Allergic rhinitis affects half a billion people globally, including a fifth of the Australian population. In Canberra, the nation’s capital, more than 1 in 3 people suffer from the disease and this can cause significant negative impacts on community wellbeing as well as the local economy. Thunderstorm Asthma events have also been recorded and have led to increasing public and government concern with regard to improving our responses to thunderstorm asthma and other environmental drivers of respiratory morbidity and mortality. Over the last decade the Canberra Pollen Monitoring Program has been monitoring airborne allergenic pollen and fungal spores in Canberra on a daily basis. These records of airborne pollen are beginning to provide a historical lens to create pollen taxa calendars, estimate the pollen season length and variability, and to provide the basis for forecast evaluation.
In this presentation we provide the latest information on the seasonal nature of the most significant airborne tree pollen, herb pollen and spore types for Canberra, Australia. The development of a citizen science approach designed to provide the public with daily airborne allergenic pollen information while allowing users to give feedback on their hay fever symptoms, is also providing insights into the impact of airborne pollen on people in the region. We also consider why Canberra is a hotspot for allergic rhinitis in Australia and discuss how pollen and spore exposure is likely to be altered by future climate change and rapid urban development.
How to cite: Haberle, S. and Keaney, B.: A decade of airborne pollen monitoring in an allergic rhinitis hotspot of Australia: insights into how climate change and urban development are altering pollen seasons and human wellbeing., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3390, https://doi.org/10.5194/egusphere-egu25-3390, 2025.
Airborne pollen significantly impacts air quality, human health, and ecosystems. Monitoring pollen trends is critical for understanding these effects, especially as pollen allergies affect millions of persons globally, reducing respiratory health and life quality. Pollen data provide relevant information about the impacts of climate change on vegetation, with shifts in flowering timing serving as indicators of ecological responses. These trends are also vital for forecasting agricultural yields, particularly in Mediterranean climates where water availability and temperature are increasingly erratic.
This study examines flowering timing and intensity trends for 12 anemophilous taxa across 12 locations in the Iberian Peninsula from 1994 to 2023. Using data from Hirst pollen monitoring method, we calculated annual trends in several flowering date indicators (e.g. peak pollen day, start of flowering, duration of pollen season) and other indicators for pollen production (e.g. peak daily pollen concentration or seasonal pollen integral). Statistical analyses assessed linear trends, comparing variations among woody and herbaceous taxa, as well as regional differences between Mediterranean and more temperate areas.
The date of maximum flowering advanced by an average of -0.054 days/year, with species-specific variations ranging from -4.2 (Amaranthaceae) to +7.3 days/year (Fraxinus). Woody taxa, especially those flowering in winter or early spring, exhibited varied responses. For instance, in some locations, Cupressus showed a slight delay with an average of -0.16 days/year, while Betula displayed a more marked delay, averaging -0.33 days/year. These delays are likely linked to insufficient chilling requirements in warm winters. Herbaceous taxa, such as Poaceae, advanced their flowering by an average of -0.13 days/year, equivalent to 1.3 days/decade, reflecting their sensitivity to rising temperatures and altered water availability. Notably, Rumex experienced a marked delay in flowering of -0.48 days/year, while Urticaceae advanced by +0.42 days/year.
Seasonal pollen integral displayed contrasting trends. Tree species such as Olea (average increase: +377.83 pollen grains/year, max: +1193.99 pollen grains/year in Jaén), Quercus (+180.81 pollen grains/year, max: +594.15 pollen grains/year in Córdoba), and Cupressus (+174.55 pollen grains/year, max: +853.45 pollen grains/year in Granada) showed significant increases. Conversely, certain herbaceous taxa, such as Poaceae (-11.89 pollen grains/year, min: -158.79 pollen grains/year in Badajoz), Amaranthaceae (-10.81 pollen grains/year, min: -30.07 pollen grains/year in Málaga) and Rumex (-6.67 pollen grains/year, min: -24.85 pollen grains/year in Badajoz), showed declining pollen loads, particularly in Mediterranean regions heavily affected by drought. In contrast, some taxa like Platanus displayed moderate increases averaging +101.11 pollen grains/year, demonstrating the complexity of pollen production responses to climatic variables.
Trends were not homogeneous across Spain. Southern regions exhibited more pronounced changes in flowering timing and intensity, aligning with greater climatic extremes. The observed trends in flowering timing and intensity underscore the complex ecological responses to climate change. Warming winters delay flowering in some taxa due to insufficient chilling, while rising temperatures and CO2 levels could have driven increased pollen production in others, particularly in trees, but not so clear evidence in herbaceous. These findings highlight the need for continued pollen monitoring to mitigate the health impacts of allergenic pollen and to adapt agricultural practices to changing climatic conditions.
How to cite: Galan, C. and Oteros, J. and the Pollen team: Airborne Pollen Trends during the 3 last decades in Spain , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21517, https://doi.org/10.5194/egusphere-egu25-21517, 2025.
The rupture of Cupressaceae pollen grains significantly affects allergenic exposure and depends on specific hydrometeorological factors. This study analysed the main meteorological variables influencing pollen rupture in three cities in southeastern Spain during two pollen seasons (2019-2020 and 2020-2021). Using data from the Aerobiological Network of the Region of Murcia and standardised sampling methods (EN 16868), the study quantified total pollen concentrations, disrupted pollen (DP) levels and percentage of disrupted pollen (DPP).
The results reveal that the main factors influencing pollen rupture are related to water, specifically relative humidity and precipitation. Higher relative humidity levels were positively correlated with increased DP and DPP, indicating that relative humidity triggers structural changes in pollen grains. Precipitation showed a dual effect, simultaneously promoting pollen swelling and disruption while reducing airborne concentrations due to its wash-out effect. The influence of relative humidity aligns with the reproductive mechanisms of Cupressaceae, which rely on hydration for pollen tube formation. The percentage of disrupted pollen was significantly higher during periods of high relative humidity, with always positive correlations.
