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 Dynamic Work Strategies
 Dynamic Work Strategies and Adaptation
 Adaptive Closure Programs

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Adaptive Closure Programs

Benefits and examples of dynamic strategies applied to closure programs.

Closure data collection programs determine when site cleanup objectives have been achieved and the site turned over for its intended reuse. For soils and sediments, closure data collection usually means collecting sufficient samples from the areas of concern to establish compliance with cleanup criteria. For groundwater, closure data collection usually means establishing that groundwater standards have been met at pre-specified points of compliance (i.e., monitoring wells).

For sites where reuse is unrestricted, closure truly means an end to CERCLA or RCRA corrective action activities. For sites where reuse is not unrestricted (e.g., industrial, recreational, etc.), there may still be a periodic review requirement (e.g., the CERCLA five year review process). Because of the significance of their conclusions, closure data collection programs usually require systematic sampling and analytical data of the highest quality to support their findings. Even in this context, however, dynamic work strategies combined with real-time measurement technologies can play a role in making the closure process more error free and efficient. Examples of dynamic strategies applied to closure programs include:

  • Pre-Verification Strategies. By their nature and the quality of data required, closure sampling programs can be expensive. While the presumption is that closure sampling would not be undertaken unless remediation is complete, because of the uncertainty associated with the remediation process there is always the possibility that a remediated area would "fail" the closure process, require additional remediation, and then need to be subjected to closure sampling again. Real-time measurement techniques can provide a means for "pre-verifying" or "pre-certifying" that cleanup standards have been attained, and that closure sampling can proceed with a high confidence that an area will meet requirements. These types of dynamic programs usually involve relatively fewer samples combined with less expensive real-time analytics.


  • Closure Sampling Program Design Support. Closure sampling programs for soils or sediments are often based on systematic sampling with sample numbers calculated with the desired statistical test in mind. Statistical tests provide equations for calculating the number of samples required to support decisions at the level of confidence desired, but these equations typically need an estimate of the average residual concentration that one would expect, and of the variability in that residual concentration over the decision unit. Historical information is not useful for estimating these parameters in areas where remediation has taken place, since historical results are no longer representative of site conditions. Absent current information on contamination status, traditional closure programs usually use conservative assumptions for calculating sample numbers, and then uniformly apply the resulting sampling protocols across the site. Dynamic work strategies with real-time measurement systems can be used to produce the information required to design a cost-effective closure sampling program that customizes systematic sample numbers based on decision unit-specific average concentration and variability estimates.


  • Hot Spot or Elevated Area Compliance Demonstration. One of the factors that can result in expensive closure programs is when there is a requirement to demonstrate compliance with an elevated area or hot spot criteria. This type of closure criteria usually is included along with an average concentration requirement, where the average requirement is applied to a decision unit. A hot spot criteria refers to demonstrating that much smaller areas with higher levels of contamination do not exist within decision units. Such areas might "average away" or be completely missed by a closure sampling program that focuses on demonstrating compliance with average requirements, but if left might still pose unacceptable residual risk concerns. The standard method for dealing with hot spots is to use some form of search algorithm that calculates required sampling densities based on assumptions about acceptable hot spot size, shape, and level of confidence that detection would occur. Because acceptable hot spot concentrations are usually much higher than the average cleanup requirement for a site, real-time systems that may have marginal detection capabilities for average cleanup requirements can perform well to address hot spot concerns. Because of the lower per sample cost of most field-based real-time systems, their use for hot spot identification can contribute to overall cost control. Finally, quickly screening a decision unit for hot spot concerns can prevent wasted additional closure analytics to address averaged cleanup goals if hot spots are identified in real-time that will require additional remediation.




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