Abstract: Despite the closure of the Semipalatinsk nuclear test site (STS) more than 30 years ago, the removal of radioactive contamination beyond the “Degelen” test site by water continues. Therefore, assessing the water resources formation at this test site is highly relevant, including for predicting the development of the radiation situation at the STS. In this case, isotope hydrology is the most promising method for understanding these processes. The aquatic environment at the “Degelen” test site consists of radioactively contaminated tunnel water, streams, and groundwater. The article presents the research results of the aquatic environment of the “Degelen” test site using the method of isotope hydrology with determination of stable isotopes of hydrogen and oxygen. The determination of the 3H concentration and the chemical composition of water were also determined. The analysis of the isotopic composition (δ2H, δ18O) of water showed that the tunnel and stream water are formed by precipitation (snow and rain). In summer, when precipitation is low, the condensation water significantly contributes to the recharge of “Degelen” test site water. The high radionuclide content of tunnel water leads to contamination to a greater extent of stream water, and, to the lesser extent, and groundwater. The 3H content in tunnel water can reach 260 kBq/L, in stream water – 58 kBq/L, which exceeds the standards established in the Republic of Kazakhstan.

Abstract: Groundwater resources management is a critical issue for populations in regions with significant growth. In arid and semiarid regions, where rainfall is scarce, groundwater is often the only source of water for all their needs. Nevertheless, groundwater characterization is virtually unknown in countries with limited infrastructure and resources. A short-term solution is to study groundwater through numerical simulation using the limited available data. We developed a GIS-integrated groundwater flow scheme based on the finite-difference method to numerically simulate a heterogeneous medium using surface geological information. We validate our implementation by comparing it with MODFLOW. We performed groundwater simulations of the Administrative Aquifer in Calera, Zacatecas, Mexico, using homogeneous and heterogeneous media to evaluate flow changes resulting from heterogeneities. We found that numerical simulations show flow barriers in low hydraulic-conductivity zones that coincide with the administrative boundaries of the aquifer; however, in high hydraulic-conductivity zones, the administrative aquifer boundaries do not match the geological limits of the aquifer. This finding gives insight into reconsidering the administrative boundaries of some aquifers in the region for their sustainability, with an integral understanding of groundwater.

Abstract: In this study, we investigated the impact of varying iron (Fe) and aluminum (Al) contents on the adsorption of phosphonates to activated sludge. Phosphonates originating from household applications account for up to 40% of the non-reactive dissolved phosphorus in domestic sewage treatment plants and thus can contribute to the eutrophication of water bodies. Although these substances are not readily degradable, substantial quantities, ranging from 40% to more than 90%, are removed by sludge adsorption.The results demonstrate a strong correlation between the adsorption of aminophosphonates and the Fe3+ content of the sludge. The maximum phosphonate loadings were 5.94 mmol g-1 Fe3+ for ATMP, 4.94 mmol g-1 Fe3+ for EDTMP and 4.74 mmol g-1 Fe3+ for DTPMP, and 2.25 mmol g-1 Fe3+ for Glyphosate. In contrast to pure ferric hydride flocs, the adsorption of phosphonates was approximately threefold higher when the hydroxides were located within activated sludge flocs. It is concluded that native sludge flocs provide larger iron surfaces than ferric hydroxide alone. Based on the weight of the adsorbents, aluminum salts were four times less efficient than ferric salts. In sludge without ferric or aluminum hydroxides, phosphonate adsorption was negligible.

Abstract: Heavy precipitation clustering is an important factor for flood risk and hydrological forecasting, but its simulation in climate models is still uncertain. This study examines European climate simulations by comparing global climate models with long-term observations. Consecutive wet days (CWD) and very wet days (R95p) were used as indicators, and clustering behavior was assessed across major regions in Europe. The results show that models capture large-scale extremes with moderate accuracy but underestimate clustering by 20–30% in Mediterranean and Alpine areas. Sensitivity tests also show that clustering depends on temporal resolution, with daily indices showing stronger underestimation than multi-day metrics. These outcomes suggest that current models reproduce intensity more reliably than persistence, which lowers confidence in hydrological forecasts and flood risk analysis. The study concludes that improvements in physical schemes and the use of convection-permitting models are needed to better simulate precipitation clustering in Europe.

