京津冀地区一次PM2.5与臭氧协同污染过程分析

doi:10.3969/j.issn.1673-6141.2024.03.007

摘要/Abstract

摘要： 2018 年4 月16—22 日我国京津冀地区发生了一次PM2.5与臭氧 (O3) 协同污染过程，为深入认识两者协同污染发生的成因及过程，利用多源数据，综合分析了本次协同污染过程的气象要素、地面形势、垂直特征等，并按两种污染物的小时质量浓度变化将污染过程分为增长、高位、下降三个过程。结果表明： (1) 本次污染过程中气温、气压、相对湿度与污染物质量浓度变化有较好对应关系，不同过程转换时风速变化明显， O3污染在每天16：00 出现最多。(2) 蒙古低压靠近京津冀且京津冀处于高压外围时的静态天气下污染物易积累传输，冷锋过境后污染物质量浓度快速下降，东北高压控制下空气质量较高。(3) 19 日凌晨气溶胶垂直分布主要包括混合型、沙尘型气溶胶，污染物主要来自京津冀南部、西南部； 19 日中午气溶胶垂直分布主要包括混合型、污染型、沙尘型气溶胶，污染物主要来自京津冀北部。高空传输易带来沙尘型气溶胶，地面污染严重时地面气团传输以外来水平传输为主。(4) PM2.5小时平均质量浓度与气温呈极显著正相关，与相对湿度呈显著正相关，与气压及风速呈极显著负相关； O3小时平均质量浓度与气温、风速呈极显著正相关，与气压、相对湿度呈极显著负相关；两种污染物间呈显著正相关，两种污染物的小时质量浓度高值大多出现在风向为135°到225°的东南至西南风范围内。

关键词: PM2.5, 臭氧, 协同污染, 京津冀

Abstract: A synergistic pollution process of PM2.5 and ozone (O3) occurred in Beijing-Tianjin-Hebei Region, China, from April 16 to 22, 2018. In order to gain a deeper understanding of the occurrence and the causes of the synergetic pollution, multi-source data were used to comprehensively analyze the meteorological elements, ground conditions and vertical characteristics of this synergistic process, and according to the hourly mass concentration changes of the two pollutants, the pollution process was divided into three processes: growth, high level, and decline. The results show that: (1) There is a good corresponding relationship between the air temperature, air pressure, relative humidity and pollutant mass concentration change during the pollution process, the wind speed changes significantly during the conversion of different processes, and O3 pollution occurs most at 16:00 every day. (2) When the Mongolian low pressure is near the Beijing-Tianjin-Hebei Region and the Beijing-Tianjin-Hebei Region is on the periphery of the high pressure, pollutants are easy to accumulate and transmit in the static weather. After the transit of the cold front, the mass concentration of pollutants drops rapidly, and the air quality is higher under the control of northeast high pressure. (3) The vertical distribution of aerosols in the early morning of April 19 mainly included mixed and sand dust aerosols, with pollutants mainly coming from the south and southwest of Beijing-Tianjin-Hebei Region, while at noon of April 19, the vertical distribution of aerosols mainly included mixed type, pollution type and sand dust type aerosols, with pollutants mainly coming from the north of Beijing-Tianjin-Hebei Region. High altitude transmission is easy to bring sand dust aerosols, and when the ground pollution is serious, the surface air mass transmission is mainly external horizontal transmission. (4) The hourly average mass concentration of PM2.5 is extremely significantly positively correlated with air temperature, significantly positively correlated with relative humidity, while extremely significantly negatively correlated with air pressure and wind speed. The hourly average mass concentration of ozone shows an extremely significant positive correlation with air temperature and wind speed, and an extremely significant negative correlation with air pressure and relative humidity. There is a significant positive correlation between PM2.5 and O3, and the high hourly mass mass concentrations of the two pollutants mostly occur in the southeast to southwest wind range with the wind directions from 135° to 225°.

Key words: PM2.5, ozone, synergetic pollution, Beijing-Tianjin-Hebei Region

中图分类号:

方文圣, 曹念文, 施欣池. 京津冀地区一次PM2.5与臭氧协同污染过程分析[J]. 大气与环境光学学报, 2024, 19(3): 342-356.

