Search Results (3,939)

Tropospheric ozone (O₃) pollution is a major concern in urban environments because of its toxicity for both people and vegetation. This paper review provides an overview of atmospheric mechanisms, as well as the potential and best management practices of urban greening for reducing O₃ pollution in cities. Urban greening has often been proposed as a cost-effective solution to reduce O₃ pollution, but its effectiveness depends on careful species selection and integration with broader air quality management strategies. Ozone is a secondary pollutant and the volatile organic compounds emitted by vegetation (BVOCs) can play a prominent role in O₃ formation. A list of recommended and to-avoid species is given here to drive future planting at city scale. Planting low BVOC-emitting species and combining greening with reductions in anthropogenic emissions are key to maximizing benefits and minimizing unintended increases in O₃. Public and non-public institutions should carefully select plant species in consultation with expert scientists from the early stages, e.g., by considering local conditions and pollutant dynamics to design effective greening interventions. Collaborative planning among urban ecologists, atmospheric scientists, and municipalities is thus crucial to ensure that greening interventions contribute to overall air quality improvements rather than inadvertently enhancing O₃ formation. Such improvements will also translate into plant protection from O₃ stress. Therefore, future directions of research and policy integration to achieve healthier, O₃-resilient urban ecosystems are also provided. Full article

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity. Full article

This study investigates the influence of urban greenery on microclimate conditions in Novi Sad, a city characterized by a temperate oceanic climate, by integrating high-resolution remote sensing data with in situ measurements from 12 urban climate stations. Sentinel-2 imagery was used to capture vegetation patterns, including tree lines and small green patches, while air temperature data were collected across two climatically contrasting years. Vegetation extent and structural characteristics were quantified using NDVI thresholds (0.6–0.8), capturing variability in vegetation activity and canopy density. Results indicate that high-activity vegetation, particularly dense tree canopies, exerts the strongest cooling effects, significantly influencing air temperatures up to 750 m from measurement sites, whereas total green area alone showed no significant effect. Cooling effects were most pronounced during summer and autumn, with temperature reductions of up to 2 °C in areas dominated by mature trees. Diurnal–nocturnal analyses revealed consistent spatial cooling patterns, while seasonal variability highlighted the role of evergreen and deciduous composition. Findings underscore that urban heat mitigation is driven more by vegetation structure and composition than by green area size, emphasizing the importance of preserving high-canopy trees in urban planning. This multidimensional approach provides actionable insights for optimizing urban greenery to enhance microclimate resilience. Full article

Urban park areas mitigate urbanization’s negative impacts by integrating environmental, social and cultural benefits. Development strategies should enable participation and consider all user groups’ needs, following sustainability principles. However, ensuring multifunctionality often generates conflicting decisions. While the universal necessity for safety is widely acknowledged, its implementation frequently results in the diminution of a crucial sense of privacy. For example, the universally recognized need for safety may compromise the willingness sense of privacy or intimacy. This can discourage those for whom this need is important and prevent urban parks from fully utilizing their social potential. This study examines how spatial configurations of plant forms within urban parks shape personal experiences. We used an intra-group design to evaluate photographs of park spaces, manipulated using Photoshop AI algorithms to examine safety, privacy, preference, and willingness to spend time. Variables included space size and shape. The study used Computer-Assisted Web Interviewing (CAWI) with 300 participants. Regression and mediation analyses showed willingness to visit derives from space attractiveness, influenced by perceived safety and privacy. Analyses revealed the following: open areas were safest but the least private, corridor spaces were the least safe but the most private; curtain screens enhanced perception better than corridor screens; small spaces with corridor screens were least attractive; space size mattered less for open spaces than screened spaces; and spatial configuration was critical in assessing small spaces. The findings of this research enhance our comprehension of the perception of park spaces. They hold potential practical implications for sustainable design, facilitating the development of plant forms that are more socially effective, particularly those with substantial environmental value, such as dense vegetation that serves as visual screens. Neglecting these preferences may result in inappropriate design decisions that fail to accommodate users’ needs and behaviors, thereby not fully capitalizing on the potential of urban green spaces. Full article

