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• W3111155009 abstract "Vol. 128, No. 12 ResearchOpen AccessInequalities in Public Water Arsenic Concentrations in Counties and Community Water Systems across the United States, 2006–2011 Anne E. Nigra, Qixuan Chen, Steven N. Chillrud, Lili Wang, David Harvey, Brian Mailloux, Pam Factor-Litvak, and Ana Navas-Acien Anne E. Nigra Address correspondence to Anne E. Nigra, Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 722 W. 168th St., 11th Floor, Room 1107, New York, NY 10032 USA. Email: E-mail Address: [email protected] Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA Search for more papers by this author , Qixuan Chen Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA Search for more papers by this author , Steven N. Chillrud Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA Search for more papers by this author , Lili Wang Office of Water, U.S. Environmental Protection Agency, Washington, DC, USA Search for more papers by this author , David Harvey United States Public Health Service, Rockville, Maryland, USA Search for more papers by this author , Brian Mailloux Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA Barnard College, Columbia University, New York, New York, USA Search for more papers by this author , Pam Factor-Litvak Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA Search for more papers by this author , and Ana Navas-Acien Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA Search for more papers by this author Published:9 December 2020CID: 127001https://doi.org/10.1289/EHP7313AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:In the United States, nationwide estimates of public drinking water arsenic exposure are not readily available. We used the U.S. Environmental Protection Agency’s (EPA) Six-Year Review contaminant occurrence data set to estimate public water arsenic exposure. We compared community water system (CWS) arsenic concentrations during 2006–2008 vs. after 2009–2011, the initial monitoring period for compliance with the U.S. EPA’s 10 μg/L arsenic maximum contaminant level (MCL).Objective:Our objective was to characterize potential inequalities in CWS arsenic exposure over time and across sociodemographic subgroups.Methods:We estimated 3-y average arsenic concentrations for 36,406 CWSs (98%) and 2,740 counties (87%) and compared differences in means and quantiles of water arsenic (via quantile regression) between both 3-y periods for U.S. regions and sociodemographic subgroups. We assigned CWSs and counties MCL compliance categories (High if above the MCL; Low if below) for each 3-y period.Results:From 2006–2008 to 2009–2011, mean and 95th percentile CWS arsenic (in micrograms per liter) declined by 10.3% (95% CI: 6.5%, 14.1%) and 11.5% (8.3%, 14.8%) nationwide, by 11.4% (4.7%, 18.1%) and 16.3% (8.1%, 24.5%) for the Southwest, and by 36.8% (7.4%, 66.1%) and 26.5% (12.1%, 40.8%) for New England, respectively. CWSs in the High/High compliance category (not MCL compliant) were more likely in the Southwest (61.1%), served by groundwater (94.7%), serving smaller populations (mean 1,102 persons), and serving Hispanic communities (38.3%).Discussion:Larger absolute declines in CWS arsenic concentrations at higher water arsenic quantiles indicate declines are related to MCL implementation. CWSs reliant on groundwater, serving smaller populations, located in the Southwest, and serving Hispanic communities were more likely to continue exceeding the arsenic MCL, raising environmental justice concerns. These estimates of public drinking water arsenic exposure can enable further surveillance and epidemiologic research, including assessing whether differential declines in water arsenic exposure resulted in differential declines in arsenic-associated disease. https://doi.org/10.1289/EHP7313IntroductionRacial/ethnic, socioeconomic, and geographic inequalities in U.S. drinking water contaminant exposures reflect structural inequality ingrained in land-use patterns, regulatory policy, facility siting, underlying geologic processes, and municipal zoning decisions (Gibson et al. 2020; Morello-Frosch et al. 2002; Nigra 2020; Wilson et al. 2008). Yet, systematic studies of inequalities