FANG Wensheng , CAO Nianwen , SHI Xinchi. Analysis of a synergistic pollution process of PM2.5 and ozone in Beijing-Tianjin-Hebei Region[J]. Journal of Atmospheric and Environmental Optics, 2024, 19(3): 342-356.

参考文献

[1]Hong W , Xu J , Meng Z , et al. A study of the meteorological causes of a prolonged and severe haze episode in January 2013 over central-eastern China[J]. Atmospheric Environment, 2014, 98(dec.):146-157.
[2]Feng, Lu, Chao, et al. Systematic review and meta-analysis of the adverse health effects of ambient PM_(2.5) and PM_(10) pollution in the Chinese population[J]. Environmental Research, 2015,136:196-204
[3]Desqueyroux H , Pujet J C , Prosper M , et al. Short-Term Effects of Low-Level Air Pollution on Respiratory Health of Adults Suffering from Moderate to Severe Asthma[J]. Environmental Research, 2002, 89(1):29-37.
[4]Oksanen E , Holopainen T . Responses of two birch (Betula pendula Roth) clones to different ozone profiles with similar AOT40 exposure[J]. Atmospheric Environment, 2001, 35(31):5245-5254.
[5]黄顺祥.大气污染与防治的过去、现在及未来[J].科学通报,2018,63(10):895-919.
[6]李红,彭良,毕方,李陵,鲍捷萌,李俊玲,张浩,柴发合.我国PM_(2. 5)与臭氧污染协同控制策略研究[J].环境科学研究,2019,32(10):1763-1778.
[7] Chang S C , Lee C T . Secondary aerosol formation through photochemical reactions estimated by using air quality monitoring data in Taipei City from 1994 to 2003[J]. Atmospheric Environment, 2007, 41(19):4002-4017.
[8] Zhu J , Chen L , Liao H , et al. Correlations between PM2.5 and Ozone over China and Associated Underlying Reasons[J]. Atmosphere, 2019, 10(7):352.
[9]毛曳,张恒德,朱彬.2016冬季京津冀一次持续重度霾天气过程分析[J].环境科学,2021,42(08):3615-3621.
[10]廉涵阳,杨欣,张普,陈义珍,杨小阳,赵妤希,何友江,赵丹婷.北京2019年冬季一次典型霾污染特征与成因分析[J].环境科学,2021,42(05):2121-2132.
[11]王耀庭,李青春,郑祚芳,窦有俊.北京春季一次霾-沙天气污染特性与成因分析[J].环境科学,2019,40(06):2582-2594.
[12]唐伟,王帅,何友江,杨小阳,孟凡.京津冀地区一次臭氧污染过程的数值模拟分析[J].环境影响评价,2017,39(05):7-12.
[13]王馨琦,张天舒,裴成磊,陈多宏,吕立慧,项衍.差分吸收激光雷达监测广州市臭氧垂直分布特征[J].中国激光,2019,46(12):279-287.
[14]裴成磊,谢雨彤,陈希,张涛,邱晓暖,王瑜,王在华,李梅.广州市冬季一次典型臭氧污染过程分析[J/OL].环境科学:1-15[2022-06-15].DOI:10.13227/j.hjkx.202110168.
[15]陈辉,厉青,李营,张连华,毛慧琴,周伟,刘伟汉.京津冀及周边地区PM_(2.5)时空变化特征遥感监测分析[J].环境科学,2019,40(01):33-43.
[16]李燕子. 中国四大污染区灰霾污染特征及气象影响因素探究[D].暨南大学,2020.
[17]朱媛媛,刘冰,桂海林,李健军,汪巍.京津冀臭氧污染特征、气象影响及基于神经网络的预报效果评估[J/OL].环境科学:1-15[2022-06-15].DOI:10.13227/j.hjkx.202111145.
[18]余益军,孟晓艳,王振,周崴,于红霞.京津冀地区城市臭氧污染趋势及原因探讨[J].环境科学,2020,41(01):106-114.
