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This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from aerosol chemical speciation monitor (ACSM) and multiwavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA (Chemical Characterization of Particles) program. The datasets contain the mass concentrations of major chemical species within submicron aerosols (PM1), namely organic aerosols (OAs), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl−), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both guidance from the French Reference Laboratory for Air Quality Monitoring (LCSQA) and the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in the aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM1 at each site (43 %–60 % of total mass), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO3 is the second most important contributor to PM1 (15 %–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO4 (8 %–14 %) and eBC (5 %–11 %) complement the major fine-aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively. A comparison with the 3D chemical transport model (CTM) CHIMERE shows high correlations between simulations and measurements, albeit with an OA concentration underestimation of 46 %–76 %. Regional discrepancies in NO3 concentration levels emphasize the importance of these datasets with respect to validating air quality models and tailoring air pollution mitigation strategies. The datasets can be found at https://doi.org/10.5281/zenodo.13318298 (Chebaicheb et al., 2024).

Fungal spore organic aerosol emissions have been recognised as a significant source of particulate matter as PM10; however, they are not widely considered in current air quality models. In this work, we have implemented the parameterisation of fungal spore organic aerosol (OA) emissions introduced by Heald and Spracklen (2009) (H&S) and further modified by Hoose et al. (2010) in the CHIMERE regional chemistry-transport model. This simple parameterisation is based on two variables, leaf area index (LAI) and specific humidity. We have validated the geographical and temporal representativeness of this parameterisation on a large scale by using yearly polyol observations and primary biogenic organic aerosol factors from positive matrix factorisation (PMF) analysis at 11 French measurement sites. For a group of sites in northern and eastern France, the seasonal variation of fungal spore emissions, displaying large summer and small winter values, is correctly depicted. However, the H&S parameterisation fails to capture fungal spore concentrations for a smaller group of Mediterranean sites with less data availability in terms of both absolute values and seasonal variability, leading to strong negative biases, especially during the autumn and winter seasons. Two years of CHIMERE simulations with the H&S parameterisation have shown a significant contribution of fungal spore OA to PM10 mass, which is lower than 10 % during winter and reaches up to 20 % during summer in high-emission zones, especially over large forested areas. In terms of contributions to organic matter (OM) concentrations, the simulated fungal spore contribution in autumn is as high as 40 % and reaches at most 30 % of the OM for the other seasons. As a conclusion, the fungal spore OA contribution to the total OM concentrations is shown to be substantial enough to be considered a major PM10 fraction and should then be included in state-of-the-art chemistry-transport models.

Biogenic volatile organic compounds (BVOCs) are emitted by vegetation and react with other compounds to form ozone and secondary organic matter (OM). In regional air quality models, biogenic emissions are often calculated using a plant functional type approach, which depends on the land use category. However, over cities, the land use is urban, so trees and their emissions are not represented. Here, we develop a bottom-up inventory of urban tree biogenic emissions in which the location of trees and their characteristics are derived from the tree database of the Paris city combined with allometric equations. Biogenic emissions are then computed for each tree based on their leaf dry biomass, tree-species-dependent emission factors, and activity factors representing the effects of light and temperature. Emissions are integrated in WRF-CHIMERE air quality simulations performed over June–July 2022. Over Paris city, the urban tree emissions have a significant impact on OM, inducing an average increase in the OM of about 5 %, reaching 14 % locally during the heatwaves. Ozone concentrations increase by 1.0 % on average and by 2.4 % during heatwaves, with a local increase of up to 6 %. The concentration increase remains spatially localized over Paris, extending to the Paris suburbs in the case of ozone during heatwaves. The inclusion of urban tree emissions improves the estimation of OM concentrations compared to in situ measurements, but they are still underestimated as trees are still missing from the inventory. OM concentrations are sensitive to terpene emissions, highlighting the importance of favoring urban tree species with low-terpene emissions.

Toluene represents a large fraction of anthropogenic emissions and significantly contributes to tropospheric ozone and secondary organic aerosol (SOA) formation. Despite the fact that toluene is one of the most studied aromatic compounds, detailed chemical mechanisms still fail to correctly reproduce the speciation of toluene gaseous and condensed oxidation products. This study aims to elucidate the role of initial experimental conditions in toluene SOA mass loadings and to investigate gas–particle partitioning of its reaction products at different relevant temperatures. Gaseous and particulate reaction products were identified and quantified using a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) coupled to a CHemical Analysis of aeRosol ONline (CHARON) inlet. The chemical system exhibited a volatility distribution mostly in the semi-volatile regime. Temperature decrease caused a shift of saturation concentration towards lower values. The CHARON–PTR-ToF-MS instrument identified and quantified approximately 60 %–80 % of the total organic mass measured by an aerosol mass spectrometer. A detailed mechanism for toluene gaseous oxidation was developed based on the Master Chemical Mechanism (MCM) and Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) deterministic mechanisms, modified following the literature. The new mechanism showed improvements in modeling oxidation product speciation with more observed species represented and more representative concentrations compared to the MCM–GECKO-A reference. Tests on partitioning processes, nonideality, and wall losses highlighted the high dependency of SOA formation on the considered processes. Our results underline the fact that volatility is not sufficient to explain the gas–particle partitioning: the organic and the aqueous phases need to be considered as well as the interactions between compounds in the particle phase.

Air pollution in the central zone of Chile is not only a public health concern but also threatens water resources and climate, in connection with the transport and deposition of black carbon (BC) from urban centers onto the glaciers of the Andes. Chemistry-transport simulations reveal a seasonal dichotomy in the flux and latitudinal pattern of BC deposition on glaciers of the central Chilean Andes. The average deposition flux of BC on glaciers between 30 and 37∘ S is 4 times larger in winter, affecting mostly low-elevation glaciers, whereas the smaller summertime flux affects glaciers evenly, irrespective of their elevation. The contribution of emissions from the city of Santiago is dominant in summertime with more than 50 % along the Andes but minor in wintertime with less than 20 % even close to the capital city. Transport at larger scales and more local sources likely account for the remaining flux. The superimposition of synoptic-scale circulation and local mountain-valley circulation along the Andes drives the differences between summertime and wintertime deposition fluxes and generates a greater meteorological export potential during summer months. Future emissions and climate projections suggest that under the RCP8.5 scenario the gap between summertime and wintertime BC export and deposition flux could decrease, thereby pointing to summertime emission control gaining relevance. The chemistry-transport modeling approach for BC deposition on the Andes sheds light on the importance of the often disregarded summertime emissions on the radiative balance of its glaciers, particularly in the vicinity of Santiago.

This paper describes the GENerator of reduced Organic Aerosol mechanism (GENOA) that produces semi-explicit mechanisms for simulating the formation and evolution of secondary organic aerosol (SOA) in air quality models. Using a series of predefined reduction strategies and evaluation criteria, GENOA trains and reduces SOA mechanisms from near-explicit chemical mechanisms (e.g., the Master Chemical Mechanism – MCM) under representative atmospheric conditions. As a consequence, these trained SOA mechanisms can preserve the accuracy of detailed gas-phase chemical mechanisms on SOA formation (e.g., molecular structures of crucial organic compounds, the effect of “non-ideality”, and the hydrophilic/hydrophobic partitioning of aerosols), with a size (in terms of reaction and species numbers) that is manageable for three-dimensional (3-D) aerosol modeling (e.g., regional chemical transport models). Applied to the degradation of sesquiterpenes (as β-caryophyllene) from MCM, GENOA builds a concise SOA mechanism (2 % of the MCM size) that consists of 23 reactions and 15 species, with 6 of them being condensable. The generated SOA mechanism has been evaluated regarding its ability to reproduce SOA concentrations under the varying atmospheric conditions encountered over Europe, with an average error lower than 3 %.

