Risk Identification Assessment and Early Warning System in Strymonas and Nestos Rivers, Greece

Transcription

1 Aristotle University of Thessaloniki School of Agriculture Laboratory of Applied Soil Science Laboratory of remote sensing and GIS «RIVER SHIELD - Protecting Rivers from Industrial Accidents» Risk Identification Assessment and Early Warning System in Strymonas and Nestos Rivers, Greece 5D189 - ΙΝTERREG III B CADSES Chronis Ioannis, MSc Agroecologist Tsirika Anastasia, PhD Biologist TakavakoglouVasileios, PhD Agronomist Zalidis George, Professor

2 Risk Identification Assessment Aim of this action Identification, prevention, confrontation and pollution risk management which results from industrial accidents, for protecting the rivers. Case Studies Strymonas River, Greece (Region of Central Macedonia) Nestos River, Greece (Region of Eastern Macedonia & Thrace)

3 Methodology Adaptation of Risk Inventory Methodology from Danube River Basin Collection of data from the local public authorities Environmental Impact Assessment A geographical database with potential risk points was created in GIS environment

4 Methodology R-phrases (Risk-phrases) of the substances were assessed Demands of WFD were examined (priority substances) (2000/60/E.C., 2001/2455/E.C.) Water Risk Class calculation

5 Methodology Problems missing data Geographic coordinates of Industrial installations were often not available to the authorities Identification of risk in the frame of WFD (some non industrial risks were also identified)

6 Results No SEVESO II industrial installations were found in both river basins In Strymonas river basin there were more detailed data available than in Nestos river basin Both river basins present similar water uses: Irrigated agriculture Dams- Hydroelectricity Non industrial uses

7 Strymonas River Basin

8 Strymonas River Basin Installations with WRC 3

9 Nestos River Basin

10 On going action Spatial analysis of the risks in each river basin The outcome is thematic maps with high risk areas according to: Distance from the river Morphology of the surrounding area (subcachments)

11 Early Warning System

12 Structure of Early Warning System 1. Collection and Transmission of field data in real time 2. Data management software (Department( of Electrical and Computer Engineering) 3. Decision Support System (cooperation with Department of Electrical and Computer Engineering)

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14 Measurement Stations in Strymonas River Basin

15 Measurement Stations in Nestos Installed station Proposed stations

16 Data Management Software Targets (1) Field data assessment (2) Near real time data (3) Easy access and distribution of results to authorities (4) Remote control of field station and synchronization

17 Decision Support System

18 Decision Support System Procedure from risk recognition to system Characteristics of the measured parameters (with time- series methods) : q trend analysis q moving average q duration of change q other quality and quantity factors

19 Characteristics of Decision Support System Fuzzy rules describe in qualitati ative matter the incidents which may involve in time, and export the risk level The description of the amounts and the structure of the rules refer to human perception and decision making The base of the rules can be formed by experienced experts The base of the rules can be expanded or diverted including different scenarios that may happen.

20 Parameter analysis in DSS Parameters profile in each river: time period, day sequence seasonal changes weather conditions the appearance of high risk installation in the area

21 Thank you for your attention