Articles | Volume 24, issue 18
https://doi.org/10.5194/acp-24-10475-2024
© 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/acp-24-10475-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report
| 
19 Sep 2024
Measurement report |
| 19 Sep 2024
| 19 Sep 2024
Measurement report: Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley – an integrated approach combining high-resolution dispersion modelling and micro-aethalometers
Giorgio Veratti
CORRESPONDING AUTHOR
Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
“Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, 41125 Modena, Italy
Alessandro Bigi
“Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, 41125 Modena, Italy
Michele Stortini
ARPAE, Regional Environmental Agency of Emilia–Romagna, 40122 Bologna, Italy
Sergio Teggi
“Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, 41125 Modena, Italy
Grazia Ghermandi
“Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, 41125 Modena, Italy
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Cited articles
ACI (Automobile Club d'Italia): Autoritratto, http://www.aci.it/laci/studi-e-ricerche/dati-e-statistiche/autoritratto.html (last access: 16 September 2024), 2023. a
Aethlabs: https://help.aethlabs.com/s/article/Define-Specific-Atte nuation-Cross-section-σATN-or-Sigma-Value-for-the-microAe
th-MA-Series-instruments (last access: 16 September 2024), 2024. a
Alas, H. D. C., Müller, T., Weinhold, K., Pfeifer, S., Glojek, K., Gregorič, A., Močnik, G., Drinovec, L., Costabile, F., Ristorini, M., and Wiedensohler, A.: Performance of microAethalometers: Real-world Field Intercomparisons from Multiple Mobile Measurement Campaigns in Different Atmospheric Environments, Aerosol Air Qual. Res., 20, 2640–2653, https://doi.org/10.4209/aaqr.2020.03.0113, 2020. a
Ali, M. U., Siyi, L., Yousaf, B., Abbas, Q., Hameed, R., Zheng, C., Kuang, X., and Wong, M. H.: Emission sources and full spectrum of health impacts of black carbon associated polycyclic aromatic hydrocarbons (PAHs) in urban environment: A review, Crit. Rev. Env. Sci. Tec., 51, 857–896, https://doi.org/10.1080/10643389.2020.1738854, 2021. a
Almbauer, R., Pucher, K., and Sturm, P. J.: Air quality modeling for the city of Graz, Meteorol. Atmos. Phys., 57, 31–42, https://doi.org/10.1007/BF01044152, 1995. a, b
Amato, F.: Non-Exhaust Emissions: An Urban Air Quality Problem for Public Health; Impact and Mitigation Measures, Academic Press, ISBN 978-0-12-811751-4, 2018. 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
Avolio, E., Federico, S., Miglietta, M. M., Lo Feudo, T., Calidonna, C. R., and Sempreviva, A. M.: Sensitivity analysis of WRF model PBL schemes in simulating boundary-layer variables in southern Italy: An experimental campaign, Atmos. Res., 192, 58–71, https://doi.org/10.1016/j.atmosres.2017.04.003, 2017. a
Bauer, J. J., Yu, X.-Y., Cary, R., Laulainen, N., and Berkowitz, C.: Characterization of the Sunset Semi-Continuous Carbon Aerosol Analyzer, J. Air Waste Manage., 59, 826–833, https://doi.org/10.3155/1047-3289.59.7.826, 2009. a
Becerril-Valle, M., Coz, E., Prévôt, A. S. H., Močnik, G., Pandis, S. N., Sánchez de la Campa, A. M., Alastuey, A., Díaz, E., Pérez, R. M., and Artíñano, B.: Characterization of atmospheric black carbon and co-pollutants in urban and rural areas of Spain, Atmos. Environ., 169, 36–53, https://doi.org/10.1016/j.atmosenv.2017.09.014, 2017. a
Belis, C. A., Pikridas, M., Lucarelli, F., Petralia, E., Cavalli, F., Calzolai, G., Berico, M., and Sciare, J.: Source apportionment of fine PM by combining high time resolution organic and inorganic chemical composition datasets, Atmospheric Environment: X, 3, 100046, https://doi.org/10.1016/j.aeaoa.2019.100046, 2019. a
Berchet, A., Zink, K., Muller, C., Oettl, D., Brunner, J., Emmenegger, L., and Brunner, D.: A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale, Atmos. Environ., 158, 181–196, https://doi.org/10.1016/j.atmosenv.2017.03.030, 2017. a
Bernardoni, V., Vecchi, R., Valli, G., Piazzalunga, A., and Fermo, P.: PM10 source apportionment in Milan (Italy) using time-resolved data, Science Total Environ., 409, 4788–4795, https://doi.org/10.1016/j.scitotenv.2011.07.048, 2011. a, b
Bernardoni, V., Elser, M., Valli, G., Valentini, S., Bigi, A., Fermo, P., Piazzalunga, A., and Vecchi, R.: Size-segregated aerosol in a hot-spot pollution urban area: Chemical composition and three-way source apportionment, Environ. Pollut., 231, 601–611, https://doi.org/10.1016/j.envpol.2017.08.040, 2017a. a
Bernardoni, V., Pileci, R. E., Caponi, L., and Massabò, D.: The Multi-Wavelength Absorption Analyzer (MWAA) Model as a Tool for Source and Component Apportionment Based on Aerosol Absorption Properties: Application to Samples Collected in Different Environments, Atmosphere, 8, 218, https://doi.org/10.3390/atmos8110218, 2017b. a, b, c, d
Bernardoni, V., Ferrero, L., Bolzacchini, E., Forello, A. C., Gregorič, A., Massabò, D., Močnik, G., Prati, P., Rigler, M., Santagostini, L., Soldan, F., Valentini, S., Valli, G., and Vecchi, R.: Determination of Aethalometer multiple-scattering enhancement parameters and impact on source apportionment during the winter 2017/18 EMEP/ACTRIS/COLOSSAL campaign in Milan, Atmos. Meas. Tech., 14, 2919–2940, https://doi.org/10.5194/amt-14-2919-2021, 2021. a, b, c, d
Bessagnet, B., Pirovano, G., Mircea, M., Cuvelier, C., Aulinger, A., Calori, G., Ciarelli, G., Manders, A., Stern, R., Tsyro, S., García Vivanco, M., Thunis, P., Pay, M.-T., Colette, A., Couvidat, F., Meleux, F., Rouïl, L., Ung, A., Aksoyoglu, S., Baldasano, J. M., Bieser, J., Briganti, G., Cappelletti, A., D'Isidoro, M., Finardi, S., Kranenburg, R., Silibello, C., Carnevale, C., Aas, W., Dupont, J.-C., Fagerli, H., Gonzalez, L., Menut, L., Prévôt, A. S. H., Roberts, P., and White, L.: Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology, Atmos. Chem. Phys., 16, 12667–12701, https://doi.org/10.5194/acp-16-12667-2016, 2016. a
Bigi, A.: Aerosol absorption data in Modena, Italy published in the journal article by Bigi et. al. (2023), Version 1.0.0, Zenodo [data set], https://doi.org/10.5281/zenodo.8140250, 2023. a
Bigi, A. and Ghermandi, G.: Trends and variability of atmospheric PM2.5 and PM10−2.5 concentration in the Po Valley, Italy, Atmos. Chem. Phys., 16, 15777–15788, https://doi.org/10.5194/acp-16-15777-2016, 2016. 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. Monitor., 14, 552–563, https://doi.org/10.1039/C1EM10728C, 2012. a, b
