Search Results (1,569)

Climate change affects surface water quality parameters, including river quality. This study analyses changes in climate parameters, specifically air temperature and solar radiation, and their impact on river water temperature. It also examines how changes in river water temperature and organic matter load affect oxygen saturation levels, a key indicator of river water quality. Using water quality data, the status as well as temporal and spatial trends of the analysed parameters were assessed for the period between 2007 and 2024 on the three largest Slovenian rivers: the Drava, Mura, and Sava. Relative importance analysis of temperature and biochemical oxygen demand (BOD) using the Random Forest machine learning method showed that water temperature in the analysed rivers has an impact ranging from 51% to 66% on predicting oxygen saturation. The selected approach to analysing watercourse quality parameters enables the assessment of the impact of these parameters on river water quality. Based on these results, it will be possible to implement appropriate measures promptly to achieve sustainable river management by establishing a strategy that, under climate change conditions, safeguards water quality and maintains ecosystem protection, ensuring long-term ecological and socio-economic benefits. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

This study evaluates the spatiotemporal variations in surface water quality in An Giang province, a key upstream region of the Vietnamese Mekong Delta (VMD), under the influence of hydrological alterations and climate change impacts. Water quality data from 2010 to 2023 were collected from 10 monitoring stations along the Tien and Hau Rivers, focusing on key parameters including pH, temperature, Dissolved Oxygen (DO), Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammonium (N-NH₄⁺), Nitrate (NO₃⁻), orthophosphate (P-PO₄³⁻), and Coliforms. The Mann–Kendall test and Sen’s slope estimator were employed to detect long-term trends and quantify the magnitude of changes. The findings indicated that the Hau River exhibits significant organic pollution, evidenced by elevated levels of BOD and COD, alongside diminished levels of DO. The Tien River exhibits elevated concentrations of NH₄⁺ and total suspended solids (TSS). The MK test indicated that BOD, COD, and NH₄⁺ levels were increasing at most locations in a statistically significant manner. This indicates that the water quality deteriorated over time. The study revealed that the majority of pollutants exhibited statistically significant increasing trends (p ≤ 0.05). The Tien River’s COD is increasing by 1.6 mg/L annually, whereas the Hau River’s COD is escalating by 1.7 mg/L per year. The biochemical oxygen demand on both rivers is increasing by 0.5 mg/L each year. The diminishing quantities of dissolved oxygen indicated a decline in water quality. Pollutant concentrations demonstrated significant positive associations with maximum temperature (r = 0.47–0.64) and hours of sunshine (r ≈ 0.50–0.64). A significant negative correlation with river discharge was observed, particularly during the dry season (r = −0.79 to −0.88), when diminished flows resulted in elevated pollution concentrations. The findings offer measurable evidence that increasing temperatures and decreasing river flows significantly affect water quality, underscoring the necessity of adapting water resource management in the Mekong Delta. Full article

Aquatic vegetation (AV) forms the structural and functional basis of lake ecosystems, providing irreplaceable ecological functions such as water self-purification and the sustenance of biodiversity. Under the “Yangtze River’s Great Protection Strategy”, the action of returning nets to the lake has significantly improved water-quality in the middle and lower reaches of the Yangtze River (MLRYR) basin. However, its ecological benefits for key biotic components, particularly AV communities, remain unclear. To address this knowledge gap, this study utilized Landsat and Sentinel-1 satellite imagery to analyze the dynamic evolution of enclosure aquaculture (EA) and AV in 25 lakes (>10 km²) within the MLRYR basin from 1989 to 2023. A U-Net deep learning model was employed to extract EA data (2016–2023), and a vegetation and bloom extraction algorithm was applied to map different AV groups (1989–2023). Results indicate that by 2023, 88% (22/25) of the lakes had completed EA removal. Over the 34-year period, floating/emergent aquatic vegetation (FEAV) exhibited fluctuating trends, while submerged aquatic vegetation (SAV) demonstrated a significant decline, particularly during the EA demolition phase (2016–2023), when its area sharply decreased from 804.8 km² to 247.3 km²—a reduction of 69.3%. Spatial comparative analysis further confirmed that SAV degradation was substantially more severe in EA removal areas than in EA retention areas. This study demonstrates that EA demolition, while beneficial for improving water quality, exerts significant short-term negative impacts on AV. These findings highlight the urgent need for lake governance policies to shift from single-objective management toward integrated strategies that equally prioritize water-quality improvement and ecological restoration. Future efforts should enhance targeted restoration in EA removal areas through active vegetation recovery and habitat reconstruction, thereby preventing catastrophic regime shifts to phytoplankton-dominated turbid-water states in lake ecosystems. Full article

