pollution are most intensive in the districts of Ha Tay and a few districts in Nam Dinh. Domestic BOD5, SS and solid waste loads to water are primarily concentrated in the districts of Hanoi with some exceptions for rural solid waste in Nam Dinh and Ha Tay. The pollution situation for craft-villages is quite clear. Focusing resources in the province of Ha Tay would serve to target over 75% of estimated craft-village water pollution for all selected indicators covered in this study (BOD5, COD, SS, Total N, Total P, Total Fe, Oil and Coliform). Each of these indicators may be interpreted separately for setting area-based targets. In addition it may be the case that specific types of pollution are better prepared to be addressed given the nature of the institutional arrangements, capacity and cost constraints. However, taken together, the indicators suggest very clear synergies in giving the provinces of Hanoi and Ha Tay special attention, as districts in these provinces rank consistently in the first or second quartile of estimated pollution load.

Pollution load in the Day/Nhue River Basin 93 Table 6.71 District level pollution estimates and significance in the Day/Nhue River Basin * District Province Ind BOD5 (kg) Ind SS (kg) Hazard Index Dom BOD5 (kg) Dom SS (kg) Dom SW (kg: urban) Dom SW (kg: rural) Hai ba Trung Hanoi 1359392 6439514 609284 4423929 3317947 97299304 0 0 0 0 0 0 0 0 0 0 0 Phu Xuyen Ha Tay 487094 1501071 133000 695515 521636 0 11334313 37976 22402397 66706 220670 83447 21319 579 163 39746 794863 Dong Da Hanoi 476271 3291321 331230 4142530 3106897 91110240 0 0 0 0 0 0 0 0 0 0 0 Ky Son Hoa Binh 215722 732814 73972 121138 90854 0 1108287 7681 2971450 0 0 0 0 0 0 0 0 Tu Liem Hanoi 179777 1913045 294695 2340605 1755454 0 33744915 7046 3292558 63888 639761 266166 10934 2582 156 34193 5634208 Phuc Tho Ha Tay 177305 601560 58651 600922 450692 0 9792810 25831 12774855 151750 501863 318758 14880 3619 72 2570 8134452 Hoan Kiem Hanoi 172288 1886694 261266 2130409 1597807 46855924 0 0 0 0 0 0 0 0 0 0 0 Hoang Mai Hanoi 137800 4124500 988562 410575 307931 9030131 0 1467 479104 0 0 0 0 0 0 0 0 Phu Ly Ha Nam 91724 150103 98514 252209 189157 4629840 0 3863 2977324 0 0 0 0 0 0 0 0 Nam Dinh Nam Dinh 83795 1621743 723251 227920 170940 8638915 0 1805 1580573 0 0 0 0 0 0 0 0 Chuong My Ha Tay 73856 48044 54378 980123 735092 0 15972377 41461 24049401 33456 54407 40497 557 66 89 4447 270876 Ba Dinh Hanoi 65565 2522537 148205 2497719 1873289 54934496 0 0 0 0 0 0 0 0 0 0 0 Ung Hoa Ha Tay 63532 1374794 21134 673640 505230 0 10977845 33386 24644258 40223 167651 102637 2862 648 186 19931 1275722 Ninh Binh Ninh Binh 56011 14154951 60081 318477 238857 5059667 0 3590 3345735 0 0 0 0 0 0 0 0 Thanh Xuan Hanoi 53392 2120057 276508 1880137 1410103 41351484 0 0 0 0 0 0 0 0 0 0 0 Ha Dong Ha Tay 49159 1159863 120870 379965 284974 13685792 0 4179 2951321 6987 83034 15129 1358 99 190 60630 138697 Tay Ho Hanoi 46019 1071976 21233 1143030 857272 25139644 0 0 0 0 0 0 0 0 0 0 0 Son Tay Ha Tay 45442 165602 69715 372964 279723 13433627 0 9324 5247491 0 0 0 0 0 0 0 0 Thanh Tri Hanoi 44993 4650317 568031 1616217 1212163 0 23301294 10662 4560877 16367 88073 40530 2112 513 16 369 1150515 Nam Truc Nam Dinh 39051 648988 20999 835273 626455 0 39742842 20393 17160498 6053 40372 6605 247 53 70 35 21895 Tam Diep Ninh Binh 25065 5216262 29778 178225 133669 4584977 0 3949 2352673 0 0 0 0 0 0 0 0 Cau Giay Hanoi 21332 239679 60712 1593764 1195323 35053028 0 0 0 0 0 0 0 0 0 0 0 Hoai Duc Ha Tay 13468 501209 135548 746914 560186 2254580 11151871 19168 9916977 329882 1042102 851561 32025 7766 101 5533 18507827 Dan Phuong Ha Tay 7451 28748 375303 514545 385909 0 8385183 15005 6605169 103786 317570 215282 10352 2408 134 41465 5266200 Duy Tien Ha Nam 6863 14294 8254 674045 505534 0 8714436 17359 13509716 17369 90068 17470 294 96 121 32 62321 H. Thanh Oai Ha Tay 6661 107482 27688 685632 514224 2616784 9989320 29289 18280984 80269 233830 265948 21665 1721 296 10725 3695777 Hai Hau Nam Dinh 3154 7344 78442 1158249 868687 0 55110218 22605 20381233 0 0 0 0 0 0 0 0 Xuan Truong Nam Dinh 3093 11525 4690 774439 580830 0 36848323 12538 11369161 0 0 0 0 0 0 0 0 Thuong Tin Ha Tay 2847 58186 49677 780559 585420 0 12720227 29610 16615769 69879 219071 145773 2951 635 320 50892 938427 Thach That Ha Tay 2190 4332189 156831 602148 451611 0 9812788 13611 10274116 106672 292248 178238 8284 1758 493 1484 2656227 Ly Nhan Ha Nam 1837 3632 12267 971927 728945 0 12565629 20993 16969697 118425 456863 228339 10207 2548 206 1740 5435281 Binh Luc Ha Nam 1433 3767 21065 719519 539639 0 9302348 19954 18849085 13896 41715 51639 1335 300 19 1088 687120 Yen Mo Ninh Binh 1412 2542 2800 536105 402079 0 5101641 13285 12107811 556 820 646 9 0 1 1 4704 Thanh Liem Ha Nam 1057 129287 14531 624871 468653 0 8078693 16055 14097314 8793 27263 35688 901 217 0 156 496802 Kim Boi Hoa Binh 862 1341 118 488471 366354 0 4468994 49854 15739780 0 0 0 0 0 0 0 0 Quoc Oai Ha Tay 730 162456 28805 570534 427900 0 9297584 18820 12089306 34236 94229 57155 1261 325 153 194 573874 Nghia Hung Nam Dinh 502 2264 30044 844470 633353 0 40180434 24953 21351221 0 0 0 0 0 0 0 0 Kim Bang Ha Nam 484 330091 3728 607891 455918 0 7859158 19759 13323083 131 926 122 2 1 1 0 148 Gia Vien Ninh Binh 380 27746 33925 526858 395144 0 5013649 13048 12388945 1308 1930 1521 21 1 4 3 11069 My Loc Nam Dinh 282 57 582 302436 226827 0 14390118 7733 5965900 373 550 433 6 0 1 638 3155 Hoa Lu Ninh Binh 270 1237116 22006 297245 222934 0 2828622 5828 5568822 0 0 0 0 0 0 0 0 Luong Son Hoa Binh 245 121729 1434 257373 193030 0 2354689 15906 5776060 0 0 0 0 0 0 0 0 My Duc Ha Tay 223 8375 12432 637350 478013 0 10386452 28760 18296830 8854 107558 10420 266 111 130 26 29993 Kim Son Ninh Binh 215 338 111 779278 584458 0 7415706 14317 14721293 0 0 0 0 0 0 0 0 Yen Thuy Hoa Binh 105 74458 937 212835 159626 0 1947216 19604 6524540 0 0 0 0 0 0 0 0 Pest Total (kg) Fert Total (kg) CV BOD5 (kg) CV COD (kg) CV SS (kg) CV Total N (kg) CV Total P (kg) CV Total Fe (kg) CV Oil (kg) CV Coliform (billion MPN)

Pollution load in the Day/Nhue River Basin 94 Truc Ninh Nam Dinh 97 394190 217 777847 583386 0 37010482 15544 14901563 28691 155180 27962 471 196 200 56 73008 H. Nho Quan Ninh Binh 84 221 83 621320 465990 0 5912563 19377 14999032 11724 36350 47584 1202 290 0 208 662403 Y Yen Nam Dinh 78 130 5373 992421 744315 0 47220010 31574 23187830 21611 70888 36663 2665 272 370 2133 274779 Lac Thuy Hoa Binh 60 84 4 168599 126450 0 1542505 15069 4855540 0 0 0 0 0 0 0 0 Yen Khanh Ninh Binh 50 1209 12430 602311 451733 0 5731669 14864 14170797 0 0 0 0 0 0 0 0 Ba Vi Ha Tay 37 76 123 913921 685441 0 14893530 47166 22974557 220712 684298 477900 22625 5458 0 3925 12469740 Giao Thuy Nam Dinh 9 57 39 879759 659820 0 41859518 16835 15222328 20029 62099 43369 2053 495 0 356 1131606 Vu ban Nam Dinh 8 26 9 569724 427293 0 27107827 19086 14194920 9863 38791 30155 144 36 39 16935 69028 * - Listed in descending order of Industrial BOD5 load (kg). ANNEX 6.1: POLLUTION LOADS BY SECTOR AND INDUSTRIAL COMPOSITION Table A6.1 Contribution of industry, domestic and craft village sources to district-level SS pollution load * Province District Ind SS (kg) Dom SS (kg) CV SS (kg) SS total % Basin SS % Ind SS % Dom SS % CV SS Ha Nam Binh Luc 3767.2 539639.0 51638.9 595045.1 0.6 0.6 90.7 8.7 Ha Nam Duy Tien 14293.9 505533.6 17469.7 537297.2 0.5 2.7 94.1 3.3 Ha Nam Kim Bang 330090.9 455918.0 121.6 786130.6 0.8 42.0 58.0 0.0 Ha Nam Ly Nhan 3631.9 728945.3 228338.5 960915.8 0.9 0.4 75.9 23.8 Ha Nam Phu Ly 150103.4 189156.9 0.0 339260.3 0.3 44.2 55.8 0.0 Ha Nam Thanh Liem 129286.9 468653.4 35688.2 633628.6 0.6 20.4 74.0 5.6 Ha Tay Ba Vi 76.5 685440.9 477900.5 1163417.9 1.1 0.0 58.9 41.1 Ha Tay Chuong My 48043.9 735092.3 40496.7 823632.9 0.8 5.8 89.3 4.9 Ha Tay Dan Phuong 28748.1 385909.0 215282.5 629939.6 0.6 4.6 61.3 34.2 Ha Tay H. Thanh Oai 107481.6 514224.0 265948.4 887654.0 0.9 12.1 57.9 30.0 Ha Tay Ha Dong 1159863.1 284974.1 15128.7 1459965.9 1.4 79.4 19.5 1.0 Ha Tay Hoai Duc 501208.7 560185.8 851560.7 1912955.1 1.9 26.2 29.3 44.5 Ha Tay My Duc 8374.9 478012.8 10419.6 496807.3 0.5 1.7 96.2 2.1 Ha Tay Phu Xuyen 1501070.7 521636.0 83446.9 2106153.5 2.1 71.3 24.8 4.0 Ha Tay Phuc Tho 601560.1 450691.8 318758.1 1371010.0 1.3 43.9 32.9 23.3 Ha Tay Quoc Oai 162455.6 427900.2 57155.2 647510.9 0.6 25.1 66.1 8.8 Ha Tay Son Tay 165602.3 279723.3 0.0 445325.6 0.4 37.2 62.8 0.0 Ha Tay Thach That 4332189.2 451611.3 178237.5 4962038.0 4.8 87.3 9.1 3.6 Ha Tay Thuong Tin 58186.3 585419.5 145773.0 789378.8 0.8 7.4 74.2 18.5 Ha Tay Ung Hoa 1374793.9 505230.4 102637.0 1982661.4 1.9 69.3 25.5 5.2 Hanoi Ba Dinh 2522536.9 1873289.4 0.0 4395826.0 4.3 57.4 42.6 0.0 Hanoi Cau Giay 239678.5 1195323.0 0.0 1435001.5 1.4 16.7 83.3 0.0 Hanoi Dong Da 3291321.3 3106897.3 0.0 6398218.5 6.2 51.4 48.6 0.0 Hanoi Hai ba Trung 6439513.9 3317947.0 0.0 9757461.0 9.5 66.0 34.0 0.0

Pollution load in the Day/Nhue River Basin 95 Province District Ind SS (kg) Dom SS (kg) CV SS (kg) SS total % Basin SS % Ind SS % Dom SS % CV SS Hanoi Hoan Kiem 1886693.6 1597806.6 0.0 3484500.3 3.4 54.2 45.9 0.0 Hanoi Hoang Mai 4124500.4 307931.2 0.0 4432431.5 4.3 93.1 7.0 0.0 Hanoi Tay Ho 1071976.1 857272.4 0.0 1929248.5 1.9 55.6 44.4 0.0 Hanoi Thanh Tri 4650317.0 1212163.0 40530.2 5903010.0 5.8 78.8 20.5 0.7 Hanoi Thanh Xuan 2120057.1 1410102.9 0.0 3530160.0 3.4 60.1 39.9 0.0 Hanoi Tu Liem 1913045.2 1755453.5 266166.1 3934664.8 3.8 48.6 44.6 6.8 Hoa Binh Kim Boi 1341.5 366353.6 0.0 367695.1 0.4 0.4 99.6 0.0 Hoa Binh Ky Son 732814.1 90853.7 0.0 823667.8 0.8 89.0 11.0 0.0 Hoa Binh Lac Thuy 84.1 126449.5 0.0 126533.6 0.1 0.1 99.9 0.0 Hoa Binh Luong Son 121728.7 193029.8 0.0 314758.4 0.3 38.7 61.3 0.0 Hoa Binh Yen Thuy 74458.4 159626.4 0.0 234084.8 0.2 31.8 68.2 0.0 Nam Dinh Giao Thuy 56.8 659819.5 43368.6 703244.9 0.7 0.0 93.8 6.2 Nam Dinh Hai Hau 7344.2 868686.5 0.0 876030.8 0.9 0.8 99.2 0.0 Nam Dinh My Loc 57.4 226827.3 433.4 227318.1 0.2 0.0 99.8 0.2 Nam Dinh Nam Dinh 1621742.9 170940.2 0.0 1792683.1 1.8 90.5 9.5 0.0 Nam Dinh Nam Truc 648988.3 626455.0 6605.2 1282048.5 1.3 50.6 48.9 0.5 Nam Dinh Nghia Hung 2263.6 633352.6 0.0 635616.2 0.6 0.4 99.6 0.0 Nam Dinh Truc Ninh 394190.0 583385.6 27962.4 1005537.9 1.0 39.2 58.0 2.8 Nam Dinh Vu ban 25.9 427292.9 30154.6 457473.3 0.5 0.0 93.4 6.6 Nam Dinh Xuan Truong 11524.9 580829.5 0.0 592354.4 0.6 2.0 98.1 0.0 Nam Dinh Y Yen 130.2 744315.4 36662.9 781108.6 0.8 0.0 95.3 4.7 Ninh Binh Gia Vien 27745.6 395143.6 1520.7 424409.9 0.4 6.5 93.1 0.4 Ninh Binh H. Nho Quan 221.0 465990.2 47584.3 513795.5 0.5 0.0 90.7 9.3 Ninh Binh Hoa Lu 1237116.4 222933.8 0.0 1460050.1 1.4 84.7 15.3 0.0 Ninh Binh Kim Son 337.6 584458.2 0.0 584795.8 0.6 0.1 99.9 0.0 Ninh Binh Ninh Binh 14154950.6 238857.4 0.0 14393808.0 14.0 98.3 1.7 0.0 Ninh Binh Tam Diep 5216262.2 133668.6 0.0 5349931.0 5.2 97.5 2.5 0.0 Ninh Binh Yen Khanh 1209.3 451733.3 0.0 452942.5 0.4 0.3 99.7 0.0 Ninh Binh Yen Mo 2542.1 402078.5 646.3 405267.0 0.4 0.6 99.2 0.2 * - Shaded rows indicate an Industrial SS share 50%. Table A6.2 Industrial sector composition in the Day/Nhue River Basin *

