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

项目摘要 准确和可靠的接触估计对任何环境健康研究的成功至关重要。的 本项目的首要目标是开发和应用统计方法，以改进暴露评估 时空环境污染场暴露不确定度量化。来实现这一点 通过将观测结果与其他数据源（包括最先进的计算机模型）进行统计整合 模拟和卫星图像。我们将开发由三个目前的研究重点在空气中的方法 污染流行病学：a）确定最易受空气污染暴露风险的易感亚群体;（B） （c）了解空气污染的来源， 制定控制策略。在目标1中，我们将开发多分辨率和多变量数据集成方法 环境空气污染浓度。我们将补充监测网络的稀疏观测， 化学传输模型和多个卫星反演参数的模拟。所提出的方法 将利用污染物之间的依赖性和每个污染物内的时空自相关性， 更好的预测在目标2中，我们将开发用于气候模式模拟的多元偏差校正方法 使用历史观察。目标是跨多个变量执行联合偏差校正，使得 在今后的预测中保留了观察到的依赖性。在目标3中，我们将开发集合源解析 细颗粒物污染（PM2.5）的方法。这些方法将估计排放源的贡献 通过结合来自几种算法的结果，这些算法结合了不同类型的外部信息和数据， 选择。我们将进一步利用计算机模型模拟，将源信息空间内插到位置 没有监视器。根据目标1、2和3制定的方法将用于创建国家数据库， 标准污染物和PM2.5主要成分的浓度估计，（2）臭氧水平预测 （3）在不同的未来排放情景下， 多种PM2.5来源，包括燃煤、道路柴油和汽油燃烧、生物质燃烧， 和再悬浮的土壤/灰尘。我们还将提供不确定性估计、详细文档和R包 以确保这些方法和估计可用于其他环境健康研究。在目标4中，我们 获取2005-2014年25个城市的个体水平急诊（艾德）就诊数据。的 数据集成产品将用于估计哮喘艾德访视与多个 空气污染物和污染源。拟议的健康研究填补了一个主要空白， 非老年易感人群，以支持制定有针对性的、有效的风险降低和预防措施， 的活动。虽然空气污染作为本项目的激励应用，但所提出的方法是 非常适用于其他环境暴露。

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) quan- tifying 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 assump- tions. 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 fills a major gap by considering both elderly and non-elderly susceptible populations to support the development of targeted, effective risk reduction and preven- tion activities. While air pollution serves as the motivating application in this project, the methods proposed are highly applicable to other environmental exposures.