Articles | Volume 19, issue 11
https://doi.org/10.5194/amt-19-3625-2026
© 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-19-3625-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
03 Jun 2026
Research article | Highlight paper |
| 03 Jun 2026
| 03 Jun 2026
From real-time to long-term source apportionment of PM10 using high-time-resolution measurements of aerosol physical properties: methodology and example application at an urban background site (Aosta, Italy)
Regional Environmental Protection Agency (ARPA) of the Aosta Valley, Loc. La Maladière 48, Saint-Christophe, 11020, Italy
Francesca Barnaba
National Research Council, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Via Fosso del Cavaliere 100, Roma, 00133, Italy
Luca Ferrero
GEMMA Center, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, Milano, 20126, Italy
Ivan K. F. Tombolato
Regional Environmental Protection Agency (ARPA) of the Aosta Valley, Loc. La Maladière 48, Saint-Christophe, 11020, Italy
Caterina Mapelli
National Research Council, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Via Fosso del Cavaliere 100, Roma, 00133, Italy
National Research Council, Institute of Methodologies for Environmental Analysis, CNR-IMAA, Contrada S. Loja, Tito Scalo, 85050, Italy
Annachiara Bellini
Regional Environmental Protection Agency (ARPA) of the Aosta Valley, Loc. La Maladière 48, Saint-Christophe, 11020, Italy
Claudia Desandré
Regional Environmental Protection Agency (ARPA) of the Aosta Valley, Loc. La Maladière 48, Saint-Christophe, 11020, Italy
Tiziana Magri
Regional Environmental Protection Agency (ARPA) of the Aosta Valley, Loc. La Maladière 48, Saint-Christophe, 11020, Italy
Manuela Zublena
Regional Environmental Protection Agency (ARPA) of the Aosta Valley, Loc. La Maladière 48, Saint-Christophe, 11020, Italy
Related authors
Stefania Gilardoni, Annachiara Bellini, Paolo Bonasoni, Henry Diémoz, Christian Gencarelli, Angela Marinoni, Eros Mariani, Luigi Mazari Villanova, Bruno Neininger, Mattia Perilli, Michael Sprenger, and Francesco Petracchini
EGUsphere, https://doi.org/10.5194/egusphere-2026-1299, https://doi.org/10.5194/egusphere-2026-1299, 2026
Short summary
Short summary
This study presents the first high-time-resolution aerosol data collected from the Testa Grigia Observatory (3,480 m) in the Italian Alps, from 2021 to 2023. We identified three transport pathways: Saharan Dust Events (SDE), which occurred with a frequency of 6 %, regional and long-range transported pollution (28 %), and clean air from the free troposphere. The timing of SDE, observed mainly in spring, and the mixing of dust particles with black carbon, enhance SDE impact on snow melting.
Costanza Civale, M. Dolores Andrés Hernández, Jean-Philippe Putaud, Francesca Barnaba, Henri Diémoz, Johannes Schneider, Helmut Ziereis, Katharina Kaiser, Jörg Schmidt, Ovid Oktavian Krüger, Bruna Holanda, Robert Baumann, Benjamin Weyland, Eric Förster, Midhun George, Yangzhuoran Liu, Daniel Sauer, Jennifer Wolf, Annachiara Bellini, Birger Bohn, Klaus Pfeilsticker, and John Philip Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2025-6458, https://doi.org/10.5194/egusphere-2025-6458, 2026
Short summary
Short summary
This study combines measurements of gases and particles at the ground and on-board an instrumented aircraft to investigate pollution transport over the Po Valley, a major atmospheric pollution hotspot in Europe. Overall, the extensive data set obtained at different altitudes provides insight into the mixing of pollution plumes and their export towards the Adriatic coast as well as with naturally occurring dust and its impact on air quality over the Po Plain.
Annachiara Bellini, Henri Diémoz, Luca Di Liberto, Gian Paolo Gobbi, Alessandro Bracci, Ferdinando Pasqualini, and Francesca Barnaba
Atmos. Meas. Tech., 17, 6119–6144, https://doi.org/10.5194/amt-17-6119-2024, https://doi.org/10.5194/amt-17-6119-2024, 2024
Short summary
Short summary
We provide a comprehensive overview of the Italian Automated LIdar-CEilometer network, ALICENET, describing its infrastructure, aerosol retrievals, and main applications. The supplement covers data-processing details. We include examples of output products, comparisons with independent data, and examples of the network capability to provide near-real-time aerosol fields over Italy. ALICENET is expected to benefit the sectors of air quality, radiative budget/solar energy, and aviation safety.
Monica Campanelli, Victor Estellés, Gaurav Kumar, Teruyuki Nakajima, Masahiro Momoi, Julian Gröbner, Stelios Kazadzis, Natalia Kouremeti, Angelos Karanikolas, Africa Barreto, Saulius Nevas, Kerstin Schwind, Philipp Schneider, Iiro Harju, Petri Kärhä, Henri Diémoz, Rei Kudo, Akihiro Uchiyama, Akihiro Yamazaki, Anna Maria Iannarelli, Gabriele Mevi, Annalisa Di Bernardino, and Stefano Casadio
Atmos. Meas. Tech., 17, 5029–5050, https://doi.org/10.5194/amt-17-5029-2024, https://doi.org/10.5194/amt-17-5029-2024, 2024
Short summary
Short summary
To retrieve columnar aerosol properties from sun photometers, some calibration factors are needed. The on-site calibrations, performed as frequently as possible to monitor changes in the machine conditions, allow operators to track and evaluate the calibration status on a continuous basis, reducing the data gaps incurred by the periodic shipments for performing centralized calibrations. The performance of the on-site calibration procedures was evaluated, providing very good results.
Vladimir Savastiouk, Henri Diémoz, and C. Thomas McElroy
Atmos. Meas. Tech., 16, 4785–4806, https://doi.org/10.5194/amt-16-4785-2023, https://doi.org/10.5194/amt-16-4785-2023, 2023
Short summary
Short summary
This paper describes a way to significantly improve ozone measurements at low sun elevations and large ozone amounts when using the Brewer ozone spectrophotometer. The proposed algorithm will allow more uniform ozone measurements across the monitoring network. This will contribute to more reliable trend analysis and support the satellite validation. This research contributes to better understanding the physics of the instrument, and the new algorithm is based on this new knowledge.
Kostas Eleftheratos, John Kapsomenakis, Ilias Fountoulakis, Christos S. Zerefos, Patrick Jöckel, Martin Dameris, Alkiviadis F. Bais, Germar Bernhard, Dimitra Kouklaki, Kleareti Tourpali, Scott Stierle, J. Ben Liley, Colette Brogniez, Frédérique Auriol, Henri Diémoz, Stana Simic, Irina Petropavlovskikh, Kaisa Lakkala, and Kostas Douvis
Atmos. Chem. Phys., 22, 12827–12855, https://doi.org/10.5194/acp-22-12827-2022, https://doi.org/10.5194/acp-22-12827-2022, 2022
Short summary
Short summary
We present the future evolution of DNA-active ultraviolet (UV) radiation in view of increasing greenhouse gases (GHGs) and decreasing ozone depleting substances (ODSs). It is shown that DNA-active UV radiation might increase after 2050 between 50° N–50° S due to GHG-induced reductions in clouds and ozone, something that is likely not to happen at high latitudes, where DNA-active UV radiation will continue its downward trend mainly due to stratospheric ozone recovery from the reduction in ODSs.
Monica Campanelli, Henri Diémoz, Anna Maria Siani, Alcide di Sarra, Anna Maria Iannarelli, Rei Kudo, Gabriele Fasano, Giampietro Casasanta, Luca Tofful, Marco Cacciani, Paolo Sanò, and Stefano Dietrich
Atmos. Meas. Tech., 15, 1171–1183, https://doi.org/10.5194/amt-15-1171-2022, https://doi.org/10.5194/amt-15-1171-2022, 2022
Short summary
Short summary
The aerosol optical depth (AOD) characteristics in an urban area of Rome were retrieved over a period of 11 years (2010–2020) to determine, for the first time, their effect on the incoming ultraviolet (UV) solar radiation. The surface forcing efficiency shows that the AOD is the primary parameter affecting the surface irradiance in Rome, and it is found to be greater for smaller zenith angles and for larger and more absorbing particles in the UV range (such as, e.g., mineral dust).
Ilias Fountoulakis, Henri Diémoz, Anna Maria Siani, Alcide di Sarra, Daniela Meloni, and Damiano M. Sferlazzo
Atmos. Chem. Phys., 21, 18689–18705, https://doi.org/10.5194/acp-21-18689-2021, https://doi.org/10.5194/acp-21-18689-2021, 2021
Short summary
Short summary
The variability and trends of solar spectral UV irradiance have been studied for the periods 1996–2020 (for Rome) and 2006–2020 (for Lampedusa, Rome, and Aosta) with respect to the variability and trends of total ozone and geopotential height. Analyses revealed increasing UV in particular months at all sites, possibly due to decreasing lower-stratospheric ozone (at Rome in 1996–2020) and decreasing attenuation by aerosols and/or clouds (at all stations in 2006–2020).
Henri Diémoz, Anna Maria Siani, Stefano Casadio, Anna Maria Iannarelli, Giuseppe Rocco Casale, Vladimir Savastiouk, Alexander Cede, Martin Tiefengraber, and Moritz Müller
Earth Syst. Sci. Data, 13, 4929–4950, https://doi.org/10.5194/essd-13-4929-2021, https://doi.org/10.5194/essd-13-4929-2021, 2021
Short summary
Short summary
A 20-year (1996–2017) record of nitrogen dioxide column densities collected in Rome by a Brewer spectrophotometer is presented, together with the novel algorithm employed to re-evaluate the series. The high quality of the data is demonstrated by comparison with reference instrumentation, including a co-located Pandora spectrometer. The data can be used for satellite validation and identification of NO2 trends. The method can be replicated on other instruments of the international Brewer network.
Panagiotis G. Kosmopoulos, Stelios Kazadzis, Alois W. Schmalwieser, Panagiotis I. Raptis, Kyriakoula Papachristopoulou, Ilias Fountoulakis, Akriti Masoom, Alkiviadis F. Bais, Julia Bilbao, Mario Blumthaler, Axel Kreuter, Anna Maria Siani, Kostas Eleftheratos, Chrysanthi Topaloglou, Julian Gröbner, Bjørn Johnsen, Tove M. Svendby, Jose Manuel Vilaplana, Lionel Doppler, Ann R. Webb, Marina Khazova, Hugo De Backer, Anu Heikkilä, Kaisa Lakkala, Janusz Jaroslawski, Charikleia Meleti, Henri Diémoz, Gregor Hülsen, Barbara Klotz, John Rimmer, and Charalampos Kontoes
Atmos. Meas. Tech., 14, 5657–5699, https://doi.org/10.5194/amt-14-5657-2021, https://doi.org/10.5194/amt-14-5657-2021, 2021
Short summary
Short summary
Large-scale retrievals of the ultraviolet index (UVI) in real time by exploiting the modern Earth observation data and techniques are capable of forming operational early warning systems that raise awareness among citizens of the health implications of high UVI doses. In this direction a novel UVI operating system, the so-called UVIOS, was introduced for massive outputs, while its performance was tested against ground-based measurements revealing a dependence on the input quality and resolution.
Luca D'Angelo, Florian Ungeheuer, Jialiang Ma, Luca Ferrero, Cristina Colombi, Eleonora Cuccia, Umberto Dal Santo, Beatrice Biffi, and Alexander L. Vogel
Atmos. Chem. Phys., 26, 6799–6823, https://doi.org/10.5194/acp-26-6799-2026, https://doi.org/10.5194/acp-26-6799-2026, 2026
Short summary
Short summary
Molecular characterization of organic aerosol in the Po Valley exhibits differences between an urban and an agricultural site, despite similar concentration of organic aerosol mass. Multivariate statistical analysis on one year of samples further reveals specific sources such as biomass burning, agricultural activities and biogenic secondary organic aerosol. Light-absorbing molecules appear to be linked to combustion, peaking at both sites during winter season.
Stefania Gilardoni, Annachiara Bellini, Paolo Bonasoni, Henry Diémoz, Christian Gencarelli, Angela Marinoni, Eros Mariani, Luigi Mazari Villanova, Bruno Neininger, Mattia Perilli, Michael Sprenger, and Francesco Petracchini
EGUsphere, https://doi.org/10.5194/egusphere-2026-1299, https://doi.org/10.5194/egusphere-2026-1299, 2026
Short summary
Short summary
This study presents the first high-time-resolution aerosol data collected from the Testa Grigia Observatory (3,480 m) in the Italian Alps, from 2021 to 2023. We identified three transport pathways: Saharan Dust Events (SDE), which occurred with a frequency of 6 %, regional and long-range transported pollution (28 %), and clean air from the free troposphere. The timing of SDE, observed mainly in spring, and the mixing of dust particles with black carbon, enhance SDE impact on snow melting.
Costanza Civale, M. Dolores Andrés Hernández, Jean-Philippe Putaud, Francesca Barnaba, Henri Diémoz, Johannes Schneider, Helmut Ziereis, Katharina Kaiser, Jörg Schmidt, Ovid Oktavian Krüger, Bruna Holanda, Robert Baumann, Benjamin Weyland, Eric Förster, Midhun George, Yangzhuoran Liu, Daniel Sauer, Jennifer Wolf, Annachiara Bellini, Birger Bohn, Klaus Pfeilsticker, and John Philip Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2025-6458, https://doi.org/10.5194/egusphere-2025-6458, 2026
Short summary
Short summary
This study combines measurements of gases and particles at the ground and on-board an instrumented aircraft to investigate pollution transport over the Po Valley, a major atmospheric pollution hotspot in Europe. Overall, the extensive data set obtained at different altitudes provides insight into the mixing of pollution plumes and their export towards the Adriatic coast as well as with naturally occurring dust and its impact on air quality over the Po Plain.
