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Spatiotemporal Distribution Characteristics And Relationship Analysis Of Aerosol Optical Depth,Urban Heat Island And Air Quality

Posted on:2022-12-21Degree:MasterType:Thesis
Country:ChinaCandidate:Y X YangFull Text:PDF
GTID:2480306782968509Subject:Environment Science and Resources Utilization
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The rapid development of urbanization has led to the agglomeration of population and industries in cities,the characteristics of urban underlying surfaces have also been changed,and artificial surfaces are constantly replacing natural surfaces.At the same time,the surge in urban population has caused a rapid increase in energy consumption,resulting in increasing air pollutant emissions,with irreversible negative impacts on the urban climate.Therefore,study on the spatiotemporal changes of urban heat island(UHI)and air pollutants,as well as on the effect of aerosol optical depth(AOD)on land surface temperature(LST)and air quality,can provide a good reference and guiding significance for the air quality management and climate change research.This paper takes the"Beijing-Tianjin-Hebei air pollution transmission channel city"("2+26"city)as the research area,uses remote sensing data to analyze the spatial agglomeration characteristics and the spatiotemporal variation characteristics of heat island intensity,air quality and aerosol optical depth in the study area from 2015 to 2019,and their correlations are also analyzed.The conclusions are as follows:(1)From 2015 to 2019,the maximum increase in daytime LST in September was4.82°C and the minimum increase in November was 0.2°C.The intensity of the heat island in spring and summer increased significantly,most of the study area was above grade III,and the area of grade V accounted for a large proportion;the heat island intensity in autumn and winter did not change significantly,mainly grades III and IV.the maximum increase in night LST in October was 2.25°C and the maximum decrease is in February,which is-2.55?.The night heat island intensity showed an increasing trend in spring and autumn,first intensified and then weakened in summer,and the change was not significant in winter.Due to the comprehensive influence of various factors,the spatial aggregation characteristic of LST did not show a regular change,but both daytime LST and night LST showed an overall increasing trend.(2)From 2015 to 2019,the average concentrations of PM2.5and PM10decreased significantly in most months,except for September,when PM2.5and PM10increased by 5.15?g/m~3and 13.24?g/m~3respectively.PM2.5and PM10had the same trend of change,showing high concentrations in winter and spring and low concentrations in summer and autumn.The overall distribution trend was:winter>spring>autumn>summer.Combined with the characteristics of spatial agglomeration,high concentrations of PM2.5and PM10were mainly concentrated in Hebei province and Shandong province.(3)From 2015 to 2019,the average value of aerosol optical depth was between 0 and 1.Except for December,January and February,where AOD increased by 0.02,0.13 and 0.25,AOD in other months decreased significantly,among which July and August decreased the most,decreasing by 0.40 and 0.46 respectively.In general,AOD was high in spring and summer and low in autumn and winter.Combined with the characteristics of spatial agglomeration,high AOD was mainly concentrated in the southern part of the study area,including cities in Henan Province,Liaocheng City,Heze City,Jining City in Shandong Province,and Shijiazhuang City,Baoding City,and Langfang City in Hebei Province.(4)The calculation results of the difference found that daytime LST mainly showed an increasing trend.The maximum growth occurred in Kaifeng City,Henan Province in spring,with an increase of 10.328°C,and the minimum growth value of 0.026°C occurred in Tianjin City in autumn.Night LST also showed an increasing trend.The maximum growth appeared in Binzhou City,Shandong Province in winter,with an increase of 5.544°C,and the minimum growth value was 0.008°C in Hengshui City,Hebei Province in winter.PM2.5decreased significantly,the degree of reduction was as follows:winter>spring>summer>autumn.The maximum reduction of PM2.5was-27.852?g/m~3,occurred in Langfang City,Hebei Province in winter,and the minimum reduction was-0.092?g/m~3,which occurred in Xinxiang City,Henan Province in autumn.PM10decreased significantly in spring,summer and winter,while increased in autumn.The decreasing trend was as follows:winter>spring>summer.The maximum reduction of PM10was-47.764?g/m~3,occurred in Liaocheng City,Shandong Province in winter,and the minimum reduction was-1.110?g/m~3,which occurred in Jincheng City,Shanxi Province in spring.The maximum growth of PM10was 15.725?g/m~3in Dezhou,Shandong Province in autumn,the minimum growth value was 7.397?g/m~3,appeared in Langfang City,Hebei Province.AOD decreased significantly,most notably in summer.The maximum reduction value appeared in Puyang City,Henan Province in summer,AOD decreased by 0.658,and the minimum reduction value was-0.003,which appeared in Tianjin City in winter.(5)From 2015 to 2019,AOD showed significant positive correlations with PM2.5,PM10,daytime LST and night LST.The correlation coefficient between AOD and PM2.5in the winter of 2017 was the highest(r=0.745**),and the lowest correlation coefficient appeared in the spring of 2019(r=0.288**);the highest correlation coefficient between AOD and PM10appeared in the winter of 2017(r=0.739**),and the lowest correlation coefficient appeared in the spring of 2015(r=0.188**);the highest correlation coefficient between AOD and daytime LST appeared in the autumn of 2018(r=0.635**),the lowest correlation coefficient appeared in the spring of 2015(r=-0.026**);the highest correlation coefficient between AOD and night LST appeared in the summer in 2017(r=0.736**),the lowest correlation coefficient occurred in the autumn of 2015(r=0.017**).In addition,the correlation coefficients between annual average AOD and PM2.5,PM10,daytime LST and night LST were 0.314**,0.340**,0.136,and 0.720**respectively.
Keywords/Search Tags:Urban heat island, Aerosol optical depth, PM2.5, PM10, spatial and temporal distribution, correlation analysis
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