In Europe a quarter of the adult population and a third of all children suffer from allergenic airborne pollen thereby decreasing the quality of life. In order to ease the pollen induced symptoms mitigation measures can be applied. This, however, requires timely information on forthcoming pollen episodes derived from early warning systems. These systems can substantially be improved when pollen observations from strategically well-chosen pollen monitoring stations are assimilated.

Here we explore the network quality (i) and network coverage (ii) of the current five pollen monitoring stations in Belgium. As reference dataset we use the spatio-temporal distributions of daily surface airborne birch and grass pollen levels as produced by the operational early warning system for pollen on the website of the Royal Meteorological Institute of Belgium. This system implements the SILAM model (System for Integrated modeLling of Atmospheric coMposition) and ECMWF meteorological data.

The ability of the network to reproduce the concentration field over the region of interest is quantified by the RMSE computed from the reference concentration field and the interpolated concentration field for each day of the pollen season. In the first step, time series of the current daily pollen observations in the network are interpolated over space by applying the radial-based function. This results in the daily interpolated concentration fields which we compare with the spatially distributed daily reference data.

For evaluating the network coverage of the current five monitoring stations we perform a footprint-based analysis. Footprints relate directly to the fraction of air reaching the monitoring device. By applying pollen emission point sources in the five stations into SILAM that is run in the backward mode (three days back), we can investigate the travelling trajectory of the captured birch and grass pollen in the air observed at the network stations. Nine pollen seasons (2013-2021) were analyzed using ECMWF ERA5 meteorology.

First results on the network quality for birch pollen show that over a period of nine pollen seasons more than 60% of the daily RMSE values derived from the interpolated daily concentrations are less than their mean value. This is an indication that the interpolated network performs well compared to the spatio-temporal reference dataset derived from SILAM. For the 2013 birch pollen season more than 80% is reached. In contrast, this is only ~40% for 2020. The applied time scale is of great importance, since at smaller time scales (days, hours) network configurations may degrade faster than on larger time steps (weeks, months, seasons).

The footprint-based analysis shows that on average the coverage of the monitoring stations for birch pollen is quite good. There are, however, large differences during the 2013-2022 seasons which might be due to the typical large inter-seasonal variation in birch pollen production. For grass pollen, the average coverage is better, and the inter-seasonal variation much lower.

How to cite: Verstraeten, W. W., Bruffaerts, N., Kouznetsov, R., Sofiev, M., and Delcloo, A.: The search for the best airborne pollen monitoring locations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5408, https://doi.org/10.5194/egusphere-egu24-5408, 2024.

Black Carbon, emitted during incomplete combustion, is harmful to human health and also an important climate forcer with a dominating warming component. When deposited on snow it decreases albedo and leads to early melting, while in the atmosphere it can absorb radiation and lead to warming. The emission sources are both anthropogenic and natural. In winter anthropogenic sources, like domestic burning are important, while during summertime black carbon from wild fires plays a major role.

Black carbon concentrations are derived from observations of the aerosol absorption coefficient and then converted to an equivalent black carbon concentration and recorded at over 20 sites in Europe. However, the measurements on these sites are not homogeneous, as different mass absorption coefficients should be applied depending on location and season. For the years 2017-2022 we obtained a unified dataset of hourly black carbon concentrations for Europe. 

A statistical method (a non negative matrix factorization based on different wavelengths) is used to split the observed black carbon into a fraction originating from fossil fuel and from biomass burning sources. The Lagrangian transport model FLEXPART is used in combination with the ECLIPSE and GFED emission inventories to model BC concentrations for the European sites and analyse the source regions for each source type.

By comparing the observations and modelled concentrations  we are able to assess the correctness of the emission inventories, and using statistical optimization we can provide an updated emission estimate. We will present the sources and their seasonality and point to weaknesses in the emission inventories.

How to cite: Eckhardt, S., Thompson, R., Evangeliou, N., Pisso, I., Cassiani, M., Yttri, K. E., and Platt, S. M.: Sources and seasonality of black carbon in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7993, https://doi.org/10.5194/egusphere-egu24-7993, 2024.

Primary biological aerosol particles (PBAP) are ubiquitous in the Earth’s atmosphere, and can make significant contributions to measured particulate matter (PM) concentrations [1,2]. They are also known to play an important role in cloud formation and have a significant impact on health [3]. The sources of PBAP, however, are many and complex (e.g., viruses, bacteria, algae, fungae, plant pollen), and PBAP can be emitted from both land and sea sources. Although modelling of pollen has been included in the EMEP MSC-W chemical transport model [4] for many years, other PBAP sources have not been included. This is mainly because of the difficulties in quantifying the magnitude and spatial and temporal distributions at both European and global scale. In this study we review some of the main sources of PBAP, and consider approaches for a more detailed inclusion of these important aerosol particles in the EMEP model.

[1] V. R. DesprÅLes et al. Primary biological aerosol particles in the atmosphere: a review, Tellus B: Chemical and Physical Meteorology, 64:1 (2012).

[2] K. Yttri et al. Trends, composition, and sources of carbonaceous aerosol at the Birkenes Observatory, northern Europe, 2001–2018, Atmos. Chem. Phys., 21, 7149–7170 (2021).

[3] J. FrÅNohlich-Nowoisky et al. Bioaerosols in the Earth system: Climate, health, and ecosystem interactions, Atmospheric Research Volume 182, 346-376 (2016).

[4] D. Simpson et al. The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865 (2012).

How to cite: Lange, G. F., Simpson, D., Yttri, K. E., Valdebenito, A., Olivie, D., van Caspel, W., and Fagerli, H.: Approaches to PBAP modelling in EMEP, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21149, https://doi.org/10.5194/egusphere-egu24-21149, 2024.

Organic aerosol (OA) constitutes a major fraction of the sub-micrometer atmospheric particulate matter and is either emitted directly into the atmosphere as primary organic aerosol (POA) or formed by the partitioning onto pre-existent particles of low vapor pressure products of the oxidation of volatile, intermediate volatility, and semivolatile organic compounds (VOCs, IVOCs, and SVOCs respectively) as secondary organic aerosol (SOA). The oxidation of these compounds results in thousands of mostly unspecified oxygenated products making our understanding of SOA formation mechanisms incomplete. The volatility basis set (VBS) is a framework that has been designed to simplify these oxidation systems and to allow SOA simulation in chemical transport models (CTMs). The VBS describes the evolution of OA using a set of surrogate species with effective saturation concentrations that vary by 1 order of magnitude (referred to as 1D-VBS). This framework was extended by a second dimension (2D-VBS) to include the oxidation state (2D-VBS), which is important to quantify the degree of oxidation. Three main reasons led to this extension. First, the disadvantage of the 1D-VBS is that compounds with similar saturation concentrations can have different properties and reactivities. Second, the available measuring techniques have increasing capabilities, and they can provide detailed information about the composition of ambient and smog chamber OA (e.g., oxidation state). Third, CTMs often have difficulties in reproducing field observations.

In this work, different parametrization schemes on the 2D-VBS framework were evaluated using measurements collected in the SPRUCE-22 field campaign in a remote forest area of the eastern Mediterranean site (Pertouli, Greece) in the summer of 2022. Field measurements suggested that most of the OA in the site was highly processed secondary anthropogenic and biogenic OA and also aged biomass burning OA.  The results of the default version of the model indicated both underprediction of the total OA level and its oxygen-to-carbon ratio (O:C). A series of hypotheses are tested involving the chemical aging of atmospheric OA to close the gap between the measurements and model predictions.  

