Articles | Volume 19, issue 17
https://doi.org/10.5194/amt-19-5729-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-5729-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
09 Sep 2026
Review article |
| 09 Sep 2026
| 09 Sep 2026
Enhancing Urban Air Quality Mapping through Novel Measurement and Modelling approaches, and Citizen Science: Actionable Insights from the RI-URBANS Project
Dimitrios Bousiotis
School of Geography, Earth, and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
School of Geography, Earth, and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
Jelle Hofman
Air Quality & Industrial Emissions Team, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium
Martine Van Poppel
Air Quality & Industrial Emissions Team, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium
Jules Kerckhoffs
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
Roy M. Harrison
School of Geography, Earth, and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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Cited articles
Bagkis, E., Hassani, A., Schneider, P., De Souza, P., Shetty, S., Kassandros, T., Salamalikis, V., Castell, N., Karatzas, K., Ahlawat, A., and Khan, J.: Evolving trends in application of low-cost air quality sensor networks: challenges and future directions, npj Clim. Atmos. Sci., 8, 335, https://doi.org/10.1038/s41612-025-01216-4, 2025.
Baklanov, A., Smith Korsholm, U., Nuterman, R., Mahura, A., Nielsen, K. P., Sass, B. H., Rasmussen, A., Zakey, A., Kaas, E., Kurganskiy, A., Sørensen, B., and González-Aparicio, I.: Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2), Geosci. Model Dev., 10, 2971–2999, https://doi.org/10.5194/gmd-10-2971-2017, 2017.
Barreira, L. M. F., Helin, A., Aurela, M., Teinilä, K., Friman, M., Kangas, L., Niemi, J. V., Portin, H., Kousa, A., Pirjola, L., Rönkkö, T., Saarikoski, S., and Timonen, H.: In-depth characterization of submicron particulate matter inter-annual variations at a street canyon site in northern Europe, Atmos. Chem. Phys., 21, 6297–6314, https://doi.org/10.5194/acp-21-6297-2021, 2021.
Baruah, A., Bousiotis, D., Damayanti, S., Bigi, A., Ghermandi, G., Ghaffarpasand, O., Harrison, R. M., and Pope, F. D.: A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques, npj Clim. Atmos. Sci., 7, https://doi.org/10.1038/s41612-024-00859-z, 2024.
Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications, Atmos. Meas. Tech., 17, 6119–6144, https://doi.org/10.5194/amt-17-6119-2024, 2024.
Bellini, A., Diémoz, H., Gobbi, G. P., Di Liberto, L., Bracci, A., and Barnaba, F.: Aerosols in the mixed layer and mid-troposphere from long-term data of the Italian Automated Lidar-Ceilometer Network (ALICENET) and comparison with the ERA5 and CAMS models, Remote Sens., 17, 372, https://doi.org/10.3390/rs17030372, 2025.
Bousiotis, D., Allison, G., Beddows, D. C. S., Harrison, R. M., and Pope, F. D.: Towards comprehensive air quality management using low-cost sensors for pollution source apportionment, npj Clim. Atmos. Sci., 6, https://doi.org/10.1038/s41612-023-00424-0, 2023.
Bousiotis, D., Damayanti, S., Baruah, A., Bigi, A., Beddows, D. C. S., Harrison, R. M., and Pope, F. D.: Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment, Environ. Int., 193, 109069, https://doi.org/10.1016/j.envint.2024.109069, 2024.
Bousiotis, D., Ademi Shaqiri, L., Sanghera, D. S., Tinker, D., and Pope, F. D.: Low-Cost Source Apportionment (LoCoSA) of air pollution – literature review of the state of the art, Sci. Tot. Environ., 998, 180257, https://doi.org/10.1016/j.scitotenv.2025.180257, 2025.
Boylan, J. W. and Russell, A. G.: PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models, Atmos. Environ., 40, 4946–4959, https://doi.org/10.1016/j.atmosenv.2005.09.087, 2006.
Cambridge Environmental Research Consultants (CERC): ADMS 6, World leading software for modelling industrial air pollution, https://www.cerc.co.uk/environmental-software/ADMS-model.html (last access: 8 July 2026), 2026.
Damayanti, S., Bousiotis, D., Baruah, A., Pope, F. D., and Harrison, R. M.: An evaluation of methods for normalising short campaign outdoor personal exposure data to improve long-term representativeness using background site measurements, Atmos. Pollut. Res., 103147, https://doi.org/10.1016/j.apr.2026.103147, 2026.
