Abstract
With the rapid development of the economy, the expressway has been used as a main mode of transportation due to its function to meet traffic demand of people and thus has been given full attention. But, at the same time, it has gradually become the main cause of pollution of traffic environment. To clarify the degree of pollution caused by expressway vehicle and improve the expressway pollution diagnosis system, upon the notion of low-carbon transportation, this paper divides expressway environmental pollution into four types: air pollution, photochemical smog pollution, noise pollution, and vibration pollution, and analyzes each of them, respectively. Then, a comprehensive diagnosis model of environmental pollution caused by running vehicles will be built. This paper monitors the pollution intensity on different spots on the expressway to obtain the single-vehicle factors of various pollutants of the motor vehicles. Combined with the geographic information system, this puts forward the diagnosis methods in terms of the environmental “air pollution,” “photochemical smog pollution,” “noise pollution” and “vibration pollution” caused by the expressway vehicles, respectively, and further establishes a diagnosis model of vehicle pollution corresponding to the characteristics of the expressway. The result of the case study on the actual monitoring data of six expressways in Jiangsu Province shows that the pollution diagnosis values of six expressways are all between (0.4, 0.6] which symbolizes “slight pollution.” The research results can provide technical support for monitoring of environmental pollution caused by expressway more comprehensively and reasonably, and provide data support for formulating effective control strategies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
References
Angelevska, B., Atanasova, V., & Andreevski, I. (2021). Urban air quality guidance based on measures categorization in road transport. Civil Engineering Journal, 7(2), 253–267. https://doi.org/10.28991/cej-2021-03091651
Binnig, S., Fuchs, S., Collantes, C. A. R., et al. (2017). Exhaust gas condensate - formation, characterization and influence on platinum measuring electrodes in diesel vehicles. Sensors and Actuators b: Chemical, 242, 1251–1258. https://doi.org/10.1016/j.snb.2016.09.082
Booto, G. K., Vignisdottir, H. R., Marinelli, G., et al. (2020). Optimizing road gradients regarding earthwork cost, fuel cost, and tank-to-wheel emissions. Journal of Transportation Engineering, Part a: Systems, 146(3), 13–16. https://doi.org/10.1061/JTEPBS.0000289
Cao, Q., Li, J., Liu, Y., et al. (2019). Construction of driving cycle based on big data and Markov chain. Journal of Northeastern University (Natural Science), 64(1), 77–81. https://doi.org/10.12068/j.issn.1005-3026.2019.01.015
Carslaw, D. C., Farren, N. J., Vaughan, A. R., et al. (2019). The diminishing importance of nitrogen dioxide emissions from road vehicle exhaust. Atmospheric Environment, 1(1), 8–13. https://doi.org/10.1016/j.aeaoa.2018.100002
Curtis, A. E., Smith, T. A., Ziganshin, B. A., et al. (2016). The mystery of the Z-score. Aorta (Stamford, Connecticut), 4(4), 124–130. https://doi.org/10.12945/j.aorta.2016.16.014
D’Angelo, M., González, A. E., & Tizze, N. R. (2018). First approach to exhaust emissions characterization of light vehicles in Montevideo, Uruguay. Science of the Total Environment, 618(3), 1071–1078. https://doi.org/10.1016/j.scitotenv.2017.09.115
Feistel, R., & Hellmuth, O. (2021). Relative humidity: A control valve of the steam engine climate. Journal of Human, Earth, and Future, 2(2), 140–182. https://doi.org/10.28991/HEF-2021-02-02-06
Han, B., Jiang, Y. H., Gao, K. H., et al. (2019). Research on a novel calculation method of regional vehicle emission. Acta Metrologica Sinica, 40(2), 343–346.