With the growing evidence that high particle number concentrations may impact health, modelling their emissions and understanding formation processes is necessary, especially in cities where many people are exposed. As emission inventories of particle numbers and size distribution over cities are usually not available, a methodology is defined to estimate them from PM2.5 emissions and ratios of PM1 / PM2.5 and PM0.1 / PM2.5 by activity sector. In this methodology, a fitting parameter αem is used to redistribute the number concentrations in the lowest emission diameter range. This parameter is chosen by comparing measured and simulated number concentrations during non-nucleation days. The emission size distribution is then finely discretised by conserving both mass and number in each of the size ranges where emissions are specified. The methodology is applied over Greater Paris during the MEGAPOLI campaign (July 2009). Three-dimensional simulations are performed using the chemistry transport model Polair3D/Polyphemus coupled to the aerosol module SSH-aerosol to represent the evolution of particles by condensation, evaporation, coagulation, and nucleation, with a sectional approach for the size distribution. The model is first compared to measurements during non-nucleation days, and the influence over the month of July 2009 of three different nucleation parameterisations is assessed, i.e. binary (sulfuric acid, water), ternary (sulfuric acid, ammonia, water), and heteromolecular (extremely low-volatility organic compounds (ELVOCs) from monoterpenes and sulfuric acid). The modelled number concentrations compare very well to measurements, with an average normalised mean error of 42 % for the daily number concentrations of particles larger than 10 nm and 37 % for the number concentrations of particles larger than 100 nm. The influence of the binary nucleation is low, and the ternary nucleation scheme leads to better simulated number concentrations (in terms of bias and error) at only one site out of three, but it systematically reduces the model to measurement correlation, suggesting that ternary nucleation may not be the dominant process in new particle formation. However, the relative bias and error, as well as the correlation at suburban sites, are systematically improved using the heteromolecular nucleation scheme involving sulfuric acid and ELVOCs from monoterpenes. This suggests that heteromolecular nucleation may be important in cities, especially at suburban sites in summer, and that a better characterisation of the emissions of ELVOC precursors from traffic is needed.

The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends detected by observations? Do the models manage to reproduce observed trends? How close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set-up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the model-simulated trends could be regarded as an indicator for modelling uncertainty. The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5 from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM10) µg m−3 (or between 10 % and 30 %) across most of Europe (by 0.5–2 µg m−3 in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM2.5, relative PM10 trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30 %–40 % over most of Europe, increasing to 50 %–60 % in the northern and eastern parts of the EDT domain. Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble-simulated trends are −0.24 and −0.22 µg m−3 yr−1 for PM10 and PM2.5, which are somewhat weaker than the observed trends of −0.35 and −0.40 µg m−3 yr−1 respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are −1.7 % yr−1 and −2.0 % yr−1 from the model ensemble and −2.1 % yr−1 and −2.9 % yr−1 from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries. The strongest decreasing PM trends and the largest number of sites with significant trends are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modelled versus observed PM trends are limited to the regions where the sites are located. The analysis reveals considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in SO4-2 concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH4+ and NO3- to PM10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appear to be a dominant factor in bringing down PM10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties (including the implications of not accounting for forest fires and natural mineral dust by some of the models) and the effect of inter-annual meteorological variability on the trend analysis.

This paper describes a 16-year dataset of air pollution concentrations and air quality indicators over France. Using a kriging method that combines background air quality measurements and modeling with the CHIMERE chemistry transport model, hourly concentrations of NO2, O3, PM10 and PM2.5 are produced with a spatial resolution of about 4 km. Regulatory indicators (annual average, SOMO35 (sum of ozone means over 35 ppb), AOT40 (accumulated ozone exposure over a threshold of 40 ppb), etc.) are also calculated from these hourly data. The NO2 and O3 datasets cover the period 2000–2015, as well as the annual PM10 data. Hourly PM10 concentrations are not available from 2000 to 2007 due to known artifacts in PM10 measurements. PM2.5 data are only available from 2009 onwards due to the limited number of measuring stations available before this date. The overall dataset was evaluated over all years by a cross-validation process against background stations (rural, sub-urban and urban) to take into account the data fusion between measurement and models in the method. The results are very good for PM10, PM2.5 and O3. They show an overestimation of NO2 concentrations in rural areas, while NO2 background values in urban areas are well represented. Maps of the main indicators are presented over several years, and trends are calculated. Finally, exposure and trends are calculated for the three main health-related indicators: annual averages of PM2.5, NO2 and SOMO35. The DOI link for the dataset is https://doi.org/10.5281/zenodo.5043645 (Real et al., 2021). We hope that the publication of this open dataset will facilitate further studies on the impacts of air pollution.

The CHIMERE chemistry-transport model v2020r1 replaces the v2017r5 version and provides numerous novelties. The most important of these is the online coupling with the Weather Research and Forecasting (WRF) meteorological model via the OASIS3 – Model Coupling Toolkit (MCT) external coupler. The model can still be used in offline mode; the online mode enables us to take into account the direct and indirect effects of aerosols on meteorology. This coupling also enables using the meteorological parameters with sub-hourly time steps. Some new parameterizations are implemented to increase the model performance and the user's choices: dimethyl sulfide (DMS) emissions, additional schemes for secondary organic aerosol (SOA) formation with volatility basis set (VBS) and H2O, improved schemes for mineral dust, biomass burning, and sea-salt emissions. The NOx emissions from lightning are added. The model also includes the possibility to use the operator-splitting integration technique. The subgrid-scale variability calculation of concentrations due to emission activity sectors is now possible. Finally, a new vertical advection scheme has been implemented, which is able to simulate more correctly long-range transport of thin pollutant plumes.

Agriculture is the main source of ammonia (NH3) in France, an important gaseous precursor of atmospheric particulate matter (PM). National and global emission inventories are known to have difficulty representing the large spatial and temporal variability inherent to atmospheric NH3. In this study, we compare NH3 emissions in France during spring 2011 from one reference inventory, the TNO inventory, and two alternative inventories that account in different manners for both the spatial and temporal variabilities of the emissions: (i) the NH3SAT satellite-derived inventory based on IASI NH3 columns and (ii) the CADASTRE-CIT inventory that combines NH3 emissions due to nitrogen fertilization calculated with the mechanistic model VOLT'AIR on the database of the CADASTRE_NH3 framework and other source emissions from the CITEPA. The total spring budgets, from March to May 2011, at the national level are higher when calculated with both alternative inventories than with the reference, the difference being more marked with CADASTRE-CIT. NH3SAT and CADASTRE-CIT inventories both yield to large NH3 spring emissions due to fertilization on soils with high pH in the northeastern part of France (65 and 135 kt NH3, respectively, vs. 48 kt NH3 for TNO-GEN), while soil properties are not accounted for by the TNO-GEN methodology. For the other parts of France, the differences are smaller. The timing of fertilization and associated ammonia emissions is closely related to the nitrogen requirements and hence the phenological stage of the crops, and therefore to the crop year's specific weather conditions. Maximum emissions are observed in March for 2011 for some regions for both alternative inventories, while April is the period with maximum emissions for the reference inventory regardless of the region or the year. Comparing the inventories at finer temporal resolutions, typically at daily scale, large differences are found. The convergence of alternative, independent and complementary methods on the spatiotemporal representation of the spring NH3 emissions, particularly over areas where the contribution of mineral fertilizer spreading to the spring budget is strong, encourages further developments in both prospective complementary directions, as this will help improve national NH3 emission inventories.

