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Ineris operates and develops the CHIMERE chemistry transport model which is now widely used throughout Europe and beyond for air quality forecasting and assessment. To maintain the model at the state of the art and improve its performances, CHIMERE needs to be regularly updated to take into account the last findings on air quality modeling. In this spirit, a new aerosol module was developed and implemented into CHIMERE. Developments include the revision the algorithms of condensation/evaporation and coagulation processes and the implementation of a new secondary organic aerosol mechanism. Concentrations of particles over Europe were simulated for the year 2013 and were compared to available PM concentrations and speciation measured at several stations in Europe. The model gives satisfactory performance in general but overestimates ammonium nitrate concentrations during late autumn (possibly due to problems in the temporal evolution of emissions) and underestimates summer organic aerosol concentrations over most of the stations (especially in northern Europe). ; L'unité de modélisation atmosphérique et cartographie environnementale de l'Ineris développe et met en oeuvre le modèle de chimie-transport CHIMERE pour ses missions d'appui aux politiques publiques, de recherche et ses activités d'expertise. La partie la plus visible de la modélisation de la qualité de l'air concerne la plateforme nationale de prévision de la qualité de l'air PREV'AIR coordonnée par l'Institut en collaboration avec Météo France, le CNRS et le Laboratoire central de surveillance de la qualité de l'air. PREV'AIR met à disposition des prévisions à courte échéance (de l'ordre de quelques jours), mais les modélisations CHIMERE servent aussi à évaluer l'efficacité des politiques de gestion de la qualité de l'air que ce soit en rétrospectif (pour des analyses de tendances) ou en projection pour évaluer l'amélioration de qualité de l'air qui est attendue en termes de baisses d'émissions programmées, comme cela avait été fait pour le Plan national de réduction des émissions de polluants atmosphériques – PREPA adopté pour la période 2017-2021 (décret et arrêté du 10 mai 2017). CHIMERE est un modèle déterministe qui résout les équations régissant le transport et l'évolution physico-chimique des particules en utilisant en entrée les données de flux d'émissions induits principalement par les activités humaines et les champs météorologiques qui influent sur l'accumulation et la dispersion des polluants

Ineris operates and develops the CHIMERE chemistry transport model which is now widely used throughout Europe and beyond for air quality forecasting and assessment. To maintain the model at the state of the art and improve its performances, CHIMERE needs to be regularly updated to take into account the last findings on air quality modeling. In this spirit, a new aerosol module was developed and implemented into CHIMERE. Developments include the revision the algorithms of condensation/evaporation and coagulation processes and the implementation of a new secondary organic aerosol mechanism. Concentrations of particles over Europe were simulated for the year 2013 and were compared to available PM concentrations and speciation measured at several stations in Europe. The model gives satisfactory performance in general but overestimates ammonium nitrate concentrations during late autumn (possibly due to problems in the temporal evolution of emissions) and underestimates summer organic aerosol concentrations over most of the stations (especially in northern Europe). ; L'unité de modélisation atmosphérique et cartographie environnementale de l'Ineris développe et met en oeuvre le modèle de chimie-transport CHIMERE pour ses missions d'appui aux politiques publiques, de recherche et ses activités d'expertise. La partie la plus visible de la modélisation de la qualité de l'air concerne la plateforme nationale de prévision de la qualité de l'air PREV'AIR coordonnée par l'Institut en collaboration avec Météo France, le CNRS et le Laboratoire central de surveillance de la qualité de l'air. PREV'AIR met à disposition des prévisions à courte échéance (de l'ordre de quelques jours), mais les modélisations CHIMERE servent aussi à évaluer l'efficacité des politiques de gestion de la qualité de l'air que ce soit en rétrospectif (pour des analyses de tendances) ou en projection pour évaluer l'amélioration de qualité de l'air qui est attendue en termes de baisses d'émissions programmées, comme cela avait été fait pour le Plan ...

