Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6465-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6465-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
30 Aug 2024
Model description paper |
| 30 Aug 2024
| 30 Aug 2024
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Giorgio Veratti
CORRESPONDING AUTHOR
Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy
Alessandro Bigi
Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy
Sergio Teggi
Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy
Grazia Ghermandi
Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy
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Cited articles
Al-Bahr, T. M., Hassan, S. A., Puan, O. C., Mashros, N., and Sukor, N. S. A.: Speed-Flow-Geometric Relationship for Urban Roads Network, Appl. Sci., 12, 4231, https://doi.org/10.3390/app12094231, 2022. a
Amato, F., Karanasiou, A., Moreno, T., Alastuey, A., Orza, J. A. G., Lumbreras, J., Borge, R., Boldo, E., Linares, C., and Querol, X.: Emission factors from road dust resuspension in a Mediterranean freeway, Atmos. Environ., 61, 580–587, https://doi.org/10.1016/j.atmosenv.2012.07.065, 2012. a, b, c
Amato, F., Favez, O., Pandolfi, M., Alastuey, A., Querol, X., Moukhtar, S., Bruge, B., Verlhac, S., Orza, J. A. G., Bonnaire, N., Le Priol, T., Petit, J. F., and Sciare, J.: Traffic induced particle resuspension in Paris: Emission factors and source contributions, Atmos. Environ., 129, 114–124, https://doi.org/10.1016/j.atmosenv.2016.01.022, 2016. a, b
Baek, B. H., Pedruzzi, R., Park, M., Wang, C.-T., Kim, Y., Song, C.-H., and Woo, J.-H.: The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model, Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, 2022. a
Beddows, D. C. S. and Harrison, R. M.: PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles, Atmos. Environ., 244, 117886, https://doi.org/10.1016/j.atmosenv.2020.117886, 2021. a
Bigi, A., Ghermandi, G., and Harrison, R. M.: Analysis of the air pollution climate at a background site in the Po valley, J. Environ. Monit., 14, 552–563, https://doi.org/10.1039/C1EM10728C, 2012. a
Bigi, A., Veratti, G., Andrews, E., Collaud Coen, M., Guerrieri, L., Bernardoni, V., Massabò, D., Ferrero, L., Teggi, S., and Ghermandi, G.: Aerosol absorption using in situ filter-based photometers and ground-based sun photometry in the Po Valley urban atmosphere, Atmos. Chem. Phys., 23, 14841–14869, https://doi.org/10.5194/acp-23-14841-2023, 2023. a, b, c
Borge, R., de Miguel, I., de la Paz, D., Lumbreras, J., Pérez, J., and Rodríguez, E.: Comparison of road traffic emission models in Madrid (Spain), Atmos. Environ., 62, 461–471, https://doi.org/10.1016/j.atmosenv.2012.08.073, 2012. a
Brilon, W. and Lohoff, J.: Speed-flow Models for Freeways, Procedia, 16, 26–36, https://doi.org/10.1016/j.sbspro.2011.04.426, 2011. a
Casotti Rienda, I. and Alves, C. A.: Road dust resuspension: A review, Atmos. Res., 261, 105740, https://doi.org/10.1016/j.atmosres.2021.105740, 2021. a, b
CCL: CORINE Land Cover, https://land.copernicus.eu/en/products/corine-land-cover (last access: 26 August 2024), 2018. a
Chan, E. C., Leitão, J., Kerschbaumer, A., and Butler, T. M.: Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions, Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023, 2023. a, b
