Urban canals in rapidly growing cities often experience water quality deterioration from wastewater inputs and stormwater runoff, with impacts that vary across space and time. This study aimed to quantify five-year spatiotemporal patterns of key water quality indicators in the Mae Kha Canal, Chiang Mai, Thailand, and to identify persistent degradation hotspots to support management. Monthly longitudinal data (2020–2024) for dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, and water temperature (WT) were collected at 18 monitoring stations and analyzed using locally estimated scatterplot smoothing (LOESS) for trend exploration, repeated-measures correlation for association between parameters, and Geographic Information Systems-based spatiotemporal mapping using inverse-distance-weighted interpolation. Results showed that DO remains very low across much of the canal, while BOD was persistently high; pH was relatively stable near neutral and WT exhibited clear seasonal variability. Spatial mapping indicated that upstream sections generally had better quality, whereas the urban middle reaches repeatedly exhibited hotspots of low DO and high BOD. BOD and DO levels positively correlate with pH level (p < 0.001). In conclusion, the Mae Kha Canal has sustained impairment over 2020–2024, highlighting the need for strengthened wastewater control, stormwater management, and targeted remediation guided by hotspot-based monitoring.

This study investigates the role of longitudinal aquifer slope in controlling stream–aquifer interaction within bounded floodplain aquifer systems. A series of numerical simulations were conducted to analyze groundwater flow patterns, hyporheic exchange fluxes, and contaminant transport behavior under varying slope conditions. The results showed that increasing slope does not simply enhance hydraulic gradients but fundamentally reorganizes subsurface flow structure. As the slope increases, groundwater flow becomes progressively aligned with the stream, reducing lateral connectivity and confining exchange to a narrow corridor adjacent to the stream. This reorganization leads to the expansion of hydraulically inactive zones and a non-linear response in hyporheic exchange. Exchange flow rates initially increase at low to moderate slopes but decline beyond a threshold at higher slopes, despite higher local gradients. The transition begins at around a 2% slope and becomes pronounced within the range of approximately 3–7%, indicating a shift in flow regime rather than a continuous scaling of interaction intensity. Particle tracking analyses further reveal that slope controls the spatial distribution of contaminant vulnerability. While the overall extent of active transport zones decreases with increasing slope, localized transport potential intensifies near the stream boundary due to higher velocities and reduced residence times. These findings demonstrate that hydraulic gradient magnitude alone is insufficient to characterize stream–aquifer interaction and highlight the importance of flow geometry and connectivity. The results provide a process-based framework for understanding slope-controlled hyporheic exchange and offer insights for improving groundwater vulnerability assessment and management in alluvial systems.

This study examines the association between tourism intensity, the free movement of people, and water quality outcomes across the European Union (EU-27) over the period 2012–2024. By integrating open-access datasets from Eurostat, the European Environment Agency (EEA), and the EXIOBASE input–output framework, the analysis estimates the direct (blue), indirect, and grey components of the tourism-related water footprint and explores their relationship with bathing water quality indicators using panel econometric models. The results indicate that tourism activity increased substantially during the study period, while the share of bathing waters classified as “excellent” also improved. The findings further show that the gray water footprint is strongly associated with variations in water quality, whereas higher wastewater treatment coverage is positively associated with improved environmental outcomes. These results highlight the importance of wastewater management and governance capacity in moderating the relationship between tourism and water quality across diverse European contexts. We find that tourism activity rose by approximately 28% during the study period; yet, through improvements in wastewater treatment infrastructure and governance, the share of bathing waters rated “excellent” also increased. Notably, the grey water footprint emerged as the strongest predictor of water quality deterioration, while wastewater treatment coverage significantly mitigated negative impacts. Comparative case studies of Spain, Greece, Croatia and Romania highlight how institutional and technological capacity are associated with differences in tourism–water relationships across diverse hydro-climatic contexts. Our findings underscore that sustainable tourism in Europe is less a matter of visitor numbers and more a question of effective water management systems. The study supports a policy shift towards integrated water-tourism planning and circular water-use strategies to support more sustainable management of tourism-related environmental pressures.

