# Modelling oxidative potential of atmospheric particle: A 2-year study over France

Matthieu Vida, Gilles Foret, Guillaume Siour, Jean-Luc Jaffrezo, Olivier Favez, Arineh Cholakian, Julie Cozic, Harry Dupont, Grégory Gille, Sonia Oppo, et al.

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## ► To cite this version:

Matthieu Vida, Gilles Foret, Guillaume Siour, Jean-Luc Jaffrezo, Olivier Favez, et al. Modelling oxidative potential of atmospheric particle: A 2-year study over France. Science of the Total Environment, 2025, 967, pp.178813. 10.1016/j.scitotenv.2025.178813. hal-05036537

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# Modelling oxidative potential of atmospheric particle: A 2-year study over France

Matthieu Vida<sup>a</sup>, Gilles Foret<sup>b,*</sup>, Guillaume Siour<sup>b</sup>, Jean-Luc Jaffrezo<sup>c</sup>, Olivier Favez<sup>d,e</sup>, Arineh Cholakian<sup>f</sup>, Julie Cozic<sup>g</sup>, Harry Dupont<sup>g</sup>, Grégory Gille<sup>h</sup>, Sonia Oppo<sup>h</sup>, Shouwen Zhang<sup>i</sup>, Florie Francony<sup>j</sup>, Cyril Pallares<sup>k</sup>, Sébastien Conil<sup>l</sup>, Gaelle Uzu<sup>c,*</sup>, Matthias Beekmann<sup>a</sup>

## HIGHLIGHTS

* Oxidative potential (OP) is as a new metric for air quality.

* We develop and describe a new method to model OP (for AA and DTT assays).

* The simulation of particulate matter and OP is evaluated against observations.

* In France, sources contributing the most to aerosol mass do not have the highest OP.

* Road traffic and biomass burning are often the sources with most OP.

## GRAPHICAL ABSTRACT

Set-up of a new method to simulate oxidative Potential (OP) based on OP observations and particulate matter chemical speciation

<figure><img src="image_5.png" /></figure>

## ARTICLE INFO

Editor: Meng Gao

## ABSTRACT

The oxidative potential (OP) of particulate matter (PM) has emerged as a promising indicator of the adverse effects of PM on human health. In particular, OP is an indicator for oxidative stress in biological media through formation of reactive oxygen species. To provide a mapping of the spatial and temporal OP variability over

<footnote><sup>a</sup> Université Paris Cité and Univ Paris Est Creteil, CNRS, LISA, F-75013 Paris, France</footnote>

<footnote><sup>b</sup> Univ Paris Est Créteil and Université Paris Cité, CNRS, LISA, F-94010 Créteil, France</footnote>

<footnote><sup>c</sup> Institut des Géosciences de l'Environnement, IGE, UMR 5001, F-38000 Grenoble, France</footnote>

<footnote><sup>d</sup> Institut National de l'Environnement Industriel et des Risques, INERIS, F-60550 Verneuil-en-Halatte, France</footnote>

<footnote><sup>e</sup> Laboratoire Central de Surveillance de la Qualité de l'air, LCSQA, F-60550 Verneuil-en-Halatte, France</footnote>

<footnote><sup>f</sup> Laboratoire de Météorologie Dynamique (LMD), Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Route de Saclay, F-91128 Palaiseau, France</footnote>

<footnote><sup>g</sup> Atmo Auvergne-Rhône-Alpes, F-69500 Bron, France</footnote>

<footnote><sup>h</sup> Atmo Sud, F-13294 Marseille, France</footnote>

<footnote><sup>i</sup> Atmo Hauts de France, F-59044 Lille, France</footnote>

<footnote><sup>j</sup> Atmo Nouvelle Aquitaine, F-33692 Mérignac, France</footnote>

<footnote><sup>k</sup> Atmo Grand Est, F-67300 Schiltigheim, France</footnote>

<footnote><sup>l</sup> ANDRA DISTEC/EES Observatoire Pérenne de l'Environnement, F-55290 Bure, France</footnote>

<footnote>* Corresponding authors.
E-mail addresses: gilles.foret@lisa.ipsl.fr (G. Foret), gaelle.uzu@ird.fr (G. Uzu).</footnote>
Keywords:

Atmospheric particles

Air quality

Oxidative potential

Numerical modelling

France, we have developed a strategy to simulate the volume-normalized oxidative potential (OP<sub>v</sub>) in the state-of-the-art CHIMERE air quality model over the metropolitan French territory for the years 2013 and 2014. To do so, we combined a measurement-derived and source specific intrinsic OP (OP<sub>i</sub>) receptor modelling approach with Particle Source Apportionment Technology (PSAT) in CHIMERE. First, the model's ability to reproduce PM<sub>10</sub> concentrations and speciation was verified using in situ observations in mainland France. Furthermore, a mostly satisfying correspondence between receptor model and PSAT outputs was obtained considering their source specific chemical profiles. Simulated versus observed OP<sub>v</sub> values showed median correlations ranging from 0.35 to 0.60 and mean fractional biases from -30% to zero, depending on the OP assay considered (ascorbic acid AA, or dithiothreitol DTT) and the PM sources taken into account (i.e. two methods with different PM sources have been used, the reduced and the extended set methods). The modelled two-year average OP<sub>v</sub> fields show greater spatial hot spots over large urban areas (especially along roadsides) compared to those for PM<sub>10</sub> distributions, due to elevated intrinsic OP<sub>i</sub> values for the primary anthropogenic sources such as traffic and biomass burning. These effects are stronger for the AA compared to the DTT assays, and for a method with a reduced set compared to an extended set of sources. Overall, through the OP apportionment, these results advocate for reinforcing action plans to reduce emissions from road traffic as well as biomass burning emissions.

# 1. Introduction

Epidemiological studies have shown that air pollution causes and exacerbates respiratory and cardiovascular diseases while also playing a role in the course of other diseases such as obesity, diabetes or psychotic disorders (Brook et al., 2010; Pignon et al., 2022; Weichenthal et al., 2014). In particular, atmospheric aerosols are considered to have the greatest impact on human health, possibly accounting for about 90% of the sanitary impact of air pollution (Lelieveld et al., 2015). For instance, in France, it is considered that around 40,000 people die prematurely every year in France because of fine particulate pollution, although Europe is far from the most polluted region in the world (Adélaïde et al., 2020).

In order to reduce the health risks linked to the inhalation of airborne particles, the World Health Organization recommends yearly concentration thresholds of 15 µg m⁻³ and 5 µg m⁻³ for particulate matter with aerodynamic diameter below 10 µm (PM₁₀) and 2,5 µm (PM₂.₅), respectively (WHO, 2021). These drastic thresholds rely solely on the mass concentration of particulate matter (PM), not considering their chemical composition or sources, which can lead to an obvious bias. Indeed, at equal mass concentrations, the aerosol toxicity can be very different, with combustion sources potentially more damaging compared to others (e.g., Park et al. (2018)). Increasing our knowledge on the sources of greater health concern would allow targeting pollutant emission reductions more efficiently. It is therefore vital to search for better indicators of aerosol toxicity rather than looking at particulate mass. With this objective in mind, the PM oxidative potential (OP) has been proposed as a new metric to be considered in air quality monitoring (e.g., Steenhof et al., 2011). Epidemiological studies also indicate that OP may be a relevant indicator of acute effects of aerosols on human health (Bates et al., 2019; Gao et al., 2020). OP is then a promising health-based metric for air quality listed as recommended in the new EU directive 2024/2881/CE.

OP measures the ability of particles to generate oxidative stress in the body induced by exogenous reactive oxygen species (ROS) such as OH, HONO or superoxide ions (Ayres et al., 2008; Cho et al., 2005; Li et al., 2009; Sauvain et al., 2008). When in excess, ROS deplete the lung antioxidants and may lead to imbalance between oxidants and antioxidants. This potentially induces oxidative stress, defined as oxidative damage to any cell material and ultimately inflammation at the origin of biological health endpoints (Costabile et al., 2019; Karavalakis et al., 2017; Steenhof et al., 2011; Weichenthal et al., 2016). The antioxidant depletion can now be measured by spectrophotometry after exposing PM filters into a simulated lung fluid according to different protocols using different lung antioxidants or surrogates such as ascorbic acid (AA) and dithiothreitol (DTT) (Calas et al., 2017). The kinetic depletion rate is then taken as the oxidative potential. To date, there is no clear consensus on which assay – or assay's combination – should be considered as the more relevant indicator, but recent epidemiological

studies show that OP<sub>v</sub><sup>AA</sup> appears to be associated to biomarkers of inflammation (Leni et al., 2020; Marsal et al., 2024) and that OP<sub>v</sub><sup>DTT</sup> is relevant for various health outcomes (Gao et al., 2020). Collective research efforts are still needed to harmonize protocols to ensure a better consistency among epidemiological studies.

As it is impossible to identify all the chemical species that make up PM and contribute to the OP, an alternative is to use their contributions from sources, which eliminates the need for such detailed knowledge. As previously developed in the China by Liu et al. (2014) and in the USA by Verma et al. (2014), attribution of the OP<sub>v</sub> to aerosol sources by a PMF receptor model including uncertainties was pioneered in France by Weber et al. (2018) allowing to extract OP per mass unit (OP<sub>i</sub>) of PM for given source at specific measurement sites. A more recent study of Weber et al. (2021) highlighted biomass burning and traffic emissions as the main OP sources at the national level. While more information is still needed from observations, one of the next steps in the research related to OP<sub>v</sub> is to be able to model aerosol OP<sub>v</sub> with chemistry transport models (CTM).