Costa, L. G., Cole, T. B., Coburn, J., Chang, Y.-C., Dao, K., and Roqué, P. J.: Neurotoxicity of traffic-related air pollution, Neurotoxicology, 59, 133–139, https://doi.org/10.1016/j.neuro.2015.11.008, 2017. a
Crippa, M., Guizzardi, D., Butler, T., Keating, T., Wu, R., Kaminski, J., Kuenen, J., Kurokawa, J., Chatani, S., Morikawa, T., Pouliot, G., Racine, J., Moran, M. D., Klimont, Z., Manseau, P. M., Mashayekhi, R., Henderson, B. H., Smith, S. J., Suchyta, H., Muntean, M., Solazzo, E., Banja, M., Schaaf, E., Pagani, F., Woo, J.-H., Kim, J., Monforti-Ferrario, F., Pisoni, E., Zhang, J., Niemi, D., Sassi, M., Ansari, T., and Foley, K.: The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies, Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, 2023. a
Crosignani, P., Nanni, A., Pepe, N., Pozzi, C., Silibello, C., Poggio, A., and Conte, M.: The Effect of Non-Compliance of Diesel Vehicle Emissions with Euro Limits on Mortality in the City of Milan, Atmosphere, 12, 342, https://doi.org/10.3390/atmos12030342, 2021. a
Degraeuwe, B., Thunis, P., Clappier, A., Weiss, M., Lefebvre, W., Janssen, S., and Vranckx, S.: Impact of passenger car NOX emissions on urban NO2 pollution – Scenario analysis for 8 European cities, Atmos. Environ., 171, 330–337, https://doi.org/10.1016/j.atmosenv.2017.10.040, 2017. a
EPA: Emission Factor Documentation for AP-42 – Final Report, https://www3.epa.gov/ttnchie1/ap42/ch13/final/c13s0201.pdf (last access: 26 August 2024), 2011. a
European Council: On Ambient Air Quality and Cleaner Air for Europe 2008/50/EC, Off. J. Eur. Union, 1, 1–44, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008L0050&from=en (last access: 26 August 2024), 2008. a
Fabbi, S., Veratti, G., Bigi, A., and Ghermandi, G.: Air quality (PM10) scenarios resulting from the expansion of hydrogen fuel cell electric vehicle in Emilia-Romagna (Northern Italy), https://www.harmo.org/Conferences/Proceedings/_Aveiro/publishedSections/00503_142_h21-100-sara-fabbi.pdf (last access: 26 August 2024), 2022. a
Flores, R. M., Mertoğlu, E., Özdemir, H., Akkoyunlu, B. O., Demir, G., Ünal, A., and Tayanç, M.: A high-time resolution study of PM2.5, organic carbon, and elemental carbon at an urban traffic site in Istanbul, Atmos. Environ., 223, 117241, https://doi.org/10.1016/j.atmosenv.2019.117241, 2020. a
Fuller, R., Landrigan, P. J., Balakrishnan, K., Bathan, G., Bose-O'Reilly, S., Brauer, M., Caravanos, J., Chiles, T., Cohen, A., Corra, L., Cropper, M., Ferraro, G., Hanna, J., Hanrahan, D., Hu, H., Hunter, D., Janata, G., Kupka, R., Lanphear, B., Lichtveld, M., Martin, K., Mustapha, A., Sanchez-Triana, E., Sandilya, K., Schaefli, L., Shaw, J., Seddon, J., Suk, W., Téllez-Rojo, M. M., and Yan, C.: Pollution and health: a progress update, The Lancet Planetary Health, 6, e535–e547, https://doi.org/10.1016/S2542-5196(22)00090-0, 2022. a
Geoportale-Emilia-Romagna: Servizi cartografici regionali, https://geoportale.regione.emilia-romagna.it (last access: 26 August 2024), 2023. a
Ghermandi, G., Fabbi, S., Bigi, A., Veratti, G., Despini, F., Teggi, S., Barbieri, C., and Torreggiani, L.: Impact assessment of vehicular exhaust emissions by microscale simulation using automatic traffic flow measurements, Atmos. Pollut. Res., 10, 1473–1481, https://doi.org/10.1016/j.apr.2019.04.004, 2019. a
