Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago
Introduction
Historical ambient air quality monitoring data permit a wide range of trend, apportionment, health risk and other analyses. In the U.S., the Interagency Monitoring of Protected Visual Environments (IMPROVE) network (Hand, 2011) and the Chemical Speciation Network (CSN) (US EPA, 2014b) have collected ambient data since the mid-1980s that can facilitate these analyses. As examples, trend analyses can help evaluate the effectiveness of mitigation and control measures, e.g., low emission zones (Jones et al., 2012), and receptor models can identify and apportion contributions of pollutant sources. Both trend and apportionment studies can help to evaluate dispersion and exposure models (Haupt, 2005). Monitoring data also have been widely used to estimate exposures for epidemiology and risk studies investigating and predicting the health consequences of pollutant exposure (Park et al., 2010). Such applications are especially important in areas with susceptible populations and where concentrations exceed ambient standards, and for those emission sources that are difficult to characterize or that have changed rapidly, e.g., on-road emissions, due to recent shifts in fuels, emission controls, and fleet mix.
This study examines focuses on Detroit, MI and Chicago, IL, two U.S. Midwestern cities that have high concentrations of industry, extensive vehicle traffic, historical exceedances of air quality standards, and large low income and minority populations that are susceptible to pollutants. These cities were selected due to the length of the data record available, and to contrast trends in the two cities (in adjacent states) potentially differentially affected by the 2008 recession. In Detroit, receptor model apportionments starting in 1985 have identified key PM2.5 sources, which include secondary sulfate aerosol (, especially in the summer), secondary nitrate (), metal processing, biomass burning, other manufacturing and industrial operations, vehicle-related emissions (including primary and secondary aerosols from tire and brake wear, and entrained dust), and crustal-derived emissions (Wolff et al., 1985, Morishita et al., 2006, Morishita et al., 2011, Buzcu-Guven et al., 2007, Williams et al., 2009, Duvall et al., 2012, Gildemeister et al., 2007, Kundu and Stone, 2014, Hammond et al., 2008). In Chicago, identified PM2.5 sources include secondary , secondary , steel operations, (seasonal) road salt, and vehicles (Rizzo and Scheff, 2007, Kim and Hopke, 2007). These apportionments, like most elsewhere, are based on relatively short periods and have not examined trends. (Recent studies in the western U.S. have investigated long term PM2.5 apportionment trends (Wang and Hopke, 2013, Hasheminassab et al., 2014)) Updated analyses are needed to account for the many changes in emissions and industrial activity that have occurred over recent decades.
This study's goal is to understand the trends in the sources contributing to PM2.5 concentrations in Detroit and Chicago. In each city, we examine emission inventories, ambient pollutant concentrations, and derive source apportionments using receptor models. Quantile regression is used to analyze trends in concentrations and receptor model apportionments, a novel application of this work. Results are compared to earlier studies, and methodological issues are discussed. The study concludes with a discussion of the changing apportionments of PM2.5 levels in the two cities and several recommendations.
Section snippets
Monitoring site description
Monitoring sites in the two cities were chosen based on the PM2.5 components measured, the duration and completeness of the monitoring record, and the diversity of nearby sources. The selected sites have speciation records that extend to the early to mid-2000s, and both are part of the Speciation Trends Network (STN), a subset of CSN monitoring sites at which measurements are taken every 3 days (US Environmental Protection Agency, 2011). Fig. 1 shows the location of these sites and nearby major
Emission inventory trends
Table 1 summarizes PM2.5 emissions reported in the 2002 through 2011 NEI data. The NEI source categories, data and emission factors have shifted over the years, resulting in large changes and some difficulty in evaluating trends. The methodological changes can greatly affect results and limit its usefulness for trend analyses, at least for certain source types. For example, fugitive emissions of PM2.5 from paved roads, unpaved roads, and construction sources are calculated by applying a factor
Conclusions
The changing contribution of PM2.5 sources is shown by long term trends in PM2.5 emissions, concentrations and PMF source apportionments in Detroit and Chicago. In both cities, PM2.5 levels have been declining, primarily due to reductions in secondary sulfate and, to a more limited extent, in nitrate sources, while the importance of emissions due to vehicles, biomass, and metals sources is increasing. This is supported by examining three data sources: county emission data, which show constant
Acknowledgements
The authors thank Dr. Steven Brown at Sonoma Technology Inc. (STI) for guidance on EPA PMF 5.0 and handling of measurement uncertainty, and staff at the Michigan Department of Environmental Quality for assistance with the air quality data. We also acknowledge the helpful comments from the reviewers. Support for this research was provided by grants from the Health Effects Institute, grant P30ES017885 from the National Institute of Environmental Health Sciences, National Institutes of Health, and
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