Emission characteristics and high-resolution spatial and temporal distribution of pollutants from motor vehicles in Chengdu, China

Considering the uncertainties of the trafficflows and emission factors, the on-road mobile source emission inventory shall be further improved to provideimportant scientific research supporting for the prevention and control of air pollution in Chengdu.

📌 3 Key Takeaways

🎯

Objective & Hypothesis:The establishment of the air pollutant emission inventory of urban motor vehicles is the key to control motor vehicle pollution; therefore, the purpose of this paper is to develop an air pollutant emission inventory of urbanmotor vehicles in Chengdu.

📊

Key Findings & Data:The IVE model localization including the actual measurement correction wascompleted, the motor vehicle emission factors on various types of roads in Chengdu's different developmentareas were calculated, and the 1 km×1 km on-road mobile source emission inventory for the year of 2016 wasestablished in the bottom-up method based on the collected data on road network, traffic flow, vehicle drivingcycle, the vehicle population in Chengdu, etc.

💡

Conclusion & Impact:Considering the uncertainties of the trafficflows and emission factors, the on-road mobile source emission inventory shall be further improved to provideimportant scientific research supporting for the prevention and control of air pollution in Chengdu.

🔬Design:Modeling & Simulation

📍Scope:2016 Verileri

⚙️Variables:Domain Parameters

Özet

The establishment of the air pollutant emission inventory of urban motor vehicles is the key to control motor vehicle pollution; therefore, the purpose of this paper is to develop an air pollutant emission inventory of urbanmotor vehicles in Chengdu. The IVE model localization including the actual measurement correction wascompleted, the motor vehicle emission factors on various types of roads in Chengdu's different developmentareas were calculated, and the 1 km×1 km on-road mobile source emission inventory for the year of 2016 wasestablished in the bottom-up method based on the collected data on road network, traffic flow, vehicle drivingcycle, the vehicle population in Chengdu, etc. The inventory showed that the CO, VOCs, NOx, SO2, PM10 and NH3emissions of Chengdu's on-road mobile sources had reached 432.2 kt, 44.8 kt, 72.4 kt, 0.4 kt, 11.3 kt and 6.2 ktrespectively in 2016. Light-duty vehicle, medium-duty vehicle, and heavy-duty vehicle were the major contributorsto CO emissions, and their CO emissions accounted for 44%, 14% and 11% of the total CO emissionrespectively; VOCs mainly came from light-duty vehicle, motorcycle, and heavy-duty truck, the VOCs emissionsof which accounted for 49%, 19% and 10% of the total VOCs emission respectively; NOx and SO2 mainly camefrom light-duty vehicle and heavy-duty truck, the NOx emissions of which accounted for 25% and 48% of thetotal NOx emission, and the SO2 emissions of which accounted for 70% and 14% of the total SO2 emissionrespectively; in addition, the heavy-duty truck caused PM10 emission accounting for 78% of the total PM10emission, while the light-duty vehicle caused NH3 emission accounting for 81% of the total NH3 emission. Thespatial distribution of on-road mobile source pollutant emissions showed that emissions were concentrated in thedowntown with a decreasing trend from the downtown to the suburbs and the outer suburbs. Based on the 95%confidence intervals, the uncertainties of CO, VOCs and NOx emission estimations were relatively low withrelative errors ranging from - 68% to 87%, 58%–84% and 97%–75%. Considering the uncertainties of the trafficflows and emission factors, the on-road mobile source emission inventory shall be further improved to provideimportant scientific research supporting for the prevention and control of air pollution in Chengdu.

🔖

Cite This Article

ZHOU, Z., TAN, Q., LIU, H., DENG, Y., WU, K., LU, C., ZHOU, X. (2019). Emission characteristics and high-resolution spatial and temporal distribution of pollutants from motor vehicles in Chengdu, China. Atmospheric Pollution Research, Cilt: 10 - Sayı: 3, 749–758. https://ajindex.com/articles/351560

References (8)

Beevers, S.D., Carslaw, D.C., 2005. The impact of congestion charging on vehicle emissions in London. Atmos. Environ. 39 (1), 1–5.
Cai, H., Xie, S.D., 2009. Tempo-spatial variation of emission inventories of speciated volatile organic compounds from on-road vehicles in China. Atmos. Chem. Phys. Discuss. 9 (3), 6983–7002.
Cao, G.L., Zhang, X.Y., Gong, S.L., An, X.Q., Wang, Y.Q., 2011. Emission inventories of primary particles and pollutant gases for China. Chin. Sci. Bull. 56 (8), 781–788.
Che, W.W., Zheng, J.Y., Zhong, L.J., 2009. Vehicle exhaust emission characteristics and contributions in the Pearl River Delta region. Res. Environ. Sci. 35 (7), 2016–2024 (in Chinese). Chen, J.H., Fan, W.B., Qian, J., Li, Y., Zhao, W.Y., 2015. Establishment of the light-duty gasoline vehicle emission inventory in Chengdu by the International Vehicle Emission model. Acta Sci. Circumstantiae 35 (7), 2016–2024 (in Chinese). Cheng, S.Y., Chen, D.S., Li, J.B., Wang, H.Y., Guo, X.R., 2007. The assessment of emissionsource contributions to air quality by using a coupled MM5-ARPS-CMAQ modeling system: a case study in the Beijing metropolitan region, China. Environ. Model. Software 22 (11), 1601–1616.
Cheng, Y., Yu, L., Wang, H.T., Hao, Y.Z., Song, G.H., 2011. Comparative study of MOBILE and COPERT emission models based on PEMS. J. Transp. Syst. Eng. Inf. Technol. 11 (3), 176–181 (in Chinese). Cook, R., Touma, J.S., Beidler, A., Strum, M., 2006. Preparing highway emissions inventories for urban scale modeling: a case study in Philadelphia. Transport. Res. Transport Environ. 11 (6), 396–407.
Cullen, A.C., Frey, H.C., 1999. The Use of Probabilistic Techniques in Exposure Assessment: a Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Plenum, New York. Fan, S.B., Tian, L.D., Zhang, D.X., Qu, S., 2015. Emission characteristics of vehicle exhaust in Beijing based on actual traffic flow information. Environ. Sci. 36 (8), 2750–2757 (in Chinese). Ferreira, J., Guevara, M., Baldasano, J.M., Tchepel, O., Schaap, M., Miranda, A.I., Borrego, C., 2013. A comparative analysis of two highly spatially resolved European atmospheric emission inventories. Atmos. Environ. 75 (4), 43–57.
Frey, H.C., Zheng, J.Y., 2002. Probabilistic analysis of driving cycle-based highway vehicle emission factors. Environ. Sci. Technol. 36, 5184–5191.
Funk, T.H., Stiefer, P.S., Chinkin, L.R., 2001. Development of Gridded Spatial Allocation Factors for the State of texas. Texas Natural Resourc