in public drinking water contaminant exposures are lacking. Previous studies have identified inequalities in public drinking water exposure estimates for nitrate (higher among Hispanic populations) and arsenic (higher among incarcerated populations in the Southwest) (Nigra and Navas-Acien 2020; Schaider et al. 2019). These studies highlight the critical need to systematically identify subgroups of the U.S. population with elevated public drinking water contaminant exposures in order to identify environmental justice concerns and to inform public health interventions and regulatory action needed to eliminate exposure inequalities.Inorganic arsenic is a human carcinogen (IARC 2004), highly toxic metalloid (Kuo et al. 2015, 2017; Milton et al. 2017; Moody et al. 2018; Moon et al. 2017; Sanchez et al. 2018), and water contaminant present in many aquifers in the United States. In 2001, the U.S. Environmental Protection Agency (EPA) Final Arsenic Rule lowered the arsenic maximum contaminant level (MCL) in public water systems from 50 to 10μg/L (U.S. EPA 2001a, 2001b). The Final Arsenic Rule went into effect on 23 January 2006 and included an initial monitoring period for compliance with the new MCL (2006–2008). Reducing the MCL from 50 to 10μg/L has prevented an estimated 200–900 cancer cases per year (Nigra et al. 2017). Evaluating the extent to which MCL implementation reduced public water arsenic exposure, and identifying population subgroups disproportionately exposed by geography, race/ethnicity, and socioeconomic status (SES), remains critical.Some evidence indicates that reductions in public water arsenic exposure were not uniform across all communities and that some public water systems remain in violation of the 10μg/L MCL, especially in the southwestern United States (Environmental Integrity Project 2016; Foster et al. 2019; Nigra et al. 2017; Welch et al. 2018). Systems serving smaller populations were more likely to be in violation of the new MCL than larger systems, likely because of the increased relative cost in treating, mixing, or switching source water per population-served size (Foster et al. 2019). Although racial/ethnic inequalities in urinary arsenic concentrations are well documented (with higher internal dose estimates for Hispanic and Asian-American subgroups), evaluating the relative contribution of diet vs. drinking water to total inorganic arsenic exposure remains challenging because no national-level estimates of public drinking water arsenic exposure are available (Awata et al. 2017a, 2017b; Cubadda et al. 2017; Jones et al. 2019; Kurzius-Spencer et al. 2014). Estimating public drinking water arsenic exposure for sociodemographic and geographic subgroups is needed to evaluate whether inequalities in arsenic exposure and compliance with the MCL persist across the United States, to inform future national- and state-level arsenic MCL deliberations, and to investigate whether inequalities in drinking water arsenic exposure by subgroup contributes to disparities in arsenic-related disease.Our objective was to estimate public drinking water arsenic exposure in community water systems (CWSs) across the United States and to identify subgroups whose public water arsenic concentrations have remained above 10μg/L after the new arsenic MCL implementation and are, therefore, at disproportionate risk of arsenic-related adverse health outcomes. We estimated arsenic exposure at the CWS level (primary analysis) and at the county level (secondary analysis), comparing arsenic concentrations during 2006–2008 vs. after 2009–2011, the initial monitoring period for compliance with the MCL, using the national contaminant occurrence database supporting the U.S. EPA’s Third Six-Year Review of drinking water regulations. This database contains monitoring records for arsenic in public drinking water systems across the United States from 2006 to 2011. We considered the following subgroups in our analysis: a) U.S. region (CWS and county level); b) sociodemographic county cluster (CWS and county level); and c) population-served size and source water type (CWS level).Materials and MethodsU.S. EPA Public Water DatabasesThe U.S. EPA compiles compliance monitoring data from public water