Search Results (8,351)

Accurate detection of chlorophyll-a (Chl-a) is critical for monitoring water quality in inland waters, where high concentrations of coloured dissolved organic matter (CDOM) complicate retrieval process. Reliable Chl-a estimation depends on the precise determination of the phytoplankton absorption coefficient (a_ph). This study evaluates Chl-a detection from in situ a_ph measurements and assesses the accuracy of phytoplankton absorption retrieval from Sentinel-2/MSI (S2) and Sentinel-3/OLCI (S3) using the Case-2-Regional-Coast-Colour (C2RCC) processor across diverse optical water types (OWTs) in boreal lakes. OWTs were classified based on remote sensing reflectance features, representing Clear, Moderate, Turbid, Very Turbid, and Brown conditions. CDOM absorption strongly influenced the underwater light field, particularly in Brown and Turbid waters. Linear relationships between a_ph and Chl-a were generally strong across OWTs, with improved relationships in the red spectral region (670 nm). Satellite-derived a_pig estimates showed a weak relationship with in situ data (R² = 0.26–0.45). Both sensors overestimated small a_ph values, while S3 underestimated larger ones. S2 underestimated a_ph in Clear and Brown OWTs, with median absolute percentage differences near 100% for all OWTs. These findings emphasize the challenges posed by bio-optical complexity in boreal lakes and highlight the need for OWT-specific algorithms to improve satellite-based absorption and Chl-a retrieval accuracy. Full article

Images obtained from imaging instruments can endure issues such as high degradation, color distortion, and weak brightness. Effective systems for enhancing these images are critically required. To improve the image quality, herein, we propose two filters based on simple functions, including cosine, sine, hyperbolic secant, and the inverse of hyperbolic cosecant. These filters are used for enhancing the images obtained from a fluorescence imaging system, a near-infrared camera, and low-light condition. The contrast is increased while the image quality is improved. They perform better than a matched filter. Moreover, the combination of our filters with the filter based on the watershed algorithm or the matched filter can be used to extract the marginal features from images generated under water environment. Furthermore, their application in image fusion is explored. Our designed filters may be potentially used for future applications on target identification and tracking. Full article

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

Wheat bran, as a major nutrient-rich agricultural by-product, is underutilized due to poor functional properties. This study investigated the synergistic effects of steam explosion (SE), enzymatic hydrolysis (EH), and SE combined with EH (SE-EH) on wheat bran to improve the dough properties, bread quality, and in vitro starch digestion. Results showed that SE destroyed the dense structure of wheat bran to form a porous surface morphology and promoted the conversion of insoluble dietary fiber (IDF) to soluble dietary fiber (SDF). This structural loosening facilitated further fiber degradation for subsequent EH and achieved the obvious improvements in hydration properties after combined treatment. For the dough system, the addition of SE-EH bran increased the water absorption, hardness, and viscosity, but reduced the development and stability time of the dough, in comparison with the control dough. These changes suggested that the modified bran altered dough hydration behavior and gluten network continuity, contributing to the increment of bread’s specific volume. The starch hydrolysis rate of bread adding SE-EH wheat bran was decreased; the slowly digestible starch (SDS) and resistant starch (RS) contents were 2.59-fold and 1.31-fold higher than the control group, respectively. Additionally, the incorporation of modified wheat bran delayed bread hardening during storage, with the SE-modified group showing the best effect. Wheat bran modification enhanced its processing functionality, providing a feasible approach for bread production to improve storage stability and nutritional quality. Full article

Accurate prediction of effluent water quality is essential for the intelligent and sustainable operation of wastewater treatment plants (WWTPs). However, this task remains challenging due to the strong nonlinearity, long-term temporal dependencies, and severe fluctuations inherent in influent characteristics. In this study, a novel data-driven framework termed the Multi-Scale Temporal Representation-Enhanced Informer (MTRE-Informer), is proposed to predict key effluent quality indicators, including total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). To ensure data quality and computational efficiency, a generative recurrent learning framework is first employed for anomaly detection and correction, followed by variance inflation factor (VIF)-based feature selection to mitigate multicollinearity. Furthermore, feature contribution analysis is conducted to improve model interpretability. Subsequently, the core MTRE-Informer architecture utilizes hierarchical multi-scale temporal representation learning to simultaneously capture local patterns and long-term dependencies within the complex dynamics of the wastewater treatment process. Experimental results demonstrate that the MTRE-Informer achieves robust and stable predictive performance across diverse operational datasets. For TN prediction, the proposed framework attains a coefficient of determination () of 0.9637 and a mean absolute percentage error (MAPE) of 3.39%. Compared with baseline approaches, the improvement ranges from 3.8% to 14.2%, validating its superior capability. To further enhance model robustness, an anomaly detection and correction strategy based on a generative recurrent learning framework is employed. In addition, feature contribution analysis and VIF-based feature selection are conducted to improve interpretability, mitigate multicollinearity, and enhance computational efficiency. Overall, this framework provides a reliable and practical solution for real-time effluent quality prediction, facilitating the intelligent management of WWTPs. Full article

