# Reassessing air pollution exposure: How daily mobility and activities shape individual risk in greater Paris

Taos Benoussaïd, Isabelle Coll, Hélène Charreire, Inès Makni, Malo Costes, Arthur Elessa Etuman

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Taos Benoussaïd, Isabelle Coll, Hélène Charreire, Inès Makni, Malo Costes, et al. Reassessing air pollution exposure: How daily mobility and activities shape individual risk in greater Paris. Computers, Environment and Urban Systems, 2025, 122, 10.1016/j.compenvurbsys.2025.102340. hal-05336612

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# Reassessing air pollution exposure: How daily mobility and activities shape individual risk in greater Paris

Taos Benoussaïd<sup>a, *,</sup> Isabelle Coll<sup>a</sup>, Hélène Charreire<sup>b, c</sup>, Inès Makni<sup>a</sup>, Malo Costes<sup>a</sup>, Arthur Elessa Etuman<sup>a, d</sup>

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

## ARTICLE INFO

### Keywords:

- Air quality modelling
- Exposure assessment
- Mobility
- Microenvironments
- Agent-based model

## ABSTRACT

Understanding individual exposure to air pollution is essential for tackling environmental inequalities and informing public policies aimed at reducing disparities. Traditional approaches often focus on residential locations, but exposure is intrinsically linked to daily mobility, activities and socio-economic profiles. This study presents new results based on a dynamic exposure modelling approach that takes these dimensions into account, offering a more realistic assessment of air pollution risk. By integrating high-resolution air quality data with detailed information on individual mobility, activities and socio-economic characteristics, we quantify the exposure of 400,000 individuals in the Île-de-France region. Our approach takes into account all the environments that individuals visit during the day and the time spent in each of them, going beyond static exposure assessments based on residential location. We compare this dynamic model with traditional exposure calculations, revealing significant differences in the spatial distributions of PM10 and NO2 exposure. Our analysis highlights how mobility patterns and daily activities contribute to total exposure, demonstrating that place of residence is only one part of reality. For example, commuting, workplaces and leisure activities play a key role in determining individual exposure levels. The results of our study show that dynamic exposure calculation provides a better understanding of exposure factors and offers a framework for understanding environmental inequalities. By shifting the focus from home-based to person-based exposure, our approach makes it possible to identify levers for action to reduce disparities and support targeted public health action. Our study redefines the way in which we assess the risks associated with air pollution, by highlighting the need to take into account mobility behaviour and individual trajectories.

## 1. Introduction

Exposure to air pollution is a major health and social concern. Even though pollution levels have fallen over the last few decades in most major cities (European Environment Agency (EEA), 2018; HEI, 2022), 91 % of the world's population lives in places where the recommended threshold limits for the protection of human health are not respected (WHO, 2006). Historically, epidemiological cohorts and toxicological studies have established statistical and causal relationships between air pollution and mortality, as well as the mechanisms of action of atmospheric pollutants on the development of pathologies, based on the estimation of exposure at the place of residence. The particularly

deleterious nature of certain mixtures, such as road combustion, has been identified, and the methodologies established make it possible to quantify the contribution of air pollution to the appearance of numerous pathologies, particularly respiratory or cardiovascular (Beelen et al., 2008; Cesaroni et al., 2013; Curtis et al., 2006; Thurston et al., 2016; WHO, 2013). These studies have led to significant advances in our knowledge of the impacts and mechanisms induced by pollution on health. This representation of exposure has been widely used in health cohorts but also in the atmospheric sciences community. However, this approach has recently been called into question because it masks very different inter- and intra-individual exposures, given that it does not take into account the individual dimension of exposure - which is due to

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

<footnote><sup>b</sup> Lab'Urba, Université Paris Est Créteil, Créteil, France</footnote>

<footnote><sup>c</sup> Institut Universitaire de France (IUF), France</footnote>

<footnote><sup>d</sup> AME-SPLOTT, Univ Gustave Eiffel, IFSTTAR, F-77447 Marne-la-Vallée, France</footnote>

<footnote>* Corresponding author at: 61 avenue du Général de Gaulle, 94000 Créteil, France.
E-mail address: taos.benoussaid@lisa.ipsl.fr (T. Benoussaïd).</footnote>
the various environments visited along daily activity loops. Indeed, with the development of high-resolution air quality modelling and low-cost pollution sensors, personal exposure has recently become a topic of debate, as people take up the issue of inequalities in exposure and their right to breathe unpolluted air (Perelló et al., 2021). To deal with this major societal expectation, environmental and health studies needed to switch from air quality evaluation to high-resolution personal exposure data, and initiate the debate on individual risks.

The pioneer meta-analysis conducted by Avery et al. (2010) showed that the use of ambient PM2.5 concentrations as a proxy for exposure is not sufficient and may tend to underestimate the actual risk of exposure. The authors suggest that the risk of exposure misclassification may be significant and that studies using this proxy should be treated with caution. Since then, a growing number of studies in the literature have explored the differences between exposure levels that include micro-environments and individual mobility, and those that consider only the place of residence (Nyhan et al., 2016; Tayarani & Rowangould, 2020; Yoo et al., 2021). The study of Thurston et al. (2016) showed that the risk of mortality from cardiovascular disease associated with PM2.5 varies according to its composition and source. In particular, it has shown that long-term exposure to particles from coal combustion and elemental carbon - a tracer of combustion sources and especially diesel traffic in urban areas - is strongly associated with an increased risk of mortality from cardiovascular disease. This highlights the need not only to consider home average concentrations as an exposure proxy, but also to take into account the diversity of urban environments when calculating exposure and assessing health impacts.

The estimation of exposure to air pollution therefore challenges the environmental sciences to provide integrated data that goes far beyond classic concentration maps. Exposure asserts itself as a composite term which needs to integrate (i) an improved knowledge of air quality, both in outdoor and indoor environments and at high spatial resolution and (ii) the capacity to integrate individual mobility practices and their relation to living, work and leisure areas. Exposure assessment thus raises two major scientific issues.

The first issue relates to the assessment of pollutant concentrations in urban environments, which can be carried out using either measurements or numerical modelling approaches. While the monitoring network measurement stations provides very precise information on local concentrations, they are limited in number, which does not give full access to high-resolution exposure over the whole city. In the 2010s, the development of portable sensors has led to a rapid increase in our knowledge of air composition at the street scale and, above all, enabled citizens to take up the issue of their exposure. These low-cost sensors have notably been deployed in parts of the world where there is a lack of monitoring networks and high-quality data (WMO, 2021). Because of their small size, relatively low cost and ease of use, these instruments can also be easily deployed in homes, providing data on concentrations in private environments. Thus, while modelling can be used to assess the exposure of a very large sample of individuals, sensors can also be used to access the real-time exposure of a group of individuals when they are worn all day, providing us with valuable information about their exposure. However, while this approach is powerful, it can cover potential biases linked to inappropriate use of the sensors by the non-expert users (European Environment Agency (EEA), 2020), remains difficult to implement on large spatial scales and still requires scientific development to overcome technical biases. The literature indeed identifies problems of data reliability and accuracy (Karagulian et al., 2019; European Environment Agency (EEA), 2020; Chojer et al., 2020) which can be sensor-specific or user-dependent (mostly due to a lack of compliance with the protocols). This is shown in the article of Lewis and Edwards (2016) which describes how sensor can present problems of specificity, for example by responding to pollutants other than those measured. Moreover, the data from these sensors are not publicly available, which makes them difficult to use.

