Evaluation of receptor and chemical transport models for PM10 source apportionment

Claudio A Belis ,S Nava,M Pandolfi,M Manousakas,G Argyropoulos,P Prati,G Calori,S Sauvage,P Lazzeri,R Vecchi,F Lucarelli,K Eleftheriadis,G Valli,B Golly,Mihaela Mircea ,Guillaume Siour ,Astrid Manders ,Elisa Venturini ,Ettore Petralia ,Krešimir Šega ,Adriana Pietrodangelo ,D Mooibroek ,Joana Ferreira ,Martijn Schaap ,Denise Pernigotti ,Daniele Contini ,Fúlvio Amato ,Guido Pirovano ,Anikó Angyal ,Fabian Lenartz ,Romà Tauler ,Marco Paglione ,Katarzyna Maciejewska ,Jeroen Kuenen ,Héctor Jorquera ,Marcelo Pinho Almeida ,Dikaia Saraga ,Gianluigi De Gennaro ,Mauro Masiol ,Ivan Bešlić ,Stefania Gilardoni ,Kai Krajsek ,Yahya Izadmanesh ,Jeań Luc Jaffrezo ,Lucyna Samek ,Daniela Cesari ,Antoine Waked ,Constantini Samara ,Petra Pokorná ,Jean-Luc Besombes ,Maria Grazia Perrone ,Richard Kranenburg ,Dalia Salameh ,Stig Hellebust ,Diogo Miguel Oliveira ,Olivier Favez ,Mika Vestenius ,Evangelia Diapouli ,Imad El Haddad ,Alessia Di Gilio ,Silvia Pillon ,Philip K Hopke ,Marta G Vivanco ,Magdalena Reizer ,L Alleman ,Fabiana Scotto ,Hugo Denier Van Der Gon ,Hendrik Elbern ,Cristina Colombi ,Paolo Brotto ,Stefano Bande ,Maria Chiara Bove ,Véronique Riffault ,Eduardo Yubero

doi:10.1016/j.aeaoa.2019.100053

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

Highlights

Source Apportionment (SA) encompasses the modelling techniques used to relate the emissions from pollutions sources to the concentra tions of such pollutants in ambient air
One result was reported with the following tools: RCMB, MLPCA-MCR-ALS (MLPCA, maximum likelihood principal com ponents analysis-multivariate curve resolution-alternating least squares, Izadmanesh et al, 2017), positive matrix factorisation (PMF) version 2 (PMF2; Paatero and Tapper, 1993; Paatero, 1997), EPA-PMF version 3 (PMF3; Norris et al, 2008) and EPA-PMF version 4 (PMF4)
The high quality of the input data for both receptor models (RMs) and chemical transport models (CTMs) and the considerable number of SA results (49) provided the basis to build up an unprecedented database, with key information to support an extensive analysis of RMs and CTMs methodologies used in Europe for SA appli cations related to PM10 SA

Summary

Introduction

Source Apportionment (SA) encompasses the modelling techniques used to relate the emissions from pollutions sources to the concentra tions of such pollutants in ambient air. The abovementioned definition of SA accommodates a wide range of techniques to obtain information about the actual influence that one or more sources have on a specific area over a specific time window Such techniques may be based on the measured concentrations of pollutants and their components (receptor-oriented models or, more re ceptor models, RMs) or on chemistry, transport and dispersion models ( known as source-oriented models, SMs). Gaussian models are used to describe only the dispersion of a pollutant near the source in a stationary way, while Lagrangian models describe it in a 3D dynamic way With both types, the chemistry is assumed to be simple and linear or negligible, while Eulerian models (commonly referred to as Chemical Transport Models, CTMs) yield a description of pollutants in terms of motion, transport and dispersion, chemistry and other physical processes, accounting for all sources that are present in a given domain (emission inventories), as well as the influence from more distant sources (boundary conditions).

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