Simulations using CONTAM (a validated multi-zone indoor air quality (IAQ) model) were employed to predict indoor exposure to PM2.5 in London dwellings in both the present day housing stock and the same stock following energy efficient refurbishments to meet greenhouse gas emissions reduction targets for 2050. To achieve these targets, measures were specified that reduced building permeability to 3m 3 m-2 hr-1 at 50 Pa, combined with the introduction of mechanical ventilation and heat recovery (MVHR) systems. It was assumed that the mean current outdoor PM2.5 concentration of 13μg.m-3 , decreased to 9μg.m-3 by 2050 due to emission control policies. Proper installation of MVHR systems with permeability reduction is associated with appreciable reductions in PM2. 5 exposure in both smoking and non-smoking dwellings. Modelling of the future scenario for nonsmoking dwellings predicts a reduction in annual average indoor exposure to PM2.5 of 24.0μg.m-3 (from 28.4 to 4.4μg.m 3) for a typical household member and a larger reduction of 52.8μg.m-3 (from 60.5 to 7.7μg.m3) for members exposed primarily to cooking-related particle emissions in the kitchen. Reductions in envelope permeability, without mechanical ventilation, produced a small increase (+5.4μg.m-3) in indoor PM2.5 concentrations. These estimates of changes in PM2.5 exposure were sensitive to assumptions about occupant behaviour, ventilation system usage and the distribution of input variables (+72% for non-smoking and +107% in smoking residences) but, if realised would result in significant health benefits.

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This paper presents an indoor overheating assessment study of 100 London dwellings during the summer of 2009. The study included physical building surveys, indoor dry bulb temperature monitoring and a questionnaire survey on occupant behaviour, including the operation of passive and active ventilation, cooling and shading systems. A theoretical London housing stock comprising 3,456 combinations of building geometry, orientations, urban patterns, fabric retrofit and external weather was simulated using the EnergyPlus thermal modelling software. A statistical meta-model of EnergyPlus was then built by regressing the independent variables (simulation input) against the dependent variables (overheating risk). The monitoring and questionnaire data were analysed to explore the relationship between self-reported behaviour and overheating, and to test the meta-model. The monitoring data indicated that London homes and, in particular, bedrooms are already at risk of indoor overheating during hot spells under the current climate. Around 70% of respondents tended to open only one or no windows at night mainly due to security reasons. An improvement in R 2 values between measured temperature and meta-model predictions was obtained only for those dwellings where occupants reported actions that was in line with the modelling assumptions, thus highlighting the importance of occupant behaviour for overheating.