A multi-pronged approach to strengthen diesel vehicle emission monitoring

China is constructing an vehicle emission monitoring system, aimed at combining remote OBD, periodic inspections, remote sensing and roadside checks. In this study, the exhaust emissions from diesel vehicles were investigated and analysed.

Vehicle Management and Emission Control and Maintenance

South Africa range 15th as the world largest CO2 emitter contributing to 1.2% of global emission. During the Kyoto Protocol of 2014, South Africa pledged to reduce its emission by 34% and 42% in 2020 and 2025 respectively. This study is a combination of literature review from South Africa with particular emphasis on road transport. The focus was on vehicle emission with reference to Limpopo Province to demonstrate how emissions from primarily the use of diesel and petrol as one of the major contributors to CO2 emission in the province are vital for the sustainability debate. The methodology used to illustrate the dangers of vehicular emissions were based on statistical estimates from the Department of Environmental Affairs (DEA) inventory report from 2000 to 2010. The information used in assessing the vehicle emission standards in Limpopo were obtained from DEA. The findings from literature reviews in general and the results from the field survey from Limpopo Province shed some light on South Africa's vehicle emissions policy issues and standards. Also the analysis focused on the impact of vehicular fleet management and carbon emissions. The article concludes by drilling down to vehicle users, motor vehicle repairs, engine over haulers, used engine collection and disposal with respect to their roles in vehicle emission and control in South Africa.

Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.

Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission

The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (DRL) which does not utilize any reward shaping, hence exposes new insights into the traffic signal control (TSC) problem. Despite the omission of the standard measures in the rewarding scheme, the proposed approach can outperform a modern actuated control method in classic performance measures such as waiting time and queue length. Moreover, the sustainability of the realized controls is also placed under investigation to evaluate their environmental impacts. Our results show that the proposed solution goes beyond the actuated control not just in the classic measures but in emission-related measures too.