# Data Integration Methods for Environmental Exposures with Applications to Air Pollution and Asthma Morbidity

> **NIH NIH R01** · EMORY UNIVERSITY · 2021 · $424,278

## Abstract

PROJECT SUMMARY
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...

## Key facts

- **NIH application ID:** 10115732
- **Project number:** 5R01ES027892-05
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Howard H Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $424,278
- **Award type:** 5
- **Project period:** 2017-05-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115732

## Citation

> US National Institutes of Health, RePORTER application 10115732, Data Integration Methods for Environmental Exposures with Applications to Air Pollution and Asthma Morbidity (5R01ES027892-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10115732. Licensed CC0.

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