Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Geoscientific Model Development - Tập 13 Số 1 - Trang 23-40
Luolin Wu1, Ming Chang2, Xuemei Wang2, Jian Hang1, Jinpu Zhang3, Liqing Wu1, Min Shao2
1School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, P.R. China
2Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, P. R. China
3Guangzhou Environmental Monitoring Center, Guangzhou 510030, P. R. China

Tóm tắt

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatiotemporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban areas by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

Từ khóa


Tài liệu tham khảo

An, X., Hou, Q., Li, N., and Zhai, S.: Assessment of human exposure level to PM10 in China, Atmos. Environ., 70, 376–386, https://doi.org/10.1016/j.atmosenv.2013.01.017, 2013.

Ashie, Y. and Kono, T.: Urban-scale CFD analysis in support of a climate-sensitive design for the Tokyo Bay area, Int. J. Climatol., 31, 174–188, https://doi.org/10.1002/joc.2226, 2011.

Britter, R. E. and Hanna, S. R.: Flow and dispersion in urban areas, Annu. Rev. Fluid Mech., 35, 469–496, https://doi.org/10.1146/annurev.fluid.35.101101.161147, 2003.

Cai, H. and Xie, S. D.: 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.

Che, W., Zheng, J., Wang, S., Zhong, L., and Lau, A.: Assessment of motor vehicle emission control policies using Model-3/CMAQ model for the Pearl River Delta region, China, Atmos. Environ., 45, 1740–1751, https://doi.org/10.1016/j.atmosenv.2010.12.050, 2011.

Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011.

Chen, R., Paristech, P., and Aguil, V.: A sensitivity study of road transportation emissions at metropolitan scale, J. Earth Sci. Geotech. Eng., 7, 151–173, 2017.

Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C., Brorousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I., Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D., Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J., Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell, N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., and Theeuwesits, N.: WUDAPT: An urban weather, climate, and environmental modeling infrastructure for the anthropocene, B. Am. Meteorol. Soc., 99, 1907–1924, https://doi.org/10.1175/BAMS-D-16-0236.1, 2018.

Cortez, P., Rio, M., Rocha, M., and Sousa, P.: Multi-scale Internet traffic forecasting using neural networks and time series methods, Expert Syst., 29, 143–155, https://doi.org/10.1111/j.1468-0394.2010.00568.x, 2012.

Davies, L., Bates, J. W., Bell, J. N. B., James, P. W., and Purvis, O. W.: Diversity and sensitivity of epiphytes to oxides of nitrogen in London, Environ. Pollut., 146, 299–310, https://doi.org/10.1016/j.envpol.2006.03.023, 2007.

Di Sabatino, S., Buccolieri, R., Pulvirenti, B., and Britter, R. E.: Flow and pollutant dispersion in street canyons using FLUENT and ADMS-Urban, Environ. Model. Assess., 13, 369–381, https://doi.org/10.1007/s10666-007-9106-6, 2008.

Dudhia, J.: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989.

EPA: User’s Guide to MOBILE6.1 and MOBILE6.2: Mobile Source Emission Factor Model, EPA420-R-03-010, Washington, DC, USA, 2003.

Fan, Q., Lan, J., Liu, Y., Wang, X., Chan, P., Hong, Y., Feng, Y., Liu, Y., Zeng, Y., and Liang, G.: Process analysis of regional aerosol pollution during spring in the Pearl River Delta region, China, Atmos. Environ., 122, 829–838, https://doi.org/10.1016/j.atmosenv.2015.09.013, 2015.

Fernando, H. J. S., Zajic, D., Di Sabatino, S., Dimitrova, R., Hedquist, B., and Dallman, A.: Flow, turbulence, and pollutant dispersion in urban atmospheres, Phys. Fluids, 22, 1–20, https://doi.org/10.1063/1.3407662, 2010.

Guangzhou Bureau of Statistics: Guangzhou Statistical Yearbook 2017, Guangzhou, People's Republic of China, 2017.

Guo, H., Zhang, Q., Shi, Y., and Wang, D.: On-road remote sensing measurements and fuel-based motor vehicle emission inventory in Hangzhou, China, Atmos. Environ., 41, 3095–3107, https://doi.org/10.1016/j.atmosenv.2006.11.045, 2007.

