Development of a probabilistic multi-zone multi-source computational model and demonstration of its applications in predicting PM concentrations indoors.

This paper highlights the development and application of the probabilistic model (IAPPEM), which predicts PM10 and PM2.5 concentrations in the indoor environments. A number of features are detailed and justified through simulated comparison, which are shown to be necessary when modelling indoor PM concentrations. A one minute resolution predicts up to 20% higher peak concentrations compared with a 15 min resolution. A modified PM10 deposition method, devised to independently analyse the PM2.5 fraction of PM10, predicts up to 56% higher mean concentrations. The application of the model is demonstrated by a number of simulations. The total PM contribution, from different indoor emission sources, was analysed in terms of both emission strength and duration. In addition, PM concentrations were examined by varying the location of the emission source. A 24-hour sample profile is simulated based on sample data, designed to demonstrate the combined functionality of the model, predicting PM10 and PM2.5 peak concentrations up to 1107±175 and 596±102 μg m(-3) respectively, whilst predicting PM10 and PM2.5 mean concentrations up to 259±21 and 166±11 μg m(-3) respectively.