Regional CTMs are tools that are already used for operational air quality forecasting in France (Rouil et al., 2009) and in Europe (Marécal et al., 2015). They can also be used for a posteriori epidemiological studies (Adélaïde et al., 2020) and future projections dedicated to support air quality and climate control policies (Cholakian et al., 2021; Fortems-Cheiney et al., 2017). Adding the oxidative potential to the list of simulated species and indicators in these models would be very useful, given its close relation to the ability of aerosols to trigger inflammatory processes and thus its capacity to evaluate health exposure and associated pollution sources (as for example done within the EU Green Deal RURBANS project for future Copernicus applications).

The first modelling of the oxidative potential of aerosols was focused on PM₂.₅ over the Eastern US using the DTT test and the CMAQ model (Bates et al., 2018). Using multiple linear regressions with the measured OP by air volume (OP<sub>v</sub>) and the source concentrations in the model, the authors were able to obtain the intrinsic oxidative potential (OP<sub>i</sub>, OP per mass of PM) of sources. While the results show a higher OP<sub>i</sub> for combustion sources, these results were obtained with a relatively limited OP<sub>v</sub> dataset available for validation (with a total number of only 187 samples from 4 sites). In Europe, the first OP<sub>v</sub> modelling effort used 3 different OP<sub>v</sub> tests (among which AA and DTT assays), with a larger number of sites and samples compared to the United States study, and was performed with the CAMx model (Daellenbach et al., 2020). Following a similar approach to the one presented in Weber et al. (2021), an intrinsic OP<sub>i</sub> value per source was estimated from a multiple linear regression between OP<sub>v</sub> measurements and PM₁₀ factor concentrations obtained from PMF studies for the same sites. The PMF factors (mainly organic) were then matched to the simulated sources to represent the oxidative potential of total PM₁₀.

The objectives of this paper are to develop an OP<sub>v</sub> modelling tool based on extensive OP<sub>v</sub> observations over France, related to source
apportionment studies, and embedded within a chemistry-transport model. The aim is to simulate OP to evaluate its spatial and temporal variability over western Europe with a focus over France, and investigated the contributions of the different sectors of activity. Such an approach is novel for air quality models. With respect to Daellenbach et al. (2020), this work is based on an enhanced observational data set, in terms of variety of sites and chemical composition, allowing to address a larger and more variable set of PM sources (Weber et al., 2018, 2021). In this study, both approaches with a reduced and an extended set of sources will be compared. The CHIMERE-v2020r3 model (Menut et al., 2021) was used, in a specific configuration allowing to quantify PM sources obtained by a Source Apportionment Technology (PSAT) method implemented into the model. Two years (2013–2014) of simulations have been performed to evaluate the model’s ability to reproduce the observed PM mass concentrations, the chemical composition and finally the measured PM oxidative potential. The current work focuses on an analysis of sources and variability of the simulated OP<sub>v</sub>.

## 2. Material & methods

### 2.1. Observations

The years 2013 and 2014 were chosen because of the largest availability of sites where concurrent chemical characterizations, PMF studies, and OP<sub>v</sub> measurements are available over France during a period suitable for such modelling work.

#### 2.1.1. PM₁₀ concentrations measurements

The PM₁₀ concentration datasets are derived from continuous measurements performed by French regional air quality monitoring networks (AQMN). Datasets from a total number of 699 stations are available in metropolitan France for 2013 and 2014. Briefly, two automatic techniques were used at that time by AQMN depending on the site: tapered element oscillating microbalances equipped with filter dynamic measurement systems (TEOM-FDMS, Thermo Scientific), and beta radiation absorption analysers (Met One BAM 1020 and ENVEA MP101M).

#### 2.1.2. Chemical composition

As described by Favez et al. (2021), the French air quality monitoring network is associated with various research projects and programs, converging into a unique operational system for the chemical characterization and subsequent source apportionment of urban aerosols at the national level, known as the CARA program. Additional measurements used in this paper also come from other research programs, like the one conducted at the OPE site (Borlaza et al., 2022).

For datasets used herein, organic and elemental carbon (OC, EC) were measured by thermo-optical analysis using the EUSAAR2 protocol (Cavalli et al., 2010); sugars by liquid chromatography using pulsed amperometric detection (Verlhac et al., 2013; Yttri et al., 2015); measurement protocols are detailed in Samaké et al., 2017, 2019a, b and Weber et al., 2021; the water-soluble inorganic fraction (NO₃⁻, SO₄²⁻, Cl⁻, Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺) was measured by ion chromatography (Jaffrezo et al., 2005); Methane sulfonic acid (MSA) as well is measured by ion chromatography (Weber et al., 2021); trace elements (Al, Ag, As, Ba, Be, Br, Bi, Cd, Ce, Co, Cr, Cs, Cu, Fe, La, Li, Mn, Mo, Na, Ni, Pb, Pd, Pt, Rb, Sb, Sc, Se, Sn, Sr, Ti, V, Zn, Zr) were determined by inductively coupled plasma using mass spectrometry or atomic emission spectroscopy (Alleman et al., 2010; Mbengue et al., 2014). These measurement protocols, including OP measurements, have been detailed in previous studies (Calas et al., 2017, Borlaza et al., 2021a, 2021b; Dominutti et al., 2023; Weber et al., 2019).

The present study is benefiting from PM₁₀ chemical speciation datasets obtained for 13 different sites and a total number of 2309 daily filter samples. The locations of these sites are illustrated in Fig. 1, while Table 1 provides details on the number of available samples and their temporality.

#### 2.1.3. Source apportionment by a receptor model

Positive Matrix Factorization (PMF) has become the most widely used receptor model for identifying factors contributing to aerosol concentrations (Belis et al., 2020; Grange et al., 2022; Paatero and Tapper, 1994; Waked et al., 2014; Weber et al., 2019). In our work, PMF analyses have been previously conducted using off-line filter-based chemical composition and factor-specific tracers as input data, using a

<figure><img src="image_6.png" /><figcaption>**Fig. 1.** Location and type of sites used for PM₁₀ chemical composition, PMF factor apportionment and oxidative potential measurements from filters over the years 2013 and 2014.</figcaption></figure>
<caption>**Table 1**</caption>

Summary of the number of daily filters analysed for PM₁₀ composition, PMF factor apportionment and oxidative potential measurements over the years 2013 and 2014 at different French sites. The measurement period and geographical coordinates (latitude; longitude; altitude) are also indicated.

<table><thead><tr><th>Sites</th><th>Coordinates (lat; lon; alt)</th><th>Measurement period</th><th>Composition</th><th>PMF</th><th>OP<sub>v</sub></th></tr></thead><tbody><tr><td>Aix-en-Provence</td><td>43.53; 5.44; 192 m</td><td>18.07.2013–13.07.2014</td><td>117</td><td>56</td><td>59</td></tr><tr><td>Andra-OPE</td><td>48.55; 5.46; 386 m</td><td>01.01.2013–29.12.2014</td><td>98</td><td>/</td><td>/</td></tr><tr><td>Grenoble</td><td>45.16; 5.74; 219 m</td><td>02.01.2013–29.12.2014</td><td>243</td><td>240</td><td>240</td></tr><tr><td>Lens</td><td>50.44; 2.83; 47 m</td><td>05.04.2013–26.09.2014</td><td>158</td><td>167</td><td>/</td></tr><tr><td>Marseille</td><td>43.30; 5.39; 73 m</td><td>01.06.2014–31.12.2014</td><td>95</td><td>/</td><td>/</td></tr><tr><td>Nice</td><td>43.70; 7.29; 11 m</td><td>04.06.2014–31.12.2014</td><td>89</td><td>77</td><td>62</td></tr><tr><td>Nogent-sur-Oise</td><td>49.28; 2.48; 28 m</td><td>02.01.2013–31.12.2014</td><td>220</td><td>155</td><td>135</td></tr><tr><td>Petit-Quevilly (Rouen)</td><td>49.43; 1.06; 9 m</td><td>01.01.2013–01.06.2014</td><td>254</td><td>/</td><td>/</td></tr><tr><td>Port-de-Bouc</td><td>43.40; 4.98; 3 m</td><td>01.06.2014–31.12.2014</td><td>84</td><td>80</td><td>70</td></tr><tr><td>Revin</td><td>49.91; 4.63; 394 m</td><td>02.01.2013–26.09.2014</td><td>192</td><td>/</td><td>/</td></tr><tr><td>Roubaix</td><td>50.71; 3.18; 31 m</td><td>20.01.2013–08.09.2014</td><td>175</td><td>/</td><td>159</td></tr><tr><td>Strasbourg</td><td>48.59; 7.74; 139 m</td><td>02.04.2013–31.12.2014</td><td>108</td><td>78</td><td>78</td></tr><tr><td>Talence (Bordeaux)</td><td>44.80; –0.59; 20 m</td><td>03.01.2013–31.12.2014</td><td>476</td><td>38</td><td>26</td></tr></tbody></table>

harmonized methodology for each site (Weber et al., 2021). In this study, source apportionment results are obtained for a total number of 891 daily samples collected at 8 sites from early 2013 to end of 2014 (Table 1). Table 2 summarizes the PMF outputs factors along with the main chemical species considered as specific tracers for each of them.