Pollution load in the Day/Nhue River Basin 96 VSIC-2 VSIC-4 VSIC-4 Description # firms Employment BOD5 % SS % 21 2101 Pulp, paper and paperboard 17 1127 1688048.1 42.0 5733254.0 9.1 15 1551 Distilling, rectifying and blending of spirits; ethyl alcohol production from fe 9 856 821518.3 20.4 1476540.2 2.3 15 1520 Dairy products 4 423 490497.5 12.2 70649.7 0.1 25 2520 Plastics products 171 8975 222955.2 5.5 4818.0 0.0 24 2411 Basic chemicals, except fertilizers and nitrogen compounds 9 371 211270.4 5.3 326557.6 0.5 17 1711 Preparation and spinning of textile fibres; weaving of textiles 57 21886 91320.4 2.3 141814.8 0.2 27 2732 Casting of non-ferrous metals 9 342 89212.2 2.2 1289569.2 2.0 21 2109 Other articles of paper and paperboard 71 4070 77481.5 1.9 76427.2 0.1 15 1542 Sugar 3 176 48093.7 1.2 68955.2 0.1 19 1920 Footwear 35 13994 37021.6 0.9 36301.1 0.1 27 2720 Basic precious and non-ferrous metals 4 120 31302.5 0.8 452480.4 0.7 24 2423 Pharmaceuticals, medicinal chemicals and botanical products 43 4799 30873.8 0.8 7740401.3 12.2 15 1553 Malt liquors and malt 40 5009 28145.4 0.7 65053.4 0.1 15 1513 Processing and preserving of fruit and vegetables 19 651 16051.2 0.4 25320.8 0.0 15 1512 Processing and preserving of fish and fish products 15 411 15834.8 0.4 26995.2 0.0 21 2102 Corrugated paper and paperboard, containers of paper and paperboard 39 2384 12316.1 0.3 21146.5 0.0 26 2695 Articles of concrete, cement and plaster 41 7082 9188.3 0.2 13479.3 0.0 17 1712 Finishing of textiles 33 2179 9092.0 0.2 14119.3 0.0 28 2899 Other fabricated metal products n.e.c. 236 6576 9079.4 0.2 261343.6 0.4 24 2424 Soap and detergents, cleaning and polishing preparations, perfumes and toilet preparations 18 525 7944.3 0.2 11220.7 0.0 20 2022 Builders' carpentry and joinery 46 1486 7492.0 0.2 35327.9 0.1 24 2412 Fertilizers and nitrogen compounds 16 819 5831.6 0.1 1134757.8 1.8 15 1543 Cocoa, chocolate and sugar confectionery 25 3489 5608.2 0.1 2692.9 0.0 24 2429 Other chemical products n.e.c. 23 4490 5367.7 0.1 7746.7 0.0 20 2010 Sawmilling and planing of wood 66 1037 5228.3 0.1 24653.5 0.0 23 2320 Refined petroleum products 1 30 3411.6 0.1 17122.1 0.0 18 1820 Dressing and dyeing of fur; articles of fur 2 458 3268.4 0.1 9990.0 0.0 20 2021 Veneer sheets; plywood, laminboard, particle board, other panels and boards 18 608 3065.4 0.1 14454.5 0.0 32 3220 Television and radio transmitters; apparatus for line telephony and telegraphy 14 1202 2441.4 0.1 3378.3 0.0 27 2710 Basic iron and steel 35 2479 2367.4 0.1 34878647.2 55.2 26 2699 Other non-metallic mineral products n.e.c. 42 1601 2077.2 0.1 3047.2 0.0 32 3230 Television and radio receivers, sound, video recording, reproducing apparatus an 20 921 1870.6 0.0 2588.5 0.0 15 1511 Production, processing and preserving of meat and meat products 11 555 1808.1 0.0 2242.2 0.0 28 2812 Tanks, reservoirs and containers of metal 13 1279 1765.9 0.0 50830.1 0.1 19 1911 Tanning and dressing of leather 3 40 1732.7 0.0 3272.1 0.0

Pollution load in the Day/Nhue River Basin 97 VSIC-2 VSIC-4 VSIC-4 Description # firms Employment BOD5 % SS % 35 3591 Motorcycles 42 5633 1650.6 0.0 9802.3 0.0 24 2413 Plastics in primary forms and of synthetic rubber 2 64 1622.0 0.0 5241.3 0.0 17 1722 Carpets and rugs 11 1563 1527.0 0.0 2568.2 0.0 28 2892 Treatment and coating of metals; general mechanical engineering 29 992 1369.6 0.0 39424.1 0.1 15 1514 Vegetable and animal oils and fats 2 30 1306.0 0.0 1475.6 0.0 22 2221 Printing 234 7418 1286.0 0.0 703.9 0.0 15 1552 Wines 6 381 1022.6 0.0 561.3 0.0 26 2691 Non-structural non-refractory ceramic ware 5 737 969.8 0.0 2406.4 0.0 24 2421 Pesticides and other agro-chemical products 6 135 961.3 0.0 187048.0 0.3 37 3720 Recycling of non-metal waste and scrap 3 28 695.6 0.0 15.0 0.0 26 2694 Cement, lime and plaster 18 6068 663.3 0.0 1454548.5 2.3 29 2929 Other special purpose machinery 24 1575 596.9 0.0 488.1 0.0 26 2696 Cutting, shaping and finishing of stone 12 433 561.8 0.0 824.1 0.0 15 1549 Other food products n.e.c. 53 1902 521.2 0.0 206.1 0.0 27 2731 Casting of iron and steel 11 470 448.8 0.0 6612732.6 10.5 16 1600 Tobacco products 1 1222 395.9 0.0 483.5 0.0 28 2811 Structural metal products 127 6897 386.7 0.0 531.2 0.0 28 2891 Forging, pressing, stamping and roll-forming of metal; powder metallurgy 12 280 386.6 0.0 11127.8 0.0 15 1533 Prepared animal feeds 74 2061 382.7 0.0 557.5 0.0 32 3210 Electronic valves and tubes and other electronic components 10 172 349.3 0.0 483.4 0.0 22 2211 Publishing of books, brochures, musical books and other publications 20 1968 341.2 0.0 186.7 0.0 29 2924 Machinery for mining, quarrying and construction 4 711 269.5 0.0 220.3 0.0 15 1554 Soft drinks; production of mineral waters 50 805 220.6 0.0 87.2 0.0 35 3592 Bicycles and invalid carriages 12 751 220.1 0.0 1306.9 0.0 26 2693 Structural non-refractory clay and ceramic products 53 10101 215.8 0.0 3850.0 0.0 17 1730 Knitted and crocheted fabrics and articles 29 3865 215.1 0.0 433.2 0.0 23 2310 Coke oven products 3 56 166.6 0.0 204.5 0.0 15 1544 Macaroni, noodles, couscous and similar farinaceous products 9 500 137.0 0.0 54.2 0.0 22 2222 Service activities related to printing 75 709 122.9 0.0 67.3 0.0 35 3599 Other transport equipment n.e.c. 22 1331 100.2 0.0 2360.3 0.0 29 2925 Machinery for food, beverage and tobacco processing 8 255 96.6 0.0 79.0 0.0 22 2219 Other publishing 3 446 77.3 0.0 42.3 0.0 29 2926 Machinery for textile, apparel and leather production 7 201 76.2 0.0 62.3 0.0 31 3120 Electricity distribution and control apparatus 12 1799 76.0 0.0 421.4 0.0 29 2915 Lifting and handling equipment 5 969 73.0 0.0 1718.3 0.0

Pollution load in the Day/Nhue River Basin 98 VSIC-2 VSIC-4 VSIC-4 Description # firms Employment BOD5 % SS % 29 2911 Engines and turbines, except aircraft, vehicle and cycle engines 3 452 54.7 0.0 0.0 0.0 28 2813 Steam generators, except central heating hot water boilers 6 143 54.2 0.0 44.3 0.0 34 3410 Motor vehicles 8 1926 50.1 0.0 251.7 0.0 29 2919 Other general purpose machinery 11 571 43.0 0.0 1012.6 0.0 33 3311 Medical and surgical equipment and orthopaedic appliances 18 1354 41.9 0.0 46.6 0.0 24 2422 Paints, varnishes and similar coatings, printing ink and mastics 39 1525 41.6 0.0 174.7 0.0 26 2610 Glass and glass products 14 507 40.6 0.0 287.6 0.0 31 3110 Electric motors, generators and transformers 9 962 40.6 0.0 225.3 0.0 31 3150 Electric lamps and lighting equipment 8 2014 35.9 0.0 220.3 0.0 31 3130 Insulated wire and cable 28 1810 32.3 0.0 197.9 0.0 15 1532 Starches and starch products 7 112 30.7 0.0 12.1 0.0 31 3190 Other electrical equipment n.e.c. 38 1622 28.9 0.0 177.4 0.0 29 2913 Bearings, gears, gearing and driving elements 13 278 20.9 0.0 493.0 0.0 25 2519 Other rubber products 27 786 20.3 0.0 95250.2 0.2 34 3430 Parts and accessories for motor vehicles and their engines 17 577 15.0 0.0 75.4 0.0 29 2922 Machine-tools 7 2011 14.3 0.0 12596.9 0.0 29 2912 Pumps, compressors, taps and valves 4 181 13.6 0.0 321.0 0.0 22 2212 Publishing of newspapers, journals and periodicals 4 77 13.3 0.0 7.3 0.0 35 3511 Building and repairing of ships 13 2375 12.9 0.0 40.9 0.0 20 2023 Wooden containers 3 91 10.6 0.0 19.1 0.0 31 3140 Accumulators, primary cells and primary batteries 3 564 10.1 0.0 61.7 0.0 15 1541 Bakery products 17 1491 9.3 0.0 10.5 0.0 36 3699 Other manufacturing n.e.c. 50 2475 7.6 0.0 46.6 0.0 22 2213 Publishing of recorded media 1 43 7.5 0.0 4.1 0.0 34 3440 Repair of motor vehicles (except maintenance) 11 263 6.8 0.0 34.4 0.0 36 3694 Games and toys 13 1895 5.8 0.0 35.7 0.0 26 2692 Refractory ceramic products 8 211 4.5 0.0 80.4 0.0 25 2511 Rubber tyres and tubes; retreading and rebuilding of rubber tyres 3 2689 4.1 0.0 1837.5 0.0 33 3312 Instruments and appliances for measuring, checking, testing, navigating and othe 4 101 3.1 0.0 3.5 0.0 35 3513 Repairing of ship, boats 3 259 1.4 0.0 4.5 0.0 35 3512 Building and repairing of pleasure and sporting boats 2 59 0.3 0.0 1.0 0.0 34 3420 Bodies (coachwork) for motor vehicles; trailers and semi-trailers 1 12 0.3 0.0 1.6 0.0 15 1531 Grain mill products 10 117 0.2 0.0 2.2 0.0 29 2923 Machinery for metallurgy 1 23 0.2 0.0 144.1 0.0 30 3010 Office, accounting and computing machinery 1 19 0.0 0.0 0.9 0.0

Pollution load in the Day/Nhue River Basin 99 VSIC-2 VSIC-4 VSIC-4 Description # firms Employment BOD5 % SS % 36 3691 Jewellery and related articles 7 288 0.0 0.0 375584.6 0.6 36 3693 Sports goods 3 306 0.0 0.0 316362.0 0.5 17 1729 Other textiles n.e.c. 52 5247 0.0 0.0 1254.5 0.0 29 2921 Agricultural and forestry machinery 19 2798 0.0 0.0 924.6 0.0 36 3610 Furniture 157 4760 0.0 0.0 155.5 0.0 19 1912 Luggage, handbags, saddlery and harness 12 1227 0.0 0.0 40.5 0.0 35 3520 Railway and tramway locomotives and rolling stock 1 103 0.0 0.0 19.7 0.0 28 2893 Cutlery, hand tools and general hardware 13 1013 0.0 0.0 19.3 0.0 30 3020 Office, accounting and computing machinery 1 1 0.0 0.0 0.0 0.0 17 1721 Made-up textile articles, except apparel 11 1476 0.0 0.0 0.0 0.0 17 1723 Cordage, rope, twine and netting 2 316 0.0 0.0 0.0 0.0 18 1810 Wearing apparel, except fur apparel 212 49367 0.0 0.0 0.0 0.0 20 2029 Other products of wood; cork, straw and plaiting materials 170 11801 0.0 0.0 0.0 0.0 29 2930 Domestic appliances n.e.c. 21 1801 0.0 0.0 0.0 0.0 33 3330 Watches and clocks 1 20 0.0 0.0 0.0 0.0 36 3692 Musical instruments 1 12 0.0 0.0 0.0 0.0 * - Listed in descending order of Industrial BOD5 load (kg).

Pollution load in the Day/Nhue River Basin 100 ANNEX 6.2: MAPS OF POLLUTION ESTIMATES Map 6.1: Industrial BOD

Pollution load in the Day/Nhue River Basin 101 Map 6.2: Industrial suspended solids

Pollution load in the Day/Nhue River Basin 102 Map 6.3: Domestic BOD

Pollution load in the Day/Nhue River Basin 103 Map 6.4: Domestic suspended solids

Pollution load in the Day/Nhue River Basin 104 Map 6.5: Pesticide Use

Pollution load in the Day/Nhue River Basin 105 Map 6.6: Fertilizer Use

Pollution load in the Day/Nhue River Basin 106 Map 6.7: Hazard Index

Dispersion of Pollutants in the Day/Nhue River Basin 107 7 DISPERSION OF POLLUTANTS IN THE DAY/NHUE RIVER BASIN 7.1 OPPORTUNITIES AND LIMITATIONS Dispersion modeling is particularly useful for understanding the impact of a pollutants release on ambient water quality. By combining physical river characteristics with information on the ambient concentration and rate of pollution loading, a model can be constructed to describe how the pollutant load disperses, or mixes, and adds to current ambient conditions of the river. There are many different versions of dispersion modeling, each varying in complexity as they attempt to mimic the physical nature of a pollutants dispersal in the aquatic environment. Each model has its own unique set of input requirements and outputs. The dispersion models used in this study are modest in terms of data requirements because real monitored information is scarce. The study utilizes the best available information on river characteristics and ambient concentrations of pollutants that were as consistent as possible in their coverage and time of monitoring. Although the results are limited in river segment and pollutant coverage, the models are valuable in identifying areas of ambient water quality stress and can be improved as further information becomes available. The first key step in the data gathering process is to segment the drainage area of the Day/Nhue River Basin into areas where river characteristics and pollution parameters could be collected. In segmenting the river one must consider both the physical and man-made features of the river system. Wherever ambient water quality changes as a consequence of these types of features (e.g. dams, weirs, diversionary structures, land use patterns and population), a new segment point should be delineated. Information on river characteristics and ambient pollutant concentrations are collected through tabulating information from reports, field missions and assembled into a database. This information is combined with pollution load calculated from the models in Section 6 and used as inputs into the dispersion models. Results are then calculated for each of the river segments in the basin. The chief limitation of the dispersion modeling is data availability on river characteristics. As the dispersion models depend on physical characteristics of the river such as river width, depth and flow rates, the absence of any parameter does not allow for the derivation of the effect on ambient concentrations. Table 7.1 below summarizes the 38 segments which were selected as reasonable partitions of the Day/Nhue River Basin. The shaded rows are the river segments which lacked one or more parameters that allow for dispersion estimation. Only 21 segments out of 38 have full information. This table is also instructive for future data gathering as it points to the precise parameters that require monitoring and tabulation. Table 7.1 River characteristics used for dispersion modeling in the Day/Nhue River Basin Segment No Location River River depth River width Flow rate 1 Nghia Do To Lich 2.50 25 1.74 2 Phuong Liet Lu 2.50 30 0.64 3 Mai Dong Kim Nguu 3.00 25 1.45 4 Cau Set Set 3.00 20 0.75 5 Thanh Liet Dam To Lich 1.90 25 5.50 6 Lien Mac Nhue 2.54 30 36.20 7 Co Nhue Nhue 35 8 Cau Dien Nhue 35 9 Ha Dong Bridge Nhue 3.60 35 24.00 10 50 m fr upstream of Thanh Liet Nhue 1.70 32 28.00 11 Cau To Nhue 2.30 44 43.80 12 Cong Than Nhue 4.10 70 63.40

Dispersion of Pollutants in the Day/Nhue River Basin 108 13 Nhat Tuu/Do Kieu/Phu Van Bridge Nhue 4.20 82 64.40 14 Van Coc Sluice/before Day Dam Day 15 Mai Linh Day 0.30 30 2.70 16 Ba Tha-ChMy Day 0.45 105 14.00 17 My Duc- Day 18 Phu Ly WTS-intake Day 6.40 77 76.80 19 Hong Phu Bridge Day 8.80 104 96.00 20 Bong Lang Day 21 Ninh Binh Thermal power Day 4.20 230 113.00 22 Ninh Phuc Post Day 23 Yen Phuong-Y Yen Day 24 Yen Nhan-Doc Bo Day 25 Yen Khanh- Khanh Phu Day 4.50 400 395.00 26 Kim Tan-Kim Son Day 27 Day Estuary Day 28 My Tam-My Loc Dao 6.80 240 280.00 29 Middle stream of Dao 7.00 250 282.00 30 Nghia Hung 31 Lam Son Bui 0.30 8 0.40 32 Kim Boi Boi 33 Lac Thuy/ Thung Tram Boi 1.00 15 4.40 34 Ben De Hoang Long 35 Gia Tan Hoang Long 36 Van Lock Van 37 Estuary of Ninh Co/to the sea Ninh Co 38 Giao Thuy Hong The second major limitation of the dispersion modeling is information on ambient concentrations of pollutants. The strategy of this study was to collect as many parameters as possible that were covered by the pollution load models presented in Section 6. Combining the pollution load information with river characteristics and ambient concentrations would then allow for dispersion modeling. It was also important to collect information on pollutants that would be relevant for water quality monitoring and discharge licensing. Despite significant effort, the collection process only yielded information for BOD5, SS, DO, iron (Fe 3+), oil and coliform (total) for each of the selected river segments. Although other pollution parameters had a few values, their coverage across each segment was too limited for use by the model (see Appendix 7 for details). Table 7.2 summarizes the information used in the models, where the shaded rows are segments where no pollutant information was available. In terms of a future monitoring strategy, Table 7.2 is indicative of the locations and parameters that could be collected and put to use immediately in the dispersion models for area-based management. Table 7.2 Ambient river quality parameters used for dispersion modeling in the Day/Nhue River Basin Segment No Location River SS (mg/l) DO (mg/l) BOD5 (mg/l) Fe 3+ (mg/l) Oil (mg/l) Coliform (MPN/100ml) 1 Nghia Do To Lich 83.6 35.00 0.78 2.20 500 2 Phuong Liet Lu 22.8 0.66 158.00 1.29 >16000 3 Mai Dong Kim Nguu 0.4 75.00 3.20 1.04 5000 4 Cau Set Set 83.5 105.00 3.10 1.28 22000 5 Thanh Liet Dam To Lich 93.0 0.31 48.60 trace 590000 6 Lien Mac Nhue 46.0 5.11 6.70 0.00 5500 7 Co Nhue Nhue 175.0 5.40 14.60 0.47 14000 8 Cau Dien Nhue 169.0 17.50 0.72 340 9 Ha Dong Bridge Nhue 38.1 26.00 0.17 1.26 270