Statistical analyses confirmed that geographical characteristics and bioclimatic indices had a limited influence compared to localised factors such as urban ornamental flora and specific hydrometeorological conditions. Differences between the cities studied were also explored using hierarchical clustering dendrograms. These visualisations highlighted different clustering patterns based on pollen concentration and meteorological variables, highlighting the importance of localised urban vegetation management in influencing allergenic risks.
The results showed the dual role of precipitation in influencing airborne allergen exposure. This duality highlights the importance of considering both weather conditions and urban planning strategies to mitigate health risks. Management of ornamental Cupressaceae in urban areas, combined with monitoring of high relative humidity periods, could significantly reduce allergen exposure.
The integration of aerobiological and meteorological data networks provides a robust framework for allergen risk forecasting. Such systems can provide real-time alerts to vulnerable populations, especially during high pollen seasons. A better understanding of factors such as relative humidity and rainfall will improve public health responses and inform sustainable urban development policies.
How to cite: Moreno, J. M., Aznar, F., Negral, L., and Moreno, S.: Hydrometeorological drivers of Cupressaceae pollen rupture in southeastern Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3092, https://doi.org/10.5194/egusphere-egu25-3092, 2025.
Cypress species and other members of the Cupressaceae family are widespread evergreen trees and shrubs, commonly used as ornamental plants. Some species, such as Mediterranean cypress (Cupressus sempervirens), Arizona cypress (Cupressus arizonica), and prickly juniper (Juniperus oxycedrus), widespread in Mediterranean, cause significant allergic reactions. This study aimed to develop a phenological model for Southern and Central Europe to predict the timing of cypress pollen release, enabling integration into atmospheric models for pollen dispersion forecasts.
A key challenge is the microscopic similarity of all Cupressaceae pollen grains, which prevents species-level identification. Consequently, pollen observations report total Cupressaceae counts, complicating phenological modeling of allergenic species.
For early-flowering species, thermal time models, such as growing degree days (GDD) or growing degree hours (GDH), are suitable. These models require defining a heat accumulation start date, a temperature threshold, and the cumulative heat required for flowering. Geographic variability and ornamental planting further influence flowering patterns, even between neighbouring locations.
Pollen data were obtained from the European Aeroallergen Network (EAN), and temperature data from the ERA5 reanalysis dataset. Testing three start dates of the accumulation revealed that the autumn equinox was too early, while January 1st was too late, as J. oxycedrus and C. arizonica may flower before the new year. November 30th was optimal for detailed analysis. GDD/GDH was calculated using thresholds of 0°C, 1°C, 2°C, and 5°C, with normalized GDH (nGDH) yielding the most accurate results.
When flowering onset was defined as 5% of the seasonal pollen index (SPI), the median GDH requirements ranged from 0.06–0.15 nGDH0 (SD 0.01–0.05) to 0.01–0.06 nGDH5 (SD 0–0.02). A 5°C threshold was too high leading to insufficient heat accumulation sensitivity, while 0°C was too low due to higher variability between years. Thresholds of 1°C and 2°C provided optimal accuracy with moderate inter-annual variability, making them suitable for forecasting the flowering onset.
How to cite: Siljamo, P., Sofiev, M., and Palamarchuk, Y.: Forecasting the Onset of Cupressus Flowering in the Mediterranean Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15510, https://doi.org/10.5194/egusphere-egu25-15510, 2025.
Grapevine (Vitis vinifera L.) cultivation is one of the oldest, most widespread and important crops in the world. Downy mildew and grey mould are fungal diseases caused by the oomycete Plasmopara viticola and the ascomycete Botrytis cinerea, respectively. These diseases have a serious negative impact on viticulture, reducing the quantity and quality of harvests. Monitoring of variables affecting the environment-host-pathogen system in important viticulture areas is necessary for the control and prevention of these diseases. The aim of this study was to analyse the dynamics and behaviour of the atmospheric content of P. viticola and B. cinerea spores, as well as their relationship with different meteorological variables and the phenology of the grapevine.
The study was conducted between May and November 2023 in a vineyard belonging to the land of the Designation of Origin Uclés, D.O Uclés, located in the west of Cuenca province (Castilla-La Mancha region, central Spain). The area has a dry Mediterranean climate. Aerobiological sampling was carried out using a Hirst volumetric spore trap placed 2 metres above the ground in a vineyard of the Syrah grape cultivar. Samples were prepared and analysed following the methodology established by the Spanish Aerobiology Network. Phenological sampling was carried out weekly on 20 Syrah grapevines close to the spore trap, using an adaptation of the BBCH scale. Meteorological data were obtained from the Spanish Meteorological Agency (AEMET). The relationship between spore concentrations and meteorological variables was analysed using Spearman's non-parametric correlation test and Principal Component Analysis (PCA). Furthermore, an intradiurnal analysis of spore concentration was carried out.
A total of 894 spores/m3 of B. cinerea and 758 spores/m3 of P. viticola were obtained during the studied period. For B. cinerea, the daily peak spore concentration was on 18 October with 54 spores/m3, for P. viticola it was on 13 November with 74 spores/m3. The phenological stages with the highest daily spore concentrations for both pathogens were flowering, ripening and senescence of the grapevine. The spore concentration of both pathogens is positively influenced by relative humidity (RH), while temperature variables (mean, maximum and minimum) have a negative influence. Intradiurnal analysis showed that the highest spore concentrations occurred between midday (11:00-12:00) and early afternoon (16:00-17:00).
Flowering and ripening are critical stages for the development of these diseases. High temperatures and drought, characteristic of the summer period in areas with a dry Mediterranean climate, inhibit sporulation. However, diseases can develop before and after this period, when conditions are favourable. Progress in the knowledge of the environment-host-pathogen system in these areas will help to decide the most appropriate times for the application of phytosanitary treatments.
This work has been funded by the Ministry of Education, Culture and Sports of the JCCM, through the project SBPLY/21/180501/000172 and by the University of Castilla-La Mancha (UCLM) through the project 2022-GRIN-34507 and a pre-doctoral contract to Guillermo Muñoz-Gómez of the “Plan Propio I+D+I” of the UCLM.
