Final 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 , Bell, Michelle L. , Dominici, Francesca , Frey, H. Christopher , Reich, Brian , Zhang, Yang
Institution: North Carolina State University , The Johns Hopkins University , Yale University
EPA Project Officer: Ilacqua, Vito
Project Period: December 1, 2008 through November 30, 2012 (Extended to November 30, 2013)
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 spatio-temporal 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 can we integrate the atmospheric models in this epidemiologic framework, while characterizing uncertainties in the epidemiological and numerical models? How can we use source apportionment approaches in national epidemiologic studies, while characterizing different sources of uncertainty in the models and the data?

 

Conclusions:

Our major findings and their significances are summarized below.

  • We have conducted source apportionment of PM2.5 for major source categories using CMAQ with the brute force direct sensitivity analysis method. The elimination of certain source categories (e.g., coal combustion in January and biogenic in July) led to a small increase in some secondary PM2.5 components, due to a greater oxidation of their gaseous precursors by radicals and oxidants or the invalidity of the additive assumption for PM2.5 over areas with nonlinear chemistry. Sensitivity simulations for 10 source categories capture a monthly mean average of 65.3% of domain-wide PM2.5 in January and 64.9% in July, and 83.4 - 100% of PM2.5 in January and 63.9 - 87.1% in July at specific sites.
  • We have analyzed predicted contributions from each source category and studied the relative importance of each source along with potential implications for emission reduction strategies and component-based epidemiological PM2.5 studies. Biomass burning emissions are the largest contributor to PM2.5 in January, contributing a monthly mean average of 13.7% to domain-wide PM2.5; Coal combustion emissions are the largest contributor to PM2.5 in July, contributing a monthly mean average of 30.8% to domain-wide PM2.5.
  • Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures of PM2.5. The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS-PM for estimation of indoor exposure to ETS. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS-PM model.
  • We have developed methods for exposure apportionment and demonstrated these for a case study of Bronx and Queens Counties in New York City in 2002. For all three sites, the mean contribution from secondary sulfate contribution is significantly greater in the summer than in winter. Conversely, the mean secondary nitrate contribution is significantly smaller in summer than in winter. Secondary sulfate and nitrate are influenced by primary SO2 emissions from coal combustion and primary NOx emissions from fuel combustion. Thus, contributions to ambient PM2.5 for secondary sulfate are likely to be representative of high sulfur emission sources such as coal power plants and furnaces. Source apportionment to nitrate is likely to represent NOx emission sources such as vehicles, power plants, and other combustion sources.
  • Overall source apportionment of total PM2.5 exposure was conducted for New York city in 2002, integrating the results of ambient source apportionment and total source exposure source apportionment by SHEDS-PM. For both smokers and nonsmokers exposed to ETS, home smoking contributes at least 69.5% of total exposure for all seasons. Home cooking contributes about 5% for smokers and 4% for nonsmokers exposed to ETS. For nonsmokers not exposed to ETS, home cooking contributes 16% to 38%. For all population groups, motor vehicle contributes the largest share of total exposure after home cooking and/or home smoking. For nonsmokers not exposed to ETS, motor vehicle contributes as high as 33% of the total exposure. Secondary sulfate and secondary nitrate contribute a notable amount. All other sources contribute less than 10% of the total exposure. The seasonal variability of non-ambient exposure source apportionment results from air exchange rates variability. High air exchange rates cause lower non-ambient exposure.
