Machine learning helps reveal key factors affecting tire wear particulate matter 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 ti...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024008110 |
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| author | Zhenyu Jia Jiawei Yin Tiange Fang Zhiwen Jiang Chongzhi Zhong Zeping Cao Lin Wu Ning Wei Zhengyu Men Lei Yang Qijun Zhang Hongjun Mao |
| author_facet | Zhenyu Jia Jiawei Yin Tiange Fang Zhiwen Jiang Chongzhi Zhong Zeping Cao Lin Wu Ning Wei Zhengyu Men Lei Yang Qijun Zhang Hongjun Mao |
| author_sort | Zhenyu Jia |
| collection | DOAJ |
| description | 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 PM2.5 accounts for about 65 % of PM10. The response relationship between TWP emissions (both PM2.5 and PM2.5-10) 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 R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 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 PM2.5 and PM2.5-10 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. |
| format | Article |
| id | doaj-art-05ba00a6ab2d4370895caf431de0a36a |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-05ba00a6ab2d4370895caf431de0a36a2025-01-24T04:44:07ZengElsevierEnvironment International0160-41202025-01-01195109224Machine learning helps reveal key factors affecting tire wear particulate matter emissionsZhenyu Jia0Jiawei Yin1Tiange Fang2Zhiwen Jiang3Chongzhi Zhong4Zeping Cao5Lin Wu6Ning Wei7Zhengyu Men8Lei Yang9Qijun Zhang10Hongjun Mao11Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaChina Automotive Technology and Research Center Co. Ltd, Tianjin 300300, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaJinchuan Group Information and Automation Engineering Co. Ltd., Jinchang 737100, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, ChinaTianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, China; Corresponding authors.Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, China; Corresponding authors.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 PM2.5 accounts for about 65 % of PM10. The response relationship between TWP emissions (both PM2.5 and PM2.5-10) 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 R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 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 PM2.5 and PM2.5-10 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.http://www.sciencedirect.com/science/article/pii/S0160412024008110Non-exhaustTire wear particlesMachine learningSimilarity networkPartial dependence plotsCentered-individual conditional expectation plots |
| spellingShingle | Zhenyu Jia Jiawei Yin Tiange Fang Zhiwen Jiang Chongzhi Zhong Zeping Cao Lin Wu Ning Wei Zhengyu Men Lei Yang Qijun Zhang Hongjun Mao Machine learning helps reveal key factors affecting tire wear particulate matter emissions Environment International Non-exhaust Tire wear particles Machine learning Similarity network Partial dependence plots Centered-individual conditional expectation plots |
| title | Machine learning helps reveal key factors affecting tire wear particulate matter emissions |
| title_full | Machine learning helps reveal key factors affecting tire wear particulate matter emissions |
| title_fullStr | Machine learning helps reveal key factors affecting tire wear particulate matter emissions |
| title_full_unstemmed | Machine learning helps reveal key factors affecting tire wear particulate matter emissions |
| title_short | Machine learning helps reveal key factors affecting tire wear particulate matter emissions |
| title_sort | machine learning helps reveal key factors affecting tire wear particulate matter emissions |
| topic | Non-exhaust Tire wear particles Machine learning Similarity network Partial dependence plots Centered-individual conditional expectation plots |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024008110 |
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