How to cite: Muñoz Gómez, G., Jiménez Jiménez, E., Lara Espinar, B., Rodriguez-Arias, R. M., Rojo Úbeda, J., Fernández González, M., Rodríguez Rajo, F. J., Fernández González, F., and Pérez Badia, R.: Atmospheric spore content of the grapevine pathogenic fungi Plasmopara viticola and Botrytis cinerea in Mediterranean vineyards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18361, https://doi.org/10.5194/egusphere-egu25-18361, 2025.
Pollen detection through automatic instruments has significantly improved over the recent years. Hirst-type traps start to be complemented with automatic instruments throughout Europe. Instead of the traditional identification of airborne pollen particles using light microscopy, data of the particles is collected and subsequently analysed by an AI-classifier. As an airflow-cytometer, SwisensPoleno instruments capture a fingerprint of each particle in-flight. Holography images, as well as fluorescence spectral data, as for SwisensPoleno Jupiter, are collected to classify measured pollen grains flowing through the device in real-time. Labelled datasets of each pollen-type of interest are required for the training of a new classifier. These are generated by collecting fresh and pure pollen from plants and aerosolizing it directly into a SwisensPoleno instrument under controlled conditions.
New classifiers are evaluated by correlating concentrations determined by the automatic instrument with daily concentrations of co-located Hirst-type traps. To evaluate the performance of classifiers throughout Europe, multiple sites in different countries are assessed. Using results of the latest pollen classifier “Swisens (2025)”, based on holography images and fluorescence spectra, we show here how different locations of the SwisensPoleno and different input data can affect the resulting correlations to Hirst data, as well as the Mean Absolute Error (MAE). Differences in performance are expected between sites which are far apart, due to many factors such as differing geography, local climatic conditions and flora. Bad performance may arise from unknown interfering particles, only abundant at one specific site, and thus not included in the training datasets.
Our results demonstrate that this effect can also occur at a sub-regional scale, in sites only 30 km apart (Figure 1A), installed with the same instrument, and analysing data with the same classifier. Fraxinus pollen concentrations for P48 (Bolzano, Italy) correlate very well with a co-located Hirst (Figure 1B); that isn’t true for P46 (San Michele all’Adige, Italy), where strong interference from other particles is present during late May and beginning of June (Figure 1C). Interfering particles, similar to the target taxa, require site specific fine-tuning, e.g. in form of additional filters specifically excluding the interfering particles. In conclusion, while automatic pollen detection instruments show great promise in improving accuracy and efficiency, our findings highlight the importance of site-specific adaptations to address geographic and environmental variability, ensuring reliable performance across diverse locations.
How to cite: Schwendimann, A., Zeder, Y., Koch, K., Graf, E., Niederberger, E., Gottardini, E., Cristofori, A., Cristofolini, F., and Widmann, M.: The influence of local particles on classifier performance for pollen monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21490, https://doi.org/10.5194/egusphere-egu25-21490, 2025.
Background: Monitoring airborne pollen measurements depends on accurate and reproducible pollen recognition and analysis. The traditional volumetric Hirst method for pollen monitoring is based on European standards. This method demands skilled technicians, and it is a time-consuming process. Furthermore, this method has a 5–10 days delay in the data reported. To overcome these problems, a transition from manual to modern automatic methodologies is needed. A Hirst-type trap has been used as a baseline and maintains historical time sequences of measured data to validate automatic samplers.
Methods: We compared both three-hourly and daily data of selected pollen types: Cupressus, Fraxinus, Pinaceae, Platanus, Poaceae, and Quercus, detected with a Hirst-type trap with a parallel retrieved concentration from three different automatic samplers: Pollen Sense APS-330, Hund BAA-500, and Swisens Poleno Jupiter. The pollen measurement campaign was conducted from 01 January 2024 to 03 June 2024 in Córdoba under the Mediterranean climate. The automatic samplers are based on different sampling and analysis methods, such as imaging-based identification, fluorescence spectroscopy and/or holographic imaging. We calculated Pearson's linear correlation coefficients (r) and daily ratio among Hirst and automatic measurements.
Results: These results indicates the statistical significant differences between Hirst and automatic samplers (p ≤ 0.001) with strong correlation coefficient (r) for APS-330 data with r > 0.74 (3h) and r > 0.89 (daily) for Quercus, r > 0.77 (3h) and r >0.83 (daily) for Cupressus, and r > 0.65 (3h) and r > 0.70 (daily) for Poaceae ; the Hund BAA-500 data shows r > 0.63 (3h) and r > 0.84 (daily) for Cupressus, r > 0.55 (3h) and r > 0.56 (daily) for Poaceae at statistically highly significant (p ≤ 0.001), and for Platanus at moderately significant (p ≤ 0.01) with r > 0.50 (daily) and non-significant (p > 0.05) with r > 0.13 (3h). As with Swisens Poleno Jupiter, the weak correlation results with r > 0.61 (3h) and r > 0.85 (daily) (p ≤ 0.001) for Platanus, r > 0.38 (3h) and r > 0.67 (daily) (p ≤ 0.001) for Poaceae, and r > 0.17 (3h) (p ≤ 0.001) and r > 0.36 (daily) (p ≤ 0.01) for Quercus. The average daily ratio for APS-330 was 2.31, for Hund BAA-500 was 2.77, and for Swisens Poleno Jupiter was 3.18.
Conclusion: This research study gives first insights into the Mediterranean environment. The results are from the pre-loaded algorithms and the companies did not include southern categories (e.g. Olea, Morus), which could lead to false positivity for already trained categories. We observed comparable concentrations provided by Hirst and both APS-330 and Hund BAA-500. The concentrations are not similar due to different measuring techniques of automatic samplers, we always noted a similar distribution curve, taking into account the use of scaling factors for the application of a homogenization index. However, further intercomparison studies, in particular, after the training of local categories and refinement of the algorithms based on digital reference datasets (DRD), the automatic samplers would show potential.
Keywords: Automated biomonitoring; Validation; Intercomparison studies; Airborne pollen
How to cite: Farooq, Q., López-Orozco, R., Martínez-Bracero, M., Galán, C., and Oteros, J.: Intercomparison studies and potential of pre-load algorithms for training and validating automated sampling systems in aerobiology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4712, https://doi.org/10.5194/egusphere-egu25-4712, 2025.
