# Modeling Exposure to Mobility-Related Pollution: Review and Key Challenges

Pascal Gastineau, Arnaud Can, Boyam Yaméogo, Leonardo Luquezi, Pierre

Hankach, Pierre-Olivier Vandanjon, Valentin Le Bescond

► To cite this version:

Pascal Gastineau, Arnaud Can, Boyam Yaméogo, Leonardo Luquezi, Pierre Hankach, et al. Modeling Exposure to Mobility-Related Pollution: Review and Key Challenges. Transportation Research Record, 2023, 10.1177/03611981231208907. hal-04312243

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# Modeling exposure to mobility-related pollution: Review and key challenges

This is a postprint version of the article originally published in Transportation Research Record, November 2023

https://doi.org/10.1177/03611981231208907

Pascal Gastineau<fnref n="1" />,<fnref n="2" />, Arnaud Can<fnref n="3" />,<fnref n="2" /> Boyam Fabrice Yaméogo<fnref n="4" />, Leonardo Galassi Luquezi<fnref n="3" />,<fnref n="2" />, Pierre Hankach<fnref n="5" />,<fnref n="2" />, Pierre-Olivier Vandanjon<fnref n="1" />,<fnref n="2" /> and Valentin Le Bescond<fnref n="1" />,<fnref n="2" />

## Abstract

The health and equity issues related to urban pollution, coupled with the need to assess the consequences of implementing transport policies or new mobility solutions, make it imperative to provide decision-makers with a relevant modeling framework. The coupling of activity-based travel models with environmental models presents a promising approach to thoroughly investigate the interconnected issues related to the impact of road traffic on individual exposure to noise and air pollution. Integrated modeling chains provide a robust framework to comprehensively assess traffic scenarios on a city scale and address issues related to environmental inequalities. This article reviews the recent work in this field in order to identify both the main contributions and key challenges still to be addressed. It illustrates how the use of a dynamic approach, that considers the intraday dynamics of individual mobilities and pollution concentrations, can contribute to more accurate estimates of the exposure of individuals to pollutants. This modeling approach also has the potential to investigate how the level of exposure depends on the characteristics of the individuals (socioeconomic factors, commuting patterns, location of residence and main activities). Nonetheless, there are still important challenges to overcome in order to further enhance the accuracy and applicability of these models: taking into account all types of traffic and transport modes, refining the modeling of exposure to pollution during travel, taking better consideration of microenvironments, etc.

## Keywords

Agent-based simulation; Activity-based model; Noise exposure; Air pollution exposure

**Corresponding author:**

Pascal Gastineau, Univ. Gustave Eiffel, Campus de Nantes, Allée des Ponts et Chaussées, CS 5004, 44344 Bouguenais Cedex, France.

Email: pascal.gastineau@univ-eiffel.fr

<footnote n="1">¹AME-SPLOTT, Univ Gustave Eiffel, F-44344 Bouguenais, France</footnote>

<footnote n="2">²IRSTV, Nantes, France</footnote>

<footnote n="3">³Univ Gustave Eiffel, CEREMA, UMRAE, F-44344 Bouguenais, France</footnote>

<footnote n="4">⁴IFPM - Université de Ouahigouya, Burkina Faso</footnote>

<footnote n="5">⁵MAST-LAMES, Univ Gustave Eiffel, F-44344 Bouguenais, France</footnote>
Introduction

Increasing urbanization is associated with a high demand for mobility, which if not well planned is responsible for the degradation of cities’ environmental quality. The health challenges are considerable. Urban air pollution increases the risk of both acute (e.g. pneumonia) and chronic (e.g. lung cancer) respiratory diseases, as well as cardiovascular diseases [Krzyżanowski et al., 2005]. Noise impacts result in more than 1 million healthy years lost across Europe, mainly due to annoyance and sleep disturbance [WHO, 2011]. These high levels of exposure also constitute the source of socio-spatial inequalities. For instance, an extensive body of research in environmental justice has consistently demonstrated that individuals with lower socio-economic status experience elevated exposure to air pollutants. This is primarily due to their tendency to reside in regions characterized by higher levels of outdoor air pollution [Boso et al., 2022], coupled with limited access to resources that can mitigate increased exposure, such as private transportation and well-built housing [Hajat et al., 2015]. The political response has been the transition towards transport systems that respect environmental quality at various urban and extra-urban scales. This step requires: (i) a better understanding of the interactions taking place between transport policies and the distribution of exposures among populations; and (ii) the development of decision support tools to estimate the health impacts of transport policies based on new forms of mobility (multi-modality, soft mobility, autonomous vehicles, etc.).

Currently, the most widespread approach consists of estimating exposure using a static model that combines this spatial distribution of pollution with places of residence (the neighborhood is sometimes used as an individual exposure area). Some studies combining exposure data with census data have revealed possible correlations between exposure to pollution and socioeconomic indicators. The estimated effects vary greatly from one study to another as they are obviously highly dependent on local conditions (type of traffic, urban configuration, etc.). For example, in the case of Montreal, Dale et al. [2015] showed that noise exposure was correlated with a number of socioeconomic indicators; in particular, the strongest correlation was found with: average household income, the proportion of individuals whose housing expenses exceeded 30 % of their income, or the proportion of individuals living below the poverty line. In the case of air pollution, for example, it was found that in Hamilton (Canada), housing values were significantly and negatively correlated with exposure to pollution [Jerrett et al., 2001]. Low income and unemployment were also significant predictors of exposure (in addition, some epidemiological work has sought to highlight this type of relationship based on record cohort study [Havard et al., 2011]). However, this approach has several limitations and does not provide a robust estimate of individuals’ actual exposure [Park and Kwan, 2017]. One reason for this shortcoming is a failure to take into account the temporal dynamics of individuals’ mobility and the pollution levels at the places they cross. Such an oversight can lead to errors in the calculation of average exposures. It may also be due to the calculated exposures being static and based on aggregate indicators that do not enable assessing the potential impact of brief exposures to high pollution levels.

Over the last decade, one approach using activity-based multi-agent methods to model travel demand has been developed in parallel to this classical approach. Since activity-based models offer a fairly detailed description of individuals (i.e. synthetic population) and their activities (i.e. activity plans) [Arentze and Timmermans, 2004], they make it possible to estimate the dynamic exposure of individuals to traffic-related pollution (noise and air pollutants), i.e. an exposure that takes into account the movements and location changes experienced by individuals during the course of their day. Moreover, this approach serves to not only more precisely quantify the number of individuals exposed in a given place at a given time, but also better characterize both the exposed population (socioeconomic characteristics) and the circumstances of exposure to traffic-related noise and air pollution (during which trip? which activity?).
Similarly, it allows for a better characterization of the exposure of individuals, categories of population or areas, and moreover provides a framework for scenario analyses [Houot et al., 2015]. As such, it potentially allows for more detailed analyses of transportation policies (traffic restrictions, pricing policies, etc.), infrastructure projects or new mobility solutions.

