Search Results (730)

Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for supporting urban design and greening strategies. The review is based on a structured literature analysis of peer-reviewed studies retrieved from major scientific databases (Scopus and Web of Science), following defined selection and screening criteria. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R² = 0.82 and 0.91) and tree height (R² = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R² = 0.73 and 0.68) and crown volume (R² = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R² = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions. Full article

Urban trees are increasingly deployed as nature-based infrastructure to mitigate heat and manage stormwater, yet quantitative guidance on how species traits and site context shape transpiration remains fragmented. We conducted a systematic metadata analysis of seven field studies that measured daily transpiration rate in urban settings using heat-pulse methods. The units and spatial scales reported were harmonized with the sap flow density across active sapwood (J_s, g H₂O/${\mathrm{cm}}^2$/day) by converting reported stand transpiration and the outer 2 cm of sapwood sap flux using established Gaussian radial distribution functions for angiosperms and gymnosperms, which account for the non-linear decline in sap flux from the vascular cambium to the heartwood boundary. We then summarized distributions and tested group differences with Kruskal–Wallis and Dunn post hoc comparisons across wood anatomy, climate, soil texture, and planting configuration. Conifers exhibited significantly lower median $J_s$ (39.76 g/${\mathrm{cm}}^2$/day) than angiosperms, while the ring-porous group (median $J_s$ = 92.25 g/${\mathrm{cm}}^2$/day) and diffuse-porous groups (median $J_s$ = 96.70 g/${\mathrm{cm}}^2$/day) had similar distributions overall. Climate-modulated responses within wood anatomy groups differed, with diffuse-porous species exhibiting the highest median $J_s$ (152.59 g/${\mathrm{cm}}^2$/day) in semi-arid regions, ring-porous species maintaining comparatively stable median $J_s$ across climates (varying slightly between 80.72 and 99.32 g/${\mathrm{cm}}^2$/day), and conifers reaching their highest median $J_s$ (69.90 g/${\mathrm{cm}}^2$/day) in humid continental sites. Soil texture effects were consistent with moisture availability: sandy loam generally reduced $J_s$ relative to loam or silt loam for conifers and diffuse-porous species. Across anatomies, single trees transpired more than clustered trees or closed canopies. For example, planting as single trees increased median $J_s$ by 86% in conifers (from 33.01 to 61.37 g/${\mathrm{cm}}^2$/day) and by 45% in diffuse-porous species (from 81.31 to 118.25 g/${\mathrm{cm}}^2$/day). These results provide actionable ranges and contrasts to inform species selection and planting design for urban greening and runoff reduction, while highlighting data gaps for future research. Ultimately, by matching specific wood anatomies and planting configurations to local soil and climatic conditions, urban planners and ecohydrologists can strategically optimize urban forests to maximize targeted ecosystem services. Full article

Urban districts along major rivers are undergoing rapid transformation, yet long-term evidence on how redevelopment reshapes land cover and vegetation structure remains limited in post-socialist cities. This study examines the spatio-temporal evolution of land use and land cover (LULC) and vegetation dynamics along the Sava River corridor in Belgrade from 1990 to 2024. CORINE Land Cover (CLC) datasets were combined with Landsat-derived NDVI and MSAVI time series, while high-resolution Esri Wayback imagery was used for visual interpretation and qualitative corroboration of the detected land-cover and vegetation patterns. Beyond conventional NDVI/LULC assessments, the study integrates multi-decadal spectral trends with functional vegetation structure classification to evaluate canopy continuity and ecological configuration under contrasting redevelopment models. Results reveal a pronounced divergence between the two riverbanks. The left bank (New Belgrade) maintains stable land-cover composition and consistently higher NDVI and MSAVI values, indicating preserved green infrastructure and sustained canopy continuity. In contrast, the right bank (Belgrade Waterfront) experienced substantial land-cover conversion after 2006, with a statistically significant decline in vegetation greenness (NDVI −0.020 dec⁻¹, p < 0.001) and a marked increase in impervious surfaces. MSAVI-based functional classes indicate a shift from mixed low vegetation to predominantly sealed land, while tree canopy remained persistently low throughout redevelopment. The findings demonstrate measurable ecological simplification and canopy loss, even where nominal green areas remain present. By providing a rare multi-decadal, spatially explicit comparison of two contrasting planning paradigms within the same river corridor, the study contributes new empirical evidence on how governance and redevelopment models shape riparian ecological trajectories and sustainable urbanism in post-socialist cities. Strengthening blue-green infrastructure and restoring native riparian vegetation are essential for enhancing climate resilience and ensuring long-term riverfront sustainability. Full article