Although bioaerosol particles are invisible to our eyes, they have a big impact in human life. Already at the beginning of the past century, the first attempts to detect the airborne pathogens causing damages in crops took place (Hirst, 1994), leading to the development of the Hirst trap (Hirst, 1952). From the agriculture field, the use of this trap jumped to the medical field, being used to monitor pollen and spores for the allergy patients. Since then, the use of manual pollen traps has spread around the world (Buters et al., 2018). However, the lack of experts and the laborious work of counting spores with a bright field microscope due to their small size and high numbers, has led to a scarcity of data on airborne fungal spores as compared to pollen.
Recently, automatic monitors have been developed and are able to identify pollen in the air (Buters et al., 2022). Some automatic monitors have proven to perform well in pollen identification (Maya-Manzano et al., 2023) and are being used in official monitoring networks (Oteros et al., 2020, Sauvageat et al., 2020, Tešendić et al., 2020). These devices have brought a new opportunity to the identification of other bioaerosols, i.e., fungal spores.
One of such instruments is the BAA500 (Hund GmbH). This instrument is designed as a cascade impactor. The air is sucked at high speed and particles between 4-80 µm arrive to a sticky plate at the end, where they are photographed with a camera attached to a microscope. Then, a software based in convolutional neural networks identifies the particles. The Validation tool (https://validation.pollenscience.eu/) is a quality control tool that allows to see the particles detected by the monitor. Within these images, we have confirmed that fungal spores are being captured by the device. However, a closer examination to evaluate their capture efficiency has not been done yet.
In order to test the efficiency of the BAA500 in the capture of fungal spores, we collocated a Hirst trap next to an automatic monitor for five months (from May to October 2024) and compared the concentration of 5 fungal spore types, comprising a wide size range challenging the detection lower and upper limits of the instrument: Ganoderma, Cladosporium, Polythrincium, Epicoccum and Alternaria.
First results show that small (R2= 0.02, 0,08) and big (R2= 0.39) spores are under captured by the automatic device as compared to the Hirst trap, probably due to their size being in the limits of the cascade impactor, whereas medium-size spores correlate nicely with Hirst concentrations, showing a correlation of R2= 0.69 and 0.76. These results confirm the potential of the BAA500 to be used as fungi monitor for medium-size spores. Spores with a size close to the detection limit will need a scaling factor.
How to cite: González-Alonso, M., Žilka, M., Schmidt-Weber, C., and Buters, J.: Evaluating the capture efficiency of fungal spores with automatic monitors: the case of BAA500 (Hund GmbH), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21470, https://doi.org/10.5194/egusphere-egu25-21470, 2025.
Several automated pollen monitors have recently become available, most using conventional and/or machine-learning algorithms to detect pollen and classify their taxa. An international intercomparison campaign was organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich (March–July 2021) (Maya-Manzano et al. 2023). The study showed that some automatic systems, especially those that have built-in correction factors to compensate for losses of sampling, detection and classification, agreed with the averaged pollen concentration of four manual Hirst-type measurements. The manual pollen counting method, relying on human operators, is still used today due to a lack of traceable metrological standards for pollen monitoring. Additionally, existing reference particle counters have been validated up to particle sizes around 20 µm only (Vasilatou et al. 2022). In this study, we evaluated the shake-the-box particle tracking/detection algorithm (Novara et al. 2023) to serve as a reference measurement of particle number concentration. A volume is illuminated with an expanded light sheet produced by a double-pulse laser, and four calibrated double-frame cameras record images of the particles inside this volume. The algorithm determines the position and velocity of each particle, allowing the measurement of particle number concentration directly in the air without sampling inlets that could influence the measurement.
Particle tracking was validated against the Inkjet Aerosol Generator at the Japanese National Metrology Institute NMIJ and the reference optical particle counter at METAS using an 11-D spectrometer (Grimm GmbH, Germany) as a transfer standard and size-certified polystyrene particles. Expanded uncertainties (coverage factor k=2) were 24 %, 10 %, and 7 % for particle sizes 15 μm, 20 μm, and 26 μm, respectively. The good agreement among the three methods shows that the shake-the-box particle tracking method can be applied as a reference for particle number concentration.
We used a laboratory-based method for characterising the performance of bioaerosol monitors as a whole unit (hardware plus identification algorithms) using the particle tracking method and, in a separate experiment, freshly sampled pollen. Experiments were carried out with the SwisensPoleno Jupiter, which combines light scattering, inline digital holography and ultraviolet laser-induced fluorescence with machine learning to classify different particles. For a pollen grain to be measured, it must be sampled, detected and correctly classified. Each stage is subject to particle losses, leading to a measurement efficiency below 100 %. Particle tracking measurements of pollen (Pinus, Zea Mays) resulted in an average counting efficiency of about 43 %, neglecting particle losses in the Sigma-2 sampling head and issues with the classification of particles. The unit-to-unit variability of the Poleno instruments was 30 % based on measurement with three units. Independent experiments with Alnus glutinosa, Betula pendula and Corylus avellana showed that the major source of losses, however, originates from the pollen classification algorithm, which is trimmed to best correlate with Hirst measurements (currently the defacto reference for pollen monitoring). This highlights the need for an independent, standardised method for evaluating classifier losses.
How to cite: Horender, S., Giannakoudaki, C., Abt, R., Auderset, K., Crouzy, B., Erb, S., Erdogdu, O., Graf, E., Iida, K., Niederberger, E., Ou, L., Sakurai, H., Schmale, J., Wälchli, C., and Vasilatou, K.: Method for calibration of bioaerosol monitors based on reference pollen aerosols and imaging-based Lagrangian particle tracking as reference: the case of the Swisens Poleno, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11529, https://doi.org/10.5194/egusphere-egu25-11529, 2025.
Pollen is one of the primary aeroallergens that negatively impacts human health during the vegetation period. Studies have demonstrated that air pollution and meteorological factors influence the severity of airborne pollen load and modify its effects on health. The Pollen Resilience Index (PRI) is a five-point index developed by integrating CAMS air pollution and pollen forecast data within a framework of legal and scientific thresholds and Humidex index values. It ranges from 1 (no health effects observed in sensitive individuals) to 5 (pollen exposure, combined with environmental factors, causes adverse health effects). The forecasted PRI values indicate the potential severity of pollen exposure under varying environmental conditions.
