Abstract. Tropospheric trace gas and aerosol pollutants have adverse effects on health, environment and climate. In order to quantify and mitigate such effects, a wide range of processes leading to the formation and transport of pollutants must be considered, understood and represented in numerical models. Regional scale pollution episodes result from the combination of several factors: high emissions (from anthropogenic or natural sources), stagnant meteorological conditions, kinetics and efficiency of the chemistry and the deposition. All these processes are highly variable in time and space, and their relative contribution to the pollutants budgets can be quantified with chemistry-transport models. The CHIMERE chemistry-transport model is dedicated to regional atmospheric pollution event studies. Since it has now reached a certain level a maturity, the new stable version, CHIMERE 2013, is described to provide a reference model paper. The successive developments of the model are reviewed on the basis of published investigations that are referenced in order to discuss the scientific choices and to provide an overview of the main results.

This study integrates i-Tree Eco model in order to estimate air pollution removal by urban trees in Strasbourg city, France. Applied for the first time in a French city, the model shows that public trees, i.e. trees managed by the city, removed about 88 metric tons of pollutants during one year period (from July 2012 to June 2013): about 1 ton for CO; 14 tons for NO2; 56 tons for O3; 12 tons for PM10coarse (particles with diameter ranging from 2.5 to 10 µm); 5 tons for PM2.5 and 1 ton for SO2. Air pollution removal varied mainly with the tree cover and the level of air pollutants concentrations. Comparison between simulated pollution removal rates and local emissions shows that public trees of Strasbourg reduce about 7 % of the emitted PM10coarse in the city's atmosphere; however, effect on other air pollutants is small. Thus, our study reveals that urban trees are a significant element to reduce air pollution but are not the only solution to this problem. It's then recommended to associate planting and managing urban forest resources to other strategies that take into account the urban environment characteristics: built structures, street design, location of local sources; etc.

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[1] The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) satellite spectrometer provides detailed information on the nitrogen dioxide (NO 2 ) content in the planetary boundary layer. NO 2 tropospheric column retrievals of SCIAMACHY and its predecessor Global Ozone Monitoring Experiment are characterized by errors of the order of 40%. We present here a new SCIAMACHY tropospheric retrieval data set for the year 2003. The cloud free satellite observations are compared to surface measurements and simulations over western Europe performed with the regional air-quality model CHIMERE. The model has a resolution of 50 km similar to the satellite observations. For these comparisons, averaging kernels are applied to the collocated model profiles to remove the dependency of the comparison on a priori NO 2 profile information used in the retrieval. The consistency of both SCIAMACHY and CHIMERE outputs over sites where surface measurements are available allows us to be confident in evaluation of the model over large areas not covered by surface observations. CHIMERE underestimates surface NO 2 concentrations for urban and suburban stations which we mainly attribute to the low representativeness of point observations. No such bias is found for rural locations. The yearly average SCIAMACHY and CHIMERE spatial NO 2 distributions show a high degree of quantitative agreement over rural and urban sites: a bias of 5% (relative to the retrievals) and a correlation coefficient of 0.87 (n = 2003). On a seasonal basis, biases are smaller than 20% and correlation coefficients are larger than 0.75. Spatial correlations between both the model and satellite columns and the European Monitoring and Evaluation Program (EMEP) emission inventory are high in summer (r = 0.74, n = 1779) and low in winter (r = 0.48, n = 1078), related to seasonal changes in lifetime and transport. On the other hand, CHIMERE and SCIAMACHY columns are mutually consistent in summer (r = 0.82) and in winter (r = 0.79). This shows that CHIMERE simulates the transport and chemical processes with a reasonable accuracy. The NO 2 columns show a high daily variability. The daily NO 2 pollution plumes observed by SCIAMACHY are often well described by CHIMERE both in extent and in location. This result demonstrates the capabilities of a satellite instrument such as SCIAMACHY to monitor the NO 2 concentrations over large areas on a daily basis. It provides evidence that present and future satellite missions, in combination with CTM and surface data, will contribute to improve quantitative air quality analyses at a continental scale.

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The characteristics of indoor light (intensity, spectral, spatial distribution) originating from outdoors have been studied using experimental and modeling tools. They are influenced by many parameters such as building location, meteorological conditions, and the type of window. They have a direct impact on indoor air quality through a change in chemical processes by varying the photolysis rates of indoor pollutants. Transmittances of different windows have been measured and exhibit different wavelength cutoffs, thus influencing the potential of different species to be photolysed. The spectral distribution of light entering indoors through the windows was measured under different conditions and was found to be weakly dependent on the time of day for indirect cloudy, direct sunshine, partly cloudy conditions contrary to the light intensity, in agreement with calculations of the transmittance as a function of the zenithal angle and the calculated outdoor spectral distribution. The same conclusion can be drawn concerning the position within the room. The impact of these light characteristics on the indoor chemistry has been studied using the INCA-Indoor model by considering the variation in the photolysis rates of key indoor species. Depending on the conditions, photolysis processes can lead to a significant production of radicals and secondary species.

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