Temperature-dependent particle mass emission rate during heating of edible oils and their regression models

Abstract

Particulate matter emitted by heated cooking oil is hazardous to human health. To develop effective mitigation strategies, it is critical to know the amount of the emitted particles. The purpose of this research is to estimate the temperature-dependent particle mass emission rates of edible oils and to develop models for source strength based on the multiple linear regression method. First, this study examined seven commonly used oils by heating experiments. The emission rates of PM2.5 and PM10 were measured, and the effects of parameters such as oil volume and surface area on the emission rates were also analysed. Following that, the starting smoke points (Ts') and aggravating smoke points (Tss') of tested oils were determined. The results showed that oils with lower smoke points had greater emission rates. Notably, the experiments performed observed that peanut, rice, rapeseed and olive oil generated PM2.5 much faster at 240 °C (2.22, 1.50, 0.82 and 0.80 mg/s, respectively, at the highest emission conditions) than that of sunflower, soybean, and corn oil (0.15, 0.12 and 0.11 mg/s, respectively). The temperature, volume, and surface area of oils all had a significant impact on the particle mass emission rate, with oil temperature being the most influential. The regression models obtained were statistically significant (P < 0.001), with the majority of R² values greater than 0.85. Using sunflower, soybean and corn oils, which have higher smoke points and lower emission rates, and smaller pans for cooking is therefore recommended based on our research findings.

Keywords: Cooking emission; Multiple linear regression; PM10; PM2.5; Particulate pollution.