Dispersion of Pollutants in the Day/Nhue River Basin 109 10 50 m fr upstream of Thanh Liet Nhue 31.0 4.11 4.90 0.00 3300 11 Cau To Nhue 48.0 2.65 19.70 0.00 410000 12 Cong Than Nhue 32.0 4.78 11.80 0.00 2900 13 Nhat Tuu/Do Kieu/Phu Van Bridge Nhue 30.0 4.55 9.30 0.00 2600 14 Van Coc Sluice/before Day Dam Day 15 Mai Linh Day 16 Ba Tha-ChMy Day 47.6 18.00 <0.01 500 17 My Duc- Day 5.0 15.00 <0.01 2.24 18 Phu Ly WTS-intake Day 28.0 4.59 5.90 0.00 19 Hong Phu Bridge Day 25.0 20 Bong Lang Day 55.0 6.47 21 Ninh Binh Thermal power Day 52.3 5.00 0.11 <0.1 500 22 Ninh Phuc Post Day 5.50 9.50 1.32 4000 23 Yen Phuong-Y Yen Day 172.0 7.80 7.00 0.01 6300 24 Yen Nhan-Doc Bo Day 101.0 7.90 8.00 <0.01 0.00 6000 25 Yen Khanh- Khanh Phu Day 25.1 7.90 6.00 0.11 Not detected 220 26 Kim Tan-Kim Son Day 14.7 5.65 13.00 2.00 1600 27 Day Estuary Day 185.0 14.20 0.35 28 My Tam-My Loc Dao 108.0 7.80 12.00 0.00 11000 29 Middle stream of Dao 144.0 7.80 15.00 0.00 5000 30 Nghia Hung 103.0 8.00 10.00 0.00 7000 31 Lam Son Bui 32 Kim Boi Boi 87.0 6.62 14.30 0.17 0.00 33 Lac Thuy/ Thung Tram Boi 67.0 6.62 14.10 0.11 0.00 Hoang 34 Ben De Long 9.8 5.10 14.00 1.76 200 35 Gia Tan Hoang Long 8.3 4.00 <0.01 17 36 Van Lock Van 3.20 31.10 0.97 17000 37 Estuary of Ninh Co/to the sea Ninh Co 38 Giao Thuy Hong

Dispersion of Pollutants in the Day/Nhue River Basin 110 ANNEX 7.1: DISPERSION OF BOD5 AND SS BY RIVER SEGMENT AND FUTURE DISPERSION MODELING Despite the data limitations encountered in the study an attempt was made to model the few parameters for which observations could be found in the river basin. This annex summarises the results. 7.2 AMBIENT BOD5 AND SS FOR SELECTED RIVER SEGMENTS To clarify the calculation of ambient concentration perfectly clear, the dispersion results below should be interpreted as net additions to the current concentrations stated in Table 7.2 (Section 7). In other words, the estimated concentrations below should be added to the ambient concentration in Table 7.2 to get the total concentration in the specific river segment. The reason for presenting the results in this fashion is to see the incremental effect each pollution source has on ambient concentration. Ambient concentrations of BOD5 and SS are estimated for the river segments which contained sufficient information for industry, domestic and craft villages. From Table A7.1, domestic sources play a significant role in defining ambient BOD5 concentrations in all river segments with the exception of Mai Dong and Phuong Liet where industrial sources are dominant. In terms of SS industry is the dominant contributor to ambient concentrations in Mai Dong and Phuong Liet, but domestic sources are important in river segments such as Ba Tha and Lac Thuy. Craft village sources contribute comparably to industry for BOD5 and SS in locations Ba Tha and the Ha Dong Bridge. Table A7.1 Ranking of segments according to the estimated impact on ambient BOD5 and SS concentrations in the Day/Nhue River Basin * Segment No Location River Total BOD5 (mg/l) Ind BOD5 (mg/l) Dom BOD5 (mg/l) CV BOD5 (mg/l) Total SS (mg/l) Ind SS (mg/l) Dom SS (mg/l) 3 Mai Dong Kim Nguu 334.44 334.44 - - 1819.53 1819.53 - - 2 Phuong Liet Lu 262.43 262.43 - - 2681.15 2681.15 - - CV SS (mg/l) 16 Ba Tha Day 84.08 6.72 68.16 9.19 89.53 19.44 51.12 18.96 33 Lac Thuy/ Thung Tram Boi 35.27 0.06 35.20-26.50 0.10 26.40-5 Thanh Liet Dam To Lich 31.62 7.94 23.67-255.55 237.79 17.75-1 Nghia Do To Lich 24.22 24.22 - - 698.74 698.74 - - 9 Ha Dong Bridge Nhue 23.14 2.35 16.75 4.04 35.98 11.52 12.56 11.89 12 Cong Than Nhue 17.93 2.52 14.09 1.31 31.70 18.07 10.57 3.06 50m from upstream of Thanh 10 Liet Nhue 9.76 0.45 9.23 0.08 19.77 12.61 6.93 0.23 18 Phu Ly WTS-intake Day 6.43 0.26 6.12 0.04 7.05 2.41 4.59 0.05 11 Cau To Nhue 5.91 0.04 5.80 0.07 30.10 25.61 4.35 0.14 6 Lien Mac Nhue 2.91 0.02 2.89-3.82 1.65 2.16-25 Yen Khanh- Khanh Phu Day 2.30 0.03 2.24 0.04 2.57 0.84 1.68 0.05 4 Cau Set Set 1.00 1.00 - - 2.54 2.54 - - 13 Nhat Tuu/Do Kieu/Phu Van Bridge Nhue 0.55 0.03 0.52-0.45 0.06 0.39-29 Middle stream of Dao Dao 0.35 0.09 0.26-2.02 1.82 0.19-28 My Tam-My Loc Dao 0.34 0.00 0.34 0.00 0.26 0.00 0.26 0.00 7 Co Nhue Nhue - - - - - - - - 8 Cau Dien Nhue - - - - - - - - Van Coc Sluice/before Day 14 Dam Day - - - - - - - - 15 Mai Linh Day - - - - - - - - 17 My Duc Day - - - - - - - -

Dispersion of Pollutants in the Day/Nhue River Basin 111 19 Hong Phu Bridge Day - - - - 2.30 0.48 1.76 0.06 20 Bong Lang Day - - - - - - - - 21 Ninh Binh Thermal power Day - - - - - - - - 22 Ninh Phuc Post Day - - - - - - - - 23 Yen Phuong Day - - - - - - - - 24 Yen Nhan-Doc Bo Day - - - - - - - - 26 Kim Tan-Kim Son Day - - - - - - - - 27 Day Estuary Day - - - - - - - - 30 Nghia Hung Dao - - - - - - - - 31 Lam Son Bui - - - - - - - - 32 Kim Boi Boi - - - - - - - - 34 Ben De Hoang Long - - - - - - - - 35 Gia Tan Hoang Long - - - - - - - - 36 Van Lock Van - - - - - - - - Estuary of Ninh Co/to the 37 sea Ninh Co - - - - - - - - 38 Giao Thuy Hong - - - - - - - - * - Listed in descending order of total BOD5 concentration. 7.3 PROSPECTS FOR FUTURE POLLUTION DISPERSION MODELING IN THE RIVER BASIN The dispersion modeling exercise undertaken by this study may, at first, appear to be quite limited in its scope and conclusions. However, there currently exist very few attempts at trying to model the entire Day/Nhue River Basin including all pollution sources. In this regard the project is ambitious and the work presented here should be viewed as a first attempt at constructing a simple framework for analysis and which can be easily added to in the future. Among the most notable areas for quick improvement would be to gather information for the river segments that currently do not have any observations. Obviously one would want to gather information in a time-consistent pattern such that all parameters would have a value in the same year and season. This applies to both river characteristics such as river depth, width and flow rates, as well as the pollution parameters outlined in Table 7.2. From a longer-term perspective once these models have identified clear areas of interest, more sophisticated dispersion models could be built to factor in other, more complex, aspects of pollutant dispersion. In this regard the tool built in this exercise can be viewed as a screening tool for quick area-based identification of pollution hotspots. Once identified there can be short-term initiatives to 1) collect data to fill in the gaps raised by this model, 2) medium-term objectives of expanding the current model to include a wider scope of pollution parameters, and finally 3) longer-term initiatives to build more complex river interactions into the model for more accurate estimation of pollution dispersion.

Water pollution and public health 112 8 WATER POLLUTION AND PUBLIC HEALTH 8.1 HAZARD ASSESSMENT 8.1.1 BIOLOGICAL POLLUTANTS This section reviews the international experience relating to the health impacts of waterborne biological pollutants. Hundreds of biological pollutants are capable of causing adverse health effects (Petterson and Ashbolt, 2006). The most common health effects are gastro-intestinal illness. This section focuses on biological pollutants that have been identified in epidemiological studies conducted in Vietnam. The health effects of exposure to biological pathogens are dependent on the immunity of the individual, the dose of the exposure and the infectivity of the pathogen (WHO, 2006). Children, the elderly and people with compromised immune systems (e.g. people living with HIV/AIDS) are at increased risk of developing illnesses after exposure to biological pathogens (WHO, 2006). Quantitative Microbiological Risk Assessment (QMRA) applies mathematical modelling to estimate the health effects of exposure to low-concentrations of biological pollutants (WHO, 2006). A QMRA has been developed to estimate the health effects of wastewater reuse for restricted crop irrigation (Leeds University, 2007). Following review of data availability, it was found that the data requirements to conduct a QMRA were beyond the scope of this study. QMRA would be a useful tool in further investigations on the health effects of biological pollutants in the Day/Nhue River Basin. Bacteria Bacteria are the most common and best-studied of waterborne biological pollutants (Wyber, 2006). Different species of bacteria can produce a range of health effects - from the aggressive attacks of Vibrio Cholera - serovar 01 which can be fatal if untreated, through to abdominal pain and discomfort caused from low-level Salmonella infection. A study of Vietnamese children living in the Red River Delta found Campylobacter coli, Shigella spp. and enterotoxigenic Escherichia coli to be the most common bacteria in diarrhoeal stool samples (Isenbarger et al., 2001). Shigella is recognised as a key cause of diarrhoea throughout developing countries (Niyogi, 2005). Isenberger et al. (2001) identified a variety of serotypes of Shigella and concluded the implementation of common vaccine programs against Shigella would provide limited impacts on diarrhoeal rates in Vietnam. Table 8.1.1.1 - Diseases caused by common bacteria identified in Vietnam (Derived from Schonning and Stenstrom (2004)) Pathogen Disease Symptoms Campylobacter coli Shigella spp. Campylobacteriosis - Diarrhoea, cramping, abdominal pain, fever, nausea, arthritis, Guillain- Barre syndrome Shigellosis dysentery (bloody diarrhoea), vomiting, cramps, fever, Reiter s syndrome Escherichia coli Abdominal pain and diarrhoea It is a common misconception that all types of E. coli are damaging to human health. In fact all humans carry E. coli within their gut. Only some strains such as E. Coli 0157:H7 cause adverse health effects. Viruses

Water pollution and public health 113 Limited epidemiological evidence was found on the prevalence and incidence of virus infections in Vietnam. One study identified rotavirus in 50% of 158 Vietnamese infants with diarrhoea in Ho Chi Minh city (Nishio et al., 2000). Rotavirus are commonly associated with childhood enteritis that can lead to the inflammation of the small intestine, diarrhoea and abdominal pain. Helminths The WHO Guidelines for safe use of wastewater, excreta and greywater recognise the use of wastewater as presenting a significant exposure route to helminth infections (WHO and UNEP, 2006). A wide body of research has been conducted on the prevalence and health risks of helminths in Vietnam. A survey of 526 households in North-Eastern Vietnam found the three most prevalent species of helminth infestations in all ethnic groups to be hookworm (52%), Trichuris trichiura (50%) and Ascaris lumbricoides (45%) (Verle et al., 2003). A risk assessment of helminth infection in wastewater-fed rice cultivation found no evidence that urban wastewater posed a risk for intestinal helminth infection in agricultural workers in Nam Dinh province (Trang et al., 2006). The authors concluded that the results of the investigation may have been confounded by poor sanitation and hygiene practices. Table 8.1.1.2 Diseases caused by common helminths identified in Vietnam (Derived from Schonning and Stenstrom (2004)) Pathogen Disease - Symptoms Ascaris spp Trichuris spp Ascariosis - Generally no or few symptoms, wheezing, coughing, fever, enteritis, pulmonary eosinophilia Trichuriasis - Unapparent through vague digestive tract distress to emaciation with dry skin and diarrhoea Ancylostoma spp. Ancylostomiasis - Itch; rash; cough; anaemia; protein deficiency Protozoa Cryptosporidium is commonly identified in contaminated waterways. Cryptosporidium parvum oocysts are extremely tolerant to a range of environmental conditions. Exposure to Cryptosporidium can cause nausea, low-grade fever, vomiting and abdominal pain (Wyber, 2006). Cryptosporidium can be lethal for people with compromised immune systems such as people living with HIV (Nel, Markotter and Weyer, 2004). 8.1.2 CHEMICAL POLLUTANTS The industrial component of the ICEM PPS estimated the top 30 chemicals by load and relative toxicity in the Day/Nhue River Basin. Toxicity and environmental fate data for the 30 chemicals were collated from the US EPA s IRIS database - http://www.epa.gov/iris/, the ToxNet database - http://toxnet.nlm.nih.gov/ and the International Toxicity Estimates for Risk http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?iter. The environmental fate of each chemical was sourced from the aquatic fate section of the National Library of Medicine s (NLM) Toxicology Data Network and the Hazardous Substances Data Bank (HSDB) - http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?hsdb. Chemicals were also categorised according to the groupings provided by the International Agency for Research on Cancer (IARC) monographs http://monographs.iarc.fr/eng/classification/crthall.php.

Water pollution and public health 114 The risk matrix identified chemicals into four groups based on the available information in the toxicity and environmental fate databases. Table 8.1.2.1 shows seven chemicals to meet the criteria as an extreme risk to human health. Annex 8.1 provides the full details of the categorisation of the top 30 pollutants. This information needs to be reviewed by DWRM water managers to ensure they appreciate the availability of data and its limitations. Table 8.1.2.1 - Human health risk of top 30 pollutants by hazard ranking in the Day/Nhue River Basin Low Moderate High Extreme Phosphoric Sodium Sulfate (Solution) Ammonia Ammonium Nitrate Acid Aluminum Oxide (Fibrous Form) Ethylene Glycol Acetone N-Butanol (N- Butyl Alcohol) Sodium Hydroxide (Solution) Hydrochloric Acid (Hydrogen Chloride) (Solution) Formaldehyde Nitric Acid Sulfuric Acid 1,2,4-Trichlorobenzene Phenol Aluminum Methanol (Methyl Alcohol) Pentachlorophenol Chloroform (Fume Or Dust) Toluene Lead (Toluol) Biphenyl (Diphenyl) Nitroglycerin (Ng) Acrylic Acid Tetrachloroethylene (Perchloroethylene) Dichloromethane (Methylene Chloride) Ethylene Oxide Ammonium Sulfate Chromium Mercury (Solution) Three factors reduce the confidence in the categorisation of the chemicals in Table 8.1.2.1. The first is the data is based on estimates of the top 30 chemicals by hazard ranking derived from the PPS model. To increase the confidence of this classification the model s estimates should be validated through water quality monitoring programmes. Second, it was rare for one chemical to match all of the qualitative measures identified in the risk matrix. A weight-of-evidence approach was used to categorise each chemical. Third, the quality and quantity of toxicity and environmental fate data was variable. Some chemicals had been subjected to limited investigation and therefore they could not be categorised with confidence. The IPPS model identified the top five districts with the highest estimated hazard ranking for the seven chemicals identified as presenting an extreme risk to human health (Table 8.1.2.2). The top five industry sectors (VSIC-4) have been identified for the top ranked district for each of the seven chemicals (Table 8.1.2.3). Table 8.1.2.2 Top five districts with the highest estimated hazard ranking for seven chemicals identified as presenting an extreme risk to human health in the Day/Nhue River Basin 1 Province and district 2 Province and district 3 Province and district 4 Province and district 5 Province and district Ammonium nitrate (solution) Ha Tay - Chuong My Hanoi - Thanh Tri Hanoi - Hai ba Trung Hanoi - Hoang Mai Hanoi Thanh Xuan Formaldehyde Hanoi Thanh Xuan Hanoi Hai ba Trung Hanoi Dong Da Hanoi Thanh Tri Ha Tay Phu Xuyen Phenol Hanoi - Hai ba Trung Hanoi Hoang Mai Hanoi Thanh Tri Hanoi Dong Da Hanoi Ba Dinh

Water pollution and public health 115 Chloroform Hanoi Hoang Mai Hanoi Hai ba Trung Ha Tay Dan Phuong Ha Tay Phu Xuyen Hanoi Tu Liem Lead Hanoi Thanh Xuan Hanoi Hai ba Trung Nam Dinh Nam Dinh Hanoi Dong Da Hanoi Hoang Mai Ethylene oxide Hanoi Dong Da Nam Dinh Nam Dinh Hanoi Hai ba Trung Hanoi Ba Dinh Ninh Binh Ninh Binh Mercury Hanoi Thanh Tri Hanoi Hoan Kiem Hanoi Dong Da Ha Tay Hoai Duc Hanoi Hoang Mai Table 8.1.2.3 Top 5 industrial sectors for the district with the highest hazard ranking for the seven chemicals identified as presenting an extreme risk to human health in the Day/Nhue River Basin Chemical District Industry Industry Industry Industry Industry Ammonium nitrate (solution) Ha Tay - Chuong My Formaldehyde Hanoi Thanh Xuan Phenol Hanoi - Hai ba Trung Chloroform Hanoi Hoang Mai Lead Hanoi Thanh Xuan Ethylene oxide Hanoi Dong Da Mercury Hanoi Thanh Tri Prepared animal feeds (VSIC-4 1533) Other chemical products, n.e.c. (VSIC-4 2429) Casting of non-ferrous metals (VSIC-4 2732) Builders' carpentry and joinery (VSIC-4 2022) Electric lamps and lighting equipment (VSIC-4 3150) Pharmaceutic als, medicinal chemicals and botanical products (VSIC-4 2423) Fertilizers and nitrogen compounds (VSIC-4 2412) Plastics in primary forms and of synthetic rubber (VSIC-4 2413) Articles of concrete, cement and plaster (VSIC-4 2695) Corrugated paper and paperboard, containers of paper and paperboard (VSIC-4 2102) Insulated wire and cable (VSIC-4 3130) Other articles of paper and paperboard (VSIC-4 2109) Pulp, paper and paperboard (VSIC-4 2101) Pulp, paper and paperboard (VSIC-4 2101) Other fabricated metal products n.e.c. (VSIC-4 2899) Builders' carpentry and joinery (VSIC-4 2022) Other chemical products n.e.c. (VSIC-4 2429) Other articles of paper and paperboard (VSIC-4 2109) Preparation and spinning of textile fibres; weaving of textiles (VSIC-4 1711) Soap and detergents, cleaning and polishing preparations, perfumes and toilet preparations (VSIC-4 2424) Basic chemicals, except fertilizers and nitrogen compounds (VSIC-4 2411) Veneer sheets; plywood, laminboard, particle board, other panels and boards (VSIC-4 2021) Basic iron and steel (VSIC-4 2710) 8.1.3 EMERGING ISSUES IN CHEMICAL EXPOSURE AND HEALTH EFFECTS

