title: Contrasting regional and national mechanisms for predicting elevated arsenic in private wells across the United States using classification and regression trees.
abstract: Arsenic contamination in groundwater is a public health and environmental concern in the United States (U.S.) particularly where monitoring is not required under the Safe Water Drinking Act. Previous studies suggest the influence of regional mechanisms for arsenic mobilization into groundwater; however, no study has examined how influencing parameters change at a continental scale spanning multiple regions. We herein examine covariates for groundwater in the western, central and eastern U.S. regions representing mechanisms associated with arsenic concentrations exceeding the U.S. Environmental Protection Agency maximum contamination level (MCL) of 10 parts per billion (ppb). Statistically significant covariates were identified via classification and regression tree (CART) analysis, and included hydrometeorological and groundwater chemical parameters. The CART analyses were performed at two scales: national and regional; for which three physiographic regions located in the western (Payette Section and the Snake River Plain), central (Osage Plains of the Central Lowlands), and eastern (Embayed Section of the Coastal Plains) U.S. were examined. Validity of each of the three regional CART models was indicated by values >85% for the area under the receiver-operating characteristic curve. Aridity (precipitation minus potential evapotranspiration) was identified as the primary covariate associated with elevated arsenic at the national scale. At the regional scale, aridity and pH were the major covariates in the arid to semi-arid (western) region; whereas dissolved iron (taken to represent chemically reducing conditions) and pH were major covariates in the temperate (eastern) region, although additional important covariates emerged, including elevated phosphate. Analysis in the central U.S. region indicated that elevated arsenic concentrations were driven by a mixture of those observed in the western and eastern regions.
authors.affiliation: Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: Logan
authors.initials: L
authors.lastname: Frederick
authors.affiliation: Department of Family and Preventative Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: James
authors.initials: J
authors.lastname: VanDerslice
authors.affiliation: Department of Family and Preventative Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: Marissa
authors.initials: M
authors.lastname: Taddie
authors.affiliation: Survey of the Health of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
authors.forename: Kristen
authors.initials: K
authors.lastname: Malecki
authors.affiliation: Department of Family and Preventative Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: Josh
authors.initials: J
authors.lastname: Gregg
authors.affiliation: Department of Family and Preventative Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Faust
authors.affiliation: Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: william.johnson@utah.edu.
authors.forename: William P
authors.initials: WP
authors.lastname: Johnson
mesh_terms: D001151:Arsenic / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D060587:Groundwater / Q000032:analysis*; D012044:Regression Analysis; D014481:United States; D014874:Water Pollutants, Chemical / Q000032:analysis*; D060986:Water Wells
chemical_list: D014874:Water Pollutants, Chemical; D001151:Arsenic
keywords: Arsenic; Classification and regression trees; Groundwater chemistry; Mechanism; Prediction