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Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale AI-based modeling.

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第一作者： Abhijit,Mukherjee

第一单位： Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur, India; School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India. Electronic address: abhijit@gg.iitkgpac.in.

作者： Abhijit,Mukherjee ^[1] ; Soumyajit,Sarkar ^[2] ; Madhumita,Chakraborty ^[3] ; Srimanti,Duttagupta ^[2] ; Animesh,Bhattacharya ^[2] ; Dipankar,Saha ^[4] ; Prosun,Bhattacharya ^[5] ; Adway,Mitra ^[6] ; Saibal,Gupta ^[3]

作者单位： Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur, India; School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India. Electronic address: abhijit@gg.iitkgpac.in. ^[1] School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India. ^[2] Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur, India. ^[3] School of Water Resources, Indian Institute of Technology Kharagpur, Kharagpur, India. ^[4] KTH-International Groundwater Arsenic Research Group, Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. ^[5] Centre of Excellence in Artificial Intelligence (AI), Indian Institute of Technology Kharagpur, Kharagpur, India. ^[6]

关键词 Arsenic Groundwater contamination India Machine learning Public health Tectonics

DOI 10.1016/j.scitotenv.2020.143511

PMID 33250253

发布时间 2021-01-12

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