The dual role of roadside trees in shaping children’s PM exposure near schools

The dual role of roadside trees in shaping children’s PM exposure near schools | 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 Article The dual role of roadside trees in shaping children’s PM exposure near schools Adrian Hoppa, Piotr Sikorski, Arkadiusz Przybysz, Edyta Łaszkiewicz, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490912/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Children’s school commutes are repeated microenvironments where exposure to traffic-related particulate matter (PM) occurs at breathing height. Roadside trees are widely promoted to mitigate PM via deposition on foliage, yet dense tree crowns may also reduce near-road ventilation and increase pedestrian-level concentrations. We quantified these opposing effects along the most frequently used access routes to primary schools in Łódź (Poland). During June 2022, we measured PM2.5 and PM10 at 1 s resolution at 1.4 m height during morning and afternoon commuting periods (n = 242). Tree crown structure was characterised using two complementary indicators: horizontal crown density, quantified as leaf area index (LAI), representing potential particulate absorption surface, and vertical crown density, quantified as the proportion of the pedestrian-level field of view occupied by tree crowns, representing canopy obstruction and reduced ventilation. Foliar PM loads were determined by sequential leaf wash-off (n = 124). Mixed-effects models showed that higher vertical crown density was associated with higher airborne PM concentrations, with each one percentage-point increase corresponding to approximately 1.9% higher PM levels along routes. In contrast, higher horizontal crown density enhanced foliar retention: a 1% increase in LAI was associated with 0.52% and 0.56% higher foliar PM₁₀ and PM₂.₅, respectively, and species identity explained additional variation. Roadside trees, therefore, provide particulate deposition benefits but may simultaneously increase exposure at pedestrian height, highlighting the need for site-specific greening designs that explicitly account for both horizontal and vertical crown density. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences air pollution children LAI index particulate matter school routes trees Figures Figure 1 Figure 2 Figure 3 1. Introduction Children are among the population groups most vulnerable to the adverse health effects of air pollution, particularly particulate matter (PM), due to ongoing lung development, higher breathing rates relative to body mass, and limited ability to avoid polluted environments 1 , 2 . Exposure to PM in early life has been associated with impaired lung development, increased risk of asthma, acute respiratory infections, and adverse cardiovascular and neurological outcomes 3 , 4 , 6 , 7 . In addition to physical health effects, polluted air has been shown to negatively affect children’s cognitive development, attention, and academic performance 8 , 9 , 10 . Importantly, children’s exposure to air pollution is shaped not only by background concentrations but also by everyday routines and repeated contact with specific environments, which can cumulatively influence long-term health outcomes. Among everyday environments, routes to school represent a particularly important yet often underestimated exposure setting for children, as they are repeated daily and occur at pedestrian height, close to traffic sources. Previous studies have shown that children spend a substantial proportion of their active time outdoors along routes to and from school, frequently in proximity to busy roads characterized by elevated PM concentrations 11 , 12 . Traffic-related air pollution near schools has been identified as a major contributor to both outdoor and indoor PM levels, with vehicle emissions during morning and afternoon peak hours playing a dominant role 13 , 14 , 15 . Unlike recreational spaces such as parks, routes to school are characterized by fixed, repeated paths used by the same group of children daily, typically five days a week and often during periods of intensified traffic. As a result, environmental conditions along these routes have the potential to influence exposure for a large proportion of children consistently and cumulatively. In response to growing concerns about urban air quality and children's health, urban planning strategies are increasingly promoting green infrastructure as a mitigation measure, particularly in areas frequented by vulnerable populations, such as school surroundings. Roadside trees are among the most commonly implemented green infrastructure elements in cities due to their potential to accumulate particulate matter on leaf surfaces and thus contribute to air purification 16 , 17 , 18 . The effectiveness of vegetation in capturing PM depends on species-specific traits, leaf morphology, and structural characteristics such as the leaf area index (LAI), as well as meteorological conditions and exposure time 19 , 20 . Consequently, tree planting near roads and schools is often regarded as an unambiguously beneficial intervention to reduce children’s exposure to air pollution. However, growing evidence suggests that the impact of vegetation on air quality at pedestrian height is highly context-dependent. In densely built environments, trees may restrict airflow and reduce pollutant dispersion, leading to the local accumulation of PM rather than its dilution, particularly under low wind conditions 21 , 22 , 23 . This creates a potential paradox in which roadside trees simultaneously act as sinks for particulate matter through foliar accumulation while increasing airborne PM concentrations in the immediate surroundings where children walk 24 , 25 . Despite increasing recognition of this dual role, empirical studies that directly link vegetation structure, airborne PM concentrations at child height, and foliar PM accumulation along routes to school remain limited. The aim of this study is therefore to assess how roadside trees influence children’s exposure to PM along school routes by combining real-time air quality measurements, vegetation structure indicators (LAI), and laboratory-based analyses of particulate matter accumulated on leaves, using the post-industrial city of Łódź as a case study. 2. Methods 2.1. Study area The study was conducted in Łódź, one of the largest cities in Poland (area: 293.25 km²), with a population of 658 444 in 2023 and a population density of 2,245 people/km² 26 . Łódź is characterized by a moderately warm climate, with a mean annual temperature of 9.0°C and average annual precipitation of 712 mm 27 . Traffic intensity is highest in the city centre and along major transport corridors, where several thousand vehicles pass daily. These characteristics, combined with a post-industrial urban structure and persistent air quality challenges, make Łódź a suitable case study for examining the interaction between roadside greenery, particulate matter (PM), and children’s exposure along routes to school. Spatial patterns of PM₁₀ emissions across the city are presented in Fig. 1 . 2.2 Selection of schools and school routes From a total of 129 public primary schools operating in Łódź in the 2022/2023 school year, 20 schools were randomly selected for detailed field analysis. The selection captured variability in urban form, traffic conditions, and roadside vegetation, including schools located in densely built central areas as well as those situated in more open suburban neighbourhoods. The schools differed in their proximity to major roads and industrial areas, ranging from locations only a few meters from high-traffic streets in the city centre to sites located approximately 300–700 m from major pollution sources on the outskirts of the city (Fig. 2 ). This diversity is representative of typical primary school environments in Łódź and allowed the analysis to encompass a wide range of environmental exposure conditions experienced by children during their daily journeys to school. For each school studied, the most frequently used access routes were identified through field observations conducted during morning and afternoon commuting hours. The analysis focused on route segments extending from the school entrance to the nearest road crossing or drop-off point. These segments represent locations where children either walk or are dropped off by car or exit public means of transport, therefore constitute areas of repeated and concentrated exposure to traffic-related air pollution and roadside vegetation. This study did not involve human participants. No children (or other individuals) were interviewed, surveyed, recruited, or observed in a way that produced identifiable information. No personal data were collected. The work consisted solely of environmental measurements (airborne particulate matter in public space, vegetation structure metrics, and foliar particulate matter loads). Environmental conditions along the selected routes were further characterized by the spatial arrangement of trees in relation to the roadway and pedestrian pathways (Fig. 3 ). The proportion of vegetation along the analyzed road sections varied considerably between schools, with some routes characterized by dense tree cover which may limit the capacity for particulate matter accumulation. Characteristics of school surroundings, including mean daily traffic intensity, degree of surrounding development are summarized in Appendix 1. 2.3. Airborne PM measurements Airborne particulate matter concentrations (PM 2.5 ​ and PM 10 ​) were measured along the analysed school-route segments using a portable Sniffer4D dust meter (Appendix 2). To approximate children’s breathing zone during the commute, the instrument inlet was positioned at 140 cm above ground level. The Sniffer4D device recorded PM concentrations continuously at 1-s resolution (µg/m − 3 ). To improve robustness at each sampling location along the route segment, measurements were performed in two series. For each series, the mean concentration was calculated from 10 measured values (i.e., consecutive 1-s readings), and the final value used in subsequent analyses was obtained by averaging across the two series. The resulting airborne PM dataset (n = 242 observations) was used to characterise children’s near-route exposure and to support the statistical modelling of factors shaping PM concentrations around schools. Field measurements were carried out in June 2022 (final weeks of the school year) under controlled meteorological conditions to reduce short-term variability unrelated to the local environment. Data collection was restricted to dry, windless days (wind speed < 0.2 m/s, verified using local meteorological stations) and conducted during peak commuting periods (7:00–9:00 and 13:00–15:00) to capture conditions most relevant to children’s routine journeys to and from school. 