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Article

Particulate Matter and Trace Metal Retention Capacities of Six Tree Species: Implications for Improving Urban Air Quality

by
Weikang Zhang
1,2,
Yu Li
1,
Qiaochu Wang
1,
Tong Zhang
1,
Huan Meng
1,2,
Jialian Gong
1,2 and
Zhi Zhang
1,2,*
1
Landscape Planning Laboratory, Department of Landscape Architecture, Shenyang Agricultural University, Shenyang 110866, China
2
Key Laboratory of Forest Tree Genetics, Breeding, and Cultivation of Liaoning Province, Shenyang 110866, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(20), 13374; https://doi.org/10.3390/su142013374
Submission received: 27 July 2022 / Revised: 7 October 2022 / Accepted: 10 October 2022 / Published: 17 October 2022
(This article belongs to the Special Issue Towards the Sustainable Development Goals: Monitoring, Assessment and Management of Eco-Environmental Space)
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Abstract

:
As effective filters for natural particulate matter (PM), plants play an important role in the reduction of PM, thus improving air quality. However, research on the relationship between leaf functional traits and PM retention capacity in different polluted environments remains limited. In this study, six tree species (Abies holophylla, Pinus tabuliformis, Juniperus chinensis, Populus berolinensis, Salix babylonica, Robinia pseudoacacia) in Shenyang city, China were selected as research objects to analyze their PM retention capacity in three different polluted environments (i.e., a busy road, an industrial area of the urban center, and a green space). Additionally, we determined the composition of trace elements associated with the different polluted environments; we also evaluated the impact of different polluted environments on leaf surface traits. The results showed that the actual amounts of PM and trace elements that accumulated on leaf surfaces differed considerably between pollution sites and plant species. The greatest accumulation of PM10 and PM2.5 deposited on the leaves of tested plants was at a traffic-related pollution site and the smallest accumulation was at a park site. There were significant differences in the PM10 and PM2.5 retention capacities of leaves among the different tree species (p < 0.05), in the following order: Abies holophylla > Pinus tabuliformis > Juniperus chinensis > Populus berolinensis > Salix babylonica > Robinia pseudoacacia. The average PM10 and PM2.5 accumulation amounts of Abies holophylla were 1.28–8.74 times higher than these of the other plants (p < 0.05). Trace element analysis showed that the elemental composition of PM accumulated on leaf surfaces was location-dependent. In conclusion, a highly polluted environment can increase the average groove width, stomatal density, and roughness compared to a low-polluted environment. In contrast, the average value of contact angle is higher at low-pollution sites than at other sites. These results suggest that Abies holophylla is the most suitable greening tree species and that its widespread use could significantly reduce PM pollution in urban environments.

