As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO₂ predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 > 0.5), especially at the source-impacted sites.

We present an Observing System Simulation Experiment (OSSE) dedicated to the evaluation of the added value of the Sentinel-4 and Sentinel-5P missions for tropospheric nitrogen dioxide (NO₂). Sentinel-4 is a geostationary (GEO) mission covering the European continent, providing observations with high temporal resolution (hourly). Sentinel-5P is a low Earth orbit (LEO) mission providing daily observations with a global coverage. The OSSE experiment has been carefully designed, with separate models for the simulation of observations and for the assimilation experiments and with conservative estimates of the total observation uncertainties. In the experiment we simulate Sentinel-4 and Sentinel-5P tropospheric NO₂ columns and surface ozone concentrations at 7 by 7 km resolution over Europe for two 3-month summer and winter periods. The synthetic observations are based on a nature run (NR) from a chemistry transport model (MOCAGE) and error estimates using instrument characteristics. We assimilate the simulated observations into a chemistry transport model (LOTOS-EUROS) independent of the NR to evaluate their impact on modelled NO₂ tropospheric columns and surface concentrations. The results are compared to an operational system where only ground-based ozone observations are ingested. Both instruments have an added value to analysed NO₂ columns and surface values, reflected in decreased biases and improved correlations. The Sentinel-4 NO₂ observations with hourly temporal resolution benefit modelled NO₂ analyses throughout the entire day where the daily Sentinel-5P NO₂ observations have a slightly lower impact that lasts up to 3–6 h after overpass. The evaluated benefits may be even higher in reality as the applied error estimates were shown to be higher than actual errors in the now operational Sentinel-5P NO₂ products. We show that an accurate representation of the NO₂ profile is crucial for the benefit of the column observations on surface values. The results support the need for having a combination of GEO and LEO missions for NO₂ analyses in view of the complementary benefits of hourly temporal resolution (GEO, Sentinel-4) and global coverage (LEO, Sentinel-5P).

The advent of satellite data has provided a source of independent information to monitor trends in tropospheric nitrogen dioxide levels. To interpret these trends, one needs to know the sensitivity of the satellite retrieved NO₂ column to anthropogenic emissions. We have applied a chemistry transport model to investigate the sensitivity of the modeled NO₂ column, sampled at the OMI (Ozone Monitoring Instrument) overpass time and location and weighted by the OMI averaging kernel, to emission sources across Europe. The most important contribution (~35%) in Western Europe is made by road transport. Off-road transport and industrial combustion each contribute 10%–15% across continental Europe. In Eastern Europe, power plant contributions are of comparable magnitude as those of road transport. To answer the question if the OMI-NO₂ trends can be translated directly into emission changes, we assessed the anticipated changes in OMI-NO₂ between 2005 and 2020. Although the results indicated that for many countries, it is indeed possible, for medium- and small-sized coastal countries, the contribution of the increasing shipping emissions in adjacent sea areas may mask a significant part of national emission reductions. This study highlights the need for a combined use of models, a priori emission estimates and satellite data to verify emission trends.

An operational multi-model forecasting system for air quality including 9 different chemical transport models has been developed and is providing daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible web site (http://www.marcopolo-panda.eu). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an inter-comparison of the different forecasts performed during a given period of time (1–15 March 2017), and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.