Deshmukh, P., Kimbrough, S., Krabbe, S., Logan, R., Isakov, V., and Baldauf, R.: Identifying air pollution source impacts in urban communities using mobile monitoring, Sci. Tot. Environ., 715, 136979, https://doi.org/10.1016/j.scitotenv.2020.136979, 2020.
Di Antonio, L., Beekmann, M., Siour, G., Michoud, V., Cantrell, C., Bauville, A., Bergé, A., Cazaunau, M., Chevaillier, S., Cirtog, M., de Brito, J. F., Formenti, P., Gaimoz, C., Garret, O., Gratien, A., Gros, V., Haeffelin, M., Hawkins, L. N., Kotthaus, S., Noyalet, G., Pereira, D. L., Petit, J.-E., Pronovost, E. D., Riffault, V., Yu, C., Foret, G., Doussin, J.-F., and Di Biagio, C.: Modelling of atmospheric variability in gas and aerosols during the ACROSS campaign 2022 of the greater Paris area: evaluation of the meteorology, dynamics and chemistry, Atmos. Chem. Phys., 25, 4803–4831, https://doi.org/10.5194/acp-25-4803-2025, 2025a.
Di Antonio, L., Di Biagio, C., Formenti, P., Gratien, A., Michoud, V., Cantrell, C., Bauville, A., Bergé, A., Cazaunau, M., Chevaillier, S., Cirtog, M., Coll, P., D'Anna, B., de Brito, J. F., De Haan, D.O., Dignum, J.R., Deshmukh, S., Favez, O., Flaud, P.-M., Gaimoz, C., Hawkins, L. N., Kammer, J., Language, B., Maisonneuve, F., Močnik, G., Perraudin, E., Petit, J.-E., Acharja, P., Poulain, L., Pouyes, P., Pronovost, E. D., Riffault, V., Roundtree, H. I., Shahin, M., Siour, G., Villenave, E., Zapf, P., Foret, G., Doussin, J.-F., and Beekman, M.: Aerosol spectral optical properties in the Paris urban area and its peri-urban and forested surroundings during summer 2022 from ACROSS surface observations, Atmos. Chem. Phys., 25, 3161–3189, https://doi.org/10.5194/acp-25-3161-2025, 2025b.
European Centre for Medium-Range Weather Forecasts (ECMWF): CAMS: Global atmospheric composition forecast data documentation, https://confluence.ecmwf.int/spaces/CKB/pages/212454117/CAMS Global atmospheric composition forecast data documentation (last access: 8 July 2026), 2026.
European Environment Agency (EEA): Harm to human health from air pollution in Europe: burden of disease status, 2024, EEA briefing no. 21/2024, ISBN: 978-92-9480-695-6, https://doi.org/10.2800/3950756, 2024.
United States Environmental Protection Agency (EPA): Air quality modeling, https://www.epa.gov/air-research/air-quality-modeling (last access: 16 February 2026), 2025.
Elomaa, T., Luoma, K., Harni, S., Virkkula, A., Timonen, H., and Petäjä, T.: The applicability and challenges of black carbon sensors in monitoring networks, Aerosol Res., 3, 293–314, https://doi.org/10.5194/ar-3-293-2025, 2025.
Faherty, T., Raymond, J. E., McFiggans, G., and Pope, F. D.: Acute particulate matter exposure diminishes executive cognitive functioning after four hours regardless of inhalation pathway, Nat. Commun., 16, https://doi.org/10.1038/s41467-025-56508-3, 2025.
Fritz, S., See, L., and Grey, F.: The grand challenges facing environmental citizen science, Front. Environ. Sci., 10, 1019628, https://doi.org/10.3389/fenvs.2022.1019628, 2022.
Fry, J. L., Ooms, P., Krol, M., Kerckhoffs, J., Vermeulen, R., Wesseling, J., and van den Elshout, S.: Effect of street trees on local air pollutant concentrations (NO2, BC, UFP, PM2.5) in Rotterdam, the Netherlands, Environ. Sci.: Atmos., 5, 394, https://doi.org/10.1039/d4ea00157e, 2025.
