中国海洋上空气溶胶监测及时空变化特征研究
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摘要
作为气候模拟中最不确定的因子，气溶胶在全球和区域气候变化中扮演着重要的角色。海洋上空气溶胶的沉降会对海洋水体产生重大的影响，海洋还是自然气溶胶、海盐气溶胶和水汽的主要来源。为了全面了解气溶胶粒子在生物地球化学循环和气候变化中的重要作用，需要在大范围内确定气溶胶的性质，而目前对于中国海域上空大气气溶胶的分布状况和辐射特性却由于数据资料的缺乏，而知之甚少。因此进行海洋气溶胶的联网监测，获得准确的海洋气溶胶的时空分布是我们急需解决的问题。
本文进行了海洋气溶胶监测关键技术研究，包括监测仪器的反演算法、数据采集和后处理、仪器定标的研究，为海洋气溶胶业务监测网的建设和运行奠定了基础。根据太阳光度计测量气溶胶光学厚度的原理，编写了利用仪器测量电信号反演气溶胶光学厚度的程序，与仪器输出的结果相比，误差小于0.005；船基走航测量海洋气溶胶时，改变了太阳光度计的默认内置参数并对测量数据进行基于COV方法的后处理，有效的减小了船体晃动和薄云对结果的影响；在原有的地物光谱仪气溶胶光学厚度算法基础上进行了改进，消除了天空光观测时使用挡板带来的误差，反演结果的误差小于WMO要求的观测精度0.05.
本文首次利用654组船基和约1万组岸岛基实测数据对中国海域气溶胶的时空分布和模态信息进行了全面的分析。渤海、北黄海、南黄海和南海四个海域相比，南海受陆地影响小且降水充沛，空气最为清洁，北黄海周边的辽宁和朝鲜工业污染较轻，空气清洁度次之；渤海受四周陆地工业污染的影响，空气最为污浊，只在渤海西侧北戴河至曹妃甸沿岸海域和渤海中部，大气比较清洁；南黄海海域受临近的山东、江苏和长三角工业污染和海雾的影响，空气也比较污浊。Angstrom指数α在莱州湾、辽东湾、渤海海峡、辽河口、威海北部、大连东部等沿岸海域随地域变化较大，而在外海变化相对较小；当空气比较清洁（β<0.2）时，气溶胶成分的变化成为AOD变化的主要原因，α与AOD一般呈正相关；而空气比较浑浊（β>0.2）时，AOD变化的影响因子也比较多，AOD与α没有明确的关系，只在某些断面α与AOD呈负相关。渤黄海海域冬季寒冷干燥，AOD为全年的极小值，春夏季受到沙尘暴和海雾的影响，AOD为全年的极大值。位于黄海的圆岛气溶胶监测站可以很好地反映这种趋势，而位于大连岸边的监测站，冬季受到供暖产生的烟雾气溶胶的影响，AOD为全年最大值，不能很好地反映渤黄海海域海洋气溶胶的变化情况。南方海域，AOD极大值出现在冬季，到了夏秋季，因为大量降水的冲刷和盛行东南风带来的海盐气溶胶，AOD为全年极小值。位于东海的三沙监测站和南海的大万山监测站都是这种情况。
根据Angstrom指数和浑浊度系数性质的不同，将中国海洋气溶胶分为三个模态（主要适用于北方海域），清洁大气型气溶胶（背景气溶胶）、沙尘型气溶胶、混合模态型气溶胶。其中清洁大气性气溶胶占总数的55%，可作为中国海域的背景气溶胶；沙尘型气溶胶占12%，主要受陆地沙尘影响，分布在沿岸海域；混合模态型气溶胶占33%。
本文利用长时间AOD序列分析了不同时间窗口下气溶胶的变化规律，为卫星数据真实性检验最佳时间窗口的选择和反演误差的分析提供了依据。气溶胶光学厚度较小时，它的变化也比较小；气溶胶光学厚度较大时，它的变化也比较大。随着时间窗口的增大，窗口下的气溶胶光学厚度序列的平均相对误差和平均绝对误差也逐渐增大。在同一个时间窗口，随着AOD的增大，平均绝对误差分布范围增大，均值增大，而平均相对误差变化不大。AOD变化小的站点，时间窗口可以选的大一些；而AOD变化较剧烈的站点，时间窗口就要相应减小。气溶胶光学厚度卫星遥感数据真实性检验在中国海域的时间窗口不应超过±0.5h。
研究期间，作者承担了海洋观测技术规范关于气溶胶观测规范的编写工作，并撰写了5篇科研论文，其中3篇已发表，1篇待发表，1篇在审稿中。
As the most uncertain factor in the climate simulation, aerosol plays an importantrole in the global and regional climate change. The deposition of aerosols over seashas significant impact on the seawater which is also the main source of natural aerosol,salt aerosol and moisture. In order to comprehensively understand the role of theaerosol in the biogeochemical cycle and climate change, the aerosol characteristicshould be investigated. But now the characteristic and distribution of the aerosol overChina Sea are not clear because of the lack of monitoring data. Therefore, it is urgentto study the monitoring technique of the marine aerosols, and put the observed data ofaerosol over China Sea into network.
