A New Approach to Compute PM2.5 for Health Impact Analysis

计算 PM2.5 以进行健康影响分析的新方法

基本信息

批准号：
8319377
负责人：
FAZLAY S FARUQUE
金额：
$ 14.59万
依托单位：
UNIVERSITY OF MISSISSIPPI MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-08-15 至 2014-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8319377
关键词：
Accounting Address Admission activity Aerosols Affect Air Air Pollutants Air Pollution Area Artificial Intelligence Asthma Birth Rate Breathing Caliber Cardiovascular Diseases Chronic Obstructive Airway Disease Communities Country Data Data Quality Data Sources Development Disease Emergency Medicine Environmental Wind Epidemiology Exposure to Extinction (Psychology)Future Goals Health Healthcare Systems Heart Diseases Height Hospitals Human Humidity Hypersensitivity Life Link Location Lung Machine Learning Malignant neoplasm of lung Maps Measurement Measures Methodology Mississippi Modeling Monitor National Institute of Environmental Health Sciences Optics Particulate Particulate Matter Personal Satisfaction Process Property Prophylactic treatment Proxy Public Health Readiness Research Research Personnel Resolution Risk Scientist Site Source Speed Surface Surveillance Program System Techniques Temperature Testing Time United States National Aeronautics and Space Administration Variant Visit Work base burden of illness design hazard improved indexing interest knowledge of results novel novel strategies particle planetary Atmosphere pressure prototype respiratory sensor statistics web based interface

项目摘要

DESCRIPTION (provided by applicant): Environmental air quality impacts human well-being and disease, but the availability of air quality data is limited to selected locations because of the complexity involved in its measurement. Among air pollutants, PM2.5 is of the greatest concern. These particles are not captured by the lungs' natural defenses and can be inhaled deeply, where they can cause health problems ranging from asthma attacks to heart disease. PM2.5 currently is measured primarily by ground monitoring stations located at approximately 320 EPA sites, providing limited local geographic coverage. However, there are satellites that make a variety of aerosol observations and provide a daily global picture of atmospheric particulates in the form of aerosol optical depth (AOD). Scientists have attempted to compute ground-level PM2.5 (GLP) from these AOD data. However, the multivariate nonlinear relationship between AOD and PM2.5 imposes limitations in computing GLP using satellite data. This project proposes to overcome these limitations by computing reliable GLP via a new methodology which has already been tested and validated. The study has two specific aims: (1) develop satellite-derived daily GLP estimates for the contiguous U.S., and (2) examine spatial and temporal associations between GLP exposure and hospital visits for asthma exacerbation in Mississippi. Using our methodology, we will generate daily GLP data for a 12-month period at a resolution of 0.10x0.10 (~10x10km2), providing approximately 82,000 data points as opposed to about 300 data points available daily from EPA ground monitoring stations within the contiguous U.S. We will address the nonlinear relationship between PM2.5 and AOD which is a function of humidity, temperature, surface pressure, surface wind speed, surface type, boundary layer height, and AOD by accounting for these variables using a machine learning process. The AOD that will be used in this process will be generated by merging AOD data from multiple satellite sensors. Meteorological data will come from NOAA NCEP. Surface type data will be obtained from the satellite-identified vegetation index. Boundary layer height, which is the mixed layer of the atmosphere closest to the ground where people live and work, will come from CALIPSO data which provides vertical profiles of atmospheric aerosol extinction. Information on GLP levels will allow the scientific community to better understand health impacts from exposure to low, moderate, or high levels of PM2.5. Moreover, in places where PM2.5 levels are elevated only occasionally, such as Mississippi, the short-term health impact of increases in PM2.5 can be studied more precisely. National GLP data will be made available to other researchers to facilitate future explorations of how PM2.5 exposure impacts a wide range of health conditions, thereby making possible more timely prophylactic treatment, improving healthcare system preparedness, and better informing public health policymaking.

描述（由申请人提供）：环境空气质量会影响人类的福祉和疾病，但由于其测量涉及的复杂性，空气质量数据的可用性仅限于选定位置。在空气污染物中，PM2.5是最关心的问题。这些颗粒不会被肺的自然防御捕获，并且可以被深深吸入，在那里它们会引起从哮喘发作到心脏病等方面的健康问题。 PM2.5目前主要是通过位于约320个EPA站点的地面监测站来衡量的，提供了有限的本地地理覆盖范围。但是，有些卫星可以进行各种气溶胶观测，并以气溶胶光学深度（AOD）形式提供每天的全球大气颗粒图。科学家试图从这些AOD数据中计算地面PM2.5（GLP）。但是，使用卫星数据在计算GLP时，AOD和PM2.5之间的多元非线性关系施加了局限性。该项目建议通过通过已经进行了测试和验证的新方法来计算可靠的GLP来克服这些局限性。这项研究具有两个具体的目的：（1）对美国连续的卫星衍生的每日GLP估计值，以及（2）检查密西西比州GLP暴露与医院就诊之间的空间和时间关联。使用我们的方法，我们将以0.10x0.10（〜10x10km2）的分辨率生成每日GLP数据，提供约82,000个数据点，而不是每天在美国内部的EPA地面监控站中提供的约300个数据点，我们将在美国连续的US中乘坐pm2.5和AOD之间的非线性关系，并构成pm2.5和aod的非线性关系。使用机器学习过程来考虑这些变量。将在此过程中使用的AOD通过从多个卫星传感器合并AOD数据来生成。气象数据将来自NOAA NCEP。表面类型数据将从卫星识别的植被指数中获得。边界层高度是最接近人们居住和工作的大气的混合层，它将来自卡利皮数据，该数据提供了大气气溶胶灭绝的垂直剖面。 GLP水平的信息将使科学界能够更好地了解暴露于低，中或高水平的PM2.5的健康影响。此外，在只有偶尔PM2.5水平升高的地方，例如密西西比州，可以更精确地研究PM2.5增加的短期健康影响。国家GLP数据将提供给其他研究人员，以促进对PM2.5暴露如何影响多种健康状况的未来探索，从而使得可能更及时的预防性治疗，改善医疗保健系统的准备，并更好地告知公共卫生政策。