Ghermandi, G., Fabbi, S., Veratti, G., Bigi, A., and Teggi, S.: Estimate of Secondary NO2 Levels at Two Urban Traffic Sites Using Observations and Modelling, Sustainability, 12, 7897, https://doi.org/10.3390/su12197897, 2020. a, b, c
Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module, Geosci. Model Dev., 12, 1885–1907, https://doi.org/10.5194/gmd-12-1885-2019, 2019. a
Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module, Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020, 2020. a
Hanna, S. and Chang, J.: Acceptance criteria for urban dispersion model evaluation, Meteorol. Atmos. Phys., 116, 133–146, https://doi.org/10.1007/s00703-011-0177-1, 2012. a
Hao, Y., Meng, X., Yu, X., Lei, M., Li, W., Yang, W., Shi, F., and Xie, S.: Quantification of primary and secondary sources to PM2.5 using an improved source regional apportionment method in an industrial city, China, Sci. Total Environ., 706, 135715, https://doi.org/10.1016/j.scitotenv.2019.135715, 2020. a
Harrison, R. M., Allan, J., Carruthers, D., Heal, M. R., Lewis, A. C., Marner, B., Murrells, T., and Williams, A.: Non-exhaust vehicle emissions of particulate matter and VOC from road traffic: A review, Atmos. Environ., 262, 118592, https://doi.org/10.1016/j.atmosenv.2021.118592, 2021. a, b
Helbing, D.: Theoretical foundation of macroscopic traffic models, Phys. A, 219, 375–390, https://doi.org/10.1016/0378-4371(95)00174-6, 1995. a
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 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018. a
Heyken Soares, P., Ahmed, L., Mao, Y., and Mumford, C. L.: Public transport network optimisation in PTV Visum using selection hyper-heuristics, Public Transport, 13, 163–196, https://doi.org/10.1007/s12469-020-00249-7, 2021. a, b
Hooftman, N., Messagie, M., Van Mierlo, J., and Coosemans, T.: A review of the European passenger car regulations – Real driving emissions vs local air quality, Renew. Sust. Energ. Rev., 86, 1–21, https://doi.org/10.1016/j.rser.2018.01.012, 2018. a
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. a
ISPRA: Italian Emission Inventory 1990–2020, Informative Inventory Report 2022, https://www.isprambiente.gov.it/it/pubblicazioni/rapporti/italian-emission-inventory-1990-2020 (last access: 26 August 2024), 2019. a
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Bergamaschi, P., Pagliari, V., Olivier, J. G. J., Peters, J. A. H. W., van Aardenne, J. A., Monni, S., Doering, U., Petrescu, A. M. R., Solazzo, E., and Oreggioni, G. D.: EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012, Earth Syst. Sci. Data, 11, 959–1002, https://doi.org/10.5194/essd-11-959-2019, 2019. a
Jeong, C.-H., Wang, J. M., Hilker, N., Debosz, J., Sofowote, U., Su, Y., Noble, M., Healy, R. M., Munoz, T., Dabek-Zlotorzynska, E., Celo, V., White, L., Audette, C., Herod, D., and Evans, G. J.: Temporal and spatial variability of traffic-related PM2.5 sources: Comparison of exhaust and non-exhaust emissions, Atmos. Environ., 198, 55–69, https://doi.org/10.1016/j.atmosenv.2018.10.038, 2019. a
Johari, M., Keyvan-Ekbatani, M., Leclercq, L., Ngoduy, D., and Mahmassani, H. S.: Macroscopic network-level traffic models: Bridging fifty years of development toward the next era, Transport. Res. C-Emer, 131, 103334, https://doi.org/10.1016/j.trc.2021.103334, 2021. a
