# Multi-scale comparison of modeling chains for traffic-related air pollutant emission assessment

Michele Tirico, Valentin Le Bescond, Delphine Sengelin, Pascal Gastineau, Arnaud Can

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Michele Tirico, Valentin Le Bescond, Delphine Sengelin, Pascal Gastineau, Arnaud Can. Multi-scale comparison of modeling chains for traffic-related air pollutant emission assessment. Sustainable Cities and Society, 2025, 129, pp.106354. 10.1016/j.scs.2025.106354. hal-05305911

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# Multi-scale comparison of modeling chains for traffic-related air pollutant emission assessment

Michele Tirico<sup>a,b,c,*</sup>, Valentin Le Bescond<sup>d</sup>, Delphine Sengelin<sup>b</sup>, Pascal Gastineau<sup>c</sup>, Arnaud Can<sup>d</sup>

## ARTICLE INFO

**Keywords:**

Emission model

Symuvia

MATSim

NOx

Traffic-related air pollution

Traffic simulation

## ABSTRACT

Traffic-related air pollutants pose major health risks in metropolitan areas, with Nitrogen Oxides (NOₓ) being particularly harmful. Reducing road traffic emissions through mobility management is crucial for achieving sustainable urban living. To assess such strategies, reliable modeling chains are essential. Traditionally, these chains couple traffic and pollutant emission models. However, this coupling can introduce significant uncertainty, particularly when traffic and pollution models are developed at different spatial scales. This paper addresses the challenge of examining the effects of spatio-temporal representation of traffic data on emission models. To this end, we compare emissions derived from two widely used European models, COPERT and HBEFA, using traffic data from the microscopic traffic model Symuvia and the agent-based model MATSim. Traffic outputs are aggregated at the trip, street, and square-cell grid levels. Despite significant variations in speed estimation due to mean speed calculation methodologies, emissions estimated at the trip level are generally closely aligned. At the street level, Symuvia is better suited for emission estimations, as it more accurately captures local variations and congested traffic conditions. Emissions estimated at the cell-grid level reveal high spatial variability, influenced by the street network form: higher values are often observed in city centers with MATSim, while Symuvia tends to show higher values on the ring road. Our findings indicate that MATSim+COPERT and Symuvia+HBEFA yield more consistent overall results, primarily due to the route assignment algorithm and the sensitivity of emission models to variations in traffic conditions. Emissions estimated with HBEFA, particularly when coupled with MATSim, tend to be higher than those from COPERT, largely due to differences in how stop-and-go traffic is represented. On the other hand, COPERT estimates are generally lower, especially when coupled with Symuvia, mainly due to the challenges of capturing congested conditions and speed variations with the mean speed representation.

## 1. Introduction

Air pollutants have become one of the most critical health problems in modern times. They are linked to severe health conditions, including cardiovascular diseases (Apte et al., 2018), respiratory illnesses (Dominici et al., 2006), colorectal cancer (Roswall et al., 2023), and cognitive decline (Zhang, Chen, & Zhang, 2018). These pollutants contribute to rising mortality rates among both children (Institute Health Effects, 2022) and adults (Di et al., 2017), while also worsening social inequalities and contributing to urban segregation (Fairburn et al., 2019; Gurram, Stuart, & Pinjari, 2019). Motorized vehicles are a major source of air pollutant emissions. These emissions primarily include Carbon Dioxide (CO₂), Carbon Monoxide (CO), Particulate Matter (PM), Nitrogen Oxides (NOₓ), and Volatile Organic Compounds (VOCs). In

particular, road transport accounts for 37% of NOₓ emissions, making it the largest contributor to this pollutant from all sources in 2020 (European Environment Agency, 2021). Reducing traffic-related emissions is therefore a key urban challenge with environmental, health, and social implications.

To achieve this goal, it is essential to provide tools that help understand and mitigate road traffic emissions (Khreis et al., 2023). However, predicting the impact of actions targeting road traffic on traffic-related emissions remains challenging. One of the most powerful approaches is through modeling chains that couple static or dynamic traffic models with emission models (Borge et al., 2012; Fallah Shorshani et al., 2015; Smit, Ntziachristos, & Boulter, 2010). These modeling chains provide a robust framework in which the traffic model captures the impact

<footnote><sup>a</sup> Univ. Lyon, INSA Lyon, CNRS, Ecole Centrale de Lyon, Univ. Claude Bernard Lyon 1, LMFA, UMR5509, 69621, Villeurbanne, France</footnote>

<footnote><sup>b</sup> LICIT-ECO7, ENTPE, Univ. Gustave Eiffel, Lyon, France</footnote>

<footnote><sup>c</sup> AME-SPLOTT, Univ. Gustave Eiffel, Bouguenais, France</footnote>

<footnote><sup>d</sup> Univ. Gustave Eiffel, CEREMA, UMRAE, F-44344 Bouguenais, France</footnote>

<footnote>* Corresponding author.
E-mail address: michele.tirico@insa-lyon.fr (M. Tirico).</footnote>
of an action on key traffic variables (such as vehicle flow and kinematics), which the emission model then uses to estimate traffic-related emissions.

Numerous studies have relied on this modeling framework to explore strategies for reducing traffic-related pollutant emissions, using various traffic and emission models. For instance, Noland and Quddus (2006) used VISSIM to assess the impact of increased network capacity on energy consumption and pollutant emissions (CO, NOₓ). Boriboonsomsin and Barth (2008) evaluated the environmental effects (CO, NOₓ, CO₂) of high-occupancy vehicle lanes on motorways with PARAMICS, while Barth and Boriboonsomsin Barth and Boriboon-somsin (2009) analyzed eco-driving impacts on CO₂ emissions using the same model. Madireddy et al. (2011) examined speed limit reductions and coordinated traffic lights in PARAMICS to assess their effects on CO₂ and NOₓ emissions. Fernandes, Coelho, and Roupailh (2017) compared the environmental impact of roundabouts versus traffic light with VISSIM, and Karioti et al. (2017) used AIMSUN to study the air emission effects (NOₓ, CO₂, VOCs, PM) of capacity restrictions due to incidents on a ring road. Yao et al. (2018) optimized traffic light plans in VISSIM based on vehicle fuel consumption, while Hülsmann, Gerike, and Ketzel (2014) employed MATSim to analyze how travel demand changes affect NO₂ and NOₓ emissions. Additionally, other studies have explored the impact of speed limit reductions (Gressai et al., 2021; Rhode, Wagner, & Ziemke, 2022), traffic signal optimization (Gunarathne, Amarasingha, & Wickramasighe, 2023; Kutlimuratov et al., 2021), fleet vehicle predictions (Okebe, Abuodha, & Ochieng, 2024), road design (Fernandes et al., 2020), and traffic control (Mei et al., 2024) on emissions.

