Studentships are open to both UK and non-UK international candidates.
Applications are invited by the Centre for Atmospheric and Climate Physics Research for a fully-funded PhD studentship at the University of Hertfordshire. Herts is committed to equality, diversity, and inclusive culture.The studentship is offered on a full-time basis for 3.5 years from September 2022 (subject to satisfactory progress) and provides a bursary (£15,609 per annum, current rate), including a full tuition fee waiver for 3.5 years.
For any informal enquiry please contact: Dr. Pushp Raj Tiwari
Closing date for applications: 31st May 2022
Project title: Improvement of bioaerosol parametrization in atmospheric models using molecular properties of bioaerosols measured by optical spectroscopy methods
Short description of the project: 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.
The successful candidate will be supervised by Dr. Pushp Raj Tiwari (modelling work) and co-supervised by Dr. Adrian Ghita and Dr. Boyan Tatarov (experimental work). It is expected for the prospective Ph.D. student to provide intellectual input, actively participate in the design, implementation or operation of various setups. The future PhD student will participate in field campaigns and should be actively engaged in the analysis of the data collected during the project to further develop his/her research expertise in the field. It is desirable to actively participate in writing/ drafting scientific papers and present his/her work at international conferences.
* A suitable 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;
Overseas applicants to have an IELTS (English proficiency) score of 6.5 or above (if
they get selected for the studentship)
- 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.