The development of modelling tools targeting the fine scale and the

representation of environments frequented by individuals opens up the possibility of diagnosing gradients in urban areas, on large samples, identifying more global exposure trends and making it possible to link practices, living areas and exposure. Over the last fifteen years, the atmospheric science community has been working on the development of models to reproduce fine-scale concentrations, which required the use of models different from the regional Chemical Transport Models (CTMs) developed up to then, and which have been the subject of several literature reviews (Bai et al., 2018; Baklanov & Zhang, 2020; Kukkonen et al., 2012; Sokhi et al., 2022; Thunis et al., 2016; Zhang et al., 2012). Various approaches have been developed, ranging from Computational Fluid Dynamic (CFD) (Kwak et al., 2015; Nuterman et al., 2021) to Large Eddy Simulation (Wolf-Grosse et al., 2017), Street-in-Grid methods (Briant & Seigneur, 2013; Hood et al., 2018) and statistical approaches (Berrocal et al., 2020; Moussiopoulos et al., 2012). All of these approaches have their own advantages and disadvantages, which make it possible to cover a wide range of research needs. However, these developments have not been designed with individual exposure in mind, so they do not address the issue of exposure in spaces uncoupled from the urban atmosphere, such as living areas, transport or confined spaces. Indoor environments are a critical element in calculating exposure, both in terms of time spent and the risk of exposure to additional sources of pollutants. Qualifying these indoor environments can be based on national measurement campaigns such as those conducted by the French Observatory on Indoor Air Quality (OQAI or targeted scientific studies, as shown in the literature review by (Vardoulakis et al., 2020)). Recent work has also proposed the use of probabilistic or deterministic models to reproduce concentrations in indoor environments (Dimitroulopoulou et al., 2017; Silva et al., 2017). These environments, particularly homes, are highly dependent on the socio-economic characteristics of individuals and are now a real research challenge (Ferguson et al., 2020).

The second issue in exposure is the knowledge of the agenda and individual trajectories of mobility. One of the challenges posed by this question is linked to the fact that there are as many types of mobility as there are individuals, and we therefore need to be able to reproduce this diversity. There are several possible approaches to accessing this level of information. Like air quality data, mobility data can be acquired by measurement - using GPS data or surveys - or by modelling people's agendas and mobility. The challenge, therefore, is not to rethink everything in order to model exposure, but to integrate these models into air quality modelling platforms, so as to be able to generate parameters relating to agendas and mobility. The scientific community has taken up this issue by producing new exposure data that now make it possible to quantify the differences between a static approach that considers concentrations at home and a dynamic approach that incorporates concentrations in all the environments crossed, and also to provide more integrated exposure analyses (by group, location or practices). The review of the literature presented above has highlighted two types of so-called 'dynamic' exposure studies: those calculating a spatially aggregated dynamic exposure by zone and those proposing an explicit dynamic exposure, i.e., one that considers exposure at each time step.

The calculation of an aggregated dynamic exposure is based on population data and their various trajectories of a different nature, such as mobility surveys (Ramacher & Karl, 2020; Setton et al., 2011; Singh et al., 2020; Tang et al., 2018), cellular/GPS data (Dewulf et al., 2016; Gariazzo et al., 2016; Liu et al., 2013) or mobility modelled data (Dhondt et al., 2012; Dons et al., 2014), crossed with modelled air quality data at various scales. Ramacher and Karl (2020) propose an innovative exposure analysis by calculating the contribution of environments to total exposure by integrating transport modes. Dhondt et al. (2012), Dons et al. (2014), Dewulf et al. (2016) and Singh et al. (2020), show that exposure is higher when mobility is considered and Gariazzo et al. (2016) highlight the loss of information of static exposure in terms of daily variability of concentrations. In parallel, other research teams have developed explicit modelling approaches based on individuals, by
coupling models based on individual activity (Activity Based Model – ABM) and/or survey data with air quality models. The first study to combine these two types of models were those by Beckx et al., (Beckx, Int Panis, Arentze, et al., 2009; Beckx, Int Panis, Van De Vel, et al., 2009), which represented a major step forward in calculating hourly dynamic individual exposure in the Netherlands. Hatzopoulou and Miller (2010), Hatzopoulou et al. (2011), and Gurram et al. (2019) also show the potential of this explicit dynamic approach by considering the specific exposure to road traffic and proposing a more detailed socio-environmental analysis of exposure for the latter. Other works have also crossed ABM data with sensor concentrations coupled with machine learning (random forest model) to calculate exposure (Lu, 2021) or survey data with modelled concentrations (Smith et al., 2016). Finally, de Nazelle et al. (2009) have developed an integrated exposure model, BESSTE (Built Environment Stochastic Spatial Temporal Exposure), the aim of which is to assess the risk associated with an increase in pollutant uptake when using active modes of transport (particularly cycling). This platform combines activity patterns derived from the CHAD (Consolidated Human Activity Database) model with concentration data derived from the interpolation of several different types of data (monitoring data, regional simulation data and micro-scale dispersion data around traffic routes). These studies provide new insights, but they only address the issue of exposure in part, and still have a number of limitations, whether in terms of the resolution of concentrations (a few kilometres) (Beckx, Int Panis, Arentze, et al., 2009; Beckx, Int Panis, Van De Vel, et al., 2009; Dons et al., 2014; Gariazzo et al., 2016; Setton et al., 2011) and also in terms of assessing people's journeys (de Nazelle et al., 2009; Hatzopoulou et al., 2011; Ramacher & Karl, 2020; Singh et al., 2020; Tang et al., 2018). Furthermore, the majority of the above-mentioned studies considered emissions decoupled from the mobility of individuals. They may also be disconnected from air quality models, like in (Lu, 2021), or even be incomplete by considering only traffic (Gurram et al., 2019). Finally, as Vallamsundar et al. (2016) highlighted, air quality in indoor environments has not been sufficiently explored, which results in a lack of overall information on the exposure of individuals, and modelling approaches suffer from this shortage.

