To investigate the optimization mechanism of nitrogen removal in anaerobic ammonium oxidation (Anammox) systems under high nitrogen loading, an up-flow reactor was operated continuously for 120 days. By stepwise increasing influent nitrogen loading and combining sludge characterization with high-throughput sequencing, we systematically analyzed nitrogen removal efficiency, sludge physicochemical properties, and microbial community succession. Results showed that at a volumetric nitrogen loading rate of 1.43 kg N/(m3·d), ammonia nitrogen, nitrite nitrogen, and total nitrogen removal efficiencies reached 82%, 95%, and 80%, respectively, with the nitrogen conversion ratio approaching the theoretical stoichiometry. High-throughput sequencing revealed that dominant phyla (Proteobacteria, Planctomycetota, and Chloroflexota) remained present throughout operation but shifted in relative abundance. Under progressively increasing nitrogen loading, the dominant AnAOB shifted from Ca. Brocadia (initial 21.3%) to the high-substrate-tolerant Ca. Kuenenia (final 28.7%). These results support a stage-wise conceptual understanding of ecology-function-performance coupling and provide engineering reference for optimizing high-load Anammox systems.
This study aimed to characterize freshwater microbial communities, environmental drivers, and anthropogenic impact patterns across three sites on Marambaia Island (southeastern Brazil) using metagenomics. Samples collected from freshwater sources used for human consumption were processed through concentration, nucleic acid extraction, and sequencing on the Illumina NextSeq 2000 platform. A total of 67.2 million reads were assembled into 89,230 bacterial contigs, mostly attributed to Gammaproteobacteria, Alphaproteobacteria, and Betaproteobacteria. Sites under lower anthropogenic influence exhibited higher microbial diversity, whereas impacted sites showed enrichment of opportunistic and fecal-associated genera. A heterogeneous anthropogenic impact profile was observed across sites, corroborated by the proposed Anthropogenic Impact Index (AII). Fourteen antimicrobial resistance genes conferring resistance to beta-lactams, quinolones, sulfonamides, tetracyclines, and macrolides were detected predominantly in sewage-impacted areas, indicating potential diffuse contamination. Redundancy analysis revealed that environmental variables explained 88.1% of microbial community variation, with conductivity, salinity, and turbidity as key drivers. These findings demonstrate the applicability of metagenomics as a powerful tool for assessing microbial diversity, ecological dynamics, and contamination risks in vulnerable freshwater systems.
Brazil is one of the world's largest sugarcane producers, and the sugar-energy industry generates large volumes of by-products such as vinasse and vinasse sludge, which require appropriate management to minimize environmental impacts. This study evaluated the agricultural disposal of sludge generated during the cleaning of vinasse storage reservoirs and its potential effects on adjacent surface water quality in Costa Rica, Mato Grosso do Sul, Brazil. Surface water quality was monitored at eight sampling points between 2020 and 2023, covering dry and rainy seasons. A total of 34 physicochemical and microbiological parameters were analyzed, and statistical tests were applied to assess spatial and seasonal variations and differences between areas with and without sludge application. The results showed significant spatial differences for calcium, electrical conductivity, dissolved iron, magnesium, manganese, ammoniacal nitrogen, and pH, while seasonal differences were observed for turbidity, total chromium, dissolved iron, and pH. Higher concentrations of several parameters were recorded at points P6 and P7; however, all monitored variables remained within the limits established by CONAMA Resolution No. 357/2005. Comparisons between areas with and without sludge application did not indicate measurable deterioration of surface water quality associated with sludge disposal. These findings suggest that the agricultural use of vinasse sludge, when conducted in accordance with environmental regulations and appropriate management practices, can be environmentally safe. Nevertheless, long-term monitoring remains essential to detect potential cumulative effects and ensure the sustainable management of water resources.
