Predicting groundwater arsenic contamination in Southeast Asia from surface parameters View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-08

AUTHORS

Lenny Winkel, Michael Berg, Manouchehr Amini, Stephan J. Hug, C. Annette Johnson

ABSTRACT

Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Although many arsenic-affected areas have been identified in recent years, a systematic evaluation of vulnerable areas remains to be carried out. Here we present maps pinpointing areas at risk of groundwater arsenic concentrations exceeding 10 μg l−1. These maps were produced by combining geological and surface soil parameters in a logistic regression model, calibrated with 1,756 aggregated and geo-referenced groundwater data points from the Bengal, Red River and Mekong deltas. We show that Holocene deltaic and organic-rich surface sediments are key indicators for arsenic risk areas and that the combination of surface parameters is a successful approach to predict groundwater arsenic contamination. Predictions are in good agreement with the known spatial distribution of arsenic contamination, and further indicate elevated risks in Sumatra and Myanmar, where no groundwater studies exist. More... »

PAGES

536

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ngeo254

DOI

http://dx.doi.org/10.1038/ngeo254

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025194437


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50 schema:description Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Although many arsenic-affected areas have been identified in recent years, a systematic evaluation of vulnerable areas remains to be carried out. Here we present maps pinpointing areas at risk of groundwater arsenic concentrations exceeding 10 μg l−1. These maps were produced by combining geological and surface soil parameters in a logistic regression model, calibrated with 1,756 aggregated and geo-referenced groundwater data points from the Bengal, Red River and Mekong deltas. We show that Holocene deltaic and organic-rich surface sediments are key indicators for arsenic risk areas and that the combination of surface parameters is a successful approach to predict groundwater arsenic contamination. Predictions are in good agreement with the known spatial distribution of arsenic contamination, and further indicate elevated risks in Sumatra and Myanmar, where no groundwater studies exist.
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