Formatted Title
Can Autonomous, High Data Density Sensors Save Time and Costs in Adaptive Management of Hydrocarbon-Impacted Sites?
Background/Objectives
Environmental site managers are increasingly turning to high-density data streams and AI-assisted technology to overcome the limitations and data gaps associated with traditional data streams at contaminated sites. High-density data allow site managers to track seasonal changes in plume extent and remedial success in real time so that site condition changes are detected earlier and estimated accurately. This is a game changer for site owners whose goal is to improve their accuracy and efficiency in analyzing, managing, and prioritizing their portfolio towards the ultimate goal of closing sites. Here we discuss an innovative suite of autonomous IoT sensors that produce the continuous data streams required to achieve that goal. Specifically, Soil Sense was designed to quantify remediation by measuring natural source zone depletion (NSZD) rates, whereas Water Sense was designed to quantify risk by tracking petroleum hydrocarbon (PHC) plume stability and extent in groundwater and integrate into adaptive management strategies.
Approach/Activities
Belowground sensor networks were deployed at 18 sites across the Canadian Prairies. Data collected at 30-minute intervals included gas flux (CO2, O2, CH4), pressure, air and soil temperatures, relative humidity, and PHC vapor concentrations. At each site, the network provided high-resolution spatial and temporal quantification of soil plume dynamics and, depending on site goals, also provided NSZD metrics and remediation system effectiveness. The high-density data allowed real-time tracking and prediction of plume areal extent, volume, and mass; NSZD rates, remedial system success, and time to closure.
Results/Lessons Learned
Deployment sites ranged from decommissioned oil and gas sites to former gas bars to active sites such as refineries and storage terminals. The data were used to develop and evaluate remediation and risk management plans. For example, a Water Sense network quantified the effectiveness of a multiphase extraction system, using NSZD data to identify when to switch to soil vapor mode or turn off the system. This real-time optimization increased mass recovery during the operating season and dramatically decreased costs. To maximize adaptive management of site risk, Soil Sense and groundwater monitoring data were imported into modflow to compare the current strategy effectiveness relative to other mitigation approaches. Water Sense networks have successfully incorporated into risk management plans by monitoring for potential off-site migration as well as potential exposure to nearby receptors. The versatility of the Sense data stream allows environmental managers to make evidence-based decisions and adapt their management strategies to achieve site objectives more efficiently and effectively.