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Volume 14
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10.3390/su14095434
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Article

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

by
Lu Wang
1,
Xue Chen
1,
Yan Xia
1,
Linhui Jiang
1,
Jianjie Ye
2,
Tangyan Hou
1,
Liqiang Wang
1,
Yibo Zhang
1,
Mengying Li
1,
Zhen Li
1,
Zhe Song
1,
Yaping Jiang
1,
Weiping Liu
1,
Pengfei Li
3,*,
Xiaoye Zhang
1,4 and
Shaocai Yu
1,*
1
Research Center for Air Pollution and Health, Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education, College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China
2
Bytedance Inc., Hangzhou 310058, China
3
College of Science and Technology, Hebei Agricultural University, Baoding 071000, China
4
Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5434; https://doi.org/10.3390/su14095434
Submission received: 16 February 2022 / Revised: 14 April 2022 / Accepted: 27 April 2022 / Published: 30 April 2022
(This article belongs to the Collection Air Pollution Control and Sustainable Development)
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Abstract

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure.
Keywords: big-data intelligent system; on-road vehicle emissions; traffic monitoring; hyperfine modelling; real-time visualization big-data intelligent system; on-road vehicle emissions; traffic monitoring; hyperfine modelling; real-time visualization

Share and Cite

MDPI and ACS Style

Wang, L.; Chen, X.; Xia, Y.; Jiang, L.; Ye, J.; Hou, T.; Wang, L.; Zhang, Y.; Li, M.; Li, Z.; et al. Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China. Sustainability 2022, 14, 5434. https://doi.org/10.3390/su14095434

AMA Style

Wang L, Chen X, Xia Y, Jiang L, Ye J, Hou T, Wang L, Zhang Y, Li M, Li Z, et al. Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China. Sustainability. 2022; 14(9):5434. https://doi.org/10.3390/su14095434

Chicago/Turabian Style

Wang, Lu, Xue Chen, Yan Xia, Linhui Jiang, Jianjie Ye, Tangyan Hou, Liqiang Wang, Yibo Zhang, Mengying Li, Zhen Li, and et al. 2022. "Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China" Sustainability 14, no. 9: 5434. https://doi.org/10.3390/su14095434

APA Style

Wang, L., Chen, X., Xia, Y., Jiang, L., Ye, J., Hou, T., Wang, L., Zhang, Y., Li, M., Li, Z., Song, Z., Jiang, Y., Liu, W., Li, P., Zhang, X., & Yu, S. (2022). Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China. Sustainability, 14(9), 5434. https://doi.org/10.3390/su14095434

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