Articles | Volume 25, issue 11
https://doi.org/10.5194/acp-25-5537-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-5537-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data
Yujia Wang
Environmental Research Institute, Shandong University, Qingdao 266237, China
Hongbin Wang
Traffic Police Detachment of Jinan Public Security Bureau, Jinan 250014, China
Bo Zhang
Traffic Police Detachment of Jinan Public Security Bureau, Jinan 250014, China
Peng Liu
Traffic Police Detachment of Jinan Public Security Bureau, Jinan 250014, China
Environmental Research Institute, Shandong University, Qingdao 266237, China
Shuchun Si
School of Physics, Shandong University, Jinan 250100, China
Likun Xue
Environmental Research Institute, Shandong University, Qingdao 266237, China
Qingzhu Zhang
Environmental Research Institute, Shandong University, Qingdao 266237, China
Qiao Wang
Environmental Research Institute, Shandong University, Qingdao 266237, China
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Atmos. Chem. Phys., 25, 4767–4783, https://doi.org/10.5194/acp-25-4767-2025, https://doi.org/10.5194/acp-25-4767-2025, 2025
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Youwei Hong, Keran Zhang, Dan Liao, Gaojie Chen, Min Zhao, Yiling Lin, Xiaoting Ji, Ke Xu, Yu Wu, Ruilian Yu, Gongren Hu, Sung-Deuk Choi, Likun Xue, and Jinsheng Chen
Atmos. Chem. Phys., 23, 10795–10807, https://doi.org/10.5194/acp-23-10795-2023, https://doi.org/10.5194/acp-23-10795-2023, 2023
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Junjun Deng, Hao Ma, Xinfeng Wang, Shujun Zhong, Zhimin Zhang, Jialei Zhu, Yanbing Fan, Wei Hu, Libin Wu, Xiaodong Li, Lujie Ren, Chandra Mouli Pavuluri, Xiaole Pan, Yele Sun, Zifa Wang, Kimitaka Kawamura, and Pingqing Fu
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Han Zang, Yue Zhao, Juntao Huo, Qianbiao Zhao, Qingyan Fu, Yusen Duan, Jingyuan Shao, Cheng Huang, Jingyu An, Likun Xue, Ziyue Li, Chenxi Li, and Huayun Xiao
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Particulate nitrate plays an important role in wintertime haze pollution in eastern China, yet quantitative constraints on detailed nitrate formation mechanisms remain limited. Here we quantified the contributions of the heterogeneous N2O5 hydrolysis (66 %) and gas-phase OH + NO2 reaction (32 %) to nitrate formation in this region and identified the atmospheric oxidation capacity (i.e., availability of O3 and OH radicals) as the driving factor of nitrate formation from both processes.
Chaoyang Xue, Can Ye, Jörg Kleffmann, Chenglong Zhang, Valéry Catoire, Fengxia Bao, Abdelwahid Mellouki, Likun Xue, Jianmin Chen, Keding Lu, Yong Zhao, Hengde Liu, Zhaoxin Guo, and Yujing Mu
Atmos. Chem. Phys., 22, 3149–3167, https://doi.org/10.5194/acp-22-3149-2022, https://doi.org/10.5194/acp-22-3149-2022, 2022
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Summertime measurements of nitrous acid (HONO) and related parameters were conducted at the foot and the summit of Mt. Tai (1534 m above sea level). We proposed a rapid vertical air mass exchange between the foot and the summit level, which enhances the role of HONO in the oxidizing capacity of the upper boundary layer. Kinetics for aerosol-derived HONO sources were constrained. HONO formation from different paths was quantified and discussed.
Taotao Liu, Youwei Hong, Mengren Li, Lingling Xu, Jinsheng Chen, Yahui Bian, Chen Yang, Yangbin Dan, Yingnan Zhang, Likun Xue, Min Zhao, Zhi Huang, and Hong Wang
Atmos. Chem. Phys., 22, 2173–2190, https://doi.org/10.5194/acp-22-2173-2022, https://doi.org/10.5194/acp-22-2173-2022, 2022
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Based on the OBM-MCM model analyses, the study aims to clarify (1) the pollution characteristics of O3 and its precursors, (2) the atmospheric oxidation capacity and radical chemistry, and (3) the O3 formation mechanism and sensitivity analysis. The results are expected to enhance the understanding of the O3 formation mechanism with low O3 precursor levels and provide scientific evidence for O3 pollution control in coastal cities.
