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1. Strengths and Weaknesses of the Current EU Situation

Author

Belis, C., Blond, N., Bouland, C., Carnevale, C., Clappier, A., Douros, J., Fragkou, E., Guariso, G., Miranda, A. I., Nahorski, Z., Pisoni, E., Ponche, J.-L., Thunis, P., Viaene, P., Volta, M., Kacprzyk, Janusz, Series editor, Guariso, Giorgio, editor, and Volta, Marialuisa, editor

Published
2017
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2. Current European AQ Planning at Regional and Local Scale

Author

Belis, C., Baldasano, J., Blond, N., Bouland, C., Buekers, J., Carnevale, C., Cherubini, A., Clappier, A., De Saeger, E., Douros, J., Finzi, G., Fragkou, E., Gama, C., Graff, A., Guariso, G., Janssen, S., Juda-Rezler, K., Karvosenoja, N., Maffeis, G., Martilli, A., Mills, S., Miranda, A. I., Moussiopoulos, N., Nahorski, Z., Pisoni, E., Ponche, J.-L., Rasoloharimahefa, M., Real, E., Reizer, M., Relvas, H., Roncolato, D., Tainio, M., Thunis, P., Viaene, P., Vlachokostas, C., Volta, M., White, L., Kacprzyk, Janusz, Series editor, Guariso, Giorgio, editor, and Volta, Marialuisa, editor

Published
2017
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12. 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

14. Wood burning as source of Benzo(a)pyrene in PM

Author

Belis C., A, Piazzalunga, A, Larsen, B, Vecchi, R, Fermo, P, Colombi, C, Gianelle, V, Belis C. A, Larsen B, Vecchi R, Fermo P, Colombi C, Gianelle V., PIAZZALUNGA, ANDREA, Belis C., A, Piazzalunga, A, Larsen, B, Vecchi, R, Fermo, P, Colombi, C, Gianelle, V, Belis C. A, Larsen B, Vecchi R, Fermo P, Colombi C, Gianelle V., and PIAZZALUNGA, ANDREA

Published
2010

15. Wood burning as source of Benzo(a)pyrene in PM

Author

Belis, C, Piazzalunga, A, Larsen, B, Vecchi, R, Fermo, P, Colombi, C, Gianelle, V, Belis C. A, Larsen B, Vecchi R, Fermo P, Colombi C, Gianelle V., PIAZZALUNGA, ANDREA, Belis, C, Piazzalunga, A, Larsen, B, Vecchi, R, Fermo, P, Colombi, C, Gianelle, V, Belis C. A, Larsen B, Vecchi R, Fermo P, Colombi C, Gianelle V., and PIAZZALUNGA, ANDREA

Published
2010

17. Network di progetti per lo studio del particolato atmosferico nell’area alpina della lombardia

Author

Belis, C, Cazzuli, O, Colombi, C, Fermo, P, Gianelle, V, Giudici, A, Lanzani, G, Larsen, B, Magnani, T, Piazzalunga, A, Vecchi, R, Belis, C. A, Vecchi, R., PIAZZALUNGA, ANDREA, Belis, C, Cazzuli, O, Colombi, C, Fermo, P, Gianelle, V, Giudici, A, Lanzani, G, Larsen, B, Magnani, T, Piazzalunga, A, Vecchi, R, Belis, C. A, Vecchi, R., and PIAZZALUNGA, ANDREA

Published
2008

18. Wood burning as source of Benzo(a)pyrene in PM

Author

Belis C. A, Larsen B, Vecchi R, Fermo P, Colombi C, Gianelle V., PIAZZALUNGA, ANDREA, Belis, C, Piazzalunga, A, Larsen, B, Vecchi, R, Fermo, P, Colombi, C, and Gianelle, V

Subjects
particulate matter, wood burning, Benzo(a)pyrene
Published
2010

19. The contribution of biomass burning to PAH levels in PM10

Author

Belis, C, Larsen, B, Roberta Vecchi, R, Colombi, C, Gianelle, V., PIAZZALUNGA, ANDREA, Belis, C, Larsen, B, Piazzalunga, A, Roberta Vecchi, R, Colombi, C, and Gianelle, V

Subjects
CHIM/12 - CHIMICA DELL'AMBIENTE E DEI BENI CULTURALI, wood burning, PAH, particulate matter
Published
2010

23. ACTRIS ACSM intercomparison - Part 2

Author

University of Helsinki, Department of Physics, Froehlich, R., Crenn, V., Setyan, A., Belis, C. A., Canonaco, F., Favez, O., Riffault, V., Slowik, J. G., Aas, W., Aijala, M., Alastuey, A., Artinano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P. L., Cubison, M. J., Esser-Gietl, J. K., Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Jayne, J. T., Lunder, C. R., Minguillon, M. C., Mocnik, G., O'Dowd, C. D., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Ripoll, A., Sarda-Esteve, R., Wiedensohler, A., Baltensperger, U., Sciare, J., Prevot, A. S. H., University of Helsinki, Department of Physics, Froehlich, R., Crenn, V., Setyan, A., Belis, C. A., Canonaco, F., Favez, O., Riffault, V., Slowik, J. G., Aas, W., Aijala, M., Alastuey, A., Artinano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P. L., Cubison, M. J., Esser-Gietl, J. K., Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Jayne, J. T., Lunder, C. R., Minguillon, M. C., Mocnik, G., O'Dowd, C. D., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Ripoll, A., Sarda-Esteve, R., Wiedensohler, A., Baltensperger, U., Sciare, J., and Prevot, A. S. H.

