|
|
长江中游城市群PM2.5污染驱动因素的地理探测
|
Abstract:
随着经济的快速发展，大气污染问题不断加重，而PM2.5作为大气污染中重要污染物之一，探究PM2.5污染的时空演变特征与驱动因素对区域大气联动治理意义重大。本文通过地理探测器模型，并结合了空间相关性分析，探究了2006~2022年长江中游城市群PM2.5污染的时空演变特征与其驱动因素。研究结果表明：长江中游城市群PM2.5浓度呈现一个先上升后下降的变化趋势，在空间上呈现显著的空间聚集和空间依赖性；其次，自然条件中的降水量、平均温度、风速均为影响PM2.5浓度的主导因子，在2022年社会经济因素中的建成区绿化覆盖率和第二产业占比的驱动力值上升。且因子交互作用q值远大于单一因子，2006年、2013年、2022年的主导交互因子分别为TEM∩URB、2 PRE∩URB、IND∩GFC。
With the rapid development of the economy, the problem of air pollution is increasing, and PM2.5 is one of the important pollutants in air pollution, so it is of great significance to explore the temporal and spatial evolution characteristics and driving factors of PM2.5 pollution for regional atmospheric linkage control. In this paper, we explored the temporal and spatial evolution characteristics and driving factors of PM2.5 pollution in the urban agglomeration of the middle reaches of the Yangtze River from 2006 to 2022 through a geographic detector model combined with spatial correlation analysis. The results show that the PM2.5 concentration in the urban agglomeration in the middle reaches of the Yangtze River shows a trend of first increasing and then decreasing, showing significant spatial aggregation and spatial dependence in space, and secondly, precipitation, average temperature and wind speed are the dominant factors affecting PM2.5 concentration under natural conditions, and the driving force values of green coverage rate and the proportion of secondary industry in built-up areas increase in 2022. The q-value of factor interaction was much larger than that of a single factor, and the dominant interaction factors in 2006, 2013, and 2022 were TEM∩URB, 2 PRE∩URB, IND∩GFC, respectively.
| [1] | Wang, H., Zhuang, Y., Wang, Y., Sun, Y., Yuan, H., Zhuang, G., et al. (2008) Long-Term Monitoring and Source Apportionment of PM2.5/PM10 in Beijing, China. Journal of Environmental Sciences, 20, 1323-1327. https://doi.org/10.1016/s1001-0742(08)62228-7 |
| [2] | 张鑫, 赵小曼, 孟雪洁, 等. 北京、新乡夏季大气颗粒物中重金属的粒径分布及人体健康风险评价[J]. 环境科学, 2018, 39(3): 997-1003. |
| [3] | Duan, J. and Tan, J. (2013) Atmospheric Heavy Metals and Arsenic in China: Situation, Sources and Control Policies. Atmospheric Environment, 74, 93-101. https://doi.org/10.1016/j.atmosenv.2013.03.031 |
| [4] | 刘媛, 张蕾, 陈娱, 等. 2003-2016年中国PM2.5浓度时空格局演变及影响因素解析[J]. 地理科学, 2023, 43(1): 152-162. |
| [5] | Zhao, H., Guo, S. and Zhao, H. (2018) Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China. Sustainability, 10, Article 2574. https://doi.org/10.3390/su10072574 |
| [6] | 王丽丽, 刘笑杰, 李丁, 等. 长江经济带PM2.5空间异质性和驱动因素的地理探测[J]. 环境科学, 2022, 43(3): 1190-1200. |
| [7] | Wu, Q., Guo, R., Luo, J. and Chen, C. (2021) Spatiotemporal Evolution and the Driving Factors of PM2.5 in Chinese Urban Agglomerations between 2000 and 2017. Ecological Indicators, 125, Article 107491. https://doi.org/10.1016/j.ecolind.2021.107491 |
| [8] | 孙梦雨. 1998-2017年黄土高原地区雾霾污染时空演变及其影响因素研究[D]: [硕士学位论文]. 西安: 陕西师范大学, 2019. |
| [9] | 成勤, 岳岩裕, 罗剑琴, 等. 地形对宜昌市大气污染特征及扩散条件影响[J]. 气象与环境科学, 2024, 47(5): 86-94. |
| [10] | 肖玉, 王硕, 李娜, 等. 北京城市绿地对大气PM2.5的削减作用[J]. 资源科学, 2015, 37(6): 1149-1155. |
| [11] | Anselin, L. (1988) Spatial Econometrics: Methods and Models. Springer. https://doi.org/10.1007/978-94-015-7799-1 |
| [12] | 王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 163-134. |
| [13] | Wei, J. and Li, Z. (2023) China High PM2.5: High-Resolution and High-Quality Ground-Level PM2.5 Dataset for China (2000-2022). National Tibetan Plateau/Third Pole Environment Data Center. https://doi.org/10.5281/zenodo.3539349 |
| [14] | 赵萍, 孙雨, 赵思逸, 等. 巢湖流域土地利用碳排放时空变化及影响因素研究[J]. 合肥工业大学学报(自然科学版), 2024, 47(4): 433-440, 457. |
| [15] | 权文婷, 周辉, 王卫东, 等. 2000-2023年关中平原城市群生态环境质量动态特征[J]. 水土保持研究, 2025, 32(1): 336-346, 357. |
| [16] | Wu, Y., Shi, K., Chen, Z., Liu, S. and Chang, Z. (2022) Developing Improved Time-Series DMSP-OLS-Like Data (1992-2019) in China by Integrating DMSP-OLS and SNPP-VIIRS. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14. https://doi.org/10.1109/tgrs.2021.3135333 |