Membrane capacitive deionization (MCDI) is a promising electrochemical technique for water desalination. However, accurately predicting its salt removal efficiency (SRE) remains challenging because desalination performance is governed by complex nonlinear interactions among operating conditions and feed-water characteristics. In this study, a laboratory-scale MCDI system was designed, fabricated, and experimentally evaluated to investigate key operational parameters (applied voltage and feed flow rate) and feed-water characteristics (total dissolved solids, salinity, pH, and temperature) on desalination performance. To address the limited number of experimental observations, 25 experimental samples were augmented to a dataset of 150 samples using a Bayesian network-based DataSynthesizer. Four ensemble machine learning algorithms, namely, random forest (RF), categorical boosting (CatBoost), eXtreme gradient boosting (XGBoost), and gradient boosting regression (GBR), were optimized using Bayesian hyperparameter optimization (Optuna). Among the investigated models, GBR achieved the highest predictive performance with training and testing R2 values of 0.999 and 0.958, respectively, outperforming RF, CatBoost, and XGBoost. To improve model interpretability, SHapley Additive exPlanations (SHAP), partial dependence plots (PDP), individual conditional expectation (ICE), and accumulated local effect (ALE) analyses were employed to quantify feature importance and nonlinear relationships. Thus, the proposed framework can serve as an interpretable artificial intelligence tool for accurately predicting SRE and supporting the design, operation, and optimization of MCDI systems.
Groundwater quality assessment in hyper arid aquifers is often limited by sparse monitoring, incomplete hydraulic data, and difficulty separating prior hydrochemical conditions from short-term meteorological forcing. This study develops a hydrochemically grounded and machine learning framework with internal transferability testing to diagnose chloride-dominated salinization in the Rawdatain-Umm Al Aish freshwater aquifers of northern Kuwait. The framework combines four components: comparison of pre-1990 baseline chemistry with 2012-2015 observations, chloride-specific salinization diagnostics, separation of predictors into meteorological, space-time, prior-chemistry, and hybrid groups, and model evaluation using random, temporal, and grouped-well validation. The analysis also integrates spatial salinity mapping, Cl-TDS analysis, Na:Cl ratios, carbonate/sulfate balance, principal component analysis (PCA), and supervised learning models to evaluate both hydrochemical change and predictive structure. This integrated analysis shows substantial deterioration relative to the historical baseline. Median TDS increased from 905.5 to 1821.0 mg/L (+101.1%), whereas median Cl, Ca, Mg, and Na increased by 228.5%, 194.6%, 227.0%, and 75.3%, respectively. HCO3 remained broadly stable, whereas NO3 declined by 43.5%. Diagnostic plots and PCA indicate chloride-rich salinization governed by mixed geochemical and mixing processes rather than simple halite dissolution alone. Random holdout models performed well, with R2 values of 0.947 for SO4, 0.913 for Na, 0.912 for Ca, 0.877 for HCO3, 0.868 for TDS, 0.808 for NO3, 0.807 for Cl, and 0.673 for Mg. However, validation performance was analyte-specific: Cl declined mainly under temporal holdout, NO3 declined under grouped-well holdout, and Mg showed the lowest random-holdout skill and largest train-test gap despite higher grouped-well performance. Predictor importance showed that spatial coordinates, elevation, and lagged chemistry were more influential than meteorological predictors alone. The framework supports targeted monitoring of chloride-rich hotspots and validation-aware groundwater quality decision support in hyper arid freshwater reserves.
Fluoride contamination in drinking water poses a significant environmental and public health challenge, necessitating the development of sustainable and efficient remediation materials. In this study, calcium oxide (CaO) nanoparticles were synthesized using banana blossom calyx residue as a renewable biotemplate through a simple precipitation-calcination approach. The porous biomass architecture facilitated calcium ion adsorption and the subsequent formation of nanostructured CaO. Characterization by XRD, TGA/DTA, UV-Vis spectroscopy, photoluminescence spectroscopy, FTIR, FESEM, and EDS confirmed the formation of crystalline CaO nanoparticles with an average crystallite size of 12 nm. FESEM analysis revealed hierarchical surface morphology with well-defined cubic and rhombohedral particle geometries ranging from 1.65 to 2.0 μm, composed of aggregated nanocrystalline domains. The synthesized nanoparticles exhibited effective fluoride removal, achieving 100% removal at an initial fluoride concentration of 3.84 mg L-1 and a maximum adsorption capacity of 25.12 mg g-1. Adsorption equilibrium followed the Langmuir isotherm model (R2 = 0.990), indicating predominant monolayer adsorption. The enhanced fluoride removal was attributed to the synergistic effects of surface adsorption and CaF2 precipitation. These findings demonstrate a facile and environmentally sustainable strategy for converting agricultural waste into functional nanomaterials for water purification applications.