2.4. Tree crown structure and crown density metrics Roadside tree crowns along the analysed school-route segments were characterised using two complementary indicators capturing distinct functional dimensions of vegetation structure: (i) horizontal crown density, quantified as leaf area index (LAI), and (ii) vertical crown density, quantified as the proportion of the pedestrian-level field of view occupied by tree crowns. Horizontal crown density was measured as LAI at 10 m intervals along each route segment, both within and outside tree-covered sections, using an SS1-COM-R4 Complete System with Radio Link (Delta-T Devices). Because LAI is expressed per unit ground area and local canopy density can vary substantially at small spatial scales, three adjacent readings were taken at each measurement location and averaged to obtain a representative LAI value. LAI was used as a proxy for the potential particulate matter absorption and interception surface provided by tree foliage along the routes. Tree species present along each route were recorded, and leaves or needles were collected for subsequent laboratory determination of particulate matter accumulated on plant surfaces (Section 2.5 ). Vertical crown density was quantified using digital photographs taken at each 10 m measurement point along the routes at a height of approximately 140 cm above ground level, corresponding to the average eye level of primary school children. Photographs were analysed using ImageJ software (imagej.net) to quantify the proportion of the image occupied by tree crowns. During image processing, non-tree vegetation (e.g. lawns and shrubs) was masked and excluded so that the resulting metric reflected tree crown obstruction at pedestrian height, rather than general greenness or visual perception. For each photograph, the percentage of canopy-related pixels was calculated and averaged across all measurement points for each route to produce a standardised vertical crown density indicator used in subsequent statistical analyses. Roadside trees sampled in this study are part of the municipal street-tree stock in Łódź, Poland (urban green infrastructure). Tree species and, where relevant, cultivars were identified in the field by Prof. Piotr Sikorski (co-author), a dendrologist with long-standing academic and professional experience in dendrology and vegetation science, including teaching dendrology and authorship of academic materials on plant communities in Poland. Identification was based on standard dendrological diagnostic features assessed in situ. To ensure transparency and reproducibility, we provide a digital voucher consisting of (i) a georeferenced inventory of sampled trees (sampling ID, coordinates, species/cultivar, sampling date) (Appendix 3). The sampled trees are long-lived and publicly accessible; if needed, additional leaf/needle material can be re-sampled from the same individuals for independent verification. 2.5. Determination of particulate matter accumulated on leaves Leaf/needle material was collected from trees growing along the analysed school-route segments. For each sampling event, aboveground plant material with a total surface area of approximately 300–400 cm² was collected in three biological replicates per species and location. Sampling was conducted during the summer period under windless and dry conditions and was preceded by at least seven consecutive days without rainfall to minimise recent wash-off effects. Samples were placed in paper envelopes and air-dried for 7 days at 21°C and approximately 60% relative humidity before laboratory analyses. The amount of particulate matter accumulated on plant surfaces was determined using the methodology described by Dzierżanowski et al. 28 , with modifications. To separate particles deposited on the leaf surface from those embedded in epicuticular waxes, each replicate was processed in two sequential rinsing steps. First, the plant material was rinsed with distilled water for 60 s to remove particles deposited on the leaf surface (surface-PM; SPM). Second, the same plant material was rinsed with chloroform for 45 s to dissolve epicuticular waxes and release particles retained within the wax layer (in-wax-PM; WPM). The resulting suspensions were filtered to quantify particle mass by size fraction. Prior to fine filtration, each suspension was passed through a 100 µm metal sieve (Haver and Boecker, Germany) to remove particles larger than 100 µm. Subsequently, suspensions were filtered using a 47 mm glass filter funnel with stopper support assembly (PALL Corp., USA) connected to a vacuum pump (KNF Neuberger, Inc., USA). Filtration was conducted sequentially through Type 91 filters (retention 10 µm), followed by Type 42 filters (retention 2.5 µm), and finally PTFE membrane filters (retention 0.2 µm) (all Whatman, UK). This procedure yielded two operational particle-size fractions: coarse (2.5–10 µm) and fine (0.2–2.5 µm). For subsequent analyses, PM 2.5 was defined as the mass of the 0.2–2.5 µm fraction, while PM10 was defined as the combined mass of the 0.2–10 µm fractions. Filters were dried at 60°C for 30 min (SANYO Laboratory Convection Oven MOV-212F), equilibrated in the weighing room for 45 min to stabilise humidity, and weighed before and after filtration using an analytical balance XS105DU (Mettler-Toledo International Inc., Switzerland). To minimise electrostatic artefacts, filters were passed through an ionising gate (HAUG, Switzerland) immediately before weighing. Leaf surface area for each replicate was measured using an Image Analysis System (Skye Instruments Ltd, UK) with SkyeLeaf software, allowing results to be expressed as particle mass per unit leaf area (µg·cm⁻²). Although leaves were rinsed on both sides, values were converted to a one-sided surface-area basis for consistency with common practice in LAI and leaf-surface measurements. 2.6. Traffic intensity Traffic intensity in the vicinity of each school-route segment was used as a site-level traffic intensity indicator. Data were obtained from the Local Traffic Management/Control System in Łódź 29 . Vehicle counts were extracted for three-time windows corresponding to the exposure sampling periods and baseline daytime traffic: morning peak (07:30–07:45), mid-day (10:30–10:45), and afternoon school-commute period (13:30–13:45). For each school, traffic intensity was assigned to the road section adjacent to the analysed route segment and summarised as the mean daily traffic across the selected time (Appendix 1). 2.7. Urban zones To account for differences in urban form and surrounding development, schools were classified into two urban zones (Metropolitan vs Suburban) based on the Study of Conditions and Directions of Spatial Development of the City of Łódź (2021). This categorical variable was used to examine whether relationships between airborne PM, traffic conditions, and vegetation indicators differed between compact central areas and less densely developed peripheral neighbourhoods (Appendix 1,2). 2.8. Statistical analysis Statistical analyses were performed to quantify relationships between (i) airborne particulate matter concentrations measured along school-route segments (APM; PM 2.5 and PM 10 ) and (ii) particulate matter accumulated on leaf/needle surfaces (LPM; PM 2.5 and PM), in relation to vegetation metrics and traffic conditions (Appendix 1–2). Predictor variables included leaf area index (LAI), vertical exposure to greenery (percentage of canopy-related green pixels), traffic intensity, and urban zone (Metropolitan vs Suburban). Airborne PM models (APM). To identify factors associated with PM concentrations in the air along school-route measurement points, separate models were fitted for PM 2.5 and PM 10 using the APM dataset (n = 242). Because multiple observations were collected within each school route, non-independence of observations was accounted for using linear mixed-effects models with school identity included as a random intercept. Leaf PM models (LPM). To assess whether vegetation structure was associated with the amount of PM retained on leaf/needle surfaces, separate models were fitted for PM 2.5 and PM 10 accumulated on leaves using the LPM dataset (n = 124). LAI was included as the primary predictor of foliar PM accumulation, and urban zone was included to account for differences in surrounding built form and environmental context. Species (tree type) model. To examine interspecific differences in foliar PM accumulation, a multiple regression model was additionally fitted with foliar PM as the response and tree species as a categorical predictor, alongside LAI and site-level covariates (urban zone and local airborne PM where available). This model was used to test whether species identity explained additional variation in PM accumulation beyond vegetation density. To improve adherence to linear-model assumptions, a Box–Cox procedure was used to guide transformation of the response variables; based on this, response variables were log-transformed prior to modelling. Residual normality was evaluated using the Jarque–Bera test, and heteroscedasticity was assessed using the Breusch–Pagan test. Where heteroscedasticity was detected, inference was based on heteroscedasticity-robust standard errors. Model simplification was performed using a stepwise procedure to retain statistically supported predictors and remove redundant terms. 3. Results Our results indicate a dual role of roadside trees: they remove particulate matter through foliar deposition, a function best represented by horizontal crown density, whose increase enhances absorption efficiency, while simultaneously increasing airborne particulate matter concentrations at pedestrian height, an effect best captured by vertical crown density. 3.1. Horizontal crown density (LAI) and species effects on foliar particulate matter accumulation Leaf area index (LAI) was a statistically significant predictor of particulate matter accumulated on leaves/needles (LPM) for both size fractions (Table 1 ). In the PM₁₀ model, higher LAI was associated with higher foliar PM loads (log(LAI) estimate = 0.519, p < 0.001), corresponding to an average increase of ~ 0.52% in PM₁₀ accumulated on leaves per 1% increase in LAI. In the PM₂.₅ model, LAI was also positively associated with foliar accumulation (log(LAI) estimate = 0.557, p = 0.0029), corresponding to ~ 0.56% increase in PM₂.