Articles | Volume 21, issue 4
https://doi.org/10.5194/acp-21-2917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-2917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2021
Research article |
| 26 Feb 2021
| 26 Feb 2021
Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Xingpei Ye
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Shibao Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Xiaojing He
School of Environment, Nanjing University, Nanjing, China
Lingyao Dong
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Ning Zhang
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Nanjing University, Nanjing, China
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Zhongrui Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Yun Ma
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Lei Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Xuguang Chi
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Aijun Ding
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Mingzhi Yao
Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
Yunpeng Li
Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
Qilin Li
Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
Ling Zhang
Hebei Saihero Environmental Protection Hi-tech. Company Ltd.,
Shijiazhuang, Hebei, China
Yongle Xiao
Hebei Saihero Environmental Protection Hi-tech. Company Ltd.,
Shijiazhuang, Hebei, China
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Total article views: 3,392 (including HTML, PDF, and XML)
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Cited
20 citations as recorded by crossref.
- MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling Y. Kim et al. 10.5194/gmd-15-7371-2022
- Air Quality-Driven Traffic Management Using High-Resolution Urban Climate Modeling Coupled with a Large Traffic Simulation J. Laudan et al. 10.3390/atmos16020128
- High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network Y. Sun et al. 10.3390/s22166005
- Investigation of the Spatiotemporal Distribution of PM10, PM2.5, and PM1 from Motor Vehicles in Roadside Environments V. Bachtiar et al. 10.48084/etasr.9281
- Dispersion of particulate matter (PM2.5) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations T. Wolf et al. 10.5194/acp-21-12463-2021
- Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown S. Wang et al. 10.5194/acp-21-7199-2021
- Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment J. Zhong et al. 10.1007/s44223-022-00008-7
- High Resolution Modelling of Traffic Emissions Using the Large Eddy Simulation Code Fluidity H. Woodward et al. 10.3390/atmos13081203
- Detached eddy simulation of traffic-induced air pollution in an urban highway with complex surrounding morphology E. Ghane-Tehrani et al. 10.1016/j.apr.2024.102331
- Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements S. Metzger et al. 10.5194/amt-14-6929-2021
- Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions A. Petrov et al. 10.3390/atmos15060638
- An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality S. Wang & Y. Zhang 10.1016/j.atmosenv.2024.120921
- Improving air quality through urban form optimization: A review study S. Li et al. 10.1016/j.buildenv.2023.110685
- Advancements in machine learning for spatiotemporal urban on-road traffic-air quality study: A review Z. Du et al. 10.1016/j.atmosenv.2025.121054
- The Effects of 2D and 3D Urban Morphology on Air Quality Y. Liu & H. Wang 10.1007/s11270-023-06592-2
- Development and application of a multi-scale modeling framework for urban high-resolution NO2 pollution mapping Z. Lv et al. 10.5194/acp-22-15685-2022
- A two-way coupled regional urban–street network air quality model system for Beijing, China T. Wang et al. 10.5194/gmd-16-5585-2023
- The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing T. Kokkonen et al. 10.1039/D1EA00035G
- Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions J. Resler et al. 10.5194/gmd-17-7513-2024
- Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data S. Wang et al. 10.1016/j.envpol.2024.123371
20 citations as recorded by crossref.
- MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling Y. Kim et al. 10.5194/gmd-15-7371-2022
- Air Quality-Driven Traffic Management Using High-Resolution Urban Climate Modeling Coupled with a Large Traffic Simulation J. Laudan et al. 10.3390/atmos16020128
- High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network Y. Sun et al. 10.3390/s22166005
- Investigation of the Spatiotemporal Distribution of PM10, PM2.5, and PM1 from Motor Vehicles in Roadside Environments V. Bachtiar et al. 10.48084/etasr.9281
- Dispersion of particulate matter (PM2.5) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations T. Wolf et al. 10.5194/acp-21-12463-2021
- Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown S. Wang et al. 10.5194/acp-21-7199-2021
- Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment J. Zhong et al. 10.1007/s44223-022-00008-7
- High Resolution Modelling of Traffic Emissions Using the Large Eddy Simulation Code Fluidity H. Woodward et al. 10.3390/atmos13081203
- Detached eddy simulation of traffic-induced air pollution in an urban highway with complex surrounding morphology E. Ghane-Tehrani et al. 10.1016/j.apr.2024.102331
- Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements S. Metzger et al. 10.5194/amt-14-6929-2021
- Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions A. Petrov et al. 10.3390/atmos15060638
- An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality S. Wang & Y. Zhang 10.1016/j.atmosenv.2024.120921
- Improving air quality through urban form optimization: A review study S. Li et al. 10.1016/j.buildenv.2023.110685
- Advancements in machine learning for spatiotemporal urban on-road traffic-air quality study: A review Z. Du et al. 10.1016/j.atmosenv.2025.121054
- The Effects of 2D and 3D Urban Morphology on Air Quality Y. Liu & H. Wang 10.1007/s11270-023-06592-2
- Development and application of a multi-scale modeling framework for urban high-resolution NO2 pollution mapping Z. Lv et al. 10.5194/acp-22-15685-2022
- A two-way coupled regional urban–street network air quality model system for Beijing, China T. Wang et al. 10.5194/gmd-16-5585-2023
- The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing T. Kokkonen et al. 10.1039/D1EA00035G
- Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions J. Resler et al. 10.5194/gmd-17-7513-2024
- Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data S. Wang et al. 10.1016/j.envpol.2024.123371
Latest update: 29 Jul 2025
Short summary
Urban air quality varies drastically at street scale, but traditional methods are too coarse to resolve it. We develop a 10 m resolution air quality model and apply it for traffic-related carbon monoxide air quality in Nanjing megacity. The model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network and agrees well with a validation dataset. The model can be a vigorous part of the smart city system and inform urban planning and air quality management.
Urban air quality varies drastically at street scale, but traditional methods are too coarse to...
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