中国北方地区两次沙尘暴对典型大城市的影响——以兰州市和北京市为例
收稿日期: 2024-06-07
修回日期: 2024-07-31
网络出版日期: 2025-05-13
基金资助
甘肃省自然科学基金(17YF1FA120)
Impact of two sandstorms on typical cities in northern China: A case of Lanzhou City and Beijing City
Received date: 2024-06-07
Revised date: 2024-07-31
Online published: 2025-05-13
李佳宸 , 巨天珍 , 李丙南 , 邱玉梦 , 曹亚群 , 王佳琦 . 中国北方地区两次沙尘暴对典型大城市的影响——以兰州市和北京市为例[J]. 干旱区地理, 2025 , 48(5) : 789 -800 . DOI: 10.12118/j.issn.1000-6060.2024.359
The complex relationship between dust storms and meteorological factors is the primary driver of particulate matter formation and distribution. These dust storms also significantly affect transportation, agriculture and animal husbandry, and population health. Using the HYSPLIT, wavelet coherence, and random forest models, the transport paths of two strong dust storms in northern China, one from March 10 to 25, 2021 (SD1) and the other from March 16 to 28, 2023 (SD2), were investigated. In addition, the effects of meteorological factors on urban PM10 were investigated. The findings revealed the following. (1) A high aerosol optical thickness (AOD) belt stretching across Xinjiang, Gansu, Shaanxi, and Shandong formed in northern China during these two dust storm events. This phenomenon was primarily attributed to the southward movement of the Mongolian cyclone and the strong westerly wind circulation in Xinjiang. (2) The overall airflow transmission moved from northwest to northeast. Lanzhou was found to be susceptible to Xinjiang cyclones, while Beijing was notably affected by Mongolian cyclones. During SD1, airflow in Lanzhou primarily originated from Qinghai and Xinjiang, with 53.64% of Beijing’s air mass being transmitted to northeast regions of China and extending into northeastern Russia. During SD2, 51.16% of Lanzhou’s airflow stemmed from Inner Mongolia, while 49.41% of Beijing’s airflow moved toward areas such as Shandong and Jiangsu. (3) PM10 variations in SD1 exhibited greater sensitivity to meteorological factors over longer time scales. In contrast, SD2 displayed diverse responses to meteorological factors over shorter time scales. (4) Lanzhou served as the collision zone for the two cyclones, showing unstable pressure and temperature changes. Conversely, Beijing acted as the endpoint for dust storm input, primarily influenced by temperature. These results facilitate a better understanding of dust storm formation mechanisms in northern China and of the interactions between meteorological factors and particulate matter.
[1] | Darvishi B A, Soleimani M, Papi R, et al. Dust and health[M]. Islamabad: COMSATS University Islamabad, 2023: 31-49. |
[2] | Yu H, Yang W, Wang X H, et al. A seriously sand storm mixed air-polluted area in the margin of Tarim Basin: Temporal-spatial distribution and potential sources[J]. Science of the Total Environment, 2019, 676: 436-446. |
[3] | Aswini M A, Tiwari S, Singh U, et al. Aeolian dust and sea salt in marine aerosols over the Arabian Sea during the southwest monsoon: Sources and spatial variability[J]. ACS Earth and Space Chemistry, 2022, 6(4): 1044-1058. |
[4] | 张娟, 姚晓军, 李净, 等. 基于多源遥感数据的甘肃省农业干旱研究[J]. 干旱区地理, 2023, 46(1): 11-22. |
