Formatted Title
Digital Integration of In Situ Sensors to Optimize Remedial Actions at a Hydrocarbon-Contaminated Site in Southeast Asia
Background/Objectives
This site in southeast Asia is characterized by the presence of hydrocarbon contamination in groundwater, resulting from a gasoline leak from a pipe associated with an underground tank. The site is situated in the middle of a dense residential area. The contaminated layer is an unconfined aquifer subject to fluctuations in water level due to excessive extraction of groundwater from nearby residential wells. The site's hydrogeology presents challenges due to a heterogeneous and generally low-permeability formation.
The lack of detailed information regarding vertical heterogeneities as well as the temporal conditions during active in situ remediation can limit the performance of remedial systems. Periodic sampling and monitoring of monitoring wells are the traditional methods of obtaining data from which a remediation system can be optimized. However, this traditional method of data collection does not provide detailed spatial or temporal data that can be used to effectively optimize a remediation system. Integration of digital, real-time in situ sensors into a remedial design offers the potential to provide such data to improve performance, reduce cost, and reduce the environmental footprint of a remediation system.
An air sparging (AS)/soil vapor extraction (SVE) system was installed at the site as the key component of the remediation system to address the hydrocarbon contamination. In situ sensors were installed to support the optimization of the AS/SVE system. The sensors included strings of oxidation reduction potential (ORP) and temperature sensors, as well as a pressure transducer. The sensors were supplied by S3NSE Tech.
Approach/Activities
The AS/SVE system included five AS wells that injected air into the saturated zone to enhance the removal of volatile contaminants as well as to provide oxygen to support biodegradation. Two vertical and two horizontal SVE wells were used to capture and treat vapors released during the sparging. The AS/SVE was operated in a pulsed mode, with continuous operations up to 10 hours per day. This reduced the power cost and reduced the environmental impacts from the power production. Three vertical sensor strings were installed in the treatment area. Each string had 12 ORP and temperature sensors spaced approximately 1 meter apart. They were installed on the outside of a PVC pipe that was placed in a borehole. Data were collected by the sensors and transmitted to the cloud every hour for storage, analytics, and visualization.
Results/Lessons Learned
The effectiveness of the remediation measures was assessed through a comprehensive monitoring program. The data collected from the S3NSE sensor strings were used to remotely track the performance of the AS/SVE system and adjust operation as necessary. For example, the changes in ORP due to the pulsed operation could be seen in the data from the sensors and allowed optimization of the pulsing. The ORP increased rapidly after the initiation of the AS system and then declined somewhat slowly when the AS system was turned off. This confirmed that the pulsed operation was adequate to maintain elevated ORP to support aerobic biodegradation. The rate of the ORP decline decreased as the remediation progressed due to the removal and degradation of the hydrocarbon. This information allowed the duration of the sparging to be reduced from 10 hours per day to 6 hours per day, further enhancing the cost-effectiveness of the system. Periodic groundwater sampling also confirmed that the hydrocarbon contaminants were being removed or degraded by the AS/SVE system. After approximately 6 months of operation, concentrations of benzene and MTBE (the two target contaminants) were below remediation criteria in all monitoring wells.
This project showcases the potential benefits of using real-time monitoring technology such as the S3NSE Tech sensors in groundwater remediation efforts. It allows for a more comprehensive and real-time understanding of subsurface conditions, enabling more informed decision-making and adaptive management of remedial actions.