Bigi, A., Bianchi, F., De Gennaro, G., Di Gilio, A., Fermo, P., Ghermandi, G., Prévôt, A. S. H., Urbani, M., Valli, G., Vecchi, R., and Piazzalunga, A.: Hourly composition of gas and particle phase pollutants at a central urban background site in Milan, Italy, Atmos. Res., 186, 83–94, https://doi.org/10.1016/j.atmosres.2016.10.025, 2017. 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
Blanco-Donado, E. P., Schneider, I. L., Artaxo, P., Lozano-Osorio, J., Portz, L., and Oliveira, M. L. S.: Source identification and global implications of black carbon, Geosci. Front., 13, 101149, https://doi.org/10.1016/j.gsf.2021.101149, 2022. a
Bond, T. C., Habib, G., and Bergstrom, R. W.: Limitations in the enhancement of visible light absorption due to mixing state, J. Geophys. Res.-Atmos., 111, D20211, https://doi.org/10.1029/2006JD007315, 2006. a
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013. a
Brasseur, O., Declerck, P., Heene, B., and Vanderstraeten, P.: Modelling Black Carbon concentrations in two busy street canyons in Brussels using CANSBC, Atmos. Environ., 101, 72–81, https://doi.org/10.1016/j.atmosenv.2014.10.049, 2015. a
Brown, S., Minor, H., O'Brien, T., Hameed, Y., Feenstra, B., Kuebler, D., Wetherell, W., Day, R., Tun, R., Landis, E., and Rice, J.: Review of Sunset OC/EC Instrument Measurements During the EPA’s Sunset Carbon Evaluation Project, Atmosphere, 10, 287, https://doi.org/10.3390/atmos10050287, 2019. a
Cappa, C. D., Onasch, T. B., Massoli, P., Worsnop, D. R., Bates, T. S., Cross, E. S., Davidovits, P., Hakala, J., Hayden, K. L., Jobson, B. T., Kolesar, K. R., Lack, D. A., Lerner, B. M., Li, S.-M., Mellon, D., Nuaaman, I., Olfert, J. S., Petäjä, T., Quinn, P. K., Song, C., Subramanian, R., Williams, E. J., and Zaveri, R. A.: Radiative Absorption Enhancements Due to the Mixing State of Atmospheric Black Carbon, Science, 337, 1078–1081, https://doi.org/10.1126/science.1223447, 2012. a
Chan, T. W., Brook, J. R., Smallwood, G. J., and Lu, G.: Time-resolved measurements of black carbon light absorption enhancement in urban and near-urban locations of southern Ontario, Canada, Atmos. Chem. Phys., 11, 10407–10432, https://doi.org/10.5194/acp-11-10407-2011, 2011. a
Chang, C.-Y., You, R., Armstrong, D., Bandi, A., Cheng, Y.-T., Burkhardt, P. M., Becerra-Dominguez, L., Madison, M. C., Tung, H.-Y., Zeng, Z., Wu, Y., Song, L., Phillips, P. E., Porter, P., Knight, J. M., Putluri, N., Yuan, X., Marcano, D. C., McHugh, E. A., Tour, J. M., Catic, A., Maneix, L., Burt, B. M., Lee, H.-S., Corry, D. B., and Kheradmand, F.: Chronic exposure to carbon black ultrafine particles reprograms macrophage metabolism and accelerates lung cancer, Science Advances, 8, eabq0615, https://doi.org/10.1126/sciadv.abq0615, 2022. a
Charbouillot, T., Janet, D. C., Schaal, P., Beynier, I., Boulat, J.-M., Grandchamp, A., and Biesse, F.: Methodology for the direct measurement of tire emission factors, Sci. Total Environ., 863, 160853, https://doi.org/10.1016/j.scitotenv.2022.160853, 2023. a, b
Cherian, R., Quaas, J., Salzmann, M., and Tomassini, L.: Black carbon indirect radiative effects in a climate model, Tellus B, 69, 1369342, https://doi.org/10.1080/16000889.2017.1369342, 2017. a
Chow, J. C., Watson, J. G., Chen, L.-W. A., Chang, M. O., Robinson, N. F., Trimble, D., and Kohl, S.: The IMPROVE_A Temperature Protocol for Thermal/Optical Carbon Analysis: Maintaining Consistency with a Long-Term Database, J. Air Waste Manage., 57, 1014–1023, https://doi.org/10.3155/1047-3289.57.9.1014, 2007. a
Chung, S. H. and Seinfeld, J. H.: Climate response of direct radiative forcing of anthropogenic black carbon, J. Geophys. Res.-Atmos., 110, D11102, https://doi.org/10.1029/2004JD005441, 2005. a
ARPAE-SIMC: LIBSIM, https://github.com/ARPA-SIMC/libsim (last access: 16 September 2024), 2023. a
Costabile, F., Alas, H., Aufderheide, M., Avino, P., Amato, F., Argentini, S., Barnaba, F., Berico, M., Bernardoni, V., Biondi, R., Casasanta, G., Ciampichetti, S., Calzolai, G., Canepari, S., Conidi, A., Cordelli, E., Di Ianni, A., Di Liberto, L., Facchini, M. C., Facci, A., Frasca, D., Gilardoni, S., Grollino, M. G., Gualtieri, M., Lucarelli, F., Malaguti, A., Manigrasso, M., Montagnoli, M., Nava, S., Perrino, C., Padoan, E., Petenko, I., Querol, X., Simonetti, G., Tranfo, G., Ubertini, S., Valli, G., Valentini, S., Vecchi, R., Volpi, F., Weinhold, K., Wiedensohler, A., Zanini, G., Gobbi, G. P., and Petralia, E.: First Results of the “Carbonaceous Aerosol in Rome and Environs (CARE)” Experiment: Beyond Current Standards for PM10, Atmosphere, 8, 249, https://doi.org/10.3390/atmos8120249, 2017a. a
Costabile, F., Gilardoni, S., Barnaba, F., Di Ianni, A., Di Liberto, L., Dionisi, D., Manigrasso, M., Paglione, M., Poluzzi, V., Rinaldi, M., Facchini, M. C., and Gobbi, G. P.: Characteristics of brown carbon in the urban Po Valley atmosphere, Atmos. Chem. Phys., 17, 313–326, https://doi.org/10.5194/acp-17-313-2017, 2017b. a
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research, Scientific Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2020. a
Curci, G., Alyuz, U., Barò, R., Bianconi, R., Bieser, J., Christensen, J. H., Colette, A., Farrow, A., Francis, X., Jiménez-Guerrero, P., Im, U., Liu, P., Manders, A., Palacios-Peña, L., Prank, M., Pozzoli, L., Sokhi, R., Solazzo, E., Tuccella, P., Unal, A., Vivanco, M. G., Hogrefe, C., and Galmarini, S.: Modelling black carbon absorption of solar radiation: combining external and internal mixing assumptions, Atmos. Chem. Phys., 19, 181–204, https://doi.org/10.5194/acp-19-181-2019, 2019. a, b
Demir, T., Karakaş, D., and Yenisoy-Karakaş, S.: Source identification of exhaust and non-exhaust traffic emissions through the elemental carbon fractions and Positive Matrix Factorization method, Environ. Res., 204, 112399, https://doi.org/10.1016/j.envres.2021.112399, 2022. a
Denby, B. R., Sundvor, I., Johansson, C., Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Kauhaniemi, M., and Omstedt, G.: A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions (NORTRIP). Part 2: Surface moisture and salt impact modelling, Atmos. Environ., 81, 485–503, https://doi.org/10.1016/j.atmosenv.2013.09.003, 2013. a, b
