Search Results (2,568)

Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH₃-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination ($R_{adj}^2$) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies. Full article

Water quality plays a central role in determining the environmental performance of pond-based tropical aquaculture systems. This study aimed to evaluate the relative environmental performance of different tropical pond-based aquaculture systems by identifying multivariate water quality patterns that allow their discrimination and comparison under commercial production conditions. Four pond-based production systems were evaluated: an aquaponic system (APS), a recirculating aquaculture system (RAS), a conventional earthen pond system (CEP), and an integrated rice–chame system (RCS). Fourteen physicochemical water quality variables were monitored throughout the production cycle under real commercial conditions using a comparative observational design. Multivariate discriminant analysis was applied to identify the variables with the highest discriminatory power and evaluate the ability of water quality patterns to correctly classify observations among production systems. The results revealed a clear multivariate separation between technologically intensive systems (APS and RAS) and less intensive and integrated systems (CEP and RCS), reflecting distinct water quality structures and environmental functioning. Variables associated with mineralization and nutrient dynamics, including electrical conductivity, dissolved solids, turbidity, phosphates, chlorides, dissolved oxygen, nitrites, and temperature, contributed most strongly to system discrimination. The discriminant functions achieved a high overall correct classification rate, demonstrating the robustness of the multivariate approach. These findings support the use of water quality variables as consistent environmental signatures for distinguishing tropical pond-based aquaculture systems, providing an operational framework for assessing their relative environmental performance. Discriminant analysis emerges as a valuable tool for system characterization and comparative evaluation, supporting environmentally informed management and optimization of chame aquaculture under tropical conditions. Although water quality represents a robust integrative indicator, it captures only one dimension of environmental performance, and additional factors such as production efficiency, energy use, and effluent characterization should be incorporated in future studies to achieve a comprehensive sustainability assessment. Full article

Sewerage systems are a main element of a city’s infrastructure. Roughness coefficients are fundamental parameters for sewage system operation. The intermittent nature of the flow leads to the appearance of deposits that become an integral part of the sewerage systems. Deposited material not only leads to the loss of hydraulic capacity and decreases the concentration of dissolved oxygen (which is found in direct relation to all quality parameters), but it also results in more transported particles being intercepted. In the design calculations, the roughness coefficient is estimated rather than calculated. It has been demonstrated that the estimation of stress within and above roughness elements improves the predictive capability for the concentration of suspended sediment. In this study, we focused on a local evaluation of the roughness coefficient when the flow velocity is below the minimum self-cleansing velocity. Some authors consider the selection of the most reliable method for estimating bed shear stress to be the main challenge. Other authors have suggested that all possible methods should be applied simultaneously to achieve a reliable bed shear stress estimation, knowing that the roughness coefficient can be determined through the shear boundary stress. We calculate the local roughness coefficient in Manning’s equation using a laboratory model, considering clear water flowing over a solid boundary with consolidated deposits, represented by artificial roughness elements (calibrated hemispheres). The European standard EN 752:2017 specifies a minimum average cross-sectional velocity of 0.7 m/s for pipe self-cleansing. This study established the range of possible roughness coefficient values when the minimum velocity design criterion is not met. The second criterion was to consider acceptable a sediment deposit occupying between 1% and 2% of the collector diameter. Velocity distributions around artificial roughness and statistical parameters of the turbulent flow were obtained using a PIV system. Five methods were implemented and the range of roughness coefficient values varied between 0.007 and 0.023. This variation is closely related to sewer performance. We selected the dissipation method as the primary reference for this study, as it is most closely aligned with the underlying physics of flow over roughness elements. This approach allows for robust validation by correlating multiple characteristic mechanisms of the turbulent cascade. Full article