Mountain orchards in southern China are characterized by fragmented and complex terrain with a wide slope variation range (5~30°), which easily leads to uneven pesticide distribution and pesticide accumulation on gentle slopes. These issues give rise to core technical bottlenecks such as low pesticide utilization rate, poor operational efficiency, and unclear atomization mechanism, hindering the optimization of pesticide application parameters, causing pesticide waste and environmental pollution, and restricting the sustainable development of the mountain fruit industry. To address this problem, this study designed a slope-classified pipeline layout and developed a high-efficiency fixed pipeline system for phytosanitary application in mountain orchards, featuring stable operation, low labor intensity, and easy intelligent transformation. Following the technical route of “theoretical design-atomization mechanism analysis-parameter optimization-laboratory verification-field application”, ruby nozzles with high wear resistance, uniform droplet distribution, and long service life were selected and optimized to meet the demand for long-term fixed pesticide application in mountain orchards. High-speed imaging technology was used to real-time capture the dynamic atomization process of nozzles, providing support for clarifying the atomization mechanism. Advanced methods such as fluorescence tracing were adopted to quantitatively evaluate key indicators including droplet deposition in canopies, and the system performance was verified through laboratory and field tests, laying a scientific foundation for its popularization and application. Field test results showed that the optimal spray pressure should not be less than 8 MPa. The XR9002 nozzle can generate fine droplets to achieve pesticide reduction while forming a stable hollow cone atomization flow. Fluorescence tracing analysis indicated that the droplet deposition on the adaxial leaf surface decreases with increasing altitude (presumably affected by wind speed), while the initial deposition on the abaxial leaf surface is low and shows no significant variation with altitude. Deposition on the adaxial leaf surface decreased with canopy height, while abaxial deposition was much lower (8.9–14.9%). This technology enables high-precision quantitative analysis of droplet deposition. The core innovations of this study are: clarifying the atomization mechanism of ruby high-pressure nozzles under pesticide application conditions in mountain orchards, constructing a slope-classified terrain-adaptive pipeline layout model, and establishing a closed-loop technical system of “atomization mechanism-pipeline layout-parameter optimization-deposition detection”. This study provides theoretical and technical support for green and precision pesticide application in mountain orchards, and has important academic value and broad application prospects for promoting the intelligent upgrading of the fruit industry in southern China. Full article

Shifting cultivation remains a primary farming system in Myanmar’s mountainous regions. However, population growth and economic pressures have disrupted its traditional balance. This study aimed to clarify historical land-use patterns and evaluate vegetation recovery in Lailenpi by integrating field surveys with multitemporal Sentinel-2 imagery from 2019 to 2025. We identified cultivation plots using NDVI differences and quantified recovery trajectories with a Bayesian hierarchical nonlinear model. Results confirmed that a systematic eight-year rotational cycle was maintained. However, the total cultivated area expanded from 0.93% to 3.13%, shifting toward steeper terrain. Bayesian modeling showed that canopy greenness recovered within 24 to 36 months. Despite this resilience, the shift to rugged terrain suggested mounting land-use pressure and soil degradation risks. These findings highlight the importance of combining field surveys with high-resolution monitoring to ensure the long-term ecological sustainability of tropical mountain regions. Full article

