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.