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

Under the global goals of carbon peaking and carbon neutrality, China’s northern agro-pastoral ecotone—an ecologically fragile transition zone with drastic land use/cover change (LUCC)—is characterized by a lack of in-depth understanding of its “land use conflict–carbon sink response” mechanism, which is essential for regional land optimization and carbon neutrality. This study quantified the spatiotemporal dynamics of carbon storage in the zone from 2000 to 2020 using the InVEST model and identified key driving factors by combining the XGBoost model (R² = 0.73–0.88) with the SHAP framework. The results showed that regional total carbon storage increased by 30.11 × 10⁶ tons (a net growth of 0.57%), mainly driven by forest carbon sinks (+65.74 × 10⁶ tons, accounting for 218.3% of the total increase), while cropland and grassland underwent continuous carbon loss (−53.87 × 10⁶ tons and −35.80 × 10⁶ tons, respectively). Spatially, this presents a pattern of “high-value agglomeration in the central–southern region and low-value fragmentation at urban–rural edges”. The Normalized Difference Vegetation Index (NDVI) was the primary driver (average SHAP value: 426.15–718.91), with its interacting temperature factor evolving from air temperature (2000) to nighttime surface temperature (2020). This study reveals the coupling mechanism of “vegetation restoration–microenvironment regulation–carbon sink gain” driven by the Grain for Green Program, providing empirical support for land use optimization and carbon neutrality in agro-pastoral areas. Full article

Urban densification intensifies the heat island effect, threatening ecological security. Green spaces, as crucial spatial elements in regulating the urban thermal environment, remain poorly understood in terms of their morphological characteristics and regulatory mechanisms, with a lack of systematic quantification and recognition of diurnal variations. This study, focusing on Shanghai’s main urban area, constructs physiological, physical, and morphological variables of green spaces based on high-resolution remote sensing data and the MSPA landscape morphology analysis framework. By integrating machine learning models with the SHAP interpretation algorithm, it analyses the influence mechanism of green spaces on Land Surface Temperature (LST) and its non-linear characteristics from the perspective of diurnal variation. The results indicate the following: (1) Green spaces exhibit pronounced diurnal variation in LST influence. Daytime cooling is primarily driven by vegetation cover, vegetation activity, and surface albedo through evapotranspiration and shading; night-time cooling depends on soil moisture and green space spatial structure and is achieved via thermal storage-radiative heat dissipation and cold air transport. (2) Green space indicators exhibit pronounced nonlinearity and threshold effects on LST. Optimal cooling efficiency occurs under moderate vegetation activity and moderate humidity conditions, whereas extreme high humidity or high vegetation activity may induce heat retention effects. (3) Day–night thermal regulation mechanisms differ markedly. Daytime cooling primarily depends on vegetation transpiration and shading to suppress surface warming; night-time cooling is dominated by soil thermal storage release, longwave radiation dissipation, and ventilation transport, enabling cold air to diffuse across the city and establishing a stable, three-dimensional nocturnal cooling effect. This study systematically reveals the distinct diurnal cooling mechanisms of high-density urban green spaces, providing theoretical support for refined urban thermal environment management. Full article

Global soil moisture has undergone significant changes in recent decades due to climate change and vegetation greening. However, the seasonal and climate zonal variations in soil moisture dynamics at different depths, driven by both climate and vegetation, remain insufficiently explored. This study provides a comprehensive analysis of the global patterns in rootzone and surface soil moisture and leaf area index (LAI) across different seasons and climate zones, utilizing satellite observations from 1982 to 2020. We investigate how climatic factors and LAI influence soil moisture variations and quantify their dominant contributions. Furthermore, by employing key vegetation phenological indicators, namely the peak of growing season (POS) and the corresponding maximum LAI (LAI_MAX), we assess the feedback effects of vegetation phenology on soil moisture dynamics. The results indicate that the greening trend (as reflected by LAI increases) from 2000 to 2020 was significantly stronger than that observed during 1982–1999 across all seasons and climate zones. Both rootzone and surface soil moisture shifted from a decreasing (drying) trend (1982–1999) to an increasing (wetting) trend (2000–2020). From 1982 to 2020, the LAI induced moistening trends in both surface and rootzone soil moisture. In arid and temperate zones, precipitation drove rootzone soil moisture increases only during the summer. Among all seasons and climate zones, solar radiation induced the strongest surface soil drying in tropical summers, with a rate of −0.04 × 10⁻³ m³m⁻³/Wm⁻². For rootzone soil moisture, LAI dominated over individual climatic factors in winter and spring globally. In contrast, solar radiation became the primary driver during summer and autumn, followed by precipitation. For surface soil moisture, precipitation exhibited the strongest control in winter, but solar radiation surpassed it as the dominant factor from spring through autumn. In the tropical autumn, the sensitivity of rootzone and surface soil moisture to POS (and LAI_MAX) was highest, at 0.059 m³m⁻³·d⁻¹ (0.256 m³m⁻³/m²m⁻²) and 0.052 m³m⁻³·d⁻¹ (0.232 m³m⁻³/m²m⁻²), respectively. This research deepens the understanding of how climate and vegetation regulate soil moisture across different climate zones and seasons. It also provides a scientific basis for improving global soil moisture prediction models and managing water resource risks in the context of climate change. Full article