Hang, J., Luo, Z., Wang, X., He, L., Wang, B., and Zhu, W.: The influence of street layouts and viaduct settings on daily carbon monoxide exposure and intake fraction in idealized urban canyons, Environ. Pollut., 220, 72–86, https://doi.org/10.1016/j.envpol.2016.09.024, 2017.

Hao, J., He, D., Wu, Y., Fu, L., and He, K.: A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing, Atmos. Environ., 34, 453–465, https://doi.org/10.1016/S1352-2310(99)00324-6, 2000.

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.

He, K., Huo, H. and Zhang, Q.: Urban Air Pollution in China: Current Status, Characteristics, and Progress, J. Allergy Clin. Immun., 27, 397–431, https://doi.org/10.1146/annurev.energy.27.122001.083421, 2002.

Hooper, E., Chapman, L., and Quinn, A.: The impact of precipitation on speed–flow relationships along a UK motorway corridor, Theor. Appl. Climatol., 117, 303–316, https://doi.org/10.1007/s00704-013-0999-5, 2014.

Huang, W., Wei, Y., Guo, J. and Cao, J.: Next-generation innovation and development of intelligent transportation system in China, Sci. China Inform. Sci., 60, 1–11, https://doi.org/10.1007/s11432-017-9182-x, 2017.

Huo, H., Zhang, Q., He, K., Wang, Q., Yao, Z., and Streets, D. G.: High-Resolution Vehicular Emission Inventory Using a Link-Based Method?: A Case Study of Light-Duty Vehicles in Beijing, Environ. Sci. Technol., 43, 2394–2399, 2009.

Ibarra-Espinosa, S., Ynoue, R., O'Sullivan, S., Pebesma, E., Andrade, M. D. F., and Osses, M.: VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories, Geosci. Model Dev., 11, 2209–2229, https://doi.org/10.5194/gmd-11-2209-2018, 2018.

Jing, B., Wu, L., Mao, H., Gong, S., He, J., Zou, C., Song, G., Li, X., and Wu, Z.: 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 1: Development and evaluation of vehicle emission inventory, Atmos. Chem. Phys., 16, 3161–3170, https://doi.org/10.5194/acp-16-3161-2016, 2016.

Kain, J. S.: The Kain–Fritsch Convective Parameterization: An Update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.

Ke, W., Zhang, S., Wu, Y., Zhao, B., Wang, S., and Hao, J.: Assessing the future vehicle fleet electrification: The impacts on regional and Urban air quality, Environ. Sci. Technol., 51, 1007–1016, https://doi.org/10.1021/acs.est.6b04253, 2017.

Kim, M. J., Park, R. J., and Kim, J. J.: Urban air quality modeling with full O3-NOx-VOC chemistry: Implications for O3 and PM air quality in a street canyon, Atmos. Environ., 47, 330–340, https://doi.org/10.1016/j.atmosenv.2011.10.059, 2012.

Kim, Y., Wu, Y., Seigneur, C., and Roustan, Y.: Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1), Geosci. Model Dev., 11, 611–629, https://doi.org/10.5194/gmd-11-611-2018, 2018.

Kuo, C.-W. and Tang, M.-L.: Relationship among service quality, corporate image, customer satisfaction and behaviroal intention for the elderly in high speed rail service, J. Adv. Transp., 47, 512–525, https://doi.org/10.1002/atr.179, 2011.

Kwak, K. H. and Baik, J. J.: Diurnal variation of NOx and ozone exchange between a street canyon and the overlying air, Atmos. Environ., 86, 120–128, https://doi.org/10.1016/j.atmosenv.2013.12.029, 2014.

Kwak, K. H., Baik, J. J., and Lee, K. Y.: Dispersion and photochemical evolution of reactive pollutants in street canyons, Atmos. Environ., 70, 98–107, https://doi.org/10.1016/j.atmosenv.2013.01.010, 2013.

Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017.

Liu, Y. H., Ma, J. L., Li, L., Lin, X. F., Xu, W. J., 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.

MEP: Technical Guide of Air Quality Model Selection (Trial), Beijing, China, 2012.

MEP: Technical Guide of Air Pollutant Emission Inventory for On Road Vehicles (Trial), Beijing, China, 2014.

Min, X., Hu, J., Chen, Q., Zhang, T., and Zhang, Y.: Short-term traffic flow forecasting of urban network based on dynamic STARIMA model, IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC, 100084, 461–466, https://doi.org/10.1109/ITSC.2009.5309741, 2009.

Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.

Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.

