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Diagnostic Investigations of Input Uncertainties
Research Programs
Source Apportionment and Inverse Modeling of Elemental Carbon Emissions
Elemental carbon emissions have a substantial amount of uncertainty both in magnitude and temporal variation. Two major sources of the emissions are biomass combustion sectors and mobile sectors (diesel and non-diesel). A source apportionment version of CMAQ has been developed to track contributions of fine particle emissions to elemental carbon and organic carbon predictions. Using this instrumented model, the elemental carbon contribution from each major emission sector is quantified. Inverse modeling methods similar to those previously applied for NH3 emissions will be used to evaluate the current estimates of elemental carbon from sectors such as biomass combustion and mobile sources. Primary PM2.5 is a particularly good candidate for inverse modeling because no chemistry is involved, so that uncertainty in modeled chemisry is not an issue in the inverse method. We will work in partnership with OAQPS on this study to consider the new 2002 USEPA inventory in the evaluation.
Model Evaluation
Diagnostic Evaluation:
Indicator Metrics & Instrumented CMAQ Investiguations for Inorganic Fine Particle System
Inverse modeling of Seasonal Ammonia Emissions
The value of inverse modeling has been successfully demonstrated for estimated seasonally varying ammonia emissions (Gilliland et al, 2003). This work also demonstrated the importance of the ammonia emission estimates for nitrate aerosol predictions. As CMAQ modeling domains have expanded to include the continguous United States and more recent years are simulated, inverse modeling will continue to be used to refine the estimated seasonal variations in ammonia emissions. Similar to the what we plan to do for elemental carbon, the method will also be tested against separate emission sources to determine whether seasonal factors can be estimated separately for sectors such as dairy and beef cattle, poultry, swine, and fertilizer.