  • To better understand the certainty of CMAQ estimates, basic model evaluation was performed comparing model results to monitored (observed) concentrations using several metrics. Both individual and spatially aggregated (county-level) exposure estimates for PM2.5 and O3 were calculated for all counties for year 2002. The advantages and limitations of the various modeling approaches were assessed. Results indicate that exposure estimates generated from CMAQ provided greater spatial coverage as well as improved spatial and temporal resolution, compared to exposures from ambient monitors. For county-level exposure estimates, the modeling approach covers 150% more population than the monitoring approach for both PM2.5 and O3. Generally, CMAQ provided reasonable estimates of PM2.5 concentrations, with a tendency to underestimate PM2.5 at low observed concentrations (e.g., <10 μg/m3) (mean fraction bias (MFB) = -1.47% and mean fractional error (MFE) = 25.1%). The model was less successful at modeling observed O3 concentrations, with the model consistently overestimating O3 levels compared to monitored concentrations (MFB = 36.0% and MFE= 37.4%). Correlation (and R2) values between modeled and observed pollutant concentrations, matched by day and location, were 0.62 (0.38) and 0.76 (0.52) for PM2.5 and O3, respectively. Counties with monitors tend to be more urban than counties without monitors, and there were also significant differences in the percentage of black residents, college graduates, population below the poverty line, as well as differences in median income and modeled pollutant concentrations. These findings are relevant to epidemiological studies of the human health consequences of air pollution because they provide insight into how air quality modeling can be used to address limitations in the traditional method of exposure assessment (i.e., use of ambient air quality monitors).
  • The mass balance approach in SHEDS-PM for estimating indoor residential PM2.5 concentration is based on the assumption that an entire residence is a single, well-mixed compartment. This assumption is evaluated by applying an indoor air quality model, RISK to compare ambient PM2.5 penetration for single-compartment and multi-compartment scenarios, and to consider sensitivity of results for indoor emission sources such as cooking and smoking. Emissions from smoking increased the indoor PM2.5 significantly, compared to cooking. In case of no indoor sources, the difference between single and multi-compartment PM2.5 concentration was less significant. Recommendations are made regarding the methodology and data that should be used for planned SHEDS-PM case studies for a variety of geographic areas and seasons.
  • We have developed an overall framework for estimating the apportionment of estimated human exposure to PM2.5 to ambient and indoor sources of PM2.5. The SHEDS model apportions exposure into categories of ambient and non-ambient. Ambient exposure is associated with outdoor exposure and with penetration of ambient air to indoor environments. Non-ambient exposure is based on indoor sources, such as ETS, cooking, clean, and “other.” We use sensitivity analysis of the SHEDS model to apportion estimated exposure to the non-ambient sources. We use Chemical Mass Balance (CMB) or Positive Matrix Factorization (PMF), applied to speciated air quality monitoring data for monitors within the geographic domain for an assessment, to apportion ambient PM2.5 to primary emission sources and secondary components (i.e., sulfate, nitrate). 
  • We have continued development of the study design to combine exposure data from various structures with health data. In particular, we have developed two types of analyses focusing on short-term exposure and long-term exposure. The long-term exposure analysis will compare long-term exposure to PM2.5 to age-adjusted mortality rate ratios at the zip code level for three exposure frameworks: (1) exposure from monitors for areas with PM2.5 monitoring available; (2) exposure from modeling/fused data for areas with PM2.5 monitoring available; and (3) exposure from modeling/fused data for all available areas. Results from the first two exposure frameworks will allow a comparison for datasets based on the same population size in order to disentangle differences in effect estimates from sample size from differences in effect estimates from variation in exposure. The third framework uses all available exposure data.
  • We have developed the statistical framework to combine CMAQ data and monitoring data in epidemiological studies.

