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Review

Effects of Leaf Trait Variability on PM Retention: A Systematic Review

by
Wenkai Xue
1,2,
Yongjun Lin
1,2,
Zhengqi Sun
1,2,
Yuchong Long
1,2,
Dele Chen
1,2 and
Shan Yin
1,2,3,*
1
School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
2
Shanghai Urban Ecosystem National Observation and Research Station, National Forestry and Grassland Administration, Shanghai 200240, China
3
Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station, Ministry of Science and Technology, Ministry of Education, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(2), 170; https://doi.org/10.3390/atmos16020170
Submission received: 28 December 2024 / Revised: 27 January 2025 / Accepted: 31 January 2025 / Published: 2 February 2025
(This article belongs to the Special Issue The 15th Anniversary of Atmosphere)
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Abstract

:
Airborne particulate matter (PM) is one of the most urgent urban environment problems in the world today. The urban ecosystem has been identified as a potentially promising solution to reduce the airborne PM based on the ability of plants to retain PM. Numerous studies have been conducted to explore the process and mechanism of atmospheric PM retention by plant leaves in the past. In this study, in order to better summarize previous research, particularly the impact of leaf traits on PM retention, and to provide guidance for the selection of tree species for nature-based urban PM solutions, a systematic review was carried out using the method recommended in the PRISMA, and a total of 49 articles were selected. It was found that: 1. Asian countries contribute the majority of the proportion (32, 65%). Following behind are European countries (13, 26.5%). The American countries contribute two cases. 2. Among all the tree species, Ginkgo biloba (16), Euonymus japonicus (11), Magnolia denudate (9), Styphnolobium japonicum (9), Magnolia grandiflora (8), and Prunus cerasifera (8) emerged as hot species in research. 3. Leaf area and shape emerged as the two most frequently discussed macro-indicators, while roughness, hairiness, and stomatal characteristics were the top three micro-indicators explored. 4. Roughness and stomata, respectively, play crucial roles in capturing larger PM particles and retaining fine and ultrafine PM through their recessed structures. Trichomes decrease the likelihood of particle resuspension and boosts the efficiency of PM retention. 5. Leaves with high rigidity and complex multi-faceted leaf shapes are typically presumed to exhibit higher PM retention efficiency for higher edge effects and increased interleaf turbulence. Furthermore, with rigidity and edge effects ensured, a larger leaf area is beneficial for retaining PM.

1. Introduction

With the current rapid urbanization, traffic and industrial activities have become more intense and frequent, while transport and industrial activities are considered to be the most important source of PM [1]. Thus, airborne particulate matter (PM, i.e., particles 0.001–100 μm in size) [2] is one of the most urgent urban environmental problems in the world today [3]. Meanwhile, the United Nations (UN) has forecasted that the urban population will break through more than 2.5 billion by 2050 [4]. This projection implies that a greater number of individuals will be at risk of developing cardiovascular and respiratory diseases due to exposure to PM10 [5,6,7]. PM10, defined as particulate matter where 50% of all particles are less than 10 μm in aerodynamic diameter, can penetrate airways and reach the upper lung regions [8].
The urban ecosystem has been identified as a potentially promising solution to reduce the airborne PM based on the ability of plants to retain PM [1,9]. It is a kind of implementation of nature-based solutions (NBS) in urban environments for their sustainability and harmlessness [10]. Modeling studies indicate that trees could potentially serve as a solution to clean the air of PM in urban environments. For instance, trees planted in the Greater London area are estimated to annually remove between 800 and 2000 tons of PM10 [11]. Similarly, public trees in Strasbourg, France, are projected to remove 11.77 tons of PM10 and 4.51 tons of PM2.5 each year [12]. In addition, a study conducted in Beijing, China, revealed that the number of PM particles on plant leaves reached up to 50,961.50 mm−2 [13]. With the huge potential benefits of urban plants in purifying airborne PM, numerous studies have been conducted to explore the process and mechanism of atmospheric particulate matter retention by plants, and leaves, as the most important organ of airborne PM retention [6,14,15].
However, despite numerous studies exploring the relationship between leaf traits and PM retention, little research has clearly summarized the connection between individual leaf traits and PM retention capacity. Moreover, systematic reviews that identify the most efficient leaf trait for capturing deposited particulate matter are notably scarce [1].
This paper provides a systematic review of the literature on the relationship between leaf trait indicators and PM retention over the past 15 years, detailing and summarizing the impact of various macroscopic and microscopic leaf traits on leaf PM retention, providing guidance for the selection of tree species for nature-based urban PM solutions.

