Search Results (278)

The global food industry is undergoing a critical shift toward sustainability, driven by high postharvest losses—reaching up to 40% for fruits and vegetables—and the need to reduce environmental impact. Sustainable postharvest innovations focus on improving quality, extending shelf life, and minimizing waste through eco-efficient technologies. Advances in non-thermal and minimal processing, including ultrasound, pulsed electric fields, and edible coatings, support nutrient preservation and food safety while reducing energy consumption. Although integrated postharvest technologies can reduce deterioration and microbial spoilage by 70–92%, significant challenges remain, including global losses of 20–40% and the high implementation costs of certain nanostructured materials. Simultaneously, eco-friendly packaging solutions based on biodegradable biopolymers and bio-composites are replacing petroleum-based plastics and enabling intelligent systems capable of monitoring freshness and detecting spoilage. Energy-efficient storage, smart sensors, and optimized cold-chain logistics further contribute to product integrity across distribution networks. In parallel, the circular bioeconomy promotes the valorization of agro-food by-products through the recovery of bioactive compounds with antioxidant and anti-inflammatory benefits. Together, these integrated strategies represent a promising pathway toward reducing postharvest losses, supporting food security, and building a resilient, environmentally responsible fresh produce system. Full article

Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate. Full article

Urban green space is essential for ecological functions, environmental quality, and human well-being, yet campus expansion can reduce vegetated areas. This study assessed UGS dynamics at Universitas Padjadjaran’s Jatinangor campus from 2015 to 2025 and evaluated an object-based machine learning approach for fine-scale land cover mapping. High-resolution WorldView-2, WorldView-3, and Legion-03 imagery were pan-sharpened, geometrically corrected, normalized, and used to compute NDVI and NDWI indices. Object-based image analysis segmented the imagery into homogeneous objects, followed by random forest classification into six land cover classes; UGS was derived from dense and sparse vegetation. Accuracy assessment included confusion matrices, overall accuracy 0.810–0.860, kappa coefficients 0.747–0.826, weighted F1 scores 0.807–0.860, and validation with 43 field points. The total UGS increased from 68.89% to 74.69%, bare land decreased from 13.49% to 5.81%, and building areas moderately increased from 10.36% to 11.52%. The maps captured vegetated and developed zones accurately, demonstrating the reliability of the classification approach. These findings indicate that campus expansion has been managed without compromising ecological integrity, providing spatially explicit, reliable data to inform sustainable campus planning and support green campus initiatives. Full article

Urban waterfront spaces are key venues for residents’ physical activity, and their spatial environment significantly impacts usage efficiency. Existing studies predominantly employ linear models and focus on single waterfront types, making it difficult to reveal differences across various types and the nonlinear mechanisms of influencing factors. To address this, this study investigates three types of waterfront spaces in Shenzhen—riverfront, lakeshore, and seafront spaces—integrating multi-source data and machine learning techniques to systematically analyze the differential impacts of the same elements on physical activity. The results indicate: (1) In terms of transportation accessibility, public transport is the most important factor for riverfront and lakeshore spaces, while road network accessibility is most critical for seafront spaces. (2) Regarding natural landscapes, the dominant factors are normalized difference vegetation index (NDVI) for riverfront spaces, green view index for lakeshore spaces, and distance to the shoreline for seafront spaces. (3) For facility services, the core factors are building density (riverfront), number of sports facilities (lakeshore), and number of leisure facilities (seafront). (4) The study further reveals nonlinear relationships and threshold effects of multiple elements. For instance, a turning point in physical activity intensity occurs when the distance to a subway station reaches 2–2.5 km. The green view index shows a threshold of 30% in the overall model, while dual-threshold phenomena are observed in the lakeshore and seafront models. (5) Synergistic effects between elements vary by waterfront type: in riverfront and seafront spaces, activity is more vibrant when areas are close to subway stations and have a low sky view index, whereas the opposite pattern is observed in lakeshore spaces. A combination of a high green view index and greater distance to the shoreline promotes activity in lakeshore spaces, while a high green view index combined with proximity to the shoreline has the most significant promotional effect in riverfront and seafront spaces. This study provides a scientific basis for health-oriented, precise planning and design of urban waterfront spaces. Full article

