Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou

Research area description

Zhengzhou, located in the center of China, has a continental monsoon climate in northern temperate zone, with four distinctive seasons. The average annual temperature ranges from 12–22 °C, the average wind is 2–2.8 m·s⁻¹, and the average annual rainfall is 640.9 mm. The texture of the soil is loamy clay.

The sampling sites were selected in Zhongzhou Avenue, Henan Agricultural University, and the Botanical Garden in Zhengzhou, China (refer to Fig. 1), and the sampling was approved by Zhengzhou Botanical Garden. The selection of these three sites was based on factors such as air pollution levels, transportation conditions, and crowd flow. Zhongzhou Avenue, an important section of the Zhengzhou Expressway, is 2.1 km long and 7 m wide. Traffic is heavy, with an approximate flow of 45 vehicles per minute (two-way). Henan Agricultural University is situated at No. 63, Nongye Road, Jinshui District, and is surrounded by schools and residential areas in an old urban area. It exhibits moderate pollution level compared to the other two sampling areas. Zhengzhou Botanical Garden is located in the suburbs along the West Fourth Ring Road. This area is designated as a clean control area with no large industries or heavy pollution sources nearby. The three sites above represent different levels of PM pollution, and the sites were classified as high, medium, and low (control) areas on the basis of the airborne total suspended particulate matter (TSP) concentrations measured at each site, which were 274.2, 191.1, and 80.4 μg·m⁻³ respectively.

Plant species and sampling collection

According to the primary survey of greening trees in all three sampling sites, five evergreen tree species, including Photinia serrulata, Euonymus japonicus, Pittosporum tobira, Ligustrum lucidum, and Eriobotrya japonica were selected. These species are widely used in northern China, and are also the representative plant species in Zhengzhou City. The details of the tested plants are described in Table 1.

Based on the climatic conditions and rainfall characteristics of Zhengzhou City, the experiment was conducted in September 2020. To ensure saturation of PM on plant leaves, samples were collected approximately 14 days after rainfall. Three healthy plants of similar age with luxuriant foliage and free from pests and diseases were randomly selected from each plant in each region. For each tree species, the canopy was divided into three layers (lower, middle and upper) and further subdivided into four directions. A total of twelve collections (three layers and four directions per canopy) were obtained from a single tree. Leaf acquisition was facilitated using a flat-topped bifurcated aluminum ladder to access all strata and directions. Each sampling was aimed at an area of 200−300 cm² of leaves, corresponding to 10–15 pieces for larger leaves and 20–30 pieces for smaller leaves, each species in triplicate. To avoid dehydration and structural changes, all samples were divided into two parts. The first part was stored at 4 °C for dust retention experiments and stomatal observation, while the second part was treated with liquid nitrogen and stored in an ultra-low temperature freezer at −80 °C (Forma905, Waltham, MA, USA) for physiological experiments.

Quantifying the PM retention on leaf surfaces

PM on leaves can be categorized into two parts: the wax layer and the leaf surface. However, the wax layer usually contains fewer particles, and the PM in the wax layer is easily souble in water, as mentioned by Dzierzanowski et al. (2011). The results of this study were only for the amount of physical, insoluble PM deposited on the leaf surface. The graded membrane filtration method was used to determine the amount of particulate matter adsorbed on the leaf surface per unit leaf area (Dzierzanowski et al., 2011). Initially, the collected leaves were soaked in distilled water for 2 h and gently cleaned with a soft brush. The leaves were then rinsed twice with approximately 200 mL of distilled water each time. The resulting suspensions from the three washes were combined and filtered using filter membranes with diameters of 10, 2.5, and 0.2 μm, respectively. The filtered membranes were then dried in a constant temperature drying oven set at 60 °C, and their mass was measured using a balance with a precision of 1/10,000. The particles retained on the filters were classified as PM_>10, PM_2.5–10, and PM_2.5 based on their respective filter diameters. The total mass of TSP was calculated as the sum of the masses on all three filters, while the mass of PM₁₀ was determined by adding the masses of PM_2.5–10 and PM_2.5. The leaf area of each plant was measured three times using a YMJ-B portable leaf area instrument (Topu Yunlong, Zhejiang Province, China). The PM retention capacity is calculated as the ratio of the amount of particles retained on the leaf surface to the leaf area.

