# Quantifying the impact of urban trees on air quality in Geneva, Switzerland

Donato Kofel, Ilann Bourgeois, Romana Paganini, Aurèle Pulfer, Charlotte

Grossiord, Julia Schmale

► To cite this version:

Donato Kofel, Ilann Bourgeois, Romana Paganini, Aurèle Pulfer, Charlotte Grossiord, et al. Quantifying the impact of urban trees on air quality in Geneva, Switzerland. Urban Forestry & Urban Greening, 2024, 101, pp.128513. 10.1016/j.ufug.2024.128513. hal-05044927

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# Quantifying the impact of urban trees on air quality in Geneva, Switzerland

Donato Kofel<sup>a,b</sup>, Ilann Bourgeois<sup>a,b,c,*</sup>, Romana Paganini<sup>a</sup>, Aurèle Pulfer<sup>d</sup>,
Charlotte Grossiord<sup>c,e</sup>, Julia Schmale<sup>a</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

Handling Editor: Dr Cecil Konijnendijk van den Bosch

**Keywords:**

Air pollution removal

Urban greening

i-Tree Eco

Ozone forming potential

Biogenic volatile organic compounds

Particulate matter

## ABSTRACT

Atmospheric pollution threatens human health worldwide, with tropospheric ozone ($O_3$) and particulate matter (PM) among the most harmful pollutants. Urban trees can reduce the concentration of air pollutants through dry deposition on their canopies and stomatal uptake. At the same time, urban trees can deteriorate air quality by emitting aerosol- and $O_3$ precursors in the form of biogenic volatile organic compounds (BVOCs). $O_3$ and PM removal, and BVOC emissions vary depending on the tree species. Therefore, the diversity and spatial distribution of urban trees significantly influence their impact on local air quality. This study employs a dual approach to assess and map the impact of urban trees on air quality in Geneva, Switzerland. Firstly, we use the i-Tree Eco model combined with a tree inventory (237,191 trees) to quantify BVOC emissions and $PM_{10}$ ($PM < 10~\mu m$) and $O_3$ removal at the genus level. Secondly, we develop a species-level parameterization using 51 common urban tree species to estimate the same variables and ozone-forming potential (OFP). Results show that the tree density is heterogeneous in the study area, leading to neighborhoods with greater biomass and, therefore, stronger influence by trees on local air quality. According to i-Tree Eco, urban trees in Geneva emitted 50 t of BVOCs, while removing 14 t of $PM_{10}$ and 52 t of $O_3$ in 2014. With the species-level parametrization, we estimate that urban trees removed about 66 ± 55 t of $PM_{10}$ and 150 ± 96 t of $O_3$ in 2014. However, they could also emit about 130 ± 52 t of BVOCs annually, which, under favorable conditions, can form 1153 ± 519 t of $O_3$. Depending on the method, urban trees removed between 4 and 19 % of the anthropogenic $PM_{10}$ emissions. The annual removal rates are comparable to findings in other European cities. The disparities between the two approaches are due to different parameterizations. This study could help urban planners to select adequate species for future planting programs in Geneva and more generally. It showed that the impact of urban trees on air quality is spatially heterogeneous but significant, and tightly linked to the species composition.

## 1. Introduction

Air pollution poses a significant environmental risk to human health (EEA, 2022a; WHO, 2016). Exposure to air pollutants such as ozone ($O_3$) and particulate matter (PM) can cause and aggravate multiple adverse health effects, such as respiratory and cardiovascular diseases (Anenberg et al., 2022; Malashock et al., 2022), cancers, and metabolic disorders (EEA, 2022b; Ritz et al., 2019). More generally, air quality significantly impacts our well-being, and living in a clean-air environment is associated with increased levels of satisfaction, self-esteem, and greater resilience to stress (Petrowski et al., 2021). In the European

Union, air pollution is still a major health concern despite reductions in pollutant emissions and ongoing improvements in air quality (EEA, 2022b). The European Environment Agency (EEA) estimated that for the year 2020, 275,000 premature deaths could be attributed to exposure to $PM_{2.5}$ (i.e., $PM < 2.5~\mu m$) levels above the World Health Organization (WHO) 2021 mean annual guideline level of $5~\mu g~m^{-3}$ (EEA, 2022a). Similarly, 71 % of the European urban population was exposed to $PM_{10}$ (i.e., $PM < 10~\mu m$) levels above the WHO 2021 mean annual guideline of $15~\mu g~m^{-3}$. The figure of 71 % highlights the importance of air pollution mitigation in urban environments. To reduce the adverse effects of air pollution, stakeholders must implement efficient long-term strategies

<footnote><sup>a</sup> Extreme Environments Research Laboratory (EERL), École Polytechnique Fédérale de Lausanne (EPFL), Sion CH-1951, Switzerland</footnote>

<footnote><sup>b</sup> Plant Ecology Research Laboratory (PERL), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland</footnote>

<footnote><sup>c</sup> Université Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains F-74200, France</footnote>

<footnote><sup>d</sup> Atelier de la Conception de l'Espace (ALICE), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland</footnote>

<footnote><sup>e</sup> Community Ecology Unit, Swiss Federal Institute for Forest Snow and Landscape WSL, Lausanne CH-1015, Switzerland</footnote>

<footnote>* Corresponding author at: Université Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains F-74200, France.
E-mail address: ilann.bourgeois@univ-smb.fr (I. Bourgeois).</footnote>
and regulations. However, air pollution is a complex problem that requires mitigation strategies at different scales (e.g., individual, companies, city-scale, regional, national) (Sofia et al., 2020). Since there is no unique solution to air pollution reduction, stakeholders need to determine a tailored combination of effective measures to improve local air quality.

Potential mitigation strategies include urban greening: urban green areas provide many ecosystem services, such as noise attenuation, cooling, rainwater retention, biodiversity promotion, and, of interest here, air quality improvement (Escobedo et al., 2011; Grote et al., 2016; Samson et al., 2017b; Schwaab et al., 2021). Several studies have evaluated to what extent urban vegetation improves air quality by removing major air pollutants from the atmosphere (e.g., Baraldi et al., 2019; Nowak et al., 2006; Riondato et al., 2020; Selmi et al., 2016). However, urban trees are also responsible for some ecosystem services, mainly through the emission of biogenic volatile organic compounds (BVOCs) that can lead to O₃ production when reacting with nitrogen oxides (NOₓ; Calfapietra et al., 2013; Fitzky et al., 2019; Loreto et al., 2014). In addition, BVOCs contribute to the formation of secondary organic aerosols, and some studies have shown that this contribution can be substantial (Aksoyoglu et al., 2011; Loreto et al., 2014; Steinbrecher et al., 2009). BVOC emission factors (EFs; the mass of BVOC emitted per leaf biomass per hour) are highly species-specific and depend on many environmental conditions, including temperature, water availability, and exposure to light (Churkina et al., 2015).

