Search Results (790)

Traditional approaches for monitoring aquatic organic carbon, such as satellite remote sensing and automated underwater sensors, are often constrained by limited temporal resolution, data gaps under cloudy conditions, maintenance requirements, and cost-effectiveness. To overcome these limitations, we developed and field-demonstrated a ground-based hyperspectral remote sensing system (GHRSS) for continuous, high-frequency monitoring of dissolved organic carbon (DOC) and particulate organic carbon (POC). The system is based on the above-water method and integrates three miniature hyperspectral spectrometers to measure water-surface radiance, sky radiance, and downwelling irradiance for deriving hyperspectral remote sensing reflectance ($R_{rs}$). The spectrometers cover 400–900 nm with a spectral resolution of 1 nm and support a minimum sampling interval of 10 s. The GHRSS also integrates solar power supply, 4G communication, and a microcomputer, enabling autonomous long-term deployment and wireless data transmission. Based on the GHRSS, retrieval models for DOC and POC were developed and validated using 90 paired in situ measurements collected from the Cao’e River. Empirical and machine learning methods were applied to retrieve DOC and POC from the measured $R_{rs}$ data. The empirical models showed limited retrieval performance, whereas partial least squares regression (PLSR) and support vector regression (SVR) substantially improved model accuracy. Among all models, SVR achieved the best performance on the independent test set, with $R^2=0.979$, RMSE = 0.031 mg/L, and MAE = 0.024 mg/L for DOC and $R^2=0.960$, RMSE = 0.152 mg/L, and MAE = 0.066 mg/L for POC. Using the optimal SVR models, minute-scale time series of DOC and POC were reconstructed from the GHRSS observations. The results revealed pronounced sub-daily variability in both parameters, with DOC varying relatively smoothly, whereas POC exhibited stronger short-term fluctuations and more rapid responses to hydrodynamic changes. These findings demonstrate that the GHRSS, combined with machine learning models, provides an effective and practical approach for continuous, high-frequency monitoring of riverine organic carbon dynamics. Full article

Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. As a result, monitoring datasets are often not available for all water quality parameters, or the datasets may be incomplete. To address such challenges, the objective of this study was to evaluate the potential of water quality index (WQI)-based assessment supported by machine learning algorithms as an alternative decision-support tool for water quality evaluation. The analysis compared four monitoring stations in the Austin and Arroyo Colorado Watersheds, with particular emphasis on one gauging station at Port Harlingen. Datasets were collected from the Texas Commission of Environmental Quality (TCEQ). A complete exploratory data analysis (EDA) was performed to understand the TCEQ water quality datasets containing sixteen parameters, and seven water quality parameters were selected based on multicollinearity checks. It was observed that seven independent water quality parameters (dissolved oxygen, ammonia, nitrate, phosphorus, temperature, fecal coliform, and residual non-filterable material concentrations) were identified as sufficient to define the WQI of the Austin monitoring stations. Moreover, U.S. Environmental Protection Agency (EPA)-based guidelines were utilized to scale individual parameters to a range of 0–100 to remove their magnitude and correlation-based bias. These parameters were further analyzed using machine learning techniques, i.e., principal component analysis, K-means, and one-class support vector machine, to compute the relative importance based on their fluctuation within the temporal dataset. Finally, the mean WQI model was developed for Port Harlingen and achieved a strong agreement with the National Sanitation Foundation (NSF) WQI (R² = 0.91). These findings demonstrate the applicability of the proposed data-driven WQI framework for regional water quality assessment and comparative analysis across watersheds. Full article

Increasing global insecurity for potable water has led to atmospheric water harvesting as a viable supplementary source. Passive dew water harvesting is simple to carry out but atmospheric conditions determine the frequency and amount of dew that can be collected, and up to 0.5 L/m²/night can be considered as an upper ceiling. Thus, active condensers using refrigeration and cooling systems have been developed to increase collection totals, requiring an electrical or solar power supply. In the last decade, adsorption/absorption techniques of water vapor have been studied to maximize collection, with the potential for low costs, portability, and high volumes, and they are operational even in arid regions with low humidity, islands, and remote regions. This could become a gamechanger in securing affordable potable water. Citizen Science is suggested for dew observation and collection data to increase observation points that could be used to improve the resolution/accuracy of local, regional, or global dew modelling. It would promote environmental and water literacy by engaging participants ranging from primary school communities to senior individuals. Teleconferencing now provides access to a worldwide audience and the inclusion of participants no matter their location. Full article

