In the rapidly evolving domain of vapor intrusion (VI) assessments, traditional methodologies encompass detailed groundwater and soil vapor sampling coupled with comprehensive laboratory measurements. These models, blending empirical data, theoretical equations, and site-specific parameters, evaluate VI risks by considering a spectrum of influential factors, from volatile organic compounds (VOC) concentrations in groundwater to nuanced soil attributes. However, the challenge of variability, influenced by dynamic ambient conditions and intricate soil properties, remains. Our study presents an advanced on-site gas sensing station geared towards real-time VOC flux monitoring, enriched with an array of ambient sensors, and spearheaded by the reliable PID sensor for VOC detection. Integrating this dynamic system with machine learning, we developed predictive models, notably the random forest regression, which boasts an R-squared value exceeding 79 % and mean relative error near 0.25, affirming its capability to predict trichloroethylene (TCE) concentrations in soil vapor accurately. By synergizing real-time monitoring and predictive insights, our methodology refines VI risk assessments, equipping communities with proactive, informed decision-making tools and bolstering environmental safety. Implementing these predictive models can simplify monitoring for residents, reducing dependence on specialized systems. Once proven effective, there's potential to repurpose monitoring stations to other VI-prone regions, expanding their reach and benefit. The developed model can leverage weather forecasting data to predict and provide alerts for future VOC events.
Keywords: Machine learning; On-site gas sensing; Random Forest regression; VOC long-term monitoring; Vapor intrusion (VI); Volatile organic compounds (VOC).
Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.