This case study was conducted to quantify the effects of urban greenspace patterns on particle matter (PM) concentration in Zhengzhou, China by using redundancy and variation partitioning analysis. Nine air-quality monitoring stations (AQMS) were selected as the central points. Six distances of 1 km, 2 km, 3 km, 4 km, 5 km, and 6 km were selected as the side lengths of the squares with each AQMS serving as the central point, respectively. We found: (1) the fine size of PM (PM_2.5) and coarse size of PM (PM₁₀) among four seasons showed significant differences; during winter, the concentration of PM_2.5 and PM₁₀ were both highest, and PM_2.5 and PM₁₀ concentration in summer were lowest. (2) To effectively reduce the PM_2.5 pollution, the percentage of greenspace, the differences in areas among greenspace patches, and the edge complexity of greenspace patches should be increased at distances of 2 km and 3 km. To effectively reduce PM₁₀, the percentage of greenspace at a distance of 4 km, the edge density at distances of 2 km and 4 km, and the average area of greenspace patches at a distance of 1 km should be increased. (3) Greenspace pattern significantly affected PM_2.5 at a distance of 3 km, and PM₁₀ at a distance of 4 km. From shorter distance to longer distance, the proportion of variance explained by greenspace showed a decline–increase–decline–increase trend for PM_2.5, and a decline–increase–decline trend for PM₁₀. At shorter distances, the composition of greenspace was more effective in reducing the PM pollution, and the configuration of greenspace played a more important role at longer distances. The results should lead to specific guidelines for more cost-effective and environmentally sound greenspace planning.

In the context of global warming, more and more cities are experiencing extreme Urban Heat Island (UHI) effects and extreme weather phenomena, but urban green spaces are proven to mitigate UHI. Most of UHI’s research focuses on the large scale and uses remote sensing methods, which do not reflect the dynamic characteristics in detail and do not detect internal influencing factors of the green space cooling effect. Therefore, this study focused on Small Green Spaces (SGS), carrying out the measurement of the meteorological parameters (temperature, relative humidity, wind direction, wind speed, photosynthetic radiation) of the 16 sites in four types of coverage (Impervious surface; Shrub-grass; Tree-grass; Tree-shrub-grass) in a university campus. At the same time, the coverage characteristic parameters, such as Canopy Density (CD), Leaf Area Index (LAI), Photosynthetically Active Radiation (PAR), Mean Leaf Angle (MLA), of each plot were analyzed and compared. The results showed that there were significant differences in temperature among different coverage types in SGS. The biggest difference was concentrated in the noon period when solar radiation is strongest during the day. The difference between the four types of coverage with vegetation at night was small. The maximum air temperature difference among the four types could reach 8.9 ℃ and the maximum relative humidity difference was 28.5%. The cooling effect of the multi-layer vegetation-covered (Tree-shrub-grass) area was the largest compared to the impervious surface, indicating that tree cover was the core factor affecting the temperature. Temperature and relative humidity had a close correlation with surface coverage types and some plant community characteristics (such as CD and LAI). The cooling and humidifying effects of plants were also related to PAR and leaf angle. The results provide suggestions for green space management and landscape design.

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (Photinia serrulata, Ligustrum lucidum, Eriobotrya japonica, Euonymus japonicus, Pittosporum tobira) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park. There were significant differences in dust retention among the five tree species, with the highest leaf dust accumulation observed for E. japonica (5.45 g·m⁻²), and the lowest for P. tobira (1.53 g·m⁻²). In addition, the increase in PM adsorption by different plants was uneven with increasing pollution levels, with significant decreases in chlorophyll content, photosynthetic and transpiration rate. From a physiological perspective, P. tobira exhibited greater potential to respond to PM pollution. Biochemical indicators suggested that PM pollution caused changes in plant protective enzyme activities, with a decrease in superoxide dismutase (SOD) and peroxidase (POD) activities, as well as promoting membrane lipid peroxidation, and appropriate stress also enables plants to counteract oxidative damage. In particular, PM exposure also induced stomatal constriction. Overall, PM retention was significantly associated with physiological and photosynthetic traits. In conclusion, our study contributes to the understanding of the effects of PM on plant physiology. Furthermore, it also provides insights into the selection of plants that are tolerant to PM pollution.

Application of LiDAR technology has greatly enhanced tree segmentation and phenotypic analysis. There are few studies in urban green spaces using tree segmentation methods. Our aim is to improve the single-plant segmentation accuracy in tree and shrub communities through segmenting algorithm optimisation based on TLS LiDAR data of the urban green space. We developed a multi-round comparative shortest-path algorithm (M-CSP) to achieve the objectives: a) tree and shrub plant layer pre-division (TSPD); b) shrub type classifications (STC) into spherical, cylindrical, and rectangular shapes. The overall detection kappa value using M-CSP is 0.933, which is 18% higher than the CSP value of 0.790. M-CSP-based overall segmentation accuracy value (F-score) is 0.886, which is 13% higher than the CSP value of 0.783. The shrub F-score using M-CSP is 0.817, which is 26% higher than the CSP (0.646). M-CSP should provide a more accurate, faster, and less costly tool to study plant communities in urban green spaces.

During urbanization in developing countries, fragmentation of green infrastructure due to increasing populations and the expansion of construction land leads to an extremely serious imbalance between the supply and demand for urban ecosystem services. In this study, the central city of Zhengzhou, a central city in central China, was selected as the study area and the excessive demand for six ecosystem services, namely, air purification, flood regulation, heat regulation, hydrological regulation, CO₂ sequestration and recreational services, was quantitatively evaluated. The entropy method was used to calculate the weights of various ecosystem services, and spatial overlay analysis was performed to obtain the comprehensive ecosystem service excessive demand. Finally, bivariate spatial autocorrelation analysis was used to explore the response of population density to comprehensive excessive demand for ESs. The results of this study indicate that: (1) The most prevalent need is for more CO₂ regulation service throughout the study area. (2) Except for hydrological regulation service, the spatial distribution of the remaining highly excessive ecosystem service demands are mostly concentrated in old neighborhoods. (3) Of the six excessively demanded economic services, rainwater regulation obtained the greatest weight, reflecting the poor urban infrastructure configuration for countering the rapidly increasing threat of flooding caused by climate change in the city. (4) The comprehensive ecosystem service excessive demand results show that there are eight priority green infrastructure implementation blocks in the central city of Zhengzhou. (5) There were three agglomeration types between population density and comprehensive excessive demand for ESs: high-high type, low-high type and low-low type. The spatial distribution characteristics of population density and comprehensive ES demand are positively correlated. The results of this study could help to provide information for decision making when delineating the priority areas and types of green infrastructure implementation in developing cities.