Indoor environments and respiratory health

This 3 year PhD project will investigate how the indoor environment can impact on both chronic obstructive pulmonary disease and asthma.

Paying particular attention to the effects of cooking and heating fuel, it will also focus on the role of fuel poverty in respiratory illness.

Humans spend a large proportion of their lives in indoor environments including at home, at work, and in school. In developed nations, many individuals spend at least 80% of their times in the indoor environment, a figure which is even higher in vulnerable groups like women, children and the elderly.

Indoor air quality is an important determinant of a health and wellbeing, yet is often taken for granted. Respiratory health is a global problem with high prevalence of both asthma and chronic obstructive pulmonary diseases (COPD) in both industrialised and developing nations .

This study will assess how indoor air pollution might impact on these diseases, and consider what role reduced ventilation – as a result of energy efficiency policies – might also play.

Special emphasis will be placed on particulate matter from cooking and heating fuel, and fuel poverty. Fuel poverty behaviours have also been found to affect around a third of low to middle income homes in the UK, leading to increased indoor dampness and associated allergenic contaminants such as moulds and house dust mites.

Specific questions to be answered in this study include:

  • Does cooking and heating type influence reporting of respiratory ill health in the English housing survey?
  • Does fuel poverty influence the reporting of damp or breathing problems in the English Housing Survey and British Household Panel Survey?
  • What is the variation in built environment and heating modes with rural and urban sectors in the context of health outcomes?
  • Can ventilation status be predicted from assessing data from surrogate measures such as in-house monitoring of indoor and outdoor temperature and humidity and heating usage?
  • Can proxy measures of the built environment and algorithms of predicting daily mean internal temperatures be extrapolated for use in social housing environments?