Since ancient times, clays have been used to protect plants from insects and excessive sunlight. Today, their potential use is being re-evaluated as a tool to mitigate the effects of climate change and to manage emerging pests. This review synthesizes and compares findings from studies conducted in different regions of the world. Kaolin forms a reflective film on leaves and fruits, lowering tissue temperature. In warm climates, this temperature reduction can contribute to improved physiological parameters including net assimilation and water use efficiency; however, these responses are strongly influenced by additional factors. It may also affect some oenological characteristics of grapes (acidity, pH, and phenol content, particularly anthocyanins), thereby improving the overall chemical composition of grapes and wines, particularly in terms of acidity, pH and phenolic content. In addition, kaolin has been shown to reduce damage caused by the grape leafhopper (Empoasca vitis, Jacobiasca lybica, among others) to levels comparable to those achieved with synthetic pesticides. However, responses vary depending on different factors, such as application timing, dose, cultivar and climate. Overall, kaolin represents a sustainable strategy for mitigating climate change effects on fruit quality and for supporting ecological pest management. Full article

Gum arabic (GA)-based edible coatings enriched with natural bioactive compounds offer a promising strategy for reducing postharvest losses and maintaining fruit quality. This study evaluated the effectiveness of GA coatings supplemented with carvacrol or caffeine in preserving the physicochemical quality, antioxidant status, and marketability of ‘Murcott’ mandarins during cold storage (5 ± 1 °C, 90–95% RH) for 60 days followed by 4 days of shelf life. Fruits were treated with distilled water (control), GA (10%), GA + imazalil (2000 ppm), GA + carvacrol (200 ppm), and GA + caffeine (200 ppm). Key quality parameters, including weight loss, decay incidence, firmness, electrolyte leakage, malondialdehyde (MDA), total soluble solids, titratable acidity, ascorbic acid, total phenolics, total flavonoids, and antioxidant enzyme activities of catalase (CAT) and peroxidase (POX), were evaluated. The results demonstrated that GA-based coatings, particularly GA + carvacrol, significantly reduced weight loss and decay while maintaining firmness and visual quality compared to the control. Coated fruits exhibited lower electrolyte leakage and MDA levels, indicating improved membrane integrity and reduced lipid peroxidation. In addition, the treatments enhanced antioxidant capacity, as reflected by increased phenolic and flavonoid contents and higher CAT and POX activities. Multivariate analysis further confirmed the strong association between coating treatments and improved quality attributes. In conclusion, GA coatings enriched with carvacrol or caffeine effectively improved postharvest quality and extended the shelf life of ‘Murcott’ mandarins, highlighting their potential as safe and eco-friendly alternatives to conventional postharvest treatments. Full article

The Horn of Africa (Kenya, Djibouti, Uganda, Eritrea, Somalia, Ethiopia, South Sudan, and Sudan) faces the highest rates of hunger and malnutrition in the world, exacerbated by conflict and adverse weather conditions. These factors have serious health, educational, social, and economic consequences, especially for children under five and pregnant women. In this context, we analyze each country’s progress toward Sustainable Development Goals (SDGs) 1, 2, 3, and 4, which are closely linked to the eradication of hunger, improved health, and access to quality education. Using comparable data from the United Nations 2030 Agenda up to 2019, the achievement of the SDGs is assessed through a multidimensional approach based on Pena’s P₂ distance method, constructing a composite indicator that allows for robust cross-country comparisons. This method helps identify the key measures needed to prevent future humanitarian crises in the Horn of Africa, including providing urgent assistance to these countries in vital areas such as water, nutrition, education, sanitation, and child and maternal immunization. Factors related to the work of qualified healthcare personnel in treating diseases and improving maternal and neonatal health, as well as facilitating access to basic services such as clean drinking water and sanitation and ensuring girls’ access to primary education, top the rankings in terms of their correlation with greater progress by these countries in achieving these four SDGs, which are crucial for improving the well-being of their populations. Full article

