Search Results (473)

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

The Lower Rio Grande Valley (LRGV) of Texas is a biodiversity hotspot due to its high alpha, beta, and gamma diversity and high regional endemism, which are at high risk of degradation. The region has lost 95% of its native thornforest habitat primarily due to agricultural and urban expansion. This study aims to evaluate the current vegetative structure and composition of restored thornforest sites located in the LRGV to identify restoration methods and site characteristics that affect forest restoration outcomes. Twelve restored thornforest sites were selected for this study that varied in time since restoration, patch size, degree of isolation, and method of restoration. Canopy, understory, and ground layer vegetation were evaluated at six survey points per restored site (n = 72), and 17 environmental variables were incorporated into univariate and multivariate analyses to identify factors influencing restored plant communities. Actively restored sites showed higher overall richness, abundance, and diversity than passively restored sites. More isolated patches had higher overall richness, abundance, and diversity, and longer times since restoration began increased richness and diversity. Higher abundances of Urochloa maxima, an invasive grass, altered community composition and reduced diversity in each forest layer and overall and reduced richness in the canopy and ground layers. Important considerations for thornforest restoration in the LRGV should include invasive grass prevalence; proximity to riparian and seasonal wetland habitats; landscape factors that influence water availability; and patch geography, including shape, size, and proximity to other forest patches. Full article

This study assesses the potential impact of sea level rise (SLR) on wood-consuming mills in coastal Georgia, a major forestry state in the southern United States. To assess the vulnerability of wood-consuming mills in coastal Georgia, two potential wood procurement zones are defined: areas within 40 miles (64.4 km) and 64 miles (103 km) of the radius of each wood-consuming mill. The projected SLR scenarios of 2 ft (0.61 m) and 6 ft (1.83 m)—approximating intermediate and high-end conditions for coastal Georgia, respectively—are then overlaid onto the procurement zones of each mill to calculate the percentage of procurement area lost to the inundation. Our findings indicate that a 2 ft rise would have a minimal impact on wood supply for most wood-consuming mills. On the other hand, some facilities in Glynn and Liberty Counties could experience a substantial loss of up to 26% of their wood procurement area under a 6 ft sea level rise with a 40-mile wood procurement zone due to proximity to inundation. A larger procurement radius of 64 miles mitigates this impact, though spatial variability persists. Woody wetlands suffer the highest proportional losses across buffers and scenarios; upland forest types remain mostly intact under 2 ft SLR and display moderate loss under 6 ft. This study emphasizes the significance of accounting for spatially variable climate change impacts when planning for mill resilience. The results inform long-term sustainability strategies for wood-consuming mills in coastal regions of Georgia and beyond. Full article

Mountain settlements represent culturally rich but environmentally fragile landscapes, shaped by enduring processes of ecological adaptation and human resilience. In western Sichuan, Jiarong Tibetan villages, with their distinctive integration of defensive stone towers and settlements, embody this coupling of culture and the environment. We hypothesize that settlement cores in these villages were shaped by natural environmental factors, with subsequent expansion reinforced by the cultural significance of towers. To test this, we applied a micro-scale spatial–environmental framework to four sample villages in Suopo Township, Danba County. High-resolution World Imagery (Esri, 0.5–1 m, 2022–2023) was classified via a Random Forest algorithm to generate detailed land-use maps, and a 100 × 100 m fishnet grid extracted topographic metrics (elevation, slope, aspect) and accessibility measures (distances to streams, roads, towers). Geographically weighted regression (GWR) was then used to examine how slope, elevation, aspect, proximity to water and roads, and tower distribution affect settlement patterns. The results show built-up density peaks on southeast-facing slopes of 15–30°, at altitudes of 2600–2800 m, and within 50–500 m of streams, co-locating with historic watchtower sites. Based on these findings, we propose four zoning strategies—a Core Protected Zone, a Construction And Development Zone, an Ecological Conservation Zone, and an Industry Development Zone—to balance preservation with growth. The resulting policy recommendations offer actionable guidance for sustaining traditional settlements in complex mountain environments. Full article

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R² = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors. Full article

This study investigates the effects of species, geographical origin, and processing on the proximate composition of frog leg meat, with a focus on developing predictive models for processing status. Data were systematically compiled from 18 published studies, yielding 32 entries across 10 edible frog species and multiple processing methods. Proximate composition parameters (moisture, protein, fat, ash) were compared between processed and unprocessed samples, and classification models were trained using moisture content as the primary predictor. Logistic regression and several machine learning algorithms, including Stochastic Gradient Descent, Support Vector Machine, Random Forest, and Decision Tree, were benchmarked under a Leave-One-Study-Out (LOSO) cross-validation framework. Results demonstrated that moisture content alone was sufficient to accurately distinguish processing status, with a critical threshold of ~73% separating processed from unprocessed frog legs. Logistic regression achieved perfect specificity and precision (100%) with an overall accuracy of 96.8%, while other classifiers also performed strongly (>90% accuracy). These findings confirm moisture as a species- and origin-independent marker of processing, offering a simple, rapid, and cost-effective tool for authenticity verification and quality control in frog meat and potentially other niche protein products. Future work should expand sample coverage, validate thresholds across processing types, and integrate biochemical and sensory quality assessments. Full article

