T. Prabhakar Clement

Limited mixing between injected amendments and contaminated groundwater remains a major challenge limiting the effectiveness of in situ remediation systems. Conventional amendment-delivery methods rely primarily on hydraulically driven advective transport and the associated dispersive effects, which often displace contaminant plumes and confine mixing to narrow interfacial regions. This study presents a novel density-driven mixing approach that uses gravitational instability to enhance mixing between a reactive amendment and a contaminant plume in porous media systems. The concept was evaluated through controlled two-dimensional laboratory experiments and qualitative density-coupled reactive transport simulations. A visible acid–base colorimetric reaction was used to track the development of a reactive mixing zone (RMZ) indicated by the pink region generated using phenolphthalein indicator. The results show that when the reactive solution was injected directly into the plume under conventional conditions, mixing was limited primarily to the interface between the two fluids, leaving substantial unmixed acidic and alkaline regions. In contrast, when a denser reactive solution was introduced above the plume, it migrated downward under gravity, generated buoyancy-driven circulation and fingering, deformed the plume interface, and produced a substantially larger RMZ. A density-coupled multispecies reactive transport model was implemented in COMSOL and the model reproduced various experimental results well. Both experimental and model results showed that the density-driven flow configuration produced substantially increased the RMZ volume. Both experimental and modeling results show that density-driven flow can substantially improve treatment amendment–contaminant contact area, thus has the potential to substantially increase the overall efficiency of in situ remediation systems. Overall, this study provides proof-of-principle evidence that engineered density gradients can be used as a design strategy for enhancing reactive mixing in groundwater remediation systems.

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve‐based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process‐based hypotheses about limitations in large scale hydrologic prediction systems.

Water flow in unsaturated soils is modeled using the Richardson–Richards (RR) equation, a nonlinear partial differential equation. Contaminant transport processes, on the other hand, are modeled using the advection‐dispersion equation (ADE) that combines a hyperbolic term and parabolic dispersion term to simulate the advection and dispersion processes. In this paper, we examine various mathematical transformations used to transform both pressure‐head‐ and water‐content‐based RR equations to a form similar to the ADE. The transformed RR equations are solved using both analytical and numerical solution methods. We evaluated the efficiency of various discretization strategies for numerically solving an infiltration test problem using this transformation approach. In addition, we also derived a new type of analytical solution for the pressure‐head form of the transformed RR equation using this approach. To test the performance of different types of ADE‐based RR solutions, we employed a standard implicit solver as a benchmark to evaluate these solutions. Results of our comparison study, assessed in terms of computational performance and accuracy, demonstrated that ADE‐based solutions produce results comparable to those produced with standard solvers. In terms of computing efficiency, the dual time step explicit–implicit solution is the most promising strategy for solving the ADE‐form of the RR equation used to simulate the infiltration problem considered in this work. However, the other solution strategies also yielded comparable results. Our findings demonstrate that the use of ADE‐based solvers, which are primarily used in the contaminant transport literature, offers a promising alternative approach for solving the RR equation.

Per-and polyfluoroalkyl substances (PFAS) present significant challenges for remediation due to their persistence in nature. Activated carbon is a widely used adsorbent for removing PFAS. In this study, three forms of activated carbon, granular activated carbon (GAC), powdered activated carbon (PAC), and ball-milled colloidal activated carbon (CAC_BM), are compared for their effectiveness in removing short and long-chain PFAS. Physical modification through ball-milling process enhanced the adsorptive properties of activated carbon, resulting in smaller particle size (d ₅₀ = 0.318 μm), increased surface area (968.59 m² g^–1), and improved suspension stability compared to conventional GAC and PAC. Kinetic experiments showed that CAC_BM demonstrated superior removal efficiencies of long-chain PFAS (up to 89% for perfluorooctanesulfonic acid (PFOS) and 73% for perfluorooctanoic acid (PFOA)), and moderate removal of short-chain PFAS (55% for perfluorobutanesulfonic acid (PFBS) and 30% for perfluorobutanoic acid (PFBA)). The pseudo-first-order model adequately described adsorption trends; however, the pseudo-second-order model provided a better fit, with intraparticle diffusion identified as the rate-limiting step. Isotherm studies indicated that PFAS adsorption aligned well with the Freundlich model. Competitive adsorption experiments revealed a hierarchical pattern. Overall, the study demonstrates CAC_BM as a promising adsorbent for remediation of PFAS-contaminated water systems.

Accurately identifying baseflow-dominant (BFD) periods in streamflow records is crucial for evaluating low-flow conditions, groundwater interactions, and other water resource management issues. While baseflow separation methods are widespread, the definition and identification of BFD flows are relatively new areas. Here, we define BFD periods as flow conditions that occur with minimal contribution from quickflow, including periods dominated by bank flow, groundwater interaction, or residual flow routing through the system. We develop a comprehensive, expert-labeled dataset of BFD periods from 182 USGS stream gages across diverse hydrological settings in the continental United States as ground truth. Using this dataset, we evaluate various automated BFD identification methods, including three new approaches, a machine learning classifier, a gradient-based method, and a statistical method, as well as two established techniques: the BN77 and Strict Baseflow methods. Our results demonstrate that the machine learning model (RF-BFD) outperforms all other approaches, achieving an F1 score of 0.92 and 92% accuracy. This study characterizes challenges in identifying BDF periods and establishes benchmarks for improving BFD identification in large-scale hydrological studies. The findings offer a pathway toward more robust and scalable BFD identification techniques, enhancing low-flow forecasting and groundwater-surface water interaction assessments.

Much of the economic growth of the U.S. in the past century is a result of scientific and technological advancements. In disregard for science, the US Supreme Court recently overruled the Chevron doctrine (known as the Loper decision) that mandated courts to defer to an administrative agency's reasonable interpretation of an ambiguous statute. This decision has empowered courts to exercise their subjective judgment even when the agency's interpretations involved an objective technical or scientific basis. Loper provides enormous power for (mostly) politically appointed Justices and judges, who typically have little or no scientific training. Data show that in the past 75 years, none of the Justices appointed to the Supreme Court bench had advanced science or engineering training. In this note, we point out some of the problems with the lack of scientific expertise in US courtrooms and recommend that the US Congress consider creative ideas to remedy this problem. Furthermore, we recommend more collaboration between US law schools and STEM programs to train scientifically literate 21st‐century legal practitioners.