Arsenic (As) is a ubiquitous and highly toxic metalloid with well-established carcinogenicity. Its accumulation and secondary release from lake sediments pose potential risks to lake ecosystem integrity and human health. Meanwhile, the ongoing intensification of lake eutrophication at the global scale has altered the sources, composition, and environmental behavior of internally derived dissolved organic matter (DOM). These changes have profoundly influenced As mobilization and transformation at the sediment-water interface (SWI). To advance understanding of the regulatory roles and underlying mechanisms of algal dissolved organic matter (ADOM) and submerged macrophyte dissolved organic matter (SMDOM) in As biogeochemical cycling under lake ecosystem regime shifts, extensive findings from the international literature were synthesized. The characteristic properties and environmental behaviors of ADOM and SMDOM were systematically compared, and their distinct regulatory pathways in lacustrine systems were further summarized. Results indicate that ADOM is typically characterized by low molecular weight, weak aromaticity, and high bioavailability. It can enhance As dissolution and mobilization from sediments through direct complexation, competition for adsorption sites, and stimulation of microbial metabolism and Fe(III) reduction. In contrast, SMDOM exhibits higher molecular weight, greater aromaticity, and a higher degree of humification. It tends to form stable complexes with mineral phases. Under the influence of radial oxygen loss (ROL) from submerged macrophyte roots during the growth phase, its capacity to promote mineral reduction is relatively limited. This process favors stable As retention in sediments. The regulatory effects of ADOM and SMDOM on As behavior are strongly modulated by environmental factors such as pH, redox potential (Eh), temperature, and light conditions, as well as by microbial communities. ADOM is more sensitive to reducing environments and photochemical processes. SMDOM, in contrast, exerts more persistent control under oxidizing conditions and at mineral-water interfaces. In addition, ADOM more readily drives microbial community shifts toward assemblages with enhanced capacities for Fe(III) reduction and As reduction or methylation. SMDOM is less likely to trigger strongly reducing processes. Based on these mechanisms, the outbreak and decay phases in algal-dominated lakes often correspond to critical periods of enhanced As mobilization and elevated ecological risk. In submerged macrophyte-dominated lakes, the decay phase may represent an important window for sedimentary As release. Finally, a conceptual framework describing the differential regulation of As biogeochemical cycling by ADOM and SMDOM is proposed. This framework provides a theoretical basis for As risk identification, the determination of critical risk periods, and the development of management strategies across lakes with different trophic states. Full article

Nutrient inputs from human activities, such as agriculture and sewage discharge, influence algal blooms in water bodies. In Ecuador, the Daule River receives wastewater discharges. In addition, poor agricultural practices, including the unsuitable use of fertilisers in combination with soil erosion and surface runoff processes, increase the nutrient load to the river. Considering this, the objective of this study was to evaluate environmental and biological variables using statistical analysis to identify the parameters that influence algal blooms in the main stem of the Daule River. The methodology consisted of two phases: (i) data collection, including water sampling and laboratory work for the analysis of nutrients and phytoplankton, and (ii) statistical analysis, which includes univariate, bivariate, inferential and multivariate analysis (STATICO technique). The results showed that pH and dissolved oxygen were the main drivers of diatoms (Polymyxus coronalis and Aulacoseira granulate) and the charophyte Mougeotia sp. Similarly, ammonium-N was the main driver of the diatom Ulnaria ulna and the cyanobacteria Planktothrix cf. agardhii. The outcomes of this study identified the main environmental variables driving blooms of the five most abundant species, providing a basis for the development of ecological models in the context of land use and climate change. Full article

