MODIS气溶胶光学厚度与南京主城区空气污染指数的关系研究
|
摘要
国民经济的快速发展，城市规模的不断扩大，城市各类工程的不断开展，机动车的不断增加，给城市空气质量带来了严峻的挑战。实践证明遥感在城市空气质量监测中发挥了重要的作用。本文利用MODIS遥感反演的气溶胶光学厚度数据和地面监测的空气污染指数数据，通过两者之间的相关分析，试图建立相应的关系模型。此外由于空气污染状况以及气溶胶光学厚度也与一定的气象条件有关，因此引进气象因子参与关系讨论，并建立相应模型，以找出空气污染指数与气溶胶光学厚度之间的较好模型，从而实现利用遥感手段监测城市空气污染状况的目的。通过研究得到如下结论：
1、全年、季节空气污染指数和气溶胶光学厚度建立回归模型，比较各模型精度，得出夏季线性模型和秋季线性模型预测精度较理想，夏季相关系数达到0.853，秋季相关系数达到0.838。
2、讨论常规的气象观测数据，选取主导气象因子为风速和气压，考虑两气象因子的作用，对全年和季节模型进行重建，发现有主导气象因子参与的多元回归模型预测精度普遍比没有气象因子参与的一元回归模型高。
3、依据主导气象因子风速和气压将样本数据分组，各组空气污染指数和气溶胶光学厚度建立回归模型，并讨论其精度，发现气压大于1020hpa且风速小于1m／s时，空气污染指数与气溶胶光学厚度之间的模型预测精度最理想，预测精度达到95.4％。
4、对季节样本数据在气象因子分级后的情况进行讨论，发现夏季和秋季使用气象因子分级模型预测API值比这两个季节本身模型预测效果要好，精度平均提高2个百分点，春季和冬季效果不是很明显。
5、通过本文的研究，发现在考虑气象因子的情况下，探讨空气污染指数和气溶胶光学厚度的关系，会得到更好的效果，更能真实地反映地面空气的污染状况。
The fast development of national economy, the unceasing expansion of city scale, the unceasing development of all kinds of urban projects, as well as the unceasing development of motor vehicles, all of which had brought the stern challenge to the city air quality. The practice proved that remote sensing had played an important role in urban air quality monitor. This paper used the MODIS Aerosol Optical Thickness data and the ground monitor Air Pollution Index data, attempting to establish the corresponding relational model by the correlation analysis between API and AOT.
In addition, the air pollution condition or the aerosol optical thickness was related to certain meteorological condition, so the meteorological factors must be discussed when studying the relationship between the API and AOT. Then established the corresponding model to find out the good model of API and AOT, thus realized the purposes of using remote sensing to monitor the status of urban air pollution. The main conclusions of this paper were drew as follows:
1. Through regression analysis to API and AOT of season or year to compare each model's precision, found that the predicted accuracy of summer and autumn model were good.
2. Through discussing the conventional meteorological observation data, the wind speed and the barometric pressure were selected as the main meteorological factor. Then further considered the above two meteorological factors in the model, which were reconstructed by the whole year and the season data, it was found that the precision of the model involved the meteorological factor was higher than which no meteorological factors involved.
3. The sample data were grouped according to the main meteorological factor such as wind speed and barometric pressure. Through regression analysis to each group's API and AOT, and discussed its accuracy. It was found that the model's precision between API and AOT was the highest and the forecast accuracy reached 95.4%, when the barometric pressure was more than 1020hpa and the wind speed was less than 1m/s.
4. Through discussing seasonal sample data after the meteorological factor grading, found that the forecast results of the predictive API by meteorological factor grading model was better than their own model in the summer and autumn, which the accuracy averagely increased of 2 percents points. The effect of spring and winter were not very significant.
5. Through the research of this paper, we can find that discussing the relationship between API and AOT with the meteorological factors will obtain a better effect, and even really reflect the air pollution condition on the ground.