Power transformers represent critical assets within the electrical power system, and their unexpected failures may result in substantial financial losses for both utilities and consumers. Dissolved Gas Analysis (DGA) is a well-established diagnostic method extensively employed to detect incipient faults in power transformers. Although several conventional and machine learning techniques have been applied to DGA, most of them focus only on fault classification and lack the capability to provide predictive scenarios that would enable proactive maintenance planning. In this context, the present study introduces a novel approach to DGA interpretation, which highlights the trends and progression of faults by exploring the feature space through the algorithms k-Nearest Neighbors (KNN) and Optimum-Path Forest (OPF). To improve accuracy, the following strategies were implemented: statistical filtering based on normal distribution to eliminate outliers from the dataset; augmentation of gas-related features; and feature selection using optimization algorithms such as Cuckoo Search and Genetic Algorithms. The approach was validated using data from several transformers, with fault diagnoses cross-checked against inspection reports provided by the utility company. The findings indicate that the proposed method offers valuable insights into the progression, proximity, and classification of faults with satisfactory accuracy, thereby supporting its recommendation as a complementary tool for diagnosing incipient transformer faults. Full article

One of the most critical issues in soil science is the content of metals and their environmental toxicity. This is especially relevant to soil contamination by metals in industrial and postindustrial areas. The region of Lower Silesia, known for exploitation of Cu and Ag deposits, along with Zn and Pb, is significantly affected by metal contamination near post-flotation waste facilities in both old and new copper districts. Metal concentrations in soils adjacent to abandoned tailings storage facilities in the copper district were measured to identify factors influencing contamination in agricultural and technogenic soils. Concentrations of copper, lead, and zinc were determined in 111 samples taken from nine soil profiles down to a depth of 1.60 m. Significant variation was observed in metal content: in agricultural soils, copper reached up to 2800 mg/kg, lead up to 150 mg/kg, and zinc up to 65 mg/kg. In forest soils, concentrations reached as high as 1700 mg/kg for copper, 1800 mg/kg for lead, and up to 1100 mg/kg for zinc. The metal content increased with proximity to the tailings storage. Soil profiles closest to the emission source showed the highest metal concentrations, while concentrations of Cu, Pb, and Zn decreased with distance. Full article

Predictive maintenance (PdM) is essential for reducing equipment downtime and enhancing operational efficiency. However, PdM datasets frequently suffer from significant class imbalance and are often limited to single-label classification, which fails to reflect the complexity of real-world industrial systems where multiple failure modes can occur simultaneously. As the main contribution, we propose the Balanced Hoeffding Tree Forest (BHTF)—a novel multi-label classification framework that combines oversampling and undersampling strategies to effectively mitigate data imbalance. BHTF leverages the binary relevance method to decompose the multi-label problem into multiple binary tasks and utilizes an ensemble of Hoeffding Trees to ensure scalability and adaptability to streaming data. In particular, BHTF unifies three learning paradigms—multi-label learning (MLL), ensemble learning (EL), and incremental learning (IL)—providing a comprehensive and scalable approach for predictive maintenance applications. The key contribution of the proposed method is that it incorporates a hybrid data preprocessing strategy, introducing a novel undersampling technique, named Proximity-Driven Undersampling (PDU), and combining it with the Synthetic Minority Oversampling Technique (SMOTE) to effectively deal with the class imbalance issue in highly skewed datasets. Experimental results on the benchmark AI4I 2020 dataset showed that BHTF achieved an average classification accuracy of 97.44%, outperformed by a margin of the state-of-the-art methods (88.94%) with an improvement of 11% on average. These findings highlight the potential of BHTF as a robust artificial intelligence-based solution for complex fault detection in manufacturing predictive maintenance applications. Full article

This study examines the perceived air quality and contributors to air pollution among residents of Merced and Stanislaus Counties in California’s San Joaquin Valley (SJV), one of the most polluted regions in the United States. A survey was conducted during the summer of 2017, gathering responses from 176 participants to assess their perceptions of air quality, sources of pollution, and behaviors related to air pollution awareness. Findings indicate that only 3.5% of participants perceived the air quality in their city as good, while 57.9% categorized it as unhealthy or unhealthy for sensitive groups. Participants identified cars and trucks as the primary sources of air pollution, followed by forest fires and factories. Seasonal differences in perception were also observed, with summer months being viewed as the most polluted. Additionally, participants living near major roadways reported higher concerns regarding air pollution’s impact on health. Multivariate regression analysis revealed that education was significantly associated with perceived air quality, while proximity to highways influenced perceptions of health risks. This study underscores the need for targeted interventions to raise awareness and promote self-protective behaviors, especially for vulnerable populations living near highways. These findings highlight the importance of localized public health strategies to address air quality concerns in SJV communities. Full article

