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International audience; Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 \unit{km} horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to -5 % for Paris and -2 % for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between -10 and -5 % while for Stockholm we observe mixed trends up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily average and maximum ozone and 20 % for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective.

A large fraction of the European population is exposed to atmospheric pollutant concentration levels above health-related thresholds, leading to excess mortality and morbidity. Densely populated urban areas are generally more concerned than rural regions due to higher emission of atmospheric pollutants especially from the road network. Atmospheric modelling is a necessary tool to assess urban scale air-quality for both research and operational purposes. It provides a spatially and temporally resolved information for several gaseous and particulate species. It is also used for forecast and scenario evaluation for policy making. Modelling atmospheric composition at street level is challenging because pollutant concentration within street-canyons depends largely on local emissions but also on the transport of polluted air masses from remote areas. Therefore, regional scale modelling and local applications must be combined to provide accurate simulations of the atmospheric composition at urban scale. In our study we compare two such strategies. In both cases the regional scale chemistry-transport model CHIMERE, fed by WRF meteorological fields, provides urban background concentrations. To simulate the local component of pollutant concentrations over roads we use i) the statistical sub grid-scale approach embedded in the chemistry-transport model and ii) the street-network model MUNICH. Simulation results over the city of Paris from both modelling approaches are compared to in-situ measurements of the local air-quality network for all available traffic monitors.The major challenge of this inter comparison exercise is to find a consistent configuration setup for both models allowing a one-to-one comparison of the simulations. To do so we had to implement the same chemical and dynamical mechanisms for gases and suspended particles in both models. We also tested several vertical discretizations to obtain a consistent first-layer depth. Different turbulence parametrizations, including or not the urban canopy model (UCM) within WRF, were compared to obtain stable results for concentrations. We show these latter are particularly sensitive to the parametrization of the anthropogenic heat flux. To obtain realistic heat fluxes and satisfactory results for both modelling strategies we have to include in the simulations all three of the following aspects i) a highly resolved land-cover database (CORINE) ; ii) a three urban class distinction in the UCM and iii) the sub-grid scale urban fraction.Results of two-month wintertime simulations for NOx, NO2, PM2.5 are discussed. The street-network approach provides better results for both gases and particles especially at high-traffic highways. The conclusion is less straightforward at low-traffic roads. Our results highlight the need to develop a consistent coupling of the street-network model MUNICH with the regional scale chemistry transport model CHIMERE to accurately simulate gases and particulate matter concentrations over the street network.

A new version of a street-network model, the Model of Urban Network of Intersecting Canyons and Highways version 2.0 (MUNICH v2.0), is presented. The comprehensive aerosol model SSH-aerosol is implemented in MUNICH v2.0 to simulate the street concentrations of multiple pollutants, including secondary aerosols. The implementation uses the application programming interface (API) technology so that the SSH-aerosol version may be easily updated. New parameterisations are also introduced in MUNICH v2.0, including a non-stationary approach to model reactive pollutants, particle deposition and resuspension, and a parameterisation of the wind at roof level. A test case over a Paris suburb is presented for model evaluation and to illustrate the impact of the new functionalities. The implementation of SSH-aerosol leads to an increase of 11 % in PM10 concentration because of secondary aerosol formation. Using the non-stationary approach rather than the stationary one leads to a decrease in NO2 concentration of 16 %. The impact of particle deposition on built surfaces and road resuspension on pollutant concentrations in the street canyons is low.