Jonson, J. E., Borken-Kleefeld, J., Simpson, D., Nyíri, A., Posch, M., and Heyes, C.: Impact of excess NOx emissions from diesel cars on air quality, public health and eutrophication in Europe, Environ. Res. Lett., 12, 094017, https://doi.org/10.1088/1748-9326/aa8850, 2017. a
Juhász, M., Koren, C., and Mátrai, T.: Analysing the Speed-flow Relationship in Urban Road Traffic, Acta Technica Jaurinensis, 9, 128–139, https://doi.org/10.14513/actatechjaur.v9.n2.403, 2016. a
Karagulian, F., Belis, C. A., Dora, C. F. C., Prüss-Ustün, A. M., Bonjour, S., Adair-Rohani, H., and Amann, M.: Contributions to cities' ambient particulate matter (PM): A systematic review of local source contributions at global level, Atmos. Environ., 120, 475–483, https://doi.org/10.1016/j.atmosenv.2015.08.087, 2015. a
Kouridis, C., Gkatzoflias, D., Kioutsoukis, I., Ntziachristos, L., Pastorello, C., and Dilara, P.: Uncertainty Estimates and Guidance for Road Transport Emission Calculations, https://doi.org/10.2788/78236, ISBN 9789279153075, 2010. a
Krajzewicz, D.: Traffic Simulation with SUMO – Simulation of Urban Mobility, in: Fundamentals of Traffic Simulation, edited by: Barceló, J., International Series in Operations Research & Management Science, Springer, New York, NY, 269–293, ISBN 978-1-4419-6142-6, https://doi.org/10.1007/978-1-4419-6142-6_7, 2010. a
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling, Earth Syst. Sci. Data, 14, 491–515, https://doi.org/10.5194/essd-14-491-2022, 2022. a
Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963–10976, https://doi.org/10.5194/acp-14-10963-2014, 2014. a
Landrigan, P. J., Fuller, R., Acosta, N. J. R., Adeyi, O., Arnold, R., Basu, N. N., Baldé, A. B., Bertollini, R., Bose-O'Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., Hanrahan, D., Hunter, D., Khare, M., Krupnick, A., Lanphear, B., Lohani, B., Martin, K., Mathiasen, K. V., McTeer, M. A., Murray, C. J. L., Ndahimananjara, J. D., Perera, F., Potočnik, J., Preker, A. S., Ramesh, J., Rockström, J., Salinas, C., Samson, L. D., Sandilya, K., Sly, P. D., Smith, K. R., Steiner, A., Stewart, R. B., Suk, W. A., van Schayck, O. C. P., Yadama, G. N., Yumkella, K., and Zhong, M.: The Lancet Commission on pollution and health, Lancet, 391, 462–512, https://doi.org/10.1016/S0140-6736(17)32345-0, 2018. a
Lejri, D. and Leclercq, L.: Are average speed emission functions scale-free?, Atmos. Environ., 224, 117324, https://doi.org/10.1016/j.atmosenv.2020.117324, 2020. a
Lejri, D., Can, A., Schiper, N., and Leclercq, L.: Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale, Transport. Res. D-Tr. E., 63, 588–603, https://doi.org/10.1016/j.trd.2018.06.023, 2018. a
Liu, Y., Chen, H., Li, Y., Gao, J., Dave, K., Chen, J., Li, T., and Tu, R.: Exhaust and non-exhaust emissions from conventional and electric vehicles: A comparison of monetary impact values, J. Clean. Product., 331, 129965, https://doi.org/10.1016/j.jclepro.2021.129965, 2022. a
Loomis, D., Grosse, Y., Lauby-Secretan, B., El Ghissassi, F., Bouvard, V., Benbrahim-Tallaa, L., Guha, N., Baan, R., Mattock, H., Straif, K., and International Agency for Research on Cancer Monograph Working Group IARC: The carcinogenicity of outdoor air pollution, Lancet. Oncol., 14, 1262–1263, https://doi.org/10.1016/s1470-2045(13)70487-x, 2013. a