[Zhang Juan, Yao Xiaojun, Li Jing, et al. Agricultural drought research based on multi- source remote sensing data in Gansu Province[J]. Arid Land Geography, 2023, 46(1): 11-22. ] | |
[5] | Yang Y, Wang Z L, Lou S J, et al. Strong ozone intrusions associated with super dust storms in East Asia[J]. Atmospheric Environment, 2022, 290: 119355, doi: 10.1016/j.atmosenv.2022.119355. |
[6] | 赵洪飞, 杨怡, 董嘉琪, 等. 基于CMIP5的中国区域气溶胶变化及其对降水的影响[J]. 干旱区研究, 2019, 36(4): 953-962. |
[Zhao Hongfei, Yang Yi, Dong Jiaqi, et al. Variation of aerosol and its effects on precipitation in China based on CMIP5 models[J]. Arid Zone Research, 2019, 36(4): 953-962. ] | |
[7] | 吕彦勋, 赵洪民, 王小军, 等. 中国西北城市沙尘天气变化特征—以兰州为例[J]. 干旱区研究, 2024, 41(7): 1112-1119. |
[Lü Yanxun, Zhao Hongmin, Wang Xiaojun, et al. Dust weather changes in northwest Chinese cities: Lanzhou as a case study[J]. Arid Zone Research, 2024, 41(7): 1112-1119. ] | |
[8] | Fattah M A, Morshed S R, Kafy A A, et al. Wavelet coherence analysis of PM2.5 variability in response to meteorological changes in South Asian cities[J]. Atmospheric Pollution Research, 2023, 14(5): 101737, doi: 10.1016/j.apr.2023.101737. |
[9] | Han X, Ge C, Tao J H, et al. Air quality modeling for a strong dust event in East Asia in March 2010[J]. Aerosol and Air Quality Research, 2012, 12(4): 615-628. |
[10] | 李玲萍, 李岩瑛, 孙占峰, 等. 河西走廊东部沙尘暴特征及地面气象因素影响机制[J]. 干旱区研究, 2019, 36(6): 1457-1465. |
[Li Lingping, Li Yanying, Sun Zhanfeng, et al. Sandstorm and its affecting meteorological factors in east Hexi Corridor[J]. Arid Zone Research, 2019, 36(6): 1457-1465. ] | |
[11] | Liu L, Wang Z L, Che H Z, et al. Climate factors influencing springtime dust activities over northern East Asia in 2021 and 2023[J]. Atmospheric Research, 2024, 303: 107342, doi: 10.1016/j.atmosres.2024.107342. |
[12] | Karimian H, Li Q, Wu C L, et al. Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations[J]. Aerosol and Air Quality Research, 2019, 19(6): 1400-1410. |
[13] | Gagliardi R V, Andenna C. Analysis of changes in pollutant concentrations levels using a meteorological normalisation technique based on a machine learning algorithm[J]. Environmental Sciences Proceedings, 2021, 8(1): 16, doi: 10.3390/ecas2021-10691. |
[14] | 焦美玲, 韩晶, 曹彦超, 等. 庆阳市空气污染及气象因子影响特征分析[J]. 干旱区地理, 2024, 47(6): 932-941. |
[Jiao Meiling, Han Jing, Cao Yanchao, et al. Characteristics of air pollution and meteorological factors in Qingyang City[J]. Arid Land Geography, 2024, 47(6): 932-941. ] | |
[15] | Kang J H, Suh M S, Kwak C H. Land cover classification over East Asian region using recent MODIS NDVI data (2006—2008)[J]. Atmosphere, 2010, 20(4): 415-426. |
[16] | Amani M, Ghorbanian A, Ahmadi S A, et al. Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 5326-5350. |
[17] | Bao C L, Yong M, Bueh C, et al. Analyses of the dust storm sources, affected areas, and moving paths in Mongolia and China in early spring[J]. Remote Sensing, 2022, 14(15): 3661, doi: 10.3390/rs14153661. |
[18] | 黎煜满, 李磊, 谢洁岚, 等. 基于KZ滤波法的韶关市O3不同时间尺度变化特征分析研究[J]. 环境科学学报, 2023, 43(1): 128-139. |