Doran, J. C., Fast, J. D., Barnard, J. C., Laskin, A., Desyaterik, Y., and Gilles, M. K.: Applications of lagrangian dispersion modeling to the analysis of changes in the specific absorption of elemental carbon, Atmos. Chem. Phys., 8, 1377–1389, https://doi.org/10.5194/acp-8-1377-2008, 2008. a
Drinovec, L., Močnik, G., Zotter, P., Prévôt, A. S. H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., and Hansen, A. D. A.: The “dual-spot” Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation, Atmos. Meas. Tech., 8, 1965–1979, https://doi.org/10.5194/amt-8-1965-2015, 2015. a, b, c
Drinovec, L., Jagodič, U., Pirker, L., Škarabot, M., Kurtjak, M., Vidović, K., Ferrero, L., Visser, B., Röhrbein, J., Weingartner, E., Kalbermatter, D. M., Vasilatou, K., Bühlmann, T., Pascale, C., Müller, T., Wiedensohler, A., and Močnik, G.: A dual-wavelength photothermal aerosol absorption monitor: design, calibration and performance, Atmos. Meas. Tech., 15, 3805–3825, https://doi.org/10.5194/amt-15-3805-2022, 2022. a, b
EEA: The application of models under the European Union's Air Quality Directive: A technical reference guide, European Environment Agency, https://www.eea.europa.eu/publications/fairmode (last access: 16 September 2024), 2011. a
EEA: 1. A Combustion, European Environment Agency, https://www.eea.europa.eu/publications/emep-eea-guidebook-2019/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion (last access: 16 September 2024), 2019. a
EPA: Meteorological Monitoring Guidance for Regulatory Modeling Applications, NEPIS | US EPA, https://nepis.epa.gov/Exe/ZyNET.exe/2000D6B8.TXT?ZyActionD=ZyDocument&Client=EPA&Index=1995+Thru+1999&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C95thru99%5CTxt%5C00000016%5C2000D6B8.txt&User=ANONYMOUS&Password=anonymous&SortMethod=h%7C-&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Display=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Results%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL (last access: 16 September 2024), 2000. 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: 16 September 2024), 2008. a
European Council: Proposal for a directive of the European Parliament and of the Council on ambient air quality and cleaner air for Europe (recast), Document 52022PC0542, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2022%3A542%3AFIN (last access: January 2023), 2022. a
Fay, B. and Neunhäuserer, L.: Evaluation of high-resolution forecasts with the non-hydrostaticnumerical weather prediction model Lokalmodell for urban air pollutionepisodes in Helsinki, Oslo and Valencia, Atmos. Chem. Phys., 6, 2107–2128, https://doi.org/10.5194/acp-6-2107-2006, 2006. a
Ferrero, L., Riccio, A., Perrone, M. G., Sangiorgi, G., Ferrini, B. S., and Bolzacchini, E.: Mixing height determination by tethered balloon-based particle soundings and modeling simulations, Atmos. Res., 102, 145–156, https://doi.org/10.1016/j.atmosres.2011.06.016, 2011. a
Ferrero, L., Močnik, G., Cogliati, S., Gregorič, A., Colombo, R., and Bolzacchini, E.: Heating Rate of Light Absorbing Aerosols: Time-Resolved Measurements, the Role of Clouds, and Source Identification, Environ. Sci. Technol., 52, 3546–3555, https://doi.org/10.1021/acs.est.7b04320, 2018. a
Ferrero, L., Bernardoni, V., Santagostini, L., Cogliati, S., Soldan, F., Valentini, S., Massabò, D., Močnik, G., Gregorič, A., Rigler, M., Prati, P., Bigogno, A., Losi, N., Valli, G., Vecchi, R., and Bolzacchini, E.: Consistent determination of the heating rate of light-absorbing aerosol using wavelength- and time-dependent Aethalometer multiple-scattering correction, Sci. Total Environ., 791, 148277, https://doi.org/10.1016/j.scitotenv.2021.148277, 2021. a
Fischer, D. A. and Smith, G. D.: A portable, four-wavelength, single-cell photoacoustic spectrometer for ambient aerosol absorption, Aerosol Sci. Tech., 52, 393–406, https://doi.org/10.1080/02786826.2017.1413231, 2018. a
Forello, A. C., Bernardoni, V., Calzolai, G., Lucarelli, F., Massabò, D., Nava, S., Pileci, R. E., Prati, P., Valentini, S., Valli, G., and Vecchi, R.: Exploiting multi-wavelength aerosol absorption coefficients in a multi-time resolution source apportionment study to retrieve source-dependent absorption parameters, Atmos. Chem. Phys., 19, 11235–11252, https://doi.org/10.5194/acp-19-11235-2019, 2019. a, b
Galperin, B., Sukoriansky, S., and Anderson, P. S.: On the critical Richardson number in stably stratified turbulence, Atmos. Sci. Lett., 8, 65–69, https://doi.org/10.1002/asl.153, 2007. a
Gehrig, R., Zeyer, K., Bukowiecki, N., Lienemann, P., Poulikakos, L. D., Furger, M., and Buchmann, B.: Mobile load simulators – A tool to distinguish between the emissions due to abrasion and resuspension of PM10 from road surfaces, Atmos. Environ., 44, 4937–4943, https://doi.org/10.1016/j.atmosenv.2010.08.020, 2010. a
Geoportale-Emilia-Romagna: Homepage, https://geoportale.regione.emilia-romagna.it (last access: 16 September 2024), 2023. a
Ghermandi, G., Fabbi, S., Arvani, B., Veratti, G., Bigi, A., and Teggi, S.: Impact Assessment of Pollutant Emissions in the Atmosphere from a Power Plant over a Complex Terrain and under Unsteady Winds, Sustainability, 9, 2076, https://doi.org/10.3390/su9112076, 2017. 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
Gilardoni, S., Massoli, P., Marinoni, A., Mazzoleni, C., Freedman, A., Lonati, G., Iuliis, S. D., and Gianelle, V.: Spatial and Temporal Variability of Carbonaceous Aerosol Absorption in the Po Valley, Aerosol Air Qual. Res., 20, 2624–2639, https://doi.org/10.4209/aaqr.2020.03.0085, 2020. a, b
Grachev, A. A., Andreas, E. L., Fairall, C. W., Guest, P. S., and Persson, P. O. G.: The Critical Richardson Number and Limits of Applicability of Local Similarity Theory in the Stable Boundary Layer, Bound.-Lay. Meteorol., 147, 51–82, https://doi.org/10.1007/s10546-012-9771-0, 2013. a
Grahame, T. J., Klemm, R., and Schlesinger, R. B.: Public health and components of particulate matter: the changing assessment of black carbon, J. Air Waste Manage., 64, 620–660, https://doi.org/10.1080/10962247.2014.912692, 2014. a