supplies for regulated drinking water contaminants every 6 y as required by the Safe Drinking Water Act (SDWA) (U.S. EPA 2016a, 2016b). Data are obtained through the Information Collection Request process (voluntarily sent in from states, territories, and tribal authorities). The U.S. EPA works directly with agencies to collect records for a given monitoring period. We used arsenic monitoring data from the Third Six-Year Review (SYR3) period (2006–2011), which included approximately 13 million analytical records from 139,000 public water systems serving 290 million people annually. Data from 46 states, Washington DC, the Navajo Nation, and American Indian tribes from U.S. EPA Regions 1, 4, 5, 8, and 9, representing 95% of all public water systems and 92% of the total population served by public water systems nationally, were included (U.S. EPA 2016a, 2016b). Agencies with primacy for implementing the SDWA at public water systems in the states of Colorado, Delaware, Georgia, and Mississippi and U.S. EPA Regions 2, 6, 7, and 10 for water systems serving American Indian tribes did not submit data for SYR3 (U.S. EPA 2016a). The U.S. EPA conducted extensive quality assurance and quality control assessments prior to publishing the final SYR3 data (U.S. EPA 2016a, 2016b, 2016c). From an initial 297,354 arsenic monitoring records from 54,845 public water systems, we excluded 67,089 records from 17,747 systems categorized as transient or nontransient noncommunity water systems. Of the remaining 230,265 records, 104,730 reported arsenic detections and 125,535 reported nondetections.We replaced arsenic values below the limit of detection (LOD) with the LOD divided by the square root of 2. Although the U.S. EPA established the maximum LOD of 0.5μg/L, many systems reported lower and higher LODs or did not report record-specific LODs. When records reported the laboratory LOD as <5μg/L, we replaced arsenic values below the LOD with the LOD divided by the square root of 2 (N=30,256 records); for 1,202 records from Wisconsin and West Virginia that likely reported LODs in incorrect units (milligrams per liter instead of micrograms per liter), we imputed the corrected LOD divided by the square root of 2 (West Virginia Department of Health and Human Services 2019; Wisconsin Department of Natural Resources 2019). We imputed the value of 0.35μg/L (the U.S. EPA LOD of 0.5μg/L divided by the square root of 2) for a) 88,640 records with arsenic levels reported below the LOD without record-specific LOD; and b) the remaining 5,437 records for which LODs were reported as ≥5μg/L because these were considered unreliable.To assign counties-served for each CWS, we merged the SYR3 arsenic monitoring data with system inventory information extracted from the U.S. EPA Safe Drinking Water Information System (SDWIS), including counties served, number of people served, type of system, and source water type (U.S. EPA 2017). Because only county served was reliably reported in SDWIS for each CWS, we could not aggregate to smaller geographic scales (e.g., census-tract levels). Of the 37,098 CWSs, we successfully merged 36,372 to SDWIS. Of the remaining CWSs, we manually assigned county-served to 452 of these systems based on ZIP code (439 via U.S. Department of Housing and Urban Development Crosswalk files, and 13 via GoogleMaps) (Figure S1 presents a flowchart of the data cleaning, data merging, and inclusion/exclusion criteria). The final sample size included 36,406 CWSs (98% of CWSs in the SYR3) serving a total of 2,740 counties (87% of 3,141 U.S. counties and county-equivalents) across 47 states and serving 254,610,301 people.Water Arsenic Exposure EstimatesWe averaged available water arsenic monitoring records to 3-y periods (2006–2008 and 2009–2011) because these sampling periods differentiated between during (2006–2008) and after (2009–2011) the initial compliance monitoring period. We also averaged to 3-y periods because the number of records reported by CWSs was differential by prior MCL exceedances and source water type and varied substantially across CWSs (range: 1–1,506; mean: 4) (U.S. EPA 2004). Averaging available records to 3-y periods also minimized missing data and facilitated the comparison of