Abstract
In Europe, more than one quarter of the adult population and one third of the children suffer from pollinosis, but the geographical variability is large. In Belgium, at least ~ 10% of the people develop allergies due to birch pollen. These patients may benefit from a forecasting system that raises alerts when episodes with huge amount of airborne birch pollen grains are expected. Such a forecast system for birch pollen was established for the Belgian territory in 2023 based on the pollen emission and transport model System for Integrated modeLling of Atmospheric coMposition (SILAM). The question, however, is which uncertainty in modelling and forecasting airborne pollen levels can be expected? Here, we assess the uncertainty in modelling airborne birch pollen levels near the surface using SILAM in a Monte Carlo error approach summarized by the relative Coefficient of Variation (CV%) as descriptive statistic for the season of 2018 in Belgium. For the major inputs that drive the birch pollen model—the amount and location of birch trees (0.1° × 0.1° map), the start and end of the birch pollen season (1° × 1° map), and the ripening temperature of birch catkins—sets of 100 randomly sampled data layers were prepared for running SILAM 100 times. For each set of model input, 100 spatio-temporal maps of airborne birch pollen levels were produced and its spread was quantified by the CV%. We show that the spatial uncertainty of pollen emissions sources in SILAM is substantially high, but that the uncertainties of the parameters determining the start and end of the season are at least equally important. By accumulating the effects of all investigated model input uncertainties including the impact of the catkins-ripening temperature, CV% values of 50% and more are obtained when quantifying the variation of the modelled airborne birch pollen levels. These errors are in line with reported values from the current reference method for monitoring airborne birch pollen grains near the surface.
Similar content being viewed by others
Data Availability
All data are available on simple request.
References
Adamov, S., & Pauling, A. (2023). A real-time calibration method for the numerical pollen forecast model COSMO-ART. Aerobiologia, 39, 327–344. https://doi.org/10.1007/s10453-023-09796-5
Adamov, S., Lemonis, N., Clot, B., Crouzy, B., Gehrig, R., Graber, M.-J-, Sallin, C., & Tummon, F. (2021). On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers. Aerobiologia. https://doi.org/10.1007/s10453-021-09724-5
Addison-Smith, B., Wraith, D., & Davies, J. M. (2020). Standardising pollen monitoring: Quantifying confidence intervals for measurements of airborne pollen concentration. Aerobiologia, 36, 605–615. https://doi.org/10.1007/s10453-020-09656-6
Beggs, P. J. (2004). Impacts of climate change on aeroallergens: Past and future. Clinical & Experimental Allergy, 34, 1507–1513.
Beven, K. (2006). A manifesto for the equifinality thesis. Journal of Hydrology, 320, 18–36.
Beven, K. J., & Freer, J. (2001). Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249(11–29), 2001.
Beven, K.J. (2001). Rainfall-runoff modelling: the Primer. Wiley, p. 360.
Bieber, T., et al. (2016). Global Allergy Forum and 3rd Davos Declaration 2015: Atopic dermatitis/Eczema: Challenges and opportunities toward precision medicine. Allergy, 71, 588–592.
Blomme, K., Tomassen, P., Lapeere, H., et al. (2013). Prevalence of allergic sensitization versus allergic rhinitis symptoms in an unselected population. International Archives of Allergy and Immunology, 160(2), 200–207.
Bousquet, J., Anto, J. M., Bachert, C., et al. (2020). Allergic rhinitis. Nature Reviews Disease Primers, 6, 95. https://doi.org/10.1038/s41572-020-00227-0
Buters, J., et al. (2022). Automatic detection of airborne pollen: An overview. Aerobiologia. https://doi.org/10.1007/s10453-022-09750-x
Campling, P., Gobin, A., Beven, K. J., & Feyen, J. (2002). Rainfall-runoff modelling of a humid tropical catchment: The TOPMODEL approach. Hydrological Processes., 16(2), 231–253.
Comtois, P., Alcazar, P., & Néron, D. (1999). Pollen counts statistics and its relevance to precision. Aerobiologia, 15, 19–28.
D’Amato, G. & D’Amato, M. (2023). Climate change, air pollution, pollen allergy and extreme atmospheric events. Current Opinion in Pediatrics 35(3), 356–366. https://doi.org/10.1097/MOP.0000000000001237
Frenz, D. A. (2000). The efect of windspeed on pollen and spore counts collected with Rotorod Sampler and Burkard spore trap. Annals of Allergy, Asthma and Immunology, 85, 392–394.
Galán, C., Smith, M., Thibaudon, M., et al. (2014). Pollen monitoring: Minimum requirements and reproducibility of analysis. Aerobiologia, 30, 385. https://doi.org/10.1007/s10453-014-9335-5
Gottardini, E., Cristofolini, F., Cristofori, A., Vannini, A., & Ferretti, M. (2009). Sampling bias and sampling errors in pollen counting in aerobiological monitoring in Italy. Journal of Environmental Monitoring, 11, 751–755. https://doi.org/10.1039/b818162b
Grant, T., & Wood, R. (2022). The influence of urban exposures and residence on childhood asthma. Pediatric Allergy and Immunology., 2022(33), e13784S.