The global food industry is undergoing a major shift driven by increasing consumer demand for clean-label and naturally preserved foods. Fresh pasta is highly vulnerable to fungal damage because of its high water activity (a_w > 0.85), typically ranging between 0.92 and 0.97, moderate to near-neutral pH (around 5.0–7.0), and nutrient-rich composition, all of which create favorable conditions for fungal growth during refrigeration, mainly by genera such as Penicillium and Aspergillus. Fungal contamination results in significant economic losses due to reduced product quality and poses potential health risks associated with mycotoxin production. Although conventional chemical preservatives are relatively effective in preventing spoilage, their use conflicts with clean-label trends and faces growing regulatory and consumer scrutiny. In this context, antifungal lactic acid bacteria (LAB) have emerged as a promising natural alternative for biopreservation. Several LAB strains, particularly those isolated from cereal-based environments (e.g., Lactobacillus plantarum and L. amylovorus), produce a broad spectrum of antifungal metabolites, including organic acids, phenylalanine-derived acids, cyclic dipeptides, and volatile compounds. These metabolites act synergistically to inhibit fungal growth through multiple mechanisms, such as cytoplasmic acidification, energy depletion, and membrane disruption. However, the application of LAB in fresh pasta production requires overcoming several challenges, including the scale-up from laboratory to industrial processes, the maintenance of metabolic activity within the complex pasta matrix, and the preservation of desirable sensory attributes. Furthermore, regulatory approval (GRAS/QPS status), economic feasibility, and effective consumer communication are crucial for successful commercial implementation. This review analyzes studies published over the past decade on fresh pasta spoilage and the antifungal activity of lactic acid bacteria (LAB), highlighting the progressive refinement of LAB-based biopreservation strategies. The literature demonstrates a transition from early descriptive studies to recent research focused on strain-specific mechanisms and technological integration. Overall, LAB-mediated biopreservation emerges as a sustainable, clean-label approach for extending the shelf life and safety of fresh pasta, with future developments relying on targeted strain selection and synergistic preservation strategies. Full article

The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning approaches for lake total nitrogen to total phosphorus ratios (TN:TP), utilizing Zhuhai-1 hyperspectral satellite imagery to develop a CNN-SVR ensemble model integrating convolutional neural networks and support vector regression for remote sensing inversion of lake TN:TP ratios. Performance is evaluated against random forest (RF) and convolutional neural network (CNN) models, systematically analyzing spatial distribution patterns and primary drivers. Results indicate that the CNN-SVR model demonstrated superior performance among the tested models, with R², RMSE, MAPD, and RPD values of 0.856, 2.675, 9.516%, and 2.390, respectively. Spatially, the nitrogen-to-phosphorus ratio in lakes during the growing season exhibits an increasing trend from the western to the eastern half of the lake, progressing from northwest to southeast. When TN:TP falls below 9, algal growth becomes nitrogen-limited, indicating a higher degree of eutrophication; when TN:TP exceeds 22.6, phosphorus becomes the limiting factor, indicating lower eutrophication levels. A similar distribution pattern is observed during the non-growing season. Regarding driving mechanisms, the nitrogen-to-phosphorus ratio during the growing season is primarily influenced by TN accumulation and shows significant correlations with dissolved oxygen (DO) and pH. During the non-growing season, while still affected by TN input, its association with other water quality parameters is weaker. The results indicate that the combined use of CNN and SVR improves feature extraction and model fitting in nitrogen-to-phosphorus ratio inversion and helps clarify its ecological significance as an indicator of algal growth. This provides methodologies and evidence for precise diagnosis and ecological management of lake eutrophication. Full article