2. Materials and Methods

2.1. Literature Screening

The systematic reviews were carried out using the method recommended in the PRISMA statement [16]. Literature searching was performed by using the database of Web of Science. The literature was screened using the following phrases: “PM retention” or “PM deposition” or “PM accumulation” or “PM detention” or “PM capture” or “PM retain” and “leaf” or “leaves”. Papers meeting the following criteria were chosen: (1) were published in a peer-reviewed journal between 2010 and 2025; (2) concentration of background particulate matter concentration was clearly quantified; (3) PM deposited on leaves was quantified and presented in concentration or number; (4) sampling location was clear; and (5) leaf traits or species were defined.

2.2. Data Analysis

Firstly, the selected literature was reviewed thoroughly to extract valuable data and information. For cases where data were not reported in the article but presented in charts, we used the WebPlotDigitizer tool (https://apps.automeris.io/wpd4/, accessed on 10 November 2024) to extract data from the charts representing the exact concentrations. Secondly, hot indicators were extracted from the selected literature and divided into two principal groups: macro-morphology and micro-morphology, according to the characteristics of the hot indicators. Thirdly, a statistical analysis was performed on these indicators by year.
Geographical scope was obtained from the selected literature, including cities and the longitude and latitude of the test sites (if the latitude and longitude of the city where the experiment was located were not specified, the geographical coordinates of its center of mass were obtained from (http://www.maps4gis.com/jsonwd/chaxun.html, accessed on 27 September 2024) as a substitute for the latitude and longitude of the city).

3. Results

After strictly following the methods recommended in the PRISMA statement, a total of 49 articles were selected that met the requirements (Figure 1, Table 1).

3.1. Geographical Scope

The 49 selected papers that met the requirements came from 16 countries, including China (25), South Korea (4), United Kingdom (4), Iran (1), Italy (2), India (1), Poland (2), Norway (1), Belgium (2), Germany (1), Brazil (1), Australia (1), Singapore (1), Colombia (1), South Africa (1), and Russia (1). Among them, Asian countries (China, South Korea, Iran, India, and Singapore) contributed the majority of the proportion (32, 65%). Following behind were European countries (United Kingdom, Italy, Poland, Norway, Belgium, Germany, and Russia) with (13, 26.5%). The American countries (Brazil and Colombia) contributed two cases. An African country (South Africa) and an Oceanian country (Australia) contributed one case each (Figure 2).

3.2. Interannual Trends in the Number of Publications and Hot Indicators

By reviewing the selected literature thoroughly, a total of 10 hot indicators of leaf traits were found, including 5 macro-indicators and 5 micro-indicators. Among them, the 5 macro-indicators are only species (OS); leaf shape (LS); leaf area (LA); one-sided leaf area per unit of dry mass (special leaf area, SLA); and length of leaf, width of leaf, and length–width ratio (L, W, L–W R). The 5 micro-indicators are stomatal conductance (SC); stoma (S, including stoma density and stoma size); hairs/trichomes (H, including length and density of leaf hairs); wax layer (WL); and roughness (R, including wrinkles, ridge, furrows, and grooves of leaf).
Over the past decade, starting from 2011, the number of publications on the relationship between leaf traits and PM retention has generally shown an upward trend, peaking in 2020 (8 papers) (Figure 3). It shows that this field has gradually attracted the attention of researchers. From the perspective of the degree of involvement of the two principal groups, scholars pay more attention to micro traits than macro traits. Meanwhile, according to the percentage accumulation chart of hot indicators over the years, except for the year 2024 when the macroindicators had one more data point than the micro-indicators and the year 2022 when they were equal, micro-indicators received more attention in other years (Figure 3). Among all the macro-indicators, LA and LS are the two most frequently discussed indicators, with a total of 14 and 21 mentions, respectively, in all the selected literature. While, among all the micro-indicators, R, H, and S are the three most frequently discussed indicators in all the selected literature, with a total of 35, 30, and 27 mentions each (Table 2).

