<figure><img src="image_26.png" /><figcaption>**Figure F1.** Observed and simulated hourly concentrations of (a) NO₂, (b) O₃, (c) PM₂.₅, and (d) OM at the Halles station. Heatwave periods are indicated by shaded orange areas.</figcaption></figure>

In this section, the NO₂, O₃, OM, PM₂.₅, and isoprene and
monoterpene concentrations simulated by CHIMERE are
compared to observations performed at different measure-
ment stations over Île-de-France. The concentrations simu-
lated in the horizontal grid cell containing the station and
in the first vertical layer are compared to the observed con-
centrations in Table F1 for the three emission scenarios REF,

REF-TX2, and REF-TX3. Two performance criteria are de-
fined by Hanna and Chang (2012), and they are used here
to evaluate the simulations performance. The strictest cri-
teria are accepted when −0.3 < FB < 0.3, 0.7 < MG < 1.3,
NMSE < 3, VG < 1.6, FAC2 ≥ 0.5, and NAD < 0.3. The
less strict criteria are accepted when −0.67 < FB < 0.67,
NMSE < 6, FAC2 ≥ 0.3, and NAD < 0.5 (where FB is for
<figure><img src="image_28.png" /><figcaption>**Figure F1.** Observed and simulated hourly concentrations of (a) NO₂, (b) O₃, (c) PM₂.₅, and (d) OM at the Halles station. Heatwave periods are indicated by shaded orange areas.</figcaption></figure>

In this section, the NO₂, O₃, OM, PM₂.₅, and isoprene and
monoterpene concentrations simulated by CHIMERE are
compared to observations performed at different measure-
ment stations over Île-de-France. The concentrations simu-
lated in the horizontal grid cell containing the station and
in the first vertical layer are compared to the observed con-
centrations in Table F1 for the three emission scenarios REF,

REF-TX2, and REF-TX3. Two performance criteria are de-
fined by Hanna and Chang (2012), and they are used here
to evaluate the simulations performance. The strictest cri-
teria are accepted when −0.3 < FB < 0.3, 0.7 < MG < 1.3,
NMSE < 3, VG < 1.6, FAC2 ≥ 0.5, and NAD < 0.3. The
less strict criteria are accepted when −0.67 < FB < 0.67,
NMSE < 6, FAC2 ≥ 0.3, and NAD < 0.5 (where FB is for
<figure><img src="image_27.png" /><figcaption>**Figure F2.** Observed and simulated hourly concentrations of (a) isoprene and (b) monoterpenes at the PRG station and (c) isoprene and (d) monoterpenes at the SIRTA station. Heatwave periods are indicated by shaded orange areas.</figcaption></figure>

fractional bias, MG is for geometric mean bias, NMSE is for normalized mean square error, VG is for geometric variance, FAC2 is for factor of 2, NAD is for normalized absolute difference, and *R* is for correlation coefficient; see Appendix G). Values that respect the strictest performance criteria are represented in bold, those that respect the acceptable performance criteria for urban areas are represented in italics, and those that do not respect any criteria are in roman.

In order to investigate in more detail the model performance in each simulation, the temporal evolution of simulated and observed concentrations in three different stations (the Halles and PRG urban stations and the SIRTA suburban station) is presented in Figs. F1 and F2.

The different hypotheses regarding terpene biogenic emissions have a low impact on NO₂ and O₃ concentrations, and all simulations present very similar concentrations and sta-
<figure><img src="image_29.png" /><figcaption>**Figure F2.** Observed and simulated hourly concentrations of (a) isoprene and (b) monoterpenes at the PRG station and (c) isoprene and (d) monoterpenes at the SIRTA station. Heatwave periods are indicated by shaded orange areas.</figcaption></figure>

fractional bias, MG is for geometric mean bias, NMSE is for normalized mean square error, VG is for geometric variance, FAC2 is for factor of 2, NAD is for normalized absolute difference, and *R* is for correlation coefficient; see Appendix G). Values that respect the strictest performance criteria are represented in bold, those that respect the acceptable performance criteria for urban areas are represented in italics, and those that do not respect any criteria are in roman.

