A predictive risk model of groundwater arsenic contamination in China applied to the Huai River Basin, with a focus on the region's cluster of elevated cancer mortalities. (February 2017)

• Record Type: Journal Article
• Authors: Li, Yongfang; Wang, Da; Liu, Yuyan; Zheng, Quanmei; Sun, Guifan
• Is Part Of: Applied geochemistry. Volume 77(2017:Feb.)
• Page Start: 178
• Page End: 183
• Publication Date: 2017-02
• Subjects: Huai River Basin -- Cancer -- Arsenic -- Predictive risk model -- Model accuracy; Environmental geochemistry -- Periodicals; Water chemistry -- Periodicals; Geochemistry -- Social aspects -- Periodicals; Geochemistry -- Periodicals; 551.9
• DOI: 10.1016/j.apgeochem.2016.05.003
• Languages: English
• ISSNs: 0883-2927

Abstract

Abstract: Recently, a statistical risk model that classifies safe and unsafe areas with respect to geogenic groundwater arsenic contamination in China has been developed. Modeling results indicate that most areas in the Huai River Basin (HRB) are associated with a considerable probability of human exposure to elevated levels of arsenic (>10 μg/L) in groundwater. The high mortality and incidence rates of cancer in the HRB have been widely reported. The predicted high probability of arsenic hazards in this region might suggest a new hypothesis that groundwater arsenic contamination contributes to this cancer cluster. Therefore, we analyzed an arsenic concentration dataset, including 69, 702 groundwater samples from 2357 villages in 70 counties, in the HRB to determine whether the region was affected by arsenic. Our findings indicate that groundwater consumed in 408 (17%) villages and 41 (59%) counties contains high levels of arsenic (>10 μg/L). Coincidentally, in some counties with severe groundwater contamination people have been found to exhibit high cancer mortality rates. Furthermore, the model correctly classified 57% of the villages as high- and low-risk areas (0.4 probability cut-off threshold). The model sensitivity (ability to correctly classify villages with arsenic concentrations of >10 μg/L) and specificity (ability to correctly classify villages with arsenic concentrations of ≤10 μg/L) at the village-level were 61% and 56%, respectively. Collectively, our findings confirmed that the HRB was affected by arsenic and suggested, at least to a certain extent, that groundwater arsenic contamination is likely associated with the HRB cancer cluster. Additionally, the data indicated that the model was well-suited to predicting high-risk arsenic exposure areas at the village-level and can thus be used as a tool to prioritize sampling in villages showing high risk. Highlights: Arsenic contamination in groundwater exists in the Huai River Basin. Some counties with high cancer mortality rates have more severe status of groundwater arsenic contamination. The predictive risk model accurately characterizes high-risk areas at the village-level.