Although informative, the main limitation of case studies on reducing traffic-related pollutants that rely on modeling chains is that they do not question the validity domain of the couplings between models. This raises doubts about some of the conclusions drawn from these studies. In particular, models designed for use at different spatial scales are often coupled. In this regard, Zhu et al. (2022) and Lu et al. (2024) emphasized the importance of carefully estimating the spatio-temporal variability of emissions to improve strategies for reducing urban CO₂ emissions. Fontes et al. (2015) noted that varying scales of representation of transport phenomena, as well as the inclusion of certain aspects of these phenomena, can significantly affect the estimation of transport-related air pollutants. In addition to these uncertainties in transport models, emission models can also differ significantly in terms of spatio-temporal scale and estimation methods (Fallah Shorshani et al., 2015).

This paper aims to improve the understanding of limitations in coupling transport and emission models. We specifically focus on how the representation of physical transport phenomena in urban areas affects emission estimates, considering both spatial and temporal factors. The goal is to improve the consistency of emission estimates to provide decision-makers with reliable modeling chains to support efforts in reducing road traffic pollution as part of sustainable urban planning. The remainder of this paper is structured as follows. Section 2 details the case study, describes the traffic models (Symuvia and MATSim) and emission models (COPERT and HBEFA) used, and introduces three methodologies (at trip level, at street level, and over a regular grid of cells) to aggregate emissions for comparison. Section 3 presents a detailed comparative analysis of our modeling chains. Section 4 discusses the implications of these results, and Section 5 offers guidelines for future estimations.

## 2. Materials

Traffic-related pollutant emissions are typically obtained by coupling a traffic model, which provides the flow or vehicle kinematics variables required for pollutant estimation, with an emission model. These models can operate at different spatial scales. In this article, traffic variables are derived from two traffic models (Symuvia and

MATSim), while pollutant emissions are estimated using two emission models (COPERT 5 and HBEFA 4.2). Traffic conditions are simulated for a given study area with the same traffic demand to focus the comparison on differences in model representation scales. This paper provides three types of emission estimates: emissions computed from individual vehicle trips, emissions derived from aggregated outputs at street level, and emissions aggregated at a square-cell grid. Comparing the emissions calculated from the two traffic models and the two emission models allows for assessing the applicability of each modeling chain approach in a given scenario.

### 2.1. The study area

Fig. 1 depicts the use case for this study. The focus area is the L63V neighborhood, located in the Northeast part of Lyon, France. It encompasses the 6th districts of Lyon, the Northwestern part of the 3rd districts of Lyon and the Western part of the city of Villeurbanne. The study area was selected because of its wide range of traffic conditions. The Northeastern part of the study area is crossed by a section of the ring road of the metropolitan area of Lyon, where the maximum authorized speed is either 70 km/h or 90 km/h. The rest of the network consists of urban roads in a dense urban area, where the maximum authorized speed is either 30 km/h or 50 km/h.

### 2.2. Traffic models for emission estimation

Static or dynamic traffic models can be coupled with emission models to estimate traffic-related emissions (Fallah Shorshani et al., 2015). Static models, usually divided into four steps (trip generation, trip distribution, mode choice and trip assignment), predict traffic volumes in subareas of the city (McNally, 2000). While static models provide a realistic macroscopic description of traffic conditions, they lack detailed spatio-temporal resolution over the transport network. Although static approaches can accurately estimate expected traffic emissions, they do not take into account the dynamics of individual mobility and do not predict intense and localized congestion phenomena of short duration, leading to less detailed estimates of traffic conditions. This results in inaccurate assessments of short-term exposure to high levels of pollutants and an underestimation of emissions in congested situations (Park & Kwan, 2017). Dynamic approaches, on the other hand, capture these details. They provide a more accurate representation of traffic flow and vehicle interactions, taking into account spatial and temporal variations in congestion levels. This leads to more accurate predictions of traffic conditions, which are crucial for accurate exposure estimates. Consequently, our focus is on dynamic traffic models.

Dynamic models are divided into three classes (Nguyen et al., 2021):

* Macroscopic traffic models. These models use the Macroscopic Fundamental Diagram (Daganzo, 2007) to estimate traffic conditions with fluid dynamics-inspired equations at the level of subareas (called reservoirs). While these models have a lower computational cost, and allow for real-time network monitoring of traffic emissions, the definition of the reservoirs affects the accuracy of traffic condition estimates (Buisson & Ladier, 2009) and total traffic emissions (Batista, Tilg, & Menéndez, 2022). Additionally, since the reservoirs often consist of a significant number of roads, the lack of detailed traffic conditions at street level prevents the identification of hotspots, which are significant sources of short-term, high pollution exposure. Consequently, macroscopic models are not the most appropriate for estimating human exposure.

* Microscopic traffic models. These models take into account individual vehicle characteristics and calculate key kinematic parameters, such as speed and acceleration, on a second-by-second basis. As noted by Fontes et al. (2015), these models require careful handling to ensure accurate spatial projection of emission
<figure><img src="image_4.png" /><figcaption>Fig. 1. The study area. On the left, map of metropolitan urban area of Lyon (marked by a black line) and of L63V neighborhood (marked by a red line). On the right, the L63V neighborhood and road features (Background map: ©OpenStreetMap).</figcaption></figure>

<table><caption>**Table 1**
Main differences between the transportation models Symuvia and MATSim.</caption><thead><tr><th></th><th>Symuvia</th><th>MATSim</th></tr></thead><tbody><tr><td>scale</td><td>Microscopic</td><td>Mesoscopic</td></tr><tr><td>model</td><td>vehicle-centered</td><td>individual-centered</td></tr><tr><td>focus</td><td>vehicle interactions</td><td>individual modal choices</td></tr><tr><td>route assignment</td><td>minimize individual travel time</td><td>maximize individual utility</td></tr><tr><td>intermodality</td><td>no</td><td>yes</td></tr><tr><td>time simulation</td><td>few hours</td><td>daily</td></tr><tr><td>area simulation</td><td>few streets or a neighborhood</td><td>metropolitan area</td></tr><tr><td>flow</td><td>vehicles explicitly modeled</td><td>link as a queue</td></tr><tr><td>output</td><td>detailed vehicles' trajectories</td><td>aggregated counting at street level</td></tr><tr><td>computational cost</td><td>expensive</td><td>reasonable</td></tr></tbody></table>

estimates. It is challenging to translate information from individual vehicles to streets. Few works addressed this challenge (Borge et al., 2012; Quaassdorff et al., 2016).

