Journal
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
Volume 125, Issue 9, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2019JD031827
Keywords
black carbon; temporal variation; source apportionment; meteorological condition; back trajectory
Categories
Funding
- Chinese Academy of Sciences [XDA13010404, ZDRW-ZS-2019-1-3-2, 2016P173307000304]
- National Key R&D Program of China [2016YFA0602602, 2016YFA0602603]
- Shanghai Science and Technology Committee [18DZ1204902, 15DZ1170600]
- National Natural Science Foundation of China [41503119, 51778601]
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Black carbon (BC) is a major light absorption material that acts as a climate change driver with high radiative forcing and as an air pollutant that reduces visibility and air quality. Thus, reducing the emission and ambient concentration of BC could help address climate change and improve air quality simultaneously. In this study, the mass concentration of atmospheric BC was continuously measured by an aethalometer in Shanghai in 2017. The annual BC concentration was 2.19 +/- 1.28 mu g/m(3), with the highest loading in winter and the lowest loading in autumn. The BC concentrations varied with year and location when compared with previous studies in different locations in Shanghai. The hourly BC concentration had a bimodal distribution, with two peaks during the morning and evening traffic rush hours. Liquid fuels, biomass, and coal combustion contributed 65.7%, 21.5%, and 12.8%, respectively, of the total BC based on the advanced aethalometer model. The three sources varied in different seasons with a high contribution of liquid source in summer and more coal and biomass emissions in winter. High BC concentrations accumulated in the stable weather conditions in the four seasons and appeared when there were high wind speeds from the northwestern direction in winter. The Yangtze River Delta region was the most likely potential source region of high BC loading in the four seasons, and long-range transport from North China in winter was another likely source region based on the results of cluster analysis and potential source contribution function analysis of backward trajectories.
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