The VBS-GECKO (volatility basis set – Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere) parameterization for secondary organic aerosol (SOA) formation was integrated into the chemistry-transport model CHIMERE. Concentrations of organic aerosol (OA) and SOA were simulated over Europe for the July–August 2013 period. Simulated concentrations with VBS-GECKO were compared to results obtained with the former H2O parameterization implemented in CHIMERE and to observations from EMEP, ACTRIS and other observations available in the EBAS database. The model configuration using the VBS-GECKO parameterization slightly improves the performances compared to the model configuration using the former H2O parameterization. The VBS-GECKO model configuration performs well for stations showing a large SOA concentration from biogenic sources, especially in northern Europe, but underestimates OA concentrations over stations close to urban areas. Simulated OA was found to be mainly secondary (∼85 %) and from terpene oxidation. Simulations show negligible contribution of the oxidation of mono-aromatic compounds to SOA production. Tests performed to examine the sensitivity of simulated OA concentrations to hydro-solubility, volatility, aging rates and NOx regime have shown that the VBS-GECKO parameterization provides consistent results, with a weak sensitivity to changes in the parameters provided by the gas-phase mechanism included in CHIMERE (e.g., HOx or NOx concentrations). Different scenarios considering intermediate-volatility organic compound (IVOC) emissions were tested to examine the contribution of IVOC oxidation to SOA production. At the continental scale, these simulations show a weak sensitivity of OA concentrations to IVOC emission variations. At the local scale, accounting for IVOC emissions was found to lead to a substantial increase in OA concentrations in the plume from urban areas. This additional OA source remains too small to explain the gap between simulated and measured values at stations where anthropogenic sources are dominant.

Modelling of mineral dust is often done using one single mean species. But for biogeochemical studies, it could be useful to access to a more detailed information on differentiated mineral species and the associated chemical composition. Differentiating between mineral species would also induce different optical properties and densities and then different radiative impact, transport and deposition. In this study, the mineralogical differentiation is implemented in the CHIMERE regional chemistry-transport model, by using global databases. The results show that this implementation does not change the results much in terms of aerosol optical depth, surface concentrations and deposition fluxes. But the information on mineralogy, with a high spatial (a few kilometres) and temporal (1 h) resolution, is now available and is ready to be used for future biogeochemical studies.

In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas – Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanalysis. For this study, long-term observations of particle sulfate (SO42-), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years (referred to as PT) but also for two 11-year subperiods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e., 1990–2000, with a 64 %–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO2 concentrations of about 34 %–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO42- concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods, even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO42- concentrations compared with their gas-phase precursor (i.e., SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28 %–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO3-) concentrations revealed that the relative reduction in HNO3 was larger than that for NO3- during the P1 period, which was mainly attributed to an increased availability of “free ammonia”. By contrast, trends in modeled HNO3 and NO3- concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emissions and monoterpene emissions during the 1990–2010 period over Fennoscandia and eastern European regions (i.e., around 14 %–27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally, a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic underestimation of the modeled SOA fractions of a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere.

This work aims at quantifying the relative contribution of secondary organic aerosol (SOA) precursors emitted by wildfires to organic aerosol (OA) formation during summer of 2007 over the Euro-Mediterranean region, where intense wildfires occurred. A new SOA formation mechanism, H2Oaro, including recently identified aromatic volatile organic compounds (VOCs) emitted from wildfires, is developed based on smog chamber experiment measurements under low- and high-NOx regimes. The aromatic VOCs included in the mechanism are toluene, xylene, benzene, phenol, cresol, catechol, furan, naphthalene, methylnaphthalene, syringol, guaiacol, and structurally assigned and unassigned compounds with at least six carbon atoms per molecule (USC>6). This mechanism H2Oaro is an extension of the H2O (hydrophilic–hydrophobic organic) aerosol mechanism: the oxidation of the precursor forms surrogate species with specific thermodynamic properties (volatility, oxidation degree and affinity to water). The SOA concentrations over the Euro-Mediterranean region in summer of 2007 are simulated using the chemistry transport model (CTM) Polair3D of the air-quality platform Polyphemus, where the mechanism H2Oaro was implemented. To estimate the relative contribution of the aromatic VOCs, intermediate volatility, semi-volatile and low-volatility organic compounds (I/S/L-VOCs), to wildfires OA concentrations, different estimations of the gaseous I/S/L-VOC emissions (from primary organic aerosol – POA – using a factor of 1.5 or from non-methanic organic gas – NMOG – using a factor of 0.36) and their ageing (one-step oxidation vs. multi-generational oxidation) are also tested in the CTM. Most of the particle OA concentrations are formed from I/S/L-VOCs. On average during the summer of 2007 and over the Euro-Mediterranean domain, they are about 10 times higher than the OA concentrations formed from VOCs. However, locally, the OA concentrations formed from VOCs can represent up to 30 % of the OA concentrations from biomass burning. Amongst the VOCs, the main contributors to SOA formation are phenol, benzene and catechol (CAT; 47 %); USC>6 compounds (23 %); and toluene and xylene (12 %). Sensitivity studies of the influence of the VOCs and the I/S/L-VOC emissions and chemical ageing mechanisms on PM2.5 concentrations show that surface PM2.5 concentrations are more sensitive to the parameterization used for gaseous I/S/L-VOC emissions than for ageing. Estimating the gaseous I/S/L-VOC emissions from POA or from NMOG has a high impact on local surface PM2.5 concentrations (reaching −30 % in the Balkans, −8 % to −16 % in the fire plume and +8 % to +16 % in Greece). Considering the VOC as SOA precursors results in a moderate increase in PM2.5 concentrations mainly in the Balkans (up to 24 %) and in the fire plume (+10 %).

The wet deposition of nitrogen and sulfur in Europe for the period 1990–2010 was estimated by six atmospheric chemistry transport models (CHIMERE, CMAQ, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI) within the framework of the EURODELTA-Trends model intercomparison. The simulated wet deposition and its trends for two 11-year periods (1990–2000 and 2000–2010) were evaluated using data from observations from the EMEP European monitoring network. For annual wet deposition of oxidised nitrogen (WNOx), model bias was within 30 % of the average of the observations for most models. There was a tendency for most models to underestimate annual wet deposition of reduced nitrogen (WNHx), although the model bias was within 40 % of the average of the observations. Model bias for WNHx was inversely correlated with model bias for atmospheric concentrations of NH3+NH4+, suggesting that an underestimation of wet deposition partially contributed to an overestimation of atmospheric concentrations. Model bias was also within about 40 % of the average of the observations for the annual wet deposition of sulfur (WSOx) for most models. Decreasing trends in WNOx were observed at most sites for both 11-year periods, with larger trends, on average, for the second period. The models also estimated predominantly decreasing trends at the monitoring sites and all but one of the models estimated larger trends, on average, for the second period. Decreasing trends were also observed at most sites for WNHx, although larger trends, on average, were observed for the first period. This pattern was not reproduced by the models, which estimated smaller decreasing trends, on average, than those observed or even small increasing trends. The largest observed trends were for WSOx, with decreasing trends at more than 80 % of the sites. On average, the observed trends were larger for the first period. All models were able to reproduce this pattern, although some models underestimated the trends (by up to a factor of 4) and others overestimated them (by up to 40 %), on average. These biases in modelled trends were directly related to the tendency of the models to under- or overestimate annual wet deposition and were smaller for the relative trends (expressed as % yr−1 relative to the deposition at the start of the period). The fact that model biases were fairly constant throughout the time series makes it possible to improve the predictions of wet deposition for future scenarios by adjusting the model estimates using a bias correction calculated from past observations. An analysis of the contributions of various factors to the modelled trends suggests that the predominantly decreasing trends in wet deposition are mostly due to reductions in emissions of the precursors NOx, NH3 and SOx. However, changes in meteorology (e.g. precipitation) and other (non-linear) interactions partially offset the decreasing trends due to emission reductions during the first period but not the second. This suggests that the emission reduction measures had a relatively larger effect on wet deposition during the second period, at least for the sites with observations.