Observation of stomata on the leaf surface

Mature leaves were carefully harvested and placed on glass microscope slides. Sections 1 cm × 1 cm were cut from each leaf, specifically from areas away from the midvein. To isolate the epidermis, both the upper epidermal layer and the mesophyll tissue were carefully removed during subsequent processing. Stomatal features such as size and number were then quantified using high-resolution imaging (1,200 x magnification) with an ultra-deep optical microscope (Leica DVM6A, Wetzlar, Germany).

Determination of leaf photosynthetic parameters

In September 2020, photosynthetic gas exchange parameters, including net photosynthetic rate (Pn), transpiration rate (E), stomatal conductance (Gs), intercellular CO₂ concentration (Ci), were measured with a portable CIRAS-3 photosynthetic apparatus (USA) from 9:00 to 11:00 am after eight consecutive days without precipitation in sunny, windless or breezy weather.

Determination of leaf physiological indices

Chlorophyll a and b contents were determined by the ethanol extraction method, Weighted 0.1 g cut fresh leaves, add 10 ml of 95% ethanol until all the leaf tissue dissolved in the solution. After dark treatment for 24 h until the tissue turned white, the absorbance of the supernatant were determined at 649 nm (A649) and 665 nm (A665) using spectrophotometer (*UV-6100). Finally, calculated the chlorophyll content according to a relevant formulae (Li et al., 2023).

Malondialdehyde (MDA) content was quantified by the thiobarbituric acid method. Approximately 0.1 g of shredded fresh leaf tissue was ground in 1.5 ml of 5% trichloroacetic acid (TCA) solution, the extracted supernatant was mixed in equal proportions with 0.67% thiobarbituric acid solution (TBA) in a boiling water bath for 15 min, cooled rapidly and the supernatant was taken to measure the absorbance at 450, 532 and 600 nm, and the MDA content was subsequently calculated according to the relevant formulae (Li et al., 2023).

About 0.1 g fresh crushed leaves were placed in 5 ml centrifuge tubes with 2 ml of distilled water. The supernatant was extracted after grinding and centrifugation. Soluble sugar content was taken by the anthrone colorimetric method, and soluble protein content was determined via Coomassie brilliant blue-G250 staining. Enzyme activity was measured by adding 2 ml of phosphate buffer for extraction, the corresponding reaction mixture was then added to determine the superoxide dismutase (SOD) and peroxidase (POD) activity. It is important to note that the reaction mixture should be added in a certain order. SOD activity was measured by NBT photochemical reduction method (Beauchamp & Fridovich, 1971), and POD activity was determined via the guaiacol method (Zhao, Li & Gao, 2015).

After drying to constant weight at 70 °C, leaves were weighed using a balance with a precision of 1/10,000, and specific leaf weight (SLW) was calculated as the ratio of dry leaf weight to leaf area (g·m⁻²) (Ashiuchi et al., 2001).

Determination of fast chlorophyll fluorescence parameters

During the experiment, a random selection of 5–7 leaves was taken from inside and around the lower canopy of different experimental tree species that exhibited healthy growth conditions. The selected leaves were then underwent a 30-min dark adaptation, and their fast chlorophyll fluorescence parameters were determined using a multifunctional plant efficiency analyzer (M-PEA, Hansatech Instruments Ltd., Pentney, UK). The blade fully covered with a 4 mm² test hole, while the measurement light source was a red light emitting a wavelength of 650 nm via six light emitting diodes, the light intensity used was 3,000 μmol·m⁻²·s⁻¹. The fluorescence signal was recorded for a duration of 1 s, and three repeated measurements were taken for each tree species. The fast chlorophyll fluorescence curves provide several key fluorescence parameters, each of which possessed important physiological significance.

T_fm: Time to reach maximal fluorescence intensity F_m;

F_v/F_m = (F_m–F_o)/F_m: Maximum photochemical efficiency of PSII;

V_j: Relative variable fluorescence intensity at the J-step, representing the rate of energy dissipation of electrons as they pass through plastiquinone A(QA);

V_i: Relative variable fluorescence intensity in the I-step, representing the rate of energy dissipation of electrons as they pass through plastiquinone B (QB);

S_m: Normalised total complementary area above the O-J-I-P transie (reflecting single-turnover QA reduction events);

N: The number of times QA was restored during the period from the start of illumination to the arrival of F_m;

TR/RC: Trapped energy flux per RC (at t = 0);

ET_o/RC: Electron transport flux per RC (at t = 0);

Φ_Ro: Reflects the relative activity of PSI;

PI_(abs): Performance index on absorption basis.