Latitudinal gradients are widely recognized as a key macro-environmental driver shaping microbial biogeographic patterns; however, the spatial organization of sediment archaeal communities in estuarine ecosystems and the mechanisms underlying their assembly remain insufficiently understood. This study is based on sediment samples collected from three representative estuarine regions spanning distinct latitudes along the Chinese coastline—the North China Sea (NCS), East China Sea (ECS), and South China Sea (SCS). Based on 16S rRNA high-throughput sequencing, combined with null-model inference and molecular ecological network (MEN) analyses, we characterized latitudinal patterns in archaeal community distributions, assembly processes, and cross-regional interaction architectures. The results showed that archaeal communities exhibited obvious spatial segregation across three regions, with both community richness and network complexity increasing significantly toward lower latitudes. Nitrate (NO₃⁻), ferric iron (Fe³⁺), and dissolved oxygen (DO) were identified as key environmental factors governing archaeal community structure. Notably, archaeal community assembly processes exhibited a clear latitudinal gradient: deterministic processes, particularly environmental filtering, were more obvious at lower latitudes, whereas the contributions of stochastic processes—including dispersal limitation and ecological drift—increased markedly at higher latitudes. A MEN analysis further revealed that archaeal networks at lower latitudes exhibited higher connectivity, modularity, and stability, suggesting that interspecific interactions may enhance ecosystem resistance to environmental disturbance under more stable environmental conditions. Overall, this study demonstrates that macro-environmental gradients jointly shape archaeal biogeographic patterns via multiple pathways, including modulation of environmental filtering, dispersal dynamics, and cross-regional interactions. These findings deepened our understanding of the stable mechanisms governing the diversity and biogeographical distribution of archaea in estuarine systems. Full article

Eukaryotic microorganisms, including microalgae, protists, fungi, and micro-metazoans, act as drivers of energy flow and nutrient cycling, collectively forming the microbial food loop, and also serve as important indicators of environmental health. To investigate the seasonal variation in eukaryotic microorganisms in a mussel farming area, a total of 96 seawater samples were collected from surface and bottom layers of water across different seasons. High-throughput sequencing of the 18S rRNA gene was employed to characterize shifts in microbial community structure and identify key influencing factors. Our results indicated significant seasonal differences in eukaryotic microbial communities between surface and bottom waters. Redundancy Analysis (RDA) revealed that seasonal variations in community structure were primarily driven by environmental factors such as temperature, dissolved oxygen (DO), and salinity. Co-occurrence network analysis indicated that surface water networks exhibited higher numbers of nodes and edges, as well as greater modularity, suggesting more distinct niche differentiation and higher natural connectivity within the community. These findings provide fundamental data for understanding the response mechanisms of eukaryotic microbial communities to seasonal changes in the mussel cultivation area of Gouqi Island. Full article

Biogeochemical processes, including the oxygen cycle, were investigated in Lake Baikal during the spring thermal bar using a coupled numerical model that takes into account the intraday variability of atmospheric parameters and contains the following variables: nitrate, ammonium, phosphate, oxygen, chlorophyll a, phytoplankton, zooplankton, and small and large detritus. Nitrification, photosynthesis, remineralization, and respiration processes describe the biochemical dynamics of oxygen in the model. As a study area, the deep-water cross-section of Lake Baikal, Boldakov River–Maloye More Strait, was considered using meteorological data for June 2024 at the lake surface. Numerical results show that the thermal bar can contribute to the transport of dissolved oxygen and phyto- and zooplankton to the deeper layers of the lake. Full article

To accurately manage precise feeding and water quality regulation in the rice–duck–crayfish integrated system (RDCI), the continuous monitoring of plankton and physicochemical parameters in the water was conducted from March 2022 to January 2023 in both the RDCI and the rice–crayfish continuous culture system (RCCC). The results showed that a total of 188 phytoplankton species and 92 zooplankton species were identified in the RDCI, whereas 152 phytoplankton species and 95 zooplankton species were detected in the RCCC. The phytoplankton community composition was similar between these two systems. For zooplankton, Rotifera was the dominant group. However, Chlorophyta and Bacillariophyta were the dominant phytoplankton groups. Compared with the RCCC, the RDCI exhibited lower plankton density during the crayfish-farming stage and overwintering stage, but higher plankton biomass during the crayfish-farming stage, overwintering stage, and rice maturity stage. The diversity indices, richness indices, and evenness indices of both phytoplankton and zooplankton in the RDCI were significantly higher than those in the RCCC. Correlation analysis indicated that water temperature, dissolved oxygen, total nitrogen, and ammonia nitrogen were the key environmental factors affecting plankton community structure. In summary, compared with the RCCC, the RDCI exhibits higher plankton diversity and better evenness, suggesting a more complex and stable community structure. The species composition of plankton and related indices indicate that the RDCI mitigates the degree of eutrophication in water during both the crayfish farming and the overwintering stages, while increasing nutrients levels during the rice planting stage. This approach is beneficial for reducing non-point-source pollution in agriculture and promoting green agricultural development. Full article

