Search Results (187)

To explore the characteristics of species diversity and phylogenetic diversity, as well as the dominant processes of community construction, in different forest types (deciduous broad-leaved forest, mixed coniferous and broad-leaved forest, and Chinese fir plantation) in subtropical regions, analyze the specific driving patterns of soil nutrients and other environmental factors on the formation of forest diversity in different forest types, and clarify the differences in response to environmental heterogeneity between natural forests and plantation forests. Based on 48 fixed monitoring plots of 50 m × 50 m in Shouchang Forest Farm, Jiande City, Zhejiang Province, woody plants with a diameter at breast height ≥5 cm were investigated. Species diversity indices (Margalef index, Shannon–Wiener index, Simpson index, and Pielou index), phylogenetic structure index (PD), and environmental factors were used to analyze the relationship between diversity characteristics and environmental factors through variance analysis, correlation analysis, and generalized linear models. Phylogenetic structural indices (NRI and NTI) were used, combined with a random zero model, to explore the mechanisms of community construction in different forest types. Research has found that (1) the deciduous broad-leaved forest had the highest species diversity (Margalef index of 4.121 ± 1.425) and phylogenetic diversity (PD index of 21.265 ± 7.796), significantly higher than the mixed coniferous and broad-leaved forest and the Chinese fir plantation (p < 0.05); (2) there is a significant positive correlation between species richness and phylogenetic diversity, with the best fit being AIC = 70.5636 and R² = 0.9419 in broad-leaved forests; however, the contribution of evenness is limited; (3) the specific effects of soil factors on different forest types: available phosphorus (AP) is negatively correlated with the diversity of deciduous broad-leaved forests (p < 0.05), total phosphorus (TP) promotes the diversity of coniferous and broad-leaved mixed forests, while the diversity of Chinese fir plantations is significantly negatively correlated with total nitrogen (TN); (4) the phylogenetic structure of three different forest types shows a divergent pattern in deciduous broad-leaved forests, indicating that competition and exclusion dominate the construction of deciduous broad-leaved forests; the aggregation mode of Chinese fir plantation indicates that environmental filtering dominates the construction of Chinese fir plantation; the mixed coniferous and broad-leaved forest is a transitional model, indicating that the mixed coniferous and broad-leaved forest is influenced by both stochastic processes and ecological niche processes. In different forest types in subtropical regions, the species and phylogenetic diversity of broad-leaved forests is significantly higher than in other forest types. The impact of soil nutrients on the diversity of different forest types varies, and the characteristics of community construction in different forest types are also different. This indicates the importance of protecting the original vegetation and provides a scientific basis for improving the ecological function of artificial forest ecosystems through structural adjustment. The research results have important practical guidance value for sustainable forest management and biodiversity conservation in the region. Full article

The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R²), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R² for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Green roofs are increasingly recognized as effective Nature-Based Solutions (NBS) for urban stormwater management, contributing to sustainable and climate-resilient cities. The Soil Conservation Service Curve Number (SCS-CN) model is commonly used to simulate their hydrological performance due to its simplicity and low data requirements. However, the standard assumption of a fixed initial abstraction ratio (Ia/S = 0.2), long debated in hydrology, has been largely overlooked in green roof applications. This study investigates the variability of Ia/S and its impact on runoff simulation accuracy for a green roof under a humid subtropical climate. Event-based analysis across multiple storms revealed Ia/S values ranging from 0.01 to 0.62, with a calibrated optimal value of 0.17. This variability is primarily driven by the physical and biological characteristics of the green roof rather than short-term rainfall conditions. Using the fixed ratio introduced consistent biases in runoff estimation, while intermediate ratios (0.17–0.22) provided higher accuracy, with the optimal ratio yielding a median Curve Number (CN) of 89 and high model performance (NSE = 0.95). Additionally, CN values followed a positively skewed Weibull distribution, highlighting the value of probabilistic modeling. Though limited to one green roof design, the findings underscore the importance of site-specific parameter calibration to improve predictive reliability. By enhancing model accuracy, this research supports better design, evaluation, and management of green roofs, reinforcing their contribution to integrated urban water systems and global sustainability goals. Full article