James D'Souza Metcalf, Ruth K. Winkless, Caterina Mapelli, C. Rob McElroy, Claudiu Roman, Cecilia Arsene, Romeo I. Olariu, Iustinian G. Bejan, and Terry J. Dillon
Atmos. Chem. Phys., 25, 9169–9181, https://doi.org/10.5194/acp-25-9169-2025, https://doi.org/10.5194/acp-25-9169-2025, 2025
Short summary
Short summary
Oxymethylene ethers are a class of sustainable compounds that could be used to replace harmful organic solvents in a range of applications. In this work, we use lab-based experiments to identify the main breakdown routes of these compounds in the atmosphere. We have determined that they likely contribute less to air pollution than the compounds that they replace.
Annachiara Bellini, Henri Diémoz, Luca Di Liberto, Gian Paolo Gobbi, Alessandro Bracci, Ferdinando Pasqualini, and Francesca Barnaba
Atmos. Meas. Tech., 17, 6119–6144, https://doi.org/10.5194/amt-17-6119-2024, https://doi.org/10.5194/amt-17-6119-2024, 2024
Short summary
Short summary
We provide a comprehensive overview of the Italian Automated LIdar-CEilometer network, ALICENET, describing its infrastructure, aerosol retrievals, and main applications. The supplement covers data-processing details. We include examples of output products, comparisons with independent data, and examples of the network capability to provide near-real-time aerosol fields over Italy. ALICENET is expected to benefit the sectors of air quality, radiative budget/solar energy, and aviation safety.
Piotr Markuszewski, E. Douglas Nilsson, Julika Zinke, E. Monica Mårtensson, Matthew Salter, Przemysław Makuch, Małgorzata Kitowska, Iwona Niedźwiecka-Wróbel, Violetta Drozdowska, Dominik Lis, Tomasz Petelski, Luca Ferrero, and Jacek Piskozub
Atmos. Chem. Phys., 24, 11227–11253, https://doi.org/10.5194/acp-24-11227-2024, https://doi.org/10.5194/acp-24-11227-2024, 2024
Short summary
Short summary
Our research provides new insights into the study of sea spray aerosol (SSA) emissions in the Baltic Sea and North Atlantic. We observed that SSA flux is suppressed during increased marine biological activity in the Baltic Sea. At the same time, the influence of wave age showed higher SSA emissions in the Baltic Sea for younger waves compared to the Atlantic Ocean. These insights underscore the complex interplay between biological activity and physical dynamics in regulating SSA emissions.
Akriti Masoom, Stelios Kazadzis, Masimo Valeri, Ioannis-Panagiotis Raptis, Gabrielle Brizzi, Kyriakoula Papachristopoulou, Francesca Barnaba, Stefano Casadio, Axel Kreuter, and Fabrizio Niro
Atmos. Meas. Tech., 17, 5525–5549, https://doi.org/10.5194/amt-17-5525-2024, https://doi.org/10.5194/amt-17-5525-2024, 2024
Short summary
Short summary
Aerosols, which have a wide impact on climate, radiative forcing, and human health, are widely represented by aerosol optical depth (AOD). AOD retrievals require Rayleigh scattering and atmospheric absorption (ozone, NO2, etc.) corrections. We analysed the NO2 (which has a high spatiotemporal variation) uncertainty impact on AOD retrievals using the synergy of co-located ground-based instruments with a long-term dataset at worldwide sites and found significant AOD over- or underestimations.
Monica Campanelli, Victor Estellés, Gaurav Kumar, Teruyuki Nakajima, Masahiro Momoi, Julian Gröbner, Stelios Kazadzis, Natalia Kouremeti, Angelos Karanikolas, Africa Barreto, Saulius Nevas, Kerstin Schwind, Philipp Schneider, Iiro Harju, Petri Kärhä, Henri Diémoz, Rei Kudo, Akihiro Uchiyama, Akihiro Yamazaki, Anna Maria Iannarelli, Gabriele Mevi, Annalisa Di Bernardino, and Stefano Casadio
Atmos. Meas. Tech., 17, 5029–5050, https://doi.org/10.5194/amt-17-5029-2024, https://doi.org/10.5194/amt-17-5029-2024, 2024
Short summary
Short summary
To retrieve columnar aerosol properties from sun photometers, some calibration factors are needed. The on-site calibrations, performed as frequently as possible to monitor changes in the machine conditions, allow operators to track and evaluate the calibration status on a continuous basis, reducing the data gaps incurred by the periodic shipments for performing centralized calibrations. The performance of the on-site calibration procedures was evaluated, providing very good results.
Alessandro Bigi, Giorgio Veratti, Elisabeth Andrews, Martine Collaud Coen, Lorenzo Guerrieri, Vera Bernardoni, Dario Massabò, Luca Ferrero, Sergio Teggi, and Grazia Ghermandi
Atmos. Chem. Phys., 23, 14841–14869, https://doi.org/10.5194/acp-23-14841-2023, https://doi.org/10.5194/acp-23-14841-2023, 2023
Short summary
Short summary
Atmospheric particles include compounds that play a key role in the greenhouse effect and air toxicity. Concurrent observations of these compounds by multiple instruments are presented, following deployment within an urban environment in the Po Valley, one of Europe's pollution hotspots. The study compares these data, highlighting the impact of ground emissions, mainly vehicular traffic and biomass burning, on the absorption of sun radiation and, ultimately, on climate change and air quality.
Vladimir Savastiouk, Henri Diémoz, and C. Thomas McElroy
Atmos. Meas. Tech., 16, 4785–4806, https://doi.org/10.5194/amt-16-4785-2023, https://doi.org/10.5194/amt-16-4785-2023, 2023
Short summary
Short summary
This paper describes a way to significantly improve ozone measurements at low sun elevations and large ozone amounts when using the Brewer ozone spectrophotometer. The proposed algorithm will allow more uniform ozone measurements across the monitoring network. This will contribute to more reliable trend analysis and support the satellite validation. This research contributes to better understanding the physics of the instrument, and the new algorithm is based on this new knowledge.
Caterina Mapelli, James K. Donnelly, Úna E. Hogan, Andrew R. Rickard, Abbie T. Robinson, Fergal Byrne, Con Rob McElroy, Basile F. E. Curchod, Daniel Hollas, and Terry J. Dillon
Atmos. Chem. Phys., 23, 7767–7779, https://doi.org/10.5194/acp-23-7767-2023, https://doi.org/10.5194/acp-23-7767-2023, 2023
Short summary
Short summary
Solvents are chemical compounds with countless uses in the chemical industry, and they also represent one of the main sources of pollution in the chemical sector. Scientists are trying to develop new
greensafer solvents which present favourable advantages when compared to traditional solvents. Since the assessment of these green solvents often lacks air quality considerations, this study aims to understand the behaviour of these compounds, investigating their reactivity in the troposphere.
Theano Drosoglou, Ioannis-Panagiotis Raptis, Massimo Valeri, Stefano Casadio, Francesca Barnaba, Marcos Herreras-Giralda, Anton Lopatin, Oleg Dubovik, Gabriele Brizzi, Fabrizio Niro, Monica Campanelli, and Stelios Kazadzis
Atmos. Meas. Tech., 16, 2989–3014, https://doi.org/10.5194/amt-16-2989-2023, https://doi.org/10.5194/amt-16-2989-2023, 2023
Short summary
Short summary
Aerosol optical properties derived from sun photometers depend on the optical depth of trace gases absorbing solar radiation at specific spectral ranges. Various networks use satellite-based climatologies to account for this or neglect their effect. In this work, we evaluate the effect of NO2 absorption in aerosol retrievals from AERONET and SKYNET over two stations in Rome, Italy, with relatively high NO2 spatiotemporal variations, using NO2 data from the Pandora network and the TROPOMI sensor.
Caterina Mapelli, Juliette V. Schleicher, Alex Hawtin, Conor D. Rankine, Fiona C. Whiting, Fergal Byrne, C. Rob McElroy, Claudiu Roman, Cecilia Arsene, Romeo I. Olariu, Iustinian G. Bejan, and Terry J. Dillon
Atmos. Chem. Phys., 22, 14589–14602, https://doi.org/10.5194/acp-22-14589-2022, https://doi.org/10.5194/acp-22-14589-2022, 2022
Short summary
Short summary
Solvents represent an important source of pollution from the chemical industry. New "green" solvents aim to replace toxic solvents with new molecules made from renewable sources and designed to be less harmful. Whilst these new molecules are selected according to toxicity and other characteristics, no consideration has yet been included on air quality. Studying the solvent breakdown in air, we found that TMO has a lower impact on air quality than traditional solvents with similar properties.
Kostas Eleftheratos, John Kapsomenakis, Ilias Fountoulakis, Christos S. Zerefos, Patrick Jöckel, Martin Dameris, Alkiviadis F. Bais, Germar Bernhard, Dimitra Kouklaki, Kleareti Tourpali, Scott Stierle, J. Ben Liley, Colette Brogniez, Frédérique Auriol, Henri Diémoz, Stana Simic, Irina Petropavlovskikh, Kaisa Lakkala, and Kostas Douvis
Atmos. Chem. Phys., 22, 12827–12855, https://doi.org/10.5194/acp-22-12827-2022, https://doi.org/10.5194/acp-22-12827-2022, 2022
Short summary
Short summary
We present the future evolution of DNA-active ultraviolet (UV) radiation in view of increasing greenhouse gases (GHGs) and decreasing ozone depleting substances (ODSs). It is shown that DNA-active UV radiation might increase after 2050 between 50° N–50° S due to GHG-induced reductions in clouds and ozone, something that is likely not to happen at high latitudes, where DNA-active UV radiation will continue its downward trend mainly due to stratospheric ozone recovery from the reduction in ODSs.
Luka Drinovec, Uroš Jagodič, Luka Pirker, Miha Škarabot, Mario Kurtjak, Kristijan Vidović, Luca Ferrero, Bradley Visser, Jannis Röhrbein, Ernest Weingartner, Daniel M. Kalbermatter, Konstantina Vasilatou, Tobias Bühlmann, Celine Pascale, Thomas Müller, Alfred Wiedensohler, and Griša Močnik
Atmos. Meas. Tech., 15, 3805–3825, https://doi.org/10.5194/amt-15-3805-2022, https://doi.org/10.5194/amt-15-3805-2022, 2022
Short summary
Short summary
A new photothermal interferometer (PTAAM-2λ) for artefact-free determination of the aerosol absorption coefficient at two wavelengths is presented. The instrument is calibrated with NO2 and polydisperse nigrosin, resulting in very low uncertainties of the absorption coefficients: 4 % at 532 nm and 6 % at 1064 nm. The instrument’s performance makes the PTAAM-2λ a strong candidate for reference measurements of the aerosol absorption coefficient.
Enza Di Tomaso, Jerónimo Escribano, Sara Basart, Paul Ginoux, Francesca Macchia, Francesca Barnaba, Francesco Benincasa, Pierre-Antoine Bretonnière, Arnau Buñuel, Miguel Castrillo, Emilio Cuevas, Paola Formenti, María Gonçalves, Oriol Jorba, Martina Klose, Lucia Mona, Gilbert Montané Pinto, Michail Mytilinaios, Vincenzo Obiso, Miriam Olid, Nick Schutgens, Athanasios Votsis, Ernest Werner, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 14, 2785–2816, https://doi.org/10.5194/essd-14-2785-2022, https://doi.org/10.5194/essd-14-2785-2022, 2022
Short summary
Short summary
MONARCH reanalysis of desert dust aerosols extends the existing observation-based information for mineral dust monitoring by providing 3-hourly upper-air, surface and total column key geophysical variables of the dust cycle over Northern Africa, the Middle East and Europe, at a 0.1° horizontal resolution in a rotated grid, from 2007 to 2016. This work provides evidence of the high accuracy of this data set and its suitability for air quality and health and climate service applications.
M. Dolores Andrés Hernández, Andreas Hilboll, Helmut Ziereis, Eric Förster, Ovid O. Krüger, Katharina Kaiser, Johannes Schneider, Francesca Barnaba, Mihalis Vrekoussis, Jörg Schmidt, Heidi Huntrieser, Anne-Marlene Blechschmidt, Midhun George, Vladyslav Nenakhov, Theresa Harlass, Bruna A. Holanda, Jennifer Wolf, Lisa Eirenschmalz, Marc Krebsbach, Mira L. Pöhlker, Anna B. Kalisz Hedegaard, Linlu Mei, Klaus Pfeilsticker, Yangzhuoran Liu, Ralf Koppmann, Hans Schlager, Birger Bohn, Ulrich Schumann, Andreas Richter, Benjamin Schreiner, Daniel Sauer, Robert Baumann, Mariano Mertens, Patrick Jöckel, Markus Kilian, Greta Stratmann, Christopher Pöhlker, Monica Campanelli, Marco Pandolfi, Michael Sicard, José L. Gómez-Amo, Manuel Pujadas, Katja Bigge, Flora Kluge, Anja Schwarz, Nikos Daskalakis, David Walter, Andreas Zahn, Ulrich Pöschl, Harald Bönisch, Stephan Borrmann, Ulrich Platt, and John P. Burrows
Atmos. Chem. Phys., 22, 5877–5924, https://doi.org/10.5194/acp-22-5877-2022, https://doi.org/10.5194/acp-22-5877-2022, 2022
Short summary
Short summary
EMeRGe provides a unique set of in situ and remote sensing airborne measurements of trace gases and aerosol particles along selected flight routes in the lower troposphere over Europe. The interpretation uses also complementary collocated ground-based and satellite measurements. The collected data help to improve the current understanding of the complex spatial distribution of trace gases and aerosol particles resulting from mixing, transport, and transformation of pollution plumes over Europe.