Santiago (33.5°S, 70.5°W), the capital of Chile, is frequently affected by extreme air pollution events during wintertime deteriorating air quality (AQ) and thus affecting the health of its population. Intense residential heating and on-road transport emissions combined with poor circulation and vertical mixing are the main factors responsible for these events. A modelling system composed of a chemistry-transport model (CHIMERE) and a meteorological model (WRF) was implemented to assess the AQ impacts of residential and transportation sources in the Santiago basin. A two-week period of July 2015 with various days with poor AQ was simulated focusing on the impact on AQ with respect to fully inhalable particles (PM2.5) and nitrogen oxides (NOX). Three emission scenarios, within the range of targeted reductions of the decontamination plan of Santiago, were tested; namely 50% reduction of residential emission, 50% reduction of transport emissions and the combination of both. An additional scenario decreasing transport emissions in 10% was carried out to examine whether a linear dependence of surface concentrations on changes in emissions exists. The system was validated against surface and vertically resolved meteorological measurements. The model reproduces the daily surface concentration variability from the AQ monitoring network of Santiago. However, the model not fully captures the emissions variations inferred from the observations which may be due to missing sources such as resuspension of dust. Results show that, during the period studied, although both residential and transportation sources contribute to observed AQ levels in Santiago, reducing transport emissions is more effective in terms of reducing the number of days with pollution events than decreasing residential combustion. This difference in impact is largely due to the spatial distribution of the emission sources. While most of the residential combustion is emitted in the outskirts of the city, most of the transport emissions occur within the city, where most of the stations from AQ monitoring network of Santiago are located. As can be expected, the largest improvement of AQ in Santiago is achieved by the combined reduction of emissions in both sectors. Sensitivity analysis with 10% reduction in transport emissions reveals a linear behavior between emissions and concentrations for NOX and approximate linear behavior for PM2.5. The absence of secondary aerosols formation and dust resuspension in the current simulation could explain this deviation from linearity for fine particles. Nevertheless, it suggests that the results can be used for mitigation policies with emissions reductions below the 50% used in this study. © 2018 Elsevier Ltd

This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). 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 exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. 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 exposure 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 PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), 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 exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.

The city of Santiago experiences extreme pollution events during winter due to particulate matter and the associated health impact depends on the exposure to this pollutant, particularly to PM2.5. We present and apply a method that estimates the exposure of users of the public transport system of Santiago by combining smart card mobility data with measured surface concentrations from the monitoring network of Santiago and simulated concentrations by the CHIMERE model. The method was applied between July 20th and 24th of 2015 to 105,588 users corresponding to 12% of the frequent users of the public transport system and approximately 2% of the total population of Santiago. During those five days, estimated exposure based on measured concentrations varied between 44 and 75 μg/m3 while exposure based on simulated concentrations varied between 45 and 89 μg/m3. Furthermore, including socioeconomic conditions suggests an inverse relationship between exposure and income when measured concentrations are used, i.e. the lower the income the higher the exposure, whereas no such relationship is observed when using simulated concentrations. Although only exposure to PM2.5 was considered in this study, the method can also be applied to estimate exposure to other urban pollutant such as ozone.

Background Ozone and PM 2.5 are current risk factors for premature death all over the globe. In coming decades, substantial improvements in public health may be achieved by reducing air pollution. To better understand the potential of emissions policies, studies are needed that assess possible future health impacts under alternative assumptions about future emissions and climate across multiple spatial scales. Method We used consistent climate–air-quality–health modeling framework across three geographical scales (World, Europe and Ile-de-France) to assess future (2030–2050) health impacts of ozone and PM 2.5 under two emissions scenarios (Current Legislation Emissions, CLE, and Maximum Feasible Reductions, MFR). Results Consistently across the scales, we found more reductions in deaths under MFR scenario compared to CLE. 1.5 [95% CI: 0.4, 2.4] million CV deaths could be delayed each year in 2030 compared to 2010 under MFR scenario, 84% of which would occur in Asia, especially in China. In Europe, the benefits under MFR scenario (219 000 CV deaths) are noticeably larger than those under CLE (109 000 CV deaths). In Ile-de-France, under MFR more than 2830 annual CV deaths associated with PM 2.5 changes could be delayed in 2050 compared to 2010. In Paris, ozone-related respiratory mortality should increase under both scenarios. Conclusion Multi-scale HIAs can illustrate the difference in direct consequences of costly mitigation policies and provide results that may help decision-makers choose between different policy alternatives at different scales.

