Search Results (468)

Drought is an increasing threat to livelihood security and sustainable development in the Vietnamese Mekong Delta (VMD), particularly in Kien Giang Province. This study examines the spatiotemporal dynamics of meteorological drought from 1992 to 2024 using daily rainfall data from 10 rain gauges. The Standardized Precipitation Index (SPI) was calculated at 3-, 6-, and 12-month timescales to assess short-, medium-, and longer-term precipitation deficits across the province. The results show that the most severe drought events were concentrated in the most recent decade, especially during the 2015–2016 and 2019–2020 dry seasons. Spatial analysis identified clear drought hotspots: the northern coastal zone, including Ha Tien and Hon Dat, exhibited the strongest long-timescale drought signal, while central inland areas such as Go Quao experienced more frequent short-timescale drought conditions. A significant negative relationship was also observed between SPI and the Oceanic Niño Index (ONI), indicating that El Niño conditions intensified drought severity, particularly in coastal areas. These findings highlight the need for spatially differentiated drought adaptation in Kien Giang Province, with stronger emphasis on water storage and water-use efficiency in inland districts and on early warning and integrated drought–salinity management in high-risk coastal zones. Full article

Oil leakage in oil-immersed power transformers poses a significant threat to grid reliability, potentially causing severe electrical accidents and environmental pollution if not detected in time. Detecting oil leakage outdoors, however, remains challenging due to the impact of weather conditions such as fog, humidity, and rain, which obscure the leakage signs and complicate real-time detection. To address these challenges, we propose a solution that integrates infrared thermal imaging with a CNN-SVM hybrid architecture. The core of this approach lies in shifting from traditional Softmax-cross-entropy-based empirical risk minimization (ERM) to maximum-margin-based structural risk minimization (SRM). A fully fine-tuned MobileNetV3 transforms low-contrast, boundary-softened infrared thermal images—often affected by fog and moisture—into a more discriminative high-dimensional feature space, where positive and negative samples become linearly separable. This is followed by replacing Softmax with a linear SVM and using hinge loss to enforce a margin constraint, which maximizes the classification margin and improves robustness to input perturbations. Experimental results show that our proposed method outperforms all compared models, achieving an accuracy of 0.990, significantly higher than ResNet50_BCE (0.908), EfficientNetB0 (0.925), YOLOv11n-CLS (0.930), and ViT (0.929). In terms of F1-Score (0.989) and AUC (0.995), MobileNetV3-SVM also demonstrates excellent performance, ensuring outstanding classification capability. Additionally, the model achieves an inference latency of only 6.3 ms, demonstrating excellent real-time inference performance, highlighting its potential for transformer oil monitoring applications. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. Full article

Slow-moving landslides can trigger severe disasters when activated by earthquakes, torrential rains, or typhoons. Early detection is crucial for mitigating loss of life and property damage. Interferometric Synthetic Aperture Radar (InSAR) technology is among the most effective techniques for detecting slow-moving landslides, though its accuracy can be further improved through integration with optical imagery and Digital Elevation Models (DEM). Current machine learning approaches that combine InSAR and optical data suffer from limited efficiency, poor transferability, and challenges in regional-scale application. To address these limitations, this study proposes a multimodal dual-path network that integrates InSAR products with textural information from optical imagery to detect slow-moving landslides. One path processes InSAR deformation rates and topographic factors, while the other incorporates texture information and auxiliary data. Together, these paths extract semantic information from high-dimensional spatial features and condense it into low-dimensional representations. A pyramid pooling module is employed to capture multi-scale features during low-level semantic extraction. For feature fusion, a rate-constrained adaptive module is introduced to enhance the contribution of deformation rates to slow-moving landslides. According to the results, the proposed method improves the F1-score for landslide detection by 6% compared to using InSAR products alone. These results provide reliable technical support for regional landslide inventory compilation and disaster management, as well as new insights for regional-scale surveys in slow-moving landslide-prone areas. Full article