In Switzerland, the city of Geneva regularly suffers from exceedances of legal limit values for certain air pollutants (e.g., O₃ and PM₁₀) (ROPAG, 2021). The sources and reasons for these exceedances are not well constrained, hindering local authorities' efforts to develop the right mitigation measures. At the same time, an initiative to increase the canopy cover up to 25 % of ground surface in the canton of Geneva is under consideration (Schlaepfer et al., 2018). However, the positive or negative impacts of the current and to be planted urban trees on air quality have not been assessed. Several tools have been developed to assess how urban trees affect local air quality. Among them, i-Tree Eco (https://www.itreetools.org/tools/i-tree-eco), originally named the

Urban Forest Effects model (UFORE), was designed to model the structure, ecosystem services and values of trees. An increasing number of studies have applied this tool in the United-States and in European cities (Cabaraban et al., 2013; Lin et al., 2021; Nowak et al., 2013, 2006; Pace et al., 2021; Riondato et al., 2020; Selmi et al., 2016; Szkop, 2020). However, no such study exists yet for Geneva. This study aims to quantify the contribution of urban trees to air pollution and abatement thereof in the canton of Geneva. Thus, we investigate air quality improvement due to PM₁₀ and O₃ removal and air pollution due to potential O₃ production from BVOC emissions. To do so, we apply two complementary approaches: i) a literature-based approach using species-specific EFs of major BVOCs as well as other traits (e.g., stomatal conductance) for the most abundant species in Geneva (hereafter referred to as the species-level approach) and ii) use of i-Tree Eco (hereafter referred to as the i-Tree Eco approach). We hypothesize that the genus-level parameterization in i-Tree Eco should yield similar estimates of BVOC emissions and PM₁₀ and O₃ removal than the species-level parametrization, but that it is not refined enough to identify key species that may disproportionately impact urban air quality, an important information for urban planners.

## 2. Methods

The study area covers the entire Canton of Geneva (hereafter referred to as the Canton) (approx. 46°13' N, 6°09' E), which spans over 282.48 km² and is in the westernmost part of Switzerland (Fig. 1). The Canton includes Geneva city (hereafter referred to as Geneva) and its conurbation, agricultural areas, and several minor cities. About 506,343 inhabitants live in the Canton, of which 203,856 live in Geneva and another 226,675 in its conurbation. The elevation varies between 369 and 458 m a.s.l. The climate is temperate, with the lowest monthly mean temperature of 2.1°C in January and the highest monthly mean temperature of 20.6°C in July (MeteoSwiss, 2023).

In the study area, there has been a general positive evolution in air quality since monitoring was implemented for the major air pollutants, as indicated in the most recent annual air quality report of the Canton

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Location of the Canton of Geneva (study area, red outline) in Switzerland with neighboring countries and the city of Geneva outlined in black. Each green dot represents an isolated tree (i.e., trees located outside of urban and rural forests) from Geneva's inventory (n = 237,191) (ICA, 2022). Basemap: ESRI Gray (light) and ESRI Ocean.</figcaption></figure>
(ROPAG, 2021). However, PM and O₃ concentrations still regularly exceed the federal thresholds. For example, O₃ exceedances above the monthly mean threshold of 100 µg m⁻³ were observed each month from April to September 2021. Similarly, the hourly mean threshold of 120 µg m⁻³ was exceeded ca. 100 times while only one exceedance is legally allowed. Regarding PM₁₀, the 2021 annual mean of 14.4 µg m⁻³ was close to the lowest value of 13.0 µg m⁻³ ever monitored since 1998, with the Swiss legal limit value being 15 µg m⁻³. The daily legal limit mean concentration of 50 µg m⁻³ was exceeded on four occasions in 2021 out of three authorized yearly exceedances and were likely linked to Saharan dust transport.

## 2.1. Geneva's tree inventory

In this study, we used an open-source tree inventory (Inventaire cantonal des arbres isolés - ICA, 2022, last accessed 05.12.2022) created by the Canton in 1976 and regularly updated since. The inventory

centralizes all surveys of isolated trees (i.e., trees located outside forests). It contains 237,191 trees with associated characteristics such as species and genus, diameter at breast height (DBH), trunk and total heights, crown height and diameter, and spatial coordinates. The trees are mainly located in public areas (e.g., streets, parks), with a minority in private properties (Fig. 1). Importantly, the inventory does not account for rural or urban forests. As a result, the inventory contains information about 25 % of the total tree number in the Canton (ca. 1075, 000) and about 50 % of the total number of isolated public and private trees (ca. 412,000) (Schlaepfer et al., 2020, 2018). Therefore, this study provides a lower bound estimate of the air quality impact of urban trees in Geneva but the comparison between the species-level approach and i-Tree eco should not be impacted as a similar number of trees is used in both methodologies (see Fig. 2).

An initial quality check was performed on the tree inventory. Overall, 45.8 % of the inventoried trees were fully informed (i.e., genus, species, and relevant metrics), 48.1 % were missing at least one

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Schematic overview of the methodology developed in this study. Rounded rectangles represent the datasets used for calculations, ovals are tools and processes applied to the datasets and rectangles are products and results of this work. The two complementary approaches are highlighted in red (i-Tree Eco approach) and blue (species-level approach).</figcaption></figure>
parameter (e.g., DBH, total height, crown size, or species), and 2.7% were discarded for complete lack of information. For the 48.1% of trees lacking at least one parameter, we completed the missing data using the genus average value for the given parameter (Table S1). This step corresponds to the “data cleaning” process in Fig. 2. At this stage, the cleaned inventory was compared to i-Tree Eco’s EFs database resulting in an inventory of 203,147 trees.

For the species-level approach, we first conducted a literature review to gather species-specific EFs and maximum stomatal conductance (gs) values for the most abundant tree species in the inventory (n=51 species encompassing 19 genera). At this stage, 102,703 trees were fully informed. Then, we used these values to calculate species-weighted average EFs and gs for each of the 19 genera as follows:

$$X_g = \frac{\sum_{i=1}^{n} w_i X_i}{\sum_{i=1}^{n} w_i} \qquad (1)$$

where $X_g$ is the species-weighted EF or gs for genus g (19 genera in total), $w_i$ is the number of trees per species i, $X_i$ is the EF or gs for species i and n is the total number of species in genus g.

Using Eq. 1, we were able to attribute an EF and gs to 103,653 trees that belonged to one of the 19 genera, but for which the species was unknown. Ultimately, we could inform a total of 206,356 trees to be used in the species-level approach, similar to the 203,147 trees used in i-Tree Eco.