Abstract: Understanding the temporal clustering of heavy precipitation is important for flood risk assessment in Europe, but current climate models show limited skill in reproducing these events. This study evaluates ten CMIP6 models against the E-OBS dataset for the period 1981–2020, focusing on consecutive days of extreme rainfall defined above the 95th percentile. Cluster features were measured using mean cluster length and maximum consecutive wet days, and model skill was assessed with correlation, mean absolute error, and Nash–Sutcliffe Efficiency. The results show that models capture large-scale spatial patterns but underestimate persistence by 15–30% in Mediterranean and Alpine regions. In contrast, performance is better in maritime climates, where correlations reach 0.55–0.65 and mean absolute error is close to one day. Sensitivity tests indicate that thresholds and linking rules strongly affect cluster metrics. Spatial analysis further highlights systematic underestimation of persistence in southern Europe. These results point to the need for better representation of persistence, convective rainfall, and orographic effects in climate models. Although the analysis is limited by the coarse resolution of observations and the small set of models, it provides useful evidence for improving model evaluation and supports more reliable flood risk management under climate change.

Abstract: Spatial heterogeneity in economic benefits of water use provides crucial evidence for the evaluation of water diversion projects and the spatial equilibrium of water resource allocation. However, research focusing on the economic benefits of water use in major industries within Chinese cities remains insufficient. We collected water use and value added data of agriculture, industry, and service sectors of 334 Chinese cities in 2017, and used the benefit allocation coefficient method to calculated the economic benefit of water use in the three sectors. Then we analyzed spatial heterogeneity in the economic benefits of water use: for agricultural sector, the high economic benefit of water use regions are primarily concentrated on both sides surrounding of the “Hu Huanyong Line”; regions with high economic benefit of industrial water use are mainly found in the North China Plain, the middle and lower Huanghe River basin, the Yangtze River Delta, the Pearl River Delta, Chongqing and Chengdu; and the economic benefit of service water use is higher in the north than in the south. Based on the fundamental distribution of water resources and the spatial heterogeneity in the economic benefits of water use, potential water diversion areas can be preliminarily identified: the Haihe River Basin in the North China Plain and some areas in the southeast coastal region are potential receiving areas, and the eastern regions of Southwest China with abundant water resources and lower elevations, along with the middle and lower reaches of the Yangtze River are potential source areas. Further research about marginal benefits and water use costs, along with dynamic updates, is required for water resource allocation of China.

Abstract: Based on multi-batch filtration and cleaning experiments, this study systematically evaluated the fouling potential of pre-treated textile dyeing wastewater by membrane bioreactor on reverse osmosis (RO) membranes and the recovery performance of fouled RO membranes after different cleaning methods. After a permeate production of 625 L/m², continuous foulants accumulation resulted in a significant decline in RO membrane permeability. Protein-like substances and sol-uble microbial products were identified as the primary organic foulants via three-dimensional fluorescence excitation-emission matrix spectrometry (3D-FEEM). The single forward flushing with pure water, acid solution (pH 3.5), alkaline solution (pH 10.5), and sodium hypochlorite with low effective chlorine concentration (1-2 mg/L) showed very limited recovery of fouled RO membrane permeability. The combined forward flushing with acid solution (pH 2) followed by alkaline solution (pH 11.5) restored fouled membrane permeability up to 87% of new RO mem-brane. The addition of pure water backwashing at transmembrane pressure of 0.5 MPa after both acid and alkaline combined forward flushing restored fouled membrane permeability up to 97% of new RO membrane but deteriorated the rejection capacity of RO membrane. The backwashing parameters were further optimized as transmembrane pressure of 0.5 MPa and crossflow veloci-ty of 0.5 m/s, achieving fouled membrane permeability up to 96% of new RO membrane and no negative effects on the rejection capacity of RO membrane. Therefore, the combined cleaning of acid forward flushing → pure water backwashing → alkaline forward flushing → pure water backwashing was proposed for RO membrane cleaning from textile dyeing wastewater reuse application.