In this study, we aimed to validate the PRI using retrospective, anonymised data collected from users of the PASYFO mobile application. The dataset comprised 9,472 symptom reports related to eye, nose, and lung symptoms from 247 participants. These responses were analysed to determine how PRI values correspond with symptom reporting. Sensitivity to airborne irritants and individuals’ ability to tolerate these effects varied considerably and were limited by personal health conditions, reporting habits, and symptom intensity. This variation introduced uncertainty into the results, complicating statistical analysis.
Our analysis revealed a strong and consistent tendency for participants to report symptoms when the PRI exceeded a value of 1. Specifically, as the PRI increased, the proportion of respondents reporting symptoms also rose. This effect was most prominent for eye and nose symptoms. For example, when the PRI was 1, 21.3% of participants reported eye and 31.1% reported nose symptoms. At the maximum PRI value of 5, the proportion of individuals reporting eye symptoms increased to 42.6%, while nose symptoms were reported by 58.7% of participants. Common symptoms, such as eye itching, watering, redness, runny nose, and sneezing, showed the most significant increase in reporting under higher PRI conditions. The effect on lung-related symptoms was less pronounced, although their reporting also increased with higher PRI levels. Respiratory symptoms, including shortness of breath and coughing, rose by less than 7% even under the worst conditions (PRI = 5). This suggests that while pollen exposure affects respiratory health, its correlation with lung symptoms is less direct compared to eye and nose symptoms.
Among the various types of pollen, Betula (birch) and Alnus (alder) were most closely associated with the development of allergic symptoms. This highlights the significance of specific pollen types in exacerbating allergic reactions. The findings underscore the value of using the PRI as a predictive tool to assess the potential health risks posed by aeroallergens and to help mitigate their impact on sensitive individuals. Our research demonstrates the effectiveness of integrating air quality, weather, and pollen data to create a tool for predicting the health impacts of aeroallergens.
This research was funded by the LMTLT agreement No. S-MIP-19-53 and supported by EO4EU, funded by the Horizon Europe RIA Programme under Grant Agreement No. 101060784.
How to cite: Sauliene, I., Sukiene, L., Daunys, G., Dubakiene, R., Rudzeviciene, O., and Stonevicius, E.: Assessing Associations Between Pollen Resilience Index Forecast Values and Allergic Health Symptoms Induced by Aeroallergens, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12519, https://doi.org/10.5194/egusphere-egu25-12519, 2025.
Airborne pollen released by plants during their flowering season can cause significant allergic symptoms impairing public health, especially if accompanied with air pollutants and/or weather phenomena (e.g., high temperature). Apart from the public health-related motivation, information on pollen in the air can be useful to monitor biodiversity, follow species migration, habitat degradation, etc. Systematic knowledge about continental-scale pollen distribution patterns is hard to obtain due to tedious manual observation methods used through decades and limited modelling experience and maturity level.
The European Pollen Reanalysis v.1.1 (EPR) is the first 43 year-long reanalysis of pollen seasons for three major allergenic tree genera in Europe: alder (Alnus), birch (Betula), and olive (Olea). The EPR has been created by the atmospheric composition model SILAM driven by the European meteorological reanalysis ERA5. SILAM predicted a Europe-wide dispersion pollen for 1980–2022. For each year, an extended 4-dimensional variational data assimilation was applied assimilating in-situ observations of aerobiological networks of 34 European countries. The assimilated variable was the total seasonal emission of pollen grains. Therefore, the EPR assimilation constitutes an inverse dispersion problem solution realized as an annual correction factor to the mean pollen production. The EPR is positioned as an input for health- and climate- related studies, biodiversity monitoring, etc.
How to cite: Sofiev, M. and Palamarchuk, J. and the SILAM modelling team and European Pollen Reanalysis data providers: A 43 years-long European pollen reanalysis for alder, birch, and olive, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12813, https://doi.org/10.5194/egusphere-egu25-12813, 2025.
Allergenic airborne pollen in Europe affect the health of a quarter of the adult population and a third of all children badly. Due to climate change even more people might suffer from pollen allergies in the future. If timely information on forthcoming pollen episodes is available, however, mitigation measures can be taken for easing off the allergy symptoms. This requires forecasting systems at the scale of the citizens that may alert people who are vulnerable for these pollen. In order to achieve this, we aim at providing modelled and forecasted airborne birch and grass pollen levels near the surface at the one by one kilometer scale.
The pollen transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) is used as backbone for modelling and forecasting airborne birch and grass pollen in Belgium. SILAM is driven by ECMWF ERA5 meteorology in a bottom-up emission approach. The dynamic vegetation component in the pollen transport model is determined by pollen emission source maps which have to be ingested every pollen season in the model. To date, these maps have 0.10° x 0.10° and 0.05° x 0.05° gridcells for birch trees and grasses, respectively. Here, we combine monthly MODIS Land Surface Temperature (LST) data on a one by one kilometer grid with vegetation maps from earlier research on top of a pollen footprint analysis. We apply daily pollen footprints produced by SILAM running in a 3-day backward mode for five locations in Belgium, coupling the fraction of air to the pollen levels monitored by the devices of the aerobiological network. The down-scaled pollen emission source maps are then applied into SILAM in the forward mode to obtain modelled birch and grass pollen concentrations near the surface for Belgium more tailored towards the scale of citizens.
Preliminary analysis indicates that late winter/early spring MODIS LST is a good proxy of the severity of the grass pollen season. The added value of LST for the birch pollen season is small. By ingesting the new maps with down-scaled pollen emissions sources into SILAM and by comparing modelled and measured time series for the 2013-2018 pollen seasons a substantial improvement (up to 210% increase in R² values for grass pollen) is found for the monitoring stations, especially at the North Sea side. This can be mainly attributed to a better separation between sea and land characteristics in the 0.01 x 0.01° grid of the pollen emission source maps compared to the coarser native gridcells.
