In Norway, children in kindergartens spend significant time outdoors under all weather conditions, and there is thus a natural concern about the quality of outdoor air. It is well known that air pollution is associated with a wide variety of adverse health impacts for children, with greater impact on children with asthma. Especially during winter and spring, kindergartens in Oslo that are situated close to streets with busy traffic, or in areas where wood burning is used for house heating, can experience many days with bad air quality. During these periods, updated information on air quality levels can help the kindergarten teachers to plan appropriate outdoor activities and thus protect children's health. We have installed 17 low-cost air quality nodes in kindergartens in Oslo. These nodes are smaller, cheaper and less complex to use than traditional equipment. Performance evaluation shows that while they are less accurate and suffer from higher uncertainty than reference equipment, they still can provide reliable coarse information about local pollution. The main challenge when using this technology is that calibration parameters might change with time depending on the atmospheric conditions. Thus, even if the sensors are calibrated a priori, once deployed, and especially if they are deployed for a long time, it is not possible to determine if a node is over- or under-estimating the concentration levels. To enhance the data from the sensors, we employed a data fusion technique that allows generating a detailed air quality map merging the data from the sensors and the data from an urban model, thus being able to offer air quality information to any location within Oslo. We arranged a focus group with the participation of local administration, kindergarten staff and parents to understand their opinion and needs related to the air quality information that was provided to the participant kindergartens. They expressed concern about the data quality but agree that having updated information on the air quality in the surroundings of kindergartens can help them to reduce children's exposure to air pollution.

Abstract. This paper describes the Eulerian urban dispersion model EPISODE. EPISODE was developed to address a need for an urban air quality model in support of policy, planning, and air quality management in the Nordic, specifically Norwegian, setting. It can be used for the calculation of a variety of airborne pollutant concentrations, but we focus here on the implementation and application of the model for NO2 pollution. EPISODE consists of an Eulerian 3D grid model with embedded sub-grid dispersion models (e.g. a Gaussian plume model) for dispersion of pollution from line (i.e. roads) and point sources (e.g. chimney stacks). It considers the atmospheric processes advection, diffusion, and an NO2 photochemistry represented using the photostationary steady-state approximation for NO2. EPISODE calculates hourly air concentrations representative of the grids and at receptor points. The latter allow EPISODE to estimate concentrations representative of the levels experienced by the population and to estimate their exposure. This methodological framework makes it suitable for simulating NO2 concentrations at fine-scale resolution (<100 m) in Nordic environments. The model can be run in an offline nested mode using output concentrations from a global or regional chemical transport model and forced by meteorology from an external numerical weather prediction model; it also can be driven by meteorological observations. We give a full description of the overall model function and its individual components. We then present a case study for six Norwegian cities whereby we simulate NO2 pollution for the entire year of 2015. The model is evaluated against in situ observations for the entire year and for specific episodes of enhanced pollution during winter. We evaluate the model performance using the FAIRMODE DELTA Tool that utilises traditional statistical metrics, e.g. root mean square error (RMSE), Pearson correlation R, and bias, along with some specialised tests for air quality model evaluation. We find that EPISODE attains the DELTA Tool model quality objective in all of the stations we evaluate against. Further, the other statistical evaluations show adequate model performance but that the model scores greatly improved correlations during winter and autumn compared to the summer. We attribute this to the use of the photostationary steady-state scheme for NO2, which should perform best in the absence of local ozone photochemical production. Oslo does not comply with the NO2 annual limit set in the 2008/50/EC directive (AQD). NO2 pollution episodes with the highest NO2 concentrations, which lead to the occurrence of exceedances of the AQD hourly limit for NO2, occur primarily in the winter and autumn in Oslo, so this strongly supports the use of EPISODE for application to these wintertime events. Overall, we conclude that the model is suitable for an assessment of annual mean NO2 concentrations and also for the study of hourly NO2 concentrations in the Nordic winter and autumn environment. Further, in this work we conclude that it is suitable for a range of policy applications specific to NO2 that include pollution episode analysis, evaluation of seasonal statistics, policy and planning support, and air quality management. Lastly, we identify a series of model developments specifically designed to address the limitations of the current model assumptions. Part 2 of this two-part paper discusses the CityChem extension to EPISODE, which includes a number of implementations such as a more comprehensive photochemical scheme suitable for describing more chemical species and a more diverse range of photochemical environments, as well as a more advanced treatment of the sub-grid dispersion.