### 2.1.4. Oxidative potential measurements

On most sites with available source apportionment, measurements of the OP were carried out, after an extraction at iso-concentration to allow sample comparison from different environments. The core protocols include extracting the aerosols from the filters in a simulated lung fluid (Gamble's solution with an additional lung surfactant) at internal body temperature (37.4 °C) without further filtration, a method that allows both the soluble and insoluble parts of the samples to be extracted. To measure the OP via the production of reactive oxygen species (ROS) by PM, these extracts are put in contact with two lung anti-oxidants or surrogates: Ascorbic acid (AA) on the one hand and dithiothreitol (DTT) on the other. ROS production is indirectly measured through AA and DTT depletion over 30 min by spectrophotometry at 265 and 412 nm, respectively. Measurement protocols are described in Calas et al. (2017), Calas et al. (2019), and Dominutti et al. (2023).

The intrinsic oxidative potential (OPᵢ) per PMF factor was determined by multiple linear regression between the PM₁₀ mass concentrations of the factors and the volume oxidative potential (OPᵥ) (Dinh Ngoc Thuy et al., 2024; Weber et al., 2018, 2019).

Finally, the dataset of OP observations comprises a total of 829 daily measurements obtained from 8 sites for the years 2013 and 2014. Table 1 provides a comprehensive breakdown of these sites used for source attribution, including details such as the time period covered and the number of data points collected at each site.

## 2.2. Regional modelling

### 2.2.1. The chemistry transport model WRF-CHIMERE

CHIMERE is a Eulerian state-of-the-art regional chemistry transport

model (Menut et al., 2021). It is used operationally by the French platform PREV'AIR and the Copernicus Atmospheric Modelling System (CAMS) (Marécal et al., 2015) to forecast and monitor air quality. The v2020r3 version of CHIMERE coupled with the Weather Research Forecast (WRF, v3.7.1) meteorological model (Skamarock et al., 2008) has been used for this study.

For organic aerosol formation and gas/particle partitioning of primary organic aerosol, the volatility basis set (VBS) for the organic species as described in Cholakian et al. (2018) was activated. Resuspension is taken into account by the model only in urban meshes and attributed to primary mineral particles (Loosmore, 2003). The EMEP anthropogenic emission database with a resolution of 10 km² provided input data for anthropogenic emissions (Mareckova et al., 2018). Biogenic volatile organic compound (VOC) emissions come from the MEGAN2.1 model (Guenther et al., 2012), NO from soils has been added from EMEP inventories, and biogenic primary organic aerosols are simulated as fungal spores using the Heald and Spracklen scheme (Heald and Spracklen, 2009). The implementation of the latter source into CHIMERE and its evaluation is presented in Vida et al. (2024). Global CAMS reanalysis (Inness et al., 2019) fields are used as initial/boundary conditions with a 3-hourly temporal resolution, providing information on chemical concentrations outside the selected area (Marécal et al., 2015). NCEP-FNL 1° × 1° (NCEP, 2000) resolution global fields are used as boundary/initial conditions for the WRF model.

The simulation has been carried out on a western European domain with a 9 × 9 km² horizontal resolution. CHIMERE was run on 9 vertical hybrid levels from ground to an upper height of 500 hPa, the height of the first layer being around 20 m. The model was run in its default 10 bin size configuration for PM size, starting from 0.01 µm and going up to 40 µm in a logarithmic sectional distribution.

As mentioned above, we chose to simulate two complete years, 2013 and 2014, with a temporal resolution 1 h for the outputs. This period was chosen to maximize the number of OPᵥ observations available while maintaining a reasonable calculation time and having the possibility of carrying out a multi-annual analysis of results.

### 2.2.2. Sources apportionment and tagging

In our approach, incorporating the PM source information into the model is essential for modelling the OP accurately. The Particulate Source Apportionment Technology (PSAT) developed by Wagstrom et al. (2008) allows to track the contribution of both primary emissions and secondary formation processes for PM concentration. Tagging was preferred to other source tracking methods such as for example brute force simulations because of its cost-effectiveness in terms of calculation time (with only approximately twice the duration of a standard simulation). The sources used for PSAT are the sectors of activity in the Selected Nomenclature for Air Pollution (SNAP) obtained through a correspondence matrix between Nomenclature For Reporting (NFR) and

<caption>**Table 2**</caption>

PMF factors and associated species used as tracers from Weber et al. (2021).

<table><thead><tr><th>Factors</th><th>Tracers</th></tr></thead><tbody><tr><td>Biomass burning</td><td>Levoglucosan, OC, EC, K, Rb</td></tr><tr><td>Primary traffic</td><td>OC, EC, Cu, Fe, Sb, Sn</td></tr><tr><td>Dust</td><td>Al, Ti, Ca</td></tr><tr><td>Primary biogenic</td><td>Arabitol, mannitol, OC</td></tr><tr><td>Aged salt</td><td>Na, Mg, SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup></td></tr><tr><td>Nitrate-rich</td><td>NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup></td></tr><tr><td>Sulfate-rich</td><td>SO<sub>4</sub><sup>2-</sup>, NH<sub>4</sub><sup>+</sup>, Se</td></tr><tr><td>MSA rich</td><td>Methane Sulfonic Acid (MSA)</td></tr></tbody></table>
SNAP. For each species represented in the model, the contribution of different activity sectors present in the emission inventories is obtained (Table 3). The model not only explicitly accounts for natural sources such as mineral dust, marine emissions (Na, Cl, SO₂⁻¹), biogenic emissions (BPOA, BSOA, NO), but also for boundary conditions and for resuspension of primary particulate matter (PPM).

## 2.3. Description of OP<sub>v</sub> modelling methods

To simulate OP<sub>v</sub> spatial and temporal variations, we express the volume OP (OP<sub>v</sub> in nmol min⁻¹ m⁻³) as the sum, for all the PM sources, of the products of intrinsic OP (OP<sub>i</sub> in nmol min⁻¹ µg⁻¹) and source mass contribution for each source (µg m⁻³) (Eq. (1)). OP<sub>v</sub> varies as a function of time (t) and the antioxidant/reducer used (a), OP<sub>i</sub> varies as a function of the sources considered (s).

$$OP_v^a(t) = \sum_s (OP_i^{s,a} * C^s(t)) \quad (1)$$

Since the intrinsic oxidative potential OP<sub>i</sub> of all individual chemical species contributing to the PM mass is not known, the general idea behind this method is to use the OP<sub>i</sub> obtained for the different PM sources composing the PM, as observed from ambient concentrations field studies (Daellenbach et al., 2020; Weber et al., 2021). OP<sub>i</sub> values will be combined with simulated source concentrations C<sup>s</sup> obtained by PSAT corresponding in a best possible way to the initial sources (from PMF) used to determine OP<sub>i</sub>. Using Eq. (1), we can thus simulate the volumetric OP<sub>v</sub>.

### 2.3.1. OP<sub>v</sub> modelling method based on a reduced set of sources

This first method to obtain the source contribution to OP is based on the work of Daellenbach et al. (2020) and is also using Eq. (1). For this method, C<sup>s</sup> in the equation is the concentration of PM sources for a specific time (t) derived by PMF. OP<sub>i</sub><sup>s</sup> represents the corresponding intrinsic oxidative potential (nmol min⁻¹ µg⁻¹), obtained by multiple linear regression between OP<sub>v</sub> measurements and PMF factor concentrations C<sup>s</sup>, as obtained by Daellenbach et al. (2020). The method is specific for a set of sources restricted to different organic aerosol fractions (see Table 4) that have been obtained from Aerosol Mass Spectrometer (AMS) measurements and PMF analysis at 5 sites in Switzerland, together with OP measurements also performed at IGE (Institut des Géosciences de l'Environnement) according to the same protocols used for our French samples. In addition, the authors added a vehicular-wear source constrained by transition metal filter-based measurements. Based on specific OP measurements from synthetic solutions of reference compounds, the authors made the hypothesis that other aerosol species (especially inorganic salts such as ammonium sulfate and ammonium nitrate) were not contributing significantly to OP<sub>v</sub>.

One objective for our work is then to simulate the sources used in

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

Emission sources in the CHIMERE model.

<table><caption>**Table 4**</caption><thead><tr><th>SNAP</th><th>Source name</th></tr></thead><tbody><tr><td>1</td><td>Energy production</td></tr><tr><td>2</td><td>Residential combustion</td></tr><tr><td>3</td><td>Industrial combustion (manufacturing)</td></tr><tr><td>4</td><td>Industrial processes (manufacturing)</td></tr><tr><td>5</td><td>Extraction &amp; distribution of fossil fuels</td></tr><tr><td>6</td><td>Solvents</td></tr><tr><td>7</td><td>Road transport</td></tr><tr><td>8</td><td>Other transports</td></tr><tr><td>9</td><td>Waste treatment</td></tr><tr><td>10</td><td>Agriculture</td></tr><tr><td>/</td><td>Resuspension</td></tr><tr><td>/</td><td>Boundary conditions</td></tr><tr><td>/</td><td>Mineral dust</td></tr><tr><td>/</td><td>Marine</td></tr><tr><td>/</td><td>Biogenic</td></tr></tbody></table>

Correspondence between PMF factors and CHIMERE sources for the reduced set method and their intrinsic oxidative potential (OP<sub>i</sub>) for DDT and AA tests with median interquartile range in nmol min⁻¹ µg⁻¹ in Daellenbach et al. (2020). SNAP numbers are indicated with “S number”.