Hao, L. J., Yin, H., Wang, J. F., et al. (2021). Potential of big data approach for remote sensing of vehicle exhaust emissions. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-84890-7
Hu, Q. Z., Deng, W., Tian, M. J., et al. (2019). Projection pursuit model of vehicle emission on air pollution at intersections based on the improved bat algorithm. Journal of Southeast University (english Edition), 35(3), 389–392. https://doi.org/10.3969/j.issn.1003-7985.2019.03.016
Iodice, P., Adamo, P., Capozzi, F., et al. (2016). Air pollution monitoring using emission inventories combined with the moss bag approach. Science of the Total Environment, 541(11), 1410–1419. https://doi.org/10.1016/j.scitotenv.2015.10.034
Javadinejad, S., Ostad-Ali-Askari, K., & Jafary, F. (2019). Using simulation model to determine the regulation and to optimize the quantity of chlorine injection in water distribution networks. Modeling Earth Systems and Environment, 5(3), 1015–1023. https://doi.org/10.1007/s40808-019-00587-x
Li, S. L., Zhang, X. D., Shi, J. Q., et al. (2019). Simulation and analysis of intersection signal control based on vehicle emission. Journal of Transporation Systems Engineering & Information Technology, 19(4), 72–78. https://doi.org/10.16097/j.cnki.1009-6744.2019.04.011
Lyu, M., Bao, X. F., Wang, Y. J., et al. (2020). Analysis of emissions from various driving cycles based on real driving measurements obtained in a high-altitude city. Proceedings of the Institution of Mechanical Engineers, Part d: Journal of Automobile Engineering, 234(6), 1563–1571. https://doi.org/10.1177/0954407019898959
Malik, L., Tiwari, G., Thakur, S., & Kumar, A. (2019). Assessment of freight vehicle characteristics and impact of future policy interventions on their emissions in Delhi. Transportation Research. Part D, 67(2), 610–627. https://doi.org/10.1016/j.trd.2019.01.007
Marathe, S., Nambi, A., Shrivastava, N., et al. (2020). Fault diagnosis system for low-cost air pollution sensors: demo abstract. SenSys'20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems, 611–612. https://doi.org/10.1145/3384419.3431191
Ropkins, K., DeFries, T. H., Pope, F., et al. (2017). Evaluation of EDAR vehicle emissions remote sensing technology. Science of the Total Environment, 609(12), 1464–1474. https://doi.org/10.1016/j.scitotenv.2017.07.137
Schultz, L., Shah, P., Giandomenico, E., & Chiera, B. (2016). Mean vehicle speed distributions for the spatiotemporal estimation of exhaust emissions. Environmental Modeling & Assessment, 21(2), 169–179. https://doi.org/10.1007/s10666-015-9463-5
Stanley, J., Ellison, R., Loader, C., et al. (2018). Reducing Australian motor vehicle greenhouse gas emissions. Transportation Research. Part A, 109(3), 76–88. https://doi.org/10.1016/j.tra.2018.01.002
Tang, W., Yang, Q., Huang, C., et al. (2018). Study on characteristics of pollutant emission from motor vehicles in Hangzhou based on large data analysis and IVE model. Acta Scientiae Circumstantiae, 38(1), 71–78. https://doi.org/10.13671/j.hjkxxb.2017.0241
Thangjai, W., Niwitpong, S. A., & Niwitpong, S. (2022). Bayesian confidence interval for ratio of the coefficients of variation of normal distributions: A practical approach in civil engineering. Civil Engineering Journal, 7, 135–147. https://doi.org/10.17576/jsm-2021-5001-25
Tong, R. P., Liu, J. F., Wang, W., et al. (2020). Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China. Atmospheric Environment, 223. https://doi.org/10.1016/j.atmosenv.2019.117258
Wang, B. G., Zhang, Y. H., Zhu, C. J., et al. (2001). A study on city motor vehicle emission factors by tunnel test. Environmental Science, 22(2), 55–59. https://doi.org/10.13227/j.hjkx.2001.02.011
Wang, M., Liu, X., Feng, X., et al. (2014). An investigation into the emission of in-use light gasoline vehicles in Beijing. Automotive Engineering, 36(1), 74–76. https://doi.org/10.19562/j.chinasae.qcgc.2014.01.017
Wang, W., Kong, S. F., Liu, H. B., et al. (2016). Sources and risk assessment of heavy metals in PM2.5 around 2014 Spring Festival in Nanjing. China Environmental Science, 36(7), 2186–2195.
Weng, J. C., Wang, R., Wang, M. J., et al. (2015). Fuel consumption and vehicle emission models for evaluating environmental impacts of the ETC system. Sustainability, 7(7), 8934–8949. https://doi.org/10.3390/su7078934
Yao, R. H., Long, M., Zhang, W. S., & Sun, L. (2019). Influence analysis for vehicle emission factors at signalized intersections. Journal of Beijing Jiaotong University, 43(1), 122–131. Chinese.
Zhai, Z. Q., Song, G. H., Liu, Y., et al. (2019). Characteristics of operating mode distributions of light duty vehicles by road type, average speed, and driver type for estimating on-road emissions: Case study of Beijing. Journal of Intelligent Transportation Systems, 23(2), 191–202. https://doi.org/10.1080/15472450.2018.1528447
Zou, C., Wu, L., Li, X. Y., et al. (2017). Relationship between traffic flow and temporal and spatial variations of NO2 and CO in Nanjing. Acta Scientiae Circumstantiae, 37(10), 3894–3905. https://doi.org/10.13671/j.hjkxxb.2017.0374
Funding
This research was mainly supported by the following projects, including: the National Natural Science Foundation of China (No. 51178157), the Six talent peaks project in Jiangsu Province (No. JXQC-021), the Ministry of education of Humanities and Social Science project (No. 18YJAZH028), and the Key Science and Technology Program in Henan Province (No. 182102310004).
Author information
Authors and Affiliations
Contributions
Qizhou Hu: Conceptualization, Methodology, Data curation, Supervision, Writing—original draft. Xiaoyu Wu: Writing—review & editing. Lishuang Bian: Visualization.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hu, Q., Wu, X. & Bian, L. Comprehensive diagnosis model of environmental impact caused by expressway vehicle emission. Environ Monit Assess 194, 796 (2022). https://doi.org/10.1007/s10661-022-10471-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-022-10471-4