Machine learning helps reveal key factors affecting tire wear particulate matter emissions

作者：Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao · 发表于：Environment International · 年份：2024 · DOI：10.1016/j.envint.2024.109224 · 被引用次数：12 · 研究领域：Vehicle emissions and performance、Air Quality Monitoring and Forecasting、Air Quality and Health Impacts

The construction and interpretation of a machine learning based model of tire wear emissions provides new insights into the refined assessment and control of non-exhaust emissions. Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wear process under real driving conditions using a tire wear simulator and custom-designed test conditions. This study shows that tire wear PM 2.5 accounts for about 65 % of PM 10 . The response relationship between TWP emissions (both PM 2.5 and PM 2.5-10 ) and factors (the radial force, the lateral force, the tangential force, speed, driving torque, tire contact area, total contour length and tire tread temperature) was obtained by machine learning (ML) method. The random forest (RF) model was developed and displayed good prediction performance with an R 2 of 0.84 and 0.78 for PM 2.5 and PM 2.5-10 on the test set, respectively. Model-related (similarity network graph) and model-unrelated (partial dependence plots and centered-individual conditional expectation plots) explainability methods were used to break the black box of ML. Model explainability results show that the feature parameters-emission response relationships for tire wear PM 2.5 and PM 2.5-10 are different. Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread tempe...