The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Profile photo of Hossein Hashemi

Hossein Hashemi

Researcher

Profile photo of Hossein Hashemi

Human-induced arsenic pollution modeling in surface waters : An integrated approach using machine learning algorithms and environmental factors

Author

  • Maziar Mohammadi
  • Seyed Amir Naghibi
  • Alireza Motevalli
  • Hossein Hashemi

Summary, in English

In recent years, assessment of sediment contamination by heavy metals, i.e., arsenic, has attracted the interest of scientists worldwide. The present study provides a new methodology to better understand the factors influencing surface water vulnerability to arsenic pollution by two advanced machine learning algorithms including boosted regression trees (BRT) and random forest (RF). Based on the sediment quality guidelines (Effects range low) polluted and non-polluted arsenic sediment samples were defined with concentrations >8 ppm and <8 ppm, respectively. Different conditioning factors such as topographical, lithology, erosion, hydrological, and anthro- pogenic factors were acquired to model surface waters’ vulnerability to arsenic. We trained and validated the models using 70 and 30% of both polluted and non-polluted samples, respectively, and generated surface vulnerability maps. To verify the maps to arsenic pollution, the receiver operating characteristics (ROC) curve was implemented. The results approved the acceptable performance of the RF and BRT algorithms with an area under ROC values of 85% and 75.6%, respectively. Further, the findings showed higher importance of precipi- tation, slope aspect, distance from residential areas, and slope length in arsenic pollution in the modeling pro- cess. Erosion, lithology, and land use maps were introduced as the least important factors. The introduced methodology can be used to define the most vulnerable areas to arsenic pollution in advance and implement proper remediation actions to reduce the damages.

Department/s

  • Division of Water Resources Engineering
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World

Publishing year

2022

Language

English

Publication/Series

Journal of Environmental Management

Volume

305

Document type

Journal article

Publisher

Elsevier

Topic

  • Water Engineering

Status

Published

ISBN/ISSN/Other

  • ISSN: 0301-4797