基于机载高光谱分辨率激光雷达的气溶胶分类研究
Aerosols play an important role in assessing radiation, climate, cloud formation, and environmental pollution. Additionally, their optical and physical properties exert a significant influence on the formation and transportation of air pollutants. Therefore, spatio-temporal distribution characteristics of tropospheric aerosols are vital for studying the uncertainties of aerosol environments and climate changes. It is of great significance to study the optical properties and vertical distribution changes of aerosols by effective observation methods. As a widely employed aerosol active detection instrument, lidar plays an irreplaceable role in detecting vertical aerosol distribution. Relevant scholars classify the aerosol types by classifying the distribution characteristics of optical parameters such as aerosol depolarization ratio, color ratio, and lidar ratio, which promotes the development of lidar detection research methods. High spectral resolution lidar (HSRL) can accurately detect optical parameters such as aerosol extinction coefficient and backscatter coefficient, and improve the inversion accuracy of aerosol optical parameters. This airborne high spectral lidar flight test is the first aerosol observation test with Air-ACDL, and the analysis results fully reflect the advantages of HSRL in detecting aerosol types and lay a foundation for spaceborne high spectral lidars to invert aerosol types.
Aerosol classification is based on the difference in optical parameters of different aerosol types to reflect their various characteristics. For example, aerosol depolarization ratio δa reflects the shape characteristics of particles, aerosol lidar ratio Sa characterizes the absorption characteristics of particles, and dual-wavelength color ratio Cr (532 nm/1064 nm) corresponds to particle size. These characteristics are the theoretical basis for aerosol classification. Generally, Sa varies with the size, shape, and composition of aerosol particles, and the value is higher for particles with strong absorption. δa is an important parameter for identifying dust aerosols, which is related to the shape regularity degree of particles. Meanwhile, the δa value of spherical particles is the smallest, and the more irregular shape leads to the greater value. The color ratio corresponds to the particle size, and generally the larger color ratio brings smaller particles. Based on these characteristics, the aerosol particle classification can be well achieved. According to the summary of the existing studies, the threshold ranges of Sa, δa, and Cr for different aerosol types are sorted out, and an aerosol classification lookup table is established based on the classification threshold standard of aerosols. Additionally, aerosols in the Shanhaiguan area are classified by combining the aerosol optical parameters detected by airborne high spectral lidar.
According to the comparison results of aerosol optical depth (AOD), the correlation between the airborne observation data, the ground-based sunphotometer, and the passive detector data carried by the satellite is greater than 0.90 (Fig. 2), Aerosol types on March 11, 2019 are classified by the established aerosol classification lookup table and detection data from airborne high spectral lidar [Fig. 6(a)]. The classification results are compared with those of CALIPSO [Fig. 6(c)], and then confirmed by combining meteorological data and backward trajectories (Figs. 4 and 7). The results show that the polluted air flow mainly comes from Mongolia, and it is prone to bring sand and dust aerosols over Shanhaiguan. In addition, since the experimental site is close to the Bohai Sea, there is marine aerosol over Shanhaiguan, and the flight path of CALIPSO passes over the Bohai Sea without marine aerosols. Thus, the classification results of the aerosol classification lookup table based on HSRL are more accurate. Then, by analyzing the aerosol classification results on March 14 and March 19, 2019, the feasibility of the proposed aerosol classification method is verified again.
We analyze the distribution characteristics of lidar ratio, depolarization ratio, and color ratio of different aerosol types, and establish the optical parameter lookup table of different aerosol types on the basis of summarizing the previous classification methods. Meanwhile, the aerosol types are divided into eight types, including ice particles, sand, mixed sand, ocean, polluted ocean, city, smoke, and fresh smoke. Based on the lookup table, the airborne observation data on March 11, 2019 are employed to achieve aerosol classification and identification in Qinhuangdao. The results show that there are aerosol types such as mixed sand and dust aerosols, marine aerosols and smoke over Tianshan Customs, and the feasibility of the aerosol classification method is verified by adopting HYSPLIT trajectory mode and meteorological data. The method applicability is verified by the correct identification of aerosol types on March 14 and March 19, 2019 during the observation period. March 14 and March 19, 2019 are polluted days, and there are dust aerosols from Mongolia over Shanhaiguan. Additionally, as Shanhaiguan is close to the Bohai Sea and the experiment is in the winter heating period, there are marine aerosols and smoke aerosol types over Shanhaiguan, and there will be ice particles in the air under large air humidity. This airborne hyperspectral lidar flight test is the first aerosol observation test with Air-ACDL. The analysis results fully reflect the advantages of HSRL in detecting aerosol types and lay a foundation for spaceborne high spectral lidars to invert aerosol types. In the future, as the ACDL spaceborne lidar data accumulate, they can be utilized to establish a more accurate and rich aerosol classification database and realize global aerosol classification.