Monica Campanelli, Henri Diémoz, Anna Maria Siani, Alcide di Sarra, Anna Maria Iannarelli, Rei Kudo, Gabriele Fasano, Giampietro Casasanta, Luca Tofful, Marco Cacciani, Paolo Sanò, and Stefano Dietrich
Atmos. Meas. Tech., 15, 1171–1183, https://doi.org/10.5194/amt-15-1171-2022, https://doi.org/10.5194/amt-15-1171-2022, 2022
Short summary
Short summary
The aerosol optical depth (AOD) characteristics in an urban area of Rome were retrieved over a period of 11 years (2010–2020) to determine, for the first time, their effect on the incoming ultraviolet (UV) solar radiation. The surface forcing efficiency shows that the AOD is the primary parameter affecting the surface irradiance in Rome, and it is found to be greater for smaller zenith angles and for larger and more absorbing particles in the UV range (such as, e.g., mineral dust).
Ilias Fountoulakis, Henri Diémoz, Anna Maria Siani, Alcide di Sarra, Daniela Meloni, and Damiano M. Sferlazzo
Atmos. Chem. Phys., 21, 18689–18705, https://doi.org/10.5194/acp-21-18689-2021, https://doi.org/10.5194/acp-21-18689-2021, 2021
Short summary
Short summary
The variability and trends of solar spectral UV irradiance have been studied for the periods 1996–2020 (for Rome) and 2006–2020 (for Lampedusa, Rome, and Aosta) with respect to the variability and trends of total ozone and geopotential height. Analyses revealed increasing UV in particular months at all sites, possibly due to decreasing lower-stratospheric ozone (at Rome in 1996–2020) and decreasing attenuation by aerosols and/or clouds (at all stations in 2006–2020).
Henri Diémoz, Anna Maria Siani, Stefano Casadio, Anna Maria Iannarelli, Giuseppe Rocco Casale, Vladimir Savastiouk, Alexander Cede, Martin Tiefengraber, and Moritz Müller
Earth Syst. Sci. Data, 13, 4929–4950, https://doi.org/10.5194/essd-13-4929-2021, https://doi.org/10.5194/essd-13-4929-2021, 2021
Short summary
Short summary
A 20-year (1996–2017) record of nitrogen dioxide column densities collected in Rome by a Brewer spectrophotometer is presented, together with the novel algorithm employed to re-evaluate the series. The high quality of the data is demonstrated by comparison with reference instrumentation, including a co-located Pandora spectrometer. The data can be used for satellite validation and identification of NO2 trends. The method can be replicated on other instruments of the international Brewer network.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Panagiotis G. Kosmopoulos, Stelios Kazadzis, Alois W. Schmalwieser, Panagiotis I. Raptis, Kyriakoula Papachristopoulou, Ilias Fountoulakis, Akriti Masoom, Alkiviadis F. Bais, Julia Bilbao, Mario Blumthaler, Axel Kreuter, Anna Maria Siani, Kostas Eleftheratos, Chrysanthi Topaloglou, Julian Gröbner, Bjørn Johnsen, Tove M. Svendby, Jose Manuel Vilaplana, Lionel Doppler, Ann R. Webb, Marina Khazova, Hugo De Backer, Anu Heikkilä, Kaisa Lakkala, Janusz Jaroslawski, Charikleia Meleti, Henri Diémoz, Gregor Hülsen, Barbara Klotz, John Rimmer, and Charalampos Kontoes
Atmos. Meas. Tech., 14, 5657–5699, https://doi.org/10.5194/amt-14-5657-2021, https://doi.org/10.5194/amt-14-5657-2021, 2021
Short summary
Short summary
Large-scale retrievals of the ultraviolet index (UVI) in real time by exploiting the modern Earth observation data and techniques are capable of forming operational early warning systems that raise awareness among citizens of the health implications of high UVI doses. In this direction a novel UVI operating system, the so-called UVIOS, was introduced for massive outputs, while its performance was tested against ground-based measurements revealing a dependence on the input quality and resolution.
Cited articles
Aan de Brugh, J. M. J., Henzing, J. S., Schaap, M., Morgan, W. T., van Heerwaarden, C. C., Weijers, E. P., Coe, H., and Krol, M. C.: Modelling the partitioning of ammonium nitrate in the convective boundary layer, Atmos. Chem. Phys., 12, 3005–3023, https://doi.org/10.5194/acp-12-3005-2012, 2012. a
Aas, W., Tsyro, S., Bieber, E., Bergström, R., Ceburnis, D., Ellermann, T., Fagerli, H., Frölich, M., Gehrig, R., Makkonen, U., Nemitz, E., Otjes, R., Perez, N., Perrino, C., Prévôt, A. S. H., Putaud, J.-P., Simpson, D., Spindler, G., Vana, M., and Yttri, K. E.: Lessons learnt from the first EMEP intensive measurement periods, Atmos. Chem. Phys., 12, 8073–8094, https://doi.org/10.5194/acp-12-8073-2012, 2012. a
Agnesod, G., De Maria, R., Fontana, M., and Zublena, M.: Determination of PAH in airborne particulate: comparison between off-line sampling techniques and an automatic analyser based on a photoelectric aerosol sensor, Sci. Total Environ., 189-190, 443–449, https://doi.org/10.1016/0048-9697(96)05244-8, 1996. a
Aktypis, A., Sippial, D. J., Vasilakopoulou, C. N., Matrali, A., Kaltsonoudis, C., Simonati, A., Paglione, M., Rinaldi, M., Decesari, S., and Pandis, S. N.: Formation and chemical evolution of secondary organic aerosol in two different environments: a dual-chamber study, Atmos. Chem. Phys., 24, 13769–13791, https://doi.org/10.5194/acp-24-13769-2024, 2024. a
Allen, G. A., Miller, P. J., Rector, L. J., Brauer, M., and Su, J. G.: Characterization of Valley Winter Woodsmoke Concentrations in Northern NY Using Highly Time-Resolved Measurements, Aerosol Air Qual. Res., 11, 519–530, https://doi.org/10.4209/aaqr.2011.03.0031, 2011. a
Amodeo, T.: Guide méthodologique pour la surveillance des PM10 et PM2,5 dans l'air ambiant par FIDAS, 219635-2767795, v1.0, Tech. rep., Ineris, https://www.lcsqa.org/system/files/media/documents/LCSQA2023-Guide%20m%C3%A9thodologique%20pour%20la%20surveillance%20des%20PM10%20et%20PM2%2C5%20dans%20l%27air%20ambiant%20par%20FIDAS%20v1.pdf (last access: 2 June 2026), 2023. a
Amodeo, T.: Méthode de validation pour la mise en œuvre des analyseurs automatiques de PM par diffusion de la lumière. Retour d'expérience des tests de mises en œuvre du FIDAS et définition d'une méthode de validation, 219632-2789152, v1.0, Tech. rep., Ineris, https://www.lcsqa.org/system/files/media/documents/LCSQA2023-FIDAS_Retour%20d%27exp%C3%A9rience_d%C3%A9finition%20de%20crit%C3%A8re%20de%20validation%20des%20tests%20de%20mise%20en%20oeuvre_v2.pdf (last access: 2 June 2026), 2024. a
Aster, R. C., Borchers, B., and Thurber, C. H.: Parameter estimation and inverse problems, 3rd edn., Elsevier, Amsterdam, Netherlands, ISBN 9780128046517, 2019. a
Aujay-Plouzeau, R.: Guide méthodologique pour la mesure du “Black Carbon” par Aethalomètre multi longueur d'onde AE33 dans l'air ambiant (Version 2020), Tech. rep., Ineris, https://www.lcsqa.org/system/files/media/documents/LCSQA2019-Guide_mesure_BlackCarbon_par_AE33_VF03-Approuv%C3%A9CPS15122020.pdf (last access: 2 June 2026), 2020. a
Balestrini, R., Diémoz, H., Freppaz, M., Delconte, C., Caschetto, M., and Matiatos, I.: Nitrogen atmospheric deposition in a high-altitude Alpine environment: A chemical and isotopic approach to investigate the influence from anthropized areas, Atmos. Environ., 328, 120513, https://doi.org/10.1016/j.atmosenv.2024.120513, 2024. a
Barnaba, F. and Gobbi, G. P.: Aerosol seasonal variability over the Mediterranean region and relative impact of maritime, continental and Saharan dust particles over the basin from MODIS data in the year 2001, Atmos. Chem. Phys., 4, 2367–2391, https://doi.org/10.5194/acp-4-2367-2004, 2004. a
Barnaba, F., Bolignano, A., Di Liberto, L., Morelli, M., Lucarelli, F., Nava, S., Perrino, C., Canepari, S., Basart, S., Costabile, F., Dionisi, D., Ciampichetti, S., Sozzi, R., and Gobbi, G. P.: Desert dust contribution to M10 loads in Italy: Methods and recommendations addressing the relevant European Commission Guidelines in support to the Air Quality Directive 2008/50, Atmos. Environ., 161, 288–305, https://doi.org/10.1016/j.atmosenv.2017.04.038, 2017. a, b
Barnaba, F., Alvan Romero, N., Bolignano, A., Basart, S., Renzi, M., and Stafoggia, M.: Multiannual assessment of the desert dust impact on air quality in Italy combining PM10 data with physics-based and geostatistical models, Environ. Int., 163, 107204, https://doi.org/10.1016/j.envint.2022.107204, 2022. a
Barreira, L. M., Aurela, M., Saarikoski, S., Li, D., Teinilä, K., Virkkula, A., Niemi, J. V., Manninen, H., Pirjola, L., Petäjä, T., Rönkkö, T., and Timonen, H.: Characterizing winter-time brown carbon: Insights into chemical and light-absorption properties in residential and traffic environments, Sci. Total Environ., 955, 177089, https://doi.org/10.1016/j.scitotenv.2024.177089, 2024. a
Beddows, D., Brean, J., Rowell, A., Merkel, M., Weinhold, K., Dall'Osto, M., and Harrison, R.: Wide-Positive Matrix Factorisation of particle number size distributions: A new approach accounting for cyclically changing source profiles, Sci. Total Environ., 998, 180231, https://doi.org/10.1016/j.scitotenv.2025.180231, 2025. a
Beddows, D. C. S. and Harrison, R. M.: Receptor modelling of both particle composition and size distribution from a background site in London, UK – a two-step approach, Atmos. Chem. Phys., 19, 4863–4876, https://doi.org/10.5194/acp-19-4863-2019, 2019. a, b, c
Beddows, D. C. S., Dall'Osto, M., Harrison, R. M., Kulmala, M., Asmi, A., Wiedensohler, A., Laj, P., Fjaeraa, A. M., Sellegri, K., Birmili, W., Bukowiecki, N., Weingartner, E., Baltensperger, U., Zdimal, V., Zikova, N., Putaud, J.-P., Marinoni, A., Tunved, P., Hansson, H.-C., Fiebig, M., Kivekäs, N., Swietlicki, E., Lihavainen, H., Asmi, E., Ulevicius, V., Aalto, P. P., Mihalopoulos, N., Kalivitis, N., Kalapov, I., Kiss, G., de Leeuw, G., Henzing, B., O'Dowd, C., Jennings, S. G., Flentje, H., Meinhardt, F., Ries, L., Denier van der Gon, H. A. C., and Visschedijk, A. J. H.: Variations in tropospheric submicron particle size distributions across the European continent 2008–2009, Atmos. Chem. Phys., 14, 4327–4348, https://doi.org/10.5194/acp-14-4327-2014, 2014. a, b
Belis, C., Favez, O., Mircea, M., Diapouli, E., Manousakas, M., Vratolis, S., Gilardoni, S., Paglione, M., Decesari, S., Mocnik, G., Mooibroek, D., Salvador, P., Takahama, S., Vecchi, R., and Paatero, P.: European guide on air pollution source apportionment with receptor models, EUR 29816 EN, Publications Office of the European Union, Luxembourg, JRC117306, ISBN 978-92-76-09001-4, https://doi.org/10.2760/439106, 2019. a, b, c, d, e
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. a, b, c
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 ERA5 and CAMS models, Remote Sens., 17, https://doi.org/10.3390/rs17030372, 2025. a, b, c, d, e
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, https://doi.org/10.3390/atmos8110218, 2017b. a, b, c
Bhandari, S., Arub, Z., Habib, G., Apte, J. S., and Hildebrandt Ruiz, L.: Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution, Atmos. Meas. Tech., 15, 6051–6074, https://doi.org/10.5194/amt-15-6051-2022, 2022. a
Bihałowicz, J. S., Rogula-Kozłowska, W., Rogula-Kopiec, P., Świsłowski, P., Rajfur, M., and Olszowski, T.: One-Year-Long, Comprehensive Analysis of PM Number and Mass Size Distributions in Warszawa (Poland), Ecol. Chem. Eng. S., 30, 541–556, https://doi.org/10.2478/eces-2023-0047, 2023. a
Bond, T. C.: Spectral dependence of visible light absorption by carbonaceous particles emitted from coal combustion, Geophys. Res. Lett., 28, 4075–4078, https://doi.org/10.1029/2001GL013652, 2001. a
Bouscasse, H., Gabet, S., Kerneis, G., Provent, A., Rieux, C., Ben Salem, N., Dupont, H., Troude, F., Mathy, S., and Slama, R.: Designing local air pollution policies focusing on mobility and heating to avoid a targeted number of pollution-related deaths: Forward and backward approaches combining air pollution modeling, health impact assessment and cost-benefit analysis, Environ. Int., 159, 107030, https://doi.org/10.1016/j.envint.2021.107030, 2022. a
