INSIDE-T: A Groundwater Contamination Transport Model for Sustainability Assessment in Remediation Practice

Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater

Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.

Pump-and-treat (P&T) vs groundwater circulation wells (GCW): Which approach delivers more sustainable and effective groundwater remediation?

Pump-and-treat (P&T) is commonly used to remediate contaminated groundwater sites. The scientific community is currently engaged in a debate regarding the long-term effectiveness and sustainability of P&T for groundwater remediation. This work aims to provide a quantitative comparative analysis of the performance of an alternative system to traditional P&T, to support the development of sustainable groundwater remediation plans. Two industrial sites with unique geological frameworks and contamination with dense non-aqueous phase liquid (DNAPL) and arsenic (As) respectively, were selected for the study. At both locations, attempts were made for decades to clean up groundwater contamination by pump-and-treat. In response to persistently high levels of pollutants, groundwater circulation wells (GCWs) were installed to explore the possibility of accelerating the remediation process in unconsolidated and rock deposits. This comparative evaluation focuses on the different mobilization patterns observed, resulting variations in contaminant concentration, mass discharge, and volume of extracted groundwater. To facilitate the fusion of multi-source data, including geological, hydrological, hydraulic, and chemical information, and enable the continuous extraction of time-sensitive information, a geodatabase-supported conceptual site model (CSM) is utilized as a dynamic and interactive interface. This approach is used to assess the performance of GCW and P&T at the investigated sites. At Site 1, the GCW stimulated microbiological reductive dichlorination and mobilized significantly higher 1,2-DCE concentrations than P&T, despite recirculating a smaller volume of groundwater. At Site 2, As removal rate by GCW resulted generally higher than pumping wells. One conventional well mobilized higher masses of As in the early stages of P&T. This reflected the P&T's impact on accessible contaminant pools in early operational periods. P&T withdrew a significantly larger volume of groundwater than the GCW. The outcomes unveil the diverse contaminant removal behavior characterizing two distinct remediation strategies in different geological environments, revealing the dynamics and decontamination mechanisms that feature GCWs and P&T and emphasizing the limitations of traditional groundwater extraction systems in targeting aged pollution sources. GCWs have been shown to reduce remediation time, increase mass removal, and minimize the significant water consumption associated with P&T. These benefits pave the way for more sustainable groundwater remediation approaches in various hydrogeochemical scenarios.

Contaminant containment for sustainable remediation of persistent contaminants in soil and groundwater

Contaminant containment measures are often necessary to prevent or minimize offsite movement of contaminated materials for disposal or other purposes when they can be buried or left in place due to extensive subsurface contamination. These measures can include physical, chemical, and biological technologies such as impermeable and permeable barriers, stabilization and solidification, and phytostabilization. Contaminant containment is advantageous because it can stop contaminant plumes from migrating further and allow for pollutant reduction at sites where the source is inaccessible or cannot be removed. Moreover, unlike other options, contaminant containment measures do not require the excavation of contaminated substrates. However, contaminant containment measures require regular inspections to monitor for contaminant mobilization and migration. This review critically evaluates the sources of persistent contaminants, the different approaches to contaminant remediation, and the various physical-chemical-biological processes of contaminant containment. Additionally, the review provides case studies of contaminant containment operations under real or simulated field conditions. In summary, contaminant containment measures are essential for preventing further contamination and reducing risks to public health and the environment. While periodic monitoring is necessary, the benefits of contaminant containment make it a valuable remediation option when other methods are not feasible.

DynSus: Dynamic sustainability assessment in groundwater remediation practice

Decision-making processes for clean-up of contaminated sites are often highly complex and inherently uncertain. It depends not only on hydrological and biogeochemical site variability, but also on the associated health, environmental, economic, and social impacts of taking, or not taking, action. These variabilities suggest that a dynamic framework is required for promoting sustainable remediation. For this, the decision support system DynSus is presented here for integrating a predeveloped contaminant fate and transport model with a sustainability assessment tool. Implemented within a system dynamics framework, the new tool uses model simulations to provide remediation scenario analysis and handling of uncertainty in various data. DynSus was applied to a site in south Sweden, contaminated with pentachlorophenol (PCP). Simulation scenarios were developed to enable a comparison between alternative remediation strategies and combinations of these. Such comparisons are provided for selected sustainability indicators and remediation performance (in terms of concentration at the recipient). This leads to identifying the most critical variables to ensure that sustainable solutions are chosen. Simulation results indicated that although passive practices, e.g., monitored natural attenuation, were more sustainable at first (5-7 years after beginning remediation measures), they failed to compete with more active practices, e.g., bioremediation, over the entire life cycle of the project (from the beginning of remedial action to achieving the target concentration at the recipient). In addition, statistical tools (clustering and genetic algorithms) were used to further assess the available hydrogeochemical data. Taken together, the results reaffirmed the suitability of the simple analytical framework that was implemented in the contaminant transport model. DynSus outcomes could therefore enable site managers to evaluate different scenarios more quickly and effectively for life cycle sustainability in such a complex and multidimensional problem.