Spatial-Temporal Distribution Characteristics of PM2.5 and PM10 in the Northeast Tibetan Plateau and the Influence of Meteorological Factors
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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.
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Keywords:
- PM2.5 /
- PM10 /
- air pollution /
- meteorological factors /
- cause analysis /
- Northeast Tibetan Plateau
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图 2 青海省各地级市（自治州）PM2.5（a）和PM10浓度（b）均值逐年变化情况
Figure 2. (a) Annual change of mean PM2.5 and (b) PM10 mass concentration in cities of Qinghai province
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图 3 青海省各地级市（自治州）PM2.5和PM10浓度均值逐年空间分布图
Figure 3. Annual spatial distribution of mean PM2.5 and PM10 mass concentration in cities of Qinghai province
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图 4 青海省各地级市（自治州）PM2.5、PM10月均浓度和降水量、温度逐月变化情况
a. 西宁市；b. 海东市；c. 海南州；d. 海西州；e. 海北州；f. 果洛州；g. 黄南州；h. 玉树州
Figure 4. Monthly mean value of PM2.5, PM10, precipitation and temperature in cities of Qinghai province
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图 5 青海省地级市（自治州）PM2.5、PM10月均浓度与气象因子相关性
**在0.01水平（双侧）上显著相关；*在0.05水平（双侧）上显著相关，N = 12
Figure 5. Correlation analysis between monthly mean value of PM2.5, PM10 and meteorological variables in Qinghai province
表 1 PM2.5与PM10多元线性回归结果
Table 1 Results of multiple linear regression between PM2.5 and PM10
污染物 地区 多元回归模型 R2 调整后R2 p值 PM2.5 西宁 24.269+0.466*×PM10−0.021×气温+0.005×降水−25.996·×风速 0.946 0.915 0.000 海东 21.599**+0.133*×PM10−0.566*×气温+0.029×降水+8.849×风速 0.968 0.950 0.000 海北 26.564***+0.242***×PM10−0.093×气温−0.013×降水−4.361·×风速 0.973 0.957 0.000 黄南 17.730+0.169·×PM10−0.857·×气温+0.053×降水+5.044×风速 0.921 0.876 0.001 海南 14.512+0.236*×PM10−0.277×气温+0.041×降水+0.370×风速 0.955 0.929 0.000 果洛 11.575+0.183×PM10+0.069×气温−0.027×降水+4.387×风速 0.793 0.675 0.015 玉树 5.463+0.413**×PM10−0.392·×气温+0.034×降水−4.285×风速 0.948 0.918 0.000 海西 8.175*+0.176**×PM10+0.038×气温−0.095×降水+4.216×风速 0.954 0.928 0.000 青海 15.047**+0.184*×PM10−0.630**×气温+0.063·×降水+2.971×风速 0.984 0.975 0.000 PM10 西宁 −12.793+1.456**×PM2.5−1.145×气温−0.006×降水+54.761*×风速 0.954 0.928 0.000 海东 −87.131·+3.667*×PM2.5+0.818×气温−0.120×降水+8.639×风速 0.931 0.892 0.000 海北 −101.720***+3.498***×PM2.5+0.200×气温+0.046×降水+22.352**×风速 0.975 0.961 0.000 黄南 −75.280·+2.232·×PM2.5−0.486×气温−0.034×降水+33.545×风速 0.885 0.819 0.002 海南 −32.042·+2.624*×PM2.5+0.377×气温−0.187×降水+14.860×风速 0.964 0.943 0.000 果洛 0.990+0.288×PM2.5−1.209*×气温−0.028×降水+29.034**×风速 0.970 0.953 0.000 玉树 1.168+1.692**×PM2.5+0.359×气温−0.086×降水+10.561*×风速 0.959 0.935 0.000 海西 −30.355+3.972**×PM2.5−0.429×气温+0.110×降水−3.438×风速 0.938 0.902 0.000 青海 −42.628+2.835×PM2.5+0.813×气温−0.207×降水+14.313×风速 0.974 0.959 0.000 注：·为p＜0.1, *为p＜0.05, **为p＜0.01, ***为p＜0.001。 
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