In this study, assumptions (ideality and thermodynamic equilibrium) commonly made in three-dimensional (3-D) air quality models were reconsidered to evaluate their impacts on secondary organic aerosol (SOA) formation over Europe. To investigate the effects of non-ideality, dynamic mass transfer and aerosol viscosity on the SOA formation, the Secondary Organic Aerosol Processor (SOAP) model was implemented in the 3-D air quality model Polyphemus. This study presents the first 3-D modeling simulation which describes the impact of aerosol viscosity on the SOA formation. The model uses either the equilibrium approach or the dynamic approach with a method specially designed for 3-D air quality models to efficiently solve particle-phase diffusion when particles are viscous. Sensitivity simulations using two organic aerosol models implemented in Polyphemus to represent mass transfer between gas and particle phases show that the computation of the absorbing aerosol mass strongly influences the SOA formation. In particular, taking into account the concentrations of inorganic aerosols and hydrophilic organic aerosols in the absorbing mass of the aqueous phase increases the average SOA concentration by 5 % and 6 %, respectively. However, inorganic aerosols influence the SOA formation not only because they constitute an absorbing mass for hydrophilic SOA, but also because they interact with organic compounds. Non-ideality (short-, medium- and long-range interactions) was found to influence SOA concentrations by about 30 %. Concerning the dynamic mass transfer for the SOA formation, if the viscosity of SOA is not taken into account and if ideality of aerosols is assumed, the dynamic approach is found to give generally similar results to the equilibrium approach (indicating that equilibrium is an efficient hypothesis for inviscid and ideal aerosols). However, when a non-ideal aerosol is assumed, taking into account the dynamic mass transfer leads to a decrease of concentrations of the hydrophilic compounds (compared to equilibrium). This decrease is due to differences in the values of activity coefficients, which are different between values computed for bulk aerosols and those for each size section. This result indicates the importance of non-ideality on the dynamic evolution of SOA. For viscous aerosols, assuming a highly viscous organic phase leads to an increase in SOA concentrations during daytime (by preventing the evaporation of the most volatile organic compounds). The partitioning of nonvolatile compounds is not affected by viscosity, but the aging of more volatile compounds (that leads to the formation of the less volatile compounds) slows down as the evaporation of those compounds is stopped due to the viscosity of the particle. These results imply that aerosol concentrations may deviate significantly from equilibrium as the gas–particle partitioning could be higher than predicted by equilibrium. Furthermore, although a compound evaporates in the simulation using the equilibrium approach, the same compound can condense in the simulation using the dynamic approach if the particles are viscous. The results of this study emphasize the need for 3-D air quality models to take into account the effect of non-ideality on SOA formation and the effect of aerosol viscosity for the more volatile fraction of semi-volatile organic compounds.

New parameterizations for the formation of organic aerosols were developed. These parameterizations cover secondary organic aerosol (SOA) formation from biogenic and anthropogenic precursors, NOx dependency, oligomerization and the reactive uptake of pinonaldehyde. These parameterizations were implemented in a box model in which the condensation and/or evaporation of semi-volatile organic compounds was simulated by the Secondary Organic Aerosol Processor (SOAP) model to take the dynamic evolution of concentrations into account.The parameterizations were tested against several experiments carried out in previous studies in the EUPHORE outdoor chamber. Two datasets of experiments were used: the anthropogenic experiments (in which SOA is formed mainly from a mixture of toluene, 1,3,5-trimethylbenzene and o-xylene) and the biogenic experiments (in which SOA is formed mainly from α-pinene and limonene).When assuming no wall deposition of organic vapors, satisfactory results (bias lower than 20 %) were obtained for the biogenic experiments and for most of the anthropogenic experiments. However, a decrease of SOA concentrations (up to 30 %) was found when taking wall deposition of organic vapors into account (with the parameters of Zhang et al., 2014). The anthropogenic experiments seem to indicate a complex NOx dependency that could not be reproduced by the model. Oligomerization was found to have a strong effect on SOA composition (oligomers were estimated to account for up to 78 % of the SOA mass) and could therefore have a strong effect on the formation of SOA. The uptake of pinonaldehyde (which is a high-volatility semi-volatile organic compound, SVOC) onto acidic aerosol was found to be too slow to be significant under atmospheric conditions (no significant amount of SOA formed after 3 days of evolution), indicating that the parameterization of Pun and Seigneur (2007) used in some air quality models may lead to an overestimation of SOA concentrations. The uptake of aldehydes could nevertheless be an important SOA formation pathway for less volatile or more reactive aldehydes than pinonaldehyde.Regarding viscosity, a low effect of viscosity on SOA concentrations was estimated by the model, although a decrease of SVOC evaporation was found when taking it into account, as well as a lower sensitivity of concentrations to changes of temperature during the experiments.

Atmospheric chambers have been widely used to study secondary organic aerosol (SOA) properties and formation from various precursors under different controlled environmental conditions and to develop parameterization to represent SOA formation in chemical transport models (CTMs). Chamber experiments are however limited in number, performed under conditions that differ from the atmosphere and can be subject to potential artefacts from chamber walls. Here, the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) modelling tool has been used in a box model under various environmental conditions to (i) explore the sensitivity of SOA formation and properties to changes on physical and chemical conditions and (ii) develop a volatility basis set (VBS)-type parameterization. The set of parent hydrocarbons includes n-alkanes and 1-alkenes with 10, 14, 18, 22 and 26 carbon atoms, α-pinene, β-pinene and limonene, benzene, toluene, o-xylene, m-xylene and p-xylene. Simulated SOA yields and their dependences on the precursor structure, organic aerosol load, temperature and NOx levels are consistent with the literature. GECKO-A was used to explore the distribution of molar mass, vaporization enthalpy, OH reaction rate and Henry's law coefficient of the millions of secondary organic compounds formed during the oxidation of the different precursors and under various conditions. From these explicit simulations, a VBS-GECKO parameterization designed to be implemented in 3-D air quality models has been tuned to represent SOA formation from the 18 precursors using GECKO-A as a reference. In evaluating the ability of VBS-GECKO to capture the temporal evolution of SOA mass, the mean relative error is less than 20 % compared to GECKO-A. The optimization procedure has been automated to facilitate the update of the VBS-GECKO on the basis of the future GECKO-A versions, its extension to other precursors and/or its modification to carry additional information.

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000–2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone–relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3 − T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty.

The evaluation and intercomparison of air quality models is key to reducing model errors and uncertainty. The projects AQMEII3 and EURODELTA-Trends, in the framework of the Task Force on Hemispheric Transport of Air Pollutants and the Task Force on Measurements and Modelling, respectively (both task forces under the UNECE Convention on the Long Range Transport of Air Pollution, LTRAP), have brought together various regional air quality models to analyze their performance in terms of air concentrations and wet deposition, as well as to address other specific objectives.This paper jointly examines the results from both project communities by intercomparing and evaluating the deposition estimates of reduced and oxidized nitrogen (N) and sulfur (S) in Europe simulated by 14 air quality model systems for the year 2010. An accurate estimate of deposition is key to an accurate simulation of atmospheric concentrations. In addition, deposition fluxes are increasingly being used to estimate ecological impacts. It is therefore important to know by how much model results differ and how well they agree with observed values, at least when comparison with observations is possible, such as in the case of wet deposition.This study reveals a large variability between the wet deposition estimates of the models, with some performing acceptably (according to previously defined criteria) and others underestimating wet deposition rates. For dry deposition, there are also considerable differences between the model estimates. An ensemble of the models with the best performance for N wet deposition was made and used to explore the implications of N deposition in the conservation of protected European habitats. Exceedances of empirical critical loads were calculated for the most common habitats at a resolution of 100  ×  100 m2 within the Natura 2000 network, and the habitats with the largest areas showing exceedances are determined.Moreover, simulations with reduced emissions in selected source areas indicated a fairly linear relationship between reductions in emissions and changes in the deposition rates of N and S. An approximate 20 % reduction in N and S deposition in Europe is found when emissions at a global scale are reduced by the same amount. European emissions are by far the main contributor to deposition in Europe, whereas the reduction in deposition due to a decrease in emissions in North America is very small and confined to the western part of the domain. Reductions in European emissions led to substantial decreases in the protected habitat areas with critical load exceedances (halving the exceeded area for certain habitats), whereas no change was found, on average, when reducing North American emissions in terms of average values per habitat.