Urban thermal environments, characterized by the interplay between indoor and outdoor conditions, pose growing challenges in high-density coastal cities. This study proposes a multi-scale, integrative framework that couples a satellite-derived land surface temperature (LST) analysis with microscale building performance simulations to holistically evaluate the high-density urban thermal environment in subtropical climates. The results reveal that compact, high-density morphologies reduce outdoor heat stress (UTCI) through self-shading but lead to significantly higher cooling loads, energy use intensity (EUI), and poorer daylight autonomy (DA) due to restricted ventilation and limited sky exposure. In contrast, more open, vegetation-rich forms improve ventilation and reduce indoor energy demand, yet exhibit higher UTCI values in exposed areas and increased lighting energy use in poorly oriented spaces. This study also proposes actionable design strategies, including optimal building spacing (≥15 m), façade orientation (30–60° offset from west), SVF regulation (0.4–0.6), and the integration of vertical greenery to balance solar access, ventilation, and shading. These findings offer evidence-based guidance for embedding morphological performance metrics into planning policies and building design codes. This work advances the integration of outdoor and indoor performance evaluation and supports climate-adaptive urban form design through quantitative, policy-relevant insights. Full article

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p < 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p < 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce. Full article

This study employs a multi-model coupling and multi-scenario simulation approach to construct a framework for identifying and optimizing avian ecological corridors in the urban core of Kunming. The framework focuses on the ecological needs of resident birds (64.72%), woodland-dependent birds (39.87%), and low-mobility birds (47.29%) to address habitat fragmentation and enhance urban biodiversity conservation. This study identifies 54 core ecological corridors, totaling 183.58 km, primarily located in forest–urban transition zones. These corridors meet the continuous habitat requirements of resident and woodland-dependent birds, providing a stable environment for species. Additionally, 55 general corridors, spanning 537.30 km, focus on facilitating short-distance movements of low-mobility birds, enhancing habitat connectivity in urban fringe areas through ecological stepping stones. Eighteen ecological pinch points (total area 5.63 km²) play a crucial role in the network. The northern pinch points, dominated by forest land, serve as vital breeding and refuge habitats for woodland-dependent and resident birds. The southern pinch points, located in wetland-forest ecotones, function as critical stopover sites for low-mobility waterbirds. Degradation of these pinch points would significantly reduce available habitat for birds. The 27 ecological barrier points (total area 89.79 km²), characterized by urban land use, severely impede the movement of woodland-dependent birds and increase the migratory energy expenditure of low-mobility birds in agricultural areas. Following optimization, resistance to resident birds in core corridors is significantly reduced, and habitat utilization by generalist species in general corridors is markedly improved. Moreover, multi-scenario optimization measures, including the addition of ecological stepping stones, barrier improvement, and pinch-point protection, have effectively increased ecological sources, met avian habitat requirements, and secured migratory pathways for waterbirds. These measures validate the scientific rationale of a multidimensional management strategy. The comprehensive framework developed in this study, integrating species needs, corridor design, and spatial optimization, provides a replicable model for avian ecological corridor construction in subtropical montane cities. Future research may incorporate bird-tracking technologies to further validate corridor efficacy and explore planning pathways for climate-adaptive corridors. Full article

In the context of global warming, building transformation takes on a dual responsibility to be more energy-efficient and sustainable for climate change mitigation and to be more climate-resilient for occupants’ comfort. The building energy retrofitting is an urgent need due to the large amount of existing building stock. Especially in high-rise and high-density cities under a subtropical climate, like Hong Kong, existing buildings with large glazed façades face the challenges of high energy consumption and overheating risks. An advanced glazing system, namely the vacuum insulating glazing (VIG), shows the potential for effective building envelope retrofitting due to its excellent thermal insulation ability. Yet, its performance for practical applications in the subtropical region has not been investigated. To enhance the energy performance and thermal comfort of existing high-rise buildings, this study proposed a novel retrofitting approach by integrating the VIG into the existing window system as secondary glazing. Field experiments were conducted in a commercial building in Hong Kong to investigate the thermal performance of the VIG retrofit application under real-world conditions. Furthermore, the energy-saving potential and thermal comfort performance of the VIG retrofit were evaluated by building energy simulations. The experimental results indicate that the VIG retrofit can effectively stabilize the fluctuation of the inside glass surface temperature and significantly reduce the heat gain by up to 85.3%. The simulation work shows the significant energy-saving potential of the VIG retrofit in Hong Kong. For the VIG retrofit cases under different scenarios, the energy-saving potential varies from 12.5% to 29.7%. In terms of occupants’ thermal comfort, the VIG retrofit can significantly reduce the overheating risk and improve thermal satisfaction by 9.2%. Due to the thermal comfort improvement, the cooling setpoint could be reset to 1 °C higher without compromising the overall thermal comfort. The average payback period for the VIG application is 5.8 years and 8.6 years for the clear glass retrofit and the coated glass retrofit, respectively. Therefore, the VIG retrofit approach provides a promising solution for building envelope retrofits under subtropical climate conditions. It not only benefits building owners and occupants but also contributes to achieving long-term climate resilience and the carbon neutrality of urban areas. Full article

