Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China

Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China Kangning Zhao, Dandan Liu, Yongfa Chen, Jiayi Feng, Dong He, Chunhua Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1717092/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Particulate air pollution is a serious threat to human health, especially in urban areas, and trees can act as biological filters and improve air quality. However, studies on greening tree species selection are rare. We measured three particular matter adsorption metrics (PM 2.5 , PM 2.5−10 and PM > 10 captured per leaf area) and six functional traits for each of fourteen species and estimated their minimum light requirements based on field surveys. We found that shade-tolerant species captured more coarse particles (PM 2.5−10 ) than light-demanding species. For traits, a strong negative correlation was found between photosynthetic capacity and adsorption capacity for all three PM size fractions, indicating that in comparison to acquisitive species, conservative species captured larger amounts of particles. Moreover, denser wood species and smaller leaves were more efficient in capturing large particles (PM > 10 ), while species with ‘expensive’ leaves (high leaf N or P) were more efficient in capturing fine particles (PM 2.5 ), indicating that capturing large and fine particles was related to mechanical stability traits and leaf surface traits, respectively. Our results demonstrated that the metabolism (e.g., photosynthetic capacity) and chemistry (e.g., leaf N and leaf P) of leaves help explain species capacity to capture PM. We encourage future studies to investigate the ecosystem functions and stress tolerance of tree species with the same framework and trait-based methods. Airborne particulate matter Phytoremediation Green infrastructure Air pollution PM retention PM2.5 Ecosystem functioning Functional traits Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Developing countries such as China have experienced fundamental changes in recent decades, while gains in wealth have been achieved at a severe cost to the environment (Liu et al. 2018 ; Xu et al. 2018 ). Air pollution is a major threat to people’s health, particularly in urban areas (Przybysz et al. 2014 ; Yan et al. 2016 ), due to particulate matter (PM) emissions linked to traffic and industry (Simon et al. 2016 ). Coarse particles can cause lung diseases, while smaller particles, such as PM 2.5 , can be inhaled more deeply into the lungs (Beckett et al. 2000 ; Liang et al. 2016 ) and have been widely recognized as more harmful to human health. Air pollution is estimated to contribute to at least five million premature deaths each year (WHO 2014). Vegetation can reduce particle concentrations and thereby improve air quality (Litschke and Kuttler 2008 ; Liu et al. 2013 ; Shi et al. 2017 ; Weerakkody et al. 2018a ). A leaf is the main organ for particle deposition (Weerakkody et al. 2018b ) and can even biodegrade or transform pollutants into less or nontoxic molecules with its habituated microbes and endophytes (Zhang et al. 2017 ). Recognizing what kind of plants are more powerful in capturing airborne PM is important but challenging because the retention capacity of leaves in terms of accumulating PM is influenced by a variety of factors, such as the concentration of atmospheric PM (Luo et al. 2020 ), days after rainfall (Xu et al. 2020 ), wind speed (Beckett et al. 2000 ), leaf stage (Nguyen et al. 2015 ) and sampling season (Zhang et al. 2017 ), which are linked with surface moisture (Wang et al. 2013 ; Sun et al. 2020 ). Therefore, it is necessary to investigate what kind of trait could improve species’ PM adsorption abilities (Janhäll 2015 ; Shao et al. 2019 ). For example, species with smaller leaves, more complex leaf structure, waxy leaves and hairier leaves usually capture PM more efficiently (Beckett et al. 2000 ; Dzierżanowski et al. 2011 ; Weerakkody et al. 2017 ; Sun et al. 2018 ; Liu et al. 2019 ). These findings have important significance for selecting greening tree species, which is the primary task in the construction of urban forests. In comparison to ecosystem functions, environmental stress tolerance is sometimes even more important in greening tree species selection (Chaudhary and Rathore 2018 , 2019 ; Przybysz et al. 2021 ). However, there are very few studies investigating species’ PM adsorption abilities in relation to species’ environmental stress tolerance, such as shade tolerance and drought tolerance. Therefore, it is of great importance to test whether species with high PM adsorption abilities are fast- or slow-growing species and whether they are resource-demanding species. Because light is the major limiting resource in moist forests (Zhao and He 2016 ), we specifically sought to determine how PM adsorption abilities are correlated with the light requirements of tree species (Question 1). Theoretically, plant traits can track environmental changes and reflect the adaptive strategies of plants (Liu et al. 2021 ). Dust accumulation reduces the light availability of leaves (Zhu et al. 2019 ) and may inhibit chlorophyll biosynthesis (Chen et al. 2015 ) and stomatal conductance (Lewis et al. 2017 ), which is important for plant photosynthesis (Hetherington and Woodward 2003 ; Yu et al. 2018 ). Consequently, species with a greater ability to accumulate PM might need to be more shade tolerant from the perspective of evolution. We therefore hypothesized that species with high PM accumulation abilities should be able to adapt to poor light environments (Hypothesis 1). In forest ecology, there is a well-established trade-off between the survival rate in deep shade and the growth rate in bright light (Adler et al. 2014 ). Fast-growing species that rapidly acquire resources thrive in rich light environments, while slow-growing species that conserve resources are dominant in poor light environments (Lohbeck et al. 2013 ). This acquisitive-conservative trade-off points to the extremes of a continuum in plant design, and the position of a species along this continuum can be quantified by its functional traits (Poorter et al. 2008 ; Wright et al. 2010 ). For instance, an acquisitive species tends to have a high photosynthetic capacity, dark respiration rate, specific leaf area (SLA) and leaf nitrogen content (LNC) and hence a high growth rate. In contrast, conservative species tend to have thicker leaves and a higher wood density (WD), reducing the volumetric stem growth rate but facilitating leaf and stem protection and high survival rates (Chave et al. 2009 ). In this study, we tested whether species with high PM adsorption abilities are characterized by conservative or acquisitive functional traits (Question 2). Conservative species commonly have high WD and leaf thickness but a low SLA and photosynthetic capacity, suggesting a low growth rate strategy (Firn et al. 2019 ). Low growth rate species tend to produce durable leaves with long life spans (Adler et al. 2014 ). The tough leaves of these species may be associated with complex leaf surface structure and waxes, which could trap PM (Dzierżanowski et al. 2011 ). Therefore, we hypothesized that species with high PM accumulation capacities should be characterized by conservative traits (Hypothesis 2). If these two hypotheses were confirmed, then we would test the third question relating to which traits are good predictors of the PM accumulation capacities of species (Question 3). The functional traits measured in this study, including photosynthetic capacity, leaf length, SLA, WD, LNC and leaf phosphorus content (LPC), represent relatively easily measured characteristics that can be obtained for large numbers of species (Li et al. 2021 ). Relationships among functional traits and PM adsorption capacities may reflect direct effects but could also result from trait coevolution (Reich et al. 2003 ). Therefore, we used structural equation modelling (SEM) to investigate the relationships among a suite of traits and PM adsorption capacities of different size fractions. To test the first two hypotheses and establish linkages between plant functional traits and ecosystem functioning (PM adsorption in this study), we sampled leaves from fourteen evergreen tree species (Fig. 1 ) and experimentally measured their PM adsorption capacities. We then quantified shade tolerance and measured six functional traits of these species and compared them with their PM adsorption abilities. Methods Study site The study was conducted in a 50-ha dynamic forest plot in the Heishiding Provincial Nature Reserve (23°27′N, 111°52′E; altitude, 150–927 m), located in Guangdong Province, southern China. This region is covered by a subtropical evergreen broad-leaved forest and has a subtropical moist monsoon climate. The annual precipitation is approximately 1700 mm, and the mean daily temperature is 19.6°C. Quantitative assessment of PM capturing ability Samples were collected from three individuals for each of the fourteen species. For each individual, 10–50 fully expanded mature leaves were collected at 2–5 metre above-ground with a high branch scissor. Samples from the same tree were then put in a plastic bag, sprinkled water, transported to the laboratory and stored in a refrigerator at 4°C until the laboratory experiment. To ensure that all samples experienced similar dust conditions, we used a dust box, a dust grinder and an air blower (Fig. 2 ) to simulate a busy road environment, an approach similar to deposition chamber experiments (Hwang et al. 2011 ) or wind tunnel experiments (Burkhardt et al. 1995 sänen et al. 2013; Zhang et al. 2017 ). First, we cleaned the samples by immersing them with 250 ml distilled water and washed them with a no-hair-loss brush. Next, the cleaned leaves were hung on a rope installed in an experimental box (1.4 m × 1.2 m × 0.8 m, Fig. 2 ). Sixty grams of soil was ground to powder, sieved and blown into the box to simulate a cloud of dust and left to set for half an hour. We then removed the dusted leaves and measured the PM retained on them. The method for rinsing and weighing was similar to that described by Dzierżanowski et al. ( 2011 ). The filters and small beakers were dried for 60 minutes at 80°C in a drying oven and then left in the weighing room for an hour to stabilize the humidity and obtain a constant weight ( W 1 ). We immersed leaves with distilled water, cleaned them softly with a nylon brush and then washed them with distilled water. In the next step, the rinse water was filtered sequentially using 10 µm and 2.5 µm filters (Whatman, UK, Types 91 and 42, respectively), and the eluent was poured into small beakers. The filters and beakers were oven dried at 80°C for 8 hours and 12 hours, respectively, and then left in the weighing room for an hour to obtain constant humidity and weight ( W 2 ). Leaf areas ( S ) of the samples were