1、全年、季节空气污染指数和气溶胶光学厚度建立回归模型，比较各模型精度，得出夏季线性模型和秋季线性模型预测精度较理想，夏季相关系数达到0.853，秋季相关系数达到0.838。
2、讨论常规的气象观测数据，选取主导气象因子为风速和气压，考虑两气象因子的作用，对全年和季节模型进行重建，发现有主导气象因子参与的多元回归模型预测精度普遍比没有气象因子参与的一元回归模型高。
3、依据主导气象因子风速和气压将样本数据分组，各组空气污染指数和气溶胶光学厚度建立回归模型，并讨论其精度，发现气压大于1020hpa且风速小于1m／s时，空气污染指数与气溶胶光学厚度之间的模型预测精度最理想，预测精度达到95.4％。
4、对季节样本数据在气象因子分级后的情况进行讨论，发现夏季和秋季使用气象因子分级模型预测API值比这两个季节本身模型预测效果要好，精度平均提高2个百分点，春季和冬季效果不是很明显。
5、通过本文的研究，发现在考虑气象因子的情况下，探讨空气污染指数和气溶胶光学厚度的关系，会得到更好的效果，更能真实地反映地面空气的污染状况。
The fast development of national economy, the unceasing expansion of city scale, the unceasing development of all kinds of urban projects, as well as the unceasing development of motor vehicles, all of which had brought the stern challenge to the city air quality. The practice proved that remote sensing had played an important role in urban air quality monitor. This paper used the MODIS Aerosol Optical Thickness data and the ground monitor Air Pollution Index data, attempting to establish the corresponding relational model by the correlation analysis between API and AOT.
In addition, the air pollution condition or the aerosol optical thickness was related to certain meteorological condition, so the meteorological factors must be discussed when studying the relationship between the API and AOT. Then established the corresponding model to find out the good model of API and AOT, thus realized the purposes of using remote sensing to monitor the status of urban air pollution. The main conclusions of this paper were drew as follows:
1. Through regression analysis to API and AOT of season or year to compare each model's precision, found that the predicted accuracy of summer and autumn model were good.
2. Through discussing the conventional meteorological observation data, the wind speed and the barometric pressure were selected as the main meteorological factor. Then further considered the above two meteorological factors in the model, which were reconstructed by the whole year and the season data, it was found that the precision of the model involved the meteorological factor was higher than which no meteorological factors involved.
3. The sample data were grouped according to the main meteorological factor such as wind speed and barometric pressure. Through regression analysis to each group's API and AOT, and discussed its accuracy. It was found that the model's precision between API and AOT was the highest and the forecast accuracy reached 95.4%, when the barometric pressure was more than 1020hpa and the wind speed was less than 1m/s.
4. Through discussing seasonal sample data after the meteorological factor grading, found that the forecast results of the predictive API by meteorological factor grading model was better than their own model in the summer and autumn, which the accuracy averagely increased of 2 percents points. The effect of spring and winter were not very significant.
5. Through the research of this paper, we can find that discussing the relationship between API and AOT with the meteorological factors will obtain a better effect, and even really reflect the air pollution condition on the ground.
引文
② 注：本节主要内容来源于：美国国家宇航局网站：http://modis.gsfc.nasa.gov/．
[1] Ackerman K L, Strabala W P, Menzel R A, Frey C, Moeller C, and L.E.Gumley. Discrimin-ating clear-sky from clouds with MODIS[J]. Geophys Res, 1998,103:32141-32158.
[2] Durkee P A, Jensen D R, Hindman E E, and Vonder T H. The relationship between marine aerosols and satellite detected radiance[J].Geophys Res, 1986,91:4063-4072.
[3] Fraser R S, and Mahoney R L. Satellite measurements of aerosol mass and transport[J]. Atmos Environ, 1984,18:2577-2584.
[4] Griggs M. Measurement of atmospheric aerosol optical thickness over water using ERTS-1 data[J].Air Pollut Contr Assoc, 1975,25:622-626.
