A geospatial machine learning prediction of arsenic distribution in the groundwater of Murshidabad district, West Bengal, India: spatio-temporal pattern and human health risk

Abstract

Arsenic (As) contamination of groundwater in parts of South and Southeast Asia is a public health disaster. Millions of people living in these regions could be chronically exposed to drinking water with As concentrations above the World Health Organizations provisional guideline of 10 μg/L. Recent field investigations have shown that the distribution of groundwater As in many shallow aquifers in India and Bangladesh is evolving rapidly due to massive irrigation pumping. This study compares a decade-old dataset of As concentration measurements in groundwater with a dataset of recent measurements using geospatial machine learning techniques. We observed that the probability of As concentrations >10 μg/L was much greater in the regions between two major rivers than in the regions close to the Ganges River on the eastern border of the study area, where As concentrations >10 μg/L had been measured prior to 2005. The greater likelihood that As is present away from the river channel and is found instead in the interfluvial regions could be attributed to the transport and flushing of aquifer As due to intense groundwater pumping for agriculture. We estimated that about 2.8 million people could be chronically exposed to As concentrations >10 μg/L. This high population-level exposure to elevated As concentrations could be reduced through targeted well-testing campaigns, promoting well-switching, provisions for safe water access, and developing plans for raising public awareness. Policymakers could use the ternary hazard map to target high-risk localities for priority house connections of piped water supply schemes to help reduce human suffering.