Dynamics of the spatiotemporal correlation between Aerosol Optical Depth satellite data and PM2.5 surface monitoring: An exploratory approach

Abstract

PM2.5 is a major air pollutant with diverse natural and anthropogenic sources, which significantly impacts human health and the environment. Remote sensing serves as a complementary resource for air-monitoring networks, providing valuable information on the spatiotemporal patterns of air pollutants at a global scale This study analyzes the feasibility of the use of MODIS MAIAC Aerosol Optical Depth (AOD) remote sensing for the estimation of surface PM2.5 and explores the dynamic relationship between AOD and PM2.5 in conjunction with regional factors such as heterogeneity of emissions sources, topography and meteorological variables in the Toluca Valley Metropolitan Area (TVMA), identified as the most polluted metropolitan area in Mexico. The five-year (2018-2023) temporal analysis during the dry seasons (Nov – May) revealed similar patterns between monthly average PM2.5 and AOD, with higher values observed during the Hot-Dry season. Spatially, 90% of the monitoring sites exhibited a moderate linear correlation (0.4 < R < 0.69) between AOD and PM2.5, while one site showed a weaker correlation (0.33); associated with a heterogeneity of local emission sources and the presence of coarse-mode aerosols. Regarding meteorological factors, higher Boundary Layer Height (BLH) and lower Relative Humidity (RH), during the Hot-Dry season corresponded to stronger correlations, as increased convection facilitates the vertical mixing of particles, in contrast, higher RH during the Cold-Dry impacts aerosol optical properties. This study highlights the complex influence of emission sources and meteorological conditions on the relationship between AOD and PM2.5, underscoring the importance of considering these factors when resorting to satellite data for air quality assessment.