[5] Kaufman Y J, Tanre D C,Remer L, et al. Operational remote sensing of tropospheric aerosol over the land from EOS-MODIS. Journal of Geophysical Research, 1997, 102 (D14): 17051-17068.
[6] Kaufman Y J, Gao B C. Remote sensing of water vapor in the near IR from EOS/MODIS[J], IEEE Trans Geos Remote Sensing, 1992, 30: 871-884.
[7] Kaufman Y J, Tanre D C, Remer L A, Vermote E, Chu A and Holben B N. Remote sensing of tropospheric aerosol from EOS-MODIS over the land using dark targets and dynamic aerosol models[J]Geophys Res, 1997a, 102:17051-17067.
[8] Kaufman Y J, and Sendra C. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery[J]. Int. Remote Sens, 1988,9:1357-1381.
[9] Kaufman Y J, and Joseph J H. Determination of surface albedos and aerosol extinction characteristics from stellite imagery[J]. Geophys Res, 1982,87:1287-1299.
[10] Kaufman Y.J, and Remer L A. Remote sensing of vegetation in the near IR from EOS/MODIS[J]. IEEE Trans.Geosci.Remote Sens. 1994,32:672-683.
[11] King M D, Kaufman Y J, Menzel W P, et al. Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS)[J].IEEE Transactions on Geoscience and Remote Sensing, 1992, 30:2-27.
[12] Legrand M, Desbois M, and Vovor K. Satellite detection of Saharan dust, Optimized imaging during nighttime[J].Climate, 1988,1:256-264.
[13] Leroy M, and Coauthors. Retrieval of aerosol properties and surface bi-directional reflectances from POLDER/ADEOS[J]. Geophys Res, 1997,102:17023-17037.
[14] Martonchik, and Diner D J. Retrieval of aerosol and land surface optical properties from multi-angle stellite imagery[J]. IEEE traps Geosci Remote Sens, 1992,30:223-230.
[15] Michael D k, Kaufman Y J, Didier Tanre, and Teruyuki Nakajima. Remote Sensing of Tropospheric Aerosol from Space:Past,Present, and Future[J].Bulletin of the American Meterological Society,1999,80 (11):2229-2257.
[16] Tanre D C, Devaux M, Herman R. Santer, and Gac J Y. Radiative properties of desert aerosols by optical ground-based measurements at solar wavelengths[J].Geophys Res 1988,93:14223-14231.
[17] Tanre D C, Kaufman Y J, Herman M, Mattoo S. Remote sensing of aerosol over oceans from EOS-MODIS[J], JGR, 1997,102 (DI4):16971-16988.
[18] Tanre D C, Remer A, Kaufman Y J, Herman S, Mattoo P V, Hobbs J, Livingston M, Russell P B and Smimov A, Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS Airborne Simulator during TARFOX[J], G.R, 1999,104 (D2):2261-2278.
[19] Tanre, Kaufman Y J, Mattoo S, VHobbs P, Livingston J M, Russell P B and Smimov A. Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS Airborne Simulator during TARFOX[J].Geophys Res. 1999,104:2261-2278
[20] Veefkind J P and Durkee P A. Retrieval of aerosol optical depth over land using two-angle view radiometry during TARFOX[J]. Geophys Res Lett, 1998,25:3135-3138.
[21] Zhou MY, Chen Z, and Hunag R, et al. Effects of two dust storms on solar radiation in the Beijing-Tianjin area. Geophys Res Lett,1994,21 (24):2697-2700.
