Leaf Macro-Morphological Traits and  Air Pollution: Comparison
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Please note this is a comparison between Version 2 by Dean Liu and Version 1 by Carlos Alfonso Zafra Mejía.

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 of this literature review  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 [35][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 of this study. Guerreiro et al. [35][1] also excluded air pollutants with a lower citation frequency from their statistical analysis. Therefore, this study focused on air pollutants with a Q index within the first and second quartiles (i.e., PM—all fractions, O3, and PM2.5).
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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 [39][2]. According to Karagulian et al. [40][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. In tThis study, 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. [40][3], Tao et al. [41][4], and Yang et al. [42][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 [40][3].
Additionally, the results showed that PM concentrations associated with studies on urban tree leaves in countries such as China and the United States [43[6][7],44], as well as the European continent, were elevated. Ostoić et al. [45][8], Selmi et al. [46][9], and Reche et al. [47][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 [48][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., [35,40,42,49][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 [50][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 [40,51][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 [39,52][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 [53][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. [54][17] and Wang et al. [55][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. [51][14], and Jia et al. [54][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. [40][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 [51,56][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 [41,42][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., [6,57][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., [58,59,60][22][23][24]). However, the use of these LMTs was also evidenced in studies on ecosystem benefits [1,6,61][20][25][26], determination of photosynthetic levels [62][27], and urban soil fertility [63][28]. It was also observed that in recent years the citation frequency for LA increased significantly compared to the other LMTs detected [64,65][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 in this study..
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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 [14,62][27][31]. It was also reported that LA is commonly used in studies on urban air pollution due to its easy measurement and quantification [64,65][29][30]. Indeed, LA and SLA are related to the determination of different ecosystem benefits. According to Hanisch et al. [6][20] and Lopez-Iglesias [66][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 [6,57][20][21]. According to Borowy and Swan [62][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 [17][33]. In addition, Kichenin et al. [67][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 [7][35]. The results also hint that these leaf traits are influenced by altitudinal gradients and the meteorological and climatic conditions of a given region [68,69][36][37].
Additionally, Singh et al. [58][22] reported that LA in tree species is directly related to PM concentrations, because at higher LA, tree species retain more PM [70][38]. Some researchers [16,17][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 [1,58,65][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 [14,71][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 [72,73][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 [60][24]. Hence, the functional diversity of native species decreased and was homogenized as invasive species increased [63][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 [10,63][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 [74][44]. Pandey and Singh [64][29] demonstrated that LA and SLA increased under high humidity conditions, possibly as an adaptive response to climate change [75][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 [64][29].

3. LMT Applications

OurThe results suggest three main applications of leaf macro-morphology in the context of urban air pollution (n = 10): green infrastructure (50.0%) [10,17,38,67[33][34][43][46][47],76], air quality management (30.0%) [36[38][48][49],70,77], and tree management (20.0%) [7,48][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 [78][50], and by their benefits on tree structure, which involved proper vegetation planning in urban spaces [79][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 [78,79,80][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 [26,81][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 [32,65,82][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 [30][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 [30,32,83][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 [20,84][59][60] to justify that its implementation provides ecosystem benefits fundamental to the survival and good quality of life of urban communities [14,17,85][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 [5,38][46][62].
The results suggest that the best way to manage air quality in urban environments is to reduce air pollutant emissions [35,40][1][3]. However, continued population growth, expansion of urbanization, and consumerism make this difficult [86][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 [87][64]. Vieira et al. [78][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 [88][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 [20,78][50][59].
Additionally, the findings suggest the importance of assessing interactions that occur between the diversity, composition, and structure of urban green areas [89,90][66][67]. Pearse et al. [91][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 [20][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 [92][69]. Strong wind increased PM10 retention by urban vegetation [93][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 [94][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 [95,96][72][73].

References

  1. Guerreiro, C.B.B.; Foltescu, V.; de Leeuw, F. Air Quality Status and Trends in Europe. Atmos. Environ. 2014, 98, 376–384.
