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2. Evaluation of receptor and chemical transport models for PM10 source apportionment

Author

Belis, C.A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J.L., Kuenen, J., Denier van Der Gon, H., Reizer, M., Riffault, V., Alleman, L.Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I., Besombes, J.-L., Bove, M.C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I., Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M. Garcia, Gilardoni, S., Golly, B., Hellebust, S., Hopke, P.K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A., and Yubero, E.

Published
2020
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4. A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

Author

Belis, C.A., Karagulian, F., Amato, F., Almeida, M., Artaxo, P., Beddows, D.C.S., Bernardoni, V., Bove, M.C., Carbone, S., Cesari, D., Contini, D., Cuccia, E., Diapouli, E., Eleftheriadis, K., Favez, O., El Haddad, I., Harrison, R.M., Hellebust, S., Hovorka, J., Jang, E., Jorquera, H., Kammermeier, T., Karl, M., Lucarelli, F., Mooibroek, D., Nava, S., Nøjgaard, J.K., Paatero, P., Pandolfi, M., Perrone, M.G., Petit, J.E., Pietrodangelo, A., Pokorná, P., Prati, P., Prevot, A.S.H., Quass, U., Querol, X., Saraga, D., Sciare, J., Sfetsos, A., Valli, G., Vecchi, R., Vestenius, M., Yubero, E., and Hopke, P.K.

Published
2015
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5. Evaluation of receptor and chemical transport models for PM10 source apportionment

Author

Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J. L., Kuenen, J., van Der Gon, H. Denier, Reizer, M., Riffault, V, Alleman, L. Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I, Besombes, J-L, Bove, M. C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I, Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M. Garcia, Gilardoni, S., Golly, B., Hellebust, S., Hopke, P. K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A., Yubero, E., Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J. L., Kuenen, J., van Der Gon, H. Denier, Reizer, M., Riffault, V, Alleman, L. Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I, Besombes, J-L, Bove, M. C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I, Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M. Garcia, Gilardoni, S., Golly, B., Hellebust, S., Hopke, P. K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A., and Yubero, E.

Abstract

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs va

Published
2020

6. European intercomparison for Receptor Models Using a Synthetic Database

Author

Belis, CA, Karagulian, F, Amato, F, Almeida, M, Argyropoulos, G, Artaxo, P, Bove, MC, Cesari, D, Contini, D, Diapouli, E, Eleftheriadis, K, El Haddad, I, Harrison, RM, Hellebust, S, Jang, E, Jorquera, H, Mooibroek, D, Nava, S, Nøjgaard, JK, Pandolfi, M, Pietrodangelo, A, Pirovano, G, Pokorná, P, Prati, P, Samara, S, Saraga, D, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, Hopke, PK, PERRONE, MARIA GRAZIA, Belis, C, Karagulian, F, Amato, F, Almeida, M, Argyropoulos, G, Artaxo, P, Bove, M, Cesari, D, Contini, D, Diapouli, E, Eleftheriadis, K, El Haddad, I, Harrison, R, Hellebust, S, Jang, E, Jorquera, H, Mooibroek, D, Nava, S, Nøjgaard, J, Pandolfi, M, Perrone, M, Pietrodangelo, A, Pirovano, G, Pokorná, P, Prati, P, Samara, S, Saraga, D, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, and Hopke, P

Subjects
intercomparison, source apportionment, receptor models, PM, CHIM/12 - CHIMICA DELL'AMBIENTE E DEI BENI CULTURALI
Published
2013

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

Author

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, Hopke, P, Belis, CA, Beddows, D. C. S, Bove, MC, Harrison, RM, Nøjgaard, J. K, PERRONE, MARIA GRAZIA, Petit, JE, Prevot, ASH, Hopke, PK, 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, Hopke, P, Belis, CA, Beddows, D. C. S, Bove, MC, Harrison, RM, Nøjgaard, J. K, PERRONE, MARIA GRAZIA, Petit, JE, Prevot, ASH, and Hopke, PK

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 Belis 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.

Published
2015

10. European intercomparison for Receptor Models Using a Synthetic Database

Author

Belis, C, Karagulian, F, Amato, F, Almeida, M, Argyropoulos, G, Artaxo, P, Bove, M, Cesari, D, Contini, D, Diapouli, E, Eleftheriadis, K, El Haddad, I, Harrison, R, Hellebust, S, Jang, E, Jorquera, H, Mooibroek, D, Nava, S, Nøjgaard, J, Pandolfi, M, Perrone, M, Pietrodangelo, A, Pirovano, G, Pokorná, P, Prati, P, Samara, S, Saraga, D, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, Hopke, P, Belis, CA, Bove, MC, Harrison, RM, Nøjgaard, JK, Hopke, PK, PERRONE, MARIA GRAZIA, Belis, C, Karagulian, F, Amato, F, Almeida, M, Argyropoulos, G, Artaxo, P, Bove, M, Cesari, D, Contini, D, Diapouli, E, Eleftheriadis, K, El Haddad, I, Harrison, R, Hellebust, S, Jang, E, Jorquera, H, Mooibroek, D, Nava, S, Nøjgaard, J, Pandolfi, M, Perrone, M, Pietrodangelo, A, Pirovano, G, Pokorná, P, Prati, P, Samara, S, Saraga, D, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, Hopke, P, Belis, CA, Bove, MC, Harrison, RM, Nøjgaard, JK, Hopke, PK, and PERRONE, MARIA GRAZIA

Published
2013

11. Long-term volatility measurements of submicron atmospheric aerosol in Hyytiala, Finland

Author

Hakkinen, S. A. K., Aijala, M., Lehtipalo, K., Junninen, H., Backman, J., Virkkula, A., Nieminen, T., Vestenius, M., Hakola, H., Ehn, M., Worsnop, D. R., Kulmala, M., Petaja, T., Riipinen, Ilona, Hakkinen, S. A. K., Aijala, M., Lehtipalo, K., Junninen, H., Backman, J., Virkkula, A., Nieminen, T., Vestenius, M., Hakola, H., Ehn, M., Worsnop, D. R., Kulmala, M., Petaja, T., and Riipinen, Ilona

Abstract

The volatility of submicron atmospheric aerosol particles was investigated at a boreal forest site in Hyytiala, Finland from January 2008 to May 2010. These long-term observations allowed for studying the seasonal behavior of aerosol evaporation with a special focus on compounds that remained in the aerosol phase at 280 degrees C. The temperature-response of evaporation was also studied by heating the aerosol sample step-wise to six temperatures ranging from 80 degrees C to 280 degrees C. The mass fraction remaining after heating (MFR) was determined from the measured particle number size distributions before and after heating assuming a constant particle density (1.6 g cm(-3)). On average 19% of the total aerosol mass remained in the particulate phase at 280 degrees C. The particles evaporated less at low ambient temperatures during winter as compared with the warmer months. Black carbon (BC) fraction of aerosol mass correlated positively with the MFR at 280 degrees C, but could not explain it completely: most of the time a notable fraction of this nonvolatile residual was something other than BC. Using additional information on ambient meteorological conditions and results from an Aerodyne aerosol mass spectrometer (AMS), the chemical composition of MFR at 280 degrees C and its seasonal behavior was further examined. Correlation analysis with ambient temperature and mass fractions of polycyclic aromatic hydrocarbons (PAHs) indicated that MFR at 280 degrees C is probably affected by anthropogenic emissions. On the other hand, results from the AMS analysis suggested that there may be very low-volatile organics, possibly organonitrates, in the non-volatile (at 280 degrees C) fraction of aerosol mass., AuthorCount:14

Published
2012
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12. European intercomparison for Receptor Models: Preliminary Results