arsenic exposure estimates across CWSs (for diagnostics regarding this analytical decision, see Tables S1 and S2). Few CWSs reported records of both raw and finished (i.e., treated) water samples within the same year (N=1,182). When the 3-y average of arsenic in finished water samples was lower than in raw concentrations (N=336 CWSs), we calculated the 3-y average with only the finished water samples.At the county level (secondary analyses), we estimated weighted average 3-y (2006–2008 and 2009–2011) water arsenic concentrations, accounting for the number of people served by each CWS (population served). For each 3-y period, the population-served weight for each CWS was calculated as the population served by that CWS divided by the total population served by all CWSs serving that county. CWSs not reporting data for a particular 3-y period did not contribute to that county’s total population served or 3-y average (Equation S1). To avoid reporting a county-level average derived from CWSs that served only small populations relative to the entire county population, the average water arsenic concentration for a given county and 3-y period was estimated only if the CWSs serving that county reported serving at least 50% or more of the public water–reliant population in the entire county. We estimated the public water–reliant population for each county using the latest nationwide U.S. Census statistic on county-level household tap water source from the 1990 U.S. Census (Ruggles et al. 2019), which was also recently used by the U.S. Geologic Survey (USGS) (Ayotte et al. 2017). For descriptive purposes, we also calculated county-level 6-y average water arsenic concentrations as the average of the two 3-y period estimations. We mapped 3- and 6-y county-level estimates of water arsenic averages across the conterminous United States using the maps package in R version 3.5.3 (Becker and Wilks 2018).Statistical Analysis: Water Arsenic Exposure EstimatesPrimary analyses were conducted at the CWS level and secondary analyses at the county level. We calculated the distribution—including percentiles, arithmetic means, and geometric means—of 6- and 3-y average water arsenic concentrations at the CWS (N=36,406) and county level (N=2,740) for the entire United States. We also compared mean differences between the two time periods. Because the reductions in average water arsenic concentrations over time occurred at the highest end of the distribution, we also performed quantile regression at the 75th, 80th, 85th, 95th, and 99th percentiles to estimate the difference in water arsenic concentration over time at these quantiles. For CWS-level models, we evaluated both crude models and models adjusting for source water type and the size of the population served to determine whether differences in water arsenic concentrations over time were related to these variables.Statistical Analysis: Analyses Stratified by SubgroupTo identify subgroups of the U.S. population whose estimated public drinking water arsenic exposures were relatively high, we stratified our analyses by the following population subgroups: region (CWS- and county-level analyses), sociodemographic county-cluster (CWS- and county-level analyses), source water type (CWS-level analyses), and size of the population served by CWSs (CWS-level analyses). Region groupings were based on USGS-identified areas with similar groundwater arsenic patterns: Pacific Northwest (Washington, Oregon, Montana, Wyoming, and Idaho), Southwest (California, Nevada, Utah, Colorado, Arizona, New Mexico, and Texas), Central Midwest (North Dakota, South Dakota, Nebraska, Kansas, and Missouri), Eastern Midwest (Wisconsin, Illinois, Indiana, Michigan, Ohio, Minnesota, and Iowa), Southeast (Oklahoma, Arizona, Louisiana, Mississippi, Alabama, Florida, Georgia, Tennessee, Kentucky, South Carolina, North Carolina, Virginia, and West Virginia), Mid-Atlantic (Pennsylvania, Maryland, District of Columbia, Delaware, New York, New Jersey, Connecticut, and Rhode Island), New England (Massachusetts, Vermont, New Hampshire, and Maine), and Alaska/Hawaii (Alaska and Hawaii) (Ayotte et al. 2017). Sociodemographic county-clusters (N=8 distinct clusters) were derived by Wallace et al. (2019) to