Abstract: Accurate streamflow forecasting is vital for sustainable water resource management but remains challenging due to pronounced spatiotemporal variability. This study evaluates two process-based models SWAT (comprehensive) and GWLF (parsimonious)—and a data-driven Random Forest (RF) model for monthly streamflow simulation in two contrasting Chinese basins: the humid southern basin (SSB) and the semi-arid northern basin (SRB). Using four statistical metrics (NSE, R2, MAE, RMSE), we assess model accuracy, robustness in capturing extremes, and sensitivity to hydrological characteristics and data availability. Results reveal consistently superior performance in the SSB across all models, with SWAT demonstrating the highest overall accuracy—especially for peak flows—due to its physically based structure. GWLF provides acceptable simulations with minimal data requirements, offering a practical alternative in data-limited regions like the SRB. RF performs well in the SSB under zero-lag conditions but requires hydrologically informed lag structures in the SRB. However, it consistently underestimates high flows due to its lack of physical constraints. The findings underscore that model selection must therefore be guided not only by predictive performance, but by the underlying hydrological context, data availability, and the need for physical realism in decision-making.

Abstract: Heavy precipitation clustering is important for flood risk in Europe, but its description in reanalysis datasets is still uncertain. This study examined how well ERA5, ERA5-Land, and JRA-55 reproduce the size and timing of extreme precipitation from 1981 to 2022. Observations from the E-OBS dataset were used as reference, with heavy events defined as daily totals above the 95th percentile. Consecutive wet days were grouped into clusters, and measures such as mean cluster length (MCL) and mean gap between clusters (MGC) were used. Correlations between reanalysis and observed MCL were 0.58–0.63 across seasons, with mean absolute errors of 0.9–1.2 days. The largest bias was found in convective areas, where MGC was underestimated by up to 0.6 days. Sensitivity tests showed that thresholds and linking rules had stronger influence on clustering than the dataset used. The results show that reanalyses reproduce large-scale patterns but tend to underestimate storm duration and event order, which affects flood modeling. Better use of data, improved physical methods, and denser observation networks are needed to reduce these limits and support climate adaptation.

Abstract: Sustainable urban water governance in rapidly transforming cities requires integrative decision-making frameworks capable of balancing social equity, economic efficiency, and environmental resilience. This study develops a multi-criteria decision-making (MCDM) model designed to support policy optimization for sustainable water management in Yerevan City, Armenia. Building upon prior AI- and GIS-based diagnostics, the proposed framework integrates quantitative indicators of social participation, economic cost-efficiency, and ecological performance into a unified analytical structure. Using AHP–TOPSIS weighting and scenario analysis, the study evaluates alternative policy strategies such as leakage reduction, demand management, and decentralized reuse systems. Results reveal the trade-offs and synergies among sustainability dimensions, highlighting that equity-prioritized weighting schemes enhance social outcomes without significantly compromising economic performance. The Yerevan case demonstrates how adaptive, data-informed governance models can strengthen resilience, improve resource allocation, and guide policy under uncertainty. The framework contributes to advancing decision science in urban water management and offers transferable insights for mid-income cities facing institutional and environmental constraints.

Abstract: This study presents an analysis of the Infrastructure Leakage Index (ILI) variability for two District Metered Areas (DMAs) in the Silesian Region (Poland), based on 2024 data. The objective was to assess the suitability of the ILI for short-term leakage evaluation and its potential to support operational decision-making. ILI values were calculated for daily, weekly, and monthly intervals using synchronized hourly data from an Advanced Me-tering Infrastructure (AMI) system and water network monitoring platforms. A key ad-vantage of the dataset was the temporal alignment of inflow, outflow, and consumption data at the DMA level. The study applied statistical measures of variability (standard deviation, variance, coeffi-cient of variation) and graphical methods (histograms, boxplots) to evaluate ILI behavior across time resolutions. The two DMAs were compared to explore the differences in ILI dynamics, range, and identify operational factors influencing leakage levels. The results confirm that ILI interpretation is highly dependent on temporal resolution. Daily data is more responsive to anomalies and operational events, while monthly data provides more stable values suitable for benchmarking. Despite the aggregation smooth-ing the variability, notable differences in system performance between zones persist. High-frequency AMI data enhances the precision and operational value of ILI analyses.

Abstract:

Water is fundamental to the economy of Pakistan, and to many of the county’s 250 million people. Increasing groundwater use in Pakistan is occurring against a backdrop of climate change, dwindling surface water, and human population growth. Historically there has been little groundwater resource management in the country, and that little has mainly related to its use, that is to demand side management. There is some potential for Managed Aquifer Recharge (MAR) to contribute to groundwater supplies, that is, to supply side management. MAR is a complex process which to be effective requires consideration of social, institutional, environmental, technical, financial, and economic parameters. This paper introduces a methodology that has been developed to assess and sum the effect of multiple parameters into single numerical value, to enable the feasibility of proposed MAR projects; the “Managed Aquifer Recharge Feasibility Index (MARFI)”. MARFI was developed using a case study of an MAR project currently being implemented by the Punjab Irrigation Department in the bed of Old Mailsi Canal. MARFI is a suitable tool for examining the pre-project feasibility of any MAR scheme for contributing to wise investment decision making.