## 2.2. Species-level approach

### 2.2.1. BVOC emissions

We conducted a literature review to gather EFs of isoprene, monoterpenes, and sesquiterpenes for the 51 most abundant tree species in Geneva. Using Eq. 1, we were able to inform 206,356 trees to serve as input in the species-level approach, roughly equal to the 203,147 trees used in i-Tree Eco (Fig. 2). All EFs found in the literature, along with the corresponding references (Aydin et al., 2014; Baraldi et al., 2019; Karl et al., 2009; Kesselmeier and Staudt, 1999; Ren et al., 2017; Steinbrecher et al., 2009; Stewart et al., 2003) are listed in Table S2. All EFs are standardized at 30°C leaf temperature and 1000 µmol m⁻² s⁻¹ Photo-synthetic Photon Flux Density (PPFD). The EFs are reported in mass of BVOC emitted per gram of leaf dry weight per hour (µg g⁻¹ h⁻¹). We calculated an average value when different EFs were found for a given species (Table S3).

The BVOC fluxes ($F_{BVOC}$) were calculated for each tree using Eq. 2:

$$F_{BVOC} = B * (EF_{ISO} + EF_{MONO} + EF_{SQT}) \qquad (2)$$

where $F_{BVOC}$ is the hourly flux of the total BVOCs emitted by the tree in µg h⁻¹; B is the tree leaf dry weight in gdw obtained using i-Tree Eco (see Section 2.3) and $EF_{ISO}$, $EF_{MONO}$ and $EF_{SQT}$ are the standardized, species-specific EFs of isoprene, monoterpenes and sesquiterpenes, respectively, in µg g⁻¹ h⁻¹. $F_{BVOC}$ was then integrated over a year as follows:

$$F_{annual} = F_{BVOC} * N * Ph \qquad (3)$$

where N is the number of days per year the tree has leaves and Ph is the photoperiod (8 h d⁻¹). We identified the leaf retention strategy (i.e., deciduous or evergreen) of the selected 51 species (Table S3). For the trees that only had their genus reported (see Section 2.1), we assigned the leaf retention strategy of the genus.

### 2.2.2. Ozone forming potential (OFP)

The OFP corresponds to the theoretical upper limit of ozone formation from a given BVOC (Benjamin and Winer, 1998) and is determined based on the maximum incremental reactivity (MIR) scale (Carter, 2009). Calculation of the OFP assumes optimal NOₓ to VOCs ratios, meaning that the spatial heterogeneity of NOₓ mixing ratios is not considered here. Actual ozone production is likely lower than the OFP

owing to either NOₓ limitation (where the NOₓ to VOCs ratio is lower than the optimum) or NOₓ titration (where the NOₓ to VOCs ratio is higher than the optimum). OFP was calculated for each tree according to the formula and method by Benjamin and Winer (1998):

$$OFP = B * \sum_{i} EF_{i} * MIR_{i} \qquad (4)$$

where B is the total leaf dry weight per tree in gdw obtained using i-Tree Eco (see Section 2.3); $EF_i$ is the species-specific EF of BVOCi (i = isoprene, monoterpenes or sesquiterpenes) in µg g⁻¹ h⁻¹ and $MIR_i$ is the maximum incremental reactivity in gO₃ gVOC of BVOCi. OFP was then integrated over a year as follows:

$$OFP_{annual} = OFP * N * Ph \qquad (5)$$

where N is the number of days per year the tree has leaves (N = 183 days for deciduous trees and 365 days for evergreens following Baraldi et al. (2019)) and Ph is the photoperiod (8 h d⁻¹).

The MIR is VOC-specific and indicates the mass of O₃ produced per mass of VOC under ideal environmental and chemical conditions (i.e., high solar flux and optimal VOC/NOₓ ratio). Since monoterpenes and sesquiterpenes encompass many different BVOC, we calculated the weighted average MIR for the two categories. To do so, we combined the Model of Emissions of Gases and Aerosols from Nature (MEGAN) database (Huson et al., 2016), which details monoterpenes and sesquiterpenes speciation, with the MIR of each of the listed BVOC. As a result, we obtain average MIR values of 10.61, 4.18, and 4.05 gO₃ gVOC for isoprene, monoterpenes, and sesquiterpenes, respectively. Note that the calculated OFP is a theoretical estimation of which the accuracy depends on photochemical conditions and atmospheric transport, not considered here.

### 2.2.3. Ozone removal

Trees can remove atmospheric gasses such as O₃ through deposition on the leaf surface, followed by stomatal absorption (Manes et al., 2012; Samson et al., 2017a). Stomatal O₃ fluxes were calculated according to the species-specific equation reported by Manes et al. (2012):

$$FO_{3} = g_{s} * [O_{3}] * 0.613 \qquad (6)$$

where $FO_3$ is the instantaneous stomatal O₃ flux in nmol m⁻² s⁻¹, gs is the species-specific stomatal conductance to water vapor (mol m⁻² s⁻¹); [O₃] is the annual mean O₃ concentration (= 19.09 +/- 11.36 nmol mol⁻¹ in Geneva in 2014; https://www.ge.ch/node/1391) and 0.613 is the diffusivity ratio between O₃ and water vapor. We used data from 2014 to compare with i-Tree Eco (see Section 2.3). The gs values were obtained through a literature review and listed in Table S2. FO₃ was then integrated over one year to calculate the annual cumulated O₃ flux for each tree as follows:

$$FO_{3 cum} = FO_{3} * Ph * N * 3600 * 10^{-9} \qquad (7)$$

Where $FO_3 cum$ is the annual cumulated stomatal O₃ flux in mol m⁻² y⁻¹ and $10^{-9}$ is a dimensional adjustment factor. Finally, we quantified the annual mass of O₃ removed by trees ($RO_3$) as follows:

$$RO_{3} = FO_{3 cum} / 0.3 * A_{L} * 47.997 \qquad (8)$$

where $RO_3$ is the annual mass of O₃ removed by trees in g y⁻¹, 0.3 a correction factor explained below; $A_L$ is the leaf area in m² and 47.997 is the molar weight of O₃ in g mol⁻¹. We applied a 0.3 correction factor following Baraldi et al. (2019) based on the assumption that the stomatal O₃ flux corresponds to 30% of the total potential O₃ removal through both stomatal and non-stomatal processes. Gerosa et al. (2009, 2005) reported that this ratio ranged from 29% to 43% for a Quercus forest, which is the most abundant tree genus in Geneva.
### 2.2.4. PM₁₀ removal

Trees can remove PM from the atmosphere through deposition on the leaf surface (Nowak et al., 2006). Deposited particles can then be resuspended in the air or washed away by precipitation (Wedding et al., 1977). The annual PM₁₀ removal per tree (RPM₁₀) was calculated according to Baraldi et al. (2019) and Bottalico et al. (2017) as follows:

$$RPM_{10} = V_d * [PM_{10}] * A_L * N * 24 * 3600 * 0.5 * 10^{-9} \quad (9)$$

where $V_d$ is the dry deposition velocity of PM₁₀, set to a median value of 0.0064 m s⁻¹ based on Lovett (1994); [PM₁₀] is the mean annual PM₁₀ concentration (= 17.8 ± 10.3 µg m⁻³) in Geneva in 2014 (https://www.ge.ch/node/1391); 0.5 is the estimated resuspension rate (Zinke, 1967) and 10⁻⁹ is a dimensional adjustment factor. Additionally, we used Geneva meteorological data (MeteoSwiss, 2023) to calculate that in 2014 the annual cumulative period of precipitation approximated 1027 hours, which were removed from the parametrization in Eq. 9 to set RPM₁₀ to zero during these periods.

