School of Architecture, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin, 150090, China.
Division of Sustainable Buildings, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Brinellvägen 23, Stockholm, 100 44, Sweden.
Environ Pollut. 2023 Apr 15;323:121221. doi: 10.1016/j.envpol.2023.121221. Epub 2023 Feb 11.
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 R 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.
加热食用油排放的颗粒物对人体健康有害。为了制定有效的缓解策略，了解排放颗粒物的数量至关重要。本研究旨在估计食用油的温度依赖性颗粒质量排放率，并基于多元线性回归方法开发源强模型。首先，本研究通过加热实验考察了七种常用油。测量了 PM2.5 和 PM10 的排放率，并分析了油体积和表面积等参数对排放率的影响。之后，测定了测试油的起始发烟点（Ts'）和加剧发烟点（Tss'）。结果表明，发烟点较低的油具有更大的排放率。值得注意的是，实验观察到在 240°C 时，花生油、稻米油、菜籽油和橄榄油产生的 PM2.5 速度明显快于葵花籽油、大豆油和玉米油（最高排放条件下，分别为 2.22、1.50、0.82 和 0.80mg/s，而 0.15、0.12 和 0.11mg/s）。油的温度、体积和表面积对颗粒物质量排放率都有显著影响，其中油温度的影响最大。获得的回归模型具有统计学意义（P<0.001），大多数 R 值大于 0.85。根据我们的研究结果，建议使用发烟点较高、排放率较低、体积较小的葵花籽油、大豆油和玉米油，并使用较小的平底锅进行烹饪。