International audience; There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1°× 0.1°. Comparing the models at a spatial resolution of 2°× 2°with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error. Plain Language Summary When comparing the black carbon model at a resolution of 2°× 2°with local measurements, the global representativeness error is 30% for AErosol RObotic NETwork sites, compared to 7% for Global Atmosphere Watch sites. It demonstrates that, in absence of high-resolution models, the current top-down estimate of black carbon direct radiative forcing is overestimated by 30%.

Ozone and PM2.5 concentrations over the city of Paris are modeled with the CHIMERE air-quality model at 4 km × 4 km horizontal resolution for two future emission scenarios. A high-resolution (1 km × 1 km) emission projection until 2020 for the greater Paris region is developed by local experts (AIRPARIF) and is further extended to year 2050 based on regional-scale emission projections developed by the Global Energy Assessment. Model evaluation is performed based on a 10-year control simulation. Ozone is in very good agreement with measurements while PM2.5 is underestimated by 20% over the urban area mainly due to a large wet bias in wintertime precipitation. A significant increase of maximum ozone relative to present-day levels over Paris is modeled under the "business-as-usual" scenario (+7 ppb) while a more optimistic "mitigation" scenario leads to a moderate ozone decrease (−3.5 ppb) in year 2050. These results are substantially different to previous regional-scale projections where 2050 ozone is found to decrease under both future scenarios. A sensitivity analysis showed that this difference is due to the fact that ozone formation over Paris at the current urban-scale study is driven by volatile organic compound (VOC)-limited chemistry, whereas at the regional-scale ozone formation occurs under NOx-sensitive conditions. This explains why the sharp NOx reductions implemented in the future scenarios have a different effect on ozone projections at different scales. In rural areas, projections at both scales yield similar results showing that the longer timescale processes of emission transport and ozone formation are less sensitive to model resolution. PM2.5 concentrations decrease by 78% and 89% under business-as-usual and mitigation scenarios, respectively, compared to the present-day period. The reduction is much more prominent over the urban part of the domain due to the effective reductions of road transport and residential emissions resulting in the smoothing of the large urban increment modeled in the control simulation.

CHIMERE is a chemistry-transport model designed for regional atmospheric composition. It can be used at a variety of scales from local to continental domains. However, due to the model design and its historical use as a regional model, major limitations had remained, hampering its use at hemispheric scale, due to the coordinate system used for transport as well as to missing processes that are important in regions outside Europe. Most of these limitations have been removed in the CHIMERE-2017 version, allowing its use in any region of the world and at any scale, from the scale of a single urban area up to hemispheric scale, with or without polar regions included. Other important improvements have been made in the treatment of the physical processes affecting aerosols and the emissions of mineral dust. From a computational point of view, the parallelization strategy of the model has also been updated in order to improve model numerical performance and reduce the code complexity. The present article describes all these changes. Statistical scores for a model simulation over continental Europe are presented, and a simulation of the circumpolar transport of volcanic ash plume from the Puyehue volcanic eruption in June 2011 in Chile provides a test case for the new model version at hemispheric scale.

International audience

International audience; Horizontal resolution of grid-based chemistry-transport models is limited to a few square kilometers which has been proved insufficient for assessing human exposure and health impact. We propose a general methodology, applicable on any kind of grid-based air-quality model, that combines subgrid scale information on emission and land-use data in order to disaggregate the grid-averaged emission flux into a set of source-specific components (subgrid-environments). Different subgrid concentrations are calculated inside each one of these environments providing a direct estimate of pollutant variability along with the 'standard' grid-averaged model output. The method was first validated over a controlled emissions case by comparing concentrations modeled in the subgrid-environments with concentrations modeled directly at higher model resolution and next over a real case-study, where subgrid concentrations were compared with monitor data from sites representing different types of urban environments (i.e. roads and residential blocks). It was shown that the method is capable to yield accurate estimates of small scale pollutant variability.

