Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

• Authors: Xuefei Hu, Emory University; Lance Waller, Emory University; Alexei Lyapustin, NASA Goddard Space Flight Center; Yujie Wang, NASA Goddard Space Flight Center; Yang Liu, Emory University
• Journal Title: Journal of Geophysical Research: Atmospheres
• Volume: Volume 119, Number 19
• Publisher: American Geophysical Union (AGU) | 2014-10-16, Pages 11375-11386
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1002/2014JD021920
• Subjects: Biology, Biostatistics; Biology, Bioinformatics; Health Sciences, Public Health
• Keywords: Science & Technology; Physical Sciences; Meteorology & Atmospheric Sciences; GROUND-LEVEL PM2.5; AEROSOL OPTICAL-THICKNESS; UNITED-STATES; AIR-QUALITY; MODIS; US; RETRIEVALS; IMPACTS

Abstract

Multiple studies have developed surface PM 2.5 (particle size less than 2.5 μm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM 2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM 2.5 . In this paper, we examined whether remotely sensed fire count data could improve PM 2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM 2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM 2.5 across the models considered. Cross validation (CV) generated an R 2 of 0.69, a mean prediction error of 2.75 μg/m 3 , and root-mean-square prediction errors (RMSPEs) of 4.29 μg/m 3 , indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 μg/m 3 , exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM 2.5 concentration estimation, especially in areas and seasons prone to fire events.