An Examination of National Cancer Risk Based on Monitored Hazardous Air Pollutants
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 3 Linguagem: Inglês
10.1289/ehp8044
ISSN1552-9924
AutoresChelsea A. Weitekamp, McKayla Lein, Madeleine Strum, Mark Morris, Ted Palma, Darcie Smith, Lukas B. Kerr, Michael J. Stewart,
Tópico(s)Environmental Justice and Health Disparities
ResumoVol. 129, No. 3 ResearchOpen AccessAn Examination of National Cancer Risk Based on Monitored Hazardous Air Pollutants Chelsea A. Weitekamp, McKayla Lein, Madeleine Strum, Mark Morris, Ted Palma, Darcie Smith, Lukas Kerr, and Michael J. Stewart Chelsea A. Weitekamp Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA , McKayla Lein Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA Oak Ridge Associated Universities, Oak Ridge, Tennessee, USA , Madeleine Strum Air Quality Assessment Division, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, North Carolina, USA , Mark Morris Health and Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, North Carolina, USA , Ted Palma Health and Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, North Carolina, USA , Darcie Smith Health and Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, North Carolina, USA , Lukas Kerr Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA Oak Ridge Associated Universities, Oak Ridge, Tennessee, USA , and Michael J. Stewart Address correspondence to Michael J. Stewart, 109 T.W. Alexander Dr., Durham, NC 27709 USA. Email: E-mail Address: [email protected] Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA Published:24 March 2021CID: 037008https://doi.org/10.1289/EHP8044AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Hazardous air pollutants, or air toxics, are pollutants known to cause cancer or other serious health effects. Nationwide cancer risk from these pollutants is estimated by the U.S. EPA National Air Toxics Assessment. However, these model estimates are limited to the totality of the emissions inventory used as inputs, and further, they cannot be used to examine spatial and temporal trends in cancer risk from hazardous air pollutants.Objectives:To complement model estimates of nationwide cancer risk, we examined trends in cancer risk using monitoring data from 2013 to 2017 across the 27 U.S. National Air Toxics Trends Stations.Methods:For each monitoring site, we estimated cancer risk by multiplying the annual concentration for each monitored pollutant by its corresponding unit risk estimate. We examined the 5-y average (2013–2017) cancer risk across sites and the population levels and demographics within 1-mi of the monitors, as well as changes in estimated cancer risk over time. Finally, we examined changes in individual pollutant concentrations and their patterns of covariance.Results:We found that the total estimated cancer risk is higher for urban vs. rural sites, with the risk at seven urban sites (of 21) above 75 in 1 million. Furthermore, while most pollutant concentrations have not changed over the time period explored, we found 38 site-pollutant combinations that significantly declined and 12 that significantly increased between 2013 and 2017. We also identified a positive correlation between estimated cancer risk and percent of the population within 1-mi of a monitor that is low income.Discussion:Long-term trends show that annual mean concentrations of most measured air toxics have declined. Our evaluation of a more recent snapshot in time finds that most pollutant concentrations have not changed from 2013 to 2017. This analysis of cancer risk based on monitored values provides an important complement to modeled nationwide cancer risk estimates and can further inform future approaches to mitigate risk from exposure to hazardous air pollutants. https://doi.org/10.1289/EHP8044IntroductionHazardous air pollutants (HAPs), also referred to as air toxics, are air pollutants known or suspected to cause cancer or other serious health effects (U.S. EPA 2020e). There are currently 187 HAPs listed under Section 112 of the Clean Air Act. These pollutants comprise four classes based on the method by which they are measured: carbonyls, volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), and inorganic metals and metalloids [speciated from particulate matter (PM)] (U.S. EPA 2016). Anthropogenic sources of HAPs include mobile sources (e.g., vehicles), relatively large stationary sources (e.g., factories, refineries, power plants), and small area sources (e.g., gas stations, dry cleaners). HAPs also arise from natural sources, such as wildfires or biogenic VOC emissions (U.S. EPA 2018d).Estimates for nationwide cancer risk from HAPs are reported in the U.S. EPA National Air Toxics