Solid waste management remains a critical challenge in rapidly urbanizing regions of the Global South, where limited infrastructure and informal disposal practices compromise environmental and public health. This study addresses the issue of illegal waste dumping in Dakar, Senegal, by integrating remote sensing, geographic information systems, and citizen science into a multi-criteria framework to identify areas most susceptible to dumping. Using Landsat 8 and Sentinel-2 imagery, indicators such as land surface temperature, vegetation, soil, and water indices were combined with demographic and infrastructural data. A citizen survey involving local university students provided social perception scores and criterion weights through the Analytic Hierarchy Process. The resulting susceptibility maps revealed that high and very high dumping probabilities are concentrated around the Mbeubeuss landfill and densely populated areas of Keur Massar, while Malika showed lower susceptibility. Sensitivity analysis confirmed the model’s robustness but highlighted the influence of thermal and social perception variables. The results show that 28–35% of the study area falls under high or very high susceptibility, with hotspots concentrated near wetlands, informal settlements, and poorly serviced road networks. The weighted model demonstrates stronger spatial coherence compared to the unweighted version, offering improved interpretability for waste monitoring. These findings provide actionable insights for the Société Nationale de Gestion Intégrée des Déchets (SONAGED) and for municipal planners to prioritize interventions in high-susceptibility zones. Rather than being entirely novel, this study builds on existing remote sensing, geographic information systems and citizen science approaches by integrating them within a multi-criteria framework specifically adapted to a West African context. Full article

Modeling urban microclimates is essential for assessing thermal comfort and the urban heat island (UHI) effect, particularly in the context of climate change. The UHI intensifies thermal discomfort, increases energy demand, and exacerbates health risks during extreme heat events. Accurate urban modeling is crucial for evaluating microclimatic conditions and developing effective mitigation strategies. However, traditional 3D modeling approaches often lack the efficiency and precision required to capture complex urban morphologies and integrating key environmental elements such as vegetation. This study presents an optimized workflow for large-scale 3D urban modeling that combines open-source geospatial data with programming and parametrisation tools to enhance the accuracy and scalability of urban studies. The methodology applied in Seville comprises data acquisition, processing, and modeling to produce a high-resolution urban environment model. Using Grasshopper and the ShrimpGIS plugin, spatial datasets of buildings and urban vegetation are processed to create a high-fidelity model. The resulting model is structured for integration into environmental analysis tools such as Ladybug Tools. This integration enables the direct assessment of design choices and morphological relationships for climate resilience, facilitating a detailed evaluation of urban microclimates and climate adaptation strategies. This approach provides urban planners and researchers with a replicable, efficient methodology to support evidence-based decisions for climate-responsive urban development. Full article

A combined Differential InSAR (D-InSAR) and Digital Elevation Model (DEM)-based analysis revealed that earthquake-triggered landslides significantly altered river morphology and intensified flooding in the Kota Belud catchment, Sabah, Malaysia. This 1386 km² catchment, home to about 120,000 people, has experienced a marked rise in flood events following the 4 June 2015 and 8 March 2018 earthquakes. Multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data and a 30 m Shuttle Radar Topography Mission (SRTM) DEM, complemented by river network information from HydroBASINS, were integrated to map sediment redistribution and model flood extent. Upstream zones exhibited extensive coseismic landslides and pronounced geomorphic disruption. Interferometric analysis showed that coherence was well preserved over stable terrain but rapidly degraded in vegetated and steep areas. Sediment aggradation, interpreted qualitatively from patterns of coherence loss and increased backscatter intensity, highlights slope failure initiation zones and depositional build-up along channels. Conversely, downstream, similar sedimentary adjustments were detected immediately upstream of areas with repeated flood incidents. Between 2015 and 2018, flood occurrences increased over fivefold, and after 2018, they increased by more than thirteenfold relative to pre-2015 conditions. DEM-based inundation simulations demonstrated that channel shallowing substantially reduced conveyance capacity and expanded flood extent. Collectively, these results confirm that earthquake-induced landslides have contributed to reshaping the geomorphology and amplified flooding in the area. Full article