measured using an Epson Expression 11000XL scanner (Epson, Inc., USA) and calculated using the Photoshop CC2019 software (Adobe Corp., USA). The amount of PM was expressed per unit area (cm 2 ) of one side of the leaf surface and calculated as ( W 2 - W 1 )/ S . Shade tolerance estimation The 50-ha plot (1000 m × 500 m) was established following the ForestGEO ( https://www.forestgeo.si.edu ) protocol in 2011, and all free-standing individuals with diameters at breast height greater than 1 cm were tagged, mapped, measured and identified to species. More than 270000 stems belonging to 245 species were recorded in the first census. The understorey light availability was estimated using hemispherical photography at 2352 locations randomly distributed within the 50-ha plot (Zhao and He 2016 ), and light availability was then interpolated to grids of 10 m × 10 m using ordinary kriging in the AUTOMAP package (Hiemstra et al. 2009 ) to estimate the environmental conditions where the individuals were located. The minimum light requirements (shade tolerance) of the species were estimated by calculating the 10th percentile of the distribution of juveniles (height < 4 m) in relation to light availability. There were at least 200 juveniles for each of the 14 species in the 50-ha plot. Functional trait measurements We measured six traits, including leaf length (LL), maximum rate of electron transport (J m ), specific leaf area (SLA), wood density (WD), leaf nitrogen concentration (LNC) and leaf phosphorus concentration (LPC). Leaf length was averaged from the samples collected for PM accumulation measurements. However, the other five traits were measured separately at other times on different sampled individuals. J m represents the light saturated photosynthetic capacity per leaf area (Rascher et al. 2000 ). For J m measurements, a total of 1056 leaves from 335 individuals of the 14 species were sampled. Details on the samplings and measurements can be found in our previous study (Feng et al. 2018 ). SLA, WD, LNC and LPC were measured in a total of 464 individuals of the fourteen species. Details on these samplings and measurements can also be found in our previous studies (Chen Y. 2015 ; He et al. 2018 ; Li et al. 2021 ). Shade-tolerant species with durable leaves tend to have lower J m , higher WD and lower SLA than those of light-demanding species with high leaf turnover rates (Walters and Reich 1999 ; Reich 2014 ). Statistical analysis One-way analysis of variance (ANOVA) was applied to test whether individual PM values differed among species. If the difference among the species was significant, then the significance of the differences between the mean values was tested using Tukey’s honest significant difference test (HSD) at α = 0.05 with the function “HSD.test” in the R package “agricolae” (R Development Core Team 2016 ). To test the relationship between shade tolerance and PM accumulation capacities, we examined the response of PM to minimum light requirements using simple linear regressions. To test the effect of functional traits on PM accumulation capacities, we also examined the response of PM to traits using simple linear regressions. To better meet the assumptions of normality and homogenous variances, PM, shade tolerance and trait values, except for WD, were log transformed before analysis. Because functional traits are correlated with each other, bivariate relationships alone provide limited information. We used a structural equation model with the function “psem” in the R package “piecewiseSEM”. Initially, all possible paths were allowed in the model, and the complex model was further simplified by removing nonsignificant paths. This approach is useful for exploring the comparative strength of relationships. A bidirectional line was used to denote the equivalent models. Results Among the 14 species studied, accumulated total suspended particles (TSP) per leaf area ranged from 1.46 g·m − 2 to 4.11 g·m − 2 , PM 2.5 ranged from 0.17 g·m − 2 to 0.82 g·m − 2 , PM 2.5−10 ranged from 0.12 g·m − 2 to 1.29 g·m − 2 , and PM > 10 ranged from 0.97 g·m − 2 to 2.44 g·m − 2 (Fig. 3 and Table 1). According to one-way ANOVA, there were significant differences in PM 2.5 , PM 2.5−10 and TSP capturing abilities ( P 10 capturing abilities among the species ( P = 0.06). Relationships between shade tolerance and PM accumulation capacities We found that all three PM accumulation metrics were negatively correlated with the minimum light requirements (shade tolerance) of the species, but only the relationship between PM 2.5−10 and shade tolerance was significant (R 2 = 0.72, P 10 accumulation capacities were negatively correlated with leaf length (Fig. 5 b, c), whereas nonsignificant correlations were observed between PM 2.5 accumulation capacity and leaf length (Fig. 5 a). For the metabolism traits, photosynthetic capacity (J m ) was negatively correlated with PM accumulation capacity for all particle size fractions (Fig. 5 d-f). For the economic traits, the relationships between specific leaf area (SLA) and PM 2.5 , PM 2.5−10 and PM > 10 accumulation capacities were nonsignificant (Fig. 6 a-c). Wood density (WD) was positively correlated with PM > 10 accumulation capacity (Fig. 6 f), whereas it was not significantly correlated with PM 2.5 and PM 2.5−10 accumulation capacities (Fig. 6 d, e). For the leaf chemical traits, leaf nitrogen content (LNC) was positively correlated with PM 2.5 (Fig. 7 a), whereas it was nonsignificantly correlated with PM 2.5−10 and PM > 10 accumulation capacities (Fig. 7 b-c). Similar patterns were found for leaf phosphorus content (LPC), where PM 2.5 accumulation capacity was positively correlated with LPC (Fig. 7 d), and the relationships between PM 2.5−10 and PM > 10 and LPC were nonsignificant (Fig. 7 e, f). Path analysis Among the six traits LNC, LPC, SLA, WD, leaf length and J m , mainly J m , leaf length and LPC influenced PM accumulation capacities. Leaf length decreased the PM > 10 accumulation capacity and LPC increased the fine particle (PM 2.5 ) accumulation capacity. The photosynthetic capacities of leaves (J m ), which may be related to cuticle thinness, decreased the PM retention capacities of all three size classes (Fig. 8 ). Discussion Leaf characteristics, including both leaf architecture and material composition, may influence the ability of a leaf surface to accumulate PM (Liang et al. 2016 ). In the present study, we found that suites of functional traits displayed a tight association the PM accumulation capacity of leaf area for each species. We showed that species with a small leaf size (low LL), low photosynthetic capacity (low J m ), high input into construction (high WD) and a conservative nutrient use strategy (high LNC and LPC) increased in PM accumulation capacities. Our results indicate that conservative species (characterized by low J m and high WD) may have higher PM accumulation capacities than acquisitive species (characterized by high J m and low WD) and that species with high PM accumulation capacities are adapted to poor light environments. Leaf size affected PM retention capacity. We found that smaller leaves captured more PM per unit leaf area than larger leaves, especially for coarse and large particles (Fig. 5 a-c). In line with our results, Weerakkody et al. ( 2018b ) found that smaller synthetic leaves accumulate more PM than larger synthetic leaves with the same surface characteristics. This correlation between leaf size and PM accumulation capacity was also supported by field surveys (Leonard et al. 2016 ) but not by the results of some other studies (Sæbø et al. 2012 ). Scanning electron microscopy images have shown that more particles are distributed near leaf edges (Weerakkody et al. 2018b ); therefore, in comparison to broadleaved species, conifer species commonly capture larger amounts of particles (Freer-Smith et al. 2005 ; Liu et al. 2012 ). However, broadleaved species have been reported to have higher leaf retained PM wash-off efficiency than conifer species (Luo et al. 2020 ), and PM wash-off events are important for the net removal of airborne PM (Xu et al. 2020 ). Shade-tolerant species captured larger amounts of PM per area than light-demanding species The stress tolerances of species (e.g., drought tolerance or shade tolerance) are sometimes even more important than the functions of species when we design vegetation barriers for air pollution abatement (Barwise and Kumar 2020 ). Species must adapt to the shade conditions created by the deposited dust on leaves, tall buildings and other vegetation. In comparison to light-demanding species, shade-tolerant species have been shown to better survive under shaded conditions, but they have lower maximum photosynthetic rates under high light conditions (Valladares and Niinemets 2008 ). We found a significantly negative correlation between minimum light requirements and PM 2.5−10 retention capacities for the species. Moreover, light-demanding species usually have higher photosynthetic capacities than shade-tolerant species. Therefore, the finding that photosynthetic capacity (J m ) was significantly negatively correlated with the PM retention capacity of the species for all particle size classes (Fig. 5 d-f) confirmed that species with high PM accumulation abilities are adapted to poor light environments. In this study, we showed that J m is a good predictor and that it alone predicted 45–64% (for different PM size classes) of interspecific variation in PM accumulation. Compared with other commonly used traits, such as hairiness, roughness and waxiness of the leaf surface, photosynthetic capacity (J m ) is objective and easily measured. Relationships between economic traits and PM accumulation capacities For leaf economics, an axis exists from ‘cheap’ tissue investment and fast returns to ‘expensive’ tissue investment and slow returns (Wright et al. 2004 ). Species with ‘expensive’ economic traits, such as low specific leaf area (SLA), tend to have a long leaf lifespan and low relative growth rates but high survival rates (Adler et al. 2014 ). These species produce durable leaves with thick cuticles (Kitajima 1994 ) where interactions between PM and leaves occur. We thereby expected that ‘expensive’ leaves should have high PM accumulation capacities, and a negative relationship between SLA and PM deposition rate had been reported by a previous wind-tunnel experiment (Chiam et al. 2019 ). Unexpectedly, nonsignificant correlations were found between SLA and all three PM size fractions in this study (Figs. 6 a-c and 8 ). This result probably occurred because we used the species mean SLA value to characterize all individuals of a species. However, intraspecific SLA variation can arise from both phenotypic plasticity and genetic diversity (Siefert et al. 2015 ). We found that species with denser wood accumulated larger amounts of large particles (PM > 10 , Fig. 4 f). Species