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3 results on '"Belis, C"'

1. ACTRIS ACSM intercomparison - Part 1: Reproducibility of concentration and fragment results from 13 individual Quadrupole Aerosol Chemical Speciation Monitors (Q-ACSM) and consistency with co-located instruments.

Author

Crenn, V., Sciare, J., Croteau, P. L., Verlhac, S., Fröhlich, R., Belis, C. A., Aas, W., Äijälä, M., Alastuey, A., Artiñano, B., Baisnée, D., Bonnaire, N., Bressi, M., Canagaratna, M., Canonaco, F., Carbone, C., Cavalli, F., Coz, E., Cubison, M. J., and Esser-Gietl, J. K.

Subjects
ATMOSPHERIC aerosols, AEROSOLS
Abstract

As part of the European ACTRIS project, the first large Quadrupole Aerosol Chemical Speciation Monitor (QACSM) intercomparison study was conducted in the region of Paris for 3 weeks during the late-fall - early-winter period (November-December 2013). The first week was dedicated to the tuning and calibration of each instrument, whereas the second and third were dedicated to side-by-side comparison in ambient conditions with co-located instruments providing independent information on submicron aerosol optical, physical, and chemical properties. Near real-time measurements of the major chemical species (organic matter, sulfate, nitrate, ammonium, and chloride) in the non-refractory submicron aerosols (NR-PM1/ were obtained here from 13 QACSM. The results show that these instruments can produce highly comparable and robust measurements of the NR-PM1 total mass and its major components. Taking the median of the 13 Q-ACSM as a reference for this study, strong correlations (r² > 0:9) were observed systematically for each individual Q-ACSM across all chemical families except for chloride for which three Q-ACSMs showing weak correlations partly due to the very low concentrations during the study. Reproducibility expanded uncertainties of Q-ACSM concentration measurements were determined using appropriate methodologies defined by the International Standard Organization (ISO 17025, 1999) and were found to be 9, 15, 19, 28, and 36% for NR-PM1, nitrate, organic matter, sulfate, and ammonium, respectively. However, discrepancies were observed in the relative concentrations of the constituent mass fragments for each chemical component. In particular, significant differences were observed for the organic fragment at mass-to-charge ratio 44, which is a key parameter describing the oxidation state of organic aerosol. Following this first major intercomparison exercise of a large number of Q-ACSMs, detailed intercomparison results are presented, along with a discussion of some recommendations about best calibration practices, standardized data processing, and data treatment. [ABSTRACT FROM AUTHOR]

Published
2015
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2. ACTRIS ACSM intercomparison - Part 2: Intercomparison of ME-2 organic source apportionment results from 15 individual, co-located aerosol mass spectrometers.

Author

Fröhlich, R., Crenn, V., Setyan, A., Belis, C. A., Canonaco, F., Favez, O., Riffault, V., Slowik, J. G., Aas, W., Aijälä, M., Alastuey, A., Artiñano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P. L., Cubison, M. J., and Esser-Gietl, J. K.

Subjects
ATMOSPHERIC aerosols, MASS spectrometers, HYDROCARBONS, AEROSOLS, AIR pollutants
Abstract

Chemically resolved atmospheric aerosol data sets from the largest inter-comparison of the Aerodyne aerosol chemical speciation monitors (ACSM) performed to date were collected at the French atmospheric super-site SIRTA. In total 13 quadrupole ACSMs (Q-ACSM) from the European ACTRIS ACSM network, one time-of-flight ACSM (ToFACSM), and one high-resolution ToF aerosol mass spectrometer (AMS) were operated in parallel for about three weeks in November and December 2013. Part 1 of this study reports on the accuracy and precision of the instruments for all the measured species. In this work we report on the inter-comparison of organic components and the results from factor analysis source apportionment by positive matrix factorisation (PMF) utilising the multi-linear engine 2 (ME-2). Except for the organic contribution of m=z 44 to the total organics (f44), which varied by factors between 0.6 and 1.3 compared to the mean, the peaks in the organic mass spectra were similar among instruments. The m=z 44 differences in the spectra resulted in a variable f44 in the source profiles extracted by ME-2, but had only a minor influence on the extracted mass contributions of the sources. The presented source apportionment yielded four factors for all 15 instruments: hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA), biomass burning-related organic aerosol (BBOA) and secondary oxygenated organic aerosol (OOA). Individual application and optimisation of the ME-2 boundary conditions (profile constraints) are discussed together with the investigation of the influence of alternative anchors (reference profiles). A comparison of the ME-2 source apportionment output of all 15 instruments resulted in relative SD from the mean between 13.7 and 22.7% of the source's average mass contribution depending on the factors (HOA: 14.3±2.2 %, COA: 15.0±3.4 %, OOA: 41.5±5.7 %, BBOA: 29.3±5.0 %). Factors which tend to be subject to minor factor mixing (in this case COA) have higher relative uncertainties than factors which are recognised more readily like the OOA. Averaged over all factors and instruments the relative first SD from the mean of a source extracted with ME-2 was 17.2 %. [ABSTRACT FROM AUTHOR]

