Electronic Supplementary Information. Environmental Life Cycle Assessment of Brackish Water Reverse Osmosis Desalination

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1 Electronic Supplementary Information (13 pages, including 1 Scheme, 6 Tables, 1 Figure) Environmental Life Cycle Assessment of Brackish Water Reverse Osmosis Desalination for Different Electricity Production Models Jin Zhou a, Victor W.-C. Chang a, *, Anthony G. Fane a a Singapore Membrane Technology Centre, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore * Corresponding author, wcchang@ntu.edu.sg, Tel: S-1

2 Contents Scheme S1. Procedure for establishing SG LCI for electricity production... 3 Table S1. Assumptions for establishing SG LCI for electricity production... 4 Table S2. Assumptions for electricity from heavy fuel oil in SG... 5 Table S3. Assumptions for electricity from natural gas in SG... 6 Table S4. Characterization models used in Life cycle impact assessment... 7 Table S5. Contribution analysis of US, SG, and ES scenarios a,b... 8 Table S5. Contribution analysis of US, SG, and ES cases a,b (continued)... 9 Table S6. The impact break down of crude oil production in ODP Figure S1. The contribution of individual processes under different salinity levels Reference S-2

3 Scheme S1. Procedure for establishing SG LCI for electricity production S-3

4 Table S1. Assumptions for establishing SG LCI for electricity production Processes Singapore electricity production mix Electricity transmission and distribution Comments (1) The assumptions for developing datasets of electricity from heavy fuel oil are summarized in Table S1-S1 (2) The assumptions for developing datasets of electricity from natural gas are summarized in Table S2-S1. The Wobbe Index of natural gas is assumed to be 45.2 MJ/m 3 1.The natural gas composition is assumed to be the same with Chan s study 2. (3) The overall performance of Orimulsion is about 9% lower than heavy fuel oil 3. Therefore, the data used to represent electricity from Orimulsion is modified from the heavy fuel oil dataset in French scenario. (4)The photovoltaic electricity generation in Singapore is modelled as German scenario (5) The datasets of electricity from waste incineration and diesel combustion are modelled as Switzerland scenario, which is the only available data in Ecoinvent database. (1) ABB is a major contractor for the management and operation of Singapore s electricity transmission and distribution network 4. Therefore, all the construction of transmission and distribution network in Singapore is assumed to use the same technology as Switzerland. However, some changes were made to simulate local context. Firstly, wood poles and wood preservative process are removed because the transmission and distribution network in Singapore are all underground cables; secondly, extra excavation process was added for digging trenches for cables. (2) The overall electricity power transmission and distribution loss in Singapore is about 5% 5. Since the transmission distance in Singapore is limited, the loss of transmitting electricity at high voltage is assumed as 2%, while the electricity loss during medium-voltage is 3% S-4

5 Table S2. Assumptions for electricity from heavy fuel oil in SG Processes Comments Electricity, oil, at power plant It is calculated based on power plant efficiency (33%) 6 Heavy fuel oil, burned in power plant Heavy fuel oil, at regional storage Heavy fuel oil, at refinery Crude oil production Senoko Power, the largest oil-fired power generation company in SG, employs the technology of Hitachi steam 7. It is appropriate to use Japanese (JP) dataset as a proxy for SG. In addition, the dataset for JP oil-fired power plant is the same as that in French (FR) scenario in Ecoinvent database. The FR dataset is modified to fit SG context. The release of NO x and SO x is reduced to 30% with the help of retaining technology This process mainly accounts for the impacts imposed by transporting heavy fuel oil from refinery to end user. Ecoinvent provide datasets for Europe (RER) and Switzerland (CH) scenario. Since the impacts of transportation is proportional to distance, the CH scenario is used as the proxy for SG scenario The technology used in SG is assumed the same with FR. Therefore, this datasets is established based on the modification of RER scenario. Share of oil imported in Singapore: Middle East (RME) - 88%, Malaysia (MY) 5%, Indonesia (ID) 2%, China(CN) 1%, Vietnam (VN) 1%, Russia (RU) 1%, others 2% 8. The transportation of crude oil from exploration site to refinery is modelled as Europe scenario. S-5