Grange, S. K., Lötscher, H., Fischer, A., Emmenegger, L., and Hueglin, C.: Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018, Atmos. Meas. Tech., 13, 1867–1885, https://doi.org/10.5194/amt-13-1867-2020, 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 2: The bottom–up module, Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020, 2020. a
Guo, X., Nakayama, T., Yamada, H., Inomata, S., Tonokura, K., and Matsumi, Y.: Measurement of the light absorbing properties of diesel exhaust particles using a three-wavelength photoacoustic spectrometer, Atmos. Environ., 94, 428–437, https://doi.org/10.1016/j.atmosenv.2014.05.042, 2014. 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
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
Helin, A., Niemi, J. V., Virkkula, A., Pirjola, L., Teinilä, K., Backman, J., Aurela, M., Saarikoski, S., Rönkkö, T., Asmi, E., and Timonen, H.: Characteristics and source apportionment of black carbon in the Helsinki metropolitan area, Finland, Atmos. Environ., 190, 87–98, https://doi.org/10.1016/j.atmosenv.2018.07.022, 2018. a, b
Hendricks, J., Kärcher, B., and Lohmann, U.: Effects of ice nuclei on cirrus clouds in a global climate model, J. Geophys. Res.-Atmos., 116, D18206, https://doi.org/10.1029/2010JD015302, 2011. a
Henzing, J. S., Olivié, D. J. L., and van Velthoven, P. F. J.: A parameterization of size resolved below cloud scavenging of aerosols by rain, Atmos. Chem. Phys., 6, 3363–3375, https://doi.org/10.5194/acp-6-3363-2006, 2006. a
Holmes, N. S. and Morawska, L.: A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available, Atmos. Environ., 40, 5902–5928, https://doi.org/10.1016/j.atmosenv.2006.06.003, 2006. a
ISPRA: Inventario Nazionale – EMISSIONI, https://emissioni.sina.isprambiente.it/inventario-nazionale/, last access: 16 September 2024, 2023. a
Janssen, N. A., Gerlofs-Nijland, M. E., Lanki, T., Salonen, R. O., Cassee, F., Hoek, G., Fischer, P., Brunekreef, B., and Krzyzanowski, M.: Health effects of black carbon, World Health Organization. Regional Office for Europe, ISBN 978-92-890-0265-3, https://apps.who.int/iris/handle/10665/352615 (last access: 16 September 2024), 2012. a
Janssen, N. A. H., Hoek, G., Simic-Lawson, M., Fischer, P., van Bree, L., ten Brink, H., Keuken, M., Atkinson, R. W., Anderson, H. R., Brunekreef, B., and Cassee, F. R.: Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5, Environ. Health Persp., 119, 1691–1699, https://doi.org/10.1289/ehp.1003369, 2011. a
Ježek, I., Blond, N., Skupinski, G., and Močnik, G.: The traffic emission-dispersion model for a Central-European city agrees with measured black carbon apportioned to traffic, Atmos. Environ., 184, 177–190, https://doi.org/10.1016/j.atmosenv.2018.04.028, 2018. a
Karakaş, D., Berberler, E., Bayramoğlu Karşı, M. B., Demir, T., Aslan, Ö., Karadeniz, H., Ağa, Ö., and Yenisoy-Karakaş, S.: Simultaneous Quantification of Real-World Elemental Contributions from the Exhaust and Non-Exhaust Vehicular Emissions Using Road Dust Enrichment Factor-Elemental Carbon Tracer Method (EFECT), Atmosphere, 14, 631, https://doi.org/10.3390/atmos14040631, 2023. a
Kaskaoutis, D. G., Grivas, G., Stavroulas, I., Bougiatioti, A., Liakakou, E., Dumka, U. C., Gerasopoulos, E., and Mihalopoulos, N.: Apportionment of black and brown carbon spectral absorption sources in the urban environment of Athens, Greece, during winter, Sci. Total Environ., 801, 149739, https://doi.org/10.1016/j.scitotenv.2021.149739, 2021a. a
Kaskaoutis, D. G., Grivas, G., Stavroulas, I., Liakakou, E., Dumka, U. C., Gerasopoulos, E., and Mihalopoulos, N.: Effect of aerosol types from various sources at an urban location on spectral curvature of scattering and absorption coefficients, Atmos. Res., 264, 105865, https://doi.org/10.1016/j.atmosres.2021.105865, 2021b. a
Davulienė, L., Šemčuk, S., Kandrotaitė, K., Minderytė, A., Davtalab, M., Uogintė, I., Skapas, M., Dudoitis, V., and Byčenkienė, S.: Integrated personal exposure and deposition of black carbon on human lungs, Air Qual. Atmos. Hlth., 17, 35–50, https://doi.org/10.1007/s11869-023-01428-8, 2024. a
Kim, J., Park, E., Moon, H., Son, H., Hong, J., Wi, E., Kwon, J.-T., Seo, D. Y., Lee, H., and Kim, Y.: Estimation of the concentration of nano-carbon black in tire-wear particles using emission factors of PM10, PM2.5, and black carbon, Chemosphere, 303, 134976, https://doi.org/10.1016/j.chemosphere.2022.134976, 2022. a
Koch, D., Balkanski, Y., Bauer, S. E., Easter, R. C., Ferrachat, S., Ghan, S. J., Hoose, C., Iversen, T., Kirkevåg, A., Kristjansson, J. E., Liu, X., Lohmann, U., Menon, S., Quaas, J., Schulz, M., Seland, Ø., Takemura, T., and Yan, N.: Soot microphysical effects on liquid clouds, a multi-model investigation, Atmos. Chem. Phys., 11, 1051–1064, https://doi.org/10.5194/acp-11-1051-2011, 2011. a, b
Kouridis, C., Gkatzoflias, D., Kioutsoukis, I., Ntziachristos, L., Pastorello, C., and Dilara, P.: Uncertainty Estimates and Guidance for Road Transport Emission Calculations, Publications Office of the European Union, Luxemburg, https://doi.org/10.2788/78236, iSBN: 9789279153075, ISSN: 1018-5593, 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
Lee, J. and Moosmüller, H.: Measurement of Light Absorbing Aerosols with Folded-Jamin Photothermal Interferometry, Sensors, 20, 2615, https://doi.org/10.3390/s20092615, 2020. a
Li, B., Wang, Y., and Li, Z.: A method for monitoring mass concentration of black carbon particulate matter using photothermal interferometry, Environ. Sci. Pollut. R., 23, 4692–4699, https://doi.org/10.1007/s11356-015-5702-1, 2016. a
Li, C., Windwer, E., Fang, Z., Nissenbaum, D., and Rudich, Y.: Correcting micro-aethalometer absorption measurements for brown carbon aerosol, Sci. Total Environ., 777, 146143, https://doi.org/10.1016/j.scitotenv.2021.146143, 2021. a
Li, F., Luo, B., Zhai, M., Liu, L., Zhao, G., Xu, H., Deng, T., Deng, X., Tan, H., Kuang, Y., and Zhao, J.: Black carbon content of traffic emissions significantly impacts black carbon mass size distributions and mixing states, Atmos. Chem. Phys., 23, 6545–6558, https://doi.org/10.5194/acp-23-6545-2023, 2023. a
Liakakou, E., Stavroulas, I., Kaskaoutis, D. G., Grivas, G., Paraskevopoulou, D., Dumka, U. C., Tsagkaraki, M., Bougiatioti, A., Oikonomou, K., Sciare, J., Gerasopoulos, E., and Mihalopoulos, N.: Long-term variability, source apportionment and spectral properties of black carbon at an urban background site in Athens, Greece, Atmos. Environ., 222, 117137, https://doi.org/10.1016/j.atmosenv.2019.117137, 2020. a, b, c