Desertification is an ever-growing global ecological and environmental problem. With the implementation of various ecological restoration initiatives, vegetation cover in many desert regions has increased substantially. Consequently, it is essential to understand the dynamics of ecosystem services (ESs) in desert ecosystems to better inform environmental management. This study integrates the InVEST model, RWEQ model, Spearman correlation analysis, trade-off and synergy coefficient method, and the Partial Least Squares Path Model (PLS-PM) to systematically assess the spatio-temporal dynamics and underlying driving mechanisms of five key ESs in the Kubuqi (KBQ) Desert, northern China. Specifically, the application of PLS-PM enables the identification of latent pathways, indirect effects, and multi-step causal relationships, which traditional correlation-based methods fail to capture. The results show that the KBQ Desert underwent substantial land use changes from 2000 to 2020: sandy land decreased by 2697.83 km², grassland increased by 1864.15 km², and cropland and urban land expanded by 519.15 km² and 257.74 km², respectively. ESs exhibited divergent trajectories. habitat quality (HQ), carbon sequestration (CS), soil conservation (SC), and water yield (WY) all showed overall increases, with WY and SC increasing particularly strongly, whereas Sand-fixation service (G) displayed a fluctuating trend. Over the past two decades, HQ–CS, HQ–G, and CS–G have shown moderately strong synergies, while CS–WY has exhibited a pronounced trade-off, and SC–G and SC–CS have displayed relatively weaker trade-offs. The spatial distribution results of trade-off and synergy relationships show that the KBQ Desert is dominated by a synergy relationship, and the main synergy relationship combinations are CS–HQ, CS–SC, and HQ–SC. The correlation coefficients between other ES pairs are generally low. Additionally, this study identifies key pathways through the PLS-PM method, such as PRE → NDVI → ES and LU → NDVI → ES, revealing the complex interactions between precipitation (PRE), land use (LU), and vegetation dynamics. The findings show that land use (LU) consistently exerts a strong negative impact on CS, while PRE and NDVI have a significant positive effect on WY. These pathways deepen our understanding of how climate and anthropogenic factors affect ESs, particularly the influence of temperature (TEMP) on evapotranspiration (ETP), which in turn affects WY. Additionally, the impact of NDVI on wind–sand fixation (G) and SC varies over time, with vegetation dynamics playing a particularly enhanced role in 2010 and 2015. These findings highlight the impact of ecological restoration and land management on regional ESs changes. A comprehensive understanding of the interactions between climate factors, LU, and vegetation dynamics will help in developing more effective intervention strategies. Full article

Cold-bent glass has been utilized in a number of landmark projects globally, owing to its cost-effectiveness and low carbon footprint. To investigate the impact of cold bending loads on the long-term performance of insulating laminated glass, this paper proposes a durability testing method for cold-bent glass. This method is based on the hypothesis that the failure of the glass sealing system is caused by the cold bending process. It is novel in its use of full-scale glass panel specimens subjected to the maximum design cold-bend curvature to replicate the worst-case sealing boundary conditions present in actual installations. This method comprises three components: cold bending, cyclic immersion in water, and high–low temperature cycling. The durability is evaluated by assessing the laminating condition and sealing performance of the insulating laminated glass before and after testing. In total, 24 insulating glass samples from an actual engineering project were studied by the proposed methodology. The results indicate the following: (1) the proposed method demonstrates strong operational feasibility, suitable for durability testing and the assessment of cold-bent insulating laminated glass across diverse dimensions; (2) no significant quality or sealing issues were observed in the tested samples during the tests, suggesting that durability is minimally affected when the glass’s cold bending warpage is controlled within certain range; and (3) a discernible trend observed in the full-scale test data is that cold bending results in increased misalignment and decreased argon content. These findings provide a valuable reference for the design and construction of cold-bent glass curtain wall projects. Full article