1. Introduction

In the past decade, the urban environment has deteriorated greatly due to rapid urbanization. Air pollution has become the most urgent environmental problem worldwide, especially in China. Particulate matter (PM), which primarily originates from vehicle exhaust, fossil fuel combustion, road dust, dust storms, and volcanic eruptions [1,2,3], is one of the main sources of air pollution [4,5]. Owing to its small size and micromass, PM can float in the atmosphere for a long time and disperse with airflow over long distances [6]. Additionally, the large specific surface area of PM allows it to adsorb heavy metals (i.e., Cd, Pb, and Cr) and toxic organic compounds easily, thereby severely affecting human health [7,8]. Numerous epidemiological studies have shown that PM, especially the aerodynamic equivalent diameter ≤2.5 μm, can enter the human respiratory system and blood circulation system, and long-term exposure to high concentrations of PM can cause serious cardiovascular and respiratory diseases [9,10,11,12]. Although regional average PM concentrations have been decreasing worldwide from 2000 to 2019, PM pollution remains a serious environmental problem in rapid urbanization countries [13,14,15,16]. For example, the main air pollutant in 163 cities in China was PM2.5 in 2020, and the national annual mean of urban PM2.5 concentrations was 39 μg·m−3, which is higher than the Chinese pollution standard of 35 μg·m−3 [17]. Therefore, reducing PM concentrations and improving air quality are critical for urban air pollution control.
Plants are considered effective natural and biological filters that can reduce atmospheric PM pollution [18,19,20]. The amount of PM2.5 and the economic value estimated for all urban forests in 86 Canadian cities was 1312 t and USD 323.93 × 106 in 2010, respectively [21]. Plants perform the function of trapping PM, depending mainly on the surface area of their leaves to capture PM [22,23,24]. The capacity for particle accumulation by plants shows considerable species-specific differences that depend on the properties of the leaf surface, such as grooves, trichomes, stomatal density, roughness, and epicuticle wax amount. Tree species with ribbed, folded, rough, and hairy leaf surfaces tend to be more effective than species with unfolded and smooth surfaces [25,26]. Some scholars have pointed out that there are differences in the PM retention capacity of different tree species [27,28]. Generally, coniferous trees are more efficient than broadleaved trees in accumulating PM. Meanwhile, this capacity is also influenced by PM exposure level, deposition velocity, and environmental conditions, as well as meteorological conditions, such as precipitation and wind [29,30,31]. For example, Lu et al. [32] found that the PM2.5 adsorption capacity of leaf surfaces was positively correlated with the pollution level; the higher the contention of atmospheric PM2.5, the higher the accumulation amount of leaf surfaces. Przybysz et al. [33] reported that the greatest accumulation of PM deposited on the leaves of tested plants was at traffic-related pollution and the smallest accumulation was at a rural site. Additionally, the PM deposited on leaf surfaces can either be resuspended into the atmosphere by wind, resulting in the redispersion of particle pollution [34,35], or washed off by precipitation, leading to a reduction in particle pollution [28,36].
As plants purify the air and mitigate atmospheric PM pollution, the amount and component of PM deposited on leaf surfaces can also change the value of leaf surface functional traits. Lu et al. [37] investigated the relationship between the PM capacities of different plants and their photosynthetic gas exchange capacities at different pollution levels and found a negative correlation. The elemental components of PM deposited on leaf surfaces were also affected by the different pollution levels. It has been shown that elements of anthropogenic origin (i.e., Cd, Pb, Cu, As, Cr, and Ni) are related to the transport, fuel combustion, and industrial processes [38,39,40], while other elements such as Al, Si, Fe, Ca, and Mn come from natural dust resuspension processes [41,42]. However, the influence of different pollution levels on leaf functional traits and PM retention ability remains inconclusive. For example, a previous study revealed a positive correlation between leaf stomatal density and PM retention ability when the atmospheric PM concentration increased [43]. However, others have pointed out that the leaves in light pollution level areas were relatively smooth compared to those in heavy pollution areas [33]. In summary, the PM retention capacities of the same tree species in different environments are different, and the capacities can change with variations in leaf characteristics.
To close these gaps and more comprehensively understand the response of leaf function traits to different PM pollution levels, we aimed to (1) investigate the retention ability of PM deposition on leaves in different tree species and urban environments, (2) analyze how different pollution levels can affect the concentrations of PM on leaf surfaces and leaf traits, and (3) explore the response of plant functional traits to PM deposition on leaves. Our study provides an important theoretical basis for the selection of greening and dust-proof tree species.

2. Materials and Methods

2.1. Study Sites

Sampling sites were located at the Shenfu Highway (SH), Xinbei Power Plant (PP), and Dongling Park (DP) in Shenyang City, Liaoning Province, China (Figure 1). The three sampling sites represented, respectively, (ⅰ) a busy road, (ⅱ) an industrial area of the urban center, and (ⅲ) a green space, where plants were grown in a clean environment.

2.2. Leaf Collection

Three deciduous and three evergreen species, namely Pinus tabuliformis, Abies holophylla, Juniperus chinensis, Salix babylonica, Robinia pseudoacacia, and Populus berolinensis, were selected as target species. These chosen species are a common choice of plant for greening urban roadsides. The basic growth status of the tested tree species, including life form, average height (AH), diameter at breast height (DBH), crown, and branch height (BH), are presented in Table 1. The experiment was conducted from July to September 2021. There was no rain during the 10 days prior to sample collection. We collected twigs of each species from branches located in the four-quadrant directions at a tree height of 1.5 m to 2 m, weighing approximately 500 g. Finally, the collected leaves were immediately placed into pre-numbered non-static polyethylene bags and transported to the laboratory for dust-binding measurement.

2.3. Leaf PM Retention

First, the collected leaves (400–1000 cm2) were placed in 500 mL distilled water and stirred for 10 min to obtain a suspension; the filter membrane without PM was weighed (W1) with one balance (i-quip-e6315, accuracy max. 160 g, d = 0.0001 g). Second, the suspension solution was filtered through a standard sieve with mesh diameters of 10 μm and 2.5 μm, respectively. The filter membrane was then dried at 80 °C for 6 h to evaporate water, and the mass of the filter membrane was weighed again (W2). The experimental flowchart is shown in Figure 2.
In this study, based on the leaf forms, the leaves were divided into broad-leaved and coniferous leaves. The average surface area of needles was calculated assuming a truncated cone and using the following equation [9]:
A = n·1/2π · (D1 + D2) [1/4(D2 − D1)2 + L2]1/2
where D1 is the average diameter of the pine needle tip, D2 is the average diameter of the pine needle bottom, L is the average length of the needle leaf, and n is the number of needles. The broad leaves were scanned using a scanner (Canon LIDE 300, Japan), and the area was calculated using the ImageJ software (1.50i; National Institutes of Health, Bethesda, USA).
W = (W2 − W1)/A
where W is the mass of the particulate matter (μg·cm−2), W1 is the mass of the filter membrane without particles (μg), W2 is the mass of the filter membrane with particles (μg), and A is the leaf area (cm2).

2.4. Leaf Characters

2.4.1. Roughness

The roughness of the leaves was determined using atomic force microscopy (AFM, Dimension Icon, Bruker). The surface of the leaves was cleaned with distilled water, and the flatter part was selected to obtain a 5 × 5 mm2 sample. The sample was scanned using a probe at room temperature (23 °C) and photographed. The scanning rate was 0.5 Hz, the lateral resolution was 0.2 nm, the vertical resolution was 0.01 nm, and the maximum scanning area was 20 × 20 μm2. Each sheet was scanned thrice. The source files were analyzed using the NanoScope analysis software, and images of the leaf surface and leaf roughness were obtained (Figure S1).

2.4.2. Contact Angle

The contact angle refers to the slope between the perimeter of a droplet and the leaf surface, indicating leaf wettability [44]. Three leaves of each tree species were sampled to determine the leaf contact angle. First, a 0.5 cm × 0.5 cm sample from the leaf was collected, and the samples were attached to a glass plate using double-sided tape. Then, a 7.5 µL droplet of distilled water was placed on the surface. Photographs were taken using a static drop contact angle meter (jc2000c1, Shanghai Zhongshan, China). Finally, the contact angles at the left and right edges of the droplet were measured using the ImageJ software (Figure S2).

2.4.3. Stomatal Density and Groove Width

The stomatal density and groove width of the leaf surfaces were determined using SEM [45]. The stomatal density was quantified by counting the number of stomata per unit area of the needle leaf surface in the SEM (scanning electron microscopy) images. The groove width was evaluated by measuring the mean width of the wrinkles from the SEM images using the ImageJ software (Figure S5).

2.5. Trace Elemental Composition

The concentrations Fe, Ca, Cr, Ni, Hg, Cd, As, Cu, Na, and Pb in the PM were identified (Table S1). PM accumulated on the leaf surfaces was collected in the same way as previously described in Section 2.3 (Figure 2). PM samples (100–200 mg) in 5 mL of 50% v/v HNO3 were digested in Milestone UltraClave and diluted to 25 mL. The samples were analyzed using high-resolution ICP-MS (Agilent 7500a, CA, USA). Measurements were performed in triplicate. The details of sampling and analysis of metal elements of PM were presented in a previous study [42].

2.6. Statistical Analysis

In this study, Microsoft Excel 2019 was used to sort the experimental data, origin 2022 was used for mapping, and IBM SPSS statistics 26 was used for a one-way ANOVA (p < 0.05) of the particle adsorption capacity of different tree species under different pollution levels, following a confirmation of normality using the Shapiro–Wilk test. Correlation and regression analyses were used to analyze the relationship between leaf surface morphological characteristics and dust retention capacity of different tree species.

