2016
DOI: 10.1016/j.watres.2016.01.023
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Contrasting regional and national mechanisms for predicting elevated arsenic in private wells across the United States using classification and regression trees

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Cited by 21 publications
(36 citation statements)
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“…Previous studies in Maine and New Jersey have focused on domestic well stewardship and voluntary testing largely among populations living in areas with high risk of exposure to arsenic or nitrates. 4, 10 In contrast, as previously mentioned, SHOW participants were randomly selected to provide both a demographically and geographically representative sample of state residents and not selected based on potential for increased exposure to particular ground water contaminants or concerns.…”
Section: Discussionmentioning
confidence: 99%
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“…Previous studies in Maine and New Jersey have focused on domestic well stewardship and voluntary testing largely among populations living in areas with high risk of exposure to arsenic or nitrates. 4, 10 In contrast, as previously mentioned, SHOW participants were randomly selected to provide both a demographically and geographically representative sample of state residents and not selected based on potential for increased exposure to particular ground water contaminants or concerns.…”
Section: Discussionmentioning
confidence: 99%
“…2–4 A USGS report testing private wells in 48 states found that more than one-fifth of all sampled wells had levels greater than health-based standards, and almost 25% of rural wells located in largely agricultural areas exceeded health based standards, with over 34% testing positive for nitrates. 2 Private well stewardship is a particularly important issue in states like Wisconsin where at least one quarter of the population is served by a private well as their primary drinking water source 5 .…”
Section: Introductionmentioning
confidence: 99%
“…National-scale studies in the United States (US) by the US Geological Survey (USGS; Focazio et al, 2000;Ryker, 2001Ryker, , 2003Welch et al, 2001; Lee and Helsel, 2005;Gronberg, 2011) and others (Lockwood et al, 2004;Frederick et al, 2016) have led to the generation of spatial distribution maps of arsenic in groundwater. To fill the voids as a result of a national-scale assessment approach, regional groundwater studies (especially for areas with insufficient data, e.g., Mid-Atlantic states), have been used to demonstrate arsenic contamination on greater spatial scales (Sanders et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…While the use of geostatistical modeling can be used with success for generating prediction maps, these techniques suffer from limitations including data availability, data quality, and local scale arsenic mobilization variations due to complex groundwater flow paths (Ayotte et al, 2011;Bretzler et al, 2017a). As outlined by Frederick et al (2016), generally when exploring potential relationships between elevated arsenic and various explanatory variables two primary modeling strategies can be used: logistic regression (e.g., Ayotte et al, 2003;Winkel et al, 2008;Gross and Low, 2013;Bretzler et al, 2017b), or decision tree analysis (Hossain and Piantanakulchai, 2013;Tesoriero et al, 2017). However, in this present study, an alternative approach to these modeling techniques, previously applied successfully by McGrory et al (2017) which used similar predictor variables in addition to detailed aqueous geochemistry data that can potentially explain the spatial distribution of arsenic in relation to groundwater controls (e.g., bedrock geology) was utilized.…”
Section: Introductionmentioning
confidence: 99%
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