After decades of poor air quality (AQ), Baltimore, MD, and its neighbor Washington, DC are within striking distance of attaining the USEPA's standards for criteria pollutants including ozone. This results from the implementation of policies based on the best available science and on forecasts of air quality using the CMAQ model. Community-scale problems with black carbon (BC), coarse particles, and ultrafine particles (UFP) have received much less scrutiny and persist. We will discuss how measurements have helped constrain and improve chemical transport models and how effective communication with policy-makers has led to targeted emissions reductions. Examples include consideration of subgrid-scale sea and bay breezes that recirculate pollutants on hot summer days, how reservoirs such as organic nitrates extend the effective lifetime of NOx, and how aerosols and clouds alter photolysis rates. Combining observations and models has also helped quantify which, when, and where VOC controls can effectively reduce the rate of ozone formation in a regime, on average NOx-limited. Despite dramatic region-wide improvements in AQ, health and environmental problems continue on the community scale. These issues present new challenges for model resolution, and measurement coverage. We will discuss how new measurements and modeling capabilities attack the problems of over-nutrification of surface waters, and health and environmental justice impacts of short-lived pollutants BC, coal dust, and UFP.
How to cite: Dickerson, R., Allen, D., Canty, T., He, H., Ring, A., and Dreessen, J.: Air Quality Modeling for the Baltimore-Washington Area: Rigorous Science for Effective Policy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3611, https://doi.org/10.5194/egusphere-egu26-3611, 2026.
Understanding intra-urban air-shed dynamics is increasingly critical for effective air quality management. While regional-scale air-shed characterisation has been widely studied, comparatively limited attention has been given to air-shed delineation at the city scale, where pollutant transport pathways, stagnation zones and recirculation patterns exert direct influence on population exposure. Identifying prominent urban air-sheds and quantifying their dependence on key meteorological drivers such as wind speed, wind direction, thermal structure and humidity can substantially improve the predictive understanding of pollutant accumulation hotspots.
With this motivation, the present study investigates the air-shed behaviour of Ahmedabad, a densely populated and industrially active city in western India having a population of around 8.2 million and spread across 464 sq km. The Weather Research and Forecasting (WRF) model was configured for high-resolution urban simulations and validated for two contrasting seasons: a five-day period in May representing hot, dry summer conditions and a five-day period in winter characterised by lower boundary-layer heights and reduced dispersion. Simulated fields of wind speed, wind direction, ambient temperature and relative humidity were generated at hourly intervals and evaluated against available meteorological observations. Using these validated simulations, the study attempts to delineate intra-city air-sheds by analysing dominant flow regimes, seasonal shifts in ventilation and stagnation patterns and the sensitivity of air-shed boundaries to changes in meteorological parameters.
The insights derived from this analysis hold significant implications for urban regulators and civic administrators. A refined understanding of air-shed structure can support targeted emission control strategies, optimise the spatial prioritisation of air action plans, and ultimately contribute to improving public health outcomes for city residents.
How to cite: Kandya, A., Patel, V., Kela, S., Gadekar, K., Baru, R., and Sharma, A.: Delineating Intra-Urban Air-sheds using High-Resolution WRF Modelling: Insights from Ahmedabad, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19623, https://doi.org/10.5194/egusphere-egu26-19623, 2026.
The hydroxyl radical (OH) plays a key role in regulating gas-phase pollutant concentrations and particle formation and composition in the global troposphere. Globally, OH formation is dominated by production of O(1D) from photolysis of ozone and subsequent reaction with water vapour. However, under conditions characteristic of the polluted wintertime Arctic, this mechanism is inhibited by limited short-wave radiation and water abundance. A lack of observations means that our understanding of radical and oxidant sources under such conditions is lacking. The Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign, made comprehensive measurements of trace gas and aerosol pollution chemistry and meteorology during January and February 2022 in the sub-Arctic city of Fairbanks, Alaska, USA. During the campaign, severe surface-based temperature inversions gave rise to several enhanced pollution events, interspersed with weakly stable periods of lower pollution levels and influence from the free troposphere. Here, we use the Dynamically Simple Model of Atmospheric Chemical Complexity (DSMACC) 0D box model incorporating the comprehensive Master Chemical Mechanism (MCM) v3.3.1 to quantify processes controlling the abundances of atmospheric oxidants in Fairbanks. We constrain the model using available measurements from the ALPACA campaign, and compare HOx radical sources and sinks during a strongly-stable heavily polluted period of the campaign with a less stable, cleaner period. OH formation from HONO photolysis is the major chemical source of HOx in the polluted, low-light environment of wintertime Fairbanks and formaldehyde is an important precursor for HO2. We find that the HOx budget is distinct between a polluted, strongly stable inversion event and a clean, weakly stable period, with influences of meteorology being important in regulating peroxynitric acid (PNA, HO2NO2) cycling. Our results help improve understanding of the unique atmospheric pollution chemistry that regulates oxidant abundances in cold and low-light conditions.
How to cite: Arnold, S. R., Hoffman, A., James, R., Heard, D. E., Whalley, L., Stone, D., Seldon, S., Stutz, J., Kuhn, J., Cooperdock, S., Temime-Roussel, B., Ijaz, A., D'Anna, B., Law, K. S., Bekki, S., Gou, F., Flynn, J., St Clair, J., Cesler-Maloney, M., and Simpson, W.: Photochemical modelling of wintertime HOx sources and sinks in a polluted sub-Arctic environment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14449, https://doi.org/10.5194/egusphere-egu26-14449, 2026.
Atmospheric particles strongly influence air quality, climate, and human health, and understanding how new particles form in the atmosphere is essential for improving predictions of these impacts. New particle formation (NPF) is one of the key processes that creates fresh aerosol particles, but its behaviour varies widely across regions and seasons. Winter conditions, with shallow boundary layers and high aerosol loading, usually suppress NPF. However, in Hyderabad, India, we observed frequent winter NPF events that occur despite these unfavourable conditions occurring on about 30% of the days. This study combines long-term particle size distribution measurements with meteorological reanalysis and high-resolution WRF-Chem simulations to investigate the meteorological drivers behind these winter events.
Particle number size distributions (10–600 nm) were collected at the University of Hyderabad between 2019 and 2022. Large-scale atmospheric conditions were analysed using ERA-5 and MERRA-2 reanalysis, and a detailed WRF-Chem simulation was conducted for a one-week period in February 2020, during which three consecutive NPF events occurred. The reanalysis data show that a persistent high-pressure system developed during these days, producing calm winds, stable conditions, and strong vertical subsidence. Under these conditions, we observed both high aerosol loading and high levels of precursor gases, which helped support NPF even during winter pollution episodes.
WRF-Chem results reveal elevated SO2 and PM2.5 concentrations up to 2–3 km altitude on NPF event days. Despite high PM2.5, the SO2/PM2.5 ratio was much higher compared to non-event days, indicating a more favourable chemical environment for nucleation. Importantly, the simulations also show that these favourable meteorological and chemical conditions extend over a large spatial region around Hyderabad covering around 500 Kms, suggesting that winter NPF is not only a local phenomenon but part of a wider regional process. Overall, the findings highlight that winter NPF in Hyderabad is strongly controlled by high-pressure-driven meteorology, vertical subsidence, enhanced precursor availability, and large-scale regional influence. These results improve our understanding of particle formation mechanisms in polluted urban environments.
How to cite: Meda, B. N. M., Sarangi, C., Sebastian, M., Chakraborthy, O., Hooda, R. K., Hyvarinen, A.-P., Cherian, P., Jokinen, T., and Kanawade, V. P.: Meteorology Induced New Particle Formation over India - A modelling approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1209, https://doi.org/10.5194/egusphere-egu26-1209, 2026.
