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

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, the bulk of ambient NO₂ 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. NO₂ 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 NO₂ formation. To address this, the authors 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 4th 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 NO₂ 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.

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  • English

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  • Accession Number: 01665852
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 26 2018 2:46PM