Dissolved oxygen (DO) is a crucial indicator for characterizing water quality and ecosystem status in freshwater lakes, and its concentration is closely correlated with the surrounding aquatic environment, particularly meteorological conditions. However, traditional DO prediction methods struggle to effectively capture the intricate coupling relationships between multi-station meteorological factors and DO concentration time series, limiting the prediction accuracy. This study proposes a multi-scale spatio-temporal graph neural network with integrated multi-meteorological factors. Taking Taihu Lake and its surrounding cities as the study area, a meteorological graph is constructed based on the geographic proximity between meteorological stations, and a dual-stage “local–global” modeling strategy is adopted to capture the spatio-temporal dependencies of DO concentration under meteorological forcing. Using R², RMSE, MAE and MAPE as evaluation metrics, we conducted single-step and multi-step DO prediction experiments on the 2023–2024 Taihu Tuoshan water quality dataset and compared the proposed model with commonly used prediction models. In the single-step prediction task, the proposed model improved R² by 2.12–20.84% and reduced RMSE, MAE, and MAPE by 3.05–40.80%, 14.97–53.26%, and 6.91–55.62%, respectively. In the 6-step-ahead and 12-step-ahead prediction tasks, RMSE and MAE were reduced by 3.79–15.75% and 6.68–23.09%, and by 5.03–10.39% and 7.13–16.46%, respectively. The experimental results provide quantitative evidence for the superiority of the proposed model in single-step and multi-step DO prediction. This study offers a novel data-driven tool for lake water quality early warning and drinking water safety, and the proposed framework can serve as a reference for water quality prediction studies driven by multi-source environmental factors. Full article

Satellite remote sensing increasingly supports water quality monitoring, yet the temporal transferability of machine learning (ML) models remains insufficiently tested, particularly in coastal shallow lakes subject to hydrological variability. This study evaluates the predictive robustness of satellite-based ML models for electrical conductivity (EC), turbidity (TUR), water temperature (WT), and dissolved oxygen (DO) in Vrana Lake, Croatia. A total of 409 in situ measurements collected during 2023–2024 and 2025 were paired with Sentinel-2 and Landsat 8–9 imagery. Pearson, Spearman, and Kendall correlation analyses were applied for parameter-specific band selection using original, inverse, quadratic, and logarithmic feature transformations. Seventeen regression algorithms were evaluated under six training–testing split strategies, including strict temporal projection. WT exhibited high robustness (R² ≈ 0.90 under temporal projection) due to its strong dependence on thermal bands, while DO achieved moderate temporal stability (R² = 0.51) using log-transformed predictors. EC and TUR demonstrated substantial performance degradation under temporal separation (R² = 0.14 and −4.62, respectively), reflecting sensitivity to distribution shifts. For parameters showing sufficient stability, interpretable band-based retrieval equations were derived using the most strongly correlated spectral predictors. These findings highlight the importance of temporally structured validation and demonstrate that model complexity does not guarantee operational robustness in shallow, dynamically evolving lake systems. Full article