1 引 言
气溶胶是由分散并悬浮在大气中的固态、液态和固液混合态微粒所形成的胶体分散系,其粒子直径在0.001~100 μm之间。作为大气的重要组成部分,气溶胶含量很低却扮演着重要的角色,对大气辐射收支平衡、气候、降水、云的形成以及环境污染方面都起着重要的作用,并且影响空气质量和人类的身体健康[1]。在气候变化的驱动因素中,气溶胶和云对气候变化的影响是最不确定的因素[2],气溶胶既可以通过吸收和散射太阳辐射以及地球的长波辐射来直接扰动地-气系统的辐射能量收支,产生直接气候效应,又可以作为云、雾的凝结核影响云、雾的光学特性,对云的形成、降水过程造成影响,产生间接气候效应。气溶胶类型是影响气溶胶气候效应的最重要因素,可以为源属性研究提供有用的信息。然而,大气气溶胶通常不是单一的类型,而是多种类型的混合物。这种混合会影响气溶胶的光学和辐射特性。因此,通过有效的观测手段开展气溶胶种类识别的研究是尤其重要的[3]。
激光雷达作为一种主动遥感探测工具,是探测大气中云和气溶胶光学特性以及其时空分布的重要手段之一[4]。使用激光雷达观测数据对气溶胶类型进行分类是研究气溶胶特性的一种重要途径。Liu等[5]在2002年对亚洲沙尘暴沙尘气溶胶的激光雷达比以及退偏振比进行了统计分析,且提供了用激光雷达直接测量亚洲尘埃的激光雷达比的方法,这有助于验证先前的理论与实验研究。Sugimoto和Lee[6]在2006年利用双波长偏振激光雷达分析了不同波长下沙尘气溶胶的退偏振比和激光雷达比,提出了一种简单的双组分理论,并将其应用于观测数据,导出了尘埃和球形气溶胶的混合比以及与后向散射相关的AngstrÖm指数。Xie等[7]在2008年运用拉曼激光雷达对北京地区的气溶胶退偏比和激光雷达比分别在轻度污染、重度污染和沙尘暴天气下的数值变化进行了总结。2009年,Winker等[8]描述了云-气溶胶激光雷达和红外探路卫星观测(CALIPSO)产品处理系统中的一种算法,运用此算法首次通过激光雷达观测在全球范围内识别气溶胶类型。Groβ等[9]于2011年在SUMUM-2的实验期间对三台同步观测的激光雷达结果进行了对比分析,主要对撒哈拉沙尘、海洋性气溶胶、生物质这三种特殊的气溶胶类型的退偏振比和激光雷达比进行了分析。Burton等[10]在2012年运用已有的大量高光谱分辨率激光雷达(HSRL)数据并且基于已知的气溶胶类型,根据数据反演得到的激光雷达比、退偏振比、色比等光学参数的阈值来建立气溶胶的分类模型。Groβ等[11]在2013年利用1998年与2008年这两年间的三次机载HSRL的实地实验数据,并结合冷凝颗粒计数器(CPC)和差分迁移率分析器(DMA)同步观测的气溶胶的微物理特性对气溶胶进行了分类,同时提出了混合气溶胶类型的识别方法。曹念文等[12]在2014年通过建立包含背景气溶胶和云两种不同类型气溶胶光学参数(后向散射系数、消光系数)的两个激光方程,推导计算出其解的表达式,并反演出两种不同类别气溶胶的光学参数,以此来区分背景气溶胶和云。刘秉义等[13]在2017年根据已有的气溶胶分类研究结果,给出了基于气溶胶光学参数的分类方法,并建立了气溶胶分类查找表。李明阳等[14]在2019年针对激光条件下探测的云和气溶胶特有的光学信息和空间分布,结合概率统计与机器学习算法,提出了一种针对云/气溶胶、云相态及气溶胶子类型识别的分类算法,实现了星载激光雷达的大气特征层的快速、有效分类。郑仰成等[15]在2021年基于臭氧检测仪(OMI)遥感产品的气溶胶特征参数,利用随机算法,将广东省2014年的气溶胶类型划分为沙尘气溶胶、生物质燃烧型气溶胶和硫酸盐型城镇-工业气溶胶3种类型,并统计分析了随机森林算法以及特征参数的重要性。周妹等[16]在2022年根据AERONET SGP站的气溶胶光学反演数据,提出了一种基于朴素贝叶斯分类器的气溶胶分类模型,以气溶胶单次散射反照率、复折射率指数等作为输入变量识别了该地区5种类型的气溶胶,分析了不同类型的光学特性。朱鑫琦等[17]在2022年提出了一种利用归一化荧光信号对气溶胶颗粒分类的方法,将其应用于以405 nm激光二极管为激发光源的荧光粒子计数器,可以对不同气溶胶粒子进行初步分类。
