In this project the relationship between home ventilation requirements from UK policies and personal exposure to pollution from common indoor sources will be explored.
Department: Construction Management & Engineering
Supervised by: Dr. Christos H. Halios (School of Built Environment, Uni Reading), Professor Sani Dimitroulopoulou (UK Health Security Agency)
The effect of the net-zero transition policies on population exposure to indoor pollution is still unclear. As homes become more energy efficient and tighter in response to a changing climate, reduced ingress of ambient pollution indoors will lead to less exposure. However, exposure to pollution from indoor sources will most likely increase as the reduced ventilation will not effectively dilute indoor pollution. This is especially relevant for vulnerable groups including children, the elderly and those with pre-existing medical conditions who spend more time at home. In this context, it is important for policy makers to clarify the relationship between ventilation related policies and personal exposures to indoor pollution. The first step towards this direction would be to evaluate the effect of past and existing standards for acceptable ventilation rates that are included in Buildings Regulations in the UK. The student will collect and evaluate secondary data of ventilation rates in UK homes published by governmental organizations; this data will then be used as input for a probabilistic model (INDAIR) that has been developed to predict air pollutant concentrations in home microenvironments in the UK. The effect of past and existing ventilation policies on the Indoor exposure to gas and particulate concentrations will be explored under common indoor emission scenarios (e.g. cooking, smoking).
A suggested time plan is given below, assuming a student is working 4 1/2 days a week for 6 weeks. Under supervision, the student will: • review targeted sections of literature, identify, collect and analyse secondary ventilation data (weeks 1 and 2). • Model application of the data (weeks 3, 4 and 5). • Writing of report and presenting the results at the research group meeting (week 6). This time plan assumes that the student is working 4 1/2 days a week for 6 weeks. The placement could comprise of two periods, with the data collection and analysis carried out in one block (two weeks), and the model application in the second bloc (four weeks).
Essential skills: • The student is expected to have a background in one or more of the following areas: environmental science/ mathematics/ physics/ meteorology/ engineering. • Competence with computers, in particular experience in a computer programming language (C, Python, R, MATLAB). • The student will need to be flexible, reliable and hardworking. • An understanding of the importance of the scientific method. • Willingness to attend the Energy and Environmental Engineering research group meetings. Desirable skills • A genuine interest and enthusiasm for the study of healthy indoor environments. • Previous experience of handling data and knowledge of statistics is desirable. • Previous experience of running computers models in C will be welcome.
• The student will gain hands on experience in several stages of the research, including reviewing the literature, collecting and analyzing the data, using a model to further investigate the relevant processes, and presenting and writing up the results. • The supervisors will work with the student in meetings several times a week at all stages to provide training and expertise in each area. • General transferable skills to be developed include independent and team-working, computing and analytical skills, report writing and presentations skills.
Chancellors Building, remote working.
9am-5pm, flexible if required.
Wednesday 01 June 2022 - Saturday 30 September 2023
The deadline to apply for this opportunity is Sunday 7th May 2023. Students should submit their CV and Cover Letter directly to the Project Supervisor (click on supervisor name at the top of the page for email). Successful candidates will be invited for an interview.