The focus then lies in understanding the modeling issues and limitations of approaches based on existing activity-based multi-agent models. Few remaining challenges that need to be addressed are identified:

1. Transport modeling: this component requires an improved representation of all modes of transport and a deeper understanding of the effects of population downscaling.

2. Air and noise pollution modeling: specifically, the treatment of microenvironments needs to be more comprehensive, including the buildings where activities take place and the modes of transport used for commuting.

3. Exposure modeling: this challenge involves enhancing the representation of the population and the activities they perform, along with a better understanding of the sensitivity of the results to the resolution of the population and pollution data.

This article provides a review of the work previously carried out in the field and identifies specific challenges for future work. The article is organized as follows. First, the main principles of modeling chains for noise and air pollution exposure assessment are described. After this, the various modeling chains identified in the literature are presented. The advantages of assessing the dynamic exposure of individuals to transport nuisances using multi-agent modeling are then discussed, followed by the limitations of such techniques. The final section offers a conclusion and addresses the avenues of both research and support for the relevant public decision-making process.

## Modeling chain for noise and air pollution exposure assessment

This section describes activity-based multi-agent models and the issues surrounding their coupling with air quality and environmental acoustics software.

### Multi-agent models and travel simulation

In the field of transport, travel demand is derived from the need to conduct activities (work, school, health, leisure, etc.). The most common approach to demand modeling in the context of a multi-agent model for travel simulation is activity-based. Activity-based models capture travel demand from a disaggregated perspective, since the various out-of-home activities are taken into account as an induced source of travel demand. These models analyze both the behavior of individuals when they interact with others while performing their daily activities and the implications of these interactions for the transport network.

Therefore, this fully disaggregated approach (Figure 1) involves the generation of a synthetic (or simulated) population of agents representative of the actual population as well as the allocation of a 24-hour activity schedule (i.e. activity plans) to each agent [de Dios Ortuzar and Willumsen, 2011]. This generation step is most often carried out based on techniques that rely on samples from different data sources (e.g. census, travel surveys) [Hörl and Balac, 2021], resulting in a disaggregated dataset at the individual level allocated to geographic areas [Lovelace and Dumont, 2016]. The behavioral diversity reflected in these activity plans is then inputted into travel models that assign itineraries and modes to achieve these activities. Travel models are capable of handling various types of assignment protocols: all-or-nothing assignment, equilibrium assignment, dynamic assignment.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Different steps of mobility modeling in an activity-based multi-agent model</figcaption></figure>

Compared to the conventional four-step models (see de Dios Ortuzar and Willumsen [2011] for a comprehensive description of the four-step travel modelling approach), activity-based multi-agent models are better able to:

* represent the relationship between activities and travel at the individual level;

* account for the behavior of individuals and their choice mechanism in terms of mobility [Rasouli and Timmermans, 2014, Kagho et al., 2020];

* consider new mobility services, such as carpooling or car-sharing, and their operational challenges [Hörl and Balac, 2020];

* estimate the environmental externalities (noise, pollution) associated with transport [Shiftan et al., 2015, Kaddoura et al., 2017a].

Many activity-based multi-agent simulation software platforms are available (e.g. SimMobility, MATSim, POLARIS, TRANSIMS, etc.). The selection of any of one of these platforms depends primarily on the research problem at hand and other criteria, including: the number of agents to be modeled, the development facilities offered (size of the community, availability of guides and support materials), the platform's capability of being associated with other types of models, and the open-source criterion (see N [Nguyen et al., 2021] and [Tzouras et al., 2023] for an overview of existing agent-based modeling platforms).

*Coupling the multi-agent model with pollution models*
Noise and air pollution impact estimates are not necessarily governed by the same constraints and do not pursue the same objectives [Gan et al., 2012, Fecht et al., 2016, Can et al., 2009]. The differences between these externalities define the key points of the coupling and moreover influence the multi-criteria approaches (evaluations) discussed in this paper. Table 1 summarizes these differences in terms of the indicators to be estimated and the spatial and temporal granularity.

<table><caption>**Table 1.** Comparison of environmental externalities. source: Can Can [2019]</caption><thead><tr><th></th><th>Noise</th><th>Air pollution</th></tr></thead><tbody><tr><td>Impacts on health</td><td>++<br>The impacts mainly concern health outcomes (cognitive disruption, sleep disturbance, etc.) and the deterioration of the quality of life.</td><td>++<br>Morbidity related to exposure to air pollutants is a health problem.</td></tr><tr><td>Spatial granularity</td><td>+++<br>The expected spatial granularity is very fine. Ideally, the spatial distribution of noise levels should be estimated with a spatial resolution of about 10 or 20 m.</td><td>++<br>The expected spatial granularity is good. However, many studies are limited to the quantities of pollutants emitted over a rather large area of the territory.</td></tr><tr><td>Temporal granularity</td><td>+++<br>The estimation of the temporal evolution of noise levels, the estimation of sound events, are target elements of the modeling, as they have a great influence on the perception of noise environments.</td><td>++<br>The temporal evolution of estimated pollutant levels is of limited importance, although some studies tend to show the danger associated with exposure to very short pollution peaks (a few seconds) at pollution peaks above average levels.</td></tr><tr><td>Emissions calculation time</td><td>+++<br>The calculation of sound power levels is very fast and simple. It is negligible compared to sound propagation calculations.</td><td>++<br>Aggregate models follow simple laws giving emissions as a function of average flow speed or possibly traffic. They are very fast.</td></tr><tr><td>Remanence</td><td>-<br>The phenomenon is not persistent: mistakes made over a period of time have no repercussions.</td><td>++<br>The pollution levels observed over a given time a given time interval may be the result of emissions that occurred several hours or days earlier.</td></tr><tr><td>Target indicators</td><td>Indicators calculated for a given receptor map and period, based on the evolution of sound levels: average indicators, statistical indicators, sound event levels.</td><td>Average levels emitted, average pollutant concentrations in a receptor map for a given period.</td></tr></tbody></table>

Note: +++ Important, ++ Moderately important, + Slightly important, - Not important

The most complete assessment of individual exposure to pollution (air pollutants or noise) using an agent-based model essentially proceeds according to 3 steps, namely :

1. Travel modeling relies on an activity-based model. The activity and travel information produced by the model includes, for each person: a list of all non-home activities undertaken in a single day and their purpose (work, shopping, leisure, etc.), the geocoded location of the areas (i.e. trip destination), and certain information about the local trips made. This dataset comprises the estimated start and end times of the trip and the selected mode of transport (single-occupant car, multi-occupant car, bus, walking, cycling). The outputs of this traffic modeling step are, at the very least, the average flows and speeds per road section, which are then used to estimate emissions.