3.3. Main Species Involved in the Study of Selected Publications

In the selected literature, a total of 268 plant species were involved, among them, broadleaf trees were the main research subjects, accounting for 93.66% (251/268) of all studied tree species, while only 17 coniferous species were reported. Through the statistical analysis of the number of reports for each tree species, it was found that: Ginkgo biloba (16 studies), Euonymus japonicus (11 studies), Magnolia denudate (9 studies), Styphnolobium japonicum (9 studies), Magnolia grandiflora (8 studies), and Prunus cerasifera (8 studies) are research hotspots (Figure 4).

4. Discussion

4.1. Macro-Morphology

Numerous studies have indicated that leaf shape and size influence the ability of plants to capture PM [36].

4.1.1. Leaf Area

The size of leaves significantly influences the density of PM retained on leaf surfaces (p < 0.05, r = −0.415, n = 36) [20]. In terms of PM capture efficiency, smaller leaves exhibit a higher density of PM retention, and needle leaves are generally believed to have a higher PM retention capacity than broad leaves [30,41,57,60]. This phenomenon is believed to be attributed to the reduced tendency of smaller leaves to move with the wind, coupled with larger edge effects (higher perimeter-to-surface area ratio) in smaller leaves, resulting in a higher rate of PM impaction [18,26]. The leaf edge is a further macromorphological characteristic together with the leaf shape, identified as driving parameters for PM capture [10]. However, some researchers have observed that the smallest leaves exhibit the least effective PM accumulation. They attribute this result to the lower rigidity of the selected leaves and their consequent reduced ability to withstand polluted airflow, which diminishes turbulence around the leaf boundary and reduces the amount of PM retained on the leaf surface [61]. That is to say, it is higher rigidity, rather than a smaller area, that makes PM retention more efficient. Therefore, species with higher rigidity and complex morphology are the most effective in retaining PM [62].

4.1.2. Leaf Shape

The influence of different leaf shapes on the PM retention capacity were studied by cutting the same plant leaf material into different shapes of the same size (28.9 cm2), and the results showed that palmate shapes seem significantly more efficient in trapping PM (including PM1, PM2.5 and PM10) compared to elliptical or linear leaves (p < 0.001), as they may create more turbulence in the boundary air layer with their complex shape and “tip-like” areas [26]. This conclusion was further confirmed by subsequent studies [10,36,63]. Xu et al. discovered that the aspect ratio exhibited a negative and significant correlation with PM retention capacity at the 0.05 significance level (r = −0.402, n = 36), suggesting that narrow leaves retain less PM compared to wider leaves [20]. This finding aligns with Weerakkody et al.’s observations that narrow leaves can bend more easily with wind flow without swaying, potentially leading to lower turbulence levels, which is disadvantageous for PM impaction onto leaves [26]. Additionally, Leonard et al. suggested that leaves with narrow bases have higher surface-specific drag and flutter more erratically than leaves with broader bases, which may cause the resuspension of PM [14]. To conclude, palmate leaves with a lower aspect ratio and broader bases are more likely to retain more PM.

