European pollen reanalysis, 1980-2022, for alder, birch, and olive

. 2024 Oct 03 ; 11 (1) : 1082. [epub] 20241003

Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články, dataset

Perzistentní odkaz   https://www.medvik.cz/link/pmid39362896

Grantová podpora
101086109 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
101057131 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
101060784 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
101086109 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
318194 Academy of Finland (Suomen Akatemia)
355851 Academy of Finland (Suomen Akatemia)
329215 Academy of Finland (Suomen Akatemia)
MK252 Ministry of Education and Science, Republic of Latvia

Odkazy

PubMed 39362896
PubMed Central PMC11450224
DOI 10.1038/s41597-024-03686-2
PII: 10.1038/s41597-024-03686-2
Knihovny.cz E-zdroje

The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980-2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.

Aeroallergen Monitoring Centre AMoC Department of Immunology and Allergy Allergy Poland

Allergen Research Center Warsaw Poland

Allergology Research Laboratory Colentina Clinical Hospital București Romania

Andalusian Institute for Earth System Research University of Granada Granada Spain

Biodiversity and Environmental Management University of León León Spain

Biodiversity Unit University of Turku Turku Finland

BioSense Institute Research Institute for Information Technologies in Biosystems University of Novi Sad Novi Sad Serbia

Bursa Uludag University Faculty of Arts and Science Department of Biology Aerobiology Laboratory 16059 Görükle Bursa Türkiye

Center of Allergy and Environment Technical University and Helmholtz Center Munich Munich Germany

Center of Allergy and Immunology Tbilisi Georgia

Clinical Department 5 Carol Davila University of Medicine Bucharest Romania

College of Natural Sciences University of Rzeszow Rzeszow Poland

Czech Hydrometeorological Institute Prague Czech Republic

Departament de Biologia Animal Biologia Vegetal i Ecologia Universitat Autònoma de Barcelona Bellaterra Spain

Department of Biological and Environmental Sciences University of Gothenburg Gothenburg Sweden

Department of Biology Animal Plant Biology and Ecology University of Jaén Jaén Spain

Department of Biology NTNU Trondheim Norway

Department of Botany and Plant Physiology Subdepartment of Aerobiology University of Life Sciences in Lublin Lublin Poland

Department of Botany and Plant Physiology University of Malaga Malaga Spain

Department of Botany University of Granada Granada Spain

Department of Construction School of Technology University of Extremadura Avda de la Universidad s n Cáceres Spain

Department of Ecology School of Biology Faculty of Sciences Aristotle University of Thessaloniki Thessaloniki Greece

Department of Hygiene and Health Prevention Agency for Health Protection of Metropolitan Area of Milan Milan Italy

Department of Otorhinolaryngology Medical University of Vienna Vienna Austria

Department of Pharmacology Pharmacognosy and Botany Faculty of Pharmacy Complutense University of Madrid Madrid Spain

Department of Pharmacy National Pirogov Memorial Medical University Vinnytsia Ukraine

Department of Pulmonology Leiden University Medical Center Leiden the Netherlands

Department of the Prevention of Environmental Hazard Allergology and Immunology Medical University of Warsaw Warsaw Poland

Elkerliek Helmond Helmond Netherlands

Estonian Environmental research Institute Tartu Estonia

Faculty of Biology Moscow State University Moscow Russia

Faculty of Biology Shenzhen MSU BIT University Shenzhen China

Federal Office of Meteorology and Climatology MeteoSwiss Zurich Switzerland

Finnish Meteorological Institute Helsinki Finland

French Aerobiological Monitoring Network Brussieu France

Health air quality and UK pollen forecasting UK Met Office Exeter UK

HUS Helsingin yliopistollinen sairaala Jyväskylä Finland

Icelandic Institute of Natural History Akureyri Iceland

Institut de Ciència i Tecnologia Ambientals Universitat Autònoma de Barcelona Bellaterra Spain

Institute of Atmospheric Sciences and Climate CNR Bologna Italy

Institute of Biology College of Natural Sciences University of Rzeszow Rzeszow Poland

Inter University Institute for Earth System Research University of Cordoba Cordoba Spain

Jagiellonian University Medical College Department of Clinical and Environmental Allergology Kraków Poland

Laboratory of Aerobiology at Teaching Institute of Public Health dr Andrija Štampar Zagreb Croatia