How to cite: Uruci, P., Skyllakou, K., Matrali, A., Pavlidis, D., Kaltsonoudis, C., and Pandis, S.: Simulation of atmospheric organic aerosol with the 2D volatility basis during the SPRUCE-22 field campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11810, https://doi.org/10.5194/egusphere-egu24-11810, 2024.

Current high-resolution models generally present a relatively flat signal that poorly represents the spatial and temporal variability of particulate pollution at the scale of a city. This study is based on the SIRANE model, an urban air quality model that enables to simulate the dispersion of atmospheric pollutants according to the city geometry at a 10-meter spatial resolution. The model outputs provided by the ATMO Bourgogne - Franche - Comté air quality monitoring agency are actually post-processed based on a physical equation defined for the PM₁₀ (d < 10 μm) and PM_2.5 (d < 2.5 μm) pollutants and based on mass concentration levels measured by 4 micro-sensors deployed in representative sites in Dijon for the year 2021.

This study first aims at verifying if the equations established could be applied to another period, taking into account any differences in atmospheric circulation. The second objective is to test if the integration of more measurement stations enables to significantly refine the physical equations and improve the SIRANE maps correction. More generally, we would like to evaluate to what extent micro-sensors can improve the information provided by high-resolution models and when with respect to the particle season.

The work is conducted in three steps: first, apply the physical equations to 2022 and 2023 SIRANE maps and compare the outputs with reference stations; second, analyse measurements from the micro-sensors implemented in Dijon in Summer 2023; third, use this dataset to select new representative traffic, background and intermediate sites in order to refine the physical equations and quantify the added value.

The first results are encouraging as the SIRANE corrected maps based on the first 4 representative micro-stations in Dijon enable a more realistic spatial variability in the city, illustrated by a clear pollution gradient from the city centre to the suburbs (with a greater range between concentration levels and a higher number of classes), and a less flat season cycle with more realistic PM levels in Winter everywhere in the city.

How to cite: Marion, S., Martiny, N., Dudek, J., Boilleaut, M., Ristori, M., and Detournay, A.: The spatio-temporal variability of particulate pollution through modelling: new insigths from a dense network of micro-sensors in urban environment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15397, https://doi.org/10.5194/egusphere-egu24-15397, 2024.

Oxygenated volatile organic (OVOCs) are important compounds in atmospheric processes. They have been seen to contribute largely to ROx and ozone formation and in remote marine regions, they contribute to diminishing the oxidative capacity of the atmosphere by reacting with OH. OVOCs are directly emitted from biogenic sources and are produced from the oxidation of hydrocarbons in the atmosphere. However, their budget remains poorly understood, due to incomplete representation of photochemical OVOC production and uncertainties in terrestrial emissions and ocean/atmosphere exchanges.

In this work, we compared model simulations with OVOC remote high-altitude measurements conducted in 2019 at Reunion Island, a subtropical French territory in the Indian Ocean. We exploit a 2-year high-temporal resolution dataset of mass spectrometry (PTR-MS) measurements of OVOC compounds at a remote high-altitude tropical site, the Maïdo Observatory (2155m asl) on Reunion Island. More precisely, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to provide an updated evaluation of the budget of OVOCs over Reunion Island, based on the PTR-MS dataset complemented with meteorological measurements and satellite (TROPOMI) retrievals of relevant compounds. The model is configured to include two domains centred on Reunion Island. The finest resolution (2.5km) in the nested domain is needed to resolve the complex orography of the island and the spatially heterogeneous distribution of reactive species. For computational reasons, the focus is on two one-month simulations in January and July 2019, allowing analysis of seasonal differences and their impacts on model performance and chemical budget.

The WRF-simulated meteorology is first evaluated against meteorological measurements at a remote site (Maïdo) and two urban sites (Saint Denis and Saint Pierre). A high-resolution (1km²) anthropogenic emission inventory for Reunion is implemented, complemented with information from global inventories. Biogenic VOC emissions (primarily isoprene) are calculated on-line using the MEGAN algorithm and amended high-resolution distributions of standard emission factors and plant functional types (PFTs). The MOZART chemical mechanism is adopted with updates to the chemistry. The chemical simulations are evaluated against (1) NO₂ and HCHO vertical columns from TROPOMI, (2) the PTR-MS OVOC dataset at Maïdo, (3) an FTIR column dataset, also at Maïdo, and (4) network air quality measurements at several sites. Those comparisons will provide new constraints on the emissions of NOx and VOCs, and will result in recommendations for further refinements. This work will lead to a better appraisal of OVOC sources and sinks over the island. The main unknowns and potential issues will be discussed.

How to cite: Poraicu, C., Müller, J.-F., Stavrakou, T., Amelynck, C., Verreyken, B., Schoon, N., Mouchel-Vallon, C., Tulet, P., and Brioude, J.: Improved assessment of OVOC sources and sinks over Reunion Island through WRF-Chem model evaluation against PTR-MS data and satellite retrievals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3576, https://doi.org/10.5194/egusphere-egu24-3576, 2024.

Reactive nitrogen (Nr) cycle in the atmosphere has an important affection on terrestrial ecosystems, which has not been fully understood and its response to the future emissions control strategy is not clear. Taking the Yangtze River Delta (YRD) as an example, we explored the regional Nr cycle (emissions, concentrations, and depositions) and its source apportionment in the atmosphere in January (winter) and July (summer) 2015 and projected its changes under emissions control by 2030 using the CMAQ model. We examined the characteristics of Nr cycle and found that Nr suspends in the air mainly as NO, NO₂, and NH₃ gases and deposits to the earth’s surface mainly as HNO₃, NH₃, NO₃^-, and NH₄⁺. Due to the higher NO_x than NH₃ emissions, oxidized nitrogen (OXN) but not reduced nitrogen (RDN) is the major component in Nr concentration and deposition, especially in January. Nr concentration and deposition show an inverse correlation, with a high concentration in January and low in July but the opposite for deposition. We further apportioned the regional Nr sources for both concentration and deposition using the Integrated Source Apportionment Method (ISAM) incorporated in the CMAQ model. It shows that local emissions are the major contributors and this characteristic is more significant in concentration than deposition, for RDN than OXN species, and in July than in January. The contribution from North China (NC) is important for Nr in YRD, especially in January. In addition, we revealed the response of Nr concentration and deposition to the emission control to achieve the target of carbon peak in the year 2030. After the emission reduction, the relative responses of OXN concentration and deposition are generally about 100% to the reduction of NO_x emissions (~50%), while the relative responses of RDN concentration are higher than 100% and the relative responses of RDN deposition are significantly lower than 100% to the reduction of NH₃ emissions (~22%). Consequently, RDN will become the major component in Nr deposition. The smaller reduction of RDN wet deposition than sulfur wet deposition and OXN wet deposition will raise the pH of precipitation and help alleviate the acid rain problem, especially in July.

How to cite: Shen, A., Liu, Y., and Fan, Q.: Modeling regional nitrogen cycle in the atmosphere: present situation and its response to the future emissions control strategy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4819, https://doi.org/10.5194/egusphere-egu24-4819, 2024.

