Search Results (47)

Regional management of the urban thermal environment is essential for sustainable development. However, both the surface urban heat island (SUHI) spatiotemporal patterns and driving mechanisms across humid–arid regions remain uncertain. Therefore, 329 cities from various humid–arid regions were selected to investigate the interannual, seasonal, and diurnal distribution characteristics of SUHIs across regions. By constructing six-dimensional influencing factors and using CatBoost-SHAP and SEM methods, the contributions and action pathways of these factors to SUHIs were analyzed across humid–arid regions. The influence mechanisms, differences in feature importance, and similarities and discrepancies in action pathways were thoroughly examined. The findings are as follows: 1. During the day, higher SUHII values occur in humid and semihumid regions, exceeding those in arid and semiarid regions by 1.521 and 0.921, respectively. At night, arid and semiarid regions exhibit UHI effects (SUHII > 0). The SUHI distribution across humid–arid regions demonstrates seasonal variations. 2. ΔSA and ΔNDVI are stable dominant influencing factors across all regions. The contribution rank varies along the humid–arid region: Pollution factors are more important in arid and semiarid regions, whereas surface features and 2D/3D dominate in humid and semihumid regions at night. 3. SUHI regulation by influencing factors across humid–arid regions follows both similar paths and regional variations. This study reveals the SUHI distribution across humid–arid regions and provides reference data for regional thermal environment management. Full article

A large share of the global population now lives in urban areas, which creates growing challenges for city life. Local authorities are seeking ways to enhance urban livability, with transportation emerging as a major focus. Developing smart public transit systems is therefore a key priority. Accurately estimating travel times is essential for managing transport operations and supporting strategic decisions. Previous studies have used statistical, mathematical, or machine learning models to predict travel time, but most examined these approaches separately. This study introduces a hybrid framework that combines statistical regression models and machine learning algorithms to predict public bus travel times. The analysis is based on 1410 bus trips recorded between November 2021 and July 2022 in Kayseri, Turkey, including detailed meteorological and operational data. A distinctive aspect of this research is the inclusion of weather variables—temperature, humidity, precipitation, air pressure, and wind speed—which are often neglected in the literature. Additionally, sensitivity analyses are conducted by varying k values in the K-nearest neighbors (KNN) algorithm and threshold values for outlier detection to test model robustness. Among the tested models, CatBoost achieved the best performance with a mean squared error (MSE) of approximately 18.4, outperforming random forest (MSE = 25.3) and XGBoost (MSE = 23.9). The empirical results show that the CatBoost algorithm consistently achieves the lowest mean squared error across different preprocessing and parameter settings. Overall, this study presents a comprehensive and environmentally aware approach to travel time prediction, contributing to the advancement of intelligent and adaptive urban transportation systems. Full article

Urban stray cats are often associated with ectoparasites and zoonotic pathogens due to their unsanitary living conditions and lack of veterinary care. Fleas, especially Ctenocephalides spp., are competent vectors of Bartonella spp., a genus of emerging bacterial pathogens with both public health and veterinary relevance. This study investigated the presence of Bartonella DNA in ectoparasitic fleas infesting stray cats in various urban habitats within the Klang Valley, Malaysia. A total of 204 fleas were collected from 89 stray cats. Fleas were identified morphologically using established taxonomic keys under a light microscope and further validated through PCR amplification of the mitochondrial cytochrome c oxidase subunit I (cox1) gene. Detection of Bartonella spp. was conducted by targeting the citrate synthase (gltA) gene. All fleas were confirmed as Ctenocephalides felis, with an infestation prevalence of 39.33% among the cats sampled. Of 118 C. felis specimens tested, 86.44% were positive for Bartonella DNA, one of the highest worldwide and significantly surpassing previous Malaysian reports. Sequencing of 12 positive samples showed identities with B. claridgeiae (58.3%), B. henselae (25.0%), an uncultured Bartonella species (8.3%) and a Bartonella isolate from a dog in Chile (8.3%). These results highlight the significant presence of Bartonella, causative agent of cat-scratch disease in stray cats, emphasizing their potential role as urban reservoirs and vectors. The findings underscore the need for ectoparasite surveillance and zoonotic pathogen control as integral components of stray animal management in Malaysia’s urban settings. Full article

