基于MODIS气溶胶光学厚度反演的PM_（10）浓度监测
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摘要
随着我国经济的快速发展,工业化和城镇化进程加速,部分地区的环境承载压力不断加大,大气污染问题日趋严重。在我国大多数地区,可吸入颗粒物PM_(10)已成为影响环境空气质量的首要污染物。针对当前大气污染的现实状况,利用卫星遥感监测空气污染的空间分布及变化趋势具有相当重要的现实意义。
本文基于研究MODIS数据在大气污染和可吸入颗粒物PM_(10)监测的作用出发,提出利用MODIS L1B数据反演气溶胶光学厚度AOD,并对同时段的PM_(10)时均值数据与AOD数据使用多种方法进行回归分析,建立相应的关系模型,以期发现能够较准确反映两者关系的模型,实现利用MODIS数据实时动态地监测大气污染数据的目的。主要结论如下:
(1)MODIS反演的气溶胶光学厚度和可吸入颗粒物PM_(10)的浓度具有较好的相关性,在一定程度上可以反映城市空气污染状况。但对PM_(10)与AOD之间进行统计回归分析只有当首要污染物为PM_(10)时,两者之间才能进行统计回归分析。如果某些日子的主要污染物不是可吸入颗粒物,而是SO_2或NO_2,则必须排除这些天的数据,才能进行统计回归分析。
(2)在对MODIS数据进行气溶胶反演,6S模式中的大气模型参数为大陆模式,此模型的使用受气象条件的影响,在某些时段有局限性(如夏季),影响反演的精度;在此情况下进行反演,最好能对气溶胶的成分进行分析,以减少误差。
(3)利用2008年9月至11月的MODIG L1B数据反演的AOD数据,与PM_(10)的建立五种类型的回归模型,通过分析确定系数R~2和F检验的值,得到较优的两个回归模型:线性模型和一元二次模型;用检验数据对两个模型进行检验,得到的平均相对误差分别为11.7%、30.6%,确定线性模型为反映AOD与PM_(10)最佳拟合模型。
With rapid development of economy and course of industrialization and urbanization in our country, the environment stress of most areas increases, the pollution of the atmosphere also becomes more and more serious. In most areas, a kind of inhalable particle—PM_(10) becomes the critical pollutant which affects the air quality. Focus on the existing reality, utilize the satellite remote sensing to monitor the space distribution and variation trend of the pollution has the most important practical significance.
The article based on the research of the monitoring function of MODIS data which affects the air pollution and PM_(10), put forward the utilization of MODIS L1B data invert the AOD (Aerosol Optical Depth). Then proceed the regression analysis by multiple methods for mean value of PM_(10) and AOD data which appear at the same period, establish relevant relational model. The purpose of the research is expected to discover the model which can reflect the relationship of the two data exactly, and realize the utilization of MODIS data to monitor the air pollution.
The main conclusions are as follows:
(1) The Aerosol Optical Depth and the inhalable particulate matter (PM_(10)) concentration have better relationship which was inverted by MODIS, this can reflect the condition of air pollution in cities to a certain extent. However, only when the critical pollutant is PM10, we can take a statistic regression analysis between PM_(10) and AOD. If in a period, the critical pollutants were not inhalable particulate matter but SO_2 or NO_2, we should eliminate the data in these days.
(2) When we proceed the aerosol inversion on the MODIS data, the atmosphere model parameter in 6S model is continent pattern, the utilization of this model has been affected by climate condition. This model has limitations in some periods (such as summer), and these limitations would affect the precision of inversion. Under these conditions, we'd better analyze the components of the aerosol in order to decrease errors.
(3) Constructed five types of regression model by the utilization of and AOD data which inverted by MODIS L1B data that generated between September and November in 2008. According to analyze these data which verified by coefficient of determination R~2 and F, we got two better regression models: linear model and quadratic model. When verified them by testing data, their average relative errors are 11.7% and 30.6%, respectively. So we can ensure that the best fitting model which can reflect the AOD and PM_(10) is linear model.
本文基于研究MODIS数据在大气污染和可吸入颗粒物PM_(10)监测的作用出发,提出利用MODIS L1B数据反演气溶胶光学厚度AOD,并对同时段的PM_(10)时均值数据与AOD数据使用多种方法进行回归分析,建立相应的关系模型,以期发现能够较准确反映两者关系的模型,实现利用MODIS数据实时动态地监测大气污染数据的目的。主要结论如下:
(1)MODIS反演的气溶胶光学厚度和可吸入颗粒物PM_(10)的浓度具有较好的相关性,在一定程度上可以反映城市空气污染状况。但对PM_(10)与AOD之间进行统计回归分析只有当首要污染物为PM_(10)时,两者之间才能进行统计回归分析。如果某些日子的主要污染物不是可吸入颗粒物,而是SO_2或NO_2,则必须排除这些天的数据,才能进行统计回归分析。
(2)在对MODIS数据进行气溶胶反演,6S模式中的大气模型参数为大陆模式,此模型的使用受气象条件的影响,在某些时段有局限性(如夏季),影响反演的精度;在此情况下进行反演,最好能对气溶胶的成分进行分析,以减少误差。
(3)利用2008年9月至11月的MODIG L1B数据反演的AOD数据,与PM_(10)的建立五种类型的回归模型,通过分析确定系数R~2和F检验的值,得到较优的两个回归模型:线性模型和一元二次模型;用检验数据对两个模型进行检验,得到的平均相对误差分别为11.7%、30.6%,确定线性模型为反映AOD与PM_(10)最佳拟合模型。
With rapid development of economy and course of industrialization and urbanization in our country, the environment stress of most areas increases, the pollution of the atmosphere also becomes more and more serious. In most areas, a kind of inhalable particle—PM_(10) becomes the critical pollutant which affects the air quality. Focus on the existing reality, utilize the satellite remote sensing to monitor the space distribution and variation trend of the pollution has the most important practical significance.
