Constanza Molina, Benjamin Jones, Michael Kent, Ian P Hall
Languages: English | Pages: 10 pp
Bibliographic info:
38th AIVC Conference "Ventilating healthy low-energy buildings", Nottingham, UK, 13-14 September 2017

There are three common methods used to analyse Indoor Air Quality in buildings: in-site measurements, laboratory measurements, or the simulation of indoor spaces using a validated computational model. Each have their advantages, but computational models are generally used to predict air quality in a wide range of indoor environments because they are quick, cheap, and non-invasive. A wide range of inputs are required to accurately simulate airflow and pollutant transport. However, this information may not exist or may only exist in abstract forms. Furthermore, the collation and processing of data can be time consuming and can introduce systematic error when it is undocumented. A documented database containing a range of building archetypes with statistically representative values of related parameters can facilitate the simulation process. The archetypes might then be used to predict and evaluate the impacts of new policies on the indoor air quality of a stock of houses.
This paper describes a method to identify sets of archetypes that are statistically representative of the Chilean housing stock. The Santiago housing stock comprises 41% of the Chilean housing stock and is well documented (55% of all dwellings are surveyed), and so it used to represent the Chilean stock. All available data on the Santiago housing stock, including CENSUS data and annual building statistics reports, are utilised. The archetypes account for elements of building design that affect indoor pollutant concentrations, which can be used for indoor environment modelling. Some similarities are found between houses belonging to Santiago and the rest of country, and although a database that encompassed both Santiago and the remaining stock is desirable, the houses outside Santiago are more difficult to categorise and so an independent analysis is required. Dwellings were categorised according to relevant factors, such as geometry and total floor area, and allocated to weighted groups by dwelling type to form a series of archetypes. Notable architectural elements and values of relevant parameters are used to identify each archetype. These can be used to model the air quality found across the entire Chilean housing stock using validated tools, such as CONTAM or EnergyPlus.
The representative archetypes provide a better understanding of the current Chilean housing stock and will enable the testing of a range interventions designed to improve indoor air quality in Chilean houses. Existing databases are non-exhaustive and contain errors and so knowledge gaps are highlighted. This information can be used to inform future surveys of the Chilean housing stock.