A persistent challenge for small-scale air quality modeling is the assessment of health impact and population exposure studies. Despite progress in computation and in the quality of model input (i.e., high-resolution information on land use and emission patterns), the uncertainty associated with input parameters cannot be eliminated. The aim of this paper is to study different sources of uncertainty that affect model results as the resolution increases. Mesoscale chemistry transport simulations at different resolutions are used and modeled 03 concentrations are compared with surface measurements. The case study consists of CHIMERE model simulations over the city of Paris. It is shown that the principal source of noise in model results is the resolution of the input emission fluxes. The O3 concentrations modeled with simulations forced by several horizontal resolutions of input emission data (from Δx = 48 km to Δx = 6 km) indicate that model results do not improve monotonously with resolution, but that after a certain point discrepancies become larger. Based on this result and as an alternative to the deterministic downscaling that resolves explicitly the finer scale (beyond the 1-km range), the authors propose a subgrid-scale approach that uses a statistical description of spatial scales finer than model resolution. As an example, the subgrid variability of modeled O3 concentration has been quantified, when modeled dry deposition processes occur over subgrid surfaces (land use fractions). The implementation of this modified calculation gives access to subgrid fluxes and subgrid surface concentrations instead of the mean values provided by the commonly used model calculation.

International audience; Black carbon (BC) contributes to global warming by absorbing sunlight. However, the size of this contribution, namely, the direct radiative forcing (RF), ranges from +0.1 to +1.0 W m À2 , largely due to differences between bottom-up and observation-based estimates. Current global models systematically underestimate BC radiation absorption relative to observations, which is often attributed to the underestimation of BC emissions. Several studies that adjusted emissions to correct biases of global aerosol models resulted in a revised upward estimate of the BC RF. However, the BC RF was never optimized against observations in a rigorous mathematical manner. Here we simulated the absorption of solar radiation by BC from all sources at the 10 km resolution by combining a highly disaggregated emission inventory with a nested aerosol climate model and a downscaling method. As a result, the normalized mean bias in BC radiation absorption was reduced from À56% to À5% in Asia and from À71% to À46% elsewhere. We applied a Bayesian method that makes the best account of all model, representativeness, and observational uncertainties to estimate the BC RF and its uncertainty. Using the new emission inventory and high-resolution model reduces uncertainty in BC RF from À109%/+172% to À77%/+78% over Asia and from À83%/+114% to À64%/+70% over other continental regions. Finally, we derived an observationally constrained BC RF of 0.53 Wm À2 (0.14 to 1.19 as 90% confidence) as our best estimate, less than previous estimates. Our estimate implies that reduction in BC emissions would contribute to slow down global warming, but the contribution could be less than previously thought.

International audience; Black carbon (BC) is increasingly recognized as a significant air pollutant with harmful effects on human health, either in its own right or as a carrier of other chemicals. The adverse impact is of particular concern in those developing regions with high emissions and a growing population density. The results of recent studies indicate that BC emissions could be underestimated by a factor of 2–3 and this is particularly true for the hot-spot Asian region. Here we present a unique inventory at 10-km resolution based on a recently published global fuel consumption data product and updated emission factor measurements. The unique inventory is coupled to an Asia-nested (∼50 km) atmospheric model and used to calculate the global population exposure to BC with fully quantified uncertainty. Evaluating the modeled surface BC concentrations against observations reveals great improvement. The bias is reduced from −88% to −35% in Asia when the unique inventory and higher-resolution model replace a previous inventory combined with a coarse-resolution model. The bias can be further reduced to −12% by downscaling to 10 km using emission as a proxy. Our estimated global population-weighted BC exposure concentration constrained by observations is 2.14 μg⋅m−3; 130% higher than that obtained using less detailed inventories and low-resolution models.