Abstract: The treatment of refractory nitrogenous organic matter poses challenges in the removal of organic matter and nitrogen. In this study, a designed novel composite hydrodynamic cavitator, which can generate cavitation twice perpass at the throat of the spiral pipe and the step drain of the cavitation cavity superior to current other cavitators with only once perpass, was used to address these issues. The composite hydrodynamic cavitator optimized using ANSYS simulation software exhibits significant advantages in energy utilization and mass transfer efficiency. Moreover, it generates a high concentration of hydroxyl free radicals, which is crucial for organic matter degradation. Batch experiments proved the good treatment of 4-aminophenol solution. Within 120 minutes, 4-aminophenol degradation efficiency reached 74.7%, total nitrogen concentration decreased from 1.28 mg/L to 1.06 mg/L, while ammonia nitrogen concentration initially increased before decreasing from its peak value of 0.82 mg/L to 0.77 mg/L. During the cavitation treatment of 4-aminophenol solution, intermediate products such as benzoquinone are generated. Under the strong oxidative action of hydroxyl radicals, the nitrogen element undergoes deamination to form ammonium ions, which are ultimately removed in the form of nitrogen gas.

Abstract: The Boudenib Oasis in southeastern Morocco depends heavily on its limited water resources for domestic use and agriculture. However, climate change is expected to intensify water scarcity through changes in precipitation and evapotranspiration (ET). This study assesses the potential impacts of climate change on precipitation (P) and evapotranspiration in the Guir watershed using the Soil and Water Assessment Tool Plus (SWAT+). Historical (1984–2015) and future (2070–2100) simulations were conducted under the high-emission Representative Concentration Pathway (RCP8.5), driven by five bias-corrected global climate models (GCMs): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. Model outputs show a projected decline in annual precipitation of approximately 25–30%, from an average of 380 mm during the historical period to about 280 mm by 2100, and a reduction in ET of 15–20%, from roughly 380 mm to 314 mm. Despite uncertainties inherent in climate projections, consistent multi-model agreement increases confidence in these results. This study represents one of the first applications of SWAT+ in southeastern Morocco, providing new insights into hydrological responses to climate change in data-scarce arid environments. The findings highlight the urgent need for adaptive water management, integration of traditional systems such as khettaras, and sustainable land-use planning to strengthen resilience under future climate conditions.

Abstract: Phenolic compounds are persistent pollutants in agro-industrial wastewaters and hinder biological treatment due to their antimicrobial activity. Activation of peroxymonosulfate (PMS) by iron species represents an efficient advanced oxidation route for their degradation. In this work, the oxidation of protocatechuic acid (pCtchA) by the Fe(II)/Fe(III)/PMS system was investigated through a mechanistic model constructed from a comprehensive set of elementary reactions describing radical and non-radical pathways. The coupled kinetic equations were numerically solved to predict the temporal evolution of PMS, iron species, and organic intermediates. Model simulations were validated with batch reactor data and accurately reproduced the effects of iron and PMS concentrations, temperature, and pH. The results confirm that Fe(III) reduction to Fe(II) is the rate-determining step, and that the ortho-dihydroxy functionality of pCtchA promotes this reaction via Fe(III)–ligand complexation, sustaining the catalytic cycle. Optimal degradation occurs near pH 3.5, where Fe(III)–pCtchA monocomplexes predominate, and the apparent activation energy for Fe(III) reduction is approximately 36 kJ mol⁻¹. This study provides a quantitative kinetic framework for the Fe(II)/Fe(III)/PMS system and demonstrates its potential as a sustainable catalytic process for the oxidation of dihydroxy-substituted phenolic pollutants in aqueous media.

