Advancing “Autonomous” sensing and prediction of the subsurface environment: a review and exploration of the challenges for soil and groundwater contamination

Abstract

Can we hope for autonomous (self-contained in situ) sensing of subsurface soil and groundwater pollutants to satisfy relevant regulatory criteria? Global advances in sensors, communications, digital technologies, and computational capacity offer this potential. Here we review past efforts to advance subsurface investigation techniques and technologies, and computational efforts to create a digital twin (representation) of subsurface processes. In the context of the potential to link measurement and sensing to a digital twin computation platform, we outline five criteria that might make it possible. Significant advances in sensors based on passive measurement devices are proposed. As an example of what might be achievable, using the five criteria, we describe the deployment of online real-time sensors and simulations for a case study of a petroleum site where natural source zone depletion (NSZD) is underway as a potential biodegradation management option, and where a high-quality conceptual site model is available. Multiple sensors targeting parameters (major gases and temperature influenced by soil moisture) relevant to the subsurface NSZD biodegradation processes are shown to offer the potential to map subsurface processes spatially and temporally and provide continuous estimates of degradation rates for management decisions, constrained by a computational platform of the key processes. Current limitations and gaps in technologies and knowledge are highlighted specific to the case study. More generally, additional key advances required to achieve autonomous sensing of subsurface soil and groundwater pollutants are outlined.