Lugon, L., Vigneron, J., Debert, C., Chrétien, O., and Sartelet, K.: Black carbon modeling in urban areas: investigating the influence of resuspension and non-exhaust emissions in streets using the Street-in-Grid model for inert particles (SinG-inert), Geosci. Model Dev., 14, 7001–7019, https://doi.org/10.5194/gmd-14-7001-2021, 2021. a
López-Aparicio, S., Guevara, M., Thunis, P., Cuvelier, K., and Tarrasón, L.: Assessment of discrepancies between bottom-up and regional emission inventories in Norwegian urban areas, Atmos. Environ., 154, 285–296, https://doi.org/10.1016/j.atmosenv.2017.02.004, 2017. a
Markiewicz, A., Björklund, K., Eriksson, E., Kalmykova, Y., Strömvall, A.-M., and Siopi, A.: Emissions of organic pollutants from traffic and roads: Priority pollutants selection and substance flow analysis, Sci. Total Environ., 580, 1162–1174, https://doi.org/10.1016/j.scitotenv.2016.12.074, 2017. a
Marongiu, A., Angelino, E., Moretti, M., Malvestiti, G., and Fossati, G.: Atmospheric Emission Sources in the Po-Basin from the LIFE-IP PREPAIR Project, Open J. Air Pollut. 11, 70–83, https://doi.org/10.4236/ojap.2022.113006, 2022. a
MASE: National Oil bulletin: Report generated by the collected data since 1996 of petroleum products through the SISEN platform (Information System for National Energy Statistics), https://dgsaie.mise.gov.it/bollettino-petrolifero?anno=2019 (last access: 26 August 2024), 2019. a
Moussiopoulos, N., Sahm, P., and Kessler, C.: Numerical simulation of photochemical smog formation in Athens, Greece – A case study, Atmos. Environ., 29, 3619–3632, https://doi.org/10.1016/1352-2310(95)00199-9, 1995. a
Nogueira, T., Souza, K. F. D., Fornaro, A., Andrade, M. D. F., and Carvalho, L. R. F. D.: On-road emissions of carbonyls from vehicles powered by biofuel blends in traffic tunnels in the Metropolitan Area of Sao Paulo, Brazil, Atmos. Environ., 108, 88–97, https://doi.org/10.1016/j.atmosenv.2015.02.064, 2015. a
Oettl, D.: Evaluation of the Revised Lagrangian Particle Model GRAL Against Wind-Tunnel and Field Observations in the Presence of Obstacles, Bound.-Lay. Meteorol., 155, 271–287, https://doi.org/10.1007/s10546-014-9993-4, 2015a. a, b, c
Oettl, D.: A multiscale modelling methodology applicable for regulatory purposes taking into account effects of complex terrain and buildings on pollutant dispersion: a case study for an inner Alpine basin, Environ. Sci. Pollut. Res., 22, 17860–17875, https://doi.org/10.1007/s11356-015-4966-9, 2015b. a, b, c
Oettl, D.: Quality assurance of the prognostic, microscale wind-field model GRAL 14.8 using wind-tunnel data provided by the German VDI guideline 3783-9, J. Wind Eng. Ind. Aerod., 142, 104–110, https://doi.org/10.1016/j.jweia.2015.03.014, 2015c. a, b, c
Oettl, D.: Development of the Mesoscale Model GRAMM-SCI: Evaluation of Simulated Highly-Resolved Flow Fields in an Alpine and Pre-Alpine Region, Atmosphere, 12, 298, https://doi.org/10.3390/atmos12030298, 2021. a
Oettl, D. and Reifeltshammer, R.: Recent developments in high-resolution wind field modeling in complex terrain for dispersion simulations using GRAMM-SCI, Air Quality, Atmos. Health, 16, 2209–2223, https://doi.org/10.1007/s11869-023-01403-3, 2023. a
Oettl, D. and Veratti, G.: A comparative study of mesoscale flow-field modelling in an Eastern Alpine region using WRF and GRAMM-SCI, Atmos. Res., 249, 105288, https://doi.org/10.1016/j.atmosres.2020.105288, 2021. a
