大气颗粒物污染对土地覆盖变化的响应
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摘要
土地利用-覆盖变化(LUCC)直接或间接影响颗粒物污染。了解颗粒物污染对LUCC的响应,对维护和改善生态环境具有重要的意义。基于卫星遥感技术,从广域的空间尺度分析颗粒物污染对LUCC的响应。使用MODIS数据分别提取与颗粒物污染相关性较高的城市用地、林地等土地利用类型,确定土地利用类型的变化趋势,利用长时间序列MODIS气溶胶光学厚度(AOD,Aerosol optical depth)产品分析颗粒物污染与土地利用类型的变化的相关性。以山东省青岛市、淄博市、济南市3个典型城市为例,研究了AOD随土地利用类型的变化趋势。同时,考虑并分析了颗粒物污染对土地利用变化响应的敏感性,以及城市区域变化对环境的影响。研究结果表明,不同的城市类型,由于决定环境变化主导因素的差异,颗粒物污染对LUCC的响应具有明显的差异。青岛市地区,由于受海洋影响显著,大气颗粒物污染与LUCC的相关性较低,如中度污染天气与林地的相关系数为-0.451;而淄博市和济南市的相关系数分别为-0.473、-0.507。
Land-use and land-cover change( LUCC) affects particulate matter pollution in the atmosphere,directly or indirectly. Atmospheric particulate matter is now the primary type of air pollutant and can seriously affect the life of people in the city. It is very important for ecological and environmental protection to understand and determine the atmospheric particulate matter pollution response to LUCC. In this paper,based on satellite remote sensing technology,we analyzed the responses of particle pollution to LUCC on a large scale. The experiments were carried out in three typical cities,Qingdao,Zibo,and Jinan,in Shandong Province. The aerosol optical depth( AOD) data in the last ten years from 2001 to 2010 from MODIS L1 B products were chosen to define atmospheric particulate matter pollution. The main land use types including forest land and urban land were obtained from MODIS land use classification product( MOD12) to estimate the land cover change of each city. According to the atmospheric visibility,the particle pollution was divided into four levels: no pollution,slight pollution,medium pollution,and heavy pollution. The numbers of effective days for each air pollution level and the areas of each land use type in the three cities were counted to analyze the correlation of particle pollution withLUCC. The sensitivity of particle pollution to LUCC and the impact of urban change on particle pollution were also considered. There are three main conclusions:( 1) the correlation between atmospheric particle matter pollution and LUCC is lower in Qingdao( R =- 0. 451) but higher in Jinan( R =- 0. 473) and Zibo( R =- 0. 507).( 2) The influence of urban land on Zibo' s atmospheric particulate matter pollution is higher than that of Qingdao. Forest land is the most important factor on particulate matter pollution in Jinan,with the correlation coefficient between the scale of forest land change and the four air pollution level weather changes of 0. 176,0. 859,0. 753,and 0. 309 respectively.( 3) The response of atmospheric particle matter pollution to LUCC is obviously different in different cities due to different dominant factors.
Land-use and land-cover change( LUCC) affects particulate matter pollution in the atmosphere,directly or indirectly. Atmospheric particulate matter is now the primary type of air pollutant and can seriously affect the life of people in the city. It is very important for ecological and environmental protection to understand and determine the atmospheric particulate matter pollution response to LUCC. In this paper,based on satellite remote sensing technology,we analyzed the responses of particle pollution to LUCC on a large scale. The experiments were carried out in three typical cities,Qingdao,Zibo,and Jinan,in Shandong Province. The aerosol optical depth( AOD) data in the last ten years from 2001 to 2010 from MODIS L1 B products were chosen to define atmospheric particulate matter pollution. The main land use types including forest land and urban land were obtained from MODIS land use classification product( MOD12) to estimate the land cover change of each city. According to the atmospheric visibility,the particle pollution was divided into four levels: no pollution,slight pollution,medium pollution,and heavy pollution. The numbers of effective days for each air pollution level and the areas of each land use type in the three cities were counted to analyze the correlation of particle pollution withLUCC. The sensitivity of particle pollution to LUCC and the impact of urban change on particle pollution were also considered. There are three main conclusions:( 1) the correlation between atmospheric particle matter pollution and LUCC is lower in Qingdao( R =- 0. 451) but higher in Jinan( R =- 0. 473) and Zibo( R =- 0. 507).( 2) The influence of urban land on Zibo' s atmospheric particulate matter pollution is higher than that of Qingdao. Forest land is the most important factor on particulate matter pollution in Jinan,with the correlation coefficient between the scale of forest land change and the four air pollution level weather changes of 0. 176,0. 859,0. 753,and 0. 309 respectively.( 3) The response of atmospheric particle matter pollution to LUCC is obviously different in different cities due to different dominant factors.