The Upper Minjiang River Basin is a critical ecological barrier in the upper Yangtze River, where forest ecosystems play a vital role in carbon sequestration, water conservation, and soil retention. Given that different forest types exhibit significant variations in community structure, species composition, and ecological processes, their ecosystem service (ES) supplies and trade-off/synergy relationships are also expected to show distinct heterogeneity. However, systematic research on the trade-offs and synergies of ESs across different forest types remains limited, constraining the development of precision forest management and differentiated management strategies. To deal with this, we used the InVEST model and calculated five key services across the basin: carbon stock (CS), water yield (WY), soil conservation (SC), habitat quality (HQ), and forest stock volume (FSV). We then applied Spearman’s correlation, root mean square deviation (RMSD), and the GeoDetector model to analyze trade-offs and uncover driving mechanisms. Finally, we used spatially constrained K-means clustering to map different management zones. The results indicate that the Upper Minjiang River Basin stored 1.78 × 10⁸ t of carbon, retained 2.98 × 10⁸ t of soil, produced 6.48 × 10⁹ m³ of water yield, maintained a mean habitat quality of 0.78, and supported a forest stock volume of 1.20 × 10⁸ m³. Coniferous forests exhibited the highest CS (181.07 t ha⁻¹) and FSV (176.37 m³ ha⁻¹), whereas shrublands contributed the largest share (52.17%) of regional water yield. At the regional scale, CS and FSV showed the strongest synergy (r = 0.71, p < 0.01), while WY displayed significant trade-offs with most other services. GeoDetector analysis revealed that forest type acts as the primary driver shaping the relationships among services, while elevation and precipitation play supporting roles. Based on the ES bundles identified via spatially constrained K-means clustering, the Upper Minjiang River Basin was divided into four distinct management zones: a carbon sequestration core zone, an ecological balance zone, an ecologically fragile zone, and a multifunctional conservation zone. Therefore, findings from the Upper Minjiang River Basin may provide insights applicable to other mountain forest ecosystems facing similar environmental and management challenges. Full article

Soil nitrogen (N) mineralization plays a pivotal role in regulating N availability in forest ecosystems, which could not only be closely related to soil nutrient supply capacity but also profoundly affect the forest carbon sequestration. Broad-leaved forests play a key role in terrestrial carbon storage; however, soil net N mineralization rates (Rmin) vary considerably among different forest types and their underlying driving mechanisms remain poorly understood. In this study, three typical broad-leaved forests in Northeast China, namely Populus davidiana Dode forest (PF), Fraxinus mandshurica Rupr forest (FF), and Betula platyphylla Suk. forest (BF), were selected. The soil Rmin, environmental parameters, physicochemical properties, and microbial community characteristics were determined among the three broad-leaved forests to explore forest type differences in soil Rmin and their associated factors. The results showed that soil inorganic N contents differed significantly among the three forest types, with significantly higher values in FF than in PF and BF (p < 0.05). Soil Rmin also differed significantly among forest types, which was highest in FF, followed by PF and BF (2.17, 1.31, and 0.95 mg kg⁻¹ day⁻¹, respectively) (p < 0.05). Soil Rmin was significantly positively correlated with soil water content (SWC), soil temperature (ST), and pH, but there was a negative correlation to soil bulk density (BD) (p < 0.05). In addition, microbial biomass carbon, nitrogen, and phosphorus were significantly higher in FF than in PF and BF (p < 0.05). Variation in soil Rmin among three broad-leaved forests was significantly associated with the abundances of Ascomycota, Basidiomycota, and Mucoromycota, but not with bacterial community, suggesting a closer association between fungi and soil Rmin. Structural equation modeling (SEM) indicated that forest type was associated with soil microbial community structure and biomass through associations with soil environmental and physicochemical properties, in relation to soil Rmin. In conclusion, this study highlights the links between vegetation type and soil Rmin in broad-leaved forests, which deepens the theoretical understanding of forest soil N-limitation in Northeast China. Full article

