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Development and Evaluation of Model Algorithms to Account for Chemical Transformation in the Nearroad Environment
Citation:
Arunachalam, S., A. Valencia, M. Snyder, V. Isakov, D. Heist, D. Carruthers, AND A. Venkatram. Development and Evaluation of Model Algorithms to Account for Chemical Transformation in the Nearroad Environment. ISES 2015 Annual Meeting, Henderson, NV, October 19 - 22, 2015.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements
Description:
We describe the development and evaluation of two new model algorithms for NOx chemistry in the R-LINE near-road dispersion model for traffic sources. With increased urbanization, there is increased mobility leading to higher amount of traffic related activity on a global scale. Most NOx from combustion sources (about 90-95%) are emitted as NO, which is then readily converted to NO2 in the ambient air, while the remainder is emitted largely as NO2. Thus, bulk of ambient NO2 is formed due to secondary production in the atmosphere, which R-LINE cannot predict in its current form. The U.S. EPA’s 1-hour form of the National Ambient Air Quality Standard (NAAQS) for NO2 promulgated in 2010 (set at 100 ppb for a 98th percentile value, averaged over 3 years) is designed to address adverse exposure due to high short-term peaks in the vicinity of the near-road environment. EPA and states are in the process of deploying an expanded network of NO2 monitors. NO2 concentrations near major roads are appreciably higher than those measured at monitors in existing networks in urban areas, motivating a need to incorporate at least a reduced-form chemical mechanism in R-LINE to account for NO2 formation. The first is an empirical approach based upon fitting a 4th order polynomial to existing near-road observations in the U.S., and the second involves a more detailed set of chemical reactions based upon the Generic Reaction Set (GRS) mechanism. We will present the performance of the new R-LINE chemistry algorithms against near-road monitoring data (such as Detroit, Las Vegas, Raleigh field-studies) focusing on the NO-to-NO2 conversion and characterization of spatial gradients of NO2 in the near-road environment. The discussion will focus on the relative merits of the two approaches in the appropriate characterization of NO2 concentrations.