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 % 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), and monitoring data. To characterize how the effect of PM2.5 and ozone affect the risk of mortality jointly, we allowed for nonlinear interaction. 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/m3increase in PM2:5 (z-score 2.7) and when using the CMAQ 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. 


Journal Articles on this Report : 45 Displayed | Download in RIS Format

Other project views: All 90 publications 49 publications in selected types All 49 journal articles
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Journal Article Anderson GB, Bell ML. Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology. Journal of Exposure Science and Environmental Epidemiology 2010;20(1):2-11. R833863 (2009)
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  • Journal Article Banerjee S, Fuentes M. Bayesian modeling for large spatial datasets. WIREs Computational Statistics 2012;4(1):59-66. R833863 (2011)
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  • Journal Article Bell ML, Ebisu K, Peng RD, Samet JM, Dominici F. Hospital admissions and chemical composition of fine particle air pollution. American Journal of Respiratory and Critical Care Medicine 2009;179(12):1115-1120. R833863 (2009)
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  • Journal Article Bell ML, Peng RD, Dominici F, Samet JM. Emergency hospital admissions for cardiovascular diseases and ambient levels of carbon monoxide: results for 126 United States urban counties, 1999-2005. Circulation 2009;120(11):949-955. R833863 (2009)
    R833863 (Final)
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  • Journal Article Bell ML, Ebisu K, Peng RD, Dominici F. Adverse health effects of particulate air pollution: modification by air conditioning. Epidemiology 2009;20(5):682-686. R833863 (2009)
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  • Journal Article Bogen KT, Cullen AC, Frey HC, Price PS. Probabilistic exposure analysis for chemical risk characterization. Toxicological Sciences 2009;109(1):4-17. R833863 (2009)
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  • 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)
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    R834798 (2013)
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  • Journal Article Bravo MA, Ebisu K, Dominici F, Wang Y, Peng RD, Bell ML. Airborne fine particles and risk of hospital admissions for understudied populations: effects by urbanicity and short-term cumulative exposures in 708 U.S. counties. Environmental Health Perspectives 2017;125(4):594-601. R833863 (Final)
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    R835871 (2016)
    R835871 (2017)
    R835871C004 (2016)
    R835871C004 (2017)
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  • 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)
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  • 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)
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  • 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)
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  • Journal Article Cao Y, Frey HC. Geographic differences in inter-individual variability of human exposure to fine particulate matter. Atmospheric Environment. 2011;45(32):5684-5691. R833863 (Final)
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  • 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)
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  • 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)
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  • Journal Article Chang HH, Zhou J, Fuentes M. Impact of climate change on ambient ozone level and mortality in Southeastern United States. International Journal of Environmental Research and Public Health 2010;7(7):2866-2880. R833863 (2010)
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  • Journal Article Che WW, Frey HC, Lau AK. 2014. Assessment of the effect of population and diary sampling methods on estimation of school-age children exposure to fine particles. Risk Analysis 2014;34(12):2066-2079. R833863 (Final)
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  • Journal Article Choi J, Fuentes M, Reich BJ, Davis JM. Multivariate spatial-temporal modeling and prediction of speciated fine particles. Journal of Statistical Theory and Practice 2009;3(2):407-418. R833863 (2009)
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  • Journal Article Choi J, Fuentes M, Reich BJ. Spatial-temporal association between fine particulate matter and daily mortality. Computational Statistics & Data Analysis 2009;53(8):2989-3000. R833863 (2009)
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  • Journal Article Dennis R, Fox T, Fuentes M, Gilliland A, Hanna S, Hogrefe C, Irwin J, Rao ST, Scheffe R, Schere K, Steyn D, Venkatram A. A framework for evaluating regional-scale numerical photochemical modeling systems. Environmental Fluid Mechanics 2010;10(4):471-489. R833863 (2009)
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  • Journal Article Fuentes M. Statistical issues in health impact assessment at the state and local levels. Air Quality, Atmosphere & Health 2009;2(1):47-55. R833863 (2009)
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  • Journal Article Fuentes M, Xi B, Cleveland WS. Trellis display for modeling data from designed experiments. Statistical Analysis and Data Mining 2011;4(1):133-145. R833863 (Final)
