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 lg/L) in the northwest, northeast and southeast 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 lg/L is estimated to be around 122,000, of which we estimate approximately 49,000 people consume groundwater exceeding 10 lg/L. Using simple previously published doseresponse 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) cancersthat 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.

Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling

“…The area under the ROC (receiver operator characteristic) curve (AUC) is 0.86, which in general can range between 0.5 (random model) and 1 (perfect model) and represents how well a binary model can predict both low and high values as assessed over numerous probability cutoff values [ 65 ]. The AUC of this model is generally a better result than that of similar regional or country-scale groundwater quality studies (where reported), for example, 0.84 for fluoride in India [ 66 ], 0.71–0.83 for arsenic in Gujarat [ 14 ], 0.82 for arsenic in the USA [ 67 ], 0.80 for arsenic in Pakistan [ 6 ], or 0.74 for arsenic in Uttar Pradesh [ 15 ]. The overall accuracy of the model as applied to the test dataset of 0.79 is significantly higher than the no information rate of 0.58 ( p value < 2.2 × 10 −16 ) and is comparable to the average accuracy with OOB samples of 0.77.…”

“…The prediction model ( Figure 2 a) captures known arsenic-prone areas in the alluvial sediments along the Ganges and Brahmaputra plains [ 27 ] and in Gujarat [ 14 ] and Punjab [ 43 ]. It also identifies less well-known or previously undocumented areas such as Haryana, Jammu and Kashmir, and central Madhya Pradesh.…”

“…This should indicate where in the country high groundwater arsenic concentrations are likely to be found where no arsenic measurements currently exist. Such an approach has been previously been carried out for the states of Gujarat [ 14 ] and Uttar Pradesh [ 15 ], as well as the entire world [ 16 ], but never solely for all of the country of India or with the substantially larger dataset of arsenic concentrations as in this study. All other factors being equal, a modeling study conducted on a smaller area allows the model to focus on, and better characterize, the arsenic occurrences in that area and thereby produce a more accurate model.…”

Groundwater Arsenic-Attributable Cardiovascular Disease (CVD) Mortality Risks in India

“…Other processes, including complexation of arsenic by dissolved humic substances, competitive desorption, oxidation of sulfide minerals, geothermal activities, and mining-related activities, may also influence arsenic concentrations in groundwaters [18,19]. Spatial geostatistical models of the distribution of groundwater arsenic have been produced at both state (Gujarat [20], Uttar Pradesh [21]) and national scales [22,23] to help better fully understand the distribution of groundwater arsenic hazard in India, particularly given the lack of all-India systematic groundwater arsenic testing. Podgorski et al [22] generated a random forest model with over a hundred thousand arsenic concentration observations and over two dozen environmental parameter predictors to identify high groundwater arsenic (>10 µg/L) in well-known arsenic-contaminated areas in the alluvial sediments along the Ganges and Brahmaputra plains and in Gujarat and Punjab states and to capture less-known or previously unrecorded areas.…”

“…The aim of this study was to estimate the magnitude of groundwater arsenic-attributable cardiovascular disease (CVD) mortality in India. These aims were achieved by (i) generating machine learning models of the probability distribution of groundwater arsenic concentrations exceeding four different thresholds (10,20,50, 80 µg/L); (ii) thereby creating a pseudo-contour map of groundwater arsenic concentrations; (iii) estimating the state/district-level population exposed to these four arsenic concentration ranges (10-20, 20-50, 50-80, >80 µg/L) based on simple exposure models; (iv) determining the state/district-level population-attributable fraction (PAF) of CVD mortality attributable to exposure to groundwater arsenic; and hence (v) determining the state-level CVD mortality attributable to groundwater arsenic. Lastly, (vi) the costs of arsenic-attributable CVD mortality were estimated for the whole of country.…”