with high wood density (WD) represent a ‘slow’ life history strategy (Wright et al. 2010 ); i.e., they have a limited potential growth rate but have low mortality risk under shaded conditions. A negative relationship between WD and leaf size was found in this study (Fig. 6 ) and has also been reported in some previous studies (Wright et al. 2007 ). This scenario occurs probably because species with denser wood usually have lower hydraulic conductivity and thereby cannot support large leaves as species with soft wood (Chave et al. 2009 ). Wet leaves more easily retain PM; however, denser wood species with lower hydraulic conductivity were found to have higher PM accumulation capacity (Fig. 6 f). The correlations between WD and PM > 10 , as the path analysis demonstrated (Fig. 8 ), were the result of a complex correlation network in the trait space. We found that leaf N and P concentrations were both positively correlated with PM 2.5 accumulation capacities (Fig. 7 a, d). N and P are the two most limiting elements to plants and have specific functions in leaves. N is important for enzymatic activity, and P affects protein synthesis. Greater leaf N and P concentrations may enhance metabolic activity and facilitate the formation of complex leaf surface structures. Except for thicker cuticles for adsorbing PM, trees with ‘expensive’ leaves (low SLA) and denser wood may have higher structural stability (Reich et al. 1991 ) and sway less in wind, which may reduce the resuspension of PM back into the air (Chiam et al. 2019 ) and further improve their PM capturing efficiencies. Accumulation of different size fractions of PM PM of different sizes has varying effects on human health (Przybysz et al. 2014 ), with the smallest fraction considered to be the most hazardous. Particles > 100 µm in diameter have not been measured in many studies (Przybysz et al. 2014 ; Xu et al. 2017 ; Cai et al. 2017 ). However, large particles near busy urban roads are not harmless, especially when they contain large amounts of heavy metals such as mercury and lead (Liu et al. 2012 ). We did not exclude PM > 100 in the washing solutions, but the ground soils used in this experiment were sieved through a metal sieve (retention 300 µm) before being blown into the experimental box. Large particles (PM > 10 ) made up the greatest mass proportion (48.9–75.3%) of total suspended particles, followed by the coarse fraction (7.8–35.6%) and then the fine fraction (9.7–36.1%). We found that the accumulation of large particles on leaves was related to leaf morphology (leaf length), while the accumulation of fine particles was related to leaf chemistry. The mechanisms behind this result need further research. We hypothesize that large particles may mainly be captured by interception, while fine particles may mainly be captured by waxes or deposited in the microstructures (e.g., grooves), which may be related to LNC and LPC. Fine particles are not easily removed, which may be a possible reason for the poor correlation between morphology representing structural stability and fine particle accumulation capacities. Trees stand regularly occur along roads in many cities, and therefore, quantifying individual-level PM accumulation capacity is needed. We encourage future studies to integrate leaf-level PM accumulation capacity with leaf density and crown size to obtain whole-plant PM accumulation capacity and link it with functional traits of species. Conclusion Our results provide encouraging evidence of correlations between species functional traits and abilities to capture airborne particulates, including PM 2.5 , PM 2.5−10 and PM > 10 . Light saturated photosynthetic capacity per leaf area (J m ) was negatively correlated with all three PM size fractions, indicating that conservative species accumulated larger amounts of PM than acquisitive species. Denser wood species and smaller leaves captured more large particles (PM > 10 ), while ‘expensive’ leaves (high leaf N or leaf P) captured more fine particles (PM 2.5 ), indicating different mechanisms for capturing particles of different sizes. Although the correlations between economic traits (specific leaf area and wood density) and PM deposition were weak, our findings highlight the great potential of functional traits as a tool for linking species’ efficiency at air purification with their growth strategies and environmental stress tolerances. Declarations Author contribution KZ designed the research. KZ, DL, JF, YC and DH performed the experiments and conducted the fieldwork. KZ analysed the data and wrote the first draft. All authors aided in revising the final manuscript. Funding This study was financially supported by the National Natural Science Foundation of China (31800366) to KZ. 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Can J For Res 46:1103–1110. https://doi.org/10.1139/cjfr-2016-0003 Zhu J, Yu Q, Zhu H et al (2019) Response of dust particle pollution and construction of a leaf dust deposition prediction model based on leaf reflection spectrum characteristics. Environ Sci Pollut Res 26:36764–36775. https://doi.org/10.1007/s11356-019-06635-4 Tables Table 1 Summary of the 14 species used in this study for which PM accumulation capacity and functional trait data were collected. PM 2.5 , PM 2.5-10 , PM >10 per leaf area (g·m -2 ); photosynthetic capacity (J m , μmol·m -2 s -1 ); wood density (WD, g·cm -3 ); specific leaf area (SLA, cm 2 ·g -1 ); leaf length (LL, cm); leaf nitrogen content (LNC, g·kg -1 ); and leaf phosphorus content (LPC, g·kg -1 ) are illustrated (mean ± SD). Species (Family) PM 2.5 (g·m -2 ) PM 2.5-10 (g·m -2 ) PM >10 (g·m -2 ) J m (μmol·m -2 s -1 ) WD (g·cm -3 ) SLA (cm 2 ·g -1 ) LL (cm) LNC (g·kg -1 ) LPC (g·kg -1 ) Cryptocarya concinna (Lauraceae) 0.82 ± 0.13 1.29 ± 0.41 2.01 ± 1.50 33 ± 17.3 0.56 ± 0.08 143.3 ± 49.5 9.2 ± 1.6 18.3 ± 2.0 0.67 ± 0.32 Xanthophyllum hainanense (Polygalaceae) 0.55 ± 0.24 0.88 ± 0.37 2.44 ± 0.56 34.1 ± 13.7 0.71 ± 0.09 149 ± 17.6 7.6 ± 1.3 22.2 ± 2.5 0.66 ± 0.07 Castanopsis chinensis (Fagaceae) 0.31 ± 0.16 0.64 ± 0.44 1.69 ± 0.84 49.4 ± 19.2 0.6 ± 0.07 110.7 ± 25.1 8.5 ± 2.5 12.8 ± 2.5 0.41 ± 0.08 Lindera chunii (Lauraceae) 0.31 ± 0.06 0.61 ± 0.22 1.63 ± 0.22 42 ± 16.3 0.56 ± 0.07 161.5 ± 29.2 10.2 ± 1.2 15 ± 1.9 0.72 ± 0.13 Rapanea neriifolia (Myrsinaceae) 0.25 ± 0.08 0.89 ± 0.21 1.39 ± 0.26 43.2 ± 17.4 0.71 ± 0.08 101.5 ± 18.5 13.2 ± 2.8 8.8 ± 1.6 0.37 ± 0.07 Ardisia quinquegona (Myrsinaceae) 0.34 ± 0.08 0.77 ± 0.38 1.35 ± 0.35 40.2 ± 24.1 0.57 ± 0.07 149.1 ± 30.4 11.8 ± 1.9 15.4 ± 1.1 0.54 ± 0.08 Machilus breviflora (Lauraceae) 0.33 ± 0.12 0.24 ± 0.00 1.62 ± 0.29 40.9 ± 12.0 0.58 ± 0.09 102.9 ± 15.4 10.1 ± 0.9 10.5 ± 0.8 0.44 ± 0.10 Canarium album (Burseraceae) 0.7 ± 0.06 0.26 ± 0.03 0.98 ± 0.17 44.3 ± 23.2 0.33 ± 0.08 219.7 ± 54 19.9 ± 4.1 20 ± 2.8 1.02 ± 0.23 Schima superb (Theaceae) 0.2 ± 0.03 0.27 ± 0.14 1.43 ± 0.42 56.1 ± 10.8 0.64 ± 0.08 109.9 ± 23.5 14.7 ± 2.0 11.5 ± 1.6 0.31 ± 0.06 Symplocos congesta (Symplocaceae) 0.24 ± 0.07 0.67 ± 0.16 0.97 ± 0.22 55.8 ± 21.3 0.53 ± 0.06 119 ± 20.6 13.9 ± 2.7 11.4 ± 1.4 0.37 ± 0.08 Lithocarpus calophyllus (Fagaceae) 0.27 ± 0.02 0.31 ± 0.09 1.13 ± 0.08 50.3 ± 17.5 0.63 ± 0.07 105.9 ± 21.3 16.2 ± 2.7 12.1 ± 1.7 0.55 ± 0.16 Itea chinensis (Saxifragaceae) 0.19 ± 0.03 0.24 ± 0.15 1.23 ± 0.39 73.9 ± 25.4 0.57 ± 0.06 178.5 ± 36.7 11.4 ± 1.3 15 ± 2.0 0.69 ± 0.20 Machilus litseifolia (Lauraceae) 0.27 ± 0.05 0.12 ± 0.04 1.17 ± 0.49 60.5 ± 17.2 0.55 ± 0.09 115.9 ± 28.3 15.1 ± 2.7 10.8 ± 2.2 0.44 ± 0.09 Schefflera octophylla (Araliaceae) 0.17 ± 0.01 0.32 ± 0.15 0.97 ± 0.23 58.6 ± 25.9 0.29 ± 0.06 179.8 ± 46 16 ± 3.5 13.7 ± 1.6 0.6 ± 0.1 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 15 Oct, 2022 Reviewers agreed at journal 01 Aug, 2022 Reviewers invited by journal 16 Jul, 2022 Editor invited by journal 16 Jun, 2022 Editor assigned by journal 07 Jun, 2022 First submitted to journal 01 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1717092","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":121635940,"identity":"b99d29c8-9109-406c-851a-406b65f798e9","order_by":0,"name":"Kangning Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCQYGxgYGGwiHhwQtaaRrOUyCFvnZzQ8fztxxPnF++wHGB2/bGOTNCWlhnHPM2HDjmduJG84kMBvObWMw3NlAQAuzRIKZ5MM2oBYJBjZp3jaGBIMDBLSwSaR/A2o5lzh/BgP7b6K08EjkmElubDuQ2HCDgY2ZKC0SEjnFhjPbko03nElslpxzTsJwAyEt8jPSNz7sbbOTnd9++OCHN2U28gRtQQLA6AFH0ygYBaNgFIwCygEAYJs/C0LZM2MAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3254-821X","institution":"University of South China","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kangning","middleName":"","lastName":"Zhao","suffix":""},{"id":121635941,"identity":"bfabf6e2-e4ec-49b7-b19c-855849a3bfbb","order_by":1,"name":"Dandan Liu","email":"","orcid":"","institution":"University of South China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Liu","suffix":""},{"id":121635942,"identity":"9d67c45d-5c04-44f7-a323-965b7a1a430f","order_by":2,"name":"Yongfa Chen","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongfa","middleName":"","lastName":"Chen","suffix":""},{"id":121635943,"identity":"05c3a7a1-281e-426f-9e54-38a298c4e0a9","order_by":3,"name":"Jiayi Feng","email":"","orcid":"","institution":"South China Botanical Garden","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Feng","suffix":""},{"id":121635944,"identity":"4bb037ef-1e5d-44f0-95aa-63a1598e9e2d","order_by":4,"name":"Dong He","email":"","orcid":"","institution":"East China Normal University School of Life Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"He","suffix":""},{"id":121635945,"identity":"87e7488a-29bf-4195-892d-8087b15e924d","order_by":5,"name":"Chunhua Huang","email":"","orcid":"","institution":"University of South China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Huang","suffix":""},{"id":121635946,"identity":"d8f7c290-4adb-4b27-8c4a-c71b25b83da0","order_by":6,"name":"Zhiyuan Wang","email":"","orcid":"","institution":"University of South China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2022-06-01 19:56:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1717092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1717092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24000323,"identity":"57fcab56-146f-4c65-81c4-0dfb186c6858","added_by":"auto","created_at":"2022-07-18 18:06:37","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1150400,"visible":true,"origin":"","legend":"\u003cp\u003eLeaves of the fourteen tree species tested in the study. The species are ordered based on their total suspended particle accumulation capacity of per leaf area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/2f9fe5acd816b7e2d0e4a02f.jpeg"},{"id":24000100,"identity":"609181d0-83fa-4201-b111-389c66da6bf6","added_by":"auto","created_at":"2022-07-18 18:01:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003echematic diagram of the experimental dust box used in this study. Leaves in the box were vertically arranged\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/549034deeacef99458c9a24a.jpeg"},{"id":24000379,"identity":"a5a28d14-9abc-40d0-ad3c-05e7759ee6ad","added_by":"auto","created_at":"2022-07-18 18:11:37","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":381872,"visible":true,"origin":"","legend":"\u003cp\u003ePM captured per leaf area by different species of plants in the experiment. Data are arranged in descending order of total suspended particles (TSP); for an explanation of the species codes, see Table 1. Error bars for PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e and TSP are presented. Different letters above the column indicate a significant difference in TSP accumulation per leaf area (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/063192356c4ad87bdad403e9.jpeg"},{"id":24000102,"identity":"9fecb3fe-b374-428e-873c-a0f317473a24","added_by":"auto","created_at":"2022-07-18 18:01:37","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":402784,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between shade tolerance (minimum light requirements) and PM capturing capacities of individual species for (a) PM\u003csub\u003e2.5\u003c/sub\u003e, (b) PM\u003csub\u003e2.5-10\u003c/sub\u003e, and (c) PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e. The light availability of each of at least 200 individuals was quantified for each species, and shade tolerance was calculated as the 10\u003csup\u003eth\u003c/sup\u003e percentile of species distribution in relation to light availability\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/e89d55cc782b79374b9a0dcc.jpeg"},{"id":24000105,"identity":"bd5624b6-c7f0-4ea8-a68b-fc80778260ff","added_by":"auto","created_at":"2022-07-18 18:01:37","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":692162,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between plant traits and PM capturing capacities of individual species: (a) leaf length and PM\u003csub\u003e2.5\u003c/sub\u003e, (b) leaf length and PM\u003csub\u003e2.5-10\u003c/sub\u003e, and leaf length and PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e, (d) maximum rate of electron transport (J\u003csub\u003em\u003c/sub\u003e) and PM\u003csub\u003e2.5\u003c/sub\u003e, (e) J\u003csub\u003em\u003c/sub\u003e and PM\u003csub\u003e2.5-10\u003c/sub\u003e and (f) J\u003csub\u003em\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e. A dashed line denotes a nonsignificant relationship between trait and PM capturing abilities\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/0343097f3c96c6995cd79ae4.jpeg"},{"id":24000325,"identity":"ce42b7ec-1354-4e5e-9962-655912c2b0be","added_by":"auto","created_at":"2022-07-18 18:06:38","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":707258,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between economic traits and the PM capturing abilities of individual species: (a) SLA and PM\u003csub\u003e2.5\u003c/sub\u003e, (b) SLA and PM\u003csub\u003e2.5-10\u003c/sub\u003e, (c) SLA and PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e, (d) WD and PM\u003csub\u003e2.5\u003c/sub\u003e, (e) WD and PM\u003csub\u003e2.5-10\u003c/sub\u003e and (f) WD and PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e. A dashed line denotes a nonsignificant relationship between trait and PM capturing abilities\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/9d6e07a4fe299963c08c0c56.jpeg"},{"id":24000106,"identity":"3e92bc4b-5b6e-47ca-a108-ddf36ade4f47","added_by":"auto","created_at":"2022-07-18 18:01:38","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":692519,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between leaf chemistry and PM capturing abilities of individual species: (a) leaf nitrogen content (LNC) and PM\u003csub\u003e2.5\u003c/sub\u003e, (b) LNC and PM\u003csub\u003e2.5-10\u003c/sub\u003e, (c) LNC and PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e, (d) leaf phosphorus content (LPC) and PM\u003csub\u003e2.5\u003c/sub\u003e, (e) LPC and PM\u003csub\u003e2.5-10\u003c/sub\u003e and (f) LPC and PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e. A dashed line denotes a nonsignificant relationship between trait and PM capturing abilities\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/c997458748d1bf5dfb966b6a.jpeg"},{"id":24000104,"identity":"95a752c4-9da4-48ec-a5c4-038d83f9dee1","added_by":"auto","created_at":"2022-07-18 18:01:37","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":204073,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram representing how leaf nitrogen content (LNC), leaf phosphorus content (LPC), specific leaf area (SLA), wood density (WD), leaf length (LL) and maximum rate of electron transport (J\u003csub\u003em\u003c/sub\u003e) influence species abilities to capture fine (PM\u003csub\u003e2.5\u003c/sub\u003e), coarse (PM\u003csub\u003e2.5-10\u003c/sub\u003e) and large (PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e) particulates in the fourteen species tested in this study. The model was tested with Fisher’s C statistic, test statistic = 11.12 with 14 model degrees of freedom and \u003cem\u003eP\u003c/em\u003e = 0.676 (indicating close model-data fit). Path coefficients were calculated based on standardized values. Solid lines represent positive paths, and dashed lines represent negative paths. Asterisks denote significance: ***,\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. Nonsignificant paths were excluded from the model\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/83bfde224ef705f421ad3386.jpeg"},{"id":24000380,"identity":"9018e5e0-bd30-49ab-8e66-c89caa716c7a","added_by":"auto","created_at":"2022-07-18 18:11:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":921171,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1717092/v1/d3c911b9-3da3-4899-88d3-0d1693f1f58b.pdf"}],"financialInterests":"","formattedTitle":"Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeveloping countries such as China have experienced fundamental changes in recent decades, while gains in wealth have been achieved at a severe cost to the environment (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Air pollution is a major threat to people\u0026rsquo;s health, particularly in urban areas (Przybysz et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), due to particulate matter (PM) emissions linked to traffic and industry (Simon et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Coarse particles can cause lung diseases, while smaller particles, such as PM\u003csub\u003e2.5\u003c/sub\u003e, can be inhaled more deeply into the lungs (Beckett et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and have been widely recognized as more harmful to human health. Air pollution is estimated to contribute to at least five million premature deaths each year (WHO 2014).\u003c/p\u003e \u003cp\u003eVegetation can reduce particle concentrations and thereby improve air quality (Litschke and Kuttler \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Weerakkody et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). A leaf is the main organ for particle deposition (Weerakkody et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e) and can even biodegrade or transform pollutants into less or nontoxic molecules with its habituated microbes and endophytes (Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Recognizing what kind of plants are more powerful in capturing airborne PM is important but challenging because the retention capacity of leaves in terms of accumulating PM is influenced by a variety of factors, such as the concentration of atmospheric PM (Luo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), days after rainfall (Xu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), wind speed (Beckett et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), leaf stage (Nguyen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and sampling season (Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which are linked with surface moisture (Wang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is necessary to investigate what kind of trait could improve species\u0026rsquo; PM adsorption abilities (Janh\u0026auml;ll \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shao et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, species with smaller leaves, more complex leaf structure, waxy leaves and hairier leaves usually capture PM more efficiently (Beckett et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Dzierżanowski et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Weerakkody et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings have important significance for selecting greening tree species, which is the primary task in the construction of urban forests.\u003c/p\u003e \u003cp\u003eIn comparison to ecosystem functions, environmental stress tolerance is sometimes even more important in greening tree species selection (Chaudhary and Rathore \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Przybysz et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there are very few studies investigating species\u0026rsquo; PM adsorption abilities in relation to species\u0026rsquo; environmental stress tolerance, such as shade tolerance and drought tolerance. Therefore, it is of great importance to test whether species with high PM adsorption abilities are fast- or slow-growing species and whether they are resource-demanding species. Because light is the major limiting resource in moist forests (Zhao and He \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we specifically sought to determine how PM adsorption abilities are correlated with the light requirements of tree species (Question 1).\u003c/p\u003e \u003cp\u003eTheoretically, plant traits can track environmental changes and reflect the adaptive strategies of plants (Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Dust accumulation reduces the light availability of leaves (Zhu et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and may inhibit chlorophyll biosynthesis (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and stomatal conductance (Lewis et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which is important for plant photosynthesis (Hetherington and Woodward \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, species with a greater ability to accumulate PM might need to be more shade tolerant from the perspective of evolution. We therefore hypothesized that species with high PM accumulation abilities should be able to adapt to poor light environments (Hypothesis 1).