Journal
ATMOSPHERIC ENVIRONMENT
Volume 222, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2019.117121
Keywords
Visibility; Chemical transport model (CTM); PM2.5; Spatial pattern; Time series; North China plain (NCP)
Funding
- National Natural Science Foundation of China [41831175, 41775115, 41425019, 41721004]
- China National Environmental Monitoring Center
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Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125 degrees longitude x 0.25 degrees latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 mu g/m(3) (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980.
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