Final Report: Modeling Relationships Between Mobile Source Particle Emissions and Population Exposures

EPA Grant Number: R827353C012
Subproject: this is subproject number 012 , established and managed by the Center Director under grant R827353
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: Mickey Leland National Urban Air Toxics Research Center (NUATRC)
Center Director: Beskid, Craig
Title: Modeling Relationships Between Mobile Source Particle Emissions and Population Exposures
Investigators: Spengler, John D. , Evans, John S. , Greco, Susan L , Levy, Jonathan , Stevens, G. , Wilson, A.
Institution: Harvard T.H. Chan School of Public Health , Harvard University
EPA Project Officer: Chung, Serena
Project Period: June 1, 1999 through May 31, 2005 (Extended to May 31, 2006)
RFA: Airborne Particulate Matter (PM) Centers (1999) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air

Objective:

Theme I: Assessing Particle Exposures for Health Effects Studies: A large data set on personal exposures and indoor and outdoor concentrations was collected for panels of susceptible individuals across the U.S. (Sarnat, et al., 2000; Sarnat, et al., 2001; Sarnat, et al., 2002; Koutrakis, et al., 2005). These investigations suggest that personal exposures to PM2.5 of ambient origin are highly correlated with outdoor concentrations. However, the regression slopes of personal exposures on outdoor concentrations, which are usually less than one, vary substantially depending on house characteristics, season, and city climatic conditions. The strong correlations between personal and ambient concentrations were unique to PM2.5, as personal exposures to O3, SO2 and NO2 were substantially lower than, and weakly correlated with, corresponding outdoor concentrations (Sarnat, et al., 2005).

The primary focus of Theme I was to assess human exposures to particles and gaseous co-pollutants in order to better understand their heath effects. As such, research conducted as part of Theme I contained five main objectives:

  1. to characterize the inter- and intra- variability in personal particulate and gaseous exposures;
  2. to identify factors affecting the relationship between personal exposures and outdoor levels;
  3. to determine the contribution of outdoor and indoor particles to personal particulate exposures;
  4. to quantify the effect of measurement error for fine particles and their co-pollutants (coarse mass and the criteria gases) on risk estimates from epidemiological studies; and
  5. to differentiate the health effects of particles from outdoor and indoor sources.

These objectives were addressed by three inter-related research projects, which made use of our database of personal, indoor, and outdoor particulate and gaseous exposures.

This project entailed extending our intake fraction (iF) methodology (Levy, et al., 2003; Levy, et al., 2002) to address motor vehicle emissions, as a way of informing PM control decisions and future analyses. Our specific objectives were to:

  • Evaluate geographic patterns in primary and secondary particulate matter iFs for mobile sources, using a national-scale source-receptor (S-R) matrix;
  • Determine the relative contributions of near-source and long-range populations to particulate matter iFs for mobile sources in different geographic locations;
  • Develop predictive regression equations for iFs to explain geographic patterns as a function of population density and meteorological covariates

Summary/Accomplishments (Outputs/Outcomes):

Results from this analysis were recently published (Greco, et al., 2007b). For primary fine particulate matter emitted from mobile sources, the intake fractions varied across source counties from 0.14 to 23 per million (median of 1.2 per million). These values were highly correlated with near-source population density; the population in the source county explained 43% of the variability in the above estimates, and a multivariate regression model with population at various radii from the source explained 86% of the variability. Spatial analyses of residuals indicated generally strong model performance, with greater errors along the coasts, where wind fields are more difficult to characterize and downwind populations may be less significant.

For secondary ammonium sulfate formed from SO2 emissions, the median intake fraction (0.43 per million) was somewhat lower than for primary PM. The variability was similar to that for primary PM, but with more regional variability rather than small-scale spatial variability. In spite of the regional influence on atmospheric chemistry, multivariate regressions with only population terms had an R2 of 0.78, indicating the significance of population patterns even in this context. However, there was relatively greater statistical significance for population beyond 200 km from the source, relative to primary PM, and relatively lower statistical significance for population within 200 km, reflecting expected concentration patterns.

Secondary ammonium nitrate formed from NOx emissions had an even lower median intake fraction (0.072 per million), with spatial variability driven somewhat by population patterns (R2 of 0.63 in multivariate regression model) but also by relative ambient concentrations of sulfate, nitrate and ammonium. Higher values tended to be found in the Midwest, where there is adequate ammonia to neutralize nitrate (and lower ambient sulfate), versus higher levels in the Ohio River Valley and Northeast for secondary sulfate and primary PM.