2. An estimation of spatio-temporal concentrations of noise and one or more pollutants is based on traffic flows, their composition and the average speeds per road section simulated in step 1. The calculation process differs for noise and air pollutants, due to the remanence of the latter and not the former. In the case of noise, the first step entails estimating the sound power per unit length of a road
section, as based on traffic data. A propagation model is then used to quantify the sound attenuation between each source/receiver pair. This in turn serves to determine the contribution of each sound source to the noise level at a receiver, which is calculated as the sum of these distinct contributions. In the case of air pollutants, the first step consists of estimating the pollutant emissions generated by traffic per unit time and unit length of a road section based on traffic data. These emissions then feed a dispersion model that calculates their evolution at each time step as regards dispersion, transport and possibly chemical recombination, in order to calculate the air pollutant concentrations in a map of receptors.

3. Exposure modeling involves the spatio-temporal locations of individuals (as simulated in step 1) with the spatio-temporal distributions of pollutant concentrations (simulated in step 2) to estimate individual-level exposure measures. In most cases, the multi-agent transport model records each individual activity sequence. These records contain the location coordinates, time and duration of activities for the fixed location activities and travel of each individual. Various types of microenvironments as well as inhalation rates can be considered.

The modeling of individual exposure to noise and air pollution is depicted in Figure 2.

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Modeling personal exposure to air pollution and noise. source: inspired by Gurram et al. Gurram et al. [2019]</figcaption></figure>
State of the art

This section provides a review of the modeling chains used to assess individual exposure to both road traffic-related air pollution and noise. It focuses on integrated frameworks that model the full chain of effects, i.e from the underlying need that generates travel demand to the exposure of the population to noise or air pollutants.

Assessment of exposure to air pollution

The integration of an activity-based model into an air quality modeling framework offers many advantages for traffic, emissions, dispersion and exposure modeling [Shiftan, 2000, Shiftan and Suhrbier, 2002]. For example, the simulated trip information can provide more accurate temporal values of trips and emissions [Beckx et al., 2009b] that depend not only on driving distance and speed, but also on the number of trips, the time between trips, and whether the engine was warm or cold at startup [Recker and Parimi, 1999]. Moreover, an activity-based model, by describing the activities performed, where, when, for how long, with whom and the mode of transport used, yields supplementary information on other key transport variables, e.g. time-of-day trips and time/location of departures. These variables, important for both vehicle emissions analysis and atmospheric modeling, are generally unavailable in conventional travel models. An activity-based approach simulates entire daily programs of activity and travel and considers local and temporal variations in travel behavior. The calibration and validation of the transport model are generally conducted by considering various factors, including mode shares, travel times, and distances [Kaddoura et al., 2017a]. The performance of the simulation is evaluated and assessed by comparing its outputs against a combination of survey data and traffic observations [Tayarani and Rowangould, 2020]. Lastly, given that an activity-based model outputs information on individuals’ location throughout the day, this method can be relevant to assess exposure to local pollutants at different places and times.

Although the potential value of an activity-based model to estimate human exposure to air pollution was identified as many as 20 years ago [Shiftan, 2000], applications are still quite recent. The most comprehensive assessment of personal exposure to air pollution using an agent-based model essentially employs the three steps indicated above, namely transport, concentration, exposure. The second step, i.e. the estimation of pollutant concentrations, is typically based on two models, one to estimate the pollutant emissions generated by vehicle travel while the other to calculate the dispersion, transport and concentrations of pollutants in air. This assessment is based on an accurate estimate of traffic and pollutant emissions from various vehicle categories. Depending on the available data, several types of models can be implemented based on: average speed (AS), traffic situation (TS), multiple linear regression, and instantaneous emissions (Inst) [Hülsmann, 2014]. A wide array of air quality models can also be used to study local and urban cases. The models used differ in scale, from micro (a few meters) and local (km) to regional (thousands of km) and global. These air pollution models may be physical, empirical, statistical, deterministic (physicochemical) or a combination of thereof. They also differ in the air pollutants considered: dispersion modeling is performed for two categories of air pollutants: gases and particles, both of which are targeted by most models. Different dispersion models can be used for various pollutants: Gaussian Plume model, Eulerian model, hybrid model, etc. [Holmes and Morawska, 2006]. The choice of dispersion model depends on the specific pollutant being modeled, the sources and characteristics of the pollutant, and the environmental and meteorological conditions. Note that some approach used a map of pollutant concentrations produced by using other transport models (or based on traffic counts) or else generated by using simplified approach [Kickhöfer and Kern, 2015] or extrapolating measurements using regression methods [Dons et al., 2011b,a, Park, 2020, Park and Kwan, 2020, Lu, 2021].
Others do not implement the full modeling chain and stop at the emissions estimation stage [Agarwal et al., 2020, Shiftan et al., 2015, Elessa Etuman and Coll, 2018]. The former approach does not allow for scenario analysis (since the concentration maps remain independent of activity-based modeling), while the latter does not allow for individual exposure assessment since pollutant concentrations are not modeled. Although these studies are of interest, they will not be described in any further detail herein.

Two studies, one in the Netherlands [Beckx et al., 2009a,b,c] the other in Canada [Hatzopoulou and Miller, 2010], constitute the first benchmark on the use of activity-based travel demand models to estimate the exposure to traffic-related air pollution by modeling the full chain, i.e. agent-based modeling coupled with a traffic model to simulate road link-specific emissions, ambient concentrations and individual exposures (Table 2). The improvement provided by the Canadian study compared to the Dutch study was specifically aimed at the use of a dynamic traffic assignment model based on agents (MATSim) in order to better account for the congestion phenomena [Hao et al., 2010, Hatzopoulou and Miller, 2010, Hatzopoulou et al., 2011]. Subsequently, Vallamsundar et al. [2016], Gurram et al. [2019] (later taken over by Kocak et al. [2021]) and Tayarani and Rowangould [2020] brought a certain number of improvements to the previous work, also by using a dynamic traffic assignment model.