Bousiotis, D., Singh, A., Haugen, M., Beddows, D. C. S., Diez, S., Murphy, K. L., Edwards, P. M., Boies, A., Harrison, R. M., and Pope, F. D.: Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study, Atmos. Meas. Tech., 14, 4139–4155, https://doi.org/10.5194/amt-14-4139-2021, 2021. a, b
Bousiotis, D., Alconcel, L.-N. S., Beddows, D. C., Harrison, R. M., and Pope, F. D.: Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors, Environ. Int., 174, 107907, https://doi.org/10.1016/j.envint.2023.107907, 2023a. a
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, 122, https://doi.org/10.1038/s41612-023-00424-0, 2023b. a
Brown, S. G., Eberly, S., Paatero, P., and Norris, G. A.: Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results, Sci. Total Environ., 518–519, 626–635, https://doi.org/10.1016/j.scitotenv.2015.01.022, 2015. a, b
Camman, J., Chazeau, B., Marchand, N., Durand, A., Gille, G., Lanzi, L., Jaffrezo, J.-L., Wortham, H., and Uzu, G.: Oxidative potential apportionment of atmospheric PM1: a new approach combining high-sensitive online analysers for chemical composition and offline OP measurement technique, Atmos. Chem. Phys., 24, 3257–3278, https://doi.org/10.5194/acp-24-3257-2024, 2024. a
Campanelli, M., Iannarelli, A., Mevi, G., Casadio, S., Diémoz, H., Finardi, S., Dinoi, A., Castelli, E., di Sarra, A., Di Bernardino, A., Casasanta, G., Bassani, C., Siani, A., Cacciani, M., Barnaba, F., Di Liberto, L., and Argentini, S.: A wide-ranging investigation of the COVID-19 lockdown effects on the atmospheric composition in various Italian urban sites (AER – LOCUS), Urban Clim., 39, 100954, https://doi.org/10.1016/j.uclim.2021.100954, 2021. a
Campanelli, M., Estellés, V., Kumar, G., Nakajima, T., Momoi, M., Gröbner, J., Kazadzis, S., Kouremeti, N., Karanikolas, A., Barreto, A., Nevas, S., Schwind, K., Schneider, P., Harju, I., Kärhä, P., Diémoz, H., Kudo, R., Uchiyama, A., Yamazaki, A., Iannarelli, A. M., Mevi, G., Di Bernardino, A., and Casadio, S.: Evaluation of on-site calibration procedures for SKYNET Prede POM sun–sky photometers, Atmos. Meas. Tech., 17, 5029–5050, https://doi.org/10.5194/amt-17-5029-2024, 2024. a
Cavalli, F., Viana, M., Yttri, K. E., Genberg, J., and Putaud, J.-P.: Toward a standardised thermal-optical protocol for measuring atmospheric organic and elemental carbon: the EUSAAR protocol, Atmos. Meas. Tech., 3, 79–89, https://doi.org/10.5194/amt-3-79-2010, 2010. a
Chan, Y.-C., Hawas, O., Hawker, D., Vowles, P., Cohen, D. D., Stelcer, E., Simpson, R., Golding, G., and Christensen, E.: Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants, Atmos. Environ., 45, 439–449, https://doi.org/10.1016/j.atmosenv.2010.09.060, 2011. a
Chen, G., Canonaco, F., Slowik, J. G., Daellenbach, K. R., Tobler, A., Petit, J.-E., Favez, O., Stavroulas, I., Mihalopoulos, N., Gerasopoulos, E., El Haddad, I., Baltensperger, U., and Prévôt, A. S. H.: Real-Time Source Apportionment of Organic Aerosols in Three European Cities, Environ. Sci. Tech., 56, 15290–15297, https://doi.org/10.1021/acs.est.2c02509, 2022. a
Chen, J. and Hoek, G.: Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis, Environ. Int., 143, 105974, https://doi.org/10.1016/j.envint.2020.105974, 2020. a
Chen, J., Dai, Q., Zhang, X., Tian, Y., Feng, Y., and Hopke, P. K.: PMF Source Contribution Uncertainty Estimation via Effective Variance Least Squares, ACS ES&T Air, 2, 3045–3053, https://doi.org/10.1021/acsestair.5c00312, 2025. a, b
Chien, C.-H., Theodore, A., Wu, C.-Y., Hsu, Y.-M., and Birky, B.: Upon correlating diameters measured by optical particle counters and aerodynamic particle sizers, J. Aerosol Sci., 101, 77–85, https://doi.org/10.1016/j.jaerosci.2016.05.011, 2016. a, b
Chueinta, W., Hopke, P. K., and Paatero, P.: Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization, Atmos. Environ., 34, 3319–3329, https://doi.org/10.1016/S1352-2310(99)00433-1, 2000. a
Ćirović, Ž., Stojanović, D. B., Davidović, M., Onjia, A., Alastuey, A., and Jovašević-Stojanović, M.: New Particle Formation and Source Apportionment of Particle Number Size Distribution in the Urban Area of the City of Belgrade, Atmosphere, 17, https://doi.org/10.3390/atmos17020205, 2026. a
Colette, A., Collin, G., Besson, F., Blot, E., Guidard, V., Meleux, F., Royer, A., Petiot, V., Miller, C., Fermond, O., Jeant, A., Adani, M., Arteta, J., Benedictow, A., Bergström, R., Bowdalo, D., Brandt, J., Briganti, G., Carvalho, A. C., Christensen, J. H., Couvidat, F., D'Elia, I., D'Isidoro, M., Denier van der Gon, H., Descombes, G., Di Tomaso, E., Douros, J., Escribano, J., Eskes, H., Fagerli, H., Fatahi, Y., Flemming, J., Friese, E., Frohn, L., Gauss, M., Geels, C., Guarnieri, G., Guevara, M., Guion, A., Guth, J., Hänninen, R., Hansen, K., Im, U., Janssen, R., Jeoffrion, M., Joly, M., Jones, L., Jorba, O., Kadantsev, E., Kahnert, M., Kaminski, J. W., Kouznetsov, R., Kranenburg, R., Kuenen, J., Lange, A. C., Langner, J., Lannuque, V., Macchia, F., Manders, A., Mircea, M., Nyiri, A., Olid, M., Pérez García-Pando, C., Palamarchuk, Y., Piersanti, A., Raux, B., Razinger, M., Robertson, L., Segers, A., Schaap, M., Siljamo, P., Simpson, D., Sofiev, M., Stangel, A., Struzewska, J., Tena, C., Timmermans, R., Tsikerdekis, T., Tsyro, S., Tyuryakov, S., Ung, A., Uppstu, A., Valdebenito, A., van Velthoven, P., Vitali, L., Ye, Z., Peuch, V.-H., and Rouïl, L.: Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0, Geosci. Model Dev., 18, 6835–6883, https://doi.org/10.5194/gmd-18-6835-2025, 2025. a
Collaud Coen, M., Weingartner, E., Schaub, D., Hueglin, C., Corrigan, C., Henning, S., Schwikowski, M., and Baltensperger, U.: Saharan dust events at the Jungfraujoch: detection by wavelength dependence of the single scattering albedo and first climatology analysis, Atmos. Chem. Phys., 4, 2465–2480, https://doi.org/10.5194/acp-4-2465-2004, 2004. a, b
Colombi, C., D'Angelo, L., Biffi, B., Cuccia, E., Dal Santo, U., and Lanzani, G.: Monitoring ammonia concentrations in more than 10 stations in the Po Valley for the period 2007–2022 in relation to the evolution of different sources, Front. Environ. Health, 3, https://doi.org/10.3389/fenvh.2024.1249457, 2024. a
Conte, M., Merico, E., Cesari, D., Dinoi, A., Grasso, F. M., Donateo, A., Guascito, M. R., and Contini, D.: Long-term characterisation of African dust advection in south-eastern Italy: Influence on fine and coarse particle concentrations, size distributions, and carbon content, Atmos. Res., 233, 104690, https://doi.org/10.1016/j.atmosres.2019.104690, 2020. a
Costabile, F., Birmili, W., Klose, S., Tuch, T., Wehner, B., Wiedensohler, A., Franck, U., König, K., and Sonntag, A.: Spatio-temporal variability and principal components of the particle number size distribution in an urban atmosphere, Atmos. Chem. Phys., 9, 3163–3195, https://doi.org/10.5194/acp-9-3163-2009, 2009. a, b, c
Coulter, C. T.: EPA-CMB8. 2 users manual, US. Environmental Protection Agency, Office of Air Quality Planning & Standards, Emissions, Monitoring & Analysis Division, Air Quality Modeling Group, https://www.epa.gov/sites/default/files/2020-10/documents/epa-cmb82manual.pdf (last access: 2 June 2026), 2004. a, b
Cuccia, E., Bernardoni, V., Massabò, D., Prati, P., Valli, G., and Vecchi, R.: An alternative way to determine the size distribution of airborne particulate matter, Atmos. Environ., 44, 3304–3313, https://doi.org/10.1016/j.atmosenv.2010.05.045, 2010. a, b, c
Curci, G., Ferrero, L., Tuccella, P., Barnaba, F., Angelini, F., Bolzacchini, E., Carbone, C., Denier van der Gon, H. A. C., Facchini, M. C., Gobbi, G. P., Kuenen, J. P. P., Landi, T. C., Perrino, C., Perrone, M. G., Sangiorgi, G., and Stocchi, P.: How much is particulate matter near the ground influenced by upper-level processes within and above the PBL? A summertime case study in Milan (Italy) evidences the distinctive role of nitrate, Atmos. Chem. Phys., 15, 2629–2649, https://doi.org/10.5194/acp-15-2629-2015, 2015. a, b
Cusack, M., Pérez, N., Pey, J., Alastuey, A., and Querol, X.: Source apportionment of fine PM and sub-micron particle number concentrations at a regional background site in the western Mediterranean: a 2.5 year study, Atmos. Chem. Phys., 13, 5173–5187, https://doi.org/10.5194/acp-13-5173-2013, 2013. a, b
Dai, Q., Liu, B., Bi, X., Wu, J., Liang, D., Zhang, Y., Feng, Y., and Hopke, P. K.: Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak, Environ. Sci. Tech., 54, 9917–9927, https://doi.org/10.1021/acs.est.0c02776, 2020. a, b
Dall'Osto, M., Beddows, D. C. S., Pey, J., Rodriguez, S., Alastuey, A., Harrison, R. M., and Querol, X.: Urban aerosol size distributions over the Mediterranean city of Barcelona, NE Spain, Atmos. Chem. Phys., 12, 10693–10707, https://doi.org/10.5194/acp-12-10693-2012, 2012. a, b, c
Damiano, R., Amoruso, S., Sannino, A., and Boselli, A.: Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy), Remote Sens., 16, https://doi.org/10.3390/rs16030538, 2024. a
Davulienė, L., Janicka, L., Minderytė, A., Kalinauskaitė, A., Poczta, P., Karasewicz, M., Hafiz, A., Pashneva, D., Dudoitis, V., Kandrotaitė, K., Valiulis, D., Böckmann, C., Schüttemeyer, D., Stachlewska, I. S., and Byčenkienė, S.: Synergic use of in-situ and remote sensing techniques for comprehensive characterization of aerosol optical and microphysical properties, Sci. Total Environ., 906, 167585, https://doi.org/10.1016/j.scitotenv.2023.167585, 2024. a
Denby, B., 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
Di Antonio, A., Popoola, O. A. M., Ouyang, B., Saffell, J., and Jones, R. L.: Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter, Sensors, 18, https://doi.org/10.3390/s18092790, 2018. a
Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., Journet, E., Nowak, S., Andreae, M. O., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E., and Doussin, J.-F.: Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content, Atmos. Chem. Phys., 19, 15503–15531, https://doi.org/10.5194/acp-19-15503-2019, 2019. a, b, c
Diémoz, H.: RASPBERRY and RASPBERRY+EVLS: Data and R code for real-time PM10 source apportionment using aerosol physical properties, Zenodo [code and data set], https://doi.org/10.5281/zenodo.20174876, 2026. a, b
Diémoz, H., Campanelli, M., and Estellés, V.: One Year of Measurements with a POM-02 Sky Radiometer at an Alpine EuroSkyRad Station, J. Meteorol. Soc. Jpn., 92A, 1–16, https://doi.org/10.2151/jmsj.2014-A01, 2014. a
Diémoz, H., Barnaba, F., Magri, T., Pession, G., Dionisi, D., Pittavino, S., Tombolato, I. K. F., Campanelli, M., Della Ceca, L. S., Hervo, M., Di Liberto, L., Ferrero, L., and Gobbi, G. P.: Transport of Po Valley aerosol pollution to the northwestern Alps – Part 1: Phenomenology, Atmos. Chem. Phys., 19, 3065–3095, https://doi.org/10.5194/acp-19-3065-2019, 2019a. a, b, c, d, e, f, g
Diémoz, H., Gobbi, G. P., Magri, T., Pession, G., Pittavino, S., Tombolato, I. K. F., Campanelli, M., and Barnaba, F.: Transport of Po Valley aerosol pollution to the northwestern Alps – Part 2: Long-term impact on air quality, Atmos. Chem. Phys., 19, 10129–10160, https://doi.org/10.5194/acp-19-10129-2019, 2019b. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Diémoz, H., Tombolato, I. K. F., Zublena, M., Magri, T., and Ferrero, L.: The impact of biomass burning emissions on PM concentration in the Greater Alpine region, in: Proceedings of 12th International Conference on Air Quality, Science and Application, p. 26, Hatfield, UK, https://doi.org/10.18745/pb.22217, 2020. a, b
