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生态学杂志 ›› 2025, Vol. 44 ›› Issue (4): 1334-1342.doi: 10.13292/j.1000-4890.202504.018

• 研究报告 • 上一篇    下一篇

胡焕庸线以东O3时空格局演变及其影响因素

宋文芳1,胡蓓蓓1*,李红柳2*,袁雪松1,肖子霜1,侯兴帅1
  

  1. 1天津师范大学地理学部, 天津 300387; 2天津市生态环境科学研究院, 天津 300191)
  • 出版日期:2025-04-10 发布日期:2025-04-15

Evolution of O3 spatial and temporal patterns in the east of the Hu Huanyong Line and its influencing factors.

SONG Wenfang1, HU Beibei1*, LI Hongliu2*, YUAN Xuesong1, XIAO Zishuang1, HOU Xingshuai1   

  1. (1Faculty of Geography, Tianjin Normal University, Tianjin 300387, China; 2Tianjin Academy of Eco-Environmental Sciences, Tianjin 300191, China).

  • Online:2025-04-10 Published:2025-04-15

摘要: 以胡焕庸线以东237个城市作为研究区域,基于2015—2020年O3质量浓度数据、空气污染物(PM2.5、SO2、NO2)浓度数据及各地社会经济数据,运用空间自相关分析和空间面板杜宾模型,对臭氧的时空演化格局和影响因子空间效应进行分析。研究表明:(1)O3污染在时序变化上呈现“M”型波动趋势,在空间格局上呈现出显著的正相关性,集聚特征明显,京津冀、中原城市、山东半岛区域始终为高值区;(2)影响因子直接效应影响程度为,人均GDP>PM2.5浓度>三产占比>二产占比>SO2浓度>NO2浓度,工业SO2排放量和公路货运量的直接效应影响不显著;影响因子间接效应影响强度为:人均GDP>三产占比>二产占比>PM2.5浓度,人均GDP呈现显著的负向效应;三产占比、二产占比和PM2.5浓度呈现显著的正向效应;SO2浓度、NO2浓度、公路货运量和工业SO2排放量影响效应不显著。本研究揭示了区域臭氧污染的复杂性,从社会经济联系的视角强调了区域联防联控对大气臭氧污染防治的重要性,为区域间协同污染治理提供参考。


关键词: O3, 胡焕庸线, 空间杜宾模型, 影响因素, 时空演变

Abstract: We examined the spatial-temporal evolution of O3 across 237 cities in the east of the Hu Huanyong Line. The socio-economic data, air pollutant (PM2.5, SO2, NO2) and O3 concentration data during the period of 2015-2020 were analyzed using the spatial autocorrelation method. The spillover effects of influencing factors were examined by employing the spatial Durbin model. The results showed that: (1) O3 pollution demonstrated an “M-shaped” fluctuation tendency. The spatial distribution of O3 presented positive associations and characteristics of significant spatial agglomeration. High-value areas of O3 dominated in the Beijing-Tianjin-Hebei, Central Plains, and Shandong Peninsula. (2) The direct effect of the influencing factors was ranked as follows: per capita GDP > PM2.5 concentration > proportion of the tertiary industry > proportion of the secondary industry > SO2 concentration > NO2 concentration. There was no significant direct effect of industrial SO2 emissions and highway freight traffic on O3. The intensities of the indirect driving factors’ effect were as follows: per capita GDP > proportion of the tertiary industry > proportion of the secondary industry > PM2.5 concentration. The PM2.5 concentration, the proportion of the secondary, and the tertiary industry exerted positive impacts on O3, while per capita GDP was the opposite. The effects of SO2 concentration, NO2 concentration, highway freight traffic, and industrial SO2 emissions on O3 were not significant. This study reveals the complexity of regional O3 pollution, and emphasizes the importance of regional joint prevention and control of atmospheric O3 pollution from the perspective of social and economic linkages, providing a reference for inter-regional collaborative pollution control.


Key words: O3, Hu Huanyong Line, spatial Dubin model, influencing factor, spatial-temporal evolution