Abstract

Chemically resolved atmospheric aerosol data sets from the largest intercomparison of the Aerodyne aerosol chemical speciation monitors (ACSMs) performed to date were collected at the French atmospheric supersite SIRTA. In total 13 quadrupole ACSMs (Q-ACSM) from the European ACTRIS ACSM network, one time-of-flight ACSM (ToF-ACSM), and one high-resolution ToF aerosol mass spectrometer (AMS) were operated in parallel for about 3 weeks in November and December similar to 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 intercomparison of organic components and the results from factor analysis source apportionment by positive matrix factorisation (PMF) utilising the multilinear engine 2 (ME-2). Except for the organic contribution of mass-to-charge ratio m/z 44 to the total organics (f(44)), 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 f(44) 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). ME-2 boundary conditions (profile constraints) were optimised individually by means of correlation to external data in order to achieve equivalent / comparable solutions for all ACSM instruments and the results 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 standard deviations (SD) from the mean between 13.7 and 22.7 % of the source's average mass contribution depen

Published
2015

24. ACTRIS ACSM intercomparison - Part 1

Author

University of Helsinki, Department of Physics, Crenn, V., Sciare, J., Croteau, P. L., Verlhac, S., Froehlich, R., Belis, C. A., Aas, W., Äijälä, M., Alastuey, A., Artinano, B., Baisnee, D., Bonnaire, N., Bressi, M., Canagaratna, M., Canonaco, F., Carbone, C., Cavalli, F., Coz, E., Cubison, M. J., Esser-Gietl, J. K., Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Lunder, C., Minguillon, M. C., Mocnik, G., O'Dowd, C. D., Ovadnevaite, J., Petit, J. -E., Petralia, E., Poulain, L., Priestman, M., Riffault, V., Ripoll, A., Sarda-Esteve, R., Slowik, J. G., Setyan, A., Wiedensohler, A., Baltensperger, U., Prevot, A. S. H., Jayne, J. T., Favez, O., University of Helsinki, Department of Physics, Crenn, V., Sciare, J., Croteau, P. L., Verlhac, S., Froehlich, R., Belis, C. A., Aas, W., Äijälä, M., Alastuey, A., Artinano, B., Baisnee, D., Bonnaire, N., Bressi, M., Canagaratna, M., Canonaco, F., Carbone, C., Cavalli, F., Coz, E., Cubison, M. J., Esser-Gietl, J. K., Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Lunder, C., Minguillon, M. C., Mocnik, G., O'Dowd, C. D., Ovadnevaite, J., Petit, J. -E., Petralia, E., Poulain, L., Priestman, M., Riffault, V., Ripoll, A., Sarda-Esteve, R., Slowik, J. G., Setyan, A., Wiedensohler, A., Baltensperger, U., Prevot, A. S. H., Jayne, J. T., and Favez, O.

Abstract

As part of the European ACTRIS project, the first large Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM) 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 Q-ACSM. 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 (r(2) > 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 reco

Published
2015

25. 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

26. 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., primary, Sciare, J., additional, Croteau, P. L., additional, Verlhac, S., additional, Fröhlich, R., additional, Belis, C. A., additional, Aas, W., additional, Äijälä, M., additional, Alastuey, A., additional, Artiñano, B., additional, Baisnée, D., additional, Bonnaire, N., additional, Bressi, M., additional, Canagaratna, M., additional, Canonaco, F., additional, Carbone, C., additional, Cavalli, F., additional, Coz, E., additional, Cubison, M. J., additional, Esser-Gietl, J. K., additional, Green, D. C., additional, Gros, V., additional, Heikkinen, L., additional, Herrmann, H., additional, Lunder, C., additional, Minguillón, M. C., additional, Močnik, G., additional, O'Dowd, C. D., additional, Ovadnevaite, J., additional, Petit, J.-E., additional, Petralia, E., additional, Poulain, L., additional, Priestman, M., additional, Riffault, V., additional, Ripoll, A., additional, Sarda-Estève, R., additional, Slowik, J. G., additional, Setyan, A., additional, Wiedensohler, A., additional, Baltensperger, U., additional, Prévôt, A. S. H., additional, Jayne, J. T., additional, and Favez, O., additional

