大连市大气污染物质量浓度与气溶胶光学厚度的相关性分析
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摘要
分别对2015年6～12月和2016年6～12月大连地区的大气污染物PM2.5、PM10、SO_2、NO_2、CO和O_3的浓度数据进行数据统计分析,基于ENVI软件平台利用MODIS数据反演大连地区的气溶胶光学厚度,通过回归建模研究气溶胶光学厚度与大连地区10个地面监测站点的大气污染物PM2.5、PM10、SO_2、NO_2、CO和O_3的浓度数据的相关性。回归建模以气溶胶光学厚度(AOD)为自变量,以大气污染物PM2.5、PM10、SO_2、NO_2、CO和O_3为因变量,在SPSS软件中分别选取线性、对数、三次、乘幂、指数5种函数类型进行研究,通过对比回归模型的拟合优度R2,选择最优拟合模型,探讨利用遥感数据反演气溶胶光学厚度监测大气污染的相关性。结果表明:气溶胶光学厚度与NO_2、PM2.5和PM10的最优拟合模型均为三次模型,其拟合优度R2分别是0.685、0.801和0.845;与O_3和SO_2的最优拟合模型为指数模型,其R2为0.367和0.482;与CO的最优拟合模型为对数模型,其拟合优度R2为0.810。该结果为分析大气气溶胶污染来源以及治理提供了数据。
The development of the economy is restricted by the gradual increase of the quality concentration of the atmospheric pollutant. The concentration data of PM2.5, PM10, SO_2, NO_2, CO and O_3 of the atmospheric pollutant in Dalian were statistically analyzed from June to December in 2015 and from June to December in2016. The aerosol optical depth in Dalian was inverted base on ENVI software platform and the data of MODIS. The correlation between aerosol optical depth and concentration data of atmospheric pollutant PM2.5,PM10, SO_2, NO_2, CO and O_3 at 10 ground monitoring stations in Dalian City were researched by regression modeling. The aerosol optical depth was used as an independent variable, and atmospheric pollutants PM2.5,PM10, SO_2, NO_2, CO, and O_3 were dependent variables. The types of functions that were linear, logarithmic,cubic, power and exponential in the SPSS software were selected to research. The best-fit model was selected by comparing the goodness of fit R2 of the regression model. The correlation was discussed between atmospheric pollution by using remote sensing data and aerosol optical depth monitoring. The results showed that the optimal fitting model of aerosol optical depth and NO_2, PM2.5 and PM10 are all cubic models, and the goodness of fit R2 is 0.685, 0.801 and 0.845 respectively. The optimal fitting model of O_3 and SO_2 is the exponential model, whose R2 is 0.367 and 0.482. The optimal fitting model of CO is the logarithmic model, and its fitting optimal R2 is 0.810. The results provided the data for the analysis of sources of atmospheric aerosol pollution and governance.
The development of the economy is restricted by the gradual increase of the quality concentration of the atmospheric pollutant. The concentration data of PM2.5, PM10, SO_2, NO_2, CO and O_3 of the atmospheric pollutant in Dalian were statistically analyzed from June to December in 2015 and from June to December in2016. The aerosol optical depth in Dalian was inverted base on ENVI software platform and the data of MODIS. The correlation between aerosol optical depth and concentration data of atmospheric pollutant PM2.5,PM10, SO_2, NO_2, CO and O_3 at 10 ground monitoring stations in Dalian City were researched by regression modeling. The aerosol optical depth was used as an independent variable, and atmospheric pollutants PM2.5,PM10, SO_2, NO_2, CO, and O_3 were dependent variables. The types of functions that were linear, logarithmic,cubic, power and exponential in the SPSS software were selected to research. The best-fit model was selected by comparing the goodness of fit R2 of the regression model. The correlation was discussed between atmospheric pollution by using remote sensing data and aerosol optical depth monitoring. The results showed that the optimal fitting model of aerosol optical depth and NO_2, PM2.5 and PM10 are all cubic models, and the goodness of fit R2 is 0.685, 0.801 and 0.845 respectively. The optimal fitting model of O_3 and SO_2 is the exponential model, whose R2 is 0.367 and 0.482. The optimal fitting model of CO is the logarithmic model, and its fitting optimal R2 is 0.810. The results provided the data for the analysis of sources of atmospheric aerosol pollution and governance.