Published
2015
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3. A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

Author

E. Cuccia, Jean-Eudes Petit, Marco Pandolfi, Franco Lucarelli, Xavier Querol, Matthias Karl, D. Mooibroek, Marcelo Pinho Almeida, Eduardo Yubero, Pentti Paatero, Jakob Klenø Nøjgaard, Olivier Favez, Silvia Nava, Daniele Contini, Konstantinos Eleftheriadis, Evangelia Diapouli, Gianluigi Valli, A. Pietrodangelo, Paulo Artaxo, V. Bernardoni, Claudio A. Belis, Maria Chiara Bove, André S. H. Prévôt, Paolo Prati, Federico Karagulian, Philip K. Hopke, Daniela Cesari, Jan Hovorka, I. El Haddad, Petra Pokorná, Fulvio Amato, David C. S. Beddows, Héctor Jorquera, Dikaia Saraga, Athanasios Sfetsos, Maria Grazia Perrone, Roy M. Harrison, Mika Vestenius, Eunhwa Jang, Samara Carbone, Ulrich Quass, Roberta Vecchi, T. Kammermeier, Jean Sciare, Stig Hellebust, JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Institute for Physics, National Centre for Atmospheric Science, University of Birmingham [Birmingham], Department of Physics, Università degli Studi di Milano = University of Milan (UNIMI), Departement of Experimental Medicine, Section of Human Physiology, Università degli studi di Genova = University of Genoa (UniGe), Istituto di Scienze dell'Atmosfera e del Clima (ISAC), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Dipartimento di Fisica, ICT Institute of Politecnico di Milano, Institute of Nuclear Technology & Radiation Protection, National Center for Scientific Research 'Demokritos' (NCSR), Institut de recherches sur la catalyse et l'environnement de Lyon (IRCELYON), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Chimie de l'environnement (LCE), Aix Marseille Université (AMU)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Tasmanian Institute of Agriculture (TIA), University of Tasmania [Hobart, Australia] (UTAS), Norwegian Institute for Air Research (NILU), Università degli Studi di Napoli 'Parthenope' = University of Naples (PARTHENOPE), INTA - Instituto Nacional de Tecnología Agropecuaria, Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Environmental Research Laboratory, National Centre for Scientific Research Demokritos, 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), Chimie Atmosphérique Expérimentale (CAE), 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)-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), University of Rochester Medical Center, Universita degli Studi di Milano & National Institute of Nuclear Physics, University of Genoa (UNIGE), Consiglio Nazionale delle Ricerche [Roma] (CNR), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Universita degli studi di Napoli 'Parthenope' [Napoli], Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Belis, C, Karagulian, F, Amato, F, Almeida, M, Artaxo, P, Beddows, D, Bernardoni, V, Bove, M, Carbone, S, Cesari, D, Contini, D, Cuccia, E, Diapouli, E, Eleftheriadis, K, Favez, O, El Haddad, I, Harrison, R, Hellebust, S, Hovorka, J, Jang, E, Jorquera, H, Kammermeier, T, Karl, M, Lucarelli, F, Mooibroek, D, Nava, S, Nøjgaard, J, Paatero, P, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Pokorná, P, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, and Hopke, P

Subjects
Atmospheric Science, Source apportionment, 010504 meteorology & atmospheric sciences, Meteorology, AIR-QUALITY, Intercomparison exercise, Source apportionment Receptor models Intercomparison exercise Model performance indicators Model uncertainty Particulate matter, 010501 environmental sciences, 114 Physical sciences, 01 natural sciences, complex mixtures, Receptor models, POLLUTION, AEROSOLS, Time windows, Apportionment, Environmental Science(all), Statistics, Air quality management, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Air quality index, 1172 Environmental sciences, 0105 earth and related environmental sciences, General Environmental Science, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, PM SOURCE APPORTIONMENT, Time trends, AREA, respiratory system, Objective quality, Model performance indicator, CHIM/12 - CHIMICA DELL'AMBIENTE E DEI BENI CULTURALI, 13. Climate action, Model uncertainty, Model performance indicators, Environmental science, Receptor model, Standard uncertainty, Particulate matter
Abstract

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Bells et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management. (C) 2015 The Authors. Published by Elsevier Ltd.

Published
2015
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