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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.
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.