How to cite: Verstraeten, W. W., Bruffaerts, N., Kouznetsov, R., Sofiev, M., and Delcloo, A.: Scaling Down Modelled Airborne Birch and Grass Pollen Levels, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2508, https://doi.org/10.5194/egusphere-egu25-2508, 2025.
Aerial microbes, which make up less than 1% of airborne entities, have often been overlooked due to challenges in traditional monitoring methods, such as culturing and microscopy. Metagenomics has addressed this gap, allowing for the exploration of species diversity through DNA extraction and culture-independent analyses. This approach is particularly pertinent for understanding enigmatic aerosols, such as pollen, where accurate DNA extraction is essential for precise metagenomic studies, microbial profiling, and aeroallergen detection. Long-read DNA sequencing technologies, such as PacBio and Oxford Nanopore, have transformed biodiversity studies by providing much more comprehensive and accurate genetic information. These technologies produce reads that can cover entire genes or genomes, making them invaluable for studying complex ecosystems. In the field of bioaerosols, long-read sequencing can help to identify new species, detect genetic diversity, and enhance our understanding of microbial community functions, also simplifying the task of genome assembly.
Metagenomic studies of the atmospheric bioaerosols face challenges due to low concentration of biological material in the air in comparison with water and soil. Overcoming this roadblock, one has to use high-volume samplers (expensive and difficult as well) and/or a highly sensitive and precise procedure of the sample treatment and sequencing. Here the second challenge is addressed by presenting an eDNA analysis procedure applicable to atmospheric samples with moderate-to-low amount of biological material.
How to cite: Salokas, J., Sofieva-Rios, S., Paatero, J., Asmi, E., Karppinen, A., and Sofiev, M.: Advancing eDNA analysis techniques of atmospheric bioaerosol samples , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19841, https://doi.org/10.5194/egusphere-egu25-19841, 2025.
Environmental metagenomic air samples contain a large amount of information that can be unraveled with a suitable toolkit. Among numerous objectives, taxonomic identification and relative abundance estimation are one of the key points of interest in metagenomic DNA analysis. This type of analysis for large sequence datasets requires considerable computing resources with the publicly available bioinformatic software and pipelines optimized for analysis of next generation sequencing data and often targeting only bacteria. The SYLVA-DNA-classifier is a pipeline developed to efficiently and accurately classify large quantities of 3rd generation sequencing data among all kingdoms, with a special focus on plants and fungi, as best known aeroallergens.
How to cite: Sofieva-Rios, S., Stangel, A., and Sofiev, M.: A glance into specie composition of biological aeroallergens: metagenomic approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19857, https://doi.org/10.5194/egusphere-egu25-19857, 2025.
The recent rise of automatic and online instrumentation for bioaerosol research has stirred the interest from diverse scientific communities and more and more monitoring stations are installed worldwide. Providing a higher time resolution, the automatic instruments promise novel insights into atmospheric processes and distribution patterns of bioaerosols. While most stations are situated well within the atmospheric boundary layer and in populated areas, we will present data from a high alpine research station, the Sonnblick Observatory, located at 3106 m asl. Such measurements at high altitudes, far away from local sources and in atmospheric regions where clouds form, are rare.
From April 2023, a SwisensPoleno Jupiter was installed at the Sonnblick Observatory alongside a manual Hirst-type pollen trap. The SwisensPoleno Jupiter is an online aerosol monitor that obtains scattered light, two holographic images and fluorescence signals of single aerosol particles. With the Hirst-type pollen trap, the particles are collected on a sticky tape and are later examined manually under a microscope, identifying pollen and fungal spores by visual inspection. For pollen, 30 percent of the tape surface and for fungal spores 10 percent is analyzed, which exceeds the requirements of the current European standard for pollen monitoring. The increased detection area for pollen takes in account the lower concentrations which are expected at high altitudes.
In a first step, time series of concentrations and fractions of fluorescent particles (as a proxy for bioaerosols) obtained from the SwisensPoleno Jupiter were analyzed to identify particularly interesting time periods and seasonal patterns. The time series of fluorescent particles show a clear increase in the concentrations and fractions of fluorescent particles over the course of the season from early spring to summer (fluorescent fraction: 0.2 in April; 0.6 in June).
In a second step, we focused on selected time periods where FLEXPART simulations indicate long-range transport of air masses such as from the Saharan region. For these periods, we took a closer look at the fluorescence properties of the particles together with the holographic images. In a previous laboratory study, we obtained representative fluorescence signals for three classes of bioaerosol particles: pollen, fungal spores and plant debris. We use these data in combination with pollen and spore counts from the Hirst-type trap to characterize the selected events and compare them with periods with stagnant conditions and stronger local influence.
How to cite: Burkart, J., Bucci, S., Kölzer, K., Stohl, A., Weinzierl, B., Ludewig, E., and Bastl, M.: Pollen, fungal spore and fluorescent particle concentrations during long-range transport events at the Sonnblick Observatory (3106m asl), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18305, https://doi.org/10.5194/egusphere-egu25-18305, 2025.
Ambient pollen identification and classification using traditional label-dependent methodologies is often time-consuming and prone to errors. Conventional methods such as bright field (BF) microscopy and including labeled techniques provide low-contrast and low signal-to-noise ratio images of pollens. These techniques are also associated with high noise due to the complex nature of ambient samples. Numerous other label-free techniques such as phase contrast, and quantitative phase imaging have been applied for ambient pollen imaging; however, these imaging methods provide better contrast images but the identification of pollens in ambient samples is very difficult due to the heterogeneous nature of ambient particles as it contains particulate matter, fungal spores, mold spores, and other ambient particles. To address these limitations, the current work employs a label-free novel application of total internal reflection (TIR) phenomenon. When the light beam undergoes TIR at an optical interface, an evanescent field is generated over the interface where the samples are prepared. The generated evanescent field illuminates the sample upto a certain depth (~500nm) only, which helps to avoid the background noise and gives high-contrast images with high SNR. TIR imaging enhances the optical properties of ambient pollen by emphasising surface and near-surface features, providing remarkable contrast in ambient pollen images. The study used TIR imaging, enabling non-invasive, no-sample preparation and precise identification of pollen in ambient samples, even with high background concentration of ambient particles. It also allows better visualization of pollen boundary and additional surface features such as the polarity and aperture patterns. Additionally, the CNN-based deep-learning model is used for pollen detection, significantly advancing ambient pollen analysis. The model demonstrates strong performance, with a high F1 score for detecting pollen (0.83) and a well-balanced overall performance (F1 score of 0.77 for all classes). The confusion matrix shows excellent classification accuracy, especially for the pollen class. The model’s mean average performance is 76.7% across all classes at a threshold of 0.5, indicating good performance. Preliminary results demonstrate the model's robust performance, even when handling complex ambient samples with high ambient concentrations of other ambient particles. Pollen monitoring is crucial due to the scarcity of comprehensive data on airborne pollen, which impacts public health. The application of TIR microscopy combined with automated analysis offers a label-free, real-time, and field-deployable solution for addressing challenges in airborne particle monitoring. These results highlight the novel potential of TIR microscopy with deep learning as a method for precise, effective, and scalable pollen monitoring.
