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Quantifying the groundwater total contamination risk using an inclusive multi-level modelling strategy.

Maryam Gharekhani1, Ata Allah Nadiri2, Rahman Khatibi3

  • 1Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.

Journal of Environmental Management
|January 30, 2023
PubMed
Summary

This study develops a new risk assessment method for aquifers facing multiple contaminants like nitrate-N, arsenic, and fluoride. The approach identifies high-risk "hotspots" for better groundwater management.

Keywords:
Anthropogenci/geogenicIntegrated risk indexMulti-level modellingMultiple contaminantsVulnerability indexing

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Area of Science:

  • Hydrogeology and Environmental Risk Assessment
  • Groundwater Contamination and Management
  • Geochemistry and Water Quality

Background:

  • Aquifers face aggregated risks from diverse contaminants, including nitrate-N, arsenic (As), boron (B), fluoride (F), and aluminium (Al).
  • Sparse data presents a challenge for effective aquifer risk assessment and management planning.
  • Existing methods often struggle to integrate multiple contaminant risks comprehensively.

Purpose of the Study:

  • To develop a novel, efficient planning tool for aggregated aquifer risk management under data scarcity.
  • To create a methodology for transforming aquifer vulnerability indices into integrated risk indices.
  • To identify and map groundwater contamination hotspots resulting from multiple pollutants.

Main Methods:

  • Aquifer vulnerability mapping using DRASTIC and SPECTR frameworks.
  • Unsupervised methods for mapping anthropogenic and geogenic contaminant risk indices.
  • A three-level modeling strategy: Artificial Neural Networks (ANN) & Support Vector Machines (SVM) (Level 1), Entropy Model Averaging (EMA) (Level 2), and contaminant risk integration (Level 3).

Main Results:

  • The developed risk indexing methodology proves fit-for-purpose for aggregated risk assessment.
  • Integrated risk maps reveal specific 'hotspots' within the study area.
  • These hotspots are simultaneously exposed to multiple contaminants: nitrate-N, arsenic, boron, fluoride, and aluminium.

Conclusions:

  • The proposed heuristic scheme effectively integrates risks from various contaminants without requiring measured integrated risk data.
  • The methodology provides valuable new data layers for transforming vulnerability into actionable risk information.
  • The identification of multi-contaminant hotspots is crucial for targeted and efficient groundwater resource management.