Using probabilistic sampling-based sensitivity analyses for indoor air quality modelling
Tutkimustuotos › › vertaisarvioitu
|Julkaisu||Building and Environment|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 1 tammikuuta 2014|
We develop a probabilistic framework for modelling indoor air quality in housing stocks, selecting appropriate sensitivity analyses to understand indoor air quality determinants, and constructing a reliable metamodel from the most relevant determinants to allow quick assessments of future intervention scenarios. The replicated Latin Hypercube sampling method is shown to be efficient at propagating variations between model input and output variables. A comparison of a range of sample-based sensitivity methods shows that an initial visual assessment can help to select appropriate sensitivity analyses, as they test for different types of relations (i.e. linear, monotonic, and non-monotonic). An advantage of linear regression methods is that the total output can be apportioned to various input variables. The advantage of tests with correlation coefficients is that the associated p-values can be used to assess whether input variables are significant. An artificial neural network constructed from a reduced set of input variables selected at a 5% level of significance is able to accurately predict indoor air quality. In the application of the framework to the modelling of winter indoor air quality in single-storey flats in England, the drivers for internally- and externally-generated PM2.5 are found to be different, therefore allowing interventions that reduce both concentrations simultaneously. Principal determinants for externally-generated PM2.5 are the internal deposition rate of PM2.5, weather-corrected volumetric infiltration rate, and ambient concentration of PM2.5, while for PM2.5 produced by gas cooking, they are the kitchen window opening area, generation rate of PM2.5, and indoor temperature.