This paper presents the results of studying the effects of chemical amelioration and periodic flooding and its effects on the agrochemical properties of chestnut soils, the structure of microbial communities, and the productivity of natural grass stands in Western Kazakhstan. The research was conducted in 2024–2025 at a site within the Ural-Kushum irrigation and water-supply system, applying gypsum (6000 and 12,000 kg/ha) and elemental sulfur (3000 and 6000 kg/ha). It was found that of ameliorants altered the agrochemical parameters of the soil, particularly in the upper soil layer (0–20 cm): pH decreased, while the content of nitrate nitrogen, available phosphorus, and sulfur increased, whereas the potassium content showed comparatively minor changes. The maximum effect was observed at application rates of 12,000 kg/ha for gypsum and 6000 kg/ha for sulfur. High-throughput sequencing revealed that microbial communities consisted of 97.70% bacteria, with approximately 2.20% of taxa remaining unclassified at the kingdom level. In the control treatment, Pseudomonadota (43.63–47.88%), Acidobacterium (13.09–18.11%), Bacillota (9.74–13.15%), and Acidobacteria (up to 33.10% in the top layer, with a sharp decline down the soil profile) dominated. The application of gypsum and sulfur induced a redistribution of taxa: a decrease in the abundance of Pseudomonadota in the top layer, an increase in Acidobacterium and Verrucomicrobium, and a reduction in Acidobacterium in deeper horizons, especially at higher rates of ameliorants. The productivity of the grass stand increased under the influence of the ameliorants, as evidenced by a growth in plant height and hay yield, the maximum values reaching 2060 kg/ha with the application of 6000 kg/ha of gypsum. A strong correlation was observed between grass stand density and yield (r = 0.99). The obtained results confirm that chemical amelioration under periodic flooding conditions exerts a comprehensive influence on soil agrochemical properties, microbial community structure, and grass stand productivity. Full article

Despite decades of research, there is still a lack of calibrated data on acoustic leak signatures typical of common types of water supply pipes. This study addresses this gap by providing leak signatures recorded with calibrated, high-sensitivity accelerometers in a controlled laboratory environment. The study also investigates how different leak configurations at nominal static pressures of 2.8–4.2 bars influence the power spectrum of the pipe-wall acceleration. The results show a great variability, i.e., 5 orders of magnitude, in the power spectrum. The amplitude and shape of this spectrum depend on whether the leak is through a valve-controlled nozzle, hole directly drilled in the pipe wall, or a longitudinal or traverse slit. The coherence in the leak signals as a function of the distance between the accelerometers is determined and used to estimate the leak signal attenuation. Crucially, the results reveal that longitudinal slits, which represent the most common failure mode in plastic pipes, produce the weakest acoustic signals, making them difficult to detect and locate using standard acoustic equipment. It is expected that the calibrated data collected from this study will support high-fidelity computer simulations and development of better signal processing algorithms to predict and to detect hidden leaks in water distribution networks in the presence of background noise and high acoustic attenuation. The recorded data are made available to a wider community through a dedicated data depository. Full article

This study assessed the performance of a rural water supply system. Performance assessment of water supply systems is important to ensure the long-term sustainability of water services. The study addressed a critical gap in assessing performance while accounting for water disruptions and their effects on water quality in nonlinear rural water supply systems. This is critical, especially in rural areas where reliable access to water is limited. A questionnaire survey was conducted to collect data on the reliability and accessibility of the water supply system. Questionnaire responses were analysed using the Statistical Package for Social Sciences version 25. Spearman’s rank correlation was used to determine the relationship between the socio-economic variables and the performance indicators. and the variables. Turbidity, electrical conductivity (EC), total dissolved solids (TDS), and pH were measured in the field. Escherichia coli (E. coli) and total coliforms were analysed using the membrane filtration method. A paired two-tailed t-test was used to determine if there is a significant difference in water quality between the dry and wet seasons. Key performance indicators on reliability and accessibility were assessed by comparing benchmarks. Most households receive an inadequate quantity of water, with 84.3% using less than the recommended basic need of 1500 L per week. Travel distances to the source exceeded the recommended benchmark of 100 m. The majority of respondents (81.4%) reported frequent water supply disruptions, indicating poor reliability of the source. EC and pH were within the South African National Standards (SANS) 241 guideline for drinking water. TDS, turbidity, and microbial parameters failed to meet safe drinking water standards, except for E. coli during the dry season. There was no significant difference in the water quality between the dry and wet seasons. The water supply system demonstrated poor performance. Measures such as implementing low-cost filtration systems to reduce turbidity, raising community awareness about water safety, and decentralising maintenance activities to improve system sustainability due to financial constraints. These interventions will reduce physical burdens and increase effective water usage. Full article

Sustainable management of hydrogeological systems that supply water and exhibit high hydrologic complexity can be studied through pragmatic numerical modeling supported by field-constrained conceptualization. This study develops a local-scale three-dimensional groundwater flow numerical model using FEFLOW for the Barranca Lebrija settlement in Aguachica town, where the Lebrija River, the Musanda floodplain lake, and groundwater system converge. The numerical model incorporates: (i) the three-dimensional distribution of geological units and lithology; (ii) water level observations from the Musanda floodplain lake; (iii) stage records from the Lebrija River; (iv) boundary conditions and flux estimates inherited from a previous regional groundwater model; and (v) hydraulic heads from two monitoring wells and five community wells. Steady-state and transient conditions were calibrated, and a sensitivity analysis was performed to identify the parameters that most strongly control surface water–groundwater exchange. The simulations reproduce seasonal groundwater level trends and demonstrate the exchange pathways among the river, floodplain lake, and groundwater system. Results indicate dual behavior: during wet periods, flooding of the Musanda floodplain lake driven by high river levels seeps into the underlying aquifer, whereas in dry periods the floodplain lake reverses its role and becomes a principal discharge boundary. This local-scale, boundary-driven approach provides a computationally tractable framework to quantify SW–GW exchange in data-scarce tropical floodplains and supports monitoring design and water-supply management. Full article