In order to support public action and the evaluation of its effects, as well as to assess the health impacts of air pollution, it is necessary to represent exposure exhaustively, taking into account its individual dimension (Caplin et al., 2019). In this sense, and although many attempts are converging, the question of exposure to urban pollution still poses technical and scientific challenges, as follows:

- The need to represent air quality on a fine scale in order to reproduce all the urban gradients observed. This is the case for the variability of concentrations at street level, which is too rarely taken into account in dynamic approaches;

- The challenges of taking account of air quality in indoor environments, which account for a significant proportion of daily exposure but are very difficult to integrate into conventional approaches to qualifying ambient air quality;

- The issues surrounding the representation of individual spatial dynamics (activities and mobility) in exposure modelling platforms, both in terms of mobility choices and distances, but also in terms of activity typologies and living locations. These latter issues require socially and spatially differentiated modelling of the factors and behaviours that contribute to dynamic exposure.

The work presented here is a continuation of this ambition and aims to deepen our knowledge of the exposure by responding to the challenges mentioned above. Our approach is to transpose all the parameters that enable us to move from a population to exposure by having several models communicate sequentially. In our platform, we have integrated these 3 dimensions by (i) simulating fine-scale air quality using a CTM, CHIMERE, whose concentration fields are downscaled using a statistical method, (ii) representing individual dynamics using our OLYMPUS

emissions model and, finally (iii) integrating concentrations specific to indoor environments into the exposure calculation.

The plan of this article is as follows. We will first present the operating principles of the OLYMPUS-CHIMERE platform and the methodology developed for modelling dynamic exposure (Section 2) and its configuration for implementation in the Paris agglomeration (Section 3). The results will be presented and discussed (Section 4) in the form of a comparison between our approach and the one conventionally used to assess exposure. We will first look at the spatial distribution and the contribution of environments to total exposure, and then we will identify various exposure levers.

## 2. Methodology

We have developed an integrated urban modelling platform, OLYMPUS-CHIMERE (Elessa Etuman & Coll, 2023) which can be used to produce fine-scale concentration fields. This chain, whose general operation and structure are described in Fig. 1, is based on the coupling of a CTM (Fig. 1 (B)) with an emissions model based on individual mobility practices upstream (Fig. 1 (A)) and on the refinement of fine-scale concentration fields downstream (Fig. 1 (B)). By combining these models, we can link mobility, emissions and air quality. This platform can be used to produce innovative air quality scenarios incorporating regional policies, behavioural levers and mobility management. In the first part, we present the different models in the chain and in the second part, the cross-referencing between the models that enables us to model exposure (Fig. 1 (C)).

### 2.1. OLYMPUS-CHIMERE

#### 2.1.1. OLYMPUS

OLYMPUS (Fig. 2 (A)) is a model designed to simulate emissions linked to individuals' mobility and heating practices in urban areas (Elessa Etuman & Coll, 2018). It is based on a set of statistical approaches in different modules.

The first module is the creation of a synthetic population whose characteristics and location are based on national census and spatialised on the territory (Fig. 2 (A1)). The approach used is based on the use of conditional probabilities to reproduce the socio-demographic properties of individuals, via the deployment of a Monte Carlo Markov Chain Simulation (MCMC) (Etuman et al., 2024).

The second module constructs a spatialised and temporalized representation of individual activity loops in the region (Fig. 2 (A2)). This stage is based firstly on determining the potential attractiveness of an area by activity: for example, an area with more jobs will be more attractive to work in. Attractiveness is calculated using a gravity model that considers an acceptable average distance for each activity. This information on the distribution of jobs and their attractiveness is combined with agents' schedules to associate a location with each activity. Agents' schedules are then constructed according to their priorities, such as work, leisure or looking after a child, in terms of starting time and duration. These agendas are linked to activity areas to spatialise transport demand. Spatialising activities makes it possible to simulate the choice of mode of transport used, which is crucial for calculating urban mobility emissions. This modal choice is based on a utility function including monetary and time costs. The final stage involves allocating mobility on the territorial network, including journeys by car and on foot, and conserving times and routes for public transport, thus defining mobility flows.

The latest OLYMPUS module (Fig. 2 (A3)) for estimating emissions is based on the EEA recommendations for small combustion emissions (European Environment Agency, 2013) and the calculation of traffic emissions is based on the COPERT methodology (Ntziachristos et al., 2009). The traffic emissions taken into account are tailpipe emissions, non-exhaust emissions and emissions related to the evaporation of petrol from vehicles (Elessa Etuman & Coll, 2018). OLYMPUS emissions are
<figure><img src="image_3.png" /><figcaption>Fig. 1. Flowchart of the urban modelling platform.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Zoom on OLYMPUS model flowchart.</figcaption></figure>

used as input data for the Eulerian chemical transport model CHIMERE. For this step, emissions are spatialised over the same domain: OLYMPUS locates the meshes in the domain that contain the road sections used, and assigns the fraction of the emission to the identified meshes.

### 2.1.2. CHIMERE and downscaling

The Eulerian chemical transport model CHIMERE forms the second block in the modelling chain (Fig. 3 (B1)). CHIMERE has been used since the early 2000s as a forecasting tool on a national scale (by air quality monitoring agencies), but also for various research purposes on air quality and atmospheric chemistry at regional to continental scales (Menut et al., 2021; Rouil et al., 2009). CHIMERE calculates the concentration fields of gaseous and particulate phase pollutants and their spatiotemporal evolution over a territory. To do this, the area is discretised into a 3D mesh with a user-defined resolution that meets the objectives of the study. In each mesh of the domain, the physicochemical

processes are reproduced, including emissions and deposition, as well as reactive transport and mixing processes induced by the dynamics of the atmosphere. For particles, CHIMERE also considers emission, nucleation, coagulation, aging and absorption of gases at their surface. In each grid cell of the domain, pollutant concentrations are calculated on the basis of the principle of conservation of mass.

For this study, we are using the 2013 version of CHIMERE (Menut et al., 2013), in which the coupling with the meteorological fields is carried out offline, either with the WRF (Weather Research and Forecasting) forecasting model, or with that of the European Centre for Medium-Range Weather Forecasts (ECMWF). A pre-processing stage is used to project and interpolate the meteorological fields onto the domain grids. Anthropogenic emissions (traffic, industry, agriculture, etc.) are pre-processed and distributed over the grids of the study area for each chemical species on an hourly basis. They are derived from local inventories (supplied in France by air quality monitoring agencies) or
<figure><img src="image_5.png" /><figcaption>Fig. 3. Zoom on the air quality modelling module (CHIMERE and downscaling). The purple colour refers to the OLYMPUS outputs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

European inventories such as EMEP, TNO or HTAP. These inventories, generally classified by sector of activity (SNAP), provide emissions of the main gaseous pollutants such as non-methane volatile organic compounds (NMVOCs), nitrogen oxides (NOx), fine and coarse particles, carbon monoxide (CO), ammonia (NH3) and sulphur oxides (SOx). Biogenic emissions are generated by the MEGAN (Model of Emissions of Gases and Aerosols from Nature) global model (Guenther et al., 2006) using a land use and vegetation cover file and calendar and meteorological data. Finally, various chemical schemes are available to describe the reactivity of gaseous species and the formation and ageing of secondary organic aerosol (SOA).

