Coupling predicted model of arsenic in groundwater with endemic arsenism occurrence in Shanxi Province, Northern China

• Published in: Journal of hazardous materials Vol. 262; pp. 1147 - 1153
• Main Authors: Zhang, Qiang, Rodriguez-Lado, Luis, Liu, Juan, Johnson, C. Annette, Zheng, Quanmei, Sun, Guifan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.11.2013
• Subjects: Arsenic; Arsenic - analysis; Arsenic - chemistry; Arsenic - toxicity; Arsenic Poisoning - epidemiology; Arsenicosis; Calibration; China - epidemiology; Endemic arsenism; Environmental Exposure; Environmental Monitoring - methods; Environmental Pollutants; Geography; Groundwater - chemistry; Humans; Linear Models; Logistic regression; Models, Statistical; Regression Analysis; Risk Assessment; Water Pollutants, Chemical - analysis; Water Supply; China; Arsenicosis; Logistic regression; Endemic arsenism; Arsenic; Risk assessment
• ISSN: 0304-3894; 1873-3336
• DOI: 10.1016/j.jhazmat.2013.02.017

► Variables from topography, soil property, hydrology, gravity and remote sensing information can be used to build the model. ► Area more than 3000 km2 was found to have high risk of arsenic contamination. ► About 800,000 people may be at risk of high arsenic exposure. ► Arsenicosis occurrence rate can be partly predicted using predictive probability of As concentration above 50μgL−1. Statistical modeling has been used to predict high risk area of arsenic (As) hazard, but information about its application on endemic arsenism is limited. In this study, we aim to link the prediction model with population census data and endemic arsenicosis in Shanxi Province, Northern China. 23 explanatory variables from different sources were compiled in the format of grid at 1km resolution in a GIS environment. Logistic regression was applied to describe the relationship between binary-coded As concentrations data and the auxiliary predictors. 61 endemic arsenism villages were geo-located and combined with output maps of the prediction model. Linear regression was used to identify the relationship between arsenicosis occurrence rate and predictive As probability at village level. Our results show that 6 explanatory environmental variables were significantly contributed to the final model. Area of 3000km2 was found to have high risk of As concentrations above 50μgL−1. The linear regression indicates that 13% of the variation in arsenicosis occurrence rate can be predicted using predictive probability of As concentration above 50μgL−1 in Shanxi Province. These results suggest that As prediction model may be helpful for identifying As-contaminated area and endemic arsenism village.