In modern livestock production, a reliable supply of high-quality forage is essential for sustaining animal productivity and product quality. Although nitrogen (N) fertilization can promote forage growth, excessive N inputs often result in low agronomic nitrogen use efficiency (NAUE) and increased environmental risks. Biochar, owing to its porous structure, high specific surface area, and physicochemical stability, can improve soil physical properties, enhance water and nutrient retention, and regulate soil N availability. However, the mechanisms by which biochar combined with reduced N rate fertilization affects NAUE in artificial grasslands remain insufficiently quantified. A two-year field experiment was conducted at the Grassland Science Experimental Station of Xinjiang Agricultural University on the northern slope of the Tianshan Mountains, Xinjiang, China. Eight treatments were established using a factorial design with two biochar rates (0 and 20 t·ha⁻¹; B0 and B20) and four N application rates (0, 75, 150, and 225 kg·ha⁻¹; N0, N75, N150, and N225). Results showed that biochar application significantly decreased soil bulk density and increased soil water content and electrical conductivity. It also elevated soil total carbon, total nitrogen, total phosphorus, NH₄⁺–N, and NO₃⁻–N concentrations, with B20N150 exhibiting the highest overall nutrient status. Plant community diversity indices did not differ significantly among treatments (p > 0.05), though B20 slightly enhanced Shannon–Wiener and Simpson indices under N0 and N75. Moderate N application significantly increased hay yield, whereas the highest N rate (225 kg·ha⁻¹) did not further improve yield and reduced NAUE. Biochar combined with N75 or N150 improved NAUE, and B20N150 achieved the best balance of high hay yield and high NAUE. Structural equation modeling revealed that soil water content (path coefficient = 0.45), NH₄⁺–N (0.27), and plant community diversity (0.20) were key positive drivers of NAUE, with biochar exerting indirect effects primarily via improving soil water and available N. Collectively, applying 20 t·ha⁻¹ biochar with 150 kg·ha⁻¹ N (B20N150) is recommended as an optimal strategy for N rate reduction and NAUE enhancement in artificial grasslands of arid and semiarid regions. Full article

Population growth, climate change, pollution, and prolonged droughts are contributing to increasing water scarcity, which poses a significant challenge to global sustainable water management. Among the available adaptation strategies, potable water reuse has emerged as a viable and increasingly adopted solution in areas facing water stress. This review examines evolution, regulatory frameworks, treatment technologies, and implementation strategies related to drinking water reuse worldwide. Through the historical review, it becomes clear that the idea of water reuse has deep roots (5000 years ago), while the analysis of modern legislative and intergovernmental approaches led to the conclusion that the rules governing water reuse vary depending on the country and are in most cases quite strict (e.g., the Urban Wastewater Treatment Directive (UWWTD) and the Water Framework Directive). To make potable water reuse possible, including direct and indirect systems, advanced wastewater treatment technologies are applied, among which membrane processes and advanced oxidation processes (AOPs) are most often chosen, while treatment trains are almost always used. The recent studies of potable water reuse presented highlight the interest of both the scientific community and the state. The economic review demonstrates that potable water reuse can be economically viable and more economical than other solutions, e.g., desalination. The review identifies the key challenges (technical, economic, institutional, and social) and opportunities for scaling up potable water reuse as a primary water supply option and discusses its potential role in enhancing long-term sustainable water management, especially in areas that are either arid or semi-arid. Full article

