Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Jan 2020
Model description paper |
| 03 Jan 2020
| 03 Jan 2020
Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data
Luolin Wu
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, P. R. China
Ming Chang
Institute for Environmental and Climate Research, Jinan University,
Guangzhou 510632, P. R. China
Xuemei Wang
CORRESPONDING AUTHOR
Institute for Environmental and Climate Research, Jinan University,
Guangzhou 510632, P. R. China
Jian Hang
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, P. R. China
Jinpu Zhang
Guangzhou Environmental Monitoring Center, Guangzhou 510030, P. R.
China
Liqing Wu
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, P. R. China
Min Shao
Institute for Environmental and Climate Research, Jinan University,
Guangzhou 510632, P. R. China
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17 citations as recorded by crossref.
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- Delineating Urban Community Life Circles for Large Chinese Cities Based on Mobile Phone Data and POI Data—The Case of Wuhan H. Jiao & M. Xiao 10.3390/ijgi11110548
- Status and quality evaluation of precursor emission inventories for PM<sub>2.5</sub> and ozone in China Z. Huang et al. 10.1360/TB-2021-0783
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- Policy-Related Gains in Urban Air Quality May Be Offset by Increased Emissions in a Warming Climate C. Cao et al. 10.1021/acs.est.2c05904
- APFoam 1.0: integrated computational fluid dynamics simulation of O<sub>3</sub>–NO<sub><i>x</i></sub>–volatile organic compound chemistry and pollutant dispersion in a typical street canyon L. Wu et al. 10.5194/gmd-14-4655-2021
- 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
- A real-time NO emission inventory from heavy-duty vehicles based on on-board diagnostics big data with acceptable quality in China Z. Lv et al. 10.1016/j.jclepro.2023.138592
- Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China L. Wang et al. 10.3390/su14095434
- Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation X. Li et al. 10.1016/j.atmosenv.2023.120059
- Influence of distinct mobility scenarios on NO2, PM2.5 and PM10 street-level concentrations — A case study in a Brazilian urban neighborhood K. Cevolani et al. 10.1016/j.apr.2024.102126
- Combined Effects of Photochemical Processes, Pollutant Sources and Urban Configuration on Photochemical Pollutant Concentrations J. Liang et al. 10.3390/su15043281
- Enhancing multi-mode transport emission inventories: Combining open-source data with traditional approaches D. Lopes et al. 10.1016/j.uclim.2024.102097
- Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data X. Chen et al. 10.1016/j.scitotenv.2022.157581
- Simulation of O<sub>3</sub> and NO<sub><i>x</i></sub> in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0) M. Gavidia-Calderón et al. 10.5194/gmd-14-3251-2021
- Enhanced commercial cooking inventories from the city scale through normalized emission factor dataset and big data P. Lin et al. 10.1016/j.envpol.2022.120320
- Air Quality Forecasting with Inversely Updated Emissions for China H. Wu et al. 10.1021/acs.estlett.3c00266
1 citations as recorded by crossref.
Latest update: 14 Dec 2024
Short summary
We developed the Real-time On-road Emission (ROE v1.0) model to obtain the street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. The results are close to other emission inventories. Meanwhile, we applied our results to a street-level air quality model for studying the impact of the national holiday traffic volume change on air quality. The model can be further extended to more districts in China or other countries.
We developed the Real-time On-road Emission (ROE v1.0) model to obtain the street-scale on-road...