Data analysis

A one-way analysis of variance (ANOVA) was conducted to analyse the differences in dust retention, leaf physiology, and stomatal characteristics among five tree species under different pollution sites. Subsequently, Pearson correlation analysis (PCA) was used to investigate the internal relationship of leaf physiological and biochemical characteristics and the associations between dust retention ability, leaf photosynthetic and physiological indicators. All statistical analyses were performed using SPSS 26.0 software (IBM., Armonk, NY, USA), with a significance level of 0.05. All charts were created using Excel 2010 (Microsoft Corp., Redmond, WA, USA) and Origin 2021 software (Origin Lab Corp., Northampton, MA, USA).

The mass of PM retained on leaf surface in different pollution areas

There were significant differences in PM adsorption capacity among five tree species with different particle sizes (TSP, PM₁₀ and PM_2.5) (P < 0.05) (refer to Fig. 2). The dust retention of TSP, PM₁₀ and PM_2.5 ranged from 0.81 to 7.33, 0.20 to 3.68, and 0.12 to 3.60 g·m⁻², respectively. Overall, E. japonica had the highest average TSP retention per unit leaf area (5.45 g·m⁻²), which was significantly higher than that of the other four species across the three sampling sites (P < 0.05), followed by E. japonicus (2.76 g·m⁻²) and P. serrulata (2.12 g·m⁻²), while L. lucidum (1.82 g·m⁻²) and P. tobira (1.53 g·m⁻²) showed the weakest dust retention ability. Similarly, it was found that E. japonica had the highest adsorption rate for PM₁₀ and PM_2.5, whereas P. tobira exhibited the weakest ability to capture PM₁₀ and PM_2.5.

There were variations in the adsorption of PM per unit leaf area under different pollution levels. Generally, as the pollution level increased, the plants showed greater adsorption of atmospheric PM. Among the three sampling areas, the order of TSP retention per unit leaf area for P. serrulata, P. tobira, L. lucidum, and E. japonica was Zhongzhou Avenue > Henan Agricultural University > Zhengzhou Botanical Garden. However, in the botanical garden, E. japonicus retained higher amount of TSP compared to the campus. The order of PM₁₀ and PM_2.5 retention for the five tree species generally followed the same pattern. In polluted areas, E. japonica adsorbed 7.8 times more PM_2.5 per unit leaf area compared to the control area. Similarly, the PM_2.5 retention of E. japonicus, P. serrulata, P. tobira, and L. lucidum on the road was 4.0, 3.1, 2.4, and 1.5 times higher, respectively, compared to the park.

Influence of atmospheric PM on the number and size of leaf surface stomata

The study examined the stomatal size and number in five different tree species. Upon magnification at 1,200 times, it was discovered that on campus, E. japonica had the longest stomatal length (34.7 μm), while in the botanical garden, P. tobira had the widest stomatal width (28.6 μm). The smallest stomatal length (17.5 μm) and width (13.6 μm) were observed in L. lucidum on the road, and the maximum number of stomata (53) was found in P. tobira on the road (refer to Fig. 3, Table 2). Furthermore, the study revealed that the stomatal characteristics of each species varied depending on the pollution levels. In all three sampling areas, P. serrulata, E. japonicus, and P. tobira exhibited downward trend in stomatal length and width as pollution levels increased. The largest stomatal area was observed in the botanical garden, while the smallest was found on the road.

The photosynthetic gas exchange parameters response of five evergreen tree species to different levels of PM pollution

As shown from Fig. 4, compared with clean area (park), the photosynthetic response indexes including net photosynthetic rate (Pn), transpiration rate (E), stomatal conductance (Gs) and intercellular CO₂ concentration (Ci) were decreased with pollution aggravation, with variations ranging from 7.04 to 13.64 μmol∙m⁻²∙s⁻¹, 0.93 to 3.93, 0.05 to 0.14 mol∙m⁻²∙s⁻¹, and 171.67 to 274.67 μmol∙mol⁻¹, respectively. P. tobira exhibited higher values for Pn, E, and Gs compared to the other four plants.

