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基于受体和化学传输的综合模型解析重庆PM2.5来源
摘要点击 621  全文点击 117  投稿时间:2021-09-26  修订日期:2021-11-16
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中文关键词  综合源解析  模型评估与应用  时空变化  PM2.5  重庆
英文关键词  hybrid source apportionment  model evaluation and application  temporal and spatial variation  PM2.5  Chongqing
作者单位E-mail
彭超 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
pengchao0623@sina.com 
李振亮 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
zhenliangli@163.com 
曹云擎 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
 
蒲茜 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
 
方维凯 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
 
王晓宸 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
 
汪凌韬 重庆市生态环境科学研究院, 重庆 401147
城市大气环境综合观测与污染防控重庆市重点实验室, 重庆 401147 
 
中文摘要
      为进一步提高PM2.5污染源解析的准确性,研究提出一种基于受体和化学传输的综合源解析模型(CTM-RM),并以重庆冬季一次典型PM2.5污染过程为例(2019年1月21~27日)开展模型评估与应用.结果表明,观测期间基于CTM-RM获得的模拟误差平方值较CAMx/PSAT低84.58%,PM2.5及其化学组分浓度的模拟相对误差值较CAMx/PSAT下降15.69%~92.86%;此外,CTM-RM还可以获取重庆市PM2.5污染源贡献的时空分布特征.观测期间,主城区PM2.5农业源、工业源、电力源、民用源、交通源和其他源的调整因子R值分别为1.39±0.38、1.54±0.48、1.01±0.13、1.02±0.58、0.86±0.59和0.58±0.67,各污染源R值的累积分布函数差异明显.民用源和工业源是主城区PM2.5的主要污染源(46.23%和28.23%).与其他源不同,污染日交通源贡献率(8.62%)同比清洁日显著上升(P<0.001),表明交通源排放是PM2.5浓度持续上升的驱动因素.主城区各污染源初始模拟浓度与R值的拟合函数适用于重庆市47个空气质量监测站的PM2.5模拟,各站点优化模拟浓度与观测浓度显著线性相关(r=0.82,P<0.001).与清洁日相比,污染日渝东北地区工业源贡献率和渝东南地区民用源贡献率上升幅度较大(17.20%和9.15%),而主城区和渝西地区交通源贡献率上升幅度较大(66.39%和84.16%).1月26日,民用源对渝东北地区PM2.5贡献较大(64.56%),而工业源对PM2.5贡献主要集中在主城区(25.26%)和渝西地区(21.20%).
英文摘要
      In order to further improve the accuracy of fine particulate matter (PM2.5) source apportionment results, a hybrid source apportionment approach (CTM-RM) combining the capabilities of a receptor model (RM) and chemical transport model (CTM) was developed. The CTM-RM method was evaluated and applied according to a typical PM2.5 pollution process from January 21 to 27, 2019 in Chongqing. The average value of square prediction error based on CTM-RM was 84.58% lower than that of CAMx/PSAT during the campaign. Compared with that of CAMx/PSAT, the fractional error of PM2.5 and its chemical component concentrations decreased by 15.69%-92.86%. Furthermore, the temporal and spatial variations in PM2.5 source impacts could be obtained using the CTM-RM method in Chongqing. The average adjustment factor (R) values were 1.39±0.38 (agriculture sources), 1.54±0.48 (industrial sources), 1.01±0.13 (power sources), 1.02±0.58 (residential sources), 0.86±0.59 (transportation sources), and 0.58±0.67 (other sources) in the main urban areas of Chongqing. Additionally, the cumulative distribution functions of R were found to be distinct among the six sources. The residential and industrial sources were the main sources of PM2.5, with contributions of 46.23% and 28.23%, respectively. In contrast to that of the other sources, the transportation source impacts of PM2.5 (8.62%) increased significantly from the clear period to pollution period (P<0.001), indicating that the increase in PM2.5 concentrations was mainly driven by vehicular emissions during the pollution period in the main urban areas of Chongqing. The fitting functions between the initial simulated concentrations and R values of each source in the main urban areas of Chongqing could be used to evaluate PM2.5 concentrations at 47 air quality monitoring stations in Chongqing, and the correlation between the refined simulated concentrations and measured concentration of PM2.5 was significant (r=0.82, P<0.001). Compared with that during the clear period, the increases in the percentages of industrial source impacts of PM2.5 in Northeast Chongqing and residential source impacts of PM2.5 in Southeast Chongqing were 17.20% and 9.15% higher, respectively, than that in other areas during the pollution period. By contrast, the increasing percentage of transportation source impacts of PM2.5 in the main urban areas of Chongqing (66.39%) and Western Chongqing (84.16%) from the clear period to the pollution period were higher than that in other areas. The results of CTM-RM on January 26 indicated that the residential source impacts in Northeast Chongqing (64.56%) were higher than those in other areas, and the industry source impacts of PM2.5 were primarily observed in the main urban areas of Chongqing and Western Chongqing, with contributions of 25.26% and 21.20%, respectively.

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