Acta Optica Sinica, Volume. 43, Issue 6, 0612009(2023)

In-situ Detection of Petroleum Hydrocarbon Pollutants in Soil by Ultraviolet-Induced Fluorescence

Jinqiang Yang1...2,3, Ruifang Yang2,3,*, Nanjing Zhao2,3,**, Gaofang Yin2,3, Mingjun Ma2,3, Li Fang2,3, Gaoyong Shi1,2,3, Liangchen Liu1,2,3, Desuo Meng4, and Wenqing Liu23 |Show fewer author(s)
Author Affiliations
  • 1University of Science and Technology of China, Hefei 230026, Anhui, China
  • 2Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, China Academy of Sciences, Hefei 230031, Anhui, China
  • 3Key Laboratory of Optical Monitoring Technology for Environment of Anhui Province, Hefei 230031, Anhui, China
  • 4Huainan Normal University, Huainan 232000, Anhui, China
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    Figures & Tables(14)
    Three-dimensional fluorescence spectra of diesel engine oil
    Emission spectra of different soil blank samples under 280 nm-LED irradiation. (a) Red soil; (b) yellow soil; (c) black soil
    Emission spectra of samples containing 10% engine oil under different soil types irradiated by 280 nm-LED
    Schematic diagram and physical picture of experimental system. (a) Schematic diagram;(b) physical picture
    Correlation between mass fraction of oil in soil and fluorescent electrical signal. (a) LIF system; (b) experimental system
    Average RSD measured by proposed system under different soil types
    Average RE measured by proposed system under different soil types
    • Table 1. RSD of signal of experimental system under different integration times

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      Table 1. RSD of signal of experimental system under different integration times

      Integration time /sRSD /%
      0.46.77
      0.65.56
      0.84.72
      1.04.57
      1.44.41
      1.64.23
      2.03.61
      2.23.67
      2.43.65
      2.63.61
      3.03.52
    • Table 2. Quantitative analysis of soil petroleum hydrocarbons for different soil types

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      Table 2. Quantitative analysis of soil petroleum hydrocarbons for different soil types

      Soil typePetroleum hydrocarbonRegression equationCorrelation coefficient R2

      Detection limit /

      (mg·kg-1

      Red soilGasoline engine oily=7121.3x+8803.120.973460.38
      Diesel engine oily=14374.4x+8791.960.994129.91
      Air compressor engine oily=49681.01x+8827.110.99538.66
      Yellow soilGasoline engine oily=6915.7x+10724.40.962562.37
      Diesel engine oily=13730.1x+11400.70.993731.39
      Air compressor engine oily=48622.8x+11641.10.99648.87
      Black soilGasoline engine oily=6001.63x+18761.290.9572104.97
      Diesel engine oily=12113.57x+18912.430.990152.01
      Air compressor engine oily=37619.13x+18317.610.992616.75
    • Table 3. Prediction results of different types of petroleum hydrocarbons in red soil

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      Table 3. Prediction results of different types of petroleum hydrocarbons in red soil

      Petroleum hydrocarbonStandard mass fractionPredicted mass fractionREAverage RERSDAverage RSD
      Gasoline engine oil0.1500.1434.675.623.703.53
      0.2500.2336.803.71
      0.3500.3716.003.42
      0.6000.5793.503.49
      0.8000.8577.133.33
      Diesel engine oil0.1500.1416.004.763.593.59
      0.2500.2572.803.61
      0.3500.3412.573.44
      0.6000.6132.174.17
      0.8000.88210.253.12
      Air compressor engine oil0.1500.1553.334.403.363.36
      0.2500.2614.403.28
      0.3500.3572.003.51
      0.6000.6315.173.55
      0.8000.8597.383.12
    • Table 4. Prediction results of different types of petroleum hydrocarbons in yellow soil

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      Table 4. Prediction results of different types of petroleum hydrocarbons in yellow soil

      Petroleum hydrocarbonStandard mass fractionPredicted mass fractionREAverage RERSDAverage RSD
      Gasoline engine oil0.1500.13410.677.693.173.70
      0.2500.2346.404.63
      0.3500.3296.003.76
      0.6000.5705.003.01
      0.8000.88310.383.92
      Diesel engine oil0.1500.1378.676.312.423.16
      0.2500.2614.403.31
      0.3500.3325.143.86
      0.6000.5675.503.01
      0.8000.8637.883.19
      Air compressor engine oil0.1500.1434.606.403.213.04
      0.2500.2327.202.01
      0.3500.3266.863.45
      0.6000.6264.333.12
      0.8000.7289.003.43
    • Table 5. Prediction results of different types of petroleum hydrocarbons in black soil

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      Table 5. Prediction results of different types of petroleum hydrocarbons in black soil

      Petroleum hydrocarbonStandard mass fractionPredicted mass fractionREAverage RERSDAverage RSD
      Gasoline engine oil0.1500.1596.008.253.333.40
      0.2500.2708.003.71
      0.3500.3777.713.12
      0.6000.67212.003.29
      0.8000.89111.383.61
      Diesel engine oil0.1500.1628.006.723.523.51
      0.2500.2635.203.90
      0.3500.3592.573.37
      0.6000.66310.503.21
      0.8000.8627.753.53
      Air compressor engine oil0.1500.1387.337.243.533.01
      0.2500.2687.203.52
      0.3500.3695.433.23
      0.6000.6549.003.09
      0.8000.8536.622.19
    • Table 6. Quantitative analysis of soil petroleum hydrocarbons in lake bottom mud

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      Table 6. Quantitative analysis of soil petroleum hydrocarbons in lake bottom mud

      Petroleum hydrocarbonRegression equationCorrelation coefficient R2

      Detection limit /

      (mg·kg-1

      Gasoline engine oily=4718.51x+46940.90.9515135.65
      Diesel engine oily=10781.53x+47113.30.979359.37
      Air compressor engine oily=32629.52x+47467.60.990119.62
    • Table 7. Prediction results of different types of petroleum hydrocarbons in lake bottom mud

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      Table 7. Prediction results of different types of petroleum hydrocarbons in lake bottom mud

      Petroleum hydrocarbonStandard mass fractionPredicted mass fractionREAverage RE
      Gasoline engine oil0.1500.17818.678.25
      0.2500.27510.00
      0.3500.3266.86
      0.6000.5655.83
      0.8000.8465.75
      Diesel engine oil0.1500.12814.676.72
      0.2500.2365.60
      0.3500.3237.71
      0.6000.5714.83
      0.8000.7644.50
      Air compressor engine oil0.1500.1638.697.24
      0.2500.2346.40
      0.3500.3296.00
      0.6000.5636.17
      0.8000.7738.63
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    Jinqiang Yang, Ruifang Yang, Nanjing Zhao, Gaofang Yin, Mingjun Ma, Li Fang, Gaoyong Shi, Liangchen Liu, Desuo Meng, Wenqing Liu. In-situ Detection of Petroleum Hydrocarbon Pollutants in Soil by Ultraviolet-Induced Fluorescence[J]. Acta Optica Sinica, 2023, 43(6): 0612009

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Jul. 26, 2022

    Accepted: Aug. 25, 2022

    Published Online: Mar. 13, 2023

    The Author Email: Ruifang Yang (rfyang@aiofm.ac.cn), Nanjing Zhao (njzhao@aiofm.ac.cn)

    DOI:10.3788/AOS221531

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