6 Table S3. Assumptions for electricity from natural gas in SG Process Electricity, natural gas, at power plant Comments It is calculated based on power plant efficiency (50%) 9 Natural gas, burning in power plant Singapore s gas-fired power plant is modelled as German (DE) scenario since two of largest power generation company in Singapore, Senoko Power 10 and Power Seraya 7, are identified as using Siemens Combined-Cycle Plants. Natural gas, high pressure, at consumer Both SG and JP are the Asian countries with highest population density 11, so the SG dataset of natural gas distribution network is calculated based on that in JP scenario, assuming the length of pipelines is proportional to the territorial area. Natural gas, production MY and ID, at long-distance pipeline About 75% of natural gas is imported from ID, while 25% is from MY 12. It is assumed that half of the natural gas pipelines transported from Malaysia /Indonesia to Singapore are onshore pipeline, while another half are offshore pipelines. The leakage of Singapore s natural gas transportation is assume to be similar to reference 13. Natural gas, at production MY and ID Natural gas, production NL The composition of natural gas is relatively constant and carries similar impacts, so the developed datasets using the similar technology is adopted to mimic the upstream process of natural gas in SG scenario. it was Royal Dutch Shell that began the gas exploration and production there 14, the production of natural gas in MY and ID is modelled as Netherlands (NL) scenario. It is assumed that 70% of natural gas is produced offshore, while another 30% is onshore. S-6

7 Table S4. Characterization models used in Life cycle impact assessment Impact category Indicator Characterization model Abiotic depletion Abiotic Depletion Potential (ADP) Guinee and Heijungs, Acidification Acidification Potential (AP) Huijbregts, Eutrophication Eutrophication Potential (EP) Heijungs et al., , 19 Global warming Global Warming Potential (GWP100) Houghton et al., 1994, 1997 Ozone layer depletion) Ozone layer Depletion Potential (ODP) WMO, 1992, 1994, Human Toxicity Human Toxicity Potential (HTP) Huijbregts, Fresh water aquatic ecotoxicity Fresh water Aquatic EcoToxicity Potential (FAETP) Huijbregts, Marine Aquatic ecotoxicity Marine Aquatic EcoToxicity Potential (MAETP) Huijbregts, Terrestrial ecotoxicity Terrestrial EcoToxicity Potential (TETP) Huijbregts, , 25 Photochemical oxidation Photochemical Ozone Creation Potential (POCP) Derwent et al., 1996, 1998 S-7

8 Table S5. Contribution analysis of US, SG, and ES scenarios a,b Process contribution (%) ADP AP EP GWP100 ODP HTP FAETP MAETP TETP POCP country code US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES ELECTRICITY Electricity from coal Hard coal mining burning ash disposal Lignite mining 4 7 burning ash disposal Electricity from heavy fuel oil extraction production transportation refinery burning Wastewater discharge Electricity from natural gas well for exploration and production extraction transportation production burning S-8

9 Table S5. Contribution analysis of US, SG, and ES cases a,b (continued) Process contribution (%) ADP AP EP GWP100 ODP HTP FAETP MAETP TETP POCP country code US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES ELECTRICITY Electricity from nuclear blasting enrichment Electricity from wood wood burning 5 2 wood -5 Transmission network wood preservatives Other processes ANTISCALING INFRASTRUCTURE PRODUCTION MEMBRANE PRODUCTION INFRASTRUCTURE DISMANTLING OTHER PROCESSES REMAINING PROCESSES a abbreviations: ADP, Abiotic Depletion Potential; AP, Acidification Potential; EP, Eutrophication Potential; GWP100, Global Warming Potential in 100 years; ODP, Ozone Depletion Potential; HTP, Human Toxicity Potential; FEATP, Fresh-water Aquatic Eco-Toxicity Potential; MAETP Marine Aquatic Eco-Toxicity Potential; TETP, Terrestrial Eco-Toxicity Potential; POCP, Photochemical Ozone Creation Potential b the blank cells stand for contribution is less than 1% S-9

10 Table S6. The impact break down of crude oil production in ODP Countries Crude oil Shares of oil Major ODP Amount ODP emission factor exploration site imported substances ( kg / kg crude oil produced) (in kg CFC-11 eq./kg) SG RME onshore 88% Halon E-8 12 Others 12% ES RME onshore 24% Halon E-8 12 NO offshore 22% Halon E RU onshore 18% Halon E-8 12 GB offshore 17% Halon E RAF onshore 10% Halon E-8 12 Others 9% S-10