4.1.3. SLA

Muhammad et al. suggested that SLA (together with the leaf dissection index, LDI) is a good predictor of PM accumulation and immobilization by vegetation, as a proxy for the complex interactions of various leaf morphological characteristics [56]. Vásquez-Bedoya et al. found that [57] leaf SLA showed a positive significant correlation with PM retention (PM10: p < 0.05; PM2.5: p < 0.001). However, Koch et al. hold the opposite conclusion that leaves with larger SLA accumulate less PM for the reason that leaves with a smaller area tend to be rigid at the same dry mass [9], which is consistent with the conclusion made by Park et al. [21]. Generally, the smaller the SLA within the same leaf area, the greater the rigidity of the leaf. However, once rigidity reaches a certain threshold, further decreases in SLA have little effect on retaining PM. Therefore, under the condition that SLA can ensure a certain level of rigidity, the larger the SLA of leaves with the same mass, the larger the area that can be formed to retain PM.

4.2. Micro-Morphology

The micro-morphology of leaves, such as stomatal density, waxy layer thickness, trichomes and roughness, is closely related to its PM retention capacity [18,30,36].

4.2.1. Stomata

The number and density of stomata were significantly positively correlated with PM retention. Li et al. found that species with better PM retention capacity have a large stomatal density, and this characteristic makes it easier for particles to gather around the stomatal aperture (PM2.5: R = 0.96, p < 0.01) [30]. In addition, larger stomatal density can make the transpiration of the leaves stronger, which makes it easier for particles to be distributed, resulting in an increased deposition rate of particles [41,64,65]. It is worth mentioning that compared with short diameter and small area of the stomata, a longer stomatal diameter has more negative effect on PM retention (PM10: R = −0.43; PM2.5: R = −0.44) [30]. However, disagreement on the impact of the presence and density of stomata on PM retention still exists. Mo et al. reported results indicating that plant leaves with low stomatal density facilitate PM retention, as minimal PM retention occurs near the stomata [66]. Shao et al. reported that a significant relationship between the size and open status of the stomata and PM retention was not found [33], while Redondo-Bermudez et al. found that stomatal density was positively correlated with PM retention only when the grooves around the stomata are deep and wide enough to retain PM [67]. To sum up, the retention of PM by stomata in leaves primarily depends on their recessed structures. Stomata that are excessively smooth cannot effectively retain PM, while, when the stomatal depression structure is effective, a higher density of stomata correlates with greater efficiency in dust retention. Additionally, a longer stomatal diameter can decrease the PM retention effect.

4.2.2. Trichomes/Hairs

Trichomes are an important trait that play a role in the deposition of PM [57]. Compared with trichomes-removed or smooth leaves, leaves with the trichomes present showed better PM retention capacity [28,33,68]. Trichomes increase the leaf area by several folds and provide an additional surface for particulate retention [69]. Moreover, leaf trichomes can reduce resuspension by wind or rain and increase the surface roughness [27,70]. By observing SEM images, leaves with larger trichomes and hairs were found responsible for capturing larger PM with size ranges higher than the respirable range, while smooth leaves with smaller leaf hairs retained smaller particles, which comes under the respirable range [39]. However, some research still exists that did not find the interaction between amount and length of trichomes and PM accumulation [9,61,71].

4.2.3. Wax Layer

The amount of wax differed significantly among species [51]. Compared with broad-leaved tree species, leaves of coniferous plants are wavy with deep regular textures, and more oil is secreted between the texture, resulting in thick wax layers, which are conducive to the trapping of more particles on the leaf surface [30]. The wax PM showed significant positive correlations with epicuticular wax loads (Kruskal–Wallis test, p < 0.001) [21,51].

4.2.4. Roughness

Roughness is a comprehensive index used to describe the protrusions and depressions on leaf surfaces caused by micro-characteristics such as wrinkles, ridges, and grooves. A rough leaf surface has a stronger PM retention capacity than a smooth surface [18,61], and high leaf roughness can enhance the PM retention capacity when other leaf traits are similar [20]. Among the features that affect the surface roughness, grooves are the focus. Dense and deep grooves can increase the roughness of the leaf surface, which is beneficial for adsorbing PM and preventing PM resuspension [72,73]. In contrast, leaves with shallower grooves are less able to absorb dust [26]. In addition, the width of grooves is also positively correlated with the retention of PM [74], for the narrower the width of grooves, the smaller the contact area between PM and the grooves, and the lower the PM retention. It is found that the particles are concentrated precisely into grooves [36].

