2020 年春节期间芜湖市典型大气污染过程分析

大气与环境光学学报 ›› 2021, Vol. 16 ›› Issue (2): 127-137.

2020 年春节期间芜湖市典型大气污染过程分析

查书平1, 李新宇2, 张东2, 王文婧2∗, 董艳3, 胡秀芳3

1 芜湖职业技术学院, 安徽芜湖 241006; 2 芜湖市生态环境局, 安徽芜湖 241006; 3 南通大学地理科学学院, 江苏南通 226007

收稿日期:2020-03-26 修回日期:2020-05-28 出版日期:2021-03-28 发布日期:2021-03-28
通讯作者: E-mail: 273540324@qq.com E-mail:273540324@qq.com
作者简介:查书平 (1974 - ), 女, 安徽安庆人, 博士, 副教授, 主要从事大气环境方面的研究。 E-mail: 523176674@qq.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然基金项目, 41475109, 41601189, 41701634), Key Research Projects of Natural Science in Colleges and Universities of Anhui Province (安徽省高校自然科学重点研究项目, KJ2019A0984), Causes of Heavy Air Pollution and Tackling Key Projects (大气重污染成因与治理攻关项目, DQGG-05-32), Science and Technology Innovation Team Project (科技创新团队项目, wzykjtd202001), Cultivation Project of Science and Technology Innovation Service Platform (科技创新服务平台培育项目, Kjcxpt202007)

Analysis of Typical Air Pollution Event in Wuhu During Spring Festival in 2020

ZHA Shuping1, LI Xinyu2, ZHANG Dong2, WANG Wenjing2∗, DONG Yan3, HU Xiufang3

1 Wuhu Institute of Technology, Wuhu 241006, China; 2 Wuhu Ecological Environment Bureau, Wuhu 241006, China; 3 School of Geography Sciences, Nantong University, Nantong 226007, China

Received:2020-03-26 Revised:2020-05-28 Published:2021-03-28 Online:2021-03-28

摘要/Abstract

摘要： 2020 年春节期间 (1 月 24 日–2 月 8 日), 受新冠肺炎疫情影响, 芜湖市居民活动水平降到最低, 但芜湖市却出现了三次不同程度的空气污染过程, 为探索芜湖市大气污染成因提供了契机。利用地面气象要素监测数据、激光雷达监测数据以及 HYSPLIT 后向轨迹模型分析了这三次污染过程的污染特征和成因。结果表明: 1 月 27 日 08:00–28 日 18:00 第一次污染过程, 是在本地基础排放量不变的情况下, 高湿静稳的大气环境诱发的 10 小时短暂性污染过程; 1 月 29 日 17:00–2 月 1 日 13:00 第二次污染过程, 是在重点企业排放量增加、大气扩散条件较不利的情况下, 受源自河北、河南、山东等地的污染气团远距离长时间的污染输送, 加剧污染程度, 最终形成 1 月 30–31 日 2 天的轻度污染; 2 月 2 日 19:00–4 日 07:00 第三次污染过程, 则是在不利的扩散条件下, 受源自江苏、浙江一带污染气团短时间输入性影响, 形成 2 月 3 日 1 天的轻度污染; 与 2019 年春节期间相比, 2020 年空气质量改善明显, 中度及以上污染天数减少了 100%, 轻度污染以上的污染时长减少 48 小时。可见, 本地源减排是改善芜湖环境空气质量的根本, 区域性联防联控是应对污染天气的有效手段。

关键词: 大气污染过程, 芜湖, 后向轨迹模型, 气溶胶激光雷达

Abstract: Affected by the COVID-19 epidemic, the activity level of Wuhu residents has been reduced to the lowest level during the Spring Festival in 2020 (from January 24 to February 8). However, there were three different air pollution events occurred in Wuhu city, which provided an opportunity for exploring the cause of air pollution in Wuhu. The pollution characteristics and causes of the three pollution events are analyzed based on the monitoring data of ground meteorological elements, lidar monitoring data and HYSPLIT backward trajectory model. The results show that the first pollution event from 8:00 on January 27 to 18:00 on January 28 is a 10-hour transient pollution process induced by the high humidity and static atmospheric environment with the local basic emissions unchanged. The second pollution event from 17:00 on January 29 to 13:00 on February 1 is caused by a long-distance and longterm transportation of polluted air masses from Hebei, Henan and Shandong provinces, which intensifies the degree of pollution, and finally induces a two-day light pollution from January 30 to 31 under the increased emissions from key enterprises and unfavorable atmospheric diffusion conditions. And the third pollution event from 19:00 on February 2 to 07:00 on February 4 is a day light pollution caused by the short-term input of pollution groups from Jiangsu and Zhejiang provinces under the unfavorable diffusion conditions. Compared with the Spring Festival in 2019, the air quality in 2020 has improved significantly, the number of days of moderate and above pollution has decreased by 100%, and the pollution duration of light pollution has decreased by 48 hours. It can be seen that local source emission reduction is the fundamental to improve the air quality of Wuhu, and regional joint prevention and control is an effective means to deal with the polluted weather.