11 (a) The energy demand of desalination has dominant contribution to environmental impacts (feed water is low-salinity brackish water with TDS of 1500 mg/l; it consumes 1 kwh of electricity to produce 1 m 3 of fresh water from feed water 27 ) (b) The energy demand of desalination has dominant contribution to environmental impacts (feed water is sea water with TDS of mg/l; it consumes 4 kwh of electricity to produce 1 m 3 of fresh water from feed water 28 ) Figure S1. The contribution of individual process under different salinity levels S-11

12 Reference 1. EMA, Gas Supply Code, accessed Sep 7, S. H. Chan and H. M. Wang, Fuel processing technology, 2000, 64, T. Hägglund, Comparative advantages of Orimulsion, LNG and Petcoke, accessed Oct 10, Business&TechnologyNews, Singapore Power Awards Repeat Contract to ABB, singaporepowerawardsrepeatcontracttoabb-asia.html, accessed Oct 5, WorldBank, World Development Indicators 2006 [online]. 6. R. Kannan, C. P. Tso, R. Osman and H. K. Ho, Energy Conversion and Management, 2004, 45, PowerSeraya, Our business, accessed Sep 28, SingaporeTradeDevelopmentBoard, Singapore trade connnections, CD ROM. 9. R. Kannan, K. C. Leong, R. Osman, H. K. Ho and C. P. Tso, Energy Conversion and Management, 2005, 46, SenokoPower, Our plant, accessed Sep 28, UN, Population, environment and development accessed Sep 28, K. Wong and D. Reinbott, in Gas Regulation 2009 Chapter 31 Singapore, Global Legal Group Ltd, London, 2009, pp A. Prabhu, C. Pham, A. Glabe and J. Duffy, Detailed California-Modified GREET Pathway for Compressed Natural Gas (CNG) from North American Natural Gas, California Environmental Protection Agency, Air Resources Board, Wikipedia, Petronas, accessed Sep 3, J. B. Guinee and R. Heijungs, Environmental Toxicology and Chemistry, 1995, 14, M. A. J. Huijbregts, Life cycle impact assessment of acidifying and eutrophying air pollutants, Interfaculty Department of Environmental Science, Faculty of Environmental Science, University of Amsterdam, Amsterdam, R. Heijungs, J. B. Guinee, G. Huppes, H. A. Lankreijer, H. A. Udo de Haes, A. M. M. Wegener Sleeswijk, P. G. Ansems, R. Eggels, v. Duin and H. P. d. Goede, Environmental Life Cycle Assessment of Products., CML, Leiden University, Leiden, J. T. Houghton, L. G. M. Filho, B. A. Callendar, N. Harris, A. Kattenberg and K. Maskell, International Journal of Climatology, 1997, 17, J. T. Houghton, L. G. M. Filho, J. Bruce, H. lee, B. A. Callendar, E. Haites, N. Harris and K. Maskell, eds., Climte change 1994., Cambridge University Press, Cambridge, WMO, Scientific assessment of ozone depletion:1991, Report Report No. 44, World Meteorological Organization, Geneva, WMO, Scientific assessment of ozone depletion:1998, Report Report No. 44, World Meteorological Organization, Geneva, WMO, Scientific assessment of ozone depletion:1995, Report Report No. 37, World Meteorological Organization, Geneva, S-12

13 23. M. A. J. Huijbregts, Priority assessment of toxic substances in LCA., IVAM environmental research, University of Amsterdam, Amsterdam, R. G. Derwent, M. E. Jenkin and S. M. Saunders, Atmospheric Environment, 1996, 30, R. G. Derwent, M. E. Jenkin, S. M. Saunders and M. J. Pilling, Atmospheric Environment, 1998, 32, M. E. Jenkin and G. D. Hayman, Atmospheric Environment, 1999, 33, G. L. Park, A. I. Schaer and B. S. Richards, Journal of Membrane Science, 2011, 370, G. Raluy, L. Serra and J. Uche, Energy, 2006, 31, S-13