Plant-specific groundwater source apportionment and health risk assessment of chemical industrial parks based on an enhanced potential pollution index framework.

Saline tracer methods for tracking leachate leakage-migration and preferential groundwater flow pathways at valley-type landfill sites.

Employing photocatalysis and electrosynthesis enabling technologies, we have developed a continuous flow synthesis of the active pharmaceutical ingredient (API) buclizine, an antihistaminic and antiemetic drug. This convergent synthesis combines recent advances in ruthenium-based photocatalysis and an electrochemical reduction of carbonyl compounds, offering significant advantages over the state of the art for this API, considering both academic and industrial synthetic routes. All steps were scaled up (gram scale) under continuous flow conditions, achieving a new synthetic approach in four steps and up to 49% overall yield (58% overall yield LLS). • A new convergent continuous flow synthesis of buclizine API is described. • Photocatalysis and electrosynthesis were used as enabling technologies. • All steps were scaled up (gram scale) under continuous flow conditions. • A four-step synthesis was developed in 49% overall yield (58% overall yield LLS).

• Multi-fidelity delayed-acceptance MCMC for PDE-based Bayesian inverse problems • Offline-trained neural networks correct heterogeneous coarse solvers • No high-fidelity calls during sampling • Improved coarse-solver accuracy yields better mixing • Groundwater and reaction-diffusion benchmarks show computational savings Inverse uncertainty quantification (UQ) tasks such as parameter estimation are computationally demanding whenever dealing with physics-based models, and typically require repeated evaluations of complex numerical solvers. When partial differential equations are involved, full-order models such as those based on the Finite Element Method can make traditional sampling approaches like Markov Chain Monte Carlo (MCMC) computationally infeasible. Although data-driven surrogate models may help reduce evaluation costs, their utility is often limited by the expense of generating high-fidelity data. In contrast, low-fidelity data can be produced more efficiently, although relying on them alone may degrade the accuracy of the inverse UQ solution. To address these challenges, we propose a Multi-Fidelity Delayed Acceptance scheme for Bayesian inverse problems involving large-scale physics-based models. Extending the Multi-Level Delayed Acceptance framework, the method introduces multi-fidelity neural networks that combine the predictions of solvers of varying fidelity, with high-fidelity evaluations restricted to an offline training stage. During the online phase, likelihood evaluations are obtained by evaluating the coarse solvers and passing their outputs to the trained neural networks, thereby avoiding additional high-fidelity simulations. This construction allows heterogeneous coarse solvers to be incorporated consistently within the hierarchy, providing greater flexibility than standard Multi-Level Delayed Acceptance. The proposed approach improves the approximation accuracy of the low-fidelity solvers, leading to longer sub-chain lengths, better mixing, and accelerated posterior inference. The effectiveness of the strategy is demonstrated on two benchmark inverse problems involving (i) steady isotropic groundwater flow, (ii) an unsteady reaction–diffusion system, for which substantial computational savings are obtained.

Autonomous inverse modeling of complex groundwater systems via a physics-integrated large language model multi-agent framework.

Performance assessment of groundwater circulation well coupled with surfactant-enhanced flushing in low-permeability zones.

• 222 Rn in groundwater exhibits strong post-monsoon amplification linked to recharge-driven fracture activation. • Enhanced radon mobilization occurs in zones marked by deep weathering and fracture-controlled groundwater flow. • A novel Radon Sensitivity Potential Index (RSPI) developed to quantitatively integrate geological, structural, and seasonal drivers of 222 Rn. • RSPI provides a robust and transferable framework for assessing radon sensitivity in across diverse geological settings. The study examines the seasonal variability of radon ( 222 Rn) with reference to the geology and hydrogeology of the Ithikkara River Basin (IRB) in Kerala, India. Groundwater samples ( n = 35) were collected during the monsoon and post-monsoon seasons of 2023. The RAD7, RAD-H 2 O accessory (Durridge Co. USA) was used to analyze 222 Rn. No correlation was observed between radon and physico-chemical parameters. 222 Rn activities ranged between 296 and 21,100 Bq/m 3 (monsoon) and 720–69,200 Bq/m 3 (post-monsoon), with up to 14.3% of post-monsoon samples exceeding the USEPA limit. The findings demonstrate that 222 Rn distribution is strongly modulated by lithological heterogeneity, fracture networks, and seasonal controls. High radon activities were observed in midland zones underlain by migmatite and khondalite lithologies with deep weathering and proximity to lineament zones. Conversely, highland aquifers with steeper slopes exhibited limited radon mobilization due to lower recharge and faster flow paths. Importantly, a novel Radon Sensitivity Potential Index (RSPI) was developed to assess sensitivity of radon in groundwater to geological and seasonal influences. The RSPI provided a clear framework to distinguish fracture-dominated flow regimes from matrix-controlled systems, offering valuable insights into recharge processes and structural controls in hard rock aquifers. This study highlights the potential of integrating 222 Rn analysis with RSPI as an effective approach for identifying hydrogeologically active zones and enhancing groundwater resource management in structurally complex terrains.