₅ per 1% increase in LAI. However, the explanatory power of LAI alone was modest (adjusted R² = 0.13), indicating that foliar accumulation is additionally shaped by factors beyond vegetation density (Table 1 ). Table 1 The influence of the LAI on PM 10 and PM 2.5 accumulated on leaves ( L PM). PM 10 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.291 0.129 17.716 0.000 *** log(LAI) 0.519 0.121 4.305 0.000 *** Adjusted R-squared: 0.1266 PM 2.5 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.908 0.204 9.335 0.000 *** log(LAI) 0.557 0.183 3.042 0.000 ** Adjusted R-squared: 0.1313 3.2. Impact of vertical crown density and traffic intensity on airborne particulate matter Linear mixed-effects models indicated that both vertical crown density and traffic intensity were positively associated with airborne PM concentrations measured at child height along school-route segments (Table 2 ). For PM₁₀, the fixed-effect estimate for vertical green density was 0.019 (t = 2.396), implying an approximately 1.9% increase in PM₁₀ per one-unit increase in the greenery metric (as defined in Methods), while traffic intensity showed a positive association (estimate = 0.006, t = 2.752). Similar patterns were observed for PM₂.₅, with vertical crown density and traffic intensity both emerging as significant predictors (estimate = 0.0006, t = 2.744) (Table 2 ). Between-school heterogeneity was substantial (random-intercept SD = 0.24 for both PM fractions), and accounting for school-level random effects yielded high conditional explanatory power (conditional R² = 0.894 for PM₁₀ and 0.900 for PM₂.₅), indicating strong site-specific differences beyond the fixed effects alone (Table 2 ). Table 2 The influence of selected factors on the amount of PM 10 and PM 2.5 in the air A PM PM 10 PM 2.5 Random effects: Groups Variance Std.Dev. Variance Std.Dev. ID school 0.056 0.237 0.059 0.243 Residual 0.009 0.097 0.009 0.096 Fixed effects : Estimate Std. Error T Estimate Std. Error t (intercept) 2.618 0.296 8.832 2.581 0.295 8,755 Vertical crown density 0.019 0.008 2.396 0.0197 0.008 2.475 Traffic 0.006 0.0002 2.752 0.0006 0.0002 2.744 Average airborne concentrations differed markedly among the 20 schools (Table 3 ). Across school-route segments, mean PM₁₀ ranged from 18.49 µg/m³ (School 20) to 58.56 µg/m³ (School 1), and mean PM₂.₅ ranged from 18.48 µg/m³ (School 20) to 57.57 µg/m³ (School 1). None of the analysed school routes met the WHO guideline levels (PM₁₀ = 15 µg/m³; PM₂.₅ = 5 µg/m³). In contrast, when compared with EU limit values, PM₁₀ levels met the EU standard at 12 schools and exceeded it at 8 schools, whereas PM₂.₅ levels met the EU standard at 8 schools and exceeded it at 12 schools (Table 3 ). Table 3 Average PM 10 and PM 2.5 concentrations along roads to schools. School ID Mean PM 10 concentration [µg/m ³ ] Std. Error PM 10 Mean PM 2.5 concentration [µg/m ³ ] Std. Error PM 2.5 PM 10 WHO Standard Exceedance [µg/m ³ ] PM 2.5 WHO Standard Exceedance [µg/m ³ ] 1. 58.56 1.20 57.57 1.52 43.56 52.57 2. 48.51 1.52 47.6 1.38 33.51 42.60 3. 46.07 1.04 45.0 1.49 31.07 40.00 4. 45.64 1.38 45.14 1.41 30.64 40.14 5. 42.46 1.02 41.50 1.34 27.46 36.50 6. 41.94 1.27 40.73 1.54 26.94 35.73 7. 46.25 1.21 40.88 1.36 31.25 35.88 8. 39.44 1.36 38.04 1.33 24.44 33.04 9. 35.77 1.33 34.86 1.25 20.77 29.86 10. 34.75 1.34 34.01 1.38 19.75 29.01 11. 31.83 1.31 30.81 1.36 16.83 25.81 12. 31.47 1.36 30.84 1.40 16.47 25.84 13. 31.44 1.31 29.91 1.34 16.44 24.91 14. 30.6 1.21 29.77 1.38 15.60 24.77 15. 30.05 1.31 29.59 1.21 15.05 24.59 16. 28.38 1.49 27.83 1.45 13.38 22.83 17. 28.31 1.25 27.55 1.38 13.31 22.55 18. 26.73 1.10 25.41 1.45 11.73 20.41 19. 21.94 0.83 21.46 1.34 6.94 16.46 20. 18.49 1.43 18.48 1.41 3.94 13.48 3.3. Species differences in foliar PM accumulation Substantial interspecific differences were observed in foliar particulate matter loads, even after accounting for vegetation density (LAI) and local airborne concentrations (Table 4 ). In the species model for PM₁₀, LAI (log(LAI) estimate = 0.292, p = 0.022) and field PM₁₀ (log(field_PM₁₀) estimate = 0.820, p = 0.001) were significant positive covariates, confirming that both horizontal canopy density and ambient exposure contribute to foliar loads, while species identity explained additional variability beyond these effects (Table 4 ). Species ranking differed by PM fraction. For PM₁₀, Pinus sylvestris showed the highest accumulation index (1.5), whereas Prunus avium showed the lowest (–2.1). For PM₂.₅, the highest accumulation index was observed for Acer campestre (1.5), while Fraxinus excelsior showed comparatively low accumulation (0.33) (Table 4 ). Overall, coniferous species accumulated approximately 70–80% more PM₁₀ and 40–50% more PM₂.₅ than deciduous species. Across all trees sampled near schools, mean foliar loads were ~ 27.5 µg/cm² for PM₁₀ and ~ 17.6 µg/cm² for PM₂.₅, illustrating that leaves/needles act as measurable particle sinks within the studied school environments. Table 4 The effectiveness of individual tree species in PM 10 and PM 2.5 accumulation PM 10 Coefficients: Mean PM 10 accumulation [µg/cm 2 ] Estimate Std. Error t value Pr(>|t|) (Intercept) - -0.185 0.940 -0.197 0.845 log(LAI) - 0.292 0.125 2.334 0.022 * log(field_PM10) - 0.820 0.244 3.361 0.001 ** Acer campestre 24.562 1.302 0.223 5.846 6.34E-08 *** Acer saccharinum 19.321 0.487 0.104 4.663 9.66E-06 *** Aesculus hippocastanum 11.669 0.495 0.188 2.629 0.01 ** Betula pendula 38.276 0.684 0.203 3.367 0.001 ** Chamaecyparis pisifera 37.428 0.797 0.107 7.454 3.32E-11 *** Picea abies 30.635 1.233 0.163 7.568 1.90E-11 *** Pinus sylvestris 57.744 1.509 0.112 13.5 < 2.2e-16 *** Populus × canadensis 10.641 0.562 0.25 2.251 0.027 * Populus × canescens 27.963 0.831 0.104 8.026 2.00E-12 *** Prunus avium 7.49 -2.167 0.162 -13.352 < 2.2e-16 *** Rhus typhina 22.191 0.905 0.191 4.746 6.92E-06 *** Salix alba 42.823 1.067 0.24 4.438 2.34E-05 *** Sorbus aucuparia 18.314 0.446 0.145 3.067 0.003 ** Tilia cordata 16.108 0.373 0.121 3.093 0.003 ** Ulmus glabra 17.564 0.335 0.092 3.649 0.0004 *** Metropolitan Zone (M) - -0.680 0.135 -5.046 2.02E-06 *** Suburban Zone (W) - -0.377 0.100 -3.754 0.0003 *** --- Signif. codes: 0 ‘ ***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 PM 2.5 Coefficients: Mean PM 2.5 accumulation [µg/cm 2 ] Estimate Std. Error t value Pr(>|t|) (Intercept) - -0.470 0.917 -0.512 0.610 log(LAI) - 0.240 0.131 1.831 0.070 . log(field_PM25) - 0.876 0.239 3.661 0.0004 *** Acer campestre 22.695 1.534 0.221 6.937 3.89E-10 *** Acer saccharinum 15.393 0.601 0.123 4.871 4.11E-06 *** Aesculus hippocastanum 7.991 0.632 0.198 3.189 0.002 ** Betula pendula 23.97 0.878 0.241 3.65 0.0004 *** Chamaecyparis pisifera 26.631 1.092 0.11 9.883 < 2.2e-16 *** Picea abies 12.803 0.685 0.165 4.15 6.95E-05 *** Pinus sylvestris 23.341 0.997 0.114 8.7 6.47E-14 *** Populus × canadensis 8.828 0.731 0.25 2.916 0.004 ** Populus × canescens 19.35 0.809 0.121 6.678 1.34E-09 *** Prunus avium 4.237 -3.002 0.163 -18.42 < 2.2e-16 *** Rhus typhina 19.315 1.328 0.186 7.138 1.48E-10 *** Salix alba 39.53 1.244 0.308 4.041 0.0001 *** Sorbus aucuparia 16.067 0.686 0.17 4.041 0.0001 *** Tilia cordata 12.514 0.358 0.129 2.786 0.006 ** Ulmus glabra 15.095 0.511 0.103 4.987 2.56E-06 *** Metropolitan Zone (M) - -0.877 0.161 -5.454 3.51E-07 *** Suburban Zone (W) - -0.603 0.120 -5.026 2.17E-06 *** Signif. codes: 0 ‘ ***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ 4. Discussion 4.1. The dual role of trees in urban areas in reducing PM The results of this study confirm that children travelling along routes with trees to school are exposed to elevated concentrations of particulate matter, frequently exceeding health-based guidelines, particularly during morning and afternoon traffic peaks. These findings are consistent with previous research showing that near-ground emissions from road traffic are a dominant source of PM exposure in the immediate vicinity of schools during the warm season, when residential heating plays a minor role 30 . The timing of exposure is especially relevant for children, as daily school commutes coincide with periods of intensified vehicle activity related both to general traffic and school drop-off and pick-up patterns. Even short-term exposure during these repeated daily journeys may therefore contribute to cumulative health risks, given children’s heightened physiological vulnerability to air pollution 2 , 3 , 31 . At the same time, our results demonstrate that roadside trees along school routes perform a measurable ecosystem service by accumulating particulate matter on leaf surfaces, with higher LAI values associated with increased foliar PM loads. This finding aligns with previous studies documenting the capacity of urban vegetation to capture airborne particles through deposition processes influenced by leaf surface characteristics and exposure duration 16 , 19 . However, the relatively modest increase in leaf-level PM accumulation compared to the increase observed in airborne PM concentrations reflects fundamental differences in temporal scale and physical processes. While airborne PM measurements capture short-term fluctuations during traffic peaks, foliar accumulation represents an integrated signal influenced by deposition, wash-off, resuspension, and saturation over longer periods 20 , 32 . Importantly, the study reveals that greater horizontal vegetation density along school routes is associated with higher concentrations of PM in the air at pedestrian height. In the mixed-effects models, a one-unit increase in the greenery metric was associated with ~ 1.9% higher airborne PM at child height, whereas a 1% increase in LAI corresponded to only modest increases in foliar accumulation (~ 0.52% for PM₁₀ and ~ 0.56% for PM₂.₅). This finding highlights the context-dependent nature of vegetation effects on air quality and supports growing evidence that dense tree canopies in street environments may restrict airflow and reduce pollutant dispersion, particularly under low wind conditions 21 , 22 , 24 , 33 . In such settings, trees simultaneously function as sinks for particulate matter through foliar accumulation and as physical barriers that promote pollutant stagnation near ground level. Rather than indicating a failure of green infrastructure, these results underscore the importance of considering airflow, street geometry, and vegetation structure when assessing children’s exposure to air pollution along school routes. Species identity explained additional variation in foliar PM loads beyond LAI and local airborne PM, indicating that species choice can modulate PM retention potential in school environments. 