Machine learning helps reveal key factors affecting tire wear particulate matter emissions.
Journal:
Environment international
PMID:
39719754
Abstract
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 accounts for about 65 % of PM. The response relationship between TWP emissions (both PM and PM) 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 of 0.84 and 0.78 for PM and PM 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 and PM are different. Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.