Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Abstract

Abstract. Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data

“…A hyperfine-resolution bottom-up model was used to calculate the on-road vehicle emissions in Xiaoshan District, following the method of Jiang et al (2021). Hourly observations of surface temperature and humidity were used to reflect local meteorological conditions.…”

“…Equations 1-8 are the calculation methods mentioned in the measurement standard required by the Ministry of Ecological Environment (MEE, 2018). The Equation 9 is widely used in the quantification of on-road vehicle emissions in China (Wu et al, 2020;Zhang et al, 2016), which also has application cases in Hangzhou (Jiang et al, 2021;Pu et al, 2015). Thus, the Equations 1-9 are well applicable for the calculation of vehicle emissions in Hangzhou.…”

Spatially resolved hourly traffic emission over megacity Delhi using advanced traffic flow data

“…The actual traffic emission depends on several dynamic factors, such as emission factors, traffic volume, speed, vehicle age, road network and infrastructure, road type, fuel, driving behaviour, congestion etc. (Pinto et al, 2020;Jiang et al, 2021;Deng et al, 2020). Traffic emission modelling has evolved and improved over recent years, however gaps still exist because of the complexity and data involved in the emission inventory development.…”

Low Transportation Emission Analysis and Projection Using LEAP: The Case of Qatar

“…This work provides new evidence to support a large-scale shift to EVs, which can help inform air pollution reduction and public health-policy decisions. Another study by Jiang et al, provides a new and comprehensive understanding of the spatial distribution of vehicle emissions from on-road vehicles [16]. Understanding this phenomenon could be used to develop more effective traffic-control strategies and improve air quality in urban areas.…”