Liu, C., Chung, C. E., Yin, Y., and Schnaiter, M.: The absorption Ångström exponent of black carbon: from numerical aspects, Atmos. Chem. Phys., 18, 6259–6273, https://doi.org/10.5194/acp-18-6259-2018, 2018. a
Liu, D., Whitehead, J., Alfarra, M. R., Reyes-Villegas, E., Spracklen, D. V., Reddington, C. L., Kong, S., Williams, P. I., Ting, Y.-C., Haslett, S., Taylor, J. W., Flynn, M. J., Morgan, W. T., McFiggans, G., Coe, H., and Allan, J. D.: Black-carbon absorption enhancement in the atmosphere determined by particle mixing state, Nat. Geosci., 10, 184–188, https://doi.org/10.1038/ngeo2901, 2017. a
Lonati, G., Ozgen, S., Ripamonti, G., and Signorini, S.: Variability of Black Carbon and Ultrafine Particle Concentration on Urban Bike Routes in a Mid-Sized City in the Po Valley (Northern Italy), Atmosphere, 8, 40, https://doi.org/10.3390/atmos8020040, 2017. 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, b, c, d, e
Lyons, R., Panofsky, H. A., and Wollaston, S.: The Critical Richardson Number and Its Implications for Forecast Problems, J. Appl. Meteor. Climatol., 3, 136–142, https://doi.org/10.1175/1520-0450(1964)003<0136:TCRNAI>2.0.CO;2, 1964. a
Lyu, Y. and Olofsson, U.: On black carbon emission from automotive disc brakes, J. Aerosol Sci., 148, 105610, https://doi.org/10.1016/j.jaerosci.2020.105610, 2020. a
Mailler, S., Menut, L., Khvorostyanov, D., Valari, M., Couvidat, F., Siour, G., Turquety, S., Briant, R., Tuccella, P., Bessagnet, B., Colette, A., Létinois, L., Markakis, K., and Meleux, F.: CHIMERE-2017: from urban to hemispheric chemistry-transport modeling, Geosci. Model Dev., 10, 2397–2423, https://doi.org/10.5194/gmd-10-2397-2017, 2017. a, b
Marongiu, A., Angelino, E., and Distefano, G.: Methodology for estimating atmospheric emissions from residential biomass heating considering technology turnover and real utilization - Black Carbon Ensemble Emission Map Estimates from national to local level, TFEIP 2024 Meeting, Dessau, Germany, 14–16 May 2024, https://www.tfeip-secretariat.org/_files/ugd/e5a9c7_2ea5dcce45ed45549185a0dfe68ee10f.pdf (last access: 16 September 2024), 2024. a
Massabò, D., Caponi, L., Bernardoni, V., Bove, M. C., Brotto, P., Calzolai, G., Cassola, F., Chiari, M., Fedi, M. E., Fermo, P., Giannoni, M., Lucarelli, F., Nava, S., Piazzalunga, A., Valli, G., Vecchi, R., and Prati, P.: Multi-wavelength optical determination of black and brown carbon in atmospheric aerosols, Atmos. Environ., 108, 1–12, https://doi.org/10.1016/j.atmosenv.2015.02.058, 2015. a, b, c, d
Mbengue, S., Zikova, N., Schwarz, J., Vodička, P., Holubová Šmejkalová, A., and Holoubek, I.: Mass absorption cross-section and absorption enhancement from long term black and elemental carbon measurements: A rural background station in Central Europe, Sci. Total Environ., 794, 148365, https://doi.org/10.1016/j.scitotenv.2021.148365, 2021. a
Menon, S., Hansen, J., Nazarenko, L., and Luo, Y.: Climate Effects of Black Carbon Aerosols in China and India, Science, 297, 2250–2253, https://doi.org/10.1126/science.1075159, 2002. a
Menut, L., Bessagnet, B., Briant, R., Cholakian, A., Couvidat, F., Mailler, S., Pennel, R., Siour, G., Tuccella, P., Turquety, S., and Valari, M.: The CHIMERE v2020r1 online chemistry-transport model, Geosci. Model Dev., 14, 6781–6811, https://doi.org/10.5194/gmd-14-6781-2021, 2021. a
Merico, E., Cesari, D., Dinoi, A., Gambaro, A., Barbaro, E., Guascito, M. R., Giannossa, L. C., Mangone, A., and Contini, D.: Inter-comparison of carbon content in PM10 and PM2.5 measured with two thermo-optical protocols on samples collected in a Mediterranean site, Environ. Sci. Pollut. R., 26, 29334–29350, https://doi.org/10.1007/s11356-019-06117-7, 2019. a
Milinković, A., Gregorič, A., Grgičin, V. D., Vidič, S., Penezić, A., Kušan, A. C., Alempijević, S. B., Kasper-Giebl, A., and Frka, S.: Variability of black carbon aerosol concentrations and sources at a Mediterranean coastal region, Atmos. Pollut. Res., 12, 101221, https://doi.org/10.1016/j.apr.2021.101221, 2021. a
Minderytė, A., Pauraite, J., Dudoitis, V., Plauškaitė, K., Kilikevičius, A., Matijošius, J., Rimkus, A., Kilikevičienė, K., Vainorius, D., and Byčenkienė, S.: Carbonaceous aerosol source apportionment and assessment of transport-related pollution, Atmos. Environ., 279, 119043, https://doi.org/10.1016/j.atmosenv.2022.119043, 2022. a
Mircea, M., Bessagnet, B., D'Isidoro, M., Pirovano, G., Aksoyoglu, S., Ciarelli, G., Tsyro, S., Manders, A., Bieser, J., Stern, R., Vivanco, M. G., Cuvelier, C., Aas, W., Prévôt, A. S. H., Aulinger, A., Briganti, G., Calori, G., Cappelletti, A., Colette, A., Couvidat, F., Fagerli, H., Finardi, S., Kranenburg, R., Rouïl, L., Silibello, C., Spindler, G., Poulain, L., Herrmann, H., Jimenez, J. L., Day, D. A., Tiitta, P., and Carbone, S.: EURODELTA III exercise: An evaluation of air quality models' capacity to reproduce the carbonaceous aerosol, Atmospheric Environment: X, 2, 100018, https://doi.org/10.1016/j.aeaoa.2019.100018, 2019. a, b, c
Moffet, R. C. and Prather, K. A.: In-situ measurements of the mixing state and optical properties of soot with implications for radiative forcing estimates, P. Natl. Acad. Sci. USA, 106, 11872–11877, https://doi.org/10.1073/pnas.0900040106, 2009. a
Moosmüller, H., Chakrabarty, R. K., and Arnott, W. P.: Aerosol light absorption and its measurement: A review, J. Quant. Spectrosc. Ra., 110, 844–878, https://doi.org/10.1016/j.jqsrt.2009.02.035, 2009. a
Mousavi, A., Sowlat, M. H., Lovett, C., Rauber, M., Szidat, S., Boffi, R., Borgini, A., De Marco, C., Ruprecht, A. A., and Sioutas, C.: Source apportionment of black carbon (BC) from fossil fuel and biomass burning in metropolitan Milan, Italy, Atmos. Environ., 203, 252–261, https://doi.org/10.1016/j.atmosenv.2019.02.009, 2019. a, b, c
Mues, A., Kuenen, J., Hendriks, C., Manders, A., Segers, A., Scholz, Y., Hueglin, C., Builtjes, P., and Schaap, M.: Sensitivity of air pollution simulations with LOTOS-EUROS to the temporal distribution of anthropogenic emissions, Atmos. Chem. Phys., 14, 939–955, https://doi.org/10.5194/acp-14-939-2014, 2014. a
