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Start Over Author "Petr Vodička" Topic environmental sciences
3 results on '"Petr Vodička"'

1. Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations

Author

Marjan Savadkoohi, Marco Pandolfi, Olivier Favez, Jean-Philippe Putaud, Konstantinos Eleftheriadis, Markus Fiebig, Philip K. Hopke, Paolo Laj, Alfred Wiedensohler, Lucas Alados-Arboledas, Susanne Bastian, Benjamin Chazeau, Álvaro Clemente María, Cristina Colombi, Francesca Costabile, David C. Green, Christoph Hueglin, Eleni Liakakou, Krista Luoma, Stefano Listrani, Nikos Mihalopoulos, Nicolas Marchand, Griša Močnik, Jarkko V. Niemi, Jakub Ondráček, Jean-Eudes Petit, Oliver V. Rattigan, Cristina Reche, Hilkka Timonen, Gloria Titos, Anja H. Tremper, Stergios Vratolis, Petr Vodička, Eduardo Yubero Funes, Naděžda Zíková, Roy M. Harrison, Tuukka Petäjä, Andrés Alastuey, and Xavier Querol

Subjects
eBC, MAC, FAPs, EC, Absorption, Site specific MAC, Environmental sciences, GE1-350
Abstract

A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial–temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.

Published
2024
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2. European aerosol phenomenology − 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets

Author

Gang Chen, Francesco Canonaco, Anna Tobler, Wenche Aas, Andres Alastuey, James Allan, Samira Atabakhsh, Minna Aurela, Urs Baltensperger, Aikaterini Bougiatioti, Joel F. De Brito, Darius Ceburnis, Benjamin Chazeau, Hasna Chebaicheb, Kaspar R. Daellenbach, Mikael Ehn, Imad El Haddad, Konstantinos Eleftheriadis, Olivier Favez, Harald Flentje, Anna Font, Kirsten Fossum, Evelyn Freney, Maria Gini, David C Green, Liine Heikkinen, Hartmut Herrmann, Athina-Cerise Kalogridis, Hannes Keernik, Radek Lhotka, Chunshui Lin, Chris Lunder, Marek Maasikmets, Manousos I. Manousakas, Nicolas Marchand, Cristina Marin, Luminita Marmureanu, Nikolaos Mihalopoulos, Griša Močnik, Jaroslaw Nęcki, Colin O'Dowd, Jurgita Ovadnevaite, Thomas Peter, Jean-Eudes Petit, Michael Pikridas, Stephen Matthew Platt, Petra Pokorná, Laurent Poulain, Max Priestman, Véronique Riffault, Matteo Rinaldi, Kazimierz Różański, Jaroslav Schwarz, Jean Sciare, Leïla Simon, Alicja Skiba, Jay G. Slowik, Yulia Sosedova, Iasonas Stavroulas, Katarzyna Styszko, Erik Teinemaa, Hilkka Timonen, Anja Tremper, Jeni Vasilescu, Marta Via, Petr Vodička, Alfred Wiedensohler, Olga Zografou, María Cruz Minguillón, and André S.H. Prévôt

Subjects
European Overview, Source apportionment, Harmonised Protocol, Organic Aerosol, Long-term Datasets, Rolling PMF, Environmental sciences, GE1-350
Abstract

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.

Published
2022
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3. European aerosol phenomenology − 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets

Author

Gang Chen, Francesco Canonaco, Anna Tobler, Wenche Aas, Andres Alastuey, James Allan, Samira Atabakhsh, Minna Aurela, Urs Baltensperger, Aikaterini Bougiatioti, Joel F. De Brito, Darius Ceburnis, Benjamin Chazeau, Hasna Chebaicheb, Kaspar R. Daellenbach, Mikael Ehn, Imad El Haddad, Konstantinos Eleftheriadis, Olivier Favez, Harald Flentje, Anna Font, Kirsten Fossum, Evelyn Freney, Maria Gini, David C Green, Liine Heikkinen, Hartmut Herrmann, Athina-Cerise Kalogridis, Hannes Keernik, Radek Lhotka, Chunshui Lin, Chris Lunder, Marek Maasikmets, Manousos I. Manousakas, Nicolas Marchand, Cristina Marin, Luminita Marmureanu, Nikolaos Mihalopoulos, Griša Močnik, Jaroslaw Nęcki, Colin O'Dowd, Jurgita Ovadnevaite, Thomas Peter, Jean-Eudes Petit, Michael Pikridas, Stephen Matthew Platt, Petra Pokorná, Laurent Poulain, Max Priestman, Véronique Riffault, Matteo Rinaldi, Kazimierz Różański, Jaroslav Schwarz, Jean Sciare, Leïla Simon, Alicja Skiba, Jay G. Slowik, Yulia Sosedova, Iasonas Stavroulas, Katarzyna Styszko, Erik Teinemaa, Hilkka Timonen, Anja Tremper, Jeni Vasilescu, Marta Via, Petr Vodička, Alfred Wiedensohler, Olga Zografou, María Cruz Minguillón, André S.H. Prévôt, Paul Scherrer Institute (PSI), Datalystica, Norwegian Institute for Air Research (NILU), Centre d'Investigació i Desenvolupament [Barcelona] (CID-CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), University of Manchester [Manchester], Leibniz Institute for Tropospheric Research (TROPOS), Finnish Meteorological Institute (FMI), National Observatory of Athens (NOA), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Nord Europe), Institut Mines-Télécom [Paris] (IMT), Martin Ryan Institute, National University of Ireland [Galway] (NUI Galway), Laboratoire Chimie de l'environnement (LCE), Aix Marseille Université (AMU)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Institut National de l'Environnement Industriel et des Risques (INERIS), Institute for Atmospheric and Earth System Research (INAR), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, National Center for Scientific Research 'Demokritos' (NCSR), Meteorologisches Observatorium Hohenpeißenberg (MOHp), Deutscher Wetterdienst [Offenbach] (DWD), Imperial College London, Laboratoire de Météorologie Physique (LaMP), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Tallinn University of Technology (TTÜ), Czech Academy of Sciences [Prague] (CAS), Estonian Environmental Research Center, Tallinn, Estonia, National Institute of Research and Development for Optoelectronics (INOE), University Politehnica of Bucharest [Romania] (UPB), Jozef Stefan Institute [Ljubljana] (IJS), University of Nova Gorica, AGH University of Science and Technology [Krakow, PL] (AGH UST), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Climate and Atmosphere Research Centre (CARE-C), The Cyprus Institute, Nicosia, 2121, Cyprus, Istituto di Scienze dell'Atmosfera e del Clima [Bologna] (ISAC), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Faculty of Physics and Applied Computer Science [Kraków] (FPACS), Aix Marseille Université (AMU), Estonian Environmental Research Centre (EKUK), Consejo Superior de Investigaciones Científicas [Spain] (CSIC), ANR-11-LABX-0005,Cappa,Physiques et Chimie de l'Environnement Atmosphérique(2011), European Project: 689443,H2020,H2020-SC5-2015-one-stage,ERA-PLANET(2016), European Project: 262254,EC:FP7:INFRA,FP7-INFRASTRUCTURES-2010-1,ACTRIS(2011), European Project: 654109,H2020,H2020-INFRAIA-2014-2015,ACTRIS-2(2015), European Project: 638703,H2020,ERC-2014-STG,COALA(2015), Natural Environment Research Council (NERC), European Commission, Ilmatieteen laitos, and Finnish Meteorological Institute

Subjects
Source apportionment, spektrometrit, FOS: Physical sciences, European overview, Harmonised protocol, Organic aerosol, Long-term datasets, Rolling PMF, pienhiukkaset, Organic Aerosol, fine particles, 114 Physical sciences, poltto, polttoturve, fuel peat, kiinteät polttoaineet, combustion (active), atmosphere (earth), Harmonised Protocol, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, General Environmental Science, aerosolit, ilmakehä, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, spectrometers, European Overview, emissions, Long-term Datasets, air quality, Physics - Atmospheric and Oceanic Physics, [SDU]Sciences of the Universe [physics], ilmanlaatu, Atmospheric and Oceanic Physics (physics.ao-ph), [SDE]Environmental Sciences, päästöt, solid fuels, aerosols, Environmental Sciences
Abstract

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models., Environment International, 166, ISSN:0160-4120, ISSN:1873-6750

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