Modeling the fate of atmospheric reduced nitrogen during the Rocky Mountain Atmospheric Nitrogen and Sulfur Study (RoMANS): Performance evaluation and diagnosis using integrated processes rate analysis

摘要

Excess wet and dry deposition of nitrogen-containing compounds is a concern at a number of national parks. The Rocky Mountain Atmospheric Nitrogen and Sulfur Study (RoMANS) was conducted during the spring and summer of 2006 to identify the overall mix of ambient and deposited sulfur and nitrogen at Rocky Mountain National Park (RMNP), in north-central Colorado. The Comprehensive Air Quality Model with extensions (CAMx) was used to simulate the fate of gaseous and particulate species subjected to multiple chemical and physical processes during RoMANS. This study presents an operational evaluation with a special emphasis on the model performance of reduced nitrogen species. The evaluation showed large negative biases and errors at RMNP and the entire domain for ammonia; therefore the model was considered inadequate for future source apportionment applications. The CAMx Integrated Processes Rate (IPR) analysis tool was used to elucidate the potential causes behind the poor model performance. IPR served as a tool to diagnose the relative contributions of individual physical and chemical processes to the final concentrations of reduced nitrogen species. The IPR analysis revealed that dry deposition is the largest sink of ammonia in the model, with some cells losing almost 100% of the available mass. Closer examination of the ammonia dry deposition velocities in CAMx found that they were up to a factor of 10 larger than those reported in the literature. A series of sensitivity simulations were then performed by changing the original deposition velocities with a simple multiplicative scaling factor. These simulations showed that even when the dry deposition values were altered to reduce their influence, the model was still unable to replicate the observed time series; i.e., it fixed the average bias, but it did not improve the precision.