The simulation of fine organic aerosols with CTMs (chemistry–transport models) in the western Mediterranean basin has not been studied until recently. The ChArMEx (the Chemistry-Aerosol Mediterranean Experiment) SOP 1b (Special Observation Period 1b) intensive field campaign in summer of 2013 gathered a large and comprehensive data set of observations, allowing the study of different aspects of the Mediterranean atmosphere including the formation of organic aerosols (OAs) in 3-D models. In this study, we used the CHIMERE CTM to perform simulations for the duration of the SAFMED (Secondary Aerosol Formation in the MEDiterranean) period (July to August 2013) of this campaign. In particular, we evaluated four schemes for the simulation of OA, including the CHIMERE standard scheme, the VBS (volatility basis set) standard scheme with two parameterizations including aging of biogenic secondary OA, and a modified version of the VBS scheme which includes fragmentation and formation of nonvolatile OA. The results from these four schemes are compared to observations at two stations in the western Mediterranean basin, located on Ersa, Cap Corse (Corsica, France), and at Cap Es Pinar (Mallorca, Spain). These observations include OA mass concentration, PMF (positive matrix factorization) results of different OA fractions, and 14C observations showing the fossil or nonfossil origins of carbonaceous particles. Because of the complex orography of the Ersa site, an original method for calculating an orographic representativeness error (ORE) has been developed. It is concluded that the modified VBS scheme is close to observations in all three aspects mentioned above; the standard VBS scheme without BSOA (biogenic secondary organic aerosol) aging also has a satisfactory performance in simulating the mass concentration of OA, but not for the source origin analysis comparisons. In addition, the OA sources over the western Mediterranean basin are explored. OA shows a major biogenic origin, especially at several hundred meters height from the surface; however over the Gulf of Genoa near the surface, the anthropogenic origin is of similar importance. A general assessment of other species was performed to evaluate the robustness of the simulations for this particular domain before evaluating OA simulation schemes. It is also shown that the Cap Corse site presents important orographic complexity, which makes comparison between model simulations and observations difficult. A method was designed to estimate an orographic representativeness error for species measured at Ersa and yields an uncertainty of between 50 and 85 % for primary pollutants, and around 2–10 % for secondary species.

A new aerosol module was developed and integrated in the air quality model CHIMERE. Developments include the use of the Model of Emissions and Gases and Aerosols from Nature (MEGAN) 2.1 for biogenic emissions, the implementation of the inorganic thermodynamic model ISORROPIA 2.1, revision of wet deposition processes and of the algorithms of condensation/evaporation and coagulation and the implementation of the secondary organic aerosol (SOA) mechanism H2O and the thermodynamic model SOAP. Concentrations of particles over Europe were simulated by the model for the year 2013. Model concentrations were compared to the European Monitoring and Evaluation Programme (EMEP) observations and other observations available in the EBAS database to evaluate the performance of the model. Performances were determined for several components of particles (sea salt, sulfate, ammonium, nitrate, organic aerosol) with a seasonal and regional analysis of results. The model gives satisfactory performance in general. For sea salt, the model succeeds in reproducing the seasonal evolution of concentrations for western and central Europe. For sulfate, except for an overestimation of sulfate in northern Europe, modeled concentrations are close to observations and the model succeeds in reproducing the seasonal evolution of concentrations. For organic aerosol, the model reproduces with satisfactory results concentrations for stations with strong modeled biogenic SOA concentrations. However, the model strongly overestimates ammonium nitrate concentrations during late autumn (possibly due to problems in the temporal evolution of emissions) and strongly underestimates summer organic aerosol concentrations over most of the stations (especially in the northern half of Europe). This underestimation could be due to a lack of anthropogenic SOA or biogenic emissions in northern Europe. A list of recommended tests and developments to improve the model is also given.

A PM2.5-capable aerosol chemical speciation monitor (Q-ACSM) was deployed in urban Nanjing, China, for the first time to measure in situ non-refractory fine particle (NR-PM2.5) composition from 20 October to 19 November 2015, along with parallel measurements of submicron aerosol (PM1) species by a standard Q-ACSM. Our results show that the NR-PM2.5 species (organics, sulfate, nitrate, and ammonium) measured by the PM2.5-Q-ACSM are highly correlated (r2 > 0.9) with those measured by a Sunset Lab OC  /  EC analyzer and a Monitor for AeRosols and GAses (MARGA). The comparisons between the two Q-ACSMs illustrated similar temporal variations in all NR species between PM1 and PM2.5, yet substantial mass fractions of aerosol species were observed in the size range of 1–2.5 µm. On average, NR-PM1−2.5 contributed 53 % of the total NR-PM2.5, with sulfate and secondary organic aerosols (SOAs) being the two largest contributors (26 and 27 %, respectively). Positive matrix factorization of organic aerosol showed similar temporal variations in both primary and secondary OAs between PM1 and PM2.5, although the mass spectra were slightly different due to more thermal decomposition on the capture vaporizer of the PM2.5-Q-ACSM. We observed an enhancement of SOA under high relative humidity conditions, which is associated with simultaneous increases in aerosol pH, gas-phase species (NO2, SO2, and NH3) concentrations and aerosol water content driven by secondary inorganic aerosols. These results likely indicate an enhanced reactive uptake of SOA precursors upon aqueous particles. Therefore, reducing anthropogenic NOx, SO2, and NH3 emissions might not only reduce secondary inorganic aerosols but also the SOA burden during haze episodes in China.

In the framework of the Chemistry-Aerosol Mediterranean Experiment, a measurement site was set up at a remote site (Ersa) on Corsica Island in the northwestern Mediterranean Sea. Measurement campaigns performed during the summers of 2012 and 2013 showed high organic aerosol concentrations, mostly from biogenic origin. This work aims to represent the organic aerosol concentrations and properties (oxidation state and hydrophilicity) using the air-quality model Polyphemus with a surrogate approach for secondary organic aerosol (SOA) formation. Biogenic precursors are isoprene, monoterpenes and sesquiterpenes. In this work, the following model oxidation products of monoterpenes are added: (i) a carboxylic acid (MBTCA) to represent multi-generation oxidation products in the low-NOx regime, (ii) organic nitrate chemistry and (iii) extremely low-volatility organic compounds (ELVOCs) formed by ozonolysis. The model shows good agreement of measurements of organic concentrations for both 2012 and 2013 summer campaigns. The modelled oxidation property and hydrophilic organic carbon properties of the organic aerosols also agree reasonably well with the measurements. The influence of the different chemical processes added to the model on the oxidation level of organics is studied. Measured and simulated water-soluble organic carbon (WSOC) concentrations show that even at a remote site next to the sea, about 64 % of the organic carbon is soluble. The concentrations of WSOC vary with the origins of the air masses and the composition of organic aerosols. The marine organic emissions only contribute to a few percent of the organic mass in PM1, with maxima above the sea.