<table><thead><tr><th>PMF factors<br>Daellenbach<br>et al. (2020)</th><th>PSAT CHIMERE</th><th>OP<sub>i</sub><sup>DTT</sup></th><th>OP<sub>i</sub><sup>AA</sup></th></tr></thead><tbody><tr><td>Vehicular wear</td><td>PPM road transport (S7) +<br>resuspension<br>POA energy production (S1) +<br>industrial combustion (S3) +<br>road transport (S7) + other<br>transports (S8)</td><td><strong>3.51</strong><br>[3.29–3.76]</td><td><strong>3.16</strong><br>[2.56–3.38]</td></tr><tr><td>HOA</td><td>POA residential combustion (S2)<br>+ waste treatment (S9)</td><td><strong>0.94</strong><br>[0.79–1.10]</td><td><strong>0.00</strong><br>[0.–0.]</td></tr><tr><td>BBOA</td><td>ASOA all anthropogenic sources<br>(S1 to S10)</td><td><strong>0.08</strong><br>[0.01–0.14]</td><td><strong>0.06</strong><br>[0.03–0.23]</td></tr><tr><td>ASOA</td><td>BSOA</td><td><strong>0.44</strong><br>[0.35–0.43]</td><td><strong>0.42</strong><br>[0.36–0.48]</td></tr><tr><td>BSOA</td><td>BSOA</td><td><strong>0.15</strong><br>[0.11–0.20]</td><td><strong>0.00</strong><br>[0.–0.]</td></tr></tbody></table>

Daellenbach et al. (2020) with the CHIMERE model, in order to obtain the terms C<sup>s</sup>, and to combine them with their intrinsic OP<sub>i</sub> values in Eq. (1) to obtain OP<sub>v</sub>. A correspondence table was therefore established between the initial sources in Daellenbach et al. (2020) and their calculation in CHIMERE based on the tagging of EMEP SNAP sectors with the PSAT method. Table 4 displays the correspondence between the PMF factors and the combination of sources tagged in the CHIMERE model as well as the values of the OP<sub>i</sub> for AA and DTT assays from Daellenbach et al. (2020). We assume that the “vehicular wear” factor directly corresponds to the simulated primary particulate matter (PPM) associated to the road transport SNAP and the resuspension. HOA (Hydrocarbonlike Organic Aerosol) represents organic aerosols originating from fossil fuel combustion sources. In our simulation, we represent it as a combination of primary organic aerosols (POA) from main combustion sources (SNAP 1, 3, 7 and 8 described in Table 3). BBOA, the biomass burning organic aerosols factor from wood and plant burning is simulated by summing primary organic aerosol concentrations from SNAP 2 and 9 (Table 3). For ASOA (Anthropogenic Secondary Organic Aerosol) and BSOA (Biogenic Secondary Organic Aerosols) factors, we use the secondary organic aerosol concentrations directly simulated by the model which are attributed to explicit model species. ASOA correlates with the oxidation products of aromatic precursors from incomplete combustion and pyrolysis of lignin (Daellenbach et al., 2019).

### 2.3.2. OP<sub>v</sub> modelling method with an extended set of sources

In addition to the first approach described in Section 2.3.1, a new method has been established and tested, directly based on the work of Weber et al. (2021). This second method enables the consideration of an extended set of aerosol sources when modelling the OP, as opposed to the reduced set of the first method. In this approach, intrinsic OP<sub>i</sub> is again obtained from multi-linear regression between observed OP<sub>v</sub> and aerosol sources obtained by PMF, but this time from 15 sites in France. In this study by Weber et al. (2021), the authors were careful to check, as much as possible, that the factors chosen were common to the different sites. This homogeneity of factors means that they can be extrapolated to the whole of France with greater confidence. The PMF results were not restricted to particular sources like in the reduced set approach for which factors associated to inorganic species were excluded.

In order to calculate OP<sub>i</sub> from Eq. (1), the challenge lies again in establishing a correspondence between the observation-based sources resulting from the PMF factors and the simulation-based sources derived from PSAT analysis (see Section 2.2.2), but in this case for an extended list of sources corresponding less directly to modelled species compared to the first approach (but much more to the phenomenology of the field measurements). The approach adopted to resolve this issue is explained
hereafter first in a general way and then source by source.

At first glance, and from common geophysical expertise, some sources can be easily reconciled like the PMF biomass combustion factor with the PSAT residential combustion source, for which the majority of carbonaceous aerosol stem from wood burning. For aged salt, secondary species from the “other transports” sector were included, because they are often located on water surfaces. The traffic emissions from road transport are identified from both PMF and PSAT as a mainly primary source. Other PMF factors are more difficult to associate directly to one specific SNAP such as nitrate-rich, sulfate-rich, or MSA-rich and it is not obvious how to directly reconcile them with emission sectors used within the CHIMERE model. For all these sources, several tests between PMF factors and combinations of PSAT sources were conducted to finally propose the matches shown in Table 5 and described in more details below. To achieve this, we performed here a comparison between the observed source chemical profiles and the model-generated profiles. Additionally, we combined specific PSAT sources and compared their temporal patterns with those obtained through PMF factors. This comparison allows for a comprehensive evaluation of the similarities and differences in the temporal behaviour of the identified sources. Eventually, the final choice was validated through a statistical analysis (correlations, biases, errors).

### 2.3.2.1. Biomass burning.
The chemical profile of the biomass combustion factor from PMF is mainly composed of organic matter with a median value of about 95 % over all sites, with very few elemental carbon and secondary inorganic aerosols contributing to this source (Fig. 2). In the PMF method, levoglucosan is a tracer for primary organic aerosol from this factor, but secondary organic aerosol formation from this source is probably included as well as long as it remains correlated with levoglucosan. It was therefore decided to create an equivalence in the model with the SNAP 2 residential combustion source, and to include both the primary and the secondary fractions. Fig. 2 illustrates the major contribution of OM to the PSAT SNAP 2 source (>50 %), as for the PMF outputs. However, there is also a significant fraction of $NO_3^-$ in PSAT due to $NO_x$ emissions from various combustion sources in the residential sector; this could not be distinguished from wood burning in the available emission data. A specific $SO_2$ source from oil fuel use was

<caption>**Table 5**</caption>

Correspondence between PMF factors and CHIMERE sources for the extended set method. For some sources, only primary (PRIM) or secondary (SEC) species have been taken into account. SNAP numbers are indicated with “S number”. The two last columns indicate the intrinsic OP values for both DTT and AA assays in nmol $min^{-1} \mu g^{-1}$ from Weber et al., 2021. Within the table PRIM stand for primary and SEC for secondary.

<table><thead><tr><th>PMF factors<br>Weber<br>et al., 2021</th><th>PSAT CHIMERE</th><th>OP<sub>I</sub><sup>DTT</sup></th><th>OP<sub>I</sub><sup>AA</sup></th></tr></thead><tbody><tr><td>Biomass<br>burning</td><td>Residential combustion (S2) without<br>SO<sub>4</sub></td><td>0.13<br>[0.10-0.18]</td><td>0.17<br>[0.13-0.26]</td></tr><tr><td>Primary<br>traffic</td><td>Road transport (S7) without NO<sub>3</sub></td><td>0.21<br>[0.17-0.27]</td><td>0.14<br>[0.07-0.23]</td></tr><tr><td>Dust</td><td>PRIM industries (S1 + S3 + S4 + S5 +<br>S6 + S9)<br>+ Agriculture (S10) + Mineral dust +<br>Resuspension</td><td>0.11<br>[0.08-0.15]</td><td>0.01<br>[0.00-0.04]</td></tr><tr><td>Primary<br>biogenic</td><td>PRIM Biogenic + Other transports<br>(S8)</td><td>0.13<br>[0.05-0.18]</td><td>0.02<br>[0.00-0.04]</td></tr><tr><td>Aged salt</td><td>Boundary conditions + Marine + SEC<br>Other transports (S8)<br>SEC Agriculture (50 % S10) + SO<sub>4</sub></td><td>0.04<br>[0.03-0.12]</td><td>0.02<br>[0.00-0.05]</td></tr><tr><td>Nitrate-rich</td><td>SEC (Residential, S2) + NO<sub>3</sub> (Road<br>transport, S7)<br>SEC Industries (S1 + S3 + S4 + S5 +<br>S6 + S9) + Agriculture (50 % S10) +<br>Biogenic</td><td>0.04<br>[0.03-0.18]</td><td>0.01<br>[0.01-0.03]</td></tr><tr><td>Sulfate-rich</td><td></td><td>0.09<br>[0.04-0.12]</td><td>0.01<br>[0.01-0.02]</td></tr><tr><td>MSA rich</td><td>Not modelled</td><td>0.12<br>[0.02-0.29]</td><td>0.00<br>[0.03-0.07]</td></tr></tbody></table>

excluded and attributed to sulfate-rich PMF factor.