* Mesoscopic traffic models. These models describe vehicle kinematics without explicitly detailing individual vehicle interactions. Mesoscopic models are often coupled with emission models (Elessa Etuman, Coll, & Rivera Salas, 2021; Rodriguez-Rey et al., 2021). In this approach, traffic conditions are aggregated, and the exact positions of vehicles are not explicitly output. Traffic emissions derived from microscopic approaches are more accurate than those estimated with mesoscopic models (Lejri et al., 2018; Quaassdorff et al., 2016).

Both microscopic and mesoscopic approaches have been used to estimate traffic-related air emissions. However, a comprehensive comparison of the impact of different traffic model formalization on emission estimation has not yet been performed. To fill this gap, we will compare traffic estimates obtained from two traffic models: the microscopic traffic model Symuvia and the mesoscopic traffic model MATSim.

## 2.3. Symuvia and MATSim traffic models

The microscopic model Symuvia and the mesoscopic model MATSim exhibit several fundamental differences, which are summarized in Table 1. Symuvia operates as a vehicle-centered model, where route selection is dictated by a dynamic traffic assignment model aimed at minimizing vehicle travel time (Leclercq, 2007). This model enforces a comprehensive set of rules governing vehicle dynamics, including principles such as pursuit laws, lane changes, and intersection crossings. By focusing primarily on vehicle interactions, Symuvia does not take into account intermodality, which refers to the ability of individuals to switch transportation modes during their trip. As a result, this detailed

approach incurs significant computational costs, limiting its application to case studies focused on a few streets or neighborhoods and to simulations covering only a few hours of a day.

In contrast, MATSim is an individual-centered model in which a synthetic population, representing the real population, is designed with agents assigned daily activities (Horni, Nagel, & Axhausen, 2016). The route selection process in MATSim is driven by the maximization of utility for each agent across different transport options, aiming to reflect the profitability of each trip that may occur. Intermodality is a key feature of MATSim, allowing agents to switch modes of transport during their journey. MATSim is designed for large-scale simulations and was developed using a computationally efficient queue-based approach (Charypar, Axhausen, & Nagel, 2007). With this approach, there is no time-step-based updating process for vehicle positions. To simulate vehicle trajectories, network links have two key attributes: storage capacity, which defines the maximum number of vehicles they can hold, and flow capacity, which determines the allowed outflow from the link. As a result, vehicles are not explicitly modeled; instead, they are placed in a waiting queue before entering each link in their planned route, and their exit time is computed based on the actual traffic flow conditions. Compared to Symuvia, MATSim requires fewer computational resources, allowing simulations that more efficiently capture the daily evolution of traffic conditions across extensive urban areas.

The differences in vehicle representation between these models should be considered when calculating emissions. A key distinction is how vehicles are modeled in MATSim. Unlike Symuvia, which provides the exact coordinates of a vehicle's location at each second, MATSim only records the times at which vehicles enter and exit roads. This
<figure><img src="image_5.png" /><figcaption>Fig. 2. Representation of an intersection in MATSim (left) and Symuvia (right). While the information on the number of lanes per direction is embedded in the network formalization in MATSim (green lines indicate two lanes per direction, red lines indicate one lane per direction), in Symuvia, this is explicitly modeled. In Symuvia, light dotted lines indicate permitted directions from one road to another.</figcaption></figure>

<table><caption>**Table 2**

Geometrical details of Symuvia and MATSim networks.</caption><thead><tr><th>Model</th><th>Type</th><th>Max speed [km/h]</th><th>Length [Km]</th><th>%</th><th>tot [km]</th></tr></thead><tbody><tr><td rowspan="5">Symuvia</td><td>pt</td><td>-</td><td>24.599</td><td>0.05</td><td rowspan="5">460.783</td></tr><tr><td rowspan="4">car</td><td>30</td><td>91.387</td><td>0.20</td></tr><tr><td>50</td><td>299.962</td><td>0.65</td></tr><tr><td>70</td><td>10.571</td><td>0.03</td></tr><tr><td>90</td><td>34.261</td><td>0.07</td></tr><tr><td rowspan="5">MATSim</td><td>pt</td><td>-</td><td>22.549</td><td>0.05</td><td rowspan="5">415.848</td></tr><tr><td rowspan="4">car</td><td>30</td><td>104.571</td><td>0.25</td></tr><tr><td>50</td><td>240.807</td><td>0.59</td></tr><tr><td>70</td><td>30.857</td><td>0.07</td></tr><tr><td>90</td><td>17.125</td><td>0.05</td></tr></tbody></table>

can lead to discrepancies in emission calculations, as MATSim may overestimate the total travel distance.

The purpose of this paper is to provide a detailed comparison of the emission estimates obtained from these two different models. In order to ensure compatibility and reduce uncertainty as much as possible, it is therefore necessary to minimize the effects of the initialization of the traffic simulation. To this end, the main aspects to be considered are: (1) the network formalization, (2) the traffic demand, and (3) the vehicle fleet.

### 2.3.1. Network formalization

The differing formalization of traffic dynamics in these models result in distinct representations of road networks. Fig. 2 illustrates how intersections are modeled in both systems, and Table 2 summarizes the geometrical details of the network. In MATSim, intersections are represented in a simplified manner; a typical four-street intersection is depicted as a node with four connecting edges. Conversely, Symuvia represents the same type of intersection with additional links that denote the permitted directions from one road to another and for each direction. This results in intersections having a more geometrically and physically accurate representation, where vehicles traverse the intersection rather than merely crossing at nodes.

