posted on 2025-08-01, 11:38authored byFM Rowney, GL Brennan, CA Skjøth, GW Griffith, RN McInnes, Y Clewlow, B Adams-Groom, A Barber, N De Vere, T Economou, M Hegarty, HM Hanlon, L Jones, A Kurganskiy, GM Petch, C Potter, AM Rafiq, A Warner, B Wheeler, NJ Osborne, S Creer
Grass (Poaceae) pollen is the most important outdoor aeroallergen, exacerbating a range of respiratory conditions,
including allergic asthma and rhinitis (‘hay fever’). Understanding the relationships between respiratory diseases and airborne grass pollen with view to improving forecasting has broad public health and socioeconomic relevance. It
is estimated that there are over 400 million people with allergic rhinitis and over 300 million with asthma, globally, often comorbidly
. In the UK, allergic asthma has an annual cost of around US$ 2.8 billion (2017). The relative
contributions of the >11,000 (worldwide) grass species to respiratory health have been unresolved, as grass
pollen cannot be readily discriminated using standard microscopy. Instead, here we used novel environmental DNA
(eDNA) sampling and quantitative PCR (qPCR) , to measure the relative abundances of airborne pollen from
common grass species, during two grass pollen seasons (2016 and 2017), across the UK. We quantitatively
demonstrate discrete spatiotemporal patterns in airborne grass pollen assemblages. Using a series of generalised
additive models (GAMs), we explore the relationship between the incidences of airborne pollen and severe asthma
exacerbations (sub-weekly) and prescribing rates of drugs for respiratory allergies (monthly). Our results indicate that
a subset of grass species may have disproportionate influence on these population-scale respiratory health responses
during peak grass pollen concentrations. The work demonstrates the need for sensitive and detailed biomonitoring of
harmful aeroallergens in order to investigate and mitigate their impacts on human health.
This is the final version. Available on open access from Elsevier via the DOI in this record
Data and Code Availability Statement:
Data collected using qPCR is archived and on NERC EIDC [https://doi.org/10.5285/28208be4-0163-45e6-912c-2db205126925]. Standard pollen monitoring ‘count’ data were sourced from the
MEDMI database, with the exception of data from Bangor which were produced as part of the
present study and are available on request. Prescribing datasets are publicly available, as are
weather, air pollution, deprivation (IMD) and rural-urban category data. Hospital
episode statistics (HES) datasets are sensitive, individual-level health data, which are subject to
strict privacy regulations and are not publicly available. The study did not generate any unique
code