Mercury ions (Hg2+), a heavy metal contaminant of strong biotoxicity, pose a serious threat to ecosystems and human health in aquatic environments. Developing highly sensitive and specific detection methods is therefore of great importance. This study presents a novel label-free fluorescent biosensor for Hg2+ by ingeniously coupling target-induced aptamer switching with rolling circle amplification (RCA). Upon Hg2+ binding, the conformational change releases a sequestered primer to initiate RCA, generating G-quadruplex-rich DNA products that produce a strong “turn-on” signal with N-methylmesoporphyrin IX (NMM). Under optimized conditions, the assay exhibits excellent linearity from 10 to 1000 nM with a detection limit of 3.2 nM, along with high selectivity over competing metal ions. Validation using spiked environmental water samples yielded accurate and reproducible recoveries in the range of 93.8% to 106.0%. With its operational simplicity, high sensitivity, and robust performance in complex matrices, this label-free strategy offers a reliable and promising platform for detecting Hg2+ in environmental waters.

Investigating soil seepage considering free surface conditions under complex geological conditions is of great significance to ensure the safety of dams. In recent years, physics-informed deep learning (PINN) has become a cross-disciplinary hotspot for solving forward and inverse problems based on partial differential equations. However, the challenges in free surface simulation have confined the majority of current PINN research to seepage problems under fixed boundary conditions. To address the above issues, we propose a physics-informed deep learning-based approach for steady seepage in dams. In the proposed method, two different free surface approximation strategies are introduced to accommodate varying boundary conditions in the dam seepage problem. The first strategy approximates the free boundary by sampling points, while the second strategy approximates the free boundaries by an additional deep neural network. To validate the proposed methods, three benchmark cases with different boundary conditions have been conducted. The results indicate that the proposed approach effectively simulates steady seepage in dams. Both point-sampling and deep neural network-based free surface approximation strategies demonstrate high accuracy in predicting the location of the phreatic surface and the discharge of the seepage. Specifically, the prediction results are comparable in accuracy to analytical solutions and advanced numerical simulation methods.

Deammonification, which is based on partial nitritation and anammox (PN/A), is a well-established sidestream treatment for nitrogen removal. However, transferring deammonification to mainstream wastewater treatment remains challenging due to low temperatures, the need to retain slow-growing anammox bacteria (AnAOB), and their competition for nitrite with nitrite-oxidizing bacteria (NOB) and heterotrophic denitrifiers. This work investigates cubic polyurethane foam carriers to promote growth and retention of AnAOB. A moving bed biofilm reactor (MBBR) and an integrated fixed-film activated sludge (IFAS) reactor were compared over a three-year experimental period at lab-scale. The feasibility of the biofilm carriers for deammonification was first evaluated under sidestream conditions, followed by a stepwise transition to mainstream operational conditions. The impact of operational parameters, including dissolved oxygen concentration, pH value, and aeration strategy, was evaluated with respect to the activity of aerobic ammonium-oxidizing bacteria (AOB), NOB, and AnAOB, as well as nitrogen removal rates. Deammonification reached nitrogen removal rates of 0.04–0.12 kg N m−3 d−1 (IFAS reactor) and 0.02–0.28 kg N m−3 d−1 (MBBR) at subphases with reactor bulk concentrations above 60 mg NH4-N L−1. Highest nitrogen removal degrees of 77 ± 6% (IFAS) and 76 ± 5% (MBBR) were achieved at reactor bulk concentrations of 96 mg NH4 L−1 and 97 mg NH4 L−1, respectively. Lower concentrations triggered NOB activity in both reactors, leading to an increase in nitrate concentration up to 22 mg NO3-N L−1. AOB and AnAOB activities were on average 6-fold higher on the carriers compared to suspended biomass throughout all experimental phases, demonstrating the feasibility of using cubic polyurethane foam carriers for deammonification. This was also confirmed by fluorescence in-situ hybridization (FISH) measurements. Median nitrogen removal rates over all experimental phases of 0.07 kg N m−3 d−1 for the IFAS reactor and 0.05 kg N m−3 d−1 for the MBBR were achieved, which are comparable to conventional activated sludge systems performing nitrogen removal via nitrification–denitrification. While at lower nitrogen concentrations, the IFAS reactor yielded superior nitrogen removal rates, peak nitrogen removal rates of 0.28 kg N m−3 d−1 were measured in the MBBR configuration. However, controlling NOB activity at lower temperatures and concentrations remains a challenge in MBBR and IFAS configurations. In our study, in the IFAS reactor NOB activities were visible on fewer days than in MBBR. At mainstream-like conditions, higher nitrogen removal rates of IFAS (0.09–0.12 kg N m−3 d−1) were achieved compared to the MBBR (0.06–0.09 kg N m−3 d−1). This demonstrates the advantage of the IFAS reactor in treating mainstream wastewater via deammonification. As an autotrophic nitrogen removal process, the implementation of deammonification in the mainstream of municipal wastewater treatment plants enables enhanced recovery of biogas from sewage organic matter. The latter would otherwise be consumed during the conventional nitrification-denitrification pathway. Consequently, the overall energy balance for wastewater treatment can be improved, contributing to a more environmentally sustainable process.

