globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2015.10.068
Scopus记录号: 2-s2.0-84946709957
论文题名:
A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises
作者: Belis C; A; , Karagulian F; , Amato F; , Almeida M; , Artaxo P; , Beddows D; C; S; , Bernardoni V; , Bove M; C; , Carbone S; , Cesari D; , Contini D; , Cuccia E; , Diapouli E; , Eleftheriadis K; , Favez O; , El Haddad I; , Harrison R; M; , Hellebust S; , Hovorka J; , Jang E; , Jorquera H; , Kammermeier T; , Karl M; , Lucarelli F; , Mooibroek D; , Nava S; , Nøjgaard J; K; , Paatero P; , Pandolfi M; , Perrone M; G; , Petit J; E; , Pietrodangelo A; , Pokorná P; , Prati P; , Prevot A; S; H; , Quass U; , Querol X; , Saraga D; , Sciare J; , Sfetsos A; , Valli G; , Vecchi R; , Vestenius M; , Yubero E; , Hopke P; K
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 123
起始页码: 240
结束页码: 250
语种: 英语
英文关键词: Intercomparison exercise ; Model performance indicators ; Model uncertainty ; Particulate matter ; Receptor models ; Source apportionment
Scopus关键词: Air quality ; Input output programs ; Quality management ; Intercomparison exercise ; Model performance ; Model uncertainties ; Particulate Matter ; Receptor model ; Source apportionment ; Uncertainty analysis ; air quality ; chemical property ; comparative study ; data set ; model validation ; participatory approach ; particulate matter ; performance assessment ; pollutant source ; uncertainty analysis ; air pollutant ; air pollution ; air quality ; air quality control ; Article ; comparative study ; controlled study ; methodology ; model ; particulate matter ; priority journal ; reference value ; source apportionment model ; time series analysis ; uncertainty ; Romania
Scopus学科分类: Environmental Science: Water Science and Technology ; Earth and Planetary Sciences: Earth-Surface Processes ; Environmental Science: Environmental Chemistry
英文摘要: 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. © 2015 The Authors.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81374
Appears in Collections:气候变化事实与影响

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作者单位: European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via Enrico Fermi 2749, Ispra (VA), Italy; Institute of Environmental Assessment and Water Research, Spanish Research Council (IDÆA-CSIC), c/Jordi Girona 18-26, Barcelona, Spain; C2TN, Instituto Superior Técnico, Universidade de Lisboa, Estrada Nacional 10 km 139.7, Bobadela LRS, Portugal; Instituto de Fisica, Universidade de Sao Paulo, Rua do Matao, Traversor 187, Sao Paulo, Brazil; Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom; Dept. of Physics, Università degli Studi di Milano and oINFN-Milan, via Celoria 16, Milan, Italy; University of Genoa, Dept. of Physics and INFN, via Dodecaneso 33, Genova, Italy; Finnish Meteorological Institute, Atmospheric Composition Research, PO Box 503, Helsinki, Finland; Istituto di Scienze dell'Atmosfera e del Clima, ISAC-CNR Str., Prv. Lecce-Monteroni km 1.2, Lecce, Italy; Institute of Nuclear and Radiological Science and Technology, Energy and Safety, N.C.S.R. Demokritos, Athens, Greece; Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France; Laboratory of Atmospheric Chemistry (LAC), Paul Scherrer Institut, Villigen, Switzerland; Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, Saudi Arabia; Centre for Research into Atmospheric Chemistry, Dept. Chemistry, University College, Cork, Ireland; Institute for Environmental Studies, Charles University in Prague, Albertov 6, Prague 2, Czech Republic; Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago, Chile; IUTA e.V., Bereich Luftreinhaltung and oNachhaltige Nanotechnologie, Institut für Energie- und Umwelttechnik e.V., Bliersheimer Strasse 60, Duisburg, Germany; Urban Environment and Industry, Norwegian Institute for Air Research (NILU), PO Box 100, Kjeller, Norway; Department of Physics and Astronomy and INFN, Firenze, Italy; National Institute of Public Health and the Environment, Centre for Environmental Quality (MIL), Department for Air and Noise Analysis (ILG), PO Box 1, Bilthoven, Netherlands; Department for Environmental Science, Aarhus University, Frederiksborgvej 399, PO Box 358, Roskilde, Denmark; Department of Physics, University of Helsinki, Rikalantie 6, Helsinki, Finland; Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza della Scienza 1, Milan, Italy; C.N.R., Institute of Atmospheric Pollution Research, Area della Ricerca di Roma 1, Via Salaria Km 29, 300, Monterotondo (RM), Italy; INRASTES, NCSR Demokritos, P. Grigoriou and Neapoleos Str, Agia Paraskevi, Greece; CNRS LSCE, France; Laboratory of Atmospheric Pollution (LCA), Miguel Hernández University, Av. de la Universidad s/n, Edif. Alcudia, Elche, Spain; Center for Air Resources Engineering and Science, Clarkson University, Box 5708, Potsdam, NY, United States

Recommended Citation:
Belis C,A,, Karagulian F,et al. A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises[J]. Atmospheric Environment,2015-01-01,123
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