Methane (CH4) is the second-largest contributor to the anthropogenic greenhouse effect, and have been the focus of recent climate summits and global commitments towards emission reductions. To mitigate and study the impact of anthropogenic CH4 emissions, the European Union started AVENGERS, a research and innovation project funded under the Horizon Europe, with 13 Partners from 6 countries, whose objective is to reconcile reported greenhouse gasses (GHGs) emissions with independent information from atmospheric observations using top-down methods and process-based models, and thereby reduce the most important uncertainties of national emission inventories.

Here, we present TOPAS-CH4, an operational service developed by TNO as part of the AVENGERS project, following the example of a previous TNO source apportionment tool for particulate matter. This online public service provides users with the relevant daily information on CH4 concentrations over the EU countries, and how much different sources contribute to these observed  concentrations. TOPAS CH4 is based on the chemical transport model, LOTOS-EUROS, that runs daily to simulate source-labelled CH4 concentrations over Europe. The predicted CH4 concentrations are compared with the Integrated Carbon Observation System (ICOS) observations on a daily basis, showing that LOTOS-EUROS is able to realistically simulate the CH4 volume mixing ratios. Furthermore, the simulated CH4 concentrations are labelled per sector and country of emission, providing the users more insights about which European sectors and countries are mostly responsible for the additional CH4 concentrations above the background for a specific region. This information can be used to identify mitigation targets and, on the longer term, monitor changes in source contributions following successful abatement. We also show the comparison results for specific ICOS stations where the LOTOS-EUROS well matches the reported concentrations, as well as, locations where the model still performs poorly, finally providing some insights on how to improve the current simulation model.

How to cite: Nanni, R., Dernier van der Gon, H., Segers, A., Schaap, M., and Tokaya, J.: TOPAS-CH4: a service for methane monitoring, source attribution and emission reduction over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8844, https://doi.org/10.5194/egusphere-egu24-8844, 2024.

The meteorological field serves as a vital input for chemical transport models (CTMs) to simulate tropospheric ozone pollution. For global CTMs, it is often directly provided by reanalysis meteorology datasets. Different choices of meteorological fields are likely to yield varied ozone levels and contributions of ozone-related processes, e.g., transport, chemical production/loss and dry deposition, to the variations of ozone pollution. However, relevant comparisons are seldom reported. Here we investigate the impact of meteorological field choices on the results of tropospheric ozone simulations performed by the TM4-ECPL global model. Two generations of meteorological reanalysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA-Interim and ERA5, are selected as input meteorological fields to drive the model for this comparative study. Results show that when driven by ERA5 meteorology, the model generates considerably higher mean mixing ratios of tropospheric ozone for the period of 2013-2017 compared to ERA-Interim-based results. This outcome is particularly pronounced in the high-latitude areas of both hemispheres and near the surface (by 5-10 ppbV). The model results by using both meteorological fields are validated against air quality monitoring data from over 10,000 sites and ozonesonde measurements globally. When using ERA5, the overestimation of near-ground O_x (ozone + NO₂) levels by the model becomes more notable than using ERA-Interim (increases from 6% to 25%). However, the underestimation of ozone levels in the middle and high troposphere is reduced. Both simulations can well reproduce the annual trends of near-surface ozone pollution, indicating a more important role of ozone precursor emissions in driving ozone changes. Furthermore, through sensitivity simulations and budget analysis, we delve into the reasons behind the considerably higher ozone levels in the ERA5-driven simulation. 

How to cite: Qu, K., Daskalakis, N., Kanakidou, M., and Vrekoussis, M.: Considerable impacts of meteorological field choices on tropospheric ozone simulations by a global chemistry transport model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9649, https://doi.org/10.5194/egusphere-egu24-9649, 2024.

The present study assesses the impact of the nitrogen oxides emissions from lightning (LiNOx) on mid-tropospheric ozone concentrations using simulations with the CHIMERE model, through a detailed evaluation of simulated tropospheric ozone (O₃) with respect to observations. LiNOx emissions have been implemented in the model (CHIMERE v2020r1). The chemistry-transport model CHIMERE is coupled online with the Weather Research and Forecasting (WRF) meteorological model. Two simulations have been carried out for year 2018, (i) including LiNOx emissions and (ii) without LiNOx emissions, in CHIMERE to examine the impact of LiNOx on the pollutant concentration in comparison to that without LiNOx emissions. In this study the simulations are performed over the northern hemisphere at a horizontal resolution of 100 km x 100 km.

The experiments show an increase in surface-level O₃ by 4-8 ppbv over most part of northern hemisphere, while a large increase by 10-20 ppbv is observed over parts of south America and Africa due to inclusion of LiNOx. The increase in simulated O₃ is high (10-20 ppbv) at middle troposphere in comparison to surface and upper troposphere. We have compared the model outputs to radiosonde measurements (World Ozone and Ultraviolet Radiation Data Centre), showing that including the NOx emissions from lightning substantially improves the realism of model simulations, significantly reducing bias and error when compared to measurements. This is particularly true in the middle troposphere. These results show that, for hemispheric or global studies, it is very important to include a realistic representation of lightning NOx emissions, because they critically influence ozone concentrations, but also the concentrations of OH, and therefore the lifetime of many greenhouse and trace gases such as methane.

How to cite: Ghosh, S., Mailler, S., Menut, L., and Cholakian, A.: Impact of NOx emissions from lightning on mid-tropospheric ozone concentrations in the North Hemisphere: a modelling study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10044, https://doi.org/10.5194/egusphere-egu24-10044, 2024.

Volatile organic compounds (VOCs) significantly contribute to air pollution, pose serious health hazards to humans, and influence ozone formation and secondary organic aerosol production. Anthropogenic sources include various human-driven activities, such as solvent use, traffic and fuel evaporation, industrial emissions, and biomass burning. Despite their importance, the uncertainties associated with representing VOCs in atmospheric emission inventories are considerably higher than other reported air pollutants. This work presents a spatiotemporal assessment and evaluation of benzene, toluene, and xylene (BTX) emissions and concentrations in Spain. We run the High-Elective Resolution Modelling Emission System (HERMESv3) model to produce gridded bottom-up emissions of BTX and use it as input in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) chemical transport model to simulate surface concentrations across Spain. The modelling results were then evaluated against official ground-based observation data for the year 2019. The intercomparison between modelled and officially reported observed levels allows for identifying sources of uncertainty in the anthropogenic emission inputs, which we further explored through specific sensitivity test runs. The largest levels of observed benzene and xylene were found in industrial sites near coke ovens, refineries and car manufacturing facilities, where the air quality modelling results show large underestimations. Official emissions reported for these facilities were replaced by alternative estimates, allowing heterogeneous improvement of the model's performance and highlighting that uncertainties representing industrial emission processes remain. For toluene, consistent overestimations in background stations were mainly related to uncertainties in the spatial disaggregation of emissions from industrial use solvent activities, mainly from wood paint applications. Observed benzene levels in Barcelona's urban traffic areas were five times larger than the ones observed in Madrid. MONARCH failed to reproduce the observed gradient between the two cities due to uncertainties in estimating emissions from motorcycles and mopeds. Our results are constrained by the spatial and temporal coverage of available BTX observations, posing a key challenge in evaluating the spatial distribution of modelled levels and associated emissions.

How to cite: Oliveira, K., Guevara, M., Jorba, O., Petetin, H., Bowdalo, D., Tena Medina, C., Montane Pinto, G., López, F., and Pérez García-Pando, C.: Spatiotemporal assessment and evaluation of aromatic VOC emissions: a case study for Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16114, https://doi.org/10.5194/egusphere-egu24-16114, 2024.

