Zhang, Ying; Li, Zhengqiang (2015). Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. REMOTE SENSING OF ENVIRONMENT, 160, 252-262.

Abstract
With the economic development during the past thirty years, the atmospheric particulate matters over eastern China have reached high levels, causing serious environmental pollution and increased threat to public health. The in situ measurement of fine particulate matter (PM2.5) mass concentration is currently widely employed even if this traditional approach has limitations with respect to wide-range coverage and spatial continuity. Satellite remote sensing provides a possibility to monitor continuously spatial coverage of atmospheric particulate matters. However, the conversion between particle mass concentration and remotely sensed optical parameters is still a big challenge. The existing correlation approaches, i.e. estimating aerosol optical depth (ACM) and surface PM2.5 relationship based on statistics with limited samples, meet more and more limitations with respect to representativeness and applicability. In this paper, we developed a relationship between fine particle extinction and its volume, and thus established a multi-parameter remote sensing formula of dry PM2.5 mass concentration near the ground. To test the method, we utilized AOD and fine mode fraction (FMF) provided by the MODerate resolution Imaging Spectroradiometer (MODIS), supplementing with planetary boundary layer height (PBLH) and relative humidity (RH) obtained from re-analysis data based on model simulation. The remote sensing results of dry PM2.5 mass concentration near the ground over polluted regions of China from October to December 2013 showed that our method revealed well the spatial distribution of PM2.5 in the study area and illustrated clearly the daily variation. The validation against 15 in situ stations showed good performance of the method formulas (mean values of 101 vs. 105 mu g/m(3) for the average of three months of data) and suggested main errors caused by the input data which could be improved in the near future with the advance of satellite remote sensing. We also discussed uncertainty sources and possible improvements. (C) 2015 Elsevier Inc. All rights reserved.

DOI:
10.1016/j.rse.2015.02.005

ISSN:
0034-4257