基于非线性最小二乘算法的气溶胶无机离子定量分析

J4 ›› 2018, Vol. 35 ›› Issue (5): 523-526.

基于非线性最小二乘算法的气溶胶无机离子定量分析

魏秀丽 , 张露2, 高闽光1

1中国科学院安徽光学精密机械研究所，中国科学院环境光学与技术重点实验室，安徽合肥 230031； 2 安阳职业技术学院，河南安阳 455000

收稿日期:2016-09-06 修回日期:2018-01-23 出版日期:2018-09-28 发布日期:2018-09-29
通讯作者: 魏秀丽(1978- )，女，河南安阳人，硕士，副研究员，主要从事气溶胶化学物理方面的研究。 E-mail:xlwei@aiofm.ac.cn
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 41375027

Quantitative analysis of aerosol inorganic ion based on nonlinear least squares algorithm

WEI Xiuli1, ZHANG Lu2, GAO Minguang1

1 Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; 2 Anyang Vocational and Technical College, Anyang 455000, China

Received:2016-09-06 Revised:2018-01-23 Published:2018-09-28 Online:2018-09-29

摘要/Abstract

摘要：

为适应快速发展的在线自动监测技术，将非线性最小二乘算法应用于多成分红外光谱信号的定量分析，通过拟合设定的气溶胶无机离子成分标准光谱和测量光谱，得到测量光谱的定量信息。结果表明非线性最小二乘法在多成分红外光谱信号定量分析方面具有一定优势，硝酸根的均方根值(RMS)为0.340%。利用计算结果分析了合肥董铺岛气溶胶无机离子浓度趋势，得到硫酸根和硝酸根的时间变化趋势。可见非线性最小二乘法可以进行气溶胶离子化学成分浓度的红外光谱定量分析，且不需实时标定。

关键词: 光谱；非线性最小二乘；红外光谱；气溶胶离子成分；定量分析

Abstract:

In order to adapt to the rapid development of online automatic monitoring technology, nonlinear least squares algorithm was applied to quantitative analysis of multi-component infrared spectral signals. The quantitative information of measured spectrum is obtained by fitting the standard spectrum and measured spectrum of aerosol inorganic ion composition. Results showed that the nonlinear least squares algorithm has some advantages in quantitative analysis of multi-component infrared spectral signals, and the root mean square (RMS) of nitrate was 0.340%。The trend of aerosol inorganic ion concentration in Dongpu Island of Hefei is analyzed using the calculation results, and obtained the temporal trends of sulfate and nitrate. It indicated that the nonlinear least squares method can be used for infrared spectroscopy quantitative analysis of the aerosol ion chemical composition concentration without real-time calibration.

Key words: spectropscopy；nonlinear least squares; infrared spectrum; aerosol ion composition; quantitative analysis

魏秀丽 , 张露2, 高闽光1. 基于非线性最小二乘算法的气溶胶无机离子定量分析[J]. J4, 2018, 35(5): 523-526.

WEI Xiuli1, ZHANG Lu2, GAO Minguang1. Quantitative analysis of aerosol inorganic ion based on nonlinear least squares algorithm[J]. J4, 2018, 35(5): 523-526.

参考文献

[1] U.S Environmental Protection Agency Office of Resrarch and Development. Clean Air Research Multi-Year Plan 2008～2012[M]. United States：Resrarch Triangle Park, 2008. [2] Giorgi F, Bi X, Qian Y. Direct radiative forcing and regional climatic effects of anthropogenic aerosols over east asia: A regional coupled climate-chemistry/aerosol model study[J]. Journal of Geophysical Research-Atmospheres, 2002, 107 (1320): AAC7/1-AAC7/18. [3] Kleindienst T E, Smith D F, Li W, et al. Secondary organic aerosol formation from the oxidation of aromatic hydrocarbons in the presence of dry submicron ammonium sulfate aerosol [J]. Atmospheric Environment, 1999, 33: 3669–3681. [4] Oh S, Andino J M. Effects of ammonium sulfate aerosols on the gas-phase reactions of the hydroxyl radical with organic compounds[J]. Atmospheric Environment, 2000, 34: 2901–2908. [5] Yu Xuechun. Determination and Characterization of Water-Soluble Species of PM2.5 in Beijing (北京PM2.5水溶性物种污染特征及来源解析)[D]. Beijing: Doctorial Dissertation of Tsinghua University, 2004(in Chinese). [6] Xu Honghui. Study on the Characteristics of Mass Concentration and Inorganic Components and the Sources of Aerosol in Beijing and its Surrounding Area(北京及周边地区大气气溶胶的质量浓度和无机组分的特征及其来源研究) [D]. Beijing: Institute of Atmospheric Physics, Chinese Academy of Sciences 2007(in Chinese). [7] Tan Jihua. The Preliminary Study of the Charcateristice of Aerosols During Haze and Its Effect on Visibility in Guangzhou(广州灰霾期间气溶胶物化特性及其对能见度影响的初步研究)[D]. Guangdong: Doctorial Dissertation of Guangzhou Institute of Geochmistry, Chinese Academy of Sciences, 2007. [8] Krivacsy Z, Barcza T, Gelencser A, et al. FTIR study of atmospheric aerosols [J]. Journal of Aerosol Science, 1997, 28: S113–S114. [9] Yin I T, Su-Ching K. Development of diffuse reflectance infrared Fourier transform spectroscopy for the rapid characterization of aerosols [J]. Atmospheric Environment, 2006, 40: 1781–1793. [10] Andrea P, Barbara J T, Cliff I, et al. Organic PM2.5: Fractionation by Polarity, FTIR Spectroscopy, and OM/OC Ratio for the Pittsburgh Aerosol[J]. Aerosol Science and Technology, 2008, 42: 233–246. [11] Liang Yongmei, Wan Meirong, Sun ruiqing. Fourier transform infrared transmission spectroscopic analysis of bisulfate in ambient aerosols[J]. Environmental Science, 1997，18(6): 9-15(in Chinese).