Urbanization exacerbates water scarcity, necessitating the adoption of deficit irrigation (DI) strategies to balance water conservation and the carbon sink function of greening plants. This study quantified the seasonal responses of CO2 fluxes (net ecosystem exchange, NEE; gross ecosystem productivity, GEP) in three common urban shrub species (Euonymus japonicus, Berberis thunbergii, Ligustrum × vicaryi) under four irrigation treatments (sufficient irrigation, SI; deficit irrigation, DI; natural rainfall, NR; and drought, DT) during spring (April 17-June 17) and summer (June 18-August 27) in Beijing, China. Our results revealed distinct seasonal dynamics of CO2 fluxes: In late spring, sufficient irrigation (SI) enhanced shrub carbon sequestration capacity to the maximum extent, with gross ecosystem productivity (GEP) ranging from 23.34 to 40.25 μmol/m2/s. During this period, shrub CO2 fluxes exhibit a more rapid response to variations in water availability. Deficit irrigation (DI) induced only negligible differences in NEE and GEP compared with sufficient irrigation (SI), except in Ligustrum × vicaryi, and summer shrub CO2 fluxes show insensitivity to irrigation levels (p > 0.05), and demonstrated nonlinear saturation responses when soil water content exceeded 0.35-0.40 m3/m3. Leaf area index (LAI) emerged as a key driver, explaining 55% and 38% of such variability in spring and summer, respectively. Additionally, air temperature and soil moisture regulated the seasonal differences in CO2 fluxes. These findings emphasize that irrigation in spring plays a critical role in enhancing the carbon sink capacity of shrubs, while summer irrigation can be reduced by 50% without significantly compromising carbon sink function. Optimizing seasonal irrigation strategies based on these quantitative thresholds can reconcile urban water conservation with carbon sequestration objectives in water-stressed regions.

In order to analyze the effect of environmental factors on the release of negative air ions (NAI) by green tree species, this study conducted an open top chamber (OTC) control test in Beijing. The tree species selected were Acer truncatum, Sophora japonica, Pinus bungeana, and Pinus tabuliformis. The experiment investigated the effects of environmental factors on NAI release under different relative humidity conditions. The results of the study showed that (1) the NAI release contribution (L), NAI release coefficient (n), NAI release rate (s), NAI instantaneous present amount (v), and total NAI release amount (Z) all showed positive responses to humidity. (2) Under constant temperature and light intensity, all five capability indicators increased with the humidity gradient (40-80%) and reached their maximum values at 80% humidity. (3) NAI release was positively correlated with humidity, and the correlation coefficients were: Pinus tabuliformis (R-2 = 0.33) > Sophora japonica (R-2 = 0.17) > Acer truncatum (R-2 = 0.15) = Pinus bungeana (R-2 = 0.15, p < 0.05). (4) Under constant temperature and light intensity, the NAI release contribution (L) and NAI release coefficient (n) responded most strongly to humidity in the 40-60% range, while the total NAI release amount (Z), NAI release rate (s), and NAI instantaneous present amount (v) responded more significantly in the 60-80% range. Acer truncatum showed the strongest response in terms of NAI release contribution (L) and NAI release coefficient (n), while Sophora japonica exhibited the most significant response in terms of NAI release rate (s), NAI instantaneous present amount (v), and total NAI release amount (Z). This study, conducted using an OTC, clarifies the independent role of humidity on NAI released by green tree species, providing a scientific basis for forest recreation and urban green space planning.

Urban canopy height is an essential indicator for characterizing vegetation structure and carbon sequestration, yet satellite LiDAR often lacks sufficient spatial resolution, airborne LiDAR is costly, and SAR has limited sensitivity to vegetation structure. This study proposes a canopy height inversion framework using high-resolution stereo pairs from the Gaofen-7 (GF-7) satellite. A 0.65 m Digital Surface Model (DSM) was generated from GF-7 data, and a relative surface height was derived by differencing the GF-7 DSM from a coarse 30 m DSM reference. Key features were selected via Boruta and Random Forest Recursive Feature Elimination (RF-RFE), and six models—linear, polynomial, support vector machine, backpropagation neural network, XGBoost, and RF—were compared. The results showed that the Boruta feature set improved average R2 by 8.2%. Among all models, RF performed best (test set R2 = 0.71, RMSE = 1.70 m) and exhibited the strongest resistance to overfitting. Canopy heights within Beijing’s Fifth Ring Road showed an “outer-high, inner-low” pattern: large parks exceeded 30 m, while the Central Business District remained below 3 m. GF-7 stereo pairs enable efficient and cost-effective retrieval of canopy height in fragmented urban green spaces, supporting ecological parameter quantification and urban green-space management.