Laboratory of Aerobiology Department of Systematic and Environmental Botany Faculty of Biology Adam Mickiewicz University Poznan Poland

Masaryk University Brno Czech Republic

Medical University of Lodz Lodz Poland

Mycology and Aerobiology Sciensano Brussels Belgium

National Center for Public Health and Pharmacy Budapest Hungary

National Laboratory of Health Environment and Food Maribor Slovenia

Palynological Laboratory Swedish Museum of Natural History Stockholm Sweden

Pollen Laboratory Department of Biological and Environmental Sciences University of Gothenburg Gothenburg Sweden

Regional Public Health Office department of medical microbiology bratislava Slovakia

Retired from Faculty of Pharmacy of the Belarusian State Medical University Minsk Belarus

Sciences Faculty University of Vigo Ourense 32002 Spain

Serbian Environmental Protection Agency Belgrade Serbia

South Karelia Allergy and Environment Institute Imatra Finland

Stiftelsen NILU Stiftelsen Norwegian Institute for Air Research Kjeller Norway

The Asthma and Allergy Association Roskilde Denmark

Unit of Immunology Allergology Centre Hospitalier de Luxembourg Luxembourg

Université de Lille CNRS UMR 8516 LASIRE Laboratoire de Spectroscopie pour les Interactions la Réactivité et l'Environnement F 59000 Lille France

University Hospital Brno Brno Czech Republic

University Institute of research in Olive Groves and Olive Oils University of Jaén Jaén Spain

University of Castilla La Mancha Institute of Environmental Sciences Toledo Spain

University of Évora School of Health and Human Development Department of Medical and Health Sciences and Institute of Earth Sciences ICT Évora Portugal

University of Innsbruck Department of Botany Innsbruck Austria

University of Latvia Riga Latvia

University of Navarra Biodiversity and Environment Institute Pamplona Spain

University of Worcester School of Science and Environment Worcester UK

Vilnius University Siauliai Academy Siauliai Lithuania

Zabludovicz Center for Autoimmune Diseases Sheba Medical Center Ramat Gan Israel

Zobrazit více v PubMed

D’Amato, G. et al. Thunderstorm allergy and asthma: state of the art. Multidis Res Med16 (2021). PubMed PMC

D’Amato, G. et al. Allergenic pollen and pollen allergy in Europe. Allergy 976–990, 10.1111/j.1398-9995.2007.01393.x (2007). PubMed

de Weger, L. et al. Impact of pollen. in Allergenic pollen. A review of the production, release, distribution and health impacts (eds. Sofiev, M. & Bergmann, K.-C. x + 247, 10.1007/978-94007-4881-1 (Springer Netherlands, Dordrecht, 2013).

Allergenic Pollen. A Review of Production, Release, Distribution and Health Impact. (Springer-Verlag Berlin, Heidelberg, 2013).

Beggs, P. J. Thunderstorm Asthma and Climate Change. JAMA331, 878 (2024). PubMed

WHO. Phenology and Human Health: Allergic Disorders. 55 (2003).

Dahl, R., Andersen, P. S., Chivato, T., Valovirta, E. & De Monchy, J. National prevalence of respiratory allergic disorders. Respiratory Medicine98, 398–403 (2004). PubMed

Pawankar, R., Canonica, G. W., Holgate, S. T., Lockey, R. F. & Blasis, M. S. WAO White Book on Allergy: Update 2013. (World Allergy Organization, Milwaukee, Wisconsin, USA, 2013).

Savouré, M. et al. Worldwide prevalence of rhinitis in adults: A review of definitions and temporal evolution. Clinical & Translational All12 (2022). PubMed PMC

Thien, F. et al. The Melbourne epidemic thunderstorm asthma event 2016: an investigation of environmental triggers, effect on health services, and patient risk factors. The Lancet Planetary Health2, e255–e263 (2018). PubMed

Strachan, D. P. Hay fever, hygiene, and household size. BMJ299, 1259–1260 (1989). PubMed PMC

Van Tilburg Bernardes, E. & Arrieta, M.-C. Hygiene Hypothesis in Asthma Development: Is Hygiene to Blame? Archives of Medical Research48, 717–726 (2017). PubMed

Akdis, C. A. Does the epithelial barrier hypothesis explain the increase in allergy, autoimmunity and other chronic conditions? Nat Rev Immunol21, 739–751 (2021). PubMed