Background/Objectives: Dengue prevention in rural Amazonian communities is shaped by knowledge, household feasibility, sociocultural dynamics, institutional continuity, and trusted communication. This study explored behavioral, sociocultural, and institutional barriers to dengue prevention and control in rural communities of the Peruvian Amazon. Methods: An exploratory qualitative study with an ethnographic orientation, informed by the Communication for Behavioural Impact (COMBI) framework, was conducted in three anonymized rural settlements in San Martín, Peru. The qualitative corpus included 120 adults, 84 in-depth interviews, six focus group discussions with 36 participants, 22 household and community observation records, 13 institutional communication materials, and seven local operational documents. Data were analyzed using an inductive thematic approach and triangulated across participant profiles, settlements, and sources. Results: Dengue was widely recognized as a mosquito-borne disease, but the central finding was a gap between general awareness and practical, routine application. Participants’ understanding of breeding sites, warning signs, and feasible source reduction was uneven. Prevention was mainly reactive, increasing after nearby cases, alerts, or fumigation, but weakening when risk was not visible. Irregular water supply, water storage, waste accumulation, gendered domestic labor, competing household priorities, reluctance to confront neighbors, and intermittent institutional action limited sustained prevention. Fumigation was perceived as the most visible institutional response, while communication was more credible when mediated by trusted local actors. Conclusions: Dengue prevention requires locally feasible household practices, safe water-storage guidance, trusted communicators, neighborhood coordination, continuous pre-outbreak engagement, and intersectoral support. Full article

This paper develops a bi-level optimization framework for community energy systems to improve grid stability and strengthen resilience against supply–demand mismatches, with potential applicability to weather-driven operational stress. By incorporating demand-side response resources, with particular emphasis on the thermal storage potential of buildings, the proposed framework enhances the operational security and regulation capability of the system. At the upper level, energy operators determine dynamic electricity pricing strategies aimed at not only maximizing economic returns but also shaping load profiles toward smoother and more stable operation. At the lower level, a building thermal dynamic model is established, and the schedulable characteristics of flexible appliances, including electric water heaters, dishwashers, and washing machines, are exploited to reduce user-side energy costs while supporting peak load mitigation. Through iterative coordination between the two levels, the proposed method enables effective joint optimization of supply and demand. Simulation results indicate that the framework increases operator revenues through differentiated pricing and, at the same time, substantially lowers users’ electricity expenditures. In addition, by aggregating distributed flexible resources as a virtual buffering capacity, the proposed strategy helps reconcile the interests of both operators and users and further improves the resilience of the local power community energy system. Full article

Investments in urban water supply should be informed by resilience management frameworks that consider traditional reliability requirements, community preparedness during system disruptions, and sustainability goals in long-term planning. Grounded in a framework (WARATA) that integrates these aspects, this paper presents a stepwise, performance-based theoretical approach to resilience quantification, supported by explanations and practical guidance. For instance, in addition to the piped infrastructure components, emergency supply options and human resources should be incorporated within the system boundaries (Step 1), and water supplied to users is recommended as a single performance measure (Step 2). During disruptions, performance at user nodes is influenced by operational rules for resource allocation (Step 3), which must be implemented in the required computer model for simulating performance (Step 4). Equations for computing withstanding, absorptive, restorative, adaptive, and transformative capabilities as time-based metrics are proposed (Step 5), enabling the analysis of results from the bottom up (Step 6) to inform resilience management. Using illustrations of performance curves at individual system nodes, this paper advocates for extended system boundaries that bridge the gap between infrastructure and community resilience; discusses challenges with the modeling of dynamic, adaptive performances; and emphasizes the importance of assessing temporal distances to fail-safe and safe-fail thresholds during disturbances. Pending case study validation and integration into tools for predictive and real-time analyses of options, the quantification approach could support infrastructure and emergency response planning and management, ultimately ensuring sustainable system designs with equitable resilience outcomes. Full article

Efficient water resource management is critically important for arid regions such as southern Kazakhstan. This paper presents a hybrid Internet of Things (IoT) and Vision-based Internet of Things (VIoT) architecture for real-time monitoring of water levels in irrigation channels. The proposed system integrates an ultrasonic water-level sensor, an IP camera with edge-based computer vision processing on a Raspberry Pi, wireless communication, an autonomous solar power supply, and discharge estimation using Manning’s equation. The VIoT subsystem applies image processing techniques, including gauge calibration, Canny edge detection, and pixel-to-metric conversion, to automatically estimate water level from captured video frames. Water-level measurements obtained from IoT sensors and video-based analysis are combined through synchronised data fusion to improve monitoring accuracy and reliability. The hybrid approach leverages the complementary strengths of IoT and VIoT by combining continuous quantitative sensing with visual verification capabilities. Field experiments conducted on the Merke River in the Zhambyl region of Kazakhstan over a 14-day observation period demonstrated stable real-time operation with RMSE = 0.311 cm, MAE = 0.279 cm, and Pearson r = 0.99 between the ultrasonic sensor and the vision-based estimates. Sensitivity analysis indicated that water level is the most influential parameter in Manning-based discharge estimation, confirming the importance of accurate level detection. The proposed system improves reliability by cross-checking independent data sources, making it applicable to monitoring water levels in agricultural regions. Full article