Influential factors for the emission inspection results of urban in-use vehicles: From an ensemble learning perspective

References

• Batunacun, W. R., Lakes T., and Nendel C. 2021. Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China. Geoscientific Model Development 14 (3).
   Web of Science ®Google Scholar
• Bruce, P., and Bruce, A. 2018. Practical statistics for data scientists. Translated by G. Lei, Beijing: Posts & Telecom Press.
   Google Scholar
• Chao, X., L. I. Jianjun, Y. Fumo, Li, H., Junhui, C., and Jiqing, W. 2020. Pollution characteristics and source apportionment of VOCs during a heavy pollution process in winter in Chengdu. Environmental Pollution & Control 42 (330 (5)):71–77+84.
   Google Scholar
• Chen, D. Y. 2020. Pandas for everyone: Python data analysis. Translated by W. Chuanhai, Beijing: Posts & Telecom Press.
   Google Scholar
• China State Administration of Market Regulation. 2019. Road Vehicle — Vehicle Identification Number (VIN) (GB 16735-2019).
   Google Scholar
• Christoph Molnar. 2021. Interpretable machine learning: A guide for making black box models explainable. Translated by Z. Mingchao, Publishing House of Electronics Industry, Beijing.
   Google Scholar
• Dong, M. A., Y. I. N. Hang, D. Yan, Hongli, W., Junfang, W., Yanjun, W., and Zhihui, H. 2016. Research on the emission status of in-use vehicle in China based on big date analysis. Environmental Pollution & Control 38 (7):42–48+55.
   Google Scholar
• DTSP (Department of Transportation of Sichuan Province). 2020. Interpretation of the Notice on the Formulation and Implementation of Vehicle Emission Inspection and Maintenance (I/M) System jointly issued by Department of Transportation of Sichuan Province and Department of Ecology and Environment of Sichuan Province. http://jtt.sc.gov.cn/jtt/c101471/2020/12/9/b5c07293c7c84064b4ac062adc6110e2.shtml
   Google Scholar
• García, M. V., and J. L. Aznarte. 2020. Shapley additive explanations for NO2 forecasting. Ecological Informatics 56:101039.
   Web of Science ®Google Scholar
• Guangtao, L., D. Gong, L. Xing, and D. Zhang. 2021. Ensemble learning based methods for crown prediction of hot-rolled strip: Forming processing and thermomechanical treatment. ISIJ International 61 (5):1603–13.
   Google Scholar
• Haoyang, S., 2019. Research on prediction of typical vehicle emission factors based on date ming. MA.Sc Thesis. Beijing Jiaotong University.
   Google Scholar
• Hu, S. X., Y. W. Zhao, and X. H. Li. 2021. Impact of ambient temperature on GDIV emission. Journal of Beijing University of Aeronautics and Astronautics 47 (4):828–34.
   Google Scholar
• Huo, H., B. Zheng, M. Wang, Q. Zhang, K.-B. He. 2015. Vehicular air pollutant emissions in China: Evaluation of past control policies and future perspectives. Mitigation and Adaptation Strategies for Global Change. 20 (5):719–33. doi:https://doi.org/10.1007/s11027-014-9613-0.
   Web of Science ®Google Scholar
• Kun, L. 2019. Research on Supervision of Motor Vehicle Inspection Institutions in Wuxi. MA.Sc Thesis, East China University of Political Science and Law.
   Google Scholar
• Lin, Y., J. Y. Wu, K. Lin, Hu, Y., and Kong, G. 2021. Prediction of intensive care unit readmission for critically ill patients based on ensemble learning. Journal of Peking University(Health Sciences) 53 (3):566–72.
   Google Scholar
• Lohit, V., M. Nikolaos, F. Schmidt Richard, David, V., Omaditya, K., Ahmad, S., Bryan, S., Al Saiegh, F., Reid, G. M., Pascal, J., et al. 2021. A machine learning approach to first pass reperfusion in mechanical thrombectomy: Prediction and feature analysis. Journal of Stroke and Cerebrovascular Diseases 30 (7):105796.
   PubMed Web of Science ®Google Scholar
• Lundberg, S. M., G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, S.-I. Lee, et al. 2020. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2 (1):56–67. doi:https://doi.org/10.1038/s42256-019-0138-9.
   PubMedGoogle Scholar
• Lundberg, S., and S.-I. Lee. 2017. A unified approach to interpreting model predictions. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems. NY, United States: Curran Associates Inc. pp. 4768–77.
   Google Scholar
• Lundberg, S. M., B. Nair, M. S. Vavilala, M. Horibe, M. J. Eisses, T. Adams, D. E. Liston, D. K.-W. Low, S.-F. Newman, J. Kim, et al. 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed, Eng 2 (10):749–60. doi:https://doi.org/10.1038/s41551-018-0304-0.
   PubMed Web of Science ®Google Scholar
• MEEPRC (Ministry of Ecology and Environment of the People’s Republic of China), 2020. China mobile source environmental management annual report.
   Google Scholar
• Mingxing, L., L. Jianhong, A. Clement, Ya, J., Minfei, M., and Xunmei, W. 2021. Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results. ISPRS Journal of Photogrammetry and Remote Sensing 174:68–86.
   Web of Science ®Google Scholar
• Ministry of Ecology and Environment of the People’s Republic of China. 2018. Limits and measurement methods for emissions from gasoline vehicles under two-speed idle conditions and short driving mode conditions(GB 18285-2018).
   Google Scholar
• Qiang, D., and W. Zhiyong. 2019. The influence of atmospheric environment on the test results of vehicle emission pollutants. Resource Conservation and Environmental Protection 9:13.
   Google Scholar
• Raquel, R. P., and B. Jürgen. 2020. Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values. J. Med. Chem. 63 (16):8761–77.
   PubMed Web of Science ®Google Scholar
• SHAP. 2019. Lundberg. https://github.com/slundberg/shap.
   Google Scholar
• Simsekler Mecit Can EmreN. H. Alhashmi, E. Azar, N. King, R. A. M. A. Luqman, A. Al Mulla. 2021. Exploring drivers of patient satisfaction using a random forest algorithm. BMC Med Inform Decis Mak, 21 (1):157. doi:https://doi.org/10.1186/s12911-021-01519-5
   PubMed Web of Science ®Google Scholar
• Tan, Q., H. Liu, S. Xie, L. Zhou, T. Song, G. Shi, W. Jiang, F. Yang, F. Wei. 2020. Temporal and spatial distribution characteristics and source origins of volatile organic compounds in a megacity of Sichuan Basin, China. China Environmental Research, 185(Issue C): 109478. doi:https://doi.org/10.1016/j.envres.2020.109478.
   PubMedGoogle Scholar
• Yan, X., L. Songdi, Z. Manni, Yi-Ran, W., Ri-Chao, W., Li-Hang, Z., Fei, Y., Zhuang-Min, Z., Li, B., Jiang-Rong, H., et al. 2020. Emission characteristics of light-duty gasoline vehicle exhaust based on acceleration simulation mode. Environmental Science 41 (7):3112–20.
   Google Scholar
• Yue, X., Y. Wu, J. Hao, Y. Pang, Y. Ma, Y. Li, B. Li, and X. Bao. 2015. Fuel quality management versus vehicle emission control in China, status quo and future perspectives. Energy Policy 79:87–98. doi:https://doi.org/10.1016/j.enpol.2015.01.009.
   Web of Science ®Google Scholar
• Yunhe, L., S. Longlin, H. Wenwei, and Mourong, G. 2013. Study on the influence factors of on-road emission characteristics of automotives. Environmental Pollution & Control 35 (10):42–45.
   Google Scholar
• Zheng, A., and Casari, A. 2019. Feature engineering for machine learning. Translated by C. Guangxin. Beijing: Posts & Telecom Press.
   Google Scholar
• Zhihua, Z. 2016. Machine learning. Beijing: Tsinghua University Press.
   Google Scholar
• Zhimei, Q., X. Yangxiong, Y. Fei, Tian, H., Deng, X., Feng, Z., Han, Y., and Bin, W. 2021. Modelling the initial emissions inspection of urban in-use vehicles with decision trees. Acta Scientiae Circumstantiae 41 (4):1574–83.
   Google Scholar