On-road vehicle emission inventory and its spatio-temporal variations in North China Plain☆
Graphical abstract
Introduction
The on-road vehicle population in China has shown rapid exponential growth since the 21st century, leading to vehicle emissions becoming an important contributor to air pollution (Liang et al., 2019). The North China Plain (NCP), the second-largest plain in China, covers ∼20% of the country (Fig. 1). It contains many urban agglomerations with dense populations, including some megacities such as Beijing, Tianjin, and Shijiazhuang. It is also one of the most polluted areas in China (Ge et al., 2018; Hao et al., 2020; Zhao et al., 2020), especially the Beijing-Tianjin-Hebei (BTH) region (Zhou et al., 2015; Yang et al., 2018). For example, vehicle emissions could contribute up to 25% of ambient PM2.5 in BTH (Gao et al., 2018). It is therefore essential to clarify the characteristics of on-road vehicle emissions in order to more effectively control air pollution in the NCP.
Vehicle emission inventories are usually estimated using models such as MOBILE6 (US EPA, 2003), EMFAC 2002 (US CARB, 2002), IVE (International Vehicle Emission) (ISSRC, 2014), and MOVES (Motor Vehicle Emission Simulators) (Koupal et al., 2002), and COPERT (Computer Programme to Estimate Emissions from Road Transport) (Ntziachristos and Samaras, 2000), of which the latter is most commonly used. For example, Mensink (2000) verified Antwerp’s urban emission inventory by comparing the emission factors simulated by COPERT v2 with the emulated traffic flow and observations. COPERT v3 and v4 have been commonly used to calculate road transportation emissions and determine accurate emission factors (Burón et al., 2004; Berkowicz et al., 2006; Choudhary and Gokhale, 2019). In China, vehicle emission inventories have been established based on emission factors simulated by models and activity data collected from field investigation and statistical analyses. Emission factors simulated by COPERT were more similar to actual emissions of on-road vehicles in China than those from other models due to the analogous engine technology and overlap vehicle emission standards (Xie et al., 2006). Cai and Xie (2010) calculated the emission factors of different vehicle types under varied national standards using COPERT and established a vehicle emission factor database for China. The model has also been used to establish national and regional single- and multi-year vehicle emission inventories (Cai and Xie, 2007; Lang et al., 2014; Liu et al., 2015; Xie et al., 2017; He et al., 2018; Li et al., 2018; Zhou et al., 2019; Jiang et al., 2020). In the NCP, research has focused on specific cities or regions. For example, Jing et al. (2016) used COPERT with near-real-time traffic data along road segments to develop a vehicle emissions inventory with high spatiotemporal resolution for the Beijing urban area. Sun et al. (2017, 2019) used COPERT to establish historical and future vehicle emission inventories in Qingdao and Tianjin. Lang et al. (2012) established an on-road vehicle emission inventory in BTH in 2014. Sun et al. (2016) combined COPERT v4 with the vehicle age distribution to develop a 0.05 ° × 0.05 ° gridded vehicle emission inventory at the prefecture level in Shandong Province from 2000 to 2014.
Overall, there has been a lack of comprehensive vehicle emission inventories in the NCP that makes it difficult to develop a systematic and detailed understanding of the emission characteristics of vehicles in this region. The narrow focus of most studies means that many parts of the NCP have not been researched in terms of vehicle emission inventories. In addition, previous studies have mostly discussed only CO, NOx, PM10 and PM2.5 emissions and have not been updated for emissions after 2014. Meanwhile, most studies have applied COPERT v3 or v4, which have less detailed and comprehensive vehicle-type classifications. In contrast, COPERT v5 has a revised methodology that is more suited for handling actual on-road vehicle conditions, making the simulated data more accurate and identification more effective, as discussed in Section 2.1 of this paper (Dey et al., 2019). Therefore, this study applied the COPERT v5 model to estimate the emission factors of 8 air pollutants (BC, CO, NH3, NMVOCs, NOx, PM10, PM2.5, and SO2) from 152 sub-sources of 18 vehicle types within the NCP. Monthly emissions in 2017 were calculated at the city level with a high spatial resolution. The main sources and areas with high emission intensities for on-road vehicles in the NCP were thus identified, which could be helpful for improved air pollution control in this region.
Section snippets
COPERT v5 model
The COPERT model originated from a research program on vehicle emission factors carried out by the European Commission, and is being continuously developed and improved. It is widely used in European countries and is compatible with varied national standards and parameters (Ntziachristos and Samaras, 2000). The working conditions and engine technology used in COPERT are similar to those in China. As European emission standards can include recent national standards for on-road vehicles in China,
Emissions in 53 cities of the NCP
On-road vehicle emissions for all eight pollutants in the 53 cities as calculated by COPERT are given in Table S4. BC totaled 38.3 kt, CO 2900 kt, NH3 21.8 kt, NMVOCs 578 kt, NOx 2460 kt, PM10 113 kt, PM2.5 85.9 kt, and SO2 4.7 kt. Xuzhou had the largest BC and NOx emissions, Beijing had the largest CO, NH3, NMVOCs, and SO2 emissions, and Zhengzhou had the largest PM10 and PM2.5 emissions.
Differences in emissions between cities were attributed to different activity data (including vehicle
Conclusions
Small and medium petrol passenger cars and diesel heavy duty trucks of 20–26 t and 40–50 t of China 3 and 4 emission standards were the dominant sources of vehicle emissions in the NCP. Diesel coaches and petrol motorcycles also had a relatively higher contribution. Emission control measures specifically for passenger cars and diesel heavy duty trucks would effectively reduce the emissions from vehicles in NCP. Higher cold-start emission factors possibly contribute to higher emissions in
Data availability
The estimated BC, CO, NH3, NMVOCs, NOx, PM10, PM2.5, and SO2 emissions by each vehicle type, fuel type, and national emission standard in 53 cities are available at https://zenodo.org/record/3970338#.XyeiAIgzapo.
Credit author statement
Peiyu Jiang: Methodology, Formal analysis, Investigation, Writing - Original Draft. Xi Zhong: Writing - Reviewing and Editing. Lingyu Li: Conceptualization, Supervision, Visualization, Writing - Reviewing and Editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by National Natural Science Foundation of China (41705098) and Science and Technology Support Plan for Youth Innovation of Colleges in Shandong Province (DC2000000961).
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This paper has been recommended for acceptance by Pavlos Kassomenos.