\u003c/p\u003e \u003cp\u003eIn forest ecology, there is a well-established trade-off between the survival rate in deep shade and the growth rate in bright light (Adler et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Fast-growing species that rapidly acquire resources thrive in rich light environments, while slow-growing species that conserve resources are dominant in poor light environments (Lohbeck et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This acquisitive-conservative trade-off points to the extremes of a continuum in plant design, and the position of a species along this continuum can be quantified by its functional traits (Poorter et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wright et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For instance, an acquisitive species tends to have a high photosynthetic capacity, dark respiration rate, specific leaf area (SLA) and leaf nitrogen content (LNC) and hence a high growth rate. In contrast, conservative species tend to have thicker leaves and a higher wood density (WD), reducing the volumetric stem growth rate but facilitating leaf and stem protection and high survival rates (Chave et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this study, we tested whether species with high PM adsorption abilities are characterized by conservative or acquisitive functional traits (Question 2).\u003c/p\u003e \u003cp\u003eConservative species commonly have high WD and leaf thickness but a low SLA and photosynthetic capacity, suggesting a low growth rate strategy (Firn et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Low growth rate species tend to produce durable leaves with long life spans (Adler et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The tough leaves of these species may be associated with complex leaf surface structure and waxes, which could trap PM (Dzierżanowski et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, we hypothesized that species with high PM accumulation capacities should be characterized by conservative traits (Hypothesis 2).\u003c/p\u003e \u003cp\u003eIf these two hypotheses were confirmed, then we would test the third question relating to which traits are good predictors of the PM accumulation capacities of species (Question 3). The functional traits measured in this study, including photosynthetic capacity, leaf length, SLA, WD, LNC and leaf phosphorus content (LPC), represent relatively easily measured characteristics that can be obtained for large numbers of species (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Relationships among functional traits and PM adsorption capacities may reflect direct effects but could also result from trait coevolution (Reich et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Therefore, we used structural equation modelling (SEM) to investigate the relationships among a suite of traits and PM adsorption capacities of different size fractions.\u003c/p\u003e \u003cp\u003eTo test the first two hypotheses and establish linkages between plant functional traits and ecosystem functioning (PM adsorption in this study), we sampled leaves from fourteen evergreen tree species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and experimentally measured their PM adsorption capacities. We then quantified shade tolerance and measured six functional traits of these species and compared them with their PM adsorption abilities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe study was conducted in a 50-ha dynamic forest plot in the Heishiding Provincial Nature Reserve (23\u0026deg;27\u0026prime;N, 111\u0026deg;52\u0026prime;E; altitude, 150\u0026ndash;927 m), located in Guangdong Province, southern China. This region is covered by a subtropical evergreen broad-leaved forest and has a subtropical moist monsoon climate. The annual precipitation is approximately 1700 mm, and the mean daily temperature is 19.6\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative assessment of PM capturing ability\u003c/h2\u003e \u003cp\u003eSamples were collected from three individuals for each of the fourteen species. For each individual, 10\u0026ndash;50 fully expanded mature leaves were collected at 2\u0026ndash;5 metre above-ground with a high branch scissor. Samples from the same tree were then put in a plastic bag, sprinkled water, transported to the laboratory and stored in a refrigerator at 4\u0026deg;C until the laboratory experiment.\u003c/p\u003e \u003cp\u003eTo ensure that all samples experienced similar dust conditions, we used a dust box, a dust grinder and an air blower (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to simulate a busy road environment, an approach similar to deposition chamber experiments (Hwang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) or wind tunnel experiments (Burkhardt et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003es\u0026auml;nen et al. 2013; Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). First, we cleaned the samples by immersing them with 250 ml distilled water and washed them with a no-hair-loss brush. Next, the cleaned leaves were hung on a rope installed in an experimental box (1.4 m \u0026times; 1.2 m \u0026times; 0.8 m, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sixty grams of soil was ground to powder, sieved and blown into the box to simulate a cloud of dust and left to set for half an hour. We then removed the dusted leaves and measured the PM retained on them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe method for rinsing and weighing was similar to that described by Dzierżanowski et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The filters and small beakers were dried for 60 minutes at 80\u0026deg;C in a drying oven and then left in the weighing room for an hour to stabilize the humidity and obtain a constant weight (\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e). We immersed leaves with distilled water, cleaned them softly with a nylon brush and then washed them with distilled water. In the next step, the rinse water was filtered sequentially using 10 \u0026micro;m and 2.5 \u0026micro;m filters (Whatman, UK, Types 91 and 42, respectively), and the eluent was poured into small beakers. The filters and beakers were oven dried at 80\u0026deg;C for 8 hours and 12 hours, respectively, and then left in the weighing room for an hour to obtain constant humidity and weight (\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e). Leaf areas (\u003cem\u003eS\u003c/em\u003e) of the samples were measured using an Epson Expression 11000XL scanner (Epson, Inc., USA) and calculated using the Photoshop CC2019 software (Adobe Corp., USA). The amount of PM was expressed per unit area (cm\u003csup\u003e2\u003c/sup\u003e) of one side of the leaf surface and calculated as (\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e)/\u003cem\u003eS\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eShade tolerance estimation\u003c/h2\u003e \u003cp\u003eThe 50-ha plot (1000 m \u0026times; 500 m) was established following the ForestGEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.forestgeo.si.edu\u003c/span\u003e\u003cspan address=\"https://www.forestgeo.si.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) protocol in 2011, and all free-standing individuals with diameters at breast height greater than 1 cm were tagged, mapped, measured and identified to species. More than 270000 stems belonging to 245 species were recorded in the first census. The understorey light availability was estimated using hemispherical photography at 2352 locations randomly distributed within the 50-ha plot (Zhao and He \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and light availability was then interpolated to grids of 10 m \u0026times; 10 m using ordinary kriging in the AUTOMAP package (Hiemstra et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to estimate the environmental conditions where the individuals were located. The minimum light requirements (shade tolerance) of the species were estimated by calculating the 10th percentile of the distribution of juveniles (height\u0026thinsp;\u0026lt;\u0026thinsp;4 m) in relation to light availability. There were at least 200 juveniles for each of the 14 species in the 50-ha plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional trait measurements\u003c/h2\u003e \u003cp\u003eWe measured six traits, including leaf length (LL), maximum rate of electron transport (J\u003csub\u003em\u003c/sub\u003e), specific leaf area (SLA), wood density (WD), leaf nitrogen concentration (LNC) and leaf phosphorus concentration (LPC). Leaf length was averaged from the samples collected for PM accumulation measurements. However, the other five traits were measured separately at other times on different sampled individuals.\u003c/p\u003e \u003cp\u003eJ\u003csub\u003em\u003c/sub\u003e represents the light saturated photosynthetic capacity per leaf area (Rascher et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For J\u003csub\u003em\u003c/sub\u003e measurements, a total of 1056 leaves from 335 individuals of the 14 species were sampled. Details on the samplings and measurements can be found in our previous study (Feng et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). SLA, WD, LNC and LPC were measured in a total of 464 individuals of the fourteen species. Details on these samplings and measurements can also be found in our previous studies (Chen Y. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; He et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Shade-tolerant species with durable leaves tend to have lower J\u003csub\u003em\u003c/sub\u003e, higher WD and lower SLA than those of light-demanding species with high leaf turnover rates (Walters and Reich \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Reich \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eOne-way analysis of variance (ANOVA) was applied to test whether individual PM values differed among species. If the difference among the species was significant, then the significance of the differences between the mean values was tested using Tukey\u0026rsquo;s honest significant difference test (HSD) at α\u0026thinsp;=\u0026thinsp;0.05 with the function \u0026ldquo;HSD.test\u0026rdquo; in the R package \u0026ldquo;agricolae\u0026rdquo; (R Development Core Team \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo test the relationship between shade tolerance and PM accumulation capacities, we examined the response of PM to minimum light requirements using simple linear regressions. To test the effect of functional traits on PM accumulation capacities, we also examined the response of PM to traits using simple linear regressions. To better meet the assumptions of normality and homogenous variances, PM, shade tolerance and trait values, except for WD, were log transformed before analysis.