Fung, P. L., Savadkoohi, M., Zaidan, M. A., Niemi, J. V., Timonen, H., Pandolfi, M., Alastuey, A., Querol, X., Hussein, T., and Petäjä, T.: Constructing transferable and interpretable machine learning models for black carbon concentrations, Environ. Int., 184, 108449, https://doi.org/10.1016/j.envint.2024.108449, 2024.
Gaydos, T. M., Pinder, R., Koo, B., Fahey, K. M., Yarwood, G., and Pandis, S. N.: Development and application of a three-dimensional aerosol chemical transport model, PMCAMx, Atmos. Environ., 41, 2594–2611, https://doi.org/10.1016/j.atmosenv.2006.11.034, 2007.
Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., McNeill, V. F., Westervelt, D. M., Beekmann, M., and Subramanian, R.: From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors, J. Aerosol. Sci., 158, 105833, https://doi.org/10.1016/j.jaerosci.2021.105833, 2021.
González-Romero, A., Escorcia Rico, P., Alastuey, A., Moreno, N., Querol, X., and Córdoba Sola, P.: Spatial variability of metal pollution in sands and sandy soils of playgrounds and parks in the Barcelona metropolitan area: assessing the impact of urban and industrial activities, Air Qual. Atmos. Health, https://doi.org/10.1007/s11869-025-01726-3, 2025.
Hofman, J., Do, T. H., Qin, X., Bonet, E. R., Philips, W., Deligiannis, N., and La Manna, V. P.: Spatiotemporal air quality inference of low-cost sensor data: Evidence from multiple sensor testbeds, Environ. Model. Softw., 149, 105306, https://doi.org/10.1016/j.envsoft.2022.105306, 2022a.
Hofman, J., Peters, J., Stroobants, C., Elst, E., Baeyens, B., Van Laer, J., Spruyt, M., Van Essche, W., Delbare, E., Roels, B., Cochez, A., Gillijns, E., and Van Poppel, M.: Air quality sensor networks for evidence-based policy making: Best practices for actionable insights, Atmosphere, 13, 944, https://doi.org/10.3390/atmos13060944, 2022b.
Hofman, J., Panzica La Manna, V., Ibarrola-Ulzurrun, E., Peters, J., Escribano Hierro, M., and Van Poppel, M.: Opportunistic mobile air quality mapping using sensors on postal vehicles: from point clouds to actionable insights, Front. Environ. Health, 2, 1232867, https://doi.org/10.3389/fenvh.2023.1232867, 2023.
Hofman, J., Lazarov, B., Stroobants, C., Elst, E., Smets, I., and Van Poppel, M.: Portable sensors for dynamic exposure assessment in urban environments: State of the science, Sensors, 24, 5653, https://doi.org/10.3390/s24175653, 2024.
Holicki, P.: Uncertainty in Integrated Modelling of Air Quality, Advanced Air Pollution, IntechOpen, ISBN: 978-953-307-511-2, https://doi.org/10.5772/710, 2011.
Idrees, Z. and Zheng, L.: Low cost air pollution monitoring systems: A review of protocols and enabling technologies, J. Ind. Inf. Integr., 17, 100123, https://doi.org/10.1016/j.jii.2019.100123, 2020.
Järvi, L., Hannuniemi, H., Hussein, T., Junninen, H., Aalto, P. P., Hillamo, R., Mäkelä, T., Keronen, P., Siivola, E., Vesala, T., and Kulmala, M.: The urban measurement station SMEAR III: continuous monitoring of air pollution and surface–atmosphere interactions in Helsinki, Finland, Boreal Environ. Res., 14, 86–109, https://www.borenv.net/BER/archive/pdfs/ber14/ber14A-086.pdf, 2009.
Järvi, L., Kurppa, M., Kuuluvainen, H., Rönkkö, T., Karttunen, S., Balling, A., Timonen, H., Niemi, J. V., and Pirjola, L.: Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone, Sci. Tot. Environ., 856, 158974, https://doi.org/10.1016/j.scitotenv.2022.158974, 2023.
Johansson, L., Karppinen, A., Kurppa, M., Kousa, A., Niemi, J. V., and Kukkonen, J.: An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation, Environ. Model. Softw., 105460, https://doi.org/10.1016/j.envsoft.2022.105460, 2022.