3. Results

3.1. PM Accumulation on Leaf Surfaces of Tested Species at Different Pollution Sites

The PM retention capacity of leaves varied significantly between plant species and pollution sites (p < 0.05), and the results are shown in Figure 3. For different pollution sites, the higher the concentration of atmospheric PM2.5 and PM10, the higher the PM2.5 and PM10 accumulation on leaf surfaces. The order of average retention amount in the leaves from high to low was SF (220.87 ± 120.86 μg·cm−2 for PM2.5, 229.61 ± 125.76 μg·cm−2 for PM10) > PP (176.29 ± 93.79 μg·cm−2 for PM2.5, 184.93 ± 98.27 μg·cm−2 for PM10) > DL (122.12 ± 959.19 μg·cm−2 for PM2.5, 130.43 ± 63.79 μg·cm−2 for PM10). These results showed that the level of pollution had a significant impact on the PM retention capacity of leaves. For the different tree species, the accumulation amounts of PM2.5 and PM10 were significantly different (p < 0.05). The PM10 accumulation amount of the six tree species ranged from 68.92 μg·cm−2 for R. pseudoacacia at a low-pollution site to 471.28 μg·cm−2 for A. holophylla at a high-pollution site, and the following sequence was: A. holophylla > P. tabuliformis > J. chinensis > P. berolinensis > S. babylonica > R. pseudoacacia. The average PM10 accumulation was the highest in A. holophylla; it was 1.30–8.45 times higher than that in the other plants (p < 0.05). The PM2.5 accumulation amounts in the six tree species ranged from 48.95 μg·cm−2 for R. pseudoacacia at a low-pollution site to 428.06 μg·cm−2 for A. holophylla at a high pollution-site. The average PM2.5 accumulation of A. holophylla was highest and 1.28–8.74 times higher than that of the other plants (p < 0.05). The variation trend for the PM2.5-accumulating capacity of leaf surfaces for different tree species was similar to that for PM10 at different pollution sites.

3.2. Changes in Leaf Traits between Different Pollution Sites

The roughness, contact angle, stomatal density, and groove width of the leaf surfaces varied significantly among the different pollution sites (Figure 4). As shown in Figure 4a–c, high pollution levels (SH and PP sites) resulted in increased average groove width, stomatal density, and roughness compared with low pollution levels (DP site). In contrast, the average value of the contact angle was higher at the low-pollution site than at other sites. S. babylonica displayed the highest, whereas P. berolinensis displayed the lowest groove width values at the DP site (Figure 4a). Although P. berolinensis showed the lowest groove width values, it had the highest average stomatal density value at the SH site (Figure 4c). As shown in Figure 4b, R. pseudoacacia had the maximum average contact angle value (128°), followed by P. berolinensis (111°), J. chinensis (108°), and S. babylonica (107°), which were defined as not readily wet, suggesting that the leaf surfaces of the four species were hydrophobic. A. holophylla and P. tabuliformis had wettable leaf surfaces with average contact angles of 60° and 63°, respectively. The average roughness value of P. tabuliformis was the highest at 605 nm at the PP site, which was 6.59 times that of R. pseudoacacia at the DP sites (Figure 4d). Through statistical analyses via a one-way ANOVA, we revealed that different tree species had significant differences for groove widths, stomatal densities, roughness, and contact angles (p < 0.05).

3.3. Quantitative Assessment of Elemental Concentrations

The amount of trace elements composing the PM on the leaf surfaces, including Hg, Cd, As, Ni, Cu, Cr, Pb, Na, Ca, and Fe, were determined at different sites (Figure 5). The six species differed in terms of the concentrations of elements on their leaf surfaces, and the highest concentrations were recorded in R. pseudoacacia. Hg and Pb were found in the highest concentrations in P. tabuliformis. The highest concentrations of Cd and Ni were found in S. babylonica, while P. berolinensis had the highest concentration of Cu. Thus, two main trends are identified. Firstly, the results showed that the element concentrations on leaf surfaces in broadleaf trees (R. pseudoacacia, S. babylonica, and P. berolinensis) were higher than those in coniferous trees (P. tabuliformis, A. holophylla, and J. chinensis). Secondly, the concentrations of most elements (Hg, Cd, As, Cu, Cr, Pb, Na, Ca, and Fe) increased from the DP site (low pollution level) to the PP site (high pollution level).