\u003c/p\u003e \u003cp\u003eBecause functional traits are correlated with each other, bivariate relationships alone provide limited information. We used a structural equation model with the function \u0026ldquo;psem\u0026rdquo; in the R package \u0026ldquo;piecewiseSEM\u0026rdquo;. Initially, all possible paths were allowed in the model, and the complex model was further simplified by removing nonsignificant paths. This approach is useful for exploring the comparative strength of relationships. A bidirectional line was used to denote the equivalent models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 14 species studied, accumulated total suspended particles (TSP) per leaf area ranged from 1.46 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 4.11 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e ranged from 0.17 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 0.82 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e ranged from 0.12 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 1.29 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e ranged from 0.97 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 2.44 g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;1). According to one-way ANOVA, there were significant differences in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and TSP capturing abilities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among the species, whereas there were no significant differences in PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e capturing abilities among the species (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between shade tolerance and PM accumulation capacities\u003c/h2\u003e \u003cp\u003eWe found that all three PM accumulation metrics were negatively correlated with the minimum light requirements (shade tolerance) of the species, but only the relationship between PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and shade tolerance was significant (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBivariate relationships between functional traits and PM accumulation capacities\u003c/h2\u003e \u003cp\u003eFor the morphological traits, we found that PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e accumulation capacities were negatively correlated with leaf length (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c), whereas nonsignificant correlations were observed between PM\u003csub\u003e2.5\u003c/sub\u003e accumulation capacity and leaf length (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). For the metabolism traits, photosynthetic capacity (J\u003csub\u003em\u003c/sub\u003e) was negatively correlated with PM accumulation capacity for all particle size fractions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the economic traits, the relationships between specific leaf area (SLA) and PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e accumulation capacities were nonsignificant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c). Wood density (WD) was positively correlated with PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e accumulation capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef), whereas it was not significantly correlated with PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e accumulation capacities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the leaf chemical traits, leaf nitrogen content (LNC) was positively correlated with PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), whereas it was nonsignificantly correlated with PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e accumulation capacities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-c). Similar patterns were found for leaf phosphorus content (LPC), where PM\u003csub\u003e2.5\u003c/sub\u003e accumulation capacity was positively correlated with LPC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), and the relationships between PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e and LPC were nonsignificant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee, f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePath analysis\u003c/h2\u003e \u003cp\u003eAmong the six traits LNC, LPC, SLA, WD, leaf length and J\u003csub\u003em\u003c/sub\u003e, mainly J\u003csub\u003em\u003c/sub\u003e, leaf length and LPC influenced PM accumulation capacities. Leaf length decreased the PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e accumulation capacity and LPC increased the fine particle (PM\u003csub\u003e2.5\u003c/sub\u003e) accumulation capacity. The photosynthetic capacities of leaves (J\u003csub\u003em\u003c/sub\u003e), which may be related to cuticle thinness, decreased the PM retention capacities of all three size classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLeaf characteristics, including both leaf architecture and material composition, may influence the ability of a leaf surface to accumulate PM (Liang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the present study, we found that suites of functional traits displayed a tight association the PM accumulation capacity of leaf area for each species. We showed that species with a small leaf size (low LL), low photosynthetic capacity (low J\u003csub\u003em\u003c/sub\u003e), high input into construction (high WD) and a conservative nutrient use strategy (high LNC and LPC) increased in PM accumulation capacities. Our results indicate that conservative species (characterized by low J\u003csub\u003em\u003c/sub\u003e and high WD) may have higher PM accumulation capacities than acquisitive species (characterized by high J\u003csub\u003em\u003c/sub\u003e and low WD) and that species with high PM accumulation capacities are adapted to poor light environments.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLeaf size affected PM retention capacity.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe found that smaller leaves captured more PM per unit leaf area than larger leaves, especially for coarse and large particles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). In line with our results, Weerakkody et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e) found that smaller synthetic leaves accumulate more PM than larger synthetic leaves with the same surface characteristics. This correlation between leaf size and PM accumulation capacity was also supported by field surveys (Leonard et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) but not by the results of some other studies (S\u0026aelig;b\u0026oslash; et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Scanning electron microscopy images have shown that more particles are distributed near leaf edges (Weerakkody et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e); therefore, in comparison to broadleaved species, conifer species commonly capture larger amounts of particles (Freer-Smith et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, broadleaved species have been reported to have higher leaf retained PM wash-off efficiency than conifer species (Luo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and PM wash-off events are important for the net removal of airborne PM (Xu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eShade-tolerant species captured larger amounts of PM per area than light-demanding species\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe stress tolerances of species (e.g., drought tolerance or shade tolerance) are sometimes even more important than the functions of species when we design vegetation barriers for air pollution abatement (Barwise and Kumar \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Species must adapt to the shade conditions created by the deposited dust on leaves, tall buildings and other vegetation. In comparison to light-demanding species, shade-tolerant species have been shown to better survive under shaded conditions, but they have lower maximum photosynthetic rates under high light conditions (Valladares and Niinemets \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We found a significantly negative correlation between minimum light requirements and PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e retention capacities for the species. Moreover, light-demanding species usually have higher photosynthetic capacities than shade-tolerant species. Therefore, the finding that photosynthetic capacity (J\u003csub\u003em\u003c/sub\u003e) was significantly negatively correlated with the PM retention capacity of the species for all particle size classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-f) confirmed that species with high PM accumulation abilities are adapted to poor light environments. In this study, we showed that J\u003csub\u003em\u003c/sub\u003e is a good predictor and that it alone predicted 45\u0026ndash;64% (for different PM size classes) of interspecific variation in PM accumulation. Compared with other commonly used traits, such as hairiness, roughness and waxiness of the leaf surface, photosynthetic capacity (J\u003csub\u003em\u003c/sub\u003e) is objective and easily measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between economic traits and PM accumulation capacities\u003c/h2\u003e \u003cp\u003eFor leaf economics, an axis exists from \u0026lsquo;cheap\u0026rsquo; tissue investment and fast returns to \u0026lsquo;expensive\u0026rsquo; tissue investment and slow returns (Wright et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Species with \u0026lsquo;expensive\u0026rsquo; economic traits, such as low specific leaf area (SLA), tend to have a long leaf lifespan and low relative growth rates but high survival rates (Adler et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These species produce durable leaves with thick cuticles (Kitajima \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) where interactions between PM and leaves occur. We thereby expected that \u0026lsquo;expensive\u0026rsquo; leaves should have high PM accumulation capacities, and a negative relationship between SLA and PM deposition rate had been reported by a previous wind-tunnel experiment (Chiam et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unexpectedly, nonsignificant correlations were found between SLA and all three PM size fractions in this study (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This result probably occurred because we used the species mean SLA value to characterize all individuals of a species. However, intraspecific SLA variation can arise from both phenotypic plasticity and genetic diversity (Siefert et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that species with denser wood accumulated larger amounts of large particles (PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Species with high wood density (WD) represent a \u0026lsquo;slow\u0026rsquo; life history strategy (Wright et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); i.e., they have a limited potential growth rate but have low mortality risk under shaded conditions. A negative relationship between WD and leaf size was found in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and has also been reported in some previous studies (Wright et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This scenario occurs probably because species with denser wood usually have lower hydraulic conductivity and thereby cannot support large leaves as species with soft wood (Chave et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Wet leaves more easily retain PM; however, denser wood species with lower hydraulic conductivity were found to have higher PM accumulation capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). The correlations between WD and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e, as the path analysis demonstrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), were the result of a complex correlation network in the trait space.