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Article

Influence of Urban-Road Green Space Plant Configurations on NO2 Concentrations in Nanjing City during Winter

by
Qianqian Sheng
1,2,3,4,
Yaou Ji
1,2,3,4,
Zhengwei Huang
5,*,
Congzhe Liu
1,2,
Anqi Dai
1,2 and
Zunling Zhu
1,2,3,4,6,*
1
College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
2
Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
3
Jin Pu Research Institute, Nanjing Forestry University, Nanjing 210037, China
4
Research Center for Digital Innovation Design, Nanjing Forestry University, Nanjing 210037, China
5
College of Information Science and Technology & College of Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China
6
College of Art and Design, Nanjing Forestry University, Nanjing 210037, China
*
Authors to whom correspondence should be addressed.
Forests 2023, 14(9), 1892; https://doi.org/10.3390/f14091892
Submission received: 20 August 2023 / Revised: 8 September 2023 / Accepted: 15 September 2023 / Published: 17 September 2023
(This article belongs to the Section Urban Forestry)
Browse Figures
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Abstract

:
The rapid urbanization and growing number of motor vehicles in China have led to a significant increase in NO2 emissions, posing a severe threat to the air quality in cities. Road traffic pollution has emerged as a significant environmental issue in China. Exploring the utilization of landscape plants for air pollutant mitigation and assessing the influence of various plant concentrations on reducing air pollution holds great significance in urban ecological environment protection and urban development. Through field surveys and data collection in January 2022, the objectives of this study are to explore the relationship between road meteorological factors and the reduction in air pollutant concentrations in Nanjing city’s road green spaces and to investigate the influence of plant configuration in road green spaces on pollutant concentration. The findings demonstrate a distinct positive correlation between road traffic volume during winter peak hours and the concentration of NO2 pollution gas. Furthermore, meteorological factors, including temperature and light intensity, strongly correlate with air pollutant concentrations. Open green spaces with ventilated structures and high tree planting density (deciduous trees are preferred) exhibit optimal purification effects. Excessive or insufficient planting density hinders the purifying function of green belts. In conclusion, our research on plant configurations and air pollutant concentrations in Nanjing City during the winter suggests that the recommended road green space plant configuration in Nanjing is a combination of arbor (deciduous tree), shrub, and grass.

1. Introduction

With the rapid urbanization and industrialization in China, the per capita ownership of motor vehicles is steadily increasing [1]. As a result, a range of issues arising from motor vehicle exhaust pollution have emerged [2]. Urban areas worldwide are commonly confronted with the significant challenge of vehicle exhaust pollution, which has emerged as a prominent environmental factor contributing to various health issues, including respiratory diseases in humans [3,4]. In recent years, the reduction in air pollution through green and environment-friendly methods has become a growing interest in road green space research [5,6,7,8,9,10], including studies regarding the influence of different microclimate conditions on pollutant dispersion and the influence of different road green space structures on pollutant diffusion [11].
Landscape plants play a crucial role in improving the urban ecological environment, facilitating road traffic organization, and enhancing the aesthetic appeal of urban landscapes. Additionally, they contribute to absorbing, intercepting, and filtering traffic pollutants. In their 2019 study, Huang et al. discovered that variations in the height of branching points in roadside trees can significantly alter the distribution of air pollutant concentrations along urban roadways [12]. AL-Dabbous et al. demonstrated that plants can reduce inhalable particle concentrations by 36% [13]. Hu et al. indicated that diverse plant communities can effectively decrease the concentration of NO2 and SO2 along footpaths by 10%~30% [14]. Sheng et al. proposed that a notable positive correlation exists between the size of the three-dimensional green volume of various plant communities and pollutants in open spaces. Moreover, external environmental factors, such as wind speed and direction, influence this correlation [15,16]. Wu conducted a study comparing pollutant concentrations from different roads and determined that the plants’ combination pattern significantly influences pollutant reduction [17]. Numerous studies have demonstrated that road vegetation can improve the road environment and mitigate pollution by absorbing harmful gases and capturing dust. Furthermore, the effectiveness of pollution purification in road green spaces can be significantly influenced by the specific vegetation, their combination patterns, and the planting parameters (including height, width, and density) [18]. Although meteorological conditions over large areas can indeed influence the air quality of an entire city, limited research has been conducted on the meteorological factors affecting small-scale green spaces and their influence on air pollutants in China [19,20,21,22]. Additionally, the comprehensive influence of various factors, such as plant configurations (reasonably arranging various plants in the garden according to their ecological habits and landscaping layout requirements to harness their ecological functions and ornamental characteristics), meteorological conditions, traffic volume, and vegetation height, on pollutant reduction has yet to be thoroughly analyzed [23,24,25,26,27,28]. Hence, additional research is needed to comprehensively evaluate the influence of road green spaces on air pollution reduction. Based on current research and field measurement data, the main objectives of this study are the following two points: On the one hand, this study investigates the influence of meteorological factors on air pollutants, including temperature, humidity, wind speed, atmospheric pressure, radiation, sunlight, and noise. On the other hand, we explore the influence of various plant configurations in road green spaces on air pollution, providing a theoretical basis for road green space construction and predicting the road environment in Nanjing City.