Changes in urban design and traffic policy, demography, climate and associated adaptation, mitigation measures and environmental policies are likely to modify both outdoor and indoor air quality and therefore public health. The project aims to improve our understanding of future exposure situations and their impact on health, from an interdisciplinary approach. This will be achieved by using various state-of-the-art atmospheric models, measurements, epidemiological studies and reviews. To assess population full exposure, an integrated view accounting both for indoor and outdoor air pollution as well as for population time activity data will be developed. New dose-response functions will be estimated between health outcome, air pollution and temperature in order to better estimate the effects on the foetus and young children. Ultimately, scenarios of future urban climate and air quality will be simulated, combining future exposure scenarios, population scenarios and exposure-response functions to describe the effects of different trends and relevant policies on relative risk and burden of illness attributed to urban pollutants and their interactions with extreme temperatures. Also the mitigation strategies that can be used to reduce urbanization and climate change effects on the local urban meteorology and air quality will be assessed. With applications in several large European cities, the project will study the impact of several alternative adaptation scenarios on urban air quality and human health to a mid-century horizon (2030–2060) accounting for the effects of a changing urban climate. Scenario-based health impact assessments will combine exposure information from climate models, emission scenarios, policy evaluation studies and concentration calculations with exposure-response functions from epidemiological studies of vulnerable groups within the project and previously published functions for mortality and hospital admissions. The effects of socioeconomic and demographic trends will be discussed, the predicted health impacts and benefits associated with different interventions and policies and other urban changes will be described.

Tropospheric trace gas and aerosol pollutants have adverse effects on health, environment and climate. In order to quantify and mitigate such effects, a wide range of processes leading to the formation and transport of pollutants must be considered, understood and represented in numerical models. Regional scale pollution episodes result from the combination of several factors: high emissions (from anthropogenic or natural sources), stagnant meteorological conditions, kinetics and efficiency of the chemistry and the deposition. All these processes are highly variable in time and space, and their relative contribution to the pollutants budgets can be quantified with chemistry-transport models. The CHIMERE chemistry-transport model is dedicated to regional atmospheric pollution event studies. Since it has now reached a certain level a maturity, the new stable version, CHIMERE 2013, is described to provide a reference model paper. The successive developments of the model are reviewed on the basis of published investigations that are referenced in order to discuss the scientific choices and to provide an overview of the main results.

Tropospheric trace gas and aerosol pollutants have adverse effects on health, environment and climate. In order to quantify and mitigate such effects, a wide range of processes leading to the formation and transport of pollutants must be considered, understood and represented in numerical models. Regional scale pollution episodes result from the combination of several factors: high emissions (from anthropogenic or natural sources), stagnant meteorological conditions, velocity and efficiency of the chemistry and the deposition. All these processes are highly variable in time and space, and their relative importance to the pollutants budgets can be quantified within a chemistry-transport models (CTM). The offline CTM CHIMERE model uses meteorological model fields and emissions fluxes and calculates deterministically their behavior in the troposphere. The calculated three-dimensional fields of chemical concentrations can be compared to measurements to analyze past periods or used to make air quality forecasts and CHIMERE has enabled a fine understanding of pollutants transport during numerous measurements campaigns. It is a part of the PREVAIR french national forecast platform, delivering pollutant concentrations up to three days in advance. The model also allows scenario studies and long term simulations for pollution trends. The modelling of photochemical air pollution has reached a good level of maturity, and the latest projects involving CHIMERE now aim at increasing our understanding of pollution impact on health at the urban scale or at the other end of the spectrum for long term air quality and climate change interlinkage studies, quantifying the emissions and transport of pollen, but also, at a larger scale, analyzing the transport of pollutants plumes emitted by volcanic eruptions and forest fires.

International audience; Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] = 10 µm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants. Copyright 2011 Air & Waste Management Association.