Multi-physics modeling of process parameter effects on powder flow convergence and thermal distribution in laser directed energy deposition

Groundwater flow is an integral part of the Earth’s water cycle and plays a key role in assessing groundwater resource potential, characterizing the upper limit of possible groundwater withdrawal over a long period without depletion. The objective of this study is a comprehensive regional assessment of groundwater flow and the natural and predicted resources of fresh and low-mineralized groundwater in Southern and Western Kazakhstan. This assessment is based on an analysis of hydrogeological conditions and water balance, taking into account climate variability and anthropogenic load, to justify sustainable water resources management in arid territories. This article provides a regional assessment and mapping of groundwater flow, taking into account climate and anthropogenic changes in Kazakhstan, to refine the predicted resources of fresh and low-mineralized groundwater. The basin balance calculation results indicate that in arid and semi-arid regions, the decline in groundwater recharge by the 2050s will generally not exceed 10%. The average layer of groundwater flow of renewable groundwater resources in the Kazakhstan part of the Zhaiyk-Caspian water management basin (WMB) is estimated at 33.4 mm/year, and the average modulus of groundwater flow is 1.06 L/s per 1 km2. The average layer of groundwater flow of renewable groundwater resources in the Kazakhstan part of the Aral-Surdarya water management basin (WMB) is estimated at 14.8 mm/year, and the average modulus of groundwater flow is 0.47 L/s per 1 km2. The average layer of groundwater flow of renewable groundwater resources in the Kazakhstan part of the Shu-Talas water management basin (WMB) is estimated at 26.5 mm/year, and the average modulus of groundwater flow is 0.84 L/s per 1 km2. For mountainous and folded regions, the average layer of groundwater flow of renewable groundwater resources in the Balkhash-Alakol water management basin (WMB) system is estimated at 70.7 mm/year, and the average modulus of groundwater flow is 2.24 L/s per 1 km2. For intermontane and foothill basins, the average layer of groundwater flow of renewable groundwater resources in the Balkhash-Alakol water management basin (WMB) is estimated at 54.3 mm/year, and the average modulus of groundwater flow is 1.72 L/s per km2.

Toward precise management of groundwater by combined heavy metal(loid)s contamination at industrial sites: A machine learning driven source-to-risk zoning framework.

Understanding trace-element mobilization in a redox mixing zone of a river-aquifer system: Insights from a multi-tracer Bayesian mixing framework.

Continental rifting in marginal seas remains debated, particularly regarding the role of mantle dynamics in lithospheric extension. The South China Sea, the largest marginal sea in the western Pacific, provides a well-constrained case for exploring this issue. Seismic observations reveal a major transition from early Paleogene distributed extension to late Paleogene hyper-extension along its northern margin, with systematic westward rift propagation. Numerical mantle flow models predict upper-mantle flows beneath Southeast Asia, comprising southwestward Pacific flow in the east and converging Pacific and Tethyan/Eurasian flows in the west, which are dynamically consistent with the westward migration of rifting. Subduction of the fossil Wharton Ridge to the southwest may have enhanced mantle-pressure gradients and influenced the transition from distributed extension to hyper-extension. Here, we show that mantle flow, together with plate kinematics, contributes to the regional stress framework of rifting, helping to explain the spatiotemporal organization of extension in marginal sea systems.