### 2.3.2. Traffic demand

MATSim operates on an activity-based model, categorizing individual activities into work, leisure, and home. By incorporating census data, behavioral and travel surveys, it is possible to generate daily activity schedules for individuals. Running a MATSim simulation with a synthetic population as input provides more detailed and realistic results. In this work, we use the open-source Eqasim tool (Hörl & Balac, 2021) to generate input data for the MATSim simulation.

The Eqasim tool (eqasim-org, 2025) generates a synthetic population through a multi-step process. First, it filters households from a large census data sample of the study area, retaining relevant socio-demographic attributes such as age and gender. Next, it assigns travel activity information to each agent based on a regional household travel survey. Finally, each agent is assigned a location based on census data,

and their daily activity locations are determined using enterprise census data.

Although Eqasim was initially designed for the Paris metropolitan area, it has since been extended to other French cities, including Nantes (Le Bescond et al., 2021), Lyon (Hörl & Puchinger, 2023), and Lille (Diallo et al., 2021). While MATSim simulations using Eqasim inputs have not yet been validated against real traffic data, Eqasim outputs have been calibrated for the French context (Balac, Hörl, & Schmid, 2024). This calibration provides confidence in its applicability to our study. For technical details on generating the demand for the Lyon case study, we refer the reader to GitHub (2025).

The process of harmonizing traffic models involves multiple steps to ensure compatibility across different modeling frameworks. We begin by generating a synthetic population for the Lyon metropolitan area, which serves as input for the MATSim simulation. The MATSim simulation produces detailed individual schedules, including the duration of each activity and the start and end times of each trip. At this stage, we focus specifically on car trips, given their significant contribution to urban emissions. This focus results in an origin-destination matrix for passenger vehicles, which includes scheduled departure times. This matrix is then used as input for the Symuvia simulation, effectively converting it into a trip-based demand format.

### 2.3.3. Fleet vehicles

To assess pollutant emissions, it is essential to specify the vehicle fleet, including the composition of technologies and engines present on the network. In the absence of more detailed information, this paper utilizes a description of the national vehicle fleet for the year 2021 as provided by the French Ministry of Ecological Transition's. The vehicle fleet used for the simulations was supplied by CITEPA (CITEPA, 2025), the organization responsible for emissions inventories in France (CITEPA (French Ministry of Ecological Transition / Technical Reference Center for Air Pollution (and) Climate Change), 2024). In 2021, 64% of the distances covered by light vehicles were driven by diesel-powered vehicles, while 35% were driven by petrol-powered vehicles. The most represented category is medium-sized EURO5 diesel passenger cars, accounting for 16%. Electric and hybrid
passenger vehicles play a marginal role, contributing 0.64% and 3.5%, respectively. This description allows for the consideration of vehicles with characteristics weighted according to the entire fleet's composition in terms of pollutant emissions at any given time and place on the network.

## 2.4. COPERT and HBEFA emission models

Fleet vehicles are responsible for various types of pollutant emissions, which can be broadly categorized into climate-change-related emissions (such as CO₂) and health-degradation-related emissions (such as NOₓ). These vehicles emit both exhaust emissions (directly from the tailpipe of internal combustion engine vehicles) and non-exhaust emissions (resulting from mechanisms other than internal combustion, such as wear and tear of vehicle components or friction between tires and the road). Since the challenges related to output representation and the definition of validity limits in modeling chains are similar for both types of pollutants and emissions, we focus on detailing the estimations of exhaust NOₓ emissions in the following sections.

### 2.4.1. Estimate emissions from vehicles' trips

To obtain emissions from vehicles' trips, there are two possible methods. The first method involves assessing emissions by calculating the instantaneous quantity of pollutants emitted using instantaneous models (Vieira da Rocha et al., 2015) such as PHEM (Hausberger, 2003). The second method aggregates trips over a given time period to match the required emission model inputs. The first method requires detailed time step information (1 s), which is not available as a MAT-Sim output. Moreover, while Symuvia's traffic outputs can be directly implemented in an emission model, MATSim requires preliminary processing. In a mesoscopic traffic model like MATSim, vehicle trips are not explicitly described. The outputs consist of the time each vehicle spends on every street it traverses, resulting in sequences of average speeds per street and per vehicle. Although the first method provides more detailed estimations (Lejri et al., 2018; Quaassdorff et al., 2016), it is not suitable for MATSim's outputs due to the flattened estimation of speeds available.

In this paper, emissions from vehicles' trips have been calculated via the second approach using the COPERT (Computer Programme to calculate Emissions from Road Transport) 5 model (Ntziachristos et al., 2009). Funded by the European Environment Agency, COPERT is widely used by European countries for compiling national emission inventories. Emission at the trip level is then obtained according to the vehicle fleet of Lyon for the year 2021 using the formula:

$$E(t) = \sum_{c}^{t} D_{c}(t) \cdot F_{NO_{x}} \cdot V_{c}(t) \quad (1)$$

where, for the time period $t = 15$ min, $F_{NO_x}$ is the unitary emission factor (g/km) of pollutant NOₓ for the vehicle c, $D_c(t)$ is the travel distance (km), and $V_c(t)$ is the speed (km/h).

### 2.4.2. Estimate emissions at street level

COPERT can be used to estimate emissions at both the trip and street levels. To estimate emission at the street level, we computed average speed and travel distance of all vehicles which pass through a street over 15 min time intervals and calculated emissions via Eq. (1).

To enhance our understanding of the features of these traffic models and to better delineate the limits of the modeling coupling, we employ a second emission model: The Handbook Emission Factors for Road Transport (HBEFA) 4.2 model (Hausberger, 2009). Similarly to COPERT, HBEFA offers detailed emission factors for various pollutants, including NOₓ, CO₂, and PM₁₀, for a broad spectrum of vehicle types. However, important differences exist between them: while COPERT provides emission factors as continuous functions of speed, HBEFA uses discrete emission factors based on predefined traffic service levels (e.g., free flow, congestion), street configurations (e.g., urban roads,

highways), areas (urban, rural), and speed limits. HBEFA provides a set of thresholds that allow for determining the traffic service level by combining the average speed on a given street with the corresponding street configuration, type of area, and speed limits. These features are available for each link in our networks.

