Yong-jian Gu; Qian-kun Luo; Min Zhang; Zhuo Ning; Lin Sun; Lei Ma; Hai-chun Ma

doi:10.26599/JGSE.2026.9280066

doi: 10.26599/JGSE.2026.9280066

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出版历程
- 收稿日期: 2025-03-19
- 录用日期: 2025-10-26
- 网络出版日期: 2025-12-05
- 刊出日期: 2026-09-15

SDPHC: A software tool for detecting petroleum hydrocarbons contamination in active industrial sites using Non-Invasive Survey (NIS) dataset

Yong-jian Gu¹,
Qian-kun Luo^{1
, ,},
Min Zhang^{2, 3
, ,},
Zhuo Ning^{2, 3},
Lin Sun^{2, 3},
Lei Ma¹,
Hai-chun Ma¹

1.
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
2.
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
3.
Key Laboratory of Groundwater Remediation of Hebei Province & China Geological Survey, Shijiazhuang 050061, China

More Information

Corresponding author: qkluo@hfut.edu.cn; minzhang205@live.cn

Appendix Content related to this article can be found at https://github.com/ArtCoff/SDPHC/tree/main/APPENDIX.

摘要

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Abstract: Conventional drilling-based methods for investigating Petroleum Hydrocarbons (PHCs) contamination in industrial parks are time-consuming, labor-intensive, and disruptive, making them often unsuitable for active industrial sites. Non-Invasive Survey (NIS) technology has emerged as a promising alternative owing to its cost-effectiveness and minimal environmental disturbance. To enhance the efficiency of NIS-based contamination surveys in active industrial sites and facilitate widespread adoption, this study developed a Software Tool for Detecting Petroleum Hydrocarbons Contamination (SDPHC). SDPHC integrates Python's scientific computing ecosystem with the PySide6 desktop Graphical User Interface (GUI) framework, achieving a scalable architecture for rapid development. The software provides three complementary analytical methods: Empirical Threshold Analysis (ETA), Background Level Analysis (BLA), and Principal Component Analysis (PCA). Each method is tailored to distinct data scenarios: ETA leverages field-validated thresholds for sites with comprehensive NIS datasets; BLA quantifies site-specific natural baselines for individual indicators to distinguish anthropogenic contamination; and PCA identifies multivariate spatial patterns from correlated soil gas variables (e.g., CO₂, O₂, CH₄), enabling robust contamination zoning even when radon or functional gene data are absent. This modular design allows users to select or combine methods based on data availability and site characteristics. Additionally, SDPHC automates report generation to enhance survey efficiency. Two case studies conducted at active petrochemical parks demonstrate the software's applicability and reliability. SDPHC is anticipated to function as a reliable and powerful tool for conducting NIS-based contamination assessments in industrial parks.
- Contamination survey /
- Natural Source Zone Depletion (NSZD) /
- Volatile Organic Compounds (VOCs) /
- Soil gases /
- Radon /
- Functional genes
Appendix Content related to this article can be found at https://github.com/ArtCoff/SDPHC/tree/main/APPENDIX.
注释:
1) Appendix:

HTML全文

Figure 1. Core advantages of PySide6 framework

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Figure 2. Diagram of software architecture

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Figure 3. Software Graphical User Interface (GUI)

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Figure 4. Operational flow diagram of SDPHC: A four-stage pipeline for NIS data analysis

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Figure 5. Overview of the NIS dataset and ETA results in the contaminated petrochemical park (a) Distribution of NIS survey points and data types; (b) Delineation of contaminated source zones; (c) Delineation of contaminated areas; Contamination assessment results are derived from SDPHC analysis.

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Figure 6. BLA result of NIS dataset (The curves in the figures are plotted based on empirical cumulative distribution function; the black × symbols represent percentiles, with their specific values annotated in the legend)

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Figure 7. Overview of the NIS dataset and PCA result in the contaminated chemical-pharmaceutical manufacturing park (a) Distribution of NIS survey points and data types; (b) Contamination assessment results based on PC1; (c)–(d) PCA analysis outcomes of soil-gas data. Panels (b), (c), and (d) are direct outputs from SDPHC.

