Exploring the effects of crop residue burning on local haze pollution in Northeast China using ground and satellite data. (15th February 2019)

• Record Type: Journal Article
• Authors: Yin, Shuai; Wang, Xiufeng; Zhang, Xirui; Zhang, Zhongxue; Xiao, Yi; Tani, Hiroshi; Sun, Zhongyi
• Is Part Of: Atmospheric environment. Volume 199(2019)
• Page Start: 189
• Page End: 201
• Publication Date: 2019-02-15
• Subjects: AOD -- Crop residue burning -- MODIS -- PM2.5 -- Primary aerosols -- Secondary aerosols; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2018.11.033
• Languages: English
• ISSNs: 1352-2310

Abstract

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. Highlights: Both ground-measured and remote sensing datasets are used for PM pollution analysis. Effective method to extract crop residue burning spots from MODIS was used. Crop residue burning contributed to the pollution episode in Northeast China. Meteorological conditions have been taken into consideration. Various factors jointly induced the worst PM pollution episode in November 2015.