Karagulian, F., Borowiak, A., Barbiere, M., Kotsev, A., Van den Broecke, J., Vonk, J., Signorini, M., and Gerboles, M.: Calibration of AirSensEUR boxes during a field study in the Netherlands, Technical Report JRC116324, European Commission, Ispra, Italy, https://www.researchgate.net/publication/344163000_Calibration_of_AirSensEUR_boxes_during_a_field_study_in_the_Netherlands (last access: 17 August 2026), 2020.
Kaskaoutis, D. G., Petrinoli, K., Grivas, G., Kalkavouras, P., Tsagkaraki, M., Tavernaraki, K., Papoutsidaki, K., Stavroulas, I., Paraskevopoulou, D., Bougiatioti, A., Liakakou, E., Rashki, A., Sotiropoulou, R. E. P., Tagaris, E., Gerasopoulos, E., and Mihalopoulos, N.: Impact of peri-urban forest fires on air quality and aerosol optical and chemical properties: The case of the August 2021 wildfires in Athens, Greece. Sci. Tot. Environ., 907, 168028, https://doi.org/10.1016/j.scitotenv.2023.168028, 2024.
Kerckhoffs, J., Khan, J., Hoek, G., Yuan, Z., Ellermann, T., Hertel, O., Ketzel, M., Jensen, S. S., Maliefste, K., and Vermeulen, R.: Mixed-effects modelling framework for Amsterdam and Copenhagen for outdoor NO2 concentrations using measurements sampled with Google Street View cars, Environ. Sci. Technol., 56, 11, 7174–7184, https://doi.org/10.1021/acs.est.1c05806, 2022.
Kerckhoffs, J., Hofman, J., Khan, J., Adams, M. D., Blanco, M. N., de Souza, P., Durant, J. L., Faridi, S., Fruin, S., Hankey, S., Hassanvand, M. S., Hatzopoulou, M., Hoek, G., de Hoogh, K., Hudda, N., Kushwaha, M., Marshall, J.D., Minet, L., Patton, A. P., Petäjä, T., Peters, J., Presto, A. A., Shairsingh, K., Sheppard, L., Simon, M. C., Vakacherla, S., Van Ryswyk, K., Van Poppel, M., Vermeulen, R. C. H., Wegener, R., Yuan, Z., and Amini, H.: Mobile monitoring of air pollution – a position paper on use cases, good practices, challenges, and opportunities, Environ. Int., 202, 109582, https://doi.org/10.1016/j.envint.2025.109582, 2025.
Kleemola, E., Marsela, A., Haataja, J., Wu, Y., Rohal, D., Koskinen, P., Harni, S., Li, D., Timonen, H., Petäjä, T., and Kangasluoma, J.: Integrated platform for mobile air quality measurements in urban environments, European Aerosol Conference 2024, Tampere, Finland, 25–30 August 2024, https://www.eac2024.fi/ (last access: 17 August 2026), 2024.
Kosmopoulos, G., Salamalikis, V., Matrali, A., Pandis, S. N., and Kazantzidis, A.: Insights about the Sources of PM2.5 in an Urban Area from Measurements of a Low-Cost Sensor Network, Atmosphere, 13, 440, https://doi.org/10.3390/atmos13030440, 2022.
Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., Bell., M., Norford, L., and Britter, R.: The rise of low-cost sensing for managing air pollution in cities, Environ. Int., 75, 199–205, https://doi.org/10.1016/j.envint.2014.11.019, 2015.
Kushta, J., Georgiou, G.K., Proestos, Y., Christoudias, T., Thunis, P., Savvides, C., Papadopoulos, C., and Lelieveld, J.: Evaluation of EU air quality standards through modelling and the FAIRMODE benchmarking methodology, Air Qual., Atmos., Health, 12, 73–86, https://doi.org/10.1007/s11869-018-0631-z, 2019.
Laboratoire de Météorologie Dynamique (LMD): Chimere, Chemistry transport model v2023r3, documentation, https://www.lmd.polytechnique.fr/chimere/ (last access: 8 July 2026), 2025.
Liu, X., Zhang, X., Wang, R., Liu, Y., Hadiatullah, H., Xu, Y., Wang, T., Bendi, J., Adam, T., Schnelle-Kreis, J., and Querol, X.: High-precision microscale particulate matter prediction in diverse environments using a long short-term memory neural network and street view imagery, Environ. Sci. Technol., 58, https://doi.org/10.1021/acs.est.3c06511, 2024.