### 2.3.2.2. Primary traffic.
The corresponding PMF chemical profile (Fig. 2) indicates an EC contribution to this source of about 20 %. Metals, some of which -such as Cu - being commonly considered as tracers of road traffic emissions, contribute to about 10 % (grouped into PPM - inorganic primary particulate matter). Fig. 2 shows that, as for biomass burning, OM is the major fraction (~55 %), and can be both primary and secondary, as long as secondary OM from this source is correlated with the specific primary tracers. Therefore, the road transport source (SNAP 7) was used, excluding nitrates from traffic related $NO_x$ emissions which were affected to the nitrate-rich PMF factor. In this way, the modelled chemical profile is similar to the observed one; OM being the major species followed by EC and PPM.

### 2.3.2.3. Aged salt.
This factor concerns mainly aerosols with a marine origin including sodium, magnesium, chloride, as specific tracers in the PMF (Fig. S1). The chemical profile of this source shows a major contribution of sodium and chloride, but also shows a contribution of nitrate (about 15 %) pointing to ageing of sea-salt (replacement of chloride by nitrate). The sulfate contribution (~10 %) could be due to secondary aerosols from ship emissions together with sea-salt-sulfate. The PSAT modelling of this source therefore takes into account the entire marine salt source as well as the SNAP 8 including sources of emissions linked to transport other than road traffic. It also includes PM from the boundary conditions, because they are often advected by westerlies from the Atlantic Ocean, and could therefore be included with the marine “aged salt” source. In the PSAT source profiles, $Na^+$ and $Cl^-$ are still important contributors (respectively ~15 % and 20 %) albeit lower than in PMF, while $NO_3^-$ makes up about 30 % indicating the secondary character of aged sea-salt.

### 2.3.2.4. Nitrate-rich.
The nitrate-rich factor is a secondary aerosol source consisting of a large fraction of ammonium nitrate as well as gathering some fractions of other secondary aerosol species. As nitrate is derived from $NO_x$ emissions in fossil fuel combustion processes, especially from transport, the link with the road transport source in the model is direct. Ammonium is considered as mainly originating from agricultural activities and the associated ammonia emissions, so the agricultural source of the PSAT has been used to simulate the Nitrate rich factor of the PMF, but only partially (50 %), as both nitrate and ammonium sulfate are secondary (Fig. S1). Both PMF and PSAT chemical profiles are in rather good agreement, $NH_4^+$ and $NO_3^-$ being the major contributors (~65 % in PMF, ~85 % in PSAT). In the PMF, OM contributes to nearly 20 %. This is probably secondary organic aerosol formed at a time scale of about a day compatible with that of nitrate formation. In our PSAT approach, this SOA is attributed to the corresponding primary sources, and especially BSOA is attributed to the sulfate rich source. Minor contributions are also coming from sulfate (~15 % in PMF, ~10 % in PSAT).

### 2.3.2.5. Sulfate-rich.
The sulfate-rich factor is a secondary aerosol source including a large fraction of ammonium sulfate or bisulfate, also including other secondary aerosol species (Fig. S1). It is probably representative of more aged secondary aerosol, as the conversion of $SO_2$ into $SO_4^{2-}$ in the gas phase takes several days or requires the presence of cloud water. It contains a specific metallic tracer, selenium, that may arise from industrial activities such as glass, cement, fertilizer or metallurgy production, but also from natural sources. The presence of secondary organic matter in this factor comes mainly from biogenic secondary organic aerosols (BSOA), as identified with a specific $\alpha$-pinene tracer, the 3-MBTCA, in a study performed on some of the same sites as ours (Borlaza et al., 2021a, 2021b). In the PSAT approach, BSOA has been attributed to this factor. Furthermore, concentrations of sulfate and oxalate (the most important organic acid by mass, indicative of aged
<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Comparison of chemical profiles of biomass combustion sources (top) and primary traffic (bottom) from MFP (red) and CHIMERE simulation with PSAT (blue). Model mean values are calculated over 2013 and 2014. The ordinate represents the relative contribution of the species included in the source. The PM10 column shows the source's contribution to PM10. Values for individual sites are shown only where variability is significant. For other sources, the figures can be found in the supplement (Figs. S1 and S2).</figcaption></figure>

organic matter) are always well correlated in many studies. Both PMF and PSAT chemical profiles are rather similar, the sum of $SO_4^{2-}$ and $NH_4^+$ accounting for respectively ~45 and ~40 % and OM for respectively ~45 and ~50 % for each case.

### 2.3.2.6. Dust.
In the PMF analysis, this factor contains metal-rich particles with aluminium, titanium and calcium as tracers. However, the major contributor is actually OM, with a smaller fraction of EC (Fig. S2). This could be due to the difficulty of the PMF analysis to fully resolve different sources, or to the fact that road dust resuspension is also strongly contributing to this PMF factor, with all the sites where this factor appears being urban ones. The chemical composition of resuspended road dust is not fully elucidated (Rienda and Alves, 2021) and probably not totally homogeneous spatially, but it can be expected that it contains organic aerosol and EC from anthropogenic sources (Charron et al., 2019), as urban aerosol generally does. In addition, important fractions of plant debris have been found in resuspended aerosol (Brighty et al., 2022). We then have simulated the PMF dust factor as mineral dust, resuspension as well as adding the primary mineral emissions from industrial and agriculture sectors (Table 5). However, as the chemical composition of resuspended aerosol is not fully known, it is uniquely affected to the PPM species in CHIMERE, even if it certainly contains a large fraction of carbonaceous aerosol. This partly explains the missing OM fraction in the PSAT chemical profile.

### 2.3.2.7. Primary biogenic.
The primary biogenic factor is composed of biogenic organic aerosols directly from vegetation. Polyols are used as tracers of this primary biogenic organic matter, which is mainly emitted from fungal spores in the PM₁₀ fraction (Samaké et al., 2019a, 2019b). Thus, OM is the major contributor (about 90 %) to this chemical profile, with a small contribution from EC (around 5 %) that could partly be associated with agricultural machinery (Fig. S2) or some soil dust resuspension. With PSAT, we combined both primary biogenic organic aerosol emissions and primary emissions from other transport. This

results in a major OM contribution (about 80 %) and a minor EC contribution (~10 %).

#### 2.3.2.8. MSA-rich.
This PMF factor is composed exclusively of biogenic secondary species linked to the biological activities of marine and (possibly) lacustrine plankton or algae. It should be noted that marine aerosol or precursor sources have not been included in the CHIMERE simulation, except the gaseous DMS (Dimethyl Sulfide). As this PMF factor is only available at some of the sites and it is always a minor of PM mass (<5 % PM₁₀), omitting this factor will only have a very limited effect on the modelling of the oxidative potential of aerosols.

In conclusion, the major chemical contributors to pairs of PMF factors and PSAT sources are in general at least in qualitative agreement but for the case of dust for which the fraction in PM₁₀ is similar between PMF and PSAT combination of sources but the contribution of different species if significantly different. Overall, these correspondences allow the use of intrinsic oxidative potential values obtained for PMF factors to the corresponding PSAT sources. However, this procedure still bears some uncertainty; nonetheless this uncertainty seems acceptable in light of other existing uncertainties (e.g. the uncertainty to quantitatively model the different PMF factors (evaluated in Section 3.2), or the uncertainty in the $OP_i$ values themselves, derived as the median of a limited number of sites (Weber et al., 2021)).

In addition to the median $OP_i$ values per source, we have indicated, for the DTT and AA tests, the interquartile ranges of these values obtained by Daellenbach et al. (2020) (Table 4) and Weber et al. (2021) (Table 5), which reflect the inter-site variability of these values. This gives an idea of the robustness of the median values we use. We can see that for the sources with the highest $OP_i$ values for the two tests, primary traffic and biomass burning, the interquartile variability is not too high compared to the median. However, for the DTT test, the primary biogenic and especially the MSA-rich sources show greater variability, which raises questions about the representativeness of the median values chosen.
### 3. Evaluation of the simulation with observations

In this section, we evaluate and analyze the ability of the CHIMERE model to reproduce daily observed PM₁₀ concentrations, PM composition, PMF factors and OPᵥ measurements. For this performance evaluation of the model, from a technical point of view, we compared the simulated daily mean values to the observations since this is what was available for the OPᵥ. The simulated PM₁₀ and species concentrations at the sites are obtained by bilinear interpolation of the results from the 4 grid cells adjacent to the site in the model.

#### 3.1. Particulate matter

##### 3.1.1. PM₁₀ concentrations

Fig. 3a and b displays the maps of the correlation coefficients (Pearson R) calculated for PM₁₀ mass for the whole simulation period (2013–2014) and all days (when available) on all PM₁₀ stations in France. It shows the overall ability of the CHIMERE model to reproduce variations of daily PM₁₀ concentrations. Indeed, we get correlations higher than 0.60 for most sites. This is quite satisfactory compared to the CAMS ensemble (Marécal et al., 2015). However, simulations for sites in mountainous areas (e.g., alpine region) exhibit lower correlations. This is partly explained by the fact that the model resolution of 9 × 9 km² does not resolve orography and related dynamical processes, such as thermal winds and inversion layers (Bessagnet et al., 2020).