Sulfate–nitrate–ammonium aerosols are the main secondary inorganic components of particulate matter, particularly PM2.5 (particulate matter with an aerodynamic diameter of 2.5 µm or less), contributing more than 40% of PM2.5 mass (Tao et al., 2017), and even up to 60% during polluted events in East Asia (Geng et al., 2017). Sulfate and nitrate are formed through the chemical oxidation of precursor gases (i.e., NOx and SO2). These oxidation pathways include homogeneous processes (i.e., in the gas and aqueous phases) and heterogeneous processes (i.e., on particle surfaces). Previous studies have emphasized the importance of heterogeneous oxidation in secondary inorganic aerosol formation during severe haze events. However, heterogeneous oxidation mechanisms are either neglected in current models or represented using empirical and oversimplified parameterizations, leading to substantial discrepancies between field observations and model simulations, particularly the underestimation of sulfate concentrations. In addition, the size distribution of sulfate–nitrate–ammonium mass fractions and aerosol acidity is influenced by heterogeneous chemistry. However, most studies focus on the fine mode (PM2.5), and the size-resolved responses of aerosol chemical composition and acidity remain poorly understood. In this study, we incorporate an updated heterogeneous oxidation scheme into the formation mechanism of sulfate–nitrate–ammonium aerosols to improve simulations over East Asia using the EMAC atmospheric chemistry–climate model. Compared with observational datasets, the inclusion of the updated heterogeneous oxidation scheme improves model performance, with the normalized mean bias of sulfate, nitrate, and ammonium decreasing from −50%, 61%, and −51% to −45%, 60%, and −35%, respectively. We find that enhanced sulfate formation promotes the partitioning of ammonium into the aerosol phase, especially in the coarse size mode (PM ≥ 2.5 µm), while increased sulfate in the coarse mode suppresses coarse nitrate formation. In addition, aerosol acidity in the fine mode shows a negligible response, whereas acidity in the coarse mode increases by approximately 0.1 pH units. These findings highlight the importance of heterogeneous oxidation mechanisms, particularly in the coarse size mode.
References
Geng, G., Zhang, Q., Tong, D., Li, M., Zheng, Y., Wang, S., and He, K.: Chemical composition of ambient PM2. 5 over China and relationship to precursor emissions during 2005–2012, Atmos. Chem. Phys., 17, 9187-9203, 10.5194/acp-17-9187-2017, 2017.
Tao, J., Zhang, L., Cao, J., and Zhang, R.: A review of current knowledge concerning PM2. 5 chemical composition, aerosol optical properties and their relationships across China, Atmos. Chem. Phys., 17, 9485-9518, 10.5194/acp-17-9485-2017, 2017.
How to cite: Wang, X., Tsimpidi, A., Kerkweg, A., and Karydis, V.: Heterogeneous oxidation shapes inorganic aerosol composition and acidity in East Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18702, https://doi.org/10.5194/egusphere-egu26-18702, 2026.
The Indo-Gangetic Plain (IGP)—one of the most polluted regions globally—experiences intense seasonal crop-residue burning, yet its contribution to ambient PM₂.₅ remains poorly quantified. Existing studies report a wide range of CRB influence (7%-78%) because satellites miss many short-lived or low-intensity fires, chemical signatures of CRB overlap with those from residential biomass burning, and bottom-up inventories lack reliable, region-specific activity data for agricultural burning. These issues highlight the need for a more rigorous, observation-driven framework to isolate and optimise CRB-related emissions robustly. To identify days when receptor-site PM2.5 was influenced by transported emissions from open biomass burning (CRB), satellite fire counts (MODIS and VIIRS) and Lagrangian particle dispersion modelling (FLEXPART) were used. For each day, weighted fire counts (WFC) were calculated by overlapping satellite fire hotspots with FLEXPART back-trajectory sensitivities, assigning greater weight to fires located within high-sensitivity regions of the footprint. The days were then ranked according to their WFC values. Based on this ranking, the study period was partitioned into biomass-burning period (23 October–16 November) and non-biomass-burning period (4–27 September; 7–11 October). PM2.5 emissions for each period were optimised using the analytical inverse modelling system FLEXINVERT, constrained by surface observations and ECLIPSE v6b as the prior emission inventory. The observational constraint was provided by a 32-station monitoring network distributed across Punjab, Haryana, Delhi NCR (National Capital Region), and western Uttar Pradesh states in the northwestern IGP. The results show that during the non-biomass-burning period, posterior emissions showed a ~25% regional increase, ndicating that anthropogenic non-biomass-burning sources—such as brick kilns, agro-processing facilities, small food-processing units, sugar mills, and agricultural energy use—are systematically underestimated in current global emission inventories. During the biomass-burning period, posterior PM2.5 emission fluxes increased up to ~300% relative to the prior. The strongest increments occurred over central Punjab, southern Haryana and the India-Pakistan border region, coinciding with known CRB hotspots. In contrast, Delhi NCR exhibited negative increments, suggesting that prior inventories overestimated emissions over Delhi NCR region while underestimating emissions in upwind agricultural states. By comparing biomass-burning and non-biomass-burning periods, CRB-related emission enhancements in posterior fluxes reached ~250% in several Punjab and Haryana grids, whereas Delhi NCR showed only a ~15% increase. The analysis is further extended to 2023 and 2024 which revealed that although satellite fire counts decreased, posterior optimized fluxes increased, suggesting that satellite fire detections alone underrepresent true CRB activity. The study further quantifies the total amount of crop residue burnt and the effective emission factor per fire to reconcile modelled and observed PM2.5. This work provides the observational constrained regional optimisation of CRB-related PM2.5 emissions over the IGP, offering new insight into the quantification, spatial distribution, and regional influence of CRB emissions in India. These improved CRB emission estimates can provide insight for air-quality mitigation and agricultural-burning policy, and provide an input for aerosol-climate modelling studies.
How to cite: Arora, A., Gadhavi, H., and K Patra, P.: Optimising crop-residue burning PM2.5 emissions over the Indo-Gangetic Plain using inverse modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1243, https://doi.org/10.5194/egusphere-egu26-1243, 2026.
This study presents the results of smoke simulations from wildfires that occurred in September 2024 in northern Ukraine. The case study focuses on peatland fires near Loshakova Huta (Chernihiv region), specifically during 19–23 September, when smoke was transported toward the capital city of Kyiv under northerly wind direction. Burnt areas were identified using multiple data sources, including Sentinel-2 and Planet satellite imagery (false-colour analysis), EFFIS (European Forest Fire Information System) polygons, land surface temperature anomalies obtained from MODIS and VIIRS satellite instruments (NASA FIRMS (Fire Information for Resource Management System) platform) and EUMETSAT LSA SAF (Land Surface Analysis Satellite Application Facility), and Sentinel-3 fire radiative power data. Emissions of major air pollutants were estimated using the Fuel Fire Tools application, which integrates the FCCS (Fuel Characteristic Classification System), the CONSUME fuel consumption model, and FEPS (Fire Emission Prediction Simulator). The FCCS module provided fire behaviour parameters and fuel loading for the identified burnt areas based on a 30-m fuel map developed for Ukraine, which had previously been modified by introducing peat fuel classes using available soil data. Total emissions and their temporal dynamics were then calculated. Smoke transport and dispersion were simulated by means of the CALPUFF Lagrangian puff model and HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) at 1 km spatial resolution. Both models were applied to simulate the transport of carbon monoxide and particulate matter (PM2.5 and PM10) using meteorological fields simulated by the WRF (Weather Research & Forecasting) model driven by ERA5 reanalysis. Model performance was evaluated by comparing background-corrected modelled pollutant concentrations with measurements from eight air-quality monitoring stations in Kyiv, using several statistical metrics. Data from seven stations were provided by the Department of Environmental Protection and Climate Change Adaptation of the Kyiv City State Administration, and data from one station were provided by the SI “Marzieiev Institute for Public Health of the National Academy of Medical Sciences of Ukraine”. The results showed acceptable accuracy for both models, despite the relatively large distance from the active fires (~60-70 km) and uncertainties related to other emission sources and surrounding conditions near the monitoring sites. Periods of substantial concentration increases were also well reproduced by the models.
