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. https://doi.org/10.4209/aaqr.200059


 

ABSTRACT 


To understand the spatial and temporal distribution characteristics of the NOx emissions of urban buses, actual road NOx emissions of the buses in Kunming City were obtained by an onboard monitoring platform. A method combining Bayesians network and probabilistic inference was used to fill in the missing data so as to form a complete data set. NOx emission heat map was generated on the basis of complete data set, and the spatial autocorrelation analysis method was used to study the spatial and temporal distribution characteristics of NOx in the test process. The results showed that Bayesian networks and probabilistic reasoning methods have high accuracy in filling in missing data. The spatial autocorrelation analysis found that the spatial autocorrelation indices for morning, noon, afternoon, and evening were respectively 0.648, 0.836, 0.935, and 0.798. NOx emissions were spatially correlated at all four time periods, and pollution emissions were spatially aggregated. The heat map showed that the highest-concentration times for NOx emissions were noon and afternoon. At each time fraction, highest emissions accumulated at road sections 1–3 and 6–9, and high emission-intensity growth rates were found at road sections 5–9.


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




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