Men Xia, Xiang Peng, Weihao Wang, Chuan Yu, Zhe Wang, Yee Jun Tham, Jianmin Chen, Hui Chen, Yujing Mu, Chenglong Zhang, Pengfei Liu, Likun Xue, Xinfeng Wang, Jian Gao, Hong Li, and Tao Wang
Atmos. Chem. Phys., 21, 15985–16000, https://doi.org/10.5194/acp-21-15985-2021, https://doi.org/10.5194/acp-21-15985-2021, 2021
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ClNO2 is an important precursor of chlorine radical that affects photochemistry. However, its production and impact are not well understood. Our study presents field observations of ClNO2 at three sites in northern China. These observations provide new insights into nighttime processes that produce ClNO2 and the significant impact of ClNO2 on secondary pollutions during daytime. The results improve the understanding of photochemical pollution in the lower part of the atmosphere.
Yingnan Zhang, Likun Xue, William P. L. Carter, Chenglei Pei, Tianshu Chen, Jiangshan Mu, Yujun Wang, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 21, 11053–11068, https://doi.org/10.5194/acp-21-11053-2021, https://doi.org/10.5194/acp-21-11053-2021, 2021
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We developed the localized incremental reactivity (IR) for VOCs in a Chinese megacity and elucidated their applications in calculating the ozone formation potential (OFP). The IR scales showed a strong dependence on chemical mechanisms. Both emission- and observation-based inputs are suitable for the MIR calculation but not the case under mixed-limited or NOx-limited O3 formation regimes. We provide suggestions for the application of IR and OFP scales to aid in VOC control in China.
Yujiao Zhu, Likun Xue, Jian Gao, Jianmin Chen, Hongyong Li, Yong Zhao, Zhaoxin Guo, Tianshu Chen, Liang Wen, Penggang Zheng, Ye Shan, Xinfeng Wang, Tao Wang, Xiaohong Yao, and Wenxing Wang
Atmos. Chem. Phys., 21, 1305–1323, https://doi.org/10.5194/acp-21-1305-2021, https://doi.org/10.5194/acp-21-1305-2021, 2021
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This work investigates the long-term changes in new particle formation (NPF) events under reduced SO2 emissions at the summit of Mt. Tai during seven campaigns from 2007 to 2018. We found the NPF intensity increased 2- to 3-fold in 2018 compared to 2007. In contrast, the probability of new particles growing to CCN size largely decreased. Changes to biogenic VOCs and anthropogenic emissions are proposed to explain the distinct NPF characteristics.
Jiarong Li, Chao Zhu, Hui Chen, Defeng Zhao, Likun Xue, Xinfeng Wang, Hongyong Li, Pengfei Liu, Junfeng Liu, Chenglong Zhang, Yujing Mu, Wenjin Zhang, Luming Zhang, Hartmut Herrmann, Kai Li, Min Liu, and Jianmin Chen
Atmos. Chem. Phys., 20, 13735–13751, https://doi.org/10.5194/acp-20-13735-2020, https://doi.org/10.5194/acp-20-13735-2020, 2020
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Based on a field study at Mt. Tai, China, the simultaneous variations of cloud microphysics, aerosol microphysics and their potential interactions during cloud life cycles were discussed. Results demonstrated that clouds on clean days were more susceptible to the concentrations of particle number, while clouds formed on polluted days might be more sensitive to meteorological parameters. Particles larger than 150 nm played important roles in forming cloud droplets with sizes of 5–10 μm.
Ying Jiang, Likun Xue, Rongrong Gu, Mengwei Jia, Yingnan Zhang, Liang Wen, Penggang Zheng, Tianshu Chen, Hongyong Li, Ye Shan, Yong Zhao, Zhaoxin Guo, Yujian Bi, Hengde Liu, Aijun Ding, Qingzhu Zhang, and Wenxing Wang
Atmos. Chem. Phys., 20, 12115–12131, https://doi.org/10.5194/acp-20-12115-2020, https://doi.org/10.5194/acp-20-12115-2020, 2020
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We analyzed the characteristics and sources of HONO in the upper boundary layer and lower free troposphere in the North China Plain, based on the field measurements at Mount Tai. Higher-than-expected levels and broad daytime peaks of HONO were observed. Without presence of ground surfaces, aerosol surface plays a key role in the heterogeneous HONO formation at high altitudes. Models without additional HONO sources largely
underestimatedthe oxidation processes in the elevation atmospheres.