Assessment (NATA), typically released in a 3–4 y cycle (U.S. EPA 2018e). These estimations start with the compilation of a national emissions inventory of outdoor air toxics sources for a particular year. Air quality models then estimate average ambient concentrations across the United States, which are used in an exposure model based on time–activity patterns to estimate potential cancer and chronic noncancer public health risks at the census tract level (U.S. EPA 2018e). For the calculation of cancer risk, the estimated exposure concentration is multiplied by the inhalation unit risk estimate for that carcinogen, an upper-bound estimate of an individual's probability of contracting cancer over a lifetime of exposure to 1 μg/m3 HAPs in air. Assuming additivity of risk, estimated total cancer risk is equal to the sum of the individual cancer risks from all HAPs to which a person is exposed (U.S. EPA 2018e). Results at the census tract level can be aggregated up to the county, state, or national level. NATA, therefore, provides a snapshot in time of cancer (as well as chronic noncancer) risk from HAPs on a variety of spatial scales.There are important limitations, however, to consider when interpreting NATA results. For example, as with any exposure model, the accuracy of the risk estimates is highly dependent on the quality and totality of the emissions used as model inputs, and this emissions completeness can vary regionally (Stewart et al. 2019). Given that emissions estimates can vary according to where and when they were produced, NATA documentation explicitly states that the assessments should not be used to compare risks between states, nor to examine trends between years (U.S. EPA 2018e).To further inform public health risk, some HAP concentrations are routinely monitored throughout the United States, with several hundred monitors that are managed by states, local agencies, and tribes (Strum and Scheffe 2016; U.S. EPA 2018f). However, given that monitoring is typically used to measure ambient concentrations of HAPs associated with a local source (e.g., emissions associated with a pulp and paper mill), the specific HAPs measured at these sites varies (Strum and Scheffe 2016). Thus, the number of monitored HAPs, the monitoring frequency, and the sampling methodology employed often vary across sites. This limits the ability to directly compare concentrations across monitors. In addition to the locally run monitors, the U.S. EPA monitors ambient levels of HAPs at National Air Toxics Trends Stations (NATTS). These stations use standardized methods and analyses to monitor a core group of 19 HAPs, with a total of 60 HAPs that are suggested to be monitored when possible (U.S. EPA 2016, 2019b). Until 2018, there were 21 urban and 6 rural NATTS (U.S. EPA 2016). Most monitors are located in or near residential areas. A calculation of cancer risk based on monitored HAP concentrations is informative, because in many cases, it allows for a comparison of spatial and temporal trends in cancer risk. Estimating cancer risk based on these ambiently monitored carcinogenic HAP concentrations is not routine; thus this analysis offers a novel complement to the public health information that NATA provides when estimating cancer risk based on modeled concentrations of HAPs for a single year.Using monitored HAPs data, the 2020 U.S. EPA report "Our Nation's Air" shows generally declining trends for HAP ambient concentrations from 2003 to 2017 (U.S. EPA 2020c). However, trends in cancer risk from HAP concentrations have not been recently examined. Analysis based on cancer risk weights the chemicals driving potential cancer risk and can point to priority pollutants for which a reduction in emissions would have the greatest impact. Here, expanding on previous studies of cancer risk trends from HAPs across years (McCarthy et al. 2009; Strum and Scheffe 2016), we apply novel analyses to examine NATTS monitoring data from 2013 to 2017 across the 27 U.S. sites. Using the 5-y annual average, we estimate total cancer risk from monitored HAPs at each site and assess the relative contribution from VOCs, PAHs, and PM-speciated metals and metalloids. Further, we conduct an analysis of population levels and demographics associated with estimated cancer risk. Next, we examine spatial and temporal trends across sites and HAPs, as well as HAP patterns of covariance. Finally, we directly compare NATA census tract modeled cancer risk estimates to those at NATTS monitors located in the same census tract and for the same year.MethodsQuality Filtering and Data PreparationAnalyses and figures were generated using R (version 3.4.3; R Development Core Team). R scripts used to process data and