引文
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[19]王宏斌,张镭,焦圣明,等.中国地区MODIS气溶胶产品的验证及反演误差分析[J].高原气象,2016,35(3):810-822.[Wang Hongbin, Zhang Lei, Jiao Shengming et al. Validation and inversion error analysis of MODIS aerosol products in China. Plateau Meteorology,2016,35(3):810-822.]
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[21]王重,刘黎明.拟合优度检验统计量的设定方法[J].知识丛林,2010,305(5):154-156.[Wang Zhong, Liu Liming. The method for setting the test statistics of goodness of fit. Knowledge Jungle,2010,305(5):154-156.]
[2]林海峰,辛金元,张文煜,等.北京市大气污染物浓度与气溶胶光学厚度相关性分析研究[J].环境科学,2013,34(3):826-834.[Lin Haifeng, Xin Jinyuan, Zhang Wenyu et al. Analysis of correlation between the concentration of particulate matter and aerosol optical thickness in Beijing. Environmental Science,2013,34(3):826-834.]
[3]郑小波,罗宇翔,赵天良,等.中国气溶胶分布的地理学和气候学特征[J].地理科学,2012,32(3):265-272.[Zheng Xiaobo, Luo Yuxiang, Zhao Tianliang et al. Geographical and climatological characteristics of aerosol distribution in China. Scientia Geographica Sinica, 2012, 32(3):265-272.]
[4]杨鹏,陈静,高祺,等.京津冀地区气溶胶光学厚度反演及其空间分布特征[J].气象与环境学报,2017,33(3):52-58.[Yang Peng, Chen Jing, Gao Qi et al. Inversion and spatial distribution of aerosol optical thickness in beijing-tianjin-hebei region. Journal of Meteorology and Environment, 2013,33(3):52-58.]
[5] Wang J, Christopher S A. Intercomparison between satellitederived aerosol optical thickness and PM2.5 mass:Implications for air quality studies[J]. Geophysical Research Letters, 2003,30(21):267-283.
[6] Donkelaar A V, Martin R V, Brauer M et al. Global estimates of ambient fine particulate matter concentrations from Satellite-based Aerosol Optical Depth:Development and application[J]. Environmental Health Perspectives, 2010, 118(6):847-855.
[7] Engel-Cox J A, Holloman C H, Coutant B W et al. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality[J]. Atmospheric Environment, 2004, 38(16):2495-2509.
[8]陈良富,陈水森,钟流举,等.卫星数据和地面观测结合的珠三角地区颗粒物质量浓度统计估算方法[J].热带地理,2015,35(1):7-12.[Chen Liangfu, Chen Shuisen, Zhong Liuju et al. Statistical estimation of particulate mass concentration in the Pearl River Delta Region combined with satellite data and ground observation. Tropical Geography, 2015, 35(1):7-12.]
[9]刘桂青,李成才,朱爱华,等.长江三角洲地区大气气溶胶光学厚度研究[J].环境保护,2003(8):50-54.[Liu Guiqing, Li Chengcai, Zhu Aihua et al. Study on the optical thickness of atmospheric aerosol in the Yangtze River Delta. Environmental Protection,2003(8):50-54.]
[10]葛邦宇,杨磊库,陈兴峰,等.暗目标法的Himawari-8静止卫星数据气溶胶反演[J].遥感学报,2018,22(1):38-50.[Ge Bangyu,Yang Leigku, Chen Xingfeng et al. The Himawari-8 stationary satellite data aerosol inversion. Journal of Remote Sensing,2011,22(1):38-50.]
