###Addressing air quality and thermal performance issues to help tackle inequalities embedded in overcrowded UK dwellings: development of enhanced household overcrowding metrics
Project ID: 2228bd1144 (You will need this ID for your application)
Research Theme: Engineering
UCL Lead department: Bartlett School of Environment, Energy and Resources
Lead Supervisor: Marcella Ucci
Project Summary:
Overcrowding in residential buildings is a growing problem in some areas of the UK, being especially prevalent in ethnic minority households and potentially leading to adverse health impacts, such as increased risk of infection transmission (e.g. COVID-19) and mental health problems. Definitions of overcrowding typically consider the amount of space/rooms available for each household/person, with local authorities utilising the ‘bedroom standard’ in particular to identify those households which could be moved to a bigger property because they are ‘overcrowded’. The bedroom standard criteria define how many people can share a bedroom, depending on their age, gender and relationship status. However, ventilation provision and thermal performance should be additional criteria when evaluating risks in overcrowded dwellings. For example, inadequate ventilation and thermal performance could further increase the risk of dampness and mould growth in overcrowded dwellings. Therefore, for the same household characteristics and number of bedrooms, health risks may be greater in those dwellings with sub-standard ventilation or thermal performance. This project seeks to develop performance metrics which could be utilised by local authorities to complement existing definitions of overcrowding to account for other relevant dwelling characteristics. The doctoral research will be supervised by Dr Marcella Ucci, with extensive expertise in healthy and sustainable building design/operation - including monitoring and modelling indoor air quality - in collaboration with Dr Phil Symonds with expertise in building stock model development and analysis of large datasets through cutting edge statistical and machine learning methods. The work will involve secondary data analysis (e.g. English Housing Survey) and performance modelling at the individual and building stock scale. The ideal candidate will have: 1) a good understanding of building physics principles, particularly ventilation and thermal performance; 2) knowledge of building performance modelling software; 3) knowledge of, or a strong interest in, statistical methods applicable to building-related datasets.