An additional step was added at the output of CHIMERE to refine the kilometric hourly concentration fields in order to reproduce urban gradients (Fig. 3 (B2)). Indeed, in CHIMERE, all input data, physical and chemical processes as well as output data are processed at the mesh level (the resolution of which is chosen by the user). Considering this, the kilometric resolution is the finest mesh resolution available to CHIMERE, which is not sufficient to reproduce local urban gradients. A

statistical downscaling method was developed to reproduce environments close to road traffic where exposure is higher, using a learning method that corrects the signal intensity and amplitude (Elessa Etuman et al., 2020). We applied the downscaling method to 2 pollutants, PM10 and NO2, which we will focus on here to calculate exposure. This choice is based on a combination of 3 criteria: (i) the pollutant has to be of interest in terms of health risk, (ii) the concentration gradient close to road traffic must be a relevant issue (iii) there should be a sufficient number of measuring stations over the studied year to be able to carry out statistical downscaling. While PM2.5 meets the first 2 criteria, there unfortunately was not enough data over Paris for statistical training in 2009. With the deployment of new monitoring stations in recent years, it will be possible to consider this pollutant in future simulations.

## 2.2. IDEM: The Individual Dynamic Exposure Model methodology

In the newly developed model IDEM (Individual Dynamic Exposure Model), the calculation of exposure is based on the cross-referencing of

<figure><img src="image_6.png" /><figcaption>Fig. 4. Description of the exposure calculation module (IDEM). The purple colour refers to OLYMPUS, the green colour to CHIMERE and downscaling and the blue one to IDEM. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
population agendas and mobility from the OLYMPUS model with the concentrations generated by CHIMERE and downscaling (Fig. 1 (C)). In this module, the calculation is divided into three stages: in the first instance, exposure during travel (Fig. 4 (C1)), then indoor exposure during activities (Fig. 4 (C2)) and finally the dynamic exposure calculation (Fig. 4 (C3)), which combines these two items to construct a daily exposure profile. Details of these three components are given in the following section.

### 2.2.1. Travel exposure

Exposure during travel, which is the first stage of IDEM, requires information on the routes taken and the type of transport used by each agent. OLYMPUS produces two types of output on the mobility of agent: a table of trips linked to the activities of individuals and a table detailing, for each trip by car, on foot and by public transport, the routes taken. This table also contains information on the type of public transport used, as well as details of the routes taken and the time spent on each section of the route.

For trips made by car, on foot or by bus, the proximity concentrations resulting from the downscaling are assigned because these are environments strongly influenced by traffic. For trips made by car, it is necessary to take into account the fact that these are confined environments in which ventilation influences indoor concentrations. To this end, average outdoor-to-indoor transfer coefficients (I/O ratio) are applied to the proximity concentrations representing the immediate outdoor environment (Table 1) (Mehel et al., 2019). For bus trips, the concentrations assigned are recovered from known points on the routes, as these are not fully documented. This method makes it possible to introduce spatial and temporal variability at sub-municipal level by ensuring that concentrations are representative at neighbourhood level, and to provide a more realistic dimension than a tabulated average concentration.

The characterisation of concentrations in underground public transport is more complex than for cars and walking because there is no exhaustive description of concentrations in the literature, which are spotty and few in number. It is therefore difficult to obtain robust representative values in these environments. However, an underground measurement network has been deployed in 4 stations on the Paris metro since 2013. We have chosen to construct hourly values averaged over 3 months for PM10 and NO2. This makes it possible to reproduce the hourly and seasonal variability observed in the measurements. Finally, for other public transport (i.e., train and tram), we applied fixed average values taken from a review of the literature (Table 1) (Makni, 2022).

These developments enable us to increase the diversity and representativeness of exposure during travel. Once the concentrations have been assigned to each segment of a trip, the exposure per individual during the trip referred as Individual cumulative exposure per trip, is calculated following this equation:

$$E_n = \sum_j C_{jn} \times T_{jn} \qquad (1)$$

where $E_n$ represents the cumulative exposure over the trip n [µg.m⁻³.h], $T_{jn}$ the time spent on segment j of trip n [h], $C_{jn}$ the concentration of

<table><caption>**Table 1**
Ratio I/O and concentrations in public transport.</caption><thead><tr><th>Environment</th><th>NO2</th><th>PM10</th></tr></thead><tbody><tr><td>Car</td><td>0,82</td><td>1,07</td></tr><tr><td>Home</td><td>0,83</td><td>1,32</td></tr><tr><td>Other</td><td>0,85</td><td>0,68</td></tr><tr><td><strong>Transport</strong></td><td></td><td></td></tr><tr><td>RER/Train</td><td>40*</td><td>220*</td></tr><tr><td>Tramway</td><td>30*</td><td>30*</td></tr><tr><td>Underground connections</td><td>55*</td><td>190*</td></tr></tbody></table>

segment j of trip n [µg.m⁻³]. This step makes it possible to obtain, for each individual, a single piece of information on the concentrations and time associated with each trip referred as Individual mean weighted concentration per trip and calculated as:

$$C_n = E_n / \sum_j T_{jn} \qquad (2)$$

where $C_n$ is the average concentration for trip n [µg.m⁻³], $E_n$ is the cumulative exposure over the trip n [µg.m⁻³.h] and $T_{jn}$ is the time spent on segment j of trip n [h].

### 2.2.2. Indoor exposure

The second stage of the model is the calculation of indoor exposure, i. e., in the activity locations (Fig. 4 (C2)). As we mentioned in the introduction, indoor environments are a critical element in calculating exposure, and indoor air quality (IAQ) modelling is currently the subject of numerous developments in our community. This is why we chose to design an approach for estimating IAQ based on I/O ratios that allow us to characterise the transfer of pollutants between outdoor air and indoor environments, as well as to reproduce the average impact of indoor sources. We developed a script that allows us to iterate over each agent and assign a pollutant concentration depending on the location of the activity and the time of day. We then weight the outdoor background concentration at the scale of the neighbourhood by an I/O ratio to calculate indoor exposure. This ratio is used to characterise the transfer of different pollutants between outdoor air and indoor environments, and also to reproduce the average impact of indoor sources. The I/O coefficient values used (Table 1) are taken from the ANSES review study (ANSES, 2019). They correspond to the averages of the median values of the I/O ratios calculated from French studies in dwellings and - as they do not exist for France - from international studies in other types of buildings.

### 2.2.3. Dynamic exposure calculation

The final step is to calculate the total dynamic exposure of each agent over a day (0h-23h), combining travel exposure and indoor exposure. A script is used to iterate through each agent's diary and calculate the actual time spent on each activity. This makes it possible to calculate exposure per environment per hour, referred as individual microenvironment exposure and calculated as:

$$E_{ij} = C_j \times T_j \qquad (3)$$

where $E_{ij}$ represents the exposure per environment j for agent i [µg.m⁻³. h], $C_j$ is the concentration in environment j [µg.m⁻³] and $T_j$ is the time spent in environment j by agent i [h]. It is also possible to calculate the cumulative exposure over a day for each individual and for each pollutant:

$$E_{i,24h} = \sum_{0}^{23} E_{ij} \qquad (4)$$

where $E_{i,24h}$ represents the cumulative exposure over 24 h for agent i [µg.m⁻³.24 h] and $E_{ij}$ is the exposure per environment j for agent i [µg.m⁻³.h].

