| 常州市冬季大气污染特征及潜在源区分析 |
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| 引用本文： | 何涛,彭燕,乔利平,滕加泉,薛银刚. 常州市冬季大气污染特征及潜在源区分析[J]. 环境科学研究, 2018, 31(3): 487-495. DOI: 10.13198/j.issn.1001-6929.2017.03.74 |
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| 作者姓名： | 何涛 彭燕 乔利平 滕加泉 薛银刚 |
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| 作者单位： | 1.常州市环境监测中心, 江苏 常州 213001 |
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| 基金项目： | 国家自然科学基金青年科学基金项目（No.21607016）；江苏省环境监测科研基金项目（No.1603）；常州市科技局科技支撑（社会发展）项目（No.CE20175022） |
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| 摘 要： | 为了解常州市冬季大气污染特征，对2013—2015年常州市冬季PM2.5、PM10、SO2、NO2、CO数据进行分析，并结合HYSPLIT 4.9模式研究不同气团来源对常州市各污染物浓度的影响及潜在污染源区分布特征.结果表明，常州市冬季以PM2.5污染为主，其占冬季首要污染物的90%以上，冬季PM2.5小时浓度对应的空气质量级别以良和轻度污染出现频次最多，冬季的ρ（PM2.5）对ρ（PM2.5）年均值的贡献率高达37.4%，不完全燃烧是颗粒物的一个重要来源.冬季ρ（PM2.5）、ρ（PM10）、ρ（SO2）、ρ（NO2）和ρ（CO）的日变化均呈双峰分布，两个峰值分别出现在交通的早高峰和晚高峰附近.ρ（NO2）在晚高峰明显大于早高峰，而ρ（SO2）和ρ（CO）表现为早高峰大于晚高峰.常州市CO/NOx和SO2/NOx的分析结果表明，常州市交通源的贡献明显，点源对常州市的空气质量的影响也较大.1和6 h的ρ（PM2.5）梯度变化可判识细颗粒物的爆发性增长.冬季常州市受到西北、西和西南等地区的大陆性气流影响较大，其对应的ρ（PM2.5）、ρ（PM10）、ρ（SO2）、ρ（NO2）和ρ（CO）平均值相对较高，且对应的污染轨迹出现概率较大.偏东方向的气流由于移动速度慢，不利于污染物扩散易造成污染累积，导致ρ（PM2.5）、ρ（SO2）和ρ（NO2）相对较高.WPSCF（源区分布概率）高值区（>0.5）集中于从芜湖至上海的长江中下游区域和杭州湾区域.PM2.5、PM10、SO2、NO2和CO潜在源区存在较大差异性，NO2、SO2和CO本地化的潜在贡献较PM2.5和PM10更明显.此外，受船舶等影响海洋源区对NO2、SO2和CO的潜在贡献较大.研究显示，长三角区域的大气污染物以本地污染为主，但远距离污染输送贡献也不容忽视.
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| 关 键 词： | 大气污染 梯度变化 后向轨迹 聚类分析 传输路径 潜在源贡献函数 |
| 收稿时间： | 2017-07-05 |
| 修稿时间： | 2017-11-10 |
Characteristics of Air Pollution and Potential Source in Winter of Changzhou City |
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| HE Tao,PENG Yan,QIAO Liping,TENG Jiaquan and XUE Yingang. Characteristics of Air Pollution and Potential Source in Winter of Changzhou City[J]. Research of Environmental Sciences, 2018, 31(3): 487-495. DOI: 10.13198/j.issn.1001-6929.2017.03.74 |
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| Authors: | HE Tao PENG Yan QIAO Liping TENG Jiaquan XUE Yingang |
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| Affiliation: | 1.Changzhou Environmental Monitoring Center, Changzhou 213001, China2.State Environmental Protection Key Lab of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China |
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| Abstract: | The characteristics of air pollution in Changzhou City were analyzed by using the measurement data of PM2.5, PM10, SO2, NO2 and CO in winter from 2013 to 2015. Hourly 48-hour backward trajectories at ground (100 m) level were used to study the effect of clusters on various pollutants. The potential source contribution function combined with weight factors (WPSCF) was applied to identify the potential sources and estimate the source contributions for air pollutions in winter. The results showed that PM2.5 was the primary pollutant in winter in Changzhou City. The number of days with PM2.5 as the primary pollutant accounted for more than 90% days in winter. The highest frequency of air quality level were good and slight polluted. The contribution of PM2.5 in winter accounted for 37.4% of PM2.5 annual mean, one reason of which was the incomplete combustion of fuel. Two peak pattern in the diurnal variations of ρ(PM2.5), ρ(PM10), ρ(SO2), ρ(NO2) and ρ(CO) were observed. The first peak value appeared in the early morning rush hours while the second one appeared around late evening, while the later peak of ρ(NO2) was obviously larger than that of the first peak, while ρ(SO2) and ρ(CO) behaved the opposite. The ratios of CO/NOx and SO2/NOx suggested that mobile sources were the major contributor to gaseous pollutants in Changzhou City and the point sources had important effects on air quality. The gradient variation of ρ(PM2.5) concentration in 1 and 6 h can be used to identify the explosive growth of ρ(PM2.5). The continental airflow from the northwest, west and southwest regions had significant impact on Changzhou City and the corresponding ρ(PM2.5), ρ(PM10), ρ(SO2), ρ(NO2) and ρ(CO) values as well as the probability of the corresponding polluted trajectories were higher than airflows from other direction. The east airflow usually moved slowly and the atmospheric diffusion conditions were poor, which could easily lead to pollution accumulation and high concentrations of ρ(PM2.5), ρ(SO2) and ρ(NO2). High WPSCF values (>0.5) were focused in the downstream of the Yangtze River from Wuhu to Shanghai and the Hangzhou Bay area. The potential sources of PM2.5, PM10, SO2, NO2 and CO were quite different. The contribution of local sources on NO2, SO2 and CO was more pronounced than that of PM2.5 and PM10. The marine pollution source would be a significant impact on NO2, SO2 and CO in the coastal area. The study showed that the local air pollution was highly related with the local anthropogenic air pollutant emission in the Yangtze River Delta region but the contribution of long-distance transport could not be ignored. |
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| Keywords: | air pollution gradient variation backward trajectory cluster analysis transport pathways potential source contribution function |
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