Concerning the mean fractional bias (MFB) visible on Fig. 3 for the same period, the average on all sites is 2.61 % for 2013 and 4.08 % for 2014 which is low. For the majority of the sites, the bias is in a range of ±30 % which can be considered as satisfactory according to the

recommendations of Boylan and Russell (2006). On the other hand, there is a North/South disparity, with overestimations that can be important for the sites in the North East of France and underestimations in the South East. Fig. 3 shows that the results are very similar from one year (2013) to another (2014). The RMSE maps (Fig. S3) show an error structure similar to that shown for the biases (Fig. 3).

The type of site (urban, suburban, rural) does not seem to impact the simulation of daily average PM₁₀ concentrations that much as opposed to the geographical situation (e.g., mountainous regions vs. flat lands and north vs. south location).

##### 3.1.2. Chemical composition of PM₁₀

Since we are interested in a health impact indicator that integrates both the chemical composition of particles and OPᵢ by PM source, it is important to assess how well the model reproduces this chemical composition. We carried out a rather complete comparison of the simulations and daily observations available for the French sites over the period of the study. Nevertheless, in the framework of this paper, we do not have the objective to go too deep in the analysis of model's capabilities but rather to be able to further analyze the potential weaknesses in the OPᵥ modelling.

From observations at the sites mentioned in Section 2.1.2, the following species concentrations have been used in the analysis: Organic carbon (OC), elemental carbon (EC), nitrate (NO₃), sulfate (SO₄²⁻), ammonium (NH₄⁺), sodium (Na⁺) calcium (Ca²⁺) and “dust”. For the comparisons with simulated concentrations, we had to propose some conversions to fit the measured species with the simulated ones. Measured OC were multiplied by a factor 1.80 to obtain the organic matter (OM) concentrations (Favez et al., 2010; Petit et al., 2015). To

<figure><img src="image_8.png" /><figcaption>Fig. 3. Scores between PM₁₀ daily mean mass concentrations measured and modelled by CHIMERE. Pearson correlation (R) is visible on the left from bottom to top respectively for 2013 and 2014. The same goes for mean fractional bias (MFB) on the right.</figcaption></figure>
obtain dust concentrations, a factor of 8 was applied to measured Ca²⁺ concentrations (Favez et al., 2008).

Fig. 4 shows that for these latter two PM components, the correlation values between simulation and observations are lower than for the other components with a median of about 0.35 for OM and about 0.30 for dust. In addition, there is a very large disparity in scores depending on the site, with R ~ 0 for Aix-en-Provence, Nice or Roubaix, and R ~ 0.58 for Lens and Revin in the case of OM. Correlations are in the same ranges for dust. We note much better performances for reproducing EC and SO₄²⁻, with median values for R respectively of 0.58 and 0.59, and more homogeneous behaviour for different sites. For NO₃⁻, NH₄⁺ and Na⁺ the scores are even better with respective median R values of 0.60, 0.62 and 0.60. These scores are in a good agreement, for the inorganic fraction, with those presented in Couvidat et al. (2018). Overall, these comparisons indicate that there are certain difficulties in reproducing OM and dust for which sources are apparently not yet well reproduced by the model according to the sites. For organic matter, we can see that the main difficulties seem to be associated with sites in the south of France (Nice, Aix, Port-de-Bouc) and mid latitude (Talence and Grenoble), which may reflect the difficulty in simulating some sources or some transport / dynamic processes in the atmosphere of these areas. In addition, a single OM/OC conversion factor may not reflect the variability in this ratio, lower for primary and higher for aged secondary aerosol. We can note that this is also consistent with the fact that it is more difficult for the model to reproduce PM₁₀ concentrations in the southeastern part of France, where OM represents a larger fraction of the PM₁₀ (Font et al., 2024).

We also note that the chemistry at some specific sites is more difficult to reproduce by the model. This is particularly the case for the Roubaix traffic site, which is perhaps influenced by sources that are too local for the current model spatial resolution, even if conversely, the chemistry at the Strasbourg traffic site is very well simulated. There also seems to be some difficulty in reproducing the PM chemistry at the ANDRA-OPE site; it is however a remote rural site and therefore it should not suffer from problems of representativeness which can penalize sites near point sources, or valley sites such as Grenoble where sources and dynamics are difficult to represent at the scale of 9 × 9 km². In the following

discussion concerning modelling of OP<sub>v</sub>, it will be necessary to be cautious in the interpretation of the simulated values, particularly depending upon the sites considered.

The bias analysis (here the mean fractional bias) is also considered and provides another aspect to take into account. Biases are in general reasonable (in the sense of the criteria by Boylan and Russell (2006)), with values largely between ±50 % for OM, EC, SO₄²⁻ and dust. Conversely, there is a strong tendency of the model to overestimate NO₃⁻ and NH₄⁺ concentrations (i.e. ammonium nitrate), particularly for the sites in the North of France with bias values between 50 and 100 %. This is consistent with the strong biases observed on the simulation of PM₁₀ concentrations in the North East of France, where nitrate is a major PM₁₀ component. This type of bias has also been observed at European level by Couvidat et al. (2018), albeit in lower proportions. A hypothesis may be considered that the issue comes from biases in the representation of ammonia emissions of the agricultural origin or in the inorganic chemistry associated with the formation of nitric acid and then the neutralization of this acid by ammonia. This is also suggested in the comparisons between NH₃ emission inventories and IASI satellite observations (Fortems-Cheiney et al., 2020). Note also that there is no specific bias in the representation of NO₂ emissions and its concentrations compared to NO₂ measurements on French sites (not shown). We also must note that negative sampling artefacts are also possible (Schaap et al., 2004), potentially leading to significant underestimations of ammonium nitrate from filter-based analysis. To complete the analysis, RMSEs are displayed in Fig. S4 and show that species with higher biases (but good correlations) such as nitrate and ammonium have the highest errors, as do species with lower biases but poorer correlations such as OM and dust. Moreover, it is interesting to see that there is a greater dispersion for nitrate in terms of RMSE.

## 3.2. Source modelling

As extensively underlined in Section 2.3.2, one of the key points of the OP simulation methods is the simulation of the PMF factors from the PSAT sources of the model. For the so-called extended method which takes into account all the factors of the PMF, we have established a

<figure><img src="image_9.png" /><figcaption>Fig. 4. Statistical scores of the chemical composition of PM₁₀ between daily mean concentrations measured and those modelled by CHIMERE. Pearson correlation (R) on the top graph and mean fractional bias (MFB) on the bottom one for 2013 and 2014.</figcaption></figure>
correspondence between PMF factors, PSAT sources and model species (Table 5) and compared their chemical profiles (Fig. 2). Here, we perform a statistical analysis (Fig. 5) (correlations and biases) of daily time series from the PMF factors and their simulation by CHIMERE with the correspondences proposed in Table 5.

The median correlations of the daily mean source specific concentrations at all sites are in the range of 0.30 to 0.70. Correlation is on the lower end for the dust factor, which is consistent with the model's difficulties in simulating soluble calcium (Section 3.1.2). The high variability, particularly the low and even negative correlations at Aix-en-Provence and Port-de-Bouc, could be partially explained by the identification of an industrial PMF factor, which was not taken into account in the final harmonized PMF because it was identified at only two sites; for these two sites, this factor was then integrated into the dust factor. For Strasbourg, the difficulty lies probably in modelling the resuspension of aerosols at a traffic site. The PSAT and PMF factors of aged salt correlate at around 0.40 on average, being not well simulated at 3 sites (Lens, Nogent-sur-Oise and Strasbourg) all located in the northern eastern part of France. The opposite is seen for the primary biogenic PMF factor, with clear difficulties for the sites of the Mediterranean coast (Aix-en-Provence, Nice, Port-de-Bouc). As for the other factors (Biomass burning, Nitrate-rich, Primary traffic and Sulfate-rich), the median correlations are >0.40 with less variability among sites than for the sources previously mentioned, reflecting a certain homogeneity in the results.

As far as biases are concerned, the differences are particularly marked for very local and mainly primary sources such as primary traffic and biomass burning. Conversely, secondary inorganic sources such as nitrates are generally overestimated; this feature is also observed on RMSE (Fig. S4). Such results are commonly observed in regional models, where accurate representation of source contributions from localized influences at specific sites remains challenging (Marecal et al., 2015). As mentioned above, underestimations from filter-based measurements due to negative sampling artefacts of semi-volatile species, such as ammonium nitrate, cannot not be excluded either. The site typology does not seem to affect the results. In addition, some PMF factors observed at specific sites only were not considered in our work as our approach needs to be general for France. This could lead to biases, as

these sources in return are affected by other sources that are represented in our PSAT approach, and then not taken into account in the PMF approach. Nevertheless, the (MFB) biases are fairly low for all the sources, within ±50 %, except sulfate-rich with a positive bias of +65 %.

Finally, PMF and simulated concentrations for the primary biogenic source show a median correlation of about 0.30 and a median MFB of about -20 %. The modelling of primary biogenic aerosols, in particular the organic fraction, by simulating fungal spores is discussed in detail in a separate paper (Vida et al., 2024).

For the method using a reduced set of sources, this correspondence is based on the one carried out in another regional transport chemistry model (CAMx) as part of the modelling of the oxidative potential of aerosols in Switzerland and Europe (Daellenbach et al., 2020). No comparisons of the time series were made due to the complexity of accessing the data. However, the source contribution maps for PM₁₀ and oxidative potential are similar between the original work of Daellenbach et al. (2020) and our application.