Idrose, N. S. et al. A Review of the Respiratory Health Burden Attributable to Short-Term Exposure to Pollen. IJERPH19, 7541 (2022). PubMed PMC

Jaakkola, J. J. K. et al. Airborne pollen concentrations and daily mortality from respiratory and cardiovascular causes. European Journal of Public Health31, 722–724 (2021). PubMed PMC

Littlefair, J. E. et al. Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales. Current Biology33, R426–R428 (2023). PubMed

Smith, M. et al. Geographic and temporal variations in pollen exposure across Europe. Allergy69, 913–23 (2014). PubMed

Ziska, L. H. et al. Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: a retrospective data analysis. The Lancet Planetary Health3, e124–e131 (2019). PubMed

Valipour Shokouhi, B., De Hoogh, K., Gehrig, R. & Eeftens, M. Estimation of historical daily airborne pollen concentrations across Switzerland using a spatio temporal random forest model. Science of The Total Environment906, 167286 (2024). PubMed

Sofiev, M. On impact of transport conditions on variability of the seasonal pollen index. Aerobiologia33, 167–179 (2016). PubMed PMC

Bocquet, M. et al. Data assimilation in atmospheric chemistry models: Current status and future prospects for coupled chemistry meteorology models. Atmospheric Chemistry and Physics15 (2015).

Elbern, H., Strunk, A., Schmidt, H. & Talagrand, O. Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmospheric Chemistry and Physics7, 3749–3769 (2007).

Elbern, H., Schmidt, H., Talagrand, O. & Ebel, A. 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environmental Modelling & Software15, 539–548 (2000).

Gaubert, B. et al. Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model. Geoscience Model Development7, 283–302 (2014).

Schwinger, J. & Elbern, H. Chemical state estimation for the middle atmosphere by four-dimensional variational data assimilation: A posteriori validation of error statistics in observation space. Journal of Geophysical Research115 (2010).

Vira, J., Carboni, E., Grainger, R. G. & Sofiev, M. Variational assimilation of IASI SO2 plume height and total column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model. Geoscientific Model Development10 (2017).

Vira, J. & Sofiev, M. On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations. Atmospheric Environment46, 318–328 (2012).

Sofiev, M. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models. Aerobiologia1 (2019).

Gurney, K. R. et al. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature415, 626–630 (2002). PubMed

Nathan, B. J. et al. Source Sector Attribution of CO 2 Emissions Using an Urban CO/CO 2 Bayesian Inversion System. JGR Atmospheres123 (2018).

Super, I., Dellaert, S. N. C., Visschedijk, A. J. H. & van der Gon, H. A. C. D. Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design. Atmos. Chem. Phys.20, 1795–1816 (2020).

Tans, P. P., Fung, I. Y. & Takahashi, T. Observational Contrains on the Global Atmospheric Co 2 Budget. Science247, 1431–1438 (1990). PubMed

Chandra, N. et al. Estimated regional CO 2 flux and uncertainty based on an ensemble of atmospheric CO 2 inversions. Atmos. Chem. Phys.22, 9215–9243 (2022).

Lian, J. et al. Can we use atmospheric CO 2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris. Atmos. Chem. Phys.23, 8823–8835 (2023).

Nalini, K. et al. High‐Resolution Lagrangian Inverse Modeling of CO 2 Emissions Over the Paris Region During the First 2020 Lockdown Period. JGR Atmospheres127, e2021JD036032 (2022).

Pauling, A., Clot, B., Menzel, A. & Jung, S. Pollen forecasts in complex topography: two case studies from the Alps using the numerical pollen forecast model COSMO-ART. Aerobiologia36, 25–30 (2020).

Siljamo, P. et al. A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis. International journal of biometeorologye-pub (2012). PubMed PMC

Sofiev, M. et al. Multi-model ensemble simulations of olive pollen distribution in Europe in 2014. Atmospheric Chemistry and Physics Discussions 1–32, 10.5194/acp-2016-1189 (2017).

Sofiev, M. et al. A numerical model of birch pollen emission and dispersion in the atmosphere. Description of the emission module. International journal of biometeorology57, 54–58 (2012). PubMed PMC

Zink, K. et al. EMPOL 1. 0: a new parameterization of pollen emission in numerical weather prediction models. Geoscience Model Development6, 1961–1975 (2013).