In order to investigate in more detail the model performance in each simulation, the temporal evolution of simulated and observed concentrations in three different stations (the Halles and PRG urban stations and the SIRTA suburban station) is presented in Figs. F1 and F2.

The different hypotheses regarding terpene biogenic emissions have a low impact on NO₂ and O₃ concentrations, and all simulations present very similar concentrations and sta-
tistical indicators (Table F1). Figure F1a shows good correlation between the NO₂ concentrations measured and observed at the Halles site in all simulations, although a few concentration peaks are underestimated. The strictest performance criteria are respected for all statistical indicators for NO₂ and for O₃. The O₃ geometric mean bias is at the limit of the acceptance criteria because of the overestimation of the low O₃ concentrations at night (see Fig. F1b). This overestimation of low O₃ concentrations has previously commonly been observed and might be related to model grid resolution (Jang et al., 1995a, b; Liang and Jacobson, 2000; Arunachalam et al., 2006).

For PM₂.₅, the less strict criteria are respected for the three simulations, but the fractional bias (FB) increases with the increase in the biogenic terpene emissions. This increase is observed mostly in rural stations. In other words, PM₂.₅ concentrations are overestimated at rural stations when the terpene biogenic emissions are increased, but the increase in the terpene biogenic emissions does not degrade the scores at urban and suburban stations, and it even improves the correlation. A PM₂.₅ concentration peak reaching 80 µg m⁻³ is observed on 19 July (not shown in Fig. F1c) and is probably due to forest fires in the southwest of France (Menut et al., 2023). Similar to PM₂.₅, the concentrations of the organic fraction of PM₁ (organic matter, OM) are strongly influenced by the terpene biogenic emission hypothesis. While OM concentrations are strongly underestimated in the REF simulation (fractional bias of -0.99), they respect all the less strict criteria in the REF-TX2 and REF-TX3 simulations (fractional bias equal to -0.46 and -0.09, respectively). As the stations where OM is measured are suburban and urban stations, this goes hand in hand with the better estimate of PM₂.₅ at urban stations (not shown). As shown in Fig. F1c, the effect of modifying biogenic terpene emissions is quite significant, even at the Halles station, which is located in a very dense urban area. This increase in the PM₂.₅ concentrations is due to the increase in OM, as shown in Fig. F1d. OM concentrations are especially high between 18 and 19 June, days with very high temperatures and high biogenic emissions. During this period, the differences between the OM concentrations in the REF, REF-TX2, and REF-TX3 simulations are the largest. The higher the terpene emissions, the better the simulated OM concentration is compared to observation, suggesting that it is essential to represent the terpene emission of suburban areas well to represent the OM concentrations well.

Regarding BVOC concentrations, no differences in the three simulations are observed for isoprene (C₅H₈) concentrations, as expected, and the mean concentration tends to be underestimated. Monoterpene concentrations are highly influenced by the biogenic terpene emissions. The higher the biogenic terpene emissions are, the smaller the fractional biases observed in the simulations will be (-0.91 for REF, -0.39 for REF-TX2, and -0.03 for REF-TX3) (Table F1). Figure F2a and c show the hourly isoprene concentrations simulated and observed at the PRG station (dense urban area) and at the SIRTA station (suburban area), respectively. Isoprene is better represented at SIRTA than at the PRG station because of the absence of biogenic emissions inside Paris in REF, REF-TX2, and REF-TX3 simulations. Figure F2b and d illustrate the hourly concentrations of monoterpenes simulated and observed at the PRG and SIRTA stations, respectively. Similar to what was observed for isoprene, monoterpene concentrations are also strongly underestimated in urban areas (PRG) and are better represented at the SIRTA suburban site. This can be justified by the absence of monoterpene biogenic emissions inside Paris, as analyzed in Sect. 4.2. The observed values in the urban PRG site point out a “regional background” of the monoterpene concentrations around 0.1 ppb.

## Appendix G: Definition of the statistical indicators

To compare the simulation results to measured data, classical statistical indicators are computed where obsᵢ and simᵢ are, respectively, the observed and simulated hourly concentrations. n is the total number of concentrations, and obs and sim are the average observed and simulated concentrations.