Weather patterns substantially influence extreme weathers in Japan. Extreme high temperature events can cause serious health problems, including heat stroke. Therefore, understanding weather patterns, along with their impacts on human health, is critically important for developing effective public health measures. This study examines the impact of weather patterns on heat stroke risk, focusing on a two-tiered high-pressure system (DH: double high) consisting of a lower tropospheric western Pacific subtropical high (WPSH) and an overlapping upper tropospheric South Asian high (SAH), which is thought to cause high-temperature events in Japan. In this study, the self-organizing map technique was utilized to investigate the relationship between pressure patterns and the number of heat stroke patients in four populous cities. The study period covers July and August from 2008 to 2021. The results show that the average number of heat stroke patients in these cities is higher on DH days than on WPSH days in which SAH is absent. The probability of an extremely high daily number of heat stroke patients is more than twice as high on DH days compared to WPSH days. Notably, this result remains true even when WPSH and DH days are compared within the same air temperature range. This is attributable to the higher humidity and stronger solar radiation under DH conditions, which enhances the risk of heat stroke. Large-scale circulation anomalies similar to the Pacific–Japan teleconnection are found on DH days, suggesting that both high humidity and cloudless conditions are among the large-scale features controlled by this teleconnection. Early countermeasures to mitigate heat stroke risk, including advisories for outdoor activities, should be taken when DH-like weather patterns are predicted. Full article

Global warming has intensified the frequency and intensity of heatwaves, particularly in urban areas, significantly affecting residents’ daily activities. Extant studies have mainly concentrated on the relationship between socio-economic attributes and the impacts of heatwaves on urban populations. However, the relationship between the built environment and the impacts of heatwaves on urban population distribution has not received much attention. Furthermore, most studies have overlooked the temporal heterogeneity in heatwave impacts on population activities and distribution. Therefore, taking the central urban area of Nanchang as the case, this study investigated the impacts of heatwaves on population distribution and their temporal heterogeneity. Moreover, it identified the nonlinear relationships between built environment factors and population changes during heatwaves by using the XGBoost model and SHAP method. The results revealed that heatwaves exerted the largest impacts on population distribution during weekend nights, followed by weekend daytime and weekday nighttime, with the least impacts observed during weekday daytime. Furthermore, location and transportation factors significantly affected population changes during heatwaves across most time periods, with their influences being associated with policy factors such as the high-temperature leave policy for workers in industrial zones located in urban fringe areas and the cooling zone establishment policy for citizens in subway stations. Moreover, land use and building form factors exhibited significant temporal heterogeneity in their impacts on population changes during heatwaves. This temporal heterogeneity was fundamentally driven by individuals’ heat adaptation behaviors, the spatiotemporal patterns of their daily activities, and the diurnal variations in the built environment’s influence on local thermal environment. These findings provide valuable insights to proactively alleviate the adverse impacts of heatwaves. Full article

Climate change amplifies heat wave effects on outdoor thermal comfort by increasing their frequency, duration, and intensity. The urban heat island effect worsens heat risks in cities and impacts resilience. Nature-based solution (NBS) with tree plantation was reported as an effective mitigation measure. This simulation study, by the well-validated ENVI-met model, aimed to investigate the impact of different tree planting strategies and building parameters on urban heat risk mitigation and microclimate during a typical hot summer day. Hypothetical skyscrapers and super high-rise buildings were assumed in the study site located in southern China. Adopting meteorological inputs from a typical year, the simulation results revealed that both mean radiant temperature (T_mrt) and physiological equivalent temperature (PET) were elevated (T_mrt > 60 °C and PET > 50 °C) in early afternoon in sunlit areas. Three mitigation approaches with different tree planting locations were investigated. While all approaches demonstrated effective cooling (PET down to <35 °C) in the proximity of trees, a superior approach for mitigating the heat risks was not evident. Within the building array, the shade of bulky structures also lowered T_mrt and PET to a thermally comfortable level in the late afternoon. Combining open-space tree planting with optimized building designs is recommended to mitigate heat risks and enhance urban resilience while promoting outdoor activities and their health benefits. Full article