The key techniques in marine aerosol monitoring which contains inversionalgorithm of the observation instrument, data acquisition and post-processing andcalibration, are investigated in this study. The observation methods of monitoringmarine aerosol by using instruments have been established. Computer program whichcalculates aerosol optical depth (AOD) from digital signals is developed based onAOD measuring principle of sun photometer; the errors of root-mean-square are lessthan0.05, compared with output results of sun photometer. When the sun photometeris used to measure AOD on ships, the default settings should be changed and the datashould be post-processed based on COV methods in order to decrease the influence ofthe poor platform stability. An improved method for measuring AOD using fieldspectrometer is proposed, which could remove the error produced by the baffle-boardused in the observation.
In this paper,654groups of data obtained using Microtops sunphotometer onship and more than10,000groups of data obtained using CE318were taken toanalyze the temporal and spatial distributions and types of the marine aerosols overChina Sea. Air over the South China Sea is cleanest due to the small influence of landand abundant precipitation. The industrial pollution of Liaoning and North Koreawhich are nearby the North Yellow Sea is lighter, so air over the North Yellow Sea isrelatively clean too. Air over Bohai Sea is the most turbid due to nearby industrialpollution, and the clean air just distributes over Beidaihe to Caofeidian in the westerncoast and the center area of Bohai Sea. Air over the South Yellow sea is relativelyturbid due to the industrial pollution of Shandong, Jiangsu and the Yangtze RiverDelta nearby and frequent sea fog. The variation of Angstrom exponent is larger overthe Laizhou Bay, Liaodong Bay, Bohai Strait, Liaohe Estuary, northern area of Weihaicity, and eastern of Dalian city while it is smaller over the areas far from land. When βis smaller than0.2, air is relatively clean, and the variation of aerosol composition isthe main reason of the variation of AOD, so there is a positive correlation between α and AOD. When β is larger than0.2, air is relatively turbid and the aerosol particlesare from sandstorm or air pollution. There are several factors which can influence thevariation of AOD, so there is not a definite correlation between α and AOD, eventhere is a negative correlation between α and AOD in some sections. In Bohai Sea andYellow sea, AOD shows minimum value in the cold and dry winter while AOD showsmaximum value in spring and summer because of the influence of sandstorm and seafog. AOD monitored at Yuandao Station located in Yellow Sea could present this trendtoo. But AOD monitored in Dalian shows a maximum in winter because of theinfluence of fuel burning for winter heating. In the southern seas, AOD shows amaximum in winter and a minimum in summer because of the the washing out oflarge amounts of precipitation and salt aerosols brought by southeast monsoon. AODmonitored over Sansha Station and Dawanshan Station shows this trend.
According to the difference of Angstrom exponent and turbidity coefficient, themarine aerosol (mainly in the northern seas) could be divided into three types: cleanair aerosol (background aerosol), dust aerosol, mixed-mode aerosol. The clean airaerosols account for55%of the total monitoring station which could be considered asthe background aerosol, dust aerosols account for12%and the mixed-mode aerosolsaccount for33%.