Oettl, D., Kuntner, M., and Hofstadler, R.: The Lagrangian Particle Model GRAL [code], https://gral.tugraz.at/, last access: 27 August 2024. a
Pachón, J. E., Galvis, B., Lombana, O., Carmona, L. G., Fajardo, S., Rincón, A., Meneses, S., Chaparro, R., Nedbor-Gross, R., and Henderson, B.: Development and Evaluation of a Comprehensive Atmospheric Emission Inventory for Air Quality Modeling in the Megacity of Bogotá, Atmosphere, 9, 49, https://doi.org/10.3390/atmos9020049, 2018. a
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. a, b
Pernigotti, D., Georgieva, E., Thunis, P., and Bessagnet, B.: Impact of meteorology on air quality modeling over the Po valley in northern Italy, Atmos. Environ., 51, 303–310, https://doi.org/10.1016/j.atmosenv.2011.12.059, 2012. a, b
Piscitello, A., Bianco, C., Casasso, A., and Sethi, R.: Non-exhaust traffic emissions: Sources, characterization, and mitigation measures, Sci. Total Environ., 766, 144440, https://doi.org/10.1016/j.scitotenv.2020.144440, 2021. a, b
PUMS: Piano Urbano Mobilità Sostenibile – Modena, https://www.comune.modena.it/argomenti/mobilita-sostenibile/pums/documenti-pums/pums-2030 (last access: 26 August 2024), 2023. a
Scotto, F., Bacco, D., Lasagni, S., Trentini, A., Poluzzi, V., and Vecchi, R.: A multi-year source apportionment of PM2.5 at multiple sites in the southern Po Valley (Italy), Atmos. Pollut. Res., 12, 101192, https://doi.org/10.1016/j.apr.2021.101192, 2021. a
Song, X., Hu, Y., Ma, Y., Jiang, L., Wang, X., Shi, A., Zhao, J., Liu, Y., Liu, Y., Tang, J., Li, X., Zhang, X., Guo, Y., and Wang, S.: Is short-term and long-term exposure to black carbon associated with cardiovascular and respiratory diseases? A systematic review and meta-analysis based on evidence reliability, BMJ Open, 12, e049516, https://doi.org/10.1136/bmjopen-2021-049516, 2022. a
Squires, F. A., Nemitz, E., Langford, B., Wild, O., Drysdale, W. S., Acton, W. J. F., Fu, P., Grimmond, C. S. B., Hamilton, J. F., Hewitt, C. N., Hollaway, M., Kotthaus, S., Lee, J., Metzger, S., Pingintha-Durden, N., Shaw, M., Vaughan, A. R., Wang, X., Wu, R., Zhang, Q., and Zhang, Y.: Measurements of traffic-dominated pollutant emissions in a Chinese megacity, Atmos. Chem. Phys., 20, 8737–8761, https://doi.org/10.5194/acp-20-8737-2020, 2020. a
Squizzato, S., Masiol, M., Brunelli, A., Pistollato, S., Tarabotti, E., Rampazzo, G., and Pavoni, B.: Factors determining the formation of secondary inorganic aerosol: a case study in the Po Valley (Italy), Atmos. Chem. Phys., 13, 1927–1939, https://doi.org/10.5194/acp-13-1927-2013, 2013. a
Tian, Y., Liu, X., Huo, R., Shi, Z., Sun, Y., Feng, Y., and Harrison, R. M.: Organic compound source profiles of PM2.5 from traffic emissions, coal combustion, industrial processes and dust, Chemosphere, 278, 130429, https://doi.org/10.1016/j.chemosphere.2021.130429, 2021. a
Trombetti, M., Thunis, P., Bessagnet, B., Clappier, A., Couvidat, F., Guevara, M., Kuenen, J., and López-Aparicio, S.: Spatial inter-comparison of Top-down emission inventories in European urban areas, Atmos. Environ., 173, 142–156, https://doi.org/10.1016/j.atmosenv.2017.10.032, 2018. a
Ullrich, B., Wankmüller, R., and Schindlbacher, S.: Inventory Review 2023, Review of emission data reported under the LRTAP Convention, https://www.ceip.at/fileadmin/inhalte/ceip/00_pdf_other/2023/dp188.pdf (last access: 26 August 2024), 2023. a