- Root mean square error (same unit as the concentration):

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\text{obs}_i - \text{sim}_i)^2}. \quad (G1)$$

- Normalized mean square error (dimensionless):

$$NMSE = \frac{\sum_{i=1}^{n} (\text{obs}_i - \text{sim}_i)^2}{\sum_{i=1}^{n} \text{obs}_i \times \sum_{i=1}^{n} \text{sim}_i}. \quad (G2)$$

- Normalized absolute difference (dimensionless):

$$NAD = \frac{\sum_{i=1}^{n} |\text{obs}_i - \text{sim}_i|}{\sum_{i=1}^{n} \text{obs}_i + \sum_{i=1}^{n} \text{sim}_i}. \quad (G3)$$

- Mean fractional error (dimensionless):

$$MFE = \frac{1}{n} \sum_{i=1}^{n} \frac{|\text{sim}_i - \text{obs}_i|}{\text{obs}_i}. \quad (G4)$$

- Mean fractional bias (same unit as the concentration):

$$MFB = \frac{1}{n} \sum_{i=1}^{n} |\text{sim}_i - \text{obs}_i|. \quad (G5)$$
tistical indicators (Table F1). Figure F1a shows good correlation between the NO₂ concentrations measured and observed at the Halles site in all simulations, although a few concentration peaks are underestimated. The strictest performance criteria are respected for all statistical indicators for NO₂ and for O₃. The O₃ geometric mean bias is at the limit of the acceptance criteria because of the overestimation of the low O₃ concentrations at night (see Fig. F1b). This overestimation of low O₃ concentrations has previously commonly been observed and might be related to model grid resolution (Jang et al., 1995a, b; Liang and Jacobson, 2000; Arunachalam et al., 2006).

For PM₂.₅, the less strict criteria are respected for the three simulations, but the fractional bias (FB) increases with the increase in the biogenic terpene emissions. This increase is observed mostly in rural stations. In other words, PM₂.₅ concentrations are overestimated at rural stations when the terpene biogenic emissions are increased, but the increase in the terpene biogenic emissions does not degrade the scores at urban and suburban stations, and it even improves the correlation. A PM₂.₅ concentration peak reaching 80 µg m⁻³ is observed on 19 July (not shown in Fig. F1c) and is probably due to forest fires in the southwest of France (Menut et al., 2023). Similar to PM₂.₅, the concentrations of the organic fraction of PM₁ (organic matter, OM) are strongly influenced by the terpene biogenic emission hypothesis. While OM concentrations are strongly underestimated in the REF simulation (fractional bias of -0.99), they respect all the less strict criteria in the REF-TX2 and REF-TX3 simulations (fractional bias equal to -0.46 and -0.09, respectively). As the stations where OM is measured are suburban and urban stations, this goes hand in hand with the better estimate of PM₂.₅ at urban stations (not shown). As shown in Fig. F1c, the effect of modifying biogenic terpene emissions is quite significant, even at the Halles station, which is located in a very dense urban area. This increase in the PM₂.₅ concentrations is due to the increase in OM, as shown in Fig. F1d. OM concentrations are especially high between 18 and 19 June, days with very high temperatures and high biogenic emissions. During this period, the differences between the OM concentrations in the REF, REF-TX2, and REF-TX3 simulations are the largest. The higher the terpene emissions, the better the simulated OM concentration is compared to observation, suggesting that it is essential to represent the terpene emission of suburban areas well to represent the OM concentrations well.

Regarding BVOC concentrations, no differences in the three simulations are observed for isoprene (C₅H₈) concentrations, as expected, and the mean concentration tends to be underestimated. Monoterpene concentrations are highly influenced by the biogenic terpene emissions. The higher the biogenic terpene emissions are, the smaller the fractional biases observed in the simulations will be (-0.91 for REF, -0.39 for REF-TX2, and -0.03 for REF-TX3) (Table F1). Figure F2a and c show the hourly isoprene concentrations simulated and observed at the PRG station (dense urban area) and at the SIRTA station (suburban area), respectively. Isoprene is better represented at SIRTA than at the PRG station because of the absence of biogenic emissions inside Paris in REF, REF-TX2, and REF-TX3 simulations. Figure F2b and d illustrate the hourly concentrations of monoterpenes simulated and observed at the PRG and SIRTA stations, respectively. Similar to what was observed for isoprene, monoterpene concentrations are also strongly underestimated in urban areas (PRG) and are better represented at the SIRTA suburban site. This can be justified by the absence of monoterpene biogenic emissions inside Paris, as analyzed in Sect. 4.2. The observed values in the urban PRG site point out a “regional background” of the monoterpene concentrations around 0.1 ppb.

