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• Speed correction curves are introduced to improve the simulation of vehicle emission factors. • A multi-year emission inventory is developed in a typical middle-sized city based on detailed data. • The comparison of emissions is made between the middle-sized city and the megacities. • Four possible emission control policies are evaluated and discussed for middle-sized cities. Vehicle emissions are regarded as an important contributor to urban air pollution in China and most previous studies focused on megacities. However, the vehicle pollution in middle-sized cities becomes more severe due to the increasing vehicle population (VP) and lagged control policy. This study takes Langfang, a typical middle-sized city bordered by two megacities (Beijing and Tianjin), as the target domain to investigate vehicle emissions. The speed correction curves (SCC) are introduced to improve the vehicle emission factors (EF) simulation in official technical guidelines on emission inventory (GEI). A multi-year vehicle emission inventory (from 2011 to 2025) is developed in Langfang. From 2011 to 2017, the total vehicle emissions in Langfang decrease for carbon monoxide (CO), but increase for volatile organic compounds (VOCs), nitrogen oxides (NO x), and inhalable particles (PM 10), respectively. From 2018 to 2025, the emissions would increase more rapidly in Langfang than in Beijing and Tianjin, indicating the middle-sized cities may become a significant contributor to air pollution in China. Four possible control policies, including VP constrained (VPC), public transportation promotion (PTP), new energy vehicles promotion (NEP), and freight transportation structure optimization (FTO) are evaluated. The most significant emissions reductions are observed under the FTO for CO, NO x , and PM 10 , and under the VPC for VOCs. The spatial distributions of vehicle emissions show a high order of heterogeneity, indicating that local conditions should be considered in policy formulation in addition to national consistency. More comprehensive policies should be implemented to mitigate the vehicle pollution in middle-sized cities. [ABSTRACT FROM AUTHOR]

[Display omitted] 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 2.5 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 R2 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning is used to build vehicle CO 2 and NO X transient models. • The models are trained on multiple vehicles and verified by a separate vehicle. • The input features of the models are more comprehensive than MOVES. • For CO 2 and NO X , how different model inputs affect the model results is analyzed. • Different models are constructed according to different pollutants. The transient simulation of CO 2 and NO X from motor vehicles has essential applications in evaluating vehicular greenhouse gas emissions and pollutant emissions. However, accurately estimating vehicular transient emissions is challenging due to the heterogeneity between different vehicles and the continuous upgrading of vehicle exhaust purification technology. To accurately characterize the transient emissions of motor vehicles, a Super-learner model is used to build CO 2 and NOx transient emission models. The actual onboard test data of 9 China VI N 2 vehicles were used to train the model, and the test data of another China VI N 2 vehicle were selected for further robustness verification. There were significant differences in the emissions between the vehicles, but the constructed transient model could capture the common law of transient emissions from China VI N 2 vehicles. The R2 values of CO 2 and NOx emission in the test data of the validation vehicle were 0.71 and 0.82, respectively. In addition, to further prove the model's robustness, the training data were synchronously modelled based on the Moves-method. The Super-learner model has a smaller RMSE on the validation set than the model based on the Moves-method, indicating that the Super-learner model has more transient simulation advantages. The marginal contributions of the model characteristics to the model results were analysed by SHapley Additive exPlanation (SHAP) value interpretation, and the marginal contributions of different pollutant characteristic parameters varied. Therefore, when establishing transient models of different pollutants, the selection of the model parameters demands considering the generation and purification process of different pollutants. The present work provides novel insights into the parameter selection, construction, and interpretation of the transient vehicle emission model. [ABSTRACT FROM AUTHOR]