Background: Semi-occluded vocal tract exercises (SOVTE) improve vocal quality and capacity. Water resistance therapy (WRT), a specific form of SOVTE with a tube submerged under water, generates increased and oscillating oral pressure through bubble formation during phonation, thereby influencing transglottal pressure and vocal fold dynamics. While the physiological effects of WRT using tube-based systems have been extensively studied, the influence of vowel-specific vocal tract configurations during WRT remains unclarified. This study examined how different vowel qualities during WRT affect vocal fold oscillation using the DoctorVox^® mask, which allows near-natural mouth opening and vowel articulation. Methods: Ten vocally healthy, untrained adults (25–50 years) performed a continuous vowel glide (/i/–/a/–/u/-/i/) at constant fundamental frequency and habitual loudness during WRT using the DoctorVox^® mask, with the tube submerged 2 cm in water. Simultaneous recordings included transnasal high-speed videoendoscopy (20,000 fps), electroglottography (EGG), acoustic signals and intra-tube oral pressure measurements. Glottal area waveforms (GAW) were derived to calculate the open quotient (OQ_GAW) and closing quotient (ClQ_GAW). Analyses were conducted separately for intra-tube pressure maxima, minima and intermediate phases within the bubble cycle during WRT. Statistical analysis used Wilcoxon signed-rank tests with Bonferroni correction. Results: In the baseline condition without WRT, significant vowel-related differences were found: /u/ showed a higher open quotient than /i/ and /a/ (p < 0.05) and a higher closing quotient than /a/ (p < 0.05). During WRT, these vowel-specific differences were no longer statistically significant. A non-significant trend toward reduced OQ_GAW during WRT was observed, most notably for /u/, while differences between pressure phases within the bubble cycle were minimal. Conclusions: WRT using the DoctorVox^® mask reduces vowel-specific differences in vocal fold vibration patterns, suggesting that for voice therapy, vowel quality modifications during WRT have little impact on vocal outcomes. Full article

Maintaining water quality and fish well-being in newly established, small, unfiltered betta (Betta splendens) aquaria is a significant challenge. To improve betta fish breeding and welfare, this study set up four groups: the Sagittaria subulata (S.su) group, the Alternanthera reineckii (A.re) group, the Wolffia globosa (W.gl) group, and the plant-free (CG) group. We evaluated the effects of aquatic plants on water quality, fish behavior, and microbial community in newly established tanks over 25 days. The results demonstrated that both the dissolved oxygen (DO) and potential of hydrogen (pH) decreased with the experimental duration, while ammonia nitrogen (NH₃-N) increased over time in all groups. Compared to the CG group, all aquatic plants significantly reduced the NH₃-N accumulation. The S.su group exhibited the lowest mean NH₃-N concentration of only 0.14 mg·L⁻¹, which was considerably lower than that of the other groups (p < 0.05). The behavioral analysis revealed that, during the 25-day randomized monitoring period, bettas in the S.su group exhibited the lowest surface breathing, with an average of only 0.36 events per 5 min, which was significantly lower than that of the CG group (p < 0.05). Additionally, the S.su and W.gl groups demonstrated longer average swimming durations than the other groups, suggesting a potential trend toward improved welfare in betta fish. Aquatic plants shaped the microbial diversity and composition within the experimental aquatic system. The W.gl group had the highest microbial diversity, and the A.re and S.su groups enriched Verrucomicrobiota. These results demonstrate the preferential shaping of microbial communities by aquatic plants, suggesting a potential pathway for enhancing water quality. In conclusion, S. subulata demonstrates the greatest benefits under the experimental conditions, making it a more suitable choice for this experiment. Full article

This study focuses on the diversity of phytoplankton in the Koh Yaa region of Thailand and their relationship with environmental variables, aiming to assess whether human activities (primarily tourism) pose potential threats to the marine ecosystem and provide scientific support for eco-sustainable tourism management decisions in the region. In April, August, and December 2024, corresponding to peak season, off-season, and shoulder season, a total of 156 discrete samples were collected from four coastal sites to analyze water quality parameters such as temperature, pH, total nitrogen (TN), and total phosphorus (TP), along with plankton diversity and abundance. Statistical analyses including two-way ANOVA with Duncan’s Multiple Range Test (DMRT), Pearson correlation analysis, and principal component analysis (PCA) were applied. The results showed a declining trend in plankton abundance over time, peaking at 1009 × 10⁶ cells/m³ in April and dropping to 281 × 10⁶ cells/m³ by December. A total of 15 types of phytoplankton were identified across four phyla: Bacillariophyta, Cyanobacteria, Dinoflagellata, and Chlorophyta. Notably, Chaetoceros from Bacillariophyta accounted for 47% of phytoplankton, while Oscillatoria from Cyanobacteria made up 29.6%. The diversity index and evenness index improved from 1.34 and 0.46 in April to 1.88 and 0.64 in December, respectively. Environmental factors like pH, temperature, and TP significantly affected phytoplankton abundance (p < 0.01), with TP levels ranging from 0.27 to 0.69 mg/L. These results indicate possible pollution in this region, and changes in phytoplankton abundance were linked to seasonal climate variations—especially during peak tourist seasons—which may exacerbate eutrophication affecting community structures. Full article