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

Author

Fröhlich, R., primary, Crenn, V., additional, Setyan, A., additional, Belis, C. A., additional, Canonaco, F., additional, Favez, O., additional, Riffault, V., additional, Slowik, J. G., additional, Aas, W., additional, Aijälä, M., additional, Alastuey, A., additional, Artiñano, B., additional, Bonnaire, N., additional, Bozzetti, C., additional, Bressi, M., additional, Carbone, C., additional, Coz, E., additional, Croteau, P. L., additional, Cubison, M. J., additional, Esser-Gietl, J. K., additional, Green, D. C., additional, Gros, V., additional, Heikkinen, L., additional, Herrmann, H., additional, Jayne, J. T., additional, Lunder, C. R., additional, Minguillón, M. C., additional, Močnik, G., additional, O'Dowd, C. D., additional, Ovadnevaite, J., additional, Petralia, E., additional, Poulain, L., additional, Priestman, M., additional, Ripoll, A., additional, Sarda-Estève, R., additional, Wiedensohler, A., additional, Baltensperger, U., additional, Sciare, J., additional, and Prévôt, A. S. H., additional

Published
2015
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28. Dioxins and dioxin-like pollutants in alpine forests

Author

Offenthaler, I., Moche, W., Futterknecht, P., Schwarzl, B., Thanner, G., Uhl, M., van Ommen, K., Bassan, R., Belis, C., Jakobi, G., Kirchner, M., Kräuchi, N., Schramm, K.-W., Levy-Lopez, W., Sedivy, I., Simoncic, P., and Weiss, P.

Abstract

PCDD/F and PCB concentrations in remote mountainous spruce stands of the Central European Alps show strong geographic variation. Independent of the matrix (0.5 year old needles, humus or mineral soil), the highest pollutant levels were always found at the lateral zones of the mountain range. High levels coincided with strong precipitation, particularly along the northern margin of the study region. The most volatile PCB congener propagated farther into the colder, drier central Alps than the heavier species. Matrices with different accumulation history (needles and humus) repeatedly reflected different spatial immission patterns. Consistent with its much longer exposure, pollutant levels in humus exceeded those of needles by LIP to two orders of magnitude. Needle contamination varied with altitude but the vertical trends were highly variable between transsects and changed between years, too.

Published
2009

29. Monitoring network in the Alpine Region for persistent and other organic pollutants : a multinational approach to investigate the contamination of the Alps with organic compounds

Author

Bassan, R., Jakobi, G., Belis, C., Heublein, D., Iozza, S., Jakl, T., Kirchner, M., Knoth, W., Kräuchi, N., Levy-Lopez, W., Luchetta, A., Magnani, T., Moche, W., Oehme, M., Offenthaler, I., Perthen - Palmisano, B., Schramm, K.-W., Schmidt, D., Schrott, H., Schröder, P., Sedivy, I., Simoncic, P., Uhl, M., Weiss, P., Reiner, E., and Alaee, M.

Subjects
Alpine region, organic pollutants, altitudinal effects, deposition
Published
2005

30. 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

31. 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

32. Sources for PM air pollution in the Po Plain, Italy: I. Critical comparison of methods for estimating biomass burning contributions to benzo(a)pyrene

Author

Belis, C, Cancelinha, J, Duane, M, Forcina, V, Pedroni, V, Passarella, R, Tanet, G, Douglas, K, Piazzalunga, A, Bolzacchini, E, Sangiorgi, G, Perrone, M, Ferrero, L, Fermo, P, Larsen, B, Belis, CA, Larsen, BR, PIAZZALUNGA, ANDREA, BOLZACCHINI, EZIO, SANGIORGI, GIORGIA MAURA LUISA, PERRONE, MARIA GRAZIA, FERRERO, LUCA, Belis, C, Cancelinha, J, Duane, M, Forcina, V, Pedroni, V, Passarella, R, Tanet, G, Douglas, K, Piazzalunga, A, Bolzacchini, E, Sangiorgi, G, Perrone, M, Ferrero, L, Fermo, P, Larsen, B, Belis, CA, Larsen, BR, PIAZZALUNGA, ANDREA, BOLZACCHINI, EZIO, SANGIORGI, GIORGIA MAURA LUISA, PERRONE, MARIA GRAZIA, and FERRERO, LUCA

Published
2011

33. Estimates of wood burning contribution to PM by the macro-tracer method using tailored emission factors

Author

Piazzalunga, A, Belis, C, Bernardoni, V, Cazzuli, O, Fermo, P, Valli, G, Vecchi, R, PIAZZALUNGA, ANDREA, Vecchi, R., Piazzalunga, A, Belis, C, Bernardoni, V, Cazzuli, O, Fermo, P, Valli, G, Vecchi, R, PIAZZALUNGA, ANDREA, and Vecchi, R.

Abstract

In this work, a methodology based on the macro-tracer approach was improved to obtain a more reliable estimate of the wood burning impact on PM10 and OC concentrations. Indeed, literature emission factors were weighed by the wood consumption data available for the investigated region and these tailored factors were used in the calculation. Moreover, an alternative approach using the chemical profile of the wood burning source obtained by the Positive Matrix Factorization was introduced. As far as we know, this is the first time that PMF-derived emission ratios instead of source emission factors are used in the macro-tracer method. A critical comparison of the results obtained by the two approaches was carried out. The results suggest that PMF-derived emission ratios can be a feasible alternative to the widely used wood smoke emission factors, which show a high variability. The proposed methodology was applied to a trial dataset of wintertime PM10 samples - characterised for anhydrosugars, organic, and elemental carbon - collected in the frame of a regional project. The results gave a preliminary estimate of the wood smoke contribution to PM10 and OC in different sites in Lombardy (Northern Italy). © 2011 Elsevier Ltd.

