Arsenic (As) contamination is well known public health concern exposing more than 137 million people to unsafe levels of As in drinking water supplies (Chakraborti et al., 2018). The over pumping of groundwater significantly impacts the natural and geochemical processes in the subsurface resulting in advertent disturbances in the local and regional conditions of aquifer resulting in As mobilization. This As contamination exposes human population to serious health consequences, such as skin lesions, keratosis, and many types of cancers. Despite this critical importance of clean drinking water, estimation of spatial extent of As contamination stayed unmapped. Numerous studies stressed upon documentation of health impacts associated with As exposure. Also, the linkages between As exposure, its health implications and socio-economic conditions of households were found missing. Based on the above background, this doctoral research has two broad objectives: (i) to develop a GIS based model to predict the areas of arsenic contamination and the extent of population at risk, and (ii) to identify the socio-economic determinants which will help in lowering arsenic poisoning.
The first objective convoluted development of database for proxy variables at high spatial resolution influencing As enrichment. Then, significant variables using univariate selection approach were used to construct multiple decision trees based on hybrid random forest model developed. Finally, theoutput was generated as the probability map of As across the study region. Using the output generated, it was found that 23.48 million people in rural areas of Uttar Pradesh are exposed to high Ascontamination in their groundwater which they use for their drinking, cooking and irrigation needs.Additionally, reductive dissolution of iron oxyhydroxides was found to be the main mechanism responsible for As enrichment in the groundwater of the study region.
The second objective involved the identification of socio-economic factors influencing As exposure at household level. This also involved understanding the level of awareness among people regarding the diseased conditions. The outcome suggests water consumption per day, social groupand occupation are significant and found positively associated with arsenic exposure. Educational status was found negatively associated with arsenic exposure. Moreover, by increasing education and awareness about As and by reducing the tobacco smoking and alcohol consumption will lead to lowering arsenic exposure among individuals. The results indicate that although rural people were unaware about the term arsenic, they were able to recognize the status of their handpump`s water with change in colour.
Thus, the doctoral research work provides a critical demarcation of no risk and high risk regions vulnerable to As contamination, and the subsequent socioeconomic factors affecting rural people`s health implications. This integrated approach will benefit the policy makers to prioritize the interventions to provide safe drinking water. This developed model and approach through this work can be further extrapolated to other parts of the world, especially to Southeast Asian regions.