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

机译：开发和评估R-LINE模型算法以解决近路环境中的化学转化问题

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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, 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.
机译：随着城市化程度的提高，机动性的增加导致全球范围内与交通有关的活动数量增加。来自燃烧源的大多数NOx（大约90-95％）以NO的形式排放，然后在周围空气中容易转化为NO2，而其余的大部分以NO2的形式排放。因此，大量的环境NO2是由于大气中的二次产生而形成的，而R-LINE仅能模拟一次空气污染物的扩散而无法预测。主要道路附近的NO2浓度明显高于城市现有网络中监测仪测得的浓度，这激发了在R-LINE中纳入一种机制以解决NO2形成的需求。为了解决这个问题，我们按照复杂度从高到低的顺序实施了三种不同的方法。第一种是基于四阶多项式拟合整个美国大陆现有近路观测值的经验方法，第二种是简化的双向反应化学方案，第三种是基于通用原理的一组更详细的化学反应反应集（GRS）机制。所有模型都能够在两个近路监测数据的两倍之内估算出超过75％的浓度，并产生了可比的性能统计数据。这些结果表明，新的R-LINE化学算法预测NO2的性能可与其他模型（即具有GRS的ADMS-Roads）相媲美，均显示出小于+/- 15％的分数偏差和小于45％的标准化均方值错误。

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