Development of a 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 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.

From Global to local: A multi-scale air quality modeling study over the Metropolitan Area of São Paulo

“…In Pinheiros neighborhood, the underprediction of NO X concentration is caused by the underprediction of NO concentration during the second half of the week. MUNICH with the adjusted emissions fulfills the performance criteria.O 3 concentrations simulated in Pinheiros are smaller than background concentrations, the same results are reported byWu et al (2020). As noted inKrecl et al (2016), this behavior is caused by the high NO X emissions inside the street urban canyons, which rapidly deplete the formed O 3 and the O 3 concentration over the rooftop (i.e, background concentration).…”

“…In Street-in-Grid model, background concentrations come from Polair3D air quality model (Boutahar et al, 2004). Wu et al (2020) chose measurements from a station located close to the study zone. Consequently, by using the mean wind field from WRF simulation for our study period, we selected Ibirapuera AQS measurements as background concentration, considering the pollutants advection from Ibirapuera to Pinheiros station.…”

Simulation of O<sub>3</sub> and NO<sub>X</sub> in Sao Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)

“…In the SinG model, background concentrations are the concentrations calculated by Polair3D, a mesoscale air quality model (Kim et al, 2018). Wu et al (2020) chose as the background concentration, measurements from a station located very close to the study zone. Consequently, we consider background concentration the concentration outside the MUNICH domain.…”

“…It solves pollutant reactions using a chemical mechanism, so it can also simulate the production of ozone inside the urban canyons. MUNICH has been used to simulate ozone (O 3 ) and nitrogen oxides (NO x ) by Wu et al (2020) in Tianhe District of Guangzhou city, and NO x as part of Street in Grid (SinG) model in Kim et al (2018), Thouron et al (2019) and Lugon et al (2020) in the Paris region.…”

Estimation of Emissions at Signalized Intersections Using an Improved MOVES Model with GPS Data

“…The improvement of microscopic traffic parameters in urban roads got multifarious fluctuation in signal-controlled road traffic flow [26]. Some model made systematic progress through improving the data assimilation methods and related models [27,28,29,30].…”