Accurate and reliable exposure estimates are crucial to the success of any environmental health study. The overarching goal of this project is to develop and apply statistical methods to improve exposure assessment and exposure uncertainty quantification for spatio-temporal environmental pollution fields. This is accomplished by statistically integrating observations with additional data sources, including state-of-the-art computer model simulations and satellite imagery. We will develop methods motivated by three current research priorities in air pollution epidemiology: a) identifying susceptible sub-populations most at risk to air pollution exposures; (b) quantifying health impacts of air pollution under a changing climate; and (c) understanding sources of air pollution to develop control strategies.
In Aim 1, we will develop multi-resolutional and multivariate data integration methods for ambient air pollution concentrations. We will supplement sparse observations from monitoring networks with simulations from a chemical transport model and multiple satellite retrieval parameters. The proposed methods will exploit the between-pollutant dependence and the spatio-temporal autocorrelation within each pollutant for better predictions.
In Aim 2, we will develop multivariate bias-correction methods for climate model simulations using historical observations. The goal is to perform joint bias-correction across multiple variables such that the observed dependence is retained in future projections.
In Aim 3, we will develop ensemble source apportionment methods for fine particulate matter pollution (PM2.5). The methods will estimate emission source contributions by combining results from several algorithms that incorporate different types of external information and assumptions. We will further utilize computer model simulations to spatially interpolate source information to locations without monitors. Methods developed from Aims 1, 2, and 3 will be used to create national databases of (1) daily concentration estimates for criteria pollutants and major constituents of PM2.5, (2) projections of ozone levels due to climate change under different future emission scenarios, and (3) daily estimates of contributions from multiple PM2.5 sources, including coal combustion, on-road diesel and gasoline combustion, biomass burning, and resuspended soil/dust. We will also provide uncertainty estimates, detailed documentation, and R packages to ensure these methods and estimates can be used in other environmental health studies.
In Aim 4, we will acquire individual-level emergency department (ED) visit data from 25 cities during the period 2005-2014. The data integration products will be used to estimate short-term associations between asthma ED visits and multiple air pollutants and pollutant sources. The proposed health study lls a major gap by considering both elderly and non-elderly susceptible populations to support the development of targeted, effective risk reduction and prevention activities. While air pollution serves as the motivating application in this project, the methods proposed are highly applicable to other environmental exposures.
This research will develop new statistical methods to combine different types of environmental data for estimating air pollution exposures. More accurate exposure estimates will increase the reliability, relevance, and reproducibility of large population-based environmental health studies, which have played an important role in guiding policies towards protecting public health.
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