| Issue |
E3S Web Conf.
Volume 360, 2022
2022 8th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2022)
|
|
|---|---|---|
| Article Number | 01027 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/e3sconf/202236001027 | |
| Published online | 23 November 2022 | |
A review of on-road vehicle emission inventory
1 Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, PR China
2 Laboratory of Transport Pollution Control and Monitoring Technology, Beijing, 100028, PR China
* Corresponding author: 674057237@qq.com
The large increase in the on-road vehicle population in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. Vehicle emission inventory is an irreplaceable tool to characterize the temporal and spatial distribution of the air pollutant and provide guidance to the policy makers with effective vehicle emission controls. This review paper reviewed two kinds of typical vehicle emission inventories. The top-down vehicle emission inventories is calculated based on the static datasets (e.g., vehicle population, vehicle kilometer traveled, and fuel consumption). These inventories could track historical emissions abatement progress and examine potential benefits from future regulations. The technological evolution in intelligent transportation systems have facilitated emission inventories to satisfy the increasing sophisticated management demand. The bottom-up link-level vehicle emission inventories are development based on the availability of the real-world traffic profiles. To simulate the temporal and spatial patterns with high-resolution, traffic demand model and machine learning methods are employed to elucidate traffic emissions.
Key words: Vehicle emission inventory / Emission factor model / Traffic demand models / Machine learning / Traffic big data
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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