Water pollution and public health 116 Recent research has identified a number of emerging issues in waterborne chemical pollutants. This section provides an overview of emerging issues in waterborne pollutants that are recognised internationally as posing a threat to human health. The emerging issues include: a) Mixtures, b) Endocrine disruptive chemicals, c) By-products of disinfection and d) Cyanobacteria toxins. The information provided in this section should be taken into account in defining water management strategies. 32 Mixtures Hazard identification utilises toxicological information that is based on exposure to one chemical. In reality exposure to a single chemical is highly unlikely. More often humans are exposed to multiple chemicals from a variety of sources (EnHealth, 2002). For example, the PPS model estimated pollution loads for 64 chemicals in one segment of the Nhue River. The toxicity of mixtures can have three outcomes: 1) Toxicity of the mixture is equal to the sum of the individual chemical additivity 2) Toxicity of the mixture is greater than the sum of the individual chemicals synergism 3) Toxicity of the mixture is less than the sum of the individual chemicals antagonism Of the three outcomes, synergism presents the greatest threat to increasing the risk of adverse health effects due to exposure to mixtures. Interest in developing regulatory approaches to chemical mixtures has increased over the last decade (EnHealth, 2002; Monosson, 2005). The US EPA has developed a guideline for chemical mixture assessment (U.S. EPA, 2000). The guidelines classify mixtures as simple (up to two identified chemicals) and complex (numerous chemicals). The Agency for Toxic Substances and Disease Registry (ATSDR, 2007) has developed a number of risk assessments for common mixtures. Endocrine disruptive chemicals (EDCs) EDCs are defined as exogenous substances that alter function(s) of the endocrine system and consequently cause adverse health effects in an intact organism, or its progeny, or (sub)- populations (WHO/IPCS, 2002). Water is the main environmental media that transports EDCs. The three main sources of EDCs to the environment are human and animal sewerage, agricultural run-off and industrial discharges. Industrial discharges that produce EDCs include wastes from the disposal of polychlorinated biphenyls (PCBs) in electrical transformers, phytoestrols from pulp and paper mills and dioxin from incineration and manufacturer of PVC (Cirimele et al., 2005). Consistent evidence is available on the harm caused to invertebrates, fish, amphibians and birds through exposure to EDCs (Green, 1998; Levy et al., 2004). A number of bio-monitoring studies have identified a variety of EDCs in blood, fat tissue, breast milk and urine of humans (CDC, 2005). However, the current epidemiological evidence of the health impacts of EDCs upon humans is weak. Ongoing research is attempting to bring new information on the risk posed by EDCs to human health. Disinfection by-products (DBPs) DBPs are chemicals that are produced during the treatment of drinking water supplies through chlorination or ozonation. The group of disinfection products that have undergone extensive 32 Persistent organic pollutants and hazardous substances have been the subject of international treaties since the 1980s. The chemicals identified in these treaties are presented in Annex 8.2.

Water pollution and public health 117 research are trihalomethanes (THMs). THMs are formed when chlorine reacts with natural organic matter in water (Ashbolt, 2004). The other significant groups include haloacetic acids, brominated compounds and nitrosamines. Exposure to chlorination DBP is associated with a statistically significant increased risk of bladder cancer (Do et al., 2005). However the association is affected by a number of environmental factors including type of organic matter, disinfection type and dose, temperature, ph, and bromine concentration (Ashbolt, 2004). The PPS model estimated high loads of BOD entering the Day and Nhue River. The combination of high organic loads with chlorination at the water supply companies suggest that the production of DBPs may be occurring. The production of DBPs in drinking water supplies does warrant attention. However, an extensive risk assessment of DBPs concluded that efforts to reduce potential health risks from DBP must not compromise pathogen control (Ashbolt, 2004). Cyanobacteria Slow-moving water contaminated with high concentrations of nutrients (nitrates and phosphates) combined with high temperatures provide ideal conditions for blooms of cyanobacteria (WHO, 2006). A number of species of cyanobacteria produce cyanotoxins (microcystin) which can cause adverse health impacts. The toxins are defined by their chemical structure. The three major groups include cyclic peptide alkaloids, the neurotoxic paralytic shellfish poisons (PSPs) and lipopolysaccharides (LPS) (Nasri et al., 2007). Concerns over these health risks have prompted WHO to adopt a provisional guideline value for microcystin-lr (MCYST-LR) of 1g L -1 drinking water (WHO, 1998). The use of dams and sluices throughout the Day/Nhue River Basin has dramatically reduced the flow rate of the rivers. During the field visit a large reservoir was observed up- and downstream of the Day Dam (Figure 8.1.3). The slow moving water, high temperatures and high nutrient loads throughout the Day and Nhue Rivers present ideal conditions for the production of cyanobacteria blooms and the resulting cyanotoxins. In areas where the Day and Nhue Rivers are used for swimming and other recreational activities the likelihood of exposure to cyanotoxins would increase. Further investigations are required to determine the health risks associated with cyanobacteria blooms throughout the Day/Nhue River Basin. Figure 8.1.3 - Reservoir downstream of the Day Dam

Water pollution and public health 118 8.2 EXPOSURE ASSESSMENT Exposure assessment often holds the greatest level of uncertainty in an environmental health risk assessment (EnHealth, 2002). The US EPA Guidelines for Exposure Assessment (1992) recommend that the level of detail of an exposure assessment is measured by the amount and resolution of the data used, and the sophistication of the analysis employed and the resources available to perform the assessment. This assessment will provide an overview of the key sources of exposure based on studies conducted in Vietnam and south-east Asia and from anecdotal information sourced during the field trips. Exposure to waterborne pollutants can occur directly or indirectly. Direct exposure to waterborne pollutants can occur through ingestion, skin contact and inhalation of fine water particles. Indirect exposure to waterborne pollutants occurs when humans are exposed to food, soil or breast milk that have bioconcentrated or biomagnified a pollutant. Table 8.2.1 identifies the sources of exposure to biological and chemical pollutants that occur in the Day/Nhue River Basin. Table 8.2.1 - Pathway, source and communities or persons of interest to exposure to biological and chemical pollutants in the Day/Nhue River Basin Pathway of exposure Source Communities or persons of interest Ingestion Drinking water from water supply companies Ha Nam, Ninh Binh, Hoa Binh and Nam Dinh Consumption of vegetables irrigated with Day/Nhue River water Periurban areas located adjacent to sewerage collection points Consumption of fish and shellfish taken from the Day/Nhue Rivers Communes with high consumption rates of shellfish and fish taken from the Day/Nhue rivers Recreational Communes with close proximity to reservoirs such as the Day Dam Accidental ingestion of soil Children due to high levels of hand to mouth contact Dermal Occupational exposure during rice paddy farming All rice farming areas adjacent to Day and Nhue River Occupational exposure during textile dying Craft villages that dye textiles Occupational exposure during harvest of vegetables grown on the Day/Nhue Rivers Communes that produce vegetables grown directly on the river Recreational Communes with reservoirs and swimming areas along the Day/Nhue Rivers Inhalation Air conditioners Higher income, urbanised areas 8.2.1 DRINKING WATER Biological pollutants Exposure to pathogenic contaminants in drinking water has been recognised as posing a significant health risk to humans (Ritter et al., 2002). Determining the level of risk from biological pollutants in source water of drinking water supplies provides managers with vital information in developing effective public health protection measures (Wyber, 2006). Due to high concentrations of arsenic in the groundwater throughout the Day/Nhue River Basin a high proportion of water supply companies source their water from the Day, Nhue or tributary rivers. The field investigation

Water pollution and public health 119 in this study identified 16 water supply companies that source their intake water from rivers in the Day/Nhue River Basin. The MONRE guidelines stipulate coliform levels for water supply systems should not exceed 5000 MPN/100mL (Decision No. TCVN 5942:1995). The PPS model estimated concentrations of coliforms in six river segments that were located upstream of a water supply intake. The estimated coliform concentration for locations adjacent to the source of intake water exceeded the coliform standard in three of the six locations (Table 8.2.1.1). Table 8.2.1.1 - Estimated coliform concentrations in upstream river segments for six water treatment plants Province District Upstream river segment PPS estimated coliform ambient water quality in river segment Nam Dinh Nam Dinh 29 5000 Equal Vu Ban 30 7000 Yes Lam Town (Y Yen) 23 6300 Yes Co Le (Truc Ninh) 22 4000 No Ninh Binh Yen Ninh (Yen 30 7000 Yes Khanh) Phat Diem (Kim Son) 26 1600 No Exceeds total coliform Surface Water Quality Standard A: TCVN: 5942-1995 (5000 MPN/100ml) The field study found that all water supply companies in the Day/Nhue River basin treat the water prior to distribution to the household. The treatment process includes flocculation using aluminium sulphate followed by sedimentation and chlorination. Monitoring data provided by the Ha Nam water supply company revealed significant reductions in coliforms and faecal coliforms as the water passes through the treatment process (Table 8.2.1.2). Table 8.2.1.2 - Coliform and faecal coliform concentrations in Ha Nam water distribution system Coliforms Faecal coliforms Inlet water 93000 4300 Sedimentation 2400 210 Chlorination 0 0 Household 1 23 0 Household 2 23 0 Source: Ha Nam Water Supply Company data taken on 9 th May, 2007. Monitoring data provided from the water supply companies showed the treatment processes were eliminating the risk of exposure to microbial pollutants (Table 8.2.1.3). The drinking water quality data supplied by the water supply companies in each province identified measured parameters did not exceed Vietnam s drinking water standard (Decision No. 1329/ 2002/BYT/QD) except for one total coliform measurement in Ha Nam. Table 8.2.1.3 - Drinking water quality monitoring of biological pollutants at the household level in four provinces in the Day/Nhue River Basin Residual chlorine Drinking Water Standards - Decision No. 1329/ 2002/BYT/QD dated 18/4/2002 Ha Nam a Ninh Binh b Hoa Binh c Unit n.a. mg/l 0.5 0.3 n.a. 0.3 Total coliforms 0 Coliforms/100mL 23 0 0 0 Nam Dinh d

Water pollution and public health 120 Faecal coliforms 0 Coliforms/100mL 0 0 0 0 Sources: a: Taken from water sampling of two households on 9/5/2007 b: Average water quality from 14 household samples taken fortnightly from 13/2/07-7/6/07 c: Water quality of one household sampled on 9/8/2006 and 17/7/2006 d: Average water quality of 11 households taken on 26/6/2007 n.a: Not available Chemical pollutants Monitoring data from the Ha Nam Water Supply Company s intake water showed that nitrate, ammonia, BOD, COD and suspended solids regularly exceeded the standard for water supply sources (TVCN 5942:1995) (Table 8.2.1.4). Table 8.2.1.4 - Average water quality parameters from bimonthly sampling (2/1/05-13/12/05) of source water for Ha Nam Water Supply Company TCVN 5942:1995 Surface water for water supply sources Average parameters from water samples (2/1/05 13/12/05) ph 6.5-8.5 7.51 TDS Not listed 159.46 + NH 4 mg/l 0.05 1.20 - NO 2 mg/l 0.01 0.09 - NO 3 mg/l 10 1.64-3 PO 4 mg/l Not listed 1.09 Cr 6+ mg/l 0.05 0.02 DO mg/l 6 5.88 Turbidity Not listed 22.38 BOD mg/l <4 13.17 COD mg/l <10 24.54 SS mg/l 20 25.83 Table 8.2.1.4 shows both biological oxygen demand (BOD) and chemical oxygen demand (COD) exceed guidance levels for intake water in Ha Nam. BOD is the measure of oxygen consumed by aquatic micro-organisms to breakdown organic material in the water. COD is the amount of oxygen consumed by the chemical breakdown of organic and inorganic matter. The parameters BOD and COD act as important proxy measures of organic and inorganic pollution in a waterway. Monitoring data of household water provided by the water supply companies in Hoa Binh, Ninh Binh, Ha Nam and Nam Dinh showed that measured chemical parameters did not exceed Vietnam s drinking water standards (Table 8.2.1.5). These results confirm the vital role of water treatment in reducing chemical pollutants in drinking water supplies. Table 8.2.1.5 - Drinking water quality monitoring of chemical pollutants at the household level of four provinces in the Day/Nhue River Basin Drinking Water Standards - Decision No. 1329/ 2002/BYT/QD dated 18/4/2002 Ha Nam a Ninh Binh b Hoa Binh c Nam Dinh d Colour 15 TCU n.a. n.a. n.a. 7.3 Turbidity 2.0 NTU n.a. 30 n.a. 0.7 ph 6.5-8.5 7 7 6 8 Hardness 300 mg/l 142 n.a. 184 43 Iron (Fe) 0.5 mg/l 0.25 n.a. 0.03 0.001 Chloride (Cl) 250 mg/l n.a. n.a. 0 12 Manganese (Mn) 0.5 mg/l n.a. n.a. 0 n.a. Nitrate (NO 3- ) 50 mg/l 10 7 0.1 1.4

Water pollution and public health 121 Nitrite (NO 2- ) 3 mg/l 0.3 0 0 0 Ammonium (NH 4+ ) 1.5 mg/l 0.8 0 n.a. n.a. Sulphate (SO 4 2- ) 250 mg/l n.a. n.a. 8 10 COD n.a. mg/l 5.8 n.a. 0.30 1.4 Sources: a: Taken from water sampling of two households on 9/5/2007 b: Average water quality from 14 household samples taken fortnightly from 13/2/07-7/6/07 c: Water quality of one household sampled on 9/8/2006 and 17/7/2006 d: Average water quality of 11 households taken on 26/6/2007 n.a: Not available 8.2.2 CONSUMPTION OF VEGETABLES AND FRUIT Biological pollutants The Day and Nhue Rivers are important sources of irrigation water in agricultural fields located within the Day/Nhue River Basin. Crops that are irrigated with river water include rice, corn, beans, leafy vegetables and fruit trees. Crops grown directly in the Day and Nhue Rivers include water spinach or morning glory (Ipomoea aquatica), water mimosa (Neptunia oleracea), dropwort (Oenanthe stolonifera) and water cress (Rorippa nasturtium-aquaticum) (Dalsgaard et al., 2006). Appropriate preparation of food crops prior to consumption can eliminate the exposure pathways of biological pollutants (Vuong et al., in prep b). Agricultural products such as rice and corn that are dried and boiled prior to consumption present limited routes of exposure of biological pollutants. Giardia and Cryptosporidium were identified as the most prevalent microbial pollutants in morning glory grown in waste water in Hanoi (Dalsgaard et al., 2006). In contrast helminths were not found to pose a risk to food safety as they settled out from the waste water and entered the sediment of the irrigation canal. Previous research has identified crops grown in wastewater present a greater likelihood of exposing farmer workers and consumers to microbial pollutants (Dalsgaard et al., 2006). Chemical pollutants Elevated concentrations of chemical pollutants have been recorded in plants grown in contaminated water and/or soil. A study of heavy metal concentrations in Phnom Penh found elevated levels of cadmium, chromium, copper, nickel, lead and zinc concentrations in water spinach (Marcussen, 2006b). The investigators concluded that based on daily consumption rates the levels would not exceed tolerable daily limits and therefore the heavy metals did not constitute a health effect to regular consumers of the water spinach (Marcussen, 2006b). Investigations of heavy metal concentrations in rice have indicated high levels of cadmium in rice grown in Nam Dinh (Simmons et al., 2006). The increased levels of cadmium were the result of naturally occurring alluvial deposits. Exposure to cadmium via rice consumption was concluded to not present a significant risk to human health (Simmons et al., 2006). 8.2.3 CONSUMPTION OF FISH AND SHELLFISH Biological pollutants Aquacultural facilities are located within and adjacent to the Day and Nhue Rivers. The commonly cultured fish are common carp (Cyprinus carpio), silver carp (Hypophthalamichthys molitrix), and

Water pollution and public health 122 Nile tilapia (Oreochromis niloticus). An investigation of microbial contamination of fish raised in wastewater in Thanh Tri district, Hanoi identified no significant levels of thermotolerant faecal coliforms in the flesh of the fish (Lan et al., 2006). Lan et al. (2006) did identify significant levels of thermotolerant faecal coliforms in the flesh sold at the market place due to cross contamination from the fish gut to the flesh during the fishmonger s preparation of the fish. Chemical pollutants Persistent and bioaccumulative chemicals such as heavy metals have been detected in the flesh and fatty tissues of fish (Weisbrod et al., 2007). A sample of 10 common carp, silver carp and tilapia fed with wastewaste in periurban Hanoi found concentrations of cadmium and lead were below European Union threshold values for the majority of fish tissues (Marcussen et al., 2006a). The only tissue to exceed cadmium and lead levels was the liver taken from the tilapia and one sample of common carp. Marcussen et al. (2006a) advised against eating the liver of tilapia due to the elevated heavy metal concentrations. 8.2.4 SOIL INTAKE Biological and chemical pollutants The potential for exposure to contaminants via soil is greater for children because they are more likely than adults to ingest soil (US EPA, 1992). Sediments in Nam Dinh were recorded to have zinc, lead, nickel, chromium, copper and cadmium within EU standards: Directive 86/278/EEC (Simmons et al., 2006). High concentrations of cadmium were discovered to exist and were linked to naturally high concentrations due to the alluvial soil in the Nam Dinh region. 8.2.5 OCCUPATIONAL EXPOSURE Biological and chemical pollutants Exposure to wastewater that enters the Day/Nhue River basin has been associated with high incidence and monthly prevalence of skin disease in Nam Dinh (Tran et al., 2006). The localisation of the skin disorders to the hands and feet provided supporting evidence of the association of skin diseases and exposure to wastewater. Communes in Hanoi that applied wastewater to fields also recorded significantly higher incidence of dermatitis for farmers and aquaculturalists (in prep. Vuong et al.). The cause of the dermatitis was not linked to a specific pollutant in either study and requires further investigation. 8.2.6 RECREATIONAL EXPOSURE Biological and chemical pollutants In the Day and Nhue Rivers recreational activities include swimming, fishing and game playing conducted mainly by young children and adolescents. Recreational activities can increase the likelihood of exposure to pollutants through contact with water and sediment. The WHO recreational health guidelines (2003) states: Many substances of potential concern are of low water solubility and will tend to migrate to sediments where they may accumulate. When the sediments are disturbed or resuspended or where water users are in intimate contact with sediment, then this may contribute to exposure.