4.2. Implications for urban planning and school environments Our results suggest that while trees can contribute to particulate matter removal, their effects on air quality at pedestrian height depend strongly on spatial configuration, vegetation density, and surrounding built form. In narrow street environments or areas with limited ventilation, dense tree canopies may unintentionally increase children’s exposure to airborne PM along frequently used routes to school 25 , 34 . Achieving a balance between high horizontal crown density (maximising particulate absorption surface) and lower vertical crown density (minimising airflow obstruction) appears to be critical for effective exposure mitigation along school routes. These findings highlight the need for a nuanced approach to greening interventions in school environments. Rather than relying on uniform tree-planting strategies, planners should consider how different vegetation forms interact with airflow and pedestrian movement. Previous studies indicate that lower vegetation elements, such as hedges or herbaceous plantings, may reduce PM concentrations at breathing height without substantially restricting ventilation, particularly when placed between traffic lanes and sidewalks 35 , 36 . Similarly, the creation of ventilation corridors and the careful placement of trees relative to prevailing wind directions and building geometry may help balance the benefits of particulate matter deposition with the need for effective pollutant dispersion 37 , 38 . Our results therefore support planning approaches that prioritise site-specific design and exposure reduction rather than assuming that increased vegetation density will universally improve air quality. Data on the differing particulate matter accumulation capacities of individual tree species provide practical guidance for urban planners and green infrastructure designers. Species identity explained additional variation in foliar PM loads beyond LAI and local airborne PM, indicating that species choice can modulate PM retention potential in school environments. The findings of this study have direct relevance for urban planning strategies aimed at improving environmental conditions in school surroundings. Roadside trees are among the most applied green infrastructure elements in cities and are often introduced with the expectation of reducing air pollution exposure among vulnerable populations. 4.3. Limitations While the analysis incorporates key factors influencing PM exposure along school routes, including traffic intensity, vegetation structure, and urban context, it does not account for all elements that may shape local air quality, such as detailed street geometry, building porosity, or dynamic wind patterns. In addition, measurements were conducted during the vegetation season, and the role of trees in modifying exposure during winter months—when emissions from residential heating dominate—requires further investigation. Nevertheless, focusing on the growing season allowed for direct assessment of foliar accumulation processes and their interaction with airborne PM concentrations. Future research should integrate high-resolution airflow modelling with empirical exposure measurements to better disentangle the relative contributions of vegetation structure and built form to pollutant dispersion near schools. Long-term monitoring across seasons would further clarify how vegetation-mediated effects on exposure vary throughout the year. Importantly, studies that link spatial exposure patterns along school routes with health or behavioural outcomes could provide valuable insights for evidence-based urban planning aimed at protecting children’s health. 5. Conclusions This study demonstrates that routes to school constitute critical exposure settings where children regularly encounter elevated concentrations of particulate matter. Roadside trees along these routes play a dual role: : horizontal crown density enhances particulate matter accumulation on foliage, while vertical crown density influences airflow and may increase airborne PM concentrations at pedestrian height. Effective planning of greener school environments should therefore explicitly consider horizontal and vertical dimensions of tree crowns, as well as species composition, rather than focusing on the presence of greenery alone. The findings highlight the importance of moving beyond simplified assumptions about the protective role of urban vegetation in school environments. Designing healthier routes to school requires careful consideration of exposure dynamics, spatial configuration, and the interaction between green infrastructure and the built environment. When focusing on locations that children traverse daily, particular attention should be paid to horizontal and vertical crown structure as well as species composition. This study provides evidence relevant for planning interventions that aim to maximise public health benefits while minimising unintended consequences. Declarations Funding This research was funded by the National Science Centre (Poland) grant no. 2020/39/B/HS4/03240. The publication of the manuscript was financed by Science Development Fund of the Warsaw University of Life Sciences – SGGW Author Contribution **A. Hoppa:** Investigation, Manuscript writing - original draft, Data visualization, Formal analysis. **P. Sikorski:** Conceptualization, Investigation, Formal analysis, Manuscript writing. **A. Przybysz:** Investigation, Formal analysis, Manuscript writing. **E. Łaszkiewicz:** Conceptualization, Funding acquisition, Project management, Formal analysis. **A. Nawrocki:** Investigation. **P. Archiciński:** Investigation. **D. Sikorska:** Investigation, Conceptualization, Manuscript writing – original draft, Supervision. 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Supplementary Files Appendices.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviews received at journal 25 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Editor invited by journal 19 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 16 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":269215,"visible":true,"origin":"","legend":"\u003cp\u003eLinear emission of PM\u003csub\u003e10\u003c/sub\u003e [Mg/year] in the city of Łódź\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8490912/v1/2cd2e266927581b6991bdc9e.png"},{"id":100929231,"identity":"e06eeae2-c496-4746-9ff8-b23f64cd33d3","added_by":"auto","created_at":"2026-01-23 00:34:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1235101,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of selected schools on the map of Łódź (Basemap: Google maps).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8490912/v1/3b53c7535282cd043bd9ff8f.png"},{"id":100951884,"identity":"d4e6d4f6-8f29-4fe4-9bc5-3acc18d20165","added_by":"auto","created_at":"2026-01-23 07:11:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":677720,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of analyzed school surroundings and route segments. Orthophotomaps and (i) school entrance, (ii) the measurement points on the route from the entrance to the nearest crossing/drop-off point, and (iii) locations of studied roadside trees along the route\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8490912/v1/f42e0d2891d6ad8884557b64.png"},{"id":100953203,"identity":"ae5c7dea-5f79-4779-aa7a-1060b18df276","added_by":"auto","created_at":"2026-01-23 07:20:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3707591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8490912/v1/4c269774-68b0-4e93-965b-7568e59827a3.pdf"},{"id":100929227,"identity":"7206d73c-f1ae-42f5-be28-bdfed3481113","added_by":"auto","created_at":"2026-01-23 00:34:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18484,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-8490912/v1/93022dea272583f04ac4976b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The dual role of roadside trees in shaping children’s PM exposure near schools","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChildren are among the population groups most vulnerable to the adverse health effects of air pollution, particularly particulate matter (PM), due to ongoing lung development, higher breathing rates relative to body mass, and limited ability to avoid polluted environments \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Exposure to PM in early life has been associated with impaired lung development, increased risk of asthma, acute respiratory infections, and adverse cardiovascular and neurological outcomes \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition to physical health effects, polluted air has been shown to negatively affect children\u0026rsquo;s cognitive development, attention, and academic performance \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Importantly, children\u0026rsquo;s exposure to air pollution is shaped not only by background concentrations but also by everyday routines and repeated contact with specific environments, which can cumulatively influence long-term health outcomes.\u003c/p\u003e \u003cp\u003eAmong everyday environments, routes to school represent a particularly important yet often underestimated exposure setting for children, as they are repeated daily and occur at pedestrian height, close to traffic sources. Previous studies have shown that children spend a substantial proportion of their active time outdoors along routes to and from school, frequently in proximity to busy roads characterized by elevated PM concentrations \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Traffic-related air pollution near schools has been identified as a major contributor to both outdoor and indoor PM levels, with vehicle emissions during morning and afternoon peak hours playing a dominant role \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Unlike recreational spaces such as parks, routes to school are characterized by fixed, repeated paths used by the same group of children daily, typically five days a week and often during periods of intensified traffic. As a result, environmental conditions along these routes have the potential to influence exposure for a large proportion of children consistently and cumulatively.\u003c/p\u003e \u003cp\u003eIn response to growing concerns about urban air quality and children's health, urban planning strategies are increasingly promoting green infrastructure as a mitigation measure, particularly in areas frequented by vulnerable populations, such as school surroundings. Roadside trees are among the most commonly implemented green infrastructure elements in cities due to their potential to accumulate particulate matter on leaf surfaces and thus contribute to air purification \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The effectiveness of vegetation in capturing PM depends on species-specific traits, leaf morphology, and structural characteristics such as the leaf area index (LAI), as well as meteorological conditions and exposure time \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Consequently, tree planting near roads and schools is often regarded as an unambiguously beneficial intervention to reduce children\u0026rsquo;s exposure to air pollution.