Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees
Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees
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DOI：
10.1016/j.atmosenv.2015.12.024
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发表时间：
2016-02-01
影响因子：
5
通讯作者：
Ropkins, Karl
中科院分区：
文献类型：
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作者：
Sayegh, Arwa;Tate, James E.;Ropkins, Karl
氮氧化物(NOx)是光化学烟雾的主要成分，其成分被认为是影响人类健康的主要交通污染物。本研究采用Boosted回归树(BRT)的统计方法，研究了NOx交通密度的背景浓度和主要气象条件对英国城市、开放高速公路和高速公路隧道现场路边NOx浓度的影响。BRT模型已经使用每个站点的每小时浓度、交通和气象数据进行了拟合。这些模型对模型变量和路边NOx浓度之间的关系进行预测、排序和可视化。路边NOx与监测到的当地本底浓度之间有很强的相关性。路边NOx和其他模型变量之间的关系已被证明受到NOx背景浓度的质量和分辨率的强烈影响，即它是否基于监测数据或模型预测。本文提出了一种直接使用站点基础图将交通数据划分为四种交通状态的方法：自由流、繁忙流、拥堵和严重拥堵。利用BRT模型，观察到车辆密度(每公里车辆)对路边NOx浓度的影响成比例，不同的交通状态有不同的回归线斜率。如果剔除其他影响，路边浓度与环境空气温度的关系显示，氮氧化物浓度在摄氏22度左右达到最低水平，而在低环境空气温度下浓度较高，这可能与大气扩散受限和/或低环境空气温度下道路交通废气排放特性的变化有关。本文利用BRT模型研究了不同的关键因素及其相对重要性如何影响路边NOx浓度的变化。该文件强调了建立本地背景连续监测仪或提高模拟的英国背景图的质量和分辨率的重要性，以及进一步调查环境空气温度对NOx排放和路边NOx浓度的影响的必要性。(C)2015爱思唯尔有限公司。保留所有权利。
Oxides of Nitrogen (NOx) is a major component of photochemical smog and its constituents are considered principal traffic-related pollutants affecting human health. This study investigates the influence of background concentrations of NOx traffic density, and prevailing meteorological conditions on roadside concentrations of NOx at UK urban, open motorway, and motorway tunnel sites using the statistical approach Boosted Regression Trees (BRT). BRT models have been fitted using hourly concentration, traffic, and meteorological data for each site. The models predict, rank, and visualise the relationship between model variables and roadside NOx concentrations. A strong relationship between roadside NOx and monitored local background concentrations is demonstrated. Relationships between roadside NOx and other model variables have been shown to be strongly influenced by the quality and resolution of background concentrations of NOx, i.e. if it were based on monitored data or modelled prediction. The paper proposes a direct method of using site-specific fundamental diagrams for splitting traffic data into four traffic states: free-flow, busy-flow, congested, and severely congested. Using BRT models, the density of traffic (vehicles per kilometre) was observed to have a proportional influence on the concentrations of roadside NOx, with different fitted regression line slopes for the different traffic states. When other influences are conditioned out, the relationship between roadside concentrations and ambient air temperature suggests NOx concentrations reach a minimum at around 22 degrees C with high concentrations at low ambient air temperatures which could be associated to restricted atmospheric dispersion and/or to changes in road traffic exhaust emission characteristics at low ambient air temperatures. This paper uses BRT models to study how different critical factors, and their relative importance, influence the variation of roadside NOx concentrations. The paper highlights the importance of either setting up local background continuous monitors or improving the quality and resolution of modelled UK background maps and the need to further investigate the influence of ambient air temperature on NOx emissions and roadside NOx concentrations. (C) 2015 Elsevier Ltd. All rights reserved.