Cited articles
Apte, J. S. and Manchanda, C.: High-resolution urban air pollution mapping, Science, 385, 380–385, https://doi.org/10.1126/science.adq3678, 2024.
Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C. H., and Hamburg, S. P.: High-resolution air pollution mapping with Google Street View cars: Exploiting big data, Environ. Sci. Technol., 51, 6999–7008, https://doi.org/10.1021/acs.est.7b00891, 2017.
Barreto, E., Holden, P. B., Edwards, N. R., and Rangel, T. F.: PALEO-PGEM-Series: A spatial time series of the global climate over the last 5 million years (Plio-Pleistocene), Global Ecol. Biogeogr., 32, 1034–1045, https://doi.org/10.1111/geb.13683, 2023.
Belalcazar, L. C., Clappier, A., Blond, N., Flassak, T., and Eichhorn, J.: An evaluation of the estimation of road traffic emission factors from tracer studies, Atmos. Environ., 44, 3814–3822, https://doi.org/10.1016/j.atmosenv.2010.06.038, 2010.
Böhm, M., Nanni, M., and Pappalardo, L.: Gross polluters and vehicle emissions reduction, Nat. Sustain., 5, 699–707, https://doi.org/10.1038/s41893-022-00903-x, 2022.
Boleti, E., Hueglin, C., Grange, S. K., Prévôt, A. S. H., and Takahama, S.: Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach, Atmos. Chem. Phys., 20, 9051–9066, https://doi.org/10.5194/acp-20-9051-2020, 2020.
Brimblecombe, P., Townsend, T., Lau, C. F., Rakowska, A., Chan, T. L., Moènik, G., and Ning, Z.: Through-tunnel estimates of vehicle fleet emission factors, Atmos. Environ., 123, 180–189, https://doi.org/10.1016/j.atmosenv.2015.10.086, 2015.
Cai, H. and Xie, S.: Estimation of vehicular emission inventories in China from 1980 to 2005, Atmos. Environ., 41, 8963–8979, https://doi.org/10.1016/j.atmosenv.2007.08.019, 2007.
Camastra, F., Capone, V., Ciaramella, A., Riccio, A., and Staiano, A.: Prediction of environmental missing data time series by support vector machine regression and correlation dimension estimation, Environ. Model. Softw. Environ. Data News, 150, 105343, https://doi.org/10.1016/j.envsoft.2022.105343, 2022.
Chen, J., Li, W., Zhang, H., Jiang, W., Li, W., Sui, Y., Song, X., and Shibasaki, R.: Mining urban sustainable performance: GPS data-based spatio-temporal analysis on on-road braking emission, J. Clean. Prod., 270, 122489, https://doi.org/10.1016/j.jclepro.2020.122489, 2020.
China State Council: Notice of the State Council on Issuing the “2024–2025 energy conservation and carbon reduction action plan” (in Chinese), https://www.gov.cn/zhengce/content/202405/content_6954322.htm, last access: 3 June 2024.
Choi, W., Ho, C.-H., and Lee, Y.: Temporal pattern classification of PM2.5 chemical compositions in Seoul, Korea using K-means clustering analysis, Sci. Total Environ., 927, 172157, https://doi.org/10.1016/j.scitotenv.2024.172157, 2024.
Conte, M. and Contini, D.: Size-resolved particle emission factors of vehicular traffic derived from urban eddy covariance measurements, Environ. Pollut., 251, 830–838, https://doi.org/10.1016/j.envpol.2019.05.029, 2019.
Davison, J., Bernard, Y., Borken-Kleefeld, J., Farren, N. J., Hausberger, S., Sjödin, Å., Tate, J. E., Vaughan, A. R., and Carslaw, D. C.: Distance-based emission factors from vehicle emission remote sensing measurements, Sci. Total Environ., 739, 139688, https://doi.org/10.1016/j.scitotenv.2020.139688, 2020.