The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

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.

A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4–0.7 and a systematic underestimation reaching −10 µg m−3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.

The aerosol speciation and size distribution is modeled during the summer 2013 and over a large area encompassing Africa, Mediterranean and western Europe. The modeled aerosol is compared to available measurements such as the AERONET aerosol optical depth (AOD) and aerosol size distribution (ASD) and the EMEP network for surface concentrations of particulate matter PM2.5, PM10 and inorganic species (nitrate, sulfate and ammonium). The main goal of this study is to quantify the model ability to realistically model the speciation and size distribution of the aerosol. Results first showed that the long-range transport pathways are well reproduced and mainly constituted by mineral dust: spatial correlation is ≈ 0.9 for AOD and Ångström exponent, when temporal correlations show that the day-to-day variability is more difficult to reproduce. Over Europe, PM2.5 and PM10 have a mean temporal correlation of ≈ 0.4 but the lowest spatial correlation ( ≈ 0.25 and 0.62, respectively), showing that the fine particles are not well localized or transported. Being short-lived species, the uncertainties on meteorology and emissions induce these lowest scores. However, time series of PM2.5 with the speciation show a good agreement between model and measurements and are useful for discriminating the aerosol composition. Using a classification from the south (Africa) to the north (northern Europe), it is shown that mineral dust relative mass contribution decreases from 50 to 10 % when nitrate increases from 0 to 20 % and all other species, sulfate, sea salt, ammonium, elemental carbon, primary organic matter, are constant. The secondary organic aerosol contribution is between 10 and 20 % with a maximum at the latitude of the Mediterranean Sea (Spanish stations). For inorganic species, it is shown that nitrate, sulfate and ammonium have a mean temporal correlation of 0.25, 0.37 and 0.17, respectively. The spatial correlation is better (0.25, 0.5 and 0.87), showing that the mean values may be biased but the spatial localization of sulfate and ammonium is well reproduced. The size distribution is compared to the AERONET product and it is shown that the model fairly reproduces the main values for the fine and coarse mode. In particular, for the fine mode, the model overestimates the aerosol mass in Africa and underestimates it in Europe.

The organic fraction of atmospheric aerosols has proven to be a critical element of air quality and climate issues. However, its composition and the aging processes it undergoes remain insufficiently understood. This work builds on laboratory knowledge to simulate the formation of oligomers from biogenic secondary organic aerosol (BSOA) in the troposphere at the continental scale. We compare the results of two different modeling approaches, a first-order kinetic process and a pH-dependent parameterization, both implemented in the CHIMERE air quality model (AQM) (www.lmd.polytechnique.fr/chimere), to simulate the spatial and temporal distribution of oligomerized secondary organic aerosol (SOA) over western Europe. We also included a comparison of organic carbon (OC) concentrations at two EMEP (European Monitoring and Evaluation Programme) stations. Our results show that there is a strong dependence of the results on the selected modeling approach: while the irreversible kinetic process leads to the oligomerization of about 50 % of the total BSOA mass, the pH-dependent approach shows a broader range of impacts, with a strong dependency on environmental parameters (pH and nature of aerosol) and the possibility for the process to be reversible. In parallel, we investigated the sensitivity of each modeling approach to the representation of SOA precursor solubility (Henry's law constant values). Finally, the pros and cons of each approach for the representation of SOA aging are discussed and recommendations are provided to improve current representations of oligomer formation in AQMs.

In this paper the Secondary Organic Aerosol Processor (SOAP v1.0) model is presented. This model determines the partitioning of organic compounds between the gas and particle phases. It is designed to be modular with different user options depending on the computation time and the complexity required by the user. This model is based on the molecular surrogate approach, in which each surrogate compound is associated with a molecular structure to estimate some properties and parameters (hygroscopicity, absorption into the aqueous phase of particles, activity coefficients and phase separation). Each surrogate can be hydrophilic (condenses only into the aqueous phase of particles), hydrophobic (condenses only into the organic phases of particles) or both (condenses into both the aqueous and the organic phases of particles). Activity coefficients are computed with the UNIFAC (UNIversal Functional group Activity Coefficient; Fredenslund et al., 1975) thermodynamic model for short-range interactions and with the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) parameterization for medium- and long-range interactions between electrolytes and organic compounds. Phase separation is determined by Gibbs energy minimization. The user can choose between an equilibrium representation and a dynamic representation of organic aerosols (OAs). In the equilibrium representation, compounds in the particle phase are assumed to be at equilibrium with the gas phase. However, recent studies show that the organic aerosol is not at equilibrium with the gas phase because the organic phases could be semi-solid (very viscous liquid phase). The condensation–evaporation of organic compounds could then be limited by the diffusion in the organic phases due to the high viscosity. An implicit dynamic representation of secondary organic aerosols (SOAs) is available in SOAP with OAs divided into layers, the first layer being at the center of the particle (slowly reaches equilibrium) and the final layer being near the interface with the gas phase (quickly reaches equilibrium). Although this dynamic implicit representation is a simplified approach to model condensation–evaporation with a low number of layers and short CPU (central processing unit) time, it shows good agreements with an explicit representation of condensation–evaporation (no significant differences after a few hours of condensation).

Beirut, the capital of Lebanon, which is located on the eastern shore of the Mediterranean basin, experiences high air pollution episodes. Annual average concentrations of coarse and fine particulate matter (PM2.5) as well as nitrogen oxides (NOx) often exceed the World Health Organization (WHO) guidelines. Therefore, improving air quality in this region is essential. The Polyphemus/Polair3D modeling system is used here to investigate air pollution episodes in Beirut during 2 to 18 July 2011. The modeling domain covers two nested grids of 1 and 5 km horizontal resolution over greater Beirut and Lebanon, respectively. The anthropogenic emission inventory was developed earlier (Waked et al., 2012). The Weather and Research Forecasting (WRF) model is used to generate the meteorological fields and the Model of Emissions of Gases and Aerosols from Nature (MEGAN) is used for biogenic emissions. The results of the study are compared to measurements from a field campaign conducted in the suburb of Beirut during 2–18 July 2011. The model reproduces satisfactorily the concentrations of most gaseous pollutants, the total mass of PM2.5 as well as PM2.5 elemental carbon (EC), organic carbon (OC), and sulfate. Ozone concentrations are overestimated and it appears that this overestimation results mainly from the boundary conditions.

A secondary organic aerosol (SOA) model, H2O (Hydrophilic/Hydrophobic Organic), is evaluated over the Paris area. This model treats the formation of SOA with two kinds of surrogate species: hydrophilic species (which condense preferentially on an aqueous phase) and hydrophobic species (which condense only on an organic phase). These surrogates species are formed from the oxidation in the atmosphere of volatile organic compounds (VOC) by radicals (HO and NO3) and ozone. These VOC are either biogenic (isoprene, monoterpenes and sesquiterpenes) or anthropogenic (mainly aromatic compounds). This model includes the formation of aerosols from different precursors (biogenic precursors, aromatics), and semi-volatile organic compounds (SVOC) from traffic. The H2O aerosol model was incorporated into the Polyphemus air quality modeling platform and applied to the Paris area and evaluated by comparison to measurements performed during the Megapoli campaign in July 2009. The comparison to measurements in the suburbs and in the city center of Paris shows that the model gives satisfactory results for both elemental carbon (EC) and organic carbon (OC). However, the model gives a peak of OC concentrations in the morning due to high emissions from traffic, which does not appear in measurements. Uncertainties in the modeled temperature, which can affect the gas-particle partitioning, in the partitioning of primary SVOC or underestimation of primary organic aerosol (POA) evaporation by the model could explain the differences between model and measurements. Moreover, using a theoretical mechanism for the oxidation of primary SVOC and intermediate volatility organic compounds (IVOC), POA concentrations were found to be likely overestimated by models due to the use of simple partitioning constants (which do not take into account the affinity of a compound with the liquid aerosol solution) or due to the assumption that the organic aerosol solution is a one-phase ideal solution. The organic aerosol in the city center of Paris was found to be originating mostly from distant sources with only 30 to 38% due to local sources.