5. Conclusions

Numerous studies have shown that the dust retention capacity of leaves is determined by both macroscopic and microscopic leaf traits (Figure 5). The real-world effectiveness of these traits in capturing and retaining PM can be enhanced or limited by other influential variables [1,19,20]. No single leaf trait can dominate all other traits to determine the retention capacity of PM [17].
Generally speaking, the PM retention per unit area of leaves is primarily related to their microscopic traits. Roughness affects the retention of larger PM, and the wider and denser the grooves, the greater the space available for retaining particles. Additionally, some tree species can secrete oils, which increases the PM retention efficiency of the wax layer. Stomata are more effective in retaining fine and ultrafine PM [21,39]. Trichomes, to some extent, increase the contact area between the leaf surface and airborne PM, thereby reducing the possibility of particle resuspension and enhancing the PM retention efficiency of the leaves.
The overall PM retention of leaves is linked to their macroscopic traits based on the unit PM retention. Large leaf areas are often considered to have lower unit PM retention efficiency, because under the same SLA conditions, large leaves tend to be softer and have lower rigidity, reducing their ability to withstand polluted airflow and increasing their tendency to move with the wind, which leads to a reduction in the amount of PM retained on the leaf surface. However, when considering overall dust retention, a larger leaf area is not necessarily disadvantageous for leaf PM retention. Regarding the leaf shape, existing research generally supports that palmate shapes seem more efficient in PM trapping compared to elliptical or linear leaves, as they may create more turbulence in the boundary air layer with their complex shape and have higher edge effects.
Different leaf traits have varying effects on the retention of particles of different sizes. Therefore, in the construction of urban forest communities, we can maximize the capture of airborne PM by combining different tree species. However, in past studies, the recommendations provided by researchers were often based on the idealized assumption that leaf traits are only related to species and overlooked the fact that leaf traits can also change with variations in pollutant concentrations. It is important to note that the concentration level of pollutants not only determines the background value of PM but also affects the development and expression of plant leaf traits. The same batch of plants will exhibit different PM retention capabilities in different PM pollution environments, showing different rankings [32]. For instance, it is found that the stomatal widths and lengths in the leaves of the 10 plant species differed considerably in different urban areas [31]. Thus, in the process of urban forest construction, the selection of dust-retention tree species not only needs to consider the combination of leaf traits but also the pollution resistance of the tree species themselves.

Author Contributions

Conceptualization, W.X., D.C. and S.Y.; methodology, W.X. and D.C.; software, W.X.; formal analysis, W.X. and Y.L. (Yongjun Lin); investigation, W.X. and Z.S.; data curation, W.X. and Y.L. (Yuchong Long); writing—original draft, W.X.; writing—review and editing, W.X. and S.Y.; visualization, W.X.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32171865).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PMParticulate matter
OSOnly species, papers solely focus on tree species without detailing specific leaf traits
LSLeaf shape
LALeaf area
SLASpecial leaf area, one-sided leaf area per unit of dry mass
L, W, L–W RLength of leaf, width of leaf, and length–width ratio
SCStomatal conductance
SStomata
HHairs/trichomes
WLWax layer of leaf surface
RRoughness of leaf surface

