中文关键词
成都PM2.5WRF-CMAQ模型区域传输行业贡献 | 英文关键词ChengduPM2.5WRF-CMAQ modelregional transportsource contributions |
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| 中文摘要 |
| 在建立成都市大气污染物排放清单的基础上，采用源开关敏感性分析法，设置8个排放情景，基于WRF-CMAQ模型模拟分析了2015年1、4、7和10月这4个典型代表月份的大气污染传输和不同行业对成都市PM2.5污染贡献.结果表明成都市PM2.5污染较重，特别是1月达到130 μg·m-3以上；浓度的高值集中在中心城区，且与周边城市PM2.5污染连接成片.由于气团比较稳定，大气污染物的区域传输能力较弱，成都市PM2.5污染以本地源的贡献为主，占比为61%.从行业贡献来看，移动源、扬尘源和生活源对成都市PM2.5年均浓度贡献率分别为29%、26%和24%，是影响PM2.5污染的主要污染源，下一步应强化对这3类源的污染控制. |
| 英文摘要 |
| This study establishes eight emission scenarios in the air pollutant emissions inventory of Chengdu City, China. We use the Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) models and a "zero-out" approach to investigate contributions of air pollution transport and sources to aerosol fine particulate matter (PM2.5) pollution in Chengdu City during January, April, July, and October 2015. The results showed that PM2.5 pollution in Chengdu City was serious during these months and reached >130 μg·m-3 in January. Highest concentrations were measured in the city center. PM2.5 pollution in Chengdu and the surrounding cities was found to exhibit regional characteristics. Since the air mass was stable during the monitoring periods, the interregional transmission capability of air pollution was poor, and thus local sources were the main contributors (61% of the annual average concentration) to PM2.5 pollution in Chengdu City. The contributions of local sources in April and July were higher than of those in January and October. We found that the main sources of PM2.5 pollution in Chengdu City were automobile emission (29% of the total), dust (26%), and domestic pollution (24%), and should be further controlled in the future. |
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https://www.hjkx.ac.cn/hjkx/ch/reader/create_pdf.aspx?file_no=20200106&flag=1&journal_id=hjkx&year_id=2020