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Table 1. A list of Python packages during the SDPHC implementation

Category	Library name	Description of the functions	Reference
Data processing	Geopandas	Geospatial data processing and analysis	(Jordahl et al., 2020)
	Shapely	Geometric object manipulation	(Shapely, 2025)
	Scikit-learn	Data standardization, principal component analysis	(Pedregosa et al., 2011)
	Scipy	Data analysis and interpolation	(Virtanen et al., 2020)
Visualization	Matplotlib	Graphics drawing and saving, provision of GUI widgets	(Hunter, 2007)
Visualization	Seaborn	Graphic drawing and beautification	(Waskom, 2021)
Report	Python-docx	Word document generation	(docx, 2025)
Report	Pillow	Image format conversion/cropping/filter operations	(Clark et al., 2015)

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Table 2. NIS indicators supported by SDPHC and their relationship to PHCs contamination

Indicator name	Common unit	Scientific significance in PHCs assessment
VOCs	ppb	Indicators of petroleum leakage; elevated levels suggest direct contamination sources
CO₂	ppm	Byproduct of aerobic microbial degradation; high concentrations reflect active biodegradation
O₂	%	Depletion indicates anaerobic conditions linked to hydrocarbon degradation processes
CH₄	%	Produced under anaerobic conditions; signals methanogenic activity in contaminated zones
H₂S	mg/L	Anaerobic metabolic byproduct; associated with sulfate reduction in LNAPL-affected areas
H₂	mg/L	Intermediate in hydrogen fermentation; reflects anaerobic metabolic activity
Radon	Bq/m³	Adsorbed by LNAPLs; reduced concentrations highlight contaminated subsurface regions
Functional genes	copies/g soil	Microbial genetic markers reflecting hydrocarbon degradation pathways and contamination dynamics

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参考文献(50)

Anshuman A, Eldho TI. 2022. Entity aware sequence to sequence learning using LSTMs for estimation of groundwater contamination release history and transport parameters. Journal of Hydrology, 608: 127662. DOI: 10.1016/j.jhydrol.2022.127662.

Arato A, Wehrer M, Biró B, et al. 2014. Integration of geophysical, geochemical and microbiological data for a comprehensive small-scale characterization of an aged LNAPL-contaminated site. Environmental Science and Pollution Research, 21(15): 8948−8963. DOI: 10.1007/s11356-013-2171-2.

Azzellino A, Colombo L, Lombi S, et al. 2019. Groundwater diffuse pollution in functional urban areas: The need to define anthropogenic diffuse pollution background levels. Science of the Total Environment, 656: 1207−1222. DOI: 10.1016/j.scitotenv.2018.11.416.

Baessa MPM, Chang HK, Teramoto EH, et al. 2024. Integrating high-resolution investigation and soil TPH analysis for accurate estimation of LNAPL volume in the subsurface. Environmental Earth Sciences, 83(13): 415. DOI: 10.1007/s12665-024-11621-2.

Bertolla L, Porsani JL, Soldovieri F, et al. 2014. GPR-4D monitoring a controlled LNAPL spill in a masonry tank at USP, Brazil. Journal of Applied Geophysics, 103: 237−244. DOI: 10.1016/j.jappgeo.2014.02.006.

Bonnière A, Khaska S, Salle CLGL, et al. 2024. Long-term impact of wastewater effluent discharge on groundwater: Identification of contaminant plume by geochemical, isotopic, and organic tracers' approach. Water Research, 257: 121637. DOI: 10.1016/j.watres.2024.121637.

Brendel CE, Dymond RL, Aguilar MF. 2020. Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding. Environmental Modelling the Software, 134: 104864. DOI: 10.1016/j.envsoft.2020.104864.

Cassidy NJ. 2008. GPR attenuation and scattering in a mature hydrocarbon spill: A modeling study. Vadose Zone Journal, 7(1): 140−159. DOI: 10.2136/vzj2006.0142.

Cecconi A, Verginelli I, Baciocchi R. 2022. Modeling of soil gas radon as an in situ partitioning tracer for quantifying LNAPL contamination. Science of the Total Environment, 806: 150593. DOI: 10.1016/j.scitotenv.2021.150593.

Chen CJ, Judge J, Hulse D. 2022. PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis. Environmental Modelling and Software, 151: 105362. DOI: 10.1016/j.envsoft.2022.105362.

Chen GD, Zhang K, Wang S, et al. 2024. PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale. Environmental Modelling the Software, 172: 105882. DOI: 10.1016/j.envsoft.2023.105882.

