Wei Ning, Men Zhengyu, Ren Chunzhe, Jia Zhenyu, Zhang Yanjie, Jin Jiaxin, Chang Junyu, Lv Zongyan, Guo Dongping, Yang Zhiwen, Guo Jiliang, Wu Lin, Peng Jianfei, Wang Ting, Du Zhuofei, Zhang Qijun, Mao Hongjun
Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
Tianjin Youmei Environment Technology, Ltd, Tianjin, 300300, China.
Environ Int. 2022 Aug;166:107386. doi: 10.1016/j.envint.2022.107386. Epub 2022 Jul 1.
Brake emissions from vehicles are increasing as the number of vehicles increases. However, current research on brake emissions, particularly the intensity and characteristics of emissions under real road conditions, is significantly inadequate compared to exhaust emissions. To this end, a dataset of 600 (200 unique real-world braking events simulated using three types of brake pads) real-world braking events (called brake pad segments) was constructed and a mapping function between the average brake emission intensity of PM from the segments and the segment features was established by five algorithms (multiple linear regression (MLR) and four machine learning algorithms). Based on the five algorithms, the importance of the different features of the fragments was discussed and brake energy intensity (BEI) and metal content (MC) of the brake pad emissions were identified as the most significant factors affecting brake emissions and used as the final modeling features. Among the five algorithms, categorical boosting (CatBoost) had the best prediction performance, with a mean R and RMSE of 0.83 and 0.039 respectively for the tenfold cross-validation. In addition, the CatBoost-based model was further compared with the MOVES model to demonstrate its applicability. The CatBoost-based model has better prediction performance than the MOVES model. The MOVES model overpredicts brake fragment emissions for urban roads and underpredicts brake fragment emissions for motorways. Furthermore, the CatBoost-based model was interpreted and visualized by an individual conditional expectation (ICE) plot to break the machine learning "black box", with BEI and MC showing nonlinear monotonic increasing relationships with braking emissions. ICE plot also provides viable technical solutions for controlling brake emissions in the future. Both avoiding aggressive braking driving behavior (e.g., the application of smart transportation technologies) and using brake pads with less metal content (e.g., using ceramic brake pads) can effectively reduce brake emissions. The construction of a machine learning-based brake emission model and the white-boxing of its model provide excellent insights for the future detailed assessment and control of brake emissions.
随着车辆数量的增加，车辆制动排放也在增加。然而，目前关于制动排放的研究，特别是实际道路条件下排放的强度和特征，与尾气排放相比明显不足。为此，构建了一个包含600个（使用三种类型刹车片模拟的200个独特的实际制动事件）实际制动事件（称为刹车片片段）的数据集，并通过五种算法（多元线性回归（MLR）和四种机器学习算法）建立了片段中PM平均制动排放强度与片段特征之间的映射函数。基于这五种算法，讨论了片段不同特征的重要性，并将刹车片排放的制动能量强度（BEI）和金属含量（MC）确定为影响制动排放的最显著因素，并用作最终的建模特征。在这五种算法中，分类提升（CatBoost）具有最佳的预测性能，十折交叉验证的平均R和RMSE分别为0.83和0.039。此外，将基于CatBoost的模型与MOVES模型进行了进一步比较，以证明其适用性。基于CatBoost的模型比MOVES模型具有更好的预测性能。MOVES模型对城市道路的制动片段排放预测过高，对高速公路的制动片段排放预测过低。此外，通过个体条件期望（ICE）图对基于CatBoost的模型进行了解释和可视化，以打破机器学习的“黑箱”，BEI和MC与制动排放呈现非线性单调递增关系。ICE图也为未来控制制动排放提供了可行的技术方案。避免激进的制动驾驶行为（例如应用智能交通技术）和使用金属含量较低的刹车片（例如使用陶瓷刹车片）都可以有效减少制动排放。基于机器学习的制动排放模型的构建及其模型的白盒化，为未来制动排放的详细评估和控制提供了很好的见解。