Several studies proposing COPERT and HBEFA as valuable emission models for coupling with microscopic and mesoscopic traffic models (see references in Fallah Shorshani et al. (2015)) and various authors have pointed out limitations in these approaches. Borge et al. (2012), in a comparison of COPERT and HBEFA for NOₓ emissions, observed significant discrepancies at smaller scales for COPERT, as well as a tendency for HBEFA to overestimate emissions, mainly due to its consideration of stop-and-go traffic situations. Similarly, Lejri et al. (2018) highlighted COPERT's sensitivity to average speed estimation, while in another study, Lejri and Leclercq (2020) discussed the bias introduced by mean speed definitions when coupling microscopic traffic models with COPERT. Samaras et al. (2014) addressed COPERT's limitations regarding link lengths, emphasizing that it struggles with links shorter than 400 m. Finally, Badin et al. (2017), in a comparison of emissions estimated with COPERT, HBEFA, and PHEM using a microscopic traffic model (Aimsun), demonstrated the ability of instantaneous emission models to better capture the variability in vehicle kinematics.

### 2.4.3. Compare emission estimations spatially

The goal of this paper is to provide a spatial and temporal comparison of emissions obtained from traffic estimations using two different traffic models. While comparing estimations temporally is relatively straightforward by considering the same time interval (in this paper, 15 min), spatial comparisons pose a greater challenge.

Due to the different network structures used in MATSim and Symuvia (see Section 2.3.1) the network cannot be used as a shared element of comparison. To overcome this limitation, we use a regular grid of 200 × 200 meter cells that overlaps both networks. We compute the percentage of the street length that overlaps each cell and use this to calculate relative travel distances, average speeds, and emissions. This approach ensures that total emissions are preserved and allows for a shared grid to facilitate pairwise comparisons of emissions between cells.

To compare our outputs, we normalize the pairwise difference between each cell using the formula:

$$D(c, t) = \frac{E_{MATSim}(c, t) - E_{Symuvia}(c, t)}{max(E_{MATSim}(c, t), E_{Symuvia}(c, t))} \quad (2)$$

where, for the cell c at time t, the normalized difference is the ratio of the difference between emissions to the maximum value over all cells at time t. Values range between $D(c,t) = -1$ (when $E_{MATSim}(c,t) = \max$ and $E_{Symuvia}(c,t) = 0$), $D(c,t) = +1$ (when $E_{MATSim}(c,t) = 0$ and $E_{Symuvia}(c,t) = \max$). The delta is $D(c,t) = 0$ when $E_{MATSim}(c,t) = E_{Symuvia}(c,t)$.

## 3. Results

In this section, we present the traffic (Section 3.1) and emissions outputs. Emissions have been aggregated at the trips level (Section 3.2), at the street level (Section 3.3), and at a square cells grid (Section 3.4).

COPERT and HBEFA provide a series of exhaust and not-exhaust emission factors for pollutants such as carbon dioxide (CO₂), nitrogen oxides (NOₓ), and particulate matter (PM) for a given vehicle fleet. In this article, only results relative to exhaust NOₓ emissions are presented.
<figure><img src="image_6.png" /><figcaption>Fig. 3. The number of vehicles generated per 15 min interval.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. The number of vehicles over the network per second.</figcaption></figure>

### 3.1. Traffic outputs

Due to inherent differences in the design of the two traffic models, discrepancies between the networks are inevitable. Recognizing these differences is crucial for understanding their impact on estimations and interpreting results accurately, highlighting the strengths and limitations of each model in estimating traffic-related emissions.

The simulation consists of 5 h (between 5:00 am and 10:00 am) representing the morning rush hour. Fig. 3 depicts the number of vehicles created per 15 min time slot. Four distinct conditions are observed: before 6 a.m., the number of vehicles is low; the distribution peaks at 8:00 a.m., decreases until 9:15 a.m., and remains relatively stable until the end of the simulation. Despite the input data being the same for both simulations, some differences are observed in the simulated outputs. Fig. 4 shows the number of vehicles over the network per second in both simulations. As expected, both charts exhibit a Gaussian-like distribution.

Fig. 5 shows the average speeds calculated at the trip level using two methodologies for both MATSim and Symuvia over 15 min intervals: (1) the arithmetic mean speed (AMS), which is the mean speed of vehicles over the time period, and (2) the spatial mean speed (SMS), which is the ratio between the total travel distance and the total travel time over the time period (Edie, 1965). These two speed metrics can yield

<figure><img src="image_8.png" /><figcaption>Fig. 5. The arithmetic mean speed (AMS) and spatial mean speed (SMS) for 15 min intervals calculated from MATSim and Symuvia outputs.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. The distribution of the total travel distance per speed class of 5 km/h aggregated at the trip level.</figcaption></figure>

significantly different results, especially under varying traffic conditions, leading to large errors in emission estimation. SMS is independent of the spatial and temporal scale of observation. Therefore, although calculating SMS can be more challenging due to data limitations, it is generally regarded as more precise for estimating traffic emissions. In Lejri et al. (2018), the authors compared various speed definitions, demonstrating that SMS is the most accurate and compatible with emission estimates, as it effectively characterizes vehicle kinematics over the entire road segment. They showed that the relative error between emissions estimated using AMS and SMS is influenced by traffic conditions. In highly congested situations, the emissions can diverge significantly.

The speeds calculated by MATSim and Symuvia obtained per 15 min intervals and for each street show high similarity, with Pearson correlation indices of 0.991 for MATSim and 0.985 for Symuvia between AMS and SMS. In both cases, speeds remain fairly constant during the initial charging section of the simulation before suddenly decreasing and then increasing again. As observed in Fig. 4, the peaks do not occur simultaneously in both simulations. The maximum difference between speeds for both models is observed at their lowest speeds (at 7:45 in
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** The distribution of the total travel distance per speed class of 5 km/h aggregated at the street level.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** NOₓ emission estimations for traffic models Symuvia and MATSim using Arithmetic Speed Mean (ASM) and spatial mean speed (SMS) over 15 min intervals with the COPERT model.</figcaption></figure>

Symuvia and 8:15 in MATSim): for Symuvia, the delta between the AMC and SMS value at 7:45 is 10.96 km/h while for MATSim at 8:15 is 3.14 km/h. This indicates that, in our study case, the method of computing average speed has less impact in MATSim than in Symuvia.

