2011 Progress Report: Spatial temporal analysis of health effects associated with sources and speciation of fine PM

EPA Grant Number: R833863
Title: Spatial temporal analysis of health effects associated with sources and speciation of fine PM
Investigators: Fuentes, Montserrat , Frey, H. Christopher , Bell, Michelle L. , Reich, Brian , Dominici, Francesca , Zhang, Yang
Institution: North Carolina State University , The Johns Hopkins University , Yale University
EPA Project Officer: Chung, Serena
Project Period: December 1, 2008 through November 30, 2012 (Extended to November 30, 2013)
Project Period Covered by this Report: December 1, 2010 through November 30,2011
Project Amount: $893,439
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text |  Recipients Lists
Research Category: Health Effects , Particulate Matter , Air

Objective:

The overall objectives of this proposed nationwide spatiotemporal analysis are to investigate the adverse health outcomes associated with population exposure to fine particulate matter (PM2.5) and speciation and to characterize geographic differences, sources, and population heterogeneity in the putatively PM2.5 mediated health effects, combining different sources of data with atmospheric models. We aim to answer the following research questions:  What is the recommended framework to integrate atmospheric models with monitoring data and other sources of information to obtain a better spatial and temporal characterization of fine PM components and sources? Can we improve the PM component-based epidemiologic studies by using atmospheric and exposure models? How to integrate the atmospheric models in this epidemiologic framework, while characterizing uncertainties in the epidemiological and numerical models? How to use source apportionment approaches in national epidemiologic studies, while characterizing different sources of uncertainty in the models and the data?

Progress Summary:

During year 3, we have accomplished most of the objectives that are critical to achieve our final goals. Our major findings and their significances are summarized below.
 
Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) and ozone (O3) for the eastern United States in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM2.5 and O3, respectively. Modeled estimates included 1,861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O3 (annual normalized mean bias = 4.30%), while modeled PM2.5 had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.
 
In the context of source-apportionment using the CMAQ model, during this project year, we focus on two important tasks: (1) the model evaluation of CMAQ and the Comprehensive Air Quality Model with Extensions (CAMx), and (2) the application of CAMx with the particle source apportionment technology (PSAT) for source apportionment and comparison with results from CMAQ with the Brute force method (BFM). Model evaluation shows that CMAQ performs better for max 1- and 8-h O3, and the wet deposition fluxes of NO3-, SO42-, and NH4+ and both models perform similarly for 24-h average PM2.5 and its components. For source apportionment in January 2002, the three most important sources of PM2.5 domainwide are coal combustion, biomass burning, and other mobile sources by CAMx/PSAT and biomass burning, miscellaneous area sources, and coal combustion by CMAQ/BFM. The two methods agree that the top three contributors in July are coal combustion, miscellaneous area sources, and industrial processes, but differing considerably in the magnitude of contributions from coal combustion. For primary PM species, the source contributions from CMAQ/BFM and CAMx/PSAT are very similar, as the concentrations of these species are linearly related to emissions. However, large discrepancies exist in the source apportionment of secondary PM species due to the fact that CMAQ/BFM accounts for the interactions of secondary species through photochemistry and aerosol formation processes, whereas CAMx/PSAT does not account for these interactions.
 
The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of mean and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for assessment of model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution, and improving the ability of these numerical models to characterize high pollution events is of critical interest for air quality management. In this work, we introduced an innovative framework to assess model performance, not only based on the two first moments of models and field data, but also on their entire distribution. Our approach also compares the spatial dependence and variability in both models and data. More specifically, we estimate the spatial quantile functions for both models and data, and we apply a nonlinear monotonic regression approach on the quantile functions taking into account the spatial dependence to compare the density functions of numerical models and field data. We use a Bayesian approach for estimation and fitting to characterize uncertainties in data and statistical models. We assess the performance of the EPA CMAQ model to characterize ozone ambient concentrations. Our approach shows a 75% reduction in the root of mean square error (RMSE) compared to the default approach based on the two moments of models and data.
 