Nowadays, environmental studies emphasize that the advancement of atmospheric transport models will persist as a significant challenge in environmental modeling for the forthcoming decades. The obtained results are increasingly instrumental in air pollution epidemiology, health burden assessment, and evaluating exposure to air pollution. In this work we have run the regional EMEP4PL atmospheric transport model, with 4km x 4km resolution with high-resolution uEMEP model (250m x 250m) for the area of Poland. The models were run for the entire year of 2022. For the first time, these two models were run using a consistent, high-resolution national emission inventory. We have analyzed two pollutants harmful to population health: PM2.5 and NOx, and qualitatively compared the differences in spatial distribution of pollutant concentrations calculated by uEMEP and EMEP4PL. The uEMEP model shows higher concentrations for the emission hot spot areas, which are averaged out in the coarse-resolution EMEP4PL model. This is observed for both PM2.5 and NOx and is noticeable especially for the areas with large spatial gradient of emission (e.g. large cities or along the main roads). The results were also compared with available measurements of PM2.5 and NOx from the national air quality network operated by Chief Inspectorate for Environmental Protection (CIEP). For PM2.5, we have additionally used the measurements from the national air quality network operated by Chief Inspectorate for Environmental Protection (CIEP) and from the low-cost sensors network established within the LIFE-Mappingair project. The results show that the uEMEP model concentrationsresults are closer to the measurements for both networks. For NOx, uEMEP is also closer to the measurements, and the differences between the uEMEP and EMEP4PL performance are larger if compared to the PM2.5.

How to cite: Wisniewska, K., Werner, M., Kryza, M., Denby, B. R., and Fagerli, H.: Comparing EMEP4PL and uEMEP models performance for PM2.5 and NOx for Poland , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5230, https://doi.org/10.5194/egusphere-egu24-5230, 2024.

We summarize tentative findings from multi air quality model ensembles for the years 2009 and 2010 in Europe (EU), and 2010 and 2016 in North America (NA), under AQMEII-4.  The model predictions of sulphur and nitrogen deposition were used to estimate exceedances of critical loads for acidification and eutrophication, to show the extent to which the ensemble members agree in the magnitude and the trend of ecologically meaningful impacts.  Model exceedance variability was analyzed using AQMEII-4 diagnostics.  Evaluation against concentration and wet deposition observations, coupled with these diagnostics, identified specific process representations as the causes for variability between model predictions and for reduced model performance. 

All models predicted reductions in ecosystem acidification impacts in North America between the years 2010 and 2016, in accord with SO₂ emissions reduction legislation which started in 2010 (SO₂ SIP) However, all models in EU and NA domains had net negative biases for wet deposition of sulphur and nitrogen relative to observations.  The wet S deposition average mean bias for the NA ensemble was -0.17 eq ha^-1 d^-1, and for the EU ensemble -1.15 eq ha^-1 d^-1.  The NA daily wet deposition average mean bias for NH₄⁺ was -0.37 eq ha^-1d^-1; EU -1.19 eq ha^-1 d^-1.  The daily NA wet NO₃^- deposition average mean bias was -0.24 eq ha^-1d^-1; EU -0.69 eq ha^-1 d^-1.  The members of the ensemble diverged (factor of 10) in their North American predictions for N_dep and consequently their eutrophication exceedances. The models with the highest eutrophication predictions also predicted the highest levels of gas-phase ammonia dry deposition (standard deviation of ammonia dry deposition flux across ensemble members was larger than the ensemble average).  These models also had negative biases of predicted ammonia concentrations; average mean biases of -0.63 (satellite NH₃) and -0.85 ppbv (surface NH₃) compared to ensemble averages of -0.30 and -0.34 ppbv.  Diagnostics showed that these differences resulted from the manner in which bidirectional ammonia fluxes were parameterized within these models.  The second largest source of NA eutrophication prediction variability were models with positive biases in particulate ammonium and nitrate concentrations, and higher particle nitrogen deposition levels ( particle ammonium concentration bias +0.35 ug m^-3; ensemble bias +0.15 ug m^-3).   We believe two factors may have led to these latter overestimates:  higher levels of fine mode particle nitrate formation compared to other models (due to the use of an inorganic heterogeneous chemistry algorithm which did not take base cation chemistry into account), and updates to particle dry deposition velocities carried out in the absence of concurrent updates to wet scavenging algorithms. 

The relative importance of dry gas, dry particulate, and wet deposition towards total sulphur and nitrogen deposition totals differed between EU and North American domains, though all models had negative biases in wet deposition as noted above.  Parallel and subsequent work suggests that multiphase hydrometeor scavenging may improve model wet deposition performance. 

An increased research focus is recommended for four model processes: multiphase hydrometeor scavenging, ammonia bidirectional fluxes, base cation chemistry and emissions, and particle dry deposition. 

How to cite: Makar, P., Cheung, P., Hogrefe, C., Ayodeji, A., Alyuz-Ozdemir, U., Bash, J., Bell, M., Bellasio, R., Bianconi, R., Butler, T., Cathcart, H., Clifton, O., Cole, A., Hodzic, A., Kioutsioukis, I., Richard, K., Lupascu, A., Lynch, J. A., Momoh, J.-K., and Perez-Camanyo, J. and the Remaining AQMEII4 Critical Load and Modelling Team Members: An ensemble investigation of the causes for regional air-quality model critical load exceedances prediction variability in European and North American domains using diagnostics from Phase 4 of the Air Quality Model Evaluation International Initiative, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6741, https://doi.org/10.5194/egusphere-egu24-6741, 2024.

Introduction

Conclusions

Strengthening air pollution legislations to the technically feasible level and phasing out fossil fuel use leads to a decrease in mortality by 35-50 % in 2050 and a decline in morbidity by 30-40% in the Arctic Council countries. The monetary related benefits in these countries are estimated at 250-750 billion euro in 2050. These benefits likely exceed the costs associated with these actions. Actions on reducing air pollution and fossil fuels are valuable input in supporting the currently proposed European Green Deal, revision of EU air quality legislation and the setting of a zero-pollution objectives for air quality.

How to cite: Rao, S., Brandt, J., Klimont, Z., Im, U., Roldin, P., and Wilson, S.: Economic Impacts of Air Pollution on Health in the Arctic Council Countries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8920, https://doi.org/10.5194/egusphere-egu24-8920, 2024.

In this work within-plume aqueous-phase chemistry is explored utilizing air quality modelling and a plume rise algorithm which includes the effects of combustion-generated water, latent heat release, and in-plume cloud droplet formation. Effluents emitted from high temperature industrial stacks usually contain large amounts of combustion-generated water in gaseous phase (vapour), resulting in high relative humidity within the emitted parcels that make up the plume. As the plume rises in the atmosphere due to buoyancy and cools, the water vapour can condense into droplets, and result in a significant amount of in-plume liquid water. The combined effects of high relative humidity and the presence of liquid-phase water can potentially impact the rate of oxidation of emitted pollutants due to aqueous-phase chemistry within cloud droplets contained within the plume parcel.  Examples include the conversion rates of sulfur dioxide to particulate sulfate and nitrogen dioxide to particulate nitrate. Accounting for in-plume aqueous-phase chemistry can be instrumental in addressing the past discrepancies between predicted and observed levels of secondary aerosols and other gaseous tracers. This work utilizes the Moist-Plume-Rise algorithm (Fathi et al., 2024, under review), which incorporates the thermodynamic effects of combustion-generated water.  The algorithm determines the final height reached by buoyant plumes while keeping track of within-plume water content (vapour, condensed, ice) as it rises. Here, the effect of aqueous phase chemistry taking place within the rising parcel’s condensed water is examined.  The newly developed model feature makes use of information on in-plume water content such as mixing ratio and physical phase over time to perform aqueous-phase chemistry calculations based on the already existing model cloud chemistry modules.  These aqueous-phase chemistry processes and other processes traditionally associated with cloud processing of gases and aerosols can potentially alter the makeup of combustion-source effluents emitted from industrial stacks before they reach neutral buoyancy and are dispersed in the atmosphere. 