已有的激光雷达气溶胶分类方法大都使用传统的米散射或偏振激光雷达,其不确定性给气溶胶分类带来了误差。HSRL利用大气气溶胶和大气分子对激光光谱的展宽不同使用窄带滤波器件滤除了气溶胶信号[18],在不需要激光雷达比的情况下,能得到独立反演的气溶胶的消光系数和后向散射系数,杜绝了引入其他因子所带来的误差,能够更加精准地探测气溶胶的光学性质。HSRL克服了传统米散射激光雷达在反演气溶胶消光系数时需要假设激光雷达比这一问题,与拉曼激光雷达相比,具有较强的散射强度,易于实现全天时观测[19-20]。自2000年开始,国内一些学者也开展了相关的HSRL技术的研究,结合滤波器技术对大气风场和气溶胶光学参数探测方法进行研究,验证了碘分子滤波器的优点、适用性以及HSRL技术在大气气溶胶探测方面的独特优势,促进了其发展和应用[21-26]。中国海洋大学的刘智深等[27]在应用碘分子滤波器的HSRL技术测量大气温度、风场方面做了不少开拓性工作。结合HSRL观测的大气气溶胶和风场结果,2010年,刘金涛等[28]对碘分子滤波器的实用性和便利性进行了相关验证和论述。自2012年开始,浙江大学的刘东及其团队[29-30]在高光谱鉴频器方面做了大量的工作,应用视场展宽的迈克尔孙干涉仪作为高光谱滤波器进行大气气溶胶、温度和风场观测,在鉴频器技术研究应用方面进行了进一步拓展。2018年,中国科学院上海光学精密机械研究所董俊发、刘继桥等[31-32]结合大气模式等系统参数选取了更优的碘分子吸收线,并于2019年开展了机载HSRL外场观测试验,对秦皇岛地区的气溶胶分布和变化进行了研究[33-34]。
由于存在固有优势,HSRL被设计为我国大气环境星的一个重要组成部分,旨在连续获取全球云、气溶胶类型和光学特性的垂直分布信息,支持全球气候变化、云与气溶胶相互作用等研究。本课题组在研制气溶胶碳探测激光雷达(Air-ACDL)的基础上,进行了多次机载观测校飞试验,首次获得Air-ACDL机载观测数据,为大气环境星数据应用效能提升做了重要支撑[33]。由于机载HSRL可以提供更多、更准的气溶胶观测信息,因此,可以实现更为准确的气溶胶分类。
本文基于Air-ACDL机载观测数据开展了气溶胶分类方法的研究,首先主要介绍了基于碘分子滤波器的HSRL系统原理与气溶胶光学参数的反演方法;然后介绍了基于现有的气溶胶类型分类的研究成果,建立了气溶胶分类查找表,利用2019年在山海关地区进行的机载观测数据,对气溶胶进行了分类研究;最后分析了气溶胶分类的个例结果,并运用混合单粒子拉格朗日积分轨道模型(HYSPLIT)轨迹模式等方法进行了验证。
2 HSRL系统、机载观测试验及数据介绍
2.1 HSRL系统
机载HSRL气溶胶通道接收系统如
图 1. HSRL气溶胶通道接收系统组成
Fig. 1. Composition of aerosol channel receiver system in HSRL system
偏振型基于碘分子吸收池的HSRL接收系统可以简化为三探测通道结构。回波光信号首先经过一个532 nm的带通滤光片,然后通过一个窄带法布里-珀罗(F-P)腔,窄带 F-P腔的中心波长为所选定的碘吸收线波长。532 nm的带通滤光片用于去除窄带F-P腔其他边带的影响,限制进入窄带F-P腔的光谱范围,使得进入窄带F-P腔的光谱在所选定的碘吸收线波长附近。窄带F-P腔用于进一步去除背景噪声,使得所选定的碘吸收线波长的透过率最大。如果后向散射的回波信号光偏振方向发生改变,则将存在垂直偏振分量和平行偏振分量。回波信号光的垂直偏振分量经过第一个偏振分光棱镜(PBS1)后会被PBS 45°反射,然后经由已经镀了532 nm增透膜的聚焦镜聚焦到垂直通道探测器中。没有发生退偏的回波或者退偏回波信号光的平行偏振分量会经过第一个PBS透射后,依次经过一个1/2波片和第二个PBS(PBS2),一部分回波信号光经过碘分子吸收池后经聚焦镜聚焦进入高光谱分子通道探测器,另一部分信号光经过聚焦镜后直接进入平行参考信道探测器。通过改变1/2波片的角度,能够改变高光谱的分子信道与平行参考信道的分光比。
接收系统包括大气气溶胶垂直信道、平行信道、高光谱信道。利用垂直和平行信道信号进行气溶胶退偏比测量,可用于气溶胶粒子的形状探测。通过高光谱通道反演气溶胶后散和消光系数,进而求解激光雷达比,以及精确测量气溶胶的散射参数特征。另外,系统中加入1064通道,可以对两个波段的气溶胶散射特性参数进行研究。