As global urbanization accelerates, biophilic urbanism has emerged as a key nature-based strategy for enhancing public health. While plants are critical active agents for psychological restoration, the specific pathways through which vegetation characteristics influence human–environment interactions remain fragmented. This knowledge gap hinders the evidence-based translation of biophilic principles into actionable urban design and governance. This study conducts a systematic bibliometric analysis of 443 peer-reviewed articles (2000–2025) at the intersection of restorative landscapes, urban settings, and plant-based interventions retrieved from the Web of Science Core Collection. Employing multiple visualization tools (VOSviewer, bibliometrix, and CiteSpace), we map publication trends, international collaborations, and thematic evolution. The results demonstrate a significant shift in the field, moving beyond the validation of foundational restorative theories (e.g., ART and SRT) to a more precise, implementation-oriented framework. This shift is characterized by the operationalization of vegetation attributes as controllable design variables, increasingly relating biophilic principles to broader nature-based solutions (NbS) agendas and evidence-informed urban governance. Thematic clustering analysis identified three core knowledge domains: (1) the role of plants as active exposure agents and behavioral mediators in psychological restoration; (2) the impact of specific plant characteristics—such as canopy structure, species diversity, and seasonal variation—on therapeutic outcomes; and (3) the integration of urban green spaces into broader governance frameworks to promote health equity and inclusive well-being. Our analysis highlights that plant-based interventions are evolving from aesthetic ornaments into precision design levers for fostering human–nature interactions. This study provides a science-based foundation for developing practical design guidelines and policy frameworks, shifting biophilic urbanism toward a robust governance strategy for creating equitable, restorative, and resilient cities. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

Against frequent extreme heat, landscaped green spaces cool, humidify, and mitigate urban heat islands, also boosting thermal comfort. Classical Chinese garden “gray spaces” are transitional gathering zones with strong microclimate-regulating potential, yet systematic research on their mechanisms in Western Sichuan memorial gardens remains limited. This study first reveals their thermal characteristics; establishes a refined classification system; uncovers nonlinear links between garden elements, spatial form, and thermal comfort; and proposes optimization strategies. Key findings: (1) Gray spaces show notable microclimate regulation. Internal air temperatures drop by 0.8–4.3 °C, relative humidity rises by 2.2–22.33%, and average PET decreases by 3.1 °C, effectively relieving thermal stress. (2) Thermal comfort is closely related to gray space types, with open halls performing best due to their strong sense of enclosement and shading. (3) Plant-dominated and hybrid spaces are superior to water-dominated ones. PET is negatively correlated with 40–70% plant canopy and 20–30% water coverage, while excess water leads to stuffiness. Hybrid spaces reach ideal blue–green synergy at 50–60% canopy and 20–30% water. (4) The summer PET comfort threshold for Western Sichuan gray spaces is 29.1–31.5 °C (neutral at 30.2 °C), higher than European standards, reflecting local adaptation to a hot–humid climate and guiding microclimate-adaptive design. Full article

Leaf Area Index (LAI) is critical for monitoring cassava growth and yield prediction, yet ground measurements are time-consuming and labor-intensive for large-scale applications. While satellite-based vegetation indices (VIs) offer a scalable alternative, their performance for cassava LAI estimation remains poorly documented, and optimal index selection for different growth stages is unclear. This study evaluated the predictive performance of 13 Sentinel-2-derived VIs for estimating ground-measured LAI across cassava growth stages. Ground-LAI was measured monthly using a SunScan Canopy Analyzer from January to June 2022 (2–7 months after planting; MAP) in 47 cassava plots in Nakhon Ratchasima Province, Thailand. Linear mixed-effects models and stage-specific regressions assessed VI predictive performance using Coefficient of determination (R²) and Root Mean Squared Error (RMSE). The Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Water Index (NDWI) demonstrated superior performance across all growth stages (R² = 0.524; RMSE = 0.350), followed by Sentinel-2 LAI Green Index (SeLI R² = 0.521, RMSE = 0.357). Stage-specific analysis revealed that Ratio Vegetation Index performed best during early growth (2 MAP, R² = 0.671; RMSE = 0.164) while GNDVI and NDWI excelled during mid-growth (3–5 MAP) and SeLI at late growth (7 MAP, R² = 0.393; RMSE = 0.422). While the presence of large trees altered the ranking of VI predictive performance, it did not substantially affect estimation errors, suggesting a relatively small impact of spatial heterogeneity on LAI estimation accuracy. These findings identify GNDVI and NDWI as the most operationally suitable Sentinel-2 indices for cassava LAI estimation and demonstrate that stage-specific index selection can improve monitoring accuracy, providing validated tools for regional-scale cassava crop monitoring using freely available satellite data. Full article

