EXPLUME v1.0: a model for personal exposures to ambient O3 and PM2.5

This paper presents the first version of the regional scale personal exposure model EXPLUME. The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate 1) space-time activity event sequences, 2) the infiltration of atmospheric contaminants indoors and 3) daily aggregated personal exposures. The model is applied over the greater Paris region at 2km x 2km resolution for the entire 2017 year. Annual averaged population exposures are discussed. We show that population mobility within the region, disregarding pollutant concentrations 5 indoors, has only a small effect on average daily exposures. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11% (population average). Moreover, accounting for PM2.5 exposure during transportation (in-vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5%. We show that the spatial distribution of PM2.5 and O3 exposures is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in 10 PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. 20% of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 μg/m), compared to 0% for people travelling by car. Finally, we develop a 2050-horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposures. We show that exposure to ozone will decrease by as much as 14% as a result of this projection, whereas there is no significant 15 impact on exposure to PM2.5.

conditions (e.g. humidity) and human manipulation are yet to be addressed before their true potential to be realized (Berchet et al., 2017).
Pollutant concentration fields simulated with atmospheric dispersion models are another possible input source for exposure models. The advantage of this approach is that simulation data may cover long time-periods to support climate studies or policy 60 applications adjusting for meteorological variability, emissions regulations, and land-use classification. Gaussian dispersion models have often been coupled with population space-time activity data for use in exposure studies (Dias and Tchepel, 2018;Korek et al., 2015;Batterman et al., 2014;Willers et al., 2013). These models, coupled with regional-scale chemistry-transport models, account simultaneously for long-range transport, regional background concentrations, and local features such as traffic emissions over the road network (Soares et al., 2014). 65 Regional scale chemistry-transport models (CTMs) such as CHIMERE (Mailler et al., 2017) or CMAQ (K. Wyat Appel et al., 2014) have achieved resolution of 1km x 1km with sufficient accuracy to be considered for use in such fine scale applications (Beevers et al., 2013). Statistical, dynamical or hybrid downscaling techniques such as kriging (Beauchamp et al., 2015) or subgrid-scale parametrizations (Valari and Menut, 2010) can be applied or coupled to these models to provide concentrations at district level. The use of CTMs instead of high-resolution Gaussian or Lagrangian models in an exposure context 70 has several advantages. The study domain may be large enough to cover an entire region, whereas typical Gaussian or Lagrangian applications cover at best, the urban agglomeration. However, a large part of the population moves in and out of the agglomeration within the day and on systematic basis. Furthermore, the enhanced chemical mechanisms of CTMs compared to the simplified chemistry (the Chapman cycle) in Gaussian or Lagrangian models gives access to refined information on the chemical speciation and size distribution of particulate matter (PM). This information is particularly relevant in the context of 75 health impact assessment, since the health impact of PM strongly depends on these properties (Atkinson et al., 2015;Cassee et al., 2013). This paper presents the first version of a regional scale model for personal exposures to O 3 and PM 2.5 . The originality of the model lies on the development of i) individual activity sequences that are defined geographically in space and time and ii) the modelling of seasonal distributions of indoor/outdoor ratios by building type and age. This latter feature is unique in personal justments are also applied for specific activities such as cycling, walking on busy roads, waiting at the subway platforms, as well as for car journeys that intersect tunnels or the Boulevard Periphèrique (ring road). Space-time activity sequences define the geographical coordinates of each member of the population at each minute of the simulation. Daily averaged personal exposures are calculated from the products of time spent by a person in different microenvironments and the time-averaged 95 pollutant concentrations occurring in those locations (Klepeis, 2006). Personal exposures are simulated for the entire 2017 year over the Ile-de-France region (greater Paris).

