A Bayesian multivariate receptor model for estimating source contributions to particulate matter pollution using national databases

Hackstadt, Amber J.; Peng, Roger D.

doi:10.1002/env.2296

Cited by 21 publications

(12 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using NEI data from 2002 through 2011 and predefined source profiles in a chemical mass balance (CMB) model in the southeast US, point source emissions showed large decreases, while mobile source emissions showed comparable or smaller decreases [50]. The largest sources identified by a Bayesian source apportionment model, which used CSN data in Boston and Phoenix from 2000 onwards, NEI 2002 data, and profiles from the SPECIATE database, were coal and oil combustion, vegetative burning, road dust, and vehicles [51]. A hybrid receptor-chemical transport model (CTM) using projected NEI 2002 data in six major US cities indicated that coal combustion and on-road gasoline emissions were the largest sources of primary and secondary PM 2.5 [52].…”

Section: Resultsmentioning

confidence: 99%

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

Milando

Huang

Batterman

2016

Atmospheric Environment

View full text Add to dashboard Cite

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 to 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 to 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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Milando

Huang

Batterman

2016

Atmospheric Environment

View full text Add to dashboard Cite

show abstract

“…PM 2.5 , constituents of which include metal oxides, sulfate, organic carbon (OC), and elemental carbon (EC) (Bell et al 2007), varies geographically in chemical composition depending on its natural and/or anthropogenic generating sources (Hackstadt and Peng 2014; Hopke et al 2006). Individual PM 2.5 chemical constituents vary in their associations with adverse health outcomes (Krall et al 2013; Ostro et al 2008; Sarnat et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

Krall

Mulholland

Russell

et al. 2017

Environ Health Perspect

131

View full text Add to dashboard Cite

Background:Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities.Objectives:We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations.Methods:We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits.Results:We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5.Conclusions:We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health.Citation:Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97–103; http://dx.doi.org/10.1289/EHP271

show abstract

“…Many sources of traffic pollution, for example brake and tire wear, are spatially and temporally correlated and separating these sources is difficult even when prior information is available. Bayesian source apportionment models provide an alternative approach for incorporating source-specific prior information ( 64 , 65 ), though they can be difficult to fit to available data.…”

Section: Discussionmentioning

confidence: 99%

Source-specific pollution exposure and associations with pulmonary response in the Atlanta Commuters Exposure Studies

Krall

Ladva

Russell

et al. 2018

J Expo Sci Environ Epidemiol

View full text Add to dashboard Cite

Concentrations of traffic-related air pollutants are frequently higher within commuting vehicles than in ambient air. Pollutants found within vehicles may include those generated by tailpipe exhaust, brake wear, and road dust sources, as well as pollutants from in-cabin sources. Source-specific pollution, compared to total pollution, may represent regulation targets that can better protect human health. We estimated source-specific pollution exposures and corresponding pulmonary response in a panel study of commuters. We used constrained positive matrix factorization to estimate source-specific pollution factors and, subsequently, mixed effects models to estimate associations between source-specific pollution and pulmonary response. We identified four pollution factors that we named: crustal, primary tailpipe traffic, non-tailpipe traffic, and secondary. Among asthmatic subjects (N=48), interquartile range increases in crustal and secondary pollution were associated with changes in lung function of −1.33% (95% confidence interval (CI): −2.45, −0.22) and −2.19% (95% CI: −3.46, −0.92) relative to baseline, respectively. Among non-asthmatic subjects (N=51), non-tailpipe pollution was associated with pulmonary response only at 2.5 hours post-commute. We found no significant associations between pulmonary response and primary tailpipe pollution. Health effects associated with traffic-related pollution may vary by source, and therefore some traffic pollution sources may require targeted interventions to protect health.

show abstract

A Bayesian multivariate receptor model for estimating source contributions to particulate matter pollution using national databases

Cited by 21 publications

References 53 publications

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

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

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

Source-specific pollution exposure and associations with pulmonary response in the Atlanta Commuters Exposure Studies

Contact Info

Product

Resources

About