## Appendix G: Definition of the statistical indicators

To compare the simulation results to measured data, classical statistical indicators are computed where obsᵢ and simᵢ are, respectively, the observed and simulated hourly concentrations. n is the total number of concentrations, and obs and sim are the average observed and simulated concentrations.

- Root mean square error (same unit as the concentration):

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\text{obs}_i - \text{sim}_i)^2}. \qquad (G1)$$

- Normalized mean square error (dimensionless):

$$NMSE = \frac{\sum_{i=1}^{n} (\text{obs}_i - \text{sim}_i)^2}{\sum_{i=1}^{n} \text{obs}_i \times \sum_{i=1}^{n} \text{sim}_i}. \qquad (G2)$$

- Normalized absolute difference (dimensionless):

$$NAD = \frac{\sum_{i=1}^{n} |\text{obs}_i - \text{sim}_i|}{\sum_{i=1}^{n} \text{obs}_i + \sum_{i=1}^{n} \text{sim}_i}. \qquad (G3)$$

- Mean fractional error (dimensionless):

$$MFE = \frac{1}{n} \sum_{i=1}^{n} \frac{|\text{sim}_i - \text{obs}_i|}{\text{obs}_i}. \qquad (G4)$$

- Mean fractional bias (same unit as the concentration):

$$MFB = \frac{1}{n} \sum_{i=1}^{n} |\text{sim}_i - \text{obs}_i|. \qquad (G5)$$
- Bias (same unit as the concentration):

$$Bias = \frac{1}{n} \sum_{i=1}^{n} (sim_i - obs_i). \qquad (G6)$$

- Fractional bias (dimensionless):

$$FB = 2 \times \frac{\sum_{i=1}^{n} sim_i - \sum_{i=1}^{n} obs_i}{\sum_{i=1}^{n} obs_i + \sum_{i=1}^{n} sim_i}. \qquad (G7)$$

- Geometric mean bias (dimensionless):

$$MG = \exp \left[ \frac{1}{n} \sum_{i=1}^{n} \ln (sim_i) - \frac{1}{n} \sum_{i=1}^{n} \ln (obs_i) \right]. \qquad (G8)$$

- Correlation coefficient (dimensionless):

$$R = \frac{\sum_{i=1}^{n} [(sim_i - \overline{sim})(obs_i - \overline{obs})]}{\sqrt{\sum_{i=1}^{n} (sim_i - \overline{sim})^2 \sum_{i=1}^{n} (obs_i - \overline{obs})^2}}. \qquad (G9)$$

- Geometric variance (dimensionless):

$$VG = \exp \left[ \frac{1}{n} \sum_{i=1}^{n} (\ln(obs_i) - \ln(sim_i))^2 \right]. \qquad (G10)$$

- Factor of 2 (dimensionless):

$$FAC2 = \text{total fraction where } 0.5 < \frac{sim_i}{obs_i} < 2.0. \qquad (G11)$$

**Code availability.** The code to process the tree database, calculate tree characteristics, and estimate biogenic emissions is available online at https://doi.org/10.5281/zenodo.10381923 (Maison et al., 2023).

The version of WRF-CHIMERE code used here is available on request to the corresponding author.

**Data availability.** ACSM data measured at the PRG site will be available after publication from AERIS data center (2024) at https://across.aeris-data.fr/catalogue/.

PTR-MS data measured at the PRG site are available from the AERIS data center at https://doi.org/10.25326/659 (Kammer et al., 2024).

