Impact/Purpose:

The overall objectives of this proposed nationwide spatiotemporal analysis are to investigate the adverse health outcomes associated with population exposure to fine particulate matter (PM_2.5) and speciation and to characterize geographic differences, sources, and population heterogeneity in the putatively PM_2.5 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?

Description:

The key scientific benefits of this work include:

1. 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,
2. better quantification of the health effects of and related population susceptibility to fine PM and speciation by integrating atmospheric models with other data;
3. improved characterization of the spatial temporal variation of PM sources by using atmospheric models, source and receptor spatial temporal analysis,
4. 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,
5. 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.

Record Details:

Record Type:PROJECT( ABSTRACT )

Start Date:12/01/2008

Completion Date:11/30/2011

Record ID: 201523

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Related Organizations:

Role :OWNER

Organization Name :JOHNS HOPKINS UNIVERSITY

Mailing Address :3400 N Charles St

Citation :Baltimore

State :MD

Zip Code :21218



Role :OWNER

Organization Name :YALE UNIVERSITY

Citation :New Haven

State :CT

Zip Code :6520



Role :OWNER

Organization Name :NORTH CAROLINA STATE UNIVERSITY

Mailing Address :P.O. Box 7001

Citation :Raleigh

State :NC

Zip Code :27695

Project Information:

Approach :

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 PM_2.5 mass and its components, while characterizing its potential sources. In Stage 1, we will map ambient PM_2.5 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 PM_2.5 and its species and sources in a regression Poisson model, accounting for potential confounders.

Cost :$893,439.00

Project IDs:

ID Code :R833863

Project type :EPA Grant