Water pollution and public health 123 8.2.7 INHALATION Air conditioners are the primary pathway of inhaled biological waterborne pollutants. Vietnam s rapid economic growth has been matched with the uptake and use of air conditioners. No evidence was available on the exposure level to pollutants transported by air conditioners. 8.3 RISK CHARACTERISATION Risk characterisation has been described as the last step in an environmental health risk assessment but the first step in risk management. Risk characterisation is the compilation of data from the hazard identification, dose-response and exposure assessment. The next section presents a short description of one biological and two chemical pollutants that data from the PPS model and existing literature have identified as presenting a potential risk to human health. The list should not be regarded as a definitive list for prioritisation of action but rather an indication of where further investigations could be directed. 8.3.1 BIOLOGICAL POLLUTANTS Drinking water can be an important exposure pathway to biological pollutants. Water quality monitoring data and the PPS outputs indicate high concentrations of total coliform are present in the intake water of the water supply companies. High concentrations of coliform suggest possible presence of pathogens and faecal contamination. The water supply companies along the Day/Nhue River Basin all treat their water through flocculation and chlorination. The treatment processes will remove the risk of exposure to the majority of biological pollutants however parasites and bacteria that are resistant to chlorination may be capable of passing through the treatment systems. Cryptosporidium Likelihood Possible Outbreaks due to drinking water contamination with Cryptosporidium have been reported in Japan, the U.K and North America (Fayer, 2004). Limited studies have been conducted on the dose and infectivity of chlorine-resistant bacteria and protozoa in drinking water supplies in Vietnam. The evidence provided from the source water monitoring data combined with the total coliform estimations of the PPS model suggest that high concentrations of chlorine-resistant biological pollutants may be capable of moving into the piped water supplies throughout the Day/Nhue River Basin. Chlorination has little or no impact on removing Cryptospordium oocysts from water. The WHO Guidelines for Drinking-Water Quality (2006) identify Cryptosporidium parvum oocysts are resistant to chlorination at conventional doses and retention times (WHO, 2006). Studies conducted in Hanoi also identified Cryptosporidium as the most prevalent biological pollutant in vegetables grown in wastewater. Consequences - Catastrophic Exposure to Cryptosporidium can cause diarrhoea, abdominal discomfort, nausea and vomitting to healthy persons. Cryptosporidium infections can be lethal for people with compromised immune systems such as people living with HIV/AIDS. In 1986, the United States Centers for Disease Centre recorded 19,817 AIDS cases had cryptosporidiosis and that their fatality rate was 61% (Fayer, 2004).

Water pollution and public health 124 Summary The WHO recommends the application of preventative management principles in protecting drinking water supplies from biological and chemical pollutants. Water safety plans present a methodology of protecting water supplies from catchment to consumer (WHO, 2006). Through protecting source water from high loads of domestic waste the level of treatment required would be reduced. This offers the twin benefits of minimising operational costs and reducing the production of disinfection by-products (WHO, 2006 - Ch.4 Water Safety Plans). Boiling tap water is an effective treatment process to remove Cryptosporidium from drinking water (WHO, 2006). During the field trips it was anecdotally reported that the majority of households boiled their tap water prior to consumption. This behaviour would dramatically reduce exposure to Cryptosporidium from drinking water. Further investigations are required to determine the risk to human health presented by Cryptosporidium to population health in the Day/Nhue River Basin. 8.3.2 CHEMICAL POLLUTANTS The risk matrix identified lead and formaldehyde as the top inorganic and organic chemical that presented an extreme health risk to humans in the Day/Nhue River Basin. These two chemicals have been arbitrarily selected to provide an in-depth examination of the threat to human health, environmental fate and identify potential exposure pathways for lead and formaldehyde in the Day/Nhue River Basin. The health and environmental fate information was taken from the Agency for Toxic Substances and Disease Registry s (ATSDR) toxicological profile information sheets and the International Programme on Chemical Safety s Environmental Health Criteria. Lead Physical and chemical properties Lead is a bluish or silvery-grey soft metal. With the exception of the nitrate, the chlorate, and, to a much lesser degree, the chloride, the salts of lead are poorly soluble in water. Lead also forms stable organic compounds (IPCS, 1985). Sources in the Day/Nhue River Basin Sources of lead include the mining and smelting of ore, manufacture of lead-containing products, combustion of coal and oil, and waste incineration. The major dispersive, non-recoverable use of lead was in the manufacture and application of alkyllead fuel additives however these have been subjected to a global phase out. Environmental fate The amount of soluble lead in surface waters depends upon the ph of the water and the dissolved salt content. In most surface waters and groundwaters, the concentration of dissolved lead is low because lead forms compounds with anions in the water such as hydroxides, carbonates, sulfates, and phosphates that have low water solubilities and will precipitate out of the water column. Lead is known to form strong complexes with humic acid and other organic matter. Plants and animals may bioconcentrate lead, but biomagnification is not expected. In general, the highest lead concentrations are found in aquatic and terrestrial organisms with habitats near lead mining, smelting, and refining facilities; storage battery recycling plants; areas affected by high

Water pollution and public health 125 automobile and truck traffic; sewage sludge and spoil disposal areas; sites where dredging has occurred; areas of heavy hunting and fishing (lead from spent shot or sinkers); and in urban and industrialized areas (ATSDR, 2005). Exposure routes The general population may be exposed to lead in ambient air, foods, drinking water, soil, and dust. Segments of the general population at highest risk of health effects from lead exposure are preschool-age children and pregnant women and their foetuses. Within these groups, relationships have been established between lead exposure and adverse health effects. Other segments of the general population at high risk include individuals living near sites where lead was produced or disposed (ATSDR, 2005). Health impacts The main target for lead toxicity is the nervous system, both in adults and children. Long-term exposure of adults to lead at work has resulted in decreased performance in some tests that measure functions of the nervous system. Lead exposure may cause weakness in fingers, wrists, or ankles, and small increases in blood pressure, particularly in middle-aged and older people. Lead exposure may also cause anaemia. At high levels of exposure, lead can severely damage the brain and kidneys in adults or children and ultimately cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. High-level exposure in men can damage the organs responsible for sperm production (ATSDR, 2005). Summary Exposure to lead can occur through ingestion of contaminated foods and vegetables. Studies conducted in south-east Asia indicate lead may be consumed via the consumption of morning glory. Elevated lead levels were also identified in the liver of fish taken from Hanoi. Lead was detected below EU guideline levels in sediments in Nam Dinh. The health impacts of lead are severe particularly for children and pregnant women. The ingestion of lead via hand to mouth contact of young children with sediment from the Day/Nhue River Basin may present a pathway of lead exposure. Formaldehyde Physical and chemical properties Formaldehyde is a flammable, colourless and readily polymerized gas at ambient temperatures. Formaldehyde decomposes at 150 C into methanol and carbon monoxide; in general it is highly reactive with other chemicals. It has a very low n -octanol/water partition coefficient as well as a low soil-absorption coefficient. The Henry constant is relatively high. Sources in the Day/Nhue River Basin Formaldehyde is produced indirectly by photochemical oxidation of hydrocarbons or other formaldehyde precursors that are released from combustion processes. Environmental fate The value of the Henry constant suggests that formaldehyde in aqueous solution is less volatile than water and that volatilization from an aquatic environment is not expected under normal environmental conditions. The high water solubility and the low N-octanol/water partition coefficient suggest that adsorption on suspended solids and partition in sediments is not significant. In water, formaldehyde is rapidly biodegraded by several species of microorganisms, provided the concentration is not too high (IPCS, 1989). No experimental data were found concerning the adsorption of formaldehyde to soil, but because of the low octanol/water partition coefficient (log Kow=0.35), little adsorption to soil or sediment is expected to occur. Experiments performed on a variety of fish and shrimp showed no evidence of bioaccumulation of formaldehyde (ATSDR, 1999).

Water pollution and public health 126 Formaldehyde is unstable in water; however, it has been detected in municipal and industrial aqueous effluents, rainwater, lake water, and some waterways (ATSDR, 1999). Exposure routes The major exposure route is inhalation of indoor air in occupational or home settings. Health impacts Increased rates of cancer-related mortality associated with occupational exposure to formaldehyde have been found in some epidemiological studies, but not in others (ATSDR, 1999). Short-term exposure to formaldehyde can irritate the skin, eyes and lungs of humans. Due to formaldehyde s high reactivity and volatility there is uncertainty associated with oral dose levels and therefore the results of ingestion toxicity tests. High dose ingestion has been reported to lead to death, respiratory, gastrointestinal and cardiac effects. No evidence of reproductive, genotoxic or developmental damage was reported in the literature after oral exposure (ATSDR, 1999). Occupational exposures to formaldehyde have been associated with dermal irritation and the diagnosis of allergic contact dermatitis by patch tests (ATSDR, 1999). Summary Formaldehyde breaks down rapidly in water. It is therefore unlikely that exposure would occur through ingestion. Areas in close proximity to discharge points of formaldehyde may present a risk of dermal exposure to farmers that are using river water to irrigate their crops or people swimming near the discharge point. Formaldehyde is identified as a human carcinogen. Dermal contact can result in skin irritations. 8.3.3 UNCERTAINTIES AND LIMITATIONS As is common in the majority of EHRAs the lack of exposure data was a key limiting factor for this assessment. The exposure assessment was based on published literature and through anecdotal interviews and observations conducted during the seven-day field trip. This data was insufficient to create a strong indication of the potential exposure pathways to biological or chemical pollutants. The identification of Cryptosporidium as a potential health risk is based on the assumption that high levels of total coliform are associated with increased levels of Cryptosporidium. There is no evidence in Vietnam to support this association. Further investigations are required to determine the amount and infectivity of Cryptosporidium in drinking water supplies. It should be noted that the detection of Cryptosporidium is expensive and requires specialised detection equipment. Any studies conducted within Vietnam may require collaboration with international research facilities to ensure cost-effectiveness. Five factors reduce the confidence in the categorisation of the top 30 chemicals pollutants. 1. The hazard index is calculated by prioritising the top chemicals by load and hazard ranking. Assumptions are made in calculating the load for each chemical pollutant as presented in Section 5 Models, methods and assumptions. The model s estimates of load should be validated through water quality monitoring programmes in the Day/Nhue River Basin. 2. The data is based on estimates of the top 30 chemicals by hazard ranking derived from the PPS model. The hazard ranking is produced through classifying the chemicals into low,

Water pollution and public health 127 medium and high hazard according to their LC50 values. LC50 values are a measure of acute exposure to very high concentrations of a chemical. This type of exposure is not environmentally realistic and the relationship between LC50 data and exposure to low-level, long-term exposure has not been established. 3. A qualitative risk matrix was applied due to the data limitations and time constraints of the assessment. The use of words or descriptions resulted in some of the chemicals matching more than one categorisation. It was necessary for the consultant to use a weight-of-evidence approach to categorise some of the chemicals. As this study represents one step in the prioritisation of priority pollutants it is recommended that water managers and policymakers should familiarise themselves with the categorisation to raise their awareness of the limitations in the classification. 4. The quality and quantity of toxicity and environmental fate data grows each year as new experiments and studies are conducted and published. The current classification is based on available data that was presented in the National Library of Medicine s Toxicology Data Network, Hazardous Substances Data Bank and the International Agency for Research on Cancer in July, 2007. These databases are regularly updated. These databases should be checked to ensure new toxicological and environmental fate data is incorporated into the prioritisation of chemicals. 5. High levels of uncertainty exist in predicting the environmental fate, movement and toxicity of chemicals in water. The environmental fate and toxicity of chemicals in water can be impacted by numerous physico-chemical properties in the water and potential interactions. For example, ph affects ionisation and toxicity of metals, organic material can adsorb/absorb dissolved organic and inorganic chemicals and sunlight can increase the rate of degradation of organic chemicals. 8.4 THE HEALTH STUDY 8.4.1 INTRODUCTION Previous studies of the Day and Nhue River Sub-basin have indicated an association between adverse health effects and people living in close proximity to the Day and Nhue Rivers. The State of the Environment Report 2006 for the Day/Nhue River basin showed provinces, districts and communes through which the Nhue River flowed had higher rates of people infected by amoeba and diarrhoea than other districts not located along the river (MONRE, 2006). A report filed by the Hanoi-based University of Medicine in April, 2007 identified that water from the seriously contaminated Nhue caused digestive, gynecological, and skin diseases among residents in Ha Nam province (Dien, 2007). These studies provided the impetus to search for any further linkages that exist between available waterborne-related health data and the outputs of Pollution Projection System. Contamination for biological contaminants is the most common form of exposure that results in health effects. The WHO and Centre for Disease Control recognises diarrhea, cholera, dysentery and shigella as waterborne diseases (CDC, 2007; WHO, 2007). The diagnosis of these diseases occurs through microbiological examination of stool samples. Laboratories for diagnosis are available at all provincial hospitals and some district-level health centres and hospitals. The system used to collect infectious disease data is well established in Vietnam. Commune-level health centres provide monthly reports to district-level health centres. The district-level centres collate the data and then send it on to the epidemiological section of the provincial-level Administration of Preventative Medicine. Although a strong reporting system exists it was noted that high variability exists in the number of staff and the

Water pollution and public health 128 training of staff at commune-level health centres. The variability in staff training may result in inconsistencies in disease diagnosis and reporting at the commune-level. 8.4.2 RESULTS AND DISCUSSION The provincial-level data provided no clear trends in infectious diseases between the six provinces. Figure 8.4.1 shows that incidence of cholera, salmonella, dystenery and amoebiasis displayed no pattern between provinces from 2001-2005. Ninh Binh recorded approximately double the rate of Salmonella in comparison to other provinces. Figure 8.4.1 - Cases per 100,000 persons of chlorea, salmonella, dystenry and amoebiasis in the six provinces of the Day/Nhue River basin 90 80 70 Cases per 100,000 persons 60 50 40 30 20 Cholera Salmonella Dysentery Amoebiasis 10 0 Hanoi Ha Tay Ha Nam Hoa Binh Ninh Binh Nam Dinh The number of cases of diarrhoea and dysentery syndrome exceeded the other waterborne infectious diseases. The highest average number of diarrhoeal cases per 100,000 persons (2001-2005) was recorded at Ha Nam and Ninh Binh. Figure 8.4.2 - Cases per 100,000 persons of dysentery syndrome and diarrhoea in the six provinces of the Day/Nhue River basin 2500 2000 1500 1000 Dysentry syndrome Diarrhoea 500 0 Hanoi Ha Tay Ha Nam Hoa Binh Ninh Binh Nam Dinh

Water pollution and public health 129 District-level diarrhoeal and dysentery syndrome cases did not show any significant differences (p>0.05) between districts adjacent to the Day or Nhue river to those not located next to the Day or Nhue river. In contrast, the commune level data from Ha Nam found a significant difference (p<0.05) in the rate of diarrhoeal cases between communes adjacent to the river (µ = 1455 cases per 100,000 people) with those that were not located adjacent to the river (µ = 919 cases per 100,000 people). Districts not located adjacent to the river had approximately one and half times more incidence of diarrhoea than districts located adjacent to the river (Figure 8.4.3). These findings do not confirm the trends reported in the Day/Nhue State of Environment Report (2006) that recorded districts located adjacent to the river had much higher rates of people infected by amoeba and diarrhoea than other districts not by the river (Environment Monitor Report, 2006). Figure 8.4.3 - Cases of diarrhoea per 100,000 in Ha Nam communes located adjacent and non-adjacent to Day and Nhue River in June 2006-June 2007 1600 1400 1200 1000 800 600 400 200 0 Adjacent Non-adjacent 8.4.3 UNCERTAINTIES AND LIMITATIONS Data limitations The data presented records only cases who sort treatment at a commune health centre. The data does not include cases that self-medicated, attended a private clinic or a traditional healer. Isenbarger et al. (2001) recorded significant levels of underreporting of diarrhoeal cases in the Red River delta. It is likely the data recorded by the APM reflects a significant underreporting of cases of gastro-intestinal illness at the commune, district and provincial level. During the field visits a number of epidemiological staff expressed reservations about the consistency of infectious disease reporting at the commune-level. The data provided in the Vietnam Health Statistic Yearbook (2005) reveals the variability in the percentage of communes that have a doctor present. In Hanoi the level is 96%, whereas Hoa Binh has only 36% of communes with a doctor (see Table 8.4.5). The presence of a doctor would increase the accuracy of the diagnosis and the reporting of waterborne diseases. Inconsistent numbers of doctors at the commune-level reduces the confidence of the findings from the health study. Table 8.4.5 - Health statistic yearbook 2005, Published by HSID Planning and Finance Department

Water pollution and public health 130 No of provincial hospitals No of district hospitals No. of medical doctors - provincial No. of medical doctors - district No. of medical doctors - commune Ha Noi 15 3 793 433 202 96 Ha Tay 5 12 392 603 252 80 Ha Nam 4 6 203 133 78 72 Hoa Binh 2 11 141 119 79 36 Ninh Binh 3 7 194 131 70 49 Nam Dinh 7 9 323 339 182 69 % of communes with doctor Undertaking a thorough examination of the likelihood of exposure pathways of pathogenic microorganisms would be a substantial undertaking and is beyond the scope of this study. Four important forms of exposure to pathogenic microorganisms that are not related to exposure to the Day and Nhue Rivers are handwashing with soap, incorrect food preparation, use of human faeces as fertiliser on agricultural crops and exposure to chemical pollutants. Handwashing with soap: Failing to wash hands with soap after defecation, handling of children s faeces, prior to breastfeeding and food preparation are exposure sources to pathogens from human faeces. A recent survey of mothers with children under the age of five living in urban and rural villages found handwashing with soap occurred in 40% of households (Indochina Research, 2006). The survey included Hung Yen province which is located close to the Day and Nhue River Basin. Age, income and education were not found to impact on handwashing with soap prevalence rates. If this data is extrapolated to the six provinces under investigation it is reasonable to suggest a high number of gastro-intestinal illness would be the result of hand-oral faecal contamination. Incorrect food handling and poor food preparation: Incorrect food preparation can result in exposure to a number of pathogens that can lead to gastro-intestinal illness. A study of pathogens in raw food prepared in Hanoi identified Salmonella, Campylobacter, and Escherichia coli (E. coli) (Dao and Pham, 2006).The contribution of foodborne contaminants to gastrointestinal disease may confound the interpretation of the statistical analysis. Use of human faeces as fertiliser: The use of human faeces as a crop fertiliser or soil conditioner is common practice throughout the Northern provinces of Vietnam. The risks of exposure to pathogenic micro-organisms is high during the removal and spreading of human excreta upon agricultural crops. Other sources of gastrointestinal illness: Gastrointestinal disease can be caused by exposure to a number of chemical pollutants. Acute exposures to pesticides, herbicides and heavy metals have all been associated with gastrointestinal illness and symptoms including diarrhoea and stomach pain. 8.4.4 CONCLUSIONS The evidence collected during the field visits does not support the hypothesis that communities living adjacent to the Day or Nhue Rivers have a higher rate of diarrhoea than those not living adjacent to the rivers. These findings are not surprising as it was recognised that high levels of variability exist in the accuracy and reliability of diarrhoeal reporting across communes, combined with numerous confounding factors such as rates of hand washing, incorrect food handling and poor food preparation and other sources of gastrointestinal illness. Prospective epidemiological

Water pollution and public health 131 studies that include regular and reliable reporting of waterborne illnesses are required to test the hypothesis that proximity to the river is associated with the incidence of waterborne illnesses.

