Valencia Alejandro, Venkatram Akula, Heist David, Carruthers David, Arunachalam Saravanan
Institute for the Environment, University of North Carolina at Chapel Hill, USA.
University of California at Riverside, Riverside, California, USA.
Transp Res D Transp Environ. 2018;59:464-477. doi: 10.1016/j.trd.2018.01.028.
With increased urbanization, there is increased mobility leading to higher amount of traffic-related activity on a global scale. Most NO from combustion sources (about 90-95%) are emitted as NO, which is then readily converted to NO in the ambient air, while the remainder is emitted largely as NO. Thus, the bulk of ambient NO 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 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 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 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 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.
随着城市化进程的加快，全球范围内的流动性增强，导致与交通相关的活动增多。燃烧源产生的大部分一氧化氮（约90 - 95%）以一氧化氮（NO）的形式排放，随后在环境空气中迅速转化为二氧化氮（NO₂），而其余部分主要以一氧化氮（NO）的形式排放。因此，环境中的二氧化氮大部分是由于大气中的二次生成形成的，鉴于R - LINE只能模拟一次空气污染物的扩散，所以它无法预测这一过程。主要道路附近的二氧化氮浓度明显高于城市现有监测网络中监测器所测浓度，这促使有必要在R - LINE中纳入一种机制来解释二氧化氮的形成。为了解决这个问题，我们按照从简单到复杂的顺序实施了三种不同的方法，实施的难度也随之增加。第一种是经验方法，基于对美国大陆现有近道路观测数据拟合四阶多项式；第二种涉及简化的双反应化学方案；第三种涉及基于通用反应集（GRS）机制的更详细的化学反应集。所有模型都能够在近道路监测数据的两倍因子范围内估计超过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化学算法在预测二氧化氮方面的性能与其他模型（即采用GRS的ADMS - Roads）相当，两者的分数偏差均小于±15%，归一化均方误差均小于45%。
