Modeling Contaminant Concentration Distributions in China’s Centralized Source Waters

Abstract

Characterizing contaminant occurrences in China's centralized source waters can provide an understanding of source water quality for stakeholders. The single-factor (i.e., worst contaminant) water-quality assessment method, commonly used in Chinese official analysis and publications, provides a qualitative summary of the country's water-quality status but does not specify the extent and degree of specific contaminant occurrences at the national level. Such information is needed for developing scientifically sound management strategies. This article presents a Bayesian hierarchical modeling approach for estimating contaminant concentration distributions in China's centralized source waters using arsenic and fluoride as examples. The data used are from the most recent national census of centralized source waters in 2006. The article uses three commonly used source water stratification methods to establish alternative hierarchical structures reflecting alternative model assumptions as well as competing management needs in characterizing pollutant occurrences. The results indicate that the probability of arsenic exceeding the standard of 0.05 mg/L is about 0.96-1.68% and the probability of fluoride exceeding 1 mg/L is about 9.56-9.96% nationally, both with strong spatial patterns. The article also discusses the use of the Bayesian approach for establishing a source water-quality information management system as well as other applications of our methods.