Harvard, http://ipl.physics.harvard.edu/wp-uploads/2013/03/PS3_Error_Propagation_sp13.pdf. Acessed on 17 October 2023.
Hirst, J. M. (1952). An automatic volumetric spore trap. Annals of Applied Biology, 39, 257–265.
Kouznetsov, R., Sofiev, M. (2012). A methodology for evaluation of vertical dispersion and dry deposition of atmospheric aerosols. Journal of Geophysical Research, 117. https://doi.org/10.1029/2011JD016366.
Kroese, D. P., Taimre, T., & Botev, Z. I. (2011). Handbook of Monte Carlo Methods. John Wiley & Sons.
Landrigan, P. J., et al. (2018). The Lancet Commission on pollution and health. Lancet, 2018(391), 462–512. https://doi.org/10.1016/S0140-6736(17)32345-0
Linkosalo, T., Ranta, H., Oksanen, A., Siljamo, P., Luomajoki, A., Kukkonen, J., & Sofiev, M. (2010). A double-threshold temperature sum model for predicting the flowering duration and relative intensity of Betula pendula and B. pubescens. Agricultural and Forest Meteorology, 150, 6–11. https://doi.org/10.1016/j.agrformet.2010.08.007
Lumnitz, S., Devisscher, T., Mayaud, J. R., Radic, V., Coops, N. C., & Griess, V. C. (2021). Mapping trees along urban street networks with deep learning and street-level imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 144–157.
Ma, Q., Lin, J., Ju, Y., Li, W., Liang, L. & Guo, Q. (2023). Individual structure mapping over six million trees for New York City USA. Scientific Data, 10:102. https://doi.org/10.1038/s41597-023-02000-w.
Maya-Manzano, J. M., FernÁndez-RodrÍguez, S., Silva-Palacios, I., Gonzalo-Garijo, Á., & Tormo-Molina, R. (2016). Comparison between two adhesives (silicone and petroleum jelly) in Hirst pollen traps in a controlled environment. Grana, 57, 137–143.
McCabe, M. F., Kalma, J. D., & Franks, S. W. (2005). Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework. HESS, 9, 467–480.
Neumann, J. E., et al. (2019). Estimates of present and future asthma emergency department visits associated with exposure to oak, birch, and grass pollen in the United States. GeoHealth, 3, 11–27.
Oteros, J., Buters, J., Laven, G., et al. (2017). Errors in determining the flow rate of Hirst-type pollen traps. Aerobiologia, 33, 201–210. https://doi.org/10.1007/s10453-016-9467-x
Pfaar, O., Bastl, K., Berger, U., Buters, J., Calderon, M. A., Clot, B., et al. (2017). Defining pollen exposure times for clinical trials of allergen immunotherapy for pollen induced rhinoconjunctivitis - an EAACI Position Paper. Allergy, 72, 713–722. https://doi.org/10.1111/all.13092
Reitsma, S., Subramaniam, S., Fokkens, W. W., & Wang, D. Y. (2018). Recent developments and highlights in rhinitis and allergen immunotherapy. Allergy, 73, 2306–2313.
RMI, https://www.meteo.be/en/weather/forecasts/pollen-allergy-and-hay-fever. Accessed on 23 March 2023.
Rojo, J., Oteros, J., Pérez-Badia, R., et al. (2019). Near-ground effect of height on pollen exposure. Environmental Research, 174(2019), 160–169. https://doi.org/10.1016/j.envres.2019.04.027
Roubelat, S., Besancenot, J.-P., Bley, D., Thibaudon, M., & Charpin, D. (2020). Inventory of the Recommendations for Patients with Pollen Allergies and Evaluation of Their Scientific Relevance. International Archives of Allergy and Immunology, 181 (11), 839–852. https://doi.org/10.1159/000510313.
Schmidt, C. W. (2016). Pollen overload: Seasonal allergies in a changing climate. Environmental Health Perspectives., 124, A71–A75.
Siljamo, P., Sofiev, M., Filatova, E., Grewling, L., Jäger, S., Khoreva, E., Linkosalo, T., Ortega Jimenez, S., Ranta, H., Rantio-Lehtimäki, A., Svetlov, A., Veriankaite, L., Yakovleva, E., & Kukkonen, J. (2012). A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis. International Journal of Biometeorology e-pub. https://doi.org/10.1007/s00484-012-0539-5.
Smith, M., Oteros, J., Schmidt-Weber, C., & Buters, J. (2018). An abbreviated method for the Quality Control of pollen counters. Grana, 58, 185–190.
