Literature DB >> 32653966

Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India.

Ruohan Wu1, Joel Podgorski1,2, Michael Berg2, David A Polya3.   

Abstract

Geogenic arsenic contamination in groundwaters poses a severe health risk to hundreds of millions of people globally. Notwithstanding the particular risks to exposed populations in the Indian sub-continent, at the time of writing, there was a paucity of geostatistically based models of the spatial distribution of groundwater hazard in India. In this study, we used logistic regression models of secondary groundwater arsenic data with research-informed secondary soil, climate and topographic variables as principal predictors generate hazard and risk maps of groundwater arsenic at a resolution of 1 km across Gujarat State. By combining models based on different arsenic concentrations, we have generated a pseudo-contour map of groundwater arsenic concentrations, which indicates greater arsenic hazard (> 10 μg/L) in the northwest, northeast and south-east parts of Kachchh District as well as northwest and southwest Banas Kantha District. The total number of people living in areas in Gujarat with groundwater arsenic concentration exceeding 10 μg/L is estimated to be around 122,000, of which we estimate approximately 49,000 people consume groundwater exceeding 10 µg/L. Using simple previously published dose-response relationships, this is estimated to have given rise to 700 (prevalence) cases of skin cancer and around 10 cases of premature avoidable mortality/annum from internal (lung, liver, bladder) cancers-that latter value is on the order of just 0.001% of internal cancers in Gujarat, reflecting the relative low groundwater arsenic hazard in Gujarat State.
© 2020. The Author(s).

Entities:  

Keywords:  Arsenic; Geostatistics; Groundwater; Gujarat; Health impacts; Logistic regression

Year:  2020        PMID: 32653966     DOI: 10.1007/s10653-020-00655-7

Source DB:  PubMed          Journal:  Environ Geochem Health        ISSN: 0269-4042            Impact factor:   4.609


  3 in total

1.  Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications.

Authors:  Bibhash Nath; Runti Chowdhury; Wenge Ni-Meister; Chandan Mahanta
Journal:  Geohealth       Date:  2022-03-01

2.  Global analysis and prediction of fluoride in groundwater.

Authors:  Joel Podgorski; Michael Berg
Journal:  Nat Commun       Date:  2022-08-01       Impact factor: 17.694

3.  Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling.

Authors:  Joel Podgorski; Ruohan Wu; Biswajit Chakravorty; David A Polya
Journal:  Int J Environ Res Public Health       Date:  2020-09-28       Impact factor: 3.390

  3 in total

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