The article based on the research of the monitoring function of MODIS data which affects the air pollution and PM_(10), put forward the utilization of MODIS L1B data invert the AOD (Aerosol Optical Depth). Then proceed the regression analysis by multiple methods for mean value of PM_(10) and AOD data which appear at the same period, establish relevant relational model. The purpose of the research is expected to discover the model which can reflect the relationship of the two data exactly, and realize the utilization of MODIS data to monitor the air pollution.
The main conclusions are as follows:
(1) The Aerosol Optical Depth and the inhalable particulate matter (PM_(10)) concentration have better relationship which was inverted by MODIS, this can reflect the condition of air pollution in cities to a certain extent. However, only when the critical pollutant is PM10, we can take a statistic regression analysis between PM_(10) and AOD. If in a period, the critical pollutants were not inhalable particulate matter but SO_2 or NO_2, we should eliminate the data in these days.
(2) When we proceed the aerosol inversion on the MODIS data, the atmosphere model parameter in 6S model is continent pattern, the utilization of this model has been affected by climate condition. This model has limitations in some periods (such as summer), and these limitations would affect the precision of inversion. Under these conditions, we'd better analyze the components of the aerosol in order to decrease errors.
(3) Constructed five types of regression model by the utilization of and AOD data which inverted by MODIS L1B data that generated between September and November in 2008. According to analyze these data which verified by coefficient of determination R~2 and F, we got two better regression models: linear model and quadratic model. When verified them by testing data, their average relative errors are 11.7% and 30.6%, respectively. So we can ensure that the best fitting model which can reflect the AOD and PM_(10) is linear model.
引文
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[10]周斌,刘文清,齐峰等.差分光学吸收光谱法测量大气污染气体的研究[J].环境科学研究,2001,14(5):23-37.
[11]魏合理,胡欢陵等.用太阳光谱遥测大气污染气体NO2的含量[J].遥感学报,2001,5(3):125-130.
[12]于意仲等.紫外差分光学吸收法测量污染气体的实验研究[J].环境科学学报,2003,23(5):78-83.
[13]王雪梅,邓孺孺等.遥感技术在大气监测中的应用[J].中山大学学报(自然科学版),2001,40(6):59-68.
[14]熊利亚,李海萍等.应用MODIS数据研究沙尘信息定量化方法探讨[J].地理科学进展,2002,21(4):327-332.
[15]王皓,查勇.MODIS气溶胶光学厚度对城市空气质量的指示[J].城市环境与城市生态,2006,19(3):21-24.
[16]夏丽华,王德辉,王芳.基于MODIS数据的广州市光化学污染预警等级研究[J].国土资源遥感,2006,4:73-76.
[17]马国欣,薛永棋,李高丰.珠江三角洲地区的灰霆监控与卫星遥感[J].科技导报,2008,26(16):98-110.
[18]Hisao F,Kiyoyuki I,Hajime N..Assessment of Air Pollution by Using Satellite Data in the Neighhorhood of Cities and Industrial Complex[J].Remote Sensing Society of Sapan,1991,11(4):21-33.
[19]Keller J,Lamprecht R.Road Dust as An Indicator for Air Pollution Transport and Deposition:An Application of SPOT Imagery[J].Remote Sensing of Enviromnent,1995,54(1):1-12.
[20]Hutchison K.D..Applications of MODIS Satellite Data and Products for Monitoring Air Quality in the State of Texas[J].Atmospheric Enviromnent,2003,3 7(17):2403- 2412.
[21]Gamiles,Don S.;Rodgers,Michael;Meeker,Charles;Olive,Brent S.;Evaluation of innovative air-monitoring technologies for the measurement of ambient concentrations of ozone and its precursors at the 1996 Summer Olympics[C].Proceedings of SPIE,1997:1045-1056.
[22]Chu D A,Kaufman Y J,Zibordi G,et al.Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS)[J].Geophys Res,2003,108(D21):61-71.
[23]Engel-Cox J A,Christopher H H,Basil W C,et al.Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality[J].Atmospheric Environment,2004,38:102-114.
[24]Wang J,Christopher S A.Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass:Implications for air quality studies[J].Geophysical Research Letters,2003,30(21):465-471.
[25]Lau Kai-Hon Alexis,Li Chengcai,Mao Jietai,et al.A new way of using MODIS data to study air pollution over the Pear River Delta[J].Proceedings of the SPIE,2003,4891
[26]刘玉洁,杨忠东等.MODIS遥感信息原理与算法[M].科学出版社,2001:2-3.
[27]黄家洁,万幼川,刘良明,MODIS的特性及其应用[J].地理空间信,2003,1(4):20-28.
[28]EOS/MODIS资料接收应用培训教材[M].中国气象局监测网络司,预测减灾 司,国家卫星气象中心,2001:11-45.
[29]MODIS数据产品分级、分类、流程参考规范[S].中科院地理科学与资源研究所全球变化信息研究中心,2004:9-52.
[30]盛裴轩,毛节泰,李建国等.大气物理学[M].北京:北京大学出版,2003:430-433.
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