[22] 程立刚，王艳姣，王耀庭．遥感技术在大气环境监测中的应用综述[J]．中国环境监测，2005，21(5)：17-23．
[23] 邓孺孺，田国良，王雪梅等．大气污染定量遥感方法及其在长江三角洲的应用．红外与毫米波学报，2003，22(3)：181-185．
[24] 方宗义，张运刚，郑新江等．用气象卫星遥感监测沙尘暴的方法和初步结果[J]．第四纪研究，2001，21(1)：48-541．
[25] 美国国家宇航局网站：http://modis.gsfc.nasa.gov/．
[26] 韩志刚．草地上空对流层气溶胶特性的卫星偏振遥感-正问题模式系统和反演初步实验[学位论文]．北京：中国科学院大气物理研究所，1999．
[27] 李成才．MODIS遥感气溶胶光学厚度及应用于区域环境大气污染研究．北京大学大气科学系，博士学位论文，2002．
[28] 李成才，毛节泰，刘启汉等．MODIS卫星遥感气溶胶产品在北京市大气污染研究中的应用[J]．中国科学D辑地球科学，2005，35(增刊Ⅰ)：177-186．
[29] 李晓静，刘玉沽，邱红等．利用MODIS资料反演北京及其周边地区气溶胶光学厚度的方法研究[J]．气象学报，2003，61(5)：580-591．
[30] 林祥明，林永登，冯宏芳等．利用地面气象资料进行福州市空气质量日预报．热带气象学报，2001，17(3)：320-326．
[31] 刘桂青，李成才，朱爱华．长江三角洲地区大气气溶胶光学厚度研究．环境保护[J]，2003，8：50-54．
[32] 刘莉．GMS 5卫星遥感气溶胶光学厚度的试验研究[学位论文]．北京：北京大学地球物理系，1999．
[33] 刘玉洁，杨忠东．《MODIS遥感信息处理原理与算法》，北京：科学出版社，2001．
[34] 毛节泰，张军华，王美华．中国大气气溶胶研究综述．气象学报，2002，60(5)：626-634．
[35] 南京市环保局．2004南京市环境状况公报，南京，2005．
[36] 南京市环保局．2005南京市环境状况公报，南京，2006．
[37] 邱金桓，林耀荣．关于中国大气气溶胶光学厚度的一个参数化模式．气象学报，2001，59(3)：368-372．
[38] 宋艳玲，郑水红，柳艳菊等．2000～2002年北京市城市大气污染特征分析．应用气象学报，2005，16(增刊)：116-122．
[39] 王浩．南京市区空气污染指数与MODIS气溶胶光学厚度的回归分析．南京师范大学地理科学学院，硕士学位论文，2006．
[40] 王京丽，刘旭林．北京市大气细粒子质量浓度与能见度定量关系初探．气象学报，2006，64(2)：222-228．
[41] 王雪梅，邓孺孺，何执谦．遥感技术在大气监测中的应用[J]．中山大学学报(自然科学)，2001，6(40)：95-981．
[42] 吴国柱，吴克勤译．卫星海洋学．北京：海洋出版社，1981．
[43] 徐祥德，施晓晖，张胜军．北京及周边城市群落气溶胶影响域及其相关气候效应．科学通报，2005，50(22)：2522-2530．
[44] 徐祥德，汤绪等．城市化环境气象学引论．北京：气象科学出版社，2002，139-185．
[45] 徐祝龄(主编)．气象学．北京：气象出版社，1994．
[46] 张军华，斯召俊，毛节泰等．GMS卫星遥感中国地区气溶胶光学厚度[J]，大气科学，2003，27(1)：23-350．
[47] 赵柏林，俞小鼎．海上大气气溶胶的卫星遥感研究[J]．科学通报，1986，31：1645-1649．
[48] 赵烨，刘芸，赵承易等．北京城区油松灰分含量与大气质量指数相关分析．北京师范大学学报(自然科学版)，2000，3(2)：281-284．
[1] Ackerman K L, Strabala W P, Menzel R A, Frey C, Moeller C, and L.E.Gumley. Discrimin-ating clear-sky from clouds with MODIS[J]. Geophys Res, 1998,103:32141-32158.
[2] Durkee P A, Jensen D R, Hindman E E, and Vonder T H. The relationship between marine aerosols and satellite detected radiance[J].Geophys Res, 1986,91:4063-4072.