Peatland wildfires pose a significant public health hazard due to their enormous emissions of air pollutants, particularly the PM2.5 fraction. This case study is valuable due to the availability of qualitive satellite-based information, on-site documentation, and reliable air-quality measurements in Kyiv. The results indicate that the CALPUFF and HYSPLIT models can adequately reproduce smoke plume transport and dispersion when peat-fire source terms are properly parameterized, especially with respect to emission rates, and when accurate meteorological input is used.
How to cite: Oshurok, D., Grabovets, D., Petrosian, A., Boldyriev, D., Maremukha, T., Morhulova, V., Chulkov, O., Yaryfa, P., and Skrynyk, O.: Smoke simulation from peatland wildfires: a case study in northern Ukraine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13492, https://doi.org/10.5194/egusphere-egu26-13492, 2026.
Accurate air quality forecasting is essential for issuing early warnings of high-concentration air pollution episodes, mitigating their adverse health impacts. This study evaluates the performance of the Korean Air Chemistry Modeling System version 2.0 (K_ACheMS v2.0), an integrated system designed to enhance the predictability of air pollutant concentrations in South Korea. The K_ACheMS v2.0 incorporates (i) a modified Weather Research and Forecasting (WRF) version 4.1.5 with machine learning (ML) based wind-speed corrections; (ii) GIST Multiscale Air Quality (GMAQ) v1.0, utilizing an updated Statewide Air Pollution Research Center 07 (SAPRC07TC) chemical mechanism; and (iii) a three-dimensional variational (3D-VAR) data assimilation method to optimize the chemical initial conditions. The 5-day PM2.5 predictability of the K_ACheMS v2.0 was evaluated against ground-based PM2.5 observations in South Korea for 2024. Furthermore, we conducted an intercomparison of the K_ACheMS v2.0 against two global real-time air quality prediction systems, the Copernicus Atmosphere Monitoring Service (CAMS) from ECMWF and the Goddard Earth Observing System Composition Forecast (GEOS-CF) from NASA GMAO. We further analyzed the chemical composition of PM2.5 to identify the key drivers of performance variability among these systems, using observations measured at two supersites in South Korea during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Our findings demonstrate that K_ACheMS v2.0 exhibits robust predictive performance for PM2.5 and its chemical components compared to the global models, achieving an Index of Agreement (IOA) of 0.71, which outperforms CAMS (0.66) and GEOS-CF (0.46).
How to cite: Youn, N., Yu, J., Han, K. M., Kim, J., and Song, C. H.: A Performance Analysis of Air Quality Prediction using the Korean Air Chemistry Modeling System version 2.0 (K_ACheMS v2.0), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3698, https://doi.org/10.5194/egusphere-egu26-3698, 2026.
Providentia is an evaluation software package designed for the in-depth analysis of in-situ surface observations and colocated model output, tailored specifically for the atmospheric modelling community.
Reproducibility is a key concern when performing any type of model evaluation. A variety of factors can affect reproducibility, including how observations are processed and filtered, and how statistics are calculated. Even two scientists within the same institution may obtain markedly different results depending on their methodologies. Providentia addresses this challenge by leveraging harmonised observational datasets, such as GHOST and ACTRIS, which are widely used by the community, and by allowing precise customisation of fully documented statistics. Critically, by using the same configuration file, two users can be confident that their evaluations are exactly the same.
Providentia offers a variety of use modes, these include an interactive dashboard for quick-look visualisations; a report mode designed for more exhaustive evaluations, generating PDF reports; a library mode that enables Providentia's backend functions to be used in scripts or Jupyter notebooks, for example for reading, filtering, or plotting data; a download mode that automatically retrieves and formats observational (e.g. GHOST and ACTRIS) and model datasets (e.g. CAMS model forecasts and reanalyses); and an interpolation mode that spatially colocates model output with observational stations.
Providentia is publicly available on GitHub (https://github.com/BSC-ES/providentia), and is fully documented on a dedicated ReadTheDocs page (https://providentia.readthedocs.io/).
How to cite: Bowdalo, D., Vilanova, A., Serrano Sierra, P., Vradi, A., Benincasa, F., Jorba, O., and Pérez García-Pando, C.: Providentia: an evaluation software package for the atmospheric modelling community , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15230, https://doi.org/10.5194/egusphere-egu26-15230, 2026.
Trace gases, such as ammonia (NH3) and nitrogen oxides (NOx), serve as key precursors for secondary inorganic aerosols and play an important role in air quality and the nitrogen cycle. The primary anthropogenic sources of NH3 and NOx in Europe are agriculture, industry and transportation. Additionally, soil NOx emissions contribute significantly to the total NOx budget. Anthropogenic emission reporting is based on “bottom-up” emission inventories, which provide gridded NH3 and NOx emissions, and are widely used for air quality modelling and effective policymaking.
However, bottom-up emission inventories rely on statistical information, activity data and emission factors, resulting to uncertainties and significant time lags in data availability. Therefore, in addition to bottom-up emission inventories, “top-down” methods have been developed using various inverse modelling techniques. These techniques make use of satellite data, which provide independent information on emissions and can be used to evaluate bottom-up emission inventories and help to identify potential unknown or unreported sources.
In this study we use the Daily Emissions Constrained by Satellite Observations (DECSO) inversion algorithm combined with Cross-track Infrared Sounder (CrIS) and TROPOspheric Monitoring Instrument (TROPOMI) satellite data to derive NH3 and NOx emissions on a spatial resolution of 0.1o x 0.1o. DECSO is coupled to Eulerian regional offline CHIMERE CTM. Our study focuses on BENELUX and Po-Valley, two regions with high agricultural emissions, as part of the Agricultural Atmospheric Emissions (AGATE) ESA project.
We validate the DECSO NH3 and NOx emissions by comparing them directly against bottom-up inventories, such as Copernicus Atmosphere Monitoring Service (CAMS) and Emissions Database for Global Atmospheric Research (EDGAR). Satellite-derived emissions are consistent with bottom-up inventories regarding the magnitude of country-region totals.
To further validate the DECSO emissions we also perform forward simulations using CHIMERE CTM with DECSO and bottom-up emission inventories. The focus of the validation here is on aerosol precursors, such as nitrogen dioxide (NO2) and NH3, and compare the model results against in-situ observations and independent satellite data. The comparison against observations shows that the model simulations using the DECSO NH3 and NOx emissions perform similarly to simulations using bottom-up inventories providing further confidence on the quality of satellite-derived emissions.
How to cite: Ioannidis, E., Ding, J., van der A, R., Wichink Kruit, R., Marongiu, A., and van Weele, M.: An inter-comparison of bottom-up and satellite-derived emissions for trace gases over agricultural regions in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17551, https://doi.org/10.5194/egusphere-egu26-17551, 2026.
