The increasing discharge of untreated and inadequately treated wastewater is severely degrading water resources, threatening aquatic ecosystems and human health. Efficient, cost-effective, and sustainable treatment technologies are therefore urgently needed. Among emerging approaches, electrolytic methods offer energy-efficient, environmentally friendly, and highly adaptable solutions for wastewater remediation. This study synthesizes recent advances in electrode materials, reactor configurations, and energy-efficient electrolytic systems, with emphasis on continuous-flow operation, renewable energy integration, resource recovery, non-passive electrodes, and hybrid treatment strategies. Through a comparative assessment of recent studies, key technological progress is evaluated alongside persistent challenges, including high operational costs, limited scalability, sludge generation, and toxic by-product formation. By consolidating recent progress and identifying critical research gaps, this review proposes future research priorities to accelerate the transition of electrolytic technologies from laboratory studies to large-scale, sustainable wastewater treatment.

Wave energy offers immense potential as a renewable energy source. However, accurately estimating the Total Absorbed Power (TAP) at various sites remains a significant challenge, requiring resource-intensive physical modelling and numerical simulations to capture the complex hydrodynamic behaviour of Wave Energy Converters (WECs) across different designs and wave conditions. To address this, we propose a novel, computationally efficient Machine Learning-Transfer Function (ML-TF) approach to estimate the TAP of Multi-Body Floating WECs (MBFWEC). The methodology integrates frequency-domain and time-domain analyses to generate a sparse dataset of MBFWEC responses under regular waves, which is used to train Machine Learning (ML) models. Wave height, wave period, and Power Take-Off (PTO) damping are the key inputs for predicting the Capture Width Ratio (CWR). Among the models tested, Multi-Layer Perceptron (MLP) model performed best (R2 = 0.995). This model was then used to derive a high-resolution CWR dataset, with error margins within +/- 6.11 %, proving its reliability for out-of-range CWR predictions. To extend the model's applicability to irregular wave conditions, a Transfer Function (TF) was developed from the CWR dataset across a desired frequency range. The TAP was subsequently estimated based on the TF, site-specific wave power spectra, and the converter's effective length. Validation using time-history simulations in uni-modal and bi-modal sea states showed excellent accuracy (4 % maximum error), while achieving an 80 % reduction in computational cost. The methodology was further applied in a real-world case study using wave data from three locations in the northern Oman Sea, to evaluate the region's year-round power potential.

Understanding lake ecological status (ES) and the environmental factors that influence it is critical for maintaining ecosystem stability. The European Union Water Framework Directive (WFD, 2000/60/EC) provides a standardised methodology for assessing ES, classifying lakes into five categories ranging from Bad to High. However, conventional assessment approaches are labour-intensive, time-consuming, and limited in temporal resolution, particularly in boreal and Arctic regions where ecosystems are increasingly affected by climate change and anthropogenic pressures. Using the official Finnish WFD ecological-status classes as the classification reference, this study developed a near-real-time, data-driven framework for large-scale ES assessment across 2418 Finnish lakes using routine water-quality variables, morphometric characteristics, and natural lake type. Machine-learning, deep-learning, and Bayesian neural-network models were trained and evaluated using stratified five-fold cross-validation within an 80% development set, with macro-F1 as the primary optimisation metric. Out-of-fold predictions from the individual models were subsequently used to train a stacking ensemble with an XGBoost meta-learner. The stacking ensemble and BNN achieved macro-F1 scores of 0.71 and 0.70, respectively; the BNN was the strongest individual model. Both were well calibrated, with expected calibration error (ECE) values of 0.037-0.040. Uncertainty was mainly aleatoric, reflecting overlaps in feature distributions, particularly for nutrient concentrations and water clarity across adjacent ES classes. Explainable AI consistently identified these variables as having the strongest relative predictive importance, aligning with established eutrophication dynamics. Overall, this approach provides a scalable, cost-effective complement to WFD-based assessment and supports national and local authorities in lake monitoring, prioritisation, and water-management decisions.