enable the direct comparison of county-level health outcomes and behaviors while accounting for the sociodemographic makeup of a county’s population. Briefly, Wallace et al. (2019) used k-means analysis to identify groups of counties with similar sociodemographic profiles (e.g., race and ethnicity, age, educational attainment, employer status, health insurance status). In the present study, we stratified public water arsenic exposure estimates by these sociodemographic county-clusters to identify characteristics of population subgroups exposed to elevated public water arsenic exposure. These sociodemographic county-clusters are as follows: Semi-Urban, High SES; Semi-Urban, Mid/Low SES; Semi-Urban, Hispanic; Mostly Rural, Mid SES; Rural, Mid/Low SES; Young, Urban, Mid/High SES; Rural, American Indian; and Rural, High SES. We also stratified CWS-level analyses by source water type (groundwater vs. surface water, as reported in SDWIS) and by the size of the population served (≤500, 501–3,300, 3,301–10,000, and >10,000 persons) (these are not relevant at the county level).We conducted sensitivity analyses for the comparison of water arsenic concentrations over the two 3-y periods. First, we replaced all arsenic concentration values below the LOD with 0.35μg/L (the standard U.S. EPA LOD divided by the square root of 2), regardless of the LOD reported in each record. Second, we excluded records with a reported LOD of ≥5μg/L. Third, we estimated county average water arsenic concentrations for a given 3-y period only if the CWSs serving that county reported serving at least 70% and 80% or more of the public water–reliant population in the entire county (instead of 50% or more as in our main analysis).MCL Compliance EvaluationWe assigned all CWSs and counties to one of four MCL compliance categories using the 10μg/L MCL cut point based on the average water arsenic concentration in the first (2006–2008) and second (2009–2011) 3-y time periods: Low/Low (<10μg/L in both periods); High/Low (>10μg/L in 2006–2008, but <10μg/L in 2009–2011); Low/High (<10μg/L in 2006–2008 but >10μg/L in 2009–2011); and High/High (>10μg/L in both periods). We also categorized compliance categories using cutoff values of 5μg/L (the current MCL for New Hampshire and New Jersey) and 1μg/L (the MCL for the Netherlands and close to the U.S. EPA’s MCL goal of 0μg/L). We compared characteristics of CWSs and counties (region, sociodemographic county-cluster, source water type, and population served) by compliance category, and we mapped these county-level compliance categories for the conterminous United States. Finally, we identified all counties and CWSs that served counties classified into the High/Low (i.e., successful in reducing water arsenic below the new MCL) and High/High (i.e., not successful in reducing water arsenic below the new MCL) compliance categories to identify counties that were successful vs. unsuccessful in reducing water arsenic concentrations. We also assessed the odds ratio (OR) of average water arsenic concentration exceeding the 10μg/L MCL at either time period (2006–2008 and 2009–2011) for CWSs in a given subgroup, compared with all CWSs not in that subgroup (reference), using logistic regression models with generalized estimating equations to account for two 3-y averages per CWS.Interactive Map and Publicly Available DataWe also created an online interactive map of county-level water arsenic concentrations for all three time periods (2006–2008, 2009–2011, and 2006–2011) and MCL compliance categories to improve accessibility and results dissemination ( https://annenigra.github.io/ColumbiaArsenicMap.html). Data sets of the 3-y and 6-y average arsenic concentrations at the CWS and county level and a reproducible R archive of analyses and data sets are available in the Supplemental Material and via GitHub ( https://github.com/annenigra/epa-public-water-arsenic).ResultsAverage CWS Arsenic Estimates Nationwide and by RegionNationwide, the mean CWS arsenic concentrations [95% confidence intervals (CIs)] in 2006–2008 and 2009–2011 were 1.89μg/L (95% CI: 1.84, 1.94) and 1.7μg/L (95% CI: 1.64, 1.75) [mean difference=−0.19 (95% CI: −0.27, −0.12); corresponding percentage decrease=10.1% (95% CI: 