PTR-MS data measured at the SIRTA station are available from the ACTRIS database at https://ebas-data-nilu.no/Pages/DataSetList.aspx?key=138B0C1CFF024EDF97682B09E9A220B2 (Simon et al., 2022b) and in the IPSL Data Catalog at https://doi.org/10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

Hourly NO₂, O₃, and PM₂.₅ concentrations measured at the Paris Châtelet–Les Halles station are available on the Airparif Open Data Portal at https://data-airparif-asso.opendata.arcgis.com/search?collection=dataset&tags=2022 (Airparif, 2023). Regional emission inventory and organic matter data for the Halles site are available on request.

Hourly meteorological variables measured by the operational station network operated by Météo-France are freely available at https://meteo.data.gouv.fr/datasets/6569b4473bedf2e7abad3b72 (Météo-France, 2024).

**Author contributions.** KS and AM were responsible for the conceptualization. AM, AT, and KS developed the tree biogenic emission inventory. LL, SJP, KS, FC, and MV prepared the input data for the WRF-CHIMERE model, and LL performed the simulations. AM, KS, LL, and SJP performed the formal analysis. KS was responsible for the supervision. JV provided the regional and traffic emission inventory. AM and LL conducted the visualization. The experimental data were provided by AB and JV for the Airparif sites; by AG, CDB, BD, JK, MS, VM, and CC for the PRG site; and by VG, JEP, CK, and LS for SIRTA. AM, LL, and KS wrote the original draft, and all authors reviewed it. KS and AT were responsible for the funding acquisition related to the modeling study.

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Special issue statement.** This article is part of the special issue "Atmospheric Chemistry of the Suburban Forest – multiplatform observational campaign of the chemistry and physics of mixed urban and biogenic emissions". It is not associated with a conference.

**Acknowledgements.** This project was provided with computer and storage resources by GENCI at TGCC (thanks to grant no. A0150114641) on the ROME partition of supercomputer Joliot-Curie. Contributions to measurements at PRG by Astrid Bauville, Mathieu Cazaunau, Lelia Hawkins, Drew Pronovost, Antonin Bergé, Ludovico Di Antonio, Franck Maisonneuve, Cécile Gaimoz, and Servanne Chevaillier are gratefully acknowledged. Contributions to measurements at SIRTA by Olivier Favez (INERIS), Nicolas Bonnaire, and François Truong (LSCE) are gratefully acknowledged.
- Bias (same unit as the concentration):

$$ \text{Bias} = \frac{1}{n} \sum_{i=1}^{n} (\text{sim}_i - \text{obs}_i). \qquad (G6) $$

- Fractional bias (dimensionless):

$$ \text{FB} = 2 \times \frac{\sum_{i=1}^{n} \text{sim}_i - \sum_{i=1}^{n} \text{obs}_i}{\sum_{i=1}^{n} \text{obs}_i + \sum_{i=1}^{n} \text{sim}_i}. \qquad (G7) $$

- Geometric mean bias (dimensionless):

$$ \text{MG} = \exp \left[ \frac{1}{n} \sum_{i=1}^{n} \ln(\text{sim}_i) - \frac{1}{n} \sum_{i=1}^{n} \ln(\text{obs}_i) \right]. \qquad (G8) $$

- Correlation coefficient (dimensionless):

$$ R = \frac{\sum_{i=1}^{n} [(\text{sim}_i - \overline{\text{sim}})(\text{obs}_i - \overline{\text{obs}})]}{\sqrt{\sum_{i=1}^{n} (\text{sim}_i - \overline{\text{sim}})^2 \sum_{i=1}^{n} (\text{obs}_i - \overline{\text{obs}})^2}}. \qquad (G9) $$

- Geometric variance (dimensionless):

$$ \text{VG} = \exp \left[ \frac{1}{n} \sum_{i=1}^{n} (\ln(\text{obs}_i) - \ln(\text{sim}_i))^2 \right]. \qquad (G10) $$

- Factor of 2 (dimensionless):

$$ \text{FAC2} = \text{total fraction where } 0.5 < \frac{\text{sim}_i}{\text{obs}_i} < 2.0. \qquad (G11) $$

**Code availability.** The code to process the tree database, calculate tree characteristics, and estimate biogenic emissions is available online at https://doi.org/10.5281/zenodo.10381923 (Maison et al., 2023).