Urban riparian areas serve as vital blue-green infrastructure for climate adaptation, yet mechanisms governing energy exchange remain underexplored. This study aims to quantify the spatiotemporal patterns of sensible heat flux (H) and latent heat flux (LE) across riparian plant communities on daily and annual scales, and to disentangle the interactive effects of vegetation structure and water bodies on these fluxes. Using year-long field monitoring (September 2020–August 2021) across seven riparian plant communities along the Danshui River in Shanghai, environmental parameters were collected at multiple distances from the river. Interpretable machine learning models (Random Forest with SHAP analysis) were employed to identify key drivers. Results reveal significant diurnal and seasonal dynamics: LE amplitude exceeded H in summer but reversed in winter, with spatial gradients in H and LE strongly influenced by proximity to water bodies in grasslands and broadleaf forests but weakened in conifers. Meteorological factors such as photosynthetically active radiation and sunshine duration dominated daily-scale fluxes, while vegetation structures such as canopy height and leaf area index (LAI) contributed >50% to annual-scale variability. These findings underscore vegetation’s role in modulating energy partitioning, providing a theoretical basis for optimizing riparian plant configurations to enhance microclimate regulation in urban riparian. Full article

Renewable energy expansion requires cost-effective strategies to integrate underutilized biomass resources into energy systems. In Australia, forest residues represent a significant but largely untapped feedstock that could contribute to a more diversified energy portfolio. This study presents an integrated geospatial and optimization decision-support model designed to minimize the total cost of forest biomass-to-bioenergy supply chains through optimal facility selection and network design. The model combined geographic information systems with mixed-integer linear programming to identify the optimal candidate facility sites based on spatial constraints, biomass availability and infrastructure proximity. These inputs then informed an optimization framework that determined the number, size, and geographical distribution of bioenergy plants. The model was applied to a case study in Queensland, Australia, evaluating two strategic scenarios: (i) a biomass-driven approach that maximizes the use of forest residues; (ii) an energydriven approach that aligns facilities with regional energy consumption patterns. Results indicated that increasing the minimum facility size reduced overall costs by capitalizing on economies of scale. Biomass collection accounted for 81%–83% of total supply chain costs (excluding capital installation), emphasizing the need for logistically efficient sourcing strategies. Furthermore, the system exhibited high sensitivity to transportation distance and biomass availability; energy demands exceeding 400 MW resulted in sharply escalating transport expenses. This study provides a scalable, data-driven framework for the strategic planning of forest-based bioenergy systems. It offers actionable insights for policymakers and industry stakeholders to support the development of robust, cost-effective, and sustainable bioenergy supply chains in Australia and other regions with similar biomass resources. Full article

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability prediction model was constructed based on the existing distribution of primary schools, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. Comprehensive evaluation, feature importance analysis, and SHAP (SHapley Additive exPlanations) interpretation were conducted to ensure model reliability and interpretability. Spatial overlay analysis, incorporating population structure and the education supply–demand ratio, identified highly suitable areas for primary school construction. The results demonstrate: (1) RF and XGBoost achieved evaluation metrics exceeding 85%, outperforming traditional single models such as Logistic Regression, SVM, KNN, and CART. Validation against actual primary school distributions yielded accuracies of 84.70% and 92.41% for RF and XGBoost, respectively. (2) SHAP analysis identified population density, proximity to other educational institutions, and accessibility to transportation facilities as the most critical factors influencing site suitability. (3) Suitable areas for primary school construction are concentrated in central Tianjin and surrounding areas, including Baoping Street (Baodi District), Huaming Street (Dongli District), and Zhongbei Town (Xiqing District), among others, to meet high-quality educational service demands. Full article

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) offers a means to assess deforestation, yet map-based verification remains technically limited and uncertain. This study addresses the lack of a systematic assessment of global Forest/Non-Forest (FNF), Tree Cover/Non-Tree Cover (TC/NTC) and Land Use/Land Cover (LULC) datasets by identifying and evaluating 21 publicly available global forest/tree cover reference maps for their alignment with EUDR criteria. This goes beyond merely treating these datasets as simply “fit” or “not fit” for the purpose of the EUDR, but rather aims to assess how well each dataset meets the needs compared to others, acknowledging strengths, weaknesses, and trade-offs. The 21 datasets are reviewed based on EUDR-related parameters (temporal proximity, spatial resolution, and forest definition) as well as accuracy metrics. From this broader review, eight datasets are shortlisted based on their alignment with key regulatory requirements. However, most datasets fail to fully meet all EUDR requirements, particularly forest definitions, with only two datasets satisfying all indicators. Notably, all datasets are unable to distinguish forests from other non-forest, tree-based systems. Reported accuracy metrics reveal a general overestimation of forest areas, while canopy height-based maps tend to underestimate tree cover, potentially excluding forested regions. Regional comparisons show more consistent estimates in South America, while Europe and North America display greater variability. These findings support informed decision-making by companies and policymakers for selecting suitable datasets, while also highlighting conflicts and challenges associated with the use of global forest/tree cover maps for regulatory compliance. Full article