These case studies helped to quantify the value of a dynamic approach to pollutant exposure compared to the usual static approaches. According to Beckx et al. [2009a], the gap between static and dynamic exposure (estimated as the time spent in an area where the PM<sub>2.5</sub> or PM<sub>10</sub> concentration exceeds a given threshold) is greatest between 9 am and 4 pm (by nearly 100 %), a period during the day when people tend to move around within the urban area. In contrast, the static approach is based on a fixed number of individuals during the day, which explains the lower exposure estimates. The results produced by Beckx et al. [2009b], on the other hand, revealed that by neglecting travel behavior, the total average exposure to NO<sub>2</sub> could be underestimated by 4 %, while hourly exposure could be underestimated by more than 30 %. Dhondt et al. [2012b] obtained more sharply contrasted results, with an average dynamic exposure value higher than the static value for NO<sub>2</sub> (21.6 µg/m<sup>3</sup> vs. 20.98 µg/m<sup>3</sup>) yet lower for ozone (79.7 µg/m<sup>3</sup> vs. 81.2 µg/m<sup>3</sup>). Hatzopoulou and Miller [2010] showed that, in the case of Toronto, the distribution of cumulative NO<sub>x</sub> concentrations throughout the day by individuals presents a larger range of values than that obtained with a static approach. Similarly, the work of Elessa Etuman et al. [2020] suggested that, in the case of Greater Paris (Grand Paris), the distribution of individual exposure to NO<sub>2</sub> (share of time spent in a place where the concentration lies at a given level (expressed in µg/m<sup>3</sup>)) is much more heterogeneous than a static approach would suggest. Let's note that the errors estimated by Tayarani and Rowangould [2020] are much higher than those of previous studies [Beckx et al., 2009a,b, Dhondt et al., 2012b, Hatzopoulou and Miller, 2010].

The differences in results between these studies have different possible explanations. These are summarised as follows.

- Microenvironments and inhalation rates -

Vallamsundar et al. [2016] showed the benefit of considering various types of microenvironments, as well as a variable inhalation rate depending on age and gender. The results of this dynamic exposure, which took into account different microenvironments, were compared by Vallamsundar et al. [2016] with the static exposure, wherein only outdoor concentration levels were inputted into the exposure calculation. Overall, the use of outdoor concentration levels alone resulted in an estimate that was, on average, 10 % higher than that obtained by taking microenvironments into account. To assess the effect of inhalation rate on exposure levels, the authors compared use of a uniform inhalation rate of 12 m³/day for the whole population with varying the inhalation rate by both age and gender. In the latter case, results showed a 20 % increase in exposure levels.

- Data resolution -
<table><caption>**Table 2.** Identified cases of coupling with an air pollution model</caption><thead><tr><th>References</th><th>Studied area</th><th>Activity model</th><th>Transport Model</th><th>Pollutants</th><th>Emissions</th><th>Dispersion</th></tr></thead><tbody><tr><td>Beckx et al. [2009a,c,b]</td><td>Urban area Utrecht (NL)</td><td>ALBATROSS</td><td>All-or-nothing assignment</td><td>PM<sub>10</sub>, PM<sub>2.5</sub></td><td>MIMOSA [AS]</td><td>AURORA</td></tr><tr><td>Dhondt et al. [2012b,a], Lefebvre et al. [2013]</td><td>Flanders (BE)</td><td>FEATHERS</td><td>Equilibrium traffic assignment</td><td>NO<sub>2</sub></td><td>MIMOSA [AS]</td><td>IFDM</td></tr><tr><td>Elessa Etzioni et al. [2020]</td><td>Grand Paris (FR)</td><td>MOIRAI</td><td></td><td>NO<sub>2</sub>, PM<sub>10</sub></td><td>VULCAN/COPERT [AS]</td><td>CHIMERE</td></tr><tr><td>Gurram et al. [2019], Kocak et al. [2021]</td><td>Tampa (Florida, US)</td><td>DaySim</td><td>MATSim</td><td>NO<sub>x</sub></td><td>MOVES [Inst.]</td><td>R-LINE</td></tr><tr><td>Hao et al. [2010], Hatzopoulou et al. [2011], Hatzopoulou and Miller [2010]</td><td>Grand Toronto (CA)</td><td>TASHA</td><td>MATSim</td><td>HC, CO, NO<sub>x</sub>, CO<sub>2</sub></td><td>Mobile [AS]</td><td>CALMET/ CALPUFF</td></tr><tr><td>Hülsmann [2014], Hülsmann et al. [2014]</td><td>Munich (DE)</td><td>-</td><td>MATSim</td><td>NO<sub>x</sub>, NO<sub>2</sub>, CO, PM<sub>10</sub></td><td>HBEFA [TS]</td><td>OSPM</td></tr><tr><td>Tayarani and Rowangould [2020]</td><td>Atlanta, Georgia metropolitan area (US)</td><td>CT-RAMPT</td><td>Equilibrium traffic assignment</td><td>PM<sub>2.5</sub></td><td>MOVES [Inst.]</td><td>AERMOD</td></tr><tr><td>Vallamsund et al. [2016]</td><td>Maricopa (Arizona, US)</td><td>County</td><td>OpenAMOS</td><td>DTALite</td><td>MOVES [Inst.]</td><td>AERMOD</td></tr></tbody></table>

AV: average speed, Inst.: instantaneous emission, TS: traffic situation

The differences between static and dynamic exposure estimates may also depend on the level of data resolution (pollutant concentration, activities or synthetic population). Indeed, since traffic-related pollution levels display large spatial variation at small scales, the use of low-resolution concentration or activity data (as is the case for example with Beckx et al. [2009a]) may leave high exposures undetected. As shown by Gurram et al. [2019], the use of low-resolution activity and concentration data (i.e. block-group level) led to an average exposure 10 % less than that obtained using high-resolution data (i.e. individual level), thus suggesting underestimation on average.

- *Exposure during travel* -

[Gurram et al., 2019] found that, in average, exposure to passenger car NOx emissions in Tampa (Florida) was lower for individuals with a shorter daily travel time and higher for individuals with a longer daily travel time (exceeding 60 minutes). Tayarani and Rowangould Tayarani and Rowangould [2020] confirmed the value of both taking into account individuals' exposure during their travels and moving from static
exposure modeling to dynamic modeling. More specifically, they found that for the Atlanta metropolitan area, an analysis incorporating personal mobility resulted in a higher estimation of exposure to primary PM<sub>2.5</sub> from vehicle exhaust and tire and brake wear than a static approach, where assessment of exposure was based on residential location. On average, exposure at home accounts for 43 % of daily exposure, while work and travel make up 27 % and 18 %, respectively. In their study, Vallamsundar et al. [Vallamsundar et al., 2016] found that exposure in-vehicle accounts for 8 % of the total intake. These estimates are in line with observations from previous studies showing that exposure during travel contributes between 6 and 24 % of total daily exposure [De Nazelle et al., 2013, Dons et al., 2011b, 2012, Gurram et al., 2015].