Decentralized graywater treatment using nature-based systems represents a sustainable, low-energy alternative to centralized wastewater technologies, particularly in water-scarce regions. This study evaluates the performance of a rain-garden-based constructed wetland implemented at Zain Park in Jerash, Jordan, for on-site graywater treatment and potential non-potable reuse. The system consists of two filtration beds with multi-layer gravel–sand media planted with ornamental vegetation to promote physical filtration, adsorption, and biologically mediated transformations. Influent and effluent samples were monitored monthly from April 2024 to January 2025 and analyzed for biodegradable and oxidizable organic fractions (BOD₅ and COD), nutrients (TN, PO₄³⁻), suspended solids, turbidity, salinity indicators, and microbial parameters (E. coli and total coliform). Average removal efficiencies reached 98% for BOD and 96% for COD, while turbidity and TSS were reduced by more than 96%, indicating effective organic degradation and particulate retention. Nutrient removal was moderate, with 40% reduction in Total Nitrogen and 74% in nitrate, reflecting partial nitrification–denitrification and plant uptake. Microbial removal was variable, with an average reduction of 0.8 log₁₀ (64.7%) for E. coli and 1.1 log₁₀ (82.6%) for total coliforms, indicating that passive filtration alone may not ensure complete pathogen attenuation. Post-treatment disinfection and substrate enhancements (aeration and plant selection) can strengthen system efficiency and support sustainable graywater reuse in water-stressed regions, contributing directly to SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production). These findings support the applicability of compact constructed wetland systems as decentralized wastewater treatment solutions in arid and semi-arid urban environments. Full article

Accurate and real-time vehicle detection is a fundamental requirement for smart urban traffic monitoring, particularly in densely populated cities where heterogeneous traffic, frequent occlusion, and severe scale variation challenge lightweight vision systems deployed at the edge. To address these issues, this paper proposes BDNet, a lightweight YOLOv12-based vehicle detection framework designed to enhance feature preservation, contextual modeling, and multi-scale representation for intelligent transportation systems. BDNet integrates three complementary architectural components: (i) HyDASE, a hybrid detail-preserving downsampling module that mitigates information loss during resolution reduction; (ii) C3k2_MogaBlock, which strengthens long-range contextual interactions through multi-order gated aggregation; and (iii) an A2C2f_FRFN neck, which refines multi-scale features by suppressing redundancy and emphasizing discriminative responses. To support evaluation under realistic developing-region traffic conditions, we introduce the Bangladeshi Road Vehicle Dataset (BRVD), comprising 10,200 annotated images across 13 native vehicle categories captured under diverse urban scenarios, including daytime, nighttime, fog, and rain. On BRVD, BDNet achieves 85.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 67.3% ${m\mathcal{A}\mathcal{P}}_{50-95}$, outperforming YOLOv12n by +1.4 and +0.7 percentage points, respectively, while maintaining a compact footprint of 2.5 M parameters, 6.0 GFLOPs, and a real-time inference speed of 285.7 FPS. Cross-dataset evaluation on VisDrone-DET2019, using models trained exclusively on BRVD, further demonstrates improved generalization, achieving 31.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 17.9% ${m\mathcal{A}\mathcal{P}}_{50-95}$. These results indicate that BDNet provides an effective and resource-efficient vehicle detection solution for smart city–scale urban traffic monitoring. Full article

Nature-based solutions, including green infrastructure (GI), are considered sustainable tools for stormwater treatment. GI elements (rain gardens, green roofs, etc.) are increasingly applied as integrated approaches for climate change mitigation and environmental pollution reduction. This study focused on investigations of rain gardens for reducing stormwater polluted by residual chlorine after the disinfection of outdoor spaces. Laboratory (column test) and field tests were carried out to evaluate the infiltration capacities of an experimental rain garden model, as well as its efficiency for retaining residual chlorine. The experiments were conducted using simulated rain garden layers composed of waste materials that remained after different production processes. The average infiltration coefficient values obtained were 2.55 × 10⁻⁵ m/s, 2.45 × 10⁻⁵ m/s, 2.24 × 10⁻⁵ m/s, 3.4 × 10⁻⁵ m/s, 1.28 × 10⁻⁵ m/s, 1.84 × 10⁻⁵ m/s (laboratory test), and 1.39 × 10⁻⁵ m/s (field test). These values correspond to the characteristics of sand–gravel substrates. A chlorine retention efficiency of 82.5–87% was obtained. Granulometric analysis confirmed fraction size suitability for rain garden filtration. This research indicates the potential of rain gardens for reducing stormwater pollution, providing a basis for future research and practical implementation. Full article