[Li Yuman, Li Lei, Xie Jielan, et al. Study on variation characteristics of O3 at different time scales in Shaoguan City based on KZ filter method[J]. Journal of Environmental Science, 2023, 43(1): 128-139. ] | |
[19] | 韩力慧, 兰童, 程水源, 等. 唐山市大气颗粒物和O3多尺度变化及影响因素[J]. 中国环境科学, 2024, 44(3): 1185-1194. |
[Han Lihun, Lan Tong, Chen Shuiyuan, et al. The variations and influencing factors of atmospheric particulate matter and O3 at multiple scales in Tangshan[J]. China Environmental Science, 2024, 44(3): 1185-1194. ] | |
[20] | Verma P, Verma R, Mallet M, et al. Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms[J]. Atmospheric Pollution Research, 2024, 102123, doi: 10.1016/j.apr.2024.102123. |
[21] | Muthukumar P, Cocom E, Nagrecha K, et al. Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data[J]. Air Quality, Atmosphere and Health, 2021, 2021: 1-14. |
[22] | Talbot N, Takada A, Bingham A, et al. An investigation of the impacts of a successful COVID-19 response and meteorology on air quality in New Zealand[J]. Air Quality and Climate Change, 2022, 56(1): 30-31. |
[23] | Wang Y J, Tang J K, Wang W H, et al. Long-term spatiotemporal characteristics and influencing factors of dust aerosols in East Asia (2000—2022)[J]. Remote Sensing, 2024, 16(2): 318, doi: 10.3390/rs16020318. |
[24] | 史建阳, 刘旻霞, 潘竟虎, 等. 黄河几字湾气溶胶对植被总初级生产力的影响[J]. 中国环境科学, 2024, 44(6): 3314-3324. |
[Shi Jianyang, Liu Minxia, Pan Jinghu, et al. The impact of aerosols in Jizi Bay of the Yellow River on the total primary productivity of vegetation[J]. China Environmental Science, 2024, 44(6): 3314-3324. ] | |
[25] | Yin Z C, Wan Y, Zhang Y J, et al. Why super sandstorm 2021 in north China?[J]. National Science Review, 2022, 9(3): 165, doi: 10.1093/nsr/nwab165. |
[26] | Meng H F, Bai G Z, Wang L W. Analysis of the spatial and temporal distribution characteristics of AOD in typical industrial cities in northwest China and the influence of meteorological factors[J]. Atmospheric Pollution Research, 2024, 15(1): 101957, doi: 10.1016/j.apr.2023.101957. |
[27] | Gao J, Ding T, Gao H. Dominant circulation pattern and moving path of the Mongolian cyclone for the severe sand and dust storm in China[J]. Atmospheric Research, 2024, 301: 107272, doi: 10.1016/j.atmosres.2024.107272. |
[28] | Filonchyk M, Peterson M P, Zhang L F, et al. An analysis of air pollution associated with the 2023 sand and dust storms over China: Aerosol properties and PM10 variability[J]. Geoscience Frontiers, 2024, 15(2): 101762, doi: 10.1016/j.gsf.2023.101762. |
[29] | 杨梅, 李岩瑛, 张春燕, 等. 河西走廊中东部春季沙尘暴变化特征及其典型个例分析[J]. 干旱区地理, 2021, 44(5): 1339-1349. |
[Yang Mei, Li Yanying, Zhang Chunyan, et al. Variation characteristics of spring sandstorm and its typical case analysis in the middle east of Hexi Corridor[J]. Arid Land Geography, 2021, 44(5): 1339-1349. ] | |
[30] | You Q L, Jiang Z H, Moore G W K, et al. Revisiting the relationship between observed warming and surface pressure in the Tibetan Plateau[J]. Journal of Climate, 2017, 30(5): 1721-1737. |
[31] | 中国气象局. 大气环境气象公报(2021年)[M]. 北京: 中国气象报社, 2021: 8-13. |
[China Meteorological Administration. Bulletin on atmospheric environment and meteorology (2021)[M]. Beijing: China Meteorological Press, 2021: 8-13. ] |
/
〈 |
|
〉 |