Crudu V. 2024. Evaluating the efficiency of python versus C++ in handling scientific computing projects. Available on https://moldstud.com/articles/p-evaluating-the-efficiency-of-python-versus-c-in-handling-scientific-computing-projects.

Davis BM, Istok JD, Semprini L. 2005. Numerical simulations of radon as an in situ partitioning tracer for quantifying NAPL contamination using push–pull tests. Journal of Contaminant Hydrology, 78(1-2): 87−103. DOI: 10.1016/j.jconhyd.2005.03.003.

De Simone G, Lucchetti C, Pompilj F, et al. 2017. Soil radon survey to assess NAPL contamination from an ancient spill. Do kerosene vapors affect radon partition? Journal of Environmental Radioactivity, 171: 138−147. DOI: 10.1016/j.jenvrad.2017.02.014.

EI. 2024. Statistical Review of World Energy. Energy Institute.

Fernández-Baca CP, Truhlar AM, Omar AH, et al. 2018. Methane and nitrous oxide cycling microbial communities in soils above septic leach fields: Abundances with depth and correlations with net surface emissions. Science of the Total Environment, 640: 429−441. DOI: 10.1016/j.scitotenv.2018.05.303.

García-Rincón J, Gatsios E, Lenhard RJ, et al. 2024. Advances in the characterisation and remediation of sites contaminated with petroleum hydrocarbons. Cham: Springer Nature.

Garg S, Newell CJ, Kulkarni PR, et al. 2017. Overview of natural source zone depletion: Processes, controlling factors, and composition change. Groundwater Monitoring and Remediation, 37(3): 62−81. DOI: 10.1111/gwmr.12219.

Han XM, Liu YR, Zheng YM, et al. 2014. Response of bacterial pdo1, nah, and C₁₂O genes to aged soil PAH pollution in a coke factory area. Environmental Science and Pollution Research, 21(16): 9754−9763. DOI: 10.1007/s11356-014-2928-2.

Hunter JD. 2007. Matplotlib: A 2D graphics environment. Computing in Science and Engineering, 9(3): 90−95. DOI: 10.1109/MCSE.2007.55.

IEEE spectrum Top programming languages. 2024. Available on https://spectrum.ieee.org/top-programming-languages-2024.

Jiang SM, Fan JH, Xia XM, et al. 2018. An effective Kalman filter-based method for groundwater pollution source identification and plume morphology characterization. Water, 10(8): 1063. DOI: 10.3390/w10081063.

Jordahl K, Bossche JV, Fleischmann M, et al. 2020. Geopandas/geopandas: v0.8.1. DOI: 10.5281/zenodo.3946761.

Kadiyala A, Kumar A. 2017. Applications of Python to evaluate environmental data science problems. Environmental Progress and Sustainable Energy, 36(6): 1580−1586. DOI: 10.1002/ep.12786.

Kulkarni PR, Walker KL, Newell CJ, et al. 2022. Natural source zone depletion (NSZD) insights from over 15 years of research and measurements: A multi-site study. Water Research, 225: 119170. DOI: 10.1016/j.watres.2022.119170.

López-Fernández A, Gómez-Vela FA, Gonzalez-Dominguez J, et al. 2024. BioScience: A new Python science library for high-performance computing bioinformatics analytics. SoftwareX, 26: 101666. DOI: 10.1016/j.softx.2024.101666.

Lucas L, Jauzein M. 2008. Use of principal component analysis to profile temporal and spatial variations of chlorinated solvent concentration in groundwater. Environmental Pollution, 151(1): 205−212. DOI: 10.1016/j.envpol.2007.01.054.

Molinari A, Guadagnini L, Marcaccio M, et al. 2019. Geostatistical multimodel approach for the assessment of the spatial distribution of natural background concentrations in large-scale groundwater bodies. Water Research, 149: 522−532. DOI: 10.1016/j.watres.2018.09.049.

Pan ZD, Lu WX, Wang H, et al. 2023. Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto XGBoost surrogate. Environmental Modelling and Software, 159: 105588. DOI: 10.1016/j.envsoft.2022.105588.

Pedregosa F, Varoquaux G, Gramfort A, et al. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12: 2825−2830

Preziosi E, Parrone D, Del Bon A, et al. 2014. Natural background level assessment in groundwaters: Probability plot versus pre-selection method. Journal of Geochemical Exploration, 143: 43−53. DOI: 10.1016/j.gexplo.2014.03.015.