Urban greenery is essential for environmental quality, visual comfort, and residents’ well-being, and it becomes especially critical in high-density residential compounds where outdoor space is limited. This study proposes a pedestrian-scale visibility framework that integrates solid 3D models (DEM, extruded buildings, water) with voxelized LiDAR point clouds to reconstruct fine-resolution outdoor scenes and to quantify visual perception indicators, including green view factor (GVF), sky view factor (SVF), and average green distance (AGD). A residential community in Nanjing is used as the case study. Line-of-sight sampling was performed on 223 viewpoints distributed across three empirically identified activity zones, and a resident questionnaire was conducted in parallel (279 valid responses). The results show that the visually open zone, characterized by relatively high SVF, moderate GVF, and larger vegetation setback (higher AGD), is also the zone most preferred by residents, whereas the zone with the highest GVF but strong enclosure is least preferred. This consistency between modeled indicators and survey responses confirms that excessive, close-range planting may reduce usability, while a balanced combination of greenery and openness better supports everyday outdoor activities. The proposed Point-Cloud-Based approach, therefore, provides a data-driven basis for planning, evaluating, and managing outdoor environments in dense urban residential areas, and ultimately reaching the purpose of more livable urban communities in the era of intelligent and sustainable cities. Full article

In Southwest China, traditional wooden buildings in historic villages commonly feature natural vegetation roofing materials, such as thatch or bamboo shingles, which are highly susceptible to fire. Existing research has primarily focused on traditional timber-frame buildings with tiled roofs, while limited attention has been given to those with natural vegetation roofs. This study, taking Wengding village in Cangyuan Wa Autonomous County, Yunnan Province, as an exemplary case, conducts a fire risk assessment and explores fire prevention strategies for buildings with bamboo-wood frames and natural vegetation roofs on the basis of Fire Dynamics Simulator (FDS): the application of fire-retardant coatings, the use of synthetic thatched roofing materials, and a combination of both. The results indicate that the strategy employing synthetic thatched roofing materials offers the best fire resistance performance. By integrating traditional fire prevention knowledge with modern technologies, this study provides a scientifically grounded reference for mitigating fire risks in historic buildings with natural vegetation roofs in China’s ethnic minority regions, aiming to enhance fire safety while preserving architectural authenticity. Full article

Urban ecosystems are increasingly shaped by multiple environmental stressors, which may threaten both biodiversity and human well-being. We summarised the current knowledge on the ecological and social consequences of seven major urban pressures: air pollution, freshwater degradation, biological invasions, noise pollution, habitat fragmentation, soil pollution and climate crisis. Air and soil pollution, largely driven by traffic and industrial activities, compromises vegetation functions, reduces ecosystem services, and affects human health. Urban freshwater systems face contamination from stormwater runoff, wastewater, and microplastics, leading to biodiversity loss, altered ecosystem processes, and reduced water availability. Biological invasions, facilitated by human activities and habitat disturbances, reshape ecological communities, outcompete native species, and impose socio-economic costs, while management requires integrated monitoring and citizen engagement. Noise pollution disrupts animal communication, alters species distributions, and poses significant risks to human physical and mental health. Simultaneously, habitat fragmentation and loss reduce ecological connectivity, impair pollination and dispersal processes, and heighten extinction risks for both plants and animals. Collectively, these stressors interact synergistically, amplifying ecological degradation and exacerbating health and social inequalities in urban populations. The cumulative impacts highlight the need for systemic and adaptive approaches to urban planning that integrate biodiversity conservation, public health, and social equity. Nature-based solutions, ecological restoration, technological innovation, and participatory governance emerge as promising strategies to enhance urban resilience. Furthermore, fostering citizen science initiatives can strengthen monitoring capacity and create community ownership of sustainable urban environments. Addressing the combined pressures of urban environmental stressors is thus pivotal for building cities that are ecologically robust, socially inclusive, and capable of coping with the challenges of the climate crisis and global urbanization. Full article