Groundwater environmental background value is a core parameter for distinguishing natural background fluctuations from anthropogenic pollution interference, underpinning the safe utilization of groundwater resources and effective water environment management. Accurately determining such values in high iron and manganese enrichment areas bears great theoretical and practical significance, yet traditional calculation methods are susceptible to anomalous data and cannot effectively differentiate geological-origin from human-induced anomalies. Taking the Yinchuan Plain as the study area, this research adopts two-factor method of absolute content and milliequivalent percentage (two-factor method), combined with hierarchical cluster analysis (HCA) and principal component analysis (PCA) to eliminate outliers based on 741 sets of shallow groundwater hydrochemical monitoring data. The background values are calculated from the normal distribution of filtered data and verified against traditional statistical methods. Results show the upper background limit and threshold of total iron (TFe) are both 0.30 mg/L, whereas those of manganese (Mn) are 0.09 and 0.10 mg/L. Groundwater Fe and Mn enrichment in the Yinchuan Plain is dominated by natural geological processes. Quaternary unconsolidated sediments provide the primary material source, whereas the flat topography induces slow runoff and a strongly reducing environment, facilitating the reductive dissolution of Fe/Mn minerals. Complexation with HCO3 - further stabilizes ionic species, collectively driving accumulation. Although human activities exert notable influences on TFe and Mn, these disturbances are localized and relatively minor compared to geological controls. This study offers a robust quantitative basis for pollution identification, water quality assessment, and groundwater management in the region.
Imidacloprid (IMI), one of the most extensively used neonicotinoid insecticides, has become a widespread environmental contaminant owing to its high water solubility, persistence, and intensive agricultural applications. Its frequent detection in soil and aquatic environments has raised growing concerns because of its adverse effects on pollinators, aquatic organisms, wildlife, and potential risks to human health. Although numerous remediation technologies have been developed, existing reviews largely focus on individual treatment approaches and provide limited comparative evaluation of their remediation efficiency, mineralization potential, transformation-product toxicity, scalability, and long-term sustainability. This review addresses this gap by critically assessing the physicochemical properties, environmental fate, toxicological impacts, and remediation technologies of IMI. Comparative analysis indicates that electrochemical and advanced oxidation processes generally achieve rapid degradation and superior mineralization; however, their practical implementation is often constrained by energy consumption, catalyst or electrode costs, and uncertainties regarding the toxicity of degradation intermediates. In contrast, adsorption and membrane-based technologies offer rapid and scalable pollutant removal but primarily transfer IMI into secondary waste streams without complete detoxification. Biological and hybrid treatment systems provide environmentally compatible alternatives with lower energy requirements, although slower degradation kinetics, environmental variability, and limited field-scale validation remain significant challenges. Collectively, the evidence suggests that integrated technology can simultaneously maximize removal efficiency, mineralization, cost-effectiveness, and environmental safety for sustainable detoxification and long-term management of IMI-contaminated environments.
Enhanced human activities have increased nitrate contamination in groundwater, threatening water-supply safety and public health. This study examined nitrate sources and transformation processes in 29 shallow groundwater samples from the lower Yellow River Basin, a region subject to intensive agricultural, industrial, and domestic pressures. The observed NO3 --N concentrations ranged from 0.37 to 177.95 mg/L. Self-organizing maps, the MixSIAR model, and hydrochemical and multi-isotope analyses identified five hydrochemical clusters. Across all samples, the mean proportional contributions of soil nitrogen and chemical fertilizers were 52.6% and 31.0%, respectively, indicating that these two sources dominated groundwater nitrate. Sewage/manure and industrial or mining wastewater made smaller overall contributions, but their relative importance increased in areas affected by stronger human activities. The isotope and hydrochemical evidence indicated that nitrification was a key process increasing groundwater nitrate concentrations; this effect was particularly pronounced in the northwestern groundwater convergence zone, where soil nitrogen accumulation was relatively high. These findings suggest that anthropogenic inputs not only introduce nitrate directly but also enhance the leaching and transformation of soil- and fertilizer-derived nitrogen. The results support targeted groundwater management through precision fertilization, soil nitrogen monitoring, wastewater control, and priority protection of vulnerable recharge and convergence areas.