Water pollution from the sugar industry is a significant environmental problem as it generates effluents containing organic compounds, solids, nutrients, and chemicals such as H₃PO₄, SO₂, and Ca (OH)₂. Mesoporous silica nanoparticles (MSNs) are a promising option for its treatment, due to their high surface area, and ease of functionalization using organically modified silanes (ORMOSIL) improving its adsorption of contaminants. The objective of this study is to remove anions (Cl⁻, SO₄²⁻, NO₂⁻, NO₃⁻) from the wastewater of a sugar mill in Campeche, Mexico and improve its physicochemical parameters (conductivity, turbidity, dissolved oxygen) using MSNs functionalized with 3-aminopropyltriethoxysilane (MSNs-APTES) or 3-(2-aminoethylamino)propyltrimethoxysilane (MSNs-3-2-A). The synthesized materials were characterized by FTIR and XPS analyses, which confirmed the incorporation of amino functional group and that MSNs-APTES exhibited a stronger N1s signal, indicating greater surface accessibility of amino groups. However, a partial surface masking under complex aqueous conditions was revealed. In contrast, MSNs-3-2-A showed lower apparent surface exposure of amino groups maintaining a more stable functional presence after exposure, likely due to its diamine structure promoting more confined interactions within the mesoporous framework. The results of removing anions and physicochemical parameters of wastewater exposed to MSNs indicate that treatments with MSNs-APTES and MSNs-3-2-A were able to significantly reduce the concentrations of SO₄²⁻, NO₂⁻ and NO₃⁻ anions, but not able to reduce the chloride ion. A decrease in turbidity and an increase in dissolved oxygen were also observed. Then, both materials proved to be functional and stable in contact with wastewater, demonstrating their potential for environmental remediation, particularly for the removal of anionic contaminants from sugar industry effluents. Full article

Zelenci is a limnocrene spring in the South-Eastern Alps, attractive for its unique structure and known among researchers for its high diatom diversity. Our aim was to assess how abiotic factors influence the structure of diatom communities in different habitats and to compare the trophic status over 10 years of investigation. Four sampling sites were chosen: two for tychoplankton and two for epipelon. Achnanthidium minutissimum was the most abundant species in both habitats, while Navicula was the most diverse genus (17 species). Planktonic diatoms dominated plankton samples, while the motile ecological type dominated the epipelon. Of all diatom taxa, 23.5% had some endangerment status. Key factors influencing the Shannon–Wiener diversity index were water level, temperature, and concentration of NH₄⁺. The most important abiotic factors for the tychoplankton community were temperature and NH₄⁺, while the most important abiotic factors influencing the structure of epipelon communities were water level, NH₄⁺, pH, and dissolved oxygen concentration. Trophic index revealed increasing inflow of nutrients to the spring; 10 years ago, they were oligotrophic to oligo-mesotrophic, whereas in the present, they are predominantly eu-mesotrophic to eutrophic, indicating human pressure from the catchment area. Full article

Pondweeds, an important component of macrophyte vegetation, are influenced by various ecological factors of the aquatic ecosystem. In turn, pondweeds affect the nutrient and sediment dynamics and provide food and shelter for other organisms. As different species have specific environmental preferences and tolerances, they can serve as indicators of the ecological status of water bodies. Here, the ecological preference of the seven most frequent pondweeds in Croatia (Potamogeton berchtoldii, P. crispus, P. lucens, P. natans, P. nodosus, P. perfoliatus and Stuckenia pectinata) for chemical and physical water parameters was studied using 218 vegetation relevés and the accompanying water parameters. CCA revealed the main environmental gradients described by six parameters (chemical oxygen demand, total nitrogen, total phosphorus, electrical conductivity, dissolved oxygen and pH), while ecological responses of the species were further explored by GAMs. Potamogeton berchtoldii, P. lucens, P. natans and P. perfoliatus prefer clean, oxygenated, oligo- to mesotrophic water, and P. crispus and S. pectinata thrived in eutrophic water with low oxygen levels, while P. nodosus is a widespread generalist. The results of this study explain the distribution patterns of Potamogeton and Stuckenia species in Croatia, and add to the general knowledge on their role as bioindicators. Full article

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R² ≥ 0.80 for all SWQPs) and demonstrated robust performance (R² ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems. Full article