Study on Concentration Distribution Reconstruction Method of Pollution Gas Column
HU Zhao-kun1，2, LI Ang1*, XIE Pin-hua1，2，3, WU Feng-cheng1, XU Jin1, YANG Lei1，2, HUANG Ye-yuan1，2
1. Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
2. Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
3. CAS Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Abstract：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.
胡肇焜，李 昂，谢品华，吴丰成，徐 晋, 杨 雷，黄业园. 车载被动DOAS的污染气体柱浓度分布重构方法[J]. 光谱学与光谱分析, 2019, 39(09): 2670-2676.
HU Zhao-kun, LI Ang, XIE Pin-hua, WU Feng-cheng, XU Jin, YANG Lei, HUANG Ye-yuan. Study on Concentration Distribution Reconstruction Method of Pollution Gas Column. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2670-2676.
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