摘要
气溶胶垂直廓线是评估污染物来源、输送等途径的必要手段。气溶胶污染对环境和人体健康带来直接的影响。该研究于2019年4—5月,利用中国科学院大气物理研究所(39.98°N,116.39°E)的地基多轴差分光学吸收光谱(MAX-DOAS)仪,对北京地区春季大气光谱垂直廓线进行了观测。凭借MAX-DOAS实时、在线、连续的观测优势,能有效的对气溶胶进行监测。MAX-DOAS基于最优估算法(OEM)以及最小二乘光谱拟合法,并以辐射传输模型SCIATRAN作为前向模型,利用海德堡廓线(HEIPRO)算法反演得到气溶胶消光系数的垂直廓线,通过对气溶胶消光系数在其路径的积分获得气溶胶光学厚度(AOD)。利用地基太阳光度计观测的AOD和高塔观测的颗粒物质量浓度垂直廓线,分别与MAX-DOAS观测的AOD和气溶胶消光系数垂直廓线进行对比,验证MAX-DOAS算法的适用性。研究结果表明,MAX-DOAS与太阳光度计观测AOD结果,相关系数为0.92,斜率为0.89。三层气溶胶消光系数与PM_(2.5)质量浓度的皮尔森相关系数从低处到高处分别达到0.69(60 m),0.77(160 m)和0.75(280 m)。并且,将气溶胶平均消光系数和对应三层(60,160和280 m)的PM_(2.5)平均质量浓度对比,发现两者趋势一致。同样的,为了验证MAX-DOAS是否具备准确识别污染物的长距离输送的能力,我们通过Angstrom指数确定沙尘天气,通过计算梯度理查森数和边界层高度确定静稳天气,分析了在特殊天气条件下,MAX-DOAS能够对沙尘和静稳天气做出及时、准确的响应。分析气溶胶平均消光系数,发现气溶胶垂直廓线随高度升高呈现指数衰减变化的趋势,并且气溶胶消光系数均值在1.5 km高度处约为近地面的50%左右,而在1.5 km以上消光系数会随着高度的增加而快速减小。当高度达到2 km左右时,气溶胶消光系数均值下降到了0.1 km^(-1)。以上结果表明MAX-DOAS探测大气气溶胶垂直廓线具有较高的适用性。
Aerosol vertical profile is a necessary means to evaluate the source and transport of pollutants.Aerosol pollution has a direct impact on the environment and human health.In April—May 2019,we observed the vertical profile of atmospheric spectra in the Beijing area in spring using the ground-based Multi-axis differential optical absorption spectroscopy(MAX-DOAS)of the Institute of Atmospheric Physics,Chinese Academy of Sciences(39.98°N,116.39°E).MAX-DOAS can effectively monitor aerosols by virtue of its real-time,online and continuous advantages.MAX-DOAS is based on the optical estimation method(OEM),and the least square spectral fitting method,the radiation transmission model SCIATRAN is used as the forward model,and the HEIPRO algorithm is used to invert the vertical profile of the aerosol extinction coefficient.The aerosol optical depth(AOD)was obtained by integrating the aerosol extinction coefficient in its path.The AOD observed by ground-based solar photometer and particle mass concentration observed by high tower was compared with the AOD and aerosol extinction coefficient observed by MAX-DOAS respectively,to verify the applicability of the MAX-DOAS algorithm.The results show that the correlation coefficient of AOD measured by MAX-DOAS and solar photometer is 0.92 with a slope of 0.89.The Pearson correlation coefficient between the extinction coefficient of three-layer aerosol and the mass concentration of PM_(2.5) reaches 0.69(60 m),0.77(160 m)and 0.75(280 m)respectively from low to high.In addition,the average aerosol extinction coefficient was compared with the average PM_(2.5) mass concentration of the corresponding three layers(60,160 and 280 m),and the trend was consistent.Similarly,to verify whether MAX-DOAS can accurately identify the long-distance transport of pollutants,we determine the sand and dust weather through Angstrom exponent and determine the static and stable weather by calculating the gradient Richardson number and boundary layer height.It is analyzed that MAX-DOAS can respond quickly to sand and static and stable weather under special weather conditions.The average extinction coefficient of aerosols was used to explore,and it was found that the vertical profile of aerosols showed an exponential decay trend with height increasing.The average extinction coefficient of aerosols at the height of 1.5 km was about 50%of that near the ground,while the extinction coefficient above 1.5 km decreased rapidly with height increasing.When the height reached about 2 km,the average extinction coefficient of the aerosol decreased to 0.1 km^(-1).The above results show that MAX-DOAS has high applicability in detecting atmospheric aerosol vertical profiles.
作者
蒋诚
唐贵谦
李启华
刘保献
王蒙
王跃思
JIANG Cheng;TANG Gui-qian;LI Qi-hua;LIU Bao-xian;WANG Meng;WANG Yue-si(Institute of Physical Science and Information Technology,Anhui University,Hefei 230601,China;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;School of Environment,Tsinghua University,Beijing 100084,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2022年第1期265-271,共7页
Spectroscopy and Spectral Analysis
基金
国家重点研发项目(2017YFC0210000,2016YFC0203302,2018YFC0213104)
国家自然科学基金项目(41705113,41877312,41722501,51778596)
中国科学院A类战略性先导科技专项(XDA23020301)资助。
