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.

We present here emissions estimated from a newly developed emission model for residential wood combustion (RWC) at high spatial and temporal resolution, which we name the MetVed model. The model estimates hourly emissions resolved on a 250 m grid resolution for several compounds, including particulate matter (PM), black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) in Norway for a 12-year period. The model uses novel input data and calculation methods that combine databases built with an unprecedented high level of detail and near-national coverage. The model establishes wood burning potential at the grid based on the dependencies between variables that influence emissions: i.e. outdoor temperature, number of and type and size of dwellings, type of available heating technologies, distribution of wood-based heating installations and their associated emission factors. RWC activity with a 1 h temporal profile was produced by combining heating degree day and hourly and weekday activity profiles reported by wood consumers in official statistics. This approach results in an improved characterisation of the spatio-temporal distribution of wood use, and subsequently of emissions, required for urban air quality assessments. Whereas most variables are calculated based on bottom-up approaches on a 250 m spatial grid, the MetVed model is set up to use official wood consumption at the county level and then distributes consumption to individual grids proportional to the physical traits of the residences within it. MetVed combines consumption with official emission factors that makes the emissions also upward scalable from the 250 m grid to the national level. The MetVed spatial distribution obtained was compared at the urban scale to other existing emissions at the same scale. The annual urban emissions, developed according to different spatial proxies, were found to have differences up to an order of magnitude. The MetVed total annual PM_2.5 emissions in the urban domains compare well to emissions adjusted based on concentration measurements. In addition, hourly PM_2.5 concentrations estimated by an Eulerian dispersion model using MetVed emissions were compared to measurements at air quality stations. Both hourly daily profiles and the seasonality of PM_2.5 show a slight overestimation of PM_2.5 levels. However, a comparison with black carbon from biomass burning and benzo(a)pyrene measurements indicates higher emissions during winter than that obtained by MetVed. The accuracy of urban emissions from RWC relies on the accuracy of the wood consumption (activity data), emission factors and the spatio-temporal distribution. While there are still knowledge gaps regarding emissions, MetVed represents a vast improvement in the spatial and temporal distribution of RWC.

Well constrained volcanic emissions inventories in chemistry transport models are necessary to study the impacts induced by these sources on the tropospheric sulfur composition and on sulfur species concentrations and depositions at the surface. In this paper, the changes induced by the update of the volcanic sulfur emissions inventory are studied using the global chemistry transport model MOCAGE (MOdèle de Chimie Atmosphérique à Grande Échelle). Unlike the previous inventory (Andres and Kasgnoc, 1998), the updated one (Carn et al., 2016, 2017) uses more accurate information and includes contributions from both passive degassing and eruptive emissions. Eruptions are provided as daily total amounts of sulfur dioxide (SO₂) emitted by volcanoes in the Carn et al. (2016, 2017) inventories, and degassing emissions are provided as annual averages with the related mean annual uncertainties of those emissions by volcano. Information on plume altitudes is also available and has been used in the model. We chose to analyze the year 2013, for which only a negligible amount of eruptive volcanic SO₂ emissions is reported, allowing us to focus the study on the impact of passive degassing emissions on the tropospheric sulfur budget. An evaluation against the Ozone Monitoring Instrument (OMI) SO₂ total column and MODIS (Moderate-Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) observations shows the improvements of the model results with the updated inventory. Because the global volcanic SO₂ flux changes from 13 Tg yr⁻¹ in Andres and Kasgnoc (1998) to 23.6 Tg yr⁻¹ in Carn et al. (2016, 2017), significant differences appear in the global sulfur budget, mainly in the free troposphere and in the tropics. Even though volcanic SO₂ emissions represent 15 % of the total annual sulfur emissions, the volcanic contribution to the tropospheric sulfate aerosol burden is 25 %, which is due to the higher altitude of emissions from volcanoes. Moreover, a sensitivity study on passive degassing emissions, using the annual uncertainties of emissions per volcano, also confirmed the nonlinear link between tropospheric sulfur species content with respect to volcanic SO₂ emissions. This study highlights the need for accurate estimates of volcanic sources in chemistry transport models in order to properly simulate tropospheric sulfur species.

In early October 2020, northern Europe experienced an episode with poor air quality due to high concentrations of particulate matter (PM). At several sites in Norway, recorded weekly values exceeded historical maximum PM₁₀ concentrations from the past 4 to 10 years. Daily mean PM₁₀ values at Norwegian sites were up to 97 µg m⁻³ and had a median value of 59 µg m⁻³. We analysed this severe pollution episode caused by long-range atmospheric transport based on surface and remote sensing observations and transport model simulations to understand its causes. Samples from three sites in mainland Norway and the Arctic remote station Zeppelin (Svalbard) showed strong contributions from mineral dust to PM₁₀ (23 %–36 % as a minimum and 31 %–45 % as a maximum) and biomass burning (8 %–16 % to 19 %–21 %). Atmospheric transport simulations indicate that Central Asia was the main source region for mineral dust observed in this episode. The biomass burning fraction can be attributed to forest fires in Ukraine and southern Russia, but we cannot exclude other sources contributing, like fires elsewhere, because the model underestimates observed concentrations. The combined use of remote sensing, surface measurements, and transport modelling proved effective in describing the episode and distinguishing its causes.

Fine particulate matter (PM_2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM_2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM_2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM_2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m³) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m³) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m³), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m³ vs. -1.5 μg/m³ for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m³, S-MESH outperforms the reanalysis (MB of -7.3 μg/m³ and -10.3 μg/m³ respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM_2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.

Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas and to the formation of secondary organic aerosols. The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we implemented the biogenic emission model MEGANv2.1 (Model of Emissions of Gases and Aerosols from Nature, version 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in French, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation-type-dependent treatment included in the SURFEX vegetation ISBA (Interaction between Soil Biosphere and Atmosphere) scheme. This scheme provides vegetation-dependent parameters such as leaf area index and soil moisture to MEGAN. This approach enables a more accurate estimation of biogenic fluxes compared to the stand-alone MEGAN model, which relies on average input values for all vegetation types. The present study focuses on the assessment of the SURFEX–MEGAN model isoprene emissions. An evaluation of the coupled SURFEX–MEGAN model results was carried out by conducting a global isoprene emission simulation in 2019 and by comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 443 Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311 to 637 Tg. The spatial distribution of SURFEX–MEGAN isoprene is consistent with other studies, with some differences located in low-isoprene-emission regions. Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a ±5 % change in isoprene emissions using MERRA (Modern-Era Retrospective analysis for Research and Applications) and IFS (Integrated Forecasting System) compared with ERA5. The impact of using different emission factor data was also investigated. The use of PFT (plant functional type) spatial coverage and PFT-dependent emission potential data resulted in a 12 % reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38 % in global isoprene emissions was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa, and South America. The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be forced with meteorological data from any period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of our work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors.