卫星遥感气溶胶光学性质在大气污染监测中的应用研究
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摘要
随着国民经济的快速发展,城市规模在不断扩大,森林面积逐年减少,大气环境保护作为环保的一个重要组成部分已经越来越受到人类的关注,大气污染监测与治理工作刻不容缓。我国幅员辽阔,大范围的设置监测网点并不现实,而依靠仅有的监测点并不能进行宏观监测。而卫星遥感技术的兴起与发展使大气污染监测开始由地面上升到高空,由局部发展为宏观,由离散变为连续。由此看来,通过遥感技术对气溶胶的研究有着巨大的潜力。大气气溶胶是由大气介质和混合于其中的固体或液体颗粒物组成的体系,其光学特性与臭氧层破坏、酸雨、烟雾事件等环境问题密切相关,被广泛应用于大气污染监测。
本论文针对不同的应用目标,分别从陆地和海洋两个方面开展了利用卫星遥感气溶胶光学性质进行大气污染监测的初步研究。
针对陆地,本论文以青岛市市南区中国海洋大学鱼山校区为研究区域,利用MODIS二级产品的大气气溶胶光学厚度(Aerosol Optical Thickness:AOT)数据、天空辐射计反演得到的AOT数据以及地面测量的空气污染指数(AIR POLLUTION INDEX:API)数据,利用统计回归分析方法,建立AOT与API之间的数学模型,考虑了湿度因子对API的影响及校正,并分季节进行了分析。在此基础上,探讨利用卫星遥感气溶胶光学厚度进行大气污染监测的可行性。
针对海洋,本论文以中国东部海域为研究对象,利用MODIS Level 1B数据,引入归一化气溶胶差异指数(Normalized Difference Aerosol Index:NDAI)的概念,结合OMI二级产品气溶胶指数(Aerosol Index:AI)数据,作为识别海上气溶胶吸收性的定量指标。利用出海测量数据以及与卫星同步观测资料的匹配分析,对结果进行了初步印证。海上吸收性气溶胶的识别也适用于海上大气污染的监测。
本论文得出的初步结论如下:
1、总体上,针对陆地,实测和卫星遥感得出的气溶胶光学厚度AOT与大气污染指数API存在一定的相关性,但相关系数不高;考虑湿度因子影响并进行校正后,对不同季节进行回归分析,AOT与API的相关程度较之前有所改善。春、秋季相关性高于夏、冬季。春季相关性低于秋季,这是因为春季天气干燥,容易扬尘,再加上春季数据最少,统计并不精确。夏、冬两季相关性低,夏季主要是因为数据较少的缘故,而冬季处于集中供暖时期,燃煤量高,导致空气中SO2和氮氧化物浓度增加,PM10浓度不能很好地用来表征大气污染指数。由于初步结果显示卫星遥感气溶胶光学厚度与空气污染指数之间的相关性较低,尚无法直接应用于陆地大气污染的监测。
2、通过对MODIS归一化气溶胶差异指数NDAI和OMI气溶胶指数AI的分析,建立一种基于NDAI的快速识别海上气溶胶吸收性的定量方法,并初步确定了NDAI的分类阈值为0.45,小于这一阈值的为吸收性气溶胶,否则为非吸收性气溶胶。通过与出海实测数据的匹配分析,明确了海上吸收性气溶胶是卫星反演气溶胶光学厚度误差的主要原因。这一初步结论既可应用于卫星遥感海上大气污染的监测,也可应用于海色遥感大气校正算法的改进。
With the rapid development of the national economy, atmospheric pollution has become an important part of environmental protection. Directly and indirectly, aerosols affect the climate, since they scatter and absorb radiation and also alter the cloud microphysical properties. And the aerosol optical properties are closely related to the environmental pollution. Traditional in-situ stations for atmospheric pollution monitoring are very limited, while satellite remote sensing technologies show great advantages with large and continuous ground coverage. Aerosol optical properties derived from satellite remote sensing have great potential on the atmospheric pollution monitoring.
Preliminary studies are carried out in this thesis, which concentrate on applications of aerosol optical properties derived from satellite remote sensing to the atmospheric pollution monitoring, both on land and over ocean respectively.
On land, the Yushan campus of Ocean University of China, Shinan District of Qingdao is selected as the study area. The aerosol optical thickness (AOT) from MODIS level-2 product, AOT from in-situ skyradiometer measurements and corresponding air pollution index (API) data are collected for statistical regression analysis. Statistical models are given between AOTs and APIs for different seasons in the year, with correction of relative humidity effect on APIs. The possibility of atmospheric pollution monitoring using aerosol optical properties derived from satellite remote sensing is discussed.
Over ocean, the East China Seas are selected as the study area. Based on MODIS level-1b product, a parameter called normalized difference aerosol index (NDAI) is proposed and calculated. NDAI is used together with OMI aerosol index (AI) to quantitative discriminate absorbing aerosols with non-absorbing aerosols. Match-up dataset between ship-borne skyradiometer observations with MODIS-derived AOTs is used for validation of the NDAI threshold. The detection of absorbing aerosols is also applicable for atmospheric pollution monitoring over the ocean.
The preliminary conclusions are as follows:
1. On land, generally there is low correlation between in-situ and MODIS-derived AOTs with APIs. After correction of relative humidity effect on APIs, the correlations are slightly improved for different seasons in the year. The correlation is usually higher in Spring and Autumn than in Summer and Winter. This may be caused by mis-interpretation of factors other than PM10 in API estimation.
