| Title | Assessing the Effect of the Long-Term Variations in Aerosol Characteristics on Satellite Remote Sensing of PM2.5 Using an Observation-Based Model |
| Authors | Lin, Changqing Lau, Alexis K. H. Fung, Jimmy C. H. Lao, Xiang Qian Li, Ying Li, Chengcai |
| Affiliation | Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong, Peoples R China Hong Kong Univ Sci & Technol, Div Environm & Sustainabil, Hong Kong, Peoples R China Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Peoples R China Chinese Univ Hong Kong, Jockey Club Sch Publ Hlth & Primary Care, Hong Kong, Peoples R China Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing 100871, Peoples R China |
| Issue Date | 2019 |
| Publisher | ENVIRONMENTAL SCIENCE & TECHNOLOGY |
| Abstract | Variations in aerosol characteristics play an essential role in satellite remote sensing of PM2.5 concentrations. The lack of measurement of aerosol characteristics, however, limits the assessment of their effects. This study presented an observation-based model that directly considered the effects of aerosol characteristics. In this model, we used an integrated humidity coefficient (gamma') and an integrated reference value (K) to delineate the effects of aerosol characteristics. We then investigated the effects of the long-term variations in aerosol characteristics on satellite remote sensing of PM2.5 concentration in Hong Kong from 2004 to 2012. The results show that the gamma' value peaked in 2009 because the percentages of highly hygroscopic components (e.g., sulfate and nitrate) in aerosols reached their peaks. The K value increased from 2004 to 2011 because of the increasing percentages of strong light-extinction components (e.g., organic matter) and the decreasing fine mode fraction in aerosols. The accuracy of PM2.5 retrieval improved greatly after accounting for the long-term variations in aerosol characteristics (e.g., correlation coefficient increased from 0.56 to 0.80). The results underscore the need to incorporate the variations in aerosol characteristics in the PM2.5 estimation models. |
| URI | http://hdl.handle.net/20.500.11897/549861 |
| ISSN | 0013-936X |
| DOI | 10.1021/acs.est.8b06358 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 物理学院 |