The temporal variations in different time window are analyzed from a long timeAOD serial, which provides a basis for selecting the optimal time window in erroranalysis of inversion algorithm for satellite data validation. When AOD is small, itsvariation is small; and when AOD is large, its variation is large. As the time windowbecomes larger, both the average relative error and absolute error of the AOD serialincrease. Under the same time window, the average absolute error increases as theAOD increases, but the average relative error changes little. At the station with littleAOD change, a large window could be selected; and at the station with large AODchange, a small window should be selected. The optimal time window could not belarge than±0.5h in the satellite validation over China Sea.
本文进行了海洋气溶胶监测关键技术研究，包括监测仪器的反演算法、数据采集和后处理、仪器定标的研究，为海洋气溶胶业务监测网的建设和运行奠定了基础。根据太阳光度计测量气溶胶光学厚度的原理，编写了利用仪器测量电信号反演气溶胶光学厚度的程序，与仪器输出的结果相比，误差小于0.005；船基走航测量海洋气溶胶时，改变了太阳光度计的默认内置参数并对测量数据进行基于COV方法的后处理，有效的减小了船体晃动和薄云对结果的影响；在原有的地物光谱仪气溶胶光学厚度算法基础上进行了改进，消除了天空光观测时使用挡板带来的误差，反演结果的误差小于WMO要求的观测精度0.05.
本文首次利用654组船基和约1万组岸岛基实测数据对中国海域气溶胶的时空分布和模态信息进行了全面的分析。渤海、北黄海、南黄海和南海四个海域相比，南海受陆地影响小且降水充沛，空气最为清洁，北黄海周边的辽宁和朝鲜工业污染较轻，空气清洁度次之；渤海受四周陆地工业污染的影响，空气最为污浊，只在渤海西侧北戴河至曹妃甸沿岸海域和渤海中部，大气比较清洁；南黄海海域受临近的山东、江苏和长三角工业污染和海雾的影响，空气也比较污浊。Angstrom指数α在莱州湾、辽东湾、渤海海峡、辽河口、威海北部、大连东部等沿岸海域随地域变化较大，而在外海变化相对较小；当空气比较清洁（β<0.2）时，气溶胶成分的变化成为AOD变化的主要原因，α与AOD一般呈正相关；而空气比较浑浊（β>0.2）时，AOD变化的影响因子也比较多，AOD与α没有明确的关系，只在某些断面α与AOD呈负相关。渤黄海海域冬季寒冷干燥，AOD为全年的极小值，春夏季受到沙尘暴和海雾的影响，AOD为全年的极大值。位于黄海的圆岛气溶胶监测站可以很好地反映这种趋势，而位于大连岸边的监测站，冬季受到供暖产生的烟雾气溶胶的影响，AOD为全年最大值，不能很好地反映渤黄海海域海洋气溶胶的变化情况。南方海域，AOD极大值出现在冬季，到了夏秋季，因为大量降水的冲刷和盛行东南风带来的海盐气溶胶，AOD为全年极小值。位于东海的三沙监测站和南海的大万山监测站都是这种情况。
根据Angstrom指数和浑浊度系数性质的不同，将中国海洋气溶胶分为三个模态（主要适用于北方海域），清洁大气型气溶胶（背景气溶胶）、沙尘型气溶胶、混合模态型气溶胶。其中清洁大气性气溶胶占总数的55%，可作为中国海域的背景气溶胶；沙尘型气溶胶占12%，主要受陆地沙尘影响，分布在沿岸海域；混合模态型气溶胶占33%。
本文利用长时间AOD序列分析了不同时间窗口下气溶胶的变化规律，为卫星数据真实性检验最佳时间窗口的选择和反演误差的分析提供了依据。气溶胶光学厚度较小时，它的变化也比较小；气溶胶光学厚度较大时，它的变化也比较大。随着时间窗口的增大，窗口下的气溶胶光学厚度序列的平均相对误差和平均绝对误差也逐渐增大。在同一个时间窗口，随着AOD的增大，平均绝对误差分布范围增大，均值增大，而平均相对误差变化不大。AOD变化小的站点，时间窗口可以选的大一些；而AOD变化较剧烈的站点，时间窗口就要相应减小。气溶胶光学厚度卫星遥感数据真实性检验在中国海域的时间窗口不应超过±0.5h。
研究期间，作者承担了海洋观测技术规范关于气溶胶观测规范的编写工作，并撰写了5篇科研论文，其中3篇已发表，1篇待发表，1篇在审稿中。
As the most uncertain factor in the climate simulation, aerosol plays an importantrole in the global and regional climate change. The deposition of aerosols over seashas significant impact on the seawater which is also the main source of natural aerosol,salt aerosol and moisture. In order to comprehensively understand the role of theaerosol in the biogeochemical cycle and climate change, the aerosol characteristicshould be investigated. But now the characteristic and distribution of the aerosol overChina Sea are not clear because of the lack of monitoring data. Therefore, it is urgentto study the monitoring technique of the marine aerosols, and put the observed data ofaerosol over China Sea into network.