Globally, the combined pollution of fine particulate matter (PM2.5) and ground-level ozone (O3) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM2.5 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 PM2.5–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 PM2.5 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 PM2.5 adsorption, wind speeds of 1.5–3.0 m s−1 are optimal for PM2.5 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.

Spring droughts, increasingly coinciding with canopy shade, interactively stress the growth of urban tree species and are poorly understood in Beijing. Three-year-old saplings of Pinus tabuliformis and Robinia pseudoacacia were subjected to comparative analysis under four drought–shade sequences, with a full-light, well-watered treatment serving as the control. During two periods encompassing the drought to wilting point and subsequent rewatering, we assessed leaf morphology, water status, photosynthetic gas exchange, and chlorophyll fluorescence. Both species exhibited losses in leaf water and carbon assimilation under drought, yet their adaptive strategies substantially differed. P. tabuliformis conserved water through the stable leaf anatomy and conservative stomatal control. In particular, P. tabuliformis under full-light and drought conditions decreased their specific leaf area (SLA) by 23%, as well as showing reductions in stomatal conductance (Gs) and transpiration rate (Tr) along with the drought duration (p < 0.01). As the duration of post-drought rewatering increased, the reductions in the net photosynthetic rates (Pn) of P. tabulaeformis showed that the shade condition intensified its photosynthetic limitation and slowed recovery after drought. Under low-light drought, R. pseudoacacia exhibited a 52% increase in SLA and a 77% decline in Gs; the latter was markedly smaller than the reduction observed under full-light drought. After rewatering, Gs displayed an overcompensation response. The rise in specific leaf area and the greater flexibility of stomatal regulation partly offset the adverse effects of drought. Nevertheless, post-drought Pn recovered to only 40%, significantly lower than the 61% recovery under full-light drought. Moreover, the negative correlation between SLA and Pn became significantly stronger, indicating that the “after-effects” of shade–drought hindered photosynthetic recovery once the stress was relieved. Drought duration eroded the phenotypic performance in both species, while the light environment during drought and subsequent rehydration determined the time trajectory and completeness of recovery. These results validate a trade-off between shade mitigation and drought legacy, and guide species selection: plant shade-tolerant R. pseudoacacia in light-limited urban pockets and reserve sun-dependent P. tabuliformis for open, high-light sites to enhance drought resilience of Beijing’s urban forests.

The correlation between negative air ions (NAI) and nitrogen oxides (NOx) exhibits significant seasonal characteristics and is non-static. Previous studies have shown that NAI concentration is highly sensitive to meteorological factors, while NOx concentration is also affected by meteorological factors, resulting in potential differences in their correlation under different meteorological conditions. To deepen the understanding of this relationship, this study explored the impact of different meteorological factors on the correlation between NAI and NOx. The main conclusions are as follows: (1) The interaction between NAI and NOx in urban forests is regulated by meteorological factors; the higher the temperature, humidity, and solar radiation, the larger the correlation coefficient, and the stronger the negative correlation between the two; (2) Under synergistic meteorological conditions, NAI concentration is high and NOx concentration is moderate, which is suitable for outdoor activities: Condition 1 is temperature > 20 °C, humidity 30–60%, air pressure > 940 kPa, solar radiation 30–60 W·m−2, wind speed < 1 m·s−1; Condition 2 is temperature > 20 °C, humidity > 60%, air pressure > 940 kPa, solar radiation > 60 W·m−2, wind speed < 1 m·s−1 (based on NAI and NOx concentration data and health standards: NAI ≥ 1000 cm−3 is beneficial to health, and NOx ≤ 80 μg/m3 meets WHO limits); (3) Temperature, humidity, and air pressure have regulatory effects on the relationship between NAI and NOx, among which air pressure exerts positive regulation, while temperature and humidity exert negative regulation.