Porous marine structures are increasingly employed as energy-dissipating elements in coastal and offshore engineering. Their hydrodynamic behaviour can be simulated using macroscopic CFD modelling approaches, in which porous effects are represented through equivalent pressure drop models. However, the predictive accuracy of such models strongly depends on the discharge coefficient (mu), which is commonly assigned a fixed empirical value or determined through computationally expensive trial-and-error calibration. This study develops a novel surrogate-assisted numerical framework that couples support vector regression with particle swarm optimisation (SVR-PSO) to provide rapid, condition-dependent recommendations of $ \mu $ mu for macroscopic CFD simulations. Two macroscopic CFD models were formulated using well-established pressure drop models proposed by Chen et al. and Molin. A total of 65 numerical simulations were conducted for each model to generate a dataset for the corresponding ML models. Four ML algorithms were trained and compared, revealing SVR-PSO as the superior model. Under a representative wave condition, the SVR-PSO framework predicted discharge coefficients of $ \mu = 0.4806 $ mu=0.4806 for the Molin model and $ \mu = 0.4874 $ mu=0.4874 for the Chen et al. model, both deviating by less than 4% from the prescribed target value of $ \mu = 0.5 $ mu=0.5. The ML-optimised mu values were incorporated into CFD models, and the resulting simulations were validated against laboratory measurements. Experimental validation demonstrated that the surrogate-assisted CFD model reduced the mean absolute percentage error (MAPE) of pressure drops from over 36% in the fixed-mu model (mu = 0.5) to below 17%. The coefficient recommendation procedure was completed within one minute, providing an efficient alternative to conventional empirical calibration in macroscopic simulations of wave interactions with perforated structures.

Understanding water quality in the deeper layers of stratified lakes is critical, as these zones govern ecosystem stability and biodiversity health. The hypolimnion, the bottommost layer of a stratified lake, is characterized by limited vertical circulation and facilitates pollutant accumulation, thus serving as an indicator of long-term lake condition. While surface water quality can now be routinely monitored using in-situ sensors and satellite observations, assessing hypolimnetic conditions remains costly and logistically challenging, particularly in the Arctic regions with severe environmental conditions and logistical constraints. Using a long-term dataset spanning 1979-2022 collected at a single monitoring station where both epilimnetic and hypolimnetic profiles are measured, this study develops and compares five machine learning and deep learning models including artificial neural networks (ANN), random forest (RF), extreme gradient boosting, support vector regression, and Kolmogorov-Arnold networks to estimate hypolimnetic total nitrogen (TN), total phosphorus (TP), and dissolved oxygen (DO) in Lake Inari, Finland, based on readily available epilimnetic water quality predictors. Model performance was evaluated using five-fold cross-validation and assessed with the Nash-Sutcliffe efficiency (NSE), normalized mean absolute error (NMAE), and coefficient of determination (R2). For TN, the RF model achieved the best overall performance, with mean cross-validation values of NSE = 0.52, NMAE = 0.11, and R2 = 0.52, outperforming the other models, which yielded NSE values of 0.38-0.52, NMAE of 0.12-0.14, and R2 of 0.47-0.51. For TP, ANN showed superior predictive skill (NSE = 0.49, NMAE = 0.13, R2 = 0.55), compared with NSE values of 0.17-0.46, NMAE of 0.13-0.18, and R2 of 0.32-0.51 for the remaining models. For DO, RF consistently outperformed all other approaches, achieving NSE = 0.76, NMAE = 0.09, and R2 = 0.77, whereas competing models produced NSE values of 0.62-0.72, NMAE of 0.10-0.11, and R2 of 0.68-0.73. Overall, all five models demonstrated their strongest performance for DO prediction. Permutation importance analysis revealed that surface TN, TP, and water temperature were key predictors for hypolimnetic TN, TP, and DO, respectively. This study demonstrates a practical, near-real-time, and cost-effective approach for assessing deep-water quality in stratified Arctic lakes, offering new opportunities for improved monitoring and management under growing climatic pressures.