Managing community cats on islands requires reconciling animal-welfare mandates with biodiversity protection under real operational constraints. In the Canary Islands (Spain), national Law 7/2023 endorses ethical, non-lethal colony management, while subsequent regional resolutions restrict TNR in and around protected areas, narrowing municipal room for action. We combine a multilevel governance assessment with stochastic demographic simulations parameterized from official records to compare three sterilization regimes over 20 years. The intensive regime (≈60–70%/year) reflects the coverage threshold previously identified by Spain-based modelling and field evaluations and adopted in national program guidance; the 20%/year regime represents the pre-resolution baseline widely observed across the archipelago up to December 2024; and the 4%/year regime reflects the post-resolution reality, with abrupt declines in sterilizations, operations largely confined to urban cores, and program suspensions in multiple municipalities. Minimal (4%) and low (20%) efforts produce rapid population growth, bringing numbers close to the assumed carrying capacity under our deliberately high-K configuration and sustaining high densities and associated welfare and ecological risks; only sustained high-coverage TNR prevents saturation and produces progressive declines across island contexts. Under insular constraints, outcomes are determined by achievable coverage rather than regulatory intent; aligning policy and implementation to secure continuous, high-coverage TNR—particularly in risk-sensitive areas with appropriate safeguards—offers a feasible pathway to meet animal-welfare obligations while limiting ecological pressure. Full article

Sensor networks generate high-dimensional temporally dependent data across healthcare, environmental monitoring, and energy management, which demands robust machine learning for reliable forecasting. While gradient boosting methods have emerged as powerful tools for sensor-based regression, systematic evaluation under realistic deployment conditions remains limited. This work provides a comprehensive review and empirical benchmark of boosting algorithms spanning classical methods (AdaBoost and GBM), modern gradient boosting frameworks (XGBoost, LightGBM, and CatBoost), and adaptive extensions for streaming data and hybrid architectures. We conduct rigorous cross-domain evaluation on continuous glucose monitoring, urban air-quality forecasting, and building-energy prediction, assessing not only predictive accuracy but also robustness under sensor degradation, temporal generalization through proper time-series validation, feature-importance stability, and computational efficiency. Our analysis reveals fundamental trade-offs challenging conventional assumptions. Algorithmic sophistication yields diminishing returns when intrinsic predictability collapses due to exogenous forcing. Random cross-validation (CV) systematically overestimates performance through temporal leakage, with magnitudes varying substantially across domains. Calibration drift emerges as the dominant failure mode, causing catastrophic degradation across all the static models regardless of sophistication. Importantly, feature-importance stability does not guarantee predictive reliability. We synthesize the findings into actionable guidelines for algorithm selection, hyperparameter configuration, and deployment strategies while identifying critical open challenges, including uncertainty quantification, physics-informed architectures, and privacy-preserving distributed learning. Full article

Urbanisation exerts profound effects on biodiversity, driving species extinctions while promoting behavioural adaptations in generalist taxa. The European badger (Meles meles) exemplifies such adaptability, exploiting anthropogenic resources and modifying activity rhythms. This study assessed badger activity within the Varese province in northern Italy, comparing an urban park and a sub-urban landscape. From August 2023 to August 2024, camera traps recorded badgers and sympatric mesocarnivores, including red foxes (Vulpes vulpes), domestic cats (Felis catus), and beech martens (Martes foina). Despite high activity overlap between sites (∆ = 0.87), the Mardia–Watson–Wheeler test revealed significant differences. Urban badgers displayed heightened nocturnality relative to sub-urban individuals, consistent with comparisons to nearby protected natural areas. This pattern indicates anthropogenic disturbance as a driver of temporal adjustment. Urban badgers are active from 18:00 to 07:00, whereas sub-urban badgers are active from 17:00 to 08:00. The later onset and earlier termination of urban activity suggest behavioural avoidance of human presence. Red foxes exhibited even greater nocturnality in urban settings, while domestic cats were primarily crepuscular and less frequently detected, particularly in sub-urban areas. Results underline the ecological plasticity of badgers, highlighting their capacity to accommodate urban pressures and providing city administrations with information to improve park management planning. Full article

