###An Integrated Indoor Enviromental Quality Exposure Inequalities Framework
Project ID: 2228bd1099 (You will need this ID for your application)
Research Theme: Energy and Decarbonisation
UCL Lead department: Bartlett School of Environment, Energy and Resources
Lead Supervisor: Anna Mavrogianni
Project Summary:
Despite the overall reduction in non-decent homes during the last decade, there are still significantly high levels of poor quality housing, including cold and damp homes. The cost of living crisis will further exacerbate health inequalities related to poor housing. It is, thus, imperative that we decarbonise our building stock to meet pressing Net Zero targets, mitigate climate change and reduce fuel poverty. However, if building performance is not tackled holistically, carbon-only focused policies can lead to unintended consequences, such as poor indoor air quality, overheating and mould risk. If built environment policies are not health oriented, such adverse effects may be disproportionately experienced by more socially disadvantaged population groups, who cannot afford remedial measures.
This PhD project will create, for the first time in the UK, an integrated residential indoor environmental quality exposure assessment framework and vulnerability index under the current and future climate. It will build on existing housing stock indoor environment modelling methods developed in the context of the BEIS CS-N0W project, expanding it by mapping mould risk, indoor air pollutant concentrations and potentially ‘hard-to-decarbonise’ homes. It will involve statistical analysis of existing databases, such as the English Housing Survey and Census socioeconomic data, a combination of dynamic thermal, air contaminant and moisture modelling, machine learning and GIS. Questionnaire surveys and focus groups will inform behaviour-related modelling inputs. The ultimate aim will be to map indoor environmental exposure inequalities and reveal potential ‘hidden fuel poverty’ clusters. It will create a tool for public health policymakers and planning authorities to identify retrofit priority areas and population groups.
Candidates should ideally have a built environment, public health or data science background, and strong analytical skills. Solid building performance modelling, programming and/or GIS experience would be desirable. Candidates should be able to engage effectively with a wide range of stakeholders.