Personal exposure calculation
The most accurate exposure assessment would rely on real-time personal monitoring devices affixed to people as they move within all the locations that are part of their daily routines (Klepeis, 2006). In practice, such equipment is too expensive to 100 affix to large cohorts. Also questions such as the calibration of the monitors and the assessment of the uncertainties still need to be tackled before such studies could be carried out at regional scale. In a modeling framework, discrete locations termed as microenvironments are considered rather than fully continuous space. In this case, the exposure trajectory of the receptor is followed explicitly. This approach has been adapted in cohort studies such as McBride et al. (2007). As in Klepeis (2006), in the exposure model developed here receptors are simulated through individuals. Further discretizing in time, we calculate 105 exposure as the sum of the product of time spent by a person in different microenvironments and the time-averaged pollutant concentrations occurring in those locations: Here T ij is the time spent in microenvironment j by person i with units in minutes, C ij is the air-pollutant concentration person i experiences in microenvironment j in units of [µg/m 3 ], E i is the integrated exposure for person i [µg/m 3 min], and m the 110 number of different microenvironments. In this formulation, concentration C ij is averaged over the corresponding time period The general structure of the model with the necessary input datasets for the exposure calculation is illustrated in Figure 1.
Outdoor pollutant concentrations are simulated with a regional scale chemistry-transport model. We use hourly averaged data over a horizontal grid with 2km spacing in both the west-east and the south-north directions (Section 3.1).   Overview of the EXPLUME model structure, from the input data to the exposure calculation.
Each journey is characterized by the origin and destination points, the motive for traveling, the duration and the means of transportation used. The mobility of the sample population is simulated with a Monte Carlo model that matches the simulated 125 data with the EGT (2010) data (Section 4). and have the largest spatial representativeness. A good temporal correlation on an hourly basis is observed for ozone, especially for summer periods on both urban background and rural locations. The correlation is lower for the winter period. Afternoon ozone concentrations are underestimated over urban background stations as also shown in Figure 2. This is due to model's horizontal resolution that is too coarse to spatially resolve the fast NO-titration near high emission sources. On the contrary, day-time ozone is slightly overestimated over rural locations see Figure S1 (supplemental material). In both urban background 145 and rural locations, night-time ozone is largely overestimated. The model keeps bringing ozone at the surface layer from the stratosphere and ozone accumulates in the surface layer in the absence of local NO emissions and dry deposition that remove it during day-time. Temporal correlation, on an hourly basis, between simulated and observed PM 2.5 concentrations is much better for winter than for summer. Pearson correlation over urban background sites drops from 0.56 for the winter period to 0.19 for summer.

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The CHIMERE model overestimates PM 2.5 concentrations over urban sites and underestimates them over traffic stations (table 1 and Figure S2). Road transport is a major source of fine particles in urban areas. The 2km x 2km horizontal resolution is insufficient to reproduce the high PM 2.5 concentrations near these sources. Another possible reason for model's underestima-tion of PM 2.5 concentrations over traffic stations is a poor representation of secondary organic aerosol formation near traffic emissions. The distribution of the above statistics across sites is shown in Figure S2 (supplemental material). As shown there, 155 the underestimation of PM 2.5 concentrations over traffic sites may be particularly high.
Globally, we assume that the CHIMERE model at 2km x 2km resolution provides reliable O 3 and PM 2.5 background concentrations, being able to spatially differentiate the urban agglomeration from peri-urban and remote rural locations for PM 2.5 (Figure 3). The formation of well-structured ozone plumes over the rural area is also well represented as shown in the top left panel, where specific date/hour surface ozone concentration map is shown. The model is also capable of reproducing 160 the diurnal cycle of ozone and PM 2.5 . Pollutant episodes induced by favorable meteorological conditions are also well-captured by the model, even though a trend to underestimate ozone peaks and overestimate PM 2.5 peaks is observed.
Based on this analysis, for the personal exposure calculation we use simulated background O 3 and PM 2.5 concentrations from the CHIMERE model grid-cell where the activity takes place. Over the road network, where we know that the 2kmx2km CHIMERE model resolution is insufficient to reproduce the high PM 2.5 concentration levels, we apply correction coefficients 165 to increase modeled concentrations. This happens in two cases: the Boulevard Periphérique (road ring) and inside road tunnels (see Section 3.2.2). Therefore, no stochastic selection operates for the estimation of outdoor pollutant concentrations.
3.2 Infiltration of outdoor O 3 and PM 2.5 indoors