Linkosalo, T. et al. A double-threshold temperature sum model for predicting the flowering duration and relative intensity of Betula pendula and B. pubescens. Agricultural and Forest Meteorology150 (2010).

Sofiev, M., Siljamo, P., Ranta, H. & Rantio-Lehtim?ki, A. Towards numerical forecasting of long-range air transport of birch pollen: Theoretical considerations and a feasibility study. International Journal of Biometeorology50 (2006). PubMed

Galán, C. et al. Recommended terminology for aerobiological studies. Aerobiologia33, 293–295 (2017).

Ritenberga, O. et al. A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe. Science of total environment615, in press (2017). PubMed

Rojo, J. et al. Effects of future climate change on birch abundance and their pollen load. Global Change Biology27, 5934–5949 (2021). PubMed

Adamov, S. & Pauling, A. A real-time calibration method for the numerical pollen forecast model COSMO-ART. Aerobiologia39, 327–344 (2023).

Sofiev, M. et al. Construction of an Eulerian atmospheric dispersion model based on the advection algorithm of M. Galperin: dynamic cores v.4 and 5 of SILAM v.5.5. Geoscientific Model Development8, 3497–3522 (2015).

Hersbach, H. et al. The ERA5 global reanalysis. Q.J.R. Meteorol. Soc.146, 1999–2049 (2020).

Kouznetsov, R., Sofiev, M., Vira, J. & Stiller, G. Simulating age of air and the distribution of SF6 in the stratosphere with the SILAM model. Atmos. Chem. Phys.20, 5837–5859 (2020).

Sofiev, M., Genikhovich, E., Keronen, P. & Vesala, T. Diagnosing the Surface Layer Parameters for Dispersion Models within the Meteorological-to-Dispersion Modeling Interface. Journal of Applied Meteorology and Climatology49, 221–233 (2010).

de Rigo, D., Caudullo, G., Houston Durrant, T. & San-Miguel-Ayanz, J. The European Atlas of Forest Tree Species: Modelling, Data and Information on Forest Tree Species. e01aa69+, https://w3id.org/mtv/FISE-Comm/v01/e01aa69 (2016).

Champeaux, J. L., Masson, V. & Chauvin, F. ECOCLIMAP: a global database of land surface parameters at I km resolution. Meteorological Applications 29–32 (2005).

Prank, M. et al. An operational model for forecasting ragweed pollen release and dispersion in Europe. Agricultural and Forest Meteorology182–183, 43–53 (2013).

Hirst, J. M. An automatic volumetric spore trap. Annals of Applied Biology39, 257–265 (1952).

Jäger, S. et al. News. Aerobiologia11, 69–70 (1995).

Galán, C. et al. Pollen monitoring: minimum requirements and reproducibility of analysis. Aerobiologia30, 385–395 (2014).

CEN. Ambient Air - Sampling and Analysis of Airborne Pollen Grains and Fungal Spores for Networks Related to Allergy—Volumetric Hirst Method, 2019. (2019).

Adamov, S. et al. On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers. Aerobiologia10.1007/s10453-021-09724-5 (2021).

Oteros, J. et al. Errors in determining the flow rate of Hirst-type pollen traps. Aerobiologia33, 201–210 (2017).

Oteros, J. et al. Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations. Science of The Total Environment688, 1263–1274 (2019). PubMed

Oteros, J. et al. Automatic and online pollen monitoring. International Archives of Allergy and Immunology167, 158–166 (2015). PubMed

Oteros, J. et al. An operational robotic pollen monitoring network based on automatic image recognition. Environmental Research191, 110031 (2020). PubMed

Maya-Manzano, J. M. et al. Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps. Science of The Total Environment866, 161220 (2023). PubMed

Sofiev, M., Siljamo, P., Valkama, I., Ilvonen, M. & Kukkonen, J. A dispersion modelling system SILAM and its evaluation against ETEX data. Atmospheric Environment40, 674–685 (2006).

Meinander, O., Kontu, A., Kouznetsov, R. & Sofiev, M. Snow Samples Combined With Long-Range Transport Modeling to Reveal the Origin and Temporal Variability of Black Carbon in Seasonal Snow in Sodankylä (67°N). Frontiers in Earth Science8, 1–11 (2020).

