Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII

Efisio Solazzo, Roberto Bianconi, Guido Pirovano, Volker Matthias, Robert Vautard, Michael D. Moran, K. Wyat Appel, Bertrand Bessagnet, Jorgen Brandt, Jesper H. Christensen, C. Chemel, Isabelle Coll, Joana Ferreira, Renate Forkel, Xavier Vazhappilly Francis, Georg Grell, Paola Grossi, Ayoe B. Hansen, Ana Isabel Miranda, Uarporn NopmongcolMarje Prank, Karine N. Sartelet, Martijn Schaap, Jeremy D. Silver, Ranjeet S. Sokhi, Julius Vira, Johannes Werhahn, Ralf Wolke, Greg Yarwood, Junhua Zhang, S. Trivikrama Rao, Stefano Galmarini

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Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental-scale domains in North America and Europe for full-year simulations of 2006 in the context of Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are model intercomparison and evaluation. Standardised modeling outputs from each group have been shared on the web-distributed ENSEMBLE system, which allows statistical and ensemble analyses to be performed. In this study, the one-year model simulations are inter-compared and evaluated with a large set of observations for ground-level particulate matter (PK10 and PM2.5) and its chemical components. Modeled concentrations of gaseous PM precursors, SO2 and NO2, have also been evaluated against observational data for both continents. Furthermore, modeled deposition (dry and wet) and emissions of several species relevant to PM are also inter-compared. The unprecedented scale of the exercise (two continents, one full year, fifteen modeling groups) allows for a detailed description of AQ model skill and uncertainty with respect to PM.

Analyses of PM10 yearly time series and mean diurnal cycle show a large underestimation throughout the year for the AQ models included in AQMEII. The possible causes of PM bias, including errors in the emissions and meteorological inputs (e.g., wind speed and precipitation), and the calculated deposition are investigated. Further analysis of the coarse PM components, PM2.5 and its major components (SO4, NH4, NO3, elemental carbon), have also been performed, and the model performance for each component evaluated against measurements. Finally, the ability of the models to capture high PM concentrations has been evaluated by examining two separate PM2.5 episodes in Europe and North America. A large variability among models in predicting emissions, deposition, and concentration of PM and its precursors during the episodes has been found. Major challenges still remain with regards to identifying and eliminating the sources of PM bias in the models. Although PM2.5 was found to be much better estimated by the models than PM10, no model was found to consistently match the observations for all locations throughout the entire year

Original languageEnglish
Pages (from-to)75-92
Number of pages18
JournalAtmospheric Environment
Early online date23 Feb 2012
Publication statusPublished - Jun 2012


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