National Bureau of Statistics of China: China Statistical Yearbook 2017, Beijing, People's Republic of China, 2017.

Pallavidino, L., Prandi, R., Bertello, A., Bracco, E., and Pavone, F.: Compilation of a road transport emission inventory for the Province of Turin: Advantages and key factors of a bottom–up approach, Atmos. Pollut. Res., 5, 648–655, https://doi.org/10.5094/APR.2014.074, 2014.

Park, S. J., Kim, J. J., Kim, M. J., Park, R. J., and Cheong, H. B.: Characteristics of flow and reactive pollutant dispersion in urban street canyons, Atmos. Environ., 108, 20–31, https://doi.org/10.1016/j.atmosenv.2015.02.065, 2015.

Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteorol. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007.

Righi, S., Lucialli, P., and Pollini, E.: Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network, Atmos. Environ., 43, 3850–3857, https://doi.org/10.1016/j.atmosenv.2009.05.016, 2009.

Saide, P., Zah, R., Osses, M., and Ossés de Eicker, M.: Spatial disaggregation of traffic emission inventories in large cities using simplified top-down methods, Atmos. Environ., 43, 4914–4923, https://doi.org/10.1016/j.atmosenv.2009.07.013, 2009.

Saikawa, E., Kurokawa, J., Takigawa, M., Borken-Kleefeld, J., Mauzerall, D. L., Horowitz, L. W., and Ohara, T.: The impact of China's vehicle emissions on regional air quality in 2000 and 2020: a scenario analysis, Atmos. Chem. Phys., 11, 9465–9484, https://doi.org/10.5194/acp-11-9465-2011, 2011.

Sanford, S. and He, D.: Some theoretical results concerning O3-NOx-VOC chemistry and NOx-VOC indicators, J. Geophys. Res., 107, 4659, https://doi.org/10.1029/2001JD001123, 2002.

Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 3, NCAR Tech. Note NCAR/TN-475+ STR, 113 pp., 2008.

Sun, S., Jiang, W., and Gao, W.: Vehicle emission trends and spatial distribution in Shandong province, China, from 2000 to 2014, Atmos. Environ., 147, 190–199, https://doi.org/10.1016/j.atmosenv.2016.09.065, 2016.

Tang, G., Chao, N., Wang, Y., and Chen, J.: Vehicular emissions in China in 2006 and 2010, J. Environ. Sci.-China, 48, 179–192, https://doi.org/10.1016/j.jes.2016.01.031, 2016.

Underwood, R. T.: Speed, volume, and density relationship: quality and theory of traffic flow, Yale Bur. Highw. Traffic, 141–188, 1961.

Vlahogianni, E. I., Karlaftis, M. G., and Golias, J. C.: Short-term traffic forecasting: Where we are and where we're going, Transp. Res. C.-Emer., 43, 3–19, https://doi.org/10.1016/j.trc.2014.01.005, 2014.

Wang, H., Chen, C., Huang, C., and Fu, L.: On-road vehicle emission inventory and its uncertainty analysis for Shanghai, China, Sci. Total Environ., 398, 60–67, https://doi.org/10.1016/j.scitotenv.2008.01.038, 2008.

Wang, H., Fu, L., and Chen, J.: Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 2-a case study in beijing, J. Air Waste Manage., 60, 1471–1475, https://doi.org/10.3155/1047-3289.60.12.1471, 2010.

Wang, H., Ni, D., Chen, Q. Y., and Li, J.: Stochastic modeling of the equilibrium speed-density relationship, J. Adv. Transp., 47, 126–150, https://doi.org/10.1002/atr.172, 2013.

Wang, N., Lyu, X. P., Deng, X. J., Guo, H., Deng, T., Li, Y., Yin, C. Q., Li, F., and Wang, S. Q.: Assessment of regional air quality resulting from emission control in the Pearl River Delta region, southern China, Sci. Total Environ., 573, 1554–1565, https://doi.org/10.1016/j.scitotenv.2016.09.013, 2016.

Wang, R., Wang, K., Zhang, F., Gao, J., Li, Y. and Yue, T.: Emission Characteristics of Vehicles from National Roads and Provincial Roads in China, Environ. Sci., 38, 3–10, https://doi.org/10.13227/j.hjkx.201701087, 2017 (in Chinese).

Wang, T. and Xie, S.: Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period, Atmos. Environ., 43, 5682–5690, https://doi.org/10.1016/j.atmosenv.2009.07.034, 2009.

