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成都平原地区空气污染及其气象特征分析
Analysis of Air Pollution and Meteorological Characteristics in the Chengdu Plain Area

DOI: 10.12677/GSER.2019.84033, PP. 312-322

Keywords: 空气污染,边界层特征,相关性
Pollution Meteorology
, Boundary Layer Characteristics, Correlation

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Abstract:

本文采用成都平原地区2014~2016年的气象要素数据及同期空气质量数据,分别从季节和月等不同时间尺度对成都平原地区的空气污染状况、主要污染物,利用相关性分析及其显著性检验的方法探讨空气污染与气象要素之间的相关性。结果表明:1) 成都平原地区空气污染状况具有鲜明的季节变化的特点:冬季最严重,秋季最轻;城市群中成都市的空气污染状况最严重,绵阳的空气状况最好;成都平原地区主要污染物为可吸入颗粒物(PM10)和细颗粒物(PM2.5);2) 成都平原地区全年主导风以NNE和NE为主,夏季风速、气温、降水量大,日照时数长,气压、水汽压、相对湿度较小,风速、相对湿度与静小风频率的季节变化不明显;3) 风速、水汽压、降水、相对湿度、温度、混合层高度与AQI呈负相关,气压、大气稳定度、逆温层强度与AQI呈正相关,风向对于污染物的扩散无太大影响。不同时间尺度上的相关性不一样。
Based on the meteorological data and the air quality data of Chengdu Plain in the period of 2014 – 2016, the air pollution situation, the ground and the boundary layer meteorological elements of different time scales were analyzed, including season and month. Correlation analysis and signi-ficance test were used to explore the correlation between air pollution and meteorological factors. The results show that: 1) The seasonal variation of air pollution in Chengdu plain area was obvious: the heaviest in winter and the lightest in autumn. And Chengdu was the city with the worst air quality, while Mianyang was the best all of the cities. The main pollutants in the plain area of Chengdu were PM10 and PM2.5. 2) In Chengdu plain, the dominant wind direction was NNE and NE. There was larger wind speed, higher ground temperature, more rainfall and sunshine in summer; On the contrary, relative humidity, air pressure and vapor pressure were lower. Ground wind ve-locity, relative humidity, small wind frequency had no obviously seasonal change. 3) The wind speed, water vapor pressure, precipitation, relative humidity, temperature and the height of at-mospheric mixed layer were significantly negative correlated with AQI. The air pressure, atmos-pheric stratification stability and inversion layer were significantly positive correlated with AQI. And the wind direction had no effect on the spread of pollutants. The correlation was different on different time scales.

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