Wind turbines operate in harsh environments where leading-edge blade erosion from particulates like sand, rain, and insects is prevalent, significantly degrading aerodynamic performance and reducing power output. To counteract this, this study proposes a novel flow-control method using detached micro-cylinders placed upstream of the leading edge of eroded S809 (a wind turbine blade profile) airfoils. The approach is inspired by the concept of symmetry recovery in disturbed flows, where strategically introduced perturbations can restore balance to an asymmetric separation pattern. The aerodynamic performance of the S809 airfoil was numerically investigated under three leading-edge erosion depths (0.2%, 0.5%, and 1% of chord length, *c*) with a fixed micro-cylinder diameter of 1% *c* positioned at fifteen different locations. Findings reveal that the strategic placement of micro-cylinders ahead of the leading edge or on the pressure side markedly enhances the aerodynamic efficiency of airfoils with 0.2% and 0.5% erosion, achieving a maximum improvement of 148.7% in the lift-to-drag ratio (L/D) difference function for the 0.5% eroded airfoil. This performance recovery is interpreted as a partial restoration of flow symmetry, disrupted by erosion-induced separation. The interaction between the cylinder wake and the spill-over stall vortex originating from the erosion groove was identified as the primary mechanism, injecting high-energy fluid into the boundary layer to suppress flow separation. This study systematically parametrizes the effect of erosion depth and cylinder placement, offering new insights for mitigating erosion-induced performance loss through controlled asymmetry introduction. Full article

The ground impedance (insulation resistance R_isol) of photovoltaic (PV) modules is usually measured only in the dry state, even though arrays frequently operate under dew-wet or rain-wet conditions, when leakage paths can change. We measured dry insulation resistance R_dry and IEC 61215 MQT 15 wet leakage resistance R_wet for N = 37 field-aged crystalline-silicon modules from utility-scale plants and related the results to the IEC 40 MΩ·m² criterion (R_wet × A ≥ 40). The measurements used 1000 V DC and a 2 min dwell; R_wet was obtained in a salted bath with a solution resistivity < 3500 Ω·cm. The median R_dry was 42.4 GΩ, whereas the median R_wet was 462.5 MΩ, resulting in a median R_dry/R_wet ratio of ~110×. Three modules (8.1%) failed the 40 MΩ·m² limit already in the dry state, whereas eight modules (21.6%) failed under IEC-wet conditions; five were dry-pass/wet-fail cases that would have passed dry screening. For a representative area A = 1.8 m², a practical conservative dry triage threshold of approximately 55.5 GΩ identifies modules needing IEC-wet verification rather than serving as a stand-alone limit. Overall, combining dry and IEC-wet measurements improves safety and supports sustainability through resource-efficient repowering/revamping and end-of-life decisions in large PV fleets, particularly in hot climates. Full article

Solar insecticidal lamp Internet of Things (SIL-IoTs) systems are widely deployed in agricultural environments, where accurate and timely rain-detection is crucial for system stability and energy-efficient operation. However, existing rain-sensing solutions rely on additional hardware, leading to increased cost and maintenance complexity. This study proposes a hardware-free rain detection method based on multivariate wireless signal feature learning, using LTE communication data. A large-scale primary dataset containing 11.84 million valid samples was collected from a real farmland SIL-IoTs deployment in Nanjing, recording RSRP, RSRQ, and RSSI at 1 Hz. To address signal heterogeneity, a signal-strength stratification strategy and a dual-rate EWMA-based adaptive signal-leveling mechanism were introduced. Four machine-learning models—Logistic Regression, Random Forest, XGBoost, and LightGBM—were trained and evaluated using both the primary dataset and an external test dataset collected in Changsha and Dongguan. Experimental results show that XGBoost achieves the highest detection accuracy, whereas LightGBM provides a favorable trade-off between performance and computational cost. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC indicates that all metrics exceed 0.975. The proposed method demonstrates strong accuracy, robustness, and cross-regional generalization, providing a practical and scalable solution for rain detection in agricultural IoT systems without additional sensing hardware. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article