Accurate representation of fine particulate matter (PM2.5) at urban scales remains a major challenge in air pollution modelling, as conventional chemical transport models (CTMs) are typically operated at kilometer-scale resolution, which may smooth out strong local concentration gradients. Such limitations can influence not only the interpretation of model results but also the application of model-derived concentration fields to exposure-related analyses.
In this study, we assess the added value of high-resolution PM2.5 concentration fields for urban-scale air pollution modelling applications by comparing population-weighted exposure (PWE) estimates derived from multiple PM₂.₅ datasets. Daily PM2.5 fields from CMAQ simulations at 9 km resolution, observation-based gridded PM2.5, and a deep-learning-based super-resolution PM2.5 product at 100 m resolution were harmonized on a common analysis grid over the Seoul metropolitan area. These concentration fields were combined with 100 m gridded population data to calculate district-level PWEs for 25 administrative districts.
The results show that CMAQ reproduces the broad spatial patterns of PM2.5 across Seoul, while the high-resolution PM2.5 product reveals localized variability that is not captured at coarser resolution, particularly in densely populated districts. Comparison with observation-based PWEs indicates that exposure estimates derived from the high-resolution PM2.5 fields are often closer to observations than those based on the original CMAQ outputs, although the magnitude of improvement varies by district. Population distribution maps further highlight that spatial heterogeneity in population density plays a key role in shaping district-level exposure patterns.
Overall, this study demonstrates that enhancing the spatial representation of PM2.5 concentration fields can provide additional insight when air pollution model outputs are applied to population exposure assessment in urban environments. The proposed framework illustrates a practical approach for evaluating the application-level benefits of high-resolution air quality products within air pollution modelling studies.
Acknowledgement
This research was supported by the Korea National Institute of Health (KNIH) research project(Project No. 2024-ER0606-01) and the Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (MOE).
How to cite: Wang, K.-H., Han, S.-H., Jang, K., and Yun, H.-Y.: Assessing the added value of high-resolution PM2.5 mapping for population exposure estimates in Seoul, South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4149, https://doi.org/10.5194/egusphere-egu26-4149, 2026.
High-resolution exposure data for particulate matter (PM) are a critical determinant of accuracy in environmental epidemiology and urban health impact assessments. However, conventional chemical transport models (CTMs) are limited in representing fine-scale spatial variability of PM at the urban scale, while purely statistical approaches often struggle to maintain physical consistency and interpretability over long-term time series.
In this study, we developed a hybrid physical–statistical reanalysis framework to construct high-resolution exposure datasets for urban PM (PM₂.₅ and PM₁₀), with complementary treatment of traffic-related NO₂, suitable for national-scale health impact studies. The proposed framework consists of three main components. First, a regional CTM (CMAQ, 9 km resolution) was used to generate national-scale background concentrations of PM and gaseous pollutants, along with meteorological reanalysis data, for the period 2013–2024. Second, physically based dispersion patterns derived from the CALPUFF model were applied to redistribute primary PM concentrations to a 100 m grid through a hybrid downscaling approach, enhancing the representation of intra-urban spatial gradients. Third, to improve the temporal accuracy of traffic-sensitive NO₂, an auxiliary XGBoost-based error correction layer was implemented to reduce model uncertainty while preserving the underlying physical structure.
The framework was applied to seven major metropolitan areas and key industrial and traffic-influenced regions in South Korea. The results demonstrate that the hybrid reanalysis effectively captures urban PM concentration gradients and roadside pollution hotspots, yielding substantial improvements over conventional coarse-resolution CTM outputs. The final exposure datasets were integrated with national health cohort data, providing multiple exposure metrics including short-term lagged PM exposures and medium- to long-term moving-average indicators.
By combining high predictive performance with physical consistency, this hybrid approach offers a robust alternative to purely data-driven downscaling methods for PM exposure assessment. The resulting high-resolution PM exposure datasets enable precision environmental health studies at both community and individual levels and provide a scientific basis for evidence-based urban and national environmental health policy development.
Acknowledgement
This research was supported by the Korea National Institute of Health (KNIH) research project (Project No. 2024-ER0606-01) and the Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (MOE).
How to cite: Yun, H., Wang, K.-H., Han, S.-H., and Jang, K.: Hybrid Physical–Statistical Reanalysis of Urban PM and NO₂ for High-Resolution Exposure Assessment in Epidemiological Studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4153, https://doi.org/10.5194/egusphere-egu26-4153, 2026.
Air pollution levels across Europe have declined significantly over the past two decades, largely due to targeted emission controls in the energy, industry, and transport sectors. Recent monitoring data (2023–2024) show that EU air quality standards are met at 99% of stations for PM2.5 and 98% for nitrogen dioxide (NO2). Despite this progress, air pollution remains the leading environmental health risk in Europe. The European Environment Agency (EEA) estimates that annual exposure to PM2.5, ozone (O3), and NO2 caused approximately 239,000, 70,000, and 48,000 premature deaths, respectively, in the EU–27. Therefore, member States are required to prepare National Air Pollution Control Programmes (NAPCPs) and local action plans, with a stronger role for air quality modeling in complementing monitoring, assessing pollutant distributions, and evaluating mitigation pathways. Air quality modeling has become indispensable for understanding pollutant dynamics, quantifying the effects of emission reductions, and designing integrated air–climate policies. Large–scale initiatives such as FAIRMODE, AQMEII, EURODELT, and HTAP have advanced knowledge on model performance and uncertainties, while emphasizing the need for harmonized approaches across Europe. These efforts show that robust policy support requires continent–wide evaluations based on consistent emissions, updated meteorological drivers, and comprehensive observational datasets. In this context, a harmonized pan-European framework based on the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models is developed and evaluated for 2019 using unified meteorological, chemical, and emission inputs. Model skill for O3 and PM2.5 is assessed against observations from over 2300 and 1700 stations, respectively, employing standard and advanced diagnostics (mean bias (MB), root mean square error (RMSE), correlation coefficient (r), Taylor, Target, and quantile-binned error (QBE) analyses). For hourly O3, r = 0.51 and MB = +1.9 ppb; for hourly PM2.5, r = 0.52 and MB = −5.0 µg m−3 (Mod–Obs). The model reproduces large-scale and seasonal patterns but underestimates PM2.5 by 4–6 µg m−3 and damps O3 variability by ~40–50 %. Taylor and Target diagnostics show that random and phase errors dominate (uRMSD/σ_obs ≈ 0.85–0.9), whereas systematic bias is modest (MB/σ_obs ≤ 0.3–0.5). QBE analysis confirms amplitude compression, with underestimation of high-O3 and high-PM2.5 events and overprediction of low-O3 levels. Overall, model skill is limited more by variance and episode representation than by mean offset. Improving boundary-layer dynamics, emission timing, and secondary aerosol processes will reduce seasonal and regional biases. Despite moderate underestimation, the framework provides a scientifically robust, harmonized basis for continental air-quality evaluation and scenario analysis, consistent with FAIRMODE, AQMEII, and the EU 2030 Air Quality Directive.
How to cite: Tagaris, E., Traka, N., Stergiou, I., Sotiropoulou, R. –. E. P., and Kaskaoutis, D.: Evaluation of European Air Quality Simulations in 2019 Using the CMAQ–WRF Modeling System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4162, https://doi.org/10.5194/egusphere-egu26-4162, 2026.