Dwellings, offices and schools
Indoor pollutant concentration levels depend on indoor sources and on outdoor pollutants entering the building through natural 170 or mechanical ventilation. As air flows through the envelope of the building, pollutants react with the surfaces over which they flow. Therefore, the actual flow indoors depends on the specific path that the air flow takes: permeability of the building shell, natural air entry, or ducts (Walker and Sherman, 2013). Other sinks of pollutants indoor are deposition on the indoor surfaces and chemical reactions with other indoor species. The relationship between these sources and sinks is expressed through equation 2 as in Walker et al. (2009). Here, -C X,in and C X,out are the concentrations of pollutant X indoors and outdoors respectively (µg/m 3 ) -P X,I is the dimensionless penetration factor for the pollutant X through leak path i, i.e the fraction of the pollutant in the infiltration air that passes through the building shell or air entrance 180 -Q in,i , Q out , Q h are, respectively the volume-normalized air flow rates into the building through path i, out of the building, and through the heating ventilating and air-conditioning equipment expressed in Air Changes per Hour units (h −1 ) η is the removal efficiency on the heating ventilating and air-conditioning equipment k d is the indoor deposition loss rate coefficient (h −1 ) chem j is the concentration of the j th chemical species reacting with the X pollutant (µg/m3)

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k j the second order rate constant for the j reaction (h −1 ) -S X is the time varying indoor production rate (µg/h) Several studies have measured indoor/outdoor ratios for different building types and meteorological conditions, in cities around the world for ozone (Collignan et al., 2012;J., 2000) and airborne particles (Cyrys et al., 2004;Matson, 2005;Monn, 190 2001). Results show a strong dependence on the building usage (residence or office/school), the air-tightness of the building, the ventilation system ,and the proximity to atmospheric pollution sources. Ozone I/O ratios generally vary between 0.2 and 0.7 (J., 2000), while for PM 2.5 , in the absence of indoor sources, between 0.5 and 1 (L. Morawska, C. He, 2003).
To account for the variability in I/O ratios due to these factors, we modelled ozone and fine airborne particles (PM 2.5 ) I/O ratios with the building ventilation model developed at the Centre Scientifique et Technique du bâtiment (CSTB), called SIREN 195 (Collignan et al., 2012). The differential equation 2 is reformulated based on three assumptions: i) no indoor sources for O 3 and PM 2.5 ; ii) no chemical reactions with other atmospheric contaminants indoors; and iii) initial concentration indoors is null. We conducted simulations for a typical dwelling and office/school.
To account for the variability of I/O ratios due to air-tightness and ventilation systems, we applied a classification of the building stock based on the construction date. This information integrates air-tightness and ventilation systems evolution based 200 on the national thermal and ventilation regulations (ADEME, 2013), the evolution of the building stock as described in (INSEE, 2014), and the use of ventilation systems in French buildings (OQAI, 2006). Table 2  Climatological conditions, temperature, pressure, and outdoor pollutant concentrations are simulated with atmospheric mod-210 els (WRF for meteorology and CHIMERE for ozone and PM 2.5 concentrations) at a 4x4km 2 horizontal resolution for a ten-year period from 1991 to 2000. Atmospheric fields are spatially averaged over the eight departments of the region. So, the atmospheric conditions database input for the SIREN model consists of ten-year period hourly data for the eight departments of the Ile-de-France region.
For each Ile-de-France department eight SIREN simulations are conducted (five for dwellings and three for office/schools) 215 at a 3 min time-step. Penetration factor is fixed to 0.8 through the building shell and 1 through air inlet based on the state of the art (Chen and Zhao, 2011;Monn, 2001;Stephens et al., 2012;Thatcher et al., 2003). Confronting numerical simulations with SIREN and I/O ratio measurements the deposition rate was fixed to 0.1 h −1 . The SIREN model output consists of a decade long database of I/O ratios for ozone and PM 2.5 at 3 min resolution for each of the eight Ile-de-France departments, for five construction date classes for dwellings and 3 construction date classes for offices and schools. This database is further 220 processed to provide seasonal I/O ratios for each pollutant, building type, construction date, and geographical zone as shown in Figure 4. Indoor/outdoor ratios for the personal exposure calculation are drawn randomly from the corresponding seasonal distribution depending on the personal profile and month.