The version of WRF-CHIMERE code used here is available on request to the corresponding author.

**Data availability.** ACSM data measured at the PRG site will be available after publication from AERIS data center (2024) at https://across.aeris-data.fr/catalogue/.

PTR-MS data measured at the PRG site are available from the AERIS data center at https://doi.org/10.25326/659 (Kammer et al., 2024).

PTR-MS data measured at the SIRTA station are available from the ACTRIS database at https://ebas-data-nil.no/Pages/DataSetList.aspx?key=138B0C1CFF024EDF97682B09E9A220B2 (Simon et al., 2022b) and in the IPSL Data Catalog at https://doi.org/10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

Hourly NO₂, O₃, and PM₂.₅ concentrations measured at the Paris Châtelet–Les Halles station are available on the Airparif Open Data Portal at https://data-airparif-asso.opendata.arcgis.com/search?collection=dataset&tags=2022 (Airparif, 2023). Regional emission inventory and organic matter data for the Halles site are available on request.

Hourly meteorological variables measured by the operational station network operated by Météo-France are freely available at https://meteo.data.gouv.fr/datasets/6569b4473bedf2e7abad3b72 (Météo-France, 2024).

**Author contributions.** KS and AM were responsible for the conceptualization, AM, AT, and KS developed the tree biogenic emission inventory. LL, SJP, KS, FC, and MV prepared the input data for the WRF-CHIMERE model, and LL performed the simulations. AM, KS, LL, and SJP performed the formal analysis. KS was responsible for the supervision. JV provided the regional and traffic emission inventory. AM and LL conducted the visualization. The experimental data were provided by AB and JV for the Airparif sites; by AG, CDB, BD, JK, MS, VM, and CC for the PRG site; and by VG, JEP, CK, and LS for SIRTA. AM, LL, and KS wrote the original draft, and all authors reviewed it. KS and AT were responsible for the funding acquisition related to the modeling study.

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Special issue statement.** This article is part of the special issue "Atmospheric Chemistry of the Suburban Forest – multiplatform observational campaign of the chemistry and physics of mixed urban and biogenic emissions". It is not associated with a conference.

**Acknowledgements.** This project was provided with computer and storage resources by GENCI at TGCC (thanks to grant no. A0150114641) on the ROME partition of supercomputer Joliot-Curie. Contributions to measurements at PRG by Astrid Bauville, Mathieu Cazaunau, Lelia Hawkins, Drew Pronovost, Antonin Bergé, Ludovico Di Antonio, Franck Maisonneuve, Cécile Gaimoz, and Servanne Chevaillier are gratefully acknowledged. Contributions to measurements at SIRTA by Olivier Favez (INERIS), Nicolas Bonnaire, and François Truong (LSCE) are gratefully acknowledged.
**Financial support.** This work benefited from the French state aid managed by the sTREEt ANR project (grant no. ANR-19-CE22-0012) and by the ANR under the "Investissements d'avenir" program (grant no. ANR-11-IDEX-0004-17-EURE-0006) with support from IPSL/Composair. The measurements at the PRG site have been supported by the ACROSS project. The ACROSS project has received funding from the French National Research Agency (ANR) under the investment program integrated into France 2030 (grant no. ANR-17-MPGA-0002), and it has been supported by the French National program LEFE (Les Enveloppes Fluides et l'Environnement) of the CNRS/INSU (Centre National de la Recherche Scientifique/Institut National des Sciences de L'Univers). Measurements at SIRTA have been supported by the European Commission (ACTRIS, grant no. 262254; ACTRIS 2, grant no. 654109), CNRS, CEA, INERIS, the French Ministry of Environment, the DIM-QI2 program from the Île-de-France region, and the research infrastructure ACTRIS-FR registered on the Roadmap of the French Ministry of Research.

**Review statement.** This paper was edited by Andrea Pozzer and reviewed by two anonymous referees.

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* Bontemps, S., Defourny, P., Van Bogaert, E., Arino, O., Kalogirou, V., and Perez, J. R.: GLOBCOVER 2009 Products Description and Validation Report, https://epic.awi.de/id/eprint/31014/16/GLOBCOVER2009_Validation_Report_2-2.pdf (last access: 5 May 2014), 2011.