• Novel device enables passive DNA tracer capture for groundwater studies. • DNA breakthrough curves align with salt and dye tracers in lab and field-scale tests. • Agarose beads with streptavidin ensure selective and stable DNA binding. • Passive devices allow high-sensitivity, low-impact tracer recovery. • Device supports low-impact, scalable hydrogeological investigations. Synthetic DNA tracers represent an innovative solution to overcome the limitations of conventional hydrological tracers, particularly in complex and large-scale hydrogeological systems. However, prolonged tracer tests still rely on manual sampling, which often provides sparse temporal coverage, increasing the risk of missing concentration peaks and biasing breakthrough curve interpretation. In addition, repeated site visits entail significant logistical effort and cost. To address these constraints, this study presents the development and validation of a novel passive device that selectively captures biotinylated synthetic DNA tracers, enabling time-integrated, semi-quantitative assessment of groundwater flow and pollutant migration. The device integrates streptavidin-functionalized superparamagnetic beads (1.0 µm; binding capacity ∼ 500pmol mg –1 ) encapsulated in 0.6% agarose hydrogel pearls and housed in a sealed hybridization chamber. Laboratory column tests (1.65 × 10 –4 L s –1 ; hydraulic gradient 1.4%) and a field-scale experiment in a natural stream (∼6 L s –1 ; 200 m reach), used as an analog for fracture flow or conduit flow in karst systems, benchmarked DNA behavior against conventional tracers (KCl, NaCl, fluorescein). Quantification by qPCR showed 100.2% efficiency and R 2 = 0.997, with sensitivity < 10 DNA molecules per reaction. Breakthrough curves from manual sampling and the passive device closely matched conservative tracer responses. The passive device produced slightly broader BTCs, consistent with time-integrated capture and detection of delayed or retained fractions. This approach provides a reproducible method for tracing hydrological pathways in karst and carbonate aquifers, thereby facilitating broader application of DNA tracers in groundwater studies.

Experimental and numerical investigations on the transport mechanisms of Ti3C2Tx MXene under variable flow and solution chemistry.

Energy tunnels represent an innovative solution for meeting heating and cooling needs through heat exchange with the ground. Among the key factors influencing their thermal performance, there is the groundwater flow which drives continuous thermal recharge of the surrounding ground. The extent to which this recharge occurs depends significantly on the flow velocity, which is mainly governed by hydraulic gradient and permeability. While previous studies typically assumed homogeneous hydraulic properties, the effect of spatial variability on the thermal exchange remains unexplored. Therefore, this study investigates how spatial variability of intrinsic permeability impacts the thermal performance of an energy tunnel under seepage. A numerical model was developed and validated against field tests data available in the literature. Then, lognormally distributed, autocorrelated random fields of intrinsic permeability were generated and incorporated into the model as a series of Monte Carlo simulations (MCS). The results indicate that assuming homogeneity is acceptable when the coefficient of variation of permeability ( C O V κ ) is below 1.0, in both heating (winter) and cooling (summer) modes. However, for higher variability ( C O V κ > 1.0), this effect becomes significant and assuming homogeneous conditions may lead to a substantial underestimation or overestimation of the heat exchange. Only in cases with a large difference between internal air temperature and fluid temperature, the homogeneous assumption may remain reasonably valid even at high C O V κ up to 4.0. The effect of spatial variability increases with increasing groundwater flow velocity up to 0.5 m/d, beyond which the variability effect remains similar. This study highlights the critical importance of field measurements of hydraulic properties in order to accurately estimate the heat exchange rates of energy tunnels.

Hydraulic conductivity is an important hydrogeological parameter that characterizes the hydraulic properties of subsurface media, and the pattern of its variation with depth is of great significance for groundwater system simulation and resource evaluation. However, the applicability of some existing theoretical and empirical models describing how hydraulic conductivity varies with depth in salt lake areas still lacks systematic validation and evaluation based on measured data. In this study, seven typical boreholes in the West Taijinar Lake area of the Qaidam Basin were selected as the study objects. Several previous representative models describing the decay of hydraulic conductivity with depth, together with a newly proposed model using the geometric square root method, were selected for comparative analysis. The applicability of these models in a salt lake depositional environment was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The results show that hydraulic conductivity in the study area generally exhibits a significant decreasing trend with depth. Different models for the decay of hydraulic conductivity with depth show different levels of applicability among the boreholes in the study area, and each model can reflect the variation characteristics of hydraulic conductivity with depth to a certain extent. Among them, the model using the geometric square root method shows the strongest applicability for all types of boreholes, although it requires the largest number of parameters. The model of Zhang etal. (2026) and the model of Kuang and Jiao (2014) rank next, and both can better describe the characteristic pattern of hydraulic conductivity in the complex salt lake depositional environment, with rapid decline in shallow layers and gradually slower change in deeper layers. The results of this study can provide a reference for the characterization of hydrogeological parameters and groundwater flow simulation in salt lake areas and also provide a scientific basis for the evaluation of deep brine resources in the Qaidam Basin.

