College of Automation & College of Artifical Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou 510275, China; Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control, Guangzhou 510275, China.
J Environ Sci (China). 2023 Aug;130:126-138. doi: 10.1016/j.jes.2022.06.034. Epub 2022 Jul 14.
Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities. Considering the variability of emissions caused by vehicles, roads, and traffic, the 24-hour change characteristics of air pollutants (CO, HC, NO, PM) on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018. The results showed that the spatial diversity of emissions caused by the unbalanced economy was obvious. The vehicle emissions in the Pearl River Delta region (PRD) with a higher economic level were approximately 1-2 times those in the non-Pearl River Delta region (non-PRD). Provincial roads with high loads became potential sources of high emissions. Therefore, emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction. Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions, contributing more than 50% in the daytime (7:00-23:00) and higher than 26% at night (0:00-6:00). Diesel trucks made up 10% of traffic, but were the dominant player at night, contributed 50%-90% to NO and PM emissions, with a marked 24-hour change rule of more than 80% at night (23:00-5:00) and less than 60% during daytime. Therefore, targeted control measures by time-section should be set up on collaborative control. These findings provide time-varying decision support for variable vehicle emission control on a large scale.
准确估算城际车辆排放将有益于多城市协同控制。考虑到车辆、道路和交通引起的排放变化，根据 2018 年 7 月每小时的实际交通车辆身份识别数据，揭示了按车辆类型和道路路段划分的广东省城际道路网络中空气污染物（CO、HC、NO、PM）的 24 小时变化特征。结果表明，经济不平衡造成的排放空间差异明显。经济水平较高的珠江三角洲地区（PRD）的车辆排放量约为非珠江三角洲地区（非 PRD）的 1-2 倍。高负荷的省级道路成为高排放的潜在来源。因此，排放控制政策必须通过出行引导来强调 PRD 和重点道路，以实现更大的减排。在 24 小时内，交通比例较大的汽油客车主导早晚高峰，是 CO 和 HC 排放的主要贡献者，白天（7:00-23:00）贡献超过 50%，夜间（0:00-6:00）高于 26%。柴油卡车占交通量的 10%，但在夜间是主要参与者，对 NO 和 PM 排放的贡献为 50%-90%，夜间（23:00-5:00）变化规律明显，超过 80%，白天（7:00-23:00）不到 60%。因此，应在协同控制方面制定按时间分段的有针对性的控制措施。这些发现为大规模可变车辆排放控制提供了随时间变化的决策支持。