The study of tropical landscape dynamics is of critical importance, particularly within protected areas, for evaluating ecosystem functioning and the effectiveness of natural conservation efforts. This study aims to identify landscape dynamics within the Dong Nai Biosphere Reserve (including Cat Tien National Park and the Ma Da Forest) using remote sensing (Landsat and others) and geographic information system methods. The analysis is based on changes in the Enhanced Vegetation Index (EVI), land cover transformations, landscape metrics (Class area, Percentage of Landscape and others), and natural landscape fragmentation, as well as a spatio-temporal assessment of anthropogenic impacts on the area. The results revealed structural changes in the landscapes of the Dong Nai Biosphere Reserve between 2000 and 2024. According to Sen’s slope estimates, a generally EVI growth was observed in both the core and buffer zones of the reserve. This trend was evident in forested areas as well as in regions of the buffer zone that were previously occupied by highly productive agricultural land. An analysis of Environmental Systems Research Institute (ESRI) Land Cover and Land Cover Climate Change Initiative (CCI) data confirms the relative stability of land cover in the core zone, while anthropogenic pressure has increased due to the expansion of agricultural lands, mosaic landscapes, and urban development. The calculation of landscape metrics revealed the growing isolation of natural forests and the dominance of artificial plantations, forming transitional zones between natural and anthropogenically modified landscapes. The human disturbance index, calculated for the years 2000 and 2024, shows only a slight change in the average value across the territory. However, the coefficient of variation increased significantly by 2024, indicating a localized rise in anthropogenic pressure within the buffer zone, while a reduction was observed in the core zone. The practical significance of the results obtained lies in the possibility of their use for the management of the Dongnai biosphere Reserve based on a differentiated approach: for the core and the buffer zone. There should be a ban on agriculture and development in the core zone, and restrictions on urbanized areas in the buffer zone. Full article

Although trap-neuter-return (TNR) is a popular method for managing free-roaming domestic cat populations, a common criticism is that sterilization fails to mitigate the public health risks posed by free-roaming cats. One of these risks is the environmental contamination of Toxoplasma gondii, a parasite that can be spread in the feces of actively infected felids (both domestic and wild). In healthy humans, toxoplasmosis tends to be mild or asymptomatic; however, the disease can have severe consequences (e.g., for pregnant women) and even be fatal in immunocompromised individuals. Previous research has examined the extent to which free-roaming domestic cats might contaminate sites frequented by young children (e.g., schools and parks). However, the model used included several assumptions that are not reflective of sterilized cats in an urban setting (e.g., smaller home range). By properly accounting for several key factors (e.g., reproductive status, home range), our modeling revealed considerably lower rates of potential incursions by sterilized free-roaming cats than those reported previously. More importantly, our results show that sterilization contributes to a considerable reduction in the risk of environmental contamination; TNR therefore appears to be a valuable harm reduction strategy in mitigating the risks of T. gondii infection. Full article

Rapid urbanisation has exacerbated habitat fragmentation and degradation, necessitating urgent improvements to urban habitat quality. However, most current studies lack an analysis of spatial differences in local ecological quality, particularly a systematic exploration of how different urban spatial characteristics drive such differences. Based on this, we use Shanghai as an example, employing the InVEST model to assess habitat quality, and utilise CatBoost machine learning models and the SHAP model to reveal the specific spatial distribution characteristics of the habitat quality spatial differences from a morphological perspective, as well as its response to changes in urban spatial form factors. The results indicate that (1) urban habitat quality exhibits significant spatial differences, with quality differences persisting even within regions of the same habitat grade, demonstrating complex spatial characteristics; (2) density-related indicators such as building density and population density have a significant negative impact on the habitat quality spatial difference value, while configuration-related indicators such as the water ratio and Normalised Difference Vegetation Index have a significant positive effect, with Population Density contributing the most among all variables (20.74%); and (3) the variables exhibit significant nonlinearity and threshold effects. For example, when building density exceeds 0.05, the positive impact becomes a negative impact. The interactions between variables further reveal the multi-dimensional coupling mechanisms underlying habitat quality performance. This study contributes to a deeper understanding of the spatial differences of urban habitat quality, providing scientific support for urban ecological zoning management, the optimised allocation of green space resources, and differentiated spatial governance while offering methodological and decision-making references for the construction of high-quality ecological cities. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

Allergic diseases, particularly respiratory allergies like asthma and allergic rhinitis, are a growing public health concern influenced by environmental factors such as climate change and air pollution. The exposome framework enables a comprehensive assessment of how lifelong environmental exposures shape immune responses and allergic sensitization. Peru’s diverse ecosystems and climates provide a unique setting to investigate regional variations in allergic sensitization. This study characterized these patterns in five Peruvian regions with distinct climatic, urbanization, and socioeconomic characteristics. A total of 268 individuals from Lima, Piura, Tarapoto, Arequipa, and Tacna were analysed for allergen-specific IgE responses using a multiplex IgE detection system. The results revealed significant geographical differences in sensitization frequencies and serodominance profiles, based on descriptive statistics and supported by Chi-square comparative analysis. House dust mites were predominant in humid regions, while Arequipa exhibited higher sensitization to cat allergens. In Tacna, olive pollen showed notable prevalence alongside house dust mites. Tarapoto’s high humidity correlated with increased fungal and cockroach allergen sensitization. Notably, some allergens traditionally considered minor, such as Der p 5 and Der p 21, reached sensitization prevalences close to or exceeding 50% in certain regions. These findings provide the most detailed molecular characterization of allergic sensitization in Peru to date, highlighting the importance of region-specific allergy management strategies. Understanding environmental influences on allergic diseases can support more effective diagnostic, therapeutic, and preventive approaches tailored to diverse geographical contexts. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article