- *Spatial context* -

Therefore, the estimation errors induced by a static approach are much greater in suburban and rural areas where exposure is underestimated, while exposure may be overestimated near busy roads and in the urban center [Tayarani and Rowangould, 2020]. The differences in results across the various studies can thus be partly explained by the specific characteristics of each study area: population density, spatial distribution of activities, commuting patterns, etc. [Vallamsundar et al., 2016].

*Assessment of noise exposure*

In order to calculate exposure to noise using an activity-based model, the approach adopted is typically made up of three distinct steps [Gerike et al., 2012, Kaddoura et al., 2017a]. In the first step, noise emissions are calculated based on: the traffic flow estimated by the multi-agent model, the share of heavy vehicles, and the average speed of the flow. Sound power levels (i.e. emissions) are calculated for each street section, in acknowledging correction factors for different road surfaces and longitudinal slopes. These emissions are discretized into source points on the road network (approximately one point every 10 meters). A receiver is then generated for each building facade. The sound levels for each receptor, i.e. each building facade, are obtained by incorporating the propagation between each 'source point/receptor' pair, and by summing the contributions of these source points. Lastly, the noise exposure model is based on keeping track of the location of each individual's activities, as well as the start and end times of each activity. Given that activities take place at the nearest receptor, the number of individuals potentially exposed to noise is recorded for each receptor and time interval (the individual may be partially taken into account if the corresponding activity starts or ends during the time interval used for recording).

Fewer works have coupled an activity-based transport model with a noise model than those focusing on air pollution (Table 3). The modeling framework most often used is that developed by Kaddoura et al. [2017a], which is based on MATSim and has one of its extensions specifically dedicated to noise modeling. As noted by Kaddoura et al. [2017a], for the sake of simplification, i.e. to keep the amount of input data small and to obtain better computational performance, several noise-related impacts need not be taken into account, such as noise shielding, single and multiple reflections, road surface and road slopes. Depending on data availability, the approach can be extended to add corrections when calculating noise emissions and immissions. The initial model which implemented German RLS-90 methodology was subsequently extended to both incorporate noise shielding [Kuehnel et al., 2019] and make use of the more recent RLS-19 method [Zwick et al., 2021]. Also, in a singular approach, the results of MATSim applied to Switzerland were combined with a characterization of noise by an indicator (N-LCA) developed within the framework of a life cycle analysis [Cucurachi et al., 2018]. Other authors have coupled a multi-agent transport model with an environmental acoustics software to allow for a more complete modeling of acoustic phenomena. The first work along these lines was conducted by Gerike et al. [2012]; these authors coupled MATSim with the noise mapping software IMMI. Thereafter, Houot et al. [2015] coupled the MobiSim model with the MITHRA-SIG sound mapping software in a case study of the french city of Besançon. Le Bescond et al. [2021] coupled MATSim with the open-source software dedicated to
environmental acoustics, NoiseModelling, which adopted the unified European noise assessment method (CNOSSOS) [Kephalopoulos et al., 2012].

<table><caption>**Table 3.** Identified cases of coupling with a noise model</caption><thead><tr><th>References</th><th>Studied area</th><th>Activity model</th><th>Transport model</th><th>Noise model</th><th>Method</th><th>Indicator</th></tr></thead><tbody><tr><td>Cucurachi et al. [2018]</td><td>Switzerland</td><td></td><td>MATSim</td><td>N-LCA</td><td>CNOSSOS</td><td>Noise Footprint</td></tr><tr><td>Gerike et al. [2012]</td><td>-</td><td></td><td>MATSim</td><td>IMMI</td><td>RLS-90</td><td>L<sub>DEN</sub></td></tr><tr><td>Houot et al. [2015]</td><td>Besançon (FR)</td><td></td><td>MobiSim</td><td>MITHRA-SIG</td><td>-</td><td>LAeq</td></tr><tr><td>Kaddoura et al. [2017a,b], Kaddoura and Nagel [2018], Kuehnel et al. [2021]</td><td>Berlin (DE)</td><td></td><td>MATSim</td><td>MATSim extension</td><td>RLS-90</td><td>L<sub>DEN</sub></td></tr><tr><td>Kuehnel et al. [2019], Kuehnel and Moeckel [2020], Kaddoura et al. [2020]</td><td>Munich (DE)</td><td></td><td>MATSim</td><td>MATSim extension</td><td>RLS-90</td><td>L<sub>DEN</sub></td></tr><tr><td>Zwick et al. [2021]†</td><td>Munich (DE)</td><td>MITO</td><td>MATSim</td><td>MATSim extension</td><td>RLS-19</td><td>L<sub>DEN</sub></td></tr><tr><td>Le Bescond et al. [2021]</td><td>Nantes (FR)</td><td>Eqasim</td><td>MATSim</td><td>NoiseModelling</td><td>CNOSSOS</td><td>sound exposure level, LAeq</td></tr></tbody></table>

†:noise shielding

The benefit of taking into account the intraday dynamics of the various population densities across a city's districts, compared to the static approach, was notably demonstrated by Kaddoura et al. [2017a] in their estimation of road noise exposure in Berlin. According to their results, in residential areas, fewer people are exposed to noise during the day than in the evening or at night, while the opposite is true in the city center (where places of work and study are located). Hence, using the number of static residents would lead to an overestimation (resp. underestimation) of the noise damage during the day in residential areas (resp. city center). In the case of the city of Nantes, the variability of individual noise exposure profiles and the bias induced by a static approach (i.e. the noise dose level estimated with a static approach does not reflect the actual level at which the individual is exposed) have been illustrated by Le Bescond et al. [2021].

## Advantages of assessing the dynamic exposure of individuals to transport pollution using multi-agent modeling

The main advantages of the modelling chains presented above can be summarised as follows.

### Scenario analysis

The various modeling frameworks previously identified have been used to study the variations in individuals' exposure to road traffic-related pollution following implementation of a pricing (e.g. congestion charging) or regulatory (e.g. Zone 30) transport policy, the introduction of a new technology [Cucurachi et al., 2018] or new mobility solution [Zwick et al., 2021], the construction of new infrastructure [Kocak et al., 2021], a modification to public transit supply or the desynchronization of individuals' activity schedules [Houot et al., 2015]. With the exception of Kaddoura et al. Kaddoura et al. [2020], all
studies merely focused on one of the two pollution categories (air or noise); at times, this pollution has been studied with another externality (congestion or CO2). The size of the study area differs from one study to another, as do the temporal and spatial scales. The approach most often adopted has consisted of comparing the exposures of a simulated baseline scenario with a scenario modeling the implementation of a policy or a change in transportation supply. For example, Zwick et al. [Zwick et al., 2021] demonstrated that switching all car journeys with a ride-sharing service that makes multiple stops resulted in a significant decrease in noise in residential areas, whereas a service that travels door-to-door might even increase noise exposure due to additional pick-up and drop-off trips and detours. Kocak et al. [Kocak et al., 2021] illustrated the utility of this type of model in analyzing the potential impacts on traffic volumes, emissions, air pollutant concentrations, exposures and exposure inequalities of a motorway expansion program.