2. Study Area

Nanjing City is situated in eastern China, at the lower reaches of the Yangtze River (31°14′ N—32°37′ N, 118°22′ E—119°14′ E). It is geographically located in a narrow north–south and east–west direction [29]. The southern part of the city features low mountains, hills, river valleys, lakeside plains, and riverbanks, collectively forming a diverse landform system. According to the Köppen and Geiger climate classification system, Nanjing City falls into the subtropical dry climate (Cfa) category. This region experiences four distinct seasons, characterized by hot and arid summers, cold and dry winters, and a relatively uniform distribution of precipitation. The annual average temperature stands at 15.4 °C, with the highest recorded temperature reaching 39.7 °C and the lowest dropping to −13.1 °C. Abundant precipitation, with an annual rainfall of 1200 mm, creates favorable conditions for vegetation growth and agricultural activities. Based on a comprehensive survey of all the roads in Nanjing, seven roads were selected for further investigation, representing five major urban areas encompassing Jianye District, Xuanwu District, Gulou District, Qixia District, and Jiangning District. The green space along these roads consists of various plant configurations, including arbors, shrubs, and grass, spanning different road grades and directions. The research scope encompasses both downtown and urban fringe areas (Figure 1). This research primarily investigates the number of lanes, the shape of green belt sections, the plant configuration, and the specific species of plants.
This study primarily focuses on the plant configuration along target roads, with the main vegetation species including arbor–shrub–grass, arbor–shrub, and arbor–grass. The prominent arbor species include Camphora officinarum Nees ex Wall., Platanus × acerifolia (Aiton) Willd., and Ginkgo biloba L. The shrubs consist of Pittosporum tobira (Thunb.) W. T. Aiton, Ligustrum lucidum Ait. f. lucidum, and Buxus sinica (Rehder & E. H. Wilson) M. Cheng, while the grasses mainly comprise Ophiopogon bodinieri H. Lév. and Zoysia japonica Steud. (Table 1). All surveys are conducted on symmetrical roads without any instances of one-way roads. The change in the cross-sectional shape of a road depends on its width, number of lanes, and slope (Table 2). The tree planting density can be classified into three structural types: a compact structure (the canopy exhibits tight integration, impeding air circulation); a ventilated structure (the canopy displays a moderate density, facilitating adequate lower-level ventilation); and a sparse structure (the canopy is notably sparse and entirely discontinuous).

3. Methods

To examine the influence of green belts on reducing road air pollutants in Nanjing, we initially conducted monitoring of road traffic volume, meteorological factors, PM2.5, PM10, NO2, SO2, NO, and O3. We further analyzed air pollutants’ spatial and temporal distribution characteristics throughout Nanjing City. Subsequently, we gathered data on the plant configuration and cluster structure of various road green spaces. We then analyzed the reduction pattern of pollutants in the entire road green space by integrating this information with the collected meteorological factor data.

3.1. Field Testing

In January 2022, air pollutant concentrations were monitored at seven urban road green spaces in Nanjing (with distinct green space plant configurations) under clear or slightly breezy conditions (wind speed below 2 m/s). Monitoring was excluded during rainy, snowy, or high-wind weather. PM2.5, PM10, NO2, and O3 served as targets for purification.
A self-developed multifunctional lifting environmental detector was employed for sampling purposes. The instrument can simultaneously measure the mass concentrations of air pollutants, including PM2.5, PM10, NO2, and O3, while recording data on temperature, humidity, wind speed, atmospheric pressure, radiation, illumination, and noise (Table 3).

3.2. Monitoring Methods

Based on surveys of traffic volume on daytime and nighttime roads and the relevant literature [30,31,32,33,34,35], monitoring was conducted at three time periods: 7:00–9:00, 12:00–14:00, and 17:00–19:00, representing morning peak, off-peak, and evening peak hours, respectively [36]. In each monitoring area of the road green space, from three to four transverse observation points perpendicular to the road were established, including one observation point in the motor vehicle lane, two in the non-motor vehicle lane, and one located behind the green belt (Figure 2) [37]. Meteorological data and gas pollutant concentrations were monitored at various heights (0 m, 0.5 m, 1.5 m, 3 m, and 6 m) using a lifting pole at each observation point. Measurements were taken for 1–3 min at each position and height, and the recorded data were averaged. Simultaneously, counters were utilized to capture the traffic volume within a five-minute time limit on the road and categorize the counts based on the types of vehicles, including trucks, buses, and electric vehicles.
The formula for calculating the percentage of pollutant purification in green belts with different plant configurations is as follows:
Pn = (Cc − C0)/Cc × 100%
Note: Pn denotes the purification percentage of the green belt for different pollutants; Cc denotes the pollutant concentration on the side of the road nearest to the green belt; and C0 denotes the pollutant concentration at a distance from the road’s edge (reference concentration).
Partial correlation analysis was used to independently assess the strength and direction of the relationship between two elements, eliminating the influence of other factors [30]. Therefore, the formula for investigating the degree of correlation between meteorological factors and air pollutants is as follows:
R x y , z   = R x y − R x z R y z   1 − R x z 2 1 − R y z 2
Rxy denotes the partial correlation coefficient between x and y after removing the influence of z, and Rxy, Rxz, and Ryz denote the correlation coefficients between two respective factors.

