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Rodríguez-Santamaría, K.;  Zafra-Mejía, C.A.;  Rondón-Quintana, H.A. Leaf Macro-Morphological Traits and Air Pollution. Encyclopedia. Available online: (accessed on 22 April 2024).
Rodríguez-Santamaría K,  Zafra-Mejía CA,  Rondón-Quintana HA. Leaf Macro-Morphological Traits and Air Pollution. Encyclopedia. Available at: Accessed April 22, 2024.
Rodríguez-Santamaría, Karen, Carlos Alfonso Zafra-Mejía, Hugo Alexander Rondón-Quintana. "Leaf Macro-Morphological Traits and Air Pollution" Encyclopedia, (accessed April 22, 2024).
Rodríguez-Santamaría, K.,  Zafra-Mejía, C.A., & Rondón-Quintana, H.A. (2022, November 17). Leaf Macro-Morphological Traits and Air Pollution. In Encyclopedia.
Rodríguez-Santamaría, Karen, et al. "Leaf Macro-Morphological Traits and Air Pollution." Encyclopedia. Web. 17 November, 2022.
Leaf Macro-Morphological Traits and Air Pollution

Urban trees provide different ecosystem benefits, such as improving air quality due to the retention of atmospheric particulate matter (PM) on their leaves. The LMTs most used for PM monitoring were leaf area (Q1) and specific leaf area (Q4). These LMTs were frequently used for their easy measurement and quantification.

