Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic

Podgorski, Joel; Araya, Dahyann; Berg, Michael

doi:10.1016/j.scitotenv.2022.155131

Cited by 43 publications

(14 citation statements)

References 52 publications

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“…The “area of applicability” of the model was checked by comparing the values of the predictors of the entire study site with those of the training datasets (Meyer and Pebesma, 2021). The calculated dissimilarity index for all predicted pixels within the study area was found to be between 0 and 1: the range considered reliable for model prediction, as suggested by Podgorski et al (2022).…”

Section: Resultsmentioning

confidence: 56%

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

Nath

Das

Roychowdhury

et al. 2022

Preprint

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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.

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Section: Resultsmentioning

confidence: 56%

“…The calculated dissimilarity index for all predicted pixels within the study area was found to be between 0 and 1: the range considered reliable for model prediction, as suggested by Podgorski et al (2022).…”

Section: Modeled Probability Of As Exceedance and Model Uncertaintymentioning

confidence: 56%

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

Nath

Das

Roychowdhury

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The “area of applicability” of the model was checked by comparing the values of the predictors of the entire study site with those of the training datasets . The calculated dissimilarity index for all predicted pixels within the study area was found to be between 0 and 1: the range considered reliable for model prediction, as suggested by Podgorski et al…”

Section: Resultsmentioning

confidence: 96%

Geospatial Machine Learning Prediction of Arsenic Distribution in the Groundwater of Murshidabad District, West Bengal, India: Analyzing Spatiotemporal Patterns to Understand Human Health Risk

et al. 2022

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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 Organization's provisional guideline of 10 μg/L. Recent field investigations have shown that the distribution of groundwater As in some shallow aquifers in India and Bangladesh is evolving rapidly because of massive irrigation pumping. This study compares a decadeold 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 higher proportions of As concentrations >10 μg/L had been observed prior to 2005. The greater likelihood that toxic concentrations of As are 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 from intense irrigation pumping. 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 presented here to target high-risk localities for priority implementation of piped water supply strategies to help reduce human suffering.

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“…An emerging trend to address the subsurface-data scarcity problem is the use of machine-learning approaches. Such approaches have been used to infer subsurface structure, properties, and functioning, including stream water quality, groundwater chemistry and permeability that can potentially be used to infer CZ thickness (Erickson, Burnett, et al, 2021;Erickson, Elliott, et al, 2021;Ouedraogo et al, 2019;Podgorski et al, 2020Podgorski et al, , 2022Wen et al, 2021). Traditional methods such as random forest or generalized boosted regression models are being used (Bergen et al, 2019;Hare et al, 2021;Li et al, 2021) as well as deep-learning models with multiple layers of neural networks that automate pattern extraction (Zhi et al, 2021;Zhi, Ouyang, et al, 2023;Zhi, Klingler, et al, 2023).…”

Section: Leveraging Machine Learning and Artificial Intelligence (Ai)mentioning

confidence: 99%

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

Singha,

Sullivan,

Billings

et al. 2024

Earth's Future

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Two major barriers hinder the holistic understanding of subsurface critical zone (CZ) evolution and its impacts: (a) an inability to measure, define, and share information and (b) a societal structure that inhibits inclusivity and creativity. In contrast to the aboveground portion of the CZ, which is visible and measurable, the bottom boundary is difficult to access and quantify. In the context of these barriers, we aim to expand the spatial reach of the CZ by highlighting existing and effective tools for research as well as the “human reach” of CZ science by expanding who performs such science and who it benefits. We do so by exploring the diversity of vocabularies and techniques used in relevant disciplines, defining terminology, and prioritizing research questions that can be addressed. Specifically, we explore geochemical, geomorphological, geophysical, and ecological measurements and modeling tools to estimate CZ base and thickness. We also outline the importance of and approaches to developing a diverse CZ workforce that looks like and harnesses the creativity of the society it serves, addressing historical legacies of exclusion. Looking forward, we suggest that to grow CZ science, we must broaden the physical spaces studied and their relationships with inhabitants, measure the “deep” CZ and make data accessible, and address the bottlenecks of scaling and data‐model integration. What is needed—and what we have tried to outline—are common and fundamental structures that can be applied anywhere and used by the diversity of researchers involved in investigating and recording CZ processes from a myriad of perspectives.

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Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic

Cited by 43 publications

References 52 publications

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

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

Geospatial Machine Learning Prediction of Arsenic Distribution in the Groundwater of Murshidabad District, West Bengal, India: Analyzing Spatiotemporal Patterns to Understand Human Health Risk

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

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