Transportation
Ambient concentrations inside the principal transportation modes are deduced from outdoor concentrations by adjusting for 225 indoor/outdoor coefficients taken from a study dedicated to evaluating the pollutant levels to which the Ile-de-France citizens are exposed while commuting to work and back during morning and evening rash hours (Delaunay C. et al., 2012). A significant   To define the indoor/outdoor ratio for each journey in the model we chose a random number within a uniform distribution between the minimum and maximum values obtained by the study of Delaunay C. et al. (2012). The extreme values of these distributions are shown in Table 3. For public transport we distinguish between waiting on the platform and journey. For the suburban train (RER), we distinguish between journeys inside the subway network in the Paris agglomeration and the rest of the network. For journeys in cars, we distinguish between the road network in the Paris agglomeration, the Boulevard Periphèrique 235 (road ring), and the rest of the network (rural).
Several studies have shown that pollutant concentrations measured inside tunnels are several times higher than concentrations over the road but outside the tunnel. Orru H, B. Lövenheim, C. Johansson, B. Forsberg (2015) conducted a study to evaluate the health impact of the exposure to traffic exhaust inside road tunnels. Here, we apply a special adjustment for car journeys Bus 0 SQUALES 0 5.5-8.5 Tram 0 SQUALES 0 5.5-8.5 On foot 0 SQUALES 0 5.5-8.5 Two wheels 0 SQUALES 0 5.5-8.5 Paris intra-muros/outside that cross tunnels. We assume that if the itinerary of an individual intersects a grid cell (2km x 2km) containing a tunnel, there 240 is a 20% probability that the driver will pass through the tunnel. Due to lack of actual data this number is assigned here in an arbitrary manner. Further investigation in traffic data could provide a more accurate estimate of this probability. Based on the measurement campaign described in AIRPARIF (2009), we assume that the PM 2.5 concentration inside road-tunnels is two times higher than the outdoors concentration (see also Section 2).
PM 2.5 concentrations in the subway train tunnels are particularly high, especially for lines with rubber-tyred railway vehicles.

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To keep a record of the air-quality in the subway platforms the RATP (Régie Autonome des Transports Parisiens) operates measurements on a 24-hour basis at two metro stations and one RER platform (SQUALES). We used hourly on-platform measurements of the SQUALES network and outdoor concentration measurements from the AIRPARIF network for the entire 2013 year to establish a diurnal cycle of the indoor /outdoor ratio inside the subway platforms ( Figure 5). For the personal exposure calculation, we draw a random value from the hourly distributions of indoor/outdoor ratios.

Other indoors
The SIREN model provides indoor /outdoor ratios for dwellings, offices and schools (Section 3.2.1). For other activities taking place indoors such as entertainment and shopping we use the same indoor/outdoor ratios that SIREN predicts for offices and schools. For the personal exposure calculation we draw random values for indoor/outdoor ratios from the seasonal distributions.
To decide whether shopping takes place indoors or outdoors we are based on statistics from the IAURIF (2006) study, following 255 which 14% of the shopping activity in the Ile-de-France region takes place outdoors. Entertainment other than exercise is assumed to always take place indoors. For exercise activities, we first chose the type of exercise activity (IAURIF, 2006) and then whether it takes place indoors or outdoors depending on the specific activity.

Population data
The methodological steps to obtain activity event sequences for the sample population are listed here:

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select the population sample size that statistically reproduces essential demographics such as population of each administrative unit.
assign attributes to the members of the population such as age, gender, principal occupation etc.
simulate the mobility of the population by matching the journeys of the EGT (2010).
Monte-Carlo sampling method is used to randomly generate a data set of simulated individuals based on these steps. The most densely populated communes are located at the outer rings of the Paris agglomeration followed by a second circle 270 of high population density at the suburbs directly attached to the agglomeration. A third highly urbanized ring is distinguished before reaching the rural areas at the outskirts of the Ile-de-France region ( Figure 6).  A certain dependency exists between the exposure factors. For example, the professional occupation strongly depends on 295 gender and age. To preserve the sub-population variability in the sample population, the random sampling of the exposure factors operates on stratified data, where exposure factors are supposed to be homogeneous. First, we assign the commune of residence, and then the other attributes in the following order: gender, age group, principal occupation, and finally the kind of contract. Once these primary attributes assigned, we then proceed to the selection of the rest, secondary characteristics. Working area is a function of the occupation and the commune of residence, the construction date of the buildings of residence depends 300 on the commune, the gender and the age group. For offices general statistics are provided by ADEME for each Ile-de-France department dividing offices in three age classes.