3.3. Data Processing and Analysis

Measurement data were processed and subjected to correlation analysis using the Excel 2021 software and OriginPro 9.1. Regression analysis was then conducted on meteorological factors, vegetation, and road purification percentages using IBM SPSS Statistics 23. Pix4D 4.5.6, Photoshop 2022, and ArcMap 10.8 were utilized to interpret the road vegetation image data, extracting detailed information to analyze the correlation between pollutants and vegetation.

4. Results

4.1. Experimental Analysis of Pollutant Concentrations on Roads in Nanjing City

Overall, the concentrations of various pollutants on Mufu South Road increase as the traffic volume increases throughout the day (Figure 3). The pollutant concentration in Nanjing City is relatively low and generally complies with the National Air Quality Standards Grade II [38]. However, there are specific disparities compared to the interim target–4 in the Air Quality Guidelines released by the World Health Organization (dotted line in Figure 3) [39]. The average daily concentration of NO2 detected at Mufu South Road was 60.4 µg/m3, which is below the National Air Quality Standards Grade II limit of 80 µg/m2. However, the average daily concentration of NO2 in the Air Quality Guidelines interim target-4 is 50 µg/m3. Specifically, the concentration of various air pollutants was significantly lower during the morning peak hours compared to the non-peak hours at noon and the evening peak hours. As shown in Figure 4, the findings demonstrate a robust positive correlation between the concentration of NO2 and the traffic volume. However, no correlation was observed between O3 concentration and traffic volume. The concentrations of inhalable particles (PM2.5 and PM10) were lower during the morning peak hours, while no significant differences were observed during the non-peak and evening peak hours. The concentration of NO2 generally showed a positive correlation with traffic volume, whereas the relationship between other pollutants and traffic flow was less apparent. These findings indicate that meteorological factors may influence the concentration fluctuations of inhalable particles and pollutants such as O3 on roads.

4.2. Correlation Analysis between Meteorological Factors and Air Pollutant Concentrations on Different Roads

A partial correlation analysis was conducted between various meteorological factors (temperature, humidity, wind speed, air pressure, sunlight, and radiation) and road pollutants on seven roads under winter conditions. The results showed no consistent correlation, suggesting that the concentration of road pollutants is affected by a combination of different meteorological factors. A comparative analysis of different roads revealed that PM10, PM2.5, and O3 exhibited significant correlations with meteorological factors and are more prone to variation in response to these factors. Both PM2.5 and PM10 exhibited significant correlations with temperature and humidity, with a stronger correlation observed in more open road sections with abundant greenery. Furthermore, PM10 showed correlations with illumination and atmospheric radiation. Apart from Xianlin Avenue, O3 pollutant concentrations on the other six roads displayed a notable negative correlation with temperature. However, there was no significant correlation between NO2 and meteorological factors (Table 4).

4.3. Influence of Different Green Space Plant Configurations on Air Pollutants

Data analysis was conducted on three representative roads (Mufu South Road, Beijing East Road, and Tai Ping North Road) out of the seven selected roads. They were chosen for their characteristic road grade, traffic flow, and different plant configurations. Regarding road grades, Mufu South Road follows a one-lane, two-greenbelt layout, while Beijing East Road and Tai Ping North Road adopt a three-lane, four-greenbelt design. Regarding traffic volume, Taiping North Road exhibits the highest flow volume with 19 vehicles/minute; Mufu South Road has the lowest with 9 vehicles/minute; and Beijing East Road records 13 vehicles/minute.
The plant configuration at the experimental site on Mufu South Road comprises grass (A1) and arbor–shrub–grass (B1) (Table 5, Figure 5). The arbor–shrub–grass green spaces exhibited a slight increase in the concentrations of the PM2.5, PM10, and O3 pollutants and a moderate reduction in NO2 gas concentration. All four pollutants showed a slight upward trend in concentrations within the grass plant configuration pattern.
The plant configuration at the experimental site on Beijing East Road consists of square (A3) and arbor–grass (B3) (Table 5, Figure 6). While the arbor–grass green space had a certain influence on reducing NO2 concentrations, its effectiveness in removing the PM2.5, PM10, and O3 pollutants was relatively limited. The square exhibited limited capacity to reduce all four pollutants.
The plant configuration at the experimental site on Taiping North Road is characterized by arbor–shrub (A4) and arbor–shrub–grass (B4) (Table 5, Figure 7). The results revealed that the arbor–shrub–grass green space showed notable pollutant reduction, with NO2 showing a particularly high reduction rate of 69.57%. While NO2 concentrations exhibited significant variation within the arbor–shrub green space, the reduction effect on all pollutants was not significant.
As shown in Table 6, complex plant configurations exhibit more substantial pollutant reduction capabilities in typical road scenarios with roadside green belts. The most optimal reduction effect is observed when deciduous trees are paired with shrubs and grasses. Meanwhile, the pollutant reduction efficiency for the green belt for street trees follows the sequence: deciduous trees + shrubs > evergreen trees + shrubs > evergreen trees.

