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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-74
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-74
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 10 Apr 2019

Submitted as: model description paper | 10 Apr 2019

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This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.

Development of a real-time on-road emission (ROE v1.0)model for street-scale air quality modeling based on dynamic traffic big data

Luolin Wu1, Ming Chang2, Xuemei Wang2, Jian Hang1, and Jinpu Zhang3 Luolin Wu et al.
  • 1School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, P. R. China
  • 2Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, P. R. China
  • 3Guangzhou Environmental Monitoring Center, Guangzhou 510030, P. R. China

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatial–temporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HC), PM10, and PM2.5 to be 35.22 × 104 Mg/a, 12.05 × 104 Mg/a, 4.10 × 104 Mg/a, 0.49 × 104 Mg/a, and 0.55 × 104 Mg/a, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive area and suburban town centers. Emission contributions show that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles in urban areas and LDVs and heavy-duty trucks in suburban areas, indicating that the traffic control policies regarding duty trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban area by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of VOCs and NOx. Thus, not only the on-road emission, but other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

Luolin Wu et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Luolin Wu et al.
Luolin Wu et al.
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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 the applied our results to 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...
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