Recent studies based on observations have shown the impact of lockdown measures taken in various European countries to contain the Covid-19 pandemic on air quality. However, these studies are often limited to compare situations without and with lockdown measures, which correspond to different time periods and then under different meteorological conditions. We propose a modelling study with the WRF-CHIMERE modelling suite for March 2020, an approach allowing to compare atmospheric composition with and without lockdown measures without the biases of meteorological conditions. This study shows that the lockdown effect on atmospheric composition, in particular through massive traffic reductions, has been important for several short-lived atmospheric trace species, with a large reduction in NO2 concentrations, a lower reduction in Particulate Matter (PM) concentrations and a mitigated effect on ozone concentrations due to non-linear chemical effects.

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 NO_x 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.

The atypical huge forest fires observed in France during the summer of 2022 are modeled using the CHIMERE model. The impact of these emissions is quantified on ozone, aerosols and aerosol optical depth (AOD). The fires also influence the surface by destroying the vegetation and creating new erodible surfaces. This increases the mineral dust emissions but also reduces the leaf area index (LAI), and then it decreases the biogenic emissions and the dry deposition of gases such as ozone. Results show that the fires induce numerous increases in surface ozone and particulate matter (PM) concentrations close to the sources but also in downwind remote sites such as the Paris area. During the period of the most intense fires in July, the impact of concentrations is mainly due to emissions themselves, and later, in August, ozone and PM concentrations continue to increase but this time due to changes in the burned surfaces.

A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated. Optimization was also performed for the management of emissions such as anthropogenic and mineral dust. The impact of fires on wind speed, soil properties and leaf area index (LAI) was added. Pollen emissions, transport and deposition were added for birch, ragweed, olive and grass. The model is validated with a simulation covering Europe with a 60 km × 60 km resolution and the entire year of 2019. Results are compared to various measurements, and statistical scores show that the model provides better results than the previous versions.

Air pollution is of major concern throughout the world and the use of modeling tools to analyze and forecast the pollutant concentrations in complex orographic areas remains challenging. This work proposes an exhaustive framework to analyze the ability of models to simulate the air quality over the French Alps up to 1.2 km resolution over Grenoble and the Arve Valley. The on-line coupled suite of models CHIMERE-WRF is used in its recent version to analyze a 1 month episode in November–December 2013. As expected, an improved resolution increases the concentrations close to the emission areas and reduced the negative bias for Particulate Matter that is the usual weakness of air quality models. However, the nitrate concentrations seem overestimated with at the same time an overestimation of surface temperature in the morning by WRF. Different WRF settings found in the literature are tested to improve the results, particularly the ability of the meteorological model to simulate the strong thermal inversions in the morning. Wood burning is one of the main contributor of air pollution during the period ranging from 80 to 90% of the Organic Matter. The activation of the on-line coupling has a moderate impact on the background concentrations but surprisingly a change of Particulate Matter (PM) concentrations in the valley will affect more the meteorology nearby high altitude areas than in the valley. This phenomenon is the result of a chain of processes involving the radiative effects and the water vapor column gradients in complex orographic areas. At last, the model confirms that the surrounding glaciers are largely impacted by long range transport of desert dust. However, in wintertime some outbreaks of anthropogenic pollution from the valley when the synoptic situation changes can be advected up to the nearby high altitude areas, then deposited.