4.4. Influence of Plant Configuration at Different Heights on Pollutants

As shown in Figure 8, at A1 on Mufu South Road, the configuration of the grass exhibited a notable reduction in pollution within a range of 0–0.5 m. However, no noticeable decrease was observed in air pollutants at 1.5 m, 3 m, and 6 m. At B1 on Mufu South Road, characterized by an arbor–shrub–grass configuration, a slight increase in pollutant concentration was observed at the branching point of the trees (1.5 m). Between the heights of the shrubs and the arbors (1.5–4 m), there was a moderate reduction in pollutant levels. However, since tree height mostly remains below 4 m, no significant changes in pollutant concentrations were observed beyond 4 m. These findings revealed that the height of vegetation leaves in road green spaces directly influences pollutant reduction. However, in the area beneath the canopy where only tree trunks are present, pollutants tend to accumulate and settle due to the absence of leaves, resulting in a deposition effect. It is noteworthy that the concentration of PM10 has consistently been significantly higher than that of PM2.5. However, within distances of 0–3 m, the maximum concentration differential between PM10 and PM2.5 amounts to 13 µg/m3, whereas at a distance of 6 m, this difference narrows to 11 µg/m3. This observation implies that PM10 particles tend to concentrate at a lower height above the ground when compared with PM2.5. Moreover, the spatial distribution of emission sources is anticipated to play a pivotal role in the dispersion of particulate matter.

5. Discussion and Conclusions

5.1. Discussion

A significant quantity of pollutant gases is emitted into the atmosphere from the exhaust pipes of vehicles on urban roads. Nevertheless, implementing a green space structure can effectively mitigate and absorb these pollutants. Both domestic and foreign scholars have extensively researched the correlation between road green space plant configurations and pollutant concentrations [30,31,32,33,34,35,36].

5.1.1. Influence of Traffic Volume on Air Pollutant Concentration

A substantial correlation has been shown between NO2, PM2.5, and PM10 in road air pollutants and traffic volume. Specifically, the concentration of NO2 exhibits a close association with traffic volume and vehicle emissions. Meanwhile, while the inhalable particles increase with the rise in traffic volume, green space plants can also accumulate and retain these particles. This provides a reasonable explanation for the observed phenomenon in our study that inhalable particle concentrations were lower in the morning and higher in the afternoon and evening [40].

5.1.2. Influence of Meteorological Factors on Air Pollutant Concentration

Concerning meteorological factors and air pollutants, temperature and light intensity exhibit a pronounced and significant correlation with pollutant gases, which is consistent with the studies conducted by Bao et al. [41] and Zhang et al. [42]. The dispersion of pollutants is believed to be influenced by turbulent motion, which is caused by temperature changes [43]. Simultaneously, increased light intensity and a longer duration of light exposure facilitate atmospheric photochemical reactions and the formation of precursor substances for ultrafine particles, promoting the catalytic conversion of pollutants. Specifically, nitrogen oxides are transformed into NO2, while O3 is catalyzed by oxygen ions in the air [44]. Moreover, atmospheric organic aerosols make up 20% to 60% of the particles, consisting of over 80 types of organic compounds, including n-alkanes, isoalkanes, and anteisoalkanes [45]. These organic compounds undergo gradual transformation into CO2, H2O, and other ions through photodegradation, which is influenced by various conditions like temperature and humidity [46].

