Abstract
Numerous studies document increased health risks from exposure to traffic and traffic-related particulate matter (PM). However, many studies use simple exposure metrics to represent traffic-related PM, and/or are limited to small geographic areas over relatively short (e.g., 1 year) time periods. We developed a modeling approach for the conterminous US from 1999 to 2011 that applies a line-source Gaussian plume dispersion model using several spatially and/or temporally varying inputs (including daily meteorology) to produce high spatial resolution estimates of primary near-road traffic-related PM levels. We compared two methods of spatially averaging traffic counts: spatial smoothing generalized additive models and kernel density. Also, we evaluated and validated the output from the line-source dispersion modeling approach in a spatio-temporal model of 24-h average PM < 2.5 μm (PM2.5) elemental carbon (EC) levels. We found that spatial smoothing of traffic count point data performed better than a kernel density approach. Predictive accuracy of the spatio-temporal model of PM2.5 EC levels was moderate for 24-h averages (cross-validation (CV) R2 = 0.532) and higher for longer averaging times (CV R2 = 0.707 and 0.795 for monthly and annual averages, respectively). PM2.5 EC levels increased monotonically with line-source dispersion model output. Predictive accuracy was higher when the spatio-temporal model of PM2.5 EC included line-source dispersion model output compared to distance to road terms. Our approach provides estimates of primary traffic-related PM levels with high spatial resolution across the conterminous US from 1999 to 2011. Spatio-temporal model predictions describe 24-h average PM2.5 EC levels at unmeasured locations well, especially over longer averaging times.
Highlights
Recent epidemiologic studies have documented increased health risks from exposure to atmospheric particulate matter (PM) (Anderson et al 2012; Beelen et al 2014; Pelucchi et al 2009; Shah et al 2013; Hart et al 2015; Heinrich et al 2013; Weuve et al 2012; Stieb et al 2012; Rich et al 2018; Golan et al 2018) for a variety of health outcomes, including nonaccidental mortality, cardiovascular disease mortality, lung cancer, neurocognitive functioning, and effects on reproduction
0.316 0.313 0.189 a automated traffic recorders (ATR) refers to the BAutomatic Traffic Recorders^ data; Weigh In Motion (WIM) refers to the BWeigh-in-Motion^ data; DOT is Department of Transportation b Data for A4 class roads was not available due to the limited number (9) and spatial coverage of stations on these roads c Obtained from linear regression of predicted traffic counts using Dynamap data and either the spatial smoothing generalized additive model (GAM) approach or the 100-km kernel density approach versus combined ATR and WIM traffic counts in the external validation data set
Comparing the two methods of spatial averaging traffic counts, we found that spatial smoothing performed better than the kernel density approach in an external validation using automated traffic recorder data
Summary
Recent epidemiologic studies have documented increased health risks from exposure to atmospheric particulate matter (PM) (Anderson et al 2012; Beelen et al 2014; Pelucchi et al 2009; Shah et al 2013; Hart et al 2015; Heinrich et al 2013; Weuve et al 2012; Stieb et al 2012; Rich et al 2018; Golan et al 2018) for a variety of health outcomes, including nonaccidental mortality, cardiovascular disease mortality, lung cancer, neurocognitive functioning, and effects on reproduction. Studies using proxy measures for exposure to trafficrelated air pollution have generally reported health effects larger than those for the mass concentration of PM < 2.5 μm (PM2.5) or PM < 10 μm (PM10) in aerodynamic diameter (Brugge et al 2013; Gehring et al 2006; Hoffmann et al 2007; Puett et al 2011; Puett et al 2014; Schikowski et al 2005). Several of these studies used simple exposure metrics for traffic-related air pollution based on distance to nearest road, or summaries of road geography within buffers of fixed radii (Eckel et al 2011; Medina-Ramon et al 2008). Uncertainty remains about whether and to what extent noise may be a confounder in analyses of health effects of traffic-related PM exposure, which underscores the need for accurate and highly spatially resolved estimation of traffic-related PM levels so these effects can be better disentangled in future epidemiologic analyses
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