Elsevier

Atmospheric Environment

Volume 129, March 2016, Pages 197-209
Atmospheric Environment

Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago

https://doi.org/10.1016/j.atmosenv.2016.01.012Get rights and content

Highlights

  • Since 2001, PM2.5 concentrations have declined and compositions have changed.

  • Changes in methodology limit interpretation of emission inventory trends.

  • Quantile regression allows for trend assessment at various relevant percentiles.

  • Emissions from vehicles, biomass, and metals sources are of growing importance.

Abstract

PM2.5 concentrations throughout much of the U.S. have decreased over the last 15 years, but emissions and concentration trends can vary by location and source type. Such trends should be understood to inform air quality management and policies. This work examines trends in emissions, concentrations and source apportionments in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. Annual and seasonal trends were investigated using National Emission Inventory (NEI) data for 2002 to 2011, speciated ambient PM2.5 data from 2001 to 2014, apportionments from positive matrix factorization (PMF) receptor modeling, and quantile regression. Over the study period, county-wide data suggest emissions from point sources decreased (Detroit) or held constant (Chicago), while emissions from on-road mobile sources were constant (Detroit) or increased (Chicago), however changes in methodology limit the interpretation of inventory trends. Ambient concentration data also suggest source and apportionment trends, e.g., annual median concentrations of PM2.5 in the two cities declined by 3.2–3.6%/yr (faster than national trends), and sulfate concentrations (due to coal-fired facilities and other point source emissions) declined even faster; in contrast, organic and elemental carbon (tracers of gasoline and diesel vehicle exhaust) declined more slowly or held constant. The PMF models identified nine sources in Detroit and eight in Chicago, the most important being secondary sulfate, secondary nitrate and vehicle emissions. A minor crustal dust source, metals sources, and a biomass source also were present in both cities. These apportionments showed that the median relative contributions from secondary sulfate sources decreased by 4.2–5.5% per year in Detroit and Chicago, while contributions from metals sources, biomass sources, and vehicles increased from 1.3 to 9.2% per year. This first application of quantile regression to trend analyses of speciated PM2.5 data reveals that source contributions to PM2.5 varied as PM2.5 concentrations decreased, and that the fraction of PM2.5 due to emissions from vehicles and other local emissions has increased. Each data source has uncertainties, but emissions, monitoring and PMF data provide complementary information that can help to discern trends and identify contributing sources. Study results emphasize the need to target specific sources in policies and regulations aimed at decreasing PM2.5 concentrations in urban areas.

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 (SO4=, especially in the summer), secondary nitrate (NO3), 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 NO3, secondary SO4=, 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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