土壤石油烃总量三维荧光光谱定量分析方法研究

谷艳红; 左兆陆; 张振振; 石朝毅; 高先和; 卢军

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

1.
合肥学院，先进制造工程学院，安徽合肥 230601
2.
中国科学技术大学，安徽合肥 230026
3.
齐鲁工业大学（山东科学院），激光所，山东济南 250103

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中图分类号: O433.4;X833
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出版历程
- 收稿日期: 2019-11-13
- 修回日期: 2019-12-09
- 网络出版日期: 2021-09-15
- 刊出日期: 2020-08-01

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

doi: 10.37188/CO.2019-0216

1.
Institute of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
2.
University of Science and Technology of China, Hefei 230026, China
3.
Laser Institute, QiLu University of Technology(Shandong Academy of Sciences), Jinan 250103, China

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. 2008085QF316)

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

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。
- 三维荧光光谱 /
- 土壤混合石油烃 /
- 平行因子法 /
- 交替三线性分解 /
- 总石油烃
Abstract: Assessment of Total Petroleum Hydrocarbons (TPHs) from contaminated soils demands reliable measurement to analyze the types and contents of mixed petroleum hydrocarbons in soils. Three-dimensional excitation-emission matrix (3DEEM) fluorescence spectroscopy has been demonstrated as a powerful technology for rapidly analyzing mixed petroleum hydrocarbons by identifying its abundant spectral information. However, detection precision in soil still demands improvement. This study investigates the correction methods of 3DEEM fluorescence spectra to correct the complicated matrix and scattering effects of soils. To improve the analytical accuracy, parallel factor analysis (PARAFAC) and the Alternating Trilinear Decomposition method (ATLD) were used to qualitatively and quantitatively analyze mixed petroleum contaminated soils. The methods were used to assess three commonly observed petroleum hydrocarbons: machine oil, lubricating oil, and diesel oil. Compared with the results of PARAFAC, the average recoveries of ATLD increased from 85% to 95%, implying that ATLD can effectively distinguish between similar fluorescence spectra and is more effective in the detection of the components and total content of petroleum in soil. This work can have applications of risk assessment and remediation techniques.
- 3DEEM fluorescence spectroscopy /
- mixed petroleum hydrocarbons in soils /
- parallel factor method /
- alternating trilinear decomposition method /
- total petroleum hydrocarbons

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Figure 1. The 3DEEM fluorescence spectral pretreatment of diesel oil in soil.

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

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

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

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

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

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

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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

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

(a) The soil samples with the mixture of two types of petroleum
calibration set		Machine oil	lubricating oil	Test set	Machine oil	lubricating oil
1#		0.11	0.21	17#	0.42	0.41
2#		0.32	0.50	18#	0.61	0.72
3#		0.51	0.52	19#	1.00	1.02
4#		0.52	0.21	20#	0.41	0.23
5#		0.22	0.79	21#	0.64	0.43
6#		0.83	0.52	22#	1.02	5.03
7#		0.82	1.54	23#	2.01	0.52
8#		1.33	2.60	24#	1.50	1.12
9#		1.51	1.12	25#	1.01	10.2
10#		3.14	5.81	26#	2.12	2.41
11#		5.02	4.08	27#	2.52	4.11
12#		3.44	4.47	28#	4.23	3.52
13#		6.78	2.10	29#	6.54	1.12
14#		2.93	5.52	30#	4.61	1.72
15#		7.82	4.22	31#	7.32	2.23
16#		4.13	7.88	32#	8.11	1.12

(b) The soil samples with the mixture of three types of petroleum
calibration set	Machine oil	lubricating oil	Diesel oil	Test set	Machine oil	lubricating oil	Diesel oil
33#	0.10	0.11	0.13	49#	0.42	0.44	0.43
34#	0.22	0.21	0.24	50#	0.71	0.51	0.62
35#	0.52	0.54	0.49	51#	0.63	0.48	0.32
36#	0.21	0.12	0.54	52#	0.22	1.31	0.42
37#	0.52	0.51	0.11	53#	0.43	1.83	0.62
38#	0.72	0.64	0.72	54#	0.51	1.02	8.01
39#	1.12	4.23	0.84	55#	1.04	8.00	0.52
40#	0.82	0.24	0.49	56#	1.02	1.01	1.01
41#	3.02	4.45	1.03	57#	1.51	0.78	1.18
42#	0.51	5.22	1.47	58#	2.42	5.19	2.11
43#	4.94	0.79	2.33	59#	4.21	1.31	1.51
44#	3.04	3.0	2.45	60#	4.41	2.98	0.64
45#	1.10	1.8	4.41	61#	2.13	0.84	5.20
46#	4.12	2.0	3.30	62#	2.04	0.82	6.41
47#	1.04	0.8	5.51	63#	2.81	0.54	6.05
48#	2.14	1.12	6.70	64#	8.01	0.51	1.03

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

Types	Left 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)