5.1.3. Influence of Plant Configuration on Air Pollutant Concentration

The stable winter weather conditions in Nanjing contribute to the accumulation of pollutants in enclosed green spaces, which hinders their dispersion. Therefore, adopting a planting pattern of deciduous trees or an appropriate density of plant configuration in winter can effectively reduce the concentration of pollutants on roads. Shen et al. [47] proposed that planting density and greening patterns influence the purification effect under a constant area and similar plant species and configurations in road green belts. For instance, well-ventilated forest belts with an optimal planting density demonstrate the most effective purification effect. Conversely, excessive or insufficient planting density is not conducive to the purifying function of forest belts. Furthermore, these findings suggest that a well-designed green belt can enhance road ventilation and mitigate on-road pollutant emissions. This conclusion aligns with the optimal pollutant reduction effect observed in this study, which involves the arbor (deciduous tree)–shrub–grass configuration.

5.2. Conclusions

Increasing the diversity of plant configurations can significantly reduce pollutants in urban road greening. In the roadside green belt, the most optimal reduction effect is observed when deciduous trees are paired with shrubs and grasses. At the same time, the pollutant reduction efficiency for the green belt for street trees follows the sequence: deciduous trees + shrubs > evergreen trees + shrubs > evergreen trees.
Among other influencing factors, traffic volume remains a significant factor and shows a significant positive correlation with NO2, leading to a lower concentration of inhalable particulate matter during morning peak hours and a higher concentration at noon and evening. Temperature has the most significant influence on the variation in air pollutant concentration among the seven meteorological factors, and the rate at which pollutants are purified is directly influenced by temperature, illumination, and wind speed.
The leaves of road green belt vegetation have a direct reducing effect on pollutants, and pollutants will gather and settle at the branching points under the tree crown. For road greening in Nanjing during the winter, ventilated green spaces with suitable planting density or deciduous trees yield the most effective purification. A well-planned plant configuration can enhance the airflow of roads, improve the microclimate environment, and, consequently, facilitate the decomposition, deposition, or dispersion of pollutants.
In conclusion, this study focused solely on analyzing air pollutants and plant configurations in the urban area of Nanjing during the winter. In the future, we will conduct research on the vegetation changes along summer-related roads in Nanjing City and collect data on summer air pollutants. We will utilize machine learning methods to perform a comparative analysis of pollution data between the winter and summer seasons from a time-series perspective, aiming to further optimize the vegetation configuration patterns in Nanjing City. Additionally, we will explore the transformation patterns of different pollutants and analyze multiple meteorological parameter indicators to provide more valuable suggestions for the plant configuration of road green spaces in Nanjing City.

Author Contributions

Methodology, Q.S. and C.L.; Formal analysis, C.L.; Data curation, Q.S., Y.J. and A.D.; Writing—original draft, Y.J.; Writing—review & editing, Q.S.; Supervision, Z.H.; Project administration, Z.H. and Z.Z.; Funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Young elite scientist sponsorship program by cast in China Association for Science and Technology: grant number YESS20220054; Ministry of Education Humanities and Social Sciences Research “Study on the new mechanism of urban green space ecological benefit Measurement and high-quality collaborative development: A case study of Nanjing Metropolitan Area”: grant number 21YJCZH131; Social Science Foundation Project of Jiangsu Province: grant number 21GLC002; National Natural Science Foundation of China: grant number 32101582; Natural Science Foundation of Jiangsu Province of China: grant number BK20210613; The Natural Science Foundation of the Jiangsu Higher Education Institutions of China: grant number 21KJB220008; The National Natural Science Foundation of China: grant number 32071832. And The APC was funded by Zunling Zhu.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 01892 g001
Figure 1. Study area location.
Figure 1. Study area location.
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Figure 2. Schematic diagram of experimental sites (elevation).
Figure 2. Schematic diagram of experimental sites (elevation).
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Figure 3. Daily variation in pollutant concentrations at the experimental sites.
Figure 3. Daily variation in pollutant concentrations at the experimental sites.
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Figure 4. Correlation analysis of pollutants and traffic volume.
Figure 4. Correlation analysis of pollutants and traffic volume.
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Figure 5. Pollutant concentration variation at experimental sites A1 and B1 on Mufu South Road.
Figure 5. Pollutant concentration variation at experimental sites A1 and B1 on Mufu South Road.
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Figure 6. Pollutant concentration variation at experimental sites A3 and B3 on Beijing East Road.
Figure 6. Pollutant concentration variation at experimental sites A3 and B3 on Beijing East Road.
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Figure 7. Pollutant concentration variation at experimental sites A4 and B4 on Tai Ping North Road.
Figure 7. Pollutant concentration variation at experimental sites A4 and B4 on Tai Ping North Road.
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Figure 8. Pollutant concentration variation at different heights for experimental sites A1 and B1 on Mufu South Road.
Figure 8. Pollutant concentration variation at different heights for experimental sites A1 and B1 on Mufu South Road.
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Table 1. Plant configuration for road experimental sites.
Table 1. Plant configuration for road experimental sites.
Road NameExperimental SitesRoadside Green Belt
Mufu South RoadA1Cynodon dactylon (L.) Persoon.
B1Malus halliana Koehne + Camphora officinarum Nees ex Wall. + Osmanthus fragrans (Thunb.) Lour. + Acer palmatum Thunb. in Murray—Photinia serratifolia (Desf.) Kalkman + Euonymus japonicus ‘Aurea-marginatus’ + Loropetalum chinense var. rubrum + Ligustrum lucidum Ait. f. lucidum—Ophiopogon bodinieri H. Lév.
ElevationForests 14 01892 i001
Elevation of experimental sites A1 and B1 on Mufu South Road
Xianlin AvenueA2Cedrus deodara (Roxb.) G. Don—Pennisetum alopecuroides (L.) Spreng.
B2Cedrus deodara (Roxb.) G. Don + Camphora officinarum Nees ex Wall.— Ophiopogon bodinieri H. Lév.
ElevationForests 14 01892 i002
Elevation of experimental sites A2 and B2 on Xianlin Avenue
Beijing East RoadA3Square.
B3Sophora japonica + Prunus subg. Cerasus sp.—Zoysia japonica Steud.
ElevationForests 14 01892 i003
Elevation of experimental sites A3 and B3 on East Beijing Road
Taiping North RoadA4Metasequoia glyptostroboides Hu & W. C. Cheng + Carya illinoinensis (Wangenh.) K. Koch + Prunus subg. Cerasus sp.—Photinia serratifolia (Desf.) Kalkman + Nandina domestica Thunb. + Mahonia bealei (Fortune) Carr.
B4Osmanthus fragrans (Thunb.) Lour. + Phyllostachys edulis (Carrière) J. Houz. + Magnolia grandiflora L. + Zelkova serrata (Thunb.) Makino + Carya illinoinensis (Wangenh.) K. Koch + Prunus subg. Cerasus sp.—Euonymus japonicus ‘Aurea-marginatus’ + Photinia × fraseri + Loropetalum chinense var. rubrum—Zoysia japonica Steud.
ElevationForests 14 01892 i004
Elevation of experimental sites A4 and B4 on Taiping North Road