Abstract: This study employed a comprehensive methodology to investigate the role of water utilities in the implementation of Nature-Based Solutions (NBS) for water management, such as Sustainable Drainage Systems. The methodological approach involved analysing the Urban Nature Atlas database to evaluate European funding sources and stakeholders associated with NBS for water management in Europe. Focusing on the Italian context, the study then conducted semi-structured interviews with Italian experts and mapped exemplary cases where Italian water utilities are actively participating in NBS implementation efforts. The results provide insight into the factors that hinder and drive NBS development by water utilities in Italy. Using a SWOT analysis, the study proposes five distinct roles that water utilities could potentially adopt to advance NBS. The article offers valuable insights for policymakers, urban planners, and water utility stakeholders, emphasising the importance of multi-stakeholder collaboration to secure financing for NBS.

Abstract: Sustainable and resilient hydrologic urban ecosystem services rely on connectivity among gray, green, and blue infrastructures. We conducted a field study of post-urban landscape hydrology in inland (River Rouge) and coastal (Detroit River) catchments on the west, and east sides, respectively, of Detroit MI. Measurements of parcel (n = 36) soil hydraulics as infiltration and drainage rates, soil taxonomic data, and depth of the groundwater table were used to determine connectivity. Hydrologic data was parsed into “stoplight” categories (green, yellow, orange, red) that at once communicate con-nectivity and best uses for sustained rendering of ecosystem services from vacant par-cels. Parcels with yellow, orange, red infiltration rates, would respond to tillage, cover cropping to increase gray-green connectivity; whereas drainage rates in the orange and red categories suggest re-development. Blue-green connectivity was constrained by high variation in drainage rate, perched versus deeper saturated zones; urban drainage affecting local groundwater gradients; and relatively little groundwater data to compare against. This assessment process can serve to optimally allocate city re-sources to effectively and efficiently identify best management practices (including redevelopment) for vacant sites, and sustain ecosystem services where they are most strongly rendered as connectivity amongst gray, green, and blue water regimes.

Abstract: The increasing amount of plastics in aquatic systems poses risks to water quality and biodiversity by transporting pathogens and antibiotic resistance genes (ARGs). This paper reviews how plastics spread and persist as vectors for these contaminants. In addition, their attachment, transport, and release mechanisms on plastic surfaces are discussed, underscoring the need for advanced detection and monitoring methods. Future research should focus on developing practical mitigation strategies and policy interventions to address plastic-mediated microbial pollution. Ultimately, this paper emphasizes the value of interdisciplinary work to protect aquatic ecosystems and public health from the adverse effects of plastic pollution and proposes potential solutions to address this global challenge.

Abstract: Urban flooding is one of the greatest challenges to sustainable development in rapidly urbanizing cities. This study applies an integrated approach that combines Sentinel-1 radar data, geomorphological analysis, and the DPSIR (Drivers–Pressures–State–Impacts–Responses) framework to assess the relationship between urbanization and flooding in Hanoi during the period 2010–2024 (with Sentinel-1 time series covering 2015-2024). A time series of Sentinel-1 images (2015–2024) was processed on the Google Earth Engine platform to detect inundation and construct maps of flood frequency, validated against 148 field survey points (overall accuracy = 87%, Kappa = 0.79). Results show that approximately 80% of newly urbanized areas are located on geomorphologically sensitive units (paleochannels, floodplains, karst), which are highly flood-prone. Meanwhile, 57% of older urban areas are mainly flooded on inner-dike floodplains, and notably, 36.5% of new urban areas have been developed on floodplains with inherently high flood risk. The DPSIR analysis highlights rapid population growth, land-use change, and inadequate drainage infrastructure as the main pressures driving both the frequency and extent of flooding. To our knowledge, this is the first study to integrate geomorphology, Sentinel-1, and DPSIR for Hanoi, providing robust scientific evidence to support sustainable urban planning and enhance resilience in the context of climate change.

Abstract:

The paper introduces a machine learning method of detecting multiple sources of water contamination caused by wildfire. The method includes changing the water flow regime, monitoring the time series of the contaminant concentration caused by regime changes, and associating the signature of the contaminant changes over time with sources locations. The contaminant signature from multiple sources starting at the moment of changing water velocity are defined by extending the approach for one contamination source. The intensity, location of each source, and diffusion coefficient are defined to satisfy the minimum square between monitoring and theoretical concentrations. The equations derived from the criteria of the best fit between experimental and modeling data are solved using the theory of hypernumbers. The initial values for hypernumber solutions are computed using the transient process of contaminant transport curve analysis. The defined in this paper algorithm can by used for detecting location of the arbitrary impurity in water network system.