Modeling the activity sequences
The second module of the model compiles 24-hour activity event sequences for each member of the sample population. Two diaries are compiled for each individual, one for weekdays and one for weekends. At each moment in time, people are either 305 at home, engaged in an activity, or in transport. Eligible activities are the six motives for transport in the EGT (2010), namely work, professional affairs, school, market, recreation or personal affairs. From this study, we deduce the number of journeys to take place at each hour in the region for each of the six aforementioned motives. Whenever an activity ends, or once every hour if the person is at home, the model checks whether the individual is about to move. Some restrictions are implemented, because not all individuals are eligible for all activities. For example, only certain age groups are eligible to go to day-care or school, only employed people are bound to go to work, etc. Once these restrictions are implemented, people will move in order to match the proportions of journeys per motive at each hour. If the person is bound to move, a number of choices are made in the following order: i) transportation mode ; ii) destination commune ; iii) travel distance ; iv) travel time ; v) activity duration (see also Figure 7): For journeys to work and back, the INSEE provides a detailed dataset with the principal modes of transport.
This information is part of the exposure factors assigned in the previous module (Section 4.1). The only stochastic choice here 315 is for the 2-wheels case that has a 40% and 60% share between bicycles and motorcycles respectively (EGT, 2010). For the rest of the journeys we match the proportions of the transportation modes per motive and hour from the EGT (2010).
In some cases, the destination commune is known (the person goes to work, to study, or back home). For other cases we only know whether the destination commune lies in the same department as the residence or in a different department. In this case, we first define the destination department based on data on the inter-departmental flows. Then we combine two pieces of 320 information to assign the destination commune: data on the average distance travelled per means of transportation. We assume a straight line connecting the centroids of the communes. Several possible destination communes are selected based on the distance criterion.
we use the information on the destination commune for the journeys in the EGT (2010) to assign a degree of attractiveness to the communes of the Ile-de-France region for each motive.

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To assign the distance of the journey we distinguish between two cases. If the destination lies in a different commune, then the journey distance is assumed to be equal to the distance over a straight line connecting the centroids of the two communes. If the destination lies within the commune of the current location then a stochastic choice is made for the traveled distance. We use statistics for the mean distance travelled per transportation means from the residents of the different departments. Depending on the transport mode, we assign a certain range around this average value and scale the limits to the commune size (radius of 330 a circle with an area equal to the commune's area) and randomly chose a travel distance within this range. To estimate the duration of the travel, we use two pieces of information at sub-communal scale. The first is the population density at 1x1km 2 resolution. Individuals are distributed over the 1x1km 2 resolution grid based on the population density.
The second information is the average speed and flow over the road segments of the traffic network. We assume a straight line linking the centers of the origin and destination cells of the 1x1km 2 grid and search for all grid-cells that intersect this 335 trajectory. The speed at which the grid cell is passed through is assigned stochastically based on the distribution of speeds over the road segments within the grid-cell. We note here that it would be more accurate to base our selection on the flows over each road segment rather than the speed distribution, but the geometry of the problem would become too complex. Given the high resolution of the application our insight is that this simplification is not bound to introduce significant errors to the transport model. The duration of the travel is then deduced from the distance and speed.