Air pollution caused by particulate matter (PM) remains one of the major environmental challenges in Europe, with fine and ultrafine particles (UFP) posing a particularly serious risk to human health and the climate system. Owing to their small size, UFP can penetrate deep into the respiratory tract and enter the bloodstream, thereby contributing to cardiovascular and pulmonary diseases. In addition, ultrafine aerosols play an important role in atmospheric chemistry and radiative processes. In Central and Eastern Europe, wintertime residential heating based on coal, peat, and wood combustion is a dominant source of elevated PM concentrations and is frequently associated with severe air pollution episodes.
While PM2.5 and PM10 have been extensively studied, much less attention has been paid to ultrafine particles. In particular, their spatial variability, their contribution to total particulate matter, and their representation in chemistry–transport models remain insufficiently constrained, especially during the winter heating season. As a result, model evaluation beyond standard mass-based PM metrics is still limited.
In this study the WRF-Chem model is used to simulate fine and ultrafine particle pollution over Poland for the period from 10 December 2024 to 3 January 2025, during which several high PM2.5 concentration events linked to residential heating emissions were observed. The simulations employ the MOZART–MOSAIC chemistry and aerosol scheme, which allows for an explicit representation of aerosol size distributions and microphysical processes relevant for combustion-related particles.
Model output is evaluated using ground-based observations from the ACTRIS research infrastructure, including size-resolved aerosol measurements, as well as routine PM2.5 observations from the Polish national air quality monitoring network (GIOŚ). The evaluation is based on spatial and temporal collocation of modeled and observed data and focuses on model performance during the winter pollution episode.
The results provide insight into the ability of WRF-Chem to reproduce wintertime PM2.5 episodes driven by residential heating emissions and into the role of ultrafine particles in shaping total PM concentrations.
This work was supported by the European Union’s programme “Support to Advanced Learning and Training (EU4Belarus- SALTII)”.
How to cite: Tabalchuk, T., Kryza, M., and Werner, M.: Modeling of fine and ultrafine particulate matter in Poland using WRF-Chem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4944, https://doi.org/10.5194/egusphere-egu26-4944, 2026.
Air quality in Italy is a major concern due to elevated pollutant concentrations, particularly in the northern regions during the winter period. Within the GRINS (Growing Resilient, Inclusive and Sustainable) project (www.grins.it), Italy aims to foster the digital transition and support decision-making within a data-driven framework. In this context, we collected air quality, climate, and emissions data for the Italian territory covering the period 2013–2023. Air quality data were obtained from ground-based monitoring stations, climate data from the ERA5 reanalysis, and emissions data from the CAMS-REG-ANT inventory. By integrating these data sources, we produced high-resolution maps of NO2 concentrations over the entire Italian territory, with daily temporal resolution on a regular 0.05° × 0.05° grid. The maps include both predicted concentrations and associated uncertainty estimates. The statistical model adopted is Fixed Rank Kriging, which is well suited for large datasets. The high-resolution maps were subsequently aggregated to the municipal level to ensure spatial consistency with other datasets available at the same administrative scale, such as health statistics. In addition to air quality data, we provide municipal-level estimates of climate variables and emissions for the same period, also at daily resolution. The dataset is freely available on Zenodo (https://zenodo.org/records/17605148) with almost 1000 downloads after few months. The corresponding code is accessible on GitHub. The data products are suitable for a wide range of applications at the intersection of environmental exposure, public health, and social impact assessment.
How to cite: Fusta Moro, A. and Fassò, A.: The GRINS Italian Large Datasets on Air Quality, Climate and Emissions for the Period 2013-2023, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10054, https://doi.org/10.5194/egusphere-egu26-10054, 2026.
Particulate matter remains a significant air quality challenge in Europe, underscoring the need for effective solutions in the context of the revised EU Ambient Air Quality Directive (EU) 2024/2881, which introduces stricter requirements for protecting human health.
In this study, the impact of national emission reduction pathways on PM10 and PM2.5 concentrations is analysed using the on-line chemical transport model GEM-AQ, which provides a detailed assessment tool for policymakers and researchers. The model is based on the Global Environmental Multiscale (GEM) numerical weather prediction system and was extended with a comprehensive tropospheric chemistry and aerosol module within the MAQNet project.
Three simulations for the base year 2022 were conducted: a baseline emission scenario and two emission reduction scenarios - Business as Usual (BAU), reflecting current legislation, and Maximum Feasible Reduction (MFR), representing the maximum technically achievable emission reductions. These scenarios enable policymakers and researchers to evaluate PM10 and PM2.5 responses to emission changes across national and urban-regional scales, supporting evidence-based decision-making.
The comparison of BAU and MFR scenarios highlights how concentrations respond to emission control efforts, emphasising the value of this research for informing policy and supporting evidence-based air quality management under evolving EU regulations.
We will present the change of the extent of exceedance areas, population exposed and number of air quality zones with exceedances with a focus on urban zones.
How to cite: Starzomska, A., Durka, P., Strużewska, J., Kamiński, J., Jeleniewicz, G., and Norowski, A.: PM10 and PM2.5 Reduction under BAU and MFR Scenarios in the EU Air Quality Directive Context, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10135, https://doi.org/10.5194/egusphere-egu26-10135, 2026.
Gaseous emissions from active volcanic systems constitute a primary natural source of global atmospheric pollution. Typically, these fluids are dominated by high concentrations of water vapor (H2O) and carbon dioxide (CO2), which together account for more than 90% of the total emission volume. These are followed by sulfur species, specifically SO2 and H2S, within a range of a few percentage points. The remaining fluid emissions comprise minor species—including HCl, HF, CO, H2, He,and N2 alongside numerous other trace elements. Many of these gases, particularly SO2, H2S, CO2, and CO, are hazardous to human health and exhibit various deleterious effects. Consequently, extensive environmental research has been conducted in recent years to evaluate the impact of these gases on public health. The primary objective of this study is to characterize the atmospheric dispersion of gaseous emissions from the volcanic system of Vulcano Island, originating from the main degassing centers: the crater area and the hydrothermal system of the Levante Bay. Atmospheric concentrations of sulfur dioxide SO2 were monitored using a quasi-continuous network based on Scanning Differential Optical Absorption Spectroscopy (Scan-DOAS) technology. Concurrently, H2S and CO2 concentrations in the Levante Bay area were measured using Multi-GAS instrumentation.By integrating SO2 flux data derived from Scan-DOAS measurements with atmospheric dispersion maps generated via AERMOD modeling software, we estimated the spatial distribution of SO2 across the volcanic crater and inhabited regions, including Vulcano Village and Vulcano Piano. This investigation provided critical insights into areas where anomalous concentrations of SO2, H2S, and CO2 exceed the threshold limits established by the World Health Organization (WHO) and the European Union (EU). Findings indicate that these thresholds are frequently surpassed within and adjacent to the crater zone.
How to cite: Inguaggiato, S., Vita, F., Schiavo, B., Inguaggiato, C., Cabassi, J., Venturi, S., and Tassi, F.: Volcanic Degassing of Hazardous Gases and Their Atmospheric Dispersion: A Case Study of Vulcano Island, Aeolian Archipelago, Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10694, https://doi.org/10.5194/egusphere-egu26-10694, 2026.
Air pollution constitutes one of the leading modifiable environmental risks to public health, and its evolution can be influenced by meteorological variability and climate change. In this context, air quality in Mediterranean coastal regions exhibits strong seasonal variability and pronounced spatial gradients, driven by urban emissions, meteorology, and complex topography. In this work, the spatiotemporal evolution of 12 atmospheric pollutants in the Valencian Community (Spain) is modelled over 2009–2023 at daily resolution, and a reproducible and transferable methodology for the spatiotemporal analysis of environmental variables is presented, applicable not only to air pollution but also to other datasets with spatial and temporal components.