Phreatic groundwater sustains ecosystems and human livelihoods in hyperarid endorheic watersheds but faces severe threats from widespread nitrogen pollution and extreme salinity. This study systematically investigates water quality suitability, nitrogen health risks, and geochemical degradation mechanisms in the Golmud River watershed on the Tibetan Plateau. The results indicate that the regional groundwater is generally alkaline and features relatively high mineralization. Water quality suitability is relatively poor, and only 37.78% (EWQI < 100) of the groundwater is suitable for daily human use. Water quality exhibits a continuous and sharp deterioration trend from pristine upstream mountainous areas to severely polluted downstream salt marsh plains. This poses extensive health risks, which mainly arise from nitrogen contamination. These risks exhibit significant population heterogeneity and spatial consistency among different groups. NO2 - is the primary nitrogen species causing health threats. Infants face the most severe health risks among all the evaluated populations. High nitrogen risks areas in the watershed are concentrated in the downstream salt marsh plain and the agricultural zones of the loess plain. Strong evaporative concentration under extreme drought and anthropogenic pollution from agricultural expansion are the core mechanisms degrading phreatic groundwater quality and increasing nitrogen risks. Intensive agricultural activities in the loess plain directly trigger NO2 - nonpoint source pollution. Extreme evaporation and weak hydrodynamics in the downstream salt marsh plain highly enrich hydrochemical components and cause long-term accumulation of industrial nitrogen from salt lake mining. These findings provide a reference for the pollution prevention and sustainable development of hypersaline groundwater globally.
Electrofusion is an emerging frontier in sustainable water purification, integrating microbial activity with electrochemical processes to achieve efficient and eco-friendly treatment. Bioeletrofusion improves microbial fuel cells (MFCs) and bioelectrochemical systems by allowing controlled membrane fusion in electric fields. This makes electron transfer pathways better and increases catalytic efficiency. This enables superior degradation of organic pollutants, pathogen elimination, and recovery of clean water with minimal chemical use. The technology stands out for its dual functionality: treating diverse municipal and industrial effluents while simultaneously generating bioelectricity, thereby advancing circular economy principles. With its low energy demand and adaptability, electrofusion offers a scalable solution well suited for decentralized and resilient water infrastructure. Current research focuses on optimizing electrode materials, engineering microbial consortia, and integrating smart monitoring systems to support real-world deployment. Collectively, bioelectrofusion holds significant promise for transforming wastewater treatment and expanding access to clean water worldwide.
The development of sustainable and effective membranes for industrial wastewater treatment is a significant challenge in environmental protection. In this work, slag and metakaolin-based geopolymer membranes were prepared and optimized for textile wastewater treatment. Three slag contents (30 wt%, 50 wt%, and 70 wt%) were considered. They were synthesized and systematically compared with respect to their structural, microstructural, mechanical, filtration, and dielectric properties. Specifically, it was found that the membrane with 50 wt% slag (GM-50) retains a good balance between porosity (34.65%), compression, and permeability results. Structural data confirmed the formation of a hybrid C-A-S-H/N-A-S-H geopolymer framework, whereas SEM measurements revealed an integrated, uniform pore structure beneficial for filtration. The GM-50 membrane exhibited excellent filtration performance, achieving high removal efficiencies of turbidity, COD, and color in textile wastewater. Apart from filtration performance, the dielectric properties of the optimized membrane were characterized using impedance spectroscopy over a broad frequency range (101-107 Hz) and temperature range (30°C-180°C). The inferred transport mechanisms arise predominantly within a structurally heterogeneous geopolymer matrix and are driven by thermally activated hopping processes. Maxwell-Wagner-Sillars interfacial polarization dominated the dielectric behavior, and non-Debye-type relaxation characteristics indicated that the material's structural heterogeneity and pore connectivity were key factors. The synergistic structural, functional, and dielectric characterization confirmed that the optimized GM-50 membrane is a feasible and sustainable option for textile wastewater treatment with direct relevance to electrical transport properties of geopolymer-based materials.
