Spatial temporal analysis of health effects associated with sources and speciation of fine PMEPA 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
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:
1) 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? 2) Can we improve the PM component-based epidemiologic studies by using atmospheric models? 3) How to integrate the atmospheric models in this epidemiologic framework, while characterizing uncertainties in the epidemiological and numerical models? 4) How to use source apportionment approaches in national epidemiologic studies, while characterizing different sources of uncertainty in the models and the data?
In this work we will develop and implement a statistical hierarchical Bayesian framework that provides a very broad, flexible approach to studying the spatiotemporal associations between mortality and morbidity and population exposure to daily PM2.5 mass and its components, while characterizing its potential sources. In Stage 1, we will map ambient PM2.5 air concentrations using all available monitoring data (Supersites, IMPROVE, STN and FRM), an air quality model (CMAQ), and satellite data (MODIS), at different spatial and temporal scales.
In Stage 2, we will conduct space-time dynamic source apportionment analysis to characterize the PM sources. We will introduce space-time source and receptor models, and we will also run source sensitivity simulations using CMAQ 12-km runs with 10 source categories. We will characterize uncertainties in the different models (stochastic and deterministic) and the data.
In stage 3, we will use exposure information and SHEDS to quantify the effect of microenvironment concentrations and characteristics, as well as human activity, to estimate individual exposures to fine PM.
In stage 4, we will examine the spatial temporal relationships between the health end-points and the exposures to PM2.5 and its species and sources in a regression Poisson model, accounting for potential confounders.
The key scientific benefits of this work include:
- development of a new flexible spatiotemporal modeling framework for predicting fine PM mass and speciation that makes the best use of available information, combining monitoring data with air quality numerical models (CMAQ), while accounting for different sources uncertainties,
- better quantification of the health effects of and related population susceptibility to fine PM and speciation by integrating atmospheric models with other data;
- improved characterization of the spatial temporal variation of PM sources by using atmospheric models, source and receptor spatial temporal analysis,
- integration in the epidemiologic analysis of our results on PM composition and estimated sources, while accounting for uncertainty in the statistical and numerical models and the data,
- better understanding of the changes in health effects estimates based on various methodologies for estimating exposure (e.g., monitoring, CMAQ).
The knowledge and modeling based developed here will be critical to assessment of the need for public policies aimed at managing fine PM air quality.