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

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.

SUBMITTER: Wu R

PROVIDER: S-EPMC8275508 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Arsenic contamination of groundwater aquifers is an issue of global concern. Among the affected sites, in several Italian groundwater aquifers arsenic levels above the WHO limits for drinking water are present, with consequent issues of public concern. In this study, for the first time, the role of microbial communities in metalloid cycling in groundwater samples from Northern Italy lying on Pleistocene sediments deriving from Alps mountains has been investigated combining environmental genomics and cultivation approaches. 16S rRNA gene libraries revealed a high number of yet uncultured species, which in some of the study sites accounted for more of the 50% of the total community. Sequences related to arsenic-resistant bacteria (arsenate-reducing and arsenite-oxidizing) were abundant in most of the sites, while arsenate-respiring bacteria were negligible. In some of the sites, sulfur-oxidizing bacteria of the genus Sulfuricurvum accounted for more than 50% of the microbial community, whereas iron-cycling bacteria were less represented. In some aquifers, arsenotrophy, growth coupled to autotrophic arsenite oxidation, was suggested by detection of arsenite monooxygenase (aioA) and 1,5-ribulose bisphosphate carboxylase (RuBisCO) cbbL genes of microorganisms belonging to Rhizobiales and Burkholderiales. Enrichment cultures established from sampled groundwaters in laboratory conditions with 1.5 mmol L-1 of arsenite as sole electron donor were able to oxidize up to 100% of arsenite, suggesting that this metabolism is active in groundwaters. The presence of heterotrophic arsenic resistant bacteria was confirmed by enrichment cultures in most of the sites. The overall results provided a first overview of the microorganisms inhabiting arsenic-contaminated aquifers in Northern Italy and suggested the importance of sulfur-cycling bacteria in the biogeochemistry of arsenic in these ecosystems. The presence of active arsenite-oxidizing bacteria indicates that biological oxidation of arsenite, in combination with arsenate-adsorbing materials, could be employed for metalloid removal.

Project description:BACKGROUND:In low-income countries it is difficult to obtain complete data that show spatial heterogeneity in the risk of tuberculosis within-and-between smaller administrative units. This may contribute to the partial effectiveness of tuberculosis control programs. The aim of this study was to estimate the spatial risk of tuberculosis distribution in Gurage Zone, Southern Ethiopia using limited spatial datasets. METHODS:A total of 1601 patient data that were retrieved from unit tuberculosis registers were included in the final analyses. The population and geo-location data were obtained from the Central Statistical Agency of Ethiopia. Altitude data were extracted from ASTER Global Digital Elevation Model Version 2. Aggregated datasets from sample of 169(40%), 254(60%) and 338(80%) kebeles were used to estimate the spatial risk of TB distribution in the Gurage Zone by using a geostatistical kriging approach. The best set of input parameters were decided based on the lowest prediction error criteria of the cross-validation technique. ArcGIS 10.2 was used for the spatial data analyses. RESULTS:The best semivariogram models were the Pentaspherical, Rational Quadratic, and K-Bessel for the 40, 60 and 80% spatial datasets, respectively. The predictive accuracies of the models have improved with the true anisotropy, altitude and latitude covariates, the change in detrending pattern from local to global, and the increase in size of spatial dataset. The risk of tuberculosis was estimated to be higher at western, northwest, southwest and southeast parts of the study area, and crossed between high and low at west-central parts. CONCLUSION:This study has underlined that the geostatistical kriging approach can be applied to estimate the spatial risk of tuberculosis distribution in data limited settings. The estimation results may help local public health authorities measure burden of the disease at all locations, identify geographical areas that require more attention, and evaluate the impacts of intervention programs.

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

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