ESR7: Hay fever and software-automated pollen metabarcoding

In short

Are you sneezing your way through spring? You're not the only one! Recent studies show that 1 in 5 people are suffering from hay fever symptoms globally. More and more people are relying on accurate hay fever forecasts to plan their outdoor activities. These are currently created manually using a microscope which is time-consuming and relies on highly trained personnel. In my project I will improve these forecasts not only by automating the microscope but also by using innovative DNA methods that will enable an even higher species identification tailored to specific client needs.

Project description

Allergic rhinitis, hay fever, is typically triggered by environmental allergens such as pet hair, dust, mold or importantly, pollen. Airborne allergen forecasts can help patients to plan their outdoor activities, and are based on data generated by "pollen sniffers" placed on the top of hospital buildings throughout Europe. Sticky traps in these instruments sample airborne pollen and spores of plants and fungi causing hay fever symptoms. These samples are surveyed microscopically, which is time consuming. In this project, automated high-throughput sequencing will be developed to supplement traditional surveys. Pollen and spore DNA barcodes and images will be compiled into a public reference collection, and metabarcoding data will subsequently be generated from pollen sniffers and compared with microscopic data for qualitative and quantitative analyses.

Objective: Software-automated species-level identification of botanical airborne allergens for web-based hay fever predictions.

Research output


Marcel Polling, Anneke T.M. ter Schure, Bas van Geel, Tom van Bokhoven, Sanne Boessenkool, Glen MacKay, Bram W. Langeveld, María Ariza, Hans van der Plicht, Albert V. Protopopov, Alexei Tikhonov, Hugo de Boer, Barbara Gravendeel. Multiproxy analysis of permafrost preserved faeces provides an unprecedented insight into the diets and habitats of extinct and extant megafauna. Quaternary Science Reviews, Volume 267, 2021, 107084, ISSN 0277-3791 


Polling, M., Li, C., Cao, L., Verbeek, F., de Weger, L., Belmonte, J., De Linares, C., Willemse, J., de Boer, H. and Gravendeel, B., 2021. Neural networks for increased accuracy of allergenic pollen monitoring. Scientific Reports 11:11357.




Polling M. Image Recognition for Improved Pollen Identifications. Part of AI4BIO hosted at Naturalis Biodiversity Center, Leiden, The Netherlands. Virtual presentation. 02 Nov. 2020.

Polling M, de Boer HJ, Donders T, Verbeek FJ, Gravendeel B, 2019. Automatic pollen species image identification. At: Biodiversity_Next. Leiden, The Netherlands. 22-25 October 2019.


Polling M, Zazula G, Heffner T, MacKay G, Tikhonov A, Mol D, Langeveld B, Geml J, van Geel B, Budding D, Eurlings M, Duijm E, Hoogeveen M, de boer H, Gravendeel B, 2019. Multilocus DNA metabarcoding of Pleistocene mammoth dung provides an integrated insight of changing Arctic vegetation. At: 8th International Barcode of Life Conference. Trondheim, Norway. 17-20 June 2019.