Microplastics (MPs) are emerging contaminants, with wastewater treatment plants (WWTPs) as principal hotspots for their release into downstream systems, including constructed wetlands (CWs), a nature-based solution for water treatment. While non-buoyant MPs readily settle, buoyant MPs risk bypassing CWs and entering aquatic environments. Biofilm formation could influence MP transport by altering buoyancy, promoting sinking, and enhancing MP retention, yet its role in CWs remains unknown. This study, for the first time, quantifies the effects of MP polymer type, particle characteristics, exposure time, and seasonality on biofilm colonisation and its impact on terminal rising velocities of initially buoyant MPs in a UK-based CW receiving partially treated wastewater. Polypropylene (PP), expanded polystyrene (PS), and low-density polyethylene (LDPE) particles (3-5 mm) in spherical, beaded, and film shapes were incubated in situ over 12 months. Sampling followed two approaches: (1) a rolling bi-monthly schedule to capture seasonal variation, and (2) a long-term deployment with subsets retrieved every two months. Biofilm biomass was quantified by crystal violet staining, surface characteristics were captured by scanning electron microscopy (SEM), and terminal rising velocity experiments measured buoyancy changes. Biofilm growth showed strong seasonality, with peak biomass in late spring showing up to a 1972 % increase compared to winter. Despite widespread colonisation, changes in terminal rising velocity were minimal and largely non-significant (p < 0.05), indicating that biofilm formation alone is insufficient to retain initially buoyant MPs in CWs. These findings are crucial for deriving MP transport models and challenge assumptions that biofilm-induced density changes drive MP retention in CWs.

Accurate monitoring of surface water quality remains challenging due to pronounced spatial heterogeneity and limited ground observations. Satellite remote sensing offers scalable solutions, yet the extent to which environmental drivers enhance predictive performance, particularly in complex river–lake systems, remains insufficiently understood. This study develops a parallel comparative river–lake modeling framework to estimate permanganate index (CODmn), total phosphorus (TP), and total nitrogen (TN) by integrating satellite spectral data with climatic and land-use variables. Four model configurations were evaluated: spectral predictors alone (M1), spectral predictors combined with climate variables (M2), spectral predictors combined with land-use information (M3), and full integration of all predictors (M4). LightGBM models were optimized using Bayesian hyperparameter tuning (Optuna) and trained over rivers (January 2021–July 2025) and lakes (January 2021–December 2024) datasets in the Dongliao Basin, China. Spectral predictors alone (M1) provided robust performance for CODmn (R2 = 0.78), with marginal improvement when land-use variables were included (M2) in rivers (R2 = 0.80). In contrast, nutrient predictions showed stronger dependence on environmental covariates. TN predictions improved substantially with land-use inputs (M3) (R2 = 0.75 in rivers and 0.63 in lakes with M2), with further gains with full integration (M4) in lakes (R2 = 0.66). TP predictions exhibited marked improvements with land-use variables in rivers (R2 = 0.76) and with full integration in lakes (R2 = 0.72). Model interpretability analysis using SHAP revealed that spectral features dominate CODmn estimation, while climatic and watershed characteristics exert greater influence on TN variability. Seasonal analysis indicated that hydrological drivers dominate during wet seasons, while land-use effects and internal biogeochemical processes become more important in dry seasons. The proposed framework advances predictive accuracy and process understanding, supporting more effective monitoring and management of water quality in complex river–lake systems.

Porous structures are widely employed as an effective means of wave energy dissipation for the protection of coastal and offshore infrastructure. This study develops a novel macroscopic numerical framework, grounded in viscous flow modelling, to simulate wave interactions with thin perforated plates. In contrast to conventional microscopic approaches that explicitly resolve pore-scale geometries, the proposed model homogenises the porous zone into an equivalent continuum. A pressure-drop formulation, accounting for both viscous friction and inertial resistance, is introduced as a momentum source term to characterise flow behaviour within the equivalent porous domain. Relative to a high-fidelity pore-scale model, the framework reduces computational cost by over 90% while maintaining deviations of less than 5% in wave transmission and pressure drop. The model is validated against controlled laboratory experiments conducted at a fixed water depth of 0.5 m, with incident wave heights ranging from 0.008 m to 0.060 m. Parametric analyses reveal that increasing wave steepness (kA = 0.0167-0.1247) enhances the reflection coefficient (from 0.155 to 0.272) and normalized pressure drop (from 0.296 to 0.632), in agreement with experimental observations. Reducing the discharge coefficient (mu) from 1.0 to 0.5 produces a 30% increase in pressure drop, highlighting its strong influence on energy dissipation. Increasing the normalized wavenumber (kd = 1.0-2.6) and porosity (tau = 0.2-0.4) improves wave transmission by 19% and 33%, respectively, whereas increasing the relative plate thickness (b/lambda > 0.02) suppresses both transmission and reflection, accompanied by asymmetric pressure distributions across the plate. These results demonstrate that the proposed macroscopic framework achieves a robust balance between computational efficiency and predictive accuracy, offering a powerful tool for the design and optimisation of porous coastal and offshore structures.