引文
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[14]Lambin E F,Linderman M.Time series of remote sensing data for land change science.IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1926-1928.
[15]Pongratz J,Reick C H,Raddatz T,Claussen M.Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change.Geophysical Research Letters,2010,37(8):L08702.
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[17]李成才,毛节泰,刘启汉,袁自冰,王美华,刘晓阳.MODIS卫星遥感气溶胶产品在北京市大气污染研究中的应用.中国科学D辑-地球科学,2005,35(增刊):177-186.
[18]陈好,顾行发,程天海,余涛,李正强.中国地区气溶胶类型特性分析.遥感学报,2013,17(6):1559-1571.
[19]Wang J,Christopher S A.Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass:Implications for air quality studies.Geophysical Research Letters,2003,30(21):doi:10.1029/2003GL018174.
[20]Lau K H,Li C C,Mao J T,Chen J C.A new way of using MODIS data to study air pollution over Hong Kong and the Pearl River Delta.Proceedings of the SPIE,2003,4891:105-114.
[21]孙娜.珠三角地区可吸入颗粒物的遥感监测及其与下垫面的相关性分析.北京:中国地质大学(北京),2013.
[22]Chu D A,Kaufman Y J,Zibordi G,Chem J D,Mao J T,Li C C,Holben B N.Global monitoring of air pollution over land from the earth observing system-terra moderate resolution imaging spectroradiometer(MODIS).Journal of Geophysical Research,2003,108(D21):4661,doi:10.1029/2002JD003179.
[23]Engel-Cox J A,Christopher H H,Basil W C,Raymond M H.Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality.Atmospheric Environment,2004,38(16):2495-2509.
[24]余梓木,周红妹,郑有飞.基于遥感和GIS的城市颗粒物污染分布研究.自然灾害学报,2004,13(3):58-64.
[25]Tsai Y I,Kuoc S C,Lee W J,Chen C L,Chen P T.Long-term visibility trends in one highly urbanized,one highly industrialized,and two rural areas of Taiwan.Science of Total Environment,2007,382(2/3):324-341.
[26]Buseck P R,Posfai M.Airborne minerals and related aerosol particles:Effects on climate and the environment.Proceedings of the National Academy of Sciences of the United States of America,1999,96(7):3372-3379.
[27]关佳欣,李成才.我国中、东部主要地区气溶胶光学厚度的分布与变化.北京大学学报:自然科学版,2010,46(2):185-191.
[28]卫亚星,王莉雯.MODIS土地覆盖产品研究进展.光谱学与光谱分析,2010,30(7):1848-1852.
[29]冉有华,李新.四种常用的全球1KM土地覆盖数据中国区域的精度评价.冰川冻土,2009,31(3):490-500.
[30]盛立芳,申莉莉,李秀镇,刘飞.水平能见度经验公式在青岛沿海地区的应用.中国海洋大学学报,2009,39(5):877-882.
[31]Yang S.On extraction and fractal of urban and rural residential spatial pat tern in developed area.Acta Geographica Sinica,2000,55(6):671-678.
[32]王斌,高会旺.中国沿海城市空气污染指数的分布特征.生态环境,2008,17(2):542-548.
[2]Huang D S,Xu J H,Zhang S Q.Valuing the health risks of particulate air pollution in the Pearl River Delta,China.Environmental Science and Policy,2012,15(1):38-47.
[3]范雪波,刘卫,王广华,林俊,伏晴艳,高松,李燕.杭州市大气颗粒物浓度及组分的粒径分布.中国环境科学,2011,31(1):13-18.
[4]Wang H,Zhuang Y,Wang Y,Sun Y,Yuan H,Zhuang G,Hao Z.Long-term monitoring and source apportionment of PM2.5/PM10 in Beijing,China.Journal of Environmental Sciences,2008,20(11):1323-1327.
[5]Gu J X,Bai Z P,Li W F,Wu L P,Liu A X,Dong H Y,Xie Y Y.Chemical composition of PM2.5 during winter in Tianjin,China.Particuology,2011,9(3):215-221.
[6]Turner B L II,Lambin E F,Reenberg A.The emergence of land change science for global environmental change and sustainability.Proceedings of the National Academy of Sciences of the United States of America,2007,104(52):20666-20671.
[7]Lambin E F,Turner B L II,Geist H,Agbola S,Angelscn A.The causes of land use and cover change:Moving beyond the myths.Global Environmental Change,2001,11(4):261-269.
[8]Ojima D S,Galvin K A,Turner B L II.The global impact of land use change.Bio Science,1994,44(5):300-304.