Published
2011

39. Variations in the chemical composition of the submicron aerosol and in the sources of the organic fraction at a regional background site of the Po Valley (Italy).

Author

Bressi, M., Cavalli, F., Belis, C. A., Putaud, J.-P., Fröhlich, R., Martins dos Santos, S., Petralia, E., Prévôt, A. S. H., Berico, M., Malaguti, A., and Canonaco, F.

Abstract

Fine particulate matter (PM) levels and resulting impacts on human health are in the Po Valley (Italy) among the highest in Europe. To build effective PM abatement strategies, it is necessary to characterize fine PM chemical composition, sources and atmospheric processes on long time scales (> months), with short time resolution (< day), and with particular emphasis on the predominant organic fraction. Although previous studies have been conducted in this region, none of them addressed all these aspects together. For the first time in the Po Valley, we investigate the chemical composition of non-refractory submicron PM (NR-PM1) with a time-resolution of 30 minutes at the regional background site of Ispra during one full year, using an Aerosol Chemical Speciation Monitor (ACSM) under the most up-to-date and stringent quality assurance protocol. The identification of the main components of the organic fraction is made using the Multilinear-Engine 2 algorithm implemented within the latest version of the SoFi toolkit. In addition, with a view of a potential implementation of ACSM measurements in European air quality networks as a replacement of traditional filter-based techniques, parallel multiple off-line analyses were carried out to assess the performance of the ACSM in the determination of PM chemical species regulated by Air Quality Directives. The annual NR-PM1 level monitored at the study site (14.2 µg/m³) is among the highest in Europe, and is even comparable to levels reported in urban areas like New York City (USA, 14.2 µg/m³) and Tokyo (Japan, 12-15 µg/m³). On the annual basis, submicron particles are primarily composed of organic aerosol (OA, 58% of NR-PM1). This fraction was apportioned into oxygenated OA (OOA, 66%), hydrocarbon-like OA (HOA, 11% of OA), and biomass burning OA (BBOA, 23%). Among the primary sources of OA, biomass burning (23%) is thus bigger than fossil fuel combustion (11%). Significant contributions of aged secondary organic aerosol (OOA) are observed throughout the year. The unexpectedly high degree of oxygenation estimated during wintertime is probably due to the contribution of secondary BBOA and the enhancement of aqueous phase production of OOA during cold months. BBOA and nitrate are the only components of which contributions increase with the NR-PM1 levels. Therefore, biomass burning and NOx emission reductions would be particularly efficient in limiting submicron aerosol pollution events. Abatement strategies conducted during cold seasons appear to be more efficient than annual-based policies. In a broader context, further studies using high-time resolution analytical techniques on a long-term basis for the characterization of fine aerosol should help better shape our future air quality policies, which constantly need refinement. [ABSTRACT FROM AUTHOR]

Published
2016
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41. Sources of carbonaceous aerosol in the Amazon Basin.

Author

Gilardoni, S., Vignati, E., Marmer, E., Cavalli, F., Belis, C., Gianelle, V., Loureiro, A., and Artaxo, P.

Abstract

The quantification of sources of carbonaceous aerosol is important to understand their atmospheric concentrations and regulating processes and to study possible effects on climate and air quality, in addition to develop mitigation strategies. In the framework of the European Aerosol Cloud Climate Interaction (EUCAARI) project fine (Dp < 2.5µm) and coarse (2.5µm < Dp < 10µm) aerosol particles were sampled from February to June (wet season) and from August to September (dry season) 2008 in the Central Amazon Basin. The mass of fine particles averaged 2.4 µg m-3 during the wet season and 4.2 µg m-3 during the dry season. The average coarse aerosol mass concentration during wet and dry periods was 7.9 and 7.6 µg m-3, respectively. The overall chemical composition of fine and coarse mass did not show any seasonality with the largest fraction of fine and coarse aerosol mass explained by organic carbon (OC); the average OC to mass ratio was 0.4 and 0.6 in fine and coarse aerosol modes, respectively. The mass absorbing cross section of soot was determined by comparison of elemental carbon and light absorption coefficient measurements and it was equal to 4.7 m² g-1 at 637 nm. Carbon aerosol sources were identified by Positive Matrix Factorization (PMF) analysis of thermograms: 43% of fine total carbon mass was assigned to biomass burning, 34% to secondary organic aerosol (SOA), and 23% to volatile species that are difficult to apportion. In the coarse mode, primary biogenic aerosol particles (PBAP) dominated the carbonaceous aerosol mass. The results confirmed the importance of PBAP in forested areas. The source apportionment results were employed to evaluate the ability of global chemistry transport models to simulate carbonaceous aerosol sources in a regional tropical background site. The comparison showed an overestimation of elemental carbon (EC) by the TM5 model during the dry season and OC both during the dry and wet periods. The overestimation was likely due to the overestimation of biomass burning emission inventories and SOA production over tropical areas. [ABSTRACT FROM AUTHOR]

Published
2010
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43. Comparison of source apportionment approaches and analysis of non-linearity in a real case model application

Author

Guido Pirovano, Jean Philippe Putaud, Claudio A. Belis, Nicola Pepe, Giuseppe Calori, Maria Gabriella Villani, Belis, C. A., Pirovano, G., Villani, M. G., Calori, G., Pepe, N., and Putaud, J. P.