Negative air ions (NAIs) can purify the atmosphere and maintain human health. In this study, we selected six tree species, Pinus tabuliformis, Pinus bungeana, Acer truncatum, Sophora japonica, Koelreuteria paniculata, Quercus variabilis, Robinia pseudoacacia, and Populus tomentosa, and we established for the first time five “capacity indicators” to characterize and judge the capacity of plants to release negative ions: they comprised the release contribution rate L, release coefficient n, release rate s, instantaneous current number v, and total level of release Z. These indicators were used to assess the ability of the plants to release NAIs by themselves. The results showed the following. (1) The daily variations in L and n show “W” and “concave” shapes, respectively, and the contribution capacity at night is significantly higher than that during the day. The diurnal variations in s, v, and Z all showed a “bimodal” pattern. The NAI release rate and release level of each tree species during the day were significantly higher than those at night. (2) The trees released the most NAIs during the day at approximately 10:00, while Robinia pseudoacacia and Populus tomentosa peaked with a 2 h lag (12:00). The NAI release capacity of each tree species was the worst at 13:00. (3) During the growing season, the self-contribution effects L and n of the plants were the strongest in May. The release rates and release levels s, v, and Z were the lowest in August. The coniferous plants released NAIs at the fastest rate in September and broad-leaved plants in July, with the highest release levels. In this study, the plants released the most NAIs from 10:00 a.m. to 11:00 a.m., which is the best time to travel. Quercus variabilis was preferentially recommended in the pairing of species of tree with the quickest NAI release and the highest total number released, followed by Koelreuteria paniculata and Sophora japonica.

Real-time, accurate and detailed monitoring of urban green space is of great significance for constructing the urban ecological environment and maximizing ecological benefits. Although high-resolution remote sensing technology provides rich ground object information, it also makes the surface information of urban green spaces more complex. Existing classification methods often struggle to meet the requirements of classification accuracy and the automation demands of high-resolution images. This study utilized GF-7 remote sensing imagery to construct an urban green space classification method for Beijing. The study used the YOLO v8 model as the framework to conduct a fine classification of urban green spaces within the Fifth Ring Road of Beijing, distinguishing between evergreen trees, deciduous trees, shrubs and grasslands. The aims were to address the limitations of insufficient model fit and coarse-grained classifications in existing studies, and to improve vegetation extraction accuracy for green spaces in northern temperate cities (with Beijing as a typical example). The results show that the overall classification accuracy of the trained YOLO v8 model is 89.60%, which is 25.3% and 28.8% higher than that of traditional machine learning methods such as Maximum Likelihood and Support Vector Machine, respectively. The model achieved extraction accuracies of 92.92%, 93.40%, 87.67%, and 93.34% for evergreen trees, deciduous trees, shrubs, and grasslands, respectively. This result confirms that the combination of deep learning and high-resolution remote sensing images can effectively enhance the classification extraction of urban green space vegetation, providing technical support and data guarantees for the refined management of green spaces and “garden cities” in megacities such as Beijing.