With the intensification of coastal hazards due to rising sea levels, increasingly frequent extreme climatic events, and reduced freeboard at coastal defenses, effective mitigation strategies for protecting coastal infrastructure are more critical than ever. Enhancing the resilience of landward critical infrastructure in tsunami-prone regions is essential for safeguarding communities and preventing catastrophic losses. This study evaluates the effectiveness of sloped dikes in mitigating tsunami-induced forces and overturning moments on coastal protection structures. A three-dimensional Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) model is developed to simulate complex tsunami wave interactions with sloped dikes. The model is rigorously validated against physical experiments, accurately capturing the hydrodynamic behavior of dam-break-generated tsunamis impacting sloped dikes. Theoretical formulations confirm the accuracy of the computed forces and moments acting on the defense structure. The study systematically examines the influence of dike geometry and crest freeboard on tsunami force attenuation across a range of Froude numbers representative of real-world conditions. Two Gaussian-based predictive equations are introduced for optimal sea dike design, offering practical insights for coastal engineering applications. Additionally, the study highlights the benefits of intermittent sea dikes, which create larger subcritical zones upstream and significantly alter downstream flow patterns. Notably, intermittent sea dikes reduce horizontal forces and overturning moments by over 30 % compared to continuous dikes, demonstrating their potential for enhancing coastal resilience.

Study region Iranian wetlands and lakes listed in Ramsar Convention Study focus Iran ranks second globally after Greece in wetland loss, with six of its wetlands listed on the Montreux Record. Despite this status, substantial gaps remain in quantifying long-term national wetland decline, associated economic losses, and the dominant drivers of desiccation. Here, we present the most comprehensive assessment to date of wetland loss in Iran over the past four decades (1986–2023), analyzing 23 Ramsar Convention-listed wetlands. We integrate Landsat 5/7/8 imagery processed with multiple water detection indices, Otsu-zero and Otsu-dynamic thresholding, Canny edge filtering, and stratified random sampling for validation. Methodological robustness is further confirmed through comparison with NASA-OPERA products and true-color images. New hydrological insights for the region We found that only six small wetlands have maintained near-natural conditions, while the remainder have experienced substantial shrinkage. Overall, wetland water-surface area declined by more than 57%, from 11,539 km2 during the baseline period (1986–1995) to 4921 km2 in 2023. This loss appears to be closely associated with state-led policies prioritizing agricultural self-sufficiency, resulting in extensive surface water diversion, groundwater over extraction, land-use changes. While our correlation analysis shows these associations, it does not establish causality, and the findings should therefore be interpreted as indicative rather than definitive. Economically, Iran now forfeits approximately US$2.415 billion annually in wetland-derived ecosystem services, while achieving a comparatively modest increase of about US$1.3 billion per year in agricultural value added. These policies are closely linked with widespread depleted aquifers across 421 of 609 national plains and disrupted river flows nationwide. The widespread desiccation of Iranian wetlands has generated profound environmental, economic, social, and public health consequences. Our findings underscore the urgent need for systemic water governance reforms to prevent further irreversible wetland loss.