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Atmosphere 16 00170 g001
Figure 1. Systematic filtration and selection process of literature. * The search formula used in the WOS database is TS = (“particulate matter” or “PM”) AND TS = (“retention” or “deposition” or “accumulation” or “detention” or “capture” or “retain”) AND TS = (“leaf” or “leaves”). Reason 1: Studies were published in a peer-reviewed journal between 2010 and 2025. Reason 2: Concentration of background particulate matter concentration was clearly quantified. Reason 3: PM deposited on leaves was quantified and presented in concentration or number. Reason 4: Sampling location was clear. Reason 5: Leaf traits or species were defined.
Figure 1. Systematic filtration and selection process of literature. * The search formula used in the WOS database is TS = (“particulate matter” or “PM”) AND TS = (“retention” or “deposition” or “accumulation” or “detention” or “capture” or “retain”) AND TS = (“leaf” or “leaves”). Reason 1: Studies were published in a peer-reviewed journal between 2010 and 2025. Reason 2: Concentration of background particulate matter concentration was clearly quantified. Reason 3: PM deposited on leaves was quantified and presented in concentration or number. Reason 4: Sampling location was clear. Reason 5: Leaf traits or species were defined.
Atmosphere 16 00170 g001
Atmosphere 16 00170 g002
Figure 2. Global sites of study on leaf PM retention.
Figure 2. Global sites of study on leaf PM retention.
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Atmosphere 16 00170 g003
Figure 3. Interannual trends in hot indicators and the number of publications. (a) Interannual trends in the number of publications on PM retention. (b) Interannual trends in hot indicators of leaf traits. Note: OS (only species); LS (leaf shape); LA (leaf area); SLA (special leaf area); L, W, L–W R (length of leaf, width of leaf, and length–width ratio); SC (stomatal conductance); S (stoma, including stoma density and stoma size); H (hairs/trichomes, including length and density of leaf hairs); WL (wax layer); R (roughness, including wrinkles, ridge, furrows, and grooves of leaf).
Figure 3. Interannual trends in hot indicators and the number of publications. (a) Interannual trends in the number of publications on PM retention. (b) Interannual trends in hot indicators of leaf traits. Note: OS (only species); LS (leaf shape); LA (leaf area); SLA (special leaf area); L, W, L–W R (length of leaf, width of leaf, and length–width ratio); SC (stomatal conductance); S (stoma, including stoma density and stoma size); H (hairs/trichomes, including length and density of leaf hairs); WL (wax layer); R (roughness, including wrinkles, ridge, furrows, and grooves of leaf).
Atmosphere 16 00170 g003
Atmosphere 16 00170 g004
Figure 4. Reported tree species and their frequencies from selected publications. Note: 1. each sector of the pie chart on the left, clockwise from dark to shallow, corresponds to a tree species arranged in a zigzag pattern on the right (as indicated by the symbol above the center of the two images). 2. The numbers above the sectors in the pie chart represent the statistical number of that tree species.
Figure 4. Reported tree species and their frequencies from selected publications. Note: 1. each sector of the pie chart on the left, clockwise from dark to shallow, corresponds to a tree species arranged in a zigzag pattern on the right (as indicated by the symbol above the center of the two images). 2. The numbers above the sectors in the pie chart represent the statistical number of that tree species.
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Atmosphere 16 00170 g005
Figure 5. A brief summary of the impact of leaf macro and micro traits on PM retention. The electron microscope images of the microscopic traits are from the following sources: Stomata [30], Roughness [20], Wax layer [38], Hairs [38].
Figure 5. A brief summary of the impact of leaf macro and micro traits on PM retention. The electron microscope images of the microscopic traits are from the following sources: Stomata [30], Roughness [20], Wax layer [38], Hairs [38].
