Urban green spaces (UGSs) are an important spatial carrier for carbon sequestration in the national land space. The urban ecosystem is a highly harmonious composite ecosystem of nature, society, and the economy. Therefore, this study constructed an evaluation system of the UGS ecological benefit based on two subsystems–the natural environment and the social economy–in order to quantitatively evaluate the construction level of the UGS ecological benefit in Nanjing City and to reveal its temporal evolution characteristics. The entropy method was applied to assess the ecological benefits of UGSs in Nanjing City, China, from 2011 to 2020. The coupling harmonious degree model was utilized to analyze the dynamic coordination relations among subsystems. The Robust regression analysis was used to verify the evaluation results. The results showed that: (i) Between 2011 and 2020, UGS ecological benefits in Nanjing City exhibited a substantial and consistent upward trend. (ii) Between 2011 and 2020, the coupling harmonious degree among the subsystems of UGS ecological benefits in Nanjing City showed an overall rising trend of fluctuation. With the enhancing of the coupling harmonious degree among the subsystems of UGSs, the ecological benefit of UGSs will be significantly improved. (iii) The comprehensive evaluation score of the social and economic subsystem of UGS in Nanjing City was higher than that of the natural environment subsystem; this highlights the significant constraints posed by the natural environment on the construction of UGSs in Nanjing City. The research conclusion provides a decision basis for realizing the collaborative optimization of the natural environment and the social economy in Nanjing City and further promotes the sustainable development of the UGS ecological environment.

As an important part of urban ecosystems, plants can reduce NO2 concentrations in the air. However, there is little evidence of the effects of different plant communities on NO2 concentrations in street-scale green spaces. We used a multifunctional lifting environmental detector to investigate the impact of environmental factors and small plant communities on NO2 concentrations in street green spaces during the summer and winter in Nanjing, China. The results showed that temperature, atmospheric pressure, and noise were significantly (P < 0.05) correlated with seasonal changes, temperature and humidity significantly (P < 0.01) influenced NO2 concentrations in winter and summer, and the average NO2 concentration in summer was generally higher than in winter. By comparing NO2 concentrations in different plant community structures and their internal spaces, we found that the plant community structure with tree-shrub-grass was more effective in reducing pollution. These findings will help predict the impact of plant communities on NO2 concentrations in urban streets and help city managers and planners effectively reduce NO2 pollution.

Determining the relationships between the structure and species of plant communities and their impact on ambient particulate matter (PM) is an important topic in city road greenbelt planning and design. The correlation between the distribution of plant communities and ambient PM concentrations in a city road greenbelt has specific spatial patterns. In this study, we selected 14 plant-community-monitoring sites on seven roads in Nanjing as research targets and monitored these roads in January 2022 for various parameters such as PM with aerodynamic diameters ≤ 10 µm (PM10) and PM with aerodynamic diameters ≤ 2.5 µm (PM2.5). We used a spatial model to analyze the relationship between the concentrations of ambient PM10 and PM2.5 and the spatial heterogeneity of plant communities. The consequences revealed that the composition and species of plant communities directly affected the concentrations of ambient PM. However, upon comparing the PM concentration patterns in the green community on the urban road, we found that the ability of the plant community structures to reduce ambient PM is in the order: trees + shrubs + grasses > trees + shrubs > trees + grasses > pure trees. Regarding the reduction in ambient PM by tree species in the plant community (conifer trees > deciduous trees > evergreen broad-leaved trees) and the result of the mixed forest abatement rate, coniferous + broad-leaved trees in mixed forests have the best reduction ability. The rates of reduction in PM10 and PM2.5 were 14.29% and 22.39%, respectively. We also found that the environmental climate indices of the road community, temperature, and traffic flow were positively correlated with ambient PM, but relative humidity was negatively correlated with ambient PM. Among them, PM2.5 and PM10 were significantly related to temperature and humidity, and the more open the green space on the road, the higher the correlation degree. PM10 is also related to light and atmospheric radiation. These characteristics of plant communities and the meteorological factors on urban roads are the foundation of urban greenery ecological services, and our research showed that the adjustment of plant communities could improve greenbelt ecological services by reducing the concentration of ambient PM.