期刊
CHINESE GEOGRAPHICAL SCIENCE
卷 30, 期 2, 页码 266-278出版社
SPRINGER
DOI: 10.1007/s11769-020-1110-7
关键词
crop residue burning; land-cover data; particular matter (PM); gaseous pollutants; emission inventory
资金
- National Key R&D Program of China [2017YFC0212303, 2017YFC0212304]
- Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDB-SSW-DQC045]
- National Natural Science Foundation of China [41775116]
- Youth Innovation Promotion Association of Chinese Academy of Sciences [2017275]
The burning of crop residues emits large quantities of atmospheric aerosols. Published studies have developed inventories of emissions from crop residue burning based on statistical data. In contrast, this study used satellite-retrieved land-cover data (1 km x 1 km) as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in {dy2015}. The emissions of PM10, PM2.5, VOCs, NOx, SO2, CO, and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5, 598.4, 584.4, 230.6, 35.4, 3329.3, and 36.1 Gg (1 Gg = 10(9) g), respectively; the corresponding emissions from burning paddy rice residues were 234.1, 229.7, 342.3, 57.5, 57.5, 1122.1, and 21.5 Gg, respectively. The emissions from crop residue burning showed large spatial and temporal variations. The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China, particularly in Shandong, Henan, Anhui, and Sichuan provinces. Emissions from burning paddy rice residue were highest in east and central China, with particularly high levels in Shandong, Jiangsu, Zhejiang, and Hunan provinces. The monthly variations in atmospheric pollutant emissions were similar among different regions, with the highest levels observed in October in north, northeast, northwest, east, and southwest China and in June and July in central and south China. The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.
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