PM_2.5 and O₃ are now the primary air pollutants in Chinese cities and pose serious risks to human health. In particular, the two- and three-dimensional patterns of urban buildings and green spaces play a crucial role in governing the dispersion of air pollutants. Using multi-source geospatial data and 2D/3D morphology metrics, this study employs an Extreme Gradient Boosting (XGBoost) model coupled with Shapley Additive Explanations (SHAP) to analyze the nonlinear effects of 2D/3D landscape and green space patterns on PM_2.5 and O₃ concentrations in the central urban area of Guiyang City. The results indicate the following findings: (1) PM_2.5 exhibits a U-shaped seasonal pattern, being higher in winter and spring and lower in summer and autumn, whereas O₃ displays an inverted U-shaped pattern, being higher in spring and summer and lower in autumn and winter. (2) PM_2.5 concentrations are higher in suburban and industrial zones and lower in central residential areas, while O₃ concentrations increase from the urban core toward the suburbs. (3) MV, BSI, BSA, BEL, BD, FAR, and BV show significant positive correlations with both PM_2.5 and O₃ (p < 0.001), whereas TH shows a significant negative correlation with PM_2.5 (p < 0.001). (4) High-density and complex building-edge patterns intensify both PM_2.5 and O₃ pollution by hindering urban ventilation and enhancing pollutant accumulation, whereas moderate vertical heterogeneity and greater tree height effectively reduce PM_2.5 concentrations but simultaneously increase O₃ concentrations due to enhanced VOC emissions. Urban form and vegetation jointly regulate air quality, highlighting the need for integrated urban planning that balances building structures and green infrastructure. The findings of this study provide practical implications for urban design and policymaking aimed at the coordinated control of PM_2.5 and O₃ pollution through the optimization of urban morphology. Full article

Alkyd resins (ARs) represent a significant development in synthetic polymers, being among the oldest ones and playing a crucial role in numerous applications, especially within the coating sector. The trend is moving towards replacing non-renewable resources in the production of ARs with bio-based alternatives, with the goal of creating more sustainable binder materials as part of the transition to a bioeconomy. 2,5-Furandicarboxylic acid (FDCA) serves as a promising biomass-derived “building block” to replace non-renewable petroleum-derived aromatic diacids and anhydrides in AR synthesis. Various vegetable oils, including sunflower seed (SFO) and linseed oils (LSO), were utilized along with pentaerythritol (P) and glycerol (G) as polyols. FTIR and ¹H NMR spectroscopies were conducted for the verification of alkyd structures. The synthesized ARs were assessed for their physico-chemical properties, including acid value, hydroxyl value, color, density, and viscosity. The performance of the resulting alkyd coatings, which are crucial for their commercial applications, was examined. Key factors such as drying time, hardness, adhesion, wettability, chemical and corrosion resistance, and UV stability were analyzed. All synthesized FDCA-based alkyd coatings demonstrate outstanding adhesion, good thermal stability up to 220 °C, and barrier properties for steel with |Z|_0.02Hz ~10⁶–10⁷ Ohm cm⁻², which render them suitable for the processing requirements of indoor coating applications. The higher temperature at 50% mass loss (T₅₀) for SFO-P (397 °C) and LSO-P (413 °C) as compared to SFO-G (380 °C) and LSO-G (394 °C) indicated greater resistance to thermal breakdown when pentaerythritol was used as a polyol. Replacing glycerol with pentaerythritol in FDCA-based ARs resulted in a viscosity increase of 1.2–2.4 times and an enhancement in hardness from 2H to 3H. FDCA-based ARs exhibited decreased tack-free time, enhanced thermomechanical properties, and similar hardness as compared to phthalic anhydride-based ARs, underscoring the potential of FDCA as a sustainable alternative to phthalic anhydride in the formulation of ARs, integrating a greater proportion of renewable components for wood coating applications. Full article