This study developed and tested a neighborhood-scale framework that integrates unmanned aerial vehicle (UAV)-based multispectral mapping and georeferenced socioeconomic data to identify inequities in urban green infrastructure and translate them into an operational prioritization tool for inclusive planning. Using object-based image analysis, impervious surfaces, low vegetation, and woody vegetation (trees and shrubs) were mapped across 33 Neighborhood Units in Temuco, Chile, and landscape metrics describing dominance, edge, isolation/connectivity, and diversity were derived. Socioeconomic conditions were summarized through Principal Component Analysis, and their relationships with vegetation metrics were evaluated using Generalized Additive Models. The results revealed strongly nonlinear and metric-specific associations, with the most robust relationships observed for woody-structure metrics, particularly total woody edge and built-environment isolation, whereas landscape diversity showed weaker but still significant dependence on resource-access gradients. To support inclusive planning, a dimensionless Green Infrastructure Prioritization Index (GIPI) was computed by combining standardized green deficit and standardized social vulnerability with equal weights. GIPI values ranged from 0.318 to 0.740 (median = 0.528), identifying 11 high-priority units characterized by higher social vulnerability and less favorable woody structure, including lower largest-patch dominance and greater isolation. Sensitivity analyses varying the deficit weight from 0.30 to 0.70 showed that 10 of the 11 high-priority units remained in the same class in at least 80% of weighting scenarios, indicating a stable priority set. Further classification of high-priority units according to dominant deficit type supported a staged intervention strategy, in which woody canopy is first increased in deficit nodes and subsequently reinforced through corridor-oriented greening to improve structural connectivity. These findings demonstrate the value of coupling fine-scale vegetation mapping with socioeconomic gradients to support more equitable urban green infrastructure planning. Full article

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

Aiming at diverse urban tree structures and difficulties in vegetation point cloud extraction and utilization, this study proposed single-tree-scale biological parameter estimation methods for urban scenarios to enhance point cloud’s application value in urban greening management. For single-tree segmentation, it constructed a method based on the constraints of the trees’ geometric features and combined the gravitational modeling characteristics, called the CGF-CG single-tree segmentation method. This method (i) combines clustering and principal direction analysis to extract trunk points, (ii) introduces canopy segmentation based on trunk positions, (iii) optimizes edge point attributes via a gravitational model. Based on CGF-CG’s accurate results, an improved random forest method for single-tree biological parameter (IRF-BP) estimation (aboveground biomass, carbon storage, leaf area index, living vegetation volume) was proposed: (i) correlation analysis with variable screening, (ii) adaptive feature selection and pigeon-inspired optimization to enhance model generalization, (iii) adopting Shapley Additive Explanations (SHAP) to improve interpretability. Based on these, a complete model for different tree species was constructed. Validation showed that CGF-CG exhibited negligible over-segmentation and under-segmentation in the selected study areas, with overall average precision, recall, and F1-score over 98.5%. Additionally, on the selected overall region, the overall mF1 score, mPTP, and mPTR of our method are 99.13%, 99.15%, and 99.12%, respectively, which are superior to Forestmetrics, lidR, PyCrown, and DBSCAN methods. IRF-BP performed well, with a highest R² of 0.81 and a lowest mean absolute percentage error of 7.5%, effectively surpassing the performance of traditional models such as RFR, GBR, KNN, and XGB. In summary, results provided theoretical and technical support for urban green resource management and evaluation. Full article

Driven by rapid global urbanization and expanding urban footprints, air pollution, particularly from industrial emissions and vehicular exhaust, has intensified, with rising concentrations of inhalable particulate matter (PM) posing direct threats to public health. To address this challenge, we conducted field measurements of ambient PM concentrations across diverse urban plant communities and quantitatively compared their capacity to mitigate four key size-fractionated pollutants: total suspended particles (TSPs), PM₁₀, PM_2.5, and PM₁. Our objective was to identify the most effective plant community type for PM abatement in urban settings. Results demonstrate that: (1) evergreen broad-leaved forests exhibit the highest overall PM removal efficiency among all studied communities; (2) removal efficacy declines markedly with decreasing particle size, indicating limited capacity to capture ultrafine particles (e.g., PM₁); and (3) seasonal performance peaks in summer, especially for deciduous broad-leaved forests attributable to maximal leaf area index, enhanced stomatal activity, and favorable meteorological conditions. By rigorously evaluating species composition, canopy structure, and seasonal dynamics, this study provides empirically grounded guidance for evidence-based urban greening strategies aimed at optimizing airborne particulate mitigation worldwide. Full article