Development and evaluation of the R-LINE model algorithms to account for chemical transformation in the near-road environment

Content Provider	Scilit
Author	Valencia, Alejandro Venkatram, Akula Heist, David Carruthers, David Arunachalam, Saravanan
Copyright Year	2018
Description	Journal: Transportation Research Part D: Transport and Environment With increased urbanization, there is increased mobility leading to higher amount of traffic-related activity on a global scale. Most $NO_{x}$ from combustion sources (about 90–95%) are emitted as NO, which is then readily converted to $NO_{2}$ in the ambient air, while the remainder is emitted largely as $NO_{2}$. Thus, the bulk of ambient $NO_{2}$ is formed due to secondary production in the atmosphere, and which R-LINE cannot predict given that it can only model the dispersion of primary air pollutants. $NO_{2}$ concentrations near major roads are appreciably higher than those measured at monitors in existing networks in urban areas, motivating a need to incorporate a mechanism in R-LINE to account for $NO_{2}$ formation. To address this, we implemented three different approaches in order of increasing degrees of complexity and barrier to implementation from simplest to more complex. The first is an empirical approach based upon fitting a $4^{th}$ order polynomial to existing near-road observations across the continental U.S., the second involves a simplified Two-reaction chemical scheme, and the third involves a more detailed set of chemical reactions based upon the Generic Reaction Set (GRS) mechanism. All models were able to estimate more than 75% of concentrations within a factor of two of the near-road monitoring data and produced comparable performance statistics. These results indicate that the performance of the new R-LINE chemistry algorithms for predicting $NO_{2}$ is comparable to other models (i.e. ADMS-Roads with GRS), both showing less than ±15% fractional bias and less than 45% normalized mean square error.
Related Links	https://escholarship.org/content/qt51n841hj/qt51n841hj.pdf?t=ptcrmc https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5954839/pdf
Ending Page	477
Page Count	14
Starting Page	464
ISSN	13619209
DOI	10.1016/j.trd.2018.01.028
Journal	Transportation Research Part D: Transport and Environment
Volume Number	59
Language	English
Publisher	Elsevier BV
Publisher Date	2018-03-01
Access Restriction	Open
Subject Keyword	Journal: Transportation Research Part D: Transport and Environment Environmental Engineering Dispersion Modeling Traffic Emissions
Content Type	Text
Resource Type	Article
Subject	Transportation Environmental Science Civil and Structural Engineering