### 2.2.5. Uncertainty propagation

The analytical uncertainty propagation method is used to estimate the variance of the output based on the variance of each input variable. The general method to calculate the propagation of uncertainty involves the total differential of a function. To estimate the uncertainty of the total annual values of BVOC emissions, OFP, PM₁₀ and O₃ removal, the uncertainty associated with each variable used for their calculations had to be defined. Table S4 summarizes these uncertainties and the underlying assumptions. Then, we propagated these uncertainties to estimate the total uncertainty associated with the calculation of BVOC emissions (40 %), OFP (45 %), PM₁₀ (83 %) and O₃ removal (64 %). Uncertainties are further discussed in Section 3.1.2.

### 2.2.6. Map generation

All maps in this study were created with the open-source geographic information system QGIS (version 3.26.3-Buenos Aires, https://www.qgis.org/en/site/). We created a uniform grid with cells of 100*100 m. For each cell, we calculated the sum of BVOC emissions, OFP, and PM₁₀ and O₃ removal as follows:

$$Cell total = \sum_{j=0}^{N} F_j \quad (10)$$

Where N is the total number of trees in a grid cell (i.e., per hectare), and $F_j$ represents either BVOC emissions, OFP, PM₁₀ or O₃ removal of tree j.

### 2.3. i-Tree Eco approach

To contrast with the species-level approach described above, we also estimated BVOC emissions and O₃ and PM₁₀ removal using i-Tree Eco (Fig. 2). i-Tree Eco has been widely applied in other urban settings (Baraldi et al., 2019; Cabaraban et al., 2013; Lin et al., 2021; Pace et al., 2021; Riondato et al., 2020; Selmi et al., 2016; Soares et al., 2011). The main difference between the two approaches resides in that i-Tree Eco calculates air quality impacts of urban trees at the genus level (as opposed to the species level) and that OFP calculation is not implemented in the model. i-Tree Eco parametrizations are thoroughly detailed elsewhere (Hirabayashi, 2012; Hirabayashi et al., 2022; Hirabayashi and Kroll, 2017; Nowak, 2021).

Briefly, individual tree leaf biomass and leaf area were computed by i-Tree Eco. They were used in the species-level approach to calculate BVOC emissions, OFP, PM₁₀, and O₃ removal (see Sections 2.2.1 to 2.2.4). Tree leaf area is calculated according to Nowak (1996) as follows:

$$\ln(Y) = -4.33 + 0.29H + 0.73D + 5.72S - 0.01C \quad (11)$$

where Y is the leaf area in m²; H is crown height in m; D is the average crown diameter in m; S is the species-specific average shading factor (i.e., percent light intensity intercepted by foliated tree crowns) and C is

the surface area of the tree crown ($πD(H + D)/2$).

Tree leaf biomass (leaf dry weight) is then calculated from the leaf area using species-specific conversion factors:

$$B = Y * f \quad (12)$$

where B is the leaf biomass, Y is the tree leaf area, and f is a species-specific conversion factor (see annex of Nowak (2021)).

i-Tree Eco uses 1-hour averaged weather and pollution data to calculate PM₁₀ and O₃ removal for each tree, according to the following equation:

$$F = V_d * C \quad (13)$$

where F is the downward pollutant flux (g m⁻² s⁻¹), $V_d$ is the deposition velocity (m s⁻¹) and C the pollutant concentration (g m⁻³). The deposition velocity $V_d$ is calculated according to the methodology described in Baldocchi et al. (1987). This method uses canopy resistances calculated from stomatal resistance, mesophyll resistance, cuticular resistance, and meteorological data to estimate $V_d$. Finally, the average hourly pollutant flux is multiplied by total tree-canopy coverage to estimate total hourly pollutant removal by trees across the study area. For our study, the weather station is in Geneva international airport, and the air monitoring station is located 5 km away from Geneva city center. We used data from 2014, the most recent year with available weather and air monitoring data already implemented in i-Tree Eco. To evaluate the representativity of 2014 meteorological and pollution data, we compared the monthly average of temperature, humidity, sunlight, and PM₁₀ and O₃ with that for 2015–2022. While the monthly mean temperatures of 2014 closely follow those of the period 2015–2022, during the summer months of July and August 2014, they were 4 and 2°C, respectively, lower than the following seven years (Fig. S1). This drop in temperature during July and August 2014 is linked with high relative humidity and low sunlight (Fig. S2). For i-Tree Eco’s estimates, this means that the emissions of BVOCs and the removal of pollutants may be lower for 2014 than for a more recent year. For air pollution data, there is no notable difference between 2014 and the period 2015–2022 (Fig. S3).

i-Tree Eco estimates BVOC (isoprene and monoterpenes only) emissions E in µg h⁻¹ according to Nowak et al., (2006):

$$E = B_E * B * \gamma \quad (14)$$

where $B_E$ is the genus-based emission factor (µg g⁻¹ h⁻¹), B is the species leaf dry weight (g) and γ is an environmental correction factor. If a genus-specific EF is unavailable, the median EF for the family, order, or superorder, depending on availability, is applied.

Currently, i-Tree Eco does not provide uncertainty estimates for most of its outputs. However, we discuss uncertainty estimates for several i-Tree Eco variables given in the literature in Section 3.2.2.

## 3. Results and discussion

Geneva’s tree inventory contains 274 tree genera, of which the three most common are Quercus (Oak), Acer (Maple), and Pinus (Pine), accounting for 10.8 %, 10.4 % and 7.5 % of the total number of trees, respectively. Note that the seven most common genera add up to 52.2 % of the total tree number in the inventory. The 274 tree genera encompass 1128 tree species, of which the three most common species are Quercus robur (Common Oak), Carpinus betulus (European hornbeam), and Fraxinus excelsior (European ash), accounting for 4.51 %, 3.98 %, and 2.27 %, respectively. That some tree genera and species are more abundant than others is common in urban environments, as observed in other studies (e.g., Bottalico et al., 2017; Selmi et al., 2016; Soares et al., 2011). Of the 51 tree species considered in the species-level approach, 43 are deciduous (87,789 trees), and 8 are evergreen (14,914 trees) (Table S3). Fig. 3 shows the spatial distribution of the urban trees
<figure><img src="image_5.png" /><figcaption>Fig. 3. Map of the tree density in the Canton of Geneva, calculated on a 1-ha grid. The color scale indicates the tree density. Areas with non-inventoried trees are in gray (i.e., where the background map is visible). Zones A-C indicate regions with high tree density. Forests are shown in dark green patches and the city, Lancy and the Canton of Geneva are delimited with a black, blue and red line, respectively. Topographic background map, for this and subsequent figures, made by the Federal Office of Topography swisstopo (https://www.swisstopo.admin.ch/en/geodata/maps.html).</figcaption></figure>

aggregated in a 1-ha grid. Values range from 0 to 408 trees per hectare (tr ha⁻¹) with an average of 8 tr ha⁻¹. The cantonal forests were not considered in this study and are shown as dark green patches.

