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

环境暴露数据集成方法及其在空气污染和哮喘发病率中的应用

• 批准号: 9291306
• 负责人: Howard H Chang
• 金额: $ 60.76万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9291306
• 关键词: Address; Air Pollutants; Air Pollution; Algorithms; Area; Asthma; Biomass; Cessation of life; Chemicals; Cities; Climate; Coal; Complex; Computer Simulation; Data; Data Set; Data Sources; Databases; Dependence; Development; Documentation; Dust; Elderly; Emergency department visit; Ensure; Environmental Epidemiology; Environmental Exposure; Environmental Health; Environmental Pollution; Epidemiology; Exposure to; Future; Gasoline; Goals; Health; Heterogeneity; Imagery; Individual; Joints; Knowledge; Link; Location; Measurement; Meteorology; Methods; Modeling; Monitor; Morbidity - disease rate; National Institute of Environmental Health Sciences; Outcome; Ozone; Particulate Matter; Play; Policies; Pollution; Population; Prevention; Process; Proxy; Public Health; Reproducibility; Research; Research Priority; Resolution; Retrieval; Risk; Risk Assessment; Risk Reduction; Role; Soil; Source; Statistical Methods; Statistical Models; Technology; Time; Toxicology; Uncertainty; Work; World Health Organization; age group; ambient air pollution; atmospheric sciences; base; climate change; data integration; evidence base; experience; extreme heat; fine particles; improved; metropolitan; models and simulation; pollutant; population based; programs; remote sensing; simulation; spatiotemporal; success; tool; tropospheric ozone

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.

项目概要 准确可靠的暴露估计对于任何环境健康研究的成功都至关重要。 该项目的总体目标是开发和应用统计方法来改进暴露评估 以及时空环境场的暴露不确定性量化，这就是完成的污染。 通过使用额外的数据源（包括最先进的计算机模型）集中观察 我们将开发由当前空气研究领域的三个重点驱动的方法。 污染流行病学：a) 确定最容易遭受空气污染暴露的人群； 明确气候变化下空气污染对健康的影响；(c) 了解空气污染的来源，以 在目标 1 中，我们将开发多分辨率和多变量数据集成方法。 我们将补充监测网络的稀疏观测数据。 化学传输模型和多个卫星检索参数的模拟。 将利用污染物之间的依赖性和每种污染物内的时空自相关性 在目标 2 中，我们将为气候模型模拟开发多元偏差校正方法。 使用历史观察结果来对多个变量进行联合偏差校正，以便 在目标 3 中，我们将开发集合源分配。 细颗粒物污染 (PM2.5) 的方法 该方法将估算排放源的贡献。 通过结合多种算法的结果，这些算法结合了不同类型的外部信息和假设 我们将进一步利用计算机模型模拟将源信息空间插值到位置。 目标 1、2 和 3 开发的方法将用于创建 (1) 每日的国家数据库。 标准污染物和 PM2.5 主要成分的浓度估算，(2) 臭氧水平预测 由于未来不同情景下的气候变化排放，以及（3）每日估算的贡献 多种 PM2.5 来源，包括煤炭燃烧、道路柴油和汽油燃烧、生物质燃烧、 我们还将提供不确定性估计、详细文档和 R 包。 为了确保这些方法和估计可以用于其他环境健康研究，我们将在目标 4 中使用。 获取 2005 年至 2014 年期间 25 个城市的个人急诊科 (ED) 就诊数据。 数据集成产品将用于估计哮喘急诊就诊与多次就诊之间的短期关联 拟议的健康研究通过考虑老年人和污染物源来填补了一项重大空白。 非老年人易感人群支持制定有针对性的、有效的风险降低和预防措施 虽然空气污染是该项目的激励应用，但提出的方法是 非常适用于其他环境暴露。