## 3. Configuration and input data

Our modelling platform was implemented for year 2009 in the Paris region with a spatial resolution of 1km² for air quality simulation. The choice of this year is based on several criteria and in particular the availability of databases. The calibration of the modules calculating modal choice and mobility is based on a survey that takes place every 10 years (EGT 2010) and which, when the platform was set up, was only available for the year 2010. In addition, the occurrence of major episodes of air pollution during the winter of 2009 provided us with contrasting situations of exposure to pollutants, which are particularly

<footnote>*in µg/m³.</footnote>
relevant for exposure studies. The updating of certain databases, in particular the latest version of EGT2020 which was released very recently, will enable simulations to be carried out in more recent years in the near future.

### 3.1. Model set up

#### 3.1.1. OLYMPUS setup

The synthetic population model is based on data from the 2009 population census conducted by the French National Institute for Statistics and Economic Studies (INSEE), with information available at sub-municipal level. Attractiveness, modal choice and mobility are based on data from the travel survey EGT2010 (DRIEA, 2010). Finally, to feed the script for calculating public transport routes (journey times, connections, etc.), we use General Transit Feed Specification (GTFS) data from the Île-de-France region. At each stage, OLYMPUS is systematically validated using data subsets that were not used in the training phase.

#### 3.1.2. CHIMERE setup

Our target for this study is to produce CHIMERE output concentration fields at a resolution of 1km². To achieve this, we set boundary conditions and performed a triple nesting simulation. CHIMERE is first implemented at the continental scale on the largest EUROPE60 domain (resolution of 0.68° x 0.46°). This simulation domain is forced to its limits by mean monthly concentration fields derived from climatological data provided by the LMDz-INCA model. Air quality is then simulated at national level on the GRANDNORD3 domain (resolution of 0.205° x 0.135°) in order to provide boundary conditions for the Île-de-France target study area, IDF1 domain at a resolution of 0.0137° x 0.009°. This latter domain covers the entire study area over the interval of longitude [1.457°-3.606°] W and latitude [48.10°-49.25°] N. Horizontally, the IDF1 domain is divided into 20,511 grid cells. It is composed of 8 vertical levels delimited between ground pressure and 750 hPa, which corresponds to the optimal configuration of CHIMERE for flat terrain applications at this scale. The meteorological data comes from an offline WRF simulation forcing all the domains. Soil type is based on the GLCF (Global Land Cover Facility) database. Emissions for the two large domains are those from the European EMEP inventory with a spatial resolution of 0.25°, widely used in CHIMERE's national to continental simulations. For emissions over the study area, we used the emissions provided by OLYMPUS with a resolution of 1km². A pre-processing step is used to combine traffic and residential emissions from OLYMPUS with the reference inventory of local emissions from the air quality monitoring agency AIRPARIF, which allows industrial emissions to be taken into account (Elessa Etuman & Coll, 2018). As mentioned before, biogenic emissions are generated by the MEGAN global model. The chemical scheme used here to describe the reactivity of gaseous species is MELCHIOR2: it considers more than 40 species and about 120 chemical reactions. It is a reduced version of the MELCHIOR1 scheme, which considers 80 species and more than 300 reactions. For particulate species, we selected 9 particle size classes between 0.001 µm and 40 µm.

The concentration fields (hourly and kilometre resolution) were subjected to our downscaling step to spatially refine the representation of air quality, and in particular to reproduce the concentration gradients linked to road emissions.

The OLYMPUS-CHIMERE combination and the downscaling step have been tested and validated previously in the Île-de-France region in this configuration (Elessa Etuman et al., 2020; Elessa Etuman & Coll, 2023).

### 3.2. Data extraction for exposure calculations

The simulations carried out targeted the period between 1 January 2009 and 31 March 2009. This allows us to explore exposure on each day, but also exposure during winter pollution episodes when atmospheric dispersion is low (early January 2009 in our case). In this paper,

however, we are interested in average exposure over the period in order to obtain a general trend in exposure. We have chosen to calculate exposure not on the entire population of Île-de-France (12 million individuals) but on a random sample of 5 % of the total population, representing around 400,000 individuals. This sampling gives us a variety of profiles covering the whole region at a reasonable cost (Appendix A). The socio-demographic characteristics of the sample are broadly representative of the general population (Appendix B). The proportion of under-14 s is slightly overestimated in our sample (+5 %), while the 30-54 age group is under-represented (-5 %). The proportion of working people is therefore underestimated compared with the population census (-8 %), to the benefit of students and retired people (+8 %). However, this sampling enables us to outline the general trend in exposure in the Paris region.

## 4. Results and discussion

The following section presents our results on the Île-de-France region. First, we look at what the dynamic approach adds in terms of knowledge about exposure, compared with the static approach that only considers exposure at the place of residence. We will consider exposure by location, but also the contribution of the different environments to total exposure. In the second section, we analyse the exposure of individuals during their journeys at certain periods of the day. Then, we will see that this method of calculating exposure can be used to identify a number of levers. To finish, we will put these results in perspective in the discussion section.

### 4.1. Spatial analysis and the contribution of environments to exposure

In this section we look at the advantages/knowledge increment of an approach that includes all locations in calculating exposure, compared with a static approach in which only concentrations at the place of residence are taken into account. In other words, we look at the differences between the exposure of individuals, i.e., to what they are really exposed to at each moment of the day, and a classic air quality map. We will first analyse the spatial distribution of exposure according to these two approaches, and then look at the contribution made to exposure by the environments through which individuals pass during the day.

#### 4.1.1. Linking concentrations to individuals rather than locations: Analysis of the spatial distribution of exposure

We first compared the 2 approaches by looking at the most polluted areas and in comparison, with the areas where the most exposed individuals live. To do this, we mapped the exposure according to the 2 approaches, for NO2 and PM10, in Fig. 5. On the maps, each neighbourhood represents the average exposure of the individuals living there. On the left are the maps of daily average hourly concentrations, which represent static exposure at home (a, c, e, g), and on the right are the maps of daily average hourly individual exposure ($\Sigma_{24 h/24 h}$) (b, d, f, h). This exposure is calculated on our sample of 400,000 individuals, and individual exposure is estimated using our dynamic calculation method, which integrates all the concentrations of the environments crossed during the day. In the remainder of this section, we will refer to this as the dynamic approach.

