RLINE model algrotihms to account for NO2 near-road chemistry data set - RLINE_N02

This data set is associated with the results found in the journal article: Valencia et al, 2018. Development and evaluation of the R-LINE model algorithms to account for chemical transformation in the near-road environment. Transportation Research Part D, https://doi.org/10.1016/j.trd.2018.01.028. To address the need to estimate near-road NO2 concentrations, 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 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. This dataset is associated with the following publication: Valencia, A., A. Venkatram, D. Heist, D. Carruthers , and S. Arunachalam. Development and evaluation of the R-LINE model algorithms to account for chemical transformation in the near-road environment. Transportation Research Part D: Transport and Environment. Elsevier BV, AMSTERDAM, NETHERLANDS, 59: 464-477, (2018).

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  "020:00"
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[
  "020:094"
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[
  "https://doi.org/10.1016/j.trd.2018.01.028"
]