Subjects
QE1-996.5, 010504 meteorology & atmospheric sciences, Geology, 010501 environmental sciences, Particulates, Combustion, Atmospheric sciences, 01 natural sciences, CAMX, Aerosol, Reduction (complexity), chemistry.chemical_compound, Nitrate, chemistry, Apportionment, Spatial ecology, Environmental science, 0105 earth and related environmental sciences
Abstract

The response of particulate matter (PM) concentrations to emission reductions was analysed by assessing the results obtained with two different source apportionment approaches. The brute force (BF) method source impacts, computed at various emission reduction levels using two chemical transport models (CAMx and FARM), were compared with the contributions obtained with the tagged species (TS) approach (CAMx with the PSAT module). The study focused on the main sources of secondary inorganic aerosol precursors in the Po Valley (northern Italy): agriculture, road transport, industry and residential combustion. The interaction terms between different sources obtained from a factor decomposition analysis were used as indicators of non-linear PM10 concentration responses to individual source emission reductions. Moreover, such interaction terms were analysed in light of the free ammonia / total nitrate gas ratio to determine the relationships between the chemical regime and the non-linearity at selected sites. The impacts of the different sources were not proportional to the emission reductions, and such non-linearity was most relevant for 100 % emission reduction levels compared with smaller reduction levels (50 % and 20 %). Such differences between emission reduction levels were connected to the extent to which they modify the chemical regime in the base case. Non-linearity was mainly associated with agriculture and the interaction of this source with road transport and, to a lesser extent, with industry. Actually, the mass concentrations of PM10 allocated to agriculture by the TS and BF approaches were significantly different when a 100 % emission reduction was applied. However, in many situations the non-linearity in PM10 annual average source allocation was negligible, and the TS and BF approaches provided comparable results. PM mass concentrations attributed to the same sources by TS and BF were highly comparable in terms of spatial patterns and quantification of the source allocation for industry, transport and residential combustion. The conclusions obtained in this study for PM10 are also applicable to PM2.5.

Published
2021

44. Source apportionment of fine PM by combining high time resolution organic and inorganic chemical composition datasets

Author

Jean Sciare, Michael Pikridas, Giulia Calzolai, M. Berico, Ettore Petralia, Franco Lucarelli, Claudio A. Belis, Fabrizia Cavalli, Belis, C. A., Pikridas, M., Lucarelli, F., Petralia, E., Cavalli, F., Calzolai, G., Berico, M., and Sciare, J.

Subjects
Online and offline, Atmospheric Science, Source apportionment, 010504 meteorology & atmospheric sciences, Ammonium nitrate, Analyser, Mineralogy, lcsh:QC851-999, 010501 environmental sciences, Inorganic ions, 01 natural sciences, chemistry.chemical_compound, On-line measurements, Particulate matter, Po valley, Positive matrix factorization, Receptor models, lcsh:Environmental pollution, On-line measurement, 0105 earth and related environmental sciences, General Environmental Science, Inorganic Chemical, Spectrometer, Aerosol, Chemical species, chemistry, lcsh:TD172-193.5, Environmental science, lcsh:Meteorology. Climatology, Receptor model, Source apportionment Particulate matter Po valley Receptor models Positive matrix factorization On-line measurements
Abstract

The use of high time resolution datasets of aerosol organic and inorganic species as input for receptor models poses a number of challenges. The estimation of uncertainties differ between different analytical methods and the number of chemical species may considerably vary among the different techniques. In this study, an approach to harmonise the uncertainties of different online datasets for their combined use in source apportionment with positive matrix factorization (PMF) is presented. The concentration of inorganic ions, organic fragments and trace elements were measured in a Po Valley background site using offline and online methods. Six-hour PM 2.5 samples were collected on filters and chemical analyses were carried out offline. Parallel hourly online measurements were made using the Xact 625 (CES LLC) XRF analyser and the Q-ACSM (Aerodyne Research Inc.) spectrometer.Online and offline methods produced comparable results for the major chemical component and some trace elements, while others (Ba, Ni, As and Se) showed limited comparability between the two methods. To ensure the consistency of the final PMF results, a multistep approach was adopted. In the first step PMF was run with only the offline dataset, in the second step only the online organic data were used and in a third step the PMF run was executed using only the online inorganic species. In the first three steps running PMF with homogeneous data made it possible to identify the main sources and produce chemical profiles to be used as internal reference for the final fourth step in which all the online species (major inorganic ions, m/z of organic fragments and trace elements) were combined. The sources of the final solution were developed using internally consistent chemical profiles and those from the literature and were validated by analysing the source diurnal variations and by comparison with external tracers. The sources identified were: biomass burning, aged biomass burning, secondary ammonium nitrate and ammonium sulphate, traffic, steel industry and waste thermal treatment. The source profiles with a large set of organic and inorganic species (87) and associated source diurnal variations resulting from this study are expected to serve as reference for future studies. Keywords: Source apportionment, Particulate matter, Po valley, Receptor models, Positive matrix factorization, On-line measurements