### 3.3. Oxidative potential modelling

The last stage of the evaluation of the model performance concerns the simulation of the OP<sub>v</sub> itself. We simulated the volume OP<sub>v</sub> for the two complementary tests, AA and DTT, as described in Section 2.3. We then compared these simulated OP<sub>v</sub> values to the daily values observed at different sites, for the two methods developed, i.e. the “reduced set” method and the “extended set” method. The overall results are presented in Fig. 6 which show the correlations and the biases between simulations and observations and Fig. S4 for the RMSE.

The medians of daily correlation values are around 0.35 and 0.50 for the reduced source set method for AA and DTT respectively. Median correlations are better for the method using a larger number of sources, approximately 0.50 for AA and 0.60 for DTT. Time series are hardly reproduced at Aix-en-Provence, where the correlations are systematically below 0.20 for AA and DTT, whatever the method (Figs. S3 and S5). The same applies to the Nice site, but with slightly better correlations for DTT. We also note that the correlation for the Grenoble site is lower than the general median in all cases. For all these sites, the results

<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Statistical scores of the modelled sources between daily mean concentrations of sources from PMF extended set method and those modelled by CHIMERE. Pearson correlation (R) on the top and mean fractional bias (MFB) on the bottom for 2013 and 2014.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Statistical scores of the modelled oxidative potential between daily mean measurements and those modelled by CHIMERE. Pearson correlation (R) on the top and mean fractional bias (MFB) on the bottom for 2013 and 2014. Station time series for AA are available in the supplement (Figs. S5 and S6), as are those for DTT (Figs. S7 and S8).</figcaption></figure>

align with the low correlations in simulating the composition of PM sources and especially OM. Conversely, for sites with less complex orography, correlations are in general much better, as for Talence, with correlation about 0.60 and 0.70 for the reduced set of sources and about 0.70 and 0.80 for the extended set, respectively for the AA and the DTT assays.

Median biases in simulated OP<sub>v</sub> are in general small, between about zero and -30 % for MFB. Particularly, for the reduced set MFB values are about zero for AA and about -10 % for DTT. For the extended set the corresponding values are a bit larger at about -30 %, both for AA and DTT, albeit with large regional differences. These small biases are an important result of this study given the various uncertainty factors contributing to simulated OP<sub>v</sub>. Negative biases can often be related to underestimations of primary sources of PM<sub>10</sub> from biomass combustion and primary traffic. These sources both have a very high intrinsic OP<sub>i</sub>, this may explain the underestimation of the OP<sub>v</sub> for the extended set method. As for PM<sub>10</sub>, biases are highly variable for different sites.

An important difference can be observed between the results obtained with the two OP assays (Fig. 6). For the AA test, it is striking to see the significant bias at the Nogent-sur-Oise site (MFB around -80 %). It is likely explained by the very low contribution of the primary traffic source at this site in the simulations, a source with one of the highest OPᵢ. For Nogent again, but also for Talence and Aix-en-Provence, the important negative bias of the biomass burning source can also explain the important biases observed for OP<sub>v</sub><sup>DTT</sup> (Fig. 6). It should be noted that RMSE (Fig. S4) exhibit very similar feature than bias in terms of ranking of the two methods and tests.

In addition, to support this analysis, we have plotted station time series for AA (Figs. S5 and S6) and for DTT (Figs. S7 and S8). Each time skill scores (MFB, R and RMSE are indicated.

As a conclusion, in comparing the reduced and the extended set methods, we can note a better correlation for the latter one, and

acceptable bias for both methods. In addition, the extended method has a broader acceptance of all kinds of sources and not excluding them a priori.

Although this study is based on a large number of OP data compared with what exists in the literature, it should be noted that the number of OP data available per station is sometimes low and does not allow a complete assessment of seasonal cycles. The number of data by type of site is sometimes unbalanced, with a lot of data for alpine sites, which limit a bit the conclusions on the performance of the methods developed. This calls for further studies based on larger volumes of data. To improve the method's representativeness, more OP and intrinsic OP data from more sources will be needed in the future, and harmonized if possible.

# 4. OP spatial distribution over France

## 4.1. Spatialization of OP<sub>v</sub>

Here we present 2D maps of simulated OP<sub>v</sub> over France as an annual average for 2013 and 2014 (Fig. 7). Globally, they show, as expected, higher oxidative burden exposure related to aerosols in large agglomeration and densely populated areas leading to higher exposure for the urban French population. Also, major roads such as motorways are prominently identifiable in the spatial distribution of OP<sub>v</sub>. However, there are differences depending on the test used (DTT or AA) and the method used (reduced or extended set).

When the same method is used (e.g. reduced or extended set), we can see that OP<sub>v</sub><sup>AA</sup> and OP<sub>v</sub><sup>DTT</sup> show similar patterns overall, but with differences in the intensity of certain sources and in spatial gradients. This can be attributed to differences in OP<sub>i</sub> between the same PMF factors for both tests. We know that OP<sub>i</sub> values are relatively larger for the AA than for the DTT assay (Tables 4 and 5) for mainly primary sources (primary traffic, primary biomass burning) as compared to secondary sources
<figure><img src="image_12.png" /><figcaption>**Fig. 7.** Volume oxidative potential (OP<sub>v</sub>) modelled with the reduced set method on the top and extended set on the bottom depending on the OP assay, OP<sub>v</sub><sup>AA</sup> on the left and OP<sub>v</sub><sup>DTT</sup> on the right. Biannual mean oxidative potential (2013–2014) in the first layer of the CHIMERE model. The black circles represent the OP<sub>v</sub> measurement sites in France.</figcaption></figure>

(nitrate-rich, sulfate-rich). This leads to relatively higher values of OP<sub>v</sub><sup>AA</sup> over larger and more urbanized areas compared to OP<sub>v</sub><sup>DTT</sup>.

In addition, OP<sub>v</sub> intensities differ between the two simulation methods using a reduced (OP<sub>v</sub><sup>AA,red</sup>, OP<sub>v</sub><sup>DTT,red</sup>) or extended (OP<sub>v</sub><sup>AA,ext</sup>,

OP<sub>v</sub><sup>DTT,ext</sup>) set of sources. Indeed, the background intensity is higher when using the extended set due to taking into account inorganic species in the analysis by including OP<sub>i</sub> values for factors such as nitrate-rich and sulfate-rich. On the contrary, OP<sub>v</sub> values for urban agglomerations

<figure><img src="image_13.png" /><figcaption>**Fig. 8.** Mass oxidative potential (OP<sub>m</sub>) modelled with the reduced (top) and extended set methods (bottom) depending on the OP<sub>m</sub><sup>AA</sup> (left) and OP<sub>m</sub><sup>DTT</sup> (right). Biannual mean oxidative potential (2013–2014) in the first layer of the CHIMERE model. The black circles represent the OP<sub>v</sub> measurement sites in France. Maps for seasonal variations of OP<sub>m</sub> are in the supplement (Fig. S12 and S13).</figcaption></figure>
are larger for the reduced set method, because of the large intrinsic OPᵢ value of primary traffic emissions. For example, in the Paris region where the differences between methods are greatest, the maps shows an OP_v AA,red approximately 2.5 times higher than the OP_v AA,ext with 5.00 and 2.00 nmol AA min⁻¹ m⁻³ respectively. The same ratio is found between the OP_v DTT,red and the OP_v DTT,ext (respectively 8.00 and 3.00 nmol DTT min⁻¹ m⁻³). However, current measurements at the Paris centre station in 2023 show that the orders of magnitude of the OP values are more like 1 nmol AA min⁻¹ m⁻³ and 2 nmol DTT min⁻¹ m⁻³, which is closer to the extended set method (Internal data upon request).

As a note of caution, the very “metallic” nature of non-exhaust PM, especially regarding copper is partly responsible for its high OPᵢ value. It is very likely that we overestimate the impact of this fraction within our

model, where we consider PPM from resuspension instead of only specific concentration of copper.

Finally, the largest OP_v values are observed outside of France, but still within the domain of the study, as for example over the Po valley or the Benelux, corresponding also to large PM₁₀ values simulated in these regions. In these areas, the OP_v DTT,red is around 8.00 nmol DTT min⁻¹ m⁻³, which is a high value rarely measured in Europe (Tassel et al., in prep, Grange et al., 2022).

The mass normalized oxidative potential (OP_m) results from normalising the OP_v by the PM₁₀ mass concentrations. Its analysis allows highlighting regions where OP_v is not only due to higher concentrations, but also due to an increased average OPᵢ of its components. Fig. 8 shows the spatialization of OP_m AA and OP_m DTT according to the two modelling

<figure><img src="image_14.png" /><figcaption>**Fig. 9.** Contribution of major extended set sources to PM₁₀ (left), OP_v AA (middle) and OP_v DTT (right) spatialized over France as a two-year average (2013–2014). Only the first vertical level of the CHIMERE model is shown. Other sources contributions are in the supplement (Fig. S9).</figcaption></figure>
methods.