14.1%, 6.5%) (Table 1)]. By region, CWS arsenic concentrations for the first time period (2006–2008) were highest in the Southwest [mean=3.59μg/L (95% CI: 3.41, 3.76)], followed by Alaska/Hawaii [mean=2.17μg/L (95% CI: 1.73, 2.61)], the Pacific Northwest [mean=2.15μg/L (95% CI: 2.03, 2.27)], and the Eastern Midwest [mean=2.03μg/L (95% CI: 1.92, 2.14)] (Table 1; Figure 1). CWSs in the New England region experienced the greatest absolute mean decline in average water arsenic between 2006–2008 and 2009–2011 [mean difference=−0.75μg/L (95% CI: −1.35, −0.15); corresponding percentage decrease=36.8% (95% CI: 7.4%, 66.1%)], followed by the Southwest [mean difference=−0.41μg/L (95% CI: −0.65, −0.17); corresponding percentage decrease=11.4% (95% CI: 4.7%, 18.1%)]; and Eastern Midwest [mean difference=−0.40μg/L (95% CI: −0.54, −0.25); corresponding percentage decrease=19.7% (95% CI: 12.2%, 26.8%)]. Detailed distributions of water arsenic concentrations in 2006–2008 and 2009–2011 stratified by region can be found in Excel Table S1. Distributions of average water arsenic from 2006–2008 were markedly bimodal in the Southwest and New England regions (Figure 1). Mean difference estimates did not change with adjustment for source water type and size of population served (Table S3).Table 1 Arithmetic means (95% CIs), mean differences (95% CIs), and corresponding percentage differences (95% CIs) of 3-y average water arsenic concentrations (μg/L) in community water systems (CWSs) from 2006–2008 and 2009–2011, stratified by CWS subgroup (total N=36,406 CWSs).Table 1, in five main columns, lists CWS Categories, 2006 to 2008, 2009 to 2011, Mean difference (95 percent confidence interval), and Corresponding percentage difference (95 percent confidence interval). The 2006 to 2008 and 2009 to 2011 columns are each subdivided into three columns, namely, Community water systems (uppercase italic n), Records (thousands) (uppercase italic n), Mean (95 percent confidence interval).CWS Categories2006–20082009–2011Mean difference (95% CI)Corresponding percentage difference (95% CI)CWSs (N)Records (thousands) (N)Mean (95% CI)CWSs (N)Records (thousands) (N)Mean (95% CI)All CWSs30,8202101.89 (1.84, 1.94)32,4812201.7 (1.64, 1.75)−0.19 (−0.27, −0.12)−10.3 (−14.1, −6.5)Source water typea150160 Groundwater26,279602.02 (1.96, 2.08)27,572611.80 (1.75, 1.86)−0.22 (−0.30, −0.14)−10.8 (−14.9, −6.7) Surface water4,5371.12 (1.04, 1.19)4,9061.08 (1.01, 1.15)−0.04 (−0.14, 0.07)−3.2 (−12.5, 6.1)Population size servedb ≤50018,001702.09 (2.01, 2.17)18,101711.89 (1.81, 1.97)−0.20 (−0.32, −0.09)−9.6 (−15.1, −4.2) 501–3,3007,190431.78 (1.70, 1.86)8,048441.59 (1.52, 1.67)−0.19 (−0.30, −0.08)−10.5 (−16.8, −4.3) 3,301–10,0002,803271.53 (1.43, 1.62)3,236271.36 (1.27, 1.45)−0.17 (−0.30, −0.03)−11.1 (−19.9, −2.2) 10,001–100,0002,485501.27 (1.19, 1.36)2,730511.19 (1.10, 1.27)−0.09 (−0.21, 0.04)−6.7 (−16.5, 3) >100,000341231.21 (1.05, 1.37)366231.22 (1.07, 1.38)0.01 (−0.21, 0.23)0.9 (−17.3, 19.1)Region Alaska/Hawaii4141.82.17 (1.73, 2.61)4261.91.65 (1.22, 2.09)−0.52 (−1.14, 0.10)−23.8 (−52.3, 4.6) Central Midwest2,2389.61.94 (1.80, 2.08)2,520101.98 (1.85, 2.11)0.04 (−0.15, 0.23)2.2 (−7.5, 12) Eastern Midwest4,878262.03 (1.92, 2.14)5,476271.63 (1.53, 1.74)−0.40 (−0.54, −0.25)−19.5 (−26.8, −12.2) Mid-Atlantic4,695250.88 (0.82, 0.94)4,611250.81 (0.75, 0.87)−0.07 (−0.15, 0.02)−7.5 (−17.3, 2.3) New England1,587112.05 (1.62, 2.47)1,592111.29 (0.87, 1.72)−0.75 (−1.35, −0.15)−36.8 (−66.1, −7.4) Pacific Northwest3,986202.15 (2.03, 2.27)3,584192.11 (1.99, 2.24)−0.04 (−0.21, 0.13)−1.8 (−9.8, 6.2) Southeast6,686300.66 (0.63, 0.70)7,243320.65 (0.62, 0.68)−0.01 (−0.06, 0.03)−2.1 (−8.6, 4.4) Southwest6,336893.59 (3.41, 3.76)7,029903.18 (3.01, 3.34)−0.41 (−0.65, −0.17)−11.4 (−18.1, −4.7)Sociodemographic county-clusterc Semi-Urban, High SES14,479781.54 (1.47, 1.61)14,479791.39 (1.32, 1.46)−0.15 (−0.25, −0.06)−9.9 (−16.2, −3.7) Semi-Urban, Mid/Low SES1,03460.63 (0.53, 0.72)1,3286.80.65 (0.57, 0.74)0.03 (−0.10, 0.15)4.3 (−16, 24.7) Semi-Urban, Hispanic3,537433.60 (3.38, 3.82)3,849443.4 (3.19, 3.62)−0.19 (−0.50, 0.11)−5.3 (−13.8, 3.2) Mostly Rural, Mid SES7,717381.44 (1.37, 1.52)8,120391.26 (1.18, 1.34)−0.19 (−0.30, −0.07)−12.9 (−20.5, −5.2) Rural, Mid/Low