A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

被引：64

作者：

Belis, C. A. ^{[1
]}

Karagulian, F. ^{[1
]}

Amato, F. ^{[2
]}

Almeida, M. ^{[3
]}

Artaxo, P. ^{[4
]}

Beddows, D. C. S. ^{[5
]}

Bernardoni, V. ^{[6
,7
]}

Bove, M. C. ^{[8
,9
]}

Carbone, S. ^{[10
]}

Cesari, D. ^{[11
]}

Contini, D. ^{[11
]}

Cuccia, E. ^{[8
,9
]}

Diapouli, E. ^{[12
]}

Eleftheriadis, K. ^{[12
]}

Favez, O. ^{[13
]}

El Haddad, I. ^{[14
]}

Harrison, R. M. ^{[5
,15
]}

Hellebust, S. ^{[16
]}

Hovorka, J. ^{[17
]}

Jang, E. ^{[5
]}

Jorquera, H. ^{[18
]}

Kammermeier, T. ^{[19
]}

Karl, M. ^{[20
]}

Lucarelli, F. ^{[21
,22
]}

Mooibroek, D. ^{[23
]}

Nava, S. ^{[21
,22
]}

Nojgaard, J. K. ^{[24
]}

Paatero, P. ^{[25
]}

Pandolfi, M. ^{[2
]}

Perrone, M. G. ^{[26
]}

Petit, J. E. ^{[14
,29
]}

Pietrodangelo, A. ^{[27
]}

Pokorna, P. ^{[17
]}

Prati, P. ^{[10
]}

Prevot, A. S. H. ^{[15
]}

Quass, U. ^{[19
]}

Querol, X. ^{[2
]}

Saraga, D. ^{[28
]}

Sciare, J. ^{[29
]}

Sfetsos, A. ^{[28
]}

Valli, G. ^{[8
,9
]}

Vecchi, R. ^{[8
,9
]}

Vestenius, M. ^{[11
]}

Yubero, E. ^{[30
]}

Hopke, P. K. ^{[31
]}

机构：

[1] Commiss European Communities, Joint Res Ctr, Inst Environm & Sustainabil, I-21027 Ispra, VA, Italy

[2] Spanish Res Council IDAEA CSIC, Inst Environm Assessment & Water Res, Barcelona 08034, Spain

[3] Univ Lisbon, Inst Super Tecn, C2TN, P-2695066 Bobadela Lrs, Portugal

[4] Univ Sao Paulo, Inst Fis, BR-05508900 Sao Paulo, Brazil

[5] Univ Birmingham, Sch Geog Earth & Environm Sci, Div Environm Hlth & Risk Management, Birmingham B15 2TT, W Midlands, England

[6] Univ Milan, Dept Phys, I-20133 Milan, Italy

[7] INFN Milan, I-20133 Milan, Italy

[8] Univ Genoa, Dept Phys, I-14146 Genoa, Italy

[9] INFN, I-14146 Genoa, Italy

[10] Finnish Meteorol Inst, Atmospher Composit Res, FI-00101 Helsinki, Finland

[11] Ist Sci Atmosfera & Clima, I-73100 Lecce, Italy

[12] NCSR Demokritos, Inst Nucl & Radiol Sci & Technol Energy & Safety, Athens 15341, Greece

[13] Inst Natl Environm Ind & Risques INERIS, Verneuil En Halatte, France

[14] Paul Scherrer Inst, LAC, Villigen, Switzerland

[15] King Abdulaziz Univ, Ctr Excellence Environm Studies, Dept Environm Sci, Jeddah 21589, Saudi Arabia

[16] Natl Univ Ireland Univ Coll Cork, Dept Chem, Ctr Res Atmospher Chem, Cork, Ireland

[17] Charles Univ Prague, Inst Environm Studies, Prague 12843 2, Czech Republic

[18] Pontificia Univ Catolica Chile, Dept Ingn Quim & Bioproc, Santiago 6904411, Chile

[19] Inst Energie & Umwelttech eV, IUTA eV, Bereich Luftreinhaltung & Nachhaltige Nanotechnol, D-47229 Duisburg, Germany

[20] Norwegian Inst Air Res NILU, Urban Environm & Ind, NO-2027 Kjeller, Norway

[21] Dept Phys & Astron, Florence, Italy

[22] Ist Nazl Fis Nucl, I-50125 Florence, Italy

[23] Natl Inst Publ Hlth & Environm, Ctr Environm Qual MIL, Dept Air & Noise Anal ILG, NL-3720 BA Bilthoven, Netherlands

[24] Aarhus Univ, Dept Environm Sci, DK-4000 Roskilde, Denmark

[25] Univ Helsinki, Dept Phys, FI-00970 Helsinki, Finland

[26] Univ Milano Bicocca, Dept Earth & Environm Sci, I-20126 Milan, Italy

[27] CNR, Inst Atmospher Pollut Res, Area Ric Roma 1, I-00015 Monterotondo, RM, Italy

[28] NCSR Demokritos, INRASTES, Aghia Paraskevi 15310, Greece

[29] CNRS LSCE, Gif Sur Yvette, France

[30] Miguel Hernandez Univ, Lab Atmospher Pollut LCA, Elche 03202, Spain

[31] Clarkson Univ, Ctr Air Resources Engn & Sci, Potsdam, NY 13699 USA

来源：

ATMOSPHERIC ENVIRONMENT | 2015年 / 123卷

关键词：

Source apportionment; Receptor models; Intercomparison exercise; Model performance indicators; Model uncertainty; Particulate matter; PM SOURCE APPORTIONMENT; PARTICULATE MATTER; RECEPTOR MODELS; AIR-QUALITY; POLLUTION;

D O I：

10.1016/j.atmosenv.2015.10.068

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

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 Bells 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. (C) 2015 The Authors. Published by Elsevier Ltd.

引用

页码：240 / 250

页数：11

共 31 条

[31] Source apportionment: Findings from the US Supersites program
Watson, John G.
Chen, L. W. Anthony
Chow, Judith C.
Doraiswamy, Prakash
Lowenthal, Douglas H.
[J]. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2008, 58 (02) : 265 - 288

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