A new model for the formation of secondary organic aerosol (SOA) from isoprene was developed. This model uses surrogate molecular species (hydroxy-hydroperoxides, tetrols, methylglyceric acid, organic nitrates) to represent SOA formation. The development of this model used available experimental data on yields and molecular composition of SOA from isoprene and methacrolein oxidation. This model reproduces the amount of particles measured in smog chambers under both low-NOx and high-NOx conditions. Under low-NOx conditions, the model reproduces the transitional formation of hydroxy-hydroperoxides particles, which are photolyzed and lead to SOA mass decrease after reaching a maximum. Under high-NOx conditions, particles are assumed to be formed mostly from the photo-oxidation of a PAN-type molecule derived from methacrolein (MPAN). This model successfully reproduces the complex NOx-dependence of isoprene oxidation and suggests a possible yield increase under some high-NOx conditions. Experimental data correspond to dry conditions (RH < 10%). However, particles formed from isoprene are expected to be highly hydrophilic, and isoprene oxidation products would likely partition between an aqueous phase and the gas phase at high humidity in the atmosphere. The model was extended to take into account the hydrophilic properties of SOA, which are relevant under atmospheric conditions, and investigate the effect of particulate liquid water on SOA formation. An important increase in SOA mass was estimated for humid conditions due to the hydrophilic properties. Experiments under high relative humidity conditions should be conducted to confirm the results of this study, which have implications for SOA modeling.

Background: While genetic, hormonal, and lifestyle factors partially elucidate the incidence of breast cancer, emerging research has underscored the potential contribution of air pollution. Polychlorinated biphenyls (PCBs) and benzo[a]pyrene (BaP) are of particular concern due to endocrine-disrupting properties and their carcinogenetic effect., Objective: To identify distinct long term trajectories of exposure to PCB153 and BaP, and estimate their associations with breast cancer risk., Methods: We used data from the XENAIR case-control study, nested within the ongoing prospective French E3N cohort which enrolled 98,995 women aged 40-65 years in 1990-1991. Cases were incident cases of primary invasive breast cancer diagnosed from cohort entry to 2011. Controls were randomly selected by incidence density sampling, and individually matched to cases on delay since cohort entry, and date, age, department of residence, and menopausal status at cohort entry. Annual mean outdoor PCB153 and BaP concentrations at residential addresses from 1990 to 2011 were estimated using the CHIMERE chemistry-transport model. Latent class mixed models were used to identify profiles of exposure trajectories from cohort entry to the index date, and conditional logistic regression to estimate their association with the odds of breast cancer., Results: 5058 cases and 5059 controls contributed to the analysis. Five profiles of trajectories of PCB153 exposure were identified. The class with the highest PCB153 concentrations had a 69% increased odds of breast cancer compared to the class with the lowest concentrations (95% CI 1.08, 2.64), after adjustment for education and matching factors. The association between identified BaP trajectories and breast cancer was weaker and suffered from large CI., Conclusions: Our results support an association between long term exposure to PCB153 and the risk of breast cancer, and encourage further studies to account for lifetime exposure to persistent organic pollutants., (© 2024. The Author(s).)

Background: Human exposure to air pollution involves complex mixtures of multiple correlated air pollutants. To date, very few studies have assessed the combined effects of exposure to multiple air pollutants on breast cancer (BC) risk., Objectives: We aimed to assess the association between combined exposures to multiple air pollutants and breast cancer risk., Methods: The study was based on a case-control study nested within the French E3N cohort (5222 incident BC cases/5222 matched controls). For each woman, the average of the mean annual exposure to eight pollutants (benzo(a)oyrene, cadmium, dioxins, polychlorinated biphenyls (PCB153), nitrogen dioxide (NO 2 ), ozone, particulate matter and fine particles (PMs)) was estimated from cohort inclusion in 1990 to the index date. We used the Bayesian Profile Regression (BPR) model, which groups individuals according to their exposure and risk levels, and assigns a risk to each cluster identified. The model was adjusted on a combination of matching variables and confounders to better consider the design of the nested case-control study. Odds ratios (OR) and their 95 % credible intervals (CrI) were estimated., Results: Among the 21 clusters identified, the cluster characterised by low exposures to all pollutants, except ozone, was taken as reference. A consistent increase in BC risk compared to the reference cluster was observed for 3 clusters: cluster 9 (OR=1.61; CrI=1.13,2.26), cluster 16 (OR=1.59; CrI=1.10,2.30) and cluster 15 (OR=1.38; CrI=1.00,1.88) characterised by high levels of NO 2 , PMs and PCB153. The other clusters showed no consistent association with BC., Discussion: This is the first study assessing the effect of exposure to a mixture of eight air pollutants on BC risk, using the BPR approach. Overall, results showed evidence of a positive joint effect of exposure to high levels to most pollutants, particularly high for NO 2 , PMs and PCB153, on the risk of BC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants' residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03-1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03-1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Nitrogen dioxide (NO 2 ) is an important air pollutant due to its adverse effects on human health. Yet, current evidence on the association between NO 2 and the risk of breast cancer lacks consistency. In this study, we investigated the association between long-term exposure to NO 2 and breast cancer risk in the French E3N cohort study. Association of breast cancer risk with NO 2 exposure was assessed in a nested case-control study within the French E3N cohort including 5222 breast cancer cases identified over the 1990-2011 follow-up period and 5222 matched controls. Annual mean concentrations of NO 2 at participants' residential addresses for each year from recruitment 1990 through 2011, were estimated using a land use regression (LUR) model. Multivariable conditional logistic regression models were used to compute odds ratios (ORs) and their 95% confidence intervals (CIs). Additional analyses were performed using NO 2 concentrations estimated by CHIMERE, a chemistry transport model. Overall, the mean NO 2 exposure was associated with an increased risk of breast cancer. In all women, for each interquartile range (IQR) increase in NO 2 levels (LUR: 17.8 μg/m 3 ), the OR of the model adjusted for confounders was 1.09 (95% CI: 1.01-1.18). The corresponding OR in the fully adjusted model (additionally adjusted for established breast cancer risk factors) was 1.07 (95% CI: 0.98-1.15). By menopausal status, results for postmenopausal women were comparable to those for all women, while no association was observed among premenopausal women. By hormone receptor status, the OR of estrogen receptor positive breast cancer = 1.07 (95% CI: 0.97-1.19) in the fully adjusted model. Additional analyses using the CHIMERE model showed slight differences in ORs estimates. The results of this study indicate an increased risk of breast cancer associated with long-term exposure to NO 2 air pollution. Observing comparable effects of NO 2 exposure estimated by two different models, reinforces these findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Airborne particulate matter (PM) is a pollutant of concern not only because of its adverse effects on human health but also on visibility and the radiative budget of the atmosphere. PM can be considered as a sum of solid/liquid species covering a wide range of particle sizes with diverse chemical composition. Organic aerosols may be emitted (primary organic aerosols, POA), or formed in the atmosphere following reaction of volatile organic compounds (secondary organic aerosols, SOA), but some of these compounds may partition between the gas and aerosol phases depending upon ambient conditions. This review focuses on carbonaceous PM and gaseous precursors emitted by road traffic, including ultrafine particles (UFP) and polycyclic aromatic hydrocarbons (PAHs) that are clearly linked to the evolution and formation of carbonaceous species. Clearly, the solid fraction of PM has been reduced during the last two decades, with the implementation of after-treatment systems abating approximately 99% of primary solid particle mass concentrations. However, the role of brown carbon and its radiative effect on climate and the generation of ultrafine particles by nucleation of organic vapour during the dilution of the exhaust remain unclear phenomena and will need further investigation. The increasing role of gasoline vehicles on carbonaceous particle emissions and formation is also highlighted, particularly through the chemical and thermodynamic evolution of organic gases and their propensity to produce particles. The remaining carbon-containing particles from brakes, tyres and road wear will still be a problem even in a future of full electrification of the vehicle fleet. Some key conclusions and recommendations are also proposed to support the decision makers in view of the next regulations on vehicle emissions worldwide., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO 2 , O 3 , PM 2.5 , and PM 10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO 2 compared to PM 2.5 and PM 10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe., (© 2022. The Author(s).)