Published
2019

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

Author

Constantini Samara, F. Lenartz, A. Di Gilio, C. Colombi, K. Maciejewska, Roberta Vecchi, Guido Pirovano, Kai Krajsek, Evangelia Diapouli, D. Mooibroek, Maria Grazia Perrone, K. Sega, Benjamin Golly, Daniele Contini, Fabiana Scotto, M. Masiol, Marcelo Pinho Almeida, E. Venturini, Giuseppe Calori, H.A.C. Denier van der Gon, Marta G. Vivanco, Daniela Cesari, Claudio A. Belis, Silvia Nava, G. Valli, Franco Lucarelli, Antoine Waked, Paolo Brotto, Véronique Riffault, Mihaela Mircea, Ettore Petralia, Eduardo Yubero, Jean-Luc Besombes, Jeroen Kuenen, M. Manousakas, Guillaume Siour, G. de Gennaro, A. Angyal, Jean-Luc Jaffrezo, Stig Hellebust, Petra Pokorná, M. Reizer, Fulvio Amato, Philip K. Hopke, Laurent Y. Alleman, Konstantinos Eleftheriadis, G. Argyropoulos, S. Bande, Paolo Prati, S. Pillon, Richard Kranenburg, Olivier Favez, Dikaia Saraga, Yahya Izadmanesh, Stefania Gilardoni, I. Beslic, Hendrik Elbern, Astrid Manders, Joana Ferreira, Romà Tauler, Stéphane Sauvage, P. Lazzeri, Mika Vestenius, Héctor Jorquera, D. Pernigotti, Lucyna Samek, Dalia Salameh, Marco Pandolfi, Marco Paglione, I. El Haddad, Martijn Schaap, A. Pietrodangelo, Maria Chiara Bove, D. Oliveira, Amato, Fulvio, Pandolfi, Marco, Tauler, Romà, European Commission - Joint Research Centre [Ispra] (JRC), Ricerca sul Sistema Energetico (RSE), Institut National de l'Environnement Industriel et des Risques (INERIS), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), TNO Climate, Air and Sustainability [Utrecht], The Netherlands Organisation for Applied Scientific Research (TNO), Warsaw University of Technology [Warsaw], Centre for Energy and Environment (CERI EE - IMT Nord Europe), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Nord Europe), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), Institut Mines-Télécom [Paris] (IMT), Centro de Ciencias e Tecnologias Nucleares, Instituto Superior Tecnico, Universidade de Lisboa, Bobadela LRS, Portugal, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute for Nuclear Research [Budapest] (ATOMKI), Hungarian Academy of Sciences (MTA), Environmental Pollution Control Laboratory, University of Thessaloniki, ARPA Piemonte Regional Agency for Environmental Protection, Institute for Medical Research and Occupational Health, Laboratoire LCME / Equipe Chimie de l'Environnement (LCME_CE), Laboratoire de Chimie Moléculaire et Environnement (LCME), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Università degli studi di Genova = University of Genoa (UniGe), ARIANET Srl, CNR Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), ARPA Lombardia, Dipartimento Sub-Provinciale Città di Milano, Department of Biology [University of Bari], Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety (INRASTES), National Center for Scientific Research 'Demokritos' (NCSR), Paul Scherrer Institute (PSI), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Universidade de Aveiro, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas [Madrid] (CIEMAT), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), University College Cork (UCC), Clarkson University, Khajeh Nasir Toosi University of Technology [Téhéran] (KNTU), Pontificia Universidad Católica de Chile (UC), Agenzia Provinciale Protezione Ambiente, Institut scientifique de service public [Liège] (ISSeP), Istituto Nazionale di Fisica Nucleare, Sezione di Firenze (INFN, Sezione di Firenze), Istituto Nazionale di Fisica Nucleare (INFN), Agenzia Nazionale per le nuove Tecnologie, l’energia e lo sviluppo economico sostenibile = Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), National Institute for Public Health and the Environment [Bilthoven] (RIVM), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Institute for Atmospheric Pollution Research, Regional Agency for Environmental Prevention and Protection of the Veneto (ARPAV), Aristotle University of Thessaloniki, Faculty of Physics and Applied Computer Science [Kraków] (FPACS), AGH University of Science and Technology [Krakow, PL] (AGH UST), ARPA Emilia-Romagna, Agenzia Regionale per la Protezione dell’Ambiente, Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Dipartimento di Fisica, Università degli studi di Milano, Istituto Nazionale di Fisica Nucleare (INFN)-Università degli Studi di Milano = University of Milan (UNIMI), University of Bologna/Università di Bologna, Finnish Meteorological Institute (FMI), Miguel Hernández University, French Ministry of Environment, 'Hauts de France' Regional Council, European Regional Development Fund (ERDF), National Research, Development and Innovation Office – NKFIH, contract number PD 125086, Grant CONICYT/FONDAP/15110020, CARA program, UID/Multi/04349/2013 project, ANR-11-LABX-0005,Cappa,Physiques et Chimie de l'Environnement Atmosphérique(2011), ANR-10-LABX-0056,OSUG@2020,Innovative strategies for observing and modelling natural systems(2010), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Centre for Energy and Environment (CERI EE), University of Genoa (UNIGE), Consiglio Nazionale delle Ricerche (CNR), Università degli studi di Bari Aldo Moro (UNIBA), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Istituto Nazionale di Fisica Nucleare (INFN)-Università degli Studi di Milano [Milano] (UNIMI), University of Bologna, Amato, Fulvio [0000-0003-1546-9154], Pandolfi, Marco [0000-0002-7493-7213], Tauler, Romà [0000-0001-8559-9670], 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. G., 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., JRC Institute for Environment and Sustainability (IES), INERIS-Parc Technologique, INERIS, Parc Technologique, ALATA BP 2 60550, Verneuil-en-Halatte, France, Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Département S.A.G.E (SAGE), École des Mines de Douai (Mines Douai EMD), Consejo Superior de Investigaciones Científicas [Spain] (CSIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA), Department of immunology and Infectious Deseases, San Raffaele Scientific Institute, Istituto di Scienze dell'Atmosfera e del Clima (ISAC), Consiglio Nazionale delle Ricerche [Roma] (CNR), Dipartimento di Fisica, ICT Institute of Politecnico di Milano, Institute of Nuclear Technology & Radiation Protection, Rhenish Institute for Environmental Research (RIU), University of Cologne, Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA), University of Rochester Medical Center, Universita degli studi di Napoli 'Parthenope' [Napoli], INTA - Instituto Nacional de Tecnología Agropecuaria, CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Environmental Research Laboratory, National Centre for Scientific Research Demokritos, Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Department of Physics, Universita degli Studi di Milano & National Institute of Nuclear Physics, 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.G., 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.