As an important part of the urban ecological environment, urban green space plays a crucial and irreplaceable role in improving air quality, promoting sustainable development, and enhancing residents’ quality of life. This study takes Beijing’s urban green space as the research object. Based on Landsat series satellite remote sensing images, the land use distribution of Beijing is obtained through supervised classification. Combined with data such as PM2.5 concentration and wind speed, the dry deposition efficiency of PM2.5 is quantitatively analyzed. The results show that: (1) Beijing’s urban green space has significant advantages in PM2.5 dry deposition. In terms of dry deposition flux, the order of annual average deposition of different land types is: forest land > farm land > grassland > impervious surface > water body = unutilized land. Among them, forest land has the best dry deposition effect, with an annual average dry deposition of 1.13 g/m2, which is 188.41 times that of impervious surface; cultivated land and grassland are 0.22 g/m2 and 0.19 g/m2 respectively, which are 37.13 times and 32.34 times that of impervious surface. (2) From 2000 to 2020, the PM2.5 removal rate of green space continued to rise, but the reduction amount showed a trend of first increasing and then decreasing. There are significant seasonal differences. The reduction amount is the highest in autumn (reaching 449.90 tons in October), followed by summer, spring, and winter (the lowest in August, at 190.27 tons). (3) In terms of spatial distribution, the high-value areas of dry deposition are concentrated in the suburbs, showing a “southwest-northeast” axial distribution, while the low-value areas are mainly located in the outer suburbs, reflecting the imbalance of green space layout and the regional differences in PM2.5 reduction. Combined with the current situation of green space in Beijing, the study puts forward targeted optimization suggestions, providing theoretical support and scientific basis for the construction of Beijing as a “garden city”.

The intensity and frequency of drought are constantly increasing, threatening the ecosystem functions of grasslands. Although drought can generally limit vegetation growth, the effect of drought timings and grassland degradation status remains unclear. We selected three grasslands with different levels of degradation (extremely, moderately and slightly degraded) in northern China and examined the effects of 30-day drought events during different timings (early, middle, and late growing seasons) on soil water content (SWC), vegetation coverages, and aboveground net primary productivity (ANPP). We found that by reducing SWC by approximately 22-75 %, drought events led to significant declines in seasonal vegetation coverage, but weaker effects on ANPP. Among different drought timings, vegetation coverages showed the minimum sensitivity to early-season droughts with positive legacy effects and the maximum sensitivity to mid-season droughts with negative legacy effects. Late season drought can lead to negative legacy effects on next spring, but positive legacy effects on next summer. Vegetation compositions in terms of the proportions of degradation indicator plants play an important role in regulating seasonal drought sensitivities in degraded grasslands. Our findings underscore that, to better understand the performance of grassland ecosystems during drought events, we must consider the impact of drought timing and grassland degradation status.

Trees can uptake nitrogen dioxide(NO2) and purify atmosphere, but the complex variation of environmental factors affects the ability of trees to purify NO2. In this study, we conducted a one-time fumigation experiment on four typical greening tree species in China, including Japanese pagoda tree (Styphnolobium japonicum), Ginkgo (Ginkgo biloba), Manchurian red pine (Pinus tabuliformis), and Bunge’s pine (Pinus bungeana), to analyze the impact of environmental factors on the ability of the trees to remove NO2 from the air by quantifying their performance under different conditions of temperature, relative humidity, and wind speed. The results showed the following: (1) Broadleaf trees were more effective at purification compared with coniferous trees. (2) With the increase of temperature, the average purification rate of each tree species showed a decreasing and then increasing trend; except Bunge’s pine, the purification amount per unit leaf area of each tree species showed an increasing and then decreasing trend. (3) With increasing relative humidity, the purification of NO2 per unit leaf area in coniferous trees increased, while the trend in broadleaf trees decreased and then increased; the average purification rate of all the tree species, except Ginkgo, also decreased and then increased. (4) Temperature inhibited plant NO2 purification capacity, relative humidity promoted plant NO2 purification capacity, and wind speed had less ability to influence. (5) Multiple linear regression equations were successfully established to predict the ability of trees to purify NO2 under different environmental factors. The study provides an important reference to purify atmosphere.

This study aimed to investigate the effect of environmental factors on the release of Negative Air Ions (NAI) by various green tree species. An open-top chamber (OTC) control experiment focusing on temperature was conducted using typical gardening tree species found in Beijing, including Acer truncatum, Sophora japonica, Pinus bungeana, and Pinus tabuliformis. The effect of temperature on the NAI release ability of green tree species was evaluated using five "capacity indicators": NAI release contribution (L), release coefficient (n), release rate (s), instantaneous present amount (v), and total release amount (Z). The effect of temperature on NAI release was clearly defined. When humidity and light intensity were unchanged, L, n, s, v, and Z tended to increase with the temperature gradient (25℃–35℃), and the five "capacity indicators" peaked when the temperature was 35℃. There was a significant positive correlation between the release of NAI and temperature, in the following order: Acer truncatum (R2 = 0.84) > Sophora japonica (R2 = 0.56) > Pinus bungeana (R2 = 0.47) > Pinus tabuliformis (R2 = 0.37). This study elucidates the independent effects of temperature on NAI release in various tree species and provides a scientific basis for tree species allocation, forest health management, and urban green space planning.