Phenology is a key factor influencing the accuracy of regional-scale winter wheat-yield estimation. This study proposes a yield-estimation modeling framework centered on phenological zoning. Based on the remote sensing monitoring results of the heading stage of winter wheat in the Huang-Huai-Hai region from 2016 to 2021, the KNN-Ward spatial constraint clustering method was adopted to divide the Huang-Huai-Hai region into four consecutive wheat phenological zones. The results indicate a consistent spatio-temporal gradient in the phenology of winter wheat across the Huang-Huai-Hai region, characterized by later development in the northern areas and earlier development in the southern areas. The median day of year (DOY) for the heading stage in each zone varies by approximately 4 to 5 days, demonstrating a high degree of interannual stability. Building upon the phenological zoning outcomes, a multi-source data-driven random forest model was developed for wheat-yield estimation by integrating remote sensing data and meteorological variables during the wheat grain filling stage. This model incorporates remote sensing vegetation indices, crop growth parameters, and climatic factors as key input variables. Results show that the phenological zoning strategy significantly improves model prediction performance. Compared with the non-zoning model (R² = 0.46, RRMSE = 13.02%), the phenological zone model shows strong performance under leave-one-year-out cross-validation, with R² ranging from 0.54 to 0.68 and RRMSE below 12.50%. The phenological zoning model also exhibits more uniform residuals and higher prediction stability than models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of phenology-based zoning for regional yield estimation and provide a reliable framework for fine-scale crop yield monitoring. The phenological zoning model also demonstrates superior residual uniformity and prediction stability compared with models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of the multi-factor-driven modeling framework based on crop phenological zoning for regional yield estimation, providing a robust methodological foundation for fine-scale yield monitoring at the regional level. Full article

The Amazon wetlands are the largest and most diverse freshwater ecosystem globally, characterized by various flooded vegetation and the Amazon River’s estuary. This critical ecosystem is vulnerable to land use changes, dam construction, mining, and climate change. While several studies have utilized remote sensing to map wetlands in this region, significant uncertainty remains, which limits the assessment of impacts and the conservation priorities for Amazon wetlands. This study aims to enhance wetland mapping by integrating existing maps, remote sensing data, expert knowledge, and cloud computing via Earth Engine. We developed a harmonized regional wetland classification system adaptable to individual countries, enabling us to train and build a random forest model to classify wetlands using a robust remote sensing dataset. In 2020, wetlands spanned 151.7 million hectares (Mha) or 22.0% of the study area, plus an additional 7.4 Mha in deforested zones. The four dominant wetland classes accounted for 98.5% of the total area: Forest Floodplain (89.0 Mha; 58.6%), Lowland Herbaceous Floodplain (29.6 Mha; 19.6%), Shrub Floodplain (16.7 Mha; 11.0%), and Open Water (14.1 Mha; 9.3%). The overall mapping accuracy was 82.2%. Of the total wetlands in 2020, 52.6% (i.e., 79.8 Mha) were protected in Indigenous Territories, Conservation Units, and Ramsar Sites. Threats to the mapped wetlands included 7.4 Mha of loss due to fires and deforestation, with an additional 800,000 ha lost from 2021 to 2024 due to agriculture, urban expansion, and gold mining. Notably, 21 Mha of wetlands were directly affected by both reduced precipitation and surface water in 2020. Our mapping efforts will help identify priorities for wetland protection and support informed decision-making by local governments and ancestral communities to implement conservation and management plans. As 47.4% of the mapped wetlands are unprotected and have some level of threats and pressure, there are also opportunities to expand protected areas and implement effective management and conservation practices. Full article

The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an ensemble machine learning approach that simultaneously considers different time periods of the week. This study reveals the impacts of four dimensions of BE variables on UV at different time periods at the scale of the community life circle. The four well-performing base models are integrated to reveal the mechanism of differential effects of BE variables on UV under different time periods in the old city of Nanjing through Shapley addition explanation. The findings reveal that (1) the seven most important built environment variables existed in different time periods of the week: floor area ratio, service POI density, remote sensing ecological index, POI mixability, average building height, fractional vegetation cover, and maximum building area; (2) The nonlinear and threshold effects of the built environment factors differed across time periods of the week; (3) There is a dominant interaction between built environment variables at different time periods of the week. This study can provide guidance for the refined management of complex urban systems. Full article