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.

To study the effects of species diversity of different urban road green space on PM2.5 reduction, and to provide a theoretical basis for the optimal design of urban road plantings. Different combinations of road plantings in Xianlin Avenue of Nanjing were used as sample areas, and 3–6 PM2.5 monitoring points were set up in each sample area. The monitoring points were setup at 10, 20, 30, 40, 50, and 60 m from the roadbed for detecting PM2.5 concentrations in different sample areas. Moreover, the living vegetation volume of each sample area was calculated. The coupling relationship between the living vegetation volumes and PM2.5 concentrations in different sample areas was evaluated by regression fitting and other methods. PM2.5 concentrations among different sample areas were significantly different. PM2.5 concentrations were higher in the morning than in the afternoon, while the differences were not significant. The living vegetation volumes of the eight sample areas varied from 2038.73 m3 to 15,032.55 m3. Affected by different plant configurations, the living vegetation volumes in the sample areas showed obvious differences. The S2 and S6 sample area, which was consisted a large number of shrubshave better PM2.5 reduction capability. The fitting curve of living vegetation volumes and PM2.5 concentrations in sample areas of S1 and S3–S8 can explain 76.4% of the change in PM2.5 concentrations, which showed significant fitting. The fitting relationship between living vegetation volumes and PM2.5 concentrations in different road green space is different owing to different compositions of plantings. With the increase in living vegetation volumes, their fitting functions first increase and then decrease in a certain range. It is speculated that only when the living vegetation volume exceeds a certain range, it will promote PM2.5 reduction.

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.

To study the effects of species diversity of different urban road green space on PM2.5 reduction, and to provide a theoretical basis for the optimal design of urban road plantings. Different combinations of road plantings in Xianlin Avenue of Nanjing were used as sample areas, and 3–6 PM2.5 monitoring points were set up in each sample area. The monitoring points were setup at 10, 20, 30, 40, 50, and 60 m from the roadbed for detecting PM2.5 concentrations in different sample areas. Moreover, the living vegetation volume of each sample area was calculated. The coupling relationship between the living vegetation volumes and PM2.5 concentrations in different sample areas was evaluated by regression fitting and other methods. PM2.5 concentrations among different sample areas were significantly different. PM2.5 concentrations were higher in the morning than in the afternoon, while the differences were not significant. The living vegetation volumes of the eight sample areas varied from 2038.73 m3 to 15032.55 m3. Affected by different plant configurations, the living vegetation volumes in the sample areas showed obvious differences with an order of S7 > S2 > S3 > S1 > S5 > S6 > S8 > S4. The S2 sample area, which was considered as control, was inside a residential area. The fitting curve of living vegetation volumes and PM2.5 concentrations in sample areas of S1 and S3–S8 can explain 76.4% of the change in PM2.5 concentrations, which showed significant fitting. The fitting relationship between living vegetation volumes and PM2.5 concentrations in different road green space is different owing to different compositions of plantings. With the increase in living vegetation volumes, their fitting functions first increase and then decrease in a certain range. It is speculated that only when the living vegetation volume exceeds a certain range, it will promote PM2.5 reduction.

To study the effects of species diversity of different urban road green space on PM2.5 reduction, and to provide a theoretical basis for the optimal design of urban road plantings. Different combinations of road plantings in Xianlin Avenue of Nanjing were used as sample areas, and 3–6 PM2.5 monitoring points were set up in each sample area. The monitoring points were setup at 10, 20, 30, 40, 50, and 60 m from the roadbed for detecting PM2.5 concentrations in different sample areas. Moreover, the living vegetation volume of each sample area was calculated. The coupling relationship between the living vegetation volumes and PM2.5 concentrations in different sample areas was evaluated by regression fitting and other methods. PM2.5 concentrations among different sample areas were significantly different. PM2.5 concentrations were higher in the morning than in the afternoon, while the differences were not significant. The living vegetation volumes of the eight sample areas varied from 2038.73 m3 to 15032.55 m3. Affected by different plant configurations, the living vegetation volumes in the sample areas showed obvious differences with an order of S7 > S2 > S3 > S1 > S5 > S6 > S8 > S4. The S2 sample area, which was considered as control, was inside a residential area. The fitting curve of living vegetation volumes and PM2.5 concentrations in sample areas of S1 and S3–S8 can explain 76.4% of the change in PM2.5 concentrations, which showed significant fitting. The fitting relationship between living vegetation volumes and PM2.5 concentrations in different road green space is different owing to different compositions of plantings. With the increase in living vegetation volumes, their fitting functions first increase and then decrease in a certain range. It is speculated that only when the living vegetation volume exceeds a certain range, it will promote PM2.5 reduction.