urban trees leaf area air quality air pollution leaf morphology

1. Urban Air Pollutants Associated with LMTs

The results show that the most studied air pollutants in the context were the following (citation frequency, Q index): PM—all fractions between 1 and 100 um (Q1 = 0.760), O3 (Q2 = 0.586), PM2.5 (Q2 = 0.504), and PM10 (Q3 = 0.423). Other air pollutants showed a lower Q index: CO2 (Q3 = 0.427), CO (Q3 = 0.317), NO2 (Q3 = 0.295), and SO2 (Q3 = 0.287). The lower citation frequency of the latter group of air pollutants was possibly because the measures implemented for their control were more effective, or that the concentrations detected worldwide had a lower impact on public health in relation to the first group of air pollutants [1]. It was noted that, in recent years, CO2 has had greater interest in studies related to air pollution compared with other pollutants (Figure 1). This trend may be related to interest in global warming. Moreover, it was evidenced that the citation frequency of all pollutants has shown an increase in recent years. However, air pollutants such as VOCs and TSPs showed a low Q index (Q4 = 0.149 and Q4 = 0.034, respectively). Thus, these air pollutants were not considered in the discussion of results. Guerreiro et al. [1] also excluded air pollutants with a lower citation frequency from their statistical analysis. Therefore, researechers focused on air pollutants with a Q index within the first and second quartiles (i.e., PM—all fractions, O3, and PM2.5).
Figure 1. Temporal trend in the citation frequency of detected urban air pollutants by the Scopus database (documents considered, n = 8181).
The findings showed, worldwide, for PM all fractions showed a higher citation frequency (Q = 0.760) under the context of urban tree leaves. For example, in China’s most industrialized cities, such as Beijing, Guangzhou, Nanjing, and Shanghai, this was the most cited air pollutant. This is probably due to high emissions from industrial activities, the use of domestic fuels, and heavy traffic [2]. According to Karagulian et al. [3], PM emissions increased worldwide, by 8.0–16.0%, during the period between 1990 and 2014. When comparing the documents analyzed, it was observed that the worldwide PM sources were similar. The order of importance of these PM sources was as follows: biomass burning (39.7%) > vehicular traffic (25.2%) > domestic fuels (20.1%) > industries (15.0%). Karagulian et al. [3], Tao et al. [4], and Yang et al. [5] reported similar results. Therefore, the measurement of PM concentration retained on the surface of urban tree leaves was considered as an effective monitor to determine air quality and human health status in different cities globally [3].
Additionally, the results showed that PM concentrations associated with studies on urban tree leaves in countries such as China and the United States [6][7], as well as the European continent, were elevated. Ostoić et al. [8], Selmi et al. [9], and Reche et al. [10] reported similar findings. In these studies, the annual PM concentrations reported exceeded the maximum standards established by the World Health Organization (WHO) of 10 μg/m3 for PM2.5 and 20 μg/m3 for PM10 [11]. On average, in the documents analyzed, the United States exceeded the maximum standards for PM2.5 and PM10 by 64% and 364%, respectively. In Europe, these were values were 2140% and 147% of the maximum standards for PM2.5 and PM10, respectively. In China, PM2.5 and PM10 concentrations were exceeded by 6210% and 96%, respectively. During the study period, PM2.5 concentrations were regulated in China only until 2012. However, China accounted for 66.0% of the studies analyzed (e.g., [1][3][5][12]). This was possibly associated with the high concentrations of urban PM reported, which were related to emissions from large industrial sectors and high traffic density. In the context of urban tree leaves as biomonitors of air pollution, the results showed that the maximum concentrations reported in China during the study period for PM2.5, PM10, and O3 were 631, 136, and 268 μg/m3, respectively. Lastly, the findings indicate that studies on LMTs of urban trees as a biomonitors of air pollution were conducted at sites with high PM concentrations.
The results show that the average concentrations of air pollutants differed comparatively between countries according to the type of air pollutant analyzed in the context of the studies detected on LMTs of urban trees. For example, China was ranked first in air pollution, as reported concentrations of PM2.5, PM10, O3, and SO2 were 154.9, 92.0, 83.6, and 10.8 μg/m3, respectively. However, for NO2 (14.4 μg/m3) and CO (0.74 μg/m3) the concentrations were in an intermediate range. These concentrations were reported in studies on urban tree leaves conducted in China’s largest and most industrialized cities (e.g., Beijing, Guangzhou, Nanjing, and Shanghai). Conversely, in European Union member countries such as Germany, Italy, France, Spain, and the Netherlands, NO2 concentrations ranked first (average = 43.7 μg/m3). This air pollutant is one of the main causes of soil acidification and eutrophication, and promotes PM and O3 formation [13]. Relative to the other air pollutants reported in Europe, the results showed that PM2.5, O3, and SO2 had comparatively intermediate concentrations of 114.5, 50.3, and 7.08 μg/m3, respectively. Lastly, in the USA the reported concentrations of PM2.5, O3, NO2, and SO2 were comparatively low at 13.9, 37.8, 0.430, and 0.52 μg/m3, respectively. Nevertheless, the highest CO concentrations were reported in the USA (41.1 µg/m3). This is possibly due to a lack of effectiveness in the control measures for human combustion sources in urban areas [3][14].
In relation to PM2.5, it was reported that this air pollutant usually had regional behavior, which implied that it could be transported from areas of high pollution to urban areas where annual concentrations did not exceed the standards allowed by WHO [2][15]. In addition, the studies showed that the high PM2.5 concentrations deposited on the tree leaves showed no significant seasonal differences, even though rainfall in winter tended to wash the deposition of this air pollutant [16]. An analysis with Spearman’s coefficient showed a very strong positive correlation (rs = 0.926; p-value < 0.001) between PM2.5 and O3 concentrations detected on the leaf surface. Jia et al. [17] and Wang et al. [18] reported that under high O3 concentrations, and in the presence of atmospheric oxidation, the formation of secondary particles such as PM2.5 were promoted. Lastly, a considerable positive correlation (rs = 0.707; p-value = 0.049) was also observed between PM10 and PM2.5 concentrations detected on the leaf surface. Gómez-Moreno et al. [14], and Jia et al. [17] reported similar results.
The findings show that there were different PM sources, both natural and artificial, in the context of tree LMTs as a biomonitor of urban air pollution. According to Karagulian et al. [3], natural sources contributed most to elevated PM10 concentrations. In contrast, combustion sources had a greater influence on the formation of PM2.5. The results suggest that high PM concentrations detected on urban tree leaves can be explained by emissions from human activities, weather conditions, and the interaction of different compounds present in the atmosphere [14][19]. The results also show that PM and O3 had a significant influence on air quality in studies on urban tree leaves. The results show in order of importance, PM—all fractions between 1–100 µm (Q1 = 0.760), O3 (Q2 = 0.586), and PM2.5 (Q2 = 0.504), were the most frequently studied air pollutants, followed by PM10 (Q3 = 0.423), CO (Q3 = 0.317), NO2 (Q3 = 0.295), and SO2 (Q3 = 0.287). This trend was most evident in the countries identified as the most industrialized (e.g., China, the United States, and Germany). PM was also of great interest due to its serious direct effects on urban public health [4][5].