How to cite: Fathi, S., Makar, P., Gong, W., and Lupu, A.: Impacts of combustion-generated water on in-plume aqueous-phase chemistry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12966, https://doi.org/10.5194/egusphere-egu24-12966, 2024.

Biogenic emissions are those emitted by natural sources such as plants, trees and soil. The main biogenic volatile organic compounds (BVOCs) involved in these emissions are isoprene and monoterpenes, which can undergo photochemical reactions in the atmosphere and contribute to the formation of tropospheric ozone. Warmer temperatures and increased solar radiation can intensify emission rates. On the other hand, different BVOCs may respond differently to water stress. For example, isoprene emissions have been observed to decrease under water stress conditions, while emissions of some monoterpenes may increase. Therefore, the study of biogenic emissions is essential for understanding the Earth's atmosphere, especially in the context of climate change and air quality. To understand the interactions between biogenic emissions and near-surface ozone, advanced atmospheric models are required.

This study presents a series of meteorology-chemistry online coupled simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to investigate the sensitivity of surface ozone concentration to changes in biogenic emissions during an extreme ozone concentration event (12-15 July 2022) over the Iberian Peninsula. Biogenic emissions are introduced into WRF-Chem using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04. The experiments consist in varying the biogenic emissions by perturbing parameters related to the emission rates and the type and amount of vegetation.

Preliminary results show that the inclusion of biogenic emissions can increase the near-surface ozone concentrations by up to 30% in some locations. Although we do not obtain a much better performance of the model in representing the observed ozone series, the observed extreme values can be better explained when biogenic emissions are considered. Therefore, it is fundamental to consider both natural and anthropogenic sources when addressing ozone pollution.

Acknowledgements: Project PID2020-115693RB-I00 funded by MCIN/ AEI /10.13039/501100011033

How to cite: Segado Moreno, L. C., Sánchez-Jiménez, F., Raluy-López, E., Montávez, J. P., and Jiménez-Guerrero, P.: Effects of biogenic emissions on an extreme event of tropospheric ozone pollution over southwestern Europe (Iberian Peninsula), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18801, https://doi.org/10.5194/egusphere-egu24-18801, 2024.

The role of air quality modelling as a supporting tool for providing information to citizens and policymakers is becoming more significant, as shown in recent research studies and the enhancement of modelling applications in a proposal for the new Ambient Air Quality Directive.

Starting in 2018, the Institute of Environmental Protection – National Research Institute (IEP-NRI) is legislated to provide modelling in support of air quality policy for Poland. This support covers modelling and its analysis required for annual air quality assessment (46 zones, including 30 urban areas), the impact of transboundary transport analysis on pollution concentrations in Poland, representativeness of monitoring stations, 5-year assessment for the purpose of zone classifications, modelled scenarios for National Air Quality Improvement Plan. An operational air quality forecast for 72 hours is also modelled every day and is being used for public information, as well as eventual air quality alerts. All simulations are calculated with the GEM-AQ model (Kaminski et al., 2008), configuration and resolution are fit for purpose and depend on application - concerning the European Commission Joint Research Centre (JRC) Forum for Air Quality Modeling (FAIRMODE) guidelines and evaluation methods. Most of the analyses cover the modelling of at least primary pollutants (NO2, SO2, O3, PM10, PM2.5) and are based on a high-resolution Central Emission Database made by The National Centre for Emissions Management (KOBiZE) for Poland. Results and analysis are provided to the State Inspectorate of Environmental Protection, Ministry of Environment and Climate and published on IEP-NRI web pages.

We will present the configuration and solutions of the modelling system built in IEP-NRI, as well as the most recent results. The strengths of the described approach work in progress, and further development plans will be shown and discussed.

How to cite: Durka, P., Kaminski, J. W., Struzewska, J., Jeleniewicz, G., Norowski, A., Gienibor, A., Kawka, M., Baginski, W., and Lobocki, L.: Air Quality Modeling for Policy Support in Poland – system overview and recent results, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18439, https://doi.org/10.5194/egusphere-egu24-18439, 2024.

Air quality in China has been significantly improved over the past decade. However, many areas still face challenges with air pollutants such as PM_2.5 and ozone. More than a quarter of the 339 cities regularly exceed the Chinese air quality standards and all of them exceed the latest World Health Organization guidelines, despite compliance efforts in reducing sulfur dioxide (SO₂), nitrogen oxides (NO_x), and carbon emissions. Continuous and deep improvements in air quality need a more in-depth quantitative assessment to understand the spatial scales (urban, regional, and national) of air pollution sources and to clarify the sequence of precursors and emission sectors that contribute to these pollutants. Here we combine the WRF-Chem model sensitivity simulations and the Screening for High Emission Reduction Potentials for Air quality (SHERPA) tool to address these challenges, particularly focusing on PM_2.5. We first assess the chemical regimes of secondary inorganic aerosols (SIA) formation by simulating emission reduction scenarios for three main precursors (SO₂, NO_x, NH₃). We find that NH₃ predominantly controls SIA formation over 60% of China during cold seasons and NO_x- or SO₂-senesitive grids only dominate in four warm months (April to July). The spatial distributions of the chemical regimes throughout the year show a distinct demarcation between eastern NH₃-NO_x-controlled area and western NH₃-SO₂(-NO_x)-controlled area. Sichuan Basin and Henan Province, however, are NO_x-sensitive throughout the year. We then train the source-receptor relationships in SHERPA using the baseline and sensitivity simulation outputs of WRF-Chem. SHERPA can well reproduce the responses of PM_2.5 concentrations to the emission changes of all precursors in China. Our results show that for yearly average PM_2.5, local actions of precursor emission reduction at the urban scale are effective for most cities and mitigations in agricultural sector are important. Our study stresses the essential role of NH₃ to further PM_2.5 mitigation strategies of whole China.

How to cite: Xu, J., Clappier, A., Zhang, L., Thunis, P., Pisoni, E., and Ye, X.: Insights into China's Air Quality: WRF-Chem and SHERPA Analysis for Effective Air Quality Solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19753, https://doi.org/10.5194/egusphere-egu24-19753, 2024.

This presentation gives an overview of the Multiscale Infrastructure for Chemistry and Aerosols (MUSICA), which is taking a fundamentally new approach to modeling and will become the next-generation community infrastructure for research on atmospheric chemistry and aerosols. MUSICA will move atmospheric chemistry modeling towards a unification of the range of scales inherent in the Earth System, allowing for the exploration of the couplings across space, time and ecosystems in a consistent manner. It follows modern software standards and is designed to be able to connect to any atmosphere model. Its capability to unify various spatio-temporal scales, coupling to other Earth System components, and process-level modularization will allow advances on topics ranging from fundamental atmospheric chemistry research to air quality to climate and couplings between ecosystems. 

