Contaminant Plume Classification System Based on Mass Discharge

Newell, Charles J.; Farhat, Shahla K.; Adamson, David T.; Looney, Brian B.

doi:10.1111/j.1745-6584.2010.00793.x

Cited by 38 publications

(34 citation statements)

References 17 publications

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“…As shown in Exhibit , the yardsticks presented in Exhibit are consistent with the plume classification approach provided by Newell et al. (), but are derived from the minimum reasonable representative yield for a typical private residential well. The suggested response for each situation in Exhibit reflects realistic expectations of what remediation (including natural attenuation) can accomplish (one OoM reduction in mass flux) in reasonably predictable timeframes (McGuire, personal communication, 2014).…”

Section: Residential Water Supply From Groundwatersupporting

confidence: 77%

Yardsticks to Integrate Risk Assessment, Risk Management, and Groundwater Remediation

Hadley

Arulanantham

Gandhi

2015

Remediation Journal

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Remediation of contaminated sites has focused largely on restoration of groundwater aquifers. Often the stated remedial goal is to achieve conditions allowing unrestricted use and unrestricted exposure. Such total groundwater cleanup has occurred at some sites, but is the exception rather than the rule. At the same time, significant effort occurs to perform risk assessments for potential exposure to contaminants in groundwater at sites, both before and after remediation. The logical synergy between risk assessment and remediation is for risk management to seek opportunities for optimal use of groundwater based upon realistic expectations of cleanup technologies and the relevant acceptable residual (postremediation) levels of contaminants. This article explores an approach to improve this synergistic relationship between risk assessment, risk management, and remediation for groundwater cleanups. ©2015 Wiley Periodicals, Inc.

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Section: Residential Water Supply From Groundwatersupporting

confidence: 77%

Yardsticks to Integrate Risk Assessment, Risk Management, and Groundwater Remediation

Hadley

Arulanantham

Gandhi

2015

Remediation Journal

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“…Collecting high‐resolution characterization data to identify contaminant and stratigraphic heterogeneity can provide a more realistic understanding of how contaminated sites will respond to remediation efforts (Parker et al ; Payne et al ). This information is critical in developing a more accurate CSM to inform remedial decisions (Newell et al ). This is particularly important at sites where a significant portion of contaminant mass is present in lower permeability zones because treatment of these zones can be difficult using conventional technologies (Sale and Newell ).…”

Section: Introductionmentioning

confidence: 99%

Membrane Interface Probe Protocol for Contaminants in Low‐Permeability Zones

et al. 2013

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Accurate characterization of contaminant mass in zones of low hydraulic conductivity (low k) is essential for site management because this difficult-to-treat mass can be a long-term secondary source. This study developed a protocol for the membrane interface probe (MIP) as a low-cost, rapid data-acquisition tool for qualitatively evaluating the location and relative distribution of mass in low-k zones. MIP operating parameters were varied systematically at high and low concentration locations at a contaminated site to evaluate the impact of the parameters on data quality relative to a detailed adjacent profile of soil concentrations. Evaluation of the relative location of maximum concentrations and the shape of the MIP vs. soil profiles led to a standard operating procedure (SOP) for the MIP to delineate contamination in low-k zones. This includes recommendations for: (1) preferred detector (ECD for low concentration zones, PID or ECD for higher concentration zones); (2) combining downlogged and uplogged data to reduce carryover; and (3) higher carrier gas flow rate in high concentration zones. Linear regression indicated scatter in all MIP-to-soil comparisons, including R(2) values using the SOP of 0.32 in the low concentration boring and 0.49 in the high concentration boring. In contrast, a control dataset with soil-to-soil correlations from borings 1-m apart exhibited an R(2) of ≥ 0.88, highlighting the uncertainty in predicting soil concentrations using MIP data. This study demonstrates that the MIP provides lower-precision contaminant distribution and heterogeneity data compared to more intensive high-resolution characterization methods. This is consistent with its use as a complementary screening tool.

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“…This fact was noted by Li et al (2007), who pointed out that ṁ uncertainty may increase after remediation when trying to measure a smaller ṁ with the same monitoring network. Likewise, the results here caution against defining an acceptable S D without regard to the magnitude of ṁ, especially given the potential range over which ṁ may vary (e.g., Newell et al, 2011). However, this is true only if J max is constant.…”

Section: Two-dimensional Control Plane Analysismentioning

confidence: 70%

“…These measurements have been used for a number of site management purposes, including assessments of degradation rates (e.g., Borden et al, 1997;Kao and Prosser, 2001;Wang, 2000, 2001;King et al, 1999;Semprini et al, 1995;Suarez and Rifai, 2002), characterization of source zones and associated plumes (e.g., Basu et al, 2006Basu et al, , 2009Einarson and Mackay, 2001;Fraser et al, 2008;Guilbeault et al, 2005;King et al, 1999;Newell et al, 2011), characterization of back diffusion from aquitards (Chapman and Parker, 2005), and assessments of benefits from partial mass removal from DNAPL source zones (Brooks et al, 2008;Cai et al, 2012;DiFilippo and Brusseau, 2008).…”

Section: Introductionmentioning

confidence: 99%

Screening-level estimates of mass discharge uncertainty from point measurement methods

Brooks

Young

Wood

et al. 2015

Journal of Contaminant Hydrology

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The uncertainty of mass discharge measurements associated with point-scale measurement techniques was investigated by deriving analytical solutions for the mass discharge coefficient of variation for two simplified, conceptual models. In the first case, a depth-averaged domain was assumed, consisting of one-dimensional groundwater flow perpendicular to a one-dimensional control plane of uniformly spaced sampling points. The contaminant flux along the control plane was assumed to be normally distributed. The second case consisted of one-dimensional groundwater flow perpendicular to a two-dimensional control plane of uniformly spaced sampling points. The contaminant flux in this case was assumed to be distributed according to a bivariate normal distribution. The center point for the flux distributions in both cases was allowed to vary in the domain of the control plane as a uniform random variable. Simplified equations for the uncertainty were investigated to facilitate screening-level evaluations of uncertainty as a function of sampling network design. Results were used to express uncertainty as a function of the length of the control plane and number of wells, or alternatively as a function of the sample spacing. Uncertainty was also expressed as a function of a new dimensionless parameter, Ω, defined as the ratio of the maximum local flux to the product of mass discharge and sample density. Expressing uncertainty as a function of Ω provided a convenient means to demonstrate the relationship between uncertainty, the magnitude of a local hot spot, magnitude of mass discharge, distribution of the contaminant across the control plane, and the sampling density.

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Contaminant Plume Classification System Based on Mass Discharge

Cited by 38 publications

References 17 publications

Yardsticks to Integrate Risk Assessment, Risk Management, and Groundwater Remediation

Yardsticks to Integrate Risk Assessment, Risk Management, and Groundwater Remediation

Membrane Interface Probe Protocol for Contaminants in Low‐Permeability Zones

Screening-level estimates of mass discharge uncertainty from point measurement methods

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