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The final step is to define the duration of the activity. For children younger than 3 years old we use statistics on the time spent at day-care. In all other cases we use statistics at department scale on the time spent by the population per activity.
A further distinction is whether the activity or transportation takes place indoors or outdoors. Certain activities may occur indoors or outdoors based on existing statistics (e.g. market and recreation). Possible means of transportation are on foot, two wheels (bicycle or motorcycle), car, bus, subway (metro), train (RER), and tramway. For public transportation, we distinguish 345 between waiting time and travel time. For tramway, bus and RER outside Paris waiting takes place outdoors.  The transport module simulates the mobility of the population. When individuals reach their destination they engage in the activity corresponding to the journey's motive. For activities other than travel we assign a mean duration. Activity event 355 sequences are simulated at 1min temporal resolution. The ambient concentrations at which people are exposed during the activity is the corresponding hour-averaged concentration modeled with the CHIMERE model at the grid-cell where the activity takes place. In the case of travelling, the model simulates the trajectory of the journey. For car journeys, we use the mean hourly traffic flows on each segment of the road network to assign probabilities to each road and assign the route trajectories. The trajectory of the journey may intersect several CHIMERE grid-cells. The corresponding outdoor concentrations are weighted 360 by the time spent in each grid-cell to estimate the aggregated exposure. Figure 8 (bottom right) shows the number of people engaged in each of the implemented activities at each hour of the simulation. Here, time-activity is modelled based on available data for the region relied on questionnaires. Modelling mobility patterns using smart phones with built-in GPS is an emerging trend in personal exposure assessment (Yu et al., 2019). Combining GPS-derived data on the trajectories of large number of individuals with information from questionnaires on the locations and activities of the population, could help overcome large 365 part of the uncertainties relating to the time-activity module developed in this study.

Results
In this section we highlight different possible applications of the exposure model. Each section looks at a different aspect of the model output as exemples of its use in applications. The spatial distribution of exposure over the Ile-de-France region is discussed in Section 5.1, the relative contribution of each microenvironment in the daily aggregated exposure is quantified in 370 Section 5.2, the variability in exposure patterns across sub-populations is studied in Section 5.3, and the impact of considering 1) the infiltration of pollutants indoors and 2) the mobility of the population is illustrated in Section 5.4. Finally, in Section 5.5 we develop a 2050 horizon projection in the building stock of the Ile-de-France region, and quantify its impact in exposure to PM 2.5 and ozone.

Exposure maps 375
Personal exposures may be spatially averaged over the communes to provide population exposure maps (Figure 9). The annual averaged exposure to ozone is three times higher for the residents of the remote rural areas compared to the exposure of the Parisians. NO emitted by cars over the dense road network in the Paris city reacts fast with O 3 to form NO 2 . This explains the absence of O 3 over the urban agglomeration. NO 2 emitted in large amounts over Paris under the influence of sunshine and in the presence of volatile organic compounds forms O 3 downwind, over the rural area (see also Figure 3). We also note 380 that the exposure to ozone is much lower than outdoors ozone concentration (30 and 15 ppb for the rural and urban areas respectively (compare to maps in Figure 3)). This difference is due to the high amount of time people spent indoors, where ozone concentrations are close to zero (see also I/O ratios for O 3 Figure 4). The traffic network is a large source of PM 2.5 , which explains why exposures to PM 2.5 are much higher in the Paris agglomeration than in the rural areas. Exposures to PM 2.5 are much closer to concentration levels because I/O ratios in buildings for 385 PM 2.5 are closer to 1 than for O 3 . Annual mean PM 2.5 concentrations are however lower than annual mean PM 2.5 exposures (compare with concentration maps in Figure 9). Even if indoor PM 2.5 sources in buildings are not yet implemented in the model and therefore concentrations in buildings are always lower than outdoor concentrations, PM 2.5 concentrations in cars, subway train or in subway platforms are several times higher than outdoor concentrations (see Table 3). Even if the time spent in transport is relatively lower than the time spent inside buildings, concentrations there are so high that the daily aggregated 390 exposures are higher than outdoor concentrations. The construction date of buildings also plays an important role, with older buildings (higher I/O ratios) contributing to exposure at higher pollutant levels. Buildings in the Paris agglomeration are in general older than buildings outside of the city center and therefore indoor exposure to PM 2.5 is higher for the residents of Paris.