* Boutahar, J., Lacour, S., Mallet, V., Quelo, D., Roustan, Y., and Sportisse, B.: Development and validation of a fully modular platform for numerical modelling of air pollution: POLAIR, Int. J. Environ. Pollut., 22, 17–28, https://doi.org/10.1504/IJEP.2004.005474, 2004.

* Burton, A. J., Pregitzer, K. S., and Reed, D. D.: Leaf Area and Foliar Biomass Relationships in Northern Hardwood Forests Located Along an 800 km Acid Deposition Gradient, Forest Sci., 37, 1041–1059, https://doi.org/10.1093/forestscience/37.4.1041, 1991.

* Byun, D. and Schere, K. L.: Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.

* Calfapietra, C.: Role of Biogenic Volatile Organic Compounds (BVOC) emitted by urban trees on ozone concentration in cities: A review, Environ. Pollut., 183, 71–80, 2013.

* Cantrell, C. and Michoud, V.: An Experiment to Study Atmospheric Oxidation Chemistry and Physics of Mixed Anthropogenic–Biogenic Air Masses in the Greater Paris Area, B. Am. Meteorol. Soc., 103, 599–603, https://doi.org/10.1175/BAMS-D-21-0115.1, 2022.

* Chang, J., Qu, Z., Xu, R., Pan, K., Xu, B., Min, Y., Ren, Y., Yang, G., and Ge, Y.: Assessing the ecosystem services provided by urban green spaces along urban center-edge gradients, Sci. Rep.-UK, 7, 11226, https://doi.org/10.1038/s41598-017-11559-5, 2017.

* Churkina, G., Kuik, F., Bonn, B., Lauer, A., Grote, R., Tomiak, K., and Butler, T. M.: Effect of VOC Emissions from Vegetation on Air Quality in Berlin during a Heatwave, Environ. Sci. Technol., 51, 6120–6130, https://doi.org/10.1021/acs.est.6b06514, 2017.

* Ciccioli, P., Silibello, C., Finardi, S., Pepe, N., Ciccioli, P., Rapparini, F., Neri, L., Fares, S., Brilli, F., Mircea, M., Magliulo, E., and Baraldi, R.: The potential impact of biogenic volatile organic compounds (BVOCs) from terrestrial vegetation on a Mediterranean area using two different emission models, Agr. Forest Meteorol., 328, 109255, https://doi.org/10.1016/j.agrformet.2022.109255, 2023.
**Financial support.** This work benefited from the French state aid managed by the sTREEt ANR project (grant no. ANR-19-CE22-0012) and by the ANR under the "Investissements d'avenir" program (grant no. ANR-11-IDEX-0004-17-EURE-0006) with support from IPSL/Composair. The measurements at the PRG site have been supported by the ACROSS project. The ACROSS project has received funding from the French National Research Agency (ANR) under the investment program integrated into France 2030 (grant no. ANR-17-MPGA-0002), and it has been supported by the French National program LEFE (Les Enveloppes Fluides et l'Environnement) of the CNRS/INSU (Centre National de la Recherche Scientifique/Institut National des Sciences de L'Univers). Measurements at SIRTA have been supported by the European Commission (ACTRIS, grant no. 262254; ACTRIS 2, grant no. 654109), CNRS, CEA, INERIS, the French Ministry of Environment, the DIM-QI2 program from the Île-de-France region, and the research infrastructure ACTRIS-FR registered on the Roadmap of the French Ministry of Research.

**Review statement.** This paper was edited by Andrea Pozzer and reviewed by two anonymous referees.

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tial evaluation over Europe, Geosci. Model Dev., 11, 165–194,
https://doi.org/10.5194/gmd-11-165-2018, 2018.

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Trees–Stress Factor Accumulation and Ecological Service Pos-
sibilities, Forests, 11, 932, https://doi.org/10.3390/f11090932,
2020.