Surface water systems are increasingly exposed to multiple pressures generated by climate variability, intensified water resource exploitation, and evolving geopolitical dynamics. This study provides a novel contribution by identifying critical threshold effects and non-linear interactions that influence nitrate concentrations through an integrated information systems framework. It develops an integrated information-system-based analytical framework that combines hydrological, climatic, geopolitical, and strategic indicators to shape the broader contextual framework within which hydrological and climatic pressures operate, rather than serving as direct predictors. Considering the nitrate concentration in rivers as a key parameter of water quality, the paper goes beyond univariate analysis of nitrite concentration, examining its relationship with four explanatory variables: the Water Exploitation Index Plus (WEI+), the number of heat stress days (Heat_Stress), the Geopolitical Risk Index (GPR), and a proxy variable representing the presence of strategic infrastructure (Nuclear_State) using a Reduced Error Pruning Tree (REPTree) decision tree algorithm with 10-fold cross-validation. The results indicate that climatic stress emerges as the primary predictor, with a critical threshold of approximately 7.83 heat stress days, beyond which nitrate concentrations increase significantly. Under conditions of high climatic stress and intensive water exploitation (WEI+ ≥ 67.39), predicted nitrate levels exceed 20 mg/L and can reach extreme values of up to 58.82 mg/L. In contrast, low hydrological pressure (WEI+ < 0.39) combined with moderate climatic stress is associated with very low nitrate concentrations, around 2.75 mg/L. The model demonstrates strong predictive performance, with a correlation coefficient of 0.976, a Mean Absolute Error (MAE) of 0.593, a Root Mean Squared Error (RMSE) of 2.046, and a Receiver Operating Characteristic (ROC) area exceeding 0.94 for classification tasks. While geopolitical and strategic variables do not act as direct predictors, they contribute to shaping the contextual framework influencing water resource management and environmental vulnerability. Overall, the study highlights the non-linear and systemic nature of water quality dynamics and demonstrates the effectiveness of decision tree-based models within integrated information systems for supporting environmental monitoring and decision-making under conditions of climate stress and geopolitical uncertainty.

Many cities adopt greening strategies to reduce contamination from combined sewer overflows (CSOs). Nonetheless, quantifying the impact of urban greening on CSO-affected water quality at the city scale remains challenging. To address this challenge, this work leveraged supervised learning to link water swimmability with the greening of a CSO shed (the drainage area of a CSO outfall), using New York City (NYC) as a case study. Random forest classification models were built to predict water swimmability after rainfall at 46 sites in NYC water bodies impacted by CSOs. A 14-feature model (AUROC =0.81, accuracy = 0.78) revealed that greening improved local water quality. However, water flow speed, antecedent rain depth, and CSO shed area were also influential. A simplified four-feature model (AUROC = 0.8, accuracy = 0.75) explored links between levels of greening and the probability of non-swimmable waters (Pns) following different 18 h rainfall depths. Increased greening was found to be most impactful in reducing Pns for CSO sheds discharging to water bodies with flow speeds < 6 cm/s. For CSO sheds discharging to water bodies with flow speeds ≥ 14.7 cm/s, urban greening had no impact on Pns. The work illustrates the utility of supervised learning in supporting citywide decisions regarding urban greening investments.

In the original publication [...]

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km2 per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies.