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腔增强吸收光谱技术作为一种高灵敏的痕量气体测量技术, 其吸收光谱的浓度反演是极其关键的环节. 为消除因吸收截面和仪器响应函数的不确定性引入的测量误差, 本文提出了一种基于标准样品吸收光谱的浓度回归算法, 该方法在浓度反演过程上进行优化, 采用标准气体样品吸收光谱直接拟合未知浓度气体吸收光谱. 采用中心波长在440 nm处的蓝色发光二极管(LED)作为光源, 建立了一套非相干光腔增强吸收光谱技术(IBBCEAS)系统, 实测腔镜反射率为99.915%, 利用NO2气体的实测吸收光谱对该算法的有效性进行了验证. 与常规吸收截面回归算法比较, 结果表明本文提出的标准样品回归算法具有显著的优越性, 测量精度提升约4倍. 利用改进的算法结合标准样品配制的多个NO2气体对实验系统性能进行了深入评估, 测量结果与理论值具有很好的一致性. Allan方差分析显示在积分时间为360 s的情况下, NO2检测限可达到5.3 ppb (1 ppb = 10–9).
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关键词:
- 非相干宽带腔增强吸收光谱 /
- NO2 /
- 可见光谱 /
- 多元线性回归
Cavity-enhanced absorption spectroscopy is a highly sensitive trace gas measurement technology, and the algorithm for retrieving gas concentrations is critical. The absorption cross-section is traditionally used to retrieve the concentration. However, the absorption cross-section used in the fitting process is affected not only by the response function of the instrument and the light source, but also by temperature and pressure. The uncertainty of the absorption cross-section will influence the accuracy of the result. Therefore, in order to eliminate the measurement error introduced by the uncertainty of the absorption cross-section and the instrument response function, a concentration regression algorithm based on the absorption spectrum of the standard sample is proposed. The process of concentration inversion is optimized. The absorption spectrum of standard gas is used to fit the unknown spectrum. In order to verify the effectiveness of the algorithm, the incoherent cavity enhanced absorption spectroscopy (IBBCEAS) system based on a blue light-emitting diode (LED) operating at 440 nm is established to analyze the absorption spectrum of NO2; and the fitting effect, measurement accuracy and stability are compared with the counter parts from the traditional absorption cross-section method. In the experiment, the measured reflectance of the cavity mirror is 99.915%. Compared with the conventional absorption cross-section regression algorithm, the standard sample regression algorithm proposed in this paper shows good results, in which the measurement accuracy is increased by about quadruple. The Allan deviation shows that a detection limit of 5.3 ppb can be achieved at an integration time of 360 s. Finally, the performance of the experimental system is evaluated by measuring the NO2 with different concentrations prepared by standard samples. The result shows good agreement with the theoretical value, which indicates that the improved spectral analysis algorithm is feasible and reliable for gas detection. This method can be used not only to measure NO2, but also to detect other gases, which shows great potential applications in monitoring the industrial emissions, atmospheric chemistry and exhaled breath analysis.-
Keywords:
- incoherent broadband cavity enhanced absorption spectroscopy /
- NO2 /
- visible spectrum /
- multiple linear regression
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Duan Q Y 2019 Environ. Dev. 31 155Google Scholar
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Wu T, Chen W D, He X D 2015 Spectrosc. Spect. Anal. 35 2989Google Scholar
[16] 凌六一, 秦敏, 谢品华, 胡仁志, 方武, 江宇, 刘建国, 刘文清 2012 物理学报 61 140703Google Scholar
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Pang X X, Deng Z C, Jia P Y, Liang W H 2011 Acta Phys. Sin. 60 125201Google Scholar
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[21] 崔厚欣, 杜振辉, 陈文亮, 齐汝宾, 徐可欣 2008 天津大学学报 41 1162
Cui H X, Du Z H, Chen W L, Qi R B, Xu K X 2008 J. Tianjin Univ. 41 1162
[22] 郑海明, 蔡小舒 2007 动力工程 27 130
Zheng H M, Cai X S 2007 J. Power Eng. 27 130
[23] Bogumil K, Orphal J, Burrows J P, Flaud J M 2001 Chem. Phys. Lett. 349 241Google Scholar
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Ling L, Wei Y, Huang Y R, Hu R Z, Xie P H 2018 Spectrosc. Spect. Anal. 38 670Google Scholar
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[30] Shardanand S, Rao A D P 1977 NASA Technical Note (Washington D.C.: National Aeronautics and Space Administration)
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表 1 不同NO2浓度对应的拟合残差的标准偏差和信噪比
Table 1. Standard deviation and signal-to-noise ratio of fitting residuals corresponding to different NO2 concentrations.
浓度/ppm 7.86 5.08 3.28 2.07 1.42 0.75 标准偏差/(10–6 cm–1) 12.35 8.13 5.27 3.44 2.62 1.57 信噪比 11.23 11.08 10.95 10.38 9.72 8.94 表 2 不同NO2浓度对应的拟合残差的标准偏差和信噪比
Table 2. Standard deviation and signal-to-noise ratio of fitting residuals corresponding to different NO2 concentrations.
浓度/ppm 7.86 5.08 3.28 2.07 1.42 0.75 标准偏差/(10–6 cm–1) 2.7 1.67 1.36 0.78 0.62 0.59 信噪比 52.6 49.9 44.6 42.7 37.4 25.1 -
[1] 段秋宴 2019 环境与发展 31 155Google Scholar
Duan Q Y 2019 Environ. Dev. 31 155Google Scholar
[2] 韩荦, 夏滑, 董凤忠, 张志荣, 庞涛, 孙鹏帅, 吴边, 崔小娟, 李哲, 余润磬 2018 中国激光 45 43Google Scholar
Han L, Xia H, Dong F Z, Zhang Z R, Pang T, Sun P S, Wu B, Cui X J, Li Z, Yu R Q 2018 Chin. J. Lasers 45 43Google Scholar
[3] Min K E, Washenfelder R A, Dubé W P, Langford A O, Edwards P, Zarzana K J, Stutz J, Lu K, Rohrer F, Zhang Y H, Brown S S 2015 Atmos. Meas. Tech. 9 11209Google Scholar
[4] Engeln R, Berden G, Peeters R, Meijei G 1998 Rev. Sci. Instrum. 69 3763Google Scholar
[5] O’keefe A 1998 Chem. Phys. Lett. 293 331Google Scholar
[6] Fiedler S E, Hoheisel G, Ruth A A, Hese A 2003 Chem. Phys. Lett. 382 447Google Scholar
[7] Wu T, Zha Q Z, Chen W D, Xu Z, Wang T, He X D 2014 Atmos. Environ. 95 544Google Scholar
[8] Nakashima Y, Sadanaga Y 2017 Anal. Sci. 33 519Google Scholar
[9] 段俊, 秦敏, 方武, 胡仁志, 卢雪, 沈兰兰, 王丹, 谢品华, 刘建国, 刘文清 2016 光谱学与光谱分析 36 466Google Scholar
Duan J, Qin M, Fang W, Hu R Z, Lu X, Shen L L, Wang D, Xie P H, Liu J G, Liu W Q 2016 Spectrosc. Spect. Anal. 36 466Google Scholar
[10] Liu J W, Li X, Yang Y M, Wang H C, Wu Y S, Lu X W, Chen M D, Hu J L, Fan X B, Zeng L M, Zhang Y H 2019 Atmos. Meas. Tech. 12 4439Google Scholar
[11] Jordan N, Ye C Z, Ghosh S, Washenfelder R A, Brown S S, Osthoff H D 2019 Atmos. Meas. Tech. 12 1277Google Scholar
[12] Jodan N, Osthoff H D 2020 Atmos. Meas. Tech. 13 285Google Scholar
[13] Duan J, Qin M, Ouyang B, Fang W, Li X, Lu K D, Tang K, Liang S X, Meng F H, Hu Z K, Xie P H, Liu W Q, Häsler R 2018 Atmos. Meas. Tech. 11 4531Google Scholar
[14] Liang S X, Qin M, Xie P H, Duan J, Fang W, He Y B, Xu J, Liu J W, Li X, Tang K, Meng F H, Ye K D, Liu J G, Liu W Q 2019 Atmos. Meas. Tech. 12 2499Google Scholar
[15] 吴涛, 陈卫东, 何兴道 2015 光谱学与光谱分析 35 2989Google Scholar
Wu T, Chen W D, He X D 2015 Spectrosc. Spect. Anal. 35 2989Google Scholar
[16] 凌六一, 秦敏, 谢品华, 胡仁志, 方武, 江宇, 刘建国, 刘文清 2012 物理学报 61 140703Google Scholar
Ling L Y, Qin M, Xie P H, Hu R Z, Fang W, Jiang Y, Liu J G, Liu W Q 2012 Acta Phys. Sin. 61 140703Google Scholar
[17] Thalman R, Zarzana K J, Tolbert M A, Volkamer R 2014 J. Quant. Spectrosc. Radiat. Transfer 147 171Google Scholar
[18] Meng L S, Wang G X, Augustin P, Fourmentin M, Gou Q, Fertein E, Ba T N, Coeur C, Tomas A, Chen W D 2020 Opt. Lett. 45 1611Google Scholar
[19] 庞学霞, 邓泽超, 贾鹏英, 梁伟华 2011 物理学报 60 125201Google Scholar
Pang X X, Deng Z C, Jia P Y, Liang W H 2011 Acta Phys. Sin. 60 125201Google Scholar
[20] 赵士彬 2018 环境与发展 30 140Google Scholar
Zhao S B 2018 Environ. Dev. 30 140Google Scholar
[21] 崔厚欣, 杜振辉, 陈文亮, 齐汝宾, 徐可欣 2008 天津大学学报 41 1162
Cui H X, Du Z H, Chen W L, Qi R B, Xu K X 2008 J. Tianjin Univ. 41 1162
[22] 郑海明, 蔡小舒 2007 动力工程 27 130
Zheng H M, Cai X S 2007 J. Power Eng. 27 130
[23] Bogumil K, Orphal J, Burrows J P, Flaud J M 2001 Chem. Phys. Lett. 349 241Google Scholar
[24] Mellqvist J, Rosen A 1996 J. Quant. Spectrosc. Radiat. Transfer 56 187Google Scholar
[25] Roehl C M, Orlando J J, Tyndall G S, Shetter R E, Vazquez G J, Cantrell C A, Calvert J G 1994 J. Phys. Chem. 98 7837Google Scholar
[26] 凌六一, 韦颖, 黄友锐, 胡仁志, 谢品华 2018 光谱学与光谱分析 38 670Google Scholar
Ling L, Wei Y, Huang Y R, Hu R Z, Xie P H 2018 Spectrosc. Spect. Anal. 38 670Google Scholar
[27] Washenfelder R A, Langford A O, Fuchs H, Brown S S 2008 Atmos. Chem. Phys. 8 7779Google Scholar
[28] Ling L Y, Xie P H, Qin M, Fang W, Jiang Y, Hu R Z, Zheng N N 2013 Chin. Opt. Lett. 11 063001Google Scholar
[29] Sneep M, Ubachs W 2005 J. Quant. Spectrosc. Radiat. Transfer 92 293Google Scholar
[30] Shardanand S, Rao A D P 1977 NASA Technical Note (Washington D.C.: National Aeronautics and Space Administration)
[31] Washenfelder R A, Attwood A R, Flores J M, Zarzana K J, Rudich Y, Brown S S 2015 Atmos. Meas. Tech. 9 41Google Scholar
[32] Wu T, Chen W D, Fertein E, Cazier F, Dewaele D, Gao X M 2012 Appl. Phys. B 106 501Google Scholar
[33] Bessant C, Saini S 2000 J. Electroanal. Chem. 489 76Google Scholar
[34] Sun J, Ding J Y, Liu N W, Yang G X, Li J S 2018 Spectrochim. Acta, Part A 191 532Google Scholar
[35] Brown S S, Stark H, Ciciora S J, Mclaughlin R J, Ravishankara A R 2002 Rev. Sci. Instrum. 73 3291Google Scholar
[36] Huang H F, Lehmann K K 2010 Appl. Opt. 49 1387Google Scholar
[37] Li J S, Deng H, Sun J, Yu B L, Fischer H 2016 Sens. Actuators, B 231 723Google Scholar
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