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上海市大气污染物时空分布特征研究
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Abstract:
为了探究近些年来上海市大气污染物的时空分布特征,利用2019~2022年上海市16个区每日的AQI和O3、SO2、CO、NO2、PM2.5、PM10环境监测数据对比分析,并用对应的平均风速和平均温度进行相关性分析。通过ArcGIS插值揭示了其空间分布特征;通过以年、季节、月为时间单位进行对比,展现了其时间变化规律;通过SPSS进行相关性分析,探究了大气污染物之间以及与气象因素的联系。研究表明:近四年AQI和PM2.5浓度最大均值出现在青浦区,PM10、SO2和NO2主要集中在宝山区,崇明区、奉贤区和金山区的O3浓度相对较高;近四年空气质量达标率为88.23%,O3是上海最常出现的首要污染物。NO2、SO2、CO、PM2.5和PM10季均浓度以及AQI季均值呈现为冬季污染重、夏季污染轻的规律,O3的季均浓度的变化规律与其他污染物相反;由相关性分析可知,大气颗粒物中PM2.5占比大,并且PM2.5和CO来源相近,6项污染物浓度以及AQI和平均风速相关性较低,SO2、CO、NO2、PM2.5、PM10和平均温度呈负相关,O3和平均温度呈正相关。
In order to explore the spatial and temporal distribution characteristics of air pollutants in Shanghai in recent years, this paper compares the daily AQI and environmental monitoring data of O3, SO2, CO, NO2, PM2.5, PM10 in 16 districts of Shanghai from 2019 to 2022, and does the correlation analysis between these corresponding average wind speed and average temperature. This paper uses ArcGIS interpolation to reveal spatial distribution characteristics, compares the time units of year, season and month to show time variation rules and explores the relationship between air pollutants and meteorological factors by correlation analysis which is carried out by SPSS. The results indicate that the maximum concentrations of AQI and PM2.5 appear in Qingpu District. PM10, SO2 and NO2 mainly concentrate in Baoshan District, while the concentration of O3 is relatively high in Chongming District, Fengxian District and Jinshan District. The air quality compliance rate of Shanghai is 88.23% and O3 is the most frequent primary pollutant there. The seasonal average concentrations of NO2, SO2, CO, PM2.5 and PM10 and the seasonal average val-ues of AQI show the regularity of heavy pollution in winter and light pollution in summer, however, the seasonal average concentrations of O3 are opposite to that of other pollutants. Then through correlation analysis, the proportion of PM2.5 in atmospheric particulate matter is large, which is similar to the source of CO, the correlation between the concentration of 6 pollutants, AQI and average wind speed is low, and SO2, CO, NO2, PM2.5, PM10 are negatively correlated with the average
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