Qikai Peng1,2, Jiaqiang Li1,2, Yanyan Wang1,2, Longqing Zhao1,2, Jianwei Tan3, Chao He This email address is being protected from spambots. You need JavaScript enabled to view it.1,2

1 School of Mechanical and Transportation Engineering, Southwest Forestry University, Kunming 650224, China
2 Key Lab of Vehicle Emission and Safety on Plateau Mountain, Yunnan Provincial Department of Education, Kunming 650224, China
3 School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China


Received: July 12, 2020
Revised: December 20, 2020
Accepted: January 29, 2021

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 


Download Citation: ||https://doi.org/10.4209/aaqr.200059  

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Cite this article:

Peng, Q., Li, J., Wang, Y., Zhao, L., Tan, J., He, C. (2021). Temporal and Spatial Distribution Characteristics of NOx Emissions of City Buses on Real Road Based on Spatial Autocorrelation. Aerosol Air Qual. Res. 21, 200059. https://doi.org/10.4209/aaqr.200059


HIGHLIGHTS

  • Thermodynamic map was used to express the emission difference of urban bus.
  • Spatial autocorrelation index was used to express the degree of NOx aggregation.
  • The urban bus pollutant emission characteristics in high-altitude areas.
  • Dense household settlements are the main factor affecting pollutant emission.
 

ABSTRACT 


To characterize the spatial and temporal distribution of NOx exhausted by urban buses, we measured real-world on-road NOx emissions from these vehicles in the city of Kunming, China, using an onboard monitoring platform. To fill the data gaps and produce a complete data set, we combined Bayesian network modeling and probabilistic inference. The complete data set was then used to generate an NOx emission heat map, and spatial autocorrelation was applied to evaluate the distribution characteristics. The results show that our method for filling in the missing data provides highly accurate values, with spatial autocorrelation indices of 0.648, 0.836, 0.935, and 0.798 for the morning, midday, afternoon, and evening, respectively. The NOx emissions showed spatial correlation during all four periods, whereas the pollutive emissions showed spatial aggregation. According to the heat map, the NOx concentrations peaked during the midday and the afternoon. Furthermore, regardless of the period, the largest emissions accumulated in Road Sections 1–3 and 6–9, and the highest as well as the fastest-growing emission intensity occurred in Road Sections 5–9.


Keywords: Buses, Spatial Autocorrelation, NOx emissions, Temporal and spatial distribution characteristics




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