EFs for isoprene and monoterpenes were found in the literature for 51 species and for sesquiterpenes for 38 species (Table S2). Fewer data on sesquiterpenes EFs may lead to a slight underestimation of a given species' OFP. Nevertheless, sesquiterpenes are usually a smaller fraction of BVOCs compared to isoprene and monoterpenes (Guenther et al., 2006), and their OFP is smaller (Carter, 2009). The species-specific isoprene emission rates range from 0 to 70 µg g<sub>dw</sub><sup>-1</sup> h<sup>-1</sup>. From the 51 tree species considered in this study, Populus nigra, Populus tremula, and Populus alba have the highest emissions of total BVOCs (isoprene + monoterpenes + sesquiterpenes). Table S3 shows the average EFs for each species and their rank according to their total BVOC emissions by applying the classification system by Benjamin et al. (1996). Based upon these definitions, 17 species (33 %) are high emitters (> 10 µg g<sub>dw</sub><sup>-1</sup> h<sup>-1</sup>), 21 (41 %) are moderate emitters (1–10 µg g<sub>dw</sub><sup>-1</sup> h<sup>-1</sup>), and 13 (26 %) are low emitters (< 1 µg g<sub>dw</sub><sup>-1</sup> h<sup>-1</sup>).

## 3.1. Urban trees' impact on air quality

### 3.1.1. Spatial distribution

Most of Geneva's trees are in urbanized zones and along the lake's shoreline. The spatial distribution is heterogeneous, with some identifiable hot spots (Fig. 3): the south-east (zone A: Chêne-Bougeries neighborhood), the south (zone B: Onex and Lancy neighborhoods), and the north part of Geneva city (zone C). In general, the density of isolated

public trees decreases with distance from the city center as agriculture becomes more important. Most cantonal forests are located outside the city and along the Rhône River. These forests were not considered in this study.

The heterogeneous spatial tree distribution directly impacts the distribution of emissions of BVOCs, OFP, and removal of PM₁₀ and O₃. The amount of BVOCs emitted by a given tree directly depends on its leaf biomass (Eq. 2) which is directly inferred from the leaf area (Eq. 12) - and on the species-specific EFs. Similarly, PM₁₀ and O₃ removal directly depend on tree leaf area (Eqs. 7 and 8). Given that the total leaf area per hectare strongly correlates to the tree density (Fig. S4), Fig. 4 exhibits hotspots of urban trees' impact on air quality colocated with areas of high tree density (Fig. 3).

### 3.1.2. Total annual quantities

Fig. 4(a) and (b) show the gridded total annual emissions of BVOCs and OFP, respectively, using the species-level approach. We estimate that Geneva's trees emit 130 ± 52 t of BVOCs per year (sum of isoprene, monoterpenes, and sesquiterpenes) that can lead to the formation of 1153 ± 519 t of O₃. Values range from 0 (i.e., trees do not emit measurable BVOCs) to 1662 kg of O₃ produced per hectare per year (kg ha⁻¹ y⁻¹), with a spatial average of 88 kg ha⁻¹ y⁻¹. Fig. 4(c) and (d) show the annual PM₁₀ and O₃ removal in 2014, respectively. In total, we estimate that trees in Geneva removed 66 ± 55 t of PM₁₀ and 150 ± 96 t of O₃. PM₁₀ and O₃ removal rates range from 0 to 84 and from 0 to 327 kg y⁻¹ ha⁻¹ with a spatial average of 5 and 11 kg y⁻¹ ha⁻¹, respectively. With the i-Tree Eco approach, BVOC emissions were
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Maps of the annual BVOC emissions (a), OFP (b), PM₁₀ removal (c) and O₃ removal (d). Forests are shown in dark green patches and the city and the Canton of Geneva are delimited with a black and red line, respectively. Areas without grid cells (background map visible) have no inventoried tree. Zones A-C indicate regions with high tree density. A: Chêne-Bougeries, B: Onex and Lancy and C: Le Petit-Saconnex.</figcaption></figure>

estimated to be 50 t y⁻¹. For PM₁₀ and O₃ removal, the model estimated removal of 14 t y⁻¹ and 52 t y⁻¹, respectively (Table 1).

The values obtained with the species-level approach are first-order estimations based on several assumptions (see Section 2). The errors associated with our estimations range from 40 % for BVOC emissions to 83 % for PM₁₀ removal, based on propagation of uncertainties (see Section 2.2.5). This approach's main sources of uncertainties come either from the parametrization (uncertainty associated with a variable) or from the dataset (error in the original tree inventory) and are given in Table S4. For instance, our species-level parametrization for BVOC emissions and OFP largely depends on BVOC EFs, which can have large uncertainties (Situ et al., 2014). Unfortunately, i-Tree Eco does not provide uncertainty estimations for its outputs. Studies such as Selmi et al. (2016) and Soares et al. (2011) that use the model do not calculate errors associated with urban forests' air pollution removal capacity. However, Lin et al. (2021) provide an uncertainty framework for i-Tree Eco. In their work, the authors evaluate the uncertainties related to input data, sampling methods, and models to estimate the overall uncertainty for i-Tree Eco. They found an average magnitude of the total uncertainty of 40.7 % for isoprene emissions, 25 % for monoterpenes

emissions, and 12.3 % for leaf area. The relative uncertainty on the total BVOC emissions (isoprene + monoterpenes) would be 47.7 % which is close to our estimation for the species-level approach (see Section 2.2.5). However, they do not characterize the uncertainties of PM₁₀ and O₃ removal.