[11]贺婧婧,张敏,陈显尧,等.香港气溶胶光学厚度与PM_(10)质量浓度的季节变异及相关性[J].中国科学:地球科学,2015,45(4):444-454.[He Jingjing, Zhang Min, Chen Xianyao et al. Seasonal variation and correlation of aerosol optical thickness and PM_(10)mass concentration in Hong Kong. Chinese Science:Earth Science,2015,45(4):444-454.]
[12]李慧娟,胡列群,李帅.北疆地区MODIS 3 km气溶胶光学厚度与PM_(10)质量浓度的相关性分析[J].环境科学学报,2018,38(3):1109-1116.[Li Huijuan, Hu Liequn, Li Shuai. Correlation analysis of the optical thickness and PM_(10)mass concentration of the MODIS 3 km aerosol in north xinjiang. Journal of Environmental Science, 2011,38(3):1109-1116.]
[13]王文欢.基于多源数据的城市地区PM2.5估算及溯源研究[D].成都:电子科技大学,2016.[Wang Wenhuan. Study on the estimation and trace-ability of PM2.5 in urban areas based on multi-source data. Chengdu:University of Electronic Science and Technology,2016.]
[14]张新蕾,黄凤荣.基于大连市MODIS数据的气溶胶光学厚度与PM2.5和PM10的相关性分析[J].国土与自然资源研究,2017(6):59-61.[Zhang Xinlei, Huang Fengrong. Analysis on the correlation between aerosol optical thickness and PM2.5 and PM10 in the data of MODIS in Dalian. Research on Land and Natural Resources,2017(6):59-61.]
[15]王旭,胡引翠,田冰,等.京津冀地区AOD和PM_(2.5)质量浓度的特征及相关性分析[J].环境工程学报,2016,10(9):5069-5074.[Wang Xu, Hu Yincui, Tian Bing et al. Characteristics and correlation analysis of quality concentration of AOD and PM_(2.5)in Beijing-Tianjin-Hebei region. Journal of Environmental Engineering, 2016,10(9):5069-5074.]
[16]付帼.大连城市建设用地扩展的时空动态变化和增长研究[D].大连:辽宁师范大学,2012.[Fu Guo. Study on the spatial and temporal dynamic changes and growth of urban construction land expansion in Dalian. Dalian:Liaoning Normal University,2012.]
[17]朱安豹.兰州市两类典型重污染个例及其边界层结构影响的数值模拟研究[D].兰州:兰州大学,2017.[Zhu Anbao. Numerical simulation study on the influence of two typical heavy pollution cases and their boundary layer structures in Lanzhou. Lanzhou:Lanzhou University,2017.]
[18]邵平,辛金元,安俊琳,等.长三角工业区夏季近地层臭氧和颗粒物污染相互关系研究[J].大气科学,2017,41(3):618-628.[Shao Ping, Xin Jinyuan, An Junlin et al. Study on the relationship between ozone and particulate pollution in summer in the Yangtze river delta. Atmospheric Science, 2011,41(3):618-628.]
[19]王宏斌,张镭,焦圣明,等.中国地区MODIS气溶胶产品的验证及反演误差分析[J].高原气象,2016,35(3):810-822.[Wang Hongbin, Zhang Lei, Jiao Shengming et al. Validation and inversion error analysis of MODIS aerosol products in China. Plateau Meteorology,2016,35(3):810-822.]
[20]陈良富.气溶胶遥感定量反演研究与应用[M].北京:科学出版社,2011.[Chen Liangfu. Research and application of aerosol remote sensing quantitative inversion. Beijing:Science Press,2011.]
[21]王重,刘黎明.拟合优度检验统计量的设定方法[J].知识丛林,2010,305(5):154-156.[Wang Zhong, Liu Liming. The method for setting the test statistics of goodness of fit. Knowledge Jungle,2010,305(5):154-156.]