Deng, F., Lv, Z., Qi, L., Wang, X., Shi, M., and Liu, H.: A big data approach to improving the vehicle emission inventory in China, Nat. Commun., 11, 2801, https://doi.org/10.1038/s41467-020-16579-w, 2020.
Ding, H., Cai, M., Lin, X., Chen, T., Li, L., and Liu, Y.: RTVEMVS: Real-time modeling and visualization system for vehicle emissions on an urban road network, J. Clean. Prod., 309, 127166, https://doi.org/10.1016/j.jclepro.2021.127166, 2021.
Ding, H., Zhao, Y., Miao, S., Chen, T., and Liu, Y.: Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data, J. Environ. Sci., 130, 126–138, https://doi.org/10.1016/j.jes.2022.06.034, 2023.
Esri: Optimized Hot Spot Analysis (Spatial Statistics), https://pro.arcgis.com/en/pro-app/3.0/tool-reference/spatial-statistics/optimized-hot-spot-analysis.htm, last access: 29 May 2024.
Fameli, K. M. and Assimakopoulos, V. D.: Development of a road transport emission inventory for Greece and the Greater Athens Area: Effects of important parameters, Sci. Total Environ., 505, 770–786, https://doi.org/10.1016/j.scitotenv.2014.10.015, 2015.
Feng, H., Ning, E., Yu, L., Wang, X., and Vladimir, Z.: The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory, Environ. Int., 181, 108287–108287, https://doi.org/10.1016/j.envint.2023.108287, 2023.
Ghaffarpasand, O., Talaie, M. R., Ahmadikia, H., Khozani, A. T., and Shalamzari, M. D.: A high-resolution spatial and temporal on-road vehicle emission inventory in an Iranian metropolitan area, Isfahan, based on detailed hourly traffic data, Atmos. Pollut. Res., 11, 1598–1609, https://doi.org/10.1016/j.apr.2020.06.006, 2020.
Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao, M., Zeng, L., Molina, M. J., and Zhang, R.: Elucidating severe urban haze formation in China, P. Natl. Acad. Sci. USA, 111, 17373–17378, https://doi.org/10.1073/pnas.1419604111, 2014.
He, J., Wu, L., Mao, H., Liu, H., Jing, B., Yu, Y., Ren, P., Feng, C., and Liu, X.: Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality, Atmos. Chem. Phys., 16, 3171–3184, https://doi.org/10.5194/acp-16-3171-2016, 2016.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, C., Tao, S., Lou, S., Hu, Q., Wang, H., Wang, Q., Li, L., Wang, H., Liu, J., Quan, Y., and Zhou, L.: Evaluation of emission factors for light-duty gasoline vehicles based on chassis dynamometer and tunnel studies in Shanghai, China, Atmos. Environ., 169, 193–203, https://doi.org/10.1016/j.atmosenv.2017.09.020, 2017.
Jaikumar, R., Shiva Nagendra, S. M., and Sivanandan, R.: Modal analysis of real-time, real world vehicular exhaust emissions under heterogeneous traffic conditions, Transport Res. D-Tr. E., 54, 397–409, https://doi.org/10.1016/j.trd.2017.06.015, 2017.
Jeong, S., Park, J., Kim, Y. M., Park, M. H., and Kim, J. Y.: Innovation of flux chamber network design for surface methane emission from landfills using spatial interpolation models, Sci. Total Environ., 688, 18–25, https://doi.org/10.1016/j.scitotenv.2019.06.142, 2019.
Jiang, L., Xia, Y., Wang, L., Chen, X., Ye, J., Hou, T., Wang, L., Zhang, Y., Li, M., Li, Z., Song, Z., Jiang, Y., Liu, W., Li, P., Rosenfeld, D., Seinfeld, J. H., and Yu, S.: Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system, Atmos. Chem. Phys., 21, 16985–17002, https://doi.org/10.5194/acp-21-16985-2021, 2021.
Liang, X., Zhang, S., Wu, Y., Xing, J., He, X., Zhang, K. M., Wang, S., and Hao, J.: Air quality and health benefits from fleet electrification in China, Nat. Sustain., 2, 962–971, https://doi.org/10.1038/s41893-019-0398-8, 2019.