2. LMTs Associated with Air Pollution Biomonitoring

The results show that in studies on leaf macro-morphology of trees and urban air pollution, the most used LMTs were LA and SLA (e.g., [20][21]). In relation to LA (Q = 1.000), SLA was Q4 (Q = 0.096). Indeed, SLA had a higher citation frequency index (Q) with respect to the other LMTs detected; for example, compared to LS (Q4 = 0.080) and LDMC (Q4 = 0.020). 64.0% of the documents detected reported LA and SLA (e.g., [22][23][24]). However, the use of these LMTs was also evidenced in studies on ecosystem benefits [20][25][26], determination of photosynthetic levels [27], and urban soil fertility [28]. It was also observed that in recent years the citation frequency for LA increased significantly compared to the other LMTs detected [29][30] (Figure 2). Lastly, the studies detected showed the use of other leaf traits with a very low citation frequency. In descending order, the Q index for these leaf traits in relation to LA was as follows: LS (Q4 = 0.080) > LDMC (Q4 = 0.020) > LT (Q4 = 0.013) > SD (Q4 = 0.001). These leaf traits were used less frequently in urban air pollution studies and were not considered in depth.
Figure 2. Temporal trend in citation frequency for LMTs detected in studies on urban air pollution from the Scopus database (n = 8181). LA = Leaf area, SLA = Specific leaf area, LDMC = Leaf dry matter content, LS = leaf surface, LT = Leaf thickness, and SD = Stomatal density.
The findings show the importance of LA as a trait that allows the study of different environmental factors, such as evapotranspiration, light interception, response to irrigation, and ecological factors such as photosynthetic efficiency and plant growth in urban green areas [27][31]. It was also reported that LA is commonly used in studies on urban air pollution due to its easy measurement and quantification [29][30]. Indeed, LA and SLA are related to the determination of different ecosystem benefits. According to Hanisch et al. [20] and Lopez-Iglesias [32], LA and SLA are multiservice traits because, in addition to being fundamental factors in air pollution decrease, they help in the study of biomass production, erosion control, soil fertility, and control of water levels [20][21]. According to Borowy and Swan [27], soil plays a fundamental role in the performance of plant functional traits, which is why both LA and SLA have a significant relationship with soil fertility. These leaf traits also make it possible to assess the response of plant species to their environment and are associated with carbon sequestration. The latter is a fundamental ecosystem service in the regulation of global warming, and is associated with the photosynthetic functions of the plants [33]. In addition, Kichenin et al. [34] reported that these leaf traits varied with the altitudinal gradient. It was shown that both LA and SLA increase as the altitude increases. This allows plant species that present these leaf traits in greater proportion to dominate other species in the ecosystem [35]. The results also hint that these leaf traits are influenced by altitudinal gradients and the meteorological and climatic conditions of a given region [36][37].
Additionally, Singh et al. [22] reported that LA in tree species is directly related to PM concentrations, because at higher LA, tree species retain more PM [38]. Some researchers [33][39] suggested that the previous trend was a response to urban pressures and that this varied with the tree species considered. Thus, the findings suggest that the identification of these variations in leaf traits may have a significant influence during the selection of tree species for the urban air pollution monitoring [22][25][30]. During the PM2.5 study, other leaf traits were also reported that allowed a better understanding of its retention by urban trees, including trichomes and stomatal density [31][40]. Previous studies have suggested the usefulness of these leaf traits in air quality analysis in the context of urban tree species. Indeed, leaf traits have become a tool for the study and management of air quality and ecosystem benefits provided by urban trees [41][42].
The results show that LMTs such as LA and SLA, and to a lesser extent LDMC, can be modified by invasive plant species. These species have leaf traits that allow them to dominate plant communities and functional structure in urban areas [24]. Hence, the functional diversity of native species decreased and was homogenized as invasive species increased [28]. In other words, the functioning and production of ecosystem benefits of native species was altered, which directly influenced the management of urban air quality [28][43]. The findings also suggested that both LA and SLA are influenced by urbanization and temperature, as these factors (anthropic and climatic) exert pressure on these LMTs. Urbanization has been associated with an increase in leaf area, due to soil conditions and the higher albedo observed in urban areas [44]. Pandey and Singh [29] demonstrated that LA and SLA increased under high humidity conditions, possibly as an adaptive response to climate change [45].
In relation to the order of importance in the use of LMTs in an air pollution context, a significant number of documents detected (≈80.0%) used LA to establish different environmental or ecological conditions, either of the same plant or of its ecosystem, possibly due to its ease of measurement. This trend was constant in the documents detected regardless of geographical location (China, Europe, United States, or Latin America), and was repeated for different tree species. Lastly, LA provided information about the different air pollutants retained on tree leaves (e.g., heavy metals, hydrocarbons, PM, CO, and O3), which supported their use in studies on urban air pollution [29].