Diémoz, H., Magri, T., Pession, G., Tarricone, C., Tombolato, I. K. F., Fasano, G., and Zublena, M.: Air Quality in the Italian Northwestern Alps during Year 2020: Assessment of the COVID-19 “Lockdown Effect” from Multi-Technique Observations and Models, Atmosphere, 12, https://doi.org/10.3390/atmos12081006, 2021. a, b, c, d, e, f, g, h, i, j
Diez, S., Lacy, S. E., Bannan, T. J., Flynn, M., Gardiner, T., Harrison, D., Marsden, N., Martin, N. A., Read, K., and Edwards, P. M.: Air pollution measurement errors: is your data fit for purpose?, Atmos. Meas. Tech., 15, 4091–4105, https://doi.org/10.5194/amt-15-4091-2022, 2022. a
Ditas, J., Ma, N., Zhang, Y., Assmann, D., Neumaier, M., Riede, H., Karu, E., Williams, J., Scharffe, D., Wang, Q., Saturno, J., Schwarz, J. P., Katich, J. M., McMeeking, G. R., Zahn, A., Hermann, M., Brenninkmeijer, C. A. M., Andreae, M. O., Pöschl, U., Su, H., and Cheng, Y.: Strong impact of wildfires on the abundance and aging of black carbon in the lowermost stratosphere, P. Natl. A. Sci., 115, E11595–E11603, https://doi.org/10.1073/pnas.1806868115, 2018. 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
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations, J. Atmos. Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002. a
Eck, T. F., Holben, B. N., Reid, J. S., Sinyuk, A., Hyer, E. J., O'Neill, N. T., Shaw, G. E., Vande Castle, J. R., Chapin, F. S., Dubovik, O., Smirnov, A., Vermote, E., Schafer, J. S., Giles, D., Slutsker, I., Sorokine, M., and Newcomb, W. W.: Optical properties of boreal region biomass burning aerosols in central Alaska and seasonal variation of aerosol optical depth at an Arctic coastal site, J. Geophys. Res., 114, https://doi.org/10.1029/2008JD010870, 2009. a
ECMWF Copernicus Atmosphere Monitoring Service: Smoke from Canadian wildfires reaches Europe, https://atmosphere.copernicus.eu/smoke-canadian-wildfires-reaches-europe (last access: 2 June 2026), 2024. a
El Haddad, I., Marchand, N., Dron, J., Temime-Roussel, B., Quivet, E., Wortham, H., Jaffrezo, J. L., Baduel, C., Voisin, D., Besombes, J. L., and Gille, G.: Comprehensive primary particulate organic characterization of vehicular exhaust emissions in France, Atmos. Environ., 43, 6190–6198, https://doi.org/10.1016/j.atmosenv.2009.09.001, 2009. a
Emami, F. and Hopke, P. K.: Effect of adding variables on rotational ambiguity in positive matrix factorization solutions, Chemometr. Intell. Lab., 162, 198–202, https://doi.org/10.1016/j.chemolab.2017.01.012, 2017. a
Estellés, V., Campanelli, M., Smyth, T. J., Utrillas, M. P., and Martínez-Lozano, J. A.: Evaluation of the new ESR network software for the retrieval of direct sun products from CIMEL CE318 and PREDE POM01 sun-sky radiometers, Atmos. Chem. Phys., 12, 11619–11630, https://doi.org/10.5194/acp-12-11619-2012, 2012. a
EUMETSAT: Canadian wildfire monitoring over the Atlantic and Europe, https://user.eumetsat.int/resources/case-studies/canadian-wildfire-monitoring-over-the-atlantic-and-europe (last access: 2 June 2026), 2023. a
European Commission: Directive 2008/50/EC of the European Parliament and of the Council on ambient air quality and cleaner air for Europe, in: 2008/50/EC, edited by: European Parliament, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02008L0050-20150918 (last access: 2 June 2026), 2008. a
European Commission: Commission staff working paper establishing guidelines for demonstration and subtraction of exceedances attributable to natural sources under the Directive 2008/50/EC on ambient air quality and cleaner air for Europe, http://register.consilium.europa.eu/doc/srv?l=EN&f=ST%206771%202011%20INIT (last access: 2 June 2026), 2011. a
European Commission: Directive 2024/2881/EC of 23 October 2024 on ambient air quality and cleaner air for Europe, in: 2024/2881/EC, edited by: European Parliament, https://eur-lex.europa.eu/eli/dir/2024/2881 (last access: 2 June 2026), 2024. a
European Environment Agency: Europe's air quality status 2024, https://www.eea.europa.eu/publications/europes-air-quality-status-2024 (last access: 2 June 2026), 2024. a
Fasano, G., Diémoz, H., Fountoulakis, I., Cassardo, C., Kudo, R., Siani, A. M., and Ferrero, L.: Vertical profile of the clear-sky aerosol direct radiative effect in an Alpine valley, by the synergy of ground-based measurements and radiative transfer simulations, Bull. Atmos. Sci. Technol., 2, 11, https://doi.org/10.1007/s42865-021-00041-w, 2021. a, b
Favaro, E., Barbaro, E., Diémoz, H., Bertinetti, S., Malandrino, M., Foretier, A., Ferrarese, S., Bellini, A., Freppaz, M., Balestrini, R., Bortolini, M., Munteanu, A., Frassati, S., Marafante, M., Mariani, E., Bonasoni, P., Petracchini, F., Mazari, L., Provenzale, A., Gilardoni, S., Gambaro, A., and Feltracco, M.: Characterizing aerosol composition, size distribution and optical properties in the Western Italian Alps: insights into pollution sources and transport processes, in: European Aerosol Conference 2025, 31 August–5 September 2025, Lecce (Italy), European Aerosol Assembly (EAA), 2025. a
Favez, O., El Haddad, I., Piot, C., Boréave, A., Abidi, E., Marchand, N., Jaffrezo, J.-L., Besombes, J.-L., Personnaz, M.-B., Sciare, J., Wortham, H., George, C., and D'Anna, B.: Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France), Atmos. Chem. Phys., 10, 5295–5314, https://doi.org/10.5194/acp-10-5295-2010, 2010. a, b
Ferrero, L., Mocnik, G., Ferrini, B., Perrone, M., Sangiorgi, G., and Bolzacchini, E.: Vertical profiles of aerosol absorption coefficient from micro-Aethalometer data and Mie calculation over Milan, Sci. Total Environ., 409, 2824–2837, https://doi.org/10.1016/j.scitotenv.2011.04.022, 2011. a
Ferrero, L., Ritter, C., Cappelletti, D., Moroni, B., Močnik, G., Mazzola, M., Lupi, A., Becagli, S., Traversi, R., Cataldi, M., Neuber, R., Vitale, V., and Bolzacchini, E.: Aerosol optical properties in the Arctic: The role of aerosol chemistry and dust composition in a closure experiment between Lidar and tethered balloon vertical profiles, Sci. Total Environ., 686, 452–467, https://doi.org/10.1016/j.scitotenv.2019.05.399, 2019. a, b
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
Ferrero, L., Losi, N., Rigler, M., Gregorič, A., Colombi, C., D'Angelo, L., Cuccia, E., Cefalì, A., Gini, I., Doldi, A., Cerri, S., Maroni, P., Cipriano, D., Markuszewski, P., and Bolzacchini, E.: Determining the Aethalometer multiple scattering enhancement factor C from the filter loading parameter, Sci. Total Environ., 917, 170221, https://doi.org/10.1016/j.scitotenv.2024.170221, 2024. a
Filonchyk, M. and Peterson, M. P.: Changes in aerosol properties at the El Arenosillo site in Southern Europe as a result of the 2023 Canadian forest fires, Environ. Res., 260, 119629, https://doi.org/10.1016/j.envres.2024.119629, 2024. 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
Forello, A. C., Cunha-Lopes, I., Almeida, S. M., Alves, C. A., Tchepel, O., Crova, F., and Vecchi, R.: Insights on the combination of off-line and on-line measurement approaches for source apportionment studies, Sci. Total Environ., 900, 165860, https://doi.org/10.1016/j.scitotenv.2023.165860, 2023. a, b, c, d, e, f
Fountoulakis, I., Diémoz, H., Siani, A. M., Hülsen, G., and Gröbner, J.: Monitoring of solar spectral ultraviolet irradiance in Aosta, Italy, Earth Syst. Sci. Data, 12, 2787–2810, https://doi.org/10.5194/essd-12-2787-2020, 2020. a
Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier van der Gon, H., Facchini, M. C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J. G., Spracklen, D. V., Vignati, E., Wild, M., Williams, M., and Gilardoni, S.: Particulate matter, air quality and climate: lessons learned and future needs, Atmos. Chem. Phys., 15, 8217–8299, https://doi.org/10.5194/acp-15-8217-2015, 2015. a
Garcia-Marlès, M., Lara, R., Reche, C., Pérez, N., Tobías, A., Savadkoohi, M., Beddows, D., Salma, I., Vörösmarty, M., Weidinger, T., Hueglin, C., Mihalopoulos, N., Grivas, G., Kalkavouras, P., Ondráček, J., Zíková, N., Niemi, J. V., Manninen, H. E., Green, D. C., Tremper, A. H., Norman, M., Vratolis, S., Eleftheriadis, K., Gómez-Moreno, F. J., Alonso-Blanco, E., Wiedensohler, A., Weinhold, K., Merkel, M., Bastian, S., Hoffmann, B., Altug, H., Petit, J.-E., Favez, O., Dos Santos, S. M., Putaud, J.-P., Dinoi, A., Contini, D., Timonen, H., Lampilahti, J., Petäjä, T., Pandolfi, M., Hopke, P. K., Harrison, R. M., Alastuey, A., and Querol, X.: Inter-annual trends of ultrafine particles in urban Europe, Environ. Int., 185, 108510, https://doi.org/10.1016/j.envint.2024.108510, 2024. a
Geller, M., Biswas, S., and Sioutas, C.: Determination of Particle Effective Density in Urban Environments with a Differential Mobility Analyzer and Aerosol Particle Mass Analyzer, Aerosol Sci. Tech., 40, 709–723, https://doi.org/10.1080/02786820600803925, 2006. a
Gialitaki, A., Tsekeri, A., Amiridis, V., Ceolato, R., Paulien, L., Kampouri, A., Gkikas, A., Solomos, S., Marinou, E., Haarig, M., Baars, H., Ansmann, A., Lapyonok, T., Lopatin, A., Dubovik, O., Groß, S., Wirth, M., Tsichla, M., Tsikoudi, I., and Balis, D.: Is the near-spherical shape the “new black” for smoke?, Atmos. Chem. Phys., 20, 14005–14021, https://doi.org/10.5194/acp-20-14005-2020, 2020. a
Gietl, J. K., Lawrence, R., Thorpe, A. J., and Harrison, R. M.: Identification of brake wear particles and derivation of a quantitative tracer for brake dust at a major road, Atmos. Environ., 44, 141–146, https://doi.org/10.1016/j.atmosenv.2009.10.016, 2010. a
Gobbi, G., Barnaba, F., Di Liberto, L., Bolignano, A., Lucarelli, F., Nava, S., Perrino, C., Pietrodangelo, A., Basart, S., Costabile, F., Dionisi, D., Rizza, U., Canepari, S., Sozzi, R., Morelli, M., Manigrasso, M., Drewnick, F., Struckmeier, C., Poenitz, K., and Wille, H.: An inclusive view of Saharan dust advections to Italy and the Central Mediterranean, Atmos. Environ., 201, 242–256, https://doi.org/10.1016/j.atmosenv.2019.01.002, 2019. a, b, c
Goi, A., Diémoz, H., Bellini, A., Bracci, A., and Barnaba, F.: Model-assisted retrievals of aerosol properties from Polarization-sensitive Automated Lidar-Ceilometers and test applications to Vaisala CL61 measurements during desert dust transport episodes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11591, https://doi.org/10.5194/egusphere-egu26-11591, 2026. a
González, R., Toledano, C., Román, R., Mateos, D., Asmi, E., Rodríguez, E., Lau, I. C., Ferrara, J., D'Elia, R., Antuña-Sánchez, J. C., Cachorro, V. E., Calle, A., and de Frutos, Á. M.: Characterization of Stratospheric Smoke Particles over the Antarctica by Remote Sensing Instruments, Remote Sens., 12, https://doi.org/10.3390/rs12223769, 2020. a
Grange, S. K. and Carslaw, D. C.: Using meteorological normalisation to detect interventions in air quality time series, Sci. Total Environ., 653, 578–588, https://doi.org/10.1016/j.scitotenv.2018.10.344, 2019. a, b, c
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
Grange, S. K., Fischer, A., Zellweger, C., Alastuey, A., Querol, X., Jaffrezo, J.-L., Weber, S., Uzu, G., and Hueglin, C.: Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions, Atmos. Environ., 12, 100145, https://doi.org/10.1016/j.aeaoa.2021.100145, 2021. a
Gu, J., Pitz, M., Schnelle-Kreis, J., Diemer, J., Reller, A., Zimmermann, R., Soentgen, J., Stoelzel, M., Wichmann, H.-E., Peters, A., and Cyrys, J.: Source apportionment of ambient particles: Comparison of positive matrix factorization analysis applied to particle size distribution and chemical composition data, Atmos. Environ., 45, 1849–1857, https://doi.org/10.1016/j.atmosenv.2011.01.009, 2011. a, b, c, d, e, f, g
