Literature DB >> 26803265

Contrasting regional and national mechanisms for predicting elevated arsenic in private wells across the United States using classification and regression trees.

Logan Frederick1, James VanDerslice2, Marissa Taddie2, Kristen Malecki3, Josh Gregg2, Nicholas Faust2, William P Johnson4.   

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.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Arsenic; Classification and regression trees; Groundwater chemistry; Mechanism; Prediction

Mesh:

Substances:

Year:  2016        PMID: 26803265     DOI: 10.1016/j.watres.2016.01.023

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  4 in total

1.  Private-well stewardship among a general population based sample of private well-owners.

Authors:  Kristen M C Malecki; Amy A Schultz; Dolores J Severtson; Henry A Anderson; James A VanDerslice
Journal:  Sci Total Environ       Date:  2017-06-09       Impact factor: 7.963

2.  Embryonic arsenic exposure reduces intestinal cell proliferation and alters hepatic IGF mRNA expression in killifish (Fundulus heteroclitus).

Authors:  Kaleigh C Sims; Katey L Schwendinger; Dana B Szymkowicz; Jonathan R Swetenberg; Lisa J Bain
Journal:  J Toxicol Environ Health A       Date:  2019-02-07

3.  Estimating the High-Arsenic Domestic-Well Population in the Conterminous United States.

Authors:  Joseph D Ayotte; Laura Medalie; Sharon L Qi; Lorraine C Backer; Bernard T Nolan
Journal:  Environ Sci Technol       Date:  2017-10-18       Impact factor: 9.028

4.  Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies.

Authors:  Melissa A Lombard; Molly Scannell Bryan; Daniel K Jones; Catherine Bulka; Paul M Bradley; Lorraine C Backer; Michael J Focazio; Debra T Silverman; Patricia Toccalino; Maria Argos; Matthew O Gribble; Joseph D Ayotte
Journal:  Environ Sci Technol       Date:  2021-03-17       Impact factor: 9.028

  4 in total

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