BVOC emissions were three times higher in the species-level approach compared to i-Tree Eco results, and a factor four and three higher, respectively, for PM₁₀ and O₃ removal. Differences in their parameterization likely cause the calculated discrepancies between the two methods. For instance, the species-level approach uses a single annual average concentration of O₃ and PM₁₀ to calculate the total annual mass removed by trees in a given year. In contrast, i-Tree Eco integrates hourly measurements of O₃ and PM₁₀ dry deposition onto trees over a year, based on tree cover data and hourly weather and pollution data (Hirabayashi et al., 2011; Nowak et al., 2014, 2013, 2006). Thus, i-Tree Eco better accounts for the temporal variability in ambient air concentrations of O₃ and PM₁₀. The rougher parameterization in the species-level approach likely overestimates the total annual O₃ and PM₁₀ mass removed by trees. Concerning BVOC emissions, i-Tree Eco estimates hourly emissions of isoprene and monoterpenes (but not
<caption>**Table 1**</caption>

Total annual BVOC emissions, OFP, PM₁₀ and O₃ removal by Geneva's urban trees, estimated using the species-level and i-Tree Eco approaches. Total and traffic-related annual VOC and PM₁₀ emissions in the Canton of Geneva for 2014 are also reported here (CADERO, 2022). Traffic-related annual OFP was calculated in this study.

<table><thead><tr><th></th><th>BVOC emissions (t y<sup>-1</sup>)</th><th>OFP (t y<sup>-1</sup>)</th><th>PM<sub>10</sub> removal (t y<sup>-1</sup>)</th><th>O<sub>3</sub> removal (t y<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Species-level approach</td><td>130 ± 52</td><td>1153 ± 519</td><td>66 ± 55</td><td>150 ± 96</td></tr><tr><td>i-Tree Eco approach</td><td>50</td><td>-</td><td>14</td><td>52</td></tr><tr><td></td><td>VOC emissions (t y<sup>-1</sup>)</td><td>OFP (t y<sup>-1</sup>)</td><td>PM<sub>10</sub> emissions (t y<sup>-1</sup>)</td><td></td></tr><tr><td>CADERO</td><td>Traffic</td><td>723</td><td>2588*</td><td>121</td></tr><tr><td></td><td>Total anthropogenic</td><td>3093</td><td>-</td><td>339</td></tr></tbody></table>

* Stemmler et al. (2005) measured the emissions of 14 species of VOC from road traffic in Switzerland. We calculated the weighted mean MIR for traffic VOC emissions from these emission factors. This MIR was then used to estimate OFP from traffic.

sesquiterpenes) using genus-based EFs, leaf biomass, and environmental parameters such as temperature and photosynthetically active radiation. BVOC emissions are then attributed to a given species proportionally to the ratio of the species biomass over the genus biomass. In the species-level approach, meteorological conditions are not accounted for, meaning that calculated BVOC emissions represent the higher limit of potential tree emissions. However, using species-specific EFs more accurately describes the large range of BVOC emissions from distinct tree species, including within a given genus (Table S3). Despite those differences, the two approaches yield results with the same order of magnitude (Table 1).

### 3.1.3. Comparison to anthropogenic emissions

By comparing the PM₁₀ removal rate with the anthropogenic PM₁₀ emissions, we can estimate the contribution of trees to air quality improvement. Data for canton-based emissions were extracted from a regional emission inventory (CADERO, 2022). The total mass of PM₁₀ emitted by human activities in 2014 was estimated at 339 t y⁻¹ (Table 1). Geneva's trees removed between 4 and 19 % of that amount according to the i-Tree Eco and species-level approaches, respectively. Even though PM levels are seldomly exceeded in Geneva (see Section 2), a slight reduction in PM₁₀ concentrations can have significant health benefits (Pérez et al., 2009).

As described in Section 2, ground-level O₃ concentrations in Geneva regularly exceed legal limit values. Although O₃ levels have remained steady in the last decade in Geneva (ROPAG, 2021), O₃ production from BVOC emissions may further deteriorate air quality in the long term as increased BVOC emissions are expected with climate change (Penuelas and Llusià, 2003) and given the revegetation considerations in the Canton of Geneva (Schlaepfer et al., 2018). Our findings suggest that the OFP from Geneva's trees is about an order of magnitude greater than their ability to remove O₃ from the atmosphere (Table 1). This result shows the importance of selecting low BVOC-emitting tree species for future revegetation plans. Additionally, we estimated traffic-related OFP (Table 1) by combining the cantonal emission inventory (CADERO, 2022) and the measured emissions of 14 species of VOC from road traffic in Switzerland (Stemmler et al., 2005). The VOC measurements were carried out in a highway tunnel near Zurich, Switzerland, in 2002. Although this study may not be fully representative of traffic VOC emissions in 2014, it provides important information on regional traffic VOC speciation. Using this speciation and the MIR database from Carter (2009), we calculated a weighted average MIR of 3.58 for traffic-emitted VOCs. This reactivity would lead to a maximum O₃ formation of 2588 t.

While traffic emits 5–15 times more VOCs than urban trees, the corresponding OFP is only 2 times greater because of the smaller reactivity of traffic VOCs compared to BVOCs, and most notably isoprene. Our findings underscore that O₃ production from BVOCs emissions should not be underestimated despite the lower emission rates than other VOC sources.

### 3.1.4. Comparison to other studies

Our results for PM₁₀ and O₃ removal rates are comparable with ranges reported for other European cities (Table 2). Using i-Tree Eco, Selmi et al. (2016) found that urban trees in Strasbourg, France, removed 12 t of PM₁₀ and 56 t of O₃ from July 2012 to June 2013. Comparing their results with local emissions revealed that Strasbourg's trees removed about 7 % of anthropogenic PM₁₀ emissions (Selmi et al., 2016), similar to the 4 % estimation in Geneva (using i-Tree Eco) in this study. Geneva and Strasbourg are comparable in many aspects, such as total population, climate, number of trees, and geographical region. Additionally, two of the three most common species in Strasbourg (Fagus sylvatica and Fraxinus excelsior) are also very common in Geneva (1.2 % and 2.2 % of the trees, respectively; Table S2). Further, the mean annual PM₁₀ and O₃ concentrations in Strasbourg were 22.1 µg m⁻³ (against 17.8 µg m⁻³ in Geneva) and 43 µg m⁻³ (against 37.4 µg m⁻³ in Geneva), respectively. Bottalico et al. (2017) found that the urban forest in Florence, Italy, removed 171 t of PM₁₀ and 78 t of O₃ in 2013 using a parametrization similar to our species-specific approach. The main tree species in Florence are Platanus spp., Celtis australis, and Pinus pinea (Pauleit et al., 2005), which rank as the 20th, 26th, and 50th most common species in Geneva, respectively (Table S2). In Florence, the annual mean PM₁₀ concentration ranged between 20 µg m⁻³ and 34 µg m⁻³ depending on the station, and the annual mean O₃ concentration was 55 µg m⁻³. The annual mean PM₁₀ and O₃ concentrations are thus higher than in 2014 in Geneva. Baró et al. (2014) applied i-Tree Eco in Barcelona, Spain, and found that the urban forest removed 166 t of PM₁₀ and 73 t of O₃ in 2008. Platanus hispanica (ranked 24th in abundance in Geneva) and Celtis australis (ranked 26th in abundance in Geneva) are the two most common species in Barcelona. According to their study, the PM₁₀ removal rate by urban trees accounted for 22 % of local anthropogenic PM₁₀ emissions, higher than our 4 % estimate using i-Tree Eco in Geneva but similar to the 19 % using the species-level approach. Finally, Nowak et al. (2006) estimated PM₁₀ and O₃ removal for 55 cities in the United States (U.S.). Annual removal rates ranged from 7 to 3570 t for PM₁₀ and 8 to 5210 for O₃. The high range of values is due to size difference between the cities and the canopy cover estimations. Geneva's urban trees are performing within these ranges.