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Table 1. Classification results and experimental location of the 49 selected papers.
Table 1. Classification results and experimental location of the 49 selected papers.
Principal SectionsLocationReferences
Macro-morphology, Micro-morphologyJinzhong, China[17,18,19,20]
Macro-morphology, Micro-morphologySeoul, South Korea[21,22]
Macro-morphology, Micro-morphologyKunming, China[23]
Macro-morphologyBeijing, China[24,25]
Macro-morphology, Micro-morphologyStafford, UK[26,27]
Micro-morphologySeoul, South Korea[28,29]
Macro-morphology, Micro-morphologyChangsha, China[30]
Micro-morphologyLanzhou, China[31]
Macro-morphologyTehran, Iran[32]
Macro-morphology, Micro-morphologyHangzhou, China[33]
Micro-morphologyBeijing, China[7,34,35]
Micro-morphologyReggio Emilia, Italy[36]
Macro-morphology, Micro-morphologyTerni, Italy[10]
Micro-morphologyShenyang, China[37]
Micro-morphologyGuiyang, China[38]
Macro-morphology, Micro-morphologySingapore[15]
Macro-morphology, Micro-morphologyChhattisgarh, India[39]
Macro-morphology, Micro-morphologyBeijing, China[40,41,42]
Macro-morphology, Micro-morphologyPoland and Norway[43]
Macro-morphologyDestelbergen, Belgium[9]
Macro-morphology, Micro-morphologyShanghai, China[6]
Macro-morphology, Micro-morphologyZhengzhou, China[44]
Micro-morphologyWuhan, China[45]
Micro-morphologyHangzhou, China[46]
Micro-morphologyShanghai, China[47]
Macro-morphology, Micro-morphologyHannover, Germany[48]
Macro-morphology, Micro-morphologyNanchang, China[49]
Micro-morphologySão Paulo, Brazil[50]
Micro-morphologyPęchcin, Poland[51]
Macro-morphologyGuildford, UK[52]
Macro-morphology, Micro-morphologyUrumqi, China[53]
Macro-morphology, Micro-morphologyBerkshire, UK[54]
Micro-morphologyWarsaw, Poland[55]
Macro-morphology, Micro-morphologySydney, Australia[14]
Macro-morphology, Micro-morphologyAntwerp, Belgium[56]
Macro-morphology, Micro-morphologyMedellin, Colombia[57]
Micro-morphologySekhukhune, South Africa[58]
Micro-morphologyIrkutsk, Russia[59]
Table 2. Annual number of literature reporting hot indicators. Note: OS (only species); LS (leaf shape); LA (leaf area); SLA (special leaf area); L, W, L–W R (length of leaf, width of leaf, and length–width ratio); SC (stomatal conductance); S (stoma, including stoma density and stoma size); H (hairs/trichomes, including length and density of leaf hairs); WL (wax layer); R (roughness, including wrinkles, ridge, furrows, and grooves of leaf).
Table 2. Annual number of literature reporting hot indicators. Note: OS (only species); LS (leaf shape); LA (leaf area); SLA (special leaf area); L, W, L–W R (length of leaf, width of leaf, and length–width ratio); SC (stomatal conductance); S (stoma, including stoma density and stoma size); H (hairs/trichomes, including length and density of leaf hairs); WL (wax layer); R (roughness, including wrinkles, ridge, furrows, and grooves of leaf).
YearOSLSLASLAL, W, L–W RSCSHWLR
20110000000010
20120011000111
20130000001111
20140010001001
20150020001211
20160100000111
20170100002202
20181220002323
20190132103514
20201211107547
20211120101223
20220341315203
20230011003313
20241341201315
Total4142178127301635
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Xue, W.; Lin, Y.; Sun, Z.; Long, Y.; Chen, D.; Yin, S. Effects of Leaf Trait Variability on PM Retention: A Systematic Review. Atmosphere 2025, 16, 170. https://doi.org/10.3390/atmos16020170

AMA Style

Xue W, Lin Y, Sun Z, Long Y, Chen D, Yin S. Effects of Leaf Trait Variability on PM Retention: A Systematic Review. Atmosphere. 2025; 16(2):170. https://doi.org/10.3390/atmos16020170

Chicago/Turabian Style

Xue, Wenkai, Yongjun Lin, Zhengqi Sun, Yuchong Long, Dele Chen, and Shan Yin. 2025. "Effects of Leaf Trait Variability on PM Retention: A Systematic Review" Atmosphere 16, no. 2: 170. https://doi.org/10.3390/atmos16020170

APA Style

Xue, W., Lin, Y., Sun, Z., Long, Y., Chen, D., & Yin, S. (2025). Effects of Leaf Trait Variability on PM Retention: A Systematic Review. Atmosphere, 16(2), 170. https://doi.org/10.3390/atmos16020170

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