Wang, W.: Practical speed-flow relationship model of highway traffic-flow, J. Southeast Univ. Sci. Ed., 33, 487–491, 2003 (in Chinese).

Wu, L.: ROE-1.0 release (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.3264859, 2019.

Wu, J., Sui, Y., and Wang, T.: Intelligent transport systems in China, P. I. Civil Eng.-Munic., 162, 25–32, https://doi.org/10.1680/muen.2009.162.1.25, 2009.

Xiong, G., Wang, K., Zhu, F., Cheng, C., An, X., and Xie, Z.: Parallel traffic management for the 2010 Asian Games, IEEE Intell. Syst., 25, 81–85, https://doi.org/10.1109/MIS.2010.87, 2010.

Xiu, A. and Pleim, J. E.: Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model, J. Appl. Meteorol., 40, 192–209, https://doi.org/10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2, 2001.

Xu, F., He, Z., Sha, Z., Zhuang, L., and Sun, W.: Assessing the Impact of Rainfall on Traffic Operation of Urban Road Network, Procedia Soc. Behav. Sci., 96, 82–89, https://doi.org/10.1016/j.sbspro.2013.08.012, 2013.

Yao, Z., Zhang, Y., Shen, X., Wang, X., Wu, Y., and He, K.: Impacts of temporary traffic control measures on vehicular emissions during the Asian Games in Guangzhou, China, J. Air Waste Manage., 63, 11–19, https://doi.org/10.1080/10962247.2012.724041, 2013.

Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z.: Updates to the carbon bond chemical mechanism: CB05, Rep. RT-0400675, 246 pp., available at: http://www.camx.com/files/cb05_final_report_120805.aspx (last access: 2 September 2019), 2005.

Ye, L., Wang, X., Fan, S., Chen, W., Chang, M., Zhou, S., Wu, Z., and Fan, Q.: Photochemical indicators of ozone sensitivity: application in the Pearl River Delta, China, Front. Environ. Sci. Eng., 10, 1–14, https://doi.org/10.1007/s11783-016-0887-1, 2016.

Zhang, F.: The current situation and development thinking of the intelligent transportation system in China, 2010 Int. Conf. Mech. Autom. Control Eng. MACE2010, 717, 2826–2829, https://doi.org/10.1109/MACE.2010.5536406, 2010.

Zhang, G., Mu, Y., Liu, J., Zhang, C., Zhang, Y., Zhang, Y., and Zhang, H.: Seasonal and diurnal variations of atmospheric peroxyacetyl nitrate, peroxypropionyl nitrate, and carbon tetrachloride in Beijing, J. Environ. Sci., 26, 65–74, https://doi.org/10.1016/S1001-0742(13)60382-4, 2014.

Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z., Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L. T., and Yao, Z. L.: Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153, https://doi.org/10.5194/acp-9-5131-2009, 2009.

Zhang, S., Wu, Y., Liu, H., Wu, X., Zhou, Y., Yao, Z., Fu, L., He, K., and Hao, J.: Historical evaluation of vehicle emission control in Guangzhou based on a multi-year emission inventory, Atmos. Environ., 76, 32–42, https://doi.org/10.1016/j.atmosenv.2012.11.047, 2013.

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, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, part I: History, techniques, and current status, Atmos. Environ., 60, 632–655, https://doi.org/10.1016/j.atmosenv.2012.06.031, 2012.

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.

Zheng, J., Che, W., Wang, X., Louie, P., and Zhong, L.: Road-network-based spatial allocation of on-road mobile source emissions in the pearl river delta region, China, and comparisons with population-based approach, J. Air Waste Manage., 59, 1405–1416, https://doi.org/10.3155/1047-3289.59.12.1405, 2009a.

Zheng, J., Zhang, L., Che, W., Zheng, Z., and Yin, S.: A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment, Atmos. Environ., 43, 5112–5122, https://doi.org/10.1016/j.atmosenv.2009.04.060, 2009b.

Zhong, J., Cai, X. M., and Bloss, W. J.: Coupling dynamics and chemistry in the air pollution modelling of street canyons: A review, Environ. Pollut., 214, 690–704, https://doi.org/10.1016/j.envpol.2016.04.052, 2016.

Thông tin
Thông tin xuất bản

Geoscientific Model Development

Tập 13 Số 1

23-40

Thông tin tác giả