Mengdu StreetA5Camphora officinarum Nees ex Wall. + Ginkgo biloba L.
B5Camphora officinarum Nees ex Wall. + Ginkgo biloba L. + Acer palmatum Thunb. in Murray—Rhododendron simsii Planch. + Pittosporum tobira (Thunb.) W. T. Aiton + Loropetalum chinense var. rubrum—Ophiopogon japonicus (L. f.) Ker Gawl. + Ophiopogon bodinieri H. Lév.
ElevationForests 14 01892 i005
Elevation of experimental sites A5 and B5 on Mengdu Street
Shuanglong AvenueA6Sapindus saponaria L. + Prunus cerasifera ‘Atropurpurea’ + Camphora officinarum Nees ex Wall. + Ginkgo biloba L.—Zoysia japonica Steud.
B6Prunus cerasifera ‘Atropurpurea’ + Ginkgo biloba L. + Cornus wilsoniana Wangerin + Albizia julibrissin Durazz. + Camphora officinarum Nees ex Wall.—Photinia serratifolia (Desf.) Kalkman—Zoysia japonica Steud.
ElevationForests 14 01892 i006
Elevation of experimental sites A6 and B6 on Shuanglong Avenue
Jiyin AvenueA7Koelreuteria paniculata Laxm. + Photinia serratifolia (Desf.) Kalkman + Camphora officinarum Nees ex Wall. + Platanaceae T. Lestib.
B7Koelreuteria paniculata Laxm. + Osmanthus fragrans (Thunb.) Lour. + Lagerstroemia indica L. + Prunus subg. Cerasus sp. + Nerium oleander L. + Prunus persica (L.) Batsch—Euonymus japonicus ‘Aurea-marginatus’ + Ligustrum × vicaryi Rehder—Ophiopogon japonicus (L. f.) Ker Gawl.
ElevationForests 14 01892 i007
Elevation of experimental sites A7 and B7 on Jiyin Avenue
Table 2. Style, planting density, and plant community structure type for road experimental sites.
Table 2. Style, planting density, and plant community structure type for road experimental sites.
Road NameRoad StyleExperimental SitePlanting DensityRoadside Green BeltGreen Belt for Street TreesGreen Belt for Lane Separator
Mufu South Roadone-lane, two-greenbeltA1sparse structuregrassarbor: Camphora officinarum Nees ex Wall.None
B1ventilated structurearbor–shrub–grass
Xianlin Avenuefour-lane, five-greenbeltA2compact structurearbor–grassarbor–shrub: Cedrus deodara (Roxb.) G. Don—Euonymus japonicus ‘Aurea-marginatus’arbor–grass: Camphora officinarum Nees ex Wall.—Zoysia japonica Steud.
B2ventilated structurearbor–grassarbor–grass: Cedrus deodara (Roxb.) G. Don—Coreopsis basalis (A. Dietr.) S. F. Blake
Beijing East Roadthree-lane, four-greenbeltA3sparse structuresquarearbor–shrub: Metasequoia glyptostroboides Hu & W. C. Cheng + Cedrus deodara (Roxb.) G. Don—Pittosporum tobira (Thunb.) W. T. Aiton + Euonymus japonicus ‘Aurea-marginatus’+ Loropetalum chinense var. rubrumNone
B3ventilated structurearbor–grass
Taiping North Roadthree-lane, four-greenbeltA4compact structurearbor–shrubarbor–shrub: Metasequoia glyptostroboides Hu & W. C. Cheng—Pittosporum tobira (Thunb.) W. T. AitonNone
B4ventilated structurearbor–shrub–grass
Mengdu Streetfour-lane, five-greenbeltA5ventilated structurearborarbor–shrub: Platanaceae T. Lestib.—Pittosporum tobira (Thunb.) W. T. Aiton + Loropetalum chinense var. rubrumshrub: Pittosporum tobira (Thunb.) W. T. Aiton + Photinia serratifolia (Desf.) Kalkman
B5ventilated structurearbor–shrub–grassarbor–shrub: Osmanthus fragrans (Thunb.) Lour.—Loropetalum chinense var. rubrum
Shuanglong Avenuefour-lane, five-greenbeltA6ventilated structurearbor–grassarbor–grass: Platanaceae T. Lestib.—Zoysia japonica Steud.arbor–grass: Camphora officinarum Nees ex Wall.—Cynodon dactylon (L.) Persoon
B6compact structurearbor–shrub–grassgrass: Ophiopogon bodinieri H. Lév.
Jiyin Avenuetwo-lane, three-greenbeltA7compact structurearborNonearbor–shrub: Prunus subg. Cerasus sp.+ Populus L.—Pittosporum tobira (Thunb.) W. T. Aiton
B7compact structurearbor–shrub
Table 3. Monitor product parameters.
Table 3. Monitor product parameters.
Testing ContentMeasuring RangeResolution RatioPrecision
PM2.50–1000 μg/m31 μg/m3±10 μg/m3
PM100–1000 μg/m31 μg/m3±10 μg/m3
NO20–100 ppm0.01 ppm≤Reading ± 3%
O30–100 ppm0.01 ppm≤Reading ± 3%
Temperature−40–80 °C0.1 °C±2 °C
Humidity0–100%RH0.1%RH±2%RH
Wind speed0–60 m/s0.1 m/s±0.5 m/s
Atmospheric pressure300–1200 hpa1 pa±1.5 hpa
Atmospheric radiation0–2000 μw/cm21±1 μw/cm2
Illumination0–300 KLux0.1 KLux±0.1 KLux
Noise30–120 dB0.1 dB<2%
Table 4. Road partial correlation analysis.
Table 4. Road partial correlation analysis.
Road NamePollutantsTemperature/°CHumidity/%Wind Speed/m/sAtmospheric Pressure/paAtmospheric Radiation/μw/cm2Light/LuxNoise/DB
Beijing East Road (winter)PM2.50.365 *0.3000.214−0.1060.028−0.0270.034
PM10−0.405 *−0.319 *−0.1450.203−0.0830.095−0.044
NO20.347 *0.0490.131−0.289−0.1510.124−0.147
O3−0.194−0.353 *−0.080−0.334 *−0.1940.188−0.217
Mufu South Road (winter)PM2.50.316 *−0.0120.0110.365 **−0.2610.355 *−0.130
PM10−0.432 **0.0010.147−0.3220.253−0.312 *0.173
NO20.1430.140−0.084−0.305 *0.146−0.212−0.273
