Evaluation of receptor and chemical transport models for PM10 source apportionment

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

doi:10.1016/j.aeaoa.2019.100053

Evaluation of receptor and chemical transport models for PM₁₀ source apportionment

C. A. Belis
, D. Pernigotti
, G. Pirovano
, O. Favez
, J. L. Jaffrezo
, J. Kuenen
, H. Denier van Der Gon
, M. Reizer
, V. Riffault
, L. Y. Alleman
, M. Almeida
, F. Amato
, A. Angyal
, G. Argyropoulos
, S. Bande
, I. Beslic
, J. L. Besombes
, M. C. Bove
, P. Brotto
, G. Calori

D. Cesari, C. Colombi, D. Contini, G. De Gennaro, A. Di Gilio, E. Diapouli, I. El Haddad, H. Elbern, K. Eleftheriadis, J. Ferreira, M. Garcia Vivanco, S. Gilardoni, B. Golly, S. Hellebust, P. K. Hopke, Y. Izadmanesh, H. Jorquera, K. Krajsek, R. Kranenburg, P. Lazzeri, F. Lenartz, F. Lucarelli, K. Maciejewska, A. Manders, M. Manousakas, M. Masiol, M. Mircea, D. Mooibroek, S. Nava, D. Oliveira, M. Paglione, M. Pandolfi, M. Perrone, E. Petralia, A. Pietrodangelo, S. Pillon, P. Pokorna, P. Prati, D. Salameh, C. Samara, L. Samek, D. Saraga, S. Sauvage, M. Schaap, F. Scotto, K. Sega, G. Siour, R. Tauler, G. Valli, R. Vecchi, E. Venturini, M. Vestenius, A. Waked, E. Yubero

School of Chemistry

European Commission Joint Research Centre
Ricerca Sul Sistema Energetico S.p.A.
Institut national de l'environnement industriel et des risques
CNRS
Netherlands Organisation for Applied Scientific Research
Warsaw University of Technology
Université de Lille
University of Lisbon
Institute for Environmental Assessment and Water Research (IDAEA-CSIC)
Institute for Nuclear Research
Aristotle University of Thessaloniki
ARPA Piemonte
Institute for Medical Research and Occupational Health
Université Savoie Mont Blanc
University of Genoa
Arianet S.r.l.
Division of Lecce
Centro Regionale per il Monitoraggio della Qualità dell'Aria
University of Bari
Demokritos National Centre for Scientific Research
Paul Scherrer Institute
University of Cologne
University of Aveiro
CIEMAT
National Research Council of Italy
Clarkson University
K.N. Toosi University of Technology
Pontificia Universidad Católica de Chile
Agenzia Provinciale Protezione Ambiente
ISSeP
National Institute for Nuclear Physics
Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile
National Institute of Public Health and the Environment
University of Milan - Bicocca
Regional Air Observatory Environmental Protection Agency of Veneto Region
AGH University of Krakow
Regional Agency for Prevention
Institut Paul Simon Laplace (IPSL)
University of Bologna
Finnish Meteorological Institute
Miguel Hernández University

Research output: Contribution to journal › Article › peer-review

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 (RMSE_u), 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 RMSE_u 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 RMSE_u 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 RMSE_u 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 RMSE_u 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.

Original language	English
Article number	100053
Journal	Atmospheric Environment: X
Volume	5
DOIs	https://doi.org/10.1016/j.aeaoa.2019.100053
Publication status	Published - Jan 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Chemical transport models
Intercomparison
Lens
PM
Receptor models
Source apportionment

Access to Document

10.1016/j.aeaoa.2019.100053

Cite this

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., ... Yubero, E. (2020). Evaluation of receptor and chemical transport models for PM₁₀ source apportionment. Atmospheric Environment: X, 5, Article 100053. https://doi.org/10.1016/j.aeaoa.2019.100053

@article{4403814d451844a2abeb2fbc82d1b38b,

title = "Evaluation of receptor and chemical transport models for PM10 source apportionment",

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.",

keywords = "Chemical transport models, Intercomparison, Lens, PM, Receptor models, Source apportionment",

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

note = "Publisher Copyright: {\textcopyright} 2019 The Authors",

year = "2020",

month = jan,

doi = "10.1016/j.aeaoa.2019.100053",

language = "English",

volume = "5",

journal = "Atmospheric Environment: X",

issn = "2590-1621",

publisher = "Elsevier Ltd",

}

Belis, CA, Pernigotti, D, Pirovano, G, Favez, O, Jaffrezo, JL, Kuenen, J, Denier van Der Gon, H, Reizer, M, Riffault, V, Alleman, LY, Almeida, M, Amato, F, Angyal, A, Argyropoulos, G, Bande, S, Beslic, I, Besombes, JL, Bove, MC, 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, MG, Gilardoni, S, Golly, B, Hellebust, S, Hopke, PK, 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 2020, 'Evaluation of receptor and chemical transport models for PM₁₀ source apportionment', Atmospheric Environment: X, vol. 5, 100053. https://doi.org/10.1016/j.aeaoa.2019.100053

TY - JOUR

T1 - Evaluation of receptor and chemical transport models for PM10 source apportionment

AU - Belis, C. A.

AU - Pernigotti, D.

AU - Pirovano, G.

AU - Favez, O.

AU - Jaffrezo, J. L.

AU - Kuenen, J.

AU - Denier van Der Gon, H.

AU - Reizer, M.

AU - Riffault, V.

AU - Alleman, L. Y.

AU - Almeida, M.

AU - Amato, F.

AU - Angyal, A.

AU - Argyropoulos, G.

AU - Bande, S.

AU - Beslic, I.

AU - Besombes, J. L.

AU - Bove, M. C.

AU - Brotto, P.

AU - Calori, G.

AU - Cesari, D.

AU - Colombi, C.

AU - Contini, D.

AU - De Gennaro, G.

AU - Di Gilio, A.

AU - Diapouli, E.

AU - El Haddad, I.

AU - Elbern, H.

AU - Eleftheriadis, K.

AU - Ferreira, J.

AU - Vivanco, M. Garcia

AU - Gilardoni, S.

AU - Golly, B.

AU - Hellebust, S.

AU - Hopke, P. K.

AU - Izadmanesh, Y.

AU - Jorquera, H.

AU - Krajsek, K.

AU - Kranenburg, R.

AU - Lazzeri, P.

AU - Lenartz, F.

AU - Lucarelli, F.

AU - Maciejewska, K.

AU - Manders, A.

AU - Manousakas, M.

AU - Masiol, M.

AU - Mircea, M.

AU - Mooibroek, D.

AU - Nava, S.

AU - Oliveira, D.

AU - Paglione, M.

AU - Pandolfi, M.

AU - Perrone, M.

AU - Petralia, E.

AU - Pietrodangelo, A.

AU - Pillon, S.

AU - Pokorna, P.

AU - Prati, P.

AU - Salameh, D.

AU - Samara, C.

AU - Samek, L.

AU - Saraga, D.

AU - Sauvage, S.

AU - Schaap, M.

AU - Scotto, F.

AU - Sega, K.

AU - Siour, G.

AU - Tauler, R.

AU - Valli, G.

AU - Vecchi, R.

AU - Venturini, E.

AU - Vestenius, M.

AU - Waked, A.

AU - Yubero, E.

PY - 2020/1

Y1 - 2020/1

N2 - 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.

AB - 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.

KW - Chemical transport models

KW - Intercomparison

KW - Lens

KW - PM

KW - Receptor models

KW - Source apportionment

UR - https://www.scopus.com/pages/publications/85074932830

U2 - 10.1016/j.aeaoa.2019.100053

DO - 10.1016/j.aeaoa.2019.100053

M3 - Article

AN - SCOPUS:85074932830

SN - 2590-1621

VL - 5

JO - Atmospheric Environment: X

JF - Atmospheric Environment: X

M1 - 100053

ER -