2. A quantitative method is proposed based on NDAI from MODIS for fast recognition of absorbing aerosols over the ocean, which overcomes the low-spatial-resolution disadvantage of AI from OMI. A NDAI threshold of 0.45 is proposed, with NDAI less than 0.45 for absorbing aerosols. This can be used for atmospheric pollution monitoring over the ocean. It is also useful for the improvement of the atmospheric correction algorithms for ocean color remote sensing.
本论文针对不同的应用目标,分别从陆地和海洋两个方面开展了利用卫星遥感气溶胶光学性质进行大气污染监测的初步研究。
针对陆地,本论文以青岛市市南区中国海洋大学鱼山校区为研究区域,利用MODIS二级产品的大气气溶胶光学厚度(Aerosol Optical Thickness:AOT)数据、天空辐射计反演得到的AOT数据以及地面测量的空气污染指数(AIR POLLUTION INDEX:API)数据,利用统计回归分析方法,建立AOT与API之间的数学模型,考虑了湿度因子对API的影响及校正,并分季节进行了分析。在此基础上,探讨利用卫星遥感气溶胶光学厚度进行大气污染监测的可行性。
针对海洋,本论文以中国东部海域为研究对象,利用MODIS Level 1B数据,引入归一化气溶胶差异指数(Normalized Difference Aerosol Index:NDAI)的概念,结合OMI二级产品气溶胶指数(Aerosol Index:AI)数据,作为识别海上气溶胶吸收性的定量指标。利用出海测量数据以及与卫星同步观测资料的匹配分析,对结果进行了初步印证。海上吸收性气溶胶的识别也适用于海上大气污染的监测。
本论文得出的初步结论如下:
1、总体上,针对陆地,实测和卫星遥感得出的气溶胶光学厚度AOT与大气污染指数API存在一定的相关性,但相关系数不高;考虑湿度因子影响并进行校正后,对不同季节进行回归分析,AOT与API的相关程度较之前有所改善。春、秋季相关性高于夏、冬季。春季相关性低于秋季,这是因为春季天气干燥,容易扬尘,再加上春季数据最少,统计并不精确。夏、冬两季相关性低,夏季主要是因为数据较少的缘故,而冬季处于集中供暖时期,燃煤量高,导致空气中SO2和氮氧化物浓度增加,PM10浓度不能很好地用来表征大气污染指数。由于初步结果显示卫星遥感气溶胶光学厚度与空气污染指数之间的相关性较低,尚无法直接应用于陆地大气污染的监测。
2、通过对MODIS归一化气溶胶差异指数NDAI和OMI气溶胶指数AI的分析,建立一种基于NDAI的快速识别海上气溶胶吸收性的定量方法,并初步确定了NDAI的分类阈值为0.45,小于这一阈值的为吸收性气溶胶,否则为非吸收性气溶胶。通过与出海实测数据的匹配分析,明确了海上吸收性气溶胶是卫星反演气溶胶光学厚度误差的主要原因。这一初步结论既可应用于卫星遥感海上大气污染的监测,也可应用于海色遥感大气校正算法的改进。
With the rapid development of the national economy, atmospheric pollution has become an important part of environmental protection. Directly and indirectly, aerosols affect the climate, since they scatter and absorb radiation and also alter the cloud microphysical properties. And the aerosol optical properties are closely related to the environmental pollution. Traditional in-situ stations for atmospheric pollution monitoring are very limited, while satellite remote sensing technologies show great advantages with large and continuous ground coverage. Aerosol optical properties derived from satellite remote sensing have great potential on the atmospheric pollution monitoring.
Preliminary studies are carried out in this thesis, which concentrate on applications of aerosol optical properties derived from satellite remote sensing to the atmospheric pollution monitoring, both on land and over ocean respectively.
On land, the Yushan campus of Ocean University of China, Shinan District of Qingdao is selected as the study area. The aerosol optical thickness (AOT) from MODIS level-2 product, AOT from in-situ skyradiometer measurements and corresponding air pollution index (API) data are collected for statistical regression analysis. Statistical models are given between AOTs and APIs for different seasons in the year, with correction of relative humidity effect on APIs. The possibility of atmospheric pollution monitoring using aerosol optical properties derived from satellite remote sensing is discussed.
Over ocean, the East China Seas are selected as the study area. Based on MODIS level-1b product, a parameter called normalized difference aerosol index (NDAI) is proposed and calculated. NDAI is used together with OMI aerosol index (AI) to quantitative discriminate absorbing aerosols with non-absorbing aerosols. Match-up dataset between ship-borne skyradiometer observations with MODIS-derived AOTs is used for validation of the NDAI threshold. The detection of absorbing aerosols is also applicable for atmospheric pollution monitoring over the ocean.
The preliminary conclusions are as follows:
1. On land, generally there is low correlation between in-situ and MODIS-derived AOTs with APIs. After correction of relative humidity effect on APIs, the correlations are slightly improved for different seasons in the year. The correlation is usually higher in Spring and Autumn than in Summer and Winter. This may be caused by mis-interpretation of factors other than PM10 in API estimation.
2. A quantitative method is proposed based on NDAI from MODIS for fast recognition of absorbing aerosols over the ocean, which overcomes the low-spatial-resolution disadvantage of AI from OMI. A NDAI threshold of 0.45 is proposed, with NDAI less than 0.45 for absorbing aerosols. This can be used for atmospheric pollution monitoring over the ocean. It is also useful for the improvement of the atmospheric correction algorithms for ocean color remote sensing.
引文
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[3]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)
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