3. LMT Applications

The results suggest three main applications of leaf macro-morphology in the context of urban air pollution (n = 10): green infrastructure (50.0%) [33][34][43][46][47], air quality management (30.0%) [38][48][49], and tree management (20.0%) [11][35]. This trend was consistent across the documents detected, regardless of the study location. These applications showed different citation frequencies, but all were possibly related to each other by their benefits in increasing ecosystem benefits, such as improving air quality and climate regulation [50], and by their benefits on tree structure, which involved proper vegetation planning in urban spaces [51]. In relation to urban green infrastructure, the findings suggest that green areas, corridors, roofs, and walls decreased air pollution concentrations and improve the quality of life of urban communities [50][51][52]. Urban planning of this green infrastructure, i.e., the definition of tree size, density, space between them, and maintenance, was a fundamental aspect for the selection of tree species [53][54]. Indeed, species-specific leaf traits (e.g., LA and SLA) had to be considered with respect to PM retention, ensuring that this green infrastructure could significantly reduce air pollutant concentrations in each area [30][55][56]. Therefore, the results suggest that urban green infrastructure can be used as a biomonitor of air pollution levels, because it oxygenates the urban environment through photosynthesis, dilutes polluted air, and absorbs and retains pollutants from urban air.
According to Barwise and Kumar [57], the interaction between green infrastructure and air quality improvement was mainly socio-ecological, and was influenced by the selection of tree LMTs for the reduction of urban air pollutant concentrations. However, it was recommended that this selection not only consider the reduction of air pollution but also consider other ecological factors related to the biological and ecological diversity of the species used for this purpose. The failure to consider these additional ecological factors could lead to inadequate management of urban trees [55][57][58]. The findings suggested that during green infrastructure design, the urban infrastructure itself be considered for the selection and location of the selected trees. The selection, location, and design of green infrastructure should not only be based on aesthetic aspects or the survivability of species, but the increasing pressures of urbanization on these urban ecosystems must be considered [59][60] to justify that its implementation provides ecosystem benefits fundamental to the survival and good quality of life of urban communities [31][33][61]. Therefore, studies on LMTs and functional diversity of trees must be increased and deepened to complement these types of sustainable urban applications [46][62].
The results suggest that the best way to manage air quality in urban environments is to reduce air pollutant emissions [1][3]. However, continued population growth, expansion of urbanization, and consumerism make this difficult [63]. Thus, strategies have been developed, e.g., control of multiple pollutants, transition to renewable energies, use of electricity to replace sources of PM2.5 emissions, and the implementation of green infrastructure in urban areas. These make it possible to manage air pollution levels in a practical, efficient, and economical way [64]. Vieira et al. [50] reported that complex structures in urban green areas (i.e., combination of trees, shrubs, and herbaceous) were an excellent strategy to decrease air pollution; not only was planting of trees important for the retention of atmospheric pollutants and climate regulation, but the adaptation of these complex urban ecosystems had to be managed [65]. Indeed, these complex ecosystems had to interact with climatic, physical, soil, biological, and ecological elements to guarantee the provision of ecosystem benefits of urban trees [50][59].
Additionally, the findings suggest the importance of assessing interactions that occur between the diversity, composition, and structure of urban green areas [66][67]. Pearse et al. [68] established that it was very important to break with the homogenization of urban vegetation, because this decreased the number of beneficial effects resulting from this type of ecosystem. Janhäll [59] reported that air pollutant concentrations in each urban area depended on the emission source and the design of urban vegetation. The studies reported that low vegetation was more effective, as its proximity to the soil surface increased the probability of atmospheric pollutant retention without the influence of wind [69]. Strong wind increased PM10 retention by urban vegetation [70], but this was different with PM2.5, because strong wind reduced the retention capacity of PM2.5 by the urban vegetation, due to its resuspension and transport through the air masses [71]. This tended to increase respiratory diseases of inhabitants, but as a mitigation strategy it was proposed to increase the use of tree species with a higher LA, which increased the surface area of retention for urban PM2.5 [72][73].


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