The extensive deployment of automatic water quality monitoring stations has generated substantial volumes of time-series data. Effectively utilizing these data is crucial for enhancing prediction accuracy. To address the limitations of existing models in capturing complex inter-indicator relationships and multi-scale temporal features, this paper proposes a hybrid prediction model integrating time series decomposition with deep learning techniques. Adopting a “decomposition–prediction–reconstruction” paradigm, the model first decomposes the raw time series into trend, seasonal, and residual components using STL (Seasonal–Trend decomposition using LOESS). For the trend component, an improved Graph Convolutional Network (GCN) is designed to explicitly model the spatial dependencies among different water quality indicators. For the seasonal component, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed for multi-scale signal analysis, followed by a coupled Long Short-Term Memory–Convolutional Neural Network (LSTM-CNN) unit to capture both long-term dependencies and local features. To validate the efficacy of the proposed model, experiments were conducted on three real-world water quality datasets from different watersheds. Experimental results demonstrate that the proposed model outperforms mainstream baseline models, including StemGCN, LSTM-CNN, CEEMDAN-LSTM-CNN, and Attention-CLX. Across the three datasets, the model consistently outperforms the best-performing baseline, achieving reductions in MAE ranging from 13.8% to 24.5% and up to a 45.3% reduction in RMSE on a single dataset, while the highest correlation coefficient between predicted and observed values reaches 0.855. These findings demonstrate that the proposed decomposition–integration framework effectively enhances the accuracy and stability of multivariate water quality prediction, offering a promising tool for supporting sustainable water resource management. Full article

The transition from starch-dominated to protein-enriched gluten-free systems represents a critical step in improving the functional and nutritional quality of composite flours. This study investigated the effects of progressive substitution of Amaranthus caudatus (amaranth) with Lupinus mutabilis (Andean lupin) on the physicochemical, rheological, and antioxidant properties of gluten-free flour blends. A multimodal dataset comprising 33 variables across six measurement domains (proximal composition, hydration properties, thermomechanical behavior, pasting profiles, particle size, and antioxidant activity) was analyzed using an integrated framework combining univariate inference (FDR-adjusted p-values), PCA, Multiple Factor Analysis (MFA), and sparse Partial Least Squares Discriminant Analysis (sPLS-DA). Results revealed that increasing lupin content (10–40%) significantly increased protein and fiber levels while decreasing starch content, leading to higher water absorption capacity and reduced peak viscosity and setback. Multivariate models showed that the protein/fiber–starch trade-off was the principal axis of compositional differentiation (PC1, ~41% variance), while PC2 captured rheological and antioxidant variability, with formulations containing higher proportions of amaranth exhibiting greater antioxidant activity. The sPLS-DA model achieved 72% separation accuracy with moisture, protein, water absorption, and torque parameters as top discriminants. These findings provide an evidence-based framework for gluten-free flour optimization using Andean crops and highlight how statistical modeling can inform targeted formulation decisions. The approach is transferable to other compositional transitions in food systems, underscoring the utility of multivariate analytics in applied food research. The multivariate framework further suggests that intermediate substitution levels may offer an optimal balance between nutritional enrichment and rheological functionality. Full article

Microplastics (MPs) are emerging pollutants of global concern, posing significant ecological and human health risks. They are frequently detected in stormwater systems, with urban runoff serving as a major transport pathway into the environment. Green stormwater infrastructure, particularly bioretention systems (BRSs), offers a promising approach to mitigate these risks by filtering and retaining various contaminants. However, the occurrence of MPs in BRSs and their capacity to retain these pollutants remain largely unexplored in the literature, despite being critical for stormwater management and water quality protection. Therefore, this study attempted to examine the occurrence, vertical distribution, and trapping of MPs within a field-installed BRS, potentially emphasizing their role in reducing microplastic (MP) transport. Therefore, field samples were collected at depths of 2, 12, and 24 inches below the surface and processed in the laboratory for MP detection and quantification. The results revealed an average concentration of 1095 particles per kg of dried sediment, with fragments (microplastics shape) accounting for 78.54% of the total MPs. Although no clear vertical distribution pattern was observed, the initial findings showed that MPs were mostly retained at 24 inches, potentially indicating their transport through the media and the retention capacity of a BRS (surface and middle layer) in capturing microplastics from stormwater environments. However, there is no direct evidence to explain the mechanisms driving the observed concentrations at greater depths. The preliminary findings of this study highlight that the concentrations of different sizes of MPs can vary with soil depth in bioretention media. Integrating a BRS into urban stormwater infrastructure likely provides the dual benefits of improved stormwater management and reduced plastic pollution. This study underscores the importance of optimizing bioretention design and media composition to enhance MP trapping from stormwater. Full article