  2. Lin, Y.; Zou, J.; Yang, W.; Li, C.-Q. A Review of Recent Advances in Research on PM2.5 in China. Int. J. Environ. Res. Public Health 2018, 15, 438.
  3. Karagulian, F.; Belis, C.A.; Dora, C.F.C.; Prüss-Ustün, A.M.; Bonjour, S.; Adair-Rohani, H.; Amann, M. Contributions to Cities’ Ambient Particulate Matter (PM): A Systematic Review of Local Source Contributions at Global Level. Atmos. Environ. 2015, 120, 475–483.
  4. Tao, J.; Gao, J.; Zhang, L.; Zhang, R.; Che, H.; Zhang, Z.; Lin, Z.; Jing, J.; Cao, J.; Hsu, S.-C. PM2.5 Pollution in a Megacity of Southwest China: Source Apportionment and Implication. Atmos. Chem. Phys. 2014, 14, 8679–8699.
  5. Yang, F.; Tan, J.; Zhao, Q.; Du, Z.; He, K.; Ma, Y.; Duan, F.; Chen, G.; Zhao, Q. Characteristics of PM2.5 Speciation in Representative Megacities and across China. Atmos. Chem. Phys. 2011, 11, 5207–5219.
  6. Kroeger, T.; McDonald, R.I.; Boucher, T.; Zhang, P.; Wang, L. Where the People Are: Current Trends and Future Potential Targeted Investments in Urban Trees for PM10 and Temperature Mitigation in 27 U.S. Cities. Landsc. Urban Plan. 2018, 177, 227–240.
  7. Kheirbek, I.; Wheeler, K.; Walters, S.; Kass, D.; Matte, T. PM2.5 and Ozone Health Impacts and Disparities in New York City: Sensitivity to Spatial and Temporal Resolution. Air Qual. Atmos. Health 2013, 6, 473–486.
  8. Ostoić, S.; Salbitano, F.; Borelli, S.; Verlič, A. Urban Forest Research in the Mediterranean: A Systematic Review. Urban For. Urban Green. 2018, 31, 185–196.
  9. Selmi, W.; Weber, C.; Rivière, E.; Blond, N.; Mehdi, L.; Nowak, D. Air Pollution Removal by Trees in Public Green Spaces in Strasbourg City, France. Urban For. Urban Green. 2016, 17, 192–201.
  10. Reche, C.; Querol, X.; Alastuey, A.; Viana, M.; Pey, J.; Moreno, T.; Rodríguez, S.; González, Y.; Fernández-Camacho, R.; de la Rosa, J.; et al. New Considerations for PM, Black Carbon and Particle Number Concentration for Air Quality Monitoring across Different European Cities. Atmos. Chem. Phys. 2011, 11, 6207–6227.
  11. World Health Organization. Guías para la Calidad del Aire Relativas al Material Particulado, el Ozono, el Dióxido de Nitrógeno y el Dióxido de Azufre; WHO: Geneva, Switzerland, 2005; pp. 5–21.
  12. Zhao, Y.; Nielsen, C.P.; Lei, Y.; McElroy, M.B.; Hao, J. Quantifying the Uncertainties of a Bottom-up Emission Inventory of Anthropogenic Atmospheric Pollutants in China. Atmos. Chem. Phys. 2011, 11, 2295–2308.
  13. Karmakar, D.; Padhy, P.K. Air Pollution Tolerance, Anticipated Performance, and Metal Accumulation Indices of Plant Species for Greenbelt Development in Urban Industrial Area. Chemosphere 2019, 237, 124522.
  14. Gómez-Moreno, F.J.; Artíñano, B.; Ramiro, E.D.; Barreiro, M.; Núñez, L.; Coz, E.; Dimitroulopoulou, C.; Vardoulakis, S.; Yagüe, C.; Maqueda, G.; et al. Urban Vegetation and Particle Air Pollution: Experimental Campaigns in a Traffic Hotspot. Environ. Pollut. 2019, 247, 195–205.
  15. Wu, J.; Wang, Y.; Qiu, S.; Peng, J. Using the Modified I-Tree Eco Model to Quantify Air Pollution Removal by Urban Vegetation. Sci. Total Environ. 2019, 688, 673–683.
