Li Hao, Li Li, Huang Cheng, An Jing-yu, Yan Ru-sha, Huang Hai-ying, Wang Yang-jun, Lu Qing, Wang Qian, Lou Sheng-rong, Wang Hong-li, Zhou Min, Tao Shi-kang, Qiao Li-ping, Chen Ming-hua
Huan Jing Ke Xue. 2015 Jan;36(1):1-10.
With the fast development of urbanization, industrialization and mobilization, the air pollutant emissions with photochemical reactivity become more obvious, causing a severe photochemical pollution with the characteristics of high ozone concentration. However, the ozone source identification is very complicated due to the high non linearity between ozone and its precursors. Thus, ways to reduce ozone is still not clear. A high ozone pollution episode occurred during July, 2013, which lasted for a long period, with large influence area and high intensity. In this paper, we selected this episode to do a case study with the application of ozone source apportionment technology(OSAT) coupled within the CAMx air quality model. In this study, 4 source regions(including Shanghai, north Zhejiang, South Jiangsu and long range transport), 7 source categories (including power plants, industrial process, industrial boilers and kilns, residential, mobile source, volatile source and biogenic emissions) are analyzed to study their contributions to surface O3 in Shanghai, Suzhou and Zhejiang. Results indicate that long range transport contribution to the surface ozone in the YRD is around 20 x 10(-9) - 40 x 10(-9) (volume fraction). The O3 concentrations can increased to 40 x 10(-9) - 100 x 10(-9) (volume fraction) due to precursors emissions in Shanghai, Jiangsu and Zhejiang. As for the regional contribution to 8 hour ozone, long range transport constitutes 42.79% +/- 10.17%, 48.57% +/- 9.97% and 60.13% +/- 7.11% of the surface ozone in Shanghai, Suzhou and Hangzhou, respectively. Regarding the high O3 in Shanghai, local contribution is 28.94% +/- 8.49%, north Zhejiang constitutes 19.83% +/- 10.55%. As for surface O3 in Suzhou, the contribution from south Jiangsu is 26.41% +/- 6.80%. Regarding the surface O3 in Hangzhou, the major regional contributor is north Zhejiang (29.56% +/- 8.33%). Contributions from the long range transport to the daily maximum O3 concentrations are slightly lower than those to the 8-hourly O3, with the contribution of 35.35%-58.04%, while local contributions increase. As for the contributions from source sectors, it is found that the major source contributors include industrial boilers and kilns (18.4%-21.11%), industrial process (19.85%-28.46%), mobile source (21.30%-23.51%), biogenic (13.01%-17.07%) and power plants (7.08%-9.75%). Thus, industrial combustion, industrial processes, and mobile source are major anthropogenic sources of high ozone pollution in summer in the YRD region.
随着城市化、工业化和机动化的快速发展,具有光化学反应活性的空气污染物排放愈发明显,引发了以高臭氧浓度为特征的严重光化学污染。然而,由于臭氧与其前体物之间存在高度非线性关系,臭氧源识别非常复杂。因此,降低臭氧的方法仍不明确。2013年7月发生了一次持续时间长、影响范围大、强度高的高臭氧污染事件。本文选取该事件,应用耦合在CAMx空气质量模型中的臭氧源解析技术(OSAT)进行案例研究。本研究分析了4个源区(包括上海、浙北、苏南和远距离传输)、7个源类别(包括发电厂、工业过程、工业锅炉和窑炉、居民源、移动源、挥发性源和生物源排放)对上海、苏州和浙江地面O3的贡献。结果表明,远距离传输对长三角地区地面臭氧的贡献约为20×10⁻⁹ - 40×10⁻⁹(体积分数)。由于上海、江苏和浙江的前体物排放,O3浓度可增至40×10⁻⁹ - 100×10⁻⁹(体积分数)。至于对8小时臭氧的区域贡献,远距离传输分别占上海、苏州和杭州地面臭氧的42.79%±10.17%、48.57%±9.97%和60.13%±7.11%。对于上海的高O3,本地贡献为28.94%±8.49%,浙北占19.83%±10.55%。对于苏州的地面O3,苏南的贡献为26.41%±6.80%。对于杭州的地面O3,主要区域贡献者是浙北(29.56%±8.33%)。远距离传输对每日最大O3浓度的贡献略低于对8小时O3的贡献,为35.35% - 58.04%,而本地贡献增加。至于源部门的贡献,发现主要源贡献者包括工业锅炉和窑炉(18.4% - 21.11%)、工业过程(19.85% - 28.46%)、移动源(21.30% - 23.51%)、生物源(13.01% - 17.07%)和发电厂(7.08% - 9.75%)。因此,工业燃烧、工业过程和移动源是长三角地区夏季高臭氧污染的主要人为源。