This study presents a comparative evaluation of solar cabinet drying and traditional open-air sun drying with respect to their influence on the retention of water-soluble vitamins and flavonoids in Plantago major L. leaves, aiming to identify an effective and sustainable drying strategy for functional food applications. Freshly harvested leaves were subjected to both drying methods under comparable environmental conditions. To account for possible structural heterogeneity, external and internal leaf tissues were analyzed separately. Qualitative and quantitative determination of bioactive compounds was performed using high-performance liquid chromatography with diode-array detection (HPLC-DAD). Flavonoids were analyzed at detection wavelengths of 254 and 276 nm, while water-soluble vitamins (C, B₂, B₃, B₆, and B₉) were determined at 250 nm. Quantification was carried out using external calibration, and results were expressed as concentrations (mg/g dry matter). The results demonstrate that solar cabinet drying provides superior preservation of oxidation- and light-sensitive bioactive compounds compared to open-air sun drying. In particular, vitamin C content in solar cabinet-dried samples reached 91.62 mg/g, which was more than three times higher than that observed after open-air drying (26.90 mg/g). Solar cabinet drying also enhanced the retention of key antioxidant flavonoids, notably dihydroquercetin (14.23 mg/g vs. 11.21 mg/g) and luteolin (0.38 mg/g vs. 0.26 mg/g). Although slightly higher concentrations of certain compounds, such as rutin and vitamins B₆ and B₉, were detected in open-air-dried samples, the overall nutraceutical profile favored solar cabinet drying. In conclusion, the controlled microclimate of the solar cabinet dryer significantly improves the stability and retention of critical water-soluble vitamins and antioxidant flavonoids in Plantago major L. leaves. These findings confirm that solar cabinet drying is a nutritionally advantageous, energy-efficient, and sustainable approach for producing high-quality plant-based ingredients suitable for functional food and nutraceutical applications. Full article

Treated wastewater (TWW) reuse mitigates water scarcity but may induce soil salinization and trace metal accumulation if improperly managed. This field study evaluated the combined effects of irrigation water quality (TWW vs. well water) and irrigation method (surface vs. subsurface drip irrigation, SDI) on soil chemical properties, okra growth, yield, and nutrient/trace element dynamics under semi-arid Mediterranean conditions. Soil pH remained stable across treatments. Electrical conductivity was not significantly affected by water quality but increased in deeper layers under surface drip irrigation, indicating salt migration. SDI promoted more uniform nutrient distribution and favored Na⁺ displacement toward deeper layers, reducing root-zone exposure. Cations stratified vertically, with Ca²⁺, Mg²⁺, and K⁺ concentrated in surface layers and Na⁺ at depth. Water quality exerted a stronger influence than irrigation method. The fertilizing effect of TWW significantly enhanced plant height (53%), leaf dry matter (43%), aboveground biomass (81%), and fruit yield (16.3%). When combined with SDI, TWW improved irrigation water use efficiency by 20%. Although fruit Cd concentrations increased under TWW irrigation, all trace metals remained below international food safety standards. These findings indicate that integrating TWW with SDI enhances productivity and water use efficiency while maintaining short-term food safety, though long-term monitoring remains essential. Full article

The blue economy contributes significantly to Brazil’s gross domestic product due to the country’s vast coastline and abundant natural resources. Oil royalties represent a major component of this wealth; yet, their association with improvements in quality of life remains unclear. The aim of this study was to analyze the performance of 193 municipalities (coastal: 101; state of Rio de Janeiro: 92) that receive more than R$5 million in royalties per semester in 2022, using the socioeconomic indices IBP (Brazilian Deprivation Index), IDEB (Basic Education Index), and IQA (Water Quality Index). The results reveal conditional, non-linear, and regionally unequal relationships between oil revenues and socioeconomic indicators. Unsupervised learning identified four groups of municipalities. The group with the largest number of municipalities (n = 45) and the best performance in socioeconomic indices had a wide range of royalties (between R$7 and R$22 million). However, supervised analyses show that this group of municipalities, mainly from the south/southeast regions, receives relatively low oil revenues but performs well in the indices, suggesting a certain autonomy in relation to royalties. The municipalities of the state of Rio de Janeiro confirm the national trend, with cities with higher education levels benefiting, but with more specific aspects of the blue economy (water quality) not being well-represented. Policies are mandatory to redirect oil revenues to these sectors with the support of more appropriate indicators. Full article

As a widely used building material, the performance of concrete has a far-reaching impact on the quality and durability of hydraulic engineering. Polycarboxylate superplasticizer (PCE) plays an increasingly important role in concrete engineering because of its unique high-efficiency water-reducing performance and the improvement effect on concrete performance. In this paper, the application and influence of polycarboxylate in concrete, including its chemical structure, action mechanism and application effect, are reviewed. It is found that polycarboxylate can greatly reduce the shrinkage of concrete and control its volume deformation. The objective of this review is to elucidate the mechanisms by which polyurethane-modified polycarboxylate (MPCE) reduces autogenous and drying shrinkage in concrete and to demonstrate its advantages over conventional PCE. On this basis, we focus on the core research object of MPCE and discuss in depth its effect on reducing the surface tension of concrete pore solution and the intrinsic mechanism of regulating volume deformation. The research clarifies the superior performance of MPCE over ordinary PCE in inhibiting autogenous shrinkage and drying shrinkage in concrete, which provides a targeted scientific basis for the practical application of MPCE in concrete volume deformation control. Full article