Guo, H., Otjes, R., Schlag, P., Kiendler-Scharr, A., Nenes, A., and Weber, R. J.: Effectiveness of ammonia reduction on control of fine particle nitrate, Atmos. Chem. Phys., 18, 12241–12256, https://doi.org/10.5194/acp-18-12241-2018, 2018. a
Hagan, D. H., Gani, S., Bhandari, S., Patel, K., Habib, G., Apte, J. S., Hildebrandt Ruiz, L., and Kroll, J. H.: Inferring Aerosol Sources from Low-Cost Air Quality Sensor Measurements: A Case Study in Delhi, India, Environ. Sci. Tech. Lett., 6, 467–472, https://doi.org/10.1021/acs.estlett.9b00393, 2019. a
Hand, J. L. and Kreidenweis, S. M.: A New Method for Retrieving Particle Refractive Index and Effective Density from Aerosol Size Distribution Data, Aerosol Sci. Tech., 36, 1012–1026, https://doi.org/10.1080/02786820290092276, 2002. a
Harni, S. D., Aurela, M., Saarikoski, S., Niemi, J. V., Portin, H., Manninen, H., Leinonen, V., Aalto, P., Hopke, P. K., Petäjä, T., Rönkkö, T., and Timonen, H.: Source apportionment of particle number size distribution at the street canyon and urban background sites, Atmos. Chem. Phys., 24, 12143–12160, https://doi.org/10.5194/acp-24-12143-2024, 2024. a
Harrison, R. M., Beddows, D. C. S., and Dall'Osto, M.: PMF Analysis of Wide-Range Particle Size Spectra Collected on a Major Highway, Environ. Sci. Tech., 45, 5522–5528, https://doi.org/10.1021/es2006622, 2011. a, b, c
Harrison, R. M., Jones, A. M., Gietl, J., Yin, J., and Green, D. C.: Estimation of the Contributions of Brake Dust, Tire Wear, and Resuspension to Nonexhaust Traffic Particles Derived from Atmospheric Measurements, Environ. Sci. Tech., 46, 6523–6529, https://doi.org/10.1021/es300894r, 2012. a
Harrison, R. M., Beddows, D. C., Jones, A. M., Calvo, A., Alves, C., and Pio, C.: An evaluation of some issues regarding the use of aethalometers to measure woodsmoke concentrations, Atmos. Environ., 80, 540–548, https://doi.org/10.1016/j.atmosenv.2013.08.026, 2013. a
Herrero del Barrio, C., Román, R., González, R., Cazorla, A., Herreras-Giralda, M., Antuña-Sánchez, J. C., Molero, F., Navas-Guzmán, F., Serrano, A., Obregón, M. Á., Sola, Y., Pandolfi, M., Herrero-Anta, S., González-Fernández, D., Muñiz-Rosado, J., Mateos, D., Calle, A., Toledano, C., Cachorro, V. E., and de Frutos, Á. M.: CAECENET: An automatic system processing photometer and ceilometer data from different networks to provide columnar and vertically-resolved aerosol properties, Plos One, 19, e0311990, https://doi.org/10.1371/journal.pone.0311990, 2024. a, b, c
Hollander, M., Wolfe, D. A., and Chicken, E.: Nonparametric Statistical Methods, John Wiley & Sons, New York, ISBN 9780470387375, https://doi.org/10.1002/9781119196037, 2015. a
Hopke, P. K., Dai, Q., Li, L., and Feng, Y.: Global review of recent source apportionments for airborne particulate matter, Sci. Total Environ., 740, 140091, https://doi.org/10.1016/j.scitotenv.2020.140091, 2020. a
Hopke, P. K., Feng, Y., and Dai, Q.: Source apportionment of particle number concentrations: A global review, Sci. Total Environ., 819, 153104, https://doi.org/10.1016/j.scitotenv.2022.153104, 2022. a
Hopke, P. K., Chen, Y., Rich, D. Q., Mooibroek, D., and Sofowote, U. M.: The application of positive matrix factorization with diagnostics to BIG DATA, Chemometr. Intell. Lab., 240, 104885, https://doi.org/10.1016/j.chemolab.2023.104885, 2023. a
Hsu, Y.-K., Holsen, T. M., and Hopke, P. K.: Comparison of hybrid receptor models to locate PCB sources in Chicago, Atmos. Environ., 37, 545–562, https://doi.org/10.1016/S1352-2310(02)00886-5, 2003. a
Hu, M., Peng, J., Sun, K., Yue, D., Guo, S., Wiedensohler, A., and Wu, Z.: Estimation of Size-Resolved Ambient Particle Density Based on the Measurement of Aerosol Number, Mass, and Chemical Size Distributions in the Winter in Beijing, Environ. Sci. Tech., 46, 9941–9947, https://doi.org/10.1021/es204073t, 2012. a
Ibrir, A., Kerchich, Y., Hadidi, N., Merabet, H., and Hentabli, M.: Prediction of the concentrations of PM1, PM2.5, PM4, and PM10 by using the hybrid dragonfly-SVM algorithm, Air Qual. Atmos. Hlth, 14, 313–323, https://doi.org/10.1007/s11869-020-00936-1, 2021. a
IPCC: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Tech. rep., IPCC, Geneva, Switzerland, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023. a
Jiang, F., Saathoff, H., Ezenobi, U., Song, J., Zhang, H., Gao, L., and Leisner, T.: Measurement report: Brown carbon aerosol in rural Germany – sources, chemistry, and diurnal variations, Atmos. Chem. Phys., 25, 1917–1930, https://doi.org/10.5194/acp-25-1917-2025, 2025. a
Jimenez, J. L., Jayne, J. T., Shi, Q., Kolb, C. E., Worsnop, D. R., Yourshaw, I., Seinfeld, J. H., Flagan, R. C., Zhang, X., Smith, K. A., Morris, J. W., and Davidovits, P.: Ambient aerosol sampling using the Aerodyne Aerosol Mass Spectrometer, J. Geophys. Res., 108, https://doi.org/10.1029/2001JD001213, 2003. a
Jones, M. W., Kelley, D. I., Burton, C. A., Di Giuseppe, F., Barbosa, M. L. F., Brambleby, E., Hartley, A. J., Lombardi, A., Mataveli, G., McNorton, J. R., Spuler, F. R., Wessel, J. B., Abatzoglou, J. T., Anderson, L. O., Andela, N., Archibald, S., Armenteras, D., Burke, E., Carmenta, R., Chuvieco, E., Clarke, H., Doerr, S. H., Fernandes, P. M., Giglio, L., Hamilton, D. S., Hantson, S., Harris, S., Jain, P., Kolden, C. A., Kurvits, T., Lampe, S., Meier, S., New, S., Parrington, M., Perron, M. M. G., Qu, Y., Ribeiro, N. S., Saharjo, B. H., San-Miguel-Ayanz, J., Shuman, J. K., Tanpipat, V., van der Werf, G. R., Veraverbeke, S., and Xanthopoulos, G.: State of Wildfires 2023–2024, Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, 2024. a
Kalkavouras, P., Grivas, G., Stavroulas, I., Petrinoli, K., Bougiatioti, A., Liakakou, E., Gerasopoulos, E., and Mihalopoulos, N.: Source apportionment of fine and ultrafine particle number concentrations in a major city of the Eastern Mediterranean, Sci. Total Environ., 915, 170042, https://doi.org/10.1016/j.scitotenv.2024.170042, 2024. a, b
Khan, M. F., Latif, M. T., Amil, N., Juneng, L., Mohamad, N., Nadzir, M. S. M., and Hoque, H. M. S.: Characterization and source apportionment of particle number concentration at a semi-urban tropical environment, Environ. Sci. Pollut. Res., 22, 13111–13126, https://doi.org/10.1007/s11356-015-4541-4, 2015. a
Kim, N. G., Jeong, S. B., Jin, H. C., Lee, J., Kim, K. H., Kim, S., Park, Y., Choi, W., Kwak, K.-H., Lee, H., Kang, G., Kim, C., Woo, S.-H., Lee, S., Kim, W. Y., Ahn, K., Lee, K.-Y., and Lee, S.-B.: Spatial and PMF analysis of particle size distributions simultaneously measured at four locations at the roadside of highways, Sci. Total Environ., 893, 164892, https://doi.org/10.1016/j.scitotenv.2023.164892, 2023. a
Kolden, C. A., Abatzoglou, J. T., Jones, M. W., and Jain, P.: Wildfires in 2023, Nat. Rev. Earth Environ., 5, 238–240, https://doi.org/10.1038/s43017-024-00544-y, 2024. a
Krecl, P., Targino, A. C., Johansson, C., and Ström, J.: Characterisation and Source Apportionment of Submicron Particle Number Size Distributions in a Busy Street Canyon, Aerosol Air Qual. Res., 15, 220–233, https://doi.org/10.4209/aaqr.2014.06.0108, 2015. a
Kudo, R., Diémoz, H., Estellés, V., Campanelli, M., Momoi, M., Marenco, F., Ryder, C. L., Ijima, O., Uchiyama, A., Nakashima, K., Yamazaki, A., Nagasawa, R., Ohkawara, N., and Ishida, H.: Optimal use of the Prede POM sky radiometer for aerosol, water vapor, and ozone retrievals, Atmos. Meas. Tech., 14, 3395–3426, https://doi.org/10.5194/amt-14-3395-2021, 2021. a
Kumar, V., Malyan, V., Sahu, M., and Biswal, B.: Aerosol sources characterization and apportionment from low-cost particle sensors in an urban environment, Atmos. Environ. X, 22, 100271, https://doi.org/10.1016/j.aeaoa.2024.100271, 2024. a
Lampilahti, J., Leino, K., Manninen, A., Poutanen, P., Franck, A., Peltola, M., Hietala, P., Beck, L., Dada, L., Quéléver, L., Öhrnberg, R., Zhou, Y., Ekblom, M., Vakkari, V., Zilitinkevich, S., Kerminen, V.-M., Petäjä, T., and Kulmala, M.: Aerosol particle formation in the upper residual layer, Atmos. Chem. Phys., 21, 7901–7915, https://doi.org/10.5194/acp-21-7901-2021, 2021. a
Leoni, C., Pokorná, P., Hovorka, J., Masiol, M., Topinka, J., Zhao, Y., Křůmal, K., Cliff, S., Mikuška, P., and Hopke, P. K.: Source apportionment of aerosol particles at a European air pollution hot spot using particle number size distributions and chemical composition, Environ. Pollut., 234, 145–154, https://doi.org/10.1016/j.envpol.2017.10.097, 2018. a, b
Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z., and He, K.-B.: Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations, Sci. Total Environ., 744, 140923, https://doi.org/10.1016/j.scitotenv.2020.140923, 2020. a
Liang, C.-S., Yue, D., Wu, H., Shi, J.-S., and He, K.-B.: Source apportionment of atmospheric particle number concentrations with wide size range by nonnegative matrix factorization (NMF), Environ. Pollut., 289, 117846, https://doi.org/10.1016/j.envpol.2021.117846, 2021. a, b
Linnet, K.: Estimation of the linear relationship between the measurements of two methods with proportional errors, Stat. Med., 9, 1463–1473, https://doi.org/10.1002/sim.4780091210, 1990. a
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
Looschelders, D., Christen, A., Grimmond, S., Kotthaus, S., Fenner, D., Dupont, J.-C., Haeffelin, M., and Morrison, W.: Inter-Instrument Variability of Vaisala CL61 Lidar-Ceilometer's Attenuated Backscatter, Cloud Properties and Mixed-Layer Height, Meteorol. Appl., 32, e70088, https://doi.org/10.1002/met.70088, 2025. a
Maher, B. A., Gonet, T., Karloukovski, V. V., Wang, H., and Bannan, T. J.: Protecting playgrounds: local-scale reduction of airborne particulate matter concentrations through particulate deposition on roadside “tredges” (green infrastructure), Sci. Rep., 12, 14236, https://doi.org/10.1038/s41598-022-18509-w, 2022. a
Marsteen, L. and Hak, C.: NILU Report 21/2021: PM10/PM2.5 comparison exercise in Oslo, Norway. Study in 2015–2016 and 2018, Tech. rep., Norwegian Institute for Air Research (NILU), ISBN 978-82-425-3059-2, 2021. a
Masiol, M., Vu, T. V., Beddows, D. C., and Harrison, R. M.: Source apportionment of wide range particle size spectra and black carbon collected at the airport of Venice (Italy), Atmos. Environ., 139, 56–74, https://doi.org/10.1016/j.atmosenv.2016.05.018, 2016. a, b, c
Masiol, M., Hopke, P., Felton, H., Frank, B., Rattigan, O., Wurth, M., and LaDuke, G.: Source apportionment of PM2.5 chemically speciated mass and particle number concentrations in New York City, Atmos. Environ., 148, 215–229, https://doi.org/10.1016/j.atmosenv.2016.10.044, 2017b. a, b, c
Massabò, D., Caponi, L., Bernardoni, V., Bove, M., Brotto, P., Calzolai, G., Cassola, F., Chiari, M., Fedi, M., 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
Mattsson, F., Neuberger, A., Heikkinen, L., Gramlich, Y., Paglione, M., Rinaldi, M., Decesari, S., Zieger, P., Riipinen, I., and Mohr, C.: Enrichment of organic nitrogen in fog residuals observed in the Italian Po Valley, Atmos. Chem. Phys., 25, 7973–7989, https://doi.org/10.5194/acp-25-7973-2025, 2025. a
Mazzei, F., Lucarelli, F., Nava, S., Prati, P., Valli, G., and Vecchi, R.: A new methodological approach: The combined use of two-stage streaker samplers and optical particle counters for the characterization of airborne particulate matter, Atmos. Environ., 41, 5525–5535, https://doi.org/10.1016/j.atmosenv.2007.04.012, 2007. a, b, c, d, e
