Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran

Ata Joodavi, Reza Aghlmand, Joel Podgorski, Reza Dehbandi, Ali Abbasi

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Abstract

Study region: Northeastern Iran. Study focus: In northeastern Iran, water needed for municipal and agricultural activities mainly comes from groundwater resources. However, it is subject to substantial anthropogenic and geogenic contamination. We characterize the sources of groundwater contamination by employing an integrated approach that can be applied to the identification of large-scale contamination sources in other regions. An existing dataset of georeferenced water quality parameters from 676 locations in northeast of Iran was analyzed to investigate the geochemical properties of groundwater. Gridding of the parameters graphically illustrates the areas affected by high concentrations of As, Cl, Cr, Fe, Mg2+, Na+, NO3, Se, and SO42-. We then identified potential anthropogenic and geogenic contamination sources by employing random forest (RF) regression modeling. New hydrological insights for the region: Random forest (RF) models show that the major ions, As, Cr, Fe, and Se content of groundwater are mainly determined by geology in the study area. Modeling also links groundwater NO3 contamination with sewage discharge into aquifers as well as the application of nitrogenous and animal-waste fertilizers. Areas of high salinity result from evaporate deposits and irrigation return flow. Medium to high non-carcinogenic health risk is found in areas with high concentrations of geogenic As and Cr in groundwater. Our approach can be applied elsewhere to analyze regional groundwater quality and associated health risks as well as identify potential sources of contamination.

Original languageEnglish
Article number100885
Pages (from-to)1-15
Number of pages15
JournalJournal of Hydrology: Regional Studies
Volume37
DOIs
Publication statusPublished - 2021

Keywords

  • Groundwater quality
  • Health risk assessment
  • Iran
  • Random forest modelling
  • Toxic elements

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