The context of global climate change, water stress has a significant impact on the ecological function and landscape value of urban greening shrubs. In this study, three typical greening shrubs (Euonymus japonicus, Ligustrum × vicaryi, and Berberis thunbergii var. atropurpurea) in North China were subjected to a two-year field-controlled experiment (2022–2023) with four water treatments: full irrigation, deficit irrigation, natural rainfall, and extreme drought. The key findings are as follows: (1) Extreme drought reduced the color indices substantially—the GCC of E. japonicus decreased by 40% (2023); the RCC of B. thunbergii var. atropurpurea declined by 35% (2022); and the color indices of L. × vicaryi remained stable (variation < 15%). (2) Early-season soil water content (SWC) strongly correlated with the color index of E. japonicus (r2 = 0.42, p < 0.05) but weakly with B. thunbergii (r2 = 0.28), suggesting species-specific drought-tolerance mechanisms like reduced leaf area. (3) Deficit irrigation (SWC ≈ 40%) maintained color indices between fully irrigated and drought-stressed levels. Notably, B. thunbergii retained high redness (RCC > 0.8) at an SWC ≈ 40%; E. japonicus required an SWC > 60% to preserve greenness (GCC). The research results provide a scientific basis for urban greening plant screening and water-saving irrigation strategies, and expand the application scenarios of color coordinates in plant physiological and ecological research.

Negative air ions (NAIs) have the effect of improving environmental quality and human health. This study for the first time constructed an evaluation system for forest release of NAIs employing five capacity indicators: release contribution rate (L), release coefficient (n), release rate (s), instantaneous standing stock (v), and total release amount (Z). These were applied to evaluate the forest’s ability to release NAIs in the suburban urban green space of Beijing—Xishan National Forest Park. The results showed that: (1) during the growing season of the forest, the value ranges of these indicators were as follows: L: 6.04~9.71%, n: 6.63~11.05%, s: 4.53 × 103~7.49 × 103/cm2/min, v: 4.48 × 104~7.34 × 104/cm2, Z: 2.70 × 105~4.40 × 105/cm2, with the spring and autumn “noon and evening” and summer “morning and evening” forests having the strongest effect and the highest release capacity of NAIs; (2) the daily changes of L, n, s, v, and Z are generally in a “bimodal” pattern, and the overall trend of “rise and fall, rise and fall” among various indicators is consistent, showing a “linkage”; (3) weather characteristics affect release capacity in the order of sunny > rainy > cloudy, with the strongest NAI release ability from forests at 6:00 on cloudy days (0.53%, 1.7%, 877.19/cm2/min, 3.56 × 104/cm2, 9.67 × 104/cm2) and at 18:00 on rainy days (4.58%, 4.83%, 3.16 × 103/cm2/min, 3.16 × 104/cm2, 1.90 × 105/cm2), with poorer NAI release ability in the afternoon on cloudy and rainy days; (4) forests can produce over 100 million levels of NAIs throughout the year, with an average daily production of over one million levels of NAIs. From 2019 to 2021, NAI production showed an increasing trend year by year, and the increase rate increased year by year to 19.6% and 56.5%.; and (5) the five indicators are significantly positively correlated with solar radiation and temperature in the range of 0–200 w/m2 and 5–20 °C, respectively. This study provides a new method to reveal the ability of forests to release NAIs, providing strong evidence for creating a livable ecological environment.

