光谱学与光谱分析, 2018, 38 (5): 1476, 网络出版: 2018-06-01   

污染气体浓度二维空间分布的紫外成像方法

An UV Imaging Methods Applicable to the Two-Dimensional Spatial Distribution of Pollutant Concentration
作者单位
1 中国科学院合肥物质科学研究院, 环境光学与技术重点实验室, 安徽 合肥 230031
2 中国科学技术大学, 安徽 合肥 230026
3 安徽新华学院, 安徽 合肥 230088
摘要
介绍了一种新型的、 针对污染源中特定污染气体在紫外波段的成像方法, 该方法基于朗伯比尔吸收定律, 结合紫外带通滤光片分光, 能以较高的时间分辨率和空间分辨率直观再现污染气体浓度在空间的二维分布。 以SO2气体作为研究对象, 在实验室内搭建了测量系统; 阐述了该成像技术的测量方法, 分析了该方法的线性响应, 实验结果表明该方法的线性响应良好, 响应系数R2高达0985; 探讨了该成像方法在不同成像区域内的灵敏度变化, 其差异在1%~3%; 讨论了成像方法获取柱浓度的准确性, 实验室分析结果显示误差约为1%, 准确性较高; 最后分析了该成像方法的检测限并且根据线性最小二乘拟合方法解析出目标气体SO2在样品池横截面上的二维空间分布。
Abstract
A new type of ultraviolet imaging method is introduced, it is useful to specific pollution gas in the pollution sources. The method is based on Lambert-Bill absorption law and reproduces the two-dimensional distribution of pollutant concentration in space with high temporal and spatial resolution by using an ultraviolet band pass filter. The measurement system is built in the laboratory to detect SO2, the measuring method about imaging technology is set forth, and the linear response and sensitivity in regard to that is analyzed. The results show that the linear response is positive and the response coefficient is as high as 0985. The sensitivity of different imaging area changes, the difference is between 1%~3%. At the same time, the accuracy of this method is discussed to get accurate SO2 column density, the result in the laboratory shows that the error about the method is around 1% and the accuracy is higher. At last, the detection limit of the method is analyzed and the space distribution of the target gas SO2 on cross section in the sample pool is parsed according to the linear least squares fitting method.

张英华, 李昂, 谢品华, 徐晋, 胡肇焜, 吴丰成, 秦敏, 方武. 污染气体浓度二维空间分布的紫外成像方法[J]. 光谱学与光谱分析, 2018, 38(5): 1476. ZHANG Ying-hua, LI Ang, XIE Pin-hua, XU Jin, HU Zhao-kun, WU Feng-cheng, QIN Min, FANG Wu. An UV Imaging Methods Applicable to the Two-Dimensional Spatial Distribution of Pollutant Concentration[J]. Spectroscopy and Spectral Analysis, 2018, 38(5): 1476.

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