The presence of Arsenic, a toxic element, in groundwater poses a threat to human health and environment. Arsenic is a serious health hazard not only in Nepal, which is the study site for this research, but in almost all the countries in South East Asia. Even though mitigation plans for arsenic is extended almost everywhere, the risk of arsenic seems to an omnipresent problem. A mitigation strategy for arsenic requires the quantification of this risk through a series of statistical modeling approaches that describe the possible relationship of arsenic with measureable physical quantities and with the water chemistry in subsurface water. This book provides a variety of prediction models to achieve this end. Among the models considered, generalized quasi-Poisson is comparatively better and quantile model is good in modeling extreme cases. Furthermore, the models with the entire dataset are not very efficient compared to models that use data sub-settings. All in all, the end result of these models equate to prediction of arsenic at unmeasured locations, quantitative assessment of health risk, optimum remediation strategies, and cost benefit analysis for safe drinking water.

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