Regarding the spatial distribution of PM10, we can see two fairly different exposure patterns that depend on the method used. Quantitatively speaking, the dynamic approach predicts higher exposure whether at regional level (a, b) or zooming in on Paris and its inner suburbs (c, d), with an average relative difference of +22 % compared with the static approach. But the dynamic approach displays more homogeneous exposures between the centre of Paris and the inner suburbs with a significant extension of the area of high exposure. In comparison, the static approach showed highly contrasted exposures in Paris with pockets of high values located in the north and west of the capital, and a clear gradient of decrease as we move away from the Paris hyper-centre.
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Region scale: Static daily mean concentrations of individuals at their residential place (ie. classic air quality map) for PM10 (a) and NO2 (e) Dynamic daily mean exposure linked to individuals mapped at their residential place for PM10 (b) and NO2 (f)

Zoom on Paris and inner suburb: Static daily mean concentrations of individuals at their residential place for PM10 (c) and NO2 (g) Dynamic daily mean exposure linked to individuals mapped at their residential place for PM10 (d) and NO2 (h).</figcaption></figure>

The dynamic approach thus underlines the fact that a large number of individuals suffer high concentrations of particles during their daily travel, which can be aggravated if they are reaching dense areas far from their homes. In the model, mobility appears to have a significant impact on exposure outside the areas of poor air quality, through the

accumulation of short periods at high concentrations and/or long periods at moderate concentrations. Yet, the impact of place of residence should not be neglected: the red blocks observed in the north and west of Paris in map (d) point out high cumulative exposures in neighbourhoods close to major traffic arteries. Finally, we can also analyse the impact of
the I/O transfer ratio applied in the dynamic approach, which includes indoor sources. This ratio, greater than 1 for PM10 (1.32 at home) is involved in the differences observed between the dynamic and static approaches with the values being pulled up for PM10 during periods at home.

Regarding NO2, whatever the approach, the model produces a clear centre-periphery exposure gradient with high values in Paris that decrease sharply towards less urbanised areas. An East-West gradient is superimposed on this structure: residents of western Paris are associated with the highest exposure values, the steepest gradients and the lowest exposures being simulated at the east of the capital in both approaches. Quantitatively, and unlike for PM10, the dynamic approach predicts lower exposures (–10 %) than the static approach: the use of an I/O transfer ratio of less than 1 (0,83) pulls the exposure values downwards, and is not compensated for by mobility periods.

In conclusion, our results show that the dynamic approach reshuffles the exposure maps and enables us to highlight trends that are not seen on conventional concentration maps. Notably, the dynamic approach shows an increase in the spatial extent of high PM10 exposure values across the entire region, that can be attributed to public transportation trips. Such a pattern was not seen in the static approach that over-estimates the impact of outdoor air quality patterns in high traffic density areas. By adding new components to daily individual exposure, the dynamic approach shows that activities and mobility do have a significant impact on the average daily exposure of people located outside the urban hyper-centre. It also puts into perspective the impact of local outdoor pollution on individual exposure, leading to attenuated exposure gradients between neighbourhoods within Paris. This is espe-cially true for NO2, as there remain some blocks that are much more

exposed to PM10 in the north of Paris than in other areas of the city centre. Also, our results underline the importance of taking into account indoor sources and their impact on exposure, through I/O ratios, when considering exposure in buildings and in confined spaces during mobility. Residential activities thus constitute a significant increment in daily exposure to PM10, while there is a negative impact on NO2 exposure during periods at home due to a low I/O concentration ratio. This latter effect is significant, since it outweighs the increment of exposure to NO2 during mobility. In any case, only the dynamic expo-sure calculation approach retains the memory of short periods of mobility with very high exposure to atmospheric pollutants, which could be of interest for sanitary issues. Finally, our approach enables us to identify the neighbourhoods where the most exposed populations live. It also allows us to see that the spatial footprint differs between approaches and also between pollutants, which implies different actions to reduce individual exposure. The fact that there are neighbourhoods in which individual exposure is much higher calls for a specific study of the most effective leverage effects for controlling exposure.

These maps have enabled us to analyse the impact of a dynamic calculation on the distribution of exposure. In particular, it allows for the observation of the mixture of effects resulting from this calculation in comparison to a static approach, which only provides information on pre-existing exposure patterns. However, we need to go further and quantify the contribution of the different environments to total expo-sure, in particular the home but also time spent on the move, in order to identify which environments have the highest weighting. This is what we will be looking at in the next section.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Circles: Relative contribution of environments to total exposure (in µg/m³ over 24 h) for NO2 (a and b) and PM10 (d and e) | Columns: Mean exposure of the sample according to approaches for NO2 (c) and PM10 (f) (µg/m3).</figcaption></figure>
### 4.1.2. Analysing the contribution of environments to gain a better understanding of exposure

To quantify the contribution of the various environments to the total exposure of individuals in our sample, we have plotted the graphs in Fig. 6. Here, the term environment refers to activities carried out in different locations and the contribution of the environments is calculated by integrating the concentrations and times spent in each environment. For NO2 and PM10, we have calculated the share of each environment (at home, in transports, at work/school and during leisure) (a, d) as well as the contribution of the modes of transport (b, e). We have also reported the average exposure of individuals according to the 2 approaches for the 2 pollutants (c, f).

First of all, we can see that the first contribution to exposure for both pollutants is home (in blue), which accounts for 57 % of the sample's total daily exposure for NO2 and 71 % for PM10. Next, for NO2, the most represented environments are work and education (20 %, grouped under the generic term work, in light green on the graphs), then leisure (13 %, in dark green) and finally travel (10 %, in red). For PM10, travel is the 2nd largest contributor to exposure (12 %), followed by work (11 %) and leisure (6 %).

The distribution of contributions is interesting for several reasons. Firstly, it highlights the fact that the proportion of total exposure attributable to travel is not negligible, contributing between 10 and 12 % depending on the pollutant. It is of the same order of magnitude as work for PM10. The contribution of mobility to total daily exposure is the result of a multitude of combinations between journey time and the intensity of concentrations in the areas concerned (vehicle cabin, underground train, etc.). Since the time spent travelling during a day is relatively short, this high proportion can be explained by the fact that the individuals in the sample are exposed to very high concentrations in these environments, and that their weight has a significant influence on the average. This observation should lead us to consider the chronicity of intense exposure in terms of its impact on health. We can already point out that this proportion may vary from one individual to another, depending on the methods used; here it represents only an average for our sample.

Secondly, we can see that exposure at work represents a much smaller fraction on average than exposure at home (10 to 20 % of the total, depending on the pollutant in question), despite a daily working time of between 7 and 9 h. There are 2 explanations for this difference. Firstly, the share of the inactive, retired and unemployed (31 %), which we saw earlier was overestimated (Appendix B), pushes up the amount of time spent at home and therefore accounts for a larger share of total exposure. The trend observed is therefore correct but exaggerated. Secondly, the absence of sources in professional buildings (I/O ratio < 1 for PM10 and NO2) results in the work environment accounting for less of the total exposure.