4. Discussion

4.1. Effects of Pollution Levels and Tree Species on the PM Retention Capacity

The pollution level and the ability of the leaf surface to capture PM2.5 and PM10 were positively correlated. In this study, the PM ranged from 51.48 μg·cm−2 (in R. pseudoacacia at Dongling Park) to 435.20 μg·cm−2 (in A. holophylla at Shenfu Highway). The average deposition was 181.63 μg·cm−2. The amount of PM accumulation on the leaf surface was considerably greater than reported in many other studies [34]. The differences between these studies may be strongly dependent on sites and species [2,46]. For all tested tree species, the greatest amount of PM was found on the leaf surface of plants grown at the most polluted site (SH site), in agreement with Przybysz et al. [33]. A. holophylla was proved the most efficient at accumulating PM, whereas P. berolinensis was the least efficient. The results showed that the average amount of PM accumulated on the pine surface was more efficient than that on the broadleaf surface. This phenomenon could be attributed to the difference between the leaf microstructures at different sites. The efficiency of PM accumulation on leaf surfaces can also be affected by species-specific leaf surface microstructures, such as surface roughness, stomatal size, density, and trichomes. Leaf structural characteristics such as surface roughness and the dimensions of the waxy layer are thought to also influence PM accumulation [47]. Most studies have shown that tree species with rough leaves, complex shapes, and high stomatal densities are more conducive to PM retention [43,48]. Leaves with ribbed, folded, or furrowed leaf surfaces can increase the surface area for PM collision and retain more particles than unfolded and smooth surfaces. Linear regression analysis (Figure 6) showed that the roughness and groove width had significant positive correlations with PM accumulation on the leaf surface (p < 0.05), and the contact angle was significantly negatively correlated (p < 0.05), which is consistent with the results of a previous study [2]. Our study showed that the roughness and groove width of conifers were higher than those of broadleaves, whereas the contact angles of conifers were lower than those of broadleaves (Figure 4). Additionally, many researchers have reported that the stomatal density is positively correlated with the PM retention ability of leaves [49,50]. However, we found that stomatal density was not significantly positively correlated with the PM retention ability of leaves (p = 0.052). It was possible to calculate the values of stomatal density, including these of the front-back side leaf. Most captured PM on the leaves of R. pseudoacacia, P. berolinensis, and S. babylonica was found on the front-side leaves compared to that on the back-side leaves. This probably resulted in an uncertain relationship between the stomatal density and the amount of PM captured on leaf surfaces.
In addition, high PM pollution based on a large amount of PM interception was observed in the same tree species at different sites, as shown in Figure 3. This is probably because pollution sources increase the concentrations of air pollutants leading to increasing PM deposition velocity [51]. In the terms of factors extrinsic to leaf structure, PM deposition is influenced by atmospheric stability as well as friction velocity [52]. There are more human and industrial activities in the PP and SH sites resulting in unstable aerodynamic conditions and larger friction velocities, which can affect the ability of plants to alleviate atmospheric PM pollution. Therefore, we suggest that the deposition velocity must be considered to comprehensively understand the retention ability of the leaf.

4.2. Effects of Different Sites on Trace Element Composition

In this study, significant differences were found in the elemental concentrations of PM deposited on leaf surfaces at different pollution sites. The concentrations of elements were mainly determined by the environmental conditions in which the plants were located. We found that more elements were present on the surfaces of leaves collected from industrial and road traffic areas than on those of leaves collected from park areas. By comparing the elemental sources of leaf-deposited PM at different sites (Figure 7), it was shown that the variations in the amounts of Cd, Cu, and Pb retained on the leaf surfaces were significantly related to human activities, especially fuel and coal combustion, and road traffic. Previous research has indicated that the elemental composition of PM deposited on leaves near the industry and road is affected by exhaust gases, vehicular fuel combustion, and the abrasion of vehicle brakes and tires [39,40], which are generally considered to be important sources of PM pollution in urban areas. PM usually contains abundant trace metals (i.e., Pb, Zn, and Cu) that are associated with traffic-related sources, which contribute to the leaf elemental composition [53,54]. The obtained results have applications in the environmental monitoring of Cd, Cu, and Pb using street trees as bioaccumulators. The tested tree species showed greater PM capturing capacities at high-pollution levels than at low-pollution levels.

4.3. Effects of Different Environments on Leaf Characters

Some studies have shown that microstructural modifications occur in the leaves of different tree species under the effect of PM pollution [27,37], and a similar conclusion has been confirmed in our research (Figure S3 and S4). We chose an open urban environment as the experimental environment, which is more conducive to accurately explaining the response of plants to PM pollution. Additionally, the reactions of different species to altered environmental conditions are strongly correlated with their structural and functional features. In our study, we found that the changes in leaf microstructure of the tested tree species were relatively obvious, especially in coniferous species (i.e., A. holophylla, P. tabuliformis, and S. babylonica). In different gradients of PM pollution, the roughness, groove width, and stomatal density gradually increased with an increase in PM deposition on the leaf surfaces, and reached significant differences in different environments, while the contact angle gradually decreased. The PM deposited on leaf surfaces has a negative effect on plants. The deposition of PM on leaves can clog stomata, and alter stomata size, cuticular waxes, trichomes, and cuticles [55]. The aggravation of environmental pollution changes the characteristics of leaves, which is mainly reflected in the damage to the wax layer, blockage of stomata, and thinning of trichomes, thus affecting their retention capacity for PM.