We also quantified the extent to which SO2 controls might free up ammonia to react with nitrate, thereby increasing ammonium nitrate concentrations. We determined that the public health benefits of SO2 emission controls (due to sulfate reductions) would be offset by ammonium nitrate increases by an average of 9%, ranging from 1% to 29% across U.S. counties.

As mentioned above, one of our primary objectives was to determine the relative importance of near-source and long-range populations. The median distances within which half of the total intake fraction was realized was about 150 km for primary PM, 450 km for secondary sulfate, and 390 km for secondary nitrate. However, these values varied substantially by setting (i.e., range for primary PM from 0 km, indicating that more than 50% of the iF was realized in the source county, to 1800 km). In dense urban areas, often a majority of the intake fraction was realized within the source county, indicating that more geographically resolved dispersion modeling may be warranted.

Comparing our results with the published literature, the magnitude of our estimates appear reasonable, and this analysis remains the first attempt to characterize spatial variability in mobile source intake fractions and to derive conclusions about the model scope and resolution needed to accurately estimate public health benefits of pollution control from mobile sources. Specifically, we concluded that a national-scale county-resolution dispersion model is likely sufficient for secondary particulate matter or primary particulate matter in rural areas with substantial downwind populations, but that more resolved models should be explored in dense urban areas or less-populated areas without significant downwind populations.

Based on the findings from Greco, et al. (2007b) we proceeded with follow-up work addressing potential within-county heterogeneity in primary PM mobile source intake fractions, as well as the questions of the spatial extent of the iF for sources within urban areas and the potential biases in estimates based on county-level resolution. We used the CAL3QHCR dispersion model (in the CALINE family of models) to simulate the influence of line-source emissions on concentrations on 23,000 road segments in the Boston area. A year’s worth of hourly intake fractions were determined for each road segment using actual meteorological conditions and residential population patterns. The annual average values for the road segments range from 0.8 to 53 per million, with a mean of 12 per million. On average, 46% of the total exposure is realized within 200 m of the road segment, though this varies from 0–93% across road segments, largely due to variable population patterns. Our findings indicate the likelihood of substantial intra-urban variability in mobile source primary PM2.5 iF, especially as taking into account population dynamics, localized meteorological conditions, and street-canyon configurations might all increase the variability in iF. These results were published as part of a doctoral thesis, and a manuscript has been submitted to Environmental Science & Technology (Greco, et al., 2007a).

Conclusions:

Specifically, we concluded that a national-scale county-resolution dispersion model is likely sufficient for secondary particulate matter or primary particulate matter in rural areas with substantial downwind populations. Our findings also indicate the likelihood of substantial intra-urban variability in mobile source primary PM2.5 iF, especially as taking into account population dynamics, localized meteorological conditions, and street-canyon configurations might all increase the variability in iF. As a result, more resolved models should be explored in dense urban areas or less-populated areas without significant downwind populations.

References:

Greco S, Wilson A, Hanna S, Levy J. Factors influencing mobile source particulate matter emissions-to-exposure relationships in the Boston urban area. Environmental Science & Technology (submitted, 2007a).

Greco SL, Wilson AM, Spengler JD, Levy JI. Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States. Atmospheric Environment 2007b;41:1011-1025.

Koutrakis P, Suh H, Sarnat J, Brown K, Coull B, Schwartz J. Characterization of particulate and gas exposures of sensitive subpopulations living in Baltimore and Boston. Health Effects Institute, Research Report. Health Effects Institute, Boston, MA, 2005.

Levy JI, Wolff SK, Evans JS. A regression-based approach for estimating primary and secondary particulate matter intake fractions. Risk Analysis 2002;22(5):895-904.

Levy JI, Wilson A, Evans JS, Spengler JD. Estimation of primary and secondary particulate matter intake fractions for power plants in Georgia. Environmental Science & Technology 2003;37(24):5528-5536.

Sarnat JA, Brown KW, Schwartz J, Coull BA, Koutrakis P. Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles. Epidemiology 2005;16(3):385-395.

Sarnat JA, Koutrakis P, Suh H. Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore. Journal of the Air & Waste Management Association 2000;50(7):1184-1198.

Sarnat JA, Long CM, Koutrakis P, Coull BA, Schwartz J, Suh HH. Using sulfur as a tracer of outdoor fine particulate matter. Environmental Science & Technology 2002;36(24):5305-5314.

Sarnat JA, Schwartz J, Catalano P, Suh H. Gaseous pollutants in particulate matter epidemiology: confounders or surrogates? Environmental Health Perspectives 2001;109(10):1053-1061.