generate figures are available on GitHub ( https://github.com/USEPA/NATTS-HAP).For HAP, annual average ambient monitoring data for 2013 to 2017 were obtained from the most recent version of the U.S. EPA Phase XIII Ambient Monitoring Archive (U.S. EPA 2020b). Updates to the archive occur every 1–2 y. Before release of the archived data, the U.S. EPA conducts quality assurance and reduction to ensure standardization and completeness of the reported data (U.S. EPA 2020a). Annual means within the archive were computed from daily averages using two different approaches: a) averaging the daily averages, treating nondetects as zeroes and b) treating the nondetects as censored values, and using the regression on order statistics (ROS) approach via the nondetects and data analysis (NADA) package in R to compute the means. We compared these means as a criterion for data inclusion and quality assurance. If the ratio of the means (approach 2:approach 1) was greater than 1.3 for a site–pollutant–year concentration, it was assumed that the nondetects (ND) data affect the annual average, and therefore we removed that site–pollutant–year combination (see Table 1 for percent included). This approach is the same one that was used for the 2014 NATA model validation (U.S. EPA 2018e), identified as allowing the greatest number of monitors to be used while avoiding values that may be overly influenced by ND data. Finally, if more than 80% of the values used to compute an annual mean were ND, we used 0 as the annual mean. Following the filtering methodology just described, means used in further analyses were from the ROS approach. For concentrations of specific HAP metals, we used the annual means from speciated particulate matter with aerodynamic diameter less than or equal to 10 μm (PM10) in our analysis, because PM10 measurements include fine particulate matter with aerodynamic diameter less than or equal to 2.5 μm (PM2.5) and had a smaller percent of data reported as below the method detection limit (MDL) or ND compared with PM2.5 alone (data included after filtering are shown in Excel Table S1).Table 1 For all national air toxics trends stations from 2013 to 2017, the total number of hazardous air pollutants (HAPs) monitored with unit risk estimates and the percent of those that met our data inclusion criterion.Table 1 has three main columns, namely, National Air Toxics Trends Station, Total number of hazardous air pollutants monitored with unit risk estimates, and percentage satisfying data inclusion criterion. The columns Total number of hazardous air pollutants monitored with unit risk estimates and percentage satisfying data inclusion criterion are each sub divided in to five columns, namely, 2013, 2014, 2015, 2016, and 2017.NATTSTotal number of HAPs monitored with unit risk estimates% satisfying data inclusion criterion2013201420152016201720132014201520162017Atlanta, GA1935353537100%94%91%86%92%Bountiful, UT404039404098%90%97%90%85%Bronx, NY393939393997%95%92%95%97%Chesterfield, SC363333293394%100%97%90%91%Chicago, IL404039404090%88%97%98%90%Detroit, MI404039404098%98%97%100%100%Grand Junction, CO404039404098%95%97%93%93%Grayson Lake, KY404039404090%90%90%93%98%Horicon, WI353535353594%89%97%91%91%Houston, TX3232323232100%94%100%94%97%Karnack, TX323232323294%84%94%94%94%La Grande, OR373636363697%100%94%83%86%Los Angeles, CA353534323597%100%100%100%97%Phoenix, AZ404039404093%93%97%90%95%Pinellas County, FL383838383897%100%100%100%100%Portland, OR373737NA3597%95%89%NA94%Providence, RI373737393997%92%100%97%92%Richmond, VA414141404098%98%98%100%98%Rochester, NY393939393992%92%90%100%90%Roxbury, MA373737393995%92%97%92%95%Rubidoux, CA3535343235100%97%100%97%100%San Jose, CA3434343435100%100%97%100%97%Seattle, WA4040394040100%100%100%100%100%St. Louis, MO414039404095%90%95%98%95%Tampa, FL3838383838100%97%97%100%97%Underhill, VT403939394095%90%82%87%95%Washington, DC393939393995%100%95%95%92%To promote standardization of monitoring methods and to explore a set of sites across representative areas of the country and a set of well-defined pollutants, we used only monitoring data from NATTS locations and included only chemicals for which there was a unit risk estimate (URE; Table 2). Note that due to an identified discordance in concentrations of ethylene dibromide at co-located monitors, as well as general high rates of erroneous measurements, we excluded this HAP from our analyses. In addition, ethylene oxide monitoring at NATTS sites started in 2019; thus measurements were not available in our data set. Although NATTS monitors aim to measure a common core group of chemicals, the number of chemicals they measure varies across sites and years (see Table 1).Table 2 Carcinogenic