Periodic Reporting for period 1 - IPLATFORM (Implementation of a dynamical Pollen Module and the transformations of the pollen including feedbacks in a numerical Weather Forecast Model)
Reporting period: 2016-06-01 to 2018-05-31
Oak pollen seasons are relatvely unexplored in large parts of Europe despite producing allergens and being a common tree in both continental and northern Europe. Analysing pollen data on species level and using DNA sequencing information is a large step forward in the scientific field of aerobiology. Currently pollen data can only be provided on genus level and is relying on labour intensive detection methods using microscopy. There is a need for methods which can provide pollen information faster and cheaper to complement current standard detection methods. High spatial and temporal resolution of pollen concentration can be extremely important to a large group of people who suffer from airway disease and pollen allergy. Furthermore, simulating pollen in advanced transport models can provide more sophisticated forecasts in the future where atmospheric feedback mechanisms can be taken into account which can show days where pollen load might be intensified due to the state of the atmosphere. Pollen load simulations done on species level adds further information to the allergy sufferer who may be allergic to a specific species. The overall objective of the project was to develop an advanced tool for modelling the feedback, dispersion and concentration of air pollutants and bio-aerosols by focusing on oak tree pollen. The focus is model development with WRF-Chem for pollen dispersal and sequencing at the species level.
An initial observation study of oak pollen at the genus level was done to characterise the oak pollen seasons across Europe (Grundström et al., 2019, doi: 10.1016/j.scitotenv.2019.01.212) revealing a complex mixture of oak species in different parts of Europe. Furthermore, oak pollen load was considerably high in southern Europe and the UK, as expected but high levles were also found in Scandinavia where the oak season is generally short and only two native species dominate the vegetation. Modelling work has been carried out to develop mechanisms for handling new external data (such as oak tree distribution maps) within the WRF pre-processing step enabling a more efficient and integral way of handling calculations of additional data internally. Extensions to an existing pollen emission model has been carried out to include mechanisms for oak pollen. New versions of GOCART parameterisations has been developed, taking the physical properties of pollen grains into account. Simulations with radiative feedback has been run on genus level of different pollen types with promising results and papers are currently being prepared for publication. Genomic work has been carried out to explore PCR based detection methods of oak pollen species collected from the air. Results from metabarcoding using next generation sequencing are promising and are expected to be published during 2019 and further used as input for model simulations of oak at the species level in 2019. It is expected that the results as well as the underlying data from the genomic work will be open access when first published.
The genomic results and the model simulations on species level is expected to go beyond the state-of-the-art and opens up the potential for more sophisticated pollen forecasts in the future where species level modelling can help medical professionals and allergy sufferers identify time periods where specific pollen species might be dominating and thus potentially be the cause for allergic symptoms. Species specific pollen allergy is known for grass pollen but could also be the case for oak pollen in regions where several species exist and contribute to the overall pollen season load. Furthermore, the WRF model is an open source model and the emission module generated in this project will also be open source thus making access to the simulated allergy status of the atmosphere available for the greater public and the scientific community. This opens up the science to more modellers and developers who can add further knowledge. Furthermore, a molecular based detection method will help advance and modernise the aerobiological field which currently relies on labour intensive and time consuming optical detection only possible at the genus level.