In Figs. 6 we show the distribution of the total travel distance per speed class of 5 km/h obtained per 15 min intervals at the trip levels. We observed that most travel distances are at 25 km/h in MATSim, accounting for 14.37% of the total travel distance, and at 85 km/h in Symuvia, accounting for 10.35% of the total travel distance. This indicates that, despite having the same origin-destination matrix for both models, a large portion of trips in Symuvia are on the ring road and a large portion of trips in MATSim are in the city center. In the next two sections, these observations help us figure out the delta observed between emission estimations.

## 3.2. Emissions from vehicles' trips

Despite differences in the shape of the travel distance distribution, this does not result in a significant difference between the estimated global emissions. The method used to compute speeds has a more

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** NOₓ emission estimations for traffic models Symuvia and MATSim using COPERT and HBEFA over 15 min intervals.</figcaption></figure>

substantial effect. Fig. 8 shows emissions per 15 min intervals at the study case scale. We observe that emissions calculated using the average speed in Symuvia are lower. At 7:45, emissions calculated with AMS are 16.45% less than those calculated with SMS. The lower values of speeds for Symuvia are around 40 km/h and 29 km/h for ASM and AMS respectively. The corresponding COPERT emission factors are: $F_{NO_x}(V = 40 \text{ km/h}) = 0.35 \text{ g/km}$ and $F_{NO_x}(V = 29 \text{ km/h}) = 0.40 \text{ g/km}$. The emission factor used for average speed is 16.07% less than the emission factor used for spatial speed.

We can conclude that, despite the differing travel distance profiles between MATSim and Symuvia, this does not significantly impact total emissions. The most relevant impact is due to the method of calculating and providing speeds to the COPERT model to obtain the emission factor.

Estimating emissions at the trip level is useful for decision-makers to understand the dangers of traffic conditions, but it does not provide enough spatial information. Therefore, we use in the next section these outputs as a benchmark for estimating emissions at the street level.

## 3.3. Emissions at street level

In this section, NOₓ emissions for passenger cars have been calculated for the vehicle fleet of Lyon in the year 2021, aggregated at the street level and for 15 min intervals. Emissions are estimated from traffic variables deriving from both traffic models and with both emission models.

Fig. 9 shows the estimated total NOₓ emissions grouped by 15 min intervals for the four combinations of traffic and emission models.

A high range of variability can be observed when comparing NOₓ emissions from the two traffic models. MATSim total emissions are significantly higher than Symuvia by 72.48% for HBEFA and 153.77% for COPERT. Moreover, emissions per street are more dispersed in Symuvia than in MATSim, particularly when emissions are higher during the simulation (8:00 for Symuvia and 8:15 for MATSim). To assess the dispersion of values, we computed the standard deviation during the congestion period. The values are as follows: $\sigma$(Symuvia, COPERT) = 9.86, $\sigma$(Symuvia, HBEFA) = 16.04, $\sigma$(MATSim, COPERT) = 4.83, and $\sigma$(MATSim, HBEFA) = 11.58. Thus, the standard deviation is higher in Symuvia than in MATSim for both emission models.

Significant differences are also observed when comparing emission models. HBEFA total emissions for the whole time simulation are higher than COPERT by 70.79% for Symuvia and 16.08% for MATSim. This confirms previous findings that HBEFA tends to overestimate NOₓ emissions. In Borge et al. (2012), the authors compared emissions
<figure><img src="image_13.png" /><figcaption>Fig. 10. NOₓ emissions for Symuvia and MATSim models using HBEFA and COPERT, aggregated at street level grouped by 15 min intervals and speed class of 5 km/h.</figcaption></figure>

estimated by COPERT and HBEFA for the city of Madrid, observing that NOₓ emissions estimated by HBEFA were 21% higher than those from COPERT. This overestimation was attributed to how HBEFA handles the classification on traffic condition from the average speeds. HBEFA uses a statistical threshold to define traffic conditions based on a given (continuous) speed estimated by emission models, often leading to an overestimation of car density. To provide further details on this crucial aspect and investigate its impact on our results, we present Fig. 7. In this figure, we grouped the total travel distances over the simulation by 5 km/h speed increments for both traffic models. The majority of total travel distance in MATSim occurs at 65 km/h (20% of total travel distance), while in Symuvia it occurs at 85 km/h (24% of total travel distance). This is primarily due to the trip assignment model in the traffic simulations, which results in more trips being routed via the ring road in Symuvia compared to MATSim. At this speed, the COPERT emission factor reaches its lower value at F(NOₓ)(V = 65 km/h) = 0.289 g/km, therefore the delta of total travel distances has low effect on the total emissions.

Fig. 10 depicts the total emissions grouped at street level, by 15 min intervals and speed class of 5 km/h. This figure helps us understand the significant differences in emissions computed from MATSim and Symuvia. The emissions for both traffic models are highest during the most congested time slots, which correspond to the periods with the highest total travel distance. For Symuvia, the highest emissions occur at 85 km/h in HBEFA. Conversely, emission obtained with COPERT at 85 km/h are significant lower (even lower than those at 45 km/h). This discrepancy is due to the different emission models: in COPERT, the emission factor F(NOₓ)(V = 85 km/h) = 0.299 g/km, while in HBEFA it ranges between 0.305 g/km and 1.217 g/km depending on traffic conditions. Similarly, at 45 km/h, the COPERT emission factor is 0.331 g/km,

whereas the HBEFA factor ranges between 0.417 g/km and 1.217 g/km. HBEFA's factors for the first class come from streets with speed limits of 90 km/h, and for the second class from streets with speed limits of 50, 70, or 90 km/h. For MATSim, emissions are similar between the two models, though HBEFA tends to overestimate emissions for lower speed services compared to COPERT. As we observed in the previous paragraph, no major range is observed at 65 km/h because the COPERT emission factor at 85 km/h is 0.289 g/km, and for HBEFA, it ranges between 0.313 g/km (free flow service at 70 km/h) and 0.305 g/km (heavy flow service at 90 km/h).

To reduce bias from different street network configurations used in the traffic models, we normalized emissions computed at street level by the length of the corresponding street (Fig. 11). The global tendencies remain consistent, but the ranges are more coherent. The difference between total emissions of the simulated scenario computed with HBEFA and COPERT normalized by street length is 55.34 g/km in Symuvia and 39.74 g/km in MATSim. Similarly, the difference between MATSim and Symuvia is 94.38 g/km for HBEFA and 110.02 g/km for COPERT.