5. Conclusions

In this study, the amount of PM accumulated on the needle leaves was higher than that on broadleaves. A. holophylla showed the highest capacity to retain PM10 and PM2.5 and can therefore be considered the most useful within the context of improving air quality in Shenyang city, China. The ability of the leaves of the same tree species to retain particulate matter was significantly affected by the degree of pollution. PM2.5 and PM10 adsorption capacity of the leaf surface was positively correlated with the pollution level; the higher the concentration of atmospheric PM, the higher the accumulation amount of leaf surfaces. Furthermore, local pollution sources affected the elemental composition of PM accumulated on leaf surfaces. Our results demonstrated that leaf characteristics could respond to environmental factors, such as atmospheric PM concentrations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142013374/s1, Figure S1: The atomic force microscopy (AFM) images of leaf surfaces, Figure S2: Measurement image of leaf surface contact angle. Note: (a) P. tabuliformis; (b) A. holophylla; (c) J. chinensis; (d) S. babylonica; (e) R. pseudoacacia; (f) P. berolinensis, Figure S3: Scanning electron microscope images of different tree species. Note: (a) P. tabuliformis, stomata; (b) A. holophylla, stomata; (c) J. chinensis, stomata; (d) S. babylonica, stomata; (e) R. pseudoacacia, stomata; (f) P. berolinensis, stomata; (g) A. holophylla, groove; (h) P. tabuliformis, groove; (i) J. chinensis, groove; (j) S. babylonica, trichome; (k) R. pseudoacacia, trichome; (l) P. berolinensis, trichome, Figure S4: Scanning electron microscope images of leaves at different locations. Note: a: Dongling Park; b: Xinbei Power Plant; c: Shenfu Highway; 1. P. tabuliformis; 2. A. holophylla; 3. J. chinensis; 4. S. babylonica; 5. R. pseudoacacia; 6. P. berolinensis, Figure S5: SEM micrographs showing different leaf traits: stomatal density and groove width; Table S1: The description of all abbreviations in this study.