Data from the Valencian Air Quality Monitoring Network (RVVCCA), collected at 87 monitoring stations, are used. Prior to processing, an exhaustive quality assessment is conducted to validate record consistency, identify and handle missing or anomalous data, and robustly establish the effective study period. Temporal variability is analysed using time-series techniques, including descriptive exploration (basic statistics, variability, and seasonal patterns) and spectral analysis, in order to identify periodicities and dominant signals across different temporal scales.
Spatial analysis follows the methodology proposed by Yao and Journel (1998), which enables the automatic computation of covariance surfaces via the Fast Fourier Transform (FFT), facilitating operational implementation for a large number of days and pollutants. Based on these covariance surfaces, a geostatistical estimation scheme based on Ordinary Kriging (OK) is implemented to generate daily gridded concentration fields over the Valencian Community, enabling the assessment of persistent spatial patterns, regional gradients, and their interannual evolution. The final outcome is an integrated workflow that combines quality control, temporal analysis, and daily geostatistical mapping, providing a solid methodological basis to study the spatiotemporal dynamics of air pollution from monitoring networks.
How to cite: Cruz Leiva, D., Rodrigo-Clavero, M.-E., Cassiraga, E., and Rodrigo-Ilarri, J.: Spatiotemporal modelling of air pollution in the Valencian Community using spectral analysis and daily geostatistical mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10716, https://doi.org/10.5194/egusphere-egu26-10716, 2026.
Ambient air pollution is the leading environmental threat to human health worldwide. In Europe, ongoing emission-control measures are expected to reduce pollutant concentrations, but rising temperatures associated with climate change may heighten human vulnerability to these pollutants. Therefore, it is important to consider effects of increasing temperatures when considering future air pollution effects on human health. To do so, however, consistent air-quality climate simulations are needed, with spatial resolution sufficient for detailed health assessment.
Here, we present an approach for a detailed health assessment for Germany considering future climate and air pollution scenarios. The approach is based on health data from the German National Cohort (NAKO, nako.de) and exposure data from model simulations with the global-regional chemistry climate model MECO(n) with resolution up to 2 km over Germany. The NAKO has more than 200 000 participants and started in 2014. First, we link MECO(n) exposure data (2014‑2021) to the health data. In a second step, health effects for future conditions are analyzed based on future exposure data.
We will present the framework in detail, with a focus on the exposure modelling. In addition, we will present first analyses of the present-day exposure data including an assessment of the temporal trends of the exposure data over the present-day period. Additionally, we will examine how emission reductions influence exposure, using model‑based source‑apportionment to demonstrate their role in present‑day exposure declines.
How to cite: Mertens, M., Götz, A., Lanteri, A., Nikolaou, N., and Schneider, A.: Air‑pollution modelling for health‑risk assessment under future climate scenarios in Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14848, https://doi.org/10.5194/egusphere-egu26-14848, 2026.
The chemistry transport model MUSCAT (Wolke et al., 2012), was coupled to the numerical weather prediction (NWP) model ICON of German Weather Service (DWD, Zängl et al., 2015). MUSCAT was previously already coupled to COSMO, the former NWP model of DWD. Since with ICON, not only the horizontal grid structure (icosahedral grid consisting of triangles) did change, but also the whole code structure. Hence, the coupling required more steps than previous updates of COSMO, both for ensuring efficiency and flexibility of the model simulations and easy further updates.
With the two model versions, COSMO-MUSCAT and ICON-MUSCAT, the whole year 2019 was simulated over Europe. Results with COSMO-MUSCAT are already published by Thürkow et al., 2024. The presentation therefore focuses on the validation of ICON-MUSCAT and a comparison to the performance of the predecessor model version. For this purpose, we utilize data of European air quality monitoring stations provided by the European Environmental Agency (EEA). The model is validated according to FAIRMODE (Janssen and Thunis, 2022) standards. Overall, ICON-MUSCAT performs well and similarly as with the previous meteorological driver. Differences between the two model versions were found related to boundary layer physics. For example, the ozone deposition was found to be sensitive to the surface temperature, which leads to night-time differences between the two model version for the ground-level ozone concentration. The comparison to traffic-related observations of NO2 concentrations in urban locations and to traffic counts suggest a revision of the prescribed emission profiles of the traffic-sector (CAMS-TEMPO, Guevara et al., 2021) as in particular morning emission peaks were simulated earlier than in occurring in the observations. In addition, different available emission inventories (amongst others CAMS, EDGAR, CEDS) were investigated to analyze the uncertainty due to the choice of emission inventory.
Guevara, M., et al., 2021, Earth Syst. Sci. Data, 13, 367–404, https://doi.org/10.5194/essd-13-367-2021.
Thürkow, M., Wolke, R., Heinold, B., Stoll, J., et al., 2023, Science of The Total Environment, Volume 906, https://doi.org/10.1016/j.scitotenv.2023.167665.
Wolke, R., Schrödner, R. et al., 2012, Atmos. Env., 53, 110–130.
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M., 2015, Q. J. R. Meteorol. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378.
Janssen, S., and Thunis, P., 2022: FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking, Version 3.3, JRC Technical Report, doi:10.2760/41988.
How to cite: Schrödner, R., Sührig, A., Stoll, J., Weger, M., Luttkus, M., Wackermann, J., Wiedenhaus, H., Schimmel, W., Knoth, O., Müller, S., Heinold, B., and Wolke, R.: The new online-coupled chemistry transport model ICON-MUSCAT: First applications and validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14979, https://doi.org/10.5194/egusphere-egu26-14979, 2026.
Wildfires inject aerosols into the atmosphere at varying altitudes, modifying long‐range transport, which affects air quality, atmospheric chemistry, human health, and Earth's radiative budget. As biomass burning is a major and recurrent environmental problem in South America, the development of the next generation of Earth System model, named Model for Ocean-laNd-Atmosphere predictioN (MONAN), needs to account for the wildfire aerosol emission fields and their effects included in the model physics formulations. The MONAN’s atmospheric component, the Model for Prediction Across Scales - Atmosphere (MPAS-A) stand alone v8.3.1, is being advanced through the coupling with the Second-Generation Goddard Chemistry Aerosol Radiation and Transport Model (GOCART-2G) described in Collow et al. (2024), forming the so-called gocartMPAS system. Building on the importance of accurate plume rise parametrization, this study involves the implementation of Freitas et al. (2007, 2011) plume rise model (PRM) within the gocartMPAS framework to assist the fire emission module with the vertical distribution of the hot smoke produced by the fires. As part of the planned experiments, we will investigate the 2024 biomass burning season over South America by conducting simulations during the July–October period on a global quasi-uniform mesh with 60 km resolution. Physical parameterizations are from the “convection_permitting" suite, with initial conditions from ERA5 reanalysis. Regarding the input data for the plume rise scheme, we employed Fire Radiative Power, Active Fire Size and biomass burning emissions from the Brazilian Biomass Burning Emission Model with Fire Radiative Power (3BEM-FRP) inventory, which was processed in a latitude-longitude grid using the Prep-Chem-Src v1.8.3 preprocessor. We will discuss the overall characteristics of plume injection heights and transport of black carbon, organic carbon and brown carbon, including an initial evaluation of the aerosol mass concentration and optical depth simulated with the CAMS reanalysis dataset. These efforts are intended to improve the representation of wildfire smoke plume rise and to increase the accuracy of wildfire aerosol transport in the gocartMPAS model.