Author

Karagulian, F, Belis, C, Amato, F, Beddows, D, Bernardoni, V, Carbone, S, Cesari, D, Cuccia, E, Contini, D, Favez, O, El Haddad, I, Harrison, R, Kammermeier, T, Karl, M, Lucarelli, F, Nava, S, Nojgaard, J, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Schuer, J, Turner, J, Paatero, P, Hopke, P, Belis, CA, Beddows, DCS, Harrison, RM, Nojgaard, JK, Petit, JE, Prevot, AH, Schuer, JJ, Turner, JR, Hopke, PK, PERRONE, MARIA GRAZIA, Karagulian, F, Belis, C, Amato, F, Beddows, D, Bernardoni, V, Carbone, S, Cesari, D, Cuccia, E, Contini, D, Favez, O, El Haddad, I, Harrison, R, Kammermeier, T, Karl, M, Lucarelli, F, Nava, S, Nojgaard, J, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Schuer, J, Turner, J, Paatero, P, Hopke, P, Belis, CA, Beddows, DCS, Harrison, RM, Nojgaard, JK, Petit, JE, Prevot, AH, Schuer, JJ, Turner, JR, Hopke, PK, and PERRONE, MARIA GRAZIA

Published
2012

14. Long-term volatility measurements of submicron atmospheric aerosol in Hyytiälä, Finland

Author

Häkkinen, S. A. K., primary, Äijälä, M., additional, Lehtipalo, K., additional, Junninen, H., additional, Backman, J., additional, Virkkula, A., additional, Nieminen, T., additional, Vestenius, M., additional, Hakola, H., additional, Ehn, M., additional, Worsnop, D. R., additional, Kulmala, M., additional, Petäjä, T., additional, and Riipinen, I., additional

Published
2012
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19. 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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20. Airborne 210 Pb, Si, Zn and Pb as tracers for atmospheric pollution in Helsinki metropolitan area.

Author

Ioannidou E, Papagiannis S, Manousakas MI, Vestenius M, Eleftheriadis K, Paatero J, Papadopoulou L, and Ioannidou A

Abstract

This study analyzed 16070 daily and 608 weekly air filter samples from the Helsinki metropolitan area collected between 1962 and 2005. The aim was to use the Potential Source Contribution Function (PSCF) to determine potential sources of silicon (Si), zinc (Zn), lead (Pb), and radioactive isotope 210 Pb. The main sources for Si and Pb were industrial activities, particularly mining, metal industry, and traffic. Common source areas for Zn and 210 Pb were identified in the eastern and southeastern parts of the measuring site., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Published
2024
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21. Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols.

Author

Moschos V, Dzepina K, Bhattu D, Lamkaddam H, Casotto R, Daellenbach KR, Canonaco F, Rai P, Aas W, Becagli S, Calzolai G, Eleftheriadis K, Moffett CE, Schnelle-Kreis J, Severi M, Sharma S, Skov H, Vestenius M, Zhang W, Hakola H, Hellén H, Huang L, Jaffrezo JL, Massling A, Nøjgaard JK, Petäjä T, Popovicheva O, Sheesley RJ, Traversi R, Yttri KE, Schmale J, Prévôt ASH, Baltensperger U, and El Haddad I

Abstract

Aerosols play an important yet uncertain role in modulating the radiation balance of the sensitive Arctic atmosphere. Organic aerosol is one of the most abundant, yet least understood, fractions of the Arctic aerosol mass. Here we use data from eight observatories that represent the entire Arctic to reveal the annual cycles in anthropogenic and biogenic sources of organic aerosol. We show that during winter, the organic aerosol in the Arctic is dominated by anthropogenic emissions, mainly from Eurasia, which consist of both direct combustion emissions and long-range transported, aged pollution. In summer, the decreasing anthropogenic pollution is replaced by natural emissions. These include marine secondary, biogenic secondary and primary biological emissions, which have the potential to be important to Arctic climate by modifying the cloud condensation nuclei properties and acting as ice-nucleating particles. Their source strength or atmospheric processing is sensitive to nutrient availability, solar radiation, temperature and snow cover. Our results provide a comprehensive understanding of the current pan-Arctic organic aerosol, which can be used to support modelling efforts that aim to quantify the climate impacts of emissions in this sensitive region., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2022.)

Published
2022
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