ChemInform Abstract: Remediation of Organic Chemicals in the Vadose Zone

Murdoch, Lawrence C.; Girke, J. S.; Rossabi, J.; Reed, J.; Conley, D.; Phelan, James M.; Falta, Ronald W.; Heath, W.O.; Hazen, Terry C.; Siegrist, R.L.

doi:10.1002/chin.200104275

Cited by 5 publications

(6 citation statements)

References 74 publications

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“…At contaminated sites, soil vapor may contain volatile organic compounds (VOCs) that can be extracted from the subsurface environment by the BPW. It is technologically simple and potentially cost-saving, and can effectively reduce the mass of VOCs in the field [13,18,22,32]. It is especially applicable at sites where gas extraction efficiency is mass-transfer controlled or where rapid removal of VOCs is not required or sites that are remote [9,18,22,24,27].…”

Section: Passive Soil Vapor Extractionmentioning

confidence: 98%

“…Since 1990, passive SVE has been used in hundreds of sites in the United States to remediate VOCs either as a stand-alone measure or in conjunction with active SVE [18,32]. However, few careful studies of its fundamentals, such as the gas flow rate and mass removal effectiveness, have been conducted [18], which has hampered the wide use of passive SVE in field applications.…”

Section: Passive Soil Vapor Extractionmentioning

confidence: 99%

“…It is especially applicable at sites where gas extraction efficiency is mass-transfer controlled or where rapid removal of VOCs is not required or sites that are remote [9,18,22,24,27]. Passive SVE can either work as a stand-alone measure or in conjunction with active SVE [18]. It has been field tested as an effective soil remediation technology at several sites, including the US Department of Energy (DOE) Savannah River site in Aiken, South Carolina, and the Hanford site in Richland, Washington [13,22,24,28].…”

Section: Passive Soil Vapor Extractionmentioning

confidence: 99%

“…However, few careful studies of its fundamentals, such as the gas flow rate and mass removal effectiveness, have been conducted [18], which has hampered the wide use of passive SVE in field applications. Present available references include the studies conducted by Ellerd et al [9], Rossabi and Falta [27], Neeper [19] and Rossabi [25].…”

Section: Passive Soil Vapor Extractionmentioning

confidence: 99%

See 3 more Smart Citations

A new solution and data analysis for gas flow to a barometric pumping well

You

Zhan

2010

Advances in Water Resources

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Section: Passive Soil Vapor Extractionmentioning

confidence: 98%

Section: Passive Soil Vapor Extractionmentioning

confidence: 99%

Section: Passive Soil Vapor Extractionmentioning

confidence: 99%

Section: Passive Soil Vapor Extractionmentioning

confidence: 99%

See 2 more Smart Citations

A new solution and data analysis for gas flow to a barometric pumping well

You

Zhan

2010

Advances in Water Resources

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“…Soil moisture information is also a key component in predicting natural disasters, such as flooding and slope failure (Tohari, Nishigaki, & Komatsu, 2007). Soil moisture is also a key parameter in determining unsaturated hydraulic conductivity, which in turn affects solute mobility and contaminant fate and transport within the vadose zone (Murdoch, 2000).…”

Section: Introductionmentioning

confidence: 99%

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

Hou¹,

Terry²,

Hubbard³

2013

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SummaryIn this study, we evaluate the possibility of monitoring soil moisture variation using tomographic ground penetrating radar travel time data through Bayesian inversion, which is integrated with entropy memory function and pilot point concepts, as well as efficient sampling approaches.It is critical to accurately estimate soil moisture content and variations in vadose zone studies.Many studies have illustrated the promise and value of GPR tomographic data for estimating soil moisture and associated changes, however, challenges still exist in the inversion of GPR tomographic data in a manner that quantifies input and predictive uncertainty, incorporates multiple data types, handles non-uniqueness and nonlinearity, and honors time-lapse tomograms collected in a series. To address these challenges, we develop a minimum relative entropy (MRE)-Bayesian based inverse modeling framework that non-subjectively defines prior probabilities, incorporates information from multiple sources, and quantifies uncertainty. The framework enables us to estimate dielectric permittivity at pilot point locations distributed within the tomogram, as well as the spatial correlation range. In the inversion framework, MRE is first used to derive prior probability distribution functions (pdfs) of dielectric permittivity based on prior information obtained from a straight-ray GPR inversion. The probability distributions are then sampled using a Quasi-Monte Carlo (QMC) approach, and the 2 sample sets provide inputs to a sequential Gaussian simulation (SGSim) algorithm that constructs a highly resolved permittivity/velocity field for evaluation with a curved-ray GPR forward model. The likelihood functions are computed as a function of misfits, and posterior pdfs are constructed using a Gaussian kernel. Inversion of subsequent time-lapse datasets combines the Bayesian estimates from the previous inversion (as a memory function) with new data. The memory function and pilot point design takes advantage of the spatial-temporal correlation of the state variables. We first apply the inversion framework to a static synthetic example and then to a time-lapse GPR tomographic dataset collected during a dynamic experiment conducted at the Hanford Site in Richland, WA. We demonstrate that the MRE-Bayesian inversion enables us to merge various data types, quantify uncertainty, evaluate nonlinear models, and produce more detailed and better resolved estimates than straight-ray based inversion; therefore, it has the potential to improve estimates of inter-wellbore dielectric permittivity and soil moisture content and to monitor their temporal dynamics more accurately.

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Optimal control of soil venting: mathematical modeling and applications

Gierke

2001

Automatica

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ChemInform Abstract: Remediation of Organic Chemicals in the Vadose Zone

Cited by 5 publications

References 74 publications

A new solution and data analysis for gas flow to a barometric pumping well

A new solution and data analysis for gas flow to a barometric pumping well

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

Optimal control of soil venting: mathematical modeling and applications

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