O3−0.407 **−0.309 *0.203−0.490 **0.064−0.1980.252
Taiping North Road (winter)PM2.50.0310.479 **0.357 *−0.424 **0.146−0.1730.072
PM10−0.172−0.603 **−0.2380.256−0.1620.187−0.024
NO20.331 *0.191−0.1420.204−0.2560.286 *−0.095
O3−0.1200.2710.420 **−0.632 **−0.0850.0410.232
Jiyin Avenue (winter)PM2.50.348 **0.437 **0.1560.2200.311 *−0.310 *0.412 **
PM10−0.255 *−0.422 **−0.053−0.111−0.347 **0.339 **−0.413 **
NO20.1700.0530.1530.246−0.0560.0430.241
O3−0.497 **−0.080−0.245−0.519 **−0.0250.017−0.040
Shuanglong Avenue (winter)PM2.5−0.0430.004−0.161−0.1850.118−0.1410.004
PM100.013−0.0360.2280.253 *−0.1140.128−0.006
NO2−0.081−0.286 *0.0240.049−0.1680.1210.187
O3−0.476 **−0.626 **−0.058−0.559 **0.288 *−0.380 **0.173
Mengdu Street (winter)PM2.5−0.334 **−0.1280.1270.0170.246−0.257 *0.297 *
PM100.268 *0.2440.023−0.219−0.341 **0.357 **−0.129
NO20.074−0.2320.111−0.088−0.0120.0150.064
O3−0.531 **0.044−0.096−0.1490.270 *−0.292 *0.101
Xianlin Street (winter)PM2.50.1080.082−0.004−0.2170.122−0.177−0.135
PM10−0.145−0.0920.1150.204−0.0630.1320.124
NO20.2140.0920.006−0.2320.0170.0140.215
O30.121−0.0390.501 **−0.536 **−0.082−0.010−0.186
Note: * p < 0.05, ** p < 0.01.
Table 5. Comparison of plant configurations for selected roads.
Table 5. Comparison of plant configurations for selected roads.
Road Experiment SitePlant Configurations and Species
Mufu South Road experimental site A1 (grass)Roadside green belt: grass (Cynodon dactylon); green belt for street trees: arbor (Camphora officinarum)
Mufu South Road experimental site B1 (arbor–shrub–grass)Roadside green belt: arbor–shrub–grass (Malus halliana + Camphora officinarum + Osmanthus fragrans + Acer palmatum—Photinia serratifolia + Euonymus japonicus + Loropetalum chinense var. rubrum + Ligustrum lucidum—Ophiopogon bodinieri); green belt for street trees: arbor (Camphora officinarum)
Beijing East Road experimental site A3 (square)Roadside green belt: square; green belt for street trees: arbor–shrub (Metasequoia glyptostroboides + Cedrus deodara—Pittosporum tobira + Euonymus japonicus + Loropetalum chinense var. rubrum)
Beijing East Road experimental site B3 (arbor–grass)Roadside green belt: arbor–grass (Sophora japonica + Prunus subg. Cerasus sp.—Zoysia japonica); green belt for street trees: arbor–shrub (Metasequoia glyptostroboides + Cedrus deodara—Pittosporum tobira + Euonymus japonicus + Loropetalum chinense var. rubrum)
Beijing East Road experimental site A4 (arbor–shrub)Roadside green belt: arbor–shrub (Metasequoia glyptostroboides + Carya illinoinensis + Prunus subg. Cerasus sp.—Photinia serratifolia + Nandina domestica + Mahonia bealei; green belt for street trees: arbor–shrub (Metasequoia glyptostroboides—Pittosporum tobira)
Beijing East Road experimental site B4 (arbor–shrub–grass)Roadside green belt: arbor–shrub–grass (Osmanthus fragrans + Phyllostachys edulis + Magnolia grandiflora + Zelkova serrata + Metasequoia glyptostroboides + Carya illinoinensis + Prunus subg. Cerasus sp.—Euonymus japonicus + Photinia × fraseri + Pittosporum tobira + Loropetalum chinense var. rubrum—Zoysia japonica); green belt for street trees: arbor–shrub (Metasequoia glyptostroboides—Pittosporum tobira)
Table 6. Comparison of road air pollutant reduction rates.
Table 6. Comparison of road air pollutant reduction rates.
Experimental SitePM2.5 (μg/m3)PM10 (μg/m3)NO2 (ppm)O3 (ppm)
Mufu South Road experimental site A1 (grass)−11.32%−4.41%−18.52%16.67%
Mufu South Road experimental site B1 (arbor–shrub–grass)−7.69%−7.91%27.27%7.69%
Beijing East Road experimental site A3 (square)15.19%9.41%29.41%−18.75%
Beijing East Road experimental site B3 (arbor–grass)−19.05%−9.93%30.00%5.56%
Beijing East Road experimental site A4 (arbor–shrub)15.38%7.33%−39.47%−9.68%
Beijing East Road experimental site B4 (arbor–shrub–grass)22.39%14.29%69.57%−11.76%
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Sheng, Q.; Ji, Y.; Huang, Z.; Liu, C.; Dai, A.; Zhu, Z. Influence of Urban-Road Green Space Plant Configurations on NO2 Concentrations in Nanjing City during Winter. Forests 2023, 14, 1892. https://doi.org/10.3390/f14091892

AMA Style

Sheng Q, Ji Y, Huang Z, Liu C, Dai A, Zhu Z. Influence of Urban-Road Green Space Plant Configurations on NO2 Concentrations in Nanjing City during Winter. Forests. 2023; 14(9):1892. https://doi.org/10.3390/f14091892

Chicago/Turabian Style

Sheng, Qianqian, Yaou Ji, Zhengwei Huang, Congzhe Liu, Anqi Dai, and Zunling Zhu. 2023. "Influence of Urban-Road Green Space Plant Configurations on NO2 Concentrations in Nanjing City during Winter" Forests 14, no. 9: 1892. https://doi.org/10.3390/f14091892

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