### 3.4. Spatial diversities of emission estimations

In the previous section, we observed significant statistical differences in emissions between the two traffic models. In this section, our aim is to show the difference between traffic models spatially.

Fig. 12 shows the normalized pairwise difference between each cell obtained with Eq. (2). The probability distributions of normalized differences are shown in Fig. 13. The mean values for the three scenarios (06:00, 08:00, and 10:00) are 0.01, 0.28, and 0.034 for HBEFA and 0.01, 0.049, and 0.011 for COPERT. This indicates that in all scenarios
<figure><img src="image_14.png" /><figcaption>Fig. 11. Normalized emissions by length of streets per 15 min intervals.</figcaption></figure>

and for both emission models, NOₓ emissions estimated with MATSim outputs tend to be higher than those estimated with Symuvia outputs, especially under congested conditions, where the mean is higher for HBEFA compared to COPERT.

The median values are also positive, ranging between 0.002 and 0.29, indicating that MATSim overestimates emissions in 56% to 79% of the cells.

To estimate the variability and symmetry of the normalized differ-
ence distribution, we compute the standard deviation (to measure the
variation from the mean) and skewness (to assess the asymmetry of
the distribution) indicators for the three scenarios in both estimations.
The standard deviations show more dispersion in congested scenarios
(0.106 in COPERT and 0.178 in HBEFA) compared to pre-congested
(0.021 in COPERT and 0.032 in HBEFA) and post-congested scenarios
(0.033 in COPERT and 0.053 in HBEFA). Skewness indicators reveal
that distributions are more skewed in COPERT (2.973 at 06:00, 3.732
at 08:00, and 0.025 at 10:00) compared to HBEFA (2.152 at 06:00,
0.618 at 08:00, and 0.929 at 10:00).

Figs. 14 and 15 depict the pairwise values of cells grouped by emission models and traffic models, respectively. The variance of the residuals between the values and the line of equality (x=y) is lowest at 06:00 (3.726 and 18.448 between COPERT and HBEFA for Symuvia and MATSim, and 16.682 and 16.624 between Symuvia and MATSim for COPERT and HBEFA) and highest at 08:00 (310.323 and 593.846 between COPERT and HBEFA for Symuvia and MATSim, and 443.014 and 527.060 between Symuvia and MATSim for COPERT and HBEFA). This indicates more heterogeneity in the congested scenario, which is less obvious in MATSim than in Symuvia.

The Pearson correlation indicators obtained by comparing MATSim and Symuvia NOₓ emissions per cell and per 15 min interval are 0.335, 0.349, and 0.115 for COPERT and 0.478, 0.539, and 0.288 for HBEFA. Similarly, between HBEFA and COPERT, the correlations are 0.702, 0.517, and 0.228 for Symuvia and 0.998, 0.995, and 0.986 for MATSim. This means that MATSim emissions calculated with both emission models are strongly correlated, while there is no significant correlation between MATSim and Symuvia estimates for both models.

4. Discussion

As stated in our objectives, this work addresses the uncertainties
arising from the coupling of traffic and emission models at different
spatial and temporal scales. The comparison of the modeling chains in
this study highlights key discussion points regarding traffic dynamics,
emissions, models validation, and the broader assessment of health
impacts.

4.1. Traffic dynamics

Regarding road traffic, the analysis reveals notable disparities be-
tween models. Although both traffic models rely on the same origin-
destination matrix, significant differences arise in the resulting traffic
conditions (see Figs. 5, 6, and 7). The study area was carefully se-
lected for its heterogeneity, including a ring road and urban subareas,
which influence the route assignment process of the models (see Fig.
1). MATSim tends to predict shorter trips through city-center areas,
while Symuvia directs vehicles towards faster trips over the ring road.
Although both models use the same origin–destination matrix, the
number of vehicles differs between them during the simulation due
to the varying time each vehicle takes to complete its trip (see Fig.
4). There are more vehicles in MATSim at any given time, leading to
greater total distances traveled at lower speeds compared to Symuvia,
where higher speeds predominate.

While both models are adept at simulating traffic-related emissions,
their features make them suitable for different types of studies. Due to
high computational requirements, microscopic models are often limited
to evaluating traffic emissions at very localized sites rather than across
entire cities or metropolitan areas (Fallah Shorshani et al., 2015).
Symuvia, with its detailed and accurate representation of vehicle condi-
tions, excels at small-scale, detailed simulations. In contrast, MATSim,
which provides a broader view of traffic conditions, is better suited for
large-scale urban simulations. Additionally, due to the different route
assignment algorithms in the traffic models (minimizing travel time in
Symuvia and maximizing individual utility in MATSim), traffic models
can be applied in various estimation of emissions, such as examin-
ing vehicle interactions in Symuvia or the impact of different modal
transport scenarios in MATSim. Understanding these differences allows
us to better appreciate the strengths and limitations of each model,
particularly in the context of estimating traffic-related emissions.

4.2. Emission estimations

Despite variations in traffic conditions, NOₓ emissions obtained per 15 min intervals and aggregated at the trip level remain similar across all simulation steps (see Fig. 8). While spatial speeds prove to be more accurate for the calculation of emissions in microscopic models, the speed calculation methodology has little effect on the calculation of emissions when mesoscopic traffic models are used.

Our observation that HBEFA traffic emissions are higher is consis-
tent with previous findings by Borge et al. (2012). Additionally, we
found that NOₓ emissions estimated with MATSim outputs are higher
than emissions estimated with Symuvia outputs at the street level for
both emission models. Symuvia’s emissions tend to be underestimated
compared to MATSim due to the prevalence of trips over the ring
road, where COPERT emission factors are lower than those used for
the majority of MATSim trips at lower speeds. In such cases, HBEFA
better accounts for emissions on the ring road. Moreover, the MATSim
estimates computed by both emission models are more similar than
those obtained by Symuvia in our study case, suggesting that MATSim
may be more steady for exposure estimation.

