Risk Based Arsenic Rational Sampling Design for Public and Environmental Health Management

TL;DR

This paper introduces a spatially adaptive sampling design for arsenic testing in groundwater, improving resource allocation and risk mitigation by detecting contamination clusters and tailoring sampling strategies accordingly.

Contribution

It proposes a novel statistical regularization method to identify spatial clusters of arsenic contamination, enabling adaptive sampling design for better health risk management.

Findings

Effective detection of arsenic contamination clusters.

Adaptive sampling improves resource efficiency.

Framework applicable to other environmental monitoring.

Abstract

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.