Water pollution and public health 132 ANNEX 8.1 RISK MATRIX OF TOP 30 CHEMICAL POLLUTANTS BY HAZARD RANKING IN THE DAY-NHUE RIVER BASIN Sodium hydroxide (solution) Likelihood of exposure Consequence of exposure Risk to human health Possible Minor Moderate Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. Moderate to high persistence in water. High solubility in water (1g/0.9ml of water). Not bio-accumulative due to very low Kow value (not measurable). No evidence of exposure in Vietnam. Exposure only recorded in occupational settings or incorrect use of domestic cleaning products. Consequence of exposure: At high concentrations exposure to alkaline solutions can cause sensation and irritation of dermal and digestive lining. If not removed from the skin, severe burns with deep ulceration will occur. Highly caustic to oesophagus in high concentrations. Not listed on IARC monographs.. Limited evidence of causing causing DNA damage, reproductive damage or developmental toxicant. No epidemiological evidence of adverse health effects in Vietnam. Sodium sulphate (solution) Likelihood of exposure Consequence of exposure Risk to human health Possible Minor Moderate Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. High persistence in water but not bio-accumulative. No evidence of contamination of food chain. High solubility in sediments. Consequences of exposure: At very high concentrations (500mg/l) can cause diarrhoea. Considered nontoxic and nonirritating to skin and mucous membranes. No evidence of causing DNA damage, reproductive damage or developmental toxicant. Not listed on IARC monographs. Ammonia Likelihood of exposure Consequence of exposure Risk to human health Rare Major High Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. Rapidly converted to nitrate in oxygenated water. Bacteria convert the ammonia to nitrate creating an oxygen demand several days after the introduction of ammonia. Highly soluble in water (482,000 mg/l at 25 0 C). Does not bio-accumulate. Key recorded exposure routes are through inhalation in occupational settings. Consequence of exposure: Toxicological data is only available for the gaseous form of ammonia. Ammonia is rapidly converted to nitrate in aquatic systems. The major acute toxic effect of nitrate and nitrite is methemoglobinemia. Methemoglobin reduces the oxygen-carrying capacity of the blood and in addition, it shifts the oxyhemoglobin dissociation curve to the left interfering with the unloading of oxygen. IARC group 2a: probably carcinogenic to humans. Sulfuric Acid Likelihood of exposure Consequence of exposure Risk to human health Possible Minor Moderate

Water pollution and public health 133 Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. Highly soluble in water Moderate to low persistence in surface water due to higher density resulting in it rapidly moving through sediment and potentially into groundwater. Soluble in water but will ultimately react with calcium and magnesium in water to form sulfate salts. Does not bio-accumulate. Consequence of exposure: Majority of toxicity testing has studied the health effects of inhalation of sulphuric acid. Ten patients with exposure to sulfuric acid ingestion were studied. All patients had oesophageal and gastric involvement but the duodenum was not harmed in the majority of patients. Complications and mortality occurred in patients with severe injury. Not listed on IARC monographs. Ammonium nitrate (solution) Likelihood of exposure Consequence of exposure Risk to human health Possible Major Extreme Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. Ammonium nitrate is highly soluble in water (250 g/100 ml at 20 deg C). Can infiltrate the soil and can migrate into the groundwater system. It can be degraded by aerobic and anaerobic bacteria. Nitrate is more persistent in water than the ammonium ion. No evidence of bioaccumulation in vegetables or fish. Consequence of exposure: Strong evidence of acute toxic effect of nitrate poisoning is methemoglobinemia. Blood is the target organ. Methemoglobin reduces the oxygen-carrying capacity of the blood and in addition, it shifts the oxyhemoglobin dissociation curve to the left interfering with the unloading of oxygen. IARC Group 2a: Probably carcinogen to humans. Formaldehyde Likelihood of exposure Consequence of exposure Risk to human health Possible Catastrophic Extreme Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. Highly soluble in water. Formaldehyde readily biodegrades under both aqueous aerobic and anaerobic conditions. Volatilization from water surfaces is expected to be low. The potential for bioaccumulation in aquatic organisms is low (log K ow = 0.35). Key exposure routes are through inhalation via domestic or occupational exposure. Consequence of exposure: Available toxicity data focuses on inhalation of gaseous form. Eye and skin irritant. Formaldehyde is classified as carcinogenic to humans (IARC Group 1). Phosphoric acid Likelihood of exposure Consequence of exposure Risk to human health Possible Insignificant Low Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. While acidity may be reduced readily by natural water hardness minerals, the phosphate may persist indefinitely. Phosphate is readily uptaken by photosynthetic bacteria such as blue-green algae. Consequence of exposure: Limited evidence of acute and chronic disease states. Eye and skin irritant. Toxicity data is predominately on inhalation route of exposure. Nitric acid Likelihood of exposure Consequence of exposure Risk to human health

Water pollution and public health 134 Possible Insignificant Low Likelihood of exposure: Top 1-15 by hazard ranking from PPS outputs. Limited data available on aquatic fate. Consequence of exposure: Limited evidence of acute and chronic disease states. Toxicity data is predominately available for inhalation route of exposure. A corrosive irritant to skin, eyes and mucous membranes. No evidence of causing DNA damage, reproductive damage or developmental toxicant. Aluminium oxide Likelihood of exposure Consequence of exposure Risk to human health Possible Insignificant Low Likelihood of exposure: Top 1-15 ranking by hazard ranking from PPS outputs. Exposure can occur through ingestion of liquids and foods. Consequence of exposure: Limited evidence of acute and chronic disease states. Some evidence of carcinogenicity but not listed on IARC monographs. Hydrochloric acid Likelihood of exposure Consequence of exposure Risk to human health Possible Moderate High Likelihood of exposure: Top 1-15 ranked by hazard ranking from PPS outputs. Hydrogen chloride in water dissociates almost completely, with the hydrogen ion captured by water molecules. Consequence of exposure: Limited evidence of causing chronic disease via ingestion. Chloride ion has very low toxicity to humans. At levels below the threshold for taste or eye irritation, can induce sneezing, laryngitis, chest pain, hoarseness, and a feeling of suffocation. Not classifiable as to its carcinogenicity to humans (IARC Group 3). Methanol Likelihood of exposure Consequence of exposure Risk to human health Unlikely Moderate Moderate Likelihood of exposure: Top 1-15 ranked by hazard ranking from PPS outputs. Volatilization from water surfaces is expected to be an important fate process based upon this compound's Henry's Law constant. Estimated volatilization half-lives for a model river was 3 days.. Biodegradation is expected to occur in natural waters since methanol is degraded quickly in soils. Bio-concentration factor (BCF) values of less than 10 suggest bio-concentration in aquatic organisms is low. Consequence of exposure: Some evidence of diseases states following acute exposure. Humans and non-human primates are uniquely sensitive to the toxic effects of methanol. IARC Group 3: not classifiable as to its carcinogenicity to humans. Chromium compounds (including CrIII and CrVI) Likelihood of exposure Consequence of exposure Risk to human health Unlikely Major High Likelihood of exposure: Top 1-15 ranked by hazard ranking from PPS outputs. Chromium is a metallic element with oxidation states ranging from chromium(-2) to chromium(+6). The important

Water pollution and public health 135 valence states of chromium are III and VI. If released into water, most chromium compounds will ultimately be deposited in sediments. The adsorption of Cr(III) and Cr(VI) on suspended solids and sediment is complicated by redox changes that can occur. Chromium(VI) predominates under highly oxidizing conditions; whereas chromium(iii) predominates under reducing conditions. According to a classification scheme, a bioconcentration for chromium(vi) in rainbow trout of one, suggests the potential for bioconcentration in aquatic organisms is low. There is no indication of biomagnification of chromium along the terrestrial food chain (soil-plant-animal). Consequence of exposure: Acute chromic acid ingestion causes an acute gastroenteritis, hepatic necrosis, bleeding, and acute tubular necrosis with renal failure. The strong oxidizing potential of chromium(vi) compounds explain much of their irritating and toxic properties. Chromium VI is a Group 1: Carcinogenic to humans. Chromium III is a Group 3: Not classifiable as to its carcinogenicity to humans Ammonium sulphate (solution) Likelihood of exposure Consequence of exposure Risk to human health Possible Minor Moderate Likelihood of exposure: Top 1-15 ranked by hazard ranking from PPS outputs. Highly soluble in water (70.6mg/l at 0 0 c). Consequence of exposure: Low acute toxicity. May cause reversible or irreversible changes to exposed tissue but not permanent injury or death. Not listed on IARC monographs. Nitroglycerin Likelihood of exposure Consequence of exposure Risk to human health Unlikely Major High Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs. Nitroglycerin is expected to adsorb to suspended solids and sediment. Volatilization from water surfaces is not expected. According to a classification an estimated BCF of 4 suggests the potential for bioconcentration in aquatic organisms is low. Nitroglycerin was found to be readily biodegradable in river water. Consequence of exposure: Evidence of acute effects can occur after ingestion. Chronic exposure can cause vasodilatation and methemoglobinemia. Venous and arterial vasodilatation causes lowering of blood pressure leading to shock. Heart, blood vessels and red blood cells are the target organs in nitrogylcerin poisoning. Not listed on IARC monographs. Phenol Likelihood of exposure Consequence of exposure Risk to human health Unlikely Catastrophic Extreme Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs. Expected to adsorb to suspended solids and sediment. Volatilization from water surfaces would not be expected to be a major fate process. Bioaccumulation of phenol is unlikely. Phenol is rapidly biodegraded in waterways. Biodegradation increases in waterways with high organic content. Consequence of exposure: Strong evidence of causing acute disease states. Phenol is toxic with a probable oral lethal dose to humans of 50-500 mg/kg. Some individuals may be hypersensitive with lethality or serious effects at very low exposures. Rapid absorption and severe systemic

Water pollution and public health 136 toxicity can occur after any route of exposure including skin. Death and severe toxicity are usually due to effects on the CNS, heart, blood vessels, lung, and kidneys. Consumption of water contaminated with phenol resulted in diarrhea, mouth sores, burning of the mouth, and dark urine. IARC Group 3 not classifiable as to its carcinogenicity to humans. 1,2,4 - trichlorobenzene Likelihood of exposure Consequence of exposure Risk to human health Possible Minor High Likelihood of exposure: Top 1-15 ranked by hazard ranking from PPS outputs. Expected to biodegrade slowly in soils and water with biodegradation half-lives ranging from several weeks to a few months. Expected to adsorb to sediment or particulate matter based on its measured Koc values. This compound is expected to volatilize rapidly from water surfaces. Estimated volatilization half-lives for a model river was 5 hours. Bioconcentration in aquatic organisms is considered high based on BCF values in the range of 120 to 2,400 measured in fish. Consequence of exposure: Limited toxicological information available on exposure via dermal contact or ingestion. Exposure can cause irritation to the skin, conjunctiva, and mucous membranes of the upper respiratory tract. Individuals who suffer from skin, liver, kidney, or chronic respiratory disease, will be at an increased risk if they are exposed to chlorobenzenes. Not listed on IARC monographs. Chloroform Likelihood of exposure Consequence of exposure Risk to human health Possible Major Extreme Likelihood of exposure: Top 1-15 ranked by hazard ranking from PPS outputs for entire basin. Not absorbed to sediment and particles. Rapidly volatises from water column and the major process to be considered in the study of fate processes for chloroform is the diffusive air/water exchange. Biodegradation of chloroform in environmental aqueous environments is not well understood. Readily absorbed through the skin of humans and animals and significant dermal absorption of chloroform from water while showering has been demonstrated. Placental transfer of chloroform has been demonstrated in several animal species and humans. Consequence of exposure: Strong evidence of association with acute and chronic disease states. Some evidence of causing DNA damage. IARC classification Group 2B: Possibly carcinogenic to humans. Pentachlorophenol Likelihood of exposure Consequence of exposure Risk to human health Likely Moderate High Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Expected to adsorb to suspended solids and sediment in water. Adsorption is expected to be greater under acidic conditions. Moderate time required for aerobic degradation. Not expected to volatise from water column. Expected to bio-accumulate due to low water solubility. Very high bioaccumulation values have been recorded in fish species. Consequence of exposure: Evidence of acute disease states including dermal and respiratory damage from occupation exposure. Not listed on IARC monographs however USEPA has

Water pollution and public health 137 identified as probable human carcinogen based on inadequate human data and sufficient evidence of carcinogenicity in animals. Aluminium Likelihood of exposure Consequence of exposure Risk to human health Likely Insignificant Moderate Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Some plants can bio-accumulate aluminium does not accumulate in aquatic organisms. Consequence of exposure: No acute effects in the general population have been described after exposure to aluminum. Although it has been hypothesized that aluminum is a risk factor for Alzheimer's disease, present epidemiological evidence does not support a causal association between Alzheimer's disease and aluminum in drinking-water. Not listed on IARC monographs. Toluene Likelihood of exposure Consequence of exposure Risk to human health Unlikely Inadequate data available Low Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Toluene is not expected to adsorb to suspended solids and sediment. Volatilization from water surfaces is expected. Bioconcentration in aquatic organisms is low to moderate. Biodegradation is moderate to rapid. The half-life of toluene in aerobic and anaerobic water reported as 4 and 56 days, respectively. Consequence of exposure: Majority of toxicity data has studied acute and chronic effects of toluene inhalation. Biphenyl Likelihood of exposure Consequence of exposure Risk to human health Unlikely Moderate Moderate Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Expected to adsorb to suspended solids and sediment. Volatilization from water surfaces is expected half-lives for a model river were estimated to be 4 hours. However, volatilization from water surfaces is expected to be attenuated by adsorption to suspended solids and sediment in the water column. The potential for bioconcentration in aquatic organisms is high to very high. Biodegradation may be an important environmental fate process under aerobic conditions, as indicated by a reported half-life of 2-3 days in a river die-away test however the compound may be resistant to biodegradation under anaerobic conditions Consequence of exposure: Majority of toxicity data has studied acute and chronic effects of biphenyl inhalation. Biphenyl vapors in the workplace results in the irritation of the eyes and inflammation of the respiratory tract. Long-term exposure for several years to high concentrations caused damage to the liver and persistent neuronal changes. Direct skin contact may have played a part, in addition to uptake through the respiratory tract. Not listed on IARC monographs. Tetrachloroethylene Likelihood of exposure Consequence of exposure Risk to human health Unlikely Moderate Moderate

Water pollution and public health 138 Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Expected to adsorb to suspended solids and sediment in water. The biodegradation half-lives of tetrachloroethylene in aerobic and anaerobic waters were reported as 180 and 98 days, respectively. Volatilization from water surfaces is expected to be an important fate process. Half-life in a model river were estimated to be 1 hour. Measured BCF values of 26-77 in fish suggest bioconcentration in aquatic organisms is low to moderate. Consequence of exposure: Strong evidence of acute and chronic disease states via inhalation. Limited toxicological or epidemiological data available on dermal contact and ingestion. IARC group 2A is probably carcinogenic to humans (Group 2A). Lead Likelihood of exposure Consequence of exposure Risk to human health Possible Major Extreme Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Strongly absorbs to soil and remains on top soil surface. Plants and animals can bioconcentrate lead. Consequence of exposure: Very strong evidence of acute and chronic disease states. Chronic exposure can cause permanent neurological damage in children. Limited evidence of reproductive damage. IARC Group 2B, possibly carcinogenic to humans. Acrylic acid Likelihood of exposure Consequence of exposure Risk to human health Unlikely Minor Low Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Not expected to adsorb to suspended solids and sediment in the water column Biodegradation under both aerobic and anaerobic conditions is expected to occur. Volatilization from water surfaces is expected to occur slowly based the estimated Henry's Law constant. Estimated volatilization half-lives for a model river are 96 days. An estimated BCF of 1 suggests the potential for bioconcentration in aquatic organisms is low. Consequence of exposure: Low to moderate acute toxicity by the oral route and moderate acute toxicity by the inhalation or dermal route. Corrosive and irritant to skin and eyes and a strong irritant to the respiratory tract. Available reproduction studies indicate that acrylic acid has no effect on reproduction. Some evidence of causing DNA damage. IARC Group 3: Not classifiable as to its carcinogenicity to human. Ethylene glycol Likelihood of exposure Consequence of exposure Risk to human health Unlikely Minor Low Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Not expected to adsorb to suspended solids and sediment. In a river die-away test, degradation was complete within 3 days at 20 deg C. Volatilization from water surfaces is not expected to be an important fate process. Low potential for bioconcentration in aquatic organisms. Consequence of exposure: Low acute toxicity in experimental animals following oral, inhalation and dermal exposure. Available data are inadequate to assess the potential adverse neurological or immunological effects associated with long term exposure to ethylene glycol, although

Water pollution and public health 139 neurobehavioral and neurological disorders have been reported in cases of acute ethylene glycol poisonings in humans. Not listed on IARC monographs. Ethylene oxide Likelihood of exposure Consequence of exposure Risk to human health Unlikely Major Extreme Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Releases into water will be removed by volatilzation, hydrolysis and to a lesser extent, biodegradation. The volatilization half-lives for its removal from a model river are 5.9 hours. Will not adsorb strongly to soil or bioconcentrate in fish. Consequence of exposure: Occupational exposure has been linked to chronic disease states. Strong evidence of causing DNA and reproductive damage in non-human species. IARC Group 1: Carcinogenic to humans based on mechanistic and other relevant data. Acetone Likelihood of exposure Consequence of exposure Risk to human health Unlikely Minor Low Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs. Not expected to adsorb to suspended solids or sediment. Volatilization from water surfaces is expected to be an important environmental fate process given its estimated Henry's Law constant. Estimated halflives for a model river are 38 hours. Experimentally determined volatilization half-lives in a shallow stream were measured in the range of 8-18 hours. Bioconcentration in aquatic organisms is considered low based upon an estimated BCF value of 1. Consequence of exposure: Experimental animal data characterizing the effects of long term oral or inhalation exposure to acetone are not available. Based on existing evidence acetone is not considered to be genotoxic or mutagenic. Not listed on IARC monographs. N-Butanol Likelihood of exposure Consequence of exposure Risk to human health Unlikely Minor Low Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Not expected to adsorb to suspended solids and sediment in water based upon the estimated Koc. Volatilization from water surfaces is expected to be an important environmental fate process based upon the compound's Henry's Law constant. Estimated volatilization half-lives for a model river are 2 days. Biodegradation is an important fate process in water. An estimated BCF of 3 suggests the potential for bioconcentration in aquatic organisms is low. Hydrolysis is not expected to be an important environmental fate process since this compound lacks functional groups that hydrolyze under environmental conditions. Consequence of exposure: Toxicological evidence is only available for exposure via inhalation. Short-term exposure of humans can cause acute symptoms including headaches and possible coma. Muscle weakness may also be observed. Possible gastrointestinal effects include nausea & vomiting. In addition, butanol liquid or vapors cause irritation to the skin and membranes in the eyes and upper respiratory tract. Not listed on IARC monographs.