  16. Xu, W.; Wu, Q.; Liu, X.; Tang, A.; Dore, A.J.; Heal, M.R. Characteristics of Ammonia, Acid Gases, and PM2.5 for Three Typical Land-Use Types in the North China Plain. Environ. Sci. Pollut. Res. 2016, 23, 1158–1172.
  17. Jia, M.; Zhao, T.; Cheng, X.; Gong, S.; Zhang, X.; Tang, L.; Liu, D.; Wu, X.; Wang, L.; Chen, Y. Inverse Relations of PM2.5 and O3 in Air Compound Pollution between Cold and Hot Seasons over an Urban Area of East China. Atmosphere 2017, 8, 59.
  18. Wang, Y.; Ying, Q.; Hu, J.; Zhang, H. Spatial and Temporal Variations of Six Criteria Air Pollutants in 31 Provincial Capital Cities in China during 2013–2014. Environ. Int. 2014, 73, 413–422.
  19. Nowak, D.J.; Hirabayashi, S.; Doyle, M.; McGovern, M.; Pasher, J. Air Pollution Removal by Urban Forests in Canada and Its Effect on Air Quality and Human Health. Urban For. Urban Green. 2018, 29, 40–48.
  20. Hanisch, M.; Schweiger, O.; Cord, A.F.; Volk, M.; Knapp, S. Plant Functional Traits Shape Multiple Ecosystem Services, Their Trade-Offs and Synergies in Grasslands. J. Appl. Ecol. 2020, 57, 1535–1550.
  21. Hodgson, J.G.; Montserrat-Martí, G.; Charles, M.; Jones, G.; Wilson, P.; Shipley, B.; Sharafi, M.; Cerabolini, B.E.L.; Cornelissen, J.H.C.; Band, S.R.; et al. Is Leaf Dry Matter Content a Better Predictor of Soil Fertility than Specific Leaf Area? Ann. Bot. 2011, 108, 1337–1345.
  22. Singh, H.; Yadav, M.; Kumar, N.; Kumar, A.; Kumar, M. Assessing Adaptation and Mitigation Potential of Roadside Trees under the Influence of Vehicular Emissions: A Case Study of Grevillea Robusta and Mangifera Indica Planted in an Urban City of India. PLoS ONE 2020, 15, e0227380.
  23. Baraldi, R.; Chieco, C.; Neri, L.; Facini, O.; Rapparini, F.; Morrone, L.; Rotondi, A.; Carriero, G. An Integrated Study on Air Mitigation Potential of Urban Vegetation: From a Multi-Trait Approach to Modeling. Urban For. Urban Green. 2019, 41, 127–138.
  24. Conway, T.M.; Almas, A.D.; Coore, D. Ecosystem Services, Ecological Integrity, and Native Species Planting: How to Balance These Ideas in Urban Forest Management? Urban For. Urban Green. 2019, 41, 1–5.
  25. Egas, C.; Naulin, P.I.; Préndez, M.; Egas, C.; Naulin, P.I.; Préndez, M. Contaminación Urbana Por Material Particulado y Su Efecto Sobre Las Características Morfo-Anatómicas de Cuatro Especies Arbóreas de Santiago de Chile. Inf. Tecnol. 2018, 29, 111–118.
  26. Jeanjean, A.P.R.; Monks, P.S.; Leigh, R.J. Modelling the Effectiveness of Urban Trees and Grass on PM2.5 Reduction via Dispersion and Deposition at a City Scale. Atmos. Environ. 2016, 147, 1–10.
  27. Borowy, D.; Swan, C.M. A Multi-Trait Comparison of an Urban Plant Species Pool Reveals the Importance of Intraspecific Trait Variation and Its Influence on Distinct Functional Responses to Soil Quality. Front. Ecol. Evol. 2020, 8, 68.
  28. Sodhi, D.S.; Livingstone, S.W.; Carboni, M.; Cadotte, M.W. Plant Invasion Alters Trait Composition and Diversity across Habitats. Ecol. Evol. 2019, 9, 6199–6210.