Maison, A., Lugon, L., Park, S.-J., Boissard, C., Faucheux, A., Gros, V., Kalacian, C., Kim, Y., Leymarie, J., Petit, J.-E., Roustan, Y., Sanchez, O., Squarcioni, A., Valari, M., Viatte, C., Vigneron, J., Tuzet, A., and Sartelet, K.: Contrasting effects of urban trees on air quality: From the aerodynamic effects in streets to impacts of biogenic emissions in cities, Sci. Tot. Environ., 946, 174116, https://doi.org/10.1016/j.scitotenv.2024.174116, 2024.
Manisalidis, I., Stavropoulou, E., Stavropoulos, A., and Bezirtzoglou, E.: Environmental and health impacts of air pollution: A review, Front. Public Health, 8, https://doi.org/10.3389/fpubh.2020.00014, 2020.
Mehanna, S., Kedad, Z., and Chachoua, M.: Completeness assessment and improvement in mobile crowd-sensing environments, SN Comput. Sci., 3, https://doi.org/10.1007/s42979-022-01104-1, 2022.
Men, Y., Li, J., Liu, X., Li, Y., Jiang, K., Luo, Z., Xiong, R., Cheng, H., Tao, S., and Shen, G.: Contributions of internal emissions to peaks and incremental indoor PM2.5 in rural coal use households, Environ. Pollut., 288, 117753, https://doi.org/10.1016/j.envpol.2021.117753, 2021.
Menut, L., Cholakian, A., Pennel, R., Siour, G., Mailler, S., Valari, M., Lugon, L., and Meurdesoif, Y.: The CHIMERE chemistry-transport model v2023r1, Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, 2024.
Morris, R. E., McNally, D. E., Tesche, T. W., Tonnesen, G., Boylan, J. W., and Brewer, P.: Preliminary evaluation of the community multiscale air quality model for 2002 over the southeastern United States, J. Air Waste Manag. Assoc., 55, 1694–1708, https://doi.org/10.1080/10473289.2005.10464765, 2005.
Motlagh, N. H., Lagerspetz, E., Nurmi, P., Li, X., Varjonen, S., and Mineraud, J.: Toward massive scale air quality monitoring, IEEE Commun. Mag., 58, https://doi.org/10.1109/mcom.001.1900515, 2020.
Mueller, M. D., Hasenfratz, D., Saukh, O., Fierz, M., and Hueglin, C.: Atmos. Environ., 126, 171–181, https://doi.org/10.1016/j.atmosenv.2015.11.033, 2016.
Myriokefalitakis, S., Karl, M., Weiss, K. A., Karagiannis, D., Athanasopoulou, E., Kakouri, A., Bougiatioti, A., Liakakou, E., Stavroulas, I., Papangelis, G., Grivas, G., Paraskevopoulou, D., Speyer, O., Mihalopoulos, N., and Gerasopoulos, E.: Analysis of secondary inorganic aerosols over the greater Athens area using the EPISODE–CityChem source dispersion and photochemistry model, Atmos. Chem. Phys., 24, 7815–7835, https://doi.org/10.5194/acp-24-7815-2024, 2024.
Nalakurthi, N. V. S. R., Abimbola, I., Ahmed, T., Anton, I., Riaz, K., Ibrahim, Q., Banerjee, A., Tiwari, A., and Gharbia, S.: Challenges and opportunities in calibrating low-cost environmental sensors, Sensors, 24, 3650, https://doi.org/10.3390/s24113650, 2024.
Nguyen, C. V. and Soulhac, L.: Data assimilation methods for urban air quality at the local scale, Atmos. Environ., 253, 118366, https://doi.org/10.1016/j.atmosenv.2021.118366, 2021.
Nicolae, D., Talianu, C., Vasilescu, J., Dandocsi, A. M., Belegante, L., Nemuc, A., Toanca, F., Ilie, A., Dandocsi, A. V., Nicolae, S. M., Ciocan, G., Vulturescu, V., and Tudose, O.G.: How does the location of power plants impact air quality in the urban area of Bucharest, Atmosphere, 16, 636, https://doi.org/10.3390/atmos16060636, 2025.
Oyola, OP., Carbone, S., Timonen, H., Torkmahalleh, M., and Lindén, J.: Editorial: Rise of low-cost sensors and citizen science in air quality studies, Front. Environ. Sci., 10, https://doi.org/10.3389/fenvs.2022.868543, 2022.