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

The Dal Lake ecosystem is a vital freshwater body situated in the heart of Srinagar, Kashmir, India. It is not only a natural asset but also a cornerstone of environmental health, economic vitality, cultural heritage, and urban sustainability. In the last few decades, the condition of the lake ecosystem and water quality has deteriorated significantly owing to the intensification of the eutrophication process. Effective integrated management of the lake is crucial for the long-term sustainable development of the region and the communities that rely on it for their livelihoods. The main reasons for eutrophication are the substantial quantity of anthropogenic pollution, especially nutrients, discharged from the catchment area of the lake and the overexploitation of the lake space and its biological resources. The research presented in this paper aimed to diagnose the state of the lake by analysing trends in eutrophication development and its long-term changes related to the catchment area and lake ecosystem relationships. The research period was 25 years, from 1997 to 2023. Land use and land cover data and water quality monitoring data, which are the basis for trophic state assessment, allowed us to analyze the long-term dynamics of eutrophication in the reservoir. For these purposes, GIS-generated thematic maps were created by using QGIS software version 3.44.1, and an appropriate methodology for quantifying eutrophication was chosen and adapted to the specifics of Dal Lake. The obtained results provide a foundation for a eutrophication management strategy that considers the specificity of the Dal Lake ecosystem and the impact of the catchment area. The outcomes highlighted the varied trophic conditions in different lake basins and the dominance of eutrophic conditions during the study period. The research highlights the complexity of the problem and underscores the need for a comprehensive lake management system. Full article

Monitoring water quality changes and identifying their driving factors are essential for the effective management of Dongjiang Lake. However, in-depth research on the spatiotemporal variations in the lake’s water quality and the complex interactions between natural and human factors remain insufficient. In this study, we aimed to characterize water quality trends and key physicochemical indicators in Dongjiang Lake by combining a 14-year water environmental dataset (2011–2024) and a correlation analysis. Our results showed that TN and COD_Mn concentrations displayed increasing trends, whereas the NH₃-N concentration showed a decreasing trend throughout the study period. The TN concentration initially decreased earlier in the year before increasing, with values ranging from 0.56 mg/L in September to 0.78 mg/L in November. The trends in COD_Mn concentration were the opposite to those of TN within the year, which first increased from 0.79 mg/L in January to 1.00 mg/L in June, and then decreased to 0.84 mg/L in December. The water level fluctuated inter-annually from 267.63 to 278.04 m during the study period, with a difference of 10.41 m. pH increased from 7.01 to 8.25, and dissolved oxygen decreased from 9.81 to 7.57. The WT fluctuates between 17.83 °C and 19.49 °C (p < 0.05). COD_Mn showed a highly significant positive correlation with transparency, pH, and water temperature, whereas NH₃-N showed a highly significant negative correlation with transparency, pH, and dissolved oxygen. Considering the importance of Dongjiang Lake as a freshwater resource and tourism hub, this study highlights the urgent need to prioritize pollution source control, while accounting for the lake’s deep-water dynamics and incorporating ecosystem-based restoration measures. Full article

In the field of underwater laser detection, turbulence causes beam wandering and intensity scintillation, which subsequently alter the angle of incidence and ultimately degrade the quality of the target echo signal. By establishing an experimental platform that simulates oceanic turbulent channels, this study investigates the correlation between turbulence location and the backscattered optical scintillation index. This work lays the foundation for developing reliable assessment techniques for laser backscattering detection channels. Using a thermally driven turbulence simulator and an off-axis blue-green laser, a backscattering model was developed via echo signal analysis. This model captures the relationship between turbulence spatial distribution and the optical scintillation coefficient, revealing distinct nonlinear behavior in this relationship. Experimental results revealed a non-monotonic trend in the optical scintillation coefficient, characterized by an initial decrease followed by an increase, with the distance from the turbulence region. While increased water turbidity preserved this overall trend, it resulted in a dampened response. The proposed model demonstrated high reliability, with R² values of 0.8579 and 0.8844 for the open-sea and coastal environments, respectively. The turbulent laser detection backscattering channel prediction model supports the evaluation of oceanic blue-green laser detection channels. Full article