A data analysis of the Chilean housing stock and the estimation of uncertainty in indoor air quality in Chilean houses - Molina, Constanza (2020)
Air pollution is currently one of the leading causes of mortality and morbidity worldwide and forecasts predict that it will remain so until 2040. Exposures to ambient fine particles (PM2.5) were responsible for 103.1 million disability–adjusted life–years (DALYs) in 2015. However, indoor concentrations of pollutants, including PM2.5, can be higher than those found outside, and so the indoor environment may have a significant effect on personal exposures. Dwellings are particularly important because people spend over 70% of their time in them. The Chilean housing stock comprises 6.4 million dwellings of great diversity, because of the country’s variable geography. Dwellings change with the local climate, occupants' lifestyles and behaviours, and with the different materials they are constructed from. They evolve as technologies are used to save energy and improve occupants' quality of life, and improve their performance in response to new policies and standards. This thesis explores the indoor air quality (IAQ) across the Chilean housing stock, by developing a set of statistically representative archetypal dwellings and quantifying uncertainty in their characteristics using available data sources, such as national censuses. Eight archetypes, representing 35% of the stock, are modelled using CONTAM and simulated using a probabilistic sampling approach to generate distributions of indoor PM2.5 concentrations, ventilation rates, and associated heat losses. A sensitivity analysis is used to identify the most influential inputs. The results show that the variability in the physical and environmental parameters and windows use influence PM2.5 exposures, ventilation rates, and energy losses. Therefore, it is important to understand how much these results vary depending on these parameters. This will help to establish effective national standards and guidelines for IAQ and energy demand reduction in order to avoid negative health impacts at a population scale. This work contributes to knowledge by (i) characterising the Chilean housing stock and presenting a set of archetypal buildings to represent it; (ii) presenting a model and modelling framework for evaluating the Chilean housing stock probabilistically; (iii) predicting uncertainties in occupant exposures to PM2.5, and dwelling ventilation rates and energy losses across the Chilean housing stock; (iv) identifying the most important parameters that affect the predictions; (v) contextualizing and interpreting the results; (vi) showing how the model and its predictions can be used to inform and evaluate the impacts of policies, and improve the IAQ and environmental performance of dwellings; and by (vii) identifying the need for future measuring, surveying, and data gathering exercises.