Panahifar, H., Bayat, F., and Hussein, T.: Simultaneous use of ground based and satellite observation to evaluate atmospheric air pollution over Amman, Jordan, Atmosphere, 14, 274, https://doi.org/10.3390/atmos14020274, 2023.
Park, S.-J., Lugon, L., Jacquot, O., Kim, Y., Baudic, A., D'Anna, B., Di Antonio, L., Di Biagio, C., Dugay, F., Favez, O., Ghersi, V., Gratien, A., Kammer, J., Petit, J.-E., Sanchez, O., Valari, M., Vigneron, J., and Sartelet, K.: Population exposure to outdoor NO2, black carbon, and ultrafine and fine particles over Paris with multi-scale modelling down to the street scale, Atmos. Chem. Phys., 25, 3363–3387, https://doi.org/10.5194/acp-25-3363-2025, 2025.
Patoulias, D., Florou, K., and Pandis, S. N.: Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2, Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, 2025.
Rathbone, C. J., Bousiotis, D., Rose, O.G., and Pope, F. D.: Using low-cost sensors to assess common air pollution sources across multiple residences, Sci. Rep., 15, 1803, https://doi.org/10.1038/s41598-025-85985-1, 2025.
Rebeiro-Hargrave, A., Fung, P. L., Varjonen, S., Huertas, A., Sillanpää, S., Luoma, K., Hussein, T., Petäjä, T., Timonen, H., Limo, J., Nousiainen, V., and Tarkoma, S.: City wide participatory sensing of air quality, Front. Environ. Sci, https://doi.org/10.3389/fenvs.2021.773778, 2021.
Relvas, H., Lopes, D., and Armengol, J. M.: Empowering communities: Advancements in air quality monitoring and citizen engagement, Urban Clim., 102344, https://doi.org/10.1016/j.uclim.2025.102344, 2025.
RI-URBANS: Expanding the implementation, Applying our approach in all environments, https://riurbans.eu/work-package-5/ (last access: 21 October 2025), 2025.
RI-URBANS: Guidance documents on measurements and modelling of novel air quality pollutants: Urban mapping and citizen science, https://riurbans.eu/wp-content/uploads/2024/11/ENV_GUIDANCE-DOCUMENT_ST13_urban-mapping_citizen_Definitive.pdf (last access: 16 February 2026), 2024.
RI-URBANS: WP2 Deliverable D13 (D2.5), Description of methodology for mobile monitoring and citizen involvement, Horizon 2020 RI-URBANS project, https://riurbans.eu/wp-content/uploads/2022/10/RI-URBANS_D13_D2.5.pdf (last access: 8 February 2026), 2022.
San José, R., Baklanov, A., Sokhi, R.S., Karatzas, K., and Perez, J. L.: Air quality modelling, Encycl. Ecol., 111–123, https://doi.org/10.1016/B978-008045405-4.00201-9, 2008.
Sartelet, K., Couvidat, F., Wang, Z., Flageul, C., and Kim, Y.: SSH-aerosol v1. 1: A modular box model to simulate the evolution of primary and secondary aerosols, Atmos., 11, 525, https://doi.org/10.3390/atmos11050525, 2020.
Sartelet, K., Kerckoffs, J., Athanasopoulou, E., Lugon, L., Vasilescu, J., Zhong, J., Hoek, G., Jolly, C., Park, S.-J., Talianu, C., van den Elshout, S., Dugay, F., Gerasopoulos, E., Ilie, A., Kim, Y., Nicolae, D., Harrison, R. M., and Petäjä, T.: Air pollution mapping and variability over five European cities, Environ. Int., 199, 109474, https://doi.org/10.1016/j.envint.2025.109474, 2025.
Savenets, M., Pysarenko, L., Krakovska, S., Mahura, A., and Petäjä, T.: Enviro-HIRLAM model estimates of elevated black carbon pollution over Ukraine resulted from forest fires, Atmos. Chem. Phys., 22, 15777–15791, https://doi.org/10.5194/acp-22-15777-2022, 2022.
Shabbir, M., Saeed, T., Saleem, A., Bhave, P., Bergin, M., and Khokhar, M. F.: A paradigm shift: Low-cost sensors for effective air quality monitoring and management in developing countries, Environ. Int., 109521, https://doi.org/10.1016/j.envint.2025.109521, 2025.