### 3.1.5. Tree species performance

To compare the performance of different tree species relative to air quality, both their ability to remove pollutants and their contribution to the formation of O₃ needs to be considered (Fig. 5). To improve air quality, species with both large PM₁₀ removal rates and positive net O₃ balance (i.e., O₃ removal > OFP) can be prioritized in future urban greening policies. Table 3 provides a summary of the three most abundant species in the dataset, along with their respective effectiveness in either improving or deteriorating air quality. In the specific case of Geneva, Quercus robur is the most common species, with a total of 10,708 recorded trees (4.5 % of total trees). However, this species is known for its high emissions of BVOCs, particularly the highly reactive compound isoprene. In comparison to the moderate BVOC emitter Carpinus betulus (3.7 % of total trees) and low BVOC emitter Fraxinus excelsior (2.2 % of total trees), Quercus robur has a mean annual OFP per tree that is approximately 50 to 150 times greater. Still, two of the three most common species in Geneva show a negative net O₃ balance. However, in terms of PM₁₀ deposition, all three species perform similarly.

In terms of net O₃ balance, Populus nigra (0.8 % of total trees), Quercus robur (4.5 % of total trees), and Salix alba (0.2 % of total trees)
<caption>**Table 2**</caption>

Comparison of total annual PM₁₀ and O₃ removal between this study and several other studies. We indicate in the brackets the percentage of anthropogenic PM₁₀ removed from the atmosphere by urban trees.

<table><thead><tr><th></th><th colspan="2">This study</th><th>Selmi et al. (2016)</th><th>Bottalico et al. (2017)</th><th>Baró et al. (2014)</th><th>Nowak et al. (2006)</th></tr></thead><tbody><tr><td>Location</td><td colspan="2">Geneva, Switzerland</td><td>Strasbourg, France</td><td>Florence, Italy</td><td>Barcelona, Spain</td><td>55 U.S. cities</td></tr><tr><td>Methodology</td><td colspan="2">Species-level</td><td>i-Tree Eco</td><td>Species-level</td><td>i-Tree Eco</td><td>i-Tree Eco</td></tr><tr><td>PM₁₀ removal (t y⁻¹)</td><td colspan="2">66 ± 55 (19 %)</td><td>14 (4 %)</td><td>12 (7 %)</td><td>166 (22 %)</td><td>7 - 3570</td></tr><tr><td>O₃ removal (t y⁻¹)</td><td colspan="2">150 ± 96</td><td>52</td><td>56</td><td>78</td><td>8 - 5210</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Total annual PM₁₀ removal vs. total annual net O₃ balance (i.e., the difference between O₃ removal and OFP) calculated for each of the 51 most abundant tree species in Geneva using the species-level approach. Each marker indicates a tree species and is scaled according to the number of trees for that species as indicated in the legend. Pink markers are for evergreen species and gray markers are for deciduous species. Abbreviated tree species are all discussed in the text. The inserted figure is a zoom of the outlined black rectangle, for clarity.</figcaption></figure>

<caption>**Table 3**</caption>

Summary of BVOC emissions, OFP, and PM₁₀ and O₃ removal rates for the three most common tree species in Geneva. All values are species averages.

<table><thead><tr><th></th><th>Number of trees</th><th>BVOC emissions [g tree<sup>-1</sup> y<sup>-1</sup>]</th><th>OFP [g tree<sup>-1</sup> y<sup>-1</sup>]</th><th>O₃ removal [g tree<sup>-1</sup> y<sup>-1</sup>]</th><th>net O₃ balance (the difference between O₃ removal and OFP) [g tree<sup>-1</sup> y<sup>-1</sup>]</th><th>PM₁₀ removal [g tree<sup>-1</sup> y<sup>-1</sup>]</th></tr></thead><tbody><tr><td>Quercus robur</td><td>10,708</td><td>2009</td><td>20,707</td><td>2426</td><td>-18,281</td><td>386</td></tr><tr><td>Carpinus betulus</td><td>9441</td><td>110</td><td>437</td><td>198</td><td>-239</td><td>316</td></tr><tr><td>Fraxinus excelsior</td><td>5391</td><td>39</td><td>141</td><td>268</td><td>127</td><td>355</td></tr></tbody></table>

are currently the three species with the highest OFP in Geneva, which largely offsets their O₃ removal rates (Fig. 5). Manzini et al. (2023) also classified *Quercus robur* and *Populus nigra* as species with highly negative net O₃ balance. Conversely, *Acer platanoides* (3362 g tree⁻¹ y⁻¹, 2.1 % of total trees), *Ulmus minor* (615 g tree⁻¹ y⁻¹, 0.3 % of total trees) and *Acer saccharinum* (560 g tree⁻¹ y⁻¹, 0.3 % of total trees) show a strong capability to remove O₃ and are characterized by low OFP. This finding is consistent with Baraldi et al. (2019), who found *Ulmus minor* and *Acer platanoides* to have the highest O₃ removal rates in their study.

The three most abundant species remove PM₁₀ from the atmosphere with intermediate efficiency (355 to 386 g tree⁻¹ y⁻¹) (Table 3). Evergreen trees show the best results for PM₁₀ removal. *Cedrus atlantica* (1932 g tree⁻¹ y⁻¹, 0.5 % of total trees) shows the highest value of deposition, followed by *Pinus nigra* (835 g tree⁻¹ y⁻¹, 1.4 % of total trees) and *Picea abies* (711 g tree⁻¹ y⁻¹, 0.6 % of total trees). Some deciduous species such as *Aesculus hippocastanum* (603 g tree⁻¹ y⁻¹) and *Tilia platyphyllos* (581 g tree⁻¹ y⁻¹) also show relatively high removal rates. Similar results were found by Manzini et al. (2023): according to their research, *Picea abies* and *Cedrus atlantica* were among the 10 best performing tree species regarding PM₁₀ removal in Florence.