Key words: air pollution event, Wuhu, back trajectory model, aerosol lidar

中图分类号:

X513

查书平, 李新宇, 张东, 王文婧∗, 董艳, 胡秀芳. 2020 年春节期间芜湖市典型大气污染过程分析[J]. 大气与环境光学学报, 2021, 16(2): 127-137.

ZHA Shuping, LI Xinyu, ZHANG Dong, WANG Wenjing∗, DONG Yan, HU Xiufang. Analysis of Typical Air Pollution Event in Wuhu During Spring Festival in 2020[J]. Journal of Atmospheric and Environmental Optics, 2021, 16(2): 127-137.

参考文献 20

[1]	Wang M Y, Cao C X, Li G S, et al. Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China [J].
	Atmospheric Environment, 2015, 102: 112-121.
[2]	Liu X G, Li J, Qu Y, et al. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China
[J]	Atmospheric Chemistry and Physics, 2013, 13(9) : 4501-4514.
[3]	Kang N, Kumar K R, Hu K, et al. Long-term (2002-2014) evolution and trend in collection 5.1 Level-2 aerosol products derived
	from the MODIS and MISR sensors over the Chinese Yangtze River Delta [J]. Atmospheric Research, 2016, 181: 29-43.
[4]	Miao Lei, Wu Hongbo. Analysis of a typical regional air pollution process [J]. Environmental Science & Technology, 2017,
40	(2): 53-58.
	苗蕾, 吴洪波. 一次典型区域大气污染事件的过程分析 [J]. 环境科学与技术, 2017, 40(2): 53-58.
[5]	Gu J X, Bai Z P, Li W F, et al. Chemical composition of PM2. 5 during winter in Tianjin, China [J]. Particuology, 2011, 9(3):
21	5-221.
[6]	Guo S, Hu M, Zamora M L, et al. Elucidating severe urban haze formation in China [J]. Proceedings of the National Academy
	of Sciences of the United States of America, 2014, 111(49): 17373-17378.
[7]	Lin Yong, Zhang Shugui, Wang Xiaoyue. The current situation, the trend and the control countermeasures of air pollution in
	Wuhu city [J]. Journal of Anhui Normal University (Natural Science), 2000, 23(1): 79-82.
	林勇, 张书贵, 王小月. 芜湖市区大气污染现状、发展趋势及防治对策 [J]. 安徽师范大学学报 (自然科学版) , 2000,
23	(1): 79-82.
[8]	Yang Lihui, Gu Liying, Su You. Grain size characteristics of the atmospheric dust fall at Wuhu city in Spring and Summer [J].
	Journal of West Anhui University, 2014, 30(2): 111-114.
	杨立辉, 顾李颖, 苏优. 芜湖市春夏季大气干降尘的粒径特征及其环境意义 [J]. 皖西学院学报, 2014, 30(2): 111-114.
[9]	Hu Gang, Yan Wujiu, Zhuang Yongxiang, et al. Study on characteristics of atmospheric environmental quality in urban fringe
	of Wuhu [J]. Territory & Natural Resources Study, 2006, 1: 38-40.
	胡刚, 阎伍玖, 庄永祥, 等. 芜湖市城市边缘区大气环境质量特征研究 [J]. 国土与自然资源研究, 2006, 1: 38-40.
[10]	Cheng Cuiyun, Yan Wujiu. Analysis of the characteristics of the atmospheric pollution of SO2 in Wuhu urban area [J]. Journal
	of Anhui University of Technology and Science, 2005, 20(4): 16-20.
	程翠云, 阎伍玖. 安徽省芜湖市市区大气 SO2 污染特征分析 [J]. 安徽工程科技学院学报, 2005, 20(4): 16-20.
[11]	Draxler R R and Hess G D. An overview of the HYSPLIT 4 modelling system for trajectories, dispersion, and deposition [J].
	Australian Meteorological Magazine, 1998, 47: 295-308.
[12]	Zhang Fang. Re-analysis and Evaluation of Atmospheric Carbon Dioxide (CO2), Methane (CH4) and Carbon Monoxide (CO)
	at Mount Waliguan, China [D]. Beijing: University of Chinese Academy of Sciences, 2011.
	张芳. 大气 CO2、 CH4 和 CO 浓度资料再分析及源汇研究 [D]. 北京: 中国科学院研究生院, 2011.
[13]	NOAA. Global Data Assimilation System (GDAS) [EB/OL]. 2015-10-12.ftp: arlftp. arlhq. noaa. gov pub archives gdas1.