Mazzi, G., Barbaro, E., Feltracco, M., Favaro, E., Diémoz, H., Milizia, L., Colombi, C., Bove, M. C., Troiano, S., Malloci, E., Ceccon, D., Baiutti, E., and Gambaro, A.: Investigation of airborne benzothiazoles in the North of Italy, Atmos. Environ., 356, 121311, https://doi.org/10.1016/j.atmosenv.2025.121311, 2025. a
McNamara, S. M., Kolesar, K. R., Wang, S., Kirpes, R. M., May, N. W., Gunsch, M. J., Cook, R. D., Fuentes, J. D., Hornbrook, R. S., Apel, E. C., China, S., Laskin, A., and Pratt, K. A.: Observation of Road Salt Aerosol Driving Inland Wintertime Atmospheric Chlorine Chemistry, ACS Cent. Sci., 6, 684–694, https://doi.org/10.1021/acscentsci.9b00994, 2020. a
Mecca, D., Boanini, C., Vaccaro, V., Gallione, D., Mastromatteo, N., and Clerico, M.: Spatial variation, temporal evolution, and source direction apportionment of PM1, PM2.5, and PM10: 3-year assessment in Turin (Po Valley), Environ. Monit. Assess., 196, 1251, https://doi.org/10.1007/s10661-024-13446-9, 2024. a
Micheli, L., Almonacid, F., Bessa, J. G., Álvaro Fernández-Solas, and Fernández, E. F.: The impact of extreme dust storms on the national photovoltaic energy supply, Sustain. Energy Technol. Assess., 62, 103607, https://doi.org/10.1016/j.seta.2024.103607, 2024. a
Mircea, M., Calori, G., Pirovano, G., and Belis, C.: European guide on air pollution source apportionment for particulate matter with source oriented models and their combined use with receptor models, EUR 30082 EN, Publications Office of the European Union, Luxembourg, JRC119067, ISBN 978-92-76-10698-2, https://doi.org/10.2760/470628, 2020. 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
NASA: Worldview earthdata website, https://worldview.earthdata.nasa.gov (last access: 2 June 2026), 2024. a
Ng, N. L., Canagaratna, M. R., Jimenez, J. L., Zhang, Q., Ulbrich, I. M., and Worsnop, D. R.: Real-Time Methods for Estimating Organic Component Mass Concentrations from Aerosol Mass Spectrometer Data, Environ. Sci. Tech., 45, 910–916, https://doi.org/10.1021/es102951k, 2011a. a
Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P. L., Onasch, T. B., Sueper, D., Worsnop, D. R., Zhang, Q., Sun, Y. L., and Jayne, J. T.: An Aerosol Chemical Speciation Monitor (ACSM) for Routine Monitoring of the Composition and Mass Concentrations of Ambient Aerosol, Aerosol Sci. Tech., 45, 780–794, https://doi.org/10.1080/02786826.2011.560211, 2011b. a
Norris, G., Duvall, R., and Brown, S.: EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide, U.S. Environmental Protection Agency Office of Research and Development Washington, DC 20460, ePA/600/R-14/108, https://www.epa.gov/sites/default/files/2015-02/documents/pmf_5.0_user_guide.pdf (last access: 2 June 2026), 2014. a, b
Ogulei, D., Hopke, P. K., and Wallace, L. A.: Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization, Indoor Air, 16, 204–215, https://doi.org/10.1111/j.1600-0668.2006.00418.x, 2006a. a
Ogulei, D., Hopke, P. K., Zhou, L., Pancras, J. P., Nair, N., and Ondov, J. M.: Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data, Atmos. Environ., 40, 396–410, https://doi.org/10.1016/j.atmosenv.2005.11.075, 2006b. a, b, c
Ogulei, D., Hopke, P. K., Chalupa, D. C., , and Utell, M. J.: Modeling Source Contributions to Submicron Particle Number Concentrations Measured in Rochester, New York, Aerosol Sci. Tech., 41, 179–201, https://doi.org/10.1080/02786820601116012, 2007. a, b, c, d
O'Neill, N. T., Dubovik, O., and Eck, T. F.: Modified Ångström exponent for the characterization of submicrometer aerosols, Appl. Opt., 40, 2368–2375, https://doi.org/10.1364/AO.40.002368, 2001. a, b
Orellano, P., Reynoso, J., Quaranta, N., Bardach, A., and Ciapponi, A.: Short-term exposure to particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and ozone (O3) and all-cause and cause-specific mortality: Systematic review and meta-analysis, Environ. Int., 142, 105876, https://doi.org/10.1016/j.envint.2020.105876, 2020. a
Ouaret, R., Ionescu, A., and Ramalho, O.: Non-negative matrix factorization for the analysis of particle number concentrations: Characterization of the temporal variability of sources in indoor workplace, Build. Environ., 203, 108055, https://doi.org/10.1016/j.buildenv.2021.108055, 2021. a
Paatero, P.: Referee comment RC2, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-784-RC2, 2018. a, b
Paatero, P. and Tapper, U.: Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, 5, 111–126, https://doi.org/10.1002/env.3170050203, 1994. a
Paatero, P., Eberly, S., Brown, S. G., and Norris, G. A.: Methods for estimating uncertainty in factor analytic solutions, Atmos. Meas. Tech., 7, 781–797, https://doi.org/10.5194/amt-7-781-2014, 2014. a, b, c, d
PALAS GmbH: Report on supplementary testing of the Fidas® 200 S respectively Fidas® 200 measuring system manufactured by PALAS GmbH for the components suspended particulate matter PM10 and PM2.5, TÜV-report: 936/21227195/C, Operating Manual Fine Dust Monitor System Fidas® – Version V0230816, TÜV Rheinland Energie und Umwelt GmbH, https://www.palas.de/file/ea8697/application/octet-stream/Palas+T%C3%9CV+Fidas+System (last access: 2 June 2026), 2016. a, b
Parworth, C., Fast, J., Mei, F., Shippert, T., Sivaraman, C., Tilp, A., Watson, T., and Zhang, Q.: Long-term measurements of submicrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol Chemical Speciation Monitor (ACSM), Atmos. Environ., 106, 43–55, https://doi.org/10.1016/j.atmosenv.2015.01.060, 2015. a
Petit, J.-E., Favez, O., and Chauvigné, A.: Deliverable D5 (D1.5) NRT Source Apportionment Service Tools for submicron carbonaceous matter (final), Tech. rep., RI-URBANS Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS (GA n. 101036245), https://riurbans.eu/wp-content/uploads/2024/12/RI-URBANS_D5_D1_5.pdf (last access: 2 June 2026), 2024. a
Pey, J., Querol, X., Alastuey, A., Rodríguez, S., Putaud, J. P., and Van Dingenen, R.: Source apportionment of urban fine and ultra-fine particle number concentration in a Western Mediterranean city, Atmos. Environ., 43, 4407–4415, https://doi.org/10.1016/j.atmosenv.2009.05.024, 2009. a, b, c
Pey, J., Querol, X., Alastuey, A., Forastiere, F., and Stafoggia, M.: African dust outbreaks over the Mediterranean Basin during 2001–2011: PM10 concentrations, phenomenology and trends, and its relation with synoptic and mesoscale meteorology, Atmos. Chem. Phys., 13, 1395–1410, https://doi.org/10.5194/acp-13-1395-2013, 2013. a
Pitz, M., Cyrys, J., Karg, E., Wiedensohler, A., Wichmann, H.-E., and Heinrich, J.: Variability of Apparent Particle Density of an Urban Aerosol, Environ. Sci. Tech., 37, 4336–4342, https://doi.org/10.1021/es034322p, 2003. a
Pokorná, P., Leoni, C., Schwarz, J., Ondráček, J., Ondráčková, L., Vodička, P., Zíková, N., Moravec, P., Bendl, J., Klán, M., Hovorka, J., Zhao, Y., Cliff, S. S., Ždímal, V., and Hopke, P. K.: Spatial-temporal variability of aerosol sources based on chemical composition and particle number size distributions in an urban settlement influenced by metallurgical industry, Environ. Sci. Pollut. Res., 27, 38631–38643, https://doi.org/10.1007/s11356-020-09694-0, 2020. a, b
Qin, X., Wei, P., Ghadikolaei, M. A., Gali, N. K., Wang, Y., and Ning, Z.: The application of a multi-channel sensor network to decompose the local and background sources and quantify their contributions, Build. Environ., 230, 110005, https://doi.org/10.1016/j.buildenv.2023.110005, 2023. a
Querol, X., Pey, J., Pandolfi, M., Alastuey, A., Cusack, M., Pérez, N., Moreno, T., Viana, M., Mihalopoulos, N., Kallos, G., and Kleanthous, S.: African dust contributions to mean ambient PM10 mass-levels across the Mediterranean Basin, Atmos. Environ., 43, 4266–4277, https://doi.org/10.1016/j.atmosenv.2009.06.013, 2009. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 2 June 2026), 2022. a
Reid, J. S., Koppmann, R., Eck, T. F., and Eleuterio, D. P.: A review of biomass burning emissions part II: intensive physical properties of biomass burning particles, Atmos. Chem. Phys., 5, 799–825, https://doi.org/10.5194/acp-5-799-2005, 2005. a
RI-URBANS: Guidance documents on measurements and modelling of novel air quality pollutants: Source apportionment of eBC, UFP, OP and VOCs, Tech. rep., Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban and Industrial AreaS (GA n. 101036245), https://riurbans.eu/wp-content/uploads/2024/11/ENV_GUIDANCE-DOCUMENT_ST11_SA_BC_UFP_OP_VOCs_Definitive.pdf (last access: 2 June 2026), 2024. a
Rivas, I., Beddows, D. C., Amato, F., Green, D. C., Järvi, L., Hueglin, C., Reche, C., Timonen, H., Fuller, G. W., Niemi, J. V., Pérez, N., Aurela, M., Hopke, P. K., Alastuey, A., Kulmala, M., Harrison, R. M., Querol, X., and Kelly, F. J.: Source apportionment of particle number size distribution in urban background and traffic stations in four European cities, Environ. Int., 135, 105345, https://doi.org/10.1016/j.envint.2019.105345, 2020. a, b, c, d, e
Rodríguez, S. and López-Darias, J.: Extreme Saharan dust events expand northward over the Atlantic and Europe, prompting record-breaking PM10 and PM2.5 episodes, Atmos. Chem. Phys., 24, 12031–12053, https://doi.org/10.5194/acp-24-12031-2024, 2024. a, b
Rovira, J., Savadkoohi, M., Chen, G. I., Močnik, G., Aas, W., Alados-Arboledas, L., Artiñano, B., Aurela, M., Backman, J., Banerji, S., Beddow, D., Brem, B., Chazeau, B., Collaud Coen, M., Colombi, C., Conil, S., Costabile, F., Coz, E., de Brito, J. F., Eleftheriadis, K., Favez, O., Flentje, H., Freney, E., Gregorič, A., Gysel-Beer, M., Harrison, R., Hueglin, C., Hyvärinen, A., Ivančič, M., Kalogridis, A.-C., Keernik, H., Konstantinos, G., Laj, P., Liakakou, E., Lin, C., Listrani, S., Luoma, K., Maasikmets, M., Manninen, H. E., Marchand, N., dos Santos, S. M., Mbengue, S., Mihalopoulos, N., Nicolae, D., Niemi, J. V., Norman, M., Ovadnevaite, J., Petit, J.-E., Platt, S., Prévôt, A. S., Pujadas, M., Putaud, J.-P., Riffault, V., Rigler, M., Rinaldi, M., Schwarz, J., Silvergren, S., Teinemaa, E., Teinilä, K., Timonen, H., Titos, G., Tobler, A., Vasilescu, J., Vratolis, S., Espen Yttri, K., Yubero, E., Zíková, N., Alastuey, A., Petäjä, T., Querol, X., Yus-Díez, J., and Pandolfi, M.: A European aerosol phenomenology – 9: Light absorption properties of carbonaceous aerosol particles across surface Europe, Environ. Int., 195, 109185, https://doi.org/10.1016/j.envint.2024.109185, 2025. a, b, c, d
Russell, P. B., Bergstrom, R. W., Shinozuka, Y., Clarke, A. D., DeCarlo, P. F., Jimenez, J. L., Livingston, J. M., Redemann, J., Dubovik, O., and Strawa, A.: Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition, Atmos. Chem. Phys., 10, 1155–1169, https://doi.org/10.5194/acp-10-1155-2010, 2010. a, b
Salvador, P., Pey, J., Pérez, N., Querol, X., and Artíñano, B.: Increasing atmospheric dust transport towards the western Mediterranean over 1948–2020, npj Clim. Atmos. Sci., 5, 34, https://doi.org/10.1038/s41612-022-00256-4, 2022. 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. Tech., 42, 3316–3323, https://doi.org/10.1021/es702253m, 2008. a, b, c, d, e
Sarafian, R., Nathan, S., Nissenbaum, D., Khan, S., and Rudich, Y.: Correction of CAMS PM10 Reanalysis Improves AI-Based Dust Event Forecast, Remote Sens., 17, https://doi.org/10.3390/rs17020222, 2025. a
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., Álvaro Clemente María, 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, c
Savadkoohi, M., Sofowote, U. M., Querol, X., Alastuey, A., Pandolfi, M., and Hopke, P. K.: Source-dependent absorption Ångström exponent in the Los Angeles Basin: Multi-time resolution factor analyses of ambient PM2.5 and aerosol optical absorption, Sci. Total Environ., 958, 178095, https://doi.org/10.1016/j.scitotenv.2024.178095, 2025. a, b, c