Singh, A. J., Ng'ang'a, D., Gatari, M., Kidane, A. W., Alemu, Z. A., Derrick, N., Webster, M. J., Bartington, S., Thomas, G. N., Avis, W. R., and Pope, F. D.: Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots, Environ. Res. Commun., 3, 075007, https://doi.org/10.1088/2515-7620/ac0e0a, 2021.
Siouti, E., Skyllakou, K., Kioutsioukis, I., Patoulias, D., Fouskas, G., and Pandis, S. N.: Development and application of the SmartAQ high-resolution air quality and source apportionment forecasting system for European urban areas, Atmosphere, 13, 1693, https://doi.org/10.3390/atmos13101693, 2022.
Siouti, E., Kilafis, K., Kioutsioukis, I., and Pandis, S. N.: Simulation of the influence of residential biomass burning on air quality in an urban area, Atmos. Environ., 309, 119897, https://doi.org/10.1016/j.atmosenv.2023.119897, 2023.
Siouti, E., Skyllakou, K., Kioutsioukis, I., Patoulias, D., Apostolopoulos, I. D., Fouskas, G., and Pandis, S. N.: Prediction of the concentration and source contributions of PM2.5 and gas-phase pollutants in an urban area with the SmartAQ Forecasting System, Atmosphere, 15, 8, https://doi.org/10.3390/atmos15010008, 2024.
Siouti, E., Skyllakou, K., Patoulias, D., Athanasopoulou, E., Mihalopoulos, N., Kuenen, J., and Pandis, S. N.: High resolution source-resolved PM2.5 spatial distribution and human exposure in a large urban area, Atmos. Environ., 355, 121277, https://doi.org/10.1016/j.atmosenv.2025.121277, 2025.
Talianu, C., Vasilescu, J., Nicolae, D., Ilie, A., Dandocsi, A., Nemuc, A., and Belegante, L.: High-resolution air quality maps for Bucharest using a mixed-effects modeling framework, Atmos. Chem. Phys., 25, 4639–4654, https://doi.org/10.5194/acp-25-4639-2025, 2025.
Teinilä, K., Saarikoski, S., Lintusaari, H., Lepistö, T., Marjanen, P., Aurela, M., Hellén, H., Tykkä, T., Lampimäki, M., Lampilahti, J., Barreirra, L., Mäkelä, T., Kangas, L., Hatakka, J., Harni, S., Kuula, J., Niemi, J.V., Portin, H., Yli-Ojanperä, J., Niemelä, V., Jäppi, M., Lehtipalo, K., Vanhanen, J., Pirjola, L., Manninen, H.E., Petäjä, T., Rönkkö, T., and Timonen, H.: Measurement report: Wintertime aerosol characterization at an urban traffic site in Helsinki, Finland, Atmos. Chem. Phys., 25, 4907–4928, https://doi.org/10.5194/acp-25-4907-2025, 2025.
Tuccella, P., Di Antonio, L., Di Muzio, A., Colaiuda, V., Lidori, R., Menut, L., Pitari, G., and Raparelli, E.: Modelling the black and brown carbon absorption and their radiative impact: The June 2023 intense Canadian Boreal wildfires case study, J. Geophys. Res.: Atmos., 130, https://doi.org/10.1029/2024JD042674, 2025.
Van Den Bossche, J., Peters, J., Verwaren, J., Botteldooren, D., Theunis, J., and De Baets, B.: Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset, Atmos. Environ., 105, 148–161, https://doi.org/10.1016/j.atmosenv.2015.01.017, 2015.
Van Den Bossche, J., Theunis, J., Elen, B., Peters, J., Botteldooren, D., and De Boets, B.: Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp, Atmos. Environ., 141, 408–421, https://doi.org/10.1016/j.atmosenv.2016.06.063, 2016.
Van Poppel, M., Peters, J., Levei, E. A., Mǎrmureanu, L., Moldovan, A., Hoaghia, M.-A., Varaticeanu, C., and Van Laer, J.: Mobile measurements of black carbon: Comparison of normal traffic with reduced traffic conditions during COVID-19 lock-down, Atmos. Environ., 297, 119594, https://doi.org/10.1016/j.atmosenv.2023.119594, 2023.
