Optimization of a remediation strategy has long been constrained by confidence in the design basis, where even the best design intentions seldom match actual field performance. Designers tend to either hedge against performance uncertainty by over-designing — resulting in needless spending — or worse, overlook the uncertainty and develop an optimistic design that fails to meet objectives, drags out remediation timelines and increases life cycle cost.
Advancements in understanding the physical, chemical and biological processes that drive contaminant behavior and remediation performance provide designers with the computational tools needed to simulate and predict contaminant behavior, transport and degradation mechanisms before and after remedy implementation. Greater insight also brings to light improved modeling techniques, analytical capabilities and high-resolution site characterization tools.
Modeling tools that account for mechanisms such as matrix diffusion, which previously contributed to significant performance and time frame uncertainty, allow designers to develop optimized remedy designs with more realistic expectations of performance. Accurate identification of key transition points, for example, can minimize the duration of high-cost remedies, supporting a transition to lower-cost passive or natural attenuation strategies much sooner.
PROGRESS, Progressive Remediation Strategies, is an integrated approach to complex site remediation that uses a data-driven, process-based conceptual site model (CSM) to accurately define and quantify site conditions and mechanisms relevant to remedy selection, design and performance. A comprehensive strategy, PROGRESS uses enhanced modeling and predictive analysis to optimize remediation technology selection and design, setting the stage for a focused remedy execution that delivers predictable results.
One could argue that the value of predictive modeling efforts has been limited more by the integrity of the inputs and assumptions used to construct the models than by the modeling applications themselves. The geologic framework is a common Achilles’ heel for groundwater and contaminant transport models used to support remediation design.
Geology is the static framework exerting the primary control on groundwater flow and contaminant distribution and is arguably the single greatest uncertainty facing any remediation project. The geologic model implicitly informs the understanding of site hydraulics because the physical characteristics of geologic materials determine parameters such as hydraulic conductivity. Additionally, the geometry of geologic deposits defines the spatial distribution and variability of the hydraulics, as well as the primary contaminant mass transport pathways and mass storage retention features.
With a sound geologic model in place, the site-specific physical, chemical, geochemical and biological processes relevant to contaminant remediation can be quantified and incorporated into the process-based CSM. This centralized hub of information is used to develop appropriate remediation performance objectives that provide a basis for evaluating remedial options. The process of developing, evaluating and selecting remedial options marks a key decision point in the life of a complex site remediation project.
Poorly developed performance objectives (e.g., unachievable goals within cost and time constraints), inaccurate performance predictions (e.g., mass removal or source depletion rates), or misinterpreted site conditions (e.g., contaminant distribution and transport routes) can significantly impact the future success of the project. Consequently, the process-based CSM is a critical tool during this phase as it is used to evaluate each remedial alternative while giving appropriate consideration to the processes having the greatest impact on the behavior of a given contaminant. It’s also used to consider the aquifer response to a given engineered treatment process and the overall performance of a given remediation technology. If necessary, modeling can be used at this stage to perform predictive performance analysis of each alternative within site-specific constraints.
Once a remedial alternative has been selected, additional predictive analysis can be conducted, as appropriate, during the remedy design process to develop design input parameters. This process often involves iterative design simulations to “test” performance as well as the impact of key processes and/or uncertainties to optimize the remedy design. Processes that are commonly scrutinized and modeled during remedy design optimization include:
- Biotic and abiotic transformation of contaminants.
- Contaminant mass flux and source mass discharge.
- Contaminant vapor generation, transport and attenuation.
- Groundwater plume dynamics — particularly processes that influence plume stability and steady state conditions.
- Matrix diffusion — particularly the release of contaminants from low permeability mass storage zones to more permeable mass transport zones.
- Non-aqueous phase liquid (NAPL) source mobility and depletion.
Founded on a quantitative process-based CSM, this design optimization approach can significantly increase confidence in design parameters, performance expectations, and cost and schedule projections. Implementation costs also are reduced as remedies are focused to achieve site-specific objectives and natural degradation and attenuation processes are leveraged to the maximum extent.
This post is one of a series explaining Progressive Remediation Strategies (PROGRESS). Through PROGRESS, enhanced modeling and predictive analytical tools are used to optimize technology selection and design. But this is just one component of PROGRESS and its comprehensive, next-generation approach to remediating complex sites.