hazardous air pollutants monitored at national air toxics trends stations with their associated inhalation unit risk estimate (URE), class, and the top three sources of cancer risk based on the source groups reported for pollutants modeled in the 2014 National Air Toxics Assessment (U.S. EPA 2018a, 2018b).Table 2 has five columns, namely, Hazardous air pollutant, uppercase c a s number, unit risk estimate (1 divided by open parenthesis micrograms per meter cubed close parenthesis), Class, and Top 3 U. S. sources from 2014 national air toxics assessment based on contribution to national risk.Hazardous air pollutantCAS numberURE (1/(μg/m3))ClassTop three U.S. sources from 2014 NATA based on contribution to national risk1,1,2-trichloroethane**79-00-51.6×10−5VOCStationary point; oil and gas operations; waste disposal1,1-dichloroethane75-34-31.6×10−6VOCWaste disposal; stationary point; oil and gas operations1,3-butadiene*106-99-03.0×10−5VOCOn-road light duty nondiesel vehicles (starts); on-road light-duty nondiesel vehicles (running); residential wood combustion2-chloro-1,3-butadiene126-99-84.8×10−4VOCStationary point; nonpoint industrial; nonpoint bulk terminals, petroleum, organic, and inorganic chemical storage and transport9h-fluorene**,#86-73-74.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)acenaphthene**,#83-32-94.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)acenaphthylene**,#208-96-84.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)acetaldehyde*75-07-02.2×10−6CarbonylSecondary; biogenics; on-road light duty nondiesel vehicles (starts)acrylonitrile**107-13-16.8×10−5VOCStationary point; waste disposal; nonpoint industrialalpha-chlorotoluene100-44-74.9×10−5VOCStationary point; nonpoint fuel combustion; waste disposalarsenic*7440-38-20.0043metal/metalloidOn-road light-duty nondiesel vehicles (running); nonpoint fuel combustion; nonpoint industrialbenzene*71-43-27.8×10−6VOCOn-road light-duty nondiesel vehicles (starts); on-road light-duty nondiesel vehicles (running); residential wood combustion;benzo(a)anthracene**,#56-55-39.6×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)benzo(a)pyrene*,#50-32-80.00096PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)benzo(b)fluoranthene**,#205-99-29.6×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light duty nondiesel vehicles (running)benzo(e)pyrene**,#192-97-24.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)benzo[ghi]perylene#191-24-24.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)benzo(k)fluoranthene**,#207-08-99.6×10−6PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)beryllium*7440-41-70.0024metal/metalloidNonpoint fuel combustion; stationary point; locomotivescadmium*7440-43-90.0018metal/metalloidNonpoint fuel combustion; stationary point; locomotivescarbon tetrachloride*56-23-56.0×10−6VOCBackground (global contribution), stationary point; waste disposalchrysene**,#218-01-99.6×10−7PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)cis-1,3,-dichloropropene**10061-01-54.0×10−6VOCSolvents and coatings; stationary point; oil and gas operationscoronene#191-07-14.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)dibenzo[a,h]anthracene**,#53-70-30.00096PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)ethylbenzene**100-41-42.5×10−6VOCOn-road light-duty nondiesel vehicles (starts); on-road light-duty nondiesel vehicles (running); nonroad recreational including pleasure craftethylene dichloride107-06-22.65×10−5VOCCommercial cooking; stationary point; nonpoint industrialfluoranthene**,#206-44-04.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)formaldehyde*50-00-01.3×10−5CarbonylSecondary; biogenics; fires (sum of prescribed, wild and agricultural)hexachloro-1,3-butadiene**87-68-32.2×10−5VOCStationary point; waste disposalhexavalent chromium**18540-29-90.012metal/metalloidStationary point; nonpoint industrial; nonpoint fuel combustionindeno[1,2,3-cd]pyrene**,#193-39-59.6×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)methyl tert-butyl ether**1634-04-42.6×10−7VOCStationary point; waste disposal; nonpoint fuel combustionmethylene chloride**75-09-21.6×10−8VOCSolvents and coatings; stationary point; waste disposalnaphthalene*91-20-33.4×10−5PAHSolvents and coatings; fires (sum of prescribed, wild, and agricultural); on-road light-duty nondiesel vehicles (starts)nickel*7440-02-00.00048metal/metalloidStationary point; on-road light-duty nondiesel vehicles (running); fuel combustionp-dichlorobenzene**106-46-71.1×10−5VOCSolvents and coatings; stationary point; agricultural livestockperylene#198-55-04.8×10−5PAH/POMFires (sum of prescribed, wild and agricultural); residential wood combustion; on-road light-duty nondiesel vehicles (running)tetrachloroethylene*127-18-42.6×10−7VOCSolvents and coatings; stationary point; waste disposaltrans-1,3-dichloropropene**10061-02-64.0×10−6VOCSolvents and coatings; stationary point; oil and gas operationstribromomethane75-25-21.1×10−6VOCStationary point; fuel combustiontrichloroethylene*79-01-64.8×10−6VOCStationary point; solvents and coatings; waste disposalvinyl chloride*75-01-48.8×10−6VOCStationary point; waste disposal; nonpoint industrialNote: Many hazardous air pollutants have several synonyms; the names used here reflect those reported at NATTS. NATTS, National Air Toxics Trends Stations; PAH/POM, polycyclic aromatic hydrocarbon/polycyclic organic matter; VOC, volatile organic compound.