Subjects
Atmospheric Science, Source apportionment, PM, 010504 meteorology & atmospheric sciences, Mean squared error, High variability, Chemical transport, Urbanisation, lcsh:QC851-999, 010501 environmental sciences, 01 natural sciences, Chemical transport model, models, Lens, Receptor models, PM10, lcsh:Environmental pollution, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Apportionment, Consistency (statistics), Chemical transport models, Intercomparison, 10, Statistics, Range (statistics), Source apportionment, PM10, Receptor models, Chemical transport models, Intercomparison, Lens, Air quality index, ComputingMilieux_MISCELLANEOUS, Settore CHIM/12 - Chimica dell'Ambiente e dei Beni Culturali, 0105 earth and related environmental sciences, General Environmental Science, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Comparability, Len, Settore GEO/08 - Geochimica e Vulcanologia, 13. Climate action, lcsh:TD172-193.5, [SDE]Environmental Sciences, Air quality, Environmental science, Receptor model, lcsh:Meteorology. Climatology, Performance indicator, Environment & Sustainability
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 varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models. © 2019 The Authors, The authors warmly thank J. Vercauteren (VMM) for providing the CHEMKAR dataset. The CARA program was funded by the French Ministry of environment . IMT Lille Douai participates in the CaPPA project funded by the ANR through the PIA under contract ANR-11-LABX-0005-01 , the “Hauts de France” Regional Council and the European Regional Development Fund (ERDF). The C2TN/IST author gratefully acknowledges the FCT support through the UID/Multi/04349/2013 project. J.L. Jaffrezo would like to thank F. Donnaz, F. Masson, and S. Ngo for the chemical analyses of the Lens samples performed at IGE (ECOC, ions, sugars). These were possible on the Air-O-Sol analytical platform supported by Labex OSUG@2020 (ANR10 LABX56). A. Angyal was supported by National Research, Development and Innovation Office – NKFIH , contract number PD 125086 . H. Jorquera acknowledges support from Grant CONICYT/FONDAP/15110020. P . Thunis commented on an early version of the manuscript.

Published
2020
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46. 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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47. Estimates of wood burning contribution to PM by the macro-tracer method using tailored emission factors

Author

Roberta Vecchi, O Cazzuli, Claudio A. Belis, Gianluigi Valli, Andrea Piazzalunga, Paola Fermo, V. Bernardoni, Piazzalunga, A, Belis, C, Bernardoni, V, Cazzuli, O, Fermo, P, Valli, G, and Vecchi, R

Subjects
Atmospheric Science, Levoglucosan, Environmental engineering, Wood smoke, Particulates, Atmospheric sciences, Northern italy, chemistry.chemical_compound, chemistry, TRACER, Environmental science, Wood burning, particulate matter, wood burning, levoglucosan, emission ratio, macro tracer, PMF, positive matrix factorization, Macro, Elemental carbon, General Environmental Science
Abstract

In this work, a methodology based on the macro-tracer approach was improved to obtain a more reliable estimate of the wood burning impact on PM10 and OC concentrations. Indeed, literature emission factors were weighed by the wood consumption data available for the investigated region and these tailored factors were used in the calculation. Moreover, an alternative approach using the chemical profile of the wood burning source obtained by the Positive Matrix Factorization was introduced. As far as we know, this is the first time that PMF-derived emission ratios instead of source emission factors are used in the macro-tracer method. A critical comparison of the results obtained by the two approaches was carried out. The results suggest that PMF-derived emission ratios can be a feasible alternative to the widely used wood smoke emission factors, which show a high variability. The proposed methodology was applied to a trial dataset of wintertime PM10 samples - characterised for anhydrosugars, organic, and elemental carbon e collected in the frame of a regional project. The results gave a preliminary estimate of the wood smoke contribution to PM10 and OC in different sites in Lombardy (Northern Italy)., JRC.H.2-Climate change and air quality

Published
2011

48. Modelling the air quality benefits of EU climate mitigation policies using two different PM2.5-related health impact methodologies.

Author

Pisoni E, Thunis P, De Meij A, Wilson J, Bessagnet B, Crippa M, Guizzardi D, Belis CA, and Van Dingenen R

Subjects
Particulate Matter analysis, Climate Change, Policy, Air Pollution analysis, Air Pollutants analysis, Greenhouse Gases analysis
Abstract

The EU, seeking to be a global leader in the fight against climate change, is moving ahead with ambitious policies to mitigate greenhouse gases emissions. In this context, the Fit for 55 package (FF55) is a set of proposals to revise and update EU legislation, to ensure that policies are in line with the climate goals of cutting emissions by at least 55% by 2030. Whilst these policies are designed for climate purposes, they will have positive side-effects (co-benefits) on air quality. Separately, additional policies are also in place to reduce emissions of related air pollutants and to improve air quality concentrations on EU territory. In this work, through a modelling study, we analyse the benefits of these policies via the health benefits arising from the resulting reductions in yearly average PM2.5 concentrations. Results are analysed by assessing and comparing morbidity and mortality impacts as computed using both the HRAPIE (Health risks of air pollution in Europe, WHO, as implemented in the CaRBonH model) and the GBD (Global Burden of Disease, as implemented in FASST-GBD model) approaches. Even when considering the uncertainty and variability in the results obtained using the two approaches, it is clear that EU policies can bring health and economic benefit in EU, with several Billions of Euro of benefits both in terms of morbidity and mortality indicators., 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 © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Published
2023
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49. Quantitative assessment of the variability in chemical profiles from source apportionment analysis of PM10 and PM2.5 at different sites within a large metropolitan area.

Author

Manousakas M, Diapouli E, Belis CΑ, Vasilatou V, Gini M, Lucarelli F, Querol X, and Eleftheriadis K

Subjects
Dust analysis, Environmental Monitoring, Nitrates, Particulate Matter analysis, Vehicle Emissions analysis, Air Pollutants analysis
Abstract

The study aims to assess the differences between the chemical profiles of the major anthropogenic and natural PM sources in two areas with different levels of urbanization and traffic density within the same urban agglomeration. A traffic site and an urban background site in the Athens Metropolitan Area have been selected for this comparison. For both sites, eight sources were identified, with seven of them being common for the two sites (Mineral Dust, non-Exhaust Emissions, Exhaust Emissions, Heavy Oil Combustion, Sulfates & Organics, Sea Salt and Biomass Burning) and one, site-specific (Nitrates for the traffic site and Aged Sea Salt for the urban background site). The similarity between the source profiles was quantified using two statistical analysis tools, Pearson correlation (PC) and Standardized Identity Distance (SID). According to Pearson coefficients five out of the eight source profiles present high (PC > 0.8) correlation (Mineral Dust, Biomass Burning, Sea Salt, Sulfates and Heavy Oil Combustion), one presented moderate (0.8 > PC > 0.6) correlation (Exhaust) and two low/no (PC < 0.6) correlation (non-Exhaust, Nitrates/Aged Sea Salt). The source profiles that appear to be more correlated are those of sources that are not expected to have high spatial variability because there are either natural/secondary and thus have a regional character or are emitted outside the urban agglomeration and are transported to both sites. According to SID four out of the eight sources have high statistical correlation (SID < 1) in the two sites (Mineral Dust, Sea salt, Sulfates, Heavy Oil Combustion). Biomass Burning was found to be the source that yielded different results from the two methodologies. The careful examination of the source profile of that source revealed the reason for this discrepancy. SID takes all the species of the profile equally into account, while PC might be disproportionally affected by a few numbers of species with very high concentrations. It is suggested, based on the findings of this work, that the combined use of both tools can lead the users to a thorough evaluation of the similarity of source profiles. This work is, to the best of our knowledge, the first time a study is focused on the quantitative comparison of the source profiles for sites inside the same urban agglomeration using statistical indicators., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Published
2021
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50. Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas.

Author

Almeida SM, Manousakas M, Diapouli E, Kertesz Z, Samek L, Hristova E, Šega K, Alvarez RP, Belis CA, and Eleftheriadis K

Subjects
Asia, Cities, Environmental Monitoring, Europe, Eastern, Seasons, Vehicle Emissions analysis, Air Pollutants analysis, Particulate Matter analysis
Abstract

This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and "other sources". From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to "other sources" and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates., 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 © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Published
2020
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