Source of Support: NIH NIEHS
Award Amount: $89,397
Period Covered: 07/01/2011 to 06/30/2013
Grant: R21ES019713-01A1
Environmental air quality has influence on human well-being and disease, but measuring this air quality is expensive and time-consuming. Among air pollutants, particulate matter smaller than 2.5 microns in diameter (PM2.5) is of the gravest concern, because the particles are so tiny they can be inhaled deeply into the lungs, dodging the lungs’ natural defenses and causing many severe health problems ranging from asthma attacks to chronic ischemic heart disease. Currently, PM2.5 is measured by ground monitoring stations located on selected sites providing only local geographic coverage. However, there are satellites observing the entire globe daily and providing data that could be processed to produce useful PM2.5 information. Scientists have attempted to measure the ground level PM2.5 (GLP) that is relevant to human respiration from available satellite data sources. However, a significant limitation of these estimations is due to the inability to properly incorporate vertical variations of particulates in the atmosphere. Most common PM2.5 estimates are derived from a satellite sensor called Moderate Resolution Imaging Spectrometers (MODIS) on the NASA Terra and Aqua satellites. Problems also occur when MODIS cannot collect data due to cloud cover or from the areas with high reflectivity such as desserts. We have developed a prototype to compute the GLP by processing and fusing multiple satellite data sources using the full meteorological context. Obtaining detailed vertical information regarding aerosols in the form of aerosol optical depth (AOD) from CALIPSO and propagating it between curtains using the 3D time varying meteorological information makes it possible to incorporate vertical variations of PM2.5 in the atmosphere and determine the fraction of the total aerosol burden resulting in computing more reliable GLP. This improved PM2.5 data product with seamless temporal and geographic coverage is expected to play a critical role in reducing disease burden associated with exposure to fine particle matters. As a byproduct of this project, areas with consistent high levels of PM2.5 can be identified which can provide critical information for policy making in taking precise initiatives to reduce PM2.5 and thereby protect the public health. Additionally, with the information on PM2.5 levels available to the healthcare providers for their service areas, they can be better prepared with proper staffing and treatment facilities. Those who are vulnerable to PM2.5 exposure can also limit their outdoor activities to minimize the exposure. Moreover, this breathing-level PM2.5 measurement will assist in enhancing the knowledge of fine particle-related disease etiology.Â
The specific aims of this study are to: (1) Generate GLP to cover a full year for the contiguous United States using intelligent fusion of the CALIPSO vertical aerosol extinction profiles with horizontal AOD data from MODIS, OMI and MISR; (2) Explore the potential of differentiating PM2.5 aerosol based on the optical properties observed by MODIS, CALIPSO and MISR for the aerosol source areas and conditions, such as, agricultural, metro and coastal areas in Mississippi; and (3) Study the spatial and temporal association of hospital visits due to asthma exacerbation with PM2.5 exposure.
Our PM2.5 product will be generated at a resolution of approximately 10 km x 10 km (0.1°x0.1°). Statewide hospital admission data due to asthma for Mississippi will first be geo-coded using street address of the patients’ residence. These data will then be resolved into the same resolution as the satellite-derived PM2.5. GIS data for demographic and socio-economic conditions will also be resolved into the same resolution. We expect that this project will develop novel methods and techniques for computing accurate GLP for further research on the impact of fine particles on health.