Exposure in different micro-environments 395
The relative contribution of exposure in different micro-environments in the aggregated daily exposure depends on outdoor concentrations, the indoor/outdoor coefficients if the activity takes place indoors, and the time spent in the micro-environment.
For the active population (between 4 and 65 years old) residential exposure accounts for about 75% of daily exposure to PM 2.5 and almost 80% of the agregated exposure to O 3 (Figure 10) reflecting the large amount of time spent at home (see also bottom right panel in Figure 8). Exposure at school represents the second largest part of total daily exposure to both pollutants (more population) than for children going to school (4-23 years old). For PM 2.5 exposures in public transportation and car also have significant contributions.

Exposure of sub-population groups
Here we study the impact of several exposure factors on personal exposures. Figure 11 shows the cumulative distribution of 405 exposure over specific sub-populations. We identify the two factors that have the largest impact on personal exposures, namely the mode of transportation and the construction date of the building of residence. Both factors seem to strongly affect exposure to PM 2.5 and ozone. People traveling with motorcycles or cyces are exposed to the highest PM 2.5 levels, while exposure in cars is the lowest. 10% of the population using 2-wheels as transportation mode is exposed to PM 2.5 levels higher than the 25 µg/m 3 EU target value related to human health. The percentage of the population exposed to PM 2.5 levels above the EU target value 410 drops to 5% for people travelling on foot, 3% for public transport and 1% for people travelling by car. The construction date of the home building also plays an important role in personal exposure. For both pollutants, exposure is higher for buildings constructed before 1974. 100% of the population living in buildings constructed after 2005 are exposed to PM 2.5 levels below the EU target value while 5% of the population living in constructions before 1974 is exposed to levels above the EU target value.
415 Figure 11. Cumulative distributions of exposures to PM2.5 (above) and O3 (below) for sub-populations distinguished by the transportation mode (left) and construction date of the building where they live.

Model sensitivity to population mobility and exposure indoors
Often, epidemiological methods estimate exposure metrics by modelling pollution concentrations at individual addresses.
However, these models do not take into account neither exposure indoors nor population mobility. To provide insight into the exposure misclassification error due to this omission we conducted several sensitivity studies. We calculated personal exposures to PM 2.5 with and without accounting for the mobility of the population and exposure indoors as follows:

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-REF The population stays at home and indoor concentrations are the same as outdoors.
-+MOBILITY The population moves but concentrations indoors are the same as outdoors.
-+INDOORS BUILDINGS The population stays at home and indoor / outdoor coefficients for buildings are applied.
-+INDOORS BUILDINGS & TRANSPORT The population moves and indoor / outdoor coefficients for both buildings and transportation are applied. study compared to a regional scale CTM in our case. Accounting for residential exposure in the +INDOORS BUILDINGS simulation strongly affects personal exposures (-11% difference with the REF in the median exposure). Accounting also for indoors exposure during transportation +INDOORS BUILDINGS & TRANSPORT leads to a 4.6% increase in the median exposure compared to only accounting for residential exposures (+INDOORS BUILDINGS). PM 2.5 concentrations during 435 transportation are higher than outdoors whereas concentrations in buildings are always lower than outdoors (no indoor sources in buildings). These results are comparable to the findings of Smith et al. (2016) who also estimate a decrease in personal exposures to PM 2.5 in the London metropolitan area when population mobility and indoor exposure are accounted for. In the REF simulation 5% of the population is exposed to concentrations above the EU target value of 25µg/m 3 while in the complete implementation of indoor exposures only 2% of the population is exposed to PM 2.5 above this threshold ( Figure 12).

2050 horizon projection of the building stock
Based on data on the evolution of the French building stock (INSEE, 2014) and the national thermal building regulation found in the 2013 report of the Agence de l'Environnement et de la Maitrise de l'Energie (ADEME, 2013), the CSTB developed a projection for the evolution of the building stock that is applied here for the 2050 horizon. To comply with thermal legislations and energy demand, buildings will tend to be more air-tight and ventilation systems more efficient. This evolution in the 445 building stock will also affect air-quality in buildings, and therefore human exposure to atmospheric contaminants.
Following this projection, in 2050 dwellings, offices and schools will still fall in the same categories presented in Table 2 but the proportions of buildings falling in each category will change due to demolition, new construction and thermal rehabilitation.
The projection developed here models the annual rate of change in the building stock as follows: For dwellings (Equation 3):

