PhD studentship: Improvement of bioaerosol parametrization in atmospheric models using molecular properties of bioaerosols measured by optical spectroscopy methods


Qualification type: Bachelor’s or Master’s degree in Physics, meteorology, atmospheric/climate science, data science, remote sensing, mathematics, computer science or engineering

Location: College Lane Campus, Hatfield

Funding for: Open to both UK and International applicants, including EU applicants

Funding Amount: £15,609

Duration: Full Time, three years

Application closing date: 31 May 2022

Programme Outline

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.


  • Principal supervisor: Dr. Raj Tiwari
  • Supervisory Team: Dr. Adrian Ghita & Dr. Boyan Tatarov


Applications are invited from individuals with a BSc (2.1 or above) and/or Master (e.g. if your BSc degree classification is 2.2) in a relevant subject e.g. physics, meteorology, atmospheric/climate science, data science, remote sensing, mathematics, computer science or engineering;

We are seeking applicants with good understanding with:

  • experience in data analysis and numerical modelling (expected but not mandatory)
  • programming skills with at least one of the following programming languages: Python, FORTRAN, C/C++, MATLAB, IDL
  • this project is a combination of both experimental and computational work. We welcome applications from candidates who have experience in or are willing to support lab/field measurements and modelling

This studentship is open to both UK and International applicants, including EU applicants.

Funding details

Annual tax-free bursary of approximately £15,609, plus tuition fees (£5,590 for UK or £ 14,905 for International and EU applicants).

Application process

To apply for the studentship please download the application form. Completed applications should be emailed to the Doctoral College Admissions Team.

With your application please also submit a research proposal not exceeding two A4 pages, a CV and cover letter. You will also need to provide:

  • two academic/researcher references to be provided by referee directly to the doctoral college
  • copies of qualification certificates and transcripts
  • certification of English language competence (minimum IELTS 6.5 or equivalent) for candidates for whom English is not their first language.

Prospective candidates should contact Prof Sugata Kaviraj for advice on making their application or for further information. For any informal enquiry please contact Dr. Raj Tiwari.

Application Deadline: 31 May 2022. Interviews will take place on the week beginning 01/06/2022.