Liu, J., Han, K., Chen, X., and Ong, G. P.: Spatial–temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data, Transport Res. C-Emer., 106, 145–165, https://doi.org/10.1016/j.trc.2019.07.005, 2019.
Liu, Y., Chen, H., Gao, J., Li, Y., Dave, K., Chen, J., Federici, M., and Perricone, G.: Comparative analysis of non-exhaust airborne particles from electric and internal combustion engine vehicles, J. Hazard. Mater., 420, 126626, https://doi.org/10.1016/j.jhazmat.2021.126626, 2021.
Liu, Y., Zhang, Y., Yu, P., Ye, T., Zhang, Y., Xu, R., Li, S., and Guo, Y.: Applying traffic camera and deep learning-based image analysis to predict PM2.5 concentrations, Sci. Total Environ., 912, 169233, https://doi.org/10.1016/j.scitotenv.2023.169233, 2024.
Liu, Y., Ma, J., Li, L., Lin, X., Xu, W., and Ding, H.: A high temporal-spatial vehicle emission inventory based on detailed hourly traffic data in a medium-sized city of China, Environ. Pollut., 236, 324–333, https://doi.org/10.1016/j.envpol.2018.01.068, 2018.
Luo, X., Dong, L., Dou, Y., Zhang, N., Ren, J., Li, Y., Sun, L., and Yao, S.: Analysis on spatial–temporal features of taxis' emissions from big data informed travel patterns: A case of Shanghai, China, J. Clean. Prod., 142, 926–935, https://doi.org/10.1016/j.jclepro.2016.05.161, 2017.
Lv, Z., Zhang, Y., Ji, Z., Deng, F., Shi, M., Li, Q., He, M., Xiao, L., Huang, Y., Liu, H., and He, K.: A real-time NO emission inventory from heavy-duty vehicles based on on-board diagnostics big data with acceptable quality in China, J. Clean. Prod., 422, 138592, https://doi.org/10.1016/j.jclepro.2023.138592, 2023.
MacQueen, J.: Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, 281–297, University of California Press, Berkeley, California, http://projecteuclid.org/euclid.bsmsp/1200512992 (last access: 25 May 2024), 1967.
Maes, A. D. S., Hoinaski, L., Meirelles, T. B., and Carlson, R. C.: A methodology for high resolution vehicular emissions inventories in metropolitan areas: Evaluating the effect of automotive technologies improvement, Transport Res. D-Tr.-E., 77, 303–319, https://doi.org/10.1016/j.trd.2019.10.007, 2019.
MEE (Ministry of Ecology and Environment of the People's Republic of China): Technical guidelines for compiling atmospheric pollutant emission inventory of road motor vehicles (Trial) (in Chinese), https://www.mee.gov.cn/gkml/hbb/bgg/201501/W020150107594587831090.pdf (last access: 17 May 2024), 2014.
MPS (Ministry of Public Security of the People's Republic of China): China had 435 million motor vehicles, 523 million drivers, and over 20 million new energy vehicles (in Chinese), https://www.gov.cn/lianbo/bumen/202401/content_6925362.htm, last access: 8 June 2024.
Olivier, R. and Hanqiang, C.: Nearest neighbor value interpolation, Int. J. Adv. Comput. Sci. Appl., 3, https://doi.org/10.14569/IJACSA.2012.030405, 2012.
Ord, J. K. and Getis, A.: Local spatial autocorrelation statistics: Distributional issues and an application, Geogr. Anal., 27, 286–306, https://doi.org/10.1111/j.1538-4632.1995.tb00912.x, 1995.
Peng, L., Liu, F., Zhou, M., Li, M., Zhang, Q., and Mauzerall, D. L.: Alternative-energy-vehicles deployment delivers climate, air quality, and health co-benefits when coupled with decarbonizing power generation in China, One Earth, 4, 1127–1140, https://doi.org/10.1016/j.oneear.2021.07.007, 2021.
Qi, Z., Zheng, Y., Feng, Y., Chen, C., Lei, Y., Xue, W., Xu, Y., Liu, Z., Ni, X., Zhang, Q., Yan, G., and Wang, J.: Co-drivers of air pollutant and CO2 emissions from on-road transportation in China 2010–2020, Environ. Sci. Technol., 57, 20992–21004, https://doi.org/10.1021/acs.est.3c08035, 2023.