Sofiev, M. (2016). On impact of transport conditions on variability of the seasonal pollen index. Aerobiologia, 33, 167–179. https://doi.org/10.1007/s10453-016-9459-x
Sofiev, M., Siljamo, P., Ranta, H., & Rantio-Lehtimäki, A. (2006). Towards numerical forecasting of long-range air transport of birch pollen: Theoretical considerations and a feasibility study. International Journal of Biometeorology, 50, 392–402. https://doi.org/10.1007/s00484-006-0027-x
Sofiev, M., Genikhovich, E., Keronen, P., & Vesala, T. (2010). Diagnosing the surface layer parameters for dispersion models within the meteorological-to-dispersion modeling interface. Journal of Applied Meteorology and Climatology, 49, 221–233. https://doi.org/10.1175/2009JAMC2210.1
Sofiev, M., Siljamo, P., Ranta, H., Linkosalo, T., Jaeger, S., Rasmussen, A., Rantio-Lehtimaki, A., Severova, E., & Kukkonen, J. (2012). A numerical model of birch pollen emission and dispersion in the atmosphere. Description of the emission module. International Journal of Biometeorology, 57, 45–58. https://doi.org/10.1007/s00484-012-0532-z
Sofiev, M., Vira, J., Kouznetsov, R., Prank, M., Soares, J., & Genikhovich, E. (2015). Construction of the SILAM Eulerian atmospheric dispersion model based on the advection algorithm of Michael Galperin. Geoscientific Model Development, 8, 3497–3522. https://doi.org/10.5194/gmd-8-3497-2015
Sofiev, M., et al. (2023). Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields. Science of the Total Environment, 900, 165800. https://doi.org/10.1016/j.scitotenv.2023.165800
Sofiev, M. (2002). Extended resistance analogy for construction of the vertical diffusion scheme for dispersion models. Journal of Geophysical Research-Atmospheres, 107, ACH 10–1-ACH 10–8. https://doi.org/10.1029/2001JD001233.
Verstraeten, W. W., Veroustraete, F., Heyns, W., Van Roey, T., & Feyen, J. (2008). On uncertainties in carbon flux modelling and remotely sensed data assimilation: The Brasschaat pixel case. Advances in Space Research, 41, 20–35.
Verstraeten, W. W., Dujardin, S., Hoebeke, L., Bruffaerts, N., Kouznetsov, R., Dendoncker, N., Hamdi, R., Linard, C., Hendrickx, M., Sofiev, M., & Delcloo, A. W. (2019). Spatio-temporal monitoring and modelling of birch pollen levels in Belgium. Aerobiologia, 35(4), 703–717. https://doi.org/10.1007/s10453-019-09607-w
Verstraeten, W. W., Kouznetsov, R., Hoebeke, L., Bruffaerts, N., Sofiev, M., & Delcloo, A. W. (2022). Reconstructing multi-decadal airborne birch pollen levels based on NDVI data and a pollen transport model. Agricultural and Forest Meteorology, 320(2), 108942. https://doi.org/10.1016/j.agrformet.2022.108942
Verstraeten, W. W., Bruffaerts, N., Kouznetsov, R., de Weger, L., Sofiev, M., & Delcloo, A. W. (2023). Attributing long-term changes in airborne birch and grass pollen concentrations to climate change and vegetation dynamics. Atmospheric Environment, 298, 119643.
Verstraeten, W.W., Boersma, K.F., Douros, J., Williams, J.E., Eskes, H., Liu, F., Beirle, S. & Delcloo, A. (2018). Top-down NOX emissions of European cities based on the downwind plume of modelled and space-borne tropospheric NO2 columns. Sensors, 18, 2893. https://doi.org/10.3390/s18092893.
West, J. S., & Kimber, R. B. E. (2015). Innovations in air sampling to detect plant pathogens. Annals of Applied Biology, 166, 4–17.
WHO. (2003). Phenology and human health: Allergic disorders. WHO.
Funding
This research was partly funded by the Belgian Science Policy Office (BELSPO) in the frame of the Belgian Research Action through Interdisciplinary Networks Brain (BRAIN.be) program—project RETROPOLLEN (B2/191/P2/RETROPOLLEN) and partly funded by the Royal Meteorological Institute of Belgium. The SILAM general development has been funded by the Academy of Finland project PS4A (Grant 318194).
Author information
Authors and Affiliations
Contributions
Author Contributions: Conceptualization was contributed by WWV; methodology was contributed by WWV; model software was contributed by WWV, AWD, RK and MS; formal analysis was contributed by WWV; investigation was contributed by WWV; resources was contributed by WWV, NB, RK, MS and AWD; data curation was contributed by WWV, NB, AWD; writing—original draft preparation, was contributed by WWV; writing—review and editing, was contributed by RK, NB, MS, AWD; visualization was contributed by WWV; project administration was contributed by WWV, NB and AWD; funding acquisition was contributed by WWV, NB and AWD. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interests
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Verstraeten, W.W., Kouznetsov, R., Bruffaerts, N. et al. Assessing uncertainty in airborne birch pollen modelling. Aerobiologia (2024). https://doi.org/10.1007/s10453-024-09818-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10453-024-09818-w