With the reduced source set method, the OP<sub>m</sub><sup>AA</sup> and OP<sub>m</sub><sup>DTT</sup> are highest in densely populated areas such as the Paris region, and along major roads. In dense cities, both OP<sub>m</sub> can be >0.10 nmol min<sup>-1</sup> µg<sup>-1</sup>, reflecting the fact that the potential toxicity of urban aerosols is significantly larger than in rural areas. Also, the OP<sub>m</sub><sup>DTT,red</sup> is larger overall than the AA counterpart, which can be explained by the larger and non-zero OP<sub>v</sub><sup>DTT</sup> value for the HOA and BSOA sources.

For the extended set of sources, OP<sub>m</sub> is still larger over urban areas with respect to rural background, but the overall distribution is more homogeneous. This is due (i) to the much lower intrinsic OP<sub>i</sub> used for the traffic source which affects the urban agglomerations and highways, and (ii) due to a larger number of secondary sources, which are relative to the primary ones more important for rural background. As a conclusion, this analysis shows especially for the reduced set but to a lesser extent with the extended set, that urban OP<sub>v</sub> is enhanced not only due to increased PM<sub>10</sub> concentrations, but also due to increased OP<sub>i</sub> values of urban type PM sources. OP<sub>m</sub> appears as a good integrative metric to illustrate these effects.

## 4.2. Source contribution to PM and OP<sub>v</sub>

Fig. 9 shows the contributions of the main sources to OP<sub>v</sub><sup>AA</sup> and OP<sub>v</sub><sup>DTT</sup> for the extended source set method averaged over two years (other sources are displayed in Fig. S9). The contribution of sources to PM<sub>10</sub> concentrations appears to be different for OP<sub>v</sub><sup>AA</sup> and OP<sub>v</sub><sup>DTT</sup>. These maps clearly show that biomass combustion and primary traffic sources drive the levels of OP<sub>v</sub><sup>AA</sup> and OP<sub>v</sub><sup>DTT</sup>, especially in large cities. For OP<sub>v</sub><sup>AA</sup>, the contribution of the other sources is negligible, while they do contribute more for OP<sub>v</sub><sup>DTT</sup>. This is due to different intrinsic OP<sub>i</sub> values for OP<sub>v</sub><sup>AA</sup> and OP<sub>v</sub><sup>DTT</sup> as pointed out in Section 2.3.

Strong differences in source contribution do appear between PM<sub>10</sub> and the two tests of OP<sub>v</sub>. These differences are particularly evident for biomass combustion and primary traffic sources, which contribute relatively less to annual total aerosol mass but significantly influence the OP<sub>v</sub>. This observation highlights the kind of information provided by the OP<sub>v</sub>, which could be crucial in the future to help guiding policies aimed at mitigating health impacts caused by aerosols.

The contributions per source for the method based on the reduced source set have also been analysed (Fig. 10). In this approach, the abrasion source associated with road transport emerges as the most significant contributor to the oxidative potential of both AA and DTT in our study, as already shown by Daellenbach et al. (2020) with the CAMx model. By addressing and minimizing these emissions, there is a potential to substantially decrease the oxidative potential of aerosols. The disparity in the contributions of sources to PM<sub>10</sub> over the French territory is even more pronounced as these sources exhibit relatively low mass contributions but high OP<sub>v</sub>. On the contrary, the biomass combustion source is not a major contributor in this approach. Similarly, the secondary biogenic source contributes much to the PM mass, but very little to oxidative potential.

The use of these two methods, based on different PMF, introduces greater complexity into the modelling of the OP, but also illustrates the potential for refining the sources taken into account. Indeed, we see the possibility of separating exhaust and non-exhaust contributions from traffic, but also of better characterization of certain PMF factors such as nitrate and especially sulfate-rich using organic matter speciation.

## 5. Conclusions

This study, one of the first attempts to model Oxidative Potential on a large spatial scale, developed and tested two methods for simulating oxidative potential (OP) in a chemistry-transport model, incorporating unprecedented numerous sources and measurements. The methods complement each other: one is species-oriented, particularly for organic aerosol, and the other is source-oriented, based on chemical tracers. The

study used a unique database of PM<sub>10</sub> composition, source attribution, and OP<sub>v</sub> measurements to identify uncertainties at each stage of the modelling framework. Over two years, the CHIMERE model accurately simulated PM<sub>10</sub> concentrations, though with limitations in areas with complex terrain like the Alps.

The methods' ability to simulate sources obtained by PMF analysis, combined with the PSAT method (adhoc approach finding the correspondence between PMF and PSAT using the chemical profiles and then testing the correlation and the biases obtained by comparing the concentrations of PM linked to these sources), shows promising results. The models' OP<sub>v</sub> simulations are encouraging but complex, especially in Mediterranean and Alpine regions. The method using a larger panel of sources correlates better with observations despite a slight bias. While the volume of data is unprecedented for this type of assessment, more data is needed in the future to cover the geographical areas investigated and the typology of sites. It will also be necessary to investigate the finer fractions of PM in terms of OP (e.g. PM<sub>2.5</sub>, PM<sub>1</sub>).

Key findings for both methods include higher OP<sub>v</sub> values in urban areas, major roads, and valleys due to traffic emissions, while densely vegetated areas contribute little to OP<sub>v</sub>. The study emphasizes the significant impact of anthropogenic sources on OP<sub>v</sub>, suggesting that public health policies should prioritize urban areas. Road transport and biomass combustion are major contributors to OP<sub>v</sub>, particularly in urban centers and along major roads. Extending this study to a larger European dataset of OP<sub>i</sub> with additional sites and years could validate the methods in different environments and improve understanding of OP<sub>v</sub> across Europe.

Finally, by focusing on reducing sources that contribute significantly to the oxidative potential in addition to PM<sub>10</sub> mass, policymakers can develop more targeted and efficient mitigation measures that prioritize public health protection while accounting for regional complexities.

### Acronyms

OP<sub>v</sub> Volume oxidative potential

OP<sub>v</sub><sup>AA</sup> Volume oxidative potential with ascorbic acid

OP<sub>v</sub><sup>DTT</sup> Volume oxidative potential with dithiothreitol

OP<sub>i</sub> Intrinsic oxidative potential of sources

OP<sub>m</sub> Mass normalized oxidative potential (OP<sub>v</sub>/PM<sub>10</sub>)

### CRediT authorship contribution statement

**Matthieu Vida:** Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. **Gilles Foret:** Writing – review & editing, Methodology, Investigation, Funding acquisition, Conceptualization. **Guillaume Siour:** Writing – review & editing, Methodology, Investigation, Formal analysis, Conceptualization. **Jean-Luc Jaffrezo:** Writing – review & editing, Methodology, Investigation, Formal analysis, Conceptualization. **Olivier Favez:** Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. **Arineh Cholakian:** Writing – review & editing, Methodology, Formal analysis, Data curation. **Julie Cozic:** Writing – review & editing, Data curation. **Harry Dupont:** Writing – review & editing, Data curation. **Grégory Gille:** Writing – review & editing, Data curation. **Sonia Oppo:** Writing – review & editing, Data curation. **Shouwen Zhang:** Writing – review & editing, Data curation. **Florie Francony:** Writing – review & editing, Data curation. **Cyril Pallares:** Writing – review & editing, Data curation. **Sébastien Conil:** Writing – review & editing, Data curation. **Gaelle Uzu:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Matthias Beekmann:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Conceptualization.

### Financial support

The PhD of MV is funded by ADEME and the Paris Region in the
<figure><img src="image_15.png" /><figcaption>**Fig. 10.** Contribution of sources for the reduced set method to PM₁₀ (left), OP<sub>v</sub><sup>AA</sup> (middle) and OP<sub>v</sub><sup>DTT</sup> (right) spatialized over France as a two-year average (2013–2014) for the reduced set of sources method. Only the first vertical level of the CHIMERE model is shown.</figcaption></figure>
framework of the research network on air quality, the DIM Qi². This work has also been supported by the EU Horizon 2020 Green Deal project RI-URBANS (grant no. 101036245). Authors want to thank the EU project #874753 REMEDIA which currently supports the activities of M. Vida.

The ANR program "Atmospheric Biogenic Sugar" ANR-21-CE01-0021-01 provided some financial support for this collaboration, while analytical aspects were also supported at IGE by the Air-O-Sol platform within Labex OSUG@2020 (ANR10 LABX56). The work at IGE for the Andra-OPE site is supported by a long-term grant from Andra. This project was partially supported for the analytical part at IGE by the ANR project ANR GetOPstandOP ANR19-CE34-002 and Idex ACME (ANR-15-IDEX-02). The CARA program is funded the French Ministry of Environment. This work was granted access to the HPC resources of TGCC under the allocation 2022-A0130107232 made by GENCI.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Reports a relationship with that includes. Has patent pending to. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The authors warmly thank the dedicated work of Robin Aujay-Plouzeau (Ineris) and the numerous AQMN technicians (Atmo AuRA, Atmo Sud, Atmo HdF, Atmo GE, ...) for collecting the filter samples in the field. All the many engineers and technicians on the Air O Sol analytical plateau at IGE are warmly thanked for their work at analysing the samples for the chemistry and OP. The authors also thank B. Mesbah (Atmos Sud) it's contribution to the present paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.178813.

## Data availability

All measurement and PMF data for this paper are archived at the IGE, and are available on request from the corresponding authors (JLJ and GU). The codes and modelling data are available from the LISA authors (MV, GF, MB, GS).

The model is available here: https://www.lmd.polytechnique.fr/chimere

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