Microplastics (MPs) provide favorable ecological niches for antimicrobial resistance (AMR) development in aquatic ecosystems. Environmental weathering transforms the inert surfaces of MPs into oxygen-functionalized, reactive interfaces that promote plastisphere formation and selective enrichment of antibiotic-resistant microorganisms. However, studies integrating natural polymer weathering, plastisphere development, and resistome profiling under ecologically relevant conditions remain scarce, particularly in South Asian freshwater ecosystems. To address this knowledge gap, low-density polyethylene (LDPE) pellets were incubated in situ in the anthropogenically impacted Haora River of Northeastern India to investigate how environmental aging-induced surface transformations shape plastisphere formation and association of AMR characteristics. Pristine, aged with biofilm, and aged without biofilm LDPE MPs were comparatively analyzed. Weathering significantly increased surface roughness, crystallinity, and carbonyl index, facilitating dense biofilm formation (OD595 = 1.47 ± 0.02) and elevated intracellular reactive oxygen species (171 net RFU per OD600 unit). Shotgun metagenomic sequencing of plastisphere biofilms was performed on the Illumina NovaSeq 6000 platform. Resistome, mobilome, and metal resistance determinants were annotated using ARG-OAP v3.0, DeepARG Galaxy v1.0.4, MobileOG-db v2.0.1, and BacMet v2.0, respectively. The plastisphere was dominated by the class Gammaproteobacteria, including opportunistic pathogens (Aeromonas, Pseudomonas aeruginosa, and Acinetobacter baumannii), together with clinically relevant antibiotic resistance genes, mobile genetic elements, and metal resistance determinants. These findings demonstrate that naturally aged microplastics act as dynamic reservoirs and vectors for AMR dissemination in riverine environments.
Sandstone-hosted uranium (U) deposits are genetically linked to hydrogeochemical processes, with groundwater serving as the principal medium for uranium activation, migration, and immobilization. To elucidate the evolutionary characteristics and primary factors of aqueous uranium, this study jointly employs hydrochemical characteristics, ion ratio analysis and geochemical modeling to characterize the hydrogeochemical evolution, the spatial distribution and speciation of aqueous uranium. The results indicate that groundwater in the study area is weakly alkaline brackish water of Cl·SO4-Na type. Its hydrochemical evolution is primarily controlled by silicate mineral dissolution coupled with intense direct cation exchange. Dissolved uranium concentrations in groundwater vary from 0.26 to 114.00 μg/L, with a mean value of 28.55 μg/L. Uranium anomalies in groundwater display pronounced stratigraphic and spatial compartmentalization: elevated uranium concentrations are predominantly observed in the K1S confined aquifer, whereas the C-P aquifer consistently exhibits low background levels. Uranium enrichment is co-driven by oxidative conditions and Ca2+-mediated complexation. Oxidants (e.g., NO3 -) promote oxidative dissolution of tetravalent uranium minerals into soluble uranyl ions. In addition, the dissolution of carbonate minerals supplied sufficient Ca2+ and HCO3 - for uranyl complexation. Abundant Ca2+ further stabilizes dissolved uranium as neutral Ca2UO2(CO3)3 complexes, which markedly enhance uranium enrichment and mobility in groundwater. The positive correlation between carbonate saturation indices and uranium concentrations provides further thermodynamic evidence for this complexation-driven enrichment pattern. These findings decipher the hydrogeochemical driving mechanisms of uranium migration and provide vital scientific guidance for uranium exploration in sandstone aquifers of the Erlian Basin and analogous basins.