  29. Pandey, S.K.; Singh, H. A Simple, Cost-Effective Method for Leaf Area Estimation. J. Bot. 2011, 2011, e658240.
  30. Li, Y.; Wang, S.; Chen, Q. Potential of Thirteen Urban Greening Plants to Capture Particulate Matter on Leaf Surfaces across Three Levels of Ambient Atmospheric Pollution. Int. J. Environ. Res. Public Health 2019, 16, 402.
  31. Tian, Y.; Zhao, F.; Wang, T.; Jim, C.Y.; Xu, T.; Jin, J. Evaluating the Ecological Services of Roof Greening Plants in Beijing Based on Functional Traits. Sustainability 2019, 11, 5310.
  32. Lopez-Iglesias, B.; Villar, R.; Poorter, L. Rasgos funcionales como indicadores de la respuesta a la sequía en plántulas de 10 especies leñosas mediterráneas. Congr. For. Español 2013, 10, 6CFE01-078.
  33. Montes-Pulido, C.R. Uso de rasgos funcionales de plantas como estimadores de carbono almacenado en biomasa aérea. Rev. Investig. Agrar. Ambient. 2014, 5, 237–243.
  34. Kichenin, E.; Wardle, D.A.; Peltzer, D.A.; Morse, C.W.; Freschet, G.T. Contrasting Effects of Plant Inter- and Intraspecific Variation on Community-Level Trait Measures along an Environmental Gradient. Funct. Ecol. 2013, 27, 1254–1261.
  35. Miedema, L.J.; Capmourteres, V.; Anand, M. Impact of Land Composition and Configuration on the Functional Trait Assembly of Forest Communities in Southern Ontario. Ecosphere 2019, 10, e02633.
  36. Easlon, H.M.; Bloom, A.J. Easy Leaf Area: Automated Digital Image Analysis for Rapid and Accurate Measurement of Leaf Area. Appl. Plant Sci. 2014, 2, 1400033.
  37. Lamanna, C.; Blonder, B.; Violle, C.; Kraft, N.J.B.; Sandel, B.; Šímová, I.; Donoghue, J.C.; Svenning, J.-C.; McGill, B.J.; Boyle, B.; et al. Functional Trait Space and the Latitudinal Diversity Gradient. Proc. Natl. Acad. Sci. USA 2014, 111, 13745–13750.
  38. Marando, F.; Salvatori, E.; Fusaro, L.; Manes, F. Removal of PM10 by Forests as a Nature-Based Solution for Air Quality Improvement in the Metropolitan City of Rome. Forests 2016, 7, 150.
  39. Rodríguez-Alarcón, S.J.; Pinzón-Pérez, L.; López-Cruz, J.; Cabrera-Amaya, D. Rasgos funcionales de plantas leñosas en áreas verdes de Bogotá, Colombia. Biota Colomb. 2020, 21, 108–133.
  40. Zha, Y.; Shi, Y.; Tang, J.; Liu, X.; Feng, C.; Zhang, Y. Spatial-Temporal Variability and Dust-Capture Capability of 8 Plants in Urban China. Pol. J. Environ. Stud. 2018, 28, 453–462.
  41. Yu, K.; Van Geel, M.; Ceulemans, T.; Geerts, W.; Ramos, M.M.; Sousa, N.; Castro, P.M.L.; Kastendeuch, P.; Najjar, G.; Ameglio, T.; et al. Foliar Optical Traits Indicate That Sealed Planting Conditions Negatively Affect Urban Tree Health. Ecol. Indic. 2018, 95, 895–906.
  42. Rodríguez García, D.; Delgado Montes, C.; López Serrano, Y.; Brooks Laverdeza, R. Cambios en rasgos funcionales de las hojas de Piper reticulatum (Piperaceae) en luz y sombra en La Selva, Costa Rica. Cuad. Investig. UNED 2020, 12, 130–136.
  43. Milanović, M.; Knapp, S.; Pyšek, P.; Kühn, I. Linking Traits of Invasive Plants with Ecosystem Services and Disservices. Ecosyst. Serv. 2020, 42, 101072.