Two versions of MUSICA are currently available, both a configuration of the Community Earth System Model (CESM) using the Community Atmosphere Model (CAM) coupled with tropospheric and stratospheric chemistry. Both versions enable global simulations with regional refinement capability. MUSICAv0 uses a hydrostatic Spectral Element dynamical core and is suitable for scales of ~5 km or higher whereas MUSICAv1 uses the non-hydrostatic Model Prediction Across Scales (MPAS) dynamical core enabling studies of regions at local scales (<5 km grid spacing). 

We will present the status of MUSICA and its partner projects including MusicBox, which is a chemical box model, and MELODIES-MONET, which is a model evaluation framework. We will also provide examples of a range of research and forecasting applications. MUSICA is being developed collaboratively by the National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and university and government researchers. The community is encouraged to participate and collaborate in MUSICA development and applications. Various resources for users including wiki pages and online tutorials are provided on the MUSICA homepage (https://www2.acom.ucar.edu/sections/multi-scale-infrastructure-chemistry-modeling-musica). 

How to cite: Pfister, G., Barth, M., Emmons, L., Dawson, M., Tang, W., Smith, W., Vitt, F., and Skamarock, B.: Next generation atmospheric chemistry modeling with the Multiscale Infrastructure for Chemistry and Aerosols (MUSICA), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11179, https://doi.org/10.5194/egusphere-egu24-11179, 2024.

The capacity of chemical transport models to accurately simulate air pollutant concentrations and their diurnal changes is essential for pollution source attribution and exposure risk assessment. The Community Earth System Model (CESM) incorporates full chemistry from the Community Atmosphere Model (CAM-chem) and horizontal mesh refinement through the spectral element (SE) dynamical core, offering an innovative framework to study air pollution impacts at various spatial scales with globally consistent dynamics and chemistry. We use this CAM-chem-SE configuration featuring a ∼14km×14km refined grid for the contiguous United States (CONUS) within a 1°×1° global horizontal mesh, referred to as the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0). Our analysis compares the standard MUSICAv0 CAMS-GLOB-ANT v5.17 emissions with the 2017 U.S. National Emissions Inventory (NEI) and examines the effects of replacing monthly with hourly anthropogenic emissions on simulated trace gas concentrations and their diurnal variations for July 2018. The study examines three scenarios: ‘base’ with global monthly CAMS emissions; ‘monthlyNEI,' which replaces monthly NEI over the CONUS but retains CAMS elsewhere; and ‘hourlyNEI,' which uses hourly instead of monthly NEI over CONUS. We divide the CONUS domain into West Coast, Mountain, Midwest, Southwest, Southeast, and Northeast for model evaluation. July daily averages from the ‘base’ model simulations (0:00 to 23:00, local time) compared with State and Local Air Monitoring Stations (SLAMS) measurements show high model biases in surface nitrogen dioxide (NO₂) concentrations of 23-40% (1-3 ppb) in all but the Mountain region where a low bias of -18% (-1 ppb) occurs, and in surface ozone (O₃) of 11-28% (6-13 ppb); and low biases of -21 to -80% (10-60 ppb) in surface carbon monoxide (CO). Modeled tropospheric vertical column densities (VCD_Trop) of formaldehyde (HCHO) and NO₂, calculated using TROPOspheric Monitoring Instrument (TROPOMI) satellite retrieval averaging kernels at 1:30 PM local time, show HCHO overestimates of 14-24% and NO₂ underestimates of 35-52% across the six regions. Integrating NEI emissions (‘monthlyNEI’ and ‘hourlyNEI’ cases) enhances agreement with observations by improving spatial correlations and reducing model mean biases for NO₂, O₃, and CO surface simulations compared to the ‘base’ case. However, improvements in the ‘monthlyNEI’ and ‘hourlyNEI’ cases are region-specific; for instance, ‘monthlyNEI’ shows a lower model O₃ bias on the West Coast but a higher bias in the Northeast than ‘hourlyNEI.' Likewise, the high bias in modeled HCHO VCD_Trop compared with TROPOMI is reduced by approximately 1-3% compared to the ‘base’ case, but the low bias in NO₂ VCD_Trop worsens by 10-20%. Subsequent work will assess diurnal variations in MUSICAv0-simulated trace gas concentrations, comparing them across the three scenarios and with observational data.

How to cite: Tao, M., Fiore, A. M., Emmons, L. K., Pfister, G. G., Jo, D. S., and Tang, W.: Evaluating the Impact of Resolving Hourly Anthropogenic Emissions on Air Pollutant Simulations in the United States Using the MUSICAv0 Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6766, https://doi.org/10.5194/egusphere-egu24-6766, 2024.

Outdoor air pollution damages the climate and causes many diseases, including cardiovascular diseases, respiratory infections, and lung damage. Understating of air pollution sources and accurate hourly forecasting of air pollution concentrations is thus of significant importance for public health, helping the citizens to plan the measures to alleviate the harmful effects of air pollution on health. This study conducts air emision inventory (EI), analyses and discusses the temporal characteristics of air pollutants at different locations in Ho Chi Minh City (HCMC), Vietnam - an economic center and a megacity in a developing country with a population of 8.99 million people and more than 8 million of private vehicles.

A combination of bottom-up and top-down approaches was employed to conduct air pollution EI, in which EMISENS model was utilized to generate the EI for road traffic sources. The results showed that the motorcycles were the main reasons of emission in HCMC, contributing 90% of CO, 68% of non-methane volatile organic compounds (NMVOC), 63% of CH₄, 41% of SO₂, 29% of NO_x, and 18% of patriculate matter (PM_2.5).

We developed several AI-based one-shot multi-step PM_2.5 forecasting models, with both an hourly forecast granularity (1h to 24h) and a 24-hour rolling mean. These Machine Learning algorithms include Stochastic Gradient Descent Regressor, hybrid 1D CNN-LSTM, eXtreme Gradient Boosting Regressor, and Prophet. We collected the data from six monitoring stations installed by the HealthyAir project partners at different locations in HCMC, including traffic, residential and industrial areas in the city. In addition, we developed a suitable model training protocol using data from a short period to address the non-stationarity of PM_2.5 time series. Our proposed PM_2.5 forecasting models achieve state-of-the-art accuracy and will be deployed in our HealthyAir mobile app to warn HCMC citizens of air pollution issues in the city. 

How to cite: Ho, Q. B., Vu, K. H. N., Nguyen, T. T., and Carbajo, R. S.:  Air Emission Inventory and AI- based Air Quality Forecasting Models for Developing Countries: A Case Study of Ho Chi Minh City, Vietnam, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10056, https://doi.org/10.5194/egusphere-egu24-10056, 2024.

NOAA is developing the next generation air quality (AQ) prediction system for the United States (U.S.) and global aerosol predictions within the Unified Forecast System (UFS) framework to better represent and forecast impacts of wildfires on AQ and impacts of aerosols globally on weather from hourly to subseasonal scales. A new regional UFS weather model is online coupled with chemistry represented by the EPA’s Community Multiscale AQ (CMAQ) modeling system with Carbon Bond VI and AERO6 mechanisms to form this new UFS-AQM system. Wildfire emissions are specified by satellite-observed  hourly Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). Anthropogenic emissions are based on U.S. EPA’s National Emissions Inventories over the contiguous 48 U.S. states and global inventories elsewhere. Lateral boundary conditions for aerosols are provided by NOAA’s Global Ensemble Forecast System which includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. A bias correction post-processing procedure is included in UFS-AQM to improve prediction accuracy. Testing is performed over a large regional domain covering the U.S., and evaluation is done in near-real time and for retrospective periods. Recent examples indicate much improved representation of impacts of wildfires on AQ predictions, especially during Quebec fires in the summer of 2023. 

