American Geophysical Union Become an AGU Member
Subscribe to AGU Journals		 AGU Home AGU Publications

Read Full Article (file size: 1608117 bytes)    Cited by

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D02303, doi:10.1029/2007JD008580, 2008

Assessment of the wintertime performance of developmental particulate matter forecasts with the Eta-Community Multiscale Air Quality modeling system

Rohit Mathur

Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA, Research Triangle Park, North Carolina, USA
Atmospheric Modeling Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA


Shaocai Yu

Science and Technology Corporation, Hampton, Virginia, North Carolina, USA


Daiwen Kang

Science and Technology Corporation, Hampton, Virginia, North Carolina, USA


Kenneth L. Schere

Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA, Research Triangle Park, North Carolina, USA
Atmospheric Modeling Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA


Abstract

It is desirable for local air quality agencies to accurately forecast tropospheric PM2.5 concentrations to alert the sensitive population of the onset, severity, and duration of unhealthy air and to encourage the public and industry to reduce emissions-producing activities. Since elevated particulate matter concentrations are encountered throughout the year, the accurate forecast of the day-to-day variability in PM2.5 and constituent concentrations over annual cycles poses considerable challenges. In efforts to characterize forecast model performance during different seasons, PM2.5 forecast simulations with the Eta-Community Multiscale Air Quality system are compared with measurements from a variety of regional surface networks, with special emphasis on performance during the winter period. The analysis suggests that while the model can capture the average spatial trends and dynamic range in PM2.5 and constituent concentrations measured at individual sites, significant variability occurs on a day-to-day basis both in the measurements and the model predictions, which are generally not well correlated when paired both in space and time. Systematic overpredictions in regional PM2.5 forecasts during the cool season are noted through comparisons with measurements from different networks. The overpredictions are typically more pronounced at urban locations, with larger errors at the higher concentration range. Variability in aerosol sulfate concentrations were captured well, as well as the relative amounts of sulfur (IV) and sulfur (VI). The mix of carbon sources as represented by the ratio of organic to elemental carbon is captured well in the southeastern United States, but the total carbonaceous aerosol mass is underestimated. On average, during the wintertime the largest overpredictions among individual PM2.5 constituents were noted for the “other” category which predominantly represents primary-emitted trace elements in the current model configuration. The systematic errors in model predictions of both total PM2.5 and its constituents during the winter period are found to arise from a combination of uncertainties in the magnitude and spatial and temporal allocation of primary PM2.5 emissions, current uncertainties in the estimation of chemical production pathways for secondary constituents (e.g., NO3 −), and the representation of the impacts of boundary layer mixing on simulated concentrations, especially during nighttime conditions.

Received 23 February 2007; accepted 17 September 2007; published 17 January 2008.

Keywords: particulate matter; air quality forecast; modeling.

Index Terms: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801, 4906); 0345 Atmospheric Composition and Structure: Pollution: urban and regional (0305, 0478, 4251); 0550 Computational Geophysics: Model verification and validation; 0365 Atmospheric Composition and Structure: Troposphere: composition and chemistry.

Read Full Article (file size: 1608117 bytes)    Cited by

Citation: Mathur, R., S. Yu, D. Kang, and K. L. Schere (2008), Assessment of the wintertime performance of developmental particulate matter forecasts with the Eta-Community Multiscale Air Quality modeling system, J. Geophys. Res., 113, D02303, doi:10.1029/2007JD008580.