This paper describes the Eulerian urban dispersion model EPISODE. EPISODE was developed to address a need for an urban air quality model in support of policy, planning, and air quality management in the Nordic, and, specifically, Norwegian setting. It can be used for the calculation of a variety of airborne pollutant concentrations, but we focus here on the implementation and application of the model for NO2 pollution. EPISODE consists of a Eulerian 3D grid model with embedded sub-grid dispersion models (e.g., a Gaussian plume model) for dispersion of pollution from line (i.e., roads) and point sources 15 (e.g., chimney stacks). It considers the atmospheric processes advection, diffusion, and a NO2 photochemistry represented using the photostationary steady state approximation for NO2. EPISODE calculates hourly air concentrations representative of the grids and at receptor points. The latter allow EPISODE to estimate concentrations representative of the levels experienced by the population and to estimate their exposure. This methodological framework makes it suitable for simulating NO2 concentrations at fine scale resolution (< 100 m) in Nordic environments. The model can be run in an offline nested mode 20 using output concentrations from a global or regional chemical transport model and forced by meteorology from an external numerical weather prediction model; but it also can be driven by meteorological observations. We give a full description of the overall model function as well as its individual components. We then present a case study for six Norwegian cities whereby we simulate NO2 pollution for the entire year of 2015. The model is evaluated against in-situ observations for the entire year and for specific episodes of enhanced pollution during winter. We evaluate the model performance using the FAIRMODE 25 DELTA Tool that utilizes traditional statistical metrics, e.g., RMSE, Pearson correlation, R, and bias along with some specialised tests for air quality model evaluation. We find that EPISODE attains the DELTA Tool model quality objective in all of the stations we evaluate against. Further, the other statistical evaluations show adequate model performance, but that the model scores greatly improved correlations during winter and autumn compared to the summer. We attribute this to the use of the photostationary steady state scheme for NO2, which should perform best in the absence of local ozone photochemical 30 production. Oslo does not comply with the NO2 annual limit set in the 2008/50/EC directive (AQD). NO2 pollution episodes with the highest NO2 concentrations, which lead to the occurrence of exceedances of the AQD hourly limit for NO2 occur primarily in the winter and autumn in Oslo, so this strongly supports the use of EPISODE in the application of these winter-

&lt;p&gt;Heatwave and drought extremes can have significant impacts on vegetation, which can in turn lead to important effects on reactive trace gas fluxes at the land-atmosphere interface that can ultimately alter atmospheric composition. We present results from the EU-funded Sentinel EO-based Emission and Deposition Service (SEEDS) project, which aimed at developing upgrades to the existing Copernicus Atmospheric Monitoring Service (CAMS) component on European air quality. In this work, we used land surface modelling (SURFEX &amp;#8211; Surface Externalis&amp;#233;e) combined with data assimilation (Extended Kalman Filter - EKF) of satellite leaf area index (LAI) to deliver improved estimation of the land surface state. The land surface model is coupled with an online model for dry deposition and an offline model (MEGANv3.1) for biogenic volatile organic compounds (BVOCs) to estimate trace gas losses and emissions, respectively. This approach exploits methods at the forefront of land surface modelling (dynamic vegetation simulation and data assimilation) and combines them with the latest algorithms to estimate trace gas fluxes at the surface. We present findings from two extreme events in Europe: the 2018 drought and the 2019 June/July heat waves. SURFEX was forced using ECMWF meteorology at 0.1&amp;#176; &amp;#215; 0.1&amp;#176; resolution that captured both events. Both extreme events provoked strong responses in the models for dry deposition velocity and BVOC emissions. The 2018 drought began in spring and endured through summer, during which dry deposition velocities declined steadily beyond seasonal norms due to increased stomatal resistance forced by the vegetation response to drought. Over continental Europe, BVOCs initially increased in the early phase of the drought, but then sharply declined into July in the worst-affected regions in Germany, Denmark, and Poland. Meanwhile, BVOCs increased in Scandinavia relative to seasonal norms due to the warmer-than-average conditions. The first episode of severe heat in 2019 arrived in late June, which initially caused a large increase in BVOC emissions compared to seasonal norms. Then drought set in during July and despite a second large heat wave BVOC emissions were lower overall compared to seasonal norms. In fact, the European-wide BVOC emissions were higher in June compared to July due to the drought effects that commenced later in the heat wave cycle. This reverses the normal seasonal cycle in BVOC emissions, and drought impacts on vegetation were the primary driver behind this. Dry deposition velocities are reduced during both heat waves, but we see a larger decline in the second heat wave in July when drought conditions are more severe.&lt;/p&gt; &lt;p&gt;Our findings suggest that these impacts on trace gas surface fluxes would have a strong effect on atmospheric composition, and on photochemical ozone formation. We, therefore, conclude that these effects likely played a contributory role to the ozone pollution episodes that occurred coincidentally in time with the heat wave events in both 2018 and 2019. The project aim within SEEDS is to eventually test the BVOC emissions and dry deposition velocities within a chemical transport model participating within the CAMS regional ensemble (MOCAGE) and to therefore evaluate the impact on ozone.&lt;/p&gt;

    , i.e. that the puffs always remain horizontally circular throughout their lifetime. Initially, r  and z  are set equal to the diameter of the stack.