Abstract
Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
Highlights
Rapid social and economic growth in China has driven the development of road transportation systems and mobility services over the past few decades
The Pearson r values range from 0.62 (LDTs) to 0.79 (LMDPVs) for the land-use random forest (LURF) models, which are higher than the corresponding correlation coefficients for the other four models
The r values of gradient-boosted decision trees (GBDT) for light- to medium-duty passenger vehicles (LMDPVs) are slightly better than those using LURF (0.81 vs. 0.79)
Summary
Rapid social and economic growth in China has driven the development of road transportation systems and mobility services over the past few decades. This macrotrend aligns with the faster pace of urban expansion and agglomeration, creating higher travel activities that are caused by urban commuting and by intercity connections. On-road transportation systems have resulted in substantial challenges regarding traffic congestion, carbon emissions, air pollution, and land-use issues (Uherek et al, 2010; Waddell, 2002; Chapman, 2007). To address traffic-related air pollution issues, previous studies have developed linklevel emission inventories for metropolitan areas or their urban cores. Nonlocal HDDTs are estimated to contribute nearly 30 %–40 % of the total on-road emissions of nitrogen oxides (NOx) and fine
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
