title: Coupling predicted model of arsenic in groundwater with endemic arsenism occurrence in Shanxi Province, Northern China.
abstract: 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 3000 km(2) was found to have high risk of As concentrations above 50 μg L(-1). The linear regression indicates that 13% of the variation in arsenicosis occurrence rate can be predicted using predictive probability of As concentration above 50 μg L(-1) in Shanxi Province. These results suggest that As prediction model may be helpful for identifying As-contaminated area and endemic arsenism village.
authors.affiliation: Department of Occupational and Environmental Health, College of Public Health, China Medical University, 110001 Shenyang, Liaoning, China; Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, 300070 Tianjin, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhang
authors.forename: Luis
authors.initials: L
authors.lastname: Rodriguez-Lado
authors.forename: Juan
authors.initials: J
authors.lastname: Liu
authors.forename: C Annette
authors.initials: CA
authors.lastname: Johnson
authors.forename: Quanmei
authors.initials: Q
authors.lastname: Zheng
authors.forename: Guifan
authors.initials: G
authors.lastname: Sun
mesh_terms: D001151:Arsenic / Q000032:analysis / Q000737:chemistry* / Q000633:toxicity*; D020261:Arsenic Poisoning / Q000453:epidemiology*; D002138:Calibration; D002681:China / Q000453:epidemiology; D004781:Environmental Exposure; D004784:Environmental Monitoring / Q000379:methods; D004785:Environmental Pollutants; D005843:Geography; D060587:Groundwater / Q000737:chemistry*; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D012044:Regression Analysis; D018570:Risk Assessment; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014881:Water Supply
chemical_list: D004785:Environmental Pollutants; D014874:Water Pollutants, Chemical; D001151:Arsenic
keywords: Arsenic; Arsenicosis; Endemic arsenism; Logistic regression; Risk assessment