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Journal Articles Atmospheric environment: X Year : 2020

Evaluation of receptor and chemical transport models for PM10 source apportionment

1 JRC - European Commission - Joint Research Centre [Ispra]
2 RSE - Ricerca sul Sistema Energetico
3 INERIS - Institut National de l'Environnement Industriel et des Risques
4 IGE - Institut des Géosciences de l’Environnement
5 TNO Climate, Air and Sustainability [Utrecht]
6 Warsaw University of Technology [Warsaw]
7 CERI EE - IMT Nord Europe - Centre for Energy and Environment
8 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai
9 Centro de Ciencias e Tecnologias Nucleares, Instituto Superior Tecnico, Universidade de Lisboa, Bobadela LRS, Portugal
10 IDAEA - Institute of Environmental Assessment and Water Research
11 ATOMKI - Institute for Nuclear Research [Budapest]
12 Environmental Pollution Control Laboratory
13 ARPA Piemonte Regional Agency for Environmental Protection
14 Institute for Medical Research and Occupational Health
15 LCME_CE - Laboratoire LCME / Equipe Chimie de l'Environnement
16 UniGe - Università degli studi di Genova = University of Genoa
17 ARIANET Srl
18 ISAC - CNR Institute of Atmospheric Sciences and Climate
19 ARPA Lombardia
20 Department of Biology [University of Bari]
21 INRASTES - Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety
22 PSI - Paul Scherrer Institute
23 FZJ - Forschungszentrum Jülich GmbH | Centre de recherche de Jülich | Jülich Research Centre
24 Universidade de Aveiro
25 CIEMAT - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
26 LCME - Laboratoire de Chimie Moléculaire et Environnement
27 UCC - University College Cork
28 Clarkson University
29 KNTU - Khajeh Nasir Toosi University of Technology [Téhéran]
30 UC - Pontificia Universidad Católica de Chile
31 TNO - The Netherlands Organisation for Applied Scientific Research
32 Agenzia Provinciale Protezione Ambiente
33 ISSeP - Institut scientifique de service public [Liège]
34 INFN, Sezione di Firenze - Istituto Nazionale di Fisica Nucleare, Sezione di Firenze
35 ENEA - Agenzia Nazionale per le nuove Tecnologie, l’energia e lo sviluppo economico sostenibile = Italian National Agency for New Technologies, Energy and Sustainable Economic Development
36 RIVM - National Institute for Public Health and the Environment [Bilthoven]
37 UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca
38 Institute for Atmospheric Pollution Research
39 ARPAV - Regional Agency for Environmental Prevention and Protection of the Veneto
40 Aristotle University of Thessaloniki
41 FPACS - Faculty of Physics and Applied Computer Science [Kraków]
42 ARPA Emilia-Romagna, Agenzia Regionale per la Protezione dell’Ambiente
43 LISA (UMR_7583) - Laboratoire Interuniversitaire des Systèmes Atmosphériques
44 IPSL (FR_636) - Institut Pierre-Simon-Laplace
45 UNIMI - Università degli Studi di Milano = University of Milan
46 UNIBO - Alma Mater Studiorum Università di Bologna = University of Bologna
47 FMI - Finnish Meteorological Institute
48 Miguel Hernández University
Giuseppe Calori
  • Function : Author
C. Colombi
  • Function : Author
P.K. Hopke
  • Function : Author
M. Masiol
  • Function : Author
P. Pokorna
  • Function : Author

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.
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Dates and versions

hal-02479476 , version 1 (07-06-2023)

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C.A. Belis, D. Pernigotti, G. Pirovano, O. Favez, J.L. Jaffrezo, et al.. Evaluation of receptor and chemical transport models for PM10 source apportionment. Atmospheric environment: X, 2020, 5, pp.100053. ⟨10.1016/j.aeaoa.2019.100053⟩. ⟨hal-02479476⟩
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