We present a novel method for estimating lithology-specific hydraulic conductivities from catchment-scale effective values, each interpreted as a linear combination of the conductivities of the underlying lithologies. The method particularly applies to discharge-derived estimates of effective conductivity, where river networks integrate the influence of predominantly shallow groundwater flows from adjacent hillslopes. Lithological area fractions are used as weights to formulate an overdetermined linear system, which is solved using a modified method of moments under the assumption of lognormally distributed conductivities to account for spatial variability. The method was validated on synthetic cases and applied to 113 catchments in the Armorican Massif (France). It shows good convergence properties and achieves high predictive accuracy: in 85% of cases, the modeled conductivities fall within a 90% confidence interval of the observed values. Convergence analysis indicates that using five catchments per lithology is sufficient to reach 75% predictive success, with mean and standard deviation parameters estimated within 6% and 30%, respectively. With 80 catchments, prediction accuracy increases to 80%, and parameter estimation errors are reduced to 3% and 15%. The method is computationally efficient and generalizable, offering a powerful tool to explore lithology-specific conductivity datasets at regional scales. It may also be adapted to other variables, such as porosity, where linear averaging applies, enabling broader applications in hydrogeological modeling. • New method to retrieve lithology-specific hydraulic conductivity from catchment values. • Effective tool to expand regional lithology-specific conductivity datasets. • Method validated with synthetic data and applied to 113 Armorican Massif catchments. • The method demonstrates high predictive accuracy in hydraulic conductivity estimation. • 85% of modeled conductivities fall within a 90% confidence interval of observed values.

Biogeochemical cycling of sulfur and iron constrains arsenic enrichment in groundwater: Microbial functionality and organic matter composition.

Abstract Lake‐groundwater systems are characterized by pronounced seasonal recharge dynamics and nutrient accumulation within low‐permeability aquifers. Microbial communities mediate nitrogen transformations in these heterogeneous systems, yet their spatiotemporal dynamics and responses to the groundwater flow regimes remain largely unresolved. In this study, the evolution of groundwater microbial communities was investigated across contrasting recharge zones in the Poyang Lake area, one of China's largest lake‐groundwater systems, during dry and wet seasons. The discharge area with the low‐permeability aquifer was enriched with NH 4 + ‐N, Fe 2+ , and total organic carbon (TOC), exhibited lower oxidation‐reduction potential (ORP), and showed limited seasonal variability across seasons compared with other aquifers experiencing stronger hydrological seasonality. Despite the hydrochemical stability, microbial communities in the low‐permeability aquifer displayed greater dry‐to‐wet seasonal shifts, suggesting a heightened biological sensitivity to even subtle hydrological variations. From dry to wet season, denitrification potential decreased, whereas nitrogen fixation potential increased, with the strongest shifts occurring in the low‐permeability aquifer. Although microbial communities in this aquifer were associated with Fe 2+ , NH 4 + ‐N, and ORP, these variables independently explained only a small proportion of the total variance (&lt;8%). Intriguingly, community assembly was primarily stochastic, with stronger drift in the low‐permeability aquifer, indicating environmental‐microbial decoupling. Microbial source tracking analysis suggested that microbial communities in the discharge area were largely associated with upstream groundwater with additional potential contributions from lake‐groundwater exchange, which may reflect delayed recharge signatures. These findings suggest that hydrological dynamics, more than static chemistry, modulate variability of microbial community assembly and nitrogen‐cycling in groundwater.

A machine learning framework integrating data augmentation and physical principles for groundwater table depth prediction (Shiyang River Basin, China)