Water pollution and public health 140 Dichloromethane Likelihood of exposure Consequence of exposure Risk to human health Unlikely Major High Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Not expected to adsorb to suspended solids and sediment in water based upon the estimated Koc. Biodegradation is possible in natural waters but will probably be very slow compared with evaporation. Volatilization from water surfaces is expected to be an important fate process based upon this compound's Henry's Law constant. Estimated volatilization half-lives for a model river are 1 hour. An estimated BCF of 2 suggests the potential for bioconcentration in aquatic organisms is low. Hydrolysis is not an important degradation process under normal environmental conditions. Consequence of exposure: Acute toxicity to laboratory mammals by inhalation and oral administration is low. In humans, large spills and ingestion have resulted in death. Moderate irritant to the skin and eyes of experimental animals. IARC Group 2B: Possibly carcinogenic to humans. Mercury Likelihood of exposure Consequence of exposure Risk to human health Likely Major Extreme Likelihood of exposure: Top 16-30 ranked by hazard ranking from PPS outputs for entire basin. Strongly absorbs to soil and does not re-enter water column once bound to soil. The most common organic form of mercury, methylmercury, is soluble, mobile, and quickly enters the aquatic food chain. Methylmercury can be biomagnified in aquatic organisms. Consequence of exposure: Very strong evidence of acute and chronic disease states. Chronic exposure can cause irreversible damage to the central nervous system. IARC Group 3: Not classifiable as to its carcinogenicity to humans. References Blatchley E.R., Gong W-L., Elleman J.E., Rose J.B. et al., 2007 Effects of Wastewater Disinfection on Waterborne Bacteria and Viruses Water Environment Research: 79 (1): 81-92

Water pollution and public health 141 ANNEX 8.2 - INTERNATIONAL TREATIES ON HAZARDOUS CHEMICALS The Stockholm Convention was adopted in 2001 to protect human health and the environment from persistent organic pollutants (POPs). POPs have an inherent ability to be transported in the environment across geographical and political borders, accumulating in water, soil, plants and animals. POPs can enter the human body via the food chain, inhalation or dermal exposure. The WHO has recognised POPs pose a higher threat to populations in developing countries (WHO, 2005). The POPs organising committee s website (www.pop.int) identifies that Vietnam has not submitted a National Implementation Plan for the introduction of the Stockholm Convention (POP, 2007). The twelve POPs identified in the Convention are bioaccumulative, toxic and persistent. The list includes pesticides (p) and industrial chemicals (i) and industrial by-products (ib). Table 1: Listed POPs on the Stockholm Convention Aldrin p Mirex p Chlordane p Toxaphene - p DDT p Hexachlorobenzene p Dieldrin p Furans ib Endrin - p Dioxins ib Heptachlor p Polychlorinated biphenyls - i The Rotterdam Convention was adopted in 1998 and established the requirements for prior informed consent procedures for certain hazardous chemicals and pesticides. The accession of the Rotterdam Convention by Vietnam occurred in May, 2007 (Rotterdam Convention, 2007). A National Action Plan was released in May, 2006. Table 2: Prior informed consent chemicals on the Rotterdam Convention 2,4,5-T 1,2-dibromoethane Mercury compounds Aldrin Dieldrin Crocidolite Captafol Dinoseb Polybrominated biphenyls Chlordane Fluoroacetamide Polychlorinated biphenyls Chlordimeform HCH Polychlorinated terphenyls Chlorobenzilate Heptachlor Tris (2,3 dibromopropyl) phosphate DDT Hexachlorobenzene Pentachlorophenol Lindane The Basel Convention on the Control of Transboundary Movements of the hazardous wastes and their disposal was adopted in 1989. Its goal is to prevent transboundary movement of hazardous waste and the development of criteria for environmentally sound management of the waste (UNEP, 2003). The accession of the Basel Convention in Vietnam occurred in 1995 (Basel Convention, 2007). The implementing government agency is Vietnam Environmental Protection Agency (VEPA) under MONRE. References Basel Convention 2007 Parties to the Basel Convention. Cited from http://www.basel.int/ratif/convention.htm on 21/8/07

Water pollution and public health 142 POP 2007 National Implementation Plans submitted pursuant to Article 7(b) of the Stockholm Convention. Cited from http://www.pops.int/documents/implementation/nips/submissions/default.htm on 21/8/07 Rotterdam Convention 2007 Ratification. Cited from http://www.pic.int/home.php?type=t&id=63&sid=17 on 21/8/07 UNEP 2003 The Hazardous Chemicals and Wastes Conventions. Secretariat to the Basel Convention. Interim secretariat of the Rotterdam Convention. Interim secretariat to the Stockholm Convention. WHO 2005 World Health Organization Statement to the First Meeting of the Conference of the Parties to the Stockholm Convention. Cited from http://www.who.int/ipcs/features/cop1/en/index.html on 21/8/07

Water pollution and public health 143 ANNEX 8.3 BENEFICIAL USERS AND EXPOSURE PATHWAYS IN THE DAY/NHUE RIVER BASIN Exposure assessments involve describing the nature and size of the population exposed to a substance and the magnitude and duration of their exposure. Limited information was available to quantify the past, current and future exposure of the surrounding communities to the Day and Nhue Rivers. Conducting a scientifically valid exposure assessment was beyond the scope of this study. However to overcome the deficit in exposure data, anecdotal observations were taken of beneficial users of the Day and Nhue Rivers. During the field visits a number of beneficial users were observed interacting with the Day and Nhue Rivers. The purpose of this section of the health component is to provide images of the users and present a short discussion of their exposure to the Day and Nhue Rivers. It is intended that these anecdotal storylines of exposure will assist future exposure assessment in prioritising their efforts. House-boat dweller washing leafy vegetables below Hong Phu Bridge, Ha Nam. Exposure pathways Boat-dwellers in Phu Ly were observed to prepare their food and wash their clothes in the Day River. Washing leafy vegetables in contaminated water has been shown to be associated with increased risk of exposure to biological contaminants (Vuong et al., 2007). Washing clothes in the river would increase the dermal exposure to river water. Domestic washing water has been shown to be a significant contributor to nutrient loads in river basins. Living in close proximity to the river would increase the likelihood of accidental exposure from river water to adults and children living aboard the house-boats. Fisherman below Day Dam, Ha Tay

Water pollution and public health 144 Exposure pathways Fishermen were observed in the reservoir on the downstream side of the Day Dam. The fisherman would have an increased dermal exposure to the Day River due to placing the nets and removing fish. It is likely the fisherman would eat the majority of their catch and this would result in them having higher than average exposure to any biological or chemical pollutants that are accumulated in the fish flesh or organs. Children swimming in Day Dam reservoir Exposure pathways: Groups of children were observed swimming along the banks of the Day Rive and in large reservoirs. Swimming is a primary source of exposure via ingestion and from skin contact. It is estimated that approximately 100ml of water are ingested per swimming session (ANZECC, 2000). The ANZECC (2000) guidelines state the median bacterial content in samples of fresh or marine waters taken over the bathing season should not exceed 150 faecal coliform organisms/100 ml and 35 enterococci organisms/100 ml. Paddy farmers that irrigate with water taken from the Day and Nhue Rivers

Water pollution and public health 145 Exposure pathways: The Day and Nhue Rivers are used extensively to irrigate rice crops throughout the Day-Nhue River Basin. Farmers are exposed to the water predominately on the hands and feet. Dermal exposure occurs throughout the planting and harvesting of rice crops. Farmers living in communes in the periurban areas surrounding Hanoi that applied wastewater recorded significantly higher incidence of dermatitis for (in prep. Vuong et al). Woman cleaning pig intestines in Nhue River Exposure pathways: This woman was observed cleaning the stomach contents of a pig on the banks of the Nhue River. The cleaning process would contribute significantly to localised concentrations of biological pollutants. Dermal contact to the hands and feet are the key sources of exposure. Pig stomach and content are commonly boiled prior to consumption and the transmission of biological pollutants via ingestion is unlikely. References ANZECC 2000 Water Quality Guidelines: Chapter 5 Guidelines for recreational water quality and aesthetics Simmons R.W., Vinh N. C. and Jensen J. R. 2006 Cadmium in paddy soil and rice grain in Nam Dinh, Vietnam: A potential public health risk in Food safety of aquatic plants and fish raised in

Water pollution and public health 146 wastewater-fed ponds. Dalsgaard A., Klank L.T., Vuong T.A., Cam P.D. Marcussen H., Jorgensen K.,Holm,P.E., Brocca D.,Simmons R., Lan T.P.L., and Mara D. www.papussa.org Tran D T, Tuan N D et al. 2006 Skin problems among farmers engaged in wastewater-fed agriculture in Nam Dinh province, Vietnam Vuong Tuan Anh, Wim van der Hoek, Annette Kjær Ersbøll, Nguyen Van Thuong Nguyen Dang Tuan, Phung Dac Cam, Anders Dalsgaard in prep Dermatitis among farmers engaged in periurban aquatic food production in Hanoi, Vietnam Vuong T.A., Tram N.T., Klank L.T., Cam P.D. and Dalsgaard A in prep b Faecal and protozoan parasite contamination of water spinach (Ipomoea aquatica) cultivated in urban wastewater in Phnom Penh, Cambodia Weisbrod A.V., Burkhard L.P., Arnot O. et al. 2007 Workgroup Report: Review of Fish Bioaccumulation Databases Used to Identify Persistent, Bioaccumulative, Toxic Substances Environmental Health Perspectives 115(2):255-261 World Health Organization 2003 Guidelines for safe recreational water environments. Volume 1, Coastal and fresh waters.

Final priorities & next steps 147 9 FINAL PRIORITIES & NEXT STEPS 9.1 SUMMARY OF MAIN FINDINGS AND CONCLUSIONS The area-based approach is an objective tool that allows the user to include a variety of pollution sources under one analytical framework. The appeal is in the ability to bring together a variety pollution sources that normally are studied separately. For policymakers and regulators who often have to take a much wider view of the problem, area-based approaches are becoming a popular choice. The pollution sources presented in this study of the Day/Nhue River Sub-basin include industry, domestic, agriculture and craft villages. Each of these sources has a very different set of pollutants and pathways which affect ambient water quality conditions of the rivers. Although modeling these pathways was not the mandate under this project, the study made an attempt to bring together first-order estimates of the pollution generated by these types of economic activity and highlight geographical and sector areas where pollution is projected to be most acute. The findings in this study show that there are several areas of significance that can be the focus of management efforts. In particular, it is found that pollution is concentrated in relatively few provinces, and in fact can be linked to only a limited number of districts. Also, within each of the pollution sources, specific sectors and enterprises can be identified as being the largest contributors to the projected pollution load calculated by the models. The following sections summarize the top areas of pollution significance according to the estimates detailed in Section 6. 9.2 RECOMMENDATIONS 9.2.1 AREAS OF INTEREST IN TERMS OF ESTIMATED POLLUTION LOAD In terms of estimated BOD5 and SS pollution loads at the basin and provincial level, domestic wastewater sources are by far the largest contributor when compared to industrial and craft village sources. On average domestic sources represent over 89% of the potential BOD5 load and industry represents over 61% of estimated SS load at the basin level. 33 However, when the analysis drills down to the district level, industrial sources play a larger role in defining the overall BOD5 and SS profile in a number of districts. For example, Phu Ly (Ha Nam), Phu Xuyen and Phuc Tho (Ha Tay), Hai ba Trung and Hoang Mai (Hanoi), Ky Son (Hoa Binh), and Nam Dinh (Nam Dinh) have estimated industrial BOD5 load shares greater than 20%. 34 Industry is even more significant in terms of its share for estimated SS load at the district level. 35 Thus depending on the level of intervention, the target sources can diversify with the unit of analysis. The estimates derived from each of the individual pollution models (IPPS, DPPS, APPS and CVPPS) suggest several areas in identifying specific areas of interest. 36 For industry and hazardous substances, the areas of common interest are largely confined to similar (and selected) districts in Hanoi and Ha Tay. This holds true for more conventional pollutants such as BOD5 and SS, as well as for substances that are more toxic to human health. Estimated domestic BOD5, SS and solid waste loads are primarily concentrated in the districts of Hanoi with some exceptions for rural solid waste in Nam Dinh and Ha Tay. Agricultural pesticide and fertilizer-related pollution are most intensive in the districts of Ha Tay and a few districts in Nam Dinh. The pollution situation for craft-villages is quite clear. Focusing resources in the province 33 See Figures 6.11 and 6.12 for details. 34 See Table 6.11 for details. 35 See Table A6.1, Annex 6.1 for details. 36 See Table 6.71 for a summary of all source indicators.

Final priorities & next steps 148 of Ha Tay would serve to target over 75% of estimated craft-village pollution for all selected indicators covered in this study (BOD5, COD, SS, Total N, Total P, Total Fe, Oil and Coliform). 9.3 FUTURE EFFORTS FOR DISPERSION MODELING The dispersion modeling exercise undertaken by this study identified several key data gaps that could be easily filled with some short-term and regular monitoring effort. In particular, parameters which lacked repeated observations included physical features of the river stretch (river width, depth and flow rates), as well as specific parameters such as dissolved oxygen. 37 Although actual results for all river segments, and for all water quality parameters was not possible in this exercise, there are currently very few studies that attempt to integrate multiple-source loads for the entire Day/Nhue River Basin. We acknowledge that the goal was ambitious and the work presented here should be viewed as a first attempt at constructing a simple area-based framework for analysis which can be easily added to, and extended in the future. Among the most notable areas for quick improvement would be to gather information for the river segments that currently do not have any observations. One rather obvious route would be to collect information on a more regular basis such that the parameters would have an observation in the same year and season. This would be applicable to both river characteristics such as river depth, width and flow rates, as well as the pollution parameters listed in Table 7.2, Appendix 7. In the longer-term more sophisticated dispersion models could be built to factor in other, more complex, aspects of pollutant dispersion. The model built in this study can be viewed as a screening tool for quick area-based identification of pollution load hotspots. Once identified there can be short-term initiatives to 1) collect data to fill in the gaps found in this study, 2) medium-term objectives of expanding the current model to include a wider scope of pollution parameters, and finally 3) longer-term initiatives to build more complex river interactions into the model for more accurate estimation of pollution dispersion. 9.4 AREAS OF INTEREST RELATING TO POLLUTION AND PUBLIC HEALTH Substances and chemicals used in industrial processes can pose significant health risks if not managed properly. Section 8 provided the details of the hazard assessment, and below are the summary findings based on the industrial pollution projection models. In terms of the chemicals that are of most concern, Table 9.21 lists seven considered to be very high risk to human health. The top five districts which contain manufacturing processes using or producing these substances are listed and in Table 9.22 the manufacturing sectors associated with these chemicals are listed. The evidence provided from the source water monitoring data combined with the total coliform estimations of the PPS model suggest that high concentrations of chlorine-resistant biological pollutants may be capable of moving into the piped water supplies throughout the Day/Nhue River Basin. Studies conducted in Hanoi identified Cryptosporidium as the most prevalent biological pollutant in vegetables grown in wastewater. The risk matrix identified lead and formaldehyde as the top inorganic and organic chemical that presented an extreme health risk to humans in the Day/Nhue River Basin. Table 9.21 Top five districts with the highest estimated pollution load for seven chemicals identified as presenting an extreme risk to human health in the Day/Nhue River Basin Chemical 1: Province/ district 2: Province/ district 3: Province/ district 4: Province/ district 5: Province/ district 37 See Tables in Annex 4.3 for details.