Ramacher, M. O. P., Matthias, V., Aulinger, A., Quante, M., Bieser, J., and Karl, M.: Contributions of traffic and shipping emissions to city-scale NOx and PM2.5 exposure in Hamburg, Atmos. Environ., 237, 117674, https://doi.org/10.1016/j.atmosenv.2020.117674, 2020.
Romero, Y., Chicchon, N., Duarte, F., Noel, J., Ratti, C., and Nyhan, M.: Quantifying and spatial disaggregation of air pollution emissions from ground transportation in a developing country context: Case study for the Lima Metropolitan Area in Peru, Sci. Total Environ., 698, 134313–134313, https://doi.org/10.1016/j.scitotenv.2019.134313, 2020.
Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987.
Secinaro, S., Calandra, D., Lanzalonga, F., and Ferraris, A.: Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda, J. Bus. Res., 150, 399–416, https://doi.org/10.1016/j.jbusres.2022.06.011, 2022.
Shi, X., Lei, Y., Xue, W., Liu, X., Li, S., Xu, Y., Lv, C., Wang, S., Wang, J., and Yan, G.: Drivers in carbon dioxide, air pollutants emissions and health benefits of China's clean vehicle fleet 2019–2035, J. Clean. Prod., 391, 136167, https://doi.org/10.1016/j.jclepro.2023.136167, 2023.
Song, H., Zhang, J., Zuo, J., Liang, X., Han, W., and Ge, J.: Subsidence detection for urban roads using mobile laser scanner data, Remote Sens.-Basel, 14, 2240, https://doi.org/10.3390/rs14092240, 2022.
Sun, S., Sun, L., Liu, G., Zou, C., Wang, Y., Wu, L., and Mao, H.: Developing a vehicle emission inventory with high temporal-spatial resolution in Tianjin, China, Sci. Total Environ., 776, 145873, https://doi.org/10.1016/j.scitotenv.2021.145873, 2021.
Tavakoli, N., Siami-Namini, S., Adl Khanghah, M., Mirza Soltani, F., and Siami Namin, A.: An autoencoder-based deep learning approach for clustering time series data, SN Appl. Sci., 2, 937, https://doi.org/10.1007/s42452-020-2584-8, 2020.
Tian, X., Huang, G., Song, Z., An, C., and Chen, Z.: Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city, Sci. Total Environ., 840, 156657, https://doi.org/10.1016/j.scitotenv.2022.156657, 2022.
Timmers, V. R. J. H. and Achten, P. A. J.: Non-exhaust PM emissions from electric vehicles, Atmos. Environ., 134, 10–17, https://doi.org/10.1016/j.atmosenv.2016.03.017, 2016.
Uherek, E., Halenka, T., Borken-Kleefeld, J., Balkanski, Y., Berntsen, T., Borrego, C., Gauss, M., Hoor, P., Juda-Rezler, K., and Lelieveld, J.: Transport impacts on atmosphere and climate: Land transport, Atmos. Environ., 44, 4772–4816, https://doi.org/10.1016/j.atmosenv.2010.01.002, 2010.
Wang, X. and Wang, Y.: Data of High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data, Mendeley Data, V1 [data set], https://doi.org/10.17632/24t54p6rj2.1, 2024.
Wang, Y., Wang, X., Zhang, B., Zhao, L., Liu, Y., Si, S., and Xue, L.: Traffic conditions on typical roads in urban Jinan and the differentiated impacts on air quality, J. Shangdong Univ. Eng. Sci., 55, 138–148, https://doi.org/10.6040/j.issn.1672-3961.0.2023.184, 2025.
Wen, Y., Liu, M., Zhang, S., Wu, X., Wu, Y., and Hao, J.: Updating on-road vehicle emissions for China: Spatial patterns, temporal trends, and mitigation drivers, Environ. Sci. Technol., 57, 14299–14309, https://doi.org/10.1021/acs.est.3c04909, 2023.
