Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, 211189, People's Republic of China.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, 211189, People's Republic of China.
Environ Sci Pollut Res Int. 2021 Jul;28(27):36092-36101. doi: 10.1007/s11356-021-12945-3. Epub 2021 Mar 8.
Buses in urban have environmental problems because they are mostly having higher emission factors and pollution levels. This study analyzed the contributing factors on bus emissions including NO, CO, HC, and CO and further evaluated the impact degree of these factors. A back-propagation neural network (BPNN) was applied, and the results showed that the composition of pollutant emissions for different fuel types was various. BPNN can be utilized to solve the multifactor, uncertainty, and nonlinearity problems without making any prior presumptions about the data distribution. Among them, diesel buses under EURO-IV and EURO-V emission standards were more likely to produce higher emissions of CO and NO. By contrast, the emission level of CO and NO for compressed natural gas bus was lower, but the emission level of CO and HC was heavier. In this study, nine variables, namely, speed, acceleration, passenger load, past speed, past acceleration, acceleration time, delay time, stops, and location were selected to investigate their effects on bus emissions. The results showed that factors delay time, location, and stops had the strongest impacts on bus emissions. By contrast, bus emissions were not sensitive to past speed and passenger load. In addition, to fully understand the influence of contributing factors, the impact degree of all these factors on bus emissions was summarized in this study.
城市公交车存在环境问题，因为它们的排放因子和污染水平通常较高。本研究分析了影响公交车排放的因素，包括 NO、CO、HC 和 CO，并进一步评估了这些因素的影响程度。应用了反向传播神经网络（BPNN），结果表明，不同燃料类型的污染物排放组成不同。BPNN 可用于解决多因素、不确定性和非线性问题，而无需对数据分布做出任何先验假设。其中，符合 EURO-IV 和 EURO-V 排放标准的柴油公交车更有可能产生更高水平的 CO 和 NO 排放。相比之下，压缩天然气公交车的 CO 和 NO 排放水平较低，但 CO 和 HC 的排放水平较重。在本研究中，选择了九个变量，即速度、加速度、乘客负荷、过去速度、过去加速度、加速度时间、延迟时间、站点和位置，以研究它们对公交车排放的影响。结果表明，延迟时间、位置和站点是对公交车排放影响最大的因素。相比之下，过去速度和乘客负荷对公交车排放的影响不敏感。此外，为了充分了解影响因素的影响，本研究总结了所有这些因素对公交车排放的影响程度。
