| Literature DB >> 34951307 |
Craig T Connolly1,2,3, Mason O Stahl4, Beck A DeYoung4, Benjamin C Bostick1.
Abstract
Chronic exposure to groundwater contaminated with geogenic arsenic (As) poses a significant threat to human health worldwide, especially for those living on floodplains in South and Southeast (S-SE) Asia. In the alluvial and deltaic aquifers of S-SE Asia, aqueous As concentrations vary sharply over small spatial scales (10-100 m), making it challenging to identify where As contamination is present and mitigate exposure. Improved mechanistic understanding of the factors that control groundwater As levels is essential to develop models that accurately predict spatially variable groundwater As concentrations. Here we demonstrate that surface flooding duration and interannual frequency are master variables that integrate key hydrologic and biogeochemical processes that affect groundwater As levels in S-SE Asia. A machine-learning model based on high-resolution, satellite-derived, long-term measures of surface flooding duration and frequency effectively predicts heterogeneous groundwater As concentrations at fine spatial scales in Cambodia, Vietnam, and Bangladesh. Our approach can be reliably applied to identify locations of safe and unsafe groundwater sources with sufficient accuracy for making management decisions by solely using remotely sensed information. This work is important to evaluate levels of As exposure, impacts to public health, and to shed light on the underlying hydrogeochemical processes that drive As mobilization into groundwater.Entities:
Keywords: arsenic; environmental predictive modeling; flooding; geospatial analysis; groundwater
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Year: 2021 PMID: 34951307 PMCID: PMC8766940 DOI: 10.1021/acs.est.1c05955
Source DB: PubMed Journal: Environ Sci Technol ISSN: 0013-936X Impact factor: 9.028