  Groundwater contaminated with arsenic has been recognized as a global threat,
which negatively impacts human health. Populations that rely on private wells
for their drinking water are vulnerable to the potential arsenic-related health
risks such as cancer and birth defects. Arsenic exposure through drinking water
is among one of the primary arsenic exposure routes that can be effectively
managed by active testing and water treatment. From the public and
environmental health management perspective, it is critical to allocate the
limited resources to establish an effective arsenic sampling and testing plan
for health risk mitigation. We present a spatially adaptive sampling design
approach based on an estimation of the spatially varying underlying
contamination distribution. The method is different from traditional sampling
design methods that often rely on a spatially constant or smoothly varying
contamination distribution. In contrast, we propose a statistical
regularization method to automatically detect spatial clusters of the
underlying contamination risk from the currently available private well arsenic
testing data in the USA, Iowa. This approach allows us to develop a sampling
design method that is adaptive to the changes in the contamination risk across
the identified clusters.
  We provide the spatially adaptive sample size calculation and sampling
location determination at different acceptance precision and confidence levels
for each cluster. The spatially adaptive sampling approach may effectively
mitigate the arsenic risk from the resource management perspectives. The model
presents a framework that can be widely used for other environmental
contaminant monitoring and sampling for public and environmental health.