Ineris operates and develops the CHIMERE chemistry transport model which is now widely used throughout Europe and beyond for air quality forecasting and assessment. Ineris also relies on this model for operational air quality forecasting in France through the prevair.org platform, but also in Europe for the Copernicus Atmospheric Monitoring Service. A new version of the model has been completed in 2017 that allows to reach out to global air quality modeling. Taking the opportunity of the 2016 computing challenge of the new high performance computer of the Centre de Calcul Recherche et Technologie, unprecedented air quality simulations was performed by Ineris over the northern hemisphere at an outstanding resolution of 10 km. The modeling results offer an excellent illustration of the global air quality issue, where fine knowledge of physics and chemistry is required over the main polluted areas that also affect the wider atmosphere throughout the globe. ; L'unité de modélisation atmosphérique et cartographie environnementale de l'Ineris développe et met en oeuvre le modèle de chimie-transport CHIMERE pour ses missions d'appui aux politiques publiques, de recherche et ses activités d'expertise. La partie la plus visible de la modélisation de la qualité de l'air concerne la plateforme nationale de prévision de la qualité de l'air PREV'AIR1 coordonnée par l'Institut en collaboration avec Météo-France, le CNRS et le LCSQA. PREV'AIR met à disposition des prévisions à courte échéance (de l'ordre de quelques jours), mais les modélisations CHIMERE servent aussi à évaluer l'efficacité des politiques de gestion de la qualité de l'air que ce soit en rétrospectif (pour des analyses de tendances) ou en projection (pour évaluer l'amélioration de qualité de l'air qui est attendue en termes de baisses d'émissions programmées, comme cela avait été fait pour le Plan national de réduction des polluants atmosphériques - PREPA). CHIMERE est un modèle déterministe. À l'inverse des modèles statistiques, il n'est pas calibré sur des ...

Ineris operates and develops the CHIMERE chemistry transport model which is now widely used throughout Europe and beyond for air quality forecasting and assessment. Ineris also relies on this model for operational air quality forecasting in France through the prevair.org platform, but also in Europe for the Copernicus Atmospheric Monitoring Service. A new version of the model has been completed in 2017 that allows to reach out to global air quality modeling. Taking the opportunity of the 2016 computing challenge of the new high performance computer of the Centre de Calcul Recherche et Technologie, unprecedented air quality simulations was performed by Ineris over the northern hemisphere at an outstanding resolution of 10 km. The modeling results offer an excellent illustration of the global air quality issue, where fine knowledge of physics and chemistry is required over the main polluted areas that also affect the wider atmosphere throughout the globe. ; L'unité de modélisation atmosphérique et cartographie environnementale de l'Ineris développe et met en oeuvre le modèle de chimie-transport CHIMERE pour ses missions d'appui aux politiques publiques, de recherche et ses activités d'expertise. La partie la plus visible de la modélisation de la qualité de l'air concerne la plateforme nationale de prévision de la qualité de l'air PREV'AIR1 coordonnée par l'Institut en collaboration avec Météo-France, le CNRS et le LCSQA. PREV'AIR met à disposition des prévisions à courte échéance (de l'ordre de quelques jours), mais les modélisations CHIMERE servent aussi à évaluer l'efficacité des politiques de gestion de la qualité de l'air que ce soit en rétrospectif (pour des analyses de tendances) ou en projection (pour évaluer l'amélioration de qualité de l'air qui est attendue en termes de baisses d'émissions programmées, comme cela avait été fait pour le Plan national de réduction des polluants atmosphériques - PREPA). CHIMERE est un modèle déterministe. À l'inverse des modèles statistiques, il n'est pas calibré sur des observations mais repose sur les équations de physique et de chimie de l'atmosphère qui régissent le transport et la transformation des polluants. Les données d'entrée qu'il requiert sont les flux d'émissions induits principalement par les activités humaines, et aussi les champs météorologiques qui influent sur l'accumulation des polluants.

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 NO2, O3, PM2.5, and PM10 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 NO2 compared to PM2.5 and PM10 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.

International audience ; 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.

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.

International audience ; 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.

International audience ; 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.

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 NO2, O3, PM2.5, and PM10 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 NO2 compared to PM2.5 and PM10 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.

International audience ; 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.

International audience ; 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.