According to our analysis, *Tilia x europaea*, *Acer saccharinum*, *Pinus sylvestris*, and *Fraxinus excelsior* showed the best results for both PM₁₀ removal and net O₃ balance (Fig. 5). By comparing the performance of abundant tree species in Geneva, our work can offer some guidelines for urban planners for future planting of trees. However, it should be noted that our analysis solely focuses on some, but not all air quality impacts from urban trees. For instance, we did not consider secondary organic aerosol formation from BVOCs which would add to the PM burden of cities (see Section 3.2.3). In addition, co-factors such as pollen allergenicity (Cariñanos et al., 2019), resistance to diseases and pests (Sicard et al., 2018), invasiveness (Dickie et al., 2014), water consumption and drought resistance (Grote et al., 2016) could be considered.

## 3.2. Limits and perspectives

### 3.2.1. Geneva's tree inventory

Other studies that have worked on air quality impacts of urban trees at the city scale did not use a large-scale inventory, but selected random plots in the city to describe local urban forests (e.g., Baró et al., 2014; Selmi et al., 2016). We show in this study that using a city-scale tree inventory provides a detailed, comprehensive, and spatially resolved assessment of urban trees' impact on air quality (Fig. 4). However, the
accuracy of this assessment relies on the quality of the inventory. In the case of Geneva's inventory (ICA, 2022), the main source of error originates in the period (1976 - today) over which the inventory was performed. For instance, trees inventoried in the 1970s were first located on paper maps that were later digitized, resulting in a spatial precision of about 25 m. Trees surveyed several decades ago include dimensional information (e.g., tree height, canopy diameter) that largely differ from their current state or sometimes do not exist anymore. Thus, a tree inventory provides a snapshot of the state of a given urban forest and requires regular updates to ensure the accuracy of studies that use it. Here, we evaluated Geneva's inventory using a subset of the data localized in Lancy, a city part of Geneva's conurbation (Fig. 3). The tree inventory subset was compared with the Digital Canopy Model (DCM) available for the entire Canton. The DCM was produced from LiDAR data (20–40 points per m²) acquired by an aerial survey in March 2019 and represents the ground surface covered by tree foliage. The comparison of both data sources (i.e., tree inventory and DCM) allows firstly to verify whether a tree of the inventory still exists (about 16 % of the trees from the inventory are not in the DCM), and secondly to estimate how many existing trees showing on the DCM are not accounted for in the inventory (about 22 % of the isolated trees from the DCM are not present in the inventory). Thirdly, the DCM could be used to update the tree's total height and crown diameter, especially where a single tree from the inventory overlaps with a polygon from the DCM containing a single detected trunk. In this case, we found that the DCM attributed height and diameter values to trees in Lancy about 30 % higher than reported in the inventory due to tree growth. This preliminary comparison underlines the potential of developing and using remote sensing methods to update existing tree inventory over time.

### 3.2.2. Species-specific parametrization

The main current limitation for i-Tree Eco is the genus-based parametrization for BVOC emissions and PM₁₀ removal, whereas this information becomes relevant for green infrastructure planning when provided at the species-level. Here, we advocate incorporating species-specific BVOC EFs and maximum stomatal conductivities in i-Tree Eco. Manzini et al. (2023) recently developed FlorTree, a modeling framework to evaluate the impact of urban tree species by combining most of i-Tree Eco parametrization, including meteorological and air pollution hourly inputs, with species-level databases of BVOC EFs and maximum stomatal conductance. Although promising, a comparison of the performance of FlorTree with i-Tree Eco is necessary to assess the added value of this new model but it was not achieved in the study. In addition, the authors evaluated the theoretical performance of tree species by using solely literature data for several parameters (e.g., leaf biomass). Using field data from city-scale tree inventories would yield a more representative comparison of tree species performance.

### 3.2.3. Secondary organic aerosols (SOA) formation

Even though BVOC emissions are important precursors for the formation of SOA, this process was not in the scope of our study. However, to obtain a complete picture of the impact of urban trees on air quality, SOA production should be considered. For instance, Gao et al. (2022) found that biogenic-induced SOA accounted for 16 % of the total SOA in urban Beijing.

From an urban greening perspective, the choice of tree species would influence SOA formation and less stress-sensitive tree types are recommended for future planting programs (Bonn et al., 2018). It is, however, a fairly new field of research, and the distinction between organic aerosols formed from anthropogenic precursors vs. those formed from natural precursors are still not fully understood (C. Minguillón et al., 2016).

## 4. Conclusion

In this study, we used two approaches to estimate annual BVOC

emissions, OFP, PM₁₀, and O₃ removal in the Canton of Geneva. We find that total annual BVOC emissions were three times higher in the species-level approach compared to i-Tree Eco results, and a factor four and three higher, respectively, for total annual PM₁₀ and O₃ removal. However, given the large uncertainties associated with both approaches, we conclude that they agree within one order of magnitude. Using the species-level approach, we estimated that Geneva's urban trees removed 66 ± 55 t of PM₁₀ in 2014, representing about 19 % of the annual anthropogenic emissions. Also, we estimated that the trees' ozone-forming potential could exceed their O₃ removal capacity by an order of magnitude. The total BVOCs emitted by urban trees represent about 4 % of all VOC emissions in the Canton of Geneva. However, BVOCs emitted by trees are about 5 times more reactive than VOCs emitted by traffic, resulting in traffic-related OFP being only twice as high as tree-related OFP. Even though the species-level approach comes with high uncertainties, we show that urban trees play an important role in removing major air pollutants but could also significantly contribute to local air quality issues under certain circumstances.

Further, we show that estimating the impact of urban trees at the species level provides crucial insights for local stakeholders regarding the nuanced impacts specific tree species have on air quality. For instance, Quercus robur is the most abundant tree species in Geneva and a strong emitter of BVOCs, resulting in a negative net O₃ balance. In Geneva, where O₃ exceedances occur more frequently than PM₁₀ exceedances, future planting programs may target species that emit low amounts of BVOC to minimize the potential formation of O₃. Therefore, incorporating species-specific BVOC EFs and maximum stomatal conductance in i-Tree Eco would be extremely beneficial worldwide for stakeholders concerned about planting of urban trees in their areas.

Our study focused mainly on the air quality impact of urban trees; for urban planning purposes, it would be equally important to consider other criteria and possible ecosystem services and disservices provided by trees (e.g., mitigation of urban heat islands, noise reduction, pollen emissions, heat and drought resistance). Finally, given that O₃ formation largely depends on the presence of other atmospheric compounds such as nitrogen oxides that are mainly emitted by anthropogenic activities, the role played by trees should not overshadow the need for anthropogenic emissions mitigation.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The authors acknowledge funding from the interdisciplinary cluster grant scheme of the ENAC faculty at Ecole Polytechnique Fédérale de Lausanne, Switzerland and the Service de l'air, du bruit et des rayonnements non ionisants (SABRA) of the Republique et Canton de Genève. J.S. holds the Ingvar Kamprad Chair for Extreme Environments Research sponsored by Ferring Pharmaceuticals.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ufug.2024.128513.

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