Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by the COVID-19 lockdown using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI), surface site measurements and simulations from the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of NO2 changes have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite NO2 column changes with both weather-normalized surface NO2 concentration changes and simulated changes by the CAMS regional ensemble, composed of 11 models, using recently published emission reductions induced by the lockdown. We show that all estimates show the same tendency on NO2 reductions. Locations where the lockdown was stricter show stronger reductions and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Regarding average reductions, estimates based on either satellite observations (−23 %) surface stations (−43 %) or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %) pointing out the importance of the variability in time of such estimates. Observation based machine learning estimates show a stronger temporal variability than the model-based estimates.

Abstract. The Paris megacity experiences frequent particulate matter (i.e.PM2.5, particulate matter with a diameter less than 2.5 µm) pollution episodes in spring (March–April). At this time of the year, large numbers of the particles consist of ammonium sulfate and nitrate which are formed from ammonia (NH3) released during fertilizer spreading practices and transported from the surrounding areas to Paris. There is still limited knowledge of the emission sources around Paris, their magnitude, and their seasonality. Using space-borne NH3 observation records of 10 years (2008–2017) and 5 years (2013–2017) provided by the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) instrument, regional patterns of NH3 variabilities (seasonal and interannual) are derived. Observations reveal identical high seasonal variability with three major NH3 hotspots found from March to August. The high interannual variability is discussed with respect to atmospheric total precipitation and temperature. A detailed analysis of the seasonal cycle is performed using both IASI and CrIS instrument data, together with outputs from the CHIMERE atmospheric model. For 2014 and 2015, the CHIMERE model shows coefficients of determination of 0.58 and 0.18 when compared to IASI and CrIS, respectively. With respect to spatial variability, the CHIMERE monthly NH3 concentrations in spring show a slight underrepresentation over Belgium and the United Kingdom and an overrepresentation in agricultural areas in the French Brittany–Pays de la Loire and Plateau du Jura region, as well as in northern Switzerland. In addition, PM2.5 concentrations derived from the CHIMERE model have been evaluated against surface measurements from the Airparif network over Paris, with which agreement was found (r2 = 0.56) with however an underestimation during spring pollution events. Using HYSPLIT cluster analysis of back trajectories, we show that NH3 total columns measured in spring over Paris are enhanced when air masses originate from the north-east (e.g. the Netherlands and Belgium), highlighting the importance of long-range transport in the NH3 budget over Paris. Variability in NH3 in the north-east region is likely to impact NH3 concentrations in the Parisian region since the cross-correlation function is above 0.3 (at lag = 0 and 1 d). Finally, we quantify the key meteorological parameters driving the specific conditions important for the formation of PM2.5 from NH3 in the Île-de-France region in spring. Data-driven results based on surface PM2.5 measurements from the Airparif network and IASI NH3 measurements show that a combination of the factors such as a low boundary layer of ∼500 m, a relatively low temperature of 5 ∘C, a high relative humidity of 70 %, and wind from the north-east contributes to a positive PM2.5 and NH3 correlation.

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

Abstract. CHIMERE is a chemistry-transport model initially designed for box-modelling of regional atmospheric composition. In the past decade, it has been converted into a 3D eulerian model that could be used at a variety of scales from local to continental domains. However, due to the model design and its historic use as a regional model, major limitations had remained, prohibiting 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 lifted in the CHIMERE-2016 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, including or not polar regions. Other important improvements have been brought 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 improved in order to improve model numerical performance. The present article describes all these changes. 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.