The physiological responses of five evergreen tree species to different levels of PM pollution

According to the findings in Fig. 5, it was observed that plant physiological traits exhibit regularly changes in response to atmospheric PM. The increase in atmospheric PM leads to a decrease in Chl a and Chl b content, soluble sugar and soluble protein content, as well as SOD and POD activity. Conversely, MDA content and SLW increase. The Chl a and Chl b content of five tree species varied from 0.10 to 0.77 and 0.03 to 0.26 mg·g⁻¹, respectively. It is worth noting that in all three regions, the Chl a and Chl b content in P. serrulata was higher compared to the other four plants. Significant differences were also observed in soluble sugar (1.3–54.72%) and soluble protein content (0.48–8.72 mg·g⁻¹) among the five tree species (P < 0.05). In addition, it was found that the soluble sugar content of E. japonica increased and then decreased with increasing pollution levels, the higher value was observed on the road, in comparison to relatively clean areas. The SOD activity ranged from 191.77–302.56 u·g⁻¹, with the highest activity observed in E. japonicus. The POD activity (0.24–8.82 u·g⁻¹) also differed significantly among the five tree species (P < 0.05), with P. serrulata showing the highest activity. In contrast to SOD, POD activity was more sensitive in three sampling areas. The MDA content (3.66–18.31 μmol·g⁻¹) and SLW (92.39–181.82 mg·cm⁻²) exhibited the highest values on the road overall, and there were significant differences among the five plants (P < 0.05).

Fast chlorophyll fluorescence analysis of different tree species

After being irradiated with saturating pulsed light, the chlorophyll fluorescence of five dark-adapted leaves rapidly increase, stabilized after OJIP, and then slightly decreased (refer to Fig. 6). Under the stress of particulate matter pollution, the JIP values of all five plants generally declined to varying extents, resulting in inhibited photosynthesis. The most significant reduction occurred at points J and I. Among these plants, P. serrulata, E. japonicus and L. lucidum exhibited similar I values at three sampling sites. However, compared to relatively clean areas, the P value of L. lucidum decreased significantly.

Mathematical analysis was used to obtain over 50 fluorescence parameters from the fast chlorophyll fluorescence curves. Ten basic fluorescence parameters related to dust retention were selected for comparison. The botanical garden fluorescence parameters were used as the control, with a parameter value of 1. A radar map (refer to Fig. 7) was used to present the ratio of the fluorescence parameters to the control parameters of different tree species. Both TR_o/RC and ET_o/RC decreased with the aggravation of particle pollution, while TR_o/RC showed only slight change. There was no significant difference in F_v/F_m under different levels of particle pollution. V_j, V_i, S_m, N, and T_fm all showed a downward trend with increasing particulate matter pollution level. The PSI energy utilization parameter Φ_Ro decreased with increasing pollution. The parameter PI_(abs) represents the comprehensive performance of the light-receiving area, which also showed a decreasing trend.

Correlation between PM, photosynthetic and physiological indicators of tree species

As shown from Fig. 8. correlation analysis showed that the TSP was positively correlated with PM₁₀, PM_2.5 and MDA content (P < 0.01), as well as SLW (P < 0.05). Additionally, it was negatively correlated with soluble sugar content, Pn, E, and Gs (P < 0.01), as well as Chl b, POD activity and Ci (P < 0.05). PM₁₀ and PM_2.5 were positively correlated with MDA content and SLW (P < 0.01). PM₁₀ was negatively correlated with Chl b, soluble sugar content, POD activity, Pn, E, Ci (P < 0.01), and Chl a and Gs (P < 0.05). PM_2.5 was negatively correlated with Chl b, soluble sugar content, POD activity, Pn, and E (P < 0.05), as well as Ci (P < 0.01). There was no significant correltaion between the soluble protein content, SOD activity, and dust retention. Among them, the response of MDA to atmospheric particulate matter was the most severe, with Pearson correlation coefficient value of 0.65.

Correlation and synergistic relationship between photosynthetic and physiological characteristics of plant leaves

Figure 9 illustrates a clear quantitative relationship between various physiological and biochemical traits under the influence of PM pollution pressure. The effect of dust treatments varied significantly among all plants’ biochemical parameters. The results revealed that MDA content was negatively correlated with chlorophyll content, osmotic regulatory substances (soluble sugar and soluble protein content) and photosynthesis indexes (including Pn, E, Gs, Ci). On the other hand, MDA content showed a positive correlation with SLW. Furthermore, SOD activity was positively correlated with Ci, and negatively correlated with other indicators.