Population-based epidemiologic studies of air pollution have traditionally relied upon imperfect surrogates of personal exposures, such as area-wide ambient air pollution levels based on readily available outdoor concentrations from central monitoring sites. This practice may introduce exposure misclassification in epidemiologic analyses for pollutants that are spatially heterogeneous, including those associated with traffic emissions (i.e., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). To investigate the potential impact of misclassification within observed health risk estimates, U.S. EPA in collaboration with Emory University is developing and evaluating several tiers of exposure metrics for ambient traffic-related and regional pollutants that vary in their approaches for modeling pollutant spatial heterogeneity. The following tiers of exposure metrics are examined: (1) central site monitoring data, (2) local scale modeling (AERMOD), and (3) combined regional and local scale modeling. Each metric is applied in two extensive, ongoing epidemiologic studies conducted by Emory University to examine ambient air pollution and acute morbidity in Atlanta, GA. We hypothesize that using the more refined exposure estimates will provide greater power to detect epidemiologic associations of interest, particularly for heterogeneous, traffic-related pollutants. This research will be useful for improving exposure assessment in future air pollution epidemiology studies, by providing alternative methods as well as by providing a further understanding of to the situations that might require refined exposure metrics.

Urban meteorology and atmospheric composition has become an issue of great public concern. Megacities are areas of great population density and also of high rates of gaseous and particulate matter emission. The health of the residents of such large agglomerations will potentially be influenced by the local air-pollution, its formation and dispersion. Efforts are made to link air-pollution and health. Several questions are open: what is the relative contribution of the ambient air-pollution compared to all other exposure factors? What is the 'real' level of exposure reaching the individuals of a population? Can we distinguish the health effects of multiple-pollutants? To answer to these questions we have developed a modeling framework that maps the exposure of the Parisian population to ozone, nitrogen dioxide and fine particles at hourly resolution. This integrated model incorporates a meteorological model, a three-dimensional Eulerian air-quality model and a human exposure module. The first results of this modeling effort suggest new epidemiological evidence of the relationship between ambient airpollution levels and mortality over the city of Paris.

La presence d'especes gazeuses et particulaires (aerosols) dans l'atmosphere peut avoir des effets nefastes a la fois sur la sante, l'environnement et le climat. Pour limiter les effets de la pollution de l'air, il faut comprendre l'ensemble des mecanismes de formation des polluants atmospheriques. Lors d'episodes de pollution a l'echelle regionale, une conjonction de phenomenes entre en jeu: de tres fortes emissions anthropiques ou naturelles, un temps defavorable a la dispersion des polluants, une chimie et des depots plus ou moins rapides et efficaces. Tous ces facteurs sont tres variables dans le temps et dans l'espace. Pour analyser finement l'ensemble des processus, il est necessaire de construire des outils numeriques integrateurs de l'ensemble des causes, les modeles de chimie-transport. Le modele CHIMERE utilise des champs meteorologiques et des flux d'emissions de polluants et calcule de maniere deterministe leur devenir dans la troposphere. Les resultats sont des champs tridimensionnels de concentrations chimiques. Ils peuvent etre compares a des mesures pour analyser des periodes passees ou servir a realiser des previsions de pollution. CHIMERE a ainsi permis d'evaluer notre comprehension du transport de polluants lors des campagnes de mesures ESQUIF, ESCOMPTE, AMMA ou MEGA-POLI. Il est integre a la plate-forme de prevision nationale PREV'AIR, delivrant des champs de polluants prevus jusqu'a trois jours a l'avance. Le modele permet aussi de realiser des etudes de scenarios et de comprendre des tendances d'evolution de la pollution sur le long terme. Si la modelisation de la pollution photo-oxydante a atteint un bon degre de maturite, les derniers projets impliquant CHIMERE visent maintenant a accroitre nos connaissances sur l'impact de la pollution sur la sante a l'echelle urbaine et sur les emissions et le transport des pollens. A plus grande echelle, ces projets ont egalement pour objectif d'analyser et de prevoir le transport de panaches de polluants engendres par les eruptions volcaniques et les feux de forets.