气候变化领域集成服务门户(CCPortal): Evaluation of receptor and chemical transport models for PM10 source apportionment

CCPortal

DOI	10.1016/j.aeaoa.2019.100053
	Evaluation of receptor and chemical transport models for PM10 source apportionment
	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.
发表日期	2020
ISSN	25901621
卷号	5
英文摘要	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
英文关键词	Chemical transport models; Intercomparison; Lens; PM10; Receptor models; Source apportionment
学科领域	Air quality; Dust; Emission control; Lenses; Mean square error; Soil testing; Time series; Chemical transport models; Intercomparisons; PM10; Receptor model; Source apportionment; Quality control; air quality; atmospheric modeling; industrial emission; particulate matter; source apportionment; air quality; article; Europe; performance; soil; time series analysis; uncertainty; Europe; United States
语种	英语
scopus关键词	Air quality; Dust; Emission control; Lenses; Mean square error; Soil testing; Time series; Chemical transport models; Intercomparisons; PM10; Receptor model; Source apportionment; Quality control; air quality; atmospheric modeling; industrial emission; particulate matter; source apportionment; air quality; article; Europe; performance; soil; time series analysis; uncertainty; Europe; United States
来源期刊	Atmospheric Environment: X
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120910
作者单位	European Commission, Joint Research Centre, via Fermi, Ispra2748 21027, Italy; Ricerca sul Sistema Energetico, RSE S.p.A, Via R. Rubattino 54, Milano, 20134, Italy; INERIS, Verneuil-en-Halatte, France; Univ. Grenoble Alpes, CNRS, IRD, G-INP, IGE, Grenoble, F-38000, France; TNO, Princetonlaan 6, Utrecht, CB NL-3584, Netherlands; Warsaw University of Technology, Faculty of Building Services, Hydro and Environmental Engineering, Nowowiejska 20 Str, Warsaw, 00-653, Poland; Département Sciences de l'Atmosphère et Génie de l'Environnement, SAGE, IMT Lille Douai, Université de Lille, Lille, 59000, France; C2TN, Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, Estrada Nacional 10, Km 139.7, Bobadela LRS2695-066, Portugal; IDÆA, CSIC, C/Jordi Girona 18-26, Barcelona, 08034, Spain; Laboratory of Ion Beam Physics, Institute for Nuclear Research, Hungarian Academy of Sciences (MTA Atomki), P. O. Box 51, Debrecen, H-4026, Hungary; Environmental Pollution Control Laborato...
推荐引用方式 GB/T 7714	Belis C.A.,Pernigotti D.,Pirovano G.,et al. Evaluation of receptor and chemical transport models for PM10 source apportionment[J],2020,5.
APA	Belis C.A..,Pernigotti D..,Pirovano G..,Favez O..,Jaffrezo J.L..,...&Yubero E..(2020).Evaluation of receptor and chemical transport models for PM10 source apportionment.Atmospheric Environment: X,5.
MLA	Belis C.A.,et al."Evaluation of receptor and chemical transport models for PM10 source apportionment".Atmospheric Environment: X 5(2020).