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INVERSE MODELING TO ESTIMATE NH3 EMISSION SEASONALLY AND THE SENSITIVITY TO UNCERTAINTY REPRESENTATIONS
Citation:
Gilliland, A B., H. K. Im, AND M. L. Stein. INVERSE MODELING TO ESTIMATE NH3 EMISSION SEASONALLY AND THE SENSITIVITY TO UNCERTAINTY REPRESENTATIONS. Presented at NARSTO Emission Inventory Workshop, Austin, TX, October 14-17, 2003.
Impact/Purpose:
The goal of this task is to thoroughly characterize the performance of the emissions, meteorological and chemical/transport modeling components of the Models-3 system, with an emphasis on the chemical/transport model, CMAQ. Emissions-based models are composed of highly complex scientific hypotheses concerning natural processes that can be evaluated through comparison with observations, but not validated. Both performance and diagnostic evaluation together with sensitivity analyses are needed to establish credibility and build confidence within the client and scientific community in the simulations results for policy and scientific applications. The characterization of the performance of Models-3/CMAQ is also a tool for the model developers to identify aspects of the modeling system that require further improvement.
Description:
Inverse modeling has been used extensively on the global scale to produce top-down estimates of emissions for chemicals such as CO and CH4. Regional scale air quality studies could also benefit from inverse modeling as a tool to evaluate current emission inventories; however, underlying assumptions such as the linearity between emission and concentration changes can limit the applicability of inverse modeling. Ammonia (NH3) has been found to be a reasonable candidate because a strong linearity exists between NH3 emission adjustments and the response of modeled ammonium wet deposition. Further, the uncertainty in the emission estimates, especially on a monthly time scale, is quite large. While we anticipate that NH3 emissions from agricultural non-point sources have a strong seasonal pattern, the intra-annual variability of these primary NH3 sources is not yet understood well-enough to incorporate into current NH3 emission inventories. Along with the USEPA Community Multiscale Air Quality (CMAQ) model and NH4+ wet concentration data, an inverse modeling approach has been used to estimate monthly adjustments to the NH3 emission field over the Eastern United States. The first series of results, presented in Gilliland et al. [2003], offer the most comprehensive estimate of seasonal NH3 emission variability to date. These seasonal variations in NH3 emissions were shown to be essential for the prediction of nitrogen-containing compounds in that study. Further tests are now being conducted where a variety of uncertainty representations are considered in the inverse modeling calculations. These sensitivity tests will provide a more thorough range of emission adjustment estimates for each month and will test the rigor of the seasonal variability suggested by Gilliland et al. [2003].