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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Using a soft computing OSPRC risk framework to analyze multiple contaminants from multiple sources; a case study from
Ata Allah Nadiri1, Fariba Sadeghi Aghdam2, Siamak Razzagh2
1Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran; Institute of Environment, University of Tabriz, Tabriz, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran; Department of Geography & Environmental Studies, Wilfrid Laurier University, Waterloo, Canada.
Groundwater contamination in Iran
Area of Science:
- Hydrogeology
- Environmental Science
- Geochemistry
Background:
- Arid and semi-arid regions like Iran face critical water shortages, exacerbating groundwater contamination issues.
- The Khoy aquifer in NW Iran is susceptible to contamination from both natural geological processes and human activities.
- Key contaminants identified include arsenic (As), nitrate (NO3-), lead (Pb), and zinc (Zn).
Purpose of the Study:
- To develop a perceptual and conceptual model of groundwater contamination in the Khoy aquifer using a soft modeling framework.
- To identify and map risk cells (RCs) within the aquifer based on contaminant distribution.
- To validate hydrogeochemical information using a combination of graphical, geological, and statistical methods.
Main Methods:
- Application of a soft modeling framework to abstract hydrogeochemical data.
- Utilizing the Origin-Source-Pathways-Receptor-Consequence (OSPRC) risk system.
- Employing graphical representations, geological surveys, and multivariate statistical analysis for data validation.
Main Results:
- The Khoy aquifer was divided into four distinct risk cells (RCs) based on contaminant levels.
- High arsenic (As) concentrations were found in RC4 (south) and RC2 (north).
- High lead (Pb) was detected in RC1 (north) and RC3 (middle), while high nitrate (NO3-) was present in RC3 and RC4. RC2 also showed high zinc (Zn).
Conclusions:
- Soft modeling provides a descriptive understanding of dominant hydrogeochemical processes in different risk cells.
- The approach effectively identifies areas with high contaminant concentrations within the Khoy aquifer.
- Further quantitative modeling may be necessary if more extensive data becomes available.
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