How to cite: Dhawan, S., Saxena, A., Kumar, A., Khare, M., and Mehta, D. S.: Label-free Ambient Pollen Identification and Classification Using Total Internal Reflection Microscopy and Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-974, https://doi.org/10.5194/egusphere-egu25-974, 2025.
The Payerne lidaR and Insitu detection of fluorescent biomass burning and dust partiCLES and their cloud impact (PERICLES) campaign (May - June 2023) took place at the rural site of Payerne, Switzerland (46.82o N, 6.94o E, 491 m a.s.l.), at the premises of MeteoSwiss station. PERICLES aimed to understand the spatio-temporal variability of different types of bioaerosols (biomass burning, pollen, dust, etc.) in the Planetary Boundary Layer and aloft (typically up to 2-5 km asl.) and their role in cloud formation. As bioaerosols play a crucial role in cloud formation and on human health, there is strong need to characterize them, both at ground level and aloft. Recently, elastic and fluorescence lidars have become important tools for characterizing bioaerosols’ types. In this study, we used a synergy of in-situ and laser remote sensing instrumentation to discriminate between pollen, dust and biomass burning bioparticles and evaluate their role in cloud formation. Biomass burning particles originated from long-range wildfires in Canada and near-range ones in Germany. High concentrations of pollen were recorded by in situ instruments (Hirst-type volumetric trap, Swisens Poleno and WIBS 5 NEO) at ground level. The EPFL elastic-laser induced fluorescence (LIF) lidar was used to provide vertical profiles of the aerosol elastic (baer) and fluorescence backscatter (bF) coefficients, along with the fluorescence capacity factor (GF), during the study period. Typical values of bF ranged from 1.5 to 8.5 x10-4 Mm-1 sr-1, while GF took values between 1-8 x 10-4. A 32-channel spectrometer detected the bioaerosol fluorescence lidar signals aloft (from ground up to 1-1.5 km height). Application of machine learning algorithms we were able to determine the percentage distribution of various pollen types (e.g. Dactylis glomerata, Quercus robur, Fagus Sylvatica and Betula pendula), which correlate well with ground-level pollen data and number concentrations of ice-nucleating particles (INPs).
How to cite: Gidarakou, M., Papayannis, A., Gao, K., Gidarakos, P., Crouzy, B., Foskinis, R., Erb, S., Zhang, C., Lieberherr, G., Hervo, M., Rösch, M., Collaud Coen, M., Sikoparija, B., Kanji, Z., Clot, B., Calpini, B., and Nenes, A.: Detection of pollen and biomass burning particles using laser- induced aerosol fluorescence and in situ techniques during the PERICLES campaign 2023 in Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20504, https://doi.org/10.5194/egusphere-egu25-20504, 2025.
The Cleancloud Helmos OrograPhic site experimeNt (CHOPIN) campaign took place in autumn 2024 at a unique high-altitude location at Mount Helmos, Greece (38.0oN, 22.2oE) ideal for targeted studies of aerosol-cloud interactions (ACI) due to its strategic location, serving as a crossroad for different aerosol transport paths. This unique position allows the detection of a wide range of particles like wildfire smoke, continental polluted, marine and mineral dust from Sahara, as well as bioparticles (pollen, bacteria, fungal spores) from near- and long-range sources. State-of-the-art laser remote sensing and in situ instrumentation was implemented at Mount Helmos at two level heights: 1700 m (remote sensing instrumentation) and 2314 m a.s.l. (in situ instrumentation) to characterize the incoming air masses in terms of their bioparticle content and study the role of bioparticles in cloud formation. In this study we will focus on data obtained by two lidar systems: the two-wavelength (532 -parallel and cross depolarization and 1064 nm) depolarization aerosol lidar system (AIAS), and the NTUA/EPFL/FORTH 4-wavelength elastic-Raman-laser induced fluorescence (LIF) lidar system (ATLAS-NEF) to provide vertical profiles of aerosol optical properties (extinction and backscatter coefficients and lidar ratio at 355 nm, backscatter Ångström exponents at 355/532nm, 532/1064 nm, particle depolarisation lidar ratio at 532 nm), water vapor mixing ratio, fluorescence capacity and fluorescence backscatter coefficients at 470 nm. We present cases of Saharan dust intrusions with increased values of the aerosol backscatter coefficient and high particle depolarization ratios (δ532 ~0.20-0.25) and increased values of lidar ratios (LR~40-55 sr), high numbers of ice-nucleation particles (INPs) obtained with a PINE instrument and strong signals obtained at the 3 channels of the Wideband Integrated Bioparticles Sensor (WIBS) operating at the (HAC)2 station indicate the presence of bioparticles able to enhance cloud formation.
How to cite: Papayannis, A. D., Gidarakou , M., Foskinis , R., Mitsios , C., Molina , C., Kawana , K., Violaki , K., Zografou , O., Gini , M., Fetfatzis, P., Granakis , K., Zieger, P., Jönsson, A., Kommpula, M., Eleftheriadis, K., Giagka, E., Zagklis, M.-A., and Nenes, A.: Detection of bioparticles by synergy of depolarisation-LIF lidars supported by in situ measurements at the high alpine station of Helmos, Greece, during the CHOPIN campaign 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21463, https://doi.org/10.5194/egusphere-egu25-21463, 2025.
