A Regional NN estimator of PM2.5 using satellite AOD and WRF meteorology measurements

Besides affecting the global energy balance, aerosols can have a significant health impact. In particular, extended exposure ultrafine particles is a major concern and regulations by the EPA are constituted to deal with this issue. Unfortunately, measuring surface aerosols over wide areas is costly and difficult so the potential of using satellite remote sensing and/or models becomes an important area of study. In this presentation, we explore the potential of combining meteorological data together with column integrated AOD within a Neural Network approach. To begin, the study is isolated to New York City where accurate AERONET AOD as well as Lidar derived PBL heights along with weather station meteorology is included. The main result of this isolated study illustrates that beyond AOD, the next important factor is the PBL height. This result motivates an extended study where MODIS mosaic AOD's are combined with WRF weather forecast model inputs including PBL height. To use WRF PBL, a matchup between WRF and Calipso is given for single layer cases illustrating strong correlations in spring and summer when PM25 is most important. In particular, we find that with seasonal training, we are able to generally improve on the existing approach utilized by the IDEA (Infusing satellite Data into Environmental air quality Applications) product which utilizes MODIS AOD and GEOS-CHEM PM25/AOD factors. In addition, we explore potential improvements that can occur if we can filter aloft plumes from the processing stream using the NAAPS air forecast model as well as the use of EOF's to fill missing gaps in the AOD spatial imagery.