Some of the planned refinements for UFS-AQM include better representation of plume rise for wildfire smoke and for point source emissions, increased resolution consistent with the Rapid Refresh Forecast System (RRFS), which is under development, and using aerosol lateral boundary conditions from a 6-way coupled atmosphere - ocean -  land - sea-ice - waves - aerosols global UFS system, also under development. Due to challenging computational requirements for UFS-AQM at high resolution, a machine learning emulator is being developed to improve computational efficiency for prediction of chemical transformations and tracer transport. Of most interest for this session, data assimilation capabilities are being developed to constrain initial conditions for pollutant concentrations in UFS-AQM. Observations being assimilated include fine particulate matter (PM2.5) observations from AirNow, VIIRS Aerosol Optical Depth (AOD) retrievals and TROPOspheric Monitoring Instrument (TROPOMI) NO2 retrievals.

How to cite: Stajner, I. and the Disaster Relief Supplemental Appropriations Act 2022 Fire 2 project team: NOAA’s Next-Generation Air Quality Predictions for the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10505, https://doi.org/10.5194/egusphere-egu24-10505, 2024.

We present the outcomes of our 2-year endeavor as part of the NOAA Joint Technology Transfer Initiative (JTTI). This project aimed to enhance the operational air quality forecasts over the United States generated by the National Air Quality Forecasting Capability (NAQFC) at NOAA/NCEP. We focused on applying machine learning (ML) post-processing techniques to refine forecasts from the Community Multi-scale Air Quality (CMAQ) model.

In particular, our efforts concentrated on extending the analog ensemble (AnEn) model, currently utilized at NAQFC, from its existing point-based application to encompass 2D gridded predictions. This approach, known for its success in weather prediction systems for various meteorological parameters, has also been applied in predicting ozone and fine particulate matter (PM2.5) concentrations at surface monitoring sites within the Environmental Protection Agency (EPA) AirNow network.

The AnEn methodology effectively mitigates systematic and random errors present in CMAQ model forecasts, as highlighted in previous studies (Djalalova et al., 2015; Delle Monache et al., 2020). Furthermore, the AnEn method has demonstrated its proficiency in providing accurate and dependable probabilistic wind speed predictions (Alessandrini et al., 2019).

The foundation of the AnEn technique relies on a training dataset comprised of predictions from the CMAQ model and corresponding observational data for the specific quantity of interest (e.g., O3 or PM2.5). This dataset is used to generate ensemble predictions for future time points based on historical observations. The ensemble construction involves selecting past CMAQ forecasts (referred to as analogs) that best match the current deterministic CMAQ forecast. This matching process considers variables including the pollutant concentration and correlated meteorological parameters such as wind, temperature, and relative humidity. 

Our study involved an initial application of the AnEn technique to correct CMAQ PM2.5 and ozone surface-gridded concentrations. This was accomplished by combining historical gridded chemical reanalysis data from the Copernicus Atmosphere Monitoring Service (CAMS) Near-Real-Time model with measurements obtained from AirNow monitoring stations. The CAMS analysis integrates satellite-derived data on various atmospheric components and is employed with the ECMWF's Integrated Forecasting System (IFS). The resulting 2D gridded CAMS analysis fields are produced every 12 hours with a spatial resolution of approximately 40 km.

The AnEn method necessitates a continuous training dataset comprising hourly observed chemical concentrations. We utilized each 12-hour CAMS analysis along with the subsequent 11 forecast hours to fulfill this requirement, creating a consistent sequence of hourly gridded observations or pseudo-observations. Through the Satellite-Enhanced Data Interpolation technique (SEDI) (Dinku et al., 2015), we merged CAMS surface PM2.5 and ozone fields with corresponding observations from the AirNow network. This technique corrects biases present in CAMS data and short-term forecasts while retaining the accuracy of AirNow measurements at their respective station locations. 

Our presentation encompasses multiple phases of validation and verification. Initially, we validated the SEDI-corrected CAMS concentrations against AirNow PM2.5 and ozone measurements obtained from stations not part of the SEDI correction process. Subsequently, we assess the performance of the entire forecasting system over the contiguous United States within the 0-72 hour lead time range. This evaluation employs standardized verification metrics applicable to both deterministic and probabilistic forecasts.

How to cite: Alessandrini, S., Meech, S., Kumar, R., Kim, J. H., Lee, J., Djalalova, I., and Wilczak, J.: Gridded Post-Processing Air Quality Predictions based of the Community Multi-scale Air Quality (CMAQ) Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6190, https://doi.org/10.5194/egusphere-egu24-6190, 2024.

Many epidemiological studies have traditionally leveraged European maps derived from fixed-site measurements to investigate health effects, primarily emphasizing inter-city variations. Recently, mobile monitoring has been demonstrated to refine the spatial resolution focusing on intra-city variations. Nevertheless, efficiently scaling mobile monitoring campaigns to cover a large spatial area remains challenging.

Tackling this challenge, we explored the transferability of mobile measurements across three European cities. We propose to adapt the traditional land use regression (LUR) models with unsupervised transfer learning algorithms. These models, named CORrelation ALignment (Coral) and its adapted form, inverse distance-weighted Coral (IDW_Coral), aim to estimate air pollution levels in Amsterdam. They rely solely on data from mobile monitoring campaigns in Copenhagen and Rotterdam, bypassing the need for local Amsterdam data itself. The first 30 collection days of mobile campaigns in Copenhagen and Rotterdam were used as the source data (training inputs). By harmonizing the feature space, Coral is designed to minimize the domain difference between the source and target areas. IDW_Coral integrates single Coral models following general geographic principles. Their performance was validated against external routine measurements and compared with a reference LUR model (AMS_SLR), fitted by sequentially increasing amounts of mobile measurements collected in Amsterdam for nitrogen dioxide (NO₂). The proposed models were also compared with our previously published mixed-effect models using all Amsterdam mobile measurements for NO₂ and Ultra Fine Particles (UFP).

For nitrogen dioxide (NO₂), IDW_Coral achieved a balanced performance with an R² of 0.35.  This accounts for 67% of the accuracy of a locally fitted Amsterdam model (AMS_SLR, R² = 0.52), developed using comprehensive mobile monitoring over 160 days in Amsterdam. The difference in absolute errors between the two models was marginal (0.75 for MAE and 0.66 µg/m³ for RMSE). The R² of IDW_Coral matches that of AMS_SLR based on 25 days of data collection, implying that a minimum of 25 days is required to gather city-specific insights through mobile monitoring. If this condition isn't met, IDW_Coral presents a more cost-effective alternative. IDW_Coral correlated strongly (Spearman correlation of 0.72 for NO₂ and 0.76 for UFP) with mixed-effect models fitted with all Amsterdam mobile measurements.

Leveraging Tobler's first law of Geography, our IDW_Coral method proficiently delineates hyperlocal air pollution in areas not directly observed. Further improvements in accuracy and applicability can be achieved by expanding mobile-monitored areas. Requiring no direct measurements in the target area, IDW_Coral has the potential for application across Europe, promising substantial savings in collection efforts.