Wu, X., Yang, D., Wu, R., Gu, J., Wen, Y., Zhang, S., Wu, R., Wang, R., Xu, H., Zhang, K. M., Wu, Y., and Hao, J.: High-resolution mapping of regional traffic emissions using land-use machine learning models, Atmos. Chem. Phys., 22, 1939–1950, https://doi.org/10.5194/acp-22-1939-2022, 2022.
Xie, D., Gou, Z., and Gui, X.: How electric vehicles benefit urban air quality improvement: A study in Wuhan, Sci. Total Environ., 906, 167584, https://doi.org/10.1016/j.scitotenv.2023.167584, 2024.
Yang, D., Zhang, S., Niu, T., Wang, Y., Xu, H., Zhang, K. M., and Wu, Y.: High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets, Atmos. Chem. Phys., 19, 8831–8843, https://doi.org/10.5194/acp-19-8831-2019, 2019.
Yang, Z., Peng, J., Wu, L., Ma, C., Zou, C., Wei, N., Zhang, Y., Liu, Y., Andre, M., Li, D., and Mao, H.: Speed-guided intelligent transportation system helps achieve low-carbon and green traffic: Evidence from real-world measurements, J. Clean. Prod., 268, 122230, https://doi.org/10.1016/j.jclepro.2020.122230, 2020.
Zhang, J., Peng, J., Song, C., Ma, C., Men, Z., Wu, J., Wu, L., Wang, T., Zhang, X., Tao, S., Gao, S., Hopke, P. K., and Mao, H.: Vehicular non-exhaust particulate emissions in Chinese megacities: Source profiles, real-world emission factors, and inventories, Environ. Pollut., 266, 115268, https://doi.org/10.1016/j.envpol.2020.115268, 2020.
Zhang, S., Wu, Y., Huang, R., Wang, J., Yan, H., Zheng, Y., and Hao, J.: High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city, Atmos. Chem. Phys., 16, 9965–9981, https://doi.org/10.5194/acp-16-9965-2016, 2016.
Zhang, S., Niu, T., Wu, Y., Zhang, K. M., Wallington, T. J., Xie, Q., Wu, X., and Xu, H.: Fine-grained vehicle emission management using intelligent transportation system data, Environ. Pollut., 241, 1027–1037, https://doi.org/10.1016/j.envpol.2018.06.016, 2018.
Zhang, S., Xiong, Y., Liang, X., Wang, F., Liang, S., and Wu, Y.: Spatial and cross-sectoral transfer of air pollutant emissions from the fleet electrification in China by 2030, Environ. Sci. Technol., 57, 21249–21259, https://doi.org/10.1021/acs.est.3c04496, 2023.
Zhang, Y., Wang, X., Li, G., Yang, W., Huang, Z., Zhang, Z., Huang, X., Deng, W., Liu, T., Huang, Z., and Zhang, Z.: Emission factors of fine particles, carbonaceous aerosols and traces gases from road vehicles: Recent tests in an urban tunnel in the Pearl River Delta, China, Atmos. Environ., 122, 876–884, https://doi.org/10.1016/j.atmosenv.2015.08.024, 2015.
Zheng, B., Huo, H., Zhang, Q., Yao, Z. L., Wang, X. T., Yang, X. F., Liu, H., and He, K. B.: High-resolution mapping of vehicle emissions in China in 2008, Atmos. Chem. Phys., 14, 9787–9805, https://doi.org/10.5194/acp-14-9787-2014, 2014.
Zhu, C., Qu, X., Qiu, M., Zhu, C., Wang, C., Wang, B., Sun, L., Yang, N., Yan, G., Xu, C., and Li, L.: High spatiotemporal resolution vehicular emission inventory in Beijing–Tianjin–Hebei and its surrounding areas (BTHSA) during 2000–2020, China, Sci. Total Environ., 873, 162389, https://doi.org/10.1016/j.scitotenv.2023.162389, 2023.
Short summary
This study established a bottom-up approach that employs real-time traffic flows and interpolation to obtain a spatially continuous on-road vehicle emission mapping for the main urban area of Jinan. The diurnal variation, spatial distribution, and emission hotspots were analyzed with clustering and hotspot analysis, showing unique fine-scale variation characteristics of on-road vehicle emissions. Future scenario analysis demonstrates remarkable benefits of electrification on emission reduction.
This study established a bottom-up approach that employs real-time traffic flows and...
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