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Modeling Atmospheric Reactive Nitrogen
Citation:
Bash, J., Johnt Walker, M. Shephard, K. Cady-Pereira, S. Napelenok, D. Henze, AND E. Cooter. Modeling Atmospheric Reactive Nitrogen. American Society of Animal Sience 2016 JAM, Salt Lake City, UT, July 19 - 23, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Nitrogen is an essential building block of all proteins and thus an essential nutrient for all life. Reactive nitrogen, which is naturally produced via enzymatic reactions, forest fires and lightning, is continually recycled and cascades through air, water, and soil media. Human activity has perturbed this cycle through the combustion of fossil fuels and synthesis of fertilizers. The anthropogenic contribution to this cycle is now larger than natural sources in the United States and globally. Until recently, little progress has been made in modeling of the nitrogen cycle in the environment due to the complexity of and uncertainty in its transport and transformation between soil, water and atmospheric media. Here we will focus on modeling of the atmospheric component of the nitrogen cascade, with an emphasis on ammonia, emerging measurement techniques, and the potential for model improvements using emerging measurements, existing networks and modeling. The U.S. EPA’s Community Mulitscale Air Quality (CMAQ) model will be evaluated against observational trends in nitrogen deposition and ambient air quality from 2002 to 2012 and the sensitivity of CMAQ to NH3 emissions will be explored. These findings will be presented with an emphasis on how the sensitivity of the modeling system to animal husbandry emissions and how the representation of these emissions can be improved.
