Experimental and Computational Investigation of the Emission and Dispersion of Fine Particulate Matter (PM2.5) During Domestic Cooking

Abstract

As the wealth of evidence grows as to the negative impact of indoor air quality on human health, it has become increasingly urgent to investigate and characterise sources of air pollution within the home. Fine particulate matter with a diameter of 2.5 µm or less (PM2.5) is a key cause for concern, and cooking is known to be one of the most significant sources of domestic PM2.5. In this study, the aim was to demonstrate the efficacy of combining experimental techniques and cutting-edge High-Performance Computing (HPC) to characterise the dispersion of PM2.5 during stir-frying within a kitchen laboratory. This was carried out using both experimental measurement with low-cost sensors and high-fidelity Computational Fluid Dynamics (CFD) modelling, in which Lagrangian Stochastic Methods were used to model particle dispersion. Experimental results showed considerable spatio-temporal variation across the kitchen, with PM2.5 mass concentrations in some regions elevated over 1000g/m3 above the baseline. This demonstrated both the impact that even a short-term cooking event can have on indoor air quality and the need to factor in such strong spatio-temporal variations when assessing exposure risk in such settings. The computational results were promising, with a reasonable approximation of the experimental data shown at the majority of monitoring points, and future improvements to and applications of the model are suggested.