Sofiev, M. et al. Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign. Environmental Research214, 113798 (2022). PubMed

Sofiev, M. et al. MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe. Atmospheric Chemistry and Physics15 (2015).

Kouznetsov, R. & Sofiev, M. A methodology for evaluation of vertical dispersion and dry deposition of atmospheric aerosols. Journal of Geophysical Research117 (2012).

Sofiev, M. Extended resistance analogy for construction of the vertical diffusion scheme for dispersion models. Journal of Geophysical Research-Atmospheres107, ACH 10-1-ACH 10-8 (2002).

Brasseur, G. P. et al. Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1. Geosci. Model Dev.12, 33–67 (2019).

Huijnen, V. et al. Comparison of OMI NO2 tropospheric columns with an ensemble of global and European regional air quality models. Atmospheric Chemistry and Physics10, 3273–3296 (2010).

Petersen, A. K. et al. Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1. Geosci. Model Dev.12, 1241–1266 (2019).

Sofiev, M., Kouznetsov, R., Hänninen, R. & Sofieva, V. F. Technical note: Intermittent reduction of the stratospheric ozone over northern Europe caused by a storm in the Atlantic Ocean. Atmos. Chem. Phys.20, 1839–1847 (2020).

Xian, P. et al. Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP). Q.J.R. Meteorol. Soc.145, 176–209 (2019). PubMed PMC

Siljamo, P., Ashbrook, K., Comont, R. F. & Skjøth, C. A. Do atmospheric events explain the arrival of an invasive ladybird (Harmonia axyridis) in the UK? PLoS ONE15, e0219335 (2020). PubMed PMC

Prank, M., Sofiev, M., Siljamo, P., Kauhaniemi, M. & European Aeroallergen Network. Increasing the Number of Allergenic Pollen Species in SILAM Forecasts. in Air Pollution Modeling and its Application XXIV, edited by D. G. Steyn and N. Chaumerliac 313–317 (Springer, 2016).

Tummon, F. et al. Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines. Aerobiologia10.1007/s10453-022-09755-6 (2022).

Hansen, P. C. Discrete Inverse Problems: Insight and Algorithms. 10.1137/1.9780898718836 (Society for Industrial and Applied Mathematics, 2010).

Hansen, P. C. Analysis of Discrete Ill-Posed Problems by Means of the L-Curve. SIAM Rev.34, 561–580 (1992).

Sofiev, M. et al. European pollen reanalysis, 1980-2022, for alder, birch, and olive, v.1.1. Finnish Meteorological Institute 10.57707/FMI-B2SHARE.85841086F9DB46B882D750EAA9E42515 (2024).

Morgado, R. et al. Drivers of irrigated olive grove expansion in Mediterranean landscapes and associated biodiversity impacts. Landscape and Urban Planning225, 104429 (2022).

Veriankaitė, L., Siljamo, P., Sofiev, M., Sauliene, I. & Kukkonen, J. Modelling analysis of source regions of long-range transported birch pollen that influences allergenic seasons in Lithuania. Aerobiologia26, 47–62 (2010).

Verstraeten, W. W. et al. Reconstructing multi-decadal airborne birch pollen levels based on NDVI data and a pollen transport model. Agricultural and Forest Meteorology320, 108942 (2022).

Verstraeten, W. W., Kouznetsov, R., Bruffaerts, N., Sofiev, M. & Delcloo, A. W. Assessing uncertainty in airborne birch pollen modelling. 10.1007/s10453-024-09818-w (2024).

Verstraeten, W. W. et al. Attributing long-term changes in airborne birch and grass pollen concentrations to climate change and vegetation dynamics. Atmospheric Environment298, 119643 (2023).

Sofiev, M., Palamarchuk, J., Kouznetsov, R., Gauss, M. & CAMS modelling teams. Annual Report on the Evaluation of the CAMS Regional Pollen Production (Daily Forecasts). January 2022-October 2022. 67, https://atmosphere.copernicus.eu/sites/default/files/custom-uploads/EQC-regional/Pollen/CAMS_pollen_eval_2022_v3.pdf (2024).

Sofiev, M. et al. Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields. Science of The Total Environment 165800, 10.1016/j.scitotenv.2023.165800 (2023). PubMed

Kouznetsov, R. Silam_v5_9 used for pollen reanalysis 2023. Zenodo10.5281/ZENODO.10351493 (2023).

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...