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

Sample	Predicted(%)			Recoveries(%)
Sample	Machine oil	Lubricating oil	TPHs	Machine oil	Lubricating oil	TPHs
17#	0.35	0.35	0.70	82.38	85.85	84.10
18#	0.52	0.60	1.12	85.74	83.61	84.59
19#	0.87	0.98	1.85	87.00	96.08	91.58
20#	0.34	0.19	0.53	83.90	83.91	83.91
21#	0.54	0.42	0.96	84.06	96.88	89.21
22#	0.82	4.41	5.23	80.00	87.59	86.31
23#	1.82	0.54	2.36	90.30	103.77	93.07
24#	1.26	0.90	2.16	84.00	80.71	82.60
25#	0.92	9.29	10.21	91.29	91.08	91.10
26#	1.70	1.84	3.54	80.38	76.35	78.23
27#	2.24	3.08	5.32	88.97	74.94	80.27
28#	3.11	3.04	6.15	73.52	86.42	79.38
29#	6.09	1.05	7.14	93.12	94.11	93.26
30#	4.13	1.49	5.62	89.61	86.86	88.86
31#	6.11	1.80	7.91	83.50	80.54	82.81
32#	6.75	0.93	7.68	83.24	83.39	83.26
Average recoveries (%)				85.06±4.80	87.01±7.52	85.78±4.70

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

Sample	Predicted(%)				Recoveries(%)
Sample	Machine oil	Lubricating oil	Diesel oil	TPHs	Machine oil	Lubricating oil	Diesel oil	TPHs
49#	0.35	0.42	0.38	1.151	83.81	95.45	88.14	89.22
50#	0.58	0.49	0.53	1.61	81.83	96.86	86.13	87.45
51#	0.53	0.42	0.26	1.21	83.97	86.67	81.25	84.27
52#	0.21	1.12	0.35	1.67	93.64	85.34	82.38	85.64
53#	0.37	1.56	0.48	2.41	85.12	85.36	78.06	83.75
54#	0.47	0.86	6.92	8.24	91.18	83.92	86.37	86.36
55#	0.99	6.50	0.45	7.94	95.00	81.25	87.31	83.08
56#	0.85	0.74	0.88	2.47	83.73	72.87	87.23	81.28
57#	1.65	0.71	0.94	3.29	108.94	90.51	79.63	94.83
58#	1.97	4.44	1.95	8.36	81.26	85.53	92.54	85.99
59#	3.50	1.01	1.51	6.02	83.16	77.33	100.00	85.69
60#	4.23	2.12	0.57	6.92	96.00	71.14	88.76	86.20
61#	1.55	0.76	4.78	7.09	72.72	90.24	92.00	86.79
62#	1.70	0.83	5.40	7.94	83.43	101.59	84.32	85.65
63#	2.53	0.49	4.97	7.99	90.18	90.30	82.09	84.98
64#	6.70	0.40	0.81	7.91	83.61	78.43	78.64	82.80
Average recoveries (%)					87.35±8.03	85.80±8.14	85.92±5.60	85.87±2.97

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

Sample	Predicted(%)			Recoveries(%)
Sample	Machine oil	Lubricating oil	TPHs	Machine oil	Lubricating oil	TPHs
17#	0.41	0.39	0.80	97.62	95.12	96.39
18#	0.59	0.69	1.28	96.72	95.83	96.24
19#	0.99	0.98	1.97	99.00	96.08	97.52
20#	0.39	0.22	0.61	95.12	95.65	95.31
21#	0.61	0.41	1.02	95.31	95.35	95.33
22#	0.98	4.95	5.93	96.08	98.41	98.02
23#	1.89	0.51	2.40	94.03	98.08	94.86
24#	1.48	1.1	2.58	98.67	98.21	98.47
25#	0.98	9.48	10.46	97.03	92.94	93.31
26#	2.05	2.36	4.41	96.70	97.93	97.35
27#	2.43	4.05	6.48	96.43	98.54	97.74
28#	4.16	3.48	7.64	98.35	98.86	98.58
29#	6.45	1.11	7.56	98.62	99.11	98.69
30#	4.53	1.68	6.21	98.26	97.67	98.10
31#	7.26	2.19	9.45	99.18	98.21	98.95
32#	8.09	1.11	9.20	99.75	99.11	99.67
Average recoveries (%)				97.30±1.59	97.19±1.74	97.16±1.70

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

Sample	Predicted(%)				Recoveries(%)
Sample	Machine oil	Lubricating oil	Diesel oil	TPHs	Machine oil	Lubricating oil	Diesel oil	TPHs
49#	0.39	0.41	0.41	1.151	92.86	93.18	95.35	93.80
50#	0.69	0.46	0.59	1.61	97.18	90.20	95.16	94.57
51#	0.59	0.47	0.29	1.21	93.65	97.92	90.63	94.41
52#	0.21	1.24	0.39	1.67	95.45	94.66	92.86	94.36
53#	0.39	1.75	0.59	2.41	90.70	95.63	95.16	94.79
54#	0.49	0.98	7.89	8.24	96.08	96.08	98.50	98.11
55#	0.99	7.49	0.51	7.94	95.19	93.62	98.08	94.04
56#	1.01	0.98	0.99	2.47	99.02	97.03	98.02	98.03
57#	1.44	0.75	1.13	3.29	95.36	96.15	95.76	95.68
58#	2.38	5.14	1.99	8.36	98.35	99.04	94.31	97.84
59#	4.01	1.24	1.5	6.02	95.25	94.66	99.34	96.02
60#	4.13	2.89	0.59	6.92	93.65	96.98	92.19	94.77
61#	2.09	0.79	5.11	7.09	98.12	94.05	98.27	97.80
62#	1.96	0.78	6.39	7.94	96.08	95.12	99.69	98.49
63#	2.69	0.5	5.69	7.99	95.73	92.59	94.05	94.47
64#	7.69	0.49	0.99	7.91	96.00	96.08	96.12	96.02
Average recoveries (%)					95.54±2.00	95.19±2.05	95.84±2.50	95.82±1.58

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