\u003c/p\u003e \u003cp\u003eHowever, growing evidence suggests that the impact of vegetation on air quality at pedestrian height is highly context-dependent. In densely built environments, trees may restrict airflow and reduce pollutant dispersion, leading to the local accumulation of PM rather than its dilution, particularly under low wind conditions \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This creates a potential paradox in which roadside trees simultaneously act as sinks for particulate matter through foliar accumulation while increasing airborne PM concentrations in the immediate surroundings where children walk \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Despite increasing recognition of this dual role, empirical studies that directly link vegetation structure, airborne PM concentrations at child height, and foliar PM accumulation along routes to school remain limited.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe aim of this study\u003c/b\u003e is therefore to assess how roadside trees influence children\u0026rsquo;s exposure to PM along school routes by combining real-time air quality measurements, vegetation structure indicators (LAI), and laboratory-based analyses of particulate matter accumulated on leaves, using the post-industrial city of Ł\u0026oacute;dź as a case study.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe study was conducted in Ł\u0026oacute;dź, one of the largest cities in Poland (area: 293.25 km\u0026sup2;), with a population of 658 444 in 2023 and a population density of 2,245 people/km\u0026sup2; \u003csup\u003e26\u003c/sup\u003e. Ł\u0026oacute;dź is characterized by a moderately warm climate, with a mean annual temperature of 9.0\u0026deg;C and average annual precipitation of 712 mm \u003csup\u003e27\u003c/sup\u003e. Traffic intensity is highest in the city centre and along major transport corridors, where several thousand vehicles pass daily. These characteristics, combined with a post-industrial urban structure and persistent air quality challenges, make Ł\u0026oacute;dź a suitable case study for examining the interaction between roadside greenery, particulate matter (PM), and children\u0026rsquo;s exposure along routes to school. Spatial patterns of PM₁₀ emissions across the city are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection of schools and school routes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom a total of 129 public primary schools operating in Ł\u0026oacute;dź in the 2022/2023 school year, 20 schools were randomly selected for detailed field analysis. The selection captured variability in urban form, traffic conditions, and roadside vegetation, including schools located in densely built central areas as well as those situated in more open suburban neighbourhoods. The schools differed in their proximity to major roads and industrial areas, ranging from locations only a few meters from high-traffic streets in the city centre to sites located approximately 300\u0026ndash;700 m from major pollution sources on the outskirts of the city (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This diversity is representative of typical primary school environments in Ł\u0026oacute;dź and allowed the analysis to encompass a wide range of environmental exposure conditions experienced by children during their daily journeys to school.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor each school studied, the most frequently used access routes were identified through field observations conducted during morning and afternoon commuting hours. The analysis focused on route segments extending from the school entrance to the nearest road crossing or drop-off point. These segments represent locations where children either walk or are dropped off by car or exit public means of transport, therefore constitute areas of repeated and concentrated exposure to traffic-related air pollution and roadside vegetation. This study did not involve human participants. No children (or other individuals) were interviewed, surveyed, recruited, or observed in a way that produced identifiable information. No personal data were collected. The work consisted solely of environmental measurements (airborne particulate matter in public space, vegetation structure metrics, and foliar particulate matter loads).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnvironmental conditions along the selected routes were further characterized by the spatial arrangement of trees in relation to the roadway and pedestrian pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The proportion of vegetation along the analyzed road sections varied considerably between schools, with some routes characterized by dense tree cover which may limit the capacity for particulate matter accumulation. Characteristics of school surroundings, including mean daily traffic intensity, degree of surrounding development are summarized in Appendix 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Airborne PM measurements\u003c/h2\u003e \u003cp\u003eAirborne particulate matter concentrations (PM\u003csub\u003e2.5\u003c/sub\u003e​ and PM\u003csub\u003e10\u003c/sub\u003e​) were measured along the analysed school-route segments using a portable Sniffer4D dust meter (Appendix 2). To approximate children\u0026rsquo;s breathing zone during the commute, the instrument inlet was positioned at 140 cm above ground level. The Sniffer4D device recorded PM concentrations continuously at 1-s resolution (\u0026micro;g/m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). To improve robustness at each sampling location along the route segment, measurements were performed in two series. For each series, the mean concentration was calculated from 10 measured values (i.e., consecutive 1-s readings), and the final value used in subsequent analyses was obtained by averaging across the two series. The resulting airborne PM dataset (n\u0026thinsp;=\u0026thinsp;242 observations) was used to characterise children\u0026rsquo;s near-route exposure and to support the statistical modelling of factors shaping PM concentrations around schools. Field measurements were carried out in June 2022 (final weeks of the school year) under controlled meteorological conditions to reduce short-term variability unrelated to the local environment. Data collection was restricted to dry, windless days (wind speed\u0026thinsp;\u0026lt;\u0026thinsp;0.2 m/s, verified using local meteorological stations) and conducted during peak commuting periods (7:00\u0026ndash;9:00 and 13:00\u0026ndash;15:00) to capture conditions most relevant to children\u0026rsquo;s routine journeys to and from school.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Tree crown structure and crown density metrics\u003c/h2\u003e \u003cp\u003eRoadside tree crowns along the analysed school-route segments were characterised using two complementary indicators capturing distinct functional dimensions of vegetation structure: (i) horizontal crown density, quantified as leaf area index (LAI), and (ii) vertical crown density, quantified as the proportion of the pedestrian-level field of view occupied by tree crowns.\u003c/p\u003e \u003cp\u003eHorizontal crown density was measured as LAI at 10 m intervals along each route segment, both within and outside tree-covered sections, using an SS1-COM-R4 Complete System with Radio Link (Delta-T Devices). Because LAI is expressed per unit ground area and local canopy density can vary substantially at small spatial scales, three adjacent readings were taken at each measurement location and averaged to obtain a representative LAI value. LAI was used as a proxy for the potential particulate matter absorption and interception surface provided by tree foliage along the routes. Tree species present along each route were recorded, and leaves or needles were collected for subsequent laboratory determination of particulate matter accumulated on plant surfaces (Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eVertical crown density was quantified using digital photographs taken at each 10 m measurement point along the routes at a height of approximately 140 cm above ground level, corresponding to the average eye level of primary school children. Photographs were analysed using ImageJ software (imagej.net) to quantify the proportion of the image occupied by tree crowns. During image processing, non-tree vegetation (e.g. lawns and shrubs) was masked and excluded so that the resulting metric reflected tree crown obstruction at pedestrian height, rather than general greenness or visual perception. For each photograph, the percentage of canopy-related pixels was calculated and averaged across all measurement points for each route to produce a standardised vertical crown density indicator used in subsequent statistical analyses.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eRoadside trees sampled in this study are part of the municipal street-tree stock in Ł\u0026oacute;dź, Poland (urban green infrastructure). Tree species and, where relevant, cultivars were identified in the field by Prof. Piotr Sikorski (co-author), a dendrologist with long-standing academic and professional experience in dendrology and vegetation science, including teaching dendrology and authorship of academic materials on plant communities in Poland. Identification was based on standard dendrological diagnostic features assessed in situ. To ensure transparency and reproducibility, we provide a digital voucher consisting of (i) a georeferenced inventory of sampled trees (sampling ID, coordinates, species/cultivar, sampling date) (Appendix 3). The sampled trees are long-lived and publicly accessible; if needed, additional leaf/needle material can be re-sampled from the same individuals for independent verification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Determination of particulate matter accumulated on leaves\u003c/h2\u003e \u003cp\u003eLeaf/needle material was collected from trees growing along the analysed school-route segments. For each sampling event, aboveground plant material with a total surface area of approximately 300\u0026ndash;400 cm\u0026sup2; was collected in three biological replicates per species and location. Sampling was conducted during the summer period under windless and dry conditions and was preceded by at least seven consecutive days without rainfall to minimise recent wash-off effects. Samples were placed in paper envelopes and air-dried for 7 days at 21\u0026deg;C and approximately 60% relative humidity before laboratory analyses.