  44. Moreno-Barreto, E.; Rubiano, K.; Moreno-Barreto, E.; Rubiano, K. Efecto del método de emplazamiento en la respuesta funcional de seis especies arbóreas de Bogotá. Colomb. For. 2020, 23, 5–19.
  45. Zhu, J.; Zhu, H.; Cao, Y.; Li, J.; Zhu, Q.; Yao, J.; Xu, C. Effect of Simulated Warming on Leaf Functional Traits of Urban Greening Plants. BMC Plant Biol. 2020, 20, 139.
  46. Maclvor, J.S.; Macivor, J.S.; Cadotte, M.W.; Livingstone, S.W.; Lundholm, J.T.; Yasui, S.-L.E. Phylogenetic Ecology and the Greening of Cities. J. Appl. Ecol. 2016, 53, 1470–1476.
  47. Lüttge, U.; Buckeridge, M. Trees: Structure and Function and the Challenges of Urbanization. Trees 2020, 20, 1–8.
  48. Grote, R.; Samson, R.; Alonso, R.; Amorim, J.H.; Cariñanos, P.; Churkina, G.; Fares, S.; Thiec, D.L.; Niinemets, Ü.; Mikkelsen, T.N.; et al. Functional Traits of Urban Trees: Air Pollution Mitigation Potential. Front. Ecol. Environ. 2016, 14, 543–550.
  49. Nowak, D.J.; Randler, P.B.; Greenfield, E.J.; Comas, S.J.; Carr, M.A.; Alig, R.J. Sustaining America’s Urban Trees and Forests: A Forests on the Edge Report; Gen. Tech. Rep. NRS-62; U.S. Department of Agriculture, Forest Service, Northern Research Station: Newtown Square, PA, USA, 2010; Volume 62, 27p.
  50. Vieira, J.; Matos, P.; Mexia, T.; Silva, P.; Lopes, N.; Freitas, C.; Correia, O.; Santos-Reis, M.; Branquinho, C.; Pinho, P. Green Spaces Are Not All the Same for the Provision of Air Purification and Climate Regulation Services: The Case of Urban Parks. Environ. Res. 2018, 160, 306–313.
  51. Jansson, M. Green Space in Compact Cities: The Benefits and Values of Urban Ecosystem Services in Planning. Nord. J. Archit. Res. 2014, 26, 139–160.
  52. Liu, H.-L.; Shen, Y.-S. The Impact of Green Space Changes on Air Pollution and Microclimates: A Case Study of the Taipei Metropolitan Area. Sustainability 2014, 6, 8827–8855.
  53. Abhijith, K.V.; Kumar, P. Field Investigations for Evaluating Green Infrastructure Effects on Air Quality in Open-Road Conditions. Atmos. Environ. 2019, 201, 132–147.
  54. Hewitt, C.N.; Ashworth, K.; MacKenzie, A.R. Using Green Infrastructure to Improve Urban Air Quality (GI4AQ). Ambio 2020, 49, 62–73.
  55. Abhijith, K.V.; Kumar, P.; Gallagher, J.; McNabola, A.; Baldauf, R.; Pilla, F.; Broderick, B.; Di Sabatino, S.; Pulvirenti, B. Air Pollution Abatement Performances of Green Infrastructure in Open Road and Built-up Street Canyon Environments—A Review. Atmos. Environ. 2017, 162, 71–86.
  56. Pinho, P.; Correia, O.; Lecoq, M.; Munzi, S.; Vasconcelos, S.; Gonçalves, P.; Rebelo, R.; Antunes, C.; Silva, P.; Freitas, C.; et al. Evaluating Green Infrastructure in Urban Environments Using a Multi-Taxa and Functional Diversity Approach. Environ. Res. 2016, 147, 601–610.
  57. Barwise, Y.; Kumar, P. Designing Vegetation Barriers for Urban Air Pollution Abatement: A Practical Review for Appropriate Plant Species Selection. NPJ Clim. Atmos. Sci. 2020, 3, 12.
  58. Gill, A.S.; Purnell, K.; Palmer, M.I.; Stein, J.; McGuire, K.L. Microbial Composition and Functional Diversity Differ Across Urban Green Infrastructure Types. Front. Microbiol. 2020, 11, 912.