Van Poppel, M., Peters, J., Vranckx, S., Van Laer, J., Hofman, J., Vandeninden, B., Vanpoucke, C., and Lefebvre, W.: Exploring the spatial variability of air pollution using mobile BC measurements in a citizen science project: A case study in Mechelen, Atmosphere, 15, 757, https://doi.org/10.3390/atmos15070757, 2024.
Vida, M., Foret, G., Siour, G., Couvidat, F., Favez, O., Uzu, G., Cholakian, A., Conil, S., Beekmann, M., and Jaffrezo, J.-L.: Modelling of atmospheric concentrations of fungal spores: a 2-year simulation over France using CHIMERE, Atmos. Chem. Phys., 24, 10601–10615, https://doi.org/10.5194/acp-24-10601-2024, 2024.
Vida, M., Foret, G., Siour, G., Jaffrezo, J.-L., Favez, O., Cholakian, A., Cozic, J., Dupont, H., Gille, G., Oppo, S., Zhang, S., Francony, F., Pallares, C., Conil, S., Uzu, G., and Beekmann, M.: Modelling oxidative potential of atmospheric particle: A 2-year study over France, Sci. Tot. Environ., 967, https://doi.org/10.1016/j.scitotenv.2025.178813, 2025.
Vos, P. E. J., Maiheu, B., Vankerkom, J., and Janssen, S.: Improving local air quality in cities: to tree or not to tree?, Environ. Pollut., 183, 113–122, https://doi.org/10.1016/j.envpol.2012.10.021, 2013.
Wagstrom, K. M., Pandis, S. N., Yarwood, G., Wilson, G. M., and Morris, R. E.: Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model, Atmos. Environ., 42, 5650–5659, https://doi.org/10.1016/j.atmosenv.2008.03.012, 2008.
Ward, F., Lowther-Payne, H. J., Halliday, E. C., Dooley, K., Joseph, N., Livesey, R., Moran, P., Kirby, S., and Cloke, J.: Engaging communities in addressing air quality: a scoping review, Environ. Health, 21, 89, https://doi.org/10.1186/s12940-022-00896-2, 2022.
Wesseling, J., Hendricx, W., de Ruiter, H., van Ratingen, S., Drukker, D., Huitema, M., Schouwenaar, C., Janssen, G., van Aken, S., Smeenk, J. W., Hof, A., and Tielemans, E.: Assessment of PM2.5 exposure during cycle trips in The Netherlands using low-cost sensors, Environ. Res. Public Health, 18, 6007, https://doi.org/10.3390/ijerph18116007, 2021.
Yuan, Z., Kerckhoffs, J., Hoek, G., and Vermeulen, R.: A knowledge transfer approach to map long-term concentrations of hyperlocal air pollution from short-term mobile measurements, Environ. Sci. Technol., 56, 19, 13820–13828, https://doi.org/10.1021/acs.est.2c05036, 2022.
Yuan, Z., Kerckhoffs, J., Li, H., Khan, J., Hoek, G., and Vermeulen, R.: Hypelocal air pollution mapping: A scalable transfer learning LUR approach for mobile monitoring, Environ. Sci. Technol., 58, 14372 – 14383, https://doi.org/10.1021/acs.est.4c06144, 2024.
Zhong, J., Harrison, R. M., Bloss, W. J., Visschedjik, A., and van der Gon, H. D.: Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model, Environ. Int., 181, 108273, https://doi.org/10.1016/j.envint.2023.108273, 2023.
Zhong, J., Stocker, J., Cai, X., Harrison, R. M., and Bloss, W. J.: Street-scale air quality modelling over the West Midlands, United Kingdom: Effect of idealised traffic reduction scenarios, Urban Clim., 55, 101961, https://doi.org/10.1016/j.uclim.2024.101961, 2024.
Zhong, J., Li, Y., Bloss, W. J., and Harrison, R. M.: Street-scale black carbon modelling over the West Midlands, United Kingdom: Sensitivity test of traffic emission factor adjustments, Environ. Int., 196, 109265, https://doi.org/10.1016/j.envint.2025.109265, 2025.
Short summary
This study explores new ways to better understand urban air pollution by combining traditional monitoring with portable sensors, mobile measurements, and public participation. Using European city case studies, the research shows that these approaches can identify pollution hotspots in greater detail and make air quality information more accessible to communities and decision-makers. The work highlights how improved monitoring supports healthier cities and more effective environmental policies.
This study explores new ways to better understand urban air pollution by combining traditional...