Sustainable Urban Drainage Systems (SUDSs) are essential for stormwater management in urban areas, with varying hydrological, social, ecological, and economic benefits. Nevertheless, choosing the SUDS most appropriate for public spaces poses a challenge when balancing details/specifications against community decisions, primarily social implications and perceptions. Building on the SUDS design pillar of the amenity, this study outlines a three-phase methodological framework for selecting SUDS based on social facilitation. The first phase introduces the application of the Partial Least Squares Structural Equation Modeling (PLS-SEM) and Classificatory Expectation–Maximization (CEM) techniques by modeling complex social interdependencies to find critical components related to urban planning. A Likert scale survey was also conducted with 440 urban dwellers in Tunja (Colombia), which identified three dimensions: Residential Satisfaction (RS), Resilience and Adaptation to Climate Change (RACC), and Community Participation (CP). In the second phase, the factors identified above were transformed into eight operational criteria, which were weighted using the Analytic Hierarchy Process (AHP) with the collaboration of 35 international experts in SUDS planning and implementation. In the third phase, these weighted criteria were used to evaluate and classify 13 types of SUDSs based on the experts’ assessments of their sub-criteria. The results deliver a clear message: cities must concentrate on solutions that will guarantee that water is managed to the best of their ability, not just safely, and that also enhance climate resilience, energy efficiency, and the ways in which public space is used. Among those options considered, infiltration ponds, green roofs, rain gardens, wetlands, and the like were the best-performing options, providing real and concrete uses in promoting a more resilient and sustainable urban water system. The methodology was also used in a real case in Tunja, Colombia. In its results, this approach proved not only pragmatic but also useful for all concerned, showing that the socio-cultural dimensions can be truly integrated into planning SUDSs and ensuring success. Full article

This study addresses the limitations of conventional highway visibility monitoring under rain–fog conditions, where fixed stations and visibility sensors provide limited spatial coverage and unstable accuracy. Considering that drivers’ visual fields are jointly affected by global fog and local spray-induced mist, a dynamic visibility recognition and risk assessment framework is proposed using roadside monocular CCTV (Closed-Circuit Television) imagery. The method integrates the Koschmieder scattering model with the dark channel prior to estimate atmospheric transmittance and derives visibility through lane-line calibration. A Monte Carlo-based coupling model simulates local visibility degradation caused by tire spray, while a safety potential field defines the low-visibility risk field force (LVRFF) combining dynamic visibility, relative speed, and collision distance. Results show that this approach achieves over 86% accuracy under heavy rain, effectively captures real-time visibility variations, and that LVRFF exhibits strong sensitivity to visibility degradation, outperforming traditional safety indicators in identifying high-risk zones. By enabling scalable, infrastructure-based visibility monitoring without additional sensing devices, the proposed framework reduces deployment cost and energy consumption while enhancing the long-term operational resilience of highway systems under adverse weather. From a sustainability perspective, the method supports safer, more reliable, and resource-efficient traffic management, contributing to the development of intelligent and sustainable transportation infrastructure. Full article