\u003c/p\u003e \u003cp\u003eWe found that leaf N and P concentrations were both positively correlated with PM\u003csub\u003e2.5\u003c/sub\u003e accumulation capacities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, d). N and P are the two most limiting elements to plants and have specific functions in leaves. N is important for enzymatic activity, and P affects protein synthesis. Greater leaf N and P concentrations may enhance metabolic activity and facilitate the formation of complex leaf surface structures.\u003c/p\u003e \u003cp\u003eExcept for thicker cuticles for adsorbing PM, trees with \u0026lsquo;expensive\u0026rsquo; leaves (low SLA) and denser wood may have higher structural stability (Reich et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and sway less in wind, which may reduce the resuspension of PM back into the air (Chiam et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and further improve their PM capturing efficiencies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAccumulation of different size fractions of PM\u003c/h2\u003e \u003cp\u003ePM of different sizes has varying effects on human health (Przybysz et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), with the smallest fraction considered to be the most hazardous. Particles\u0026thinsp;\u0026gt;\u0026thinsp;100 \u0026micro;m in diameter have not been measured in many studies (Przybysz et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, large particles near busy urban roads are not harmless, especially when they contain large amounts of heavy metals such as mercury and lead (Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We did not exclude PM\u003csub\u003e\u0026gt;\u0026thinsp;100\u003c/sub\u003e in the washing solutions, but the ground soils used in this experiment were sieved through a metal sieve (retention 300 \u0026micro;m) before being blown into the experimental box. Large particles (PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e) made up the greatest mass proportion (48.9\u0026ndash;75.3%) of total suspended particles, followed by the coarse fraction (7.8\u0026ndash;35.6%) and then the fine fraction (9.7\u0026ndash;36.1%). We found that the accumulation of large particles on leaves was related to leaf morphology (leaf length), while the accumulation of fine particles was related to leaf chemistry. The mechanisms behind this result need further research. We hypothesize that large particles may mainly be captured by interception, while fine particles may mainly be captured by waxes or deposited in the microstructures (e.g., grooves), which may be related to LNC and LPC. Fine particles are not easily removed, which may be a possible reason for the poor correlation between morphology representing structural stability and fine particle accumulation capacities.\u003c/p\u003e \u003cp\u003eTrees stand regularly occur along roads in many cities, and therefore, quantifying individual-level PM accumulation capacity is needed. We encourage future studies to integrate leaf-level PM accumulation capacity with leaf density and crown size to obtain whole-plant PM accumulation capacity and link it with functional traits of species.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results provide encouraging evidence of correlations between species functional traits and abilities to capture airborne particulates, including PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e. Light saturated photosynthetic capacity per leaf area (J\u003csub\u003em\u003c/sub\u003e) was negatively correlated with all three PM size fractions, indicating that conservative species accumulated larger amounts of PM than acquisitive species. Denser wood species and smaller leaves captured more large particles (PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e), while \u0026lsquo;expensive\u0026rsquo; leaves (high leaf N or leaf P) captured more fine particles (PM\u003csub\u003e2.5\u003c/sub\u003e), indicating different mechanisms for capturing particles of different sizes. Although the correlations between economic traits (specific leaf area and wood density) and PM deposition were weak, our findings highlight the great potential of functional traits as a tool for linking species\u0026rsquo; efficiency at air purification with their growth strategies and environmental stress tolerances.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003e contribution \u003c/strong\u003eKZ designed the research. KZ, DL, JF, YC and DH performed the experiments and conducted the fieldwork. KZ analysed the data and wrote the first draft. All authors aided in revising the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003eThis study was financially supported by the National Natural Science Foundation of China (31800366) to KZ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003eData associated with this study are deposited in the TRY Plant Trait Database: https://www.try-db.org/TryWeb/Data.php#68.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval. \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate. \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication. \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdler PB, Salguero-G\u0026oacute;mez R, Compagnoni A et al (2014) Functional traits explain variation in plant life history strategies. 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Environ Sci Pollut Res 26:36764\u0026ndash;36775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-019-06635-4\u003c/span\u003e\u003cspan address=\"10.1007/s11356-019-06635-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary of the 14 species used in this study for which PM accumulation capacity and functional trait data were collected. PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e2.5-10\u003c/sub\u003e, PM\u003csub\u003e\u0026gt;10\u003c/sub\u003e per leaf area (g\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e); photosynthetic capacity (J\u003csub\u003em\u003c/sub\u003e, \u0026mu;mol\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e); wood density (WD, g\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e); specific leaf area (SLA, cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;g\u003csup\u003e-1\u003c/sup\u003e); leaf length (LL, cm); leaf nitrogen content (LNC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e); and leaf phosphorus content (LPC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) are illustrated (mean \u0026plusmn; SD).\u003c/p\u003e\n\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003eSpecies\u003c/p\u003e\n\u003cp\u003e(Family)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e(g\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5-10\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e(g\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003ePM\u003csub\u003e\u0026gt;10\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e(g\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eJ\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e(\u0026mu;mol\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eWD\u003c/p\u003e\n\u003cp\u003e(g\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eSLA\u003c/p\u003e\n\u003cp\u003e(cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;g\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eLL\u003c/p\u003e\n\u003cp\u003e(cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eLNC\u003c/p\u003e\n\u003cp\u003e(g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eLPC\u003c/p\u003e\n\u003cp\u003e(g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eCryptocarya concinna\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Lauraceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.82 \u0026plusmn; 0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.29 \u0026plusmn; 0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e2.01 \u0026plusmn; 1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e33 \u0026plusmn; 17.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.56 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e143.3 \u0026plusmn; 49.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e9.2 \u0026plusmn; 1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e18.3 \u0026plusmn; 2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.67 \u0026plusmn; 0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eXanthophyllum hainanense\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Polygalaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.55 \u0026plusmn; 0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.88 \u0026plusmn; 0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e2.44 \u0026plusmn; 0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e34.1 \u0026plusmn; 13.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.71 \u0026plusmn; 0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e149 \u0026plusmn; 17.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e7.6 \u0026plusmn; 1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e22.2 \u0026plusmn; 2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.66 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eCastanopsis chinensis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Fagaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.31 \u0026plusmn; 0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.64 \u0026plusmn; 0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.69 \u0026plusmn; 0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e49.4 \u0026plusmn; 19.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.6 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e110.7 \u0026plusmn; 25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e8.5 \u0026plusmn; 2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e12.8 \u0026plusmn; 2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.41 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eLindera chunii\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Lauraceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.31 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.61 \u0026plusmn; 0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.63 \u0026plusmn; 0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e42 \u0026plusmn; 16.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.56 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e161.5 \u0026plusmn; 29.