Enzyme immobilisation is a key step in translating biocatalysts into practical water treatment technologies by enhancing stability and reusability. In this study, a crude laccase extract from the white-rot fungus Trametes versicolor was immobilised via physical adsorption onto a copper-based metal–organic framework (HKUST-1) integrated with magnetic nanoparticles (Fe₃O₄/HKUST-1), yielding a stable and magnetically recoverable biocatalyst for combined adsorption and enzymatic transformation of contaminants such as p-Nitrophenol (PNP). In contrast to most reported MOF–laccase systems that rely on purified enzymes, the use of crude laccase offers a simpler and more cost-effective approach while maintaining strong catalytic performance. Physicochemical characterization (XRD, FTIR, SEM, VSM, and BET) confirmed successful immobilisation while preserving structural integrity and magnetic responsiveness. BET analysis showed decreased surface area and pore volume, consistent with partial enzyme occupation, and an immobilisation yield of 74.3% was achieved. Compared with free laccase, the immobilised system exhibited improved catalytic performance, with Km decreasing from 0.155 to 0.131 mM and Vmax increasing 2.6-fold. Under optimal conditions (pH 4.0, 50 °C), it showed 1.17-fold higher activity and enhanced thermal stability, retaining 76.7% activity after 1 h at 50 °C versus 52.5% for the free enzyme. In batch tests (20 mg/L PNP), the composite achieved 98.2% removal within 1 h and retained 67.7% efficiency after ten reuse cycles, while adsorption alone declined sharply. These results demonstrate the effectiveness of Fe₃O₄/HKUST-1 as a support for crude laccase for removing contaminants such as PNP.

Coral reefs serve as critical habitats for diverse marine species and function as natural barriers against coastal flooding. However, these ecosystems are increasingly threatened by anthropogenic activities and climate change. To mitigate such impacts, bio-mimetic technologies, particularly single bio-mimetic sponges inspired by natural forms, have been proposed as protective interventions for coral reefs. This study investigates the influence of dual bio-mimetic tubular sponges arranged in various configurations (parallel, perpendicular, and at a 45 degrees angle) and spacing on wave-induced hydrodynamics. A three-dimensional Reynolds-Averaged Navier-Stokes (RANS) model employing the standard k-omega SST turbulence closure was utilized to simulate the complex flow dynamics associated with these structures. To enhance visualization and qualitatively illustrate flow patterns and regions of vortical motions, Surface Line Integral Convolution (SurfaceLIC) and three-dimensional stream tracing techniques were applied. The results demonstrate that dual sponge configurations can increase turbulent kinetic energy (TKE) by approximately 50 % while reducing wave energy dissipation by up to 54 %. Image processing analyses further reveal that both the spatial arrangement and interspacing of the sponges significantly influence the morphology of the effluent cloud (EC), which transitions from symmetric, rounded forms to elongated or mirror-imaged patterns. Notably, longitudinal sponge arrangements generate double-paired recirculating vortices, which may exacerbate seabed scouring on sandy substrates. These findings offer novel insights into the hydrodynamic behavior induced by dual bio-mimetic tubular sponges, contributing to a broader understanding of fluid-structure interactions and informing future strategies for coastal ecosystem management.

Climate change poses significant threats to water resource sustainability and security, necessitating robust evaluation models to quantify the risks. Robust frameworks for assessing climate change impacts on coupled surface and groundwater systems remain limited, particularly in data-scarce, semi-arid regions. This study introduces an integrated climate–hydrological assessment framework that combines process-based modelling with multi-dimensional drought diagnostics. The framework integrates the Soil and Water Assessment Tool (SWAT) with CMIP6 climate projections (SSP3-7.0 and SSP5-8.5) and a suite of novel and traditional drought indices, including the newly developed Standardized Groundwater Recharge Index (SGRI), the Reconnaissance Drought Index (RDI), the Standardized Precipitation Index (SPI), and the Standardized Runoff Index (SRI). The utility of the framework is demonstrated through a case study in the Damghanroud watershed, central Iran, for the period 2031–2059. Results reveal a projected 59–68

