YU Ruixuan,JIANG Tao,LIU Zhihong,et al.Spatial and Temporal Variation and Pollution Transport Characteristics of PM2.5 in Winter in Luzhou Nearly a Decade[J].Journal of Chengdu University of Information Technology,2025,40(04):563-570.[doi:10.16836/j.cnki.jcuit.2025.04.023]
近10年泸州市冬季PM2.5时空变化及污染传输特征研究
- Title:
- Spatial and Temporal Variation and Pollution Transport Characteristics of PM2.5 in Winter in Luzhou Nearly a Decade
- 文章编号:
- 2096-1618(2025)04-0563-08
- Keywords:
- PM2.5; Luzhou City; temporal and spatial distribution; HYSPLIT
- 分类号:
- X513
- 文献标志码:
- A
- 摘要:
- 利用2014年12月至2022年2月近10年泸州市PM2.5环境监测点数据,分析泸州市冬季PM2.5浓度、逐月平均值及冬季的日平均变化,得到PM2.5的时间变化特征,同时利用遥感图像得到PM2.5空间分布特征。利用后向轨迹模型对泸州市近3年PM2.5传输及输送轨迹进行模拟分析,并通过后向轨迹模型探究一次典型PM2.5污染过程的传输特征。结果表明:冬季是PM2.5污染最严重的季节; 早高峰、晚高峰及辐射和风场等因素造成PM2.5日变化明显; 在2018年之后对于PM2.5的治理成效相对较小,泸州市空间分布特征表现为北高南低,且在近3年表现出逐年递增趋势; 对泸州市PM2.5污染影响较大的是重庆西部及与泸州市相邻的四川其余城市。
- Abstract:
- Based on the data of PM2.5 environmental monitoring sites in Luzhou in the past ten years from December 2014 to February 2022,the paper analyzed the PM2.5 concentration,monthly average,and daily average changes in winter in Luzhou to obtain the temporal variation characteristics ofPM2.5.Meanwhile,remote sensing images were used to obtain the spatial distribution characteristics of PM2.5.Hysplit was used to simulate and analyze the transport trajectory of PM2.5 in Luzhou in the last three years.WRF and split were used to explore the transport characteristics of a typical PM2.5 pollution process.The results show that winter is the most serious season forPM2.5 pollution; The morning peak,evening peak,radiation and wind field caused the obvious daily variation of PM2.5.After 2018,the control effect of PM2.5 is relatively small,and the spatial distribution characteristics of Luzhou are higher in the north and lower in the south,and show an increasing trend in the past three years.Only western Chongqing and other cities in neighboring Sichuan province have a greater impact on PM2.5 pollution in Luzhou.
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备注/Memo
收稿日期:2023-11-16
基金项目:四川省科技计划资助项目(2023YFS0383); 四川省自然科学基金资助项目(2023NSFSC0745)
通信作者:姜涛.E-mail: 3488226@qq.com