Urban free-roaming cats present challenges like noise, urination, defecation, property damage, public health risks, and wildlife predation. Traditional enforcement methods, such as containment laws and impounding, are ineffective, especially in low-income areas, where many free-roaming cats live. These cats are often cared for by “semi-owners”, who feed them without formal ownership. Financial barriers to sterilization for owned and semi-owned cats in these areas result in unplanned litters, sustaining the free-roaming population and burdening local authorities and animal welfare organizations. Cats causing complaints are frequently impounded and euthanized, affecting the mental health of veterinary, shelter, and council staff. This paper critiques punitive, compliance-driven strategies and highlights the success of assistive Community Cat Programs offering free sterilization, microchipping, and registration. In Banyule, Victoria, such a program reduced cat impoundments by 66%, euthanasia by 82%, and complaints by 36% between 2013 and 2021. Two other programs in large cities and rural towns in NSW and a rural town in Queensland have now reported similar results. Based on the One Welfare framework, these programs address the interconnectedness of animal welfare, human well-being, and environmental health. By removing financial barriers, they build trust between authorities and caregivers, improving compliance and welfare for cats, communities, and wildlife. However, following the loss of key program staff and the reintroduction of financial barriers in Banyule, cat intake rose by 140% between 2022 and 2024, demonstrating the detrimental impact of financial barriers and punitive approaches. This underscores the importance of sustained, community-based solutions and legislative reforms that prioritize humane, barrier-free strategies. Understanding the critical success factors for Community Cat Programs is essential for effective cat management. Full article

Traditional methods for managing free-roaming cats in Australia primarily depend on legislation and enforcement to achieve compliance. State laws and local regulations mandate confinement, sterilization, registration, and identification and limit the number of cats kept, with penalties for breaches. However, these strategies fail to address underlying issues like financial constraints in low-income areas and the prevalence of semi-owned cats. Containment mandates often result in increased complaints, shelter intake, and euthanasia, without effectively reducing cat-related problems. Research shows that these approaches are expensive, difficult to enforce, and place a disproportionate burden on disadvantaged communities. Moreover, they negatively affect the mental health of shelter staff and animal management officers, who are frequently exposed to euthanasia and ongoing challenges. An alternative “One Welfare” framework, which recognizes the interconnectedness of animal, human, and environmental welfare, has proven more effective. Programs that provide support and resources, particularly for cat sterilization and microchipping, while fostering the human–animal bond improve outcomes for both cats and caregivers. Shifting from punitive measures to collaborative, community-driven strategies is crucial for managing free-roaming cats in a way that benefits animals, people, and the broader community, while protecting wildlife. Full article

As global climate change intensifies, the frequency and severity of extreme weather events continue to rise. However, research on semi-outdoor and transitional spaces remains limited, and transportation stations are typically not fully enclosed. Therefore, it is crucial to gain a deeper understanding of the environmental needs of users in these spaces. This study employs machine learning (ML) algorithms and the SHAP (SHapley Additive exPlanations) methodology to identify and rank the critical factors influencing outdoor thermal comfort at tram stations. We collected microclimatic data from tram stations in Guangzhou, along with passenger comfort feedback, to construct a comprehensive dataset encompassing environmental parameters, individual perceptions, and design characteristics. A variety of ML models, including Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Random Forest (RF), and K-Nearest Neighbors (KNNs), were trained and validated, with SHAP analysis facilitating the ranking of significant factors. The results indicate that the LightGBM and CatBoost models performed exceptionally well, identifying key determinants such as relative humidity (RH), outdoor air temperature (Ta), mean radiant temperature (Tmrt), clothing insulation (Clo), gender, age, body mass index (BMI), and the location of the space occupied in the past 20 min prior to waiting (SOP20). Notably, the significance of physical parameters surpassed that of physiological and behavioral factors. This research provides clear strategic guidance for urban planners, public transport managers, and designers to enhance thermal comfort at tram stations while offering a data-driven approach to optimizing outdoor spaces and promoting sustainable urban development. Full article