[3] Fraser R S, and Mahoney R L. Satellite measurements of aerosol mass and transport[J]. Atmos Environ, 1984,18:2577-2584.
[4] Griggs M. Measurement of atmospheric aerosol optical thickness over water using ERTS-1 data[J].Air Pollut Contr Assoc, 1975,25:622-626.
[5] Kaufman Y J, Tanre D C,Remer L, et al. Operational remote sensing of tropospheric aerosol over the land from EOS-MODIS. Journal of Geophysical Research, 1997, 102 (D14): 17051-17068.
[6] Kaufman Y J, Gao B C. Remote sensing of water vapor in the near IR from EOS/MODIS[J], IEEE Trans Geos Remote Sensing, 1992, 30: 871-884.
[7] Kaufman Y J, Tanre D C, Remer L A, Vermote E, Chu A and Holben B N. Remote sensing of tropospheric aerosol from EOS-MODIS over the land using dark targets and dynamic aerosol models[J]Geophys Res, 1997a, 102:17051-17067.
[8] Kaufman Y J, and Sendra C. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery[J]. Int. Remote Sens, 1988,9:1357-1381.
[9] Kaufman Y J, and Joseph J H. Determination of surface albedos and aerosol extinction characteristics from stellite imagery[J]. Geophys Res, 1982,87:1287-1299.
[10] Kaufman Y.J, and Remer L A. Remote sensing of vegetation in the near IR from EOS/MODIS[J]. IEEE Trans.Geosci.Remote Sens. 1994,32:672-683.
[11] King M D, Kaufman Y J, Menzel W P, et al. Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS)[J].IEEE Transactions on Geoscience and Remote Sensing, 1992, 30:2-27.
[12] Legrand M, Desbois M, and Vovor K. Satellite detection of Saharan dust, Optimized imaging during nighttime[J].Climate, 1988,1:256-264.
[13] Leroy M, and Coauthors. Retrieval of aerosol properties and surface bi-directional reflectances from POLDER/ADEOS[J]. Geophys Res, 1997,102:17023-17037.
[14] Martonchik, and Diner D J. Retrieval of aerosol and land surface optical properties from multi-angle stellite imagery[J]. IEEE traps Geosci Remote Sens, 1992,30:223-230.
[15] Michael D k, Kaufman Y J, Didier Tanre, and Teruyuki Nakajima. Remote Sensing of Tropospheric Aerosol from Space:Past,Present, and Future[J].Bulletin of the American Meterological Society,1999,80 (11):2229-2257.
[16] Tanre D C, Devaux M, Herman R. Santer, and Gac J Y. Radiative properties of desert aerosols by optical ground-based measurements at solar wavelengths[J].Geophys Res 1988,93:14223-14231.
[17] Tanre D C, Kaufman Y J, Herman M, Mattoo S. Remote sensing of aerosol over oceans from EOS-MODIS[J], JGR, 1997,102 (DI4):16971-16988.
[18] Tanre D C, Remer A, Kaufman Y J, Herman S, Mattoo P V, Hobbs J, Livingston M, Russell P B and Smimov A, Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS Airborne Simulator during TARFOX[J], G.R, 1999,104 (D2):2261-2278.
[19] Tanre, Kaufman Y J, Mattoo S, VHobbs P, Livingston J M, Russell P B and Smimov A. Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS Airborne Simulator during TARFOX[J].Geophys Res. 1999,104:2261-2278
[20] Veefkind J P and Durkee P A. Retrieval of aerosol optical depth over land using two-angle view radiometry during TARFOX[J]. Geophys Res Lett, 1998,25:3135-3138.
[21] Zhou MY, Chen Z, and Hunag R, et al. Effects of two dust storms on solar radiation in the Beijing-Tianjin area. Geophys Res Lett,1994,21 (24):2697-2700.