Low-cost sensors (LCSs) emerge as a popular tool for urban micro-climate studies by offering dense observational coverage. This study evaluates the performance of PurpleAir (PA) sensors for ambient temperature monitoring—a key but underexplored aspect of their use. While widely used for particulate matter, PA sensors’ temperature data remain underutilized and lack thorough validation. For the first time, this research evaluates their accuracy by comparing PA temperature measurements with collocated high-precision temperature data loggers across a dense urban network in a humid subtropical U.S. county. Results show a moderate correlation with reference data (r = 0.86) but an average overestimation of 3.77 °C, indicating PA sensors are better suited for identifying temperature trends but not for precise applications like extreme heat events. We also developed and compared eight calibration methods to create a replicable model using readily available crowdsourced data. The best-performing model reduced RMSE and MAE by 51% and 47%, respectively, and achieved an R² of 0.89 compared to the uncalibrated scenario. Finally, the practical application of PA temperature data for identifying heat wave events was investigated, including an assessment of associated uncertainties. In sum, this work provides a crucial evaluation of PA’s temperature monitoring capabilities, offering a pathway for improved heat mapping, multi-hazard vulnerability assessments, and public health interventions in the development of climate-resilient cities. Full article

Enhancing greenspace cooling efficiency (GCE) is a cost-effective nature-based solution to improve the urban thermal environment. The spatiotemporal patterns of GCE and their driving factors have been investigated mainly based on land surface temperature in a spatial comparison perspective. However, the diurnal change in GCE based on air temperature (AT) and its non-linear responses to meteorological factors are far from thoroughly understood. Taking the subtropical Chinese city of Changsha as an example, we quantified the hourly GCE based on AT in the hottest month of 2020, investigated its diurnal changes, and uncovered its non-linear responses to meteorological change using the Generalized Additive Model. The results showed that (1) the hourly GCE displayed a U-shaped temporal pattern with an average of 0.0128 °C%⁻¹. The nighttime GCE (0.0134 °C%⁻¹) was significantly higher than the daytime GCE (0.012 °C%⁻¹). (2) Meteorological factors (i.e., temperature, relative humidity, and wind speed) significantly and non-linearly impacted GCE. (3) The responses of GCE to changes in relative humidity and wind speed followed an inverted U-shaped pattern, with the maximum values appearing at a relative humidity of 70% and a wind speed of 6m/s, respectively. GCE responded to temperature change more complexly, i.e., a negative response (<28 °C), then a positive response (30–35 °C), and finally a negative response (>35 °C). These findings extend our understanding of the diurnal variations of GCE and the non-linear responses to meteorological change and can help effective urban greenspace planning and management in Changsha, China, and other cities with similar climates in an era of rapid climate change. For example, expanding greenspace coverage as well as optimizing greenspace spatial configuration should be a priority action in areas where the AT is higher than 35 °C currently and will be in the future. Full article

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R² = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications. Full article

Urban parks play a key role in mitigating heat stress and improving outdoor thermal comfort, especially in tropical and subtropical cities. This study evaluates thermal comfort in Nuevo Bosque Park (Montería, Colombia) through a multiperspective approach that combines perception surveys (n = 99), in situ microclimatic measurements, and spatial mapping. Surface temperatures ranged from 32.0 °C in the morning to 51.7 °C at midday in sun-exposed areas, while vegetated zones remained up to 10 °C cooler. Heat Index (HI) and Temperature–Humidity Index (THI) values confirmed severe thermal stress, with HI reaching 32 °C and THI peaking at 55.0 °C in some zones. Subjective responses showed that 69.69% of users reported thermal discomfort, especially in areas with impermeable surfaces and little shade. In contrast, 90.91% of respondents stated that tree cover improved their thermal experience. The results indicate a strong correlation between vegetation density, surface type, and users’ perceived comfort. Additionally, urban furniture location and natural ventilation emerged as key factors influencing thermal sensation. The integration of objective and subjective data has enabled the identification of microclimatic risk zones and informed evidence-based recommendations for climate-adaptive park design. This study offers practical insights for sustainable urban planning in tropical climates, demonstrating the importance of thermal comfort assessments that consider both human perception and environmental conditions to enhance the resilience and usability of public spaces. Full article