Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications
Abstract Arsenic (As) is a well‐known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techni...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2022-03-01
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| Series: | GeoHealth |
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| Online Access: | https://doi.org/10.1029/2021GH000585 |
| _version_ | 1818484760345837568 |
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| author | Bibhash Nath Runti Chowdhury Wenge Ni‐Meister Chandan Mahanta |
| author_facet | Bibhash Nath Runti Chowdhury Wenge Ni‐Meister Chandan Mahanta |
| author_sort | Bibhash Nath |
| collection | DOAJ |
| description | Abstract Arsenic (As) is a well‐known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techniques have been used to predict the location of safe and unsafe areas of groundwater As. We used a similar machine learning technique and developed a habitation‐level (spatial resolution 250 m) predictive model to determine the risk and extent of As >10 μg/L in groundwater in the two most affected districts of Assam, India, with an aim to advise policymakers on targeted interventions. A random forest model was employed in Python environments to predict the probabilities of As at concentrations >10 μg/L using intrinsic and extrinsic predictor variables, which were selected for their inherent relationship with As occurrence in groundwater. The relationships between predictor variables and proportions of As occurrences >10 μg/L follow the well‐documented processes leading to As release in groundwater. We identified potential As hotspots based on a probability of ≥0.7 for As >10 μg/L, including regions not previously surveyed and extending beyond previously known As hotspots. Of the total land area (6,500 km2), 25% was identified as a high‐risk zone, with an estimated 155,000 people potentially consuming As through drinking water or cooking food. The ternary hazard probability map (showing high, moderate, and low risk for As >10 μg/L) could inform policymakers on establishing newer drinking water treatment plants and providing safe drinking water connections to rural households. |
| first_indexed | 2024-12-10T15:59:13Z |
| format | Article |
| id | doaj.art-bd9a95514fed410aacab6bff983850bd |
| institution | Directory Open Access Journal |
| issn | 2471-1403 |
| language | English |
| last_indexed | 2024-12-10T15:59:13Z |
| publishDate | 2022-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | GeoHealth |
| spelling | doaj.art-bd9a95514fed410aacab6bff983850bd2022-12-22T01:42:29ZengAmerican Geophysical Union (AGU)GeoHealth2471-14032022-03-0163n/an/a10.1029/2021GH000585Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health ImplicationsBibhash Nath0Runti Chowdhury1Wenge Ni‐Meister2Chandan Mahanta3Department of Geography and Environmental Science Hunter College of City University of New York New York NY USADepartment of Geological Sciences Gauhati University Guwahati IndiaDepartment of Geography and Environmental Science Hunter College of City University of New York New York NY USADepartment of Civil Engineering Indian Institute of Technology Guwahati Guwahati IndiaAbstract Arsenic (As) is a well‐known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techniques have been used to predict the location of safe and unsafe areas of groundwater As. We used a similar machine learning technique and developed a habitation‐level (spatial resolution 250 m) predictive model to determine the risk and extent of As >10 μg/L in groundwater in the two most affected districts of Assam, India, with an aim to advise policymakers on targeted interventions. A random forest model was employed in Python environments to predict the probabilities of As at concentrations >10 μg/L using intrinsic and extrinsic predictor variables, which were selected for their inherent relationship with As occurrence in groundwater. The relationships between predictor variables and proportions of As occurrences >10 μg/L follow the well‐documented processes leading to As release in groundwater. We identified potential As hotspots based on a probability of ≥0.7 for As >10 μg/L, including regions not previously surveyed and extending beyond previously known As hotspots. Of the total land area (6,500 km2), 25% was identified as a high‐risk zone, with an estimated 155,000 people potentially consuming As through drinking water or cooking food. The ternary hazard probability map (showing high, moderate, and low risk for As >10 μg/L) could inform policymakers on establishing newer drinking water treatment plants and providing safe drinking water connections to rural households.https://doi.org/10.1029/2021GH000585arsenicpredictive modelingrandom forest modelmachine learningAssam |
| spellingShingle | Bibhash Nath Runti Chowdhury Wenge Ni‐Meister Chandan Mahanta Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications GeoHealth arsenic predictive modeling random forest model machine learning Assam |
| title | Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications |
| title_full | Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications |
| title_fullStr | Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications |
| title_full_unstemmed | Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications |
| title_short | Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications |
| title_sort | predicting the distribution of arsenic in groundwater by a geospatial machine learning technique in the two most affected districts of assam india the public health implications |
| topic | arsenic predictive modeling random forest model machine learning Assam |
| url | https://doi.org/10.1029/2021GH000585 |
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