Technological progress and the timely availability of Earth Observation (EO) data have rapidly changed pollen research. Innovative solutions enable the development of instruments for airborne pollen identification, and integrating an increasing amount of remotely acquired EO data improves pollen forecasts. Nowadays, data about pollen in the air are available from different types of devices. Pollen data gathered using Hirst-type volumetric spore traps is especially valuable as they are long-term and can be used to evaluate climate peculiarities. The air samples were collected over 20 years. Meanwhile, data on pollen spread has recently been monitored using more sophisticated devices. The new generation devices collect data about airborne pollen automatically in near real-time. Multiple statistical methods are essential in data homogenisation, especially when integrating heterogeneous data to handle long-term observation challenges. This study aims to demonstrate the feasibility of statistical methods used to integrate records about airborne pollen from automated devices into long-term data collected with Hirst-type volumetric spore traps.
The research is based on 20 years of Betula pollen data (2005-2024) collected with a Hirst-type trap (so-named manual data) and the short-term pollen data from SwisensPoleno Mars records (so-named automatic data) covered by several years. Both devices are operational and located in Vilnius, Lithuania. Overlapping datasets from 2022 to 2024 were used in this research. We chose the data modelling pathway to assess the integration of automated device records with long-term data. Several statistical modelling approaches were tested: simple linear regression, polynomial multiple regression, generalised additive model, Prophet model, random forest model and their combinations.
Multivariate polynomial regression enables the estimation of non-linear relationships and local data heterogeneity. Heterogeneity in local pollen data records can be caused by peculiarities of flowering time and/or local weather patterns, which require models to evaluate differences. Generalized Additive Model (GAM’s) handless non-linear and seasonal patterns of airborne pollen. The Prophet model concept was also applied to estimate long-term trends and seasonality of datasets. Statistical data analysis of long-term Betula pollen data was used to make corrections to the data collected by the automatic devices and to compare the corrected bias with the observational data to assess the performance and applicability of the methods. Considering variations in mean bias error (MBE), mean absolute error (MAE) and root mean square error (RMSE), the tested models were demonstrated to highlight different causal pathways for the inconsistency between the long-term manual data and the short-term automatic data. The knowledge gained is valuable for integrating observational data into current forecasting tools, such as PASYFO, which forecasts allergy symptoms, as well as homogenising heterogeneous airborne pollen data.
This research is supported by the projects EO4EU and SYLVA, funded by the Horizon Europe RIA Programme under Grant Agreements No. 101060784 and No. 101086109.
How to cite: Šukienė, L., Šaulienė, I., Stonevičius, E., Vaitkevičius, L., and Daunys, G.: Betula pollen observation: integration of automated device records into long-term datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14796, https://doi.org/10.5194/egusphere-egu25-14796, 2025.
The measurement of pollen concentrations began in the 1960s, initiated by medical doctors seeking to address allergenic concerns. Historically, pollen monitoring networks relied on the manual identification of daily samples collected on tapes in Hirst-type traps using optical microscopy. This approach persisted until a recent paradigm shift towards automatic, in situ monitoring solutions.
The new generation of automatic measurement systems employs advanced techniques, such as automated microscopy (e.g., BAA 500) or digital holography combined with fluorescence measurements (Swisens Poleno). These are augmented by AI-based identification algorithms, enabling real-time pollen monitoring with temporal resolutions of at least one hour. However, manual and automatic measurement systems exhibit different sampling efficiencies for various pollen species, stemming from disparities in instrumentation characteristics such as flow rates, identification and data processing methods, and temporal resolution. This technological transition has introduced a discontinuity in the historical pollen concentration time series, which are crucial for forecasting models.
In this study, we analyze and compare manual and automatic pollen concentration time series from the Swiss national pollen monitoring network for four major allergenic pollen types: alder, birch, grasses, and oak—species significant across different regions of Europe. Data from 2022 to 2024, collected simultaneously using both methods in rural and urban settings in Switzerland, are evaluated. Machine learning regression algorithms (Random Forest, GRNN) are leveraged to establish a transfer function that relates automatic and manual pollen data. The model incorporates environmental variables likely to influence pollen concentrations, including temperature, wind velocity, elevation, and particulate matter (PM) concentrations.
How to cite: Lbadaoui-Darvas, M., Gehrig, R., Sauliene, I., Sukiene, L., and Oteros, J.: Bridging Automatic and Manual Pollen Monitoring: A Path Towards Homogenized Long-Term Time Series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17983, https://doi.org/10.5194/egusphere-egu25-17983, 2025.
The presence of aerosols of biological origin in the atmosphere is one of the key factors affecting the quality of human life on a daily basis. The spread and diversity of bioaerosols are heavily influenced by anthropogenic activity and significantly modulated by changing climate. A constantly growing number of allergy sufferers in Europe sets new demands to available information about the aeroallergen sources and their evolution. The existing monitoring activities and practices for bioaerosol observations are very fragmented and country specific. Limited access to the highly demanding manual counts slows down cross-disciplinary research and development of prevention measures (effective strategies) to minimize the environmental bioaerosol impact. At the same time, the recent technological progress in the automatic particle counters paved the way to the volunteering consolidation of European aerobiologists to establish and to drive the EUMETNET AutoPollen Programme (www.autopollen.ne). The extremely active collaboration within AutoPollen set a solid background for the dedicated innovative project SYLVA (A SYstem for reaL-time obserVation of Aeroallergens, https://sylva.bioaerosol.eu). Development of the standards and guidelines for the aeroallergen measurements within AutoPollen and technological solutions within SYLVA (newly created and updated and verified existing solutions) are integrated in a complex synergy of the prototype of European network and infrastructure for the real-time monitoring of bioaerosols.
How to cite: Palamarchuk, Y., Sofiev, M., Clot, B., Kadansev, E., and Saarto, A.: The European infrastructure for real-time monitoring of bioaerosols: collaborative solutions of AutoPollen Programme and SYLVA project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13392, https://doi.org/10.5194/egusphere-egu25-13392, 2025.
