Quantifying PM2.5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 158; pp. 90 - 98
• Main Authors: Liu, Ming, Zhou, Gaoxiang, Saari, Rebecca K., Li, Sabrina, Liu, Xiangnan, Li, Jonathan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2019
• Subjects: Aerosol optical depth; aerosols; algorithms; China; governmental programs and projects; human health; land use; meteorology; moderate resolution imaging spectroradiometer; MODIS; Particle radius; particle size; particulates; PM2.5; population density; remote sensing; satellites; summer; topography; China; Aerosol optical depth; Particle radius; China; MODIS; PM2.5
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2019.10.010

Although satellite-based approaches have been developed and adopted for estimating the concentration of fine particulate matter (PM2.5) with promising accuracy, few studies have considered mass concentration and particle radius simultaneously, even though particle size is significant for human health impacts. We developed a satellite-based PM2.5 retrieval method using optical-mass relationships via aerosol microphysical characteristics. Satellite data from the MODerate resolution Imaging Spectroradiometer (MODIS) instrument, combined with parameters from meteorological reanalysis, were processed to calculate particle radii and retrieve PM2.5 mass concentrations over China in 2017. Our study is the first to identify the spatial pattern of mean PM2.5 radius over China, which was validated against observations from AERONET (RMSE = 0.11 μm). Mean particle size over eastern China is smaller than in the west, depicting a clear bifurcation across the country, especially in summertime. This finding is attributed to variations in topography, meteorology, land use and population density, which affects the properties of emitted aerosols as well as their fate and transport. A statistically significant correlation (R = 0.82) was observed between estimated and measured annual PM2.5, with RMSE = 9.25 μg/m3, MAE = 6.98 μg/m3, MBE = −1.98 μg/m3 and RPE = 17.69% (N = 1270). The spatiotemporal distributions of resulting PM2.5 are consistent with previous findings, indicating the effectiveness and applicability of our method. Our approach quantifies PM2.5 mass concentrations without introducing regionally-specific fitting parameters, which can be efficiently applied across various spatial and temporal domains.