2009 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 , Bell, Michelle L. , Dominici, Francesca , Frey, H. Christopher , Zhang, Yang
Current Investigators: Fuentes, Montserrat , Bell, Michelle L. , Dominici, Francesca , Frey, H. Christopher , Reich, Brian , Zhang, Yang
Institution: North Carolina State University , Harvard University , Yale University
Current 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 Period Covered by this Report: December 1, 2008 through November 30,2009
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 1, we have accomplished several that are critical to achieve our goals.  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 non-linear chemistry. Sensitivity simulations for ten 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 PM2.5. The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) model developed by the US 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. A mass-balance based methodology for estimating in-vehicle ETS PM2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS-PM model.
  • 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 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 and implemented the statistical framework to combine CMAQ data and monitoring data in epidemiological studies. The results show an increase in the power to detect a significant association between ambient  PM2.5  and mortality.
  • We have developed 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 to 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 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.

Future Activities:

During year 2, we plan to conduct the following tasks

  1. Evaluate the CMAQ model performance of the 2002 January and July baseline simulations at 12-km using surface and satellite observations;
  2. Complete the CMAQ source apportionment analysis from the 22 one-month simulations; 
  3. Conduct CMAQ source apportionment using the Particulate Source Apportionment Technolog(PSAT) in the Comprehensive Air Quality Model with Extensions (CAMx) (ENVIRON, 2006), if resources and time permitted. .
  4. We plan to further apply the exposure apportionment methodology to a wider scope of geographic areas, including New York City, Harris County, Texas, and North Carolina.  For these case studies, we will update the model inputs to reflect local conditions to the extent possible.  We will conduct the case studies for calendar year 2002 using air quality data from CMAQ and a fusion of air quality and monitoring data obtained from the U.S. EPA.
     
  5. We will process the exposure and source apportionment data from the CMAQ modeling system at 12-km resolution and to generate estimated exposures at the community-level, based on area-weighted averages. Exposures under different frameworks will be calculated (e.g., exposures from the CMAQ system, SHEDS, exposures based on ambient monitoring data). These exposures will be combined with health data to generate health effects estimates under different exposure scenarios.
     
  6. In addition, 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 : 14 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 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)
R833863 (2010)
R833863 (Final)
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  • Full-text: JESEE-Full Text PDF
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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)
    R833863 (Final)
    R832417 (Final)
    R832417C001 (2009)
    R832417C001 (Final)
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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)
    R832417 (Final)
    R832417C001 (2009)
    R832417C001 (Final)
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  • Full-text: Circulation-Full Text PDF
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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)
    R833863 (Final)
    R832417 (Final)
    R832417C001 (Final)
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  • Full-text: Epidemiology-Full Text PDF
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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)
    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)
    R833863 (Final)
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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)
    R833863 (Final)
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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)
    R833863 (2010)
    R833863 (Final)
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  • Full-text: Springer-Full Text PDF
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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)
    R833863 (Final)
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  • Journal Article Ozkaynak H, Frey HC, Burke J, Pinder RW. Analysis of coupled model uncertainties in source-to-dose modeling of human exposures to ambient air pollution: a PM2.5 case study. Atmospheric Environment 2009;43(9):1641-1649. R833863 (2009)
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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)
    R833863 (Final)
    R832417 (Final)
    R832417C001 (2009)
    R832417C001 (Final)
    R833622 (Final)
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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)
    R833863 (Final)
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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)
    R833863 (2010)
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  • Journal Article Woodruff TJ, Parker JD, Darrow LA, Slama R, Bell ML, Choi H, Glinianaia S, Hoggatt KJ, Karr CJ, Lobdell DT, Wilhelm M. Methodological issues in studies of air pollution and reproductive health. Environmental Research 2009;109(3):311-320. R833863 (2009)
    R834514 (2012)
    R834514 (2013)
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  • Supplemental Keywords:

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

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    Progress and Final Reports:

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