How to cite: Pereira, J., Freitas, S., Barth, M., Skamarock, W., Ha, S., Kumar, R., Lacey, F., Pereira, G., and Oliveira, V.: Integration of a Smoke Plume Rise Scheme and 3BEM-FRP Emission Inventory into gocartMPAS: Application to the 2024 South America Wildfire Season, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15578, https://doi.org/10.5194/egusphere-egu26-15578, 2026.
Air pollution poses significant risks to human health and ecosystems, being strongly influenced by meteorological conditions and climate-driven extremes variability. In southern Europe, climate change is expected to intensify heatwaves, droughts, and wildfire activity, thereby increasing the likelihood of compound events and exacerbating the severity of air pollution episodes. This study analyses the spatiotemporal variability of atmospheric pollutants over the mainland Iberian Peninsula for the period 2012–2025, using data provided by Copernicus Atmosphere Monitoring Service (CAMS). The use of CAMS data enables a spatially consistent assessment of air quality across regions that are less covered by national monitoring networks. The applied analysis focuses on key pollutants, namely carbon monoxide CO, PM₁₀, and PM₂.₅, examining their intra-annual and inter-annual variability, inter-pollutant relationships, and associations with meteorological conditions conducive to extreme events.
The methodology used includes statistical and spatial analysis to identify the spatiotemporal patterns of key pollutants (CO, PM10, PM2.5) from CAMS reanalysis data and in situ measurements for the study period from 2012 to 2025. The influence of atmospheric conditions on the dispersion and concentration of pollutants was also addressed, with particular attention to atmospheric circulation patterns that can influence the occurrence of forest fires and consequent episodes of pollutant concentration exceedances. Results show marked seasonal cycles and substantial inter-annual variability in pollutant concentrations, with extreme pollution episodes frequently co-occurring with heatwaves, droughts, and periods of intense wildfire activity. Confirming that these compound events are characterised by simultaneous meteorological drivers and elevated pollutant concentrations, leading to repeated exceedances of air quality thresholds. CAMS data successfully captures the spatial and temporal signatures of these extremes, particularly for CO and particulate matter, highlighting regions recurrently affected by compound fire–air pollution events. By linking air pollution extremes to climate-related extremes, this work advances understanding of compound climate–air quality events and provides a basis for future attribution studies assessing the role of climate change in modulating air pollution extremes in the Iberian Peninsula.
How to cite: Durao, R., Simões, M., Russo, A., and Gouveia, C.: Climate-driven air pollution extremes over Portugal (2012–2025): insights from CAMS data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19966, https://doi.org/10.5194/egusphere-egu26-19966, 2026.
Cosmogenic beryllium-7 (⁷Be) is a valuable natural radiotracer for constraining atmospheric transport, vertical exchange, and aerosol removal processes in chemical transport models. We present a comprehensive implementation of 7Be in the Danish Eulerian Hemispheric Model (DEHM), a regional chemical transport model, and present model output for 2000-2025 evaluated against long-term 7Be observations over Europe.
The ⁷Be production rates are prescribed from the CRAC:7Be (CRAC:Be) model, which includes the main geophysical controls on atmospheric cosmogenic ⁷Be production. Three-dimensional production fields are mapped to the DEHM grid with 75×75 km resolution horizontally and 29 vertical layers from surface to 100 hPa, and coupled with the advection–diffusion, boundary-layer mixing, and wet/dry deposition schemes in DEHM to simulate near-surface ⁷Be concentrations. Meteorological fields driving DEHM are simulated using the Weather Research and Forecasting (WRF) model based on ERA5 reanalysis data. ⁷Be is treated as particle-bound accumulation-mode aerosol in all the model processes and with a representative particle diameter of 0.33 μm for dry deposition.
The model is evaluated against observations from 18 European sampling sites spanning contrasting climatic and dynamical regimes (mid- and high-latitude stations), with heterogeneous sampling strategies (daily to weekly/monthly). Model performance is assessed using temporal Pearson correlation (r), root mean square error (RMSE) and mean absolute percentage error (MAPE).
DEHM reproduces observed ⁷Be temporal variability with strongly site-dependent skill. Mid-latitude stations, particularly in central Europe, show high temporal correlations and lower bias, indicating that synoptic-scale transport and mixing are reasonably represented at 75 km resolution. In contrast, northern and southern Europe stations show larger MAPE and more pronounced regional mean biases. These discrepancies likely reflect challenges in representing wet scavenging, boundary-layer dynamics, and sub-grid processes including orographic precipitation, coastal effects, and convective activity. The larger errors over southern Europe may also be partly linked to episodic coarse-mode aerosol conditions (e.g., dust outbreaks), which are not explicitly resolved in the accumulation-mode ⁷Be aerosol size representation.
To better understand these discrepancies, we stratify model performance by precipitation regime to constrain wet scavenging and by season to assess boundary layer mixing and stratosphere-troposphere exchange. We quantify wet versus dry deposition contributions on temporal variability and assess resolution/representativeness effects at coastal and complex-terrain sites. Sensitivity tests of different particle size assumptions on Saharan dust events to assess the role of size-dependent removal processes. These analyses advance our understanding on 7Be cycling mechanisms and demonstrate the value of cosmogenic tracers for evaluating transport and deposition processes in regional models.
How to cite: Chham, E., Orza, J. A. G., Christensen, J. H., and Ye, Z.: Long-term simulation and evaluation of cosmogenic Beryllium-7: insights into atmospheric transport and deposition processes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20989, https://doi.org/10.5194/egusphere-egu26-20989, 2026.
In this work we are expanding upon our previous statistical modeling methodology for reconstruction of daily courses of SO42-, NO3- and NH4+ concentrations in cumulative precipitation samples (Hunova et al 2022, Hunova et al 2024). Here, we investigate relationship between the wet concentrations and concentrations of gaseous pollutants (NOx, SO2, NH3) in a detailed way from several viewpoints using modern and highly flexible statistical approach based on GAM (Generalzied Additive Model) with complexity-penalized spline components. This framework allows for decomposition of influences upon wet concentration into several easily interpretable components as well as for the nonlinearity dictated by basic physical consideration (such as saturation phenomena). The GAM model is formulated in such a way that it deals appropriately with the time-aggregated collections whose length changed historically (from 7 days to 1 day). Since it is quite obvious that the wet concentration in a spatial point sample is related to the gas concentration in a much broader area, we work not only with gaseous concentrations measured at the wet-sample-collection location, but also with numerical air pollution model (CAMx) aerial average estimates in our simultaneous models. Here, it is not clear a priori, how large neighborhood we should take for the most informative CAMx output spatial average and similarly whether it is more natural to take CAMx concentrations at surface or in 50 m. Therefore, we assess both of these features in a formalized way (via AIC model comparison). The modelling approach is illustrated at four professional Czech Hydrometeorological Institute (CHMI) stations (ALIB, JKOS, PPRM, TBKR) with long-term data (2016-2021). There, it turns out that both local measurement and relatively large area CAMx gaseous concentration averages influence water sample ion concentrations significantly. Hight and spatial aggregation differs for different ions. Further analysis using time-varying (TVAR) framework then shows that the influence of the concentration measurements is highly seasonal (local slope changes smoothly but very substantially, favoring spring to summer influences). The work has been done in cooperation with the CHMI and is related to the Technology Agency Czech Republic project ARAMIS, SS02030031).
