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Algorithmic study of total petroleum hydrocarbons in contaminated soil by three-dimensional excitation-emission matrix fluorescence spectroscopy

GU Yan-hong ZUO Zhao-lu ZHANG Zhen-zhen SHI Chao-yi GAO Xian-he LU Jun

谷艳红, 左兆陆, 张振振, 石朝毅, 高先和, 卢军. 土壤石油烃总量三维荧光光谱定量分析方法研究[J]. 中国光学, 2020, 13(4): 852-865. doi: 10.37188/CO.2019-0216
引用本文: 谷艳红, 左兆陆, 张振振, 石朝毅, 高先和, 卢军. 土壤石油烃总量三维荧光光谱定量分析方法研究[J]. 中国光学, 2020, 13(4): 852-865. doi: 10.37188/CO.2019-0216
GU Yan-hong, ZUO Zhao-lu, ZHANG Zhen-zhen, SHI Chao-yi, GAO Xian-he, LU Jun. Algorithmic study of total petroleum hydrocarbons in contaminated soil by three-dimensional excitation-emission matrix fluorescence spectroscopy[J]. Chinese Optics, 2020, 13(4): 852-865. doi: 10.37188/CO.2019-0216
Citation: GU Yan-hong, ZUO Zhao-lu, ZHANG Zhen-zhen, SHI Chao-yi, GAO Xian-he, LU Jun. Algorithmic study of total petroleum hydrocarbons in contaminated soil by three-dimensional excitation-emission matrix fluorescence spectroscopy[J]. Chinese Optics, 2020, 13(4): 852-865. doi: 10.37188/CO.2019-0216

土壤石油烃总量三维荧光光谱定量分析方法研究

doi: 10.37188/CO.2019-0216
详细信息
  • 中图分类号: O433.4;X833

Algorithmic study of total petroleum hydrocarbons in contaminated soil by three-dimensional excitation-emission matrix fluorescence spectroscopy

Funds: Supported by the Anhui Province Natural Key Science and Technology Projects (No. KJ2018A0547); Talent Research Foundation of Hefei University (No. 18-19RC47); Natural Science Foundation of Anhui Province (No. 200808QF316)
More Information
    Author Bio:

    Gu Yanhong (1989—), female, born in Jining, Shandong province. Ph.D. She received her Ph.D. from The University of Science and Technology of China in 2017. She mainly engaged in the research of spectral detection and analysis. E-mail:guyanhong66@163.com

    Zuo Zhaolu (1984—), male, born in Changchun, Jilin province. He received his Ph.D. from The University of Science and Technology of China, and now he is a postdoctor of Anhui Institute of Circular Economy Technology. He is mainly engaged in the research of optical detection and analysis. E-mail:zlzuo@hfcas.ac.cn

    Corresponding author: zlzuo@hfcas.ac.cn
  • 摘要: 混合石油烃污染土壤中准确的种类识别和含量检测有助于土壤石油烃污染总量的检测。石油烃是多种化合物的混合,而三维激发发射荧光光谱技术含有大量的荧光光谱信息,故被用于快速定性和定量检测土壤石油烃污染,但该技术仍面临着石油烃组分的准确识别以及土壤背景干扰引起的定量分析问题。本文研究了土壤石油烃污染物的三维荧光光谱复杂基质和散射效应校正方法,最大程度地保留了光谱信息。为了提高土壤石油烃定性识别和含量检测精度,本文以机油、润滑油和柴油为例,采用平行因子和交替三线性分解法对不同类型混合土壤石油烃污染进行定性和定量分析。实验结果表明,与平行因子法对土壤混合石油烃污染的检测结果相比,交替三线性分解法将土壤混合石油烃污染的平均回收率由85%提高至95%,说明交替三线性分解法能更好地分离相似荧光光谱,对土壤中石油成分和总含量的检测更有效,其可为土壤石油烃污染风险评估提供快速检测方法。
  • Figure  1.  The 3DEEM fluorescence spectral pretreatment of diesel oil in soil.

