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Retrievals of fine mode light-absorbing carbonaceous aerosols from POLDER/PARASOL observations over East and South Asia
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.rse.2020.111913
Lei Li , Huizheng Che , Yevgeny Derimian , Oleg Dubovik , Gregory L. Schuster , Cheng Chen , Qiuyue Li , Yaqiang Wang , Bin Guo , Xiaoye Zhang

Abstract A new aerosol component approach that infers aerosol species fractions from satellite observations was developed and incorporated by Li et al. (2019) into Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm. In the current work we employ this aerosol component approach for derivation and analysis of temporal and spatial characteristics of black and brown carbon (BC and BrC) concentrations in East and South Asia using POLDER/PARASOL satellite observations during 2005–2013. The inter-comparison of the satellite retrieved and in situ measured BC in China shows a reasonably good agreement for both monthly averaged and daily values. The distribution of light-absorbing carbon (BC and BrC) shows a significant temporal variation cycle with increases matching the months known for strong biomass burning or fossil fuels combustion emissions. The main concentrations of BC are found near the fire or anthropogenic emission regions, while concentrations of BrC are predominantly distanced from the emission sources. Seasonal means of BC/(BC + BrC) ratio in East and South Asia are also presented. High ratios (above 0.5) in Northeast India and North China during December, January, and February can be attributed to fossil fuels combustion and anthropogenic emissions. Medium ratios (around 0.2–0.3) over the Indo-China Peninsula and Northeast China during March, April, and May are associated with the biomass burning (e.g., forest fires, agriculture waste burning). The provided observationally-based retrievals of BC, BrC, and BC/(BC + BrC) are believed to be of high importance for constraining the aerosol modeling and reducing the uncertainties of the model estimations.
更新日期:2020-09-01
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