References:
Hunova,I.-Brabec,M.-Maly,M. (2024): Major ions in Central European precipitation – insight into changes in NO3-/SO42-, NH4+/NO3- and NH4+/SO42- ratios over the last four decades. Chemosphere 349 (2024) 140986
Hunova,I.-Brabec,M.-Maly,M.-Skachova,H. (2022): Reconstruction of daily courses of SO42-, NO3-, NH4+ concentrations in precipitation from cumulative samples. Atmosphere 2022, 13, 1049. https://doi.org/10.3390/atmos13071049
How to cite: Brabec, M., Hunova, I., and Maly, M.: Time-varying relationship between SO42-, NO3-, NH4+ concentrations in cumulative precipitation samples and gaseous pollutants, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1950, https://doi.org/10.5194/egusphere-egu26-1950, 2026.
High PM2.5 concentrations can be detrimental to human and ecosystem health, as long-term exposure is associated with the aggravation of asthma and, in some cases, increased mortality. Chemical transport models (CTMs) have long been used to complement observation-based understanding of atmospheric conditions. These models are invaluable tools for scientists, as they provide spatially and temporally comprehensive representations of atmospheric states. The Community Multiscale Air Quality (CMAQ) model was developed by the U.S. Environmental Protection Agency (US EPA) and has been extensively used to investigate complex air quality issues from regional to hemispheric scales.
In this study, the nested-down approach of chemical transport modeling is employed to analyze the target region using finer grid resolutions. However, the nested-down approach may introduce unrealistic inflow and outflow of chemical species across the boundary regions. East Asia includes China, South Korea, and Japan, and China is one of the largest emitters globally. South Korea, which is the primary focus of this study, is located downwind of China; therefore, it is essential to represent boundary influences as realistically as possible.
In this work, East Asia was configured as a single domain with a horizontal resolution of 9 km, and an additional nested-down configuration with 27 km to 9 km horizontal resolution was constructed. Furthermore, two vertical configurations were applied: 22 vertical layers with a model top at 200 hPa and 29 vertical layers with a model top at 50 hPa. CMAQ simulations were conducted using an offline approach, in which meteorological and air quality models were run separately. The Weather Research and Forecasting (WRF, version 4.4.2) model and CMAQ (version 5.3.1) were used, with meteorological initial and boundary conditions provided by the Korea Integrated Model (KIM).
The representation of modeled meteorological variables—including near-surface temperature, humidity, wind speed, and wind direction—generally improved with increasing horizontal resolution across all cases examined in this study. Most variables showed the largest improvements when the grid spacing was reduced from 27 km to 9 km. PM2.5 statistical performance was best at the 9 km resolution, while little difference in model performance was observed with respect to vertical resolution.
Acknowledgment
"This research was supported by Particulate Matter Management Speciallized Graduate Program throu the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)"
How to cite: Choi, D.-R., Lee, J., Han, S., and Kang, J.: Performance Evaluation of PM2.5 Forecasting Using Multiscale WRF–CMAQ over East Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3714, https://doi.org/10.5194/egusphere-egu26-3714, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 5
EGU26-5251 | ECS | Posters virtual | VPS3
AtmoSTEM: A high-resolution spatiotemporal emission model for urban air quality applicationsTue, 05 May, 14:48–14:51 (CEST) vPoster spot 5
As growing environmental pressures challenge urban resilience, sustainability, and human well-being, the lack of high-resolution geospatial information at the urban or intra-urban scale remains a critical limitation for effective and targeted decision-making. In the context of air quality and associated health impacts, this study addresses this gap by developing the Atmospheric SpatioTemporal Emissions Model (AtmoSTEM), a high-resolution spatiotemporal framework for representing atmospheric emissions, pollutant concentrations, and population exposure at 1 km2 resolution. The application focuses on the Ioannina basin in Greece, where residential biomass burning (BB) constitutes a dominant emission source, especially during the cold season, frequently leading to pollution levels exceeding the World Health Organization (WHO) Air Quality Guidelines (AQG) and EU thresholds and underscoring the need for targeted interventions.
For this purpose, a high spatiotemporal emission inventory for Residential Heating is developed. The Copernicus Atmosphere Monitoring Service (CAMS) regional emission inventory, structured according to the GNFR classification and provided at a spatial resolution of 0.05° × 0.1°, serves as the baseline dataset. The downscaling process is based on publicly available, open-access, GNFR-dependent high-resolution spatial proxies, including the Coordination of Information on population density data from Global Human Settlement (GHSL), land-use classifications from the Copernicus Land Monitoring Service (CLC 2018), the OpenStreetMap (OSM) road network, and, where applicable in coastal and maritime domains, marine traffic density from the European Marine Observation and Data Network (EMODNet). Particular emphasis is placed on refining pollutant fields that are more relevant to BB activities, thereby improving the spatial representativeness of BB emissions within urban and peri-urban environments.
To capture the temporal variability of the emissions, CAMS is combined with CAMS temporal Regional Profiles (CAMS-TEMPO), enabling the generation of analytically resolved, hourly emission estimates. Pollutant concentrations are then estimated using a Random Forest machine learning model that integrates AtmoSTEM’s high-resolution emissions, with meteorological, satellite-derived, and spatial data, as well as in-situ air quality measurements provided by the University of Ioannina. The resulting high-resolution concentration fields are evaluated against independent in-situ measurements. Additionally, BB-related PM2.5 fields are derived and analyzed, enabling improved source-specific characterization of residential heating contributions and providing a physically consistent basis for air-quality and exposure assessments.
How to cite: Kakouri, A., Filippis, G., Korras-Carassa, M.-B., Kuenen, J., Hatzianastassiou, N., Matsoukas, C., and Kontos, T.: AtmoSTEM: A high-resolution spatiotemporal emission model for urban air quality applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5251, https://doi.org/10.5194/egusphere-egu26-5251, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 5
EGU26-5792 | ECS | Posters virtual | VPS4
Sector-segregated anthropogenic impacts on PM2.5 over western India based on regional air quality modelingWed, 06 May, 14:51–14:54 (CEST) vPoster spot 5
Air quality over western India is impacted by both regional emissions and transport from the Indo-Gangetic Plain (IGP). Effective mitigation requires identifying the dominant emission sectors that govern regional air quality during different seasons. In this regard, the present study examines the impact of emissions from major anthropogenic sectors (power, residential, transport and industries) on ambient fine particulate matter (PM2.5)concentrations over the western Indian region. For this, high-resolution (12km x 12km) regional model (WRF-Chem v3.9.1) simulations have been conducted using the EDGAR v5.0 inventory for anthropogenic emissions. Simulations are conducted for the year 2019, for winter and post-monsoon seasons when PM2.5 concentrations are typically elevated in this region, and anthropogenic emissions effects are the highest. During winter, the residential sector is seen to dominate, contributing 20% to PM2.5 concentration over western India, followed by power (9%) and industry (∼8%). The trans-regional pollution from the IGP and central India is also dominated by residential emissions (25%), followed by the power sector (∼8%). In contrast to winter, the dominant source during post-monsoon is the power sector (∼14%), followed by the industry (∼12%) and the residential (∼9%) sectors. Trans-regional impact also shows a similar pattern and dominance of power (∼15%) and industrial (∼10%) sectors. This seasonal shift in the dominant sector is driven by the seasonal variation in emissions. The results also reveal large spatial heterogeneity in sectoral influence, highlighting that dominant emission sources vary at the local scale within western India in both seasons. The study offers model-based insights for more effective planning of regional air pollution mitigation in western India.
How to cite: Shekhar, S., Dhaka, S., Vaishya, A., Ojha, N., Pozzer, A., and Sharma, A.: Sector-segregated anthropogenic impacts on PM2.5 over western India based on regional air quality modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5792, https://doi.org/10.5194/egusphere-egu26-5792, 2026.