## Exposure inequalities and environmental justice

Furthermore, although exposure inequalities have been explored in the past, multi-agent modeling has thus far been little used to explore issues such as environmental justice [Campbell et al., 2015]. Within the body of work cited above, the assessment of the Dutch population's exposure to NO2 at different times, in different locations, for different sub-populations (gender, socio-economic status) and during different activities (residential, professional, transport, shopping) by Beckx et al. [2009b] highlighted the interest of their modeling framework. A more detailed analysis of exposure reveals intra-day variations in exposure estimates, along with the presence of wide differences in exposure between activities (in-transport > work > shopping > home) and between sub-populations (male > female, lower socioeconomic class > higher socioeconomic class). The disparities in exposure to NOx from road traffic between population subgroups in the study area were documented by Gurram et al. [2019]. In a study of equity with respect to individuals' socioeconomic criteria, their activities and their locations, Gurram et al. [2019] demonstrated that in the case of Tampa (Florida, US), the average exposure of certain population categories (living below the poverty line, a daily commute time exceeding one hour, etc.) was higher than that for the population as a whole (up to 16% higher). Using the same modeling framework, Kocak et al. [2021] contributed to the understanding of the exposure to air pollution and its impacts on inequalities (with respect to three distinct criteria, namely: subgroup inequity index (SII), comparative environmental risk index (CERI), and toxic demographic quotient index (TDQI)) of a large-scale transport infrastructure program involving road expansion and implementation of a toll. Their results show that while such programs may lead to small reductions in average exposure (at least over the short term), they may also increase certain vulnerabilities for some groups and increase the exposure differences between social groups, e.g. higher exposure for those living below the poverty line and the black population, lower exposure for high-income people and whites. Let's note that although the possibility of obtaining individual exposure trajectories has been proven [Le Bescond et al., 2021], the majority of studies do reaggregate individuals according to social status, ethnicity or location (residential or otherwise) criteria.

## Economic evaluation of transport-related externalities

Finally, this type of coupling also offers new possibilities in the economic evaluation of transport-related externalities. Since multi-agent modeling makes it possible to keep track of the identity of each agent (and their vehicle) during the simulation, it is indeed possible to carry out various analyses [Gerike et al., 2012], i.e.: calculation of average, total and marginal external costs by class of vehicle, zone or time of day (night/day); calculation of daily costs per agent or vehicle-kilometer, evaluation of the influence of spatial or socioeconomic differences on the generation of external costs; and simulation of various strategies for internalizing transport externalities [Agarwal and Kickhöfer, 2018, Kickhöfer et al., 2019]. In the case of noise, a noise internalization approach has been proposed for the Greater Berlin area by
Kaddoura et al. [2017b]. Their approach uses a MATSim-based transport simulation to calculate noise and population density levels, as well as to allocate noise damage costs to the originating road segments and transport users. By jointly studying the external effects due to congestion and those due to noise, Kaddoura and Nagel [2018] highlighted the benefit of adopting a dynamic approach (i.e. changing incentives over the course of the day) in the development of transport policies. In the Berlin case, during peak hours, congestion is the most significant externality, while in the evening, night and morning, noise becomes most significant. Hence, to decrease noise during off-peak hours, traffic should be directed towards the major roads such as the motorways in the urban area. In contrast, during peak hours, congestion is reduced by redirecting city center motorway users onto smaller roads.

## Analysis of pollution causation

It should be noted that the modeling frameworks presented here may also be useful and relevant (for the public decision-maker) in analyzing pollution causation (i.e. amount of damage induced by an agent or a trip) [Lefebvre et al., 2013, Kuehnel et al., 2021]. This type of modeling can in fact enable the identification of: trips, trip purpose, and those individuals who are not only sources of pollution but above all at the origin of its impacts (i.e. exposure caused to other individuals). The added value of using an activity-based model in the present modeling chain is not only to predict where and when people move, but also to understand the trip purpose. As a result, Lefebvre et al. [2013] were able to analyze the contribution of each type of travel to the daily NO₂ cycle. Recently, for example, in the case of road traffic noise in Berlin, it was shown that both exposure and causation are unevenly distributed (according to the Gini index) within the population [Kuehnel et al., 2021]. However, because not everyone owns and drives a car, but nearly all homes are situated close to roads, the disparity in causation is much greater than the disparity in exposure.

## Current limitations on the existing modeling frameworks

The limitations of the modeling frameworks currently in use are still numerous and concern both the transportation, pollution and exposure components of the modeling chain (see Figure 2).

### Transportation modeling

Some limitations are directly linked to the transportation modeling step.

- *Incomplete description of modes of transport* -

Certain types of traffic (transit, freight) or modes of transport (motorized two-wheelers) are sometimes imperfectly taken into account (or even ignored, e.g. Hatzopoulou and Miller [2010]), although they can have a significant impact from the standpoint of both causation and exposure to pollution or noise (see Agarwal and Kaddoura [2020] for a comparison of traffic related air pollution exposure of motorists, cyclists and motorcyclists). Moreover, the description of the vehicle fleet is often imprecise (e.g. use of a regional vehicle fleet in the case of V Vallamsundar et al. [2016]), which has a direct consequence on the estimated emissions. This limitation becomes even more critical if we consider the diffusion of alternative technologies (e.g. electric, hybrid or gas-powered vehicles) whose emission levels, noise or air pollutants, are significantly different from those of conventional vehicles. For instance, considering the characteristics of electric vehicles in terms of noise emission (i.e. at low speed, electric vehicles have lower sound emissions than cars with combustion engines) [Cucurachi et al., 2018] and [Zwick et al., 2021] shown the potential interest of the diffusion of this type of technologies.

- *Impact of the population downscaling* -
In order to reduce the computational costs, the traffic simulations used to base studies may be conducted by using a synthetic population sample, e.g. 1 % for Hülsmann et al. [2014], 5 % for Hatzopoulou and Miller [2010], Hatzopoulou et al. [2011] and Vallamsundar et al. [2016]. Others are based on a complete sample (i.e. 100 %) [Beckx et al., 2009a, Dhondt et al., 2012b, Gurram et al., 2019]. The sensitivity of traffic simulations (especially for the MATSim modeling platform) has recently been the subject of a number of studies. According to Zhuge et al. [2019], nearly all output of interest in a MATSim simulation (e.g. average speeds) is highly sensitive to the population sampling parameter. According to Llorca and Moeckel [2019], however, policy scenario requirements can influence the most appropriate scaling parameter and network density. To test a scenario in which results are analyzed at only a very aggregated level, smaller scaling factors may be sufficient, whereas traffic flow analyses for a single corridor, for example, are likely to require a 100 % sample size. Ben-Dor et al. [2021] concluded that downscaling to the 20 % level maintains a relatively low estimation bias compared to the 100 % scenario. Downscaling to 10-15 %, as commonly applied in MATSim, may lead to a larger but still acceptable bias, while downscaling below 10 % may prove to be risky. In contrast, Zhuge et al. [2019] believed that choosing a 50 % value allows for an acceptable tradeoff between efficiency and accuracy of the simulation. Nevertheless, the consequences of such choices on the evaluation of emissions (of pollutants and noise) and, thus, on the exposure of individuals to environmental road traffic-related pollution are still relatively unknown. None of the studies listed in Tables 2 and 3 propose a sensitivity analysis of their results relative to this parameter.

## Noise and air pollution modeling

Other limitations are specific to noise and air pollution modeling. As regards air pollution, the analysis is generally limited to a single pollutant and road traffic emissions only. However, the concentration patterns of pollutants may differ among one another, hence the study of a single pollutant is not necessarily representative of individuals' overall exposure. In addition, some studies neglect travel-related exposure (e.g. Le Bescond et al. [2021]), which may be significant for users of non-motorized modes of transport. The other limitations can be summarised as follows.

- Poor consideration of microenvironments where activities take place -

Buildings (indoor exposure) are assigned the ambient concentrations of their geographic areas [Tayarani and Rowangould, 2020]. For example, as regards noise exposure, the methodologies neglect indoor noise sources and noise propagation within buildings; moreover, they assume that all people participating in the given type of activity are exposed the same to the calculated noise level. However, not everyone is necessarily impacted by traffic noise, for instance, due to offices or rooms that are not located on the most exposed side of a building (see, for example, Murphy and King [2010] and Eriksson et al. [2013]), or other sources of noise at the location of activity (e.g. a machine operating). As for air pollution, the relative difference in concentration levels between outdoor (as predicted by air quality models) and microenvironments tends to be ignored. However, the relative difference between outdoor and other microenvironments can indeed be quite large (e.g. a factor of between 0.8 and 3 for PM<sub>2.5</sub>) depending on the microenvironment and other factors (e.g. pollutant penetration factor, indoor air exchange rate, etc.) [Vallamsundar et al., 2016].

- Poor consideration of exposure during travel -

Pollution exposure is calculated using simulated ambient concentrations in the areas being traversed. As shown by [Singh et al., 2021], air pollution exposure is most pronounced during transportation compared to other daily activities. It is higher in open transport modes as opposed to closed ones. Pedestrians and cyclists experience the greatest impact due to their increased respiration rates and proximity to the streets. Therefore, do not model exposures during travel may underestimate exposures. [Vallamsundar et al., 2016] and [Agarwal and Kaddoura, 2020] illustrated the value of refining the modeling of exposure to
air pollutants during travel by considering a breathing rate (or inhalation rate) and a penetration rate (or input/output ratio). Lastly, none of the studies listed in the Table 3 include the exposure of agents to noise during their travels. The development of noise exposure modeling during travel and the improvement of its equivalent for air pollution are two research gaps yet to be addressed.

- *Constant inhalation rate* -

In addition, let's note that most studies assume a constant inhalation rate (when taken into account) for the entire population. In reality, inhalation rates vary by age, gender and activity level (e.g. Panis et al. [2010]). Also note that, given the state of available data, the time spent outside buildings or vehicles is not distinguished. Furthermore, due to a lack of detailed knowledge of the characteristics of the vehicle fleet or the building stock, their influence on emissions (and exposure) is only imperfectly taken into account [Tayarani and Rowangould, 2020].

- *Sensitivity to road traffic representation* -

The pollution sources are represented in the form of a traffic flow described per road segment by means of both flow rate and average speed. This approach can lead to differences near intersections, where vehicle speeds are not uniform and acceleration phases affect emissions (for air pollutants and noise). Vehicle kinematics are generally simplified, e.g. lane changes, acceleration phases, driver decisions, overtaking or adaptive traffic light controls are not being modeled here. Studies have highlighted the impact a road traffic representation not representing vehicle kinematics can have when coupled with environmental models. For instance, Can et al. [2009] estimated for noise the error in a case study of an urban boulevard with traffic lights at over 3 dB(A). da Rocha et al. [2013] and Lejri et al. [2018] showed the high sensitivity of calculated pollutant emissions to the definition of speed and trajectories. Coupling large-scale activity-based model with microscopic traffic modeling seems a promising way to overcome some of these limitations (see for example [Triebke et al., 2022] who combine the two open-source tools MATSim and SUMO). Finally, [Gräbe and Joubert, 2022] recently showed that, at macro-level, pollutants emissions (CO, CO₂, NOₓ) are significantly underestimated by MATSim simulation (when comparing with real driving emissions tests), especially for heavy vehicles.

- *Limits of the calculation methods* -

In addition, the simplifications adopted to allow calculating of noise propagation and pollutant dispersion over a large area are known sources of error [Holmes and Morawska, 2006]. The sensitivity study proposed by Aumond et al. [2021] revealed the strong impact of certain parameters, e.g. the diffraction at the horizontal edges, on the estimated noise levels in streets, with those receivers not directly impacted by sound sources being the most heavily affected. Other limitations are inherent in the calculation methods; for example, the German RLS-90 methodology applied by Kaddoura et al. [2020] does not explicitly take into account speed levels below 30 km/h, at which point engine noise dominates road noise and aerodynamic noise, hence the type of vehicle engine can have a significant effect on noise level. Therefore, when studying the scenarios of a massive development of electric mobility, the variation in noise levels may be poorly estimated, especially along small roads with speeds below or close to 30 km/h.

Moreover, there are several limitations to the calculation methods for air pollutant concentrations: uncertainties in input data, simplifying assumptions, limited spatial and temporal resolution, interaction between pollutants, etc. [Holmes and Morawska, 2006]. The estimation of a background concentration poses a significant challenge when modeling pollutant levels at the local urban scale. Background pollution considers all pollutant sources, particularly those outside the study area, that have a potential impact on the pollution levels within the study area [Soulhac et al., 2017]. Estimating these values can be accomplished through two approaches: one involves taking measurements at monitoring stations located at the domain's border, far from traffic routes [Hülsmann et al., 2014]; the other entails running a dispersion model over a larger (regional) domain [Elessa Etuman and Coll, 2018]. When taken into account, the background value may notably influence some of the conclusions of the analysis of air pollution exposure
[Agarwal and Kaddoura, 2020]. For noise, a given constant value (e.g. 35 dB(A)) can be added at each receptor to mimic urban background noise [Aumond et al., 2021]. More complex background models can also be used [Wei et al., 2014]. However, due to traffic being widely recognized as the primary noise source in urban context, it can be reasonably inferred that the measurement of noise exposure is less sensitive to the choice of the background value compared to certain air pollutants.