HSRL反演气溶胶光学参数的具体反演方法可参考文献[34]。退偏比通过垂直信道与平行信道的信号比值与分子退偏比值的差所得:
式中:
通过Fernald算法计算1064 nm波段的气溶胶后向散射系数
2.2 机载观测试验
2019年3月,在河北省秦皇岛市进行了飞行试验,此次飞行试验区域临近渤海湾,试验地表包括海洋、城镇、厂区、山地等多种地表类型,飞行区域为118°~122°E,38°~42°N。此次正式的机载HSRL系统飞行试验,共飞行7个架次,累计飞行时间为30 h左右。利用机载HSRL系统对短时间内秦皇岛地区不同下垫面类型下的气溶胶分布和气溶胶特征展开了大范围的观测。
2.3 数据来源
机载HSRL系统提供了在山海关上空进行飞行试验得到的532 nm和1064 nm波长通道的观测数据,根据这些数据可以反演气溶胶的后向散射系数、消光系数、激光雷达比、退偏等光学参数。基于反演得到的光学参数可以对气溶胶进行分类。本次机载试验获取了海洋区域、城镇区域、山地区域等不同地表类型以及不同飞行高度和不同天气污染条件下的探测数据,具体飞行试验进程如
表 1. 机载HSRL系统飞行试验进程
Table 1. Airborne HSRL system flight test process
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为了验证气溶胶分类方法的准确性,使用CALIOP的Level2 VFM(Vertical Feature Mask)产品(V4版本)进行对比验证。CALIOP的Level2 VFM产品使用云-气溶胶分类识别算法与气溶胶分类算法判别接收的信号,识别出垂直方向上不同高度的气溶胶类型,并将其分为清洁大陆型、清洁海洋型、烟尘、沙尘、污染沙尘、污染大陆型以及污染海洋型7种类型[36-37]。同时利用来自中国国家环境检测中心的PM2.5与PM10数据、HYSPLIT轨迹模式以及Aura卫星OMI数据对分类结果进行辅助验证。
HYSPLIT即混合单粒子拉格朗日综合轨迹模型,是由美国国家海洋和大气管理局空气资源实验室联合澳大利亚气象局,共同研发的一种用于气团传输轨迹追踪、计算分析大气污染、扩散和沉降过程轨迹的专业模型[38]。本文主要运用HYSPLIT模型分析山海关地区24 h的后向轨迹,主要分析山海关上空500、1500、3000 m这3个高度的污染气团来源。
OMIAuraAER是美国国家航空航天局(NASA)公布的一组近紫外型气溶胶产品,该产品是由搭载在Aura卫星上的OMI传感器观测数据反演得到的,包含多项气溶胶特征参数以及气溶胶类型产品,其中,α指数和紫外线气溶胶指数(UVAI)是气溶胶重要的分类指标[15]。
3 基于HSRL的气溶胶分类方法
气溶胶分类是基于不同类型气溶胶光学参数的差异反映其不同特性,气溶胶退偏振比
要实现气溶胶的分类需要基于大量的实验观测,在大量数据分析、定标的基础上结合地域因素,才能实现气溶胶的合理分类。本文在总结已有工作的基础上,对不同气溶胶类型的
表 2. 分类测量的气溶胶强度参数测量范围
Table 2. Measurement range of aerosol strength parameters for categorical measurements
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基于对
4 基于HSRL的气溶胶分类结果
4.1 机载HSRL测量的AOD对比验证
本次观测试验期间,除了将HSRL安装在飞机上进行机载观测之外,还在飞机航线上布置相应的地面观测站点[33-34]。为了验证机载HSRL观测数据的可靠性,将机载HSRL测量的AOD与地面站点太阳光度计及星载探测器的观测结果进行对比分析,其中星载数据探测器包括Aqua卫星、Terra卫星、VIIRS卫星以及Aura卫星,对比结果如
图 2. HSRL、CE318和星载探测器的AOD的相关性分析。(a)HSRL与CE318的AOD的相关性;(b)HSRL与星载探测器的AOD的相关性
Fig. 2. Correlation analysis of AODs of HSRL, CE318, and spaceborne detector. (a) AOD correlation between HSRL and CE318; (b) AOD correlation between HSRL and spaceborne detector