\u003c/p\u003e \u003cp\u003eThe amount of particulate matter accumulated on plant surfaces was determined using the methodology described by Dzierżanowski et al.\u003csup\u003e28\u003c/sup\u003e, with modifications. To separate particles deposited on the leaf surface from those embedded in epicuticular waxes, each replicate was processed in two sequential rinsing steps. First, the plant material was rinsed with distilled water for 60 s to remove particles deposited on the leaf surface (surface-PM; SPM). Second, the same plant material was rinsed with chloroform for 45 s to dissolve epicuticular waxes and release particles retained within the wax layer (in-wax-PM; WPM). The resulting suspensions were filtered to quantify particle mass by size fraction. Prior to fine filtration, each suspension was passed through a 100 \u0026micro;m metal sieve (Haver and Boecker, Germany) to remove particles larger than 100 \u0026micro;m. Subsequently, suspensions were filtered using a 47 mm glass filter funnel with stopper support assembly (PALL Corp., USA) connected to a vacuum pump (KNF Neuberger, Inc., USA). Filtration was conducted sequentially through Type 91 filters (retention 10 \u0026micro;m), followed by Type 42 filters (retention 2.5 \u0026micro;m), and finally PTFE membrane filters (retention 0.2 \u0026micro;m) (all Whatman, UK). This procedure yielded two operational particle-size fractions: coarse (2.5\u0026ndash;10 \u0026micro;m) and fine (0.2\u0026ndash;2.5 \u0026micro;m). For subsequent analyses, PM\u003csub\u003e2.5\u003c/sub\u003e was defined as the mass of the 0.2\u0026ndash;2.5 \u0026micro;m fraction, while PM10 was defined as the combined mass of the 0.2\u0026ndash;10 \u0026micro;m fractions. Filters were dried at 60\u0026deg;C for 30 min (SANYO Laboratory Convection Oven MOV-212F), equilibrated in the weighing room for 45 min to stabilise humidity, and weighed before and after filtration using an analytical balance XS105DU (Mettler-Toledo International Inc., Switzerland). To minimise electrostatic artefacts, filters were passed through an ionising gate (HAUG, Switzerland) immediately before weighing. Leaf surface area for each replicate was measured using an Image Analysis System (Skye Instruments Ltd, UK) with SkyeLeaf software, allowing results to be expressed as particle mass per unit leaf area (\u0026micro;g\u0026middot;cm⁻\u0026sup2;). Although leaves were rinsed on both sides, values were converted to a one-sided surface-area basis for consistency with common practice in LAI and leaf-surface measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Traffic intensity\u003c/h2\u003e \u003cp\u003eTraffic intensity in the vicinity of each school-route segment was used as a site-level traffic intensity indicator. Data were obtained from the Local Traffic Management/Control System in Ł\u0026oacute;dź \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Vehicle counts were extracted for three-time windows corresponding to the exposure sampling periods and baseline daytime traffic: morning peak (07:30\u0026ndash;07:45), mid-day (10:30\u0026ndash;10:45), and afternoon school-commute period (13:30\u0026ndash;13:45). For each school, traffic intensity was assigned to the road section adjacent to the analysed route segment and summarised as the mean daily traffic across the selected time (Appendix 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Urban zones\u003c/h2\u003e \u003cp\u003eTo account for differences in urban form and surrounding development, schools were classified into two urban zones (Metropolitan vs Suburban) based on the Study of Conditions and Directions of Spatial Development of the City of Ł\u0026oacute;dź (2021). This categorical variable was used to examine whether relationships between airborne PM, traffic conditions, and vegetation indicators differed between compact central areas and less densely developed peripheral neighbourhoods (Appendix 1,2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed to quantify relationships between (i) airborne particulate matter concentrations measured along school-route segments (APM; PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e) and (ii) particulate matter accumulated on leaf/needle surfaces (LPM; PM\u003csub\u003e2.5\u003c/sub\u003e and PM), in relation to vegetation metrics and traffic conditions (Appendix 1\u0026ndash;2). Predictor variables included leaf area index (LAI), vertical exposure to greenery (percentage of canopy-related green pixels), traffic intensity, and urban zone (Metropolitan vs Suburban).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAirborne PM models (APM). To identify factors associated with PM concentrations in the air along school-route measurement points, separate models were fitted for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e using the APM dataset (n\u0026thinsp;=\u0026thinsp;242). Because multiple observations were collected within each school route, non-independence of observations was accounted for using linear mixed-effects models with school identity included as a random intercept.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLeaf PM models (LPM). To assess whether vegetation structure was associated with the amount of PM retained on leaf/needle surfaces, separate models were fitted for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e accumulated on leaves using the LPM dataset (n\u0026thinsp;=\u0026thinsp;124). LAI was included as the primary predictor of foliar PM accumulation, and urban zone was included to account for differences in surrounding built form and environmental context.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSpecies (tree type) model. To examine interspecific differences in foliar PM accumulation, a multiple regression model was additionally fitted with foliar PM as the response and tree species as a categorical predictor, alongside LAI and site-level covariates (urban zone and local airborne PM where available). This model was used to test whether species identity explained additional variation in PM accumulation beyond vegetation density.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo improve adherence to linear-model assumptions, a Box\u0026ndash;Cox procedure was used to guide transformation of the response variables; based on this, response variables were log-transformed prior to modelling. Residual normality was evaluated using the Jarque\u0026ndash;Bera test, and heteroscedasticity was assessed using the Breusch\u0026ndash;Pagan test. Where heteroscedasticity was detected, inference was based on heteroscedasticity-robust standard errors. Model simplification was performed using a stepwise procedure to retain statistically supported predictors and remove redundant terms.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOur results indicate a dual role of roadside trees: they remove particulate matter through foliar deposition, a function best represented by horizontal crown density, whose increase enhances absorption efficiency, while simultaneously increasing airborne particulate matter concentrations at pedestrian height, an effect best captured by vertical crown density.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Horizontal crown density (LAI) and species effects on foliar particulate matter accumulation\u003c/h2\u003e \u003cp\u003eLeaf area index (LAI) was a statistically significant predictor of particulate matter accumulated on leaves/needles (LPM) for both size fractions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the PM₁₀ model, higher LAI was associated with higher foliar PM loads (log(LAI) estimate\u0026thinsp;=\u0026thinsp;0.519, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corresponding to an average increase of ~\u0026thinsp;0.52% in PM₁₀ accumulated on leaves per 1% increase in LAI. In the PM₂.₅ model, LAI was also positively associated with foliar accumulation (log(LAI) estimate\u0026thinsp;=\u0026thinsp;0.557, p\u0026thinsp;=\u0026thinsp;0.0029), corresponding to ~\u0026thinsp;0.56% increase in PM₂.₅ per 1% increase in LAI. However, the explanatory power of LAI alone was modest (adjusted R\u0026sup2; = 0.13), indicating that foliar accumulation is additionally shaped by factors beyond vegetation density (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe influence of the LAI on PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e accumulated on leaves (\u003csub\u003eL\u003c/sub\u003ePM).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(LAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared: 0.1266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(LAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared: 0.1313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Impact of vertical crown density and traffic intensity on airborne particulate matter\u003c/h2\u003e \u003cp\u003eLinear mixed-effects models indicated that both vertical crown density and traffic intensity were positively associated with airborne PM concentrations measured at child height along school-route segments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For PM₁₀, the fixed-effect estimate for vertical green density was 0.019 (t\u0026thinsp;=\u0026thinsp;2.396), implying an approximately 1.9% increase in PM₁₀ per one-unit increase in the greenery metric (as defined in Methods), while traffic intensity showed a positive association (estimate\u0026thinsp;=\u0026thinsp;0.006, t\u0026thinsp;=\u0026thinsp;2.752). Similar patterns were observed for PM₂.₅, with vertical crown density and traffic intensity both emerging as significant predictors (estimate\u0026thinsp;=\u0026thinsp;0.0006, t\u0026thinsp;=\u0026thinsp;2.744) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Between-school heterogeneity was substantial (random-intercept SD\u0026thinsp;=\u0026thinsp;0.24 for both PM fractions), and accounting for school-level random effects yielded high conditional explanatory power (conditional R\u0026sup2; = 0.894 for PM₁₀ and 0.900 for PM₂.₅), indicating strong site-specific differences beyond the fixed effects alone (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe influence of selected factors on the amount of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e in the air \u003csub\u003eA\u003c/sub\u003ePM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effects:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGroups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd.Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eID school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed effects\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(intercept)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVertical crown\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003edensity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraffic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAverage airborne concentrations differed markedly among the 20 schools (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Across school-route segments, mean PM₁₀ ranged from 18.49 \u0026micro;g/m\u0026sup3; (School 20) to 58.56 \u0026micro;g/m\u0026sup3; (School 1), and mean PM₂.₅ ranged from 18.48 \u0026micro;g/m\u0026sup3; (School 20) to 57.57 \u0026micro;g/m\u0026sup3; (School 1). None of the analysed school routes met the WHO guideline levels (PM₁₀ = 15 \u0026micro;g/m\u0026sup3;; PM₂.₅ = 5 \u0026micro;g/m\u0026sup3;). In contrast, when compared with EU limit values, PM₁₀ levels met the EU standard at 12 schools and exceeded it at 8 schools, whereas PM₂.₅ levels met the EU standard at 8 schools and exceeded it at 12 schools (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations along roads to schools.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean PM\u003csub\u003e10\u003c/sub\u003e concentration [\u0026micro;g/m\u003csup\u003e\u0026sup3;\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error PM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean PM\u003csub\u003e2.5\u003c/sub\u003e concentration [\u0026micro;g/m\u003csup\u003e\u0026sup3;\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWHO\u003c/p\u003e \u003cp\u003eStandard\u003c/p\u003e \u003cp\u003eExceedance\u003c/p\u003e \u003cp\u003e[\u0026micro;g/m\u003csup\u003e\u0026sup3;\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWHO\u003c/p\u003e \u003cp\u003eStandard\u003c/p\u003e \u003cp\u003eExceedance\u003c/p\u003e \u003cp\u003e[\u0026micro;g/m\u003csup\u003e\u0026sup3;\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Species differences in foliar PM accumulation\u003c/h2\u003e \u003cp\u003eSubstantial interspecific differences were observed in foliar particulate matter loads, even after accounting for vegetation density (LAI) and local airborne concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the species model for PM₁₀, LAI (log(LAI) estimate\u0026thinsp;=\u0026thinsp;0.292, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and field PM₁₀ (log(field_PM₁₀) estimate\u0026thinsp;=\u0026thinsp;0.820, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were significant positive covariates, confirming that both horizontal canopy density and ambient exposure contribute to foliar loads, while species identity explained additional variability beyond these effects (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Species ranking differed by PM fraction. For PM₁₀, \u003cem\u003ePinus sylvestris\u003c/em\u003e showed the highest accumulation index (1.5), whereas \u003cem\u003ePrunus avium\u003c/em\u003e showed the lowest (\u0026ndash;2.1). For PM₂.₅, the highest accumulation index was observed for \u003cem\u003eAcer campestre\u003c/em\u003e (1.5), while \u003cem\u003eFraxinus excelsior\u003c/em\u003e showed comparatively low accumulation (0.33) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Overall, coniferous species accumulated approximately 70\u0026ndash;80% more PM₁₀ and 40\u0026ndash;50% more PM₂.₅ than deciduous species. Across all trees sampled near schools, mean foliar loads were ~\u0026thinsp;27.5 \u0026micro;g/cm\u0026sup2; for PM₁₀ and ~\u0026thinsp;17.6 \u0026micro;g/cm\u0026sup2; for PM₂.₅, illustrating that leaves/needles act as measurable particle sinks within the studied school environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effectiveness of individual tree species in PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e accumulation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean PM\u003csub\u003e10\u003c/sub\u003e accumulation [\u0026micro;g/cm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(LAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(field_PM10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcer campestre\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.34E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcer saccharinum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.66E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAesculus hippocastanum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBetula pendula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChamaecyparis pisifera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.32E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePicea abies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.90E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePinus sylvestris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePopulus \u0026times; canadensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePopulus \u0026times; canescens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus avium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhus typhina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.92E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSalix alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSorbus aucuparia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTilia cordata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUlmus glabra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan Zone (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.02E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuburban Zone (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignif. codes: 0 \u0026lsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u0026rsquo; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lsquo;**\u0026rsquo; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lsquo;*\u0026rsquo; 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean PM\u003csub\u003e2.5\u003c/sub\u003e accumulation [\u0026micro;g/cm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(LAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(field_PM25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcer campestre\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcer saccharinum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.11E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAesculus hippocastanum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBetula pendula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChamaecyparis pisifera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePicea abies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.95E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePinus sylvestris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.47E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePopulus \u0026times; canadensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePopulus \u0026times; canescens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus avium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhus typhina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSalix alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSorbus aucuparia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTilia cordata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUlmus glabra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.56E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan Zone (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.51E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuburban Zone (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignif. codes: 0 \u0026lsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u0026rsquo; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lsquo;**\u0026rsquo; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lsquo;*\u0026rsquo; 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The dual role of trees in urban areas in reducing PM\u003c/h2\u003e \u003cp\u003eThe results of this study confirm that children travelling along routes with trees to school are exposed to elevated concentrations of particulate matter, frequently exceeding health-based guidelines, particularly during morning and afternoon traffic peaks. These findings are consistent with previous research showing that near-ground emissions from road traffic are a dominant source of PM exposure in the immediate vicinity of schools during the warm season, when residential heating plays a minor role \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The timing of exposure is especially relevant for children, as daily school commutes coincide with periods of intensified vehicle activity related both to general traffic and school drop-off and pick-up patterns. Even short-term exposure during these repeated daily journeys may therefore contribute to cumulative health risks, given children\u0026rsquo;s heightened physiological vulnerability to air pollution \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the same time, our results demonstrate that roadside trees along school routes perform a measurable ecosystem service by accumulating particulate matter on leaf surfaces, with higher LAI values associated with increased foliar PM loads. This finding aligns with previous studies documenting the capacity of urban vegetation to capture airborne particles through deposition processes influenced by leaf surface characteristics and exposure duration \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, the relatively modest increase in leaf-level PM accumulation compared to the increase observed in airborne PM concentrations reflects fundamental differences in temporal scale and physical processes. While airborne PM measurements capture short-term fluctuations during traffic peaks, foliar accumulation represents an integrated signal influenced by deposition, wash-off, resuspension, and saturation over longer periods \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImportantly, the study reveals that greater horizontal vegetation density along school routes is associated with higher concentrations of PM in the air at pedestrian height. In the mixed-effects models, a one-unit increase in the greenery metric was associated with ~\u0026thinsp;1.9% higher airborne PM at child height, whereas a 1% increase in LAI corresponded to only modest increases in foliar accumulation (~\u0026thinsp;0.52% for PM₁₀ and ~\u0026thinsp;0.56% for PM₂.