Real‐Time Remediation Performance Monitoring with <scp>ORP</scp> Sensors

Blotevogel, Jens; Askarani, Kayvan Karimi; Hanson, A.S.; Gallo, Sam; Carling, Brian; Mowder, Carol; Spain, Jim C.; Hartten, Andrew; Sale, Tom

doi:10.1111/gwmr.12479

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…With the rapid emergence of sensor‐based collection of better and cheaper data, this revolution is imminent. Applicability of sensors to high‐resolution monitoring of contaminated sites has been recently demonstrated (Sale et al 2019; Sale et al 2020; Blotevogel et al 2021). This study presents results of the performance of only select forecasting algorithms—more research in this area will be necessary to take advantage of the full potential of advanced statistical and machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

McConnell

Askarani

Cognac

et al. 2022

Groundwater Monitoring Rem

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This column reviews the general features of PHT3D Version 2, a reactive multicomponent transport model that couples the geochemical modeling software PHREEQC-2 (Parkhurst and Appelo 1999) with three-dimensional groundwater flow and transport simulators MODFLOW-2000 and MT3DMS (Zheng and Wang 1999). The original version of PHT3D was developed by Henning Prommer and Version 2 by Henning Prommer and Vincent Post (Prommer and Post 2010). More detailed information about PHT3D is available at the website http://www.pht3d.org.The review was conducted separately by two reviewers. This column is presented in two parts.

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Section: Discussionmentioning

confidence: 99%

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

McConnell

Askarani

Cognac

et al. 2022

Groundwater Monitoring Rem

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“…These have been described earlier. Whilst surrogates such as reduction potential changes have been deployed to investigated groundwater remedial processes (Blotevogel et al 2021 ), missing or less well-developed technologies are contaminant specific sensors that are readily deployable into the subsurface, and robust enough to have a prolonged sensor life. Ideally, probes would consist of such sensors that auto-analyze chemical trends and telemetry data directly back from the subsurface to an enabled data base attached to an interpretive computational platform.…”

Section: Possible Way Forwardmentioning

confidence: 99%

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

Davis

Rayner

Donn

2023

Environ Sci Pollut Res

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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.

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Natural Source Zone Depletion of Petroleum Hydrocarbon NAPL

Karimi Askarani,

Sale,

Palaia

2023

Environmental Contamination Remediation and Management

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In the last decade, it has become widely recognized that petroleum found in soil and groundwater in the form of non-aqueous phase liquid (NAPL) is depleted by naturally occurring microbial communities. Losses of petroleum NAPL via natural processes are referred to as natural source zone depletion (NSZD). The natural loss rates of petroleum NAPL are large enough that they can often be the primary component of a site management strategy. Losses of NAPL through NSZD processes provide by-products such as CO2, CH4, and heat. As such, based on consumption of O2, production of CO2 and CH4, generation of heat, or changes in petroleum NAPL chemical composition over time, a variety of methods have been developed to measure NSZD rates. Each method has advantages and limitations. Therefore, care is needed to select the method that best fits site conditions and site- and project-specific data quality objectives.

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Real‐Time Remediation Performance Monitoring with ORP Sensors

Cited by 5 publications

References 2 publications

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

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

Natural Source Zone Depletion of Petroleum Hydrocarbon NAPL

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