Ning, Z., Chan, K. L., Wong, K. C., Westerdahl, D., Močnik, G., Zhou, J. H., and Cheung, C. S.: Black carbon mass size distributions of diesel exhaust and urban aerosols measured using differential mobility analyzer in tandem with Aethalometer, Atmos. Environ., 80, 31–40, https://doi.org/10.1016/j.atmosenv.2013.07.037, 2013. 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
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. R., 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 Qual. Atmos. Hlth., 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
Paglione, M., Gilardoni, S., Rinaldi, M., Decesari, S., Zanca, N., Sandrini, S., Giulianelli, L., Bacco, D., Ferrari, S., Poluzzi, V., Scotto, F., Trentini, A., Poulain, L., Herrmann, H., Wiedensohler, A., Canonaco, F., Prévôt, A. S. H., Massoli, P., Carbone, C., Facchini, M. C., and Fuzzi, S.: The impact of biomass burning and aqueous-phase processing on air quality: a multi-year source apportionment study in the Po Valley, Italy, Atmos. Chem. Phys., 20, 1233–1254, https://doi.org/10.5194/acp-20-1233-2020, 2020. a
Pandolfo, J. P.: Motions with Inertial and Diurnal Period, J. Mar. Res., 27, 301–317, 1969. a
Pani, S. K., Wang, S.-H., Lin, N.-H., Chantara, S., Lee, C.-T., and Thepnuan, D.: Black carbon over an urban atmosphere in northern peninsular Southeast Asia: Characteristics, source apportionment, and associated health risks, Environ. Pollut., 259, 113871, https://doi.org/10.1016/j.envpol.2019.113871, 2020. a
Park, S. S., Hansen, A. D. A., and Cho, S. Y.: Measurement of real time black carbon for investigating spot loading effects of Aethalometer data, Atmos. Environ., 44, 1449–1455, https://doi.org/10.1016/j.atmosenv.2010.01.025, 2010. a
Pashneva, D., Minderytė, A., Davulienė, L., Dudoitis, V., and Byčenkienė, S.: Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment, Atmosphere, 15, 832, https://doi.org/10.3390/atmos15070832, 2024. a, b
Peng, J., Hu, M., Guo, S., Du, Z., Zheng, J., Shang, D., Levy Zamora, M., Zeng, L., Shao, M., Wu, Y.-S., Zheng, J., Wang, Y., Glen, C. R., Collins, D. R., Molina, M. J., and Zhang, R.: Markedly enhanced absorption and direct radiative forcing of black carbon under polluted urban environments, P. Natl. Acad. Sci. USA, 113, 4266–4271, https://doi.org/10.1073/pnas.1602310113, 2016. a
Pepe, N., Pirovano, G., Balzarini, A., Toppetti, A., Riva, G. M., Amato, F., and Lonati, G.: Enhanced CAMx source apportionment analysis at an urban receptor in Milan based on source categories and emission regions, Atmospheric Environment: X, 2, 100020, https://doi.org/10.1016/j.aeaoa.2019.100020, 2019. a
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, 2012a. a
Pernigotti, D., Georgieva, E., Thunis, P., Cuvelier, C., and de Meij, A.: The Impact of Meteorology on Air Quality Simulations over the Po Valley in Northern Italy, in: Air Pollution Modeling and its Application XXI, edited by: Steyn, D. G. and Trini Castelli, S., NATO Science for Peace and Security Series C: Environmental Security, Springer Netherlands, Dordrecht, 485–490, https://doi.org/10.1007/978-94-007-1359-8_81, ISBN 978-94-007-1359-8, 2012b. a
Pernigotti, D., Thunis, P., Cuvelier, C., Georgieva, E., Gsella, A., De Meij, A., Pirovano, G., Balzarini, A., Riva, G. M., Carnevale, C., Pisoni, E., Volta, M., Bessagnet, B., Kerschbaumer, A., Viaene, P., De Ridder, K., Nyiri, A., and Wind, P.: POMI: a model inter-comparison exercise over the Po Valley, Air Qual. Atmos. Hlth., 6, 701–715, https://doi.org/10.1007/s11869-013-0211-1, 2013. a
Perrino, C., Catrambone, M., Dalla Torre, S., Rantica, E., Sargolini, T., and Canepari, S.: Seasonal variations in the chemical composition of particulate matter: a case study in the Po Valley. Part I: macro-components and mass closure, Environ. Sci. Pollut. R., 21, 3999–4009, https://doi.org/10.1007/s11356-013-2067-1, 2014. a
Petzold, A. and Niessner, R.: Photoacoustic soot sensor for in-situ black carbon monitoring, Appl. Phys. B, 63, 191–197, https://doi.org/10.1007/BF01095272, 1996. a
Petzold, A., Ogren, J. A., Fiebig, M., Laj, P., Li, S.-M., Baltensperger, U., Holzer-Popp, T., Kinne, S., Pappalardo, G., Sugimoto, N., Wehrli, C., Wiedensohler, A., and Zhang, X.-Y.: Recommendations for reporting “black carbon” measurements, Atmos. Chem. Phys., 13, 8365–8379, https://doi.org/10.5194/acp-13-8365-2013, 2013. a, b, c, d
Pirovano, G., Balzarini, A., Bessagnet, B., Emery, C., Kallos, G., Meleux, F., Mitsakou, C., Nopmongcol, U., Riva, G. M., and Yarwood, G.: Investigating impacts of chemistry and transport model formulation on model performance at European scale, Atmos. Environ., 53, 93–109, https://doi.org/10.1016/j.atmosenv.2011.12.052, 2012. a
Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to black carbon, Nat. Geosci., 1, 221–227, https://doi.org/10.1038/ngeo156, 2008. a
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols, Climate, and the Hydrological Cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001. a
Reche, C., Querol, X., Alastuey, A., Viana, M., Pey, J., Moreno, T., Rodríguez, S., González, Y., Fernández-Camacho, R., de la Rosa, J., Dall'Osto, M., Prévôt, A. S. H., Hueglin, C., Harrison, R. M., and Quincey, P.: New considerations for PM, Black Carbon and particle number concentration for air quality monitoring across different European cities, Atmos. Chem. Phys., 11, 6207–6227, https://doi.org/10.5194/acp-11-6207-2011, 2011. a
Roberts, D. L. and Jones, A.: Climate sensitivity to black carbon aerosol from fossil fuel combustion, J. Geophys. Res.-Atmos., 109, D16202, https://doi.org/10.1029/2004JD004676, 2004. a
Rohr, A. and McDonald, J.: Health effects of carbon-containing particulate matter: focus on sources and recent research program results, Crit. Rev. Toxicol., 46, 97–137, https://doi.org/10.3109/10408444.2015.1107024, 2016. a
Saide, P. E., Carmichael, G. R., Spak, S. N., Gallardo, L., Osses, A. E., Mena-Carrasco, M. A., and Pagowski, M.: Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF–Chem CO tracer model, Atmos. Environ., 45, 2769–2780, https://doi.org/10.1016/j.atmosenv.2011.02.001, 2011. a
Sandradewi, J., Prévôt, A. S. H., Szidat, S., Perron, N., Alfarra, M. R., Lanz, V. A., Weingartner, E., and Baltensperger, U.: Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter, Environ. Sci. Technol., 42, 3316–3323, https://doi.org/10.1021/es702253m, 2008. a
