Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements

The satellite-based estimation of dry PM2.5 mass concentration near the surface is a big challenge in the aerosol remote sensing fields, but urgently needed by the environmental monitoring. We present an experimental validation of a physical PM2.5 remote sensing (PMRS) method which is not dependent on geographical location, based on ground-based remote sensing measurements at Jinhua City, a typical middle size city in East of China. The PMRS method is designed to employ currently available satellite remote sensing parameters as many as possible, including aerosol optical depth (ADD), fine mode fraction (FMF), planetary boundary layer height (PBLH) and atmospheric relative humidity (RH), and thus be capable of deriving PM2.5 from instantaneous remote sensing measurements under different pollution levels. The key processes of the PM2.5 method including size cutting, volume visualization, bottom isolation and particle drying are directly validated by comparing with reference parameters. We found that the size cutting of the PMRS method has a significant bias (about 0.86) resulting from the use of fine mode fraction to estimate PM2.5 among all size of aerosol particles, which should be systematically corrected. The validation results of the volume visualization and particle drying of the PMRS method are quite satisfied while the bottom isolation procedure brings currently the maximum uncertainty to the PM2.5 remote sensing. The improved PMRS method shows good performance on the remote sensing of hourly PM2.5 with an average error of about 38 mu g/m(3) and relative error of about 31%. The correlation coefficient between remote sensing and in situ hourly PM2.5 is about 0.67 with a linear slope of 1.03 and intercept of 11 mu g/m(3) while the means are very close (110.7 mu g/m(3) versus 118.6 mu g/m(3)). The validation study also helps find out future improvement directions and demonstrates the possible application to ground-based remote sensing data. (C) 2015 Elsevier Inc. All rights reserved.