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

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

doi:10.26599/JGSE.2026.9280066

Volume 14 Issue 3

Sep. 2026

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Article Navigation > Journal of Groundwater Science and Engineering > 2026 > 14(3): 342-355

Gu YJ, Luo QK, Zhang M, et al. 2026. SDPHC: A software tool for detecting petroleum hydrocarbons contamination in active industrial sites using Non-Invasive Survey (NIS) dataset. Journal of Groundwater Science and Engineering, 14(3): 342-355 doi: 10.26599/JGSE.2026.9280066

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

doi: 10.26599/JGSE.2026.9280066

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.
Received Date: 2025-03-19
Accepted Date: 2025-10-26

Available Online: 2025-12-05

Publish Date: 2026-09-15

Abstract

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

https://github.com/ArtCoff/SDPHC/tree/main/APPENDIX

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References(50)

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