Python-docx. 2025. Python-docx documentation. Available on https://python-docx.readthedocs.io/en/latest/

Schubert M. 2015. Using radon as environmental tracer for the assessment of subsurface Non-Aqueous Phase Liquid (NAPL) contamination–A review. The European Physical Journal Special Topics, 224(4): 717−730. DOI: 10.1140/epjst/e2015-02402-3.

Schubert M, Paschke A, Lau S, et al. 2007. Radon as a naturally occurring tracer for the assessment of residual NAPL contamination of aquifers. Environmental Pollution, 145(3): 920−927. DOI: 10.1016/j.envpol.2006.04.029.

Shahi A, Aydin S, Ince B, et al. 2016. Evaluation of microbial population and functional genes during the bioremediation of petroleum-contaminated soil as an effective monitoring approach. Ecotoxicology and Environmental Safety, 125: 153−160. DOI: 10.1016/j.ecoenv.2015.11.029.

Shao S, Gao C, Guo XJ, et al. 2019. Mapping the contaminant plume of an abandoned hydrocarbon disposal site with geophysical and geochemical methods, Jiangsu, China. Environmental Science and Pollution Research, 26(24): 24645−24657. DOI: 10.1007/s11356-019-05780-0.

Shapely. 2025. The Shapely User Manual. Available on https://shapely.readthedocs.io/en/stable/manual.html.

Smith JWN, Davis GB, DeVaull GE, et al. 2022. Natural source zone depletion (NSZD): From process understanding to effective implementation at LNAPL-impacted sites. Quarterly Journal of Engineering Geology and Hydrogeology, 55(4): 2021−2166. DOI: 10.1144/qjegh2021-166.

Sookhak Lari K, Davis GB, Rayner JL, et al. 2019. Natural source zone depletion of LNAPL: A critical review supporting modelling approaches. Water Research, 157: 630−646. DOI: 10.1016/j.watres.2019.04.001a.

Sookhak Lari K, Rayner JL, Davis GB. 2019. Toward optimizing LNAPL remediation. Water Resources Research, 55(2): 923−936. DOI: 10.1029/2018WR023380.

Táncsics A, Szoboszlay S, Szabó I, et al. 2012. Quantification of subfamily I. 2. C catechol 2, 3-dioxygenase mRNA transcripts in groundwater samples of an oxygen-limited BTEX-contaminated site. Environmental Science and Technology, 46(1): 232−240. DOI: 10.1021/es201842h.

Tomlinson DW, Rivett MO, Wealthall GP, et al. 2017. Understanding complex LNAPL sites: Illustrated handbook of LNAPL transport and fate in the subsurface. Journal of Environmental Management, 204: 748−756. DOI: 10.1016/j.jenvman.2017.08.015.

Tsai YJ, Chou YC, Wu YS, et al. 2020. Noninvasive survey technology for LNAPL-contaminated site investigation. Journal of Hydrology, 587: 125002. DOI: 10.1016/j.jhydrol.2020.125002.

Verginelli I, Lahvis MA, Jourabchi P, et al. 2024. Soil gas gradient method for estimating natural source zone depletion rates of LNAPL and specific chemicals of concern. Water Research, 267: 122559. DOI: 10.1016/j.watres.2024.122559.

Virtanen P, Gommers R, Oliphant TE, et al. 2020. SciPy 1. 0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3): 261−272. DOI: 10.1038/s41592-019-0686-2.

Wang CQ, Dou Z, Zhu Y, et al. 2024. Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification. Journal of Hydrology, 642: 131759. DOI: 10.1016/j.jhydrol.2024.131759.

Waskom M. 2021. Seaborn: Statistical data visualization. Journal of Open Source Software, 6(60): 3021. DOI: 10.21105/joss.03021.

Xia T, Dong YH, Mao DQ, et al. 2021. Delineation of LNAPL contaminant plumes at a former perfumery plant using electrical resistivity tomography. Hydrogeology Journal, 29(3): 1189−1201. DOI: 10.1007/s10040-021-02311-5.

Zhang JJ, Li WX, Zeng LZ, et al. 2016. An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems. Water Resources Research, 52(8): 5971−5984. DOI: 10.1002/2016WR018598.

Zhu LZ, Lu WX, Luo CM, et al. 2024. An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source. Journal of Contaminant Hydrology, 267: 104437. DOI: 10.1016/j.jconhyd.2024.104437.

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