Efficient nitrogen (N) management is critical for improving productivity and sustainability in intercropping systems, especially in semi-arid regions. Maize and peanut, the two dominant local crops, were selected to represent a typical cereal/legume intercropping system with contrasting nitrogen acquisition strategies. To investigate how spatial configuration regulates nitrogen uptake and nitrogen use efficiency in maize/peanut intercropping systems, a 3-year field (2022–2024) experiment was conducted on sandy soils in semi-arid northwest Liaoning, China. Six cropping systems were evaluated, including sole maize, sole peanut, and four intercropping configurations differing in strip width and crop proportion, including M2P2 (two rows of maize intercrop with two rows of peanut, M indicates maize and P indicates peanut), M2P4, M4P4, and M8P8. The total land equivalent ratio (LER) varied from 0.65 to 1.09, indicating that yield advantages were highly dependent on spatial configuration. Maize consistently exhibited stronger competitiveness than peanut, resulting in suppressed peanut growth in narrow-strip systems. Increasing strip width and peanut proportion alleviated interspecific competition and improved fertilizer nitrogen equivalent ratio (FNER) and nitrogen equivalent ratio (NER) in intercrops. Although intercropping did not consistently enhance total nitrogen uptake, nitrogen use efficiency was significantly improved. Narrow-strip systems (M2P2 and M2P4) increased nitrogen use efficiency, whereas wide-strip systems (M4P4 and M8P8) achieved yield benefits mainly through enhanced nitrogen uptake. Overall, the results highlight that spatial configuration plays a key role in regulating nitrogen uptake and interspecific competition in maize/peanut intercropping under semi-arid sandy conditions. Optimizing strip width and crop proportion is therefore critical for stabilizing yield and improving resource use efficiency in maize/peanut intercropping systems in dryland agriculture. Full article

With the advancement of modern agriculture and increasing scarcity of water and fertilizer resources, determining optimal water and nitrogen (N) management strategies for intercropping systems is critical for ensuring system productivity and enhancing resource-use efficiency. This study employed field experiments to investigate the effects of different water and N treatments on grain yield, aboveground biomass, leaf area index (LAI), photosynthetic parameters, chlorophyll fluorescence characteristics, and radiation use efficiency (RUE) in a maize–soybean intercropping system. The experiment consisted of three cropping systems (maize monoculture, soybean monoculture, and maize–soybean intercropping), two irrigation regimes (rain-fed and supplemental irrigation), and three N-application rates for maize (240, 180, and 120 kgN ha⁻¹). The results demonstrated that supplementary irrigation significantly enhanced the LAI and photosynthetic capacity of both maize and soybean during critical growth stages, thereby promoting increases in grain yield and aboveground biomass. Intercropping significantly improved the productivity and photosynthetic performance of maize compared to monoculture, whereas soybean exhibited a reduction under intercropping conditions. Furthermore, irrigation regime and N rate had significant interactive effects on the photosynthetic performance of maize at the tasseling stage. In the intercropping system, a 25% reduction in the conventional application rate of N for maize maintained system productivity, whereas a 50% reduction substantially decreased maize yield and photosynthetic performance. The intercropping system achieved land equivalent ratios (LERs) ranging from 1.06 to 1.11 and RUE advantages (ΔRUE) of 1.52 to 1.64, demonstrating significant superiority in land and light resource utilization. Considering both productivity and resource-use efficiency, supplemental irrigation combined with 180 kgN ha⁻¹ N application for maize represents the optimal water and N management strategy for achieving high yield and efficiency in maize–soybean intercropping systems in the Guanzhong plain. Full article

The aim of this paper is to investigate the measures adopted by higher education institutions (HEIs) for sustainable water management in university campuses. Rain and storm water harvesting and treatment, rain and storm water reuse, wastewater treatment and reuse and technologies for runoff reduction were found to be frequently undertaken. Sustainable approaches to water supply such as water-efficient appliances, irrigation algorithms and the use of drought-resistant plants have been adopted as well. In support, monitoring of consumed water and of rain and storm waters has been a widespread practice. Important considerations were given to the impact of the identified measures on campuses’ energy consumption and greenhouse gas emissions. Nature-based solutions, employment of renewable energies and sustainable disinfection methods are measures to prioritize. Some wastewater technologies may deserve priority in virtue of their positive contribution to circular economy. Drawbacks such as groundwater and soil contamination due to wastewater reuse and the release of pollutants from fertilized nature-based technologies were identified. Despite their variety, it must be noted that many of these measures have generally involved rather limited portions of campuses, taken more for demonstration or pilot/full-scale research purposes. Additional measures not identified in the current review—for instance the prevention of pollution from micropollutants and waste mismanagement—should be implemented to boost HEIs’ environmental sustainability. The findings of this review pave the way for a more structured implementation of water sustainability measures in university campuses. Full article