₅). This finding highlights the context-dependent nature of vegetation effects on air quality and supports growing evidence that dense tree canopies in street environments may restrict airflow and reduce pollutant dispersion, particularly under low wind conditions \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In such settings, trees simultaneously function as sinks for particulate matter through foliar accumulation and as physical barriers that promote pollutant stagnation near ground level. Rather than indicating a failure of green infrastructure, these results underscore the importance of considering airflow, street geometry, and vegetation structure when assessing children\u0026rsquo;s exposure to air pollution along school routes. Species identity explained additional variation in foliar PM loads beyond LAI and local airborne PM, indicating that species choice can modulate PM retention potential in school environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Implications for urban planning and school environments\u003c/h2\u003e \u003cp\u003eOur results suggest that while trees can contribute to particulate matter removal, their effects on air quality at pedestrian height depend strongly on spatial configuration, vegetation density, and surrounding built form. In narrow street environments or areas with limited ventilation, dense tree canopies may unintentionally increase children\u0026rsquo;s exposure to airborne PM along frequently used routes to school \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Achieving a balance between high horizontal crown density (maximising particulate absorption surface) and lower vertical crown density (minimising airflow obstruction) appears to be critical for effective exposure mitigation along school routes. These findings highlight the need for a nuanced approach to greening interventions in school environments. Rather than relying on uniform tree-planting strategies, planners should consider how different vegetation forms interact with airflow and pedestrian movement. Previous studies indicate that lower vegetation elements, such as hedges or herbaceous plantings, may reduce PM concentrations at breathing height without substantially restricting ventilation, particularly when placed between traffic lanes and sidewalks \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Similarly, the creation of ventilation corridors and the careful placement of trees relative to prevailing wind directions and building geometry may help balance the benefits of particulate matter deposition with the need for effective pollutant dispersion \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Our results therefore support planning approaches that prioritise site-specific design and exposure reduction rather than assuming that increased vegetation density will universally improve air quality.\u003c/p\u003e \u003cp\u003eData on the differing particulate matter accumulation capacities of individual tree species provide practical guidance for urban planners and green infrastructure designers. Species identity explained additional variation in foliar PM loads beyond LAI and local airborne PM, indicating that species choice can modulate PM retention potential in school environments. The findings of this study have direct relevance for urban planning strategies aimed at improving environmental conditions in school surroundings. Roadside trees are among the most applied green infrastructure elements in cities and are often introduced with the expectation of reducing air pollution exposure among vulnerable populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations\u003c/h2\u003e \u003cp\u003eWhile the analysis incorporates key factors influencing PM exposure along school routes, including traffic intensity, vegetation structure, and urban context, it does not account for all elements that may shape local air quality, such as detailed street geometry, building porosity, or dynamic wind patterns. In addition, measurements were conducted during the vegetation season, and the role of trees in modifying exposure during winter months\u0026mdash;when emissions from residential heating dominate\u0026mdash;requires further investigation. Nevertheless, focusing on the growing season allowed for direct assessment of foliar accumulation processes and their interaction with airborne PM concentrations.\u003c/p\u003e \u003cp\u003eFuture research should integrate high-resolution airflow modelling with empirical exposure measurements to better disentangle the relative contributions of vegetation structure and built form to pollutant dispersion near schools. Long-term monitoring across seasons would further clarify how vegetation-mediated effects on exposure vary throughout the year. Importantly, studies that link spatial exposure patterns along school routes with health or behavioural outcomes could provide valuable insights for evidence-based urban planning aimed at protecting children\u0026rsquo;s health.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrates that routes to school constitute critical exposure settings where children regularly encounter elevated concentrations of particulate matter. Roadside trees along these routes play a dual role: : horizontal crown density enhances particulate matter accumulation on foliage, while vertical crown density influences airflow and may increase airborne PM concentrations at pedestrian height. Effective planning of greener school environments should therefore explicitly consider horizontal and vertical dimensions of tree crowns, as well as species composition, rather than focusing on the presence of greenery alone.\u003c/p\u003e \u003cp\u003eThe findings highlight the importance of moving beyond simplified assumptions about the protective role of urban vegetation in school environments. Designing healthier routes to school requires careful consideration of exposure dynamics, spatial configuration, and the interaction between green infrastructure and the built environment. When focusing on locations that children traverse daily, particular attention should be paid to horizontal and vertical crown structure as well as species composition. This study provides evidence relevant for planning interventions that aim to maximise public health benefits while minimising unintended consequences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the National Science Centre (Poland) grant no. 2020/39/B/HS4/03240. The publication of the manuscript was financed by Science Development Fund of the Warsaw University of Life Sciences \u0026ndash; SGGW\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**A. Hoppa:** Investigation, Manuscript writing - original draft, Data visualization, Formal analysis. **P. Sikorski:** Conceptualization, Investigation, Formal analysis, Manuscript writing. **A. Przybysz:** Investigation, Formal analysis, Manuscript writing. **E. Łaszkiewicz:** Conceptualization, Funding acquisition, Project management, Formal analysis. **A. Nawrocki:** Investigation. **P. Archiciński:** Investigation. **D. Sikorska:** Investigation, Conceptualization, Manuscript writing \u0026ndash; original draft, Supervision.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCinar, N. \u0026amp; Dede, C. 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Environ.: X\u003c/em\u003e 13, 100151. doi:10.1016/j.aeaoa.2022.100151 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"air pollution, children, LAI index, particulate matter, school routes, trees","lastPublishedDoi":"10.21203/rs.3.rs-8490912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8490912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChildren\u0026rsquo;s school commutes are repeated microenvironments where exposure to traffic-related particulate matter (PM) occurs at breathing height. Roadside trees are widely promoted to mitigate PM via deposition on foliage, yet dense tree crowns may also reduce near-road ventilation and increase pedestrian-level concentrations. We quantified these opposing effects along the most frequently used access routes to primary schools in Ł\u0026oacute;dź (Poland). During June 2022, we measured PM2.5 and PM10 at 1 s resolution at 1.4 m height during morning and afternoon commuting periods (n\u0026thinsp;=\u0026thinsp;242). Tree crown structure was characterised using two complementary indicators: horizontal crown density, quantified as leaf area index (LAI), representing potential particulate absorption surface, and vertical crown density, quantified as the proportion of the pedestrian-level field of view occupied by tree crowns, representing canopy obstruction and reduced ventilation. Foliar PM loads were determined by sequential leaf wash-off (n\u0026thinsp;=\u0026thinsp;124). Mixed-effects models showed that higher vertical crown density was associated with higher airborne PM concentrations, with each one percentage-point increase corresponding to approximately 1.9% higher PM levels along routes. In contrast, higher horizontal crown density enhanced foliar retention: a 1% increase in LAI was associated with 0.52% and 0.56% higher foliar PM₁₀ and PM₂.₅, respectively, and species identity explained additional variation. Roadside trees, therefore, provide particulate deposition benefits but may simultaneously increase exposure at pedestrian height, highlighting the need for site-specific greening designs that explicitly account for both horizontal and vertical crown density.\u003c/p\u003e","manuscriptTitle":"The dual role of roadside trees in shaping children’s PM exposure near schools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 00:34:36","doi":"10.21203/rs.3.rs-8490912/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T19:11:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T22:44:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T01:58:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123598103067391265746627593558150546709","date":"2026-01-22T13:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253915339081634729065839789979160522959","date":"2026-01-22T00:17:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306891021315182447852218127053449024816","date":"2026-01-21T02:42:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T11:32:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T23:22:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T18:16:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T21:20:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-16T21:14:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5be997a-0ed5-48f8-883f-a8e57e46c13d","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":61541721,"name":"Biological sciences/Ecology"},{"id":61541722,"name":"Earth and environmental sciences/Ecology"},{"id":61541723,"name":"Earth and environmental sciences/Environmental sciences"},{"id":61541724,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-03-23T06:24:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 00:34:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8490912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8490912","identity":"rs-8490912","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}