Eichler, P., Müller, M., D'Anna, B., and Wisthaler, A.: A novel
inlet system for online chemical analysis of semi-volatile sub-
micron particulate matter, Atmos. Meas. Tech., 8, 1353–1360,
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44fc-b22b-415f42acfdf0, 2020.

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T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of
Emissions of Gases and Aerosols from Nature version 2.1
(MEGAN2.1): an extended and updated framework for mod-
eling biogenic emissions, Geosci. Model Dev., 5, 1471–1492,
https://doi.org/10.5194/gmd-5-1471-2012, 2012.

Haeffelin, M., Barthès, L., Bock, O., Boitel, C., Bony, S., Bouniol,
D., Chepfer, H., Chiriaco, M., Cuesta, J., Delanoë, J., Drobinski,
P., Dufresne, J.-L., Flamant, C., Grall, M., Hodzic, A., Hourdin,
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Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedic-
tow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R.,
Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z.,
Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy,
S., Schulz, M., and Suttie, M.: The CAMS reanalysis of at-
mospheric composition, Atmos. Chem. Phys., 19, 3515–3556,
https://doi.org/10.5194/acp-19-3515-2019, 2019.

IPCC: Climate Change 2021: The Physical Science Basis. Con-
tribution of Working Group I to the Sixth Assessment Re-
port of the Intergovernmental Panel on Climate Change, Re-
port, Intergovernmental Panel on Climate Change, United Na-
tions, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Con-
nors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Gold-
farb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E.,
Matthews, J. B. R., Maycock, T. K., Waterfield T., Yelekçi
O., Yu, R., and Zhou, B., Cambridge University Press, Cam-
bridge, United Kingdom and New York, NY, USA, 2391 pp.,
https://doi.org/10.1017/9781009157896, 2021.

Jamei, E., Rajagopalan, P., Seyedmahmoudian, M., and Jamei,
Y.: Review on the impact of urban geometry and pedes-
trian level greening on outdoor thermal comfort, Renew-
able and Sustainable Energy Reviews, 54, 1002–1017,
https://doi.org/10.1016/j.rser.2015.10.104, 2016.

Jang, J.-C. C., Jeffries, H. E., Byun, D., and Pleim, J. E.: Sensitivity of ozone to model grid resolution—I. Application of high-resolution regional acid deposition model, Atmos. Environ., 29, 3085–3100, https://doi.org/10.1016/1352-2310(95)00118-I, 1995a.

Jang, J.-C. C., Jeffries, H. E., and Tonnesen, S.: Sensitivity of
ozone to model grid resolution – II. Detailed process anal-
ysis for ozone chemistry, Atmos. Environ., 29, 3101–3114,
https://doi.org/10.1016/1352-2310(95)00119-J, 1995b.

Jiang, J., Aksoyoglu, S., Ciarelli, G., Oikonomakis, E., El-Haddad,
I., Canonaco, F., O'Dowd, C., Ovadnevaite, J., Minguillón, M.
C., Baltensperger, U., and Prévôt, A. S. H.: Effects of two dif-
ferent biogenic emission models on modelled ozone and aerosol
concentrations in Europe, Atmos. Chem. Phys., 19, 3747–3768,
https://doi.org/10.5194/acp-19-3747-2019, 2019.

Jiang, X., Guenther, A., Potosnak, M., Geron, C., Seco,
R., Karl, T., Kim, S., Gu, L., and Pallardy, S.: Iso-
prene emission response to drought and the impact on
global atmospheric chemistry, Atmos. Environ., 183, 69–83,
https://doi.org/10.1016/j.atmosenv.2018.01.026, 2018.

Jo, O., Han, J. Q., Askari, A., Abbatt, J. P. D., and Chan,
A. W. H.: Investigation of Anthropogenic Monoterpenes in
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Wang, H., Lu, X., Seco, R., Stavrakou, T., Karl, T., Jiang, X., Gu, L., and Guenther, A. B.: Modeling Isoprene Emission Response to Drought and Heatwaves Within MEGAN Using Evapotranspiration Data and by Coupling With the Community Land Model, J. Adv. Model. Earth Sy., 14, e2022MS003174, https://doi.org/10.1029/2022MS003174, 2022.

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