This study developed an integrated and uncertainty-aware machine learning framework to predict and optimize methylene blue (MB) removal efficiency using biochar adsorbents. A dataset comprising 504 literature-derived adsorption experiments was compiled using five operational parameters, namely, initial dye concentration, contact time, initial pH, adsorbent dosage, and temperature. Four advanced ensemble learning models, namely, XGBoost, CatBoost, LightGBM, and stacking ensemble, were developed and evaluated. The stacked ensemble model achieved the highest predictive performance with R2 = 0.7740, RMSE = 7.95, and MAPE = 7.04%, demonstrating the ability to capture complex nonlinear adsorption processes. Explainable artificial intelligence (XAI) analysis using SHAP identified temperature, contact time, and adsorbent dosage as the most influential variables governing MB removal. Sensitivity analysis further confirmed the significant influence of contact time, temperature, and adsorbent dosage on prediction variability. Two Bayesian optimization strategies were investigated, mathematical optimization, performed over the complete range of the literature-derived dataset, and engineering-constrained optimization, performed within practically feasible operating ranges reported in previous adsorption studies, and both cases reported a maximum predicted removal efficiency of 99.99% with varying input variables. Monte Carlo uncertainty analysis indicated a mean predicted efficiency of 75.05% with a 95% confidence interval of 71.85%-79.54%. Residual diagnostics and Bland-Altman analysis demonstrated satisfactory agreement between predicted and observed values within the investigated dataset. Overall, the proposed framework integrates prediction, explainability, uncertainty quantification, and optimization into a unified workflow, providing a promising decision-support tool for biochar-based wastewater treatment.
This study evaluated the predictive capability of long short-term memory (LSTM) neural network models for forecasting key operational variables in a membrane bioreactor (MBR) using a comparatively modest training dataset, a limited number of sensors, and a basic model architecture, with practical implementation in mind. Time-series data for dissolved oxygen (DO), pH, transmembrane pressure (TMP), mixed liquor suspended solids (MLSS), and airflow rate were collected from a laboratory-scale MBR over several months of continuous operation. Separate training and testing periods were used to develop and evaluate the models. The LSTM models achieved good predictive accuracy for DO, pH, and TMP over a 6-h prediction horizon and retained useful predictive capability at 12 h. However, the 12-h predictions showed a systematic delay in responding to process changes, indicating reduced reliability at longer horizons. Correlation analysis between airflow rate and the other operational variables further showed that the LSTM models did not fully capture the dynamic responses of the MBR to changes in aeration. This limitation likely reflects the complex and nonlinear behavior of activated sludge processes. Overall, the results indicate that a basic LSTM architecture trained on a comparatively modest dataset can support operational forecasting over horizons of 6 and 12 h. However, improved representation of aeration-related dynamics is required before the model can be used reliably for advanced operational decision support.
Water utilities are looking for creative solutions that can lower sludge output while maintaining process dependability and environmental compliance due to the increasing operational and regulatory difficulties related to managing sewage sludge. In order to minimize sludge, this study presents the first full-scale implementation of a thermophilic pure-oxygen biological process integrated with ceramic ultrafiltration (UF) membranes in Europe. The system uses thermophilic conditions (50°C-55°C) to treat thickened municipal sludge, and membrane separation ensures complete biomass retention. Long-term monitoring (2024-2025) revealed high biodegradation performance, with VS removal exceeding 85% and very low biomass yields (0.01 kg VS kg-1 COD_removed). COD removal efficiencies were slightly lower due to the selective permeation of soluble organic matter, which is beneficially reused as an external carbon source for downstream denitrification. Oxygen- and energy-based indicators confirmed that process efficiency was strongly influenced by membrane hydraulic performance, particularly membrane fouling, permeability decline, and the associated membrane maintenance requirements. Despite higher energy demand compared to conventional mesophilic processes, the substantial sludge reduction and recovery of soluble COD make the system economically competitive in a regulatory context where sludge disposal options are increasingly limited. The results demonstrate the robustness and sustainability of thermophilic UF-based sludge reduction and highlight its potential as a strategic solution for modern wastewater treatment plants seeking resilient, circular, and regulation-proof sludge management pathways.