How to cite: Yuan, Z., Kerckhoffs, J., Li, H., Hoek, G., and Vermeulen, R.: Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10495, https://doi.org/10.5194/egusphere-egu24-10495, 2024.

The Weather Research and Forecasting model with solar extensions (WRF-Solar) has demonstrated potential and reliability in employing an aerosol-aware microphysics scheme as a moderate-cost alternative for aerosol-cloud-radiation and solar power simulations. However, it has not been integrated with any aerosol model due to computational cost considerations. In this study, we fully couple the Thompson and Eidhammer aerosol-aware microphysics scheme with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, incorporating online aerosol-cloud-radiation interactions into the WRF-Solar model. We then evaluate its performance on shortwave radiation in China in March 2021. The results show a general improvement in the model's Aerosol Optical Depth performance across most areas in China, thereby enhancing the simulation of clear-sky global horizontal irradiance (GHI). The most significant enhancement is observed in northern regions, with reductions of 24% in RMSE and 44% in BIAS. Subsequently, cloud properties and all-sky GHI are examined, revealing improvements in almost all areas except Tibet, with the most notable improvement in the Western region (56.18% reduction in BIAS for all-sky GHI). The observed underestimations in northwest areas are attributed to the overestimations of dust. This study not only provides better GHI forecasting results but also deepens the understanding of aerosol-cloud-radiation interactions in climate prediction

How to cite: Wang, S., Huang, G., and Dai, T.: Improving the Aerosol-cloud-radiation Interactions in WRF-Chem-Solar and its preliminary application in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14350, https://doi.org/10.5194/egusphere-egu24-14350, 2024.

Monitoring greenhouse gas (GHG) emissions is essential to face global warming and climate change. The ITMS project (“Integriertes Treibhausgas Monitoringsystem”, in English “integrated GHG monitoring system”)[1], is designed to establish at the German Meteorological Service (DWD) an operational GHG data assimilation service based on the model system ICON-ART[2] to enable Germany to operationally monitor the sources and sinks of three important GHGs: carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O).

In the first phase of the ITMS project DWD together with the Karlsruhe Institute of Technology (KIT) and other partners are focusing on the emission, distribution and depletion of methane. In the troposphere, methane is mainly depleted by the chemical reaction with the OH-radical. Tropospheric OH is created mostly by the photolytic destruction of ozone (O₃) and thus its abundance depends on the available solar radiation and the ozone concentration (i.a.). The calculation of this chemical system is computationally expensive. Therefore a simplified calculation of the OH chemistry has to be included in the ICON-ART forward model.

Here, we present the current state of a super-simplified OH-chemistry for ICON-ART, a data-driven approach based on Minschwaner et al., 2011[3]. The OH concentration is hereby estimated based on the solar zenith angle (SZA) at the respective grid cell, as well as two parameters which are trained priorly on existing OH and SZA data.

[1] www.itms-germany.de

[2] Schröter, J., Rieger, D., Stassen, C., Vogel, H., Weimer, M., Werchner, S., Förstner, J., Prill, F., Reinert, D., Zängl, G., Giorgetta, M., Ruhnke, R., Vogel, B., and Braesicke, P.: ICON-ART 2.1: a flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations, Geosci. Model Dev., 11, 4043–4068, https://doi.org/10.5194/gmd-11-4043-2018, 2018.

[3] Minschwaner, K., Manney, G. L., Wang, S. H., and Harwood, R. S.: Hydroxyl in the stratosphere and mesosphere – Part 1: Diurnal variability, Atmos. Chem. Phys., 11, 955–962, https://doi.org/10.5194/acp-11-955-2011, 2011.

How to cite: Dietz, P., Hanft, V., Reimus, T., Versick, S., Ruhnke, R., and Braesicke, P.: Towards a super-simplified OH chemistry for ICON-ART, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17670, https://doi.org/10.5194/egusphere-egu24-17670, 2024.

The Middle East faces important challenges from severe air pollution, marked by natural factors from frequent dust storms and human-induced emissions, notably SO₂ from power and desalination plants. These emissions significantly degrade air quality and contribute to sulfate aerosol formation, impacting climate and cloud formation. Accurate SO₂ emissions representation in this challenging environment is crucial. We aim to enhance the current SO₂ emission inventory by integrating satellite SO₂ observations and the FLEXPART-WRF model, driven by meteorological data from the WRF 10km resolution model run in 2016. In particular, we adapted the WRF-Chem’s code for simulating the major SO₂ sinks (in cloud scavenging, dry and wet deposition, SO₂ oxidation by OH and H₂O₂) into the FLEXPART-WRF model. It allowed us to exclude the “background” SO₂ column loadings caused by the spatially distributed emissions and to invert the SO₂ emissions from the strong point sources on a daily basis. The improved SO₂ emission inventory is open to the community.

How to cite: Ukhov, A., Hoteit, I., and Stenchikov, G.: Improving SO2 emissions from the point sources over the Middle East using satellite observations and inverse modeling., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18415, https://doi.org/10.5194/egusphere-egu24-18415, 2024.

Photolysis reactions are an essential component of atmospheric chemistry, with the accurate representation of solar radiation and its interactions with clouds and aerosols being a fundamental part of atmospheric chemistry-transport models (CTMs). The photolysis rate calculation scheme in the EMEP MSC-W CTM has recently been updated to the interactive Cloud-J v7.3e scheme, replacing the old system based on tabulated values (van Caspel, et. al., 2023). The current work highlights the comparison of the photolysis rate systems against aerial observations from the ATom-1 campaign over the Pacific Ocean (Hall, et. al., 2018). This comparison includes sensitivity analysis investigating the impact of model resolution, meteorological input model, and cloud averaging scheme. For air quality simulations, the Cloud-J photolysis rates lead to a clear shift in the partitioning of reactive oxygen into the ozone component, with simulations of surface ozone and carbon monoxide showing a general increase in performance. Annual mass-weighted tropospheric hydroxyl concentrations are increased by 26%, while the photolytic impact of aerosols is mostly limited to tropical biomass burning regions. The model run-time penalty of the interactive photolysis rate calculations is 15% at most, with the run-time of regional (forecasting) simulations being increased by no more than 3%.

van Caspel, W. E., Simpson, D., Jonson, J. E., Benedictow, A. M., Ge, Y., di Sarra, A., Pace, G, Vieno, M, Walker, H.L., & Heal, M. R. (2023). Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemistry-transport model using Cloud-J v7. 3e. Geoscientific Model Development, 16(24), 7433-7459. https://doi.org/10.5194/gmd-16-7433-2023

Hall, S. R., Ullmann, K., Prather, M. J., Flynn, C. M., Murray, L. T., Fiore, A. M., Correa, G., Strode, S. A., Steenrod, S. D., Lamarque, J.-F., Guth, J., Josse, B., Flemming, J., Huijnen, V., Abraham, N. L., and Archibald, A. T. (2018): Cloud impacts on photochemistry: building a climatology of photolysis rates from the Atmospheric Tomography mission, Atmos. Chem. Phys., 18, 16809–16828, https://doi.org/10.5194/acp-18-16809-2018

How to cite: van Caspel, W., Simpson, D., Ge, Y., and Vieno, M.: Implementation and evaluation of updated photolysis rates in the EMEP model using Cloud-J v7.3e, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21019, https://doi.org/10.5194/egusphere-egu24-21019, 2024.