Fusing Regional Satellite-Derived PM2.5 and Near-Road Dispersion Modeling Fields for Community Scale Fine Particulate Exposure Assessment

Aerosol Optical Depth (AOD) from NASA MODIS retrievals is commonly used as an input to geostatistical models to construct regional PM2.5 spatial fields for fine particulate exposure assessment. The advantage of using AOD is its uniform spatial coverage in cloud-free conditions, allowing better spatial gap filling of the fields. These regional PM2.5 fields, however, do not have fine enough spatial resolution to explicitly resolve enhanced PM around major roadways, known to be associated with increased health risk. The need for PM fields with more uniform and finer scale spatial coverage is also motivated by the recent California AB617 legislation, which aims to reduce disparities at community scale in air pollution concentrations. At AMS 2019, we will present our NASA Health and Air Quality Applied Science Team research group’s progress to date on developing a modeling system that fuses regional PM2.5 fields utilizing AOD with dispersion modeling fields of near-roadway PM2.5 and finer sized particulate components. Regional PM2.5 fields over California utilizing the MODIS Dark Target (3-km) and MAIAC (1-km) AOD retrieval algorithms will be presented, along with evaluation against independent air monitors across the state. The dispersion model to generate near-roadway fields, its a priori calibration against field study data from the South Coast Air Quality Management District Interstate 710 study, and the method of fusing the dispersion fields into regional fields will then be described. Results to date from the fused system around major freeway interchanges in California will then be presented, along with some preliminary assessment of these fields against measurements from available Purple Air low-cost optical PM sensors deployed around these interchanges. By presenting of this work at the Aerosols, Aeroallergens and Health session of the AMS 10th Conference on Environment and Health, we hope to get feedback from health practitioners and others interested in potentially utilizing our system, allowing optimization of its design to facilitate this end use.