Song, C., Dall'Osto, M., Lupi, A., Mazzola, M., Traversi, R., Becagli, S., Gilardoni, S., Vratolis, S., Yttri, K. E., Beddows, D. C. S., Schmale, J., Brean, J., Kramawijaya, A. G., Harrison, R. M., and Shi, Z.: Differentiation of coarse-mode anthropogenic, marine and dust particles in the High Arctic islands of Svalbard, Atmos. Chem. Phys., 21, 11317–11335, https://doi.org/10.5194/acp-21-11317-2021, 2021. a
Stirnberg, R., Cermak, J., Kotthaus, S., Haeffelin, M., Andersen, H., Fuchs, J., Kim, M., Petit, J.-E., and Favez, O.: Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning, Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, 2021. a
Struckmeier, C., Drewnick, F., Fachinger, F., Gobbi, G. P., and Borrmann, S.: Atmospheric aerosols in Rome, Italy: sources, dynamics and spatial variations during two seasons, Atmos. Chem. Phys., 16, 15277–15299, https://doi.org/10.5194/acp-16-15277-2016, 2016. a
Targa, J., Colina, M., Banyuls, L., González Ortiz, A., and Soares, J.: Status report of air quality in Europe for year 2023, using validated and up-to-date data (ETC-HE Report 2024/5), Tech. rep., European Topic Centre on Human Health and the Environment, https://www.eionet.europa.eu/etcs/etc-he/products/etc-he-products/etc-he-reports/etc-he-report-2024-5-status-report-of-air-quality-in-europe-for-year-2023-using-validated-and-up-to-date-data (last access: 2 June 2026), 2024. a
Tesche, M., Ansmann, A., Müller, D., Althausen, D., Engelmann, R., Freudenthaler, V., and Groß, S.: Vertically resolved separation of dust and smoke over Cape Verde using multiwavelength Raman and polarization lidars during Saharan Mineral Dust Experiment 2008, J. Geophys. Res., 114, https://doi.org/10.1029/2009JD011862, 2009. a
Thimmaiah, D., Hovorka, J., and Hopke, P. K.: Source apportionment of winter submicron prague aerosols from combined particle number size distribution and gaseous composition data, Aerosol Air Qual. Res., 9, 209–236, https://doi.org/10.4209/aaqr.2008.11.0055, 2009. a, b
Tohidi, R., Farahani, V. J., and Sioutas, C.: Real-time measurements of mineral dust concentration in coarse particulate matter (PM10–2.5) by employing a novel optical-based technique in Los Angeles, Sci. Total Environ., 838, 156215, https://doi.org/10.1016/j.scitotenv.2022.156215, 2022. a, b
TÜV Rheinland Energie und Umwelt GmbH: Report on supplementary testing of the Fidas® 200 S respectively idas® 200 measuring system manufactured by PALAS GmbH for the components suspended particulate matter PM10 and PM2.5, TÜV-report: 936/21227195/C, Tech. rep., TÜV Rheinland Energie und Umwelt GmbH, https://www.palas.de/file/ea8697/application/octet-stream/Palas+T%C3%9CV+Fidas+System (last access: 2 June 2026), 2016. a, b
Valentini, S., Barnaba, F., Bernardoni, V., Calzolai, G., Costabile, F., Di Liberto, L., Forello, A. C., Gobbi, G. P., Gualtieri, M., Lucarelli, F., Nava, S., Petralia, E., Valli, G., Wiedensohler, A., and Vecchi, R.: Classifying aerosol particles through the combination of optical and physical-chemical properties: results from a wintertime campaign in Rome (Italy), Atmos. Res., 235, 104799, https://doi.org/10.1016/j.atmosres.2019.104799, 2020. a
Van Poppel, M., Schneider, P., Peters, J., Yatkin, S., Gerboles, M., Matheeussen, C., Bartonova, A., Davila, S., Signorini, M., Vogt, M., Dauge, F. R., Skaar, J. S., and Haugen, R.: SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems, Sci. Data, 10, 322, https://doi.org/10.1038/s41597-023-02135-w, 2023. a
Vecchi, R.: Cutting-Edge Approaches for Aerosol Source Apportionment by Receptor Modelling, vol. European Aerosol Conference (EAC2023), https://www.dfmf.uned.es/EAC2023/service.php?myabstract=5659643a&uuid=51568afba12da4238d10bdc62aa4ddf7 (last access: 2 June 2026), 2023. a
Via, M., Chen, G., Canonaco, F., Daellenbach, K. R., Chazeau, B., Chebaicheb, H., Jiang, J., Keernik, H., Lin, C., Marchand, N., Marin, C., O'Dowd, C., Ovadnevaite, J., Petit, J.-E., Pikridas, M., Riffault, V., Sciare, J., Slowik, J. G., Simon, L., Vasilescu, J., Zhang, Y., Favez, O., Prévôt, A. S. H., Alastuey, A., and Cruz Minguillón, M.: Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison, Atmos. Meas. Tech., 15, 5479–5495, https://doi.org/10.5194/amt-15-5479-2022, 2022. a
Via, M., Yus-Díez, J., Canonaco, F., Petit, J.-E., Hopke, P., Reche, C., Pandolfi, M., Ivančič, M., Rigler, M., Prevôt, A. S., Querol, X., Alastuey, A., and Minguillón, M. C.: Towards a better understanding of fine PM sources: Online and offline datasets combination in a single PMF, Environ. Int., 177, 108006, https://doi.org/10.1016/j.envint.2023.108006, 2023. a
Vörösmarty, M., Hopke, P. K., and Salma, I.: Attribution of aerosol particle number size distributions to main sources using an 11-year urban dataset, Atmos. Chem. Phys., 24, 5695–5712, https://doi.org/10.5194/acp-24-5695-2024, 2024. a, b, c
Vu, T. V., Delgado-Saborit, J. M., and Harrison, R. M.: Review: Particle number size distributions from seven major sources and implications for source apportionment studies, Atmos. Environ., 122, 114–132, https://doi.org/10.1016/j.atmosenv.2015.09.027, 2015. a, b, c
Wang, P., Wang, Q., Jia, Y., Ma, J., Wang, C., Qiao, L., Fu, Q., Mellouki, A., Chen, H., and Li, L.: A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges, Atmosphere, 15, https://doi.org/10.3390/atmos15080955, 2024a. a, b
Wang, Y., Hopke, P. K., Rattigan, O. V., Xia, X., Chalupa, D. C., and Utell, M. J.: Characterization of Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer, Environ. Sci. Tech., 45, 7387–7393, https://doi.org/10.1021/es2013984, 2011. a
Wang, Y., Hopke, P. K., Rattigan, O. V., Chalupa, D. C., and Utell, M. J.: Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester, New York, J. Air Waste Ma., 62, 880–887, https://doi.org/10.1080/10962247.2012.671792, 2012. a
Wang, Z., Wang, Z., Zou, Z., Chen, X., Wu, H., Wang, W., Su, H., Li, F., Xu, W., Liu, Z., and Zhu, J.: Severe Global Environmental Issues Caused by Canada's Record-Breaking Wildfires in 2023, Adv. Atmos.Sci., 41, 565–571, https://doi.org/10.1007/s00376-023-3241-0, 2024b. a
Wang, Z. B., Hu, M., Wu, Z. J., Yue, D. L., He, L. Y., Huang, X. F., Liu, X. G., and Wiedensohler, A.: Long-term measurements of particle number size distributions and the relationships with air mass history and source apportionment in the summer of Beijing, Atmos. Chem. Phys., 13, 10159–10170, https://doi.org/10.5194/acp-13-10159-2013, 2013. a, b
Watson, J. G., Cooper, J. A., and Huntzicker, J. J.: The effective variance weighting for least squares calculations applied to the mass balance receptor model, Atmos. Environ., 18, 1347–1355, https://doi.org/10.1016/0004-6981(84)90043-X, 1984. a, b
Watson, J. G., Chow, J. C., and Pace, T. G.: Chapter 4 – Chemical Mass Balance, in: Receptor modeling for air quality management, edited by: Hopke, P. K., vol. 7 of Data Handling in Science and Technology, Elsevier, 83–116, https://doi.org/10.1016/S0922-3487(08)70127-8, 1991. a
Wegner, T., Hussein, T., Hämeri, K., Vesala, T., Kulmala, M., and Weber, S.: Properties of aerosol signature size distributions in the urban environment as derived by cluster analysis, Atmos. Environ., 61, 350–360, https://doi.org/10.1016/j.atmosenv.2012.07.048, 2012. a
World Health Organization: WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, ISBN 9789240034228, 2021. a
Wu, H., Li, Z., Jiang, M., Liang, C., Zhang, D., Wu, T., Wang, Y., and Cribb, M.: Contributions of traffic emissions and new particle formation to the ultrafine particle size distribution in the megacity of Beijing, Atmos. Environ., 262, 118652, https://doi.org/10.1016/j.atmosenv.2021.118652, 2021. a, b
Yang, M., Howell, S. G., Zhuang, J., and Huebert, B. J.: Attribution of aerosol light absorption to black carbon, brown carbon, and dust in China – interpretations of atmospheric measurements during EAST-AIRE, Atmos. Chem. Phys., 9, 2035–2050, https://doi.org/10.5194/acp-9-2035-2009, 2009. a, b
York, D., Evensen, N. M., Martínez, M. L., and De Basabe Delgado, J.: Unified equations for the slope, intercept, and standard errors of the best straight line, Am. J. Phys., 72, 367–375, https://doi.org/10.1119/1.1632486, 2004. a
Yue, W., Stölzel, M., Cyrys, J., Pitz, M., Heinrich, J., Kreyling, W. G., Wichmann, H.-E., Peters, A., Wang, S., and Hopke, P. K.: Source apportionment of ambient fine particle size distribution using positive matrix factorization in Erfurt, Germany, Sci. Total Environ., 398, 133–144, https://doi.org/10.1016/j.scitotenv.2008.02.049, 2008. a, b, c, d, e, f
Zanatta, M., Gysel, M., Bukowiecki, N., Müller, T., Weingartner, E., Areskoug, H., Fiebig, M., Yttri, K., Mihalopoulos, N., Kouvarakis, G., Beddows, D., Harrison, R., Cavalli, F., Putaud, J., Spindler, G., Wiedensohler, A., Alastuey, A., Pandolfi, M., Sellegri, K., Swietlicki, E., Jaffrezo, J., Baltensperger, U., and Laj, P.: A European aerosol phenomenology-5: Climatology of black carbon optical properties at 9 regional background sites across Europe, Atmos. Environ., 145, 346–364, https://doi.org/10.1016/j.atmosenv.2016.09.035, 2016. a
Zhou, L., Hopke, P. K., Paatero, P., Ondov, J. M., Pancras, J., Pekney, N. J., and Davidson, C. I.: Advanced factor analysis for multiple time resolution aerosol composition data, Atmos. Environ., 38, 4909–4920, https://doi.org/10.1016/j.atmosenv.2004.05.040, 2004. a, b, c, d
Zhou, L., Hopke, P. K., Stanier, C. O., Pandis, S. N., Ondov, J. M., and Pancras, J. P.: Investigation of the relationship between chemical composition and size distribution of airborne particles by partial least squares and positive matrix factorization, J. Geophys. Res., 110, https://doi.org/10.1029/2004JD005050, 2005a. a, b
Zhou, L., Kim, E., Hopke, P. K., Stanier, C., and Pandis, S. N.: Mining airborne particulate size distribution data by positive matrix factorization, J. Geophys. Res., 110, https://doi.org/10.1029/2004JD004707, 2005b. 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, b, c, d, e
Editorial statement
This work describes a technique for the real-time determination of aerosol sources using high temporal resolution measurements to determine rapid changes in particle size distribution in accumulation and coarse modes, along with measurements of spectrally resolved light absorption measurements in the near-UV to near-IR range. The paper presents relevant real-time applications, including emergency surveillance during accidental events and the rapid identification of long-range transport of secondary particles, desert dust, and smoke. The documented approach is transferable to air quality networks involved in aerosol mass source apportionment as it relies on optical instruments commonly employed by regulatory government agencies.
This work describes a technique for the real-time determination of aerosol sources using high...
Short summary
RASPBERRY is a new method to identify aerosol emission sources using physical properties (particle size and light absorption) measured at high time resolution by cost-effective optical instruments, instead of labour-intensive chemical analyses. Applied over five years in Aosta, Italy, it identified six main sources – traffic, biomass burning, two types of secondary particles, desert dust, and local resuspension. Validation against chemical apportionment and real-time applications are presented.
RASPBERRY is a new method to identify aerosol emission sources using physical properties...