[22] 程立刚，王艳姣，王耀庭．遥感技术在大气环境监测中的应用综述[J]．中国环境监测，2005，21(5)：17-23．
[23] 邓孺孺，田国良，王雪梅等．大气污染定量遥感方法及其在长江三角洲的应用．红外与毫米波学报，2003，22(3)：181-185．
[24] 方宗义，张运刚，郑新江等．用气象卫星遥感监测沙尘暴的方法和初步结果[J]．第四纪研究，2001，21(1)：48-541．
[25] 美国国家宇航局网站：http://modis.gsfc.nasa.gov/．
[26] 韩志刚．草地上空对流层气溶胶特性的卫星偏振遥感-正问题模式系统和反演初步实验[学位论文]．北京：中国科学院大气物理研究所，1999．
[27] 李成才．MODIS遥感气溶胶光学厚度及应用于区域环境大气污染研究．北京大学大气科学系，博士学位论文，2002．
[28] 李成才，毛节泰，刘启汉等．MODIS卫星遥感气溶胶产品在北京市大气污染研究中的应用[J]．中国科学D辑地球科学，2005，35(增刊Ⅰ)：177-186．
[29] 李晓静，刘玉沽，邱红等．利用MODIS资料反演北京及其周边地区气溶胶光学厚度的方法研究[J]．气象学报，2003，61(5)：580-591．
[30] 林祥明，林永登，冯宏芳等．利用地面气象资料进行福州市空气质量日预报．热带气象学报，2001，17(3)：320-326．
[31] 刘桂青，李成才，朱爱华．长江三角洲地区大气气溶胶光学厚度研究．环境保护[J]，2003，8：50-54．
[32] 刘莉．GMS 5卫星遥感气溶胶光学厚度的试验研究[学位论文]．北京：北京大学地球物理系，1999．
[33] 刘玉洁，杨忠东．《MODIS遥感信息处理原理与算法》，北京：科学出版社，2001．
[34] 毛节泰，张军华，王美华．中国大气气溶胶研究综述．气象学报，2002，60(5)：626-634．
[35] 南京市环保局．2004南京市环境状况公报，南京，2005．
[36] 南京市环保局．2005南京市环境状况公报，南京，2006．
[37] 邱金桓，林耀荣．关于中国大气气溶胶光学厚度的一个参数化模式．气象学报，2001，59(3)：368-372．
[38] 宋艳玲，郑水红，柳艳菊等．2000～2002年北京市城市大气污染特征分析．应用气象学报，2005，16(增刊)：116-122．
[39] 王浩．南京市区空气污染指数与MODIS气溶胶光学厚度的回归分析．南京师范大学地理科学学院，硕士学位论文，2006．
[40] 王京丽，刘旭林．北京市大气细粒子质量浓度与能见度定量关系初探．气象学报，2006，64(2)：222-228．
[41] 王雪梅，邓孺孺，何执谦．遥感技术在大气监测中的应用[J]．中山大学学报(自然科学)，2001，6(40)：95-981．
[42] 吴国柱，吴克勤译．卫星海洋学．北京：海洋出版社，1981．
[43] 徐祥德，施晓晖，张胜军．北京及周边城市群落气溶胶影响域及其相关气候效应．科学通报，2005，50(22)：2522-2530．
[44] 徐祥德，汤绪等．城市化环境气象学引论．北京：气象科学出版社，2002，139-185．
[45] 徐祝龄(主编)．气象学．北京：气象出版社，1994．
[46] 张军华，斯召俊，毛节泰等．GMS卫星遥感中国地区气溶胶光学厚度[J]，大气科学，2003，27(1)：23-350．
[47] 赵柏林，俞小鼎．海上大气气溶胶的卫星遥感研究[J]．科学通报，1986，31：1645-1649．
[48] 赵烨，刘芸，赵承易等．北京城区油松灰分含量与大气质量指数相关分析．北京师范大学学报(自然科学版)，2000，3(2)：281-284．