Negative air ions (NAI) represent an important ecological value indicator for green tree species. Flow of sap is a crucial indicator for water utilization and physiological state of trees. Although there have been some advancements in studies on the correlation between the release of NAI by plants and sap flow in recent years, it is still unclear how the release of NAI by plants changes during drought stress and recovery processes, as well as the coupling effect between the release of NAI by plants and sap flow under drought stress. In this context, four typical green tree species, Robinia pseudoacacia, Quercus variabilis, Pinus tabulaeformis, and Platycladus orientalis, were selected as experimental materials. A drought stress and recovery control experiment was conducted based on OTC. The dynamic data of negative air ion concentration (NAIC) and sap flow rate during the process of drought stress and recovery were monitored to clarify the characteristics and correlations of NAI and sap flow changes in the experimental tree species under drought stress and recovery. The main research results are as follows: (1) At the end of the drought period, the NAI and sap flow in the drought treatment group significantly decreased (p < 0.01), compared with the control group (CK), and the reduction rate of sap flow (77.73 ± 4.96%) for each tree species was higher than that of NAI (47.78% ± 4.96%). (2) At 1 day after rehydration, the recovery amplitudes of NAI and sap flow for all tree species were the greatest; at 7 days after rehydration, the NAI and sap flow of the drought treatment group recovered to the levels of the control group (p > 0.05). (3) During different stages of drought rehydration, the response degree of NAI to sap flow varied. The study found that in the drought-rehydration stage, the correlation between the NAI released by each tree species and sap flow was the lowest at the drought endpoint. In conclusion, this research clarifies the changing patterns of plant NAI release and sap flow during drought-rehydration, as well as the response changes of NAI to sap flow. It provides a theoretical basis for selecting drought-tolerant tree species in arid regions.

To uncover the internal mechanisms of various drought stress intensities affecting the soluble sugar content in organs and its regulation by endogenous abscisic acid (ABA), we selected the saplings of Platycladus orientalis, a typical tree species in the Beijing area, as our research subject. We investigated the correlation between tree soluble sugars and endogenous ABA in the organs (comprised of leaf, branch, stem, coarse root, and fine root) under two water treatments. One water treatment was defined as T1, which stopped watering until the potted soil volumetric water content (SWC) reached the wilting coefficient and then rewatered the sapling. The other water treatment, named T2, replenished 95% of the total water loss of one potted sapling every day and irrigated the above-mentioned sapling after its SWC reached the wilt coefficients. The results revealed that (1) the photosynthetic physiological parameters of P. orientalis were significantly reduced (p < 0.05) under fast and slow drought processes. The photosynthetic physiological parameters of P. orientalis in the fast drought-rehydration treatment group recovered faster relative to the slow drought-rehydration treatment group. (2) The fast and slow drought treatments significantly (p < 0.05) increased the ABA and soluble sugar contents in all organs. The roots of the P. orientalis exhibited higher sensitivity in ABA and soluble sugar content to changes in soil moisture dynamics compared to other organs. (3) ABA and soluble sugar content of P. orientalis showed a significant positive correlation (p < 0.05) under fast and slow drought conditions. During the rehydration stage, the two were significantly correlated in the T2 treatment (p < 0.05). In summary, soil drought rhythms significantly affected the photosynthetic parameters, organ ABA, and soluble sugar content of P. orientalis. This study elucidates the adaptive mechanisms of P. orientalis plants to drought and rehydration under the above-mentioned two water drought treatments, offering theoretical insights for selecting and cultivating drought-tolerant tree species.

The development of green spaces in urban parks can significantly enhance the quality of the urban and ecological environment. This paper utilizes 2021 Gaofen-7 (GF-7) satellite remote sensing images as its primary data source and uses deep learning algorithms for the precise extraction of the green space coverage within Beijing’s fifth ring road. It also incorporates the park points of interest (POI) information, road data, and other auxiliary data to extract green park space details. The analysis focuses on examining the relationship between supply and demand in the spatial allocation of green park spaces from an accessibility perspective. The main findings are as follows: (1) The application of deep learning algorithms improves the accuracy of green space extraction by 10.68% compared to conventional machine methods. (2) The distribution of parks and green spaces within the fifth ring road of Beijing is uneven, showing a clear pattern of “more in the north and less in the south”. The accessibility within a five-minute service radius achieves a coverage rate of 46.65%, with a discernible blind zone in the southeast. (3) There is an imbalance in the per capita green space location entropy within the fifth ring road of Beijing, there is a big difference in per capita green space location entropy (44.19), and social fairness needs to be improved. The study’s outcomes unveil the intricate relationship between service capacity and spatial allocation, shedding light on the supply and demand dynamics of parks and green spaces within Beijing’s fifth ring road. This insight will contribute to the construction of ecologically sustainable and aesthetically pleasing living spaces in modern megacities.