[19]李红丽. 中国东部地区PM_（2.5）和臭氧污染的协同规律及驱动因素研究[D].上海大学,2021.
[20]罗悦函,赵天良,孟凯,王宏,龚康佳,辛雨珊,卢硕.华北平原和山区城市PM_(2.5)和O_3变化关系比较分析[J].中国环境科学,2021,41(09):3981-3989.
[21]孙金金,黄琳,龚康佳,王雪颖,李婧祎,秦墨梅,于兴娜,胡建林.2014—2019年北京和南京地区PM_(2.5)和臭氧质量浓度相关性研究[J].南京信息工程大学学报(自然科学版),2020,12(06):656-664.
[22]毛卓成,许建明,杨丹丹,余钟奇,瞿元昊,周广强.上海地区PM_(2.5)-O_3复合污染特征及气象成因分析[J].中国环境科学,2019,39(07):2730-2738.
[23]张宇静,赵天良,殷翀之,王自发,葛宝珠,刘端阳,杜欣欣.徐州市大气PM_(2.5)与O_3作用关系的季节变化[J].中国环境科学,2019,39(06):2267-2272.
[24]崔梦瑞. 京津冀地区臭氧时空分布特征与影响因素分析[D].重庆交通大学,2020.
[25]邱昀,李令军,姜磊,王新辉,赵文慧,张立坤,鹿海峰.京津冀一次污染过程的星地同步动态监测分析[J].环境科学,2019,40(03):1111-1119.
[26]中华人民共和国生态环境部. 2020中国生态环境状况公报[EB/OL] . https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/202105/P020210526572756184785.pdf,2021-05-26
[27]中华人民共和国生态环境部. 2021中国生态环境状况公报[EB/OL] . https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/202205/P020220608338202870777.pdf,2022-05-27
[28]李欢欢,张凯,牛璨,罗宇骞,王涛,支敏康,张迎仲,玄兆坤.保定市PM_(2.5)和臭氧污染特征分析[J].环境科学研究,2022,35(03):683-690.
[29]李珊珊,程念亮,潘涛,孟凡,柴发合.2014年10月京津冀一次空气重污染过程分析[J].环境科学与技术,2016,39(03):162-169.
[30]Liu Z , Liu D , Huang J , et al. Airborne dust distributions over the Tibetan Plateau and surrounding areas derived from the first year of CALIPSO lidar observations[J]. Atmospheric Chemistry and Physics, 2008, 8(16).
[31]高星星,陈艳,张镭,张武.基于CALIPSO卫星数据的全球大气边界层高度时空分布特征[J].兰州大学学报(自然科学版),2018,54(05):646-653.
[32]孙强,范学花,夏祥鳌.华北地区气溶胶垂直分布特征的观测与分析[J].气象与环境科学,2016,39(01):26-33.
[33]尹晓惠,时少英,张明英,李靖.北京沙尘天气的变化特征及其沙尘源地分析[J].高原气象,2007(05):1039-1044.
[34]Dai H , Zhu J , Liao H , et al. Co-occurrence of ozone and PM 2.5 pollution in the Yangtze River Delta over 2013–2019: Spatiotemporal distribution and meteorological conditions[J]. Atmospheric Research, 2020, 249.
[35]崔梦瑞,白林燕,冯建中,林孝松,李华林,高旺旺,李紫微.京津唐地区臭氧时空分布特征与气象因子的关联性研究[J].环境科学学报,2021,41(02):373-385.
[36]李慧,王淑兰,张文杰,王涵,王涵,王少博,李海生.京津冀及周边地区“2+26”城市空气质量特征及其影响因素[J].环境科学研究,2021,34(01):172-184.
[37]Shao M , Wang W , Yuan B , et al. Quantifying the role of PM2.5 dropping in variations of ground-level ozone: Inter-comparison between Beijing and Los Angeles[J]. Science of The Total Environment, 2021, 788:147712.
[38]BIWU CHU, QINGXIN MA, JUN LIU, et al. Air Pollutant Correlations in China: Secondary Air Pollutant Responses to NOx and SO2 Control[J]. Environmental Science & Technology Letters,2020,7(10):695-700.