Exposure modeling

The exposure modelling stage is particularly sensitive to the description of individuals and their activities.

- Spatial allocation of individuals and activities -

The quality of the individual exposure estimate is directly linked to the accuracy of the individuals' locations; the processes to spatially allocate individuals to their respective dwellings and the location of activities are therefore critical and still imperfect steps. For example, Dhondt et al. [2012b] and Tayarani and Rowangould [2020] aggregated the location data of agents and households at the scale of traffic analysis zones (TAZ) and estimated exposure based on the average concentration in each zone. This relative imprecision in the location of individuals introduces error into the exposure estimates, e.g. an overestimation of PM₂.₅ exposure in the largest and most rural TAZ, where a large share of the population lives far from heavily trafficked roads [Tayarani and Rowangould, 2020].

- Data resolution -

To ensure data compatibility within their modeling framework, [Vallamsundar et al., 2016] aligned the spatial and temporal resolutions of the various data. Nevertheless, this 'resolution matching' procedure alters the spatial resolution of their analysis. According to Gurram et al. [2019], the influence of low-resolution activity data on exposure accuracy is outweighed by the impact of low concentration data. In general, approaches that disaggregate exposures using low-resolution concentration and activity data may result in underestimated average exposures. In situations where resources are limited, prioritizing high-resolution concentration data acquisition may be more crucial in minimizing errors in exposure estimation. However, in their guidance framework for air pollution and environmental justice methods, [Gardner-Frolick et al., 2022] stressed the potential risk associated with using relatively low-resolution social data to identify and analyze environmental justice due to the modifiable unit area problem (MAUP).

- Synthetic population generation -

Let's emphasize that greater effort in describing the population would allow for further exposure analyses, e.g. occupational activities could be differentiated according to the type of work (e.g. exposure of mobile workers); domestic activities could be differentiated on the basis of person-specific attributes relevant to noise sensitivity (e.g. children vs. adults); or the typology of activities could be more detailed (e.g. additional activities, such as leisure, could be incorporated). As pointed out by Kuehnel et al. [2021], the simplified allocation of residents to buildings also remains a limitation.

It should also be noted that, beyond issues specific to the generation of the synthetic population (i.e. precision, location), in their current state, analyses are often limited to counting the number of individuals exposed (to a given pollution level), due to a lack of suitable indicators, since those available are based on 'average' data and therefore not to be used with disaggregated data. Beyond studying the exposure of a population subgroup (as predefined according to socioeconomic, activity or location criteria) over a given period, such a modeling framework would allow for the study of individual daily exposure profiles (as illustrated for example by Le Bescond et al. [2021]). Therefore, without defining categories a priori, it would become possible to study the various individual profiles in order to deduce a profile classification and then determine which characteristics best explain membership in one or another class [Salonen et al., 2019].
Lastly, the representativeness of the population of workers, their vehicles, trips and activities depends directly on available data and the methods used to generate the synthetic population and the activity plans. For example, most surveys are conducted on workdays; no research, to the best of our knowledge, has reported on individuals' exposure to noise and pollution on a day representative of the weekend or holidays.

## Conclusion

When planning transport programs, policy makers and practitioners should conduct more in-depth analyses of the inequality impacts related to environmental equity issues, rather than just focusing on average emissions and exposures [Kocak et al., 2021]. To do so, integrated modeling chains capable of determining the space and time variations of pollution levels (noise and atmospheric pollutants), as based on traffic models capturing the effects of mobility policies, are needed. This review helped to identify the main contributions and key challenges still to be addressed. Activity-based multi-agent models make it possible to integrate these dimensions; each simulated agent is an agent whose trips are defined by a daily activity plan and constrained by traffic conditions on the network. In addition, each agent is characterized by its socioeconomic attributes, allowing for an assessment of environmental inequalities. By capturing the daily activity of agents, multi-agent models also improve exposure assessment at a high level of spatiotemporal resolution [Lund et al., 2020]. Also, multi-agent models are run at the city scale and thus enable evaluating traffic scenarios at this scale.

Nevertheless, beyond the limitations specific to the modeling of traffic and physical (or chemical) phenomena, in the current state of practice, one limitation to estimating individual exposure to pollutants and noise from road traffic is currently linked to the lack of precision/reliability on the agents' characteristics (socioeconomic descriptors, location of residences and activities, etc.), vehicle fleet and building stock in the model. Improving this part of the model would give rise to a more in-depth and relevant study of individuals' exposure to road traffic-related pollution. Furthermore, methodological frameworks and indicators for multi-criteria assessments of mobility policies, combining disaggregated exposure data with sensitivity to environmental justice issues, are still lacking.

Once the actual modeling shortcomings will be addressed, the next step will consist in identifying the salient differences in exposure between individuals. A consideration of individual mobility in vehicle emissions exposure studies could serve to identify fair and efficient planning strategies [Tayarani and Rowangould, 2020]. As argued by Vallée [2017], it is necessary to take individual mobilities into account in order to either reach the entire population in need of support (instead of simply targeting those residing in areas with high pollutant concentrations) or revise the negative characteristics of the area whose population is actually exposed. Interventions targeting housing conditions or interventions implemented at night can be effectively defined using a residential-based approach (as previously seen). However, this approach cannot be implemented for interventions that seek to alter the environment in which people are exposed on a daily basis or for actions applied during the day [Vallée, 2017]. The dynamic approaches will be of great help to planners and decision-makers in identifying disadvantaged populations for whom exposures are generally poorly reported. This, in turn, leads to targeted policy and planning implications [Lu, 2021].

## Author Contribution Statement

The authors confirm contribution to the paper as follows: study conception and design: P. Gastineau, A. Can, B.F. Yaméogo, L.G. Galassi, P. Hankach, P.-O. Vandanjon, V. Le Bescond; data collection: P. Gastineau, B.F. Yaméogo; analysis and interpretation of results: P. Gastineau, A. Can, B.F. Yaméogo; draft manuscript
preparation: P. Gastineau, A. Can, B.F. Yaméogo, L.G. Galassi, P. Hankach, P.-O. Vandanjon, V. Le Bescond.
All authors reviewed the results and approved the final version of the manuscript.

**Conflict of Interest statement**

The authors do not have any conflicts of interest to declare.

**Acknowledgements**

This work was supported by the Agence Nationale de la Recherche [Symexpo, ANR-21-CE22-0022].

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