Author Contributions

W.Z.: Funding acquisition, Supervision, Project administration, Writing–review and editing, Validation. Y.L.: Writing—Review and Editing. Q.W.: Investigation, Resources, Software, Data curation. T.Z.: Software, Data curation. Z.Z.: Conceptualization, Methodology, Formal analysis, Data curation, Writing—original draft, Writing—Review and Editing. H.M.: Data curation, Software. J.G.: Investigation, Software, Data curation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Foundation of China (31901361).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of Shenyang City, Liaoning Province, China, showing the location of the study sites.
Figure 1. Map of Shenyang City, Liaoning Province, China, showing the location of the study sites.
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Figure 2. Flowchart for the collection of particles deposited on the leaf surfaces.
Figure 2. Flowchart for the collection of particles deposited on the leaf surfaces.
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Figure 3. Comparison of PM retention on leaf surfaces at different pollution sites. Data are presented as mean ± SE, n = 9. Different lowercase letters indicate a significant difference (p < 0.05).
Figure 3. Comparison of PM retention on leaf surfaces at different pollution sites. Data are presented as mean ± SE, n = 9. Different lowercase letters indicate a significant difference (p < 0.05).
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Figure 4. Leaf trait changes in the tested tree species at different pollution sites. (a) groove width; (b) contact angle; (c) stomatal density; (d) leaf roughness. Data are presented as mean ± SE, n = 9 and were analyzed using a one-way ANOVA. * p ≤ 0.05, ns = no significance. DP: Dongling Park, SH: Shenfu Highway, PP: Xinbei Power Plant.
Figure 4. Leaf trait changes in the tested tree species at different pollution sites. (a) groove width; (b) contact angle; (c) stomatal density; (d) leaf roughness. Data are presented as mean ± SE, n = 9 and were analyzed using a one-way ANOVA. * p ≤ 0.05, ns = no significance. DP: Dongling Park, SH: Shenfu Highway, PP: Xinbei Power Plant.
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Figure 5. Concentrations of selected elements in the six tested tree species at three sites differ in pollution levels.
Figure 5. Concentrations of selected elements in the six tested tree species at three sites differ in pollution levels.
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Figure 6. Linear regression analysis of PM accumulation amount and contact angle, stomatal density, roughness, and groove width of all the tested tree species.
Figure 6. Linear regression analysis of PM accumulation amount and contact angle, stomatal density, roughness, and groove width of all the tested tree species.
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Figure 7. Concentrations of selected elements in different pollution sites. Data are presented as mean ± SE, n = 3. Data were analyzed using a one-way ANOVA. * p ≤ 0.05, ** p ≤ 0.01.
Figure 7. Concentrations of selected elements in different pollution sites. Data are presented as mean ± SE, n = 3. Data were analyzed using a one-way ANOVA. * p ≤ 0.05, ** p ≤ 0.01.
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Table
Table 1. Sample collection sites and tree species.
Table 1. Sample collection sites and tree species.
SitesSpeciesLife FormAH
(m)		DBH
(cm)		Crown
(m)		BH
(m)
DPP. tabuliformisEvergreen4.6 ± 0.420.0 ± 1.64.8 ± 0.22.0 ± 0.2
A. holophyllaEvergreen14.2 ± 1.341.8 ± 1.18.6 ± 0.52.1 ± 0.1
J. chinensisEvergreen7.7 ± 0.59.2 ± 0.41.3 ± 0.10.3 ± 0.1
S. babylonicaDeciduous8.3 ± 0.720.2 ± 4.76.7 ± 1.32.5 ± 0.2
R. pseudoacaciaDeciduous7.9 ± 0.213.2 ± 2.24.2 ± 0.32.0 ± 0.4
P. berolinensisDeciduous11.5 ± 1.312.2 ± 2.25.1 ± 0.84.6 ± 2.1
SHP. tabuliformisEvergreen4.3 ± 0.414.3 ± 1.14.9 ± 0.52.1 ± 0.2
A. holophyllaEvergreen7.8 ± 0.912.1 ± 0.93.4 ± 0.40.8 ± 0.1
J. chinensisEvergreen4.6 ± 0.17.0 ± 0.20.8 ± 0.10.2 ± 0.1
S. babylonicaDeciduous10.1±1.925.1 ± 1.56.6 ± 0.52.8 ± 0.3
R. pseudoacaciaDeciduous8.1 ± 0.922.6 ± 5.74.6 ± 0.52.0 ± 0.2
P. berolinensisDeciduous11.8 ± 1.433.5 ± 3.47.0 ± 1.34.0 ± 0.3
PPP. tabuliformisEvergreen4.5 ± 0.616.6 ± 1.34.0 ± 0.51.3 ± 0.2
A. holophyllaEvergreen11.7 ± 3.418.6 ± 1.75.6 ± 1.01.9 ± 0.5
J. chinensisEvergreen7.9 ± 0.310.6 ± 1.30.8 ± 0.10.4 ± 0.1
S. babylonicaDeciduous11.0 ± 1.423.3 ± 1.16.8 ± 0.72.7 ± 0.2
R. pseudoacaciaDeciduous10.9 ± 1.121.8 ± 1.66.3 ± 0.52.6 ± 0.6
P. berolinensisDeciduous12.6 ± 0.611.5 ± 0.64.7 ± 0.63.3 ± 1.3
Note: AH presents average height; DBH presents diameter at breast height (DBH); BH presents branch height. Data are presented as mean ± SE, n = 3.
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Zhang, W.; Li, Y.; Wang, Q.; Zhang, T.; Meng, H.; Gong, J.; Zhang, Z. Particulate Matter and Trace Metal Retention Capacities of Six Tree Species: Implications for Improving Urban Air Quality. Sustainability 2022, 14, 13374. https://doi.org/10.3390/su142013374

AMA Style

Zhang W, Li Y, Wang Q, Zhang T, Meng H, Gong J, Zhang Z. Particulate Matter and Trace Metal Retention Capacities of Six Tree Species: Implications for Improving Urban Air Quality. Sustainability. 2022; 14(20):13374. https://doi.org/10.3390/su142013374

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Zhang, Weikang, Yu Li, Qiaochu Wang, Tong Zhang, Huan Meng, Jialian Gong, and Zhi Zhang. 2022. "Particulate Matter and Trace Metal Retention Capacities of Six Tree Species: Implications for Improving Urban Air Quality" Sustainability 14, no. 20: 13374. https://doi.org/10.3390/su142013374

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