Saputra, D., Yoon, J.-H., Park, H., Heo, Y., Yang, H., Lee, E. J., Lee, S., Song, C.-W., and Lee, K.: Inhalation of Carbon Black Nanoparticles Aggravates Pulmonary Inflammation in Mice, Toxicological Research, 30, 83–90, https://doi.org/10.5487/TR.2014.30.2.083, 2014. a
Savadkoohi, M., Pandolfi, M., Reche, C., Niemi, J. V., Mooibroek, D., Titos, G., Green, D. C., Tremper, A. H., Hueglin, C., Liakakou, E., Mihalopoulos, N., Stavroulas, I., Artiñano, B., Coz, E., Alados-Arboledas, L., Beddows, D., Riffault, V., De Brito, J. F., Bastian, S., Baudic, A., Colombi, C., Costabile, F., Chazeau, B., Marchand, N., Gómez-Amo, J. L., Estellés, V., Matos, V., van der Gaag, E., Gille, G., Luoma, K., Manninen, H. E., Norman, M., Silvergren, S., Petit, J.-E., Putaud, J.-P., Rattigan, O. V., Timonen, H., Tuch, T., Merkel, M., Weinhold, K., Vratolis, S., Vasilescu, J., Favez, O., Harrison, R. M., Laj, P., Wiedensohler, A., Hopke, P. K., Petäjä, T., Alastuey, A., and Querol, X.: The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe, Environ. Int., 178, 108081, https://doi.org/10.1016/j.envint.2023.108081, 2023. a, b, c, d
Savadkoohi, M., Pandolfi, M., Favez, O., Putaud, J.-P., Eleftheriadis, K., Fiebig, M., Hopke, P. K., Laj, P., Wiedensohler, A., Alados-Arboledas, L., Bastian, S., Chazeau, B., María, Á. C., Colombi, C., Costabile, F., Green, D. C., Hueglin, C., Liakakou, E., Luoma, K., Listrani, S., Mihalopoulos, N., Marchand, N., Močnik, G., Niemi, J. V., Ondráček, J., Petit, J.-E., Rattigan, O. V., Reche, C., Timonen, H., Titos, G., Tremper, A. H., Vratolis, S., Vodička, P., Funes, E. Y., Zíková, N., Harrison, R. M., Petäjä, T., Alastuey, A., and Querol, X.: Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations, Environ. Int., 185, 108553, https://doi.org/10.1016/j.envint.2024.108553, 2024. a, b
Schwarz, J. P., Gao, R. S., Spackman, J. R., Watts, L. A., Thomson, D. S., Fahey, D. W., Ryerson, T. B., Peischl, J., Holloway, J. S., Trainer, M., Frost, G. J., Baynard, T., Lack, D. A., de Gouw, J. A., Warneke, C., and Del Negro, L. A.: Measurement of the mixing state, mass, and optical size of individual black carbon particles in urban and biomass burning emissions, Geophys. Res. Lett., 35, L13810, https://doi.org/10.1029/2008GL033968, 2008. a, b
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
Segersson, D., Eneroth, K., Gidhagen, L., Johansson, C., Omstedt, G., Nylén, A. E., and Forsberg, B.: Health Impact of PM10, PM2.5 and Black Carbon Exposure Due to Different Source Sectors in Stockholm, Gothenburg and Umea, Sweden, Int. J. Env. Res. Pub. He., 14, 742, https://doi.org/10.3390/ijerph14070742, 2017. a
Singh, V., Ravindra, K., Sahu, L., and Sokhi, R.: Trends of atmospheric black carbon concentration over the United Kingdom, Atmos. Environ., 178, 148–157, https://doi.org/10.1016/j.atmosenv.2018.01.030, 2018. a
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.: NCAR Tech. Note NCAR/TN-475+STR: A Description of the Advanced Research WRF Version 3, https://doi.org/10.5065/D68S4MVH, 2008. a
Sokhi, R. S., Moussiopoulos, N., Baklanov, A., Bartzis, J., Coll, I., Finardi, S., Friedrich, R., Geels, C., Grönholm, T., Halenka, T., Ketzel, M., Maragkidou, A., Matthias, V., Moldanova, J., Ntziachristos, L., Schäfer, K., Suppan, P., Tsegas, G., Carmichael, G., Franco, V., Hanna, S., Jalkanen, J.-P., Velders, G. J. M., and Kukkonen, J.: Advances in air quality research – current and emerging challenges, Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, 2022. 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
Stavroulas, I., Pikridas, M., Grivas, G., Bezantakos, S., Liakakou, E., Kalkavouras, P., Veratti, G., Bigi, A., Gerasopoulos, E., Sciare, J., and Mihalopoulos, N.: Field evaluation of miniature absorption photometers in an Eastern Mediterranean urban environment, in: 11th International Aerosol Conference (IAC 2022), Athens, Greece, 4–9 September 2022. a
Stortini, M., Arvani, B., and Deserti, M.: Operational Forecast and Daily Assessment of the Air Quality in Italy: A Copernicus-CAMS Downstream Service, Atmosphere, 11, 447, https://doi.org/10.3390/atmos11050447, 2020. a
Tang, J., Cheng, W., Gao, J., Li, Y., Yao, R., Rothman, N., Lan, Q., Campen, M. J., Zheng, Y., and Leng, S.: Occupational exposure to carbon black nanoparticles increases inflammatory vascular disease risk: an implication of an ex vivo biosensor assay, Part. Fibre Toxicol., 17, 47, https://doi.org/10.1186/s12989-020-00378-8, 2020. a
Team, C. W., Lee, H., and Romero, J. (Eds.): Climate Change 2023: Synthesis Report, Intergovernmental Panel on Climate Change, Geneva, Switzerland, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023. a
Thorpe, A. and Harrison, R. M.: Sources and properties of non-exhaust particulate matter from road traffic: A review, Sci. Total Environ., 400, 270–282, https://doi.org/10.1016/j.scitotenv.2008.06.007, 2008. a
Thouron, L., Seigneur, C., Kim, Y., Mahé, F., André, M., Lejri, D., Villegas, D., Bruge, B., Chanut, H., and Pellan, Y.: Intercomparison of three modeling approaches for traffic-related road dust resuspension using two experimental data sets, Transport. Res. D-Tr. E., 58, 108–121, https://doi.org/10.1016/j.trd.2017.11.003, 2018. a
Thunis, P., Galmarini, S., Martilli, A., Clappier, A., Andronopoulos, S., Bartzis, J., Vlachogiannis, D., De Ridder, K., Moussiopoulos, N., Sahm, P., Almbauer, R., Sturm, P., Oettl, D., Dierer, S., and Schlünzen, K. H.: An inter-comparison exercise of mesoscale flow models applied to an ideal case simulation, Atmos. Environ., 37, 363–382, https://doi.org/10.1016/S1352-2310(02)00888-9, 2003. a
Thunis, P., Clappier, A., Beekmann, M., Putaud, J. P., Cuvelier, C., Madrazo, J., and de Meij, A.: Non-linear response of PM2.5 to changes in NOx and NH3 emissions in the Po basin (Italy): consequences for air quality plans, Atmos. Chem. Phys., 21, 9309–9327, https://doi.org/10.5194/acp-21-9309-2021, 2021. a
Titos, G., del Águila, A., Cazorla, A., Lyamani, H., Casquero-Vera, J. A., Colombi, C., Cuccia, E., Gianelle, V., Močnik, G., Alastuey, A., Olmo, F. J., and Alados-Arboledas, L.: Spatial and temporal variability of carbonaceous aerosols: Assessing the impact of biomass burning in the urban environment, Sci. Total Environ., 578, 613–625, https://doi.org/10.1016/j.scitotenv.2016.11.007, 2017. a