Changes in permafrost in cold regions directly affect groundwater circulation and biogeochemical processes. To accurately capture these complex permafrost-groundwater interactions, precise identification of permafrost thermal regimes is of paramount importance. In large-scale permafrost modeling, the model for the temperature at the top of permafrost (TTOP) has been widely applied due to its clear physical mechanisms and computational efficiency. However, most studies directly employ satellite-based land surface temperature (LST) products as forcing data, which often leads to biases in simulating permafrost distribution. In this study, we used a Random Forest (RF) regression model to reconstruct spatially continuous ground surface temperature (GST) data as the driving input to simulate permafrost characteristics in the Source Area of the Yellow River (SAYR) from 2000 to 2020. The results indicate that over the past two decades, the permafrost area has decreased by 2252 km2, while the active layer thickness (ALT) has increased by 1.6 cm/10a, and the maximum depth of seasonally frozen ground (MFDSFG) has decreased by 7.4 cm/10a. Extensive ground ice thaw directly reshapes local aquifer frameworks, subsequently impacting groundwater routing and cycling processes. Compared with measured data from 132 boreholes, the accuracy of our permafrost mapping reached 79.5%. Validation against field observations reveals that incorporating GST as the forcing data for SAYR permafrost identification enhances the TTOP model accuracy by 6.4%. However, the result indicates that directly using LST products to drive the TTOP model in SAYR tends to overestimate the permafrost area by 5583 km2, particularly in the marginal zones of permafrost degradation and around large lakes.
This study investigates simultaneous nutrient removal and recovery from nondigested livestock manure wastewater. To manage high suspended solids, chitosan-mediated coagulation was utilized as a pretreatment for effective solid-liquid separation. Subsequent precipitation experiments evaluated the influence of pH, initial molar ratios, temperature, and multi-ionic interference. Optimal conditions were identified at pH 9.5, a Mg:N:P molar ratio of 1.2:1:1, and 30°C, achieving aqueous reductions of 95% for PO4-P, 91% for Mg, and 97% for NH4-N. The primary scientific contribution of this study is the identification of Hazenite (KNaMg2(PO4)2.14H2O) as the recovered solid phase, rather than conventional magnesium ammonium phosphate (struvite). Structural characterizations via XRD and SEM-EDX revealed the absence of nitrogen in the recovered precipitate. These analyses indicate that the elevated background potassium and sodium concentrations drove the crystallization of this specific alkali-magnesium phosphate. Thermodynamic and elemental analyses indicate that the extensive depletion of aqueous NH4-N was primarily driven by pH-induced ammonia volatilization rather than solid-phase recovery. Furthermore, ionic competition and kinetic inhibition restricted both ammonium and calcium integration into the crystal lattice. These findings demonstrate that in complex, alkali-rich effluents, the precipitation pathway fundamentally shifts from standard struvite toward Hazenite recovery, providing an alternative for nutrient upcycling into high-value mineral phases.
A wide range of pollutants enter rivers from cities located on their banks, causing structural and functional changes in the riverine microbial communities. Although microorganisms play important roles in aquatic environments and are sensitive indicators of water pollution, the anthropogenic transformations of riverine bacterioplankton remain insufficiently studied. Using microscopy and 16S rRNA gene metabarcoding, we investigated the distribution, size-morphological structure, taxonomic composition, and potential metabolic functions of bacterioplankton in the Tura River near a major regional center (Tyumen city, Western Siberia, Russia). The river has experienced progressive shallowing and prolonged low water conditions for several consecutive years, substantially exacerbating pollution impact. Bacterial abundance, structure, diversity, and potential metabolic functions differed markedly among river sections located upstream, within, and downstream of the city. The greatest number of bacterial pathogens and pollution indicators was detected downstream of the city. Under anthropogenic impact, rare and moderate taxa benefit, whereas typically dominant freshwater taxa are more sensitive to pollution and eutrophication. Within the city, increases in bacterial abundance, taxonomic diversity, and predicted metabolic functions, including pollutant biodegradation, suggest both community adaptation and bioremediation potential, as well as functional instability and a shift toward allochthonous or opportunistic taxa. These results highlight the important role of bacterioplankton in urban river ecosystems along pollution gradients.
