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疫情前后太原市主城区空气质量时空变化特征及影响因素研究

Spatio-temporal variation and influencing factors of air quality in the main urban area of Taiyuan before and after the COVID-19
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摘要 深入探讨城市新冠疫情管控期间大气环境质量及影响因素,对空气质量的改善与治理具有重要意义。以太原市主城区为研究对象,采用克里金插值法、Pearson相关性分析、HYSPLIT轨迹模型对太原市主城区2022年疫情前后空气质量时空变化特征及影响因素进行研究。结果表明:(1)在时间上,O3浓度呈逐月递增趋势。其他污染物在管控期间浓度有所下降,与2021年同期相比,PM2.5下降了23.6%,PM10下降了32.7%,NO2下降了2.6%。(2)在空间分布上,PM2.5、CO、SO2呈“西北低、东南高”的分布特点,PM10浓度则呈“西部低、东部高”的特点。(3)太原市污染物在疫情前及管控期间主要受来自西北方向长距离传输及山西省内晋中地区短距离传输影响。在常态化时期,气流轨迹更分散,来自晋中市和临汾市的短距离传输占比最高,为21.45%。(4)管控期间重点工业企业仍保持较快增长,工业用电量达到28.33亿k W·h,工业活动受管控影响较小;但移动源污染排放减少,缓解了大气污染。 Background,aim,and scope The recurrence of the COVID-19 has caused a major impact on people’s lives worldwide,and many cities around the world have implemented control measures to significantly reduce air pollutant emissions.The implementation of man-made control measures provides a favorable opportunity to study air quality and comparative analysis of the influencing factors.Taiyuan,located in the central part of Shanxi Province and the northern part of the Jinzhong Basin,adopted strict control measures during the COVID-19 in Mar.and Apr.2022,but the spatio-temporal variation and influencing factors of air quality in the main urban area of Taiyuan are still unclear.This study focuses on the air quality characteristics and pollutant causal mechanisms in the main urban area of Taiyuan before and after the COVID-19,in order to provide data support and reference for understanding the urban air quality conditions and formulating environmental management.Materials and methods This study used several methods,including Kriging interpolation,Pearson correlation analysis and HYSPLIT trajectory model,to analyze the characteristics of spatio-temporal changes in air quality before and after the COVID-19 in the main urban area of Taiyuan in 2022,as well as its influencing factors.Results During the COVID-19,PM2.5 and PM10 decreased significantly compared with the same period in 2021.O3 showed a trend of increasing month by month,the concentration of other pollutants changed less,and the overall air quality improved.In addition,the PM2.5,CO,and SO2 showing the distribution characteristics of“low in the northwest and high in the southeast”,and PM10 concentration is characterized by“low in the west and high in the east”.Discussion During the COVID-19,due to multiple factors such as reduced human activities and increased spring wind,the contribution rate of PM2.5 pollution decreased compared to that of before.However,the contribution rate of PM10 has increased,suggesting that primarily affected by the dust weather in northern China during spring.Notably,the contribution rate of NO2 pollution showed a downward trend,which may be mainly related to the reduction of industrial and mobile sources.It is interesting that the contribution rate of O3 was less affected during the COVID-19 and was affected by the warmer temperatures.The air flow causes the transmission of pollutants between different regions.Taiyuan was principally affected by the long-distance transport in the northwest direction and the short-distance transport in Jinzhong in the south of Shanxi before and during the COVID-19.Conclusions The air quality was better during the COVID-19 due to the reduction of mobile source pollution emissions and the influence of meteorological factors.Recommendations and perspectives This study suggests air quality can be improved by reducing emission sources through multiple measures.However,meteorological factors also have an impact on air quality.Thus,the human and natural factors should be considered comprehensively to control air quality during a public security emergency period.These insights offer a scientific foundation for future strategies in mitigating air pollution in cities located in north-central China.
作者 陈锋 赵娇燕 班凤梅 CHEN Feng;ZHAO Jiaoyan;BAN Fengmei(School of Resources and Environment,Shanxi University of Finance and Economics,Taiyuan 030006,China)
出处 《地球环境学报》 CSCD 2024年第1期140-153,共14页 Journal of Earth Environment
基金 黄土与第四纪地质国家重点实验室开放基金(SKLLQG2221)。
关键词 新冠肺炎 太原市 空气质量 HYSPLIT轨迹模型 COVID-19 Taiyuan air quality HYSPLIT trajectory model
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