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-Buildings belonging to the 5 th class (construction date >2012) will increase -Buildings belonging in the 1 st class (<1974 not rehabilitated) will decrease due to demolition and thermal rehabilitation -Buildings belonging to the 2 nd class (<1974 rehabilitated) will increase due to thermal rehabilitation of buildings in the For offices and schools (Equation 4): -Buildings belonging to the 3 rd class (2006-2012) will increase -Buildings belonging to the 1 st class (<1974 not rehabilitated) will decrease due to demolition The projection is applied to the Ile-de-France building stock, and we simulate personal exposures to quantify its impact. Due

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to new buildings being more air-tight with a better control of air renewal using more efficient ventilation systems, even less ozone penetrates the building shell. The resulting reduction in annual average exposure to O 3 is up to 14% (Figure 13). The change in annual averaged PM 2.5 exposure is very small (not shown).

Conclusions
We developed a regional scale model for personal exposures to PM 2.5 and O 3 . The model uses simulated outdoor pollutant 465 concentrations and models the infiltration of outdoor contaminants indoors in buildings with a ventilation mass-balance model.
Three building types are considered: dwellings, schools and offices. It also models population mobility inside the region considering the different possible transportation modes and adjusts for pollutant concentrations inside cars, buses, tram, subway train and regional trains. A special treatment for concentrations in subway platforms is applied considering online measurements on the platform and outdoors. An adjustment for ambient concentrations inside road-tunnels is also applied from data 470 from the literature. The model also uses data from the road traffic network to estimate the most probable trajectory for travel, as well as mean travel speed and duration.
We show that considering the population daily movement inside the region without accounting for the penetration of outdoor pollution indoors or indoor concentration during transportation has a small negative impact on annual averaged personal exposures. This is in contrast with the previous study of Shekarrizfard et al. (2016) who found an increase in exposures to 475 NO 2 in the Montreal metropolitan area when population mobility is accounted for. However, the two models are not directly comparable since they look at different pollutants, at different time-scales and use different air-quality models.
We show that, accounting for the penetration of outdoor pollution indoors in buildings without considering population movement decreases annual averaged personal exposures by 11% for PM 2.5 . This decrease stems only from the buildings' envelope acting as barrier to pollution infiltration indoors. When accounting also for population movement, annual averaged with CHIMERE (2km x 2km). However, if this resolution is not enough to solve concentration gradients at the proximity of local sources such as roads, it is capable to distinguish between urban, suburban and rural concentrations. Most of the daily movement in the region crosses these boundaries (e.g. people living at the suburbs work in Paris and vice-versa).
We conclude that both infiltration of pollutant indoors and population movement need to be considered to estimate the aggregated daily exposure. We note here that, so far, the model does not implement indoor sources of PM 2.5 in buildings. 490 We are aware that PM 2.5 indoors may be several times higher than outdoor concentrations (as is the case during transport).
However, in this version of the model we were more interested to see how different building types and characteristics affect personal exposures independent of human activity that would drive indoor sources. The CSTB is working actively to develop parametrizations accounting for indoor emission sources of PM 2.5 as well as their resuspension due to human activity.
Several applications of the model are presented. We first show the maps of exposure to O 3 and PM 2.5 over the region.

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The spatial distribution of the exposure field is very similar to the concentration one, showing the strong correlation of the aggregated exposure to outdoor concentration. However, we show that if we focus on specific sub-population groups, such as people using bicycles or motorcycles systematically in their daily journeys or people living in houses built before 1974, the upper percentiles of exposure are much higher than the general population. To study the impact of buildings' characteristics on personal exposures we implemented a 2050-horizon projection of the building stock in the Ile-de-France region. Following this 500 projection, older buildings will be demolished or rehabilitated to comply with the thermal regulation and newer constructions will have modernized characteristics. The share of people living in the different building categories is modified to match this projection and personal exposures are simulated. 2050-horizon personal exposures to O 3 are decreased by as much as 14% according to this projection.