Our calculation method allows us to differentiate the modes of transport used by individuals to make their journeys, so we wanted to know how much each mode contributed to travel. To do this, we plotted the contribution to exposure during journeys of each mode of transport (Fig. 6, b and e) and we can see that they differ between pollutants. For NO2 (b), we can see that the shares are evenly distributed between the different modes, with a preponderance of car use and walking due to the fact that NO2 is a pollutant found close to traffic. For PM10 (e), we see a completely different distribution of modes, with an overwhelming share linked to rail transport (58 %), followed by the car (19 %) and walking (17 %).

Finally, we can see that the average exposure of our sample is sensitive to the choice of method used. For NO2 (c), we can see that the estimate by the static method (in green) is stronger than that by the dynamic method (in grey blue), while the estimate by the dynamic method for PM10 tends to increase exposure, as we have observed in Fig. 5.

### 4.2. Identifying levers of exposure: Mobility vs living areas

In this section, we are interested in exposure during mobility, because as we have just seen, this activity contributes between 10 and 12 % of total exposure, while the time spent is relatively low on a daily scale. We will first look at the exposure of individuals travelling between 5 pm and 8 pm, by comparing the information provided by our dynamic calculation method with that provided by a static approach. Secondly, we will look at the influence of the location of the home and workplace on the exposure of the individuals in our sample.

#### 4.2.1. From daily exposure to sub-hourly exposure: Distribution of exposure during a peak hour

Our dynamic calculation method allows us to look at the distribution of exposure at different times of the day, whereas it is often analysed on a yearly basis using a static calculation method. Here, we are interested in the nature of short exposures to high concentrations, particularly when travelling. We have chosen to look at the distribution of exposure of individuals from the main sample travelling between 5 pm and 8 pm, which represent the peak hours in the Ile-de-France region. Here we are comparing a purely geographical/spatial approach to air quality with a dynamic individual approach.

In Fig. 7, we have plotted the hourly distribution of exposure to NO2 (a) and PM10 (a) for individuals travelling, using the static (green) and dynamic (grey blue) approaches. We see here that our calculation method predicts significantly higher median exposures than the static approach, regardless of the pollutant, with a distribution tail that stretches towards the highest values. For NO2, we can see that the tail of the distribution of the dynamic approach stretches well towards the highest values, with maximums of up to 112 µg/m3.h at 5 pm compared with 76.6 µg/m3.h in the static approach at 6 pm and median values of 13.5 µg/m3.h and 8.5 µg/m3.h. For PM10, the distribution is also more spread out towards the high values, with a maximum exposure value of 188.5 µg/m3.h at 7 pm compared with 19 µg/m3 at 6 pm for the static approach. Similarly, the median over all hours is higher than for the static approach (4.4 µg/m3 vs 2.7 µg/m3).

These exposure values include time-weighted concentrations, so in order to highlight the intensity of the concentrations to which individuals are exposed over these short periods of time, we have plotted the distribution of concentrations per hour for the two pollutants on graphs (c) and (d) in Fig. 7. We can see that the concentrations experienced during journeys are extremely high, with 50 % of values above 60 µg/m3 for NO2 and 19.5 µg/m3 for PM10, compared with 43.2 µg/m3 and 12.3 µg/m3 respectively in the static approach.

These differences confirm that the approaches are not equivalent, either in terms of estimating intensities or in terms of exposure distributions. We can see here that the dynamic approach enables us to highlight temporal variability and the influence of certain environments crossed on exposure over the course of a day, or even a single hour, which cannot be observed with the static approach. Above all, it provides us with information on the intense concentrations experienced by the population when travelling, and highlights the importance of taking transport into account when estimating exposure. Indeed, our method provides data to help in the assessment of the health issues potentially hidden behind intense exposure over short and recurrent periods. In its last review of the literature on health effects (REVIHAAP), the World Health Organisation concludes that clinical and toxicological studies, as well as epidemiological observations, show that short-term peaks of exposure (from less than an hour to a few hours) to particles from combustion lead to immediate physiological changes in the cardiovascular and respiratory systems (WHO, 2013). Since then, other studies have shown a link between high sub-hourly exposure and short-term health impact for various pollutants (UFP, PM2.5, NO2) (Mills et al., 2015; Rich et al., 2016).

It is therefore necessary to take into account all the variability of exposures over the course of the day in order to produce the data needed
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Distribution of exposure of individuals in the sample travelling during the hour in grey blue (using a dynamic approach), and exposure of the same individuals at home during the same hour (using a static approach) for NO2 (a) and PM10 (b). Distribution of concentrations according to the 2 approaches for NO2 (c) and PM10 (d). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

for the health assessment of short, intense and repeated exposures to urban primary pollutants. However, this point is still a key issue, particularly in the case of underground road transport, where access to data in these environments is limited. These results could be even clearer once we are able to refine the concentrations in public transport: this is an element that could give even greater scope to the exposure suffered

during travel.

### 4.2.2. Identifying levers for action: The impact of living environments on exposure

Another way of taking advantage of our method of calculating exposure is to look at the influence of place of work on exposure,

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Distribution of average exposures (µg/m3) to PM10 (a) and NO2 (b) according to place of residence and place of work. The y-axis represents place of residence and the colour of the boxplot indicates place of work. Grey boxes indicate those who live and work in the same area. Nc = No data: it is representative because there is very low attractiveness of Paris to the outer suburbs in terms of work.</figcaption></figure>
depending on the individual's place of residence. To do this, we have plotted in Fig. 8 the average exposure of individuals according to their place of residence and their place of work. The design of the figure is as follows: for each pollutant (PM10 (a) and NO2 (b)), the y-axis gives us information on the individual's area of residence and the colour of the boxplot indicates their place of work. The grey boxes show the exposure of individuals who work in their area of residence. The information contained in this figure is multi-faceted, since it allows us to look at how exposure is distributed according to place of work for each place of residence. We have shaded the boxplots corresponding to the exposure of individuals working in their area of residence.

First let's look at those individuals who live and work in the same suburbs. For PM10, we can see that for all zones, individuals are less exposed than those who commute in other zones, while for NO2 the pattern is more contrasted. In fact, those who live and work in Paris are more exposed than those who leave, and in the inner suburbs they are less exposed.

If we now look at those who work in another suburb, we see for the 2 pollutants the effect of Paris on exposure: the closer people work to the centre, the more exposed they are. Conversely, the further away they work, the less exposed they are. This is particularly true for NO2.

This analysis enables us first of all to highlight the impact of transport on exposure to PM10, as we have seen that individuals who do not move are less exposed to PM10, which is closely linked to underground transport. Secondly, we can also see the effect of place of residence for NO2, with in particular an escape from the dense zone for individuals who work outside Paris.

In conclusion, although the patterns of exposure are complex, we can still see that the places where people live, both at home and at work, are very important in structuring exposure. So, we can see the complex leverage effect on exposure, which requires a global approach to both transport and living environments.

