| 上海市PM_(10)浓度四季遥感模型研究 |
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| 引用本文： | 李薛,龚绍琦,付东洋,张莹,刘大召,丁又专,李懿超,孙庆飞,顾超,许智棋.上海市PM_(10)浓度四季遥感模型研究[J].大气科学学报,2016,39(1):126-132. |
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| 作者姓名： | 李薛 龚绍琦 付东洋 张莹 刘大召 丁又专 李懿超 孙庆飞 顾超 许智棋 |
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| 作者单位： | 广东海洋大学海洋遥感与信息技术实验室, 广东湛江 524088;南京信息工程大学, 江苏南京 210044;南京信息工程大学, 江苏南京 210044;南京师范大学虚拟地理环境教育部重点实验室, 江苏南京 210046;广东海洋大学海洋遥感与信息技术实验室, 广东湛江 524088;广东海洋大学海洋遥感与信息技术实验室, 广东湛江 524088;广东海洋大学海洋遥感与信息技术实验室, 广东湛江 524088;广东海洋大学海洋遥感与信息技术实验室, 广东湛江 524088;南京信息工程大学, 江苏南京 210044;南京信息工程大学, 江苏南京 210044;南京信息工程大学, 江苏南京 210044;南京信息工程大学, 江苏南京 210044 |
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| 基金项目： | 国家海洋公益专项(201305019);广东省自然科学基金资助项目(2014A030313603);广东省科技计划项目(2013B030200002);浙江省博士后基金资助项目(BSH1301015);广东海洋大学创新强校项目(GDOU2014050226);南京师范大学虚拟地理环境教育部重点实验室开放课题;江苏省级大学生实践创新训练计划项目(201313982015Y) |
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| 摘 要： | 通过分析2001—2012年上海市PM_(10)浓度(由API(Air Pollution Index)转化得到)的变化规律,构建了上海市PM_(10)浓度的遥感反演模型。结果表明:1)上海市PM_(10)浓度存在季节性变化,应分别建立遥感反演模型。2)分析MODIS气溶胶光学厚度(Aerosol Optical Depth,AOD)产品与PM_(10)浓度之间的相关性发现,AOD须经过垂直和湿度订正才可与PM_(10)建立较好的关系。3)结合垂直和湿度订正分别建立的上海市PM_(10)浓度春夏秋冬四季的遥感反演模型均通过了拟合度检验,其中春季模型采用指数函数、夏季和秋季模型采用二次多项式函数、冬季采用幂函数、全年采用二次多项式函数,利用此四季模型反演上海市PM_(10)浓度具有较高的可信度。
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| 关 键 词： | API PM10浓度 MODIS AOD 遥感模型 |
| 收稿时间： | 2014/11/10 0:00:00 |
| 修稿时间： | 2015/8/31 0:00:00 |
Study on the four-season remote sensing models of PM10 concentration in Shanghai |
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| LI Xue,GONG Shaoqi,FU Dongyang,ZHANG Ying,LIU Dazhao,DING Youzhuan,LI Yichao,SUN Qingfei,GU Chao and XU Zhiqi.Study on the four-season remote sensing models of PM10 concentration in Shanghai[J].大气科学学报,2016,39(1):126-132. |
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| Authors: | LI Xue GONG Shaoqi FU Dongyang ZHANG Ying LIU Dazhao DING Youzhuan LI Yichao SUN Qingfei GU Chao and XU Zhiqi |
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| Institution: | Ocean Remote Sensing and Information Technology Laboratory, Guangdong Ocean University, Zhanjiang 524088, China;Nanjing University of Information Science and Technology, Nanjing 210044, China;Nanjing University of Information Science and Technology, Nanjing 210044, China;Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210046, China;Ocean Remote Sensing and Information Technology Laboratory, Guangdong Ocean University, Zhanjiang 524088, China;Ocean Remote Sensing and Information Technology Laboratory, Guangdong Ocean University, Zhanjiang 524088, China;Ocean Remote Sensing and Information Technology Laboratory, Guangdong Ocean University, Zhanjiang 524088, China;Ocean Remote Sensing and Information Technology Laboratory, Guangdong Ocean University, Zhanjiang 524088, China;Nanjing University of Information Science and Technology, Nanjing 210044, China;Nanjing University of Information Science and Technology, Nanjing 210044, China;Nanjing University of Information Science and Technology, Nanjing 210044, China;Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Abstract: | By analyzing the variation rule of PM10 concentration(concluded by a conversion of API(Air Pollution Index)) in Shanghai from 2001 to 2012,this paper establishes the remote sensing inversion model to measure PM10 concentration in Shanghai.As indicated by the results,the following conclusions can be arrived at:1)There is a seasonal variation for PM10 concentration in Shanghai,thus remote sensing inversion models in four seasons should be established,respectively.2)By probing into the correlation between MODIS AOD(Aerosol Optical Depth) products and PM10 concentration,this paper arrives at the conclusion that only by going through aerosol vertical distribution and relative humidity error correction can AOD establish a preferable correlation with PM10 concentration.3)Based on the aerosol vertical distribution and relative humidity error correction,all the four-season remote sensing inversion models established to measure the PM10concentration in Shanghai have passed the test of fitting degree.Among them,the spring model uses the exponential function,the summer and autumn models apply the quadratic polynomial function,the winter model adopts the power function,and the yearly model uses the quadratic polynomial function.It is comparatively highly reliable to invert Shanghai PM10 concentration using the four-season remote sensing inversion models. |
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| Keywords: | API PM10 concentration MODIS AOD remote sensing model |
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