摘要
在我国长期稳定的甲醛观测站点十分缺失,卫星平台高时频、大面积覆盖等优势使得通过卫星遥感探测大气甲醛成为了一种重要的研究手段.本文讨论了现有载荷的反演理论和方法,分析了我国大气甲醛的研究现状及不足.简述了从20世纪至今可用于甲醛探测的主要载荷:GOME/ERS-2,SCIAMACHY/ENVISAT,OMI/Aura,GOME-2/Met Op-A(B),OMPS/Suomi-NPP,总结归纳了各个卫星载荷仪器的轨道信息、时间空间分辨率等相关参数,以及各个传感器在大气甲醛遥感反演中的可行性.由于卫星自上而下的观测方式与地基平台不同,其反演方法也有不同之处,因此本文针对卫星平台综合论述了两种甲醛反演算法:传统的差分吸收光谱法(DOAS)和针对于甲醛反演的一系列改进算法以及近几年提出的主成分分析法(PCA);另外,本文针对现有反演算法和时空分布在我国中东部地区的研究现状和不足进行了综合讨论,并给出了一定的改进策略.
Formaldehyde(HCHO)is a toxic trace gas and carcinogen that mainly concentrates in atmospheric planetary layer.The lifetime of HCHO is short at an order of several hours.Anthropogenic VOC gradually becomes a negligible part of air pollution in China and HCHO is an important indicator of VOC.Apparently,the investigation of HCHO is very valuable.Due to the lack of in situ observations for HCHO,satellite-based remote sensing proving frequent and large coverage measurements becomes a significant alternative.In this study,the payloads for HCHO detection and retrieval theories were discussed and analyses concerning the research status in China were provided along with corresponding shortcomings.From the last century,payloads commonly available for HCHO detection were GOME/ERS-2,SCIAMACHY/ENVISAT,OMI/Aura,GOME-2/Met Op-A(B)and OMPS/Suomi-NPP.Relative information were reviewed about satellite orbits,spatiotemporal resolution and appliance in HCHO retrieval.Because of the top-down observation of satellite platform which is different to the bottom-up of in situ observation,the retrieval algorithms are different as well.Apparently,the main focus here is two majorly adopted methods for HCHO retrieval:differential optical absorption spectroscopy(DOAS)and recently proposed principal component analysis(PCA).
作者
朱松岩
余超
李小英
陈良富
祝好
ZHU Song-yan1,2, YU Chao3, LI Xiao-ying1, CHEN Liang-fu1, ZHU Hao4(1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; 2.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; 3.State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; 4.College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610103, China)
出处
《中国环境科学》
EI
CAS
CSSCI
CSCD
北大核心
2018年第5期1685-1694,共10页
China Environmental Science
基金
国家自然科学基金资助项目(41501476)
国家重点研发计划项目(2016YFC0201507)