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王红果, 燕利利, 张建平, 等. 2023. 太行山沿线典型工业城市2021年秋冬季PM2.5阶段性污染特征[J]. 气候与环境研究, 28(6): 652−664. doi: 10.3878/j.issn.1006-9585.2023.23013
引用本文: 王红果, 燕利利, 张建平, 等. 2023. 太行山沿线典型工业城市2021年秋冬季PM2.5阶段性污染特征[J]. 气候与环境研究, 28(6): 652−664. doi: 10.3878/j.issn.1006-9585.2023.23013
WANG Hongguo, YAN Lili, ZHANG Jianping, et al. 2023. Periodic Pollution Characteristics of PM2.5 in Typical Industrial Cities along Taihang Mountains in Autumn and Winter of 2021 [J]. Climatic and Environmental Research (in Chinese), 28 (6): 652−664. doi: 10.3878/j.issn.1006-9585.2023.23013
Citation: WANG Hongguo, YAN Lili, ZHANG Jianping, et al. 2023. Periodic Pollution Characteristics of PM2.5 in Typical Industrial Cities along Taihang Mountains in Autumn and Winter of 2021 [J]. Climatic and Environmental Research (in Chinese), 28 (6): 652−664. doi: 10.3878/j.issn.1006-9585.2023.23013

太行山沿线典型工业城市2021年秋冬季PM2.5阶段性污染特征

Periodic Pollution Characteristics of PM2.5 in Typical Industrial Cities along Taihang Mountains in Autumn and Winter of 2021

  • 摘要: 为探究太行山沿线城市秋冬季细颗粒物(PM2.5)污染特征,选取典型工业城市济源市为研究对象,依据不同时间段内气象因子对PM2.5小时浓度变化的影响特征、PM2.5污染等级、冬季采暖开始(2021年11月15日)前后污染物增长速率的差异等,将2021年10月至2022年3月划分为4个阶段,并运用多元线性回归模型对不同阶段PM2.5与气象因子小时数据进行分析。结果表明,第一阶段(2021年10月1日至11月14日)26.1%的PM2.5小时浓度变化由气象因子决定,单因子与PM2.5的相关性均在36%以下;第二阶段(2021年11月15日至12月31日)72.4%的PM2.5小时浓度变化由气象因子决定,风向、相对湿度、能见度3项因子对PM2.5影响显著,且PM2.5与相对湿度和能见度的相关性均达到最高(61.5%和73.1%);第三阶段(2022年1月1~31日)53.2%的PM2.5小时浓度变化由气象因素决定,相对湿度和风速对PM2.5的小时值变化影响不显著,这与该阶段大气稳定性高、污染物与大气扩散能力双向反馈效应显著有关;第四阶段(2022年2月1日至3月31日)32.2%的PM2.5小时浓度变化由气象因素决定,受沙尘及扬尘影响,风速对PM2.5小时变化影响不显著。济源市秋冬季污染过程中颗粒物组分以NO3 、NH4+、OC、SO42−为主,其中二次无机离子(SO42−、NO3、NH4+)占比在65.7%以上,二次污染严重。颗粒物组分浓度增速整体表现为NO3、S、EC、Cl随污染程度加重增速减缓,SO42−、OC 、K+、NH4+增速随污染程度加重呈现“慢快慢”的变化趋势。

     

    Abstract: Based on the use of multiple linear regression models, the air quality of Jiyuan City, a typical industrial city, was analyzed at different stages from October 2021 to March 2022. The impact characteristics of the change of meteorological factors on PM2.5, PM2.5 pollution level, and the difference in the growth rate of pollutant concentration before and after the start of winter heating (15 Nov 2021) were studied to explore the characteristics of PM2.5 pollution in cities along Taihang Mountain in autumn and winter. Results reveal that in the first stage (1 Oct–14 Nov 2021), 26.1% of PM2.5 hourly concentration change was determined using meteorological factors, and the correlation between any single factor and PM2.5 was <36%. In the second stage (15 Nov–31 Dec 2021), 72.4% of the hourly concentration change of PM2.5 was determined using meteorological factors. Wind direction, relative humidity, and visibility had considerable influences on PM2.5, and the correlation between PM2.5 and relative humidity and visibility was the highest (61.5% correlation with relative humidity and 73.1% correlation with visibility). In the third stage (1–31 Jan 2022), 53.2% of hourly concentration change of PM2.5 was determined using meteorological factors, and the relative humidity and wind speed had no considerable effect on the hourly change of PM2.5, which is associated with the considerable effect of long-range migration and detention of pollution clusters in this stage. In the fourth stage (1 Feb–31 Mar 2022), 32.2% of PM2.5 hourly concentration change was determined using meteorological factors. Wind speed had no considerable effect on PM2.5 hourly change because it was affected by sand and dust. During the pollution period in autumn and winter in Jiyuan City, the main components of particulate matter were NO3, NH4+, OC, and SO42−, with the proportion of secondary inorganic ions (SO42−, NO3, and NH4+) being>65.7%, and the secondary pollution was severe. Thus, the growth rates of particle component concentrations show that, with the increase in pollution, the growth rates of NO3, S, EC, and Cl decrease while those of SO42−, OC, K+, and NH4+ exhibit a “slow–fast” trend.

     

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