Final priorities & next steps 149 Ammonium nitrate (solution) Ha Tay - Chuong My Hanoi - Thanh Tri Hanoi - Hai ba Trung Hanoi - Hoang Mai Hanoi Thanh Xuan Formaldehyde Hanoi Thanh Xuan Phenol Hanoi - Hai ba Trung Chloroform Hanoi Hoang Mai Lead Hanoi Thanh Xuan Hanoi Hai ba Trung Hanoi Hoang Mai Hanoi Hai ba Trung Hanoi Hai ba Trung Hanoi Dong Da Hanoi Thanh Tri Ha Tay Dan Phuong Nam Dinh Nam Dinh Hanoi Thanh Tri Hanoi Dong Da Ha Tay Phu Xuyen Hanoi Dong Da Ha Tay Phu Xuyen Hanoi Ba Dinh Hanoi Tu Liem Hanoi Hoang Mai Ethylene oxide Hanoi Dong Da Mercury Hanoi Thanh Tri Nam Dinh Nam Dinh Hanoi Hoan Kiem Hanoi Hai ba Trung Hanoi Dong Da Hanoi Ba Dinh Ha Tay Hoai Duc Ninh Binh Ninh Binh Hanoi Hoang Mai Table 9.22 Top 5 industrial sectors for the district with the highest hazard ranking for the seven chemicals identified as presenting an extreme risk to human health in the Day/Nhue River Basin Chemical District VSIC-4 VSIC-4 description Ha Tay - Ammonium nitrate Chuong My 1533 Prepared animal feeds (solution) Formaldehyde Phenol Chloroform Lead Ethylene oxide Mercury Hanoi Thanh Xuan Hanoi - Hai ba Trung Hanoi Hoang Mai Hanoi Thanh Xuan Hanoi Dong Da Hanoi Thanh Tri 2429 Other chemical products, n.e.c. 2413 Plastics in primary forms and of synthetic rubber 2109 Other articles of paper and paperboard 2022 Builders' carpentry and joinery 2424 Soap and detergents, cleaning and polishing preparations, perfumes and toilet preparations 2732 Casting of non-ferrous metals 2695 Articles of concrete, cement and plaster 2101 Pulp, paper and paperboard 2429 Other chemical products n.e.c. 2411 Basic chemicals, except fertilizers and nitrogen compounds 2022 Builders' carpentry and joinery 2102 Corrugated paper and paperboard, containers of paper and paperboard 2101 Pulp, paper and paperboard 2109 Other articles of paper and paperboard 2021 Veneer sheets; plywood, laminboard, particle board, other panels and boards 3150 Electric lamps and lighting equipment 3130 Insulated wire and cable 2899 Other fabricated metal products n.e.c. 1711 Preparation and spinning of textile fibres; weaving of textiles 2710 Basic iron and steel 2423 Pharmaceuticals, medicinal chemicals and botanical products 2412 Fertilizers and nitrogen compounds 9.5 RECOMMENDED NEXT STEPS IN THE BASIN Several initiatives can be pursued within the Day/Nhue River Sub-basin: 1. Feed the results of this study into the pollution management priority setting process as part of the integrated management of water quality in the basin. This study has identified areas, pollutants and sectors which require special attention because of their

Final priorities & next steps 150 relatively high estimated pollution loads and contribution to pollution in the basin. Those hotspots need to be the target of intensive follow up field validation and management action. 2. Target chemical and metal pollutants known to be toxic or bio-accumulative in humans and the environment. The problem of persistent toxic substances is becoming one of the most serious environmental issues in the basin. They need to be high on the list of priorities for action. 3. Target the most heavily polluting industry sectors. Each of the top polluting industry sectors should be required to prepare environmental management plans which take a product life cycle and integrated pollution management approach. 4. Build internal capacity on area-based approaches to pollution analysis and management, particularly in the provinces and districts with high estimated pollution loads and where more local data can be leveraged for priority setting; 5. The development of each of the models in the area-based approach adopted in this study can be furthered by filling any current data gaps and by refining the pollution coefficients; 6. Gather further information on river characteristics and water quality parameters, where current gaps exist; continue efforts to collect data on parameters such as COD, DO, SS, TDS, heavy metals, NH4, oil & grease in the same locations where BOD5 and NH4 are being monitored; 7. Investigate other sources of information with respect to pollution monitoring and data availability for manufacturing processes, the linkages between agricultural pollution and runoff, the pollution content of domestic sewage and the waste content of pollution from craft village activities; 8. Monitoring locations should take into consideration the increasing role of industrial parks and their influence on ambient water quality; 9. Develop a strategy of integrating other water quality research findings into the area-based approach; 10. Finalize an area-based model which can more readily handle seasonal variation and economic growth for projecting future pollution pressures. 9.6 RECOMMENDED NEXT STEPS IN TAKING THE MODELING TO OTHER BASINS The models developed in this study for estimating pollution releases from industry, agriculture, domestic sources and craft villages need to be applied to other basins. The PPS method and the linked ranking tools for priority setting are an important innovation to contribute to priority setting and pollution management on a river basin level. The method could be more widely promoted, developed and used to help DWRM and other MONRE/DONRE units and sector managers alike focus their limited resources on the most important pollutants, sectors and areas affected by pollution. 1. Adopt the PPS approach to making regular pollution estimates in helping set priorities for action in river basins: The methodology provides a systematic picture of pollution potential in river basins. Despite its noted limitations, when applied basin wide the PPS models correlates well with actual pollution-intensity observations for specific areas and sectors. 2. Calibrate results from the PPS approach: There is a high degree of correlation between pollution estimates obtained from using PPS and site and sector specific data available for the basin. A high degree of correlation is important for accurate priority setting. However the PPS estimates are not sufficiently accurate to be used, for example, to determine water licensing or pollution charges paid by industrial facilities under Decree 67/2003. As additional information industrial pollution discharge data becomes available (in quantity, quality, and reliability), the PPS could be developed further to utilize sector specific information. In so doing, the methodology can evolve to accurately report the pollution characteristics of river basins.

Final priorities & next steps 151 3. Develop a consensus among practitioners on the application of the models in other river basins and, for each, decide which assumptions will require localized information, and which can be transferred ; 4. Complete an small, initial data survey on river characteristics and water quality parameters for other basins to determine which basins could feasibly be analyzed using an area-based approach; 5. In the long-run, integrate the information from each basin simulation and widen the scope to inform policy at a national scale of implementation.

References 152 REFERENCES Ashbolt N.J. 2004 Risk analysis of drinking water microbial contamination versus disinfection byproducts (DBPs) Toxicology 198: 255 262 ATSDR 1999 Formaldehyde: Toxicological profile information sheet. Cited from http://www.atsdr.cdc.gov/toxprofiles/tp111.html on 29/8/07 ATSDR 2005 Lead: Toxicology profile information sheet (draft). Cited from http://www.atsdr.cdc.gov/toxprofiles/tp13.html on 29/8/07 ATSDR 2007 Interactions profiles for toxic substances. Cited from http://www.atsdr.cdc.gov/interactionprofiles/ on 7/8/07 CDC 2007 Environmental Public Health Indicators Project. Cited from http://www.cdc.gov/nceh/indicators/sources.htm on 6/8/07 CDC 2005 Third national report on human exposure to environmental chemicals. National Centre for Environmental Health Cirimele V., Villian M. and Kintz P. 2005 Dioxins and furans determination in post-mortem blood by gas chromatography high resolution mass spectrometry. Journal of Analytical Toxicology 29:90-94 Dalsgaard, A., Klank, L.T., Vuong, T.A., Phung Dac Cam, P.D. Marcussen, H., Jorgensen, K.,Holm, P.E., Brocca, D.,Simmons, R., Lan, T.P.L., and Mara, D. 2006 Food safety of aquatic plants and fish raised in wastewater-fed ponds. Cited from www.papussa.org on 21/8/07 Dasgupta, S., B. Laplante, H. Wang and D. Wheeler, 2002. Confronting the Environmental Kuznets Curve. Journal of Economic Perspectives, 16 (1): 147-168. Dasgupta, S., B. Laplante and C. Meisner, 1998. Accounting for Toxicity in Pollution Control: Does it Matter? World Bank Policy Research Working Paper No. 2002. World Bank: Washington DC. Available at: http://econ.worldbank.org/docs/887.pdf. Dien A. 2007 Environmental News Northern Vietnam rivers devastated by pollution, action vowed cited on 26/4/07 Source: Thanh Nien, Sai Gon Giai Phong Do T.M., Birkett N.J., Johnson K.C., Krewski D., Villeneuve P. and the Canadian Cancer Registries Epidemiology Research Group 2005 Chlorination disinfection by-products and pancreatic cancer risk Environmental Health Perspectives 113(4): 418 424. EnHealth 2002 Environmental Health Risk Assessment: Guidelines for assessing human health risks from environmental hazards Environment Monitor Report 2006 Water quality in the Cau, Nhue-Day and Dong Nai River Basin. Ch 3. Impacts of water pollution in the 3 river basins Environmental News Service 2006 Effects of China's Songhua River Chemical Spill Still Emerging Cited from http://www.ens-newswire.com/ens/jan2006/2006-01-13-05.asp on 9/8/07 Fayer, R. 2004, Cryptosporidium: a water-borne zoonotic parasite, Veterinary Parasitology, vol. 126, pp. 37-56.

References 153 Green R.E. 1998 Long-term decline in the thickness of eggshells of thrushes, Turdus spp. In Britian. Proceedings of the Royal Society of London, series B 265: 679-684 Hettige, M., P. Martin, M. Singh and D. Wheeler, 1995. The Industrial Pollution Projection System, World Bank Policy Research Working Paper No. 1431, World Bank: Washington, DC. Horvath, A., C. T. Hendrickson, L. B. Lave, F. C. McMichael and T-S. Wu, 1995. Toxic Emissions Indices for Green Design and Inventory, Environmental Science & Technology, 29, 2: 86A-90A. IPCS 1985 Lead: Environmental Health Criteria 85. Cited from http://www.inchem.org/documents/ehc/ehc/ehc85.htm on 29/8/07 IPCS 1989 Formaldehyde: Environmental Health Criteria 89. Cited from http://www.inchem.org/documents/ehc/ehc/ehc89.htm on 29/8/07 Isenbarger D.W., Hien B.T., Ha. H.T., Ha T.T., Bodhidatta L., Pang L.W. and Cam P.D. 2001 Prospective study of the incidence of diarrhoea and prevalence of bacterial pathogens in a cohort of Vietnamese children along the Red River Epidemiology and Infection 127: 229-236 Lan N.T.P., Dalsgaard A., Cam P.D. and Mara D. 2006 Microbiological quality of fish grown in wastewater-fed and nonwastewater-fed fishponds in Hanoi, Vietnam: influence of hygiene practices in local retail markets in Food safety of aquatic plants and fish raised in wastewaterfed ponds 2006 eds. Dalsgaard, A., Klank, L.T., Vuong, T.A., Phung Dac Cam, P.D. Marcussen, H., Jorgensen, K.,Holm, P.E., Brocca, D.,Simmons, R., Lan, T.P.L., and Mara, D. www.papussa.org Laplante, B. and C. Meisner, 2001. Estimating Conventional Industrial Water Pollution in Thailand. Mimeo. World Bank: Washington DC. Available at: http://www.worldbank.org/nipr/thailand/estimatingconventionalindustrialwater.htm. Laplante, B. and K. Smits, 1998. Estimating Industrial Pollution in Latvia. Mimeo. World Bank: Washington DC. Available at: http://www.worldbank.org/nipr/work_paper/latvia/index.htm. Leeds University 2007 Application of multi-trial Monte Carlo simulations to the estimation of risks to human health associated with the use of wastewaters for restricted crop irrigation. Cited from http://www.efm.leeds.ac.uk/cive/mcarlo/mcarlo-healthrisk03-restricted.html on 9/8/07 Levy G., Lutz L., Kruger A. and Kloas W. 2004 Bisphenol A induces feminisation in Xenopus laevis tadpoles. Environmental Research 94:102-111 Mara D.D., Sleigh P.A., et al. (2007). "Health risks in wastewater irrigation: Comparing estimates from quantitative microbial risk analyses and epidemiological studies." Water and Health 5(1). Marcussen H., Dalsgaard A. and Holm P. 2006a Arsenic, cadmium and lead in fish from pond production systems in Hanoi, Vietnam in Food safety of aquatic plants and fish raised in wastewater-fed ponds 2006 eds. Dalsgaard, A., Klank, L.T., Vuong, T.A., Phung Dac Cam, P.D. Marcussen, H., Jorgensen, K.,Holm, P.E., Brocca, D.,Simmons, R., Lan, T.P.L., and Mara, D. www.papussa.org Marcussen H., Jorgensen K., Holm P.E., Brocca D., Simmons R.W. and Dalsgaard A. 2006b Elemental content and food safety of water spinach (Ipomoea aquatica Forssk.) cultivated with wastewater in Hanoi, Vietnam in Food safety of aquatic plants and fish raised in wastewater-fed ponds 2006 eds. Dalsgaard, A., Klank, L.T., Vuong, T.A., Phung Dac Cam, P.D. Marcussen, H., Jorgensen, K.,Holm, P.E., Brocca, D.,Simmons, R., Lan, T.P.L., and Mara, D. www.papussa.org

References 154 Moeller D.W. 2004 Environmental Health 3 rd Ed. Harvard University Press Monosson E. 2005 Chemical Mixtures: Considering the Evolution of Toxicology and Chemical Assessment. Environmental Health Perspectives vol 113:4 p382-390 MONRE, 2006 National Report on the State of the Environment State Report 2006: The current state of water environment in three river basins of Cau, Nhue-Day and Dong Nai Nasri H.M.,Bouaı cha N. and Harche M.K. 2007 A New Morphospecies of Microcystis sp. Forming Bloom in the Cheffia Dam (Algeria): Seasonal Variation of Microcystin Concentrations in Raw Water and Their Removal in a Full-Scale Treatment Plant. Environmental Toxicology: Vol. 22(4): 347-356 Nel L.H., Markotter W. and Weyer J. 2004, Emerging infectious waterborne diseases: protozoan agents in Microbial Waterborne Pathogens, eds Cloete T.E., Rose J., Nel L.H. and Ford T. IWA Publishing, London, pp. 89-99. Nishio O., Matsui K., Lan D.T., Ushijima H. and Isomura S. 2000 Rotavirus infections among infants with diarrhoea in Veitnam. Pediatrics International 42: 422-424 Niyogi S. K. 2005 Shigellosis. Journal of Microbiology 43(2):133-43. O Rourke D. in press Community-driven regulation: Towards an improved model of environmental regulation in Vietnam in Livable Cities: The Politics of Urban Livelihood and Sustainability, eds Evans P. Berkeley: University of California Press, California. Ch. 4 Petterson S. A. and Ashbolt N. J. 2006 WHO Guidelines for the Safe Use of Wastewater and Excreta in Agriculture: Microbial Risk Assessment Section Ritter L., Solomon K. and Sibley P. 2002 Sources, pathways, and relative risks of contaminants in surface water and groundwater: A perspective prepared for the Walkerton inquiry. Journal of Toxicology and Environmental Health, Part A, 65:1-142 Schonning C. and Stenstrom T.A. (2004). Guidelines for the safe use of urine and faeces in ecological sanitation systems, EcoSanRes Publication Series. Simmons, R.W.; Vinh, Nguyen Cong; Jensen, Jens Raunsø 2006 Cadmium in paddy soils and rice grain in Nam Dinh, Vietnam: a potential public health risk? Conference paper Snow S.J. 2002 Commentary: Sutherland, Snow and water: the transmission of cholera in the nineteenth century International Journal of Epidemiology 31:908-911 Stewart PW, Reihman J, Lonky EI, Darvill TJ, Pagano J. 2003. Cognitive development in preschool children prenatally exposed to PCBs and MeHg. Neurotoxicol Teratol 25:11 22. Swanson, M., G. A. Davis, L. E. Kincaid, T. W. Schultz, J. E. Bartmess, S.L. Jones and E. L. George, 1997. A Screening Method for Ranking and Scoring Chemicals by Potential Human Health and Environmental Impacts, Environmental Toxicology and Chemistry, 16, 2: 372-383. Tran D T, Tuan N D et al. 2006 Skin problems among farmers engaged in wastewater-fed agriculture in Nam Dinh province, Vietnam Trang D.T., van der Hoek W., et al. (2006). Low risk of helminth infection in wastewater-fed rice cultivation in Vietnam.. Wastewater reuse in agriculture in Vietnam, Hanoi. US EPA 1992 Guidelines for exposure assessment: FRL-4129-5

References 155 U.S. EPA. 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures. EPA 630/R-00/002. Washington, DC:U.S. Environmental Protection Agency, Risk Assessment Forum. Verle P., Kongs A., De N.V., Thieu N.Q., Depraetere K., Kim H.T. and Dorny P. (2003) Prevalence of intestinal parasitic infections in northern Vietnam Tropical Medicine & International Health 8 (10): 961-964 Vietnam National Environmental Performance Assessment (EPA) Report (2006) Ministry of Natural Resources and Environment, Vietnam and Project Secretariat UNEP Regional Resource Center for Asia and the Pacific. Available at: http://www.rrcap.unep.org/sef/pub.htm. Vuong T.A..,van der Hoek W., Ersbøll A.K.,Thuong N.V., Tuan N.D., Cam P.D. and Dalsgaard A. in press Dermatitis among farmers engaged in peri-urban aquatic food production in Hanoi, Vietnam Vuong T.A., Tram N.T., Klank L.T., Cam P.D. and Dalsgaard A. in prep b Faecal and protozoan parasite contamination of water spinach (Ipomoea aquatica) cultivated in urban wastewater in Phnom Penh, Cambodia Weisbrod A.V., Burkhard L.P., Arnot J., Mekenyan O., Howard P.H., Russom C., Boethling R. and Sakuratani Y. 2007 Workgroup Report: Review of Fish Bioaccumulation Databases Used to Identify Persistent, Bioaccumulative, Toxic Substances Environmental Health Perspectives Vol 115(2): 255-261 WHO 1998. Guidelines for Drinking-Water Quality. Addendum to Volume 2. Geneva, Switzerland: WHO. 416 p. WHO 2003 Guidelines for safe recreational water environments. Volume 1, Coastal and fresh waters. WHO 2006 Guidelines for Drinking-water quality 3 rd Edition. Cited from http://www.who.int/water_sanitation_health/dwq/gdwq3rev/en/ on 10/8/07 WHO 2007 Water-related diseases. Cited from http://www.who.int/water_sanitation_health/diseases/en/ on 6/8/07 WHO 2007a Environmental Health Criteria 237: Principles for evaluating health risks in children associated with exposure to chemicals WHO and IPCS 2002 Global assessment of the state-of-science of endocrine disruptors WHO and UNEP (2006). WHO guidelines for the safe use of wastewater, excreta and greywater. Volume 1: Policy and regulatory aspects. Wyber 2006 Indicators of faecal contamination. Masters Thesis. Curtin University of Technology.