Military activity is a current blind spot in global climate policy. This systematic review synthesises evidence from 263 studies and 36 reports published between 2014 and 2025. Following PRISMA framework, the review assesses the environmental impacts across pre-conflict, active conflict, and post-conflict, with a focus on four interconnected domains namely greenhouse gas (GHG) emissions, water systems, agricultural land and food security, and natural ecosystems. This study finds that military operations contribute an estimated 5.5% of total global greenhouse gas emissions, a carbon footprint that would make the world's militaries collectively the fourth-largest emitter if counted as a nation. Yet, current reporting to the United Nations Framework Convention on Climate Change covers less than one-tenth of actual emissions due to current policies and voluntary disclosure. The review identifies extensive hidden emissions from pre-conflict mobilisation and weapons manufacturing to active combat, supply chains, refugee displacement, and post-conflict reconstruction. These processes generate cumulative and cascading effects that exacerbate climate change, degrade water and soil systems, disrupt ecosystems, and undermine food security. These underreported emissions intensify climate change as well as undermines global efforts towards the Paris Agreement targets. By integrating fragmented evidence across environmental domains and conflict phases, this review advances understanding of the conflict-climate nexus and identifies structural gaps in existing climate accounting and governance frameworks. The study findings highlight an urgent need for mandatory, transparent, and standardised military emission reporting and evaluation frameworks. Incorporating military accountability into global climate policy is both feasible and essential to safeguard the planets future. Without it, any vision for a sustainable and peaceful climate future remains incomplete.

We present a methodology to modulate immiscible fluid-fluid invasion patterns in patterned porous media during drainage in the capillary fingering flow regime. A 2D patterned porous medium is generated by a sequential deposition algorithm of disks/grains of two distinct size ranges (dual-porosity media). To attain patterned porous media, selected regions are packed with small grains, elsewhere packed with larger grains. By tuning the ratio of viscous to capillary forces, as defined by the capillary number (Ca), we control the degree to which the underlying microstructure affects the invasion morphology in the patterned porous medium. A decrease in Ca amplifies the influence of the underlying pore structure, resulting in a more pronounced alignment of the flow pattern with the porous medium's geometry. For these "structured" flows, the drainage within zones packed with smaller grains (having relatively smaller pores) is less than 10%. In contrast, an increase in Ca promotes more "random" displacement patterns with significant invasion in fine (more than 10%) and coarse pores. The possibility to modulate multiphase flows using patterned porous media can have practical implications in engineered membrane designs for fuel cells, selective transport, and filtering applications.

Nonpoint source pollution threatens global water quality, primarily by accelerating the eutrophication of freshwater ecosystems. This study presents a global-scale assessment of synthetic fertilizer-derived phosphorus loss (SFDPL) in runoff, aiming to (1) characterize the spatio-temporal dynamics of SFDPL and identify critical hotspots across 520 major river basins, and (2) evaluate the impact of input data uncertainties on SFDPL estimations. Using an empirical model at 0.5-degree spatial resolution, we quantified SFDPL from 1994 to 2001. Results showed a global average of 6.94 kg P/ha/yr and a median of 9.35 kg P/ha/yr, with major hotspots in southeastern Asia, Europe, South America, Japan, and New Zealand. Temporal analysis revealed increasing phosphorus loss risks in basins such as the Yangtze, Ganges, and Danube. To assess the impact of input data uncertainty, we conducted an ensemble of simulations using eight precipitation, four runoff, and two fertilizer datasets. Our findings indicated high uncertainty in approximately 62