[9]De Fries R,Bounoua L,Collatz G J.Human modification of the landscape and surface climate in the next fifty years.Global Change Biology,2002,8(5):438-458.
[10]Garik G,Janetos A C,Justice C O,Moran E F,Mustard J F,Rindfuss R R,Skole D,Turner B L II,Cochrane M A.Land Change Science:Observing,Monitoring and Understanding Trajectories of Change on the Earth's Surface.Netherlands:Kluwer Academic Publishers,2004.
[11]刘纪远,张增祥,庄大方,王一谋,周万村,张树文,李仁东,江南,吴世新.20世纪90年代中国土地利用变化时空特征及其成因分析.地理研究,2003,22(1):1-12.
[12]刘纪远,张增祥,徐新良,匡文慧,周万村,张树文,李仁东,颜长珍,于东升,吴世新,江南.21世纪初中国土地利用变化的空间格局与驱动力分析.地理学报,2009,64(12):1411-1420.
[13]刘纪远,邵全琴,延晓冬,樊江文,邓祥征,战金艳,高学杰,黄麟,徐新良,胡云峰,王军邦,匡文慧.土地利用变化对全球气候影响的研究进展与方法初探.地球科学进展,2011,26(10):1015-1022.
[14]Lambin E F,Linderman M.Time series of remote sensing data for land change science.IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1926-1928.
[15]Pongratz J,Reick C H,Raddatz T,Claussen M.Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change.Geophysical Research Letters,2010,37(8):L08702.
[16]李成才,毛节泰,刘启汉,陈介中,袁自冰,刘晓阳,朱爱华,刘桂青.利用MODIS研究中国东部地区气溶胶光学厚度的分布和季节变化特征.科学通报,2003,48(19):2094-2100.
[17]李成才,毛节泰,刘启汉,袁自冰,王美华,刘晓阳.MODIS卫星遥感气溶胶产品在北京市大气污染研究中的应用.中国科学D辑-地球科学,2005,35(增刊):177-186.
[18]陈好,顾行发,程天海,余涛,李正强.中国地区气溶胶类型特性分析.遥感学报,2013,17(6):1559-1571.
[19]Wang J,Christopher S A.Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass:Implications for air quality studies.Geophysical Research Letters,2003,30(21):doi:10.1029/2003GL018174.
[20]Lau K H,Li C C,Mao J T,Chen J C.A new way of using MODIS data to study air pollution over Hong Kong and the Pearl River Delta.Proceedings of the SPIE,2003,4891:105-114.
[21]孙娜.珠三角地区可吸入颗粒物的遥感监测及其与下垫面的相关性分析.北京:中国地质大学(北京),2013.
[22]Chu D A,Kaufman Y J,Zibordi G,Chem J D,Mao J T,Li C C,Holben B N.Global monitoring of air pollution over land from the earth observing system-terra moderate resolution imaging spectroradiometer(MODIS).Journal of Geophysical Research,2003,108(D21):4661,doi:10.1029/2002JD003179.
[23]Engel-Cox J A,Christopher H H,Basil W C,Raymond M H.Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality.Atmospheric Environment,2004,38(16):2495-2509.
[24]余梓木,周红妹,郑有飞.基于遥感和GIS的城市颗粒物污染分布研究.自然灾害学报,2004,13(3):58-64.
[25]Tsai Y I,Kuoc S C,Lee W J,Chen C L,Chen P T.Long-term visibility trends in one highly urbanized,one highly industrialized,and two rural areas of Taiwan.Science of Total Environment,2007,382(2/3):324-341.
[26]Buseck P R,Posfai M.Airborne minerals and related aerosol particles:Effects on climate and the environment.Proceedings of the National Academy of Sciences of the United States of America,1999,96(7):3372-3379.
[27]关佳欣,李成才.我国中、东部主要地区气溶胶光学厚度的分布与变化.北京大学学报:自然科学版,2010,46(2):185-191.
[28]卫亚星,王莉雯.MODIS土地覆盖产品研究进展.光谱学与光谱分析,2010,30(7):1848-1852.
[29]冉有华,李新.四种常用的全球1KM土地覆盖数据中国区域的精度评价.冰川冻土,2009,31(3):490-500.
[30]盛立芳,申莉莉,李秀镇,刘飞.水平能见度经验公式在青岛沿海地区的应用.中国海洋大学学报,2009,39(5):877-882.
[31]Yang S.On extraction and fractal of urban and rural residential spatial pat tern in developed area.Acta Geographica Sinica,2000,55(6):671-678.
[32]王斌,高会旺.中国沿海城市空气污染指数的分布特征.生态环境,2008,17(2):542-548.