SDPHC: A software tool for detecting petroleum hydrocarbons contamination in active industrial sites using non-invasive survey (NIS) dataset

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.