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

Plants can effectively purify PM2.5 in the air, thereby improving air quality. Understanding the mechanisms of the uptake and distribution of PM2.5 in plants is crucial for enhancing their ecological benefits. In this study, the uptake and distribution of the water-soluble inorganic compounds ammonium (NH4+) and nitrate (NO3-) ions in PM2.5 by the two native Chinese conifers Manchurian red pine (Pinus tabuliformis) and Bunge's pine (P. bungeana) were investigated using a one-time aerosol treatment method combined with N-15 tracing. The results showed the following: (1) Plants can efficiently uptake NH4+ (0.08-0.21 mu g/g) and NO3- (0.03-0.68 mu g/g) from PM2.5. Manchurian red pine uptakes these compounds more effectively with increases of 2.01-fold for NH4+ and 1.02-fold for NO3- compared with Bunge's pine. (2) The aboveground organs of the plants uptake and distribute more N-15 than the belowground organs. The branches had the highest unit mass uptake (0.08-1.60 mu g/g) and rate of distribution (16.91-53.60 %) for NH4+, while the leaves had the highest unit mass uptake (0.15-1.18 mu g/g) and rate of distribution (50.78-84.88 %) for NO3-. (3) The ability of the aboveground organs to uptake N-15 is influenced by the concentration of PM2.5, which showed an overall increase with increasing concentrations with some fluctuations in specific organs. However, the belowground organs were not affected by the concentration of PM2.5. (4) A larger specific leaf area, root-shoot ratio, branch biomass ratio, coarse root biomass ratio, and lower trunk biomass ratio favors the uptake of NH4+ from PM2.5, whereas these traits had a minimal influence on the uptake of NO3-. Manchurian red pine uptaked significantly more NH4+ compared with Bunge's pine, which benefited from the traits described above. These findings further revealed the mechanism of PM2.5 uptake by plants and its relationship with PM2.5 concentration and plant traits, and provided a scientific basis for how to effectively utilize plants to reduce PM2.5 pollution and purify the environment in areas with different pollution concentrations.

In this study, we analyzed the uptake of nitrogen dioxide (NO2) and its processes of distribution in various organs of different typical greening species of trees and the differences in its uptake by these different species using the N-15 stable isotope tracer method under the gradient of three NO2 concentration treatments, namely, low, medium and high. These experiments were conducted using one-time artificial fumigation to provide necessary data and theoretical support for the selection and application of species of greening trees in urban gardens. The treatments of fumigation with different concentrations of NO2 showed that the leaves of the six species of trees had the highest content of N-15. The organs that were the most effective at taking up N-15 were the leaves in broadleaf trees and the branches in conifers. The content of N-15 per unit of the leaves (0.0058-2.0486 mu g/g) increased in parallel with the concentration of fumigant and then was rapidly transported to various organs in the tree. This caused different degrees of changes in other organs. The total content of N-15 per unit was higher in the broadleaf species (0.0129-2.3171 mu g/g) than in the conifers (0.0296-0.1260 mu g/g). The leaves of broadleaf trees were the most effective at taking up N-15 (0.0054-1.3228 %) under medium and high concentrations of fumigant with the exception of ginkgo. The ability of each organ of the other broadleaf species was leaves>branches>trunks>roots, and the branches of conifers were the most effective parts of the trees at taking up NO2 under three concentrations of fumigants (0.0789-0.4005 %). The highest rate of distribution of N-15 was found in the leaves (48.14-99.53 %) in all six species under different concentrations of fumigant, and the distribution in the other organs varied to different degrees.