University of Hertfordshire 2022 PhD Studentships in Improvement of Bioaerosol Parametrization in Atmospheric Models in UK

Previous research on bioaerosol dispersal has employed analytical dispersion models or focused on short-range dispersion (order of 100 m) using single-point weather observations. Further analytical models or Lagrangian stochastic (LS) don’t consider the full variability of the atmosphere or bioaerosol processes as these models assume horizontal homogeneity (Sadys et al 2014) of the flow field, which are questionable even at short ranges in complex terrain (e.g., hilly terrain). Unlike analytical models, state-of-the-art 3-D atmospheric models (AM) provide a means to `fill in the gaps’ that analytical/LS models leave in our understanding of the physical mechanisms and transportation of bioaerosols. Atmospheric models (AM) used in the project will accurately represent all of the important fine-scale (<100 m) and large-scale flows. These fine-scale flows are key for predicting bioaerosol dispersal since they lead to, for example, deep convection systems (such as rainstorms), which remove (`wash out’) bioaerosol (spores/pollen) from the atmosphere. In this project, we will incorporate data from molecular profiling of bioaerosol (pollen) samples measured in-vitro from Raman/autofluorescence/FTIR by general ART (Attenuation/ Reflection/Transmission) or microscopic techniques. The data employed in the project are collected from lab equipment and/or from the field deployed bioaerosol monitoring equipment to be used for the improvement of the bioaerosol parametrization in AM. Furthermore, these data set will be used to evaluate the AM performance in real-time. Finally, the improved model will be used to simulate (future projections) the spatial and temporal distributions of bioaerosols. The results will be evaluated in the context of emission scenarios published by the Intergovernmental Panel on Climate Change (IPCC) AR6.