  59. Janhäll, S. Review on Urban Vegetation and Particle Air Pollution—Deposition and Dispersion. Atmos. Environ. 2015, 105, 130–137.
  60. Matthews, T.; Lo, A.Y.; Byrne, J.A. Reconceptualizing Green Infrastructure for Climate Change Adaptation: Barriers to Adoption and Drivers for Uptake by Spatial Planners. Landsc. Urban Plan. 2015, 138, 155–163.
  61. Gómez-Baggethun, E.; Barton, D.N. Classifying and Valuing Ecosystem Services for Urban Planning. Ecol. Econ. 2013, 86, 235–245.
  62. Cameron, R.W.F.; Blanuša, T. Green Infrastructure and Ecosystem Services—Is the Devil in the Detail? Ann. Bot. 2016, 118, 377–391.
  63. Kumar, P.; Khare, M.; Harrison, R.M.; Bloss, W.J.; Lewis, A.C.; Coe, H.; Morawska, L. New Directions: Air Pollution Challenges for Developing Megacities like Delhi. Atmos. Environ. 2015, 122, 657–661.
  64. Ou, Y.; West, J.J.; Smith, S.J.; Nolte, C.G.; Loughlin, D.H. Air Pollution Control Strategies Directly Limiting National Health Damages in the US. Nat. Commun. 2020, 11, 957.
  65. Santos, A.; Pinho, P.; Munzi, S.; Botelho, M.J.; Palma-Oliveira, J.M.; Branquinho, C. The Role of Forest in Mitigating the Impact of Atmospheric Dust Pollution in a Mixed Landscape. Environ. Sci. Pollut. Res. 2017, 24, 12038–12048.
  66. Newbold, T.; Hudson, L.N.; Hill, S.L.L.; Contu, S.; Lysenko, I.; Senior, R.A.; Börger, L.; Bennett, D.J.; Choimes, A.; Collen, B.; et al. Global Effects of Land Use on Local Terrestrial Biodiversity. Nature 2015, 520, 45–50.
  67. Nock, C.A.; Paquette, A.; Follett, M.; Nowak, D.J.; Messier, C. Effects of Urbanization on Tree Species Functional Diversity in Eastern North America. Ecosystems 2013, 16, 1487–1497.
  68. Pearse, W.D.; Cavender-Bares, J.; Hobbie, S.E.; Avolio, M.L.; Bettez, N.; Roy Chowdhury, R.; Darling, L.E.; Groffman, P.M.; Grove, J.M.; Hall, S.J.; et al. Homogenization of Plant Diversity, Composition, and Structure in North American Urban Yards. Ecosphere 2018, 9, e02105.
  69. Speak, A.F.; Rothwell, J.J.; Lindley, S.J.; Smith, C.L. Urban Particulate Pollution Reduction by Four Species of Green Roof Vegetation in a UK City. Atmos. Environ. 2012, 61, 283–293.
  70. Lin, M.-Y.; Khlystov, A. Investigation of Ultrafine Particle Deposition to Vegetation Branches in a Wind Tunnel. Aerosol Sci. Technol. 2012, 46, 465–472.
  71. Sæbø, A.; Popek, R.; Nawrot, B.; Hanslin, H.M.; Gawronska, H.; Gawronski, S.W. Plant Species Differences in Particulate Matter Accumulation on Leaf Surfaces. Sci. Total Environ. 2012, 427–428, 347–354.
  72. Dzierżanowski, K.; Popek, R.; Gawrońska, H.; Sæbø, A.; Gawroński, S.W. Deposition of Particulate Matter of Different Size Fractions on Leaf Surfaces and in Waxes of Urban Forest Species. Int. J. Phytoremediation 2011, 13, 1037–1046.
  73. Przybysz, A.; Sæbø, A.; Hanslin, H.M.; Gawroński, S.W. Accumulation of Particulate Matter and Trace Elements on Vegetation as Affected by Pollution Level, Rainfall and the Passage of Time. Sci. Total Environ. 2014, 481, 360–369.
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