School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853.
Environ Sci Technol. 2010 Feb 15;44(4):1320-6. doi: 10.1021/es901913k.
Collecting and analyzing high frequency emission measurements has become very usual during the past decade as significantly more information with respect to formation conditions can be collected than from regulated bag measurements. A challenging issue for researchers is the accurate time-alignment between tailpipe measurements and engine operating variables. An alignment procedure should take into account both the reaction time of the analyzers and the dynamics of gas transport in the exhaust and measurement systems. This paper discusses a statistical modeling framework that compensates for variable exhaust transport delay while relating tailpipe measurements with engine operating covariates. Specifically it is shown that some variants of the smooth transition regression model allow for transport delays that vary smoothly as functions of the exhaust flow rate. These functions are characterized by a pair of coefficients that can be estimated via a least-squares procedure. The proposed models can be adapted to encompass inherent nonlinearities that were implicit in previous instantaneous emissions modeling efforts. This article describes the methodology and presents an illustrative application which uses data collected from a diesel bus under real-world driving conditions.
在过去的十年中，收集和分析高频排放测量已变得非常普遍，因为与受规管的袋式测量相比，可以收集到更多有关形成条件的信息。对于研究人员来说，一个具有挑战性的问题是排气管测量和发动机运行变量之间的准确时间对准。对准程序应同时考虑到分析仪的反应时间和废气及测量系统中气体传输的动态。本文讨论了一种统计建模框架，该框架补偿了可变的排气传输延迟，同时将排气管测量与发动机运行协变量相关联。具体来说，表明平滑过渡回归模型的某些变体允许作为排气流量函数的平滑变化的传输延迟。这些函数由一对可以通过最小二乘程序进行估计的系数来描述。所提出的模型可以进行修改以包含先前瞬时排放建模工作中隐含的固有非线性。本文描述了该方法，并介绍了一个使用在实际驾驶条件下从柴油公共汽车收集的数据的说明性应用。