Venkatram, A.: A critique of empirical emission factor models: a case study of the AP-42 model for estimating PM10 emissions from paved roads, Atmos. Environ., 34, 1–11, https://doi.org/10.1016/S1352-2310(99)00330-1, 2000. a
Veratti, G.: GRAMM-GRAL modelling system, Zenodo [code], https://doi.org/10.5281/zenodo.10728500, 2024a. a
Veratti, G.: VERT 1.0: an R package for estimating road transport emissions from traffic flows, Zenodo [code], https://doi.org/10.5281/zenodo.12549513, 2024b. a, b
Veratti, G., Fabbi, S., Tinarelli, G., Bigi, A., Teggi, S., Brusasca, G., and Ghermandi, G.: μ-MO assessing the contribution of NOX traffic emission to atmospheric pollution in modena by microscale dispersion modelling, vol. 2017-October, 606–610, https://www.harmo.org/Conferences/Proceedings/_Bologna/publishedSections/H18-195-Veratti.pdf (last access: 26 August 2024), 2017. a
Veratti, G., Bigi, A., Fabbi, S., and Ghermandi, G.: PMSS and GRAL inter-comparison: Strengths and weaknesses of the two models in reproducing Urban NOx levels in a real case application, https://www.harmo.org/Conferences/Proceedings/_Tartu/publishedSections/H20-101_giorgio_veratti.pdf (last access: 26 August 2024), 2020a. a
Veratti, G., Fabbi, S., Bigi, A., Lupascu, A., Tinarelli, G., Teggi, S., Brusasca, G., Butler, T. M., and Ghermandi, G.: Towards the coupling of a chemical transport model with a micro-scale Lagrangian modelling system for evaluation of urban NOx levels in a European hotspot, Atmos. Environ., 223, 117285, https://doi.org/10.1016/j.atmosenv.2020.117285, 2020b. a
Veratti, G., Bigi, A., Lupascu, A., Butler, T. M., and Ghermandi, G.: Urban population exposure forecast system to predict NO2 impact by a building-resolving multi-scale model approach, Atmos. Environ., 261, 118566, https://doi.org/10.1016/j.atmosenv.2021.118566, 2021. a, b
Veratti, G., Stortini, M., Amorati, R., Bressan, L., Giovannini, G., Bande, S., Bissardella, F., Ghigo, S., Angelino, E., Colombo, L., Fossati, G., Malvestiti, G., Marongiu, A., Dalla Fontana, A., Intini, B., and Pillon, S.: Impact of NOx and NH3 Emission Reduction on Particulate Matter across Po Valley: A LIFE-IP-PREPAIR Study, Atmosphere, 14, 762, https://doi.org/10.3390/atmos14050762, 2023. a
Verhoef, E. T.: Speed-flow relations and cost functions for congested traffic: Theory and empirical analysis, Transport. Res. A-Pol., 39, 792–812, https://doi.org/10.1016/j.tra.2005.02.023, 2005. a
Yao, Z., Wei, H., Perugu, H., Liu, H., and Li, Z.: Sensitivity analysis of project level MOVES running emission rates for light and heavy duty vehicles, Journal of Traffic and Transportation Engineering (English Edition), 1, 81–96, https://doi.org/10.1016/S2095-7564(15)30092-1, 2014. a
Yolton, K., Khoury, J. C., Burkle, J., LeMasters, G., Cecil, K., and Ryan, P.: lifetime exposure to traffic-related air pollution and symptoms of depression and anxiety at age 12 years, Environ. Res., 173, 199–206, https://doi.org/10.1016/j.envres.2019.03.005, 2019. a
Zamboni, G., André, M., Roveda, A., and Capobianco, M.: Experimental evaluation of Heavy Duty Vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions, Transport. Res. D-Tr. E., 35, 1–10, https://doi.org/10.1016/j.trd.2014.11.024, 2015. a
Short summary
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to...