According to Table 3, two principal components were extracted based on the criterion that the eigenvalue exceeded 1 (Specifically, the eigenvalues were 6.05 and 1.79, respectively). These two principal components accounted for 50.4% and 14.9% of the total contribution, respectively. In combination, their cumulative contribution rate was 65.3%. This suggests that these two principal components significantly influenced the variation of leaf physiological and biochemical traits. The initial factor loading matrix of the principal components (Table 3) and the loading diagram of principal component analysis (PCA) were utilized to analyse the data. The results show that chlorophyll content, osmotic regulatory substances (soluble sugar and soluble protein), POD activity and photosynthesis indices (Pn, E, Gs, Ci) were positively correlated with the first principal component (PC1). On the other hand, MDA content, SLW and SOD activity showed negative contributions. The ranking of magnitude of correlation (absolute value) is as follows: Pn > E > Gs > Chl b > Chl a > POD > Ci > SS > SLW > SP > MDA > SOD. These findings suggest that the indicators significantly related to the first principal component can serve as key indicators of leaf photosynthetic and physiological traits. The principal component contrasts Pn, E, Gs, Chlb and Chla exhibit the greatest variation.

Comparison of PM adsorption of five tree species under different pollution levels

Using five common evergreen species in Zhengzhou as research subjects, the present study showed that the PM adsorption per unit leaf area of five plants followed the order E. japonica > E. japonicus > P. serrulata > L. lucidum > P. tobira. This indicates that E. japonica had the highest dust retention capability compared to other four species, possibly due to the presence of distinct grooves and folds on its leaf surface (Chen et al., 2017). On the other hand, the leaf surface of P. tobira was relatively smooth, resulting in the lowest PM absorption capacity. The effect of micromorphological characteristics on PM retention requires further investigation. Most of the particles trapped by plant leaves were predominantly coarse particles (PM_>10), which is consistent with the findings of Dang et al. (2022). Moreover, we also observed that the increase in PM_2.5 retention varies among plant species as increasing pollution, E. japonica showed an even greater increase. This suggests that the particle retention of the same plant is strongly affected by the environmental conditions, while the dust retention capacity of different tree species mainly determined by the morphological characteristics of the leaf surface (Li et al., 2023; Zhu & Xu, 2021).

The dust retention ability of plants is correlated with environmental pollution levels and pollutant sources. Previous studies have observed that the dust retention capacity of the same tree species varies in different urban functional areas, with industrial zones having the strongest dust retention capacity, followed by transport hub areas, residential areas, and clean areas (Aliya et al., 2014). In this study, the average PM (TSP, PM₁₀, PM_2.5) retention of five tree species was found to be highest on the road, followed by campus and the botanical garden, consistent with previous findings. Zhongzhou Avenue serves as a major pollution source due to exhaust emissions and the substantial dust generation. At the same time, the presence of active human activities contributes to the highest dust retention on roads (Zhu & Xu, 2021; Jeanjean, Monks & Leigh, 2016). Followed by the campus, mainly from non-motorised vehicles and infrequent human movement, resulting in relatively lower PM pollution levels in the atmosphere (Abhijith & Kumar, 2020). The botanical garden has the lowest pollution levels, due to its high vegetation coverage and the distance from the central city, there are no pollution sources nearby.

The effects of PM pollution on plant stomata, photosynthetic indexes and chlorophyll fluorescence parameters

The indicators relevant to photosynthesis, including Pn, E, Gs, and Ci, decreased with the increase of pollution, and were significantly negatively correlated with PM retention. The accumulation of particles on the leaf surface may lead to partial stomatal closure, thereby reducing the transpiration rate and carbon assimilation in photosynthesis (Khalid et al., 2018; Kumar et al., 2022). Previous studies have found that the size of plant stomata in polluted areas are smaller, and more in number, compared to control area (Kardel et al., 2010), which is consistent with the results of this study. While the stomata of most plants shrink in polluted areas, plants in these environments have developed some adaptations to assimilate more CO₂ due to the increased concentrations of CO₂ and other pollutants. This results in an increased number of stomata per square millimeter (Stevovic, Mikovilovic & Calic-Dragosavac, 2010; Kumar et al., 2022), which also explains the decrease in Gs, which in turn affects the physiological and biochemical processess of plants (Ghafari et al., 2021). A significant amount of dust had accumulated on the leaf surface, covering both the blade surface and the outer area, interrupting the gas exchange. This accumulation was particularly prominent on the leaf area responsible for photosynthesis, resulting in a reduced photosynthetic rate (Li et al., 2023). Additionally, the decrease in chlorophyll content disrupted the plant’s ability to carry out primary photochemical reactions, thus affecting its photosynthetic capacity (Mand et al., 2013; Sharma & Singh, 2022). This article agrees that the reduction in photosynthetic rate includes a variety of factors.