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  • Journal Article Fuentes M, Reich B. Multivariate spatial nonparametric modelling via kernel processes mixing. Statistica Sinica 2013;23(1):75-97. R833863 (Final)
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  • 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)
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  • Journal Article Jiao W, Frey HC, Cao Y. Assessment of inter-individual, geographic, and seasonal variability in estimated human exposure to fine particles. Environmental Science & Technology 2012;46(22):12519-12646. R833863 (Final)
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  • 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)
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  • 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)
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  • Journal Article Mannshardt E, Sucic K, Jiao W, Dominici F, Frey HC, Reich B, Fuentes M. Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions. Journal of Exposure Science and Environmental Epidemiology 2013;23(6):627-636. R833863 (2012)
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  • Journal Article Modlin D, Fuentes M, Reich B. Circular conditional autoregressive modeling of vector fields. Environmetrics 2012;23(1):46-53. R833863 (2011)
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  • Journal Article Peng RD, Bell ML, Geyh AS, McDermott A, Zeger SL, Samet JM, Dominici F. Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environmental Health Perspectives 2009;117(6):957-963. R833863 (2009)
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  • Journal Article Reich BJ, Fuentes M, Burke J. Analysis of the effects of ultrafine particulate matter while accounting for human exposure. Environmetrics 2009;20(2):131-146. R833863 (2009)
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  • Journal Article Reich BJ, Fuentes M, Herring AH, Evenson KR. Bayesian variable selection for multivariate spatially varying coefficient regression. Biometrics 2010;66(3):772-782. R833863 (2009)
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  • Journal Article Reich BJ, Fuentes M, Dunson DB. Bayesian spatial quantile regression. Journal of the American Statistical Association 2011;106(493):6-20. R833863 (2010)
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  • 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)
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  • 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)
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  • Journal Article Reich BJ, Fuentes M. Nonparametric Bayesian models for a spatial covariance. Statistical Methodology 2012;9(1-2):265-274. R833863 (2011)
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  • Journal Article Smith LB, Reich BJ, Herring AH, Langlois PH, Fuentes M. Multilevel quantile function modeling with application to birth outcomes. Biometrics 2015;71(2):508-519. R833863 (Final)
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  • Journal Article Stingone JA, Luben TJ, Daniels JL, Fuentes M, Richardson DB, Aylsworth AS, Herring AH, Anderka M, Botto L, Correa A, Gilboa SM, Langlois PH, Mosley B, Shaw GM, Siffel C, Olshan AF. Maternal exposure to criteria air pollutants and congenital heart defects in offspring: Results from the National Birth Defects Prevention Study. Environmental Health Perspectives 2014;122(8):863-872. R833863 (Final)
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  • Journal Article Sun Y, Wang HJ, Fuentes M. Fused adaptive lasso for spatial and temporal quantile function estimation. Technometrics 2016;58(1):127-137. R833863 (Final)
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  • Journal Article Wang K, Zhang Y. Application, evaluation, and process analysis of the US EPA's 2002 Multiple-Pollutant Air Quality Modeling platform. Atmospheric and Climate Sciences 2012;2(3):254-289. R833863 (Final)
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  • Journal Article Warren JL, Fuentes M, Herring AH, Langlois PH. Air pollution metric analysis while determining susceptible periods of pregnancy for low birth weight. ISRN Obstetrics and Gynecology 2013;2013:387452. R833863 (2012)
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  • Journal Article Warren J, Fuentes M, Herring A, Langlois P. Bayesian spatial-temporal model for cardiac congenital anomalies and ambient air pollution risk assessment. Environmetrics 2012;23(8):673-684. R833863 (2012)
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  • 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)
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  • Journal Article Zhang Y, Wang W, Wu SY, Wang K, Minoura H, Wang Z. Impacts of updated emission inventories on source apportionment of fine particle and ozone over the southeastern U.S. Atmospheric Environment 2014;88:133-154. R833863 (Final)
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  • 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)
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  • Journal Article Zhou J, Chang HH, Fuentes M. Estimating the health impact of climate change with calibrated climate model output. Journal of Agricultural, Biological, and Environmental Statistics 2012;17(3):377-394. R833863 (2012)
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  • Supplemental Keywords:

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

    Progress and Final Reports:

    Original Abstract
  • 2009 Progress Report
  • 2010 Progress Report
  • 2011 Progress Report
  • 2012 Progress Report