To relate adverse health effects to PM2.5 sources and to develop effective control strategy for PM2.5, source apportionment studies for exposure of PMPM2.5 are needed. Limitations of existing source apportionment studies were identified. The objectives of this work were to: (1) demonstrate and evaluate a methodology for apportioning human exposure to PM2.5 emission sources; (2) quantify the variability of exposure apportionment for different sub-groups of the population; (3) identify seasonal differences in source contributions to total PM2.5 exposure; and (4) analyze effects of infiltration factors on source apportionment. The Stochastic Human Exposure and Dose Simulation Model for PM2.5 (SHEDS-PM) was used to apportion non-ambient PM sources and to determine the contribution of ambient exposure to total exposure. Two receptor modeling approaches were used to apportion ambient concentrations to sources: chemical mass balance (CMB) and Positive Matrix Factorization (PMF). U.S. EPA Speciation Trends Network (STN) speciated PM2.5 fixed site monitoring data for two counties in New York City (Bronx and Queens) were used to characterize the composition of ambient PM2.5 over space and the study periods are April, July, October, and December 2002. Secondary aerosols and motor vehicles contribute most among ambient sources. Smoking contributes most among non-ambient sources to the exposure for smokers and non-smokers living with smokers. Home cooking contributes most among non-ambient for non-smokers that do not live with smokers. Seasonal variability in ambient source apportionment is affected by air exchange rate and secondary PM formation. Sensitivity analysis on infiltration factors indicates that source-specific infiltration factors lead to significant differences in source apportionment. The exposure apportionment method was further demonstrated based on case studies for exposure modeling domains in North Carolina along the I-40 corridor and for Harris County, Texas.
 
We introduced a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model was used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of less than 2.5 µm in aerodynamic diameter (PM2.5) and daily mortality in the New York City metropolitan area during the period 2001 to 2005. Personal PM2.5 exposures were simulated from the Stochastic Human Exposure and Dose Simulation (SHEDS). Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 µg/m3 increase in personal exposure to PM2.5 from outdoor sources on the previous day. The corresponding estimates per a 10 µg/m3 increase in PM2.5 ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM2.5 also were higher during the summer months.
 
We studied the impact of speciated PM2.5 exposure on premature mortality, using daily mortality data at the county level nationwide for years 1999-2005, and monitoring data. Overall, a 10-μg/m3 increase in PM2.5 at lag 1 is associated with 0.25 (95 percent interval -0.08, 0.58) in all-cause mortality. We conducted similar analysis for the PM components; the organic carbon and the silicon were the components that appeared to have a significant impact on mortality. We also evaluated the impact of different metrics to characterize exposure on the estimated risk, using air quality model (CMAQ), monitoring data, and combining CMAQ with monitoring data, by introducing a new statistical framework to fuse data. To characterize the multipollutant effect, we introduced joint models using a basis representation that allowed for nonlinear interactions between pollutants. We found that the metric for PM2.5 can have a substantial impact on the results. For example, using monitoring data in North Carolina we found a 1.5% increase in risk for a 10 μg/m3 increase in PM2.5 (z-score 2.7) and when using the CMA-monitoring combined surfaces the estimated increase is 3.2% (z-score 6.0). Also, the use of the general interaction between PM2.5 and ozone shows dramatic interactions that are not observed in the linear main effects analysis.

Future Activities:

This is the last year of the award. We will continue to disseminate our research results at national/international conferences/workshops and prepare manuscripts for publications in peer-reviewed journals.


