ISSN 1009-6248CN 61-1149/P 双月刊

主管单位:中国地质调查局

主办单位:中国地质调查局西安地质调查中心
中国地质学会

    高级检索
    梁明武, 李慧婷, 李魏龙, 贾晓丹, 汪铭媛, 冯朝晖. 青藏高原东北部地区城市PM2.5和PM10时空分布特征及气象因素的影响[J/OL]. 西北地质,2023: 1-12. doi: 10.12401/j.nwg.2023184
    引用本文: 梁明武, 李慧婷, 李魏龙, 贾晓丹, 汪铭媛, 冯朝晖. 青藏高原东北部地区城市PM2.5和PM10时空分布特征及气象因素的影响[J/OL]. 西北地质,2023: 1-12. doi: 10.12401/j.nwg.2023184
    LIANG Mingwu, LI Huiting, LI Weilong, JIA Xiaodan, WANG Mingyuan, FENG Zhaohui. Spatial-temporal Distribution Characteristics of PM2.5 and PM10 in the Northeast Tibetan Plateau and the Influence of Meteorological Factors[J/OL]. Northwestern Geology,2023: 1-12. doi: 10.12401/j.nwg.2023184
    Citation: LIANG Mingwu, LI Huiting, LI Weilong, JIA Xiaodan, WANG Mingyuan, FENG Zhaohui. Spatial-temporal Distribution Characteristics of PM2.5 and PM10 in the Northeast Tibetan Plateau and the Influence of Meteorological Factors[J/OL]. Northwestern Geology,2023: 1-12. doi: 10.12401/j.nwg.2023184

    青藏高原东北部地区城市PM2.5和PM10时空分布特征及气象因素的影响

    Spatial-temporal Distribution Characteristics of PM2.5 and PM10 in the Northeast Tibetan Plateau and the Influence of Meteorological Factors

    • 摘要: 随着工业化进程的迅速发展,大气污染问题已不容忽视,青藏高原东北部作为中国重要的生态安全战略地区,由于其常年多风的地理特点导致土地荒漠化,而土地荒漠化会带来颗粒物污染的问题。笔者以青藏高原东北部地区逐月可吸入颗粒物(PM10)和细颗粒物(PM2.5)浓度为基础,分析了大气颗粒物PM2.5和PM10时空分布特征,与气象因素(降水量、气温和风速)之间的相关性及受气象因素的影响程度。结果表明:①东部人口密集和经济发达的西宁市、海东市和黄南州PM2.5和PM10较高,以上3个市州的PM2.5平均水平分别为44.2 μg/m3,44.7 μg/m3和36.5 μg/m3,PM10平均水平分别为99.1 μg/m3,99.7 μg/m3和72.2 μg/m3;2015~2019年的时间分布上各地区颗粒物浓度呈现逐年下降的趋势;空间分布表明PM2.5呈现从西到东逐渐升高的趋势,PM10则呈东高西低分布。②各地区气温和降水量的峰值均出现在夏季,呈现出“Λ”型的分布规律;而各地区的PM2.5、PM10逐月浓度变化整体呈现出“V”型的分布规律,非采暖季颗粒物浓度最低、采暖季颗粒物浓度最高。③各种气象因素的影响中,PM2.5和PM10与降水量、气温、风速均呈负相关,并且PM2.5浓度受到风速的负向影响,而PM10浓度受到风速的显著正向影响,表明风起扬尘对该区域大气污染贡献突出但风速与污染物浓度的作用机制复杂。本研究可为典型地区空气质量的改善与预测提供理论基础与参考依据。

       

      Abstract: With the rapid development of industrialization, the problem of air pollution can not be ignored. As an important ecological security strategic area in China, the Northeast Tibetan Plateau had prominent particulate pollution caused by soil desertification. Based on the monthly concentrations of inhalable particulate matter (PM10) and fine particulate matter (PM2.5) in 8 cities (prefectures) from 2015 to 2019 and the meteorological data, this study analyzed the spatial-temporal distribution characteristics of PM2.5 and PM10, the relationships between them and meteorological factors (precipitation, temperature and wind speed) and the degree of influence of meteorological factors. The results showed that: ①Xining city, Haidong city and Huangnan prefecture, which were densely populated and economically developed in the east of Qinghai province, had higher PM2.5 and PM10 concentrations, with an average level of 44.2 μg/m3 (99.1 μg/m3)、44.7 μg/m3 (99.7 μg/m3) and 36.5 μg/m3 (72.2 μg/m3) respectively. In terms of time distribution, the concentration of particulate matter in each region showed a downward trend year by year. The spatial distribution showed that PM2.5 gradually increased from the west to the east, and PM10 was high in the east and low in the west. ② The peak values of temperature and precipitation appeared in summer, showing a "Λ" distribution law. While the monthly concentration changes of PM2.5 and PM10 in various regions showed a "V" distribution law. The particle concentration in non-heating season was the lowest and that in heating season was the highest. ③ Among the effects of various meteorological factors, the concentrations of PM2.5 and PM10 were strongly or moderately negatively correlated with precipitation and temperature, and were negatively affected by temperature. The concentration of particulate matter was negatively correlated with wind speed. The concentration of PM2.5 was negatively affected by wind speed, while the concentration of PM10 was significantly positively affected by wind speed, indicating that wind-induced dust had a prominent contribution to air pollution, but the action mechanism between wind speed and pollutant concentration was complex. The results of this study could provide reference and theoretical basis for the improvement and prediction of air quality in typical regions.

       

    /

    返回文章
    返回