Quinoa (Chenopodium quinoa Willd) is a nutrient-rich multipurpose crop. Its grains are used as a cereal, green leaves as a vegetable for humans, and the whole green plant as an alternate forage for livestock. Recently, whole-plant quinoa forage has been evaluated in several countries in Asia and Europe for its potential use as an alternative forage for livestock; however, this has not been performed in the United States. We investigated forage yield and related agronomic traits, nutritional composition, and feed quality-related traits in 60-day-old quinoa whole plants of four quinoa lines over a two-year period. The goal was to evaluate the feasibility of quinoa forage production in Missouri, a drought-prone midwestern state of the USA. Morphological traits (height and fresh and dry weight per plant), chemical composition (fiber contents), and nutritive quality (digestible nutrient contents) of forages were affected by quinoa genotype and year of planting. The crude protein content of quinoa forage averaged 16.23% and fiber 22.08%, which was similar to the values reported in Asia and Europe, but was slightly lower than that of alfalfa. Calcium (1.26%) and phosphorus (0.47% dry weight) were significantly higher than those reported in published quinoa forage results and are comparable to those in published alfalfa minerals. Lysine (0.98%) and methionine (0.25%) were higher than the published results for quinoa and alfalfa. Neutral detergent fiber (34.10%) and acid detergent fiber (25.01%) were lower than those of alfalfa, indicating better digestibility of the quinoa forage. The calculated digestible dry matter (69.40%), dry matter intake (3.56%), relative food value (192%), and total digestible nutrient (70.33%) were higher than those of alfalfa and comparable with published results for quinoa forage. Our preliminary results indicate that the quinoa lines evaluated in this study have excellent potential to be used as a non-traditional alternative forage, especially in environmentally stressed areas where the production of other forage crops is limited. Further research should explore the full multipurpose benefits of quinoa, including its use as grains, leafy green, and whole-plant forage. Full article

The increasing production of fruit and vegetable by-products from the food processing industry presents both environmental challenges and opportunities for valorisation as sources of bioactive compounds. These by-products, including peels, seeds, pomace, and leaves, are rich in polyphenols, carotenoids, dietary fibres, glucosinolates, phytosterols, and essential oils, which exhibit antioxidant, anti-inflammatory, antimicrobial, and prebiotic activities. Recent advances in green extraction technologies, including ultrasound-, microwave-, supercritical fluid-, and cold plasma-assisted extraction, allow for an efficient and sustainable recovery of these compounds, while preserving their bioactivity. Incorporation of by-product-derived extracts into functional foods and nutraceuticals offers health-promoting benefits and supports circular bioeconomy strategies. However, challenges remain in standardisation, safety assessment, and regulatory approval, among others. This review summarises current progress and outlines future directions for the sustainable utilisation of fruit and vegetable by-products in health-oriented applications. Full article

Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. Full article

Pollinators play a crucial role in global biodiversity, providing essential ecosystem services such as crop pollination. However, their abundance and diversity have been gradually decreasing in recent years. Despite increasing interest in sustainable agriculture, information on vegetable crops that attract insect pollinators remains limited. We hypothesize that variation in floral traits among vegetable crop cultivars, especially nectar volume, nectar sugar concentration, and pollen characteristics, significantly influences visitation patterns and species composition. To test this, we evaluated multiple cultivars of six vegetable crops (cowpea, sweet potato, eggplant, green bean, mustard, and chickpea) over two years, focusing on five key pollinator groups (honey bees, bumble bees, carpenter bees, sweat bees, and wasps). Cowpea and sweet potato consistently attracted the most pollinators, whereas chickpea attracted the fewest. In 2022, nectar volume was highest in sweet potato (16.45 ± 0.37 µL) and lowest in chickpea (1.18 ± 0.75 µL). Similarly, in 2023, sweet potato recorded the highest nectar volume (8.33 ± 2.95 µL), and chickpea the lowest (0.02 ± 0.01 µL). However, chickpea (31.00 ± 1.58 °Bx) and mustard (30.10 ± 1.12 °Bx) recorded the highest nectar sugar concentration in both years, and chickpea and eggplant produced significantly more pollen grains. A significant positive correlation was observed between nectar volume and pollinator abundance. Comprehensively, this two-year study demonstrates the complex relationship between floral traits and pollinator preferences. These findings offer growers practical guidance on selecting vegetable intercrops that attract specific pollinators, thereby enhancing pollination services, supporting biodiversity, and improving the yield of pollinator-dependent crops. Full article

This study aimed to assess the potential of machine learning models applied to high spatial resolution images from UAVs for estimating the aboveground biomass (AGB) of forage grass cultivated in the Brazilian semiarid region. The fresh and dry AGB were determined in Cenchrus ciliare plots with an area of 0.04 m². Spectral data were obtained using a multispectral sensor (Red, Green, and NIR) mounted on a UAV, from which 45 vegetation indices were derived, in addition to a structural variable representing plant height (H95). Among these, H95, GDVI, GSAVI2, GSAVI, GOSAVI, GRDVI, and CTVI exhibited the strongest correlations with biomass. Following multicollinearity analysis, eight variables (R, G, NIR, H95, CVI, MCARI, RGR, and Norm G) were selected to train Random Forest (RF), Support Vector Machine (SVM), and XGBoost models. RF and XGBoost yielded the highest predictive performance, both achieving an R² of 0.80 for AGB—Fresh. Their superiority was maintained for AGB—Dry estimation, with R² values of 0.69 for XGBoost and 0.67 for RF. Although SVM produced higher estimation errors, it showed a satisfactory ability to capture variability, including extreme values. In modeling, the incorporation of plant height, combined with spectral data obtained from high spatial resolution imagery, makes AGB estimation models more reliable. The findings highlight the feasibility of integrating UAV-based remote sensing and machine learning algorithms for non-destructive biomass estimation in forage systems, with promising applications in pasture monitoring and agricultural land management in semi-arid environments. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

Vegetative propagation through stem cuttings and in vitro microcuttings enables large-scale multiplication of superior genotypes in various crop species. This approach is widely used both to propagate and select trees with desirable genetic traits as well as to preserve a significant proportion of genetic diversity. However, successful plant regeneration using this technique requires the development of an adventitious root (AR) system at the base of cuttings or microcuttings. Reduced root formation and functionality strongly limit the application of vegetative propagation, both in vivo and in vitro. The complex process of AR development is greatly influenced by the physiological state of the donor plant, as well as by genetic and environmental factors. Among the environmental factors involved, light quality and intensity have been mainly studied empirically. This review summarizes advances in understanding how light quantity and quality influence in vitro rooting of micropropagated plants, emphasizing species-specific responses. Furthermore, medium components such as sugars and growth regulators, which interact significantly with light, are also considered. Based on existing studies across different plant species, particularly in the absence of growth regulators, the most effective spectrum for root induction is a temporary enrichment of red light, either alone or combined with small amounts of blue or green light. An efficient root growth occurs when the explants are re-exposed to white light, typically at intensities of 40–50 μmol m⁻² s⁻¹. After root development, exposing the microcuttings to higher intensities could help acclimatization. Finally, considering its capacity to precisely regulate light quality and intensity, LED technology offers a valuable tool for optimizing the rooting process and reducing production costs. Full article