Journal Articles on this Report : 18 Displayed | Download in RIS Format

Other project views: All 90 publications 49 publications in selected types All 49 journal articles
Type Citation Project Document Sources
Journal Article Banerjee S, Fuentes M. Bayesian modeling for large spatial datasets. WIREs Computational Statistics 2012;4(1):59-66. R833863 (2011)
R833863 (2012)
R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley - Abstract
    Exit
  • Journal Article Bravo MA, Fuentes M, Zhang Y, Burr MJ, Bell ML. Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation. Environmental Research 2012;116:1-10. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
    R834798 (2013)
    R834798 (2014)
    R834798 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Burr MJ, Zhang Y. Source apportionment of fine particulate matter over the Eastern U.S. Part II: source apportionment simulations using CAMx/PSAT and comparisons with CMAQ source sensitivity simulations. Atmospheric Pollution Research 2011;2(3):318‐336. R833863 (2011)
    R833863 (Final)
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Burr MJ, Zhang Y. Source apportionment of fine particulate matter over the Eastern U.S. Part I: source sensitivity simulations using CMAQ with the brute force method. Atmospheric Pollution Research 2011;2(3):300-317. R833863 (2010)
    R833863 (2011)
    R833863 (Final)
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Cao Y, Frey HC. Assessment of interindividual and geographic variability in human exposure to fine particulate matter in environmental tobacco smoke. Risk Analysis 2011;31(4):578-591. R833863 (2010)
    R833863 (2011)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley - Abstract
    Exit
  • Journal Article Cao Y, Frey HC. Modeling of human exposure to in-vehicle PM2.5 from environmental tobacco smoke. Human and Ecological Risk Assessment 2012;8(3):608-626. R833863 (2011)
    R833863 (Final)
  • Full-text: Research Gate-Abstract & Full Text PDF
    Exit
  • Abstract: Taylor & Francis-Abstract
    Exit
  • Journal Article Chang HH, Reich BJ, Miranda ML. Time-to-event analysis of fine particle air pollution and preterm birth: results from North Carolina, 2001-2005. American Journal of Epidemiology 2012;175(2):91-98. R833863 (2011)
    R833293 (2010)
    R833293 (2011)
    R833293 (Final)
    R833293C001 (2010)
    R833293C001 (2011)
    R833293C001 (Final)
    R833293C002 (2011)
    R833293C002 (Final)
  • Abstract from PubMed
  • Full-text: AJE - Full Text HTML
    Exit
  • Abstract: AJE - Abstract
    Exit
  • Other: AJE - Full Text PDF
    Exit
  • Journal Article Chang HH, Fuentes M, Frey HC. Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator. Journal of Exposure Science and Environmental Epidemiology 2012;22(5):483-488. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: JESEE-Full Text PDF
    Exit
  • Abstract: JESEE-Abstract & Full Text HTML
    Exit
  • Journal Article Fuentes M, Henry J, Reich B. Nonparametric spatial models for extremes: application to extreme temperature data. Extremes 2013;16(1):75-101. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Springer-Abstract
    Exit
  • Journal Article Liu X, Frey HC, Cao Y. Estimating in-vehicle concentration of and exposure to fine particulate matter: near-roadway ambient air quality and variability in vehicle operation. Transportation Research Record 2010;2158:105-112. R833863 (2010)
    R833863 (2011)
    R833863 (Final)
  • Full-text: TRB-Full Text PDF
    Exit
  • Abstract: TRB-Abstract HTML
    Exit
  • Journal Article Liu X, Frey HC. Modeling of in-vehicle human exposure to ambient fine particulate matter. Atmospheric Environment 2011;45(27):4745-4752. R833863 (2011)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Modlin D, Fuentes M, Reich B. Circular conditional autoregressive modeling of vector fields. Environmetrics 2012;23(1):46-53. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley-Abstract
    Exit
  • Journal Article Reich BJ. Spatiotemporal quantile regression for detecting distributional changes in environmental processes. Journal of the Royal Statistical Society Series C–Applied Statistics 2012;61(4):535-553. R833863 (2011)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley-Abstract
    Exit
  • Journal Article Reich BJ, Kalendra E, Storlie CB, Bondell HD, Fuentes M. Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation. Journal of the Royal Statistical Society:Series C–Applied Statistics 2012;61(1):47-66. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
  • Abstract: Wiley-Abstract
    Exit
  • Other: NCSU-Prepublication Paper PDF
    Exit
  • Journal Article Reich BJ, Fuentes M. Nonparametric Bayesian models for a spatial covariance. Statistical Methodology 2012;9(1-2):265-274. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: ScienceDirect-Abstract
    Exit
  • Journal Article Reich BJ, Chang HH, Strickland MJ. Spatial health effects analysis with uncertain residential locations. Statistical Methods in Medical Research 2014;23(2):156-168. R833863 (2011)
    R834799 (2012)
    R834799 (2013)
    R834799 (2014)
    R834799 (2015)
    R834799 (2016)
    R834799 (Final)
    R834799C003 (2012)
    R834799C003 (2013)
    R834799C003 (2014)
    R834799C003 (2015)
    R834799C003 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: SAGE-Full Text HTML
    Exit
  • Abstract: SAGE-Abstract
    Exit
  • Other: SAGE-Full Text PDF
    Exit
  • Journal Article Warren J, Fuentes M, Herring A, Langlois P. Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure. Biometrics 2012;68(4):1157-1167. R833863 (2011)
    R833863 (2012)
    R833863 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley - Abstract
    Exit
  • Journal Article Zhou J, Fuentes M, Davis J. Calibration of numerical model output using nonparametric spatial density functions. Journal of Agricultural, Biological, and Environmental Statistics 2011;16(4):531-553. R833863 (2011)
    R833863 (Final)
  • Full-text: NC State-Full Text Prepublication HTML
    Exit
  • Abstract: Springer-Abstract
    Exit
  • Supplemental Keywords:

    Bayesian inference, epidemiology, public health data, particulate matter, pollution exposure, risk assessment, statistical modeling

    Relevant Websites:

    Progress and Final Reports:

    Original Abstract
  • 2009 Progress Report
  • 2010 Progress Report
  • 2012 Progress Report
  • Final Report