Background: Although the genetic and hormonal risk factors of breast cancer are well identified, they cannot fully explain the occurrence of all cases. Epidemiological and experimental studies have suggested that exposure to environmental pollutants, especially those with potential estrogenic properties, as polychlorinated biphenyls (PCBs) may have a role in breast cancer development. Being the most abundantly detected in human tissues and in the environment, congener 153 (PCB153) is widely used in epidemiological studies as indicator for total PCBs exposure., Objectives: We aimed to estimate the association between cumulative atmospheric exposure to PCB153 and breast cancer risk., Methods: We conducted a case-control study of 5222 cases and 5222 matched controls nested within the French E3N cohort from 1990 to 2011. Annual atmospheric PCB153 concentrations were simulated with the deterministic chemistry-transport model (CHIMERE) and were assigned to women using their geocoded residential history. Their cumulative PCB153 exposure was calculated for each woman from their cohort inclusion to their index date. Breast cancer odds ratios (ORs) associated with cumulative PCB153 exposure and their 95% confidence intervals (95% CIs) were estimated using multivariate conditional logistic regression models., Results: Overall, our results showed a statistically significant linear increase in breast cancer risk related to cumulative atmospheric exposure to PCB153 as a continuous variable (adjusted OR = 1.19; 95% CI: 1.08-1.31, for an increment of one standard deviation among controls (55 pg/m 3 )). Among women who became postmenopausal during follow-up, the association remained statistically significant (adjusted OR = 1.23; 95% CI: 1.09-1.39). In analyses by hormone receptors status, the positive association remained significant only for ER-positive breast cancer (adjusted OR = 1.18; 95% CI: 1.05-1.33)., Discussion: This study is the first to have estimated the impact of atmospheric exposure to PCB153 on breast cancer risk. Our results showed a statistically significant increase in breast cancer risk, which may be limited to ER-positive breast cancer. These results warrant confirmation in further independent studies but raise the possibility that exposure to PCB153 increase breast cancer risk., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Benzo[a]pyrene (BaP) is an endocrine-disrupting pollutant formed during incomplete combustion of organic materials. It has been recognized as a reproductive and developmental toxicant, however epidemiological evidence of the long-term effect of ambient air BaP on breast cancer (BC) is limited. Thus we evaluated associations between ambient air BaP exposure and risk of BC, overall and according to menopausal status and molecular subtypes (estrogen receptor negative/positive (ER-/ER+) and progesterone receptor negative/positive (PR-/PR+)), stage and grade of differentiation of BC in the French E3N cohort study., Methods: Within a nested case-control study of 5222 incident BC cases and 5222 matched controls, annual BaP exposure was estimated using a chemistry-transport model (CHIMERE) and was assigned to the geocoded residential addresses of participants for each year during the 1990-2011 follow-up period. Multivariable conditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs)., Results: Overall, cumulative airborne BaP exposure was significantly associated with the overall risk of BC, for each 1 interquartile range (IQR) increase in the concentration levels of BaP (1.42 ng/m 3 ), the OR = 1.15 (95% CI: 1.04-1.27). However, by menopausal status, the significant positive association remained only in women who underwent menopausal transition (i.e. premenopausal women at inclusion who became postmenopausal at diagnosis), OR per 1 IQR = 1.20 (95% CI: 1.03-1.40). By hormone receptor status, positive associations were observed for ER+, PR + and ER + PR + BC, with ORs = 1.17 (95% CI: 1.04-1.32), 1.16 (95% CI: 1.01-1.33), and 1.17 (95% CI: 1.01-1.36) per 1 IQR, respectively. There was also a borderline positive association between BaP and grade 3 BC (OR per 1 IQR = 1.15 (95% CI: 0.99-1.34)., Conclusions: We provide evidence of increased risk of BC associated with cumulative BaP exposure, which varied according to menopausal status, hormone receptor status, and grade of differentiation of BC. Our results add further epidemiological evidence to the previous experimental studies suggesting the adverse effects of BaP., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Breast cancer is the most frequent cancer in women in industrialized countries. Lifestyle and environmental factors, particularly endocrine-disrupting pollutants, have been suggested to play a role in breast cancer risk. Current epidemiological studies, although not fully consistent, suggest a positive association of breast cancer risk with exposure to several International Agency for Research on Cancer Group 1 air-pollutant carcinogens, such as particulate matter, polychlorinated biphenyls (PCB), dioxins, Benzo[a]pyrene (BaP), and cadmium. However, epidemiological studies remain scarce and inconsistent. It has been proposed that the menopausal status could modify the relationship between pollutants and breast cancer and that the association varies with hormone receptor status., Objective: The XENAIR project will investigate the association of breast cancer risk (overall and by hormone receptor status) with chronic exposure to selected air pollutants, including particulate matter, nitrogen dioxide (NO2), ozone (O3), BaP, dioxins, PCB-153, and cadmium., Methods: Our research is based on a case-control study nested within the French national E3N cohort of 5222 invasive breast cancer cases identified during follow-up from 1990 to 2011, and 5222 matched controls. A questionnaire was sent to all participants to collect their lifetime residential addresses and information on indoor pollution. We will assess these exposures using complementary models of land-use regression, atmospheric dispersion, and regional chemistry-transport (CHIMERE) models, via a Geographic Information System. Associations with breast cancer risk will be modeled using conditional logistic regression models. We will also study the impact of exposure on DNA methylation and interactions with genetic polymorphisms. Appropriate statistical methods, including Bayesian modeling, principal component analysis, and cluster analysis, will be used to assess the impact of multipollutant exposure. The fraction of breast cancer cases attributable to air pollution will be estimated., Results: The XENAIR project will contribute to current knowledge on the health effects of air pollution and identify and understand environmental modifiable risk factors related to breast cancer risk., Conclusions: The results will provide relevant evidence to governments and policy-makers to improve effective public health prevention strategies on air pollution. The XENAIR dataset can be used in future efforts to study the effects of exposure to air pollution associated with other chronic conditions., International Registered Report Identifier (irrid): DERR1-10.2196/15167., (©Amina Amadou, Thomas Coudon, Delphine Praud, Pietro Salizzoni, Karen Leffondre, Emilie Lévêque, Marie-Christine Boutron-Ruault, Aurélie M N Danjou, Xavier Morelli, Charlotte Le Cornet, Lionel Perrier, Florian Couvidat, Bertrand Bessagnet, Julien Caudeville, Elodie Faure, Francesca Romana Mancini, John Gulliver, Gianluca Severi, Béatrice Fervers. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 15.09.2020.)