This study observed a decrease in the JIP values of the OJIP curve due to air pollution. This suggests that particulate stress inhibits the absorption and transmission of light energy in plant photosynthesis (Pourkhabbaz et al., 2010). When comparing the fluorescence parameters, it was found that PI_(abs) significantly decreased with increasing pollution, indicating that particulate matter negatively affected the overall performance of the leaf photosynthetic mechanism. This hindered the progress of photochemical reactions and the accumulation of organic matter (Cuba et al., 2021). Although F_v/F_m did not show a significant difference, the results suggest that PI_(abs) more accurately reflects the state of the plant photosynthetic mechanism and is more sensitive to certain stresses compared to F_v/F_m. Therefore, PI_(abs) can better indicate the effect of stress on the photosynthetic mechanism (Appenroth et al., 2001; van Heerden et al., 2003; van Heerden, Strasser & Kruger, 2004). Additionally, TR_o/RC and ET_o/RC exhibited a decreasing trend with increasing pollution, indicating that PM pollution affected the absorption, transformation, and dissipation of light energy by the plant photosynthetic organs, as well as the related loss of electron transfer (van Heerden, Kruger & Louw, 2007). Similarly, other parameters such as V_j, V_i, S_m, and N also showed a downward trend.

The physiological response of plant leaves toward PM pollution

Chlorophyll is one of the important pigments involved in photochemical reactions (Kumar et al., 2021). In the polluted areas, the five plant species had lower levels of chlorophyll compared to the control site. This is probably due to the dust accumulation on the leaf surface, which alters the microenvironment and leads to damage and degradation of chlorophyll in the leaves. As a result, the synthesis of chlorophyll is affected, ultimately causing a decrease in its content (Karmakar & Padhy, 2019). Antioxidant enzymes, such as SOD and POD, play a crucial role in protecting cells from oxidative stress (Rai, 2016). The decrease in SOD and POD enzyme activity suggests that dust pollution stress may inhibit their synthesis in leaves. Consequently, the oxygen radicals produced by the plant exceed the scavenging capacity of the antioxidant enzyme system, leading to damage to the structure and function of the membrane system (Chaudhary & Rathore, 2019). In addition, there was a general increase in MDA content as increasing pollution levels in all five plants. This can be attributed to the higher level of damage to the cell membrane caused by the plants’ exposure to more severe road pollution (Lyu et al., 2023). However, no significant correlation was found between SOD activity and TSP, PM₁₀, and PM_2.5 retention per unit leaf area (P > 0.05), which differs from the results reported by Gao (2016), suggesting that it may not be susceptible to the stress caused by dust accumulation.

The soluble sugar content of the five plants showed different trends, and the soluble sugar content of E. japonica showed a trend of first increasing and then decreasing. This suggests that mild stress can stimulate the plant’s defence response mechanism to remove excess free radicals, giving the plant the ability to resist stress. When dust stress exceeds the tolerance limit of plants, membrane lipid peroxidation is exacerbated, normal cell metabolism is disrupted, protein synthesis is inhibited, and plant growth is impaired (Lee Hee & Lee Bum, 2000; Yin et al., 2023). Additionally, the decrease in leaf soluble sugar content may be associated with the inhibition of photosynthesis (Tzvetkova & Kolarov, 1996). We therefore speculate that this may also be the reason for the decrease in soluble sugar content of the leaves. Further research has shown that there is a positive correlation between specific leaf weight (SLW) and leaf surface dust retention (P < 0.05). Specific leaf weight (SLW) refers to the dry mass of leaves per unit area. In this study, we found that the relatively high SLW of urban plants in high pollution areas resulted from their long-term adaptation to urban air particulate pollution (Zhu & Xu, 2021).