Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Abstract

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 spatiotemporal 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 (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding 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 areas 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 volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also 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.

IAQMS-street v2.0: a two-way coupled regional-urban–street-network model system for Beijing, China

“…To obtain dynamic high-resolution traffic emission inventories, a real-time on-road traffic emission model (ROE) was developed by Wu et al (2020), the street network traffic emissions was calculated by using real-time traffic-speed data, traffic volume, and vehicle emission factors.. Navigation application such as Gaode Map and Baidu Map provide the original traffic-speed data. Based on traffic big data, the traffic volume was calculated by using the Underwood speed-volume calculation formula (Greenshields et al, 1961), with the proportion of different vehicles on the road being set before using the ROE.…”

“…Based on traffic big data, the traffic volume was calculated by using the Underwood speed-volume calculation formula (Greenshields et al, 1961), with the proportion of different vehicles on the road being set before using the ROE. For a detailed description of ROE, we refer to Wu et al (2020); for the model configuration of traffic emissions in Beijing, we refer to Wang et al (2022).…”

Simulation Based-MCDM Approach for Evaluating Traffic Solutions

“…There are several technical considerations that can reduce emissions and improve safety [31][32][33]for example, the COVID-19 lockdown improved air quality [34]. Consequently, finding a solution and an appropriate methodology for measuring environmental issues is critical [31,34,35]. Furthermore, the cost of congestion is very high when measured in terms of wasted time and fuel by drivers and vehicles, respectively, in addition to the effects of pollution [36].…”

APFoam-1.0: integrated CFD simulation of O<sub>3</sub>–NO<sub>x</sub>–VOCs chemistry and pollutant dispersion in typical street canyon

“…Emissions area is set up at the bottom of street canyon with the pollutant source size of 18 m (width, WE) × 30 m (length, LE) × 0.3 m (height, HE), representing the traffic emission near street ground, and emission data are obtained from the our previous work (Wu et al, 2020). In this study, the emissions of NOx, VOCs and CO are 4.37 × 10 -8 , 2.34 × 10 -8 and 2.03 × 10 -7 kg m -3 s -1 , respectively.…”

“…From the perspective of the cause of urban air pollution, traffic-related emissions are the major part of airborne pollutant sources, including the precursors of O3, NOx ( = NO + NO2) and volatile organic compounds (VOCs) (Degraeuwe et al, 2017;Kangasniemi et al, 2019;Keyte et al, 2016;Pu and Yang, 2014;Wild et al, 2017;Wu et al, 2020). It has been believed that the production of O3 is from the NO2 photolysis.…”