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基于迭代逼近算法的土壤中机油和柴油混合物荧光信号重叠特性研究

Study on the Overlapping Characteristics of Fluorescence Signals of Machine Oil and Diesel Mixtures in Soil Based on Iterative Approximation Algorithm

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摘要

随着我国经济的迅速发展, 石油制品需求量与日俱增, 伴随着工农业生产活动, 大量石油制品进入土壤, 造成严重的土壤石油污染。 土壤中的石油污染物会对植物生长产生危害, 并通过食物链威胁人类健康,因此需对土壤中的石油污染物进行现场、 快速检测。 激光诱导荧光技术(Laser-Induced fluorescence, LIF)具有检测速度快、 灵敏度高、 可现场检测等优点, 但在检测土壤中有机污染物时, 面临着荧光光谱重叠严重等问题。 为了研究土壤中机油和柴油混合物荧光信号的重叠特性, 制备了10种含有不同浓度机油、 柴油混合物的土壤样品。 通过搭建LIF实验系统, 获取不同混合浓度的机油和柴油的荧光光谱, 对油类荧光光谱进行了最大值归一化处理, 建立土壤中机油、 柴油混合光谱的反演关系, 以最小残差平方和为指标, 使用迭代逼近算法计算出土壤荧光光谱中柴油和机油样品的荧光贡献率。 分别使用了全谱法和截取特征光谱两种方法计算机油和柴油的荧光贡献率。 全谱法是在混合油样的全波段光谱(200~600 nm)范围进行迭代逼近, 截取特征光谱方法是在截取油样光谱(330~460 nm)段进行迭代逼近。 (330~460 nm)范围内包含了混合油样的所有光谱特征。 用计算出的机油的荧光贡献率与机油样品浓度做线性拟合时发现, 截取特征光谱法的拟合系数R为0.989, 优于全谱法的0.923。 分别用全谱法、 截取特征光谱法计算出的荧光贡献率以及归一化机油、 柴油光谱合成混合油归一化光谱, 与实际归一化混合光谱比较, 截取特征光谱法计算的平均相对误差为3.38%, 优于全谱的8.79%, 其原因是全谱法比截取特征光谱法引入了更多的噪声信号, 所以在计算油类荧光贡献率时产生了较大的误差。 选取机油和柴油归一化光谱上300, 350, 400, 450和500 nm等 5个位置的荧光强度与归一化混合油光谱做多元线性回归拟合, 计算出平均相对误差为10.31%。 结果表明截取特征光谱方法优于多元线性回归方法; 土壤中机油和柴油的荧光贡献率与自身的浓度之间成良好的线性关系, 说明在土壤中机油和柴油混合后各自的化学性质保持稳定, 在土壤中的荧光信号重叠特性是线性叠加的。 这种这种方法同样可以用于其他石油类混合物的解离。 通过该研究提高了LIF技术在土壤中石油烃类污染物定性与定量检测的准确性。 为土壤中石油烃现场快速检测提供了方法支撑。

Abstract

Petroleum hydrocarbons such as machine oil and diesel are important components of soil pollution, and are of great significance for rapid and accurate detection of organic pollutants such as machine oil and diesel in soil. Laser-induced fluorescence (LIF) technology has the advantages of fast detection speed, high sensitivity and on-site detection. However, when detecting organic pollutants in soil, it faces serious problems such as overlapping fluorescence spectra. In order to study the overlapping characteristic of the fluorescence signals of the machine oil and diesel mixture in the soil, 10 soil samples containing different concentrations of machine oil and diesel mixture were prepared. By establishing the LIF experimental system, the fluorescence signals of different mixing concentrations of machine oil and diesel were obtained, and the inversion relationship between the mixed spectra of machine oil and diesel was established. The iterative approximation algorithm was used to calculate the fluorescence contribution rate of diesel and machine oil samples in soil fluorescence spectra. In the process of calculating the fluorescence contribution rate, the two methods of full spectrum and intercepted characteristic spectrum were compared. When linearly fitting with the machine oil sample concentration, the fitting coefficient R of the intercepted characteristic spectrum method was 0.989, and the average relative error was 3.38%, which was better than the full spectrum of 0.923, 8.79%. At the time of verification, the average relative error of multiple linear regressions was 10.11% compared with the multiple linear regression method, which prove that the intercepted characteristic spectroscopy method is still excellent. There was a good linear relationship between the fluorescence contribution rate of machine oil and diesel in soil and its own concentration, indicating that there is no chemical reaction after mixing machine oil and diesel in soil, and the overlapping characteristic of fluorescence signals in soil are linearly superimposed. The method is equally applicable to the separation of fluorescence spectra of other petroleum hydrocarbon mixtures in the soil. Through the research in this paper, the accuracy of qualitative and quantitative detection of petroleum hydrocarbon pollutants in soil by LIF technology was improved. It provided method support for rapid detection of petroleum hydrocarbons in the soil.

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中图分类号:S151.9

DOI:10.3964/j.issn.1000-0593(2020)01-0310-06

基金项目:National Natural Science Foundation of China (61705238) , Anhui Science and Technology Major Projects(16030801117), The National Key Research and Development Program of China (2016YFD0800902-2), Foundation of Director of AIOF(AGHH201602, AGHH201601)

收稿日期:2018-12-06

修改稿日期:2019-04-02

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左兆陆:中国科学院环境光学与技术重点实验室, 中国科学院安徽光学精密机械研究所, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
赵南京:中国科学院环境光学与技术重点实验室, 中国科学院安徽光学精密机械研究所, 安徽 合肥 230031安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
孟德硕:中国科学院环境光学与技术重点实验室, 中国科学院安徽光学精密机械研究所, 安徽 合肥 230031安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
黄 尧:中国科学院环境光学与技术重点实验室, 中国科学院安徽光学精密机械研究所, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
殷高方:中国科学院环境光学与技术重点实验室, 中国科学院安徽光学精密机械研究所, 安徽 合肥 230031安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
刘建国:中国科学院环境光学与技术重点实验室, 中国科学院安徽光学精密机械研究所, 安徽 合肥 230031安徽省环境光学监测技术重点实验室, 安徽 合肥 230031

联系人作者:左兆陆(zuozhaolu@aiofm.ac.cn)

备注:ZUO Zhao-lu, (1984—), Pursuing Doctor’s Degree in Key Laboratory of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences

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引用该论文

ZUO Zhao-lu,ZHAO Nan-jing,MENG De-shuo,HUANG Yao,YIN Gao-fang,LIU Jian-guo. Study on the Overlapping Characteristics of Fluorescence Signals of Machine Oil and Diesel Mixtures in Soil Based on Iterative Approximation Algorithm[J]. Spectroscopy and Spectral Analysis, 2020, 40(1): 310-315

左兆陆,赵南京,孟德硕,黄 尧,殷高方,刘建国. 基于迭代逼近算法的土壤中机油和柴油混合物荧光信号重叠特性研究[J]. 光谱学与光谱分析, 2020, 40(1): 310-315

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