[14]	Yu X N, Kuma K R, LV R, et al. Changes in column aerosol optical properties during extreme haze-fog episodes in January
20	13 over urban Beijing [J]. Environmental Pollution, 2016, 210: 217-226.
[15]	Chen Benqing, Yang Yanming. Remote sensing of the spatio-temporal pattern of aerosol over Taiwan Strait and its adjacent
	sea areas [J]. Acta Scientiae Circumstantiae, 2008, 28(12): 2597-2604.
	陈本清, 杨燕明. 台湾海峡及周边海区气溶胶时空分布特征的遥感分析 [J]. 环境科学学报, 2008, 28(12): 2597-2604.
[16]	Yu Xingna, Ma Jia, Zhu Bin, et al. Effects of relative humidity and aerosol physicochemical properties on atmospheric visibility
	in northern suburb of Nanjing [J]. Environmental Science, 2015, 36(6): 1919-1925.
	于兴娜, 马佳, 朱彬, 等. 南京北郊秋冬季相对湿度及气溶胶理化特性对大气能见度的影响 [J]. 环境科学, 2015, 36(6):
19	19-1925.
[17]	Zheng G.J., Duan F.K., Su H., et al. Exploring the severe winter haze in Beijing: The impact of synoptic weather, regional
	transport and heterogeneous reactions [J]. Atmospheric Chemistry & Physics, 2015, 15(6): 2969-2983.
[18]	Zhang Qiang, Xue Di, Wang Shuang, et al. Analysis on the evolution of PM2:5 heavy air pollution process in Qingdao [J].
	China Environmental Science, 2017, 37(10): 3623-3635.
	张强, 薛迪, 王爽, 等. 青岛市 PM2:5 重污染天气演变过程分析 [J]. 中国环境科学, 2017, 37(10): 3623-3635.
[19]	Tao M H, Chen L F, Xiong X Z, et al. Formation process of the widespread extreme haze pollution over northern China in
	January 2013: Implications for regional air quality and climate [J]. Atmospheric Environment, 2014, 98: 417-425.
[20]	Zhang J K, Sun Y, Liu Z R, et al. Characterization of submicron aerosols during a month of serious pollution in Beijing [J].
	Atmospheric Chemistry and Physics, 2013, 14: 2887-2903.
[21]	Li Li, Liu Ruiling, Zhai Chongzhi. Detection and analysis of heavy air pollution using lidar in Chongqing urban area [J].
	Environmental Engineering, 2013, 7(11): 4469-4474.
	李礼, 刘芮伶, 翟崇治. 重庆主城区大气重污染形势的激光雷达探测与分析 [J]. 环境工程学报, 2013, 7(11): 4469-4474.
[22]	Zhao Xiujuan, Jiang Hua, Wang Litao, et al. Quantifying the source of haze pollution in southern Hebei cities using CMAQ
	model [J]. Acta Scientiae Circumstantiae, 2012, 32(10): 2559-2567.
	赵秀娟, 姜华, 王丽涛, 等. 应用 CMAQ 模型解析河北南部城市的霾污染来源 [J]. 环境科学学报, 2012, 32(10):
25	59-2567.
[23]	Lin Elin, Wu Xihong, Jiang Yuncheng, et al. Data analysis of Xiamen aerosol lidar from February to March 2017 [J]. Straits
	Science, 2017, 12: 75-80.
	林阿玲, 巫锡洪, 蒋永成, 等. 2017 年 2–3 月厦门气溶胶激光雷达数据分析 [J]. 海峡科学, 2017, 12: 75-80.
[24]	Liu J G, Diamond J. China′s environment in a globalizing world [J]. Nature, 2005, 435(7046): 1179-1186.
[25]	Zhang Q, He K B, Huo H. Cleaning China′s air [J]. Nature, 2012, 484(7393): 161-162.
[26]	Dimitriou K, Kassomenos P. A study on the reconstitution of daily PM10 and PM2:5 levels in Paris with a multivariate linear
	regression model [J]. Atmospheric Environment, 2014, 98: 648-654.
[27]	Huang Yi, Liu Chunqiong, Xie Zhihui, et al. A self-organized analysis model for PM2:5 evolution investigation during a typical
	haze period in Chengdu based on the backward trajectories [J]. Journal of Safety and Environment, 2016, 16(1): 313-319.
	黄毅, 刘春琼, 谢志辉, 等. 基于后向轨迹模型的成都市典型灰霾期间 PM2:5 演化的自组织分析 [J]. 安全与环境学报,
20	16, 16(1): 313-319.