    Figure  2.  The typical fluorescence spectra of three kinds of pure petroleum in soils

    Figure  3.  3DEEM fluorescence spectra of mixed oil in soil (Machine oil:Lubricating oil:Diesel oil)

    Figure  4.  The core consistency diagnosis results for mixtures of two petroleum hydrocarbons. (a) N = 2; (b) N = 3

    Figure  5.  Classifying the mixture of two petroleum hydrocarbons by PARAFAC. (a) Classification of emission spectra; (b) classification of excitation spectra

    Figure  6.  The core consistency diagnosis results for mixtures of three petroleum hydrocarbons. (a) N = 3; (b) N = 4

    Figure  7.  Classifying the mixture of three petroleum hydrocarbons by PARAFAC. (a) Classification of emission spectra; (b) classification of excitation spectra

    Figure  8.  Classifying the mixtures of petroleum hydrocarbons by ATLD. (a) Classification of emission spectra and (b) classification of excitation spectra for the mixture of two; (c) classification of emission spectra and (d) classification of excitation spectra for the mixture of three

    Table  1.   Contents of mixed petroleum in the configured soil samples(%)

    (a) The soil samples with the mixture of two types of petroleum
    calibration setMachine oillubricating oilTest setMachine oillubricating oil
    1#0.110.2117#0.420.41
    2#0.320.5018#0.610.72
    3#0.510.5219#1.001.02
    4#0.520.2120#0.410.23
    5#0.220.7921#0.640.43
    6#0.830.5222#1.025.03
    7#0.821.5423#2.010.52
    8#1.332.6024#1.501.12
    9#1.511.1225#1.0110.2
    10#3.145.8126#2.122.41
    11#5.024.0827#2.524.11
    12#3.444.4728#4.233.52
    13#6.782.1029#6.541.12
    14#2.935.5230#4.611.72
    15#7.824.2231#7.322.23
    16#4.137.8832#8.111.12
    (b) The soil samples with the mixture of three types of petroleum
    calibration setMachine oillubricating oilDiesel oilTest setMachine oillubricating oilDiesel oil
    33#0.100.110.1349#0.420.440.43
    34#0.220.210.2450#0.710.510.62
    35#0.520.540.4951#0.630.480.32
    36#0.210.120.5452#0.221.310.42
    37#0.520.510.1153#0.431.830.62
    38#0.720.640.7254#0.511.028.01
    39#1.124.230.8455#1.048.000.52
    40#0.820.240.4956#1.021.011.01
    41#3.024.451.0357#1.510.781.18
    42#0.515.221.4758#2.425.192.11
    43#4.940.792.3359#4.211.311.51
    44#3.043.02.4560#4.412.980.64
    45#1.101.84.4161#2.130.845.20
    46#4.122.03.3062#2.040.826.41
    47#1.040.85.5163#2.810.546.05
    48#2.141.126.7064#8.010.511.03
    下载: 导出CSV

    Table  2.   The coordinates of peak positions in three petroleum hydrocarbons

    TypesLeft area (EX, EM)Right area (EX, EM)
    Diesel oil(232,334)(288,334)
    Lubricating oil(236,336)(290,348)
    Machine oil(238,346)(270,372)
    下载: 导出CSV

    Table  3.   The predicted results of two types of petroleum mixture using PARAFAC

    SamplePredicted(%)Recoveries(%)
    Machine oilLubricating oilTPHsMachine oilLubricating oilTPHs
    17#0.350.350.7082.3885.8584.10
    18#0.520.601.1285.7483.6184.59
    19#0.870.981.8587.0096.0891.58
    20#0.340.190.5383.9083.9183.91
    21#0.540.420.9684.0696.8889.21
    22#0.824.415.2380.0087.5986.31
    23#1.820.542.3690.30103.7793.07
    24#1.260.902.1684.0080.7182.60
    25#0.929.2910.2191.2991.0891.10
    26#1.701.843.5480.3876.3578.23
    27#2.243.085.3288.9774.9480.27
    28#3.113.046.1573.5286.4279.38
    29#6.091.057.1493.1294.1193.26
    30#4.131.495.6289.6186.8688.86
    31#6.111.807.9183.5080.5482.81
    32#6.750.937.6883.2483.3983.26
    Average recoveries (%)85.06±4.8087.01±7.5285.78±4.70
    下载: 导出CSV

