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A Stochastic Approach to Estimate Uncertainty in Pollutant Concentrations in an Archetypal Chilean House

Constanza Molina, Benjamin Jones, Michael Kent, Ian P Hall, 2018
indoor air quality | Indoor sources | modelling | sensitivity analysis | Fine Particulate Matter
Bibliographic info: 39th AIVC Conference "Smart Ventilation for Buildings", Antibes Juan-Les-Pins, France, 18-19 September 2018
Languages: English Pages (count): 11

As policy makers strive to reduce the energy demands of houses by reducing infiltration rates, an unintended consequence could be a fall in the quality of indoor air with corresponding negative health effects at a population scale. Measuring pollutant concentrations in-situ is difficult, expensive, invasive, and time consuming and so the simulation of indoor conditions, using representative models of a housing stock, is a more common method of investigation.  The AIVC asserts that fine particulate matter (PM2.5) from indoor sources poses the greatest risk to the health of occupants of dwellings. Accordingly, this paper uses a stochastic modelling approach to assess the relative influence of several sources of PM2.5 in the housing stock of Santiago, Chile. A single multi-zone archetype is modelled in CONTAM where model inputs are randomly varied between known limits to produce a distribution of mean PM2.5 concentrations over the heating season weighted by the time spent in the kitchen, living rooms, and bedrooms (PM2.5). Ambient air is only exchanged by infiltration. Three sources of PM2.5 are investigated: a heater commonly found in Chile that burns wood or paraffin, the cooking of meals, and the toasting of bread. A range of data sources are used to inform environmental, geometric, physical, and pollutant data inputs, and a sensitivity analysis is used to rank them by their influence on the model output. 

The median PM2.5 in Santiago houses over the heating season is predicted to be 107μg/m3: 90% credible intervals [5, 883μg/m3]. The WHO annual mean average threshold is exceeded in 77% of houses and so they could require remediation measures to protect occupant health. Cooking is shown to be the most important model input and so at-source interventions, such as a range hood, may be the most appropriate. 

The modelling approach can now be expanded to consider a more archetypes and pollutant sources, to give a better indication of the uncertainty in pollutant concentrations found in Chilean houses. The outputs can be used to inform future standards and guidelines for Chilean houses that simultaneously focus on energy demand reduction and occupant health. 


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