The key techniques in marine aerosol monitoring which contains inversionalgorithm of the observation instrument, data acquisition and post-processing andcalibration, are investigated in this study. The observation methods of monitoringmarine aerosol by using instruments have been established. Computer program whichcalculates aerosol optical depth (AOD) from digital signals is developed based onAOD measuring principle of sun photometer; the errors of root-mean-square are lessthan0.05, compared with output results of sun photometer. When the sun photometeris used to measure AOD on ships, the default settings should be changed and the datashould be post-processed based on COV methods in order to decrease the influence ofthe poor platform stability. An improved method for measuring AOD using fieldspectrometer is proposed, which could remove the error produced by the baffle-boardused in the observation.
In this paper,654groups of data obtained using Microtops sunphotometer onship and more than10,000groups of data obtained using CE318were taken toanalyze the temporal and spatial distributions and types of the marine aerosols overChina Sea. Air over the South China Sea is cleanest due to the small influence of landand abundant precipitation. The industrial pollution of Liaoning and North Koreawhich are nearby the North Yellow Sea is lighter, so air over the North Yellow Sea isrelatively clean too. Air over Bohai Sea is the most turbid due to nearby industrialpollution, and the clean air just distributes over Beidaihe to Caofeidian in the westerncoast and the center area of Bohai Sea. Air over the South Yellow sea is relativelyturbid due to the industrial pollution of Shandong, Jiangsu and the Yangtze RiverDelta nearby and frequent sea fog. The variation of Angstrom exponent is larger overthe Laizhou Bay, Liaodong Bay, Bohai Strait, Liaohe Estuary, northern area of Weihaicity, and eastern of Dalian city while it is smaller over the areas far from land. When βis smaller than0.2, air is relatively clean, and the variation of aerosol composition isthe main reason of the variation of AOD, so there is a positive correlation between α and AOD. When β is larger than0.2, air is relatively turbid and the aerosol particlesare from sandstorm or air pollution. There are several factors which can influence thevariation of AOD, so there is not a definite correlation between α and AOD, eventhere is a negative correlation between α and AOD in some sections. In Bohai Sea andYellow sea, AOD shows minimum value in the cold and dry winter while AOD showsmaximum value in spring and summer because of the influence of sandstorm and seafog. AOD monitored at Yuandao Station located in Yellow Sea could present this trendtoo. But AOD monitored in Dalian shows a maximum in winter because of theinfluence of fuel burning for winter heating. In the southern seas, AOD shows amaximum in winter and a minimum in summer because of the the washing out oflarge amounts of precipitation and salt aerosols brought by southeast monsoon. AODmonitored over Sansha Station and Dawanshan Station shows this trend.
According to the difference of Angstrom exponent and turbidity coefficient, themarine aerosol (mainly in the northern seas) could be divided into three types: cleanair aerosol (background aerosol), dust aerosol, mixed-mode aerosol. The clean airaerosols account for55%of the total monitoring station which could be considered asthe background aerosol, dust aerosols account for12%and the mixed-mode aerosolsaccount for33%.
The temporal variations in different time window are analyzed from a long timeAOD serial, which provides a basis for selecting the optimal time window in erroranalysis of inversion algorithm for satellite data validation. When AOD is small, itsvariation is small; and when AOD is large, its variation is large. As the time windowbecomes larger, both the average relative error and absolute error of the AOD serialincrease. Under the same time window, the average absolute error increases as theAOD increases, but the average relative error changes little. At the station with littleAOD change, a large window could be selected; and at the station with large AODchange, a small window should be selected. The optimal time window could not belarge than±0.5h in the satellite validation over China Sea.
引文
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