Citation:

Solazzo, E., R. Bianconi, G. Pirovano, V. Matthias, R. Vautard, W. APPEL, B. Bessagner, J. Brandt, J. H. Christensen, C. Chemel, I. Coll, J. Ferreira, R. Forkel, X. V. Francis, G. Grell, P. Grossi, A. Hansen, A. Miranda, M. D. Moran, U. Nopmongeol, M. Parnk, K. N. Sartelet, M. Schaap, J. D. Silver, R. S. Sokhi, J. Vira, J. Werhahn, R. Wolke, G. Yarwood, J. Zhang, S. T. RAO, AND S. Galmarini. Operational Model Evaluation for Particulate Matter in Europe and North America in the Context of the AQMEII Project. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 53(June):75-92, (2012).

Impact/Purpose:

The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Description:

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 (PM₁₀ and PM_2.5) 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 ofseveral 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, PM_2.5 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 PM_2.5 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 PM_2.5 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.

URLs/Downloads:

Record Details:

Planning Links: