出版物
共 12 篇，最后更新于 2026-07-19
Cao Z, Guo L, Yu Z, Zhao X, Yin J, et al. (2026). Identify emission-abnormal heavy-duty diesel vehicles via on-board diagnostic urea analysis. Journal of Environmental Sciences. DOI: 10.1016/j.jes.2026.05.005
本研究通过分析车载诊断系统尿素消耗数据，识别重型柴油车排放异常。方法基于尿素喷射规律与排放关联性，发现尿素消耗异常车辆排放超标，为远程监管提供新手段。
Sun B, Zhang Q, Cao Z, Zhi D, Mao H. (2025). Optimizing road attributes for urban traffic decarbonization: algorithm to trend analysis. Sustainable Energy Technologies and Assessments. DOI: 10.1016/j.seta.2025.104694
本研究通过算法优化城市道路属性，结合趋势分析探索脱碳路径。结论表明，合理调整道路结构可显著降低碳排放，为交通减排提供量化依据。
Gao J, Liu Y, Yang N, Cao Z, Zhao J, et al. (2025). Spatiotemporal patterns of super-emitting diesel vehicles: A scalable remote sensing framework for urban emission hotspot mitigation. Atmospheric Pollution Research. DOI: 10.1016/j.apr.2025.102782
本研究开发了一种可扩展的远程感知框架，用于识别柴油车超级排放的时空模式，进而为城市排放热点缓解提供支持。
Jia Z, Cao Z, Yin J, Wu L, Wei N, et al. (2025). A transferable vehicle energy consumption evaluation framework for quantifying vehicle electrification energy benefits. npj Sustainable Mobility and Transport. DOI: 10.1038/s44333-025-00040-w
本文基于3-D马尔可夫链机器学习方法，构建了可迁移的车辆能耗评估框架，并以天津和西宁数据验证。研究发现，电动汽车可实现16.4%~77.8%的节能效益，但存在显著区域差异，标准化评估对推广至关重要。
Cao Z, Wang Y, Zhao X, Yin J, Jia Z, et al. (2025). Reconstructing missing NOx emissions in heavy-duty diesel vehicle OBD data: A machine learning approach. Journal of Hazardous Materials. DOI: 10.1016/j.jhazmat.2025.138619
本研究提出一种基于机器学习的方法，重建重型柴油车OBD数据中缺失的NOx排放记录。结果表明，该方法能有效提高数据完整性，为尾气排放监测提供可靠支持。
Yin J, Zhou X, Wei W, Jia Z, Fang T, et al. (2025). Laboratory measurement and machine learning-based analysis of driving factors for brake wear particle emissions from light-duty electric vehicles and heavy-duty vehicles. Journal of Hazardous Materials. DOI: 10.1016/j.jhazmat.2025.137433
通过实验室测量与机器学习分析，识别了轻型电动车与重型车辆刹车磨损颗粒物排放的关键驱动因素。结果表明，车辆类型、制动工况及材料特性显著影响排放特征。
Cao Z, Shi K, Qin H, Xu Z, Zhao X, et al. (2025). A comprehensive OBD data analysis framework: Identification and factor analysis of high-emission heavy-duty vehicles. Environmental Pollution. DOI: 10.1016/j.envpol.2025.125751
本研究基于车载诊断系统(OBD)数据，构建了重型柴油车高排放识别与因子分析框架，揭示了高排放车辆的关键影响因子，为精准减排提供了数据支撑。
Zhang Q, Yin J, Cao Z, Fang T, Peng J, et al. (2025). Size distribution, chemical composition and influencing factors of vehicle tire wear particles based on a novel test cycle. Environmental Research. DOI: 10.1016/j.envres.2025.120817
本研究基于新型测试循环，分析了轮胎磨损颗粒的粒径分布、化学组成及影响因素，揭示了颗粒排放特征与环境相关性。
Jia Z, Yin J, Fang T, Jiang Z, Zhong C, et al. (2024). Machine learning helps reveal key factors affecting tire wear particulate matter emissions. Environment International. DOI: 10.1016/j.envint.2024.109224
本研究采用机器学习方法识别影响轮胎磨损颗粒物排放的关键因素，发现避免剧烈驾驶行为、降低胎面温度及减少小花纹数量可有效减少排放。
Jia Z, Yin J, Cao Z, Wei N, Jiang Z, et al. (2024). Sustainable transportation emission reduction through intelligent transportation systems: Mitigation drivers, and temporal trends. Environmental Impact Assessment Review. DOI: 10.1016/j.eiar.2024.107767
本研究基于融合机器学习排放模型与真实轨迹数据，采用K-Means聚类分析驾驶行为。结果表明，ITSGS可减少CO2 6.09%-9.24%、NOx 11.39%-18.17%，且在市中心和非高峰时段效果更优。
Jia Z, Yin J, Cao Z, Wu L, Wei N, et al. (2024). Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China. Applied Energy. DOI: 10.1016/j.apenergy.2024.124626
本研究提出区域车辆能源消耗评估框架，以中国西宁为例，量化高原城市车辆电动化效益。方法整合车辆活动与能源模型，发现电动化可显著降低化石燃料消耗与碳排放。
Jia Z, Yin J, Cao Z, Wei N, Jiang Z, et al. (2024). Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation. The Science of The Total Environment. DOI: 10.1016/j.scitotenv.2024.174724
本文通过大规模部署智能交通系统，结合大数据优化交通流和信号控制，显著降低了碳排放与污染物浓度，验证了该技术在实现可持续交通中的有效性。