A license plate recognition data to estimate and visualise the restriction policy for diesel vehicles on urban air quality: A case study of Shenzhen. (1st March 2022)

• Record Type: Journal Article
• Authors: Huang, Wenke; Xu, Xiaoxiao; Hu, Mingwei; Huang, Wenwei
• Is Part Of: Journal of cleaner production. Volume 338(2022)
• Publication Date: 2022-03-01
• Subjects: Diesel vehicle -- License plate recognition data -- Environmental effect -- Traffic policy; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.130401
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Diesel vehicles for road freight are primary contributors to PM2.5 and NOX emissions in numerous cities. Shenzhen, which is a megacity in China, has made efforts to promote the transition to green transport by implementing license plate restrictions. Nevertheless, it is still unclear whether the restrictions have greatly improved urban air quality. An effective framework for accurately estimating and visualising the effect of restriction policy on a large scale is still lacking. Therefore, this study aims to develop a novel method to visualise and evaluate the effect of license plate restriction policy by bridging diesel truck's license plate recognition data to emission inventories. The results reveal that the impact of the peak restriction on air quality was limited if it only affected nonlocal diesel vehicles. While the promotion of eco-friendly vehicles could reduce PM2.5 and NOX emissions. The findings could provide references for other cities or countries to estimate air pollution from diesel vehicles and recognise high emission zones at a large scale and thus create effective policies and initiatives. Highlights: The environmental effect of license plate restriction on nonlocal diesel vehicles is limited. Urban unrestricted roads in city centres have low emissions reduction potential. The highest emissions factor was generally located at intersections and connectors. The replacement of 30% local LDTs and buses with eco-friendly trucks can reduce 12.4% of PM2.5