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Exploring the effects of crop residue burning on local haze pollution in Northeast China using ground and satellite data
Atmospheric Environment ( IF 5 ) Pub Date : 2019-02-01 , DOI: 10.1016/j.atmosenv.2018.11.033 Shuai Yin , Xiufeng Wang , Xirui Zhang , Zhongxue Zhang , Yi Xiao , Hiroshi Tani , Zhongyi Sun
Atmospheric Environment ( IF 5 ) Pub Date : 2019-02-01 , DOI: 10.1016/j.atmosenv.2018.11.033 Shuai Yin , Xiufeng Wang , Xirui Zhang , Zhongxue Zhang , Yi Xiao , Hiroshi Tani , Zhongyi Sun
Abstract In this study, we used ground-measured air pollutants and various remote sensing and meteorological datasets to explore the possible causes of the severe particulate matter (PM) pollution episodes of October and November 2015 in Northeast China. The three pollution episodes in different regions were elaborately characterized by analyzing the time variation of PM2.5 and other pollutants, e.g., CO, NO2, and SO2. The results from MODerate-resolution Imaging Spectrometer (MODIS) revealed that during the study period, dramatic crop residue burning occurred in Northeast China. Combining its time variation and spatial distribution with air pollutants, the crop residue burning was inferred to have a direct influence on the first and second pollution episodes, especially in Heilongjiang Province. Since a cold wave swept Northeast China beginning on 4th November, the PM pollutants were assumed to be transported southward. This assumption was further verified by the backward and forward trajectory models. Additionally, the suddenly increased relative humidity beginning on 7th November and the week of no wind after the cold wave also greatly contributed to the most severe pollution of Liaoning and Jilin on approximately 8th November. Lastly, we compared air quality indexes and pollutants from remote sensing with ground-measured datasets; the results indicated that there were certain correlations and spatial consistency between the two types of datasets, except for UVAI, which is meaningful to the area without an effective ground monitoring network.
更新日期：2019-02-01
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