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e10.2 \u0026plusmn; 1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e15 \u0026plusmn; 1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.72 \u0026plusmn; 0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eRapanea neriifolia\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Myrsinaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.25 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.89 \u0026plusmn; 0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.39 \u0026plusmn; 0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e43.2 \u0026plusmn; 17.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.71 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e101.5 \u0026plusmn; 18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e13.2 \u0026plusmn; 2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e8.8 \u0026plusmn; 1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.37 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eArdisia quinquegona\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Myrsinaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.34 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.77 \u0026plusmn; 0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.35 \u0026plusmn; 0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e40.2 \u0026plusmn; 24.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.57 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e149.1 \u0026plusmn; 30.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e11.8 \u0026plusmn; 1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e15.4 \u0026plusmn; 1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.54 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eMachilus breviflora\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Lauraceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.33 \u0026plusmn; 0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.24 \u0026plusmn; 0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.62 \u0026plusmn; 0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e40.9 \u0026plusmn; 12.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.58 \u0026plusmn; 0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e102.9 \u0026plusmn; 15.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e10.1 \u0026plusmn; 0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e10.5 \u0026plusmn; 0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.44 \u0026plusmn; 0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eCanarium album\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Burseraceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.26 \u0026plusmn; 0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.98 \u0026plusmn; 0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e44.3 \u0026plusmn; 23.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.33 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e219.7 \u0026plusmn; 54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e19.9 \u0026plusmn; 4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e20 \u0026plusmn; 2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e1.02 \u0026plusmn; 0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eSchima superb\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Theaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.2 \u0026plusmn; 0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.27 \u0026plusmn; 0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.43 \u0026plusmn; 0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e56.1 \u0026plusmn; 10.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.64 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e109.9 \u0026plusmn; 23.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e14.7 \u0026plusmn; 2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e11.5 \u0026plusmn; 1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.31 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eSymplocos congesta\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Symplocaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.24 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.67 \u0026plusmn; 0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.97 \u0026plusmn; 0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e55.8 \u0026plusmn; 21.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.53 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e119 \u0026plusmn; 20.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e13.9 \u0026plusmn; 2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e11.4 \u0026plusmn; 1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.37 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eLithocarpus calophyllus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Fagaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.27 \u0026plusmn; 0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.31 \u0026plusmn; 0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.13 \u0026plusmn; 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e50.3 \u0026plusmn; 17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.63 \u0026plusmn; 0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e105.9 \u0026plusmn; 21.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e16.2 \u0026plusmn; 2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e12.1 \u0026plusmn; 1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.55 \u0026plusmn; 0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eItea chinensis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Saxifragaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.19 \u0026plusmn; 0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.24 \u0026plusmn; 0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.23 \u0026plusmn; 0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e73.9 \u0026plusmn; 25.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.57 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e178.5 \u0026plusmn; 36.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e11.4 \u0026plusmn; 1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e15 \u0026plusmn; 2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.69 \u0026plusmn; 0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eMachilus litseifolia\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Lauraceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.27 \u0026plusmn; 0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.12 \u0026plusmn; 0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.17 \u0026plusmn; 0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e60.5 \u0026plusmn; 17.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.55 \u0026plusmn; 0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e115.9 \u0026plusmn; 28.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e15.1 \u0026plusmn; 2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e10.8 \u0026plusmn; 2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.44 \u0026plusmn; 0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"209\"\u003e\n\u003cp\u003e\u003cem\u003eSchefflera octophylla\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(Araliaceae)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.17 \u0026plusmn; 0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.32 \u0026plusmn; 0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e0.97 \u0026plusmn; 0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e58.6 \u0026plusmn; 25.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.29 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e179.8 \u0026plusmn; 46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e16 \u0026plusmn; 3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e13.7 \u0026plusmn; 1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.6 \u0026plusmn; 0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Airborne particulate matter, Phytoremediation, Green infrastructure, Air pollution, PM retention, PM2.5, Ecosystem functioning, Functional traits","lastPublishedDoi":"10.21203/rs.3.rs-1717092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1717092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParticulate air pollution is a serious threat to human health, especially in urban areas, and trees can act as biological filters and improve air quality. However, studies on greening tree species selection are rare. We measured three particular matter adsorption metrics (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e captured per leaf area) and six functional traits for each of fourteen species and estimated their minimum light requirements based on field surveys. We found that shade-tolerant species captured more coarse particles (PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e) than light-demanding species. For traits, a strong negative correlation was found between photosynthetic capacity and adsorption capacity for all three PM size fractions, indicating that in comparison to acquisitive species, conservative species captured larger amounts of particles. Moreover, denser wood species and smaller leaves were more efficient in capturing large particles (PM\u003csub\u003e\u0026gt;\u0026thinsp;10\u003c/sub\u003e), while species with \u0026lsquo;expensive\u0026rsquo; leaves (high leaf N or P) were more efficient in capturing fine particles (PM\u003csub\u003e2.5\u003c/sub\u003e), indicating that capturing large and fine particles was related to mechanical stability traits and leaf surface traits, respectively. Our results demonstrated that the metabolism (e.g., photosynthetic capacity) and chemistry (e.g., leaf N and leaf P) of leaves help explain species capacity to capture PM. We encourage future studies to investigate the ecosystem functions and stress tolerance of tree species with the same framework and trait-based methods.\u003c/p\u003e","manuscriptTitle":"Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-18 18:01:35","doi":"10.21203/rs.3.rs-1717092/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2022-10-15T15:38:15+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-08-01T11:47:46+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-16T21:30:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2022-06-16T13:56:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-06-07T05:53:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2022-06-01T15:54:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36041085-f41a-4cf0-93f6-99ba9542a6b7","owner":[],"postedDate":"July 18th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-12-03T18:49:16+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-18 18:01:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1717092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1717092","identity":"rs-1717092","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}