Spatial comparisons of traffic emissions confirm that emissions with MATSim are generally higher than with Symuvia. Higher values are often observed in city centers with MATSim, while Symuvia tends to show higher values on the ring road. However, in highly congested situations, Symuvia's estimates exceed those of MATSim in city center areas. This could be due to Symuvia's better capacity to reproduce high vehicle interactions and stop-and-go situations. Furthermore, HBEFA shows lower deltas than COPERT, and emissions computed with MATSim are strongly correlated between emission models.
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Normalized pairwise difference of NOₓ emissions between Symuvia and MATSim models computed with COPERT and HBEFA models. Green color indicate cells where MATSim overestimates Symuvia emissions.</figcaption></figure>

### 4.3. Modeling chain validation

Beyond the inherent differences between the modeling chains highlighted in this study, a logical next step would be to confront them with experimental data, both for the traffic module and the overall modeling chain.

Validating traffic models is a complex and often debated process. Readers interested in this topic can find examples of the technical and methodological challenges involved in the validation and calibration of various types of models, including microscopic (Brockfeld, Kühne, & Wagner, 2004; Hafram, Valery, & Hasim, 2023; Punzo, Montanino, & Ciuffo, 2014), mesoscopic (Presinger, 2021), macroscopic (Jenelius, Kristoffersson, & Fransson, 2017), stochastic (Wagner, Buisson, & Nipold, 2016), and agent-based models (Bastarianto et al., 2023; Zhang, Osorio, & Flötteröd, 2017), for both large (Shafiei, Gu, & Saberi, 2018) and small networks (Elesawey & Sayed, 2011). One key challenge is that most traffic models are typically designed for a representative day

of the year, often overlooking weekly or seasonal variations. As a result, in cities with fluctuating commuting patterns, traffic models may significantly diverge from real-world conditions, complicating validation efforts.

Further research will focus on validating the entire modeling chain for pollutant concentrations through a dedicated measurement campaign. A more accurate assessment of exposure to pollutants—which directly results from differences in pollutant emissions—would require the use of a dispersion model tailored to the spatial scales under study. Dispersion models vary in spatial and temporal scales, ranging from fine-scale Computational Fluid Dynamics (CFD) models to mesoscopic and macroscopic models for neighborhood-, city-, and regional-level studies. Each model incorporates different physical mechanisms, such as weather conditions and chemical reactions, adding further complexity. Extending the analysis to include the effects of the studied representations on health impacts would require a more detailed sensitivity analysis, incorporating pollutant dispersion calculations and
<figure><img src="image_16.png" /><figcaption>Fig. 13. Probability distributions of normalized differences in NOₓ emissions between Symuvia and MATSim models for HBEFA and COPERT.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 14. Correlation of NOₓ emissions per cell between COPERT and HBEFA models at different times.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 15. Correlation of NOₓ emissions per cell between Symuvia and MATSim models at different times.</figcaption></figure>

the laws linking exposure to effects (including their variability and uncertainty).

Finally, we would like to emphasize the most important difference between the two traffic models. While Symuvia is a vehicle-centered model that provides finely detailed information on vehicle trajectories, MATSim is an individual-centered model that reproduces individual modal choices through walking, cycling, individual vehicles, shared vehicles, and public transport. For the exposure study, where it is crucial to consider the daily evolution of population density over the urban area, MATSim, despite its lack of detailed information on instantaneous emissions, can provide significant support by integrating individual conditions into the exposure estimation.

## 5. Conclusion

Estimating human exposure to traffic-related air pollutant emissions is a challenging task that requires highly detailed spatial and temporal information. Modeling chains are often used to investigate these phenomena. Traffic models differ in their scales of representation and mechanisms for reproducing physical phenomena, leading to heterogeneous results. Moreover, emission models, which have distinct characteristics, can produce significant differences even when fed with the same traffic condition estimates. Despite their promising results and the extensive literature offering guidelines for designing modeling chains, the precise limits of validity in the coupling between traffic and emission models remain not fully defined.
The aim of this paper was to provide a consistent foundation for decision-makers and modelers in estimating traffic emissions when investigating human exposure to air pollutants. Therefore, we evaluated the coupling of two traffic models, Symuvia and MATSim, with two emission models, COPERT and HBEFA, for emission estimation, setting the stage for future human exposure assessments. To highlight these differences, we chose to retain all traffic model distinctions, such as network geometry and route assignment choice, over the same study case and with the same traffic demand. Emissions were computed at the trip, street, and 2D grid cell levels. Despite significant differences in traffic conditions, global emissions computed at the trip level were similar, confirming that both approaches are useful for emission estimation. However, when spatial factors were considered, more discrepancies emerged, leading to significant differences in the estimated emissions.

Comprehensive validation of traffic models is a challenging task at a large scale. This validation requires detailed traffic data, which is typically unavailable, especially at the street-level detail necessary for accurate exposure estimation. However, despite these limitations, confronting the entire modeling chain with real traffic and air pollutant data should be the next step in this research. One of the major challenges will lie in the heterogeneity of the input data for this modeling, coming from time periods spanning several years, for a network that is itself constantly evolving. Additionally, spatial analysis of emissions tends to be more uncertain than global emission estimates. Therefore, using trip-level emission estimates as a benchmark for spatially distributed estimations is recommended. We show that different methods of estimating emissions at the trip level lead to similar results, whereas estimates at the street level can differ significantly. Two couplings, MATSim+COPERT and Symuvia+HBEFA, showed particular promise, as they more closely aligned with the aggregated emissions at the trip level. These findings give us confidence in extending the modeling chain to include pollutant concentration estimates.

The aim of this work is to provide generalizations from a specific case study that could be applicable in other contexts. Based on our findings and a review of the literature, we expect to observe the following in other contexts: (1) Microscopic traffic models offer greater sensitivity to congestion, making them more accurate for street-level emission estimates compared to mesoscopic models. (2) HBEFA tends to overestimate emissions in low-speed, congested conditions, particularly when applied to mesoscopic models. (3) COPERT, which relies on average speed data, is less accurate for microscopic models and often underestimates emissions. Further research, including validation of traffic models and comparisons with real emissions measurements, is needed to strengthen the confidence in these generalizations.

## CRediT authorship contribution statement

**Michele Tirico:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Valentin Le Bescond:** Writing – review & editing, Validation, Software, Resources, Methodology, Formal analysis, Data curation. **Delphine Sengein:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Pascal Gastineau:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Arnaud Can:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

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

## Data availability

Data will be made available on request.

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