Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling

Abstract

Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18–30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs.

Groundwater Arsenic-Attributable Cardiovascular Disease (CVD) Mortality Risks in India

“…A pseudo-contour map of groundwater arsenic concentrations for the whole of India was produced, based on the machine learning approach of Podgorski et al [22] incorporating the novel pseudo-contour approach of Wu et al [20]. The reader is referred to those studies [20,22] and the references therein for a brief account of the advantages, disadvantages, and limitations of machine learning models.…”

“…Other processes, including complexation of arsenic by dissolved humic substances, competitive desorption, oxidation of sulfide minerals, geothermal activities, and mining-related activities, may also influence arsenic concentrations in groundwaters [18,19]. Spatial geostatistical models of the distribution of groundwater arsenic have been produced at both state (Gujarat [20], Uttar Pradesh [21]) and national scales [22,23] to help better fully understand the distribution of groundwater arsenic hazard in India, particularly given the lack of all-India systematic groundwater arsenic testing. Podgorski et al [22] generated a random forest model with over a hundred thousand arsenic concentration observations and over two dozen environmental parameter predictors to identify high groundwater arsenic (>10 µg/L) in well-known arsenic-contaminated areas in the alluvial sediments along the Ganges and Brahmaputra plains and in Gujarat and Punjab states and to capture less-known or previously unrecorded areas.…”

“…Spatial geostatistical models of the distribution of groundwater arsenic have been produced at both state (Gujarat [20], Uttar Pradesh [21]) and national scales [22,23] to help better fully understand the distribution of groundwater arsenic hazard in India, particularly given the lack of all-India systematic groundwater arsenic testing. Podgorski et al [22] generated a random forest model with over a hundred thousand arsenic concentration observations and over two dozen environmental parameter predictors to identify high groundwater arsenic (>10 µg/L) in well-known arsenic-contaminated areas in the alluvial sediments along the Ganges and Brahmaputra plains and in Gujarat and Punjab states and to capture less-known or previously unrecorded areas. Estimates of the population numbers in India exposed to high arsenic through drinking groundwaters range from 18 to 30 million [22] to 90 million [23].…”

“…Podgorski et al [22] generated a random forest model with over a hundred thousand arsenic concentration observations and over two dozen environmental parameter predictors to identify high groundwater arsenic (>10 µg/L) in well-known arsenic-contaminated areas in the alluvial sediments along the Ganges and Brahmaputra plains and in Gujarat and Punjab states and to capture less-known or previously unrecorded areas. Estimates of the population numbers in India exposed to high arsenic through drinking groundwaters range from 18 to 30 million [22] to 90 million [23].…”

Application of Geostatistical Analysis and Random Forest for Source Analysis and Human Health Risk Assessment of Potentially Toxic Elements (PTEs) in Arable Land Soil

“…Random forest was proposed as a fusion algorithm based on decision tree classifiers [ 11 ]. The algorithm applies the bootstrap resampling modus to abstract diverse samples from the original samples, establishes a decision tree for each bootstrap sample, and then uses the average of all decision tree predictions as the final prediction result [ 12 ]. Random forest improves the prediction accuracy without significantly increasing the number of calculations.…”