    Table  4.   The predicted results of three types of petroleum mixture using PARAFAC

    SamplePredicted(%)Recoveries(%)
    Machine oilLubricating oilDiesel oilTPHsMachine oilLubricating oilDiesel oilTPHs
    49#0.350.420.381.15183.8195.4588.1489.22
    50#0.580.490.531.6181.8396.8686.1387.45
    51#0.530.420.261.2183.9786.6781.2584.27
    52#0.211.120.351.6793.6485.3482.3885.64
    53#0.371.560.482.4185.1285.3678.0683.75
    54#0.470.866.928.2491.1883.9286.3786.36
    55#0.996.500.457.9495.0081.2587.3183.08
    56#0.850.740.882.4783.7372.8787.2381.28
    57#1.650.710.943.29108.9490.5179.6394.83
    58#1.974.441.958.3681.2685.5392.5485.99
    59#3.501.011.516.0283.1677.33100.0085.69
    60#4.232.120.576.9296.0071.1488.7686.20
    61#1.550.764.787.0972.7290.2492.0086.79
    62#1.700.835.407.9483.43101.5984.3285.65
    63#2.530.494.977.9990.1890.3082.0984.98
    64#6.700.400.817.9183.6178.4378.6482.80
    Average recoveries (%)87.35±8.0385.80±8.1485.92±5.6085.87±2.97
    下载: 导出CSV

    Table  6.   The predicted results of three types of petroleum mixtures using ATLD

    SamplePredicted(%)Recoveries(%)
    Machine oilLubricating oilDiesel oilTPHsMachine oilLubricating oilDiesel oilTPHs
    49#0.390.410.411.15192.8693.1895.3593.80
    50#0.690.460.591.6197.1890.2095.1694.57
    51#0.590.470.291.2193.6597.9290.6394.41
    52#0.211.240.391.6795.4594.6692.8694.36
    53#0.391.750.592.4190.7095.6395.1694.79
    54#0.490.987.898.2496.0896.0898.5098.11
    55#0.997.490.517.9495.1993.6298.0894.04
    56#1.010.980.992.4799.0297.0398.0298.03
    57#1.440.751.133.2995.3696.1595.7695.68
    58#2.385.141.998.3698.3599.0494.3197.84
    59#4.011.241.56.0295.2594.6699.3496.02
    60#4.132.890.596.9293.6596.9892.1994.77
    61#2.090.795.117.0998.1294.0598.2797.80
    62#1.960.786.397.9496.0895.1299.6998.49
    63#2.690.55.697.9995.7392.5994.0594.47
    64#7.690.490.997.9196.0096.0896.1296.02
    Average recoveries (%)95.54±2.0095.19±2.0595.84±2.5095.82±1.58
    下载: 导出CSV

    Table  5.   The predicted results of two types of petroleum mixtures using ATLD

    SamplePredicted(%)Recoveries(%)
    Machine oilLubricating oilTPHsMachine oilLubricating oilTPHs
    17#0.410.390.8097.6295.1296.39
    18#0.590.691.2896.7295.8396.24
    19#0.990.981.9799.0096.0897.52
    20#0.390.220.6195.1295.6595.31
    21#0.610.411.0295.3195.3595.33
    22#0.984.955.9396.0898.4198.02
    23#1.890.512.4094.0398.0894.86
    24#1.481.12.5898.6798.2198.47
    25#0.989.4810.4697.0392.9493.31
    26#2.052.364.4196.7097.9397.35
    27#2.434.056.4896.4398.5497.74
    28#4.163.487.6498.3598.8698.58
    29#6.451.117.5698.6299.1198.69
    30#4.531.686.2198.2697.6798.10
    31#7.262.199.4599.1898.2198.95
    32#8.091.119.2099.7599.1199.67
    Average recoveries (%)97.30±1.5997.19±1.7497.16±1.70
    下载: 导出CSV
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  • 收稿日期:  2019-11-13
  • 修回日期:  2019-12-09
  • 刊出日期:  2020-08-01

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