### 4.3. Discussion

The results presented here allow us to identify the general trends in exposure provided by our method for the Île-de-France region. The exposure values are representative of an average winter day, on a typical weekday. Exposure may be higher on days when there are low winds or pollution episodes and temporal variations need to be investigated by carrying out the analysis over a year.

First, our results on the comparison of average daily static and dynamic exposures in the region show that taking mobility into account sheds new light on the way in which exposure can be understood. As the dynamic approach is more comprehensive, it enables us to reproduce more realistically the exposure experienced by individuals, but overall, it allows us to address the issue of exposure levers in a more relevant way, by calculating several exposure components based on the accumulation of daily activities.

Our work also provides a fundamental comparison of static and dynamic approaches, facilitating a deeper understanding of the potentials and limitations inherent in static exposure assessments. This is an important point to emphasise here, as in some cases it may be difficult to access data other than static. Both approaches give comparable results and give credit to the static approach for dimensioning the intensity of exposure. However, they point out strong limitations for the static approach to derive differences in exposure between individuals, identify the location of vulnerable individuals or assess the impact of living environments on exposure. Indeed, our results show that all types of activity (home, work, leisure, commuting) have a significant influence on people's exposure, and the respective proportions of exposure at home and on commuting routes vary according to place of residence and pollutant, which is critical for public action design.

This study provides an initial assessment of dynamic exposure, which opens up new perspectives on exposure risk management. It is based on an innovative modelling chain made up of three sequential models

combining probabilistic and deterministic approaches. In this process, each output is systematically evaluated against reference or field data in order to maintain a high level of confidence along the chain. This step includes OLYMPUS data referring to population, agendas, mobility and emission totals. Notably, the latter are compared by SNAP sector and by pollutant with a reference inventory. Their spatial distribution across the region is also considered in the comparison. The outputs of the CHIMERE model – regular and downscaled data – are compared with concentrations from measurement stations located over the territory. With the development of monitoring sites, the calculation of exposure may be extended to other pollutants of interest such as PM2.5, which would meet the current expectations of health-environment studies to increase the range of pollutants considered in exposure.

Regarding the intrinsic quality of input data, there exist some ways of improvement that should be considered in future studies. A large part of them relies on advances in knowledge about indoor environments, and in the first place the refinement of pollutant concentrations during activities (at home, at work) and during travel in confined environments (underground, car cabin). In our work we have chosen to use a transfer coefficient (I/E ratio) to estimate pollutant concentrations during activities. The coefficients we used are based on a review of the literature and allow us to simulate the observed average balance between indoor and outdoor sources of pollutants in order to provide more realistic average exposure values. The main point is that they do not consider the variety of individual practices (opening windows, use of household products, cooking methods) which have a significant influence on indoor air quality (Lewis & Edwards, 2016). Modelling based on such practices could prove to be an effective tool for discriminating exposure between individuals, but there are currently no tools for incorporating these aspects into our modelling. In the shorter term, a refinement of the I/O coefficients could be reasonably considered as we had to consider non-differentiating coefficients (all dwellings have the same I/O ratio) in the absence of better data. Increasing this coefficient for old buildings with poor external insulation, as an example, could contribute to giving even greater prominence to individuals living near heavy roads, who are more often socially disadvantaged and already subject to other nuisances such as noise. Taking into account the quality of building insulation, or even the quality of heating equipment used, would then put even more emphasis on these individuals, or bring to light other subgroups of population with high-risk exposure practices.

Air quality assessment, both indoor and outdoor, represents a major avenue for improvement in modelling exposure studies, and personal sensors certainly have a role to play in future developments. Combined with a tracking of the actions of individuals in their homes, they may provide us with information on pollutant levels and establish a link with practices. Thus, they would pave the way for developing realistic simulations of indoor concentrations over a territory, which is currently out of reach. At the same time, the use of exposure data from cohorts of individuals equipped with individual sensors would make it possible to validate fine-scale simulations of urban exposure.

### 5. Conclusion

The aim of this paper is to propose a method for calculating exposure that meet the scientific and technical challenges identified in the literature, which are (i) the spatial and temporal representation of individual mobility and activities along the day, (ii) the need to represent local gradients of urban air quality, and (iii) the specificity of air quality in indoor environments.

The methodology developed in our exposure model IDEM gives us an innovative view on exposure, as its final value includes a variety of exposure components, relating to the plurality of environments crossed by individuals over the course of a day. Several points are particularly important. Firstly, the effect of time weighting, i.e., the amount of time spent in each environment at a given concentration has a significant influence on the final exposure value. Secondly, mobility choices emerge
as a determining exposure component. As shown in Section 4.1.2, urban travel contributes between 10 and 12 % of the total daily exposure with the largest contributors being rail transport for PM10 exposure and carbon-based modes for NO2 exposure.

Our simulations highlight very high exposure values during transport. These data are critical for sanitary studies, which could draw on our values to study the effects of short and intense recurrent exposure, an emerging issue in the environmental health community. Also, our simulation approach enables us to identify the population subgroups that are most exposed, which makes it possible to envision a socio-spatial analysis of exposure addressing the issue of environmental inequalities and socially determined vulnerabilities.

To feed into the debate on environmental public action, it is planned to compare 2009 to 2019 exposure in the Paris metropolis. This would make it possible to assess the evolution of environmental pressure under a decade of regional public policies. In this regard, the respective location of employment and residential areas in relation to the configuration and accessibility of urban services represent a significant challenge to local planners, as it may compel individuals to adopt carbon-intensive modes of transportation. Furthermore, our simulations can provide information on how exposure inequalities evolve over time as a result of the accumulation of exposure risks throughout the day, as well as the efficiency of public policies in reducing these inequalities. In this respect, dynamic and individual exposure data can provide inspiration for policymakers.

### CRediT authorship contribution statement

**Taos Benoussaïd:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis. **Isabelle Coll:** Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. **Hélène Charreire:** Supervision. **Inès Makni:** Methodology, Investigation. **Malo Costes:** Investigation, Formal analysis. **Arthur Elessa Etuman:** Supervision, Software, 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.

### Acknowledgements

This research received funding from the French Environment and Energy Management Agency (ADEME), the PRIMEQUAL program (POLL-EXPO/Convention 1862C0012), the Île-de-France region (DIM QI²) and the Conseil départemental du Val-de-Marne (2018/0069). Malo Costes was funded by the EUR LIVE Graduate School of Research "Life Trajectories and Health Vulnerability". The EUR LIVE is a project PIA / ANR-18-EUR-0011. The work was granted access to the HPC Resources of TGCC under the allocation A0090107232 made by GENCI. We acknowledge AIRPARIF for data supply.

## Appendix A. Appendix

<figure><img src="image_11.png" /><figcaption>Appendix A. Number of individuals per neighbourhood.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Appendix B. Comparison between census data and population sample of age (a), gender (b), professional status (c) and socio-professional categories (d) at regional scale.</figcaption></figure>

## Data availability

Data will be made available on request.

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