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.

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 PM_2.5 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. PM_2.5 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, PM_2.5 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 PM_2.5, 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.

A new aerosol module was developed and integrated in the air quality model CHIMERE. Developments include an update of biogenic emissions and of the inorganic thermodynamic model ISORROPIA, revision of wet deposition processes and of the algorithms of condensation/evaporation and coagulation and the implementation of the 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 EMEP program observations and other observations available in the EBAS database to evaluate the performances of the model. Performances were determined for several components of particles (sea salts, sulfate, ammonium, nitrate, organic aerosol) with a seasonal and regional analysis of results. The model gives good performances in general. For sea salts, 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 with a good seasonal evolution of concentrations. For organic aerosol, the model performs well 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 SOA concentrations over most of stations (especially in the Northern half of Europe). This underestimation could be due to a lack of anthropogenic SOA in the model. A list of recommended tests and developments to improve the model is also given.

This paper describes a 16-year datasets of air pollution concentrations and air quality indicators over France. Using a kriging method that combines background measurements of air quality and modeling with the Chemistry Transport Model CHIMERE, hourly concentrations of NO₂, O₃, PM₁₀ and PM_2.5 are produced with a spatial resolution about 4 kilometers. Regulatory indicators (annual average, SOMO35, AOT40 etc…) are also calculated from these hourly data. NO₂ and O₃ datasets cover the period 2000–2015, as well as PM₁₀ annual data. PM₁₀ hourly concentrations are not available from 2000 to 2007 due to known artefact in PM₁₀ measurements. PM_2.5 data are only available from 2009 because of the lack of measurement stations before. The overall dataset has been evaluated over all the years through a cross-validation process against background measurement stations (rural, sub-urban and urban), to account for the data fusion between measurement and models in the method. Results are very good for PM₁₀, PM_2.5 and O₃. It shows an overestimation of NO₂ concentrations in rural area, while background NO₂ values in urban areas are well represented. Maps of the main indicators are shown over years and trends are calculated. Finally, country exposure and trends of three main health related indicators: yearly averaged PM_2.5 NO₂ and SOMO35 are calculated. The DOI link for the dataset is http://doi.org/10.5281/zenodo.5043645 (Real et al., 2021). We hope that the publication of this dataset in open access will facilitate further studies on the impacts of air pollution.

In a previous study, we assessed the efficiency of reducing either traffic or industrial emissions on various ozone metrics for several cities in Europe, based on the Air Control Toolbox surrogate model. Here, we perform various model parametrisation sensitivity analyses in order to assess the robustness of our results. We find that increasing the model resolution has a limited impact on the ozone response to emission changes when focusing on concentration peaks but strongly changes the response of the ozone daily mean with a switch to a titration regime for all zones with significant nitrogen oxide (NO_x) emissions. The impact of pollution imported from outside the simulation domain was also studied and we show that if the first lever for action on ozone peaks remains as the reduction of local and regional emissions, in order to achieve higher levels of reduction, it is necessary to act at a European level. We also explore more up-to-date temporal profiles and sectoral emission speciation and find a shift towards a more NO_x-limited regime in a number of cities. Overall, these sensitivity tests show that most of the differences are simulated in cities with high NO_x emissions and little solar radiation but do not change the overall conclusions that were previously obtained.