*NATTS "core" analyte (Tier I).**NATTS principal analyte (Tier II).#Sources based on risk reported for grouped PAHs/POMs.In each of the following cases, data were combined for analysis. The two sites at Grand Junction, Colorado, measured different HAPs and were co-located (∼200 ft apart). In addition, there were several location changes for NATTS between 2013 and 2017. In 2017, the site at La Grande, Oregon (latitude 45.338972, longitude −118.094497), moved approximately 1.3 mi to La Grande Hall (latitude 45.3235, longitude −118.0778). The site is simply referred to as La Grande in this analysis. In 2016, the site at Portland, Oregon, moved approximately 0.3 mi (latitude 45.56137, longitude −122.6679 to latitude 45.558081, longitude −122.670985).To calculate cancer risk from individual HAPs, we multiplied the unit risk estimate by the annual mean concentration from the monitoring data described above. UREs were obtained from the set of acute and chronic dose–response values compiled by the U.S. EPA Office of Air Quality and Planning Standards and used in the U.S. EPA Human Exposure Model (Table 2) (U.S. EPA 2018a). UREs are derived by the U.S. EPA from the slope of the cancer dose–response curve, which is estimated using a linearized multistage statistical model based on the low-dose region of the curve (U.S. EPA 2018e). These estimates represent a plausible upper limit to the true value and are intended to be health protective. Note, cancer is a collection of diseases that develop through changes in cells and tissues over time (U.S. EPA 2005). Cancer dose–response assessments can be based on tumor incidence data, as well as measures of key precursor events that are part of the carcinogenic process (U.S. EPA 2005). Thus, though cancer is not a single disease, it is treated as such for the purpose of deriving dose–response values.Trends at NATTSWe used the most recent 5-y averages at the time of analysis (2013–2017) of HAP concentrations to estimate cancer risk. For each site, we calculated the 5-y average cancer risk from monitored HAP by summing the 5-y means of cancer risk for individual HAP. Note, there were missing data for some HAP-site-year combinations. Some HAP were either removed at the filtering stage described above or not reported in the ambient monitoring archive (see Excel Table S2 for a breakdown of HAP included by site and year). For this analysis, we used all available data, including if data meeting our inclusion criteria were available for fewer than 5 y [e.g., if annual average data were available for a given HAP for only two of the 5 y with cancer risks of x and y, the 5-y average was taken as (x + y)/2].To visualize spatial trends in cancer risk associated with HAP concentrations, geospatial analysis was done in Python 3.8.1 (Python Software Foundation) with libraries basemap and matplotlib. Pie charts were created by grouping individual HAPs by their classification (carbonyl, VOC, PAH, or PM-speciated metal/metalloid) using the 5-y average data. The size of the pie charts across sites varies in accordance with total cancer risk at each site (5-y average). To further examine spatial trends across sites, we created a stacked bar plot. For each site, the top ten HAPs with the highest contribution to cancer risk at that site are shown, as well as an "other" group that pooled all other HAPs. To test whether total cancer risk differed between urban and rural sites, we used a Welch two sample t-test (α≤0.05). The designation of a site as urban or rural was determined by the local air quality agency that operates that site (U.S. EPA 2019a). Urban sites are intended to allow an assessment of the range of population exposures across urban areas. Rural sites are intended to allow for the characterization of exposure of nonurban populations and background concentrations (U.S. EPA 2016). For example, a rural site may be used to measure HAP concentrations outside of a metropolitan statistical area.To estimate the number of people exposed, we used ArcGIS Pro (10.4, Esri) to examine population levels within a 0.25-, 0.5-, and 1- mi radius from each NATTS monitor. Population values were based on the U.S. Census Bureau 2010 data set (U.S. Census Bureau 2010). Populations for census blocks within the specified distances were joined to the monitor sites if the block centroid was within the given distance of the monitor. The finest resolution of demographic data available was at the census block group level, downloaded from U.S. EPA EJSCREEN. We used the 2019 EJSCREEN data, based on the 5-y American Community Survey from the Census Bureau, which was for years 2013–2017 (U.S. EPA 2019c). For this demographic analysis, we included data for all persons living in block groups where the block group's population-weighted center falls within the 1-mi radius of a NATTS monitor (QGIS 3.16). Three rural sites (Chesterfield, South Carolina; Grayson Lake, Kentucky; and Karnack, Texas) did not have population-weighted census block group centroids within 1-mi of the monitor and therefore were not included in this analysis. Using an average across block groups for each site, we conducted linear regression to examine potential relationships between two demographic variables, percent of the population that is low income (where the household income is less than or equal to twice the federal "poverty level") and percent of the population that is minority status (racial status as a race other than White alone and/or ethnicity as Hispanic or Latino), and the 5-y average estimates of cancer risk in 1 million from monitored HAPs (U.S. EPA 2019c). Because the American Community Survey data represent a 5-y estimate (2013–2017), the federal poverty thresholds correspond to the year of data input (U.S. Census Bureau 2020a, 2020b).To visually examine temporal trends, we created a time series of total cancer risk change over time (relative to 2013 values), which was calculated using only chemicals that were measured and which met the data inclusion criterion outlined above for all 5 y within a given site (see Excel Table S3 for included chemicals). To examine whether these changes over time were statistically significant, we calculated Spearman's rank correlation coefficients (R version 3.4.3). Finally, to determine whether there were statistically significant changes in cancer risk from individual HAPs over the 5-y period, we calculated Spearman's rank correlation coefficients for individual HAPs at all sites. For this analysis, we included only pollutants measured in at least 30% of sites (8 sites), resulting in analysis for 32 HAPs. For Spearman's rank correlations, a significance level of 0.05, uncorrected for Type I error, was used as the criterion of statistical significance.Covariance Patterns between HAP ConcentrationsTo examine whether patterns of covariance differ between rural and urban sites, we created clustered correlation matrices using the 5-y average concentration data for HAPs. Only pollutants that were measured at more than 75% of sites (i.e., fewer than 25% missing values) were included in the analysis. Missing values were imputed using the R package MICE: multivariate imputation by chained equations. Pollutants with 80% or more ND (zeros at all sites) were discarded. To generate heat maps, we used R packages ggplot2 and pvclust. Data were clustered using the complete linkage method and correlation was used as the distance metric.Comparison with Modeled Cancer Risk Estimates at the Census TractTo compare the 2014 NATA census tract modeled cancer risk estimates to those based on measured concentrations at NATTS monitors in 2014, we first obtained the NATA cancer risk at the census tracts containing a NATTS monitor by entering the latitude and longitude of the NATTS monitors into the NATA web map application (U.S. EPA 2018b). (Formaldehyde concentrations at the Atlanta, Georgia, NATTS monitor were not reported in the monitoring archive for 2014.) Because the cancer risk estimates produced by NATA are based on exposure concentrations, rather than ambient concentrations, we sought to assess whether differences between the monitored and modeled estimates may be reflective of adjustments from the exposure model used in NATA. Given that formaldehyde is a primary driver of cancer risk, as a case study we examined differences between 2014 NATA exposure and ambient concentrations at the census tract and 2014 NATTS concentrations measured at the monitor. Exposure and ambient formaldehyde concentrations estimated by NATA were obtained by matching the NATTS census tract code (U.S. EPA 2018c).ResultsSpatial TrendsFigure 1 shows the spatial distribution of estimated cancer risk at t
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