Tobler, A. K., Skiba, A., Canonaco, F., Močnik, G., Rai, P., Chen, G., Bartyzel, J., Zimnoch, M., Styszko, K., Nęcki, J., Furger, M., Różański, K., Baltensperger, U., Slowik, J. G., and Prevot, A. S. H.: Characterization of non-refractory (NR) PM1 and source apportionment of organic aerosol in Kraków, Poland, Atmos. Chem. Phys., 21, 14893–14906, https://doi.org/10.5194/acp-21-14893-2021, 2021. a
Tominaga, Y. and Stathopoulos, T.: Ten questions concerning modeling of near-field pollutant dispersion in the built environment, Build. Environ., 105, 390–402, https://doi.org/10.1016/j.buildenv.2016.06.027, 2016. a
Travis, K. R., Crawford, J. H., Chen, G., Jordan, C. E., Nault, B. A., Kim, H., Jimenez, J. L., Campuzano-Jost, P., Dibb, J. E., Woo, J.-H., Kim, Y., Zhai, S., Wang, X., McDuffie, E. E., Luo, G., Yu, F., Kim, S., Simpson, I. J., Blake, D. R., Chang, L., and Kim, M. J.: Limitations in representation of physical processes prevent successful simulation of PM2.5 during KORUS-AQ, Atmos. Chem. Phys., 22, 7933–7958, https://doi.org/10.5194/acp-22-7933-2022, 2022. a
Troen, I. B. and Mahrt, L.: A simple model of the atmospheric boundary layer; sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148, https://doi.org/10.1007/BF00122760, 1986. a, b
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: 16 September 2024), 2023. a
Van Leer, B.: Towards the ultimate conservative difference scheme. IV. A new approach to numerical convection, J. Comput. Phys., 23, 276–299, https://doi.org/10.1016/0021-9991(77)90095-X, 1977. a
Vecchi, R., Bernardoni, V., Valentini, S., Piazzalunga, A., Fermo, P., and Valli, G.: Assessment of light extinction at a European polluted urban area during wintertime: Impact of PM1 composition and sources, Environ. Pollut., 233, 679–689, https://doi.org/10.1016/j.envpol.2017.10.059, 2018. a
Veratti, G.: Diurnal variability of Black Carbon in Modena: Modeled concentration maps for winter 2020 and 2021, Version v1, Zenodo [video], https://doi.org/10.5281/zenodo.12960786, 2024a. a
Veratti, G.: Advanced Tools for Black Carbon Dispersion Modelling, Version v1, Zenodo [code], https://doi.org/10.5281/zenodo.13255628, 2024b. 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, 2020. a, b, c
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, c, d
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, b, c
Veratti, G., Bigi, A., Teggi, S., and Ghermandi, G.: Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows, Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, 2024. a
Virkkula, A., Mäkelä, T., Hillamo, R., Yli-Tuomi, T., Hirsikko, A., Hämeri, K., and Koponen, I. K.: A Simple Procedure for Correcting Loading Effects of Aethalometer Data, J. Air Waste Manage., 57, 1214–1222, https://doi.org/10.3155/1047-3289.57.10.1214, 2007. a
Visser, B., Röhrbein, J., Steigmeier, P., Drinovec, L., Močnik, G., and Weingartner, E.: A single-beam photothermal interferometer for in situ measurements of aerosol light absorption, Atmos. Meas. Tech., 13, 7097–7111, https://doi.org/10.5194/amt-13-7097-2020, 2020. a
Visser, B., Bilal, J., Flöry, N., Wipf, M., Steigmeier, P., Rüggeberg, T., Betschon, F., and Weingartner, E.: Waveguide based passively demodulated photothermal interferometer for light absorption measurements of trace substances, Appl. Optics, 62, 374–384, https://doi.org/10.1364/AO.476868, 2023. a, b
Vitali, L., Cuvelier, K., Piersanti, A., Monteiro, A., Adani, M., Amorati, R., Bartocha, A., D'Ausilio, A., Durka, P., Gama, C., Giovannini, G., Janssen, S., Przybyła, T., Stortini, M., Vranckx, S., and Thunis, P.: A standardized methodology for the validation of air quality forecast applications (F-MQO): lessons learnt from its application across Europe, Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, 2023. a
Wang, C.: A modeling study on the climate impacts of black carbon aerosols, J. Geophys. Res.-Atmos., 109, D03106, https://doi.org/10.1029/2003JD004084, 2004. a
Wang, R., Balkanski, Y., Boucher, O., Ciais, P., Schuster, G. L., Chevallier, F., Samset, B. H., Liu, J., Piao, S., Valari, M., and Tao, S.: Estimation of global black carbon direct radiative forcing and its uncertainty constrained by observations, J. Geophys. Res.-Atmos., 121, 5948–5971, https://doi.org/10.1002/2015JD024326, 2016. a
Weingartner, E., Saathoff, H., Schnaiter, M., Streit, N., Bitnar, B., and Baltensperger, U.: Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers, J. Aerosol Sci., 34, 1445–1463, https://doi.org/10.1016/S0021-8502(03)00359-8, 2003. a
WHO: Global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, ISBN 978-92-4-003422-8, https://iris.who.int/handle/10665/345329 (last access: 16 September 2024), 2021. a
Woo, S.-H., Jang, H., Lee, S.-B., and Lee, S.: Comparison of total PM emissions emitted from electric and internal combustion engine vehicles: An experimental analysis, Sci. Total Environ., 842, 156961, https://doi.org/10.1016/j.scitotenv.2022.156961, 2022. a
Yus-Díez, J., Bernardoni, V., Močnik, G., Alastuey, A., Ciniglia, D., Ivančič, M., Querol, X., Perez, N., Reche, C., Rigler, M., Vecchi, R., Valentini, S., and Pandolfi, M.: Determination of the multiple-scattering correction factor and its cross-sensitivity to scattering and wavelength dependence for different AE33 Aethalometer filter tapes: a multi-instrumental approach, Atmos. Meas. Tech., 14, 6335–6355, https://doi.org/10.5194/amt-14-6335-2021, 2021. a, b
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001. a
Zotter, P., Herich, H., Gysel, M., El-Haddad, I., Zhang, Y., Močnik, G., Hüglin, C., Baltensperger, U., Szidat, S., and Prévôt, A. S. H.: Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol, Atmos. Chem. Phys., 17, 4229–4249, https://doi.org/10.5194/acp-17-4229-2017, 2017. a
Short summary
In a study of two consecutive winter seasons, we used measurements and modelling tools to identify the levels and sources of black carbon pollution in a medium-sized urban area of the Po Valley, Italy. Our findings show that biomass burning and traffic-related emissions (especially from Euro 4 diesel cars) significantly contribute to BC concentrations. This research offers crucial insights for policymakers and urban planners aiming to improve air quality in cities.
In a study of two consecutive winter seasons, we used measurements and modelling tools to...
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