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Risk assessment and ranking of heavy metals concentration in Iran’s Rayen groundwater basin using linear assignment method

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Abstract

Groundwater is an important source of freshwater for domestic, agricultural and industrial uses in Iran. Groundwater quality assessment and environmental evaluation are considered as critical issues in recent years. Intensive human activities have resulted in significant changes in environment leading to serious groundwater contamination. This research proposes a two-part systematic approach to tackle heavy metals contamination problem in Rayen Basin (southeast Iran). The first part consists of determining geochemical characteristics and evaluating groundwater quality through application of water quality index and heavy metal pollution indices (i.e. HPI and MI). The second part includes ranking sampling stations based on heavy metals concentration in groundwater using linear assignment method. Six types of water could be identified according to the dominant cations and anions in samples: Ca–HCO3, Ca–SO4, Na–Cl, Na–HCO3, Na–SO4 and mixed water type. Calculation of indices revealed that natural and anthropogenic activities are playing a vital role in degrading groundwater quality in the study area. The proposed methodology can help in groundwater resource management and preventative activities by identifying risk factors and recognizing their pollution level. The results of this research provide useful and effective information for water pollution control and management and can be used in environmental studies in order to protect groundwater resources in the future.

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Acknowledgements

The authors would like to thank the Amirkabir University of Technology (Polytechnic Tehran) for supporting this research. The contributions of Adonis Fard Mousavi and Samira Rezaei are highly appreciated.

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Correspondence to Hossein Hassani.

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Rezaei, A., Hassani, H., Hayati, M. et al. Risk assessment and ranking of heavy metals concentration in Iran’s Rayen groundwater basin using linear assignment method. Stoch Environ Res Risk Assess 32, 1317–1336 (2018). https://doi.org/10.1007/s00477-017-1477-x

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