温州市空气质量变化趋势研究
Study on the Trend of Air Quality Changes in Wenzhou
DOI: 10.12677/AEP.2023.132042, PDF,    科研立项经费支持
作者: 钱仁川, 白洪扬*:温州市数据管理发展集团有限公司,浙江 温州;温州市数据管理发展集团有限公司,博士创新站,浙江 温州;吴倩文, 柯毓泰:温州大学建筑工程学院,浙江 温州
关键词: 温州市空气污染PM2.5时空变化Mann-Kendall检验Wenzhou City Air Pollution PM2.5 Temporal and Spatial Changes Mann-Kendall Test
摘要: 空气污染与人类活动、城市扩张等因素密切相关。本研究中利用中国区域1 km分辨率PM2.5数据,分析浙江省与温州市2000~2020年PM2.5浓度空间分布特征、年均值分布特征;应用Mann-Kendall检验法分析PM2.5浓度年平均值变化趋势。结果表明,浙江省与温州市2000~2020年PM2.5浓度均呈现先升高再降低的趋势,且PM2.5浓度年均值的降低和“十二五”国家规划高度相关;浙江省与温州市趋势突变点都出现在2017年,温州市PM2.5浓度年平均值的变化趋势显著大于浙江省。该研究有助于了解和掌握我国大气环境质量的演化发展情况,为区域的空气污染防治、城市发展规划提供参考借鉴。
Abstract: Air pollution is closely related to human activities, urban expansion and other factors. In this study, the spatial distribution characteristics and annual average distribution characteristics of PM2.5 concentration in Zhejiang Province and Wenzhou City from 2000 to 2020 were analyzed using the 1 km resolution PM2.5 data in China; Mann-Kendall test was used to analyze the annual average change trend of PM2.5 concentration. The results showed that the PM2.5 concentration in Zhejiang Province and Wenzhou City increased first and then decreased from 2000 to 2020, and the reduction of the annual mean PM2.5 concentration was highly related to the “Twelfth Five Year Plan”. The trend mutation points of Zhejiang Province and Wenzhou City both appeared in 2017, and the change trend of annual average PM2.5 concentration in Wenzhou City was significantly greater than that in Zhejiang Province. This study is helpful to understand and master the evolution and development of atmospheric environment quality in China, and provide reference for regional air pollution prevention and urban development planning.
文章引用:钱仁川, 白洪扬, 吴倩文, 柯毓泰. 温州市空气质量变化趋势研究[J]. 环境保护前沿, 2023, 13(2): 322-332. https://doi.org/10.12677/AEP.2023.132042

参考文献

[1] 时燕, 刘瑞梅, 罗毅, 杨昆. 近20年来中国PM2.5污染演变的时空过程[J]. 环境科学, 2020, 41(1): 1-13.
[2] 郦嘉诚, 高庆先, 李亮, 等. 对首要污染物所揭示的京津冀环境空气质量状况的认识启迪与对策建议[J]. 环境科学研究, 2018, 31(10): 1651-1661.
[3] 穆泉, 张世秋. 中国2001-2013年PM2.5重污染的历史变化与健康影响的经济损失评估[J]. 北京大学学报(自然科学版), 2015, 51(4): 694-706.
[4] 尚勇. 全球问题的中外关联——以大气污染为例[J]. 改革与开放, 2015(15): 46+51.
[5] 李怀川. 浙江省空气重污染过程统计特征及浙南重污染预报方法的探讨[D]: [硕士学位论文]. 兰州: 兰州大学, 2017.
[6] 赵燕, 李大伟, 翟宇虹, 方晓丹. 2014年-2021年珠海市环境空气质量变化趋势及污染特征研究[J]. 环境科学与管理, 2022, 47(12): 144-149.
[7] Remer, L.A., Kaufman, Y.J., Tanré, D., et al. (2005) The MODIS Aerosol Algorithm, Products, and Validation. Journal of the Atmospheric Sciences, 62, 947-973. [Google Scholar] [CrossRef
[8] Lyapustin, A., Wang, Y., Laszlo, I., et al. (2011) Multiangle Implementation of Atmospheric Correction (MAIAC): 2. Aerosol Algorithm. Journal of Geophysical Research: Atmospheres, 116, Article No. D03211. [Google Scholar] [CrossRef
[9] Lyapustin, A., Wang, Y., Laszlo, I., et al. (2012) Multi-Angle Im-plementation of Atmospheric Correction for MODIS (MAIAC): 3. Atmospheric Correction. Remote Sensing of Envi-ronment, 127, 385-393. [Google Scholar] [CrossRef
[10] Zheng, Y., Zhang, Q., Liu, Y., Geng, G. and He, K. (2016) Esti-mating Ground-Level PM2.5 Concentrations over Three Megalopolises in China Using Satellite-Derived Aerosol Optical Depth Measurements. Atmospheric Environment, 124, 232-242. [Google Scholar] [CrossRef
[11] van Donkelaar, A., Martin, R.V., Levy, R.C., et al. (2011) Satellite-Based Estimates of Ground-Level Fine Particulate Matter during Extreme Events: A Case Study of the Moscow Fires in 2010. Atmospheric Environment, 45, 6225-6232. [Google Scholar] [CrossRef
[12] Lv, B., Hu, Y., Chang, H.H., et al. (2017) Daily Estimation of Ground-Level PM2.5 Concentrations at 4 km Resolution Over Beijing-Tianjin-Hebei by Fusing MODIS AOD and Ground Observations. Science of the Total Environment, 580, 235-244. [Google Scholar] [CrossRef] [PubMed]
[13] Just, A.C., Wright, R.O., Schwartz, J., et al. (2015) Using High-Resolution Satellite Aerosol Optical Depth to Estimate Daily PM2.5 Geographical Distribution in Mexico City. Environmental Science & Technology, 49, 8576-8584. [Google Scholar] [CrossRef] [PubMed]
[14] Xiao, Q., Wang, Y., Chang, H.H., et al. (2017) Full-Coverage High-Resolution Daily PM2.5 Estimation Using MAIAC AOD in the Yangtze River Delta of China. Remote Sensing of Environment, 199, 437-446. [Google Scholar] [CrossRef
[15] Huang, K., Xiao, Q., Meng, X., et al. (2018) Predicting Monthly High-Resolution PM2.5 Concentrations with Random Forest Model in the North China Plain. Environmental Pollution, 242, 675-683. [Google Scholar] [CrossRef] [PubMed]
[16] 晨澄, 杨友健, 白直旭. 瓯江流域感潮河段潮位变化趋势分析[J]. 陕西水利, 2022(7): 1-5+11.
[17] 刘亦文. 碳减排约束政策对中国城市空气质量的影响研究[J]. 湖南大学学报(社会科学版), 2022, 36(2): 73-81.