Predicting geogenic groundwater arsenic contamination risk in floodplains using interpretable machine-learning model

Ruiyu Fan1, Yamin Deng1, Yao Du1

  • 1MOE Key Laboratory of Groundwater Quality and Health, China University of Geosciences, Wuhan, 430078, China; State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution & School of Environmental Studies, China University of Geosciences, Wuhan, 430078, China.

Summary

Geogenic arsenic (As) in groundwater is a public health risk, often linked to ammonium. A predictive model using hydrogeochemical data accurately identifies high-As groundwater zones, aiding health and environmental management.

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