Journal Articles on this Report : 3 Displayed | Download in RIS Format

Other subproject views: All 3 publications 3 publications in selected types All 3 journal articles
Other center views: All 200 publications 198 publications in selected types All 197 journal articles
Type Citation Sub Project Document Sources
Journal Article Greco SL, Wilson AM, Spengler JD, Levy JI. Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States. Atmospheric Environment 2007;41(5):1011-1025. R827353 (Final)
R827353C012 (Final)
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  • Journal Article Levy JI, Wolff SK, Evans JS. A regression-based approach for estimating primary and secondary particulate matter intake fractions. Risk Analysis 2002;22(5):895-904. R827353 (Final)
    R827353C012 (Final)
    R827353C015 (Final)
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  • Journal Article Levy JI, Wilson AM, Evans JS, Spengler JD. Estimation of primary and secondary particulate matter intake fractions for power plants in Georgia. Environmental Science & Technology 2003;37(24):5528-5536. R827353 (Final)
    R827353C012 (Final)
    R827353C015 (Final)
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  • Supplemental Keywords:

    RFA, Health, Scientific Discipline, PHYSICAL ASPECTS, Air, ENVIRONMENTAL MANAGEMENT, HUMAN HEALTH, Air Pollution Monitoring, particulate matter, Toxicology, air toxics, Environmental Chemistry, Epidemiology, Air Pollution Effects, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, Environmental Monitoring, Health Effects, Physical Processes, Children's Health, genetic susceptability, indoor air, tropospheric ozone, Molecular Biology/Genetics, Biology, Environmental Engineering, Risk Assessment, particulates, microbiology, ambient air quality, chemical exposure, interindividual variability, molecular epidemiology, sensitive populations, monitoring, cardiopulmonary responses, human health effects, ambient air monitoring, indoor exposure, health risks, air pollutants, exposure and effects, ambient air, biological response, ambient measurement methods, pulmonary disease, developmental effects, epidemelogy, respiratory disease, exposure, lead, air pollution, children, Human Health Risk Assessment, particle exposure, biological mechanism , ambient monitoring, mobile sources, inhalation, pulmonary, assessment of exposure, susceptibility, particulate exposure, human exposure, cardiopulmonary response, ambient particle health effects, environmental health hazard, epidemeology, inhalation toxicology, human susceptibility, PM, indoor air quality, measurement methods , inhaled particles, cardiopulmonary, modeling studies, air quality, respiratory, dosimetry, animal inhalation study, cardiovascular disease, genetic susceptibility, human health risk

    Relevant Websites:

    http://www.hsph.harvard.edu/epacenter/epa_center_99-05/index.html Exit

    Progress and Final Reports:

    Original Abstract
  • 1999
  • 2000
  • 2001
  • 2002
  • 2003
  • 2004 Progress Report

  • Main Center Abstract and Reports:

    R827353    Mickey Leland National Urban Air Toxics Research Center (NUATRC)

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R827353C001 Assessing Human Exposures to Particulate and Gaseous Air Pollutants
    R827353C002 Quantifying Exposure Error and its Effect on Epidemiological Studies
    R827353C003 St. Louis Bus, Steubenville and Atlanta Studies
    R827353C004 Examining Conditions That Predispose Towards Acute Adverse Effects of Particulate Exposures
    R827353C005 Assessing Life-Shortening Associated with Exposure to Particulate Matter
    R827353C006 Investigating Chronic Effects of Exposure to Particulate Matter
    R827353C007 Determining the Effects of Particle Characteristics on Respiratory Health of Children
    R827353C008 Differentiating the Roles of Particle Size, Particle Composition, and Gaseous Co-Pollutants on Cardiac Ischemia
    R827353C009 Assessing Deposition of Ambient Particles in the Lung
    R827353C010 Relating Changes in Blood Viscosity, Other Clotting Parameters, Heart Rate, and Heart Rate Variability to Particulate and Criteria Gas Exposures
    R827353C011 Studies of Oxidant Mechanisms
    R827353C012 Modeling Relationships Between Mobile Source Particle Emissions and Population Exposures
    R827353C013 Toxicological Evaluation of Realistic Emissions of Source Aerosols (TERESA) Study
    R827353C014 Identifying the Physical and Chemical Properties of Particulate Matter Responsible for the Observed Adverse Health Effects
    R827353C015 Research Coordination Core
    R827353C016 Analytical and Facilities Core
    R827353C017 Technology Development and Transfer Core