Effective and sustainable management of coastal ecosystems relies on accurate water quality monitoring and forecasting, particularly in regions facing heavy pollutant loads. Phosphate and nitrate play crucial roles in coastal water quality and ecosystem health. However, predicting their concentrations remains challenging due to complex spatiotemporally varying environmental interactions. Leveraging advanced numerical modelling and artificial intelligence (AI), this study introduces a novel hybrid machine learning (ML) framework based on least-squares support vector regression (LSSVR) to simulate coastal water quality using physically grounded simulation data generated by the TELEMAC-WAQTEL numerical framework, calibrated against field-measured boundary and initial conditions. The proposed model is applied to Ha Long Bay, Vietnam, utilizing a dataset comprising 8760 hourly samples collected over one year (2021-2022). The data was generated through a two-dimensional TELEMAC hydrodynamic model integrated with a water quality module. Key hydrodynamic variables, including free surface elevation (FS), velocity (U), and flow direction (α), serve as inputs to predict nitrate (NO3) and phosphate (PO4) concentrations. To optimize the LSSVR hyperparameters, we introduced the Improved Dragonfly Algorithm (IDA), which integrates Cauchy walk mutation and adaptive inertia weighting to enhance convergence, global search efficiency, and robustness. Benchmarking against Gaussian Process Regression (GPR), ε- LSSVR, ANN, Random Forest, and Model Tree demonstrate the superior predictive capability of IDA-LSSVR. On an independent testing dataset comprising 20% of the total samples (1752 observations), IDA-LSSVR achieved the lowest RMSE (NO3 = 1.55E-02 mg/L, PO4 = 5.88E-04 mg/L), MAE (NO3 = 3.45E-07 mg/L, PO4 = 2.39E-04 mg/L), and MAPE (NO3 = 3.41, PO4 = 4.49E-04), coupled with the highest R2 (NO3 = 0.95, PO4 = 0.97). By coupling high-resolution physics-based hydrodynamic simulations with advanced machine learning and optimisation techniques, the proposed framework provides an accurate, computationally efficient, and reliable tool for coastal water quality forecasting. The integration of physically consistent simulation data with robust uncertainty-aware prediction offers valuable decision-support capabilities for nutrient management, eutrophication risk assessment, and environmental monitoring in dynamic coastal environments. The framework therefore presents a promising approach for supporting sustainable coastal ecosystem management and protecting vulnerable marine systems from nutrient-driven degradation.

This study presents and validates a geomorphological-based framework for estimating the river reaeration coefficient (K-r), specifically addressing the challenges of data-scarce basins where conventional hydraulic measurements are unavailable. K-r is a critical parameter governing dissolved oxygen recovery, self-purification capacity, and hypoxia risk, yet conventional estimation methods depend on transient and data-intensive field observations. Here, a GIS-based approach is proposed that utilizes only static geomorphological predictors, including the sinuosity index (SI), cumulative distance from source (CDS), and channel slope (S). As a proof-of-concept framework, the proposed geomorphological models were developed using K-r values calculated from established empirical reaeration equations and subsequently fitted through nonlinear power-law regression for the Simineh River basin, Iran. Model performance and robustness were evaluated through Monte Carlo uncertainty simulations, sensitivity analysis, and Bland-Altman evaluation. Results demonstrate that hydraulic geometry-based formulations can be effectively represented using SI and CDS, achieving strong predictive performance (R-2=0.781 and 0.606). For stream-power-based formulations, the inclusion of channel slope markedly improves model accuracy (R-2 > 0.94 and p < 0.001), with an estimated slope exponent of 0.49, closely aligning with the theoretical expectation (0.5), while SI becomes statistically insignificant. Monte Carlo simulations reveal a consistent downstream decline in K-r, and sensitivity analysis identifies CDS and S as the dominant controls on K-r variability, depending on model structure. The proposed framework serves as a rapid and low-cost screening tool for estimating reaeration potential and assessing hypoxia risk in semi-arid and ungauged river systems, providing a foundation for future validation and refinement using direct in-situ measurements of K-r.

Transport of solute species under unsaturated conditions, where multiple immiscible fluids coexist, is a common occurrence in various environmental and engineering applications within subsurface porous media. In this study, we integrate microfluidic experiments and direct simulation to analyze the effect of successive drainage-imbibition cycles on solute transport, a process that is not yet well understood in the context of unsaturated porous media. The spatial distribution of water and air is found by experiments, and the transport process is modeled by high-fidelity direct numerical simulation, remarkably reducing computational costs and enabling individual investigation of injection cycles. We show that cycles of non-wetting and wetting phases increase the rate of solute spreading non-monotonically by altering the volume and tortuosity of the percolating pathways of the carrier fluid (where transport occurs). Drainage-imbibition cycles reduce the saturation of the carrier fluid by entrapping a higher volume of the non-wetting phase, thereby decreasing the magnitude of mobile pathways. Simultaneously, cyclic injection increases the length of the pathways that solute species must travel through the hysteresis phenomenon. Through the analysis of mobile and immobile pathways, we demonstrate that the effect of drainage-imbibition cycles on the mixing of solute species becomes negligible after 1-2 cycles. These results advance our understanding of the complex dynamics of unsaturated transport, providing new insights into the impacts of cyclic variations in the soil water content on contaminants and nutrients transport.