车载被动DOAS的污染气体柱浓度分布重构方法

大气污染的综合防治需要从不同尺度的区域出发，充分研究区域的环境特点，需要对空气质量有作用的多种因素进行全面系统的分析，获取大气污染物浓度时空分布是了解区域污染特征的重要途径。获取高空间分辨的大气污染物柱浓度分布情况是掌握区域污染程度的重要前提。由大气扩散模型，排放源周边的大气污染物的柱浓度服从高斯分布。将车载被动差分光学吸收光谱（DOAS）获取的对流层污染气体垂直柱浓度空间分布信息结合序贯高斯模拟方法重构了高空间分辨率的区域污染物柱浓度分布及其误差分布。分别选取工业园区（钢铁企业）、城市区域（北京市怀柔城区、北京市通州城区）等典型区块进行走航观测，获取观测路径上的NO₂和HCHO柱浓度，结合地理信息网格化车载观测数据，利用序贯高斯模拟获取了观测区域的NO₂和HCHO柱浓度分布以及污染物柱浓度误差分布，重点分析了该方法在排放特征不同的区域柱浓度分布模拟重构的可行性及重构结果的不确定性。某钢铁企业、怀柔城区、通州城区内污染源依次减少，气态污染物分布的结构复杂性依次降低。由半方差分析结果，某钢铁企业由于NO₂排放源多，污染物柱浓度空间依赖性略弱，城市区域污染物柱浓度表现出强烈的空间相关性，并且整体呈现出了区域污染源越复杂，空间相关性的范围越小的特点。基于立体监测数据获取了观测区域百米空间分辨的污染物垂直柱浓度分布及误差分布，在不依赖下垫面数据、源清单数据或人口分布数据的基础上基于实测数据低成本地获取了重点工业区或城市区域气态污染物的分布细节，同已有的卫星遥感等方法获取污染气体垂直柱浓度分布相比，空间分辨率提高了2～3个数量级，同时通过柱浓度误差分布定量评估了模拟重构的准确性。针对不同排放特征的重点区域大气污染状况，提供了新的准确性可评价的实测手段，该方法对了解区域污染状况、污染控制对策及控制效果的评估具有重要作用。

Study on Concentration Distribution Reconstruction Method of Pollution Gas Column

The comprehensive prevention and control of atmospheric pollution need to proceed from different scale areas. It is necessary to fully study the environmental characteristics of the area and conduct a comprehensive and systematic analysis of various factors that have an effect on the air quality. Obtaining the spatio-temporal distribution of atmospheric pollutant concentrations is important for understanding the characteristics of regional pollution way. Getting high spatial resolution of atmospheric pollutant concentration distribution is an important prerequisite for grasping the degree of regional pollution. From the atmospheric diffusion model, the column concentration of atmospheric pollutants around the emission source obeys the Gaussian distribution. In this paper, the spatial distribution of the vertical column concentration of the contaminated gas in the troposphere obtained by the mobile passive differential optical absorption spectroscopy (DOAS) is combined with the sequential Gaussian simulation method to reconstruct the spatial distribution of the pollutant column concentration and its error distribution in high spatial resolution. The typical blocks such as industrial parks (steel companies) and urban areas (Huairou, Beijing and Tongzhou, Beijing) were selected to conduct the navigation and observation to obtain the concentration of NO₂ and HCHO on the observation path respectively. Combined with the geographic information gridded on-board observation Data, the concentration distribution of NO₂ and HCHO columns in the observation area and the error distribution of the concentration of pollutant column were obtained by using sequential Gaussian simulation. The feasibility and reconstruction results of the simulation of column concentration distribution in the area with different emission characteristics were analyzed emphatically. The pollution sources in a steel enterprise, Huairou and Tongzhou area decreased in turn, and the structural complexity of distribution of gaseous pollutants decreased in turn. According to the results of semi-variance analysis, due to the large amount of NO₂ emission sources, the space dependence of pollutant column concentration is slightly weak in an iron and steel enterprise. The concentration of pollutant column in urban area shows a strong spatial correlation, and it shows as a whole that the more complex pollutant source in the area, the smaller the scope of the spatial correlation. Based on the three-dimensional monitoring data, the spatial distribution of the vertical column concentration and the error distribution of the pollutants in the hundred meters of the observation area were obtained. Based on the measured data and without dependency on the underlying surface data, source inventory data or population distribution data, the distribution of gaseous pollutants in key industrial areas or urban areas is improved by 2~3 orders of magnitude compared with the existing methods such as satellite remote sensing to obtain the vertical column concentration distribution of polluting gases. Meanwhile, through the column concentration error distribution, we quantitatively assessed the accuracy of simulation reconstruction. Based on the situation of air pollution in key areas with different emission characteristics, a new measurable measure of accuracy is provided. This method plays an important role in understanding regional pollution status, pollution control strategies and assessment of control effect.