SES3851.71.22 (0.92, 1.52)47921.11 (0.84, 1.38)−0.11 (−0.51, 0.29)−9.1 (−42.1, 23.8) Young, Urban, Mid/High SES901262.89 (2.59, 3.19)912262.71 (2.41, 3.01)−0.18 (−0.60, 0.25)−6.1 (−20.9, 8.6) Rural, American Indian40422.95 (2.46, 3.44)4122.12.50 (2.02, 2.99)−0.45 (−1.13, 0.24)−15.1 (−38.4, 8.1) Rural, High SES4,423222.32 (2.14, 2.50)4,565221.98 (1.80, 2.16)−0.34 (−0.59, −0.09)−14.7 (−25.6, −3.8)Note: Records indicates the total number of individual monitoring records contributing to a given estimate. CI, confidence interval; EPA, Environmental Protection Agency; SES, socioeconomic status.aGroundwater is considered CWSs served by surface water under the influence of groundwater and groundwater under the influence of surface water.bCategories of population served are standard U.S. EPA categories. Population served is adjusted total population served, which accounts for systems that sell or purchase water and avoids overcounting.cA total of 172 CWSs served more than one county; of these, approximately half served counties categorized to different sociodemographic county-clusters (e.g., NY7003493 serves New York, New York (Young, Urban, Mid/High SES) and Bronx, New York (Semi-Urban, Hispanic). These CWSs are represented for each county that they serve in the sociodemographic county-cluster analyses (N=36,674).Figure 1. Distribution of average arsenic concentrations (μg/L) in community water systems (CWSs) stratified by region for the period of 2006–2008. Filled polygons represent density plots. Box plot upper, middle, and lower hinges correspond to the 25th, 50th, and 75th percentiles, respectively. Box plots for the Southeast and Mid-Atlantic are difficult to visualize because of an extreme right skew. The 3-y average arsenic concentration for each CWS is represented by a dot. Mean region-specific concentrations are indicated by the outlined white circle; mean (95% confidence intervals) are also listed for each region in text. The 10μg/L maximum contaminant level is indicated by the red dashed line. The x-axis is truncated at 54.60μg/L. The R code for this figure was adapted from Allen et al. (2019).Figure 2 illustrates the change in CWS water arsenic concentrations at a given quantile over the two time periods, stratified by region. Regions were ordered by the mean arsenic concentration from 2006–2008. Changes in CWS water arsenic concentrations nationwide from 2006–2008 to 2009–2011 were larger at increasing quantiles of the water arsenic distribution; significant changes occurred at the 80th quantile [decline of 0.13μg/L (95% CI: 0.23, 0.03)] through the 99th quantile [decline of 4.20μg/L (95% CI: 5.56, 2.84)] (Figure 2; Table S4). When stratified by region, quantile regression models indicated larger declines at the 99th percentile for New England [decline of 7.99μg/L (95% CI: 16.00, 0.02); corresponding percentage decrease=45.3%], the Eastern Midwest [decline of 8.44μg/L (95% CI: 11.83, 5.05); corresponding percentage decrease=40.5%], the Southwest [decline of 3.46μg/L (95% CI: 9.00, 2.08); corresponding percentage decrease=11.5%], and the Mid-Atlantic [decline of 2.43μg/L (95% CI: 4.46, 0.40); corresponding percentage decrease=23.1%] (Figure 2; Table S5).Figure 2. Change in water arsenic concentrations (μg/L) at a given quantile from 2006–2008 vs. 2009–2011, stratified by region (quantile regression results). Regions are ordered by increasing mean arsenic concentrations in 2006–2008. The x-axis indicates the 80th, 85th, 90th, 95th, 96th, 97th, 98th, and 99th percentiles for all CWSs within a region. Shaded areas indicate the quantile regression confidence interval. The red dashed line indicates zero change in water arsenic concentration across the two time periods. Alaska/Hawaii was not included in this figure because of small sample size and unstable estimates (see Table S4). Note: CWS, community water system.At the county level, nationwide weighted aver" @default.
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• W3111155009 title "Inequalities in Public Water Arsenic Concentrations in Counties and Community Water Systems across the United States, 2006–2011" @default.
• W3111155009 cites W2007433322 @default.
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