4.2 基于HSRL的气溶胶分类方法与CALIPSO的气溶胶分类方法对比
以2019年3月11日的飞行架次为例对气溶胶进行分类。
图 3. 四个站点PM2.5和PM10的变化。(a)PM2.5;(b)PM10
Fig. 3. Changes of PM2.5 and PM10 at four sites. (a) PM2.5; (b) PM10
图 4. 三种下垫面上空气溶胶光学参数廓线结果。(a)激光雷达比廓线;(b)退偏廓线;(c)色比廓线
Fig. 4. Profiles of aerosol optical parameters on three underlying surfaces. (a) Lidar ratio profile; (b) depolarization profile; (c) color ratio profile
基于上文总结的气溶胶分类方法,从
4.3 气溶胶分类结果及个例分析
4.3.1 2019年3月14日气溶胶类型分类结果个例分析
图 8. 四个站点PM2.5和PM10的变化图。(a)PM2.5;(b)PM10
Fig. 8. Changes of PM2.5 and PM10 at four sites. (a) PM2.5; (b) PM10
4.3.2 2019年3月19日气溶胶类型分类结果个例分析
如
图 11. 四个站点PM2.5与PM10的变化图。(a)PM2.5;(b)PM10
Fig. 11. Changes of PM2.5 and PM10 at four sites. (a) PM2.5; (b) PM10
为了进一步验证结果的准确性,本文利用了Aura卫星OMI传感器在2019年3月19日这天对紫外波段吸收性气溶胶的识别能力。基于郑仰成[15]的研究可知,α指数偏小且UVAI指数较大为沙尘气溶胶。该天退偏比偏大,粒径分布较大,可推知α指数偏小,且该天秦皇岛的UVAI大于零,表明秦皇岛在3月19日这天存在沙尘气溶胶。综上,3月19日山海关上空识别出混合沙尘气溶胶、海洋气溶胶以及烟雾和城市气溶胶是合理的。
5 结 论
本文分析了不同类型气溶胶的激光雷达比、退偏比以及色比的分布特征,在总结前人的分类方法的基础上建立了不同类型气溶胶光学参数查找表,将气溶胶类型分为冰粒子、沙尘、混合沙尘、海洋、污染海洋、城市、烟雾以及新鲜烟雾8种类型。基于该查找表利用2019年3月11日的机载观测数据实现了对秦皇岛地区的气溶胶分类识别,结果表明,该天山海关上空存在混合沙尘气溶胶、海洋气溶胶以及烟雾等气溶胶类型,并利用HYSPLIT轨迹模式以及气象数据验证了该气溶胶分类方法的可行性。通过对观测期间3月14日与3月19日这两天的气溶胶类型的正确识别验证了该方法的适用性,3月14日与3月19日两天为污染天,山海关上空均存在来自内蒙古地区的沙尘气溶胶,除此之外,由于山海关濒临渤海且试验期间正值冬季供暖期,故在山海关上空有海洋气溶胶与烟雾气溶胶类型的存在,当空气湿度较大时,空气中也会有冰粒子。
本次机载HSRL飞行试验是第一次用Air-ACDL进行的气溶胶观测试验,分析结果充分体现了HSRL探测气溶胶类型方面的优势,也为星载HSRL在反演气溶胶类型方面奠定了基础。未来,随着ACDL星载激光雷达数据的积累,可以利用ACDL数据建立更为准确、更为丰富的气溶胶分类数据库,进而实现全球气溶胶的分类。
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Article Outline
姚娜, 张苗苗, 卜令兵, 郜海阳, 王勤. 基于机载高光谱分辨率激光雷达的气溶胶分类研究[J]. 光学学报, 2023, 43(24): 2428005. Na Yao, Miaomiao Zhang, Lingbing Bu, Haiyang Gao, Qin Wang. Aerosol Classification Based on Airborne High Spectral Resolution Lidar[J]. Acta Optica Sinica, 2023, 43(24): 2428005.