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Prediction of gas-particle partitioning of PAHs based on M5’ model trees

机译:基于M5模型树的多环芳烃气体颗粒分配预测

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摘要

During the thermal combustion processes of carbon-enriched organic compounds, emission of polycyclic aromatic hydrocarbons into ambient air occurs. Previous studies of atmospheric distribution of polycyclic aromatic hydrocarbons showed low correlation between the experimental values and Junge-Pankow theoretical adsorption model, suggesting that other approaches should be used to describe the partitioning phenomena. The paper evaluates the applicability of multivariate piece-wise-linear M5’ model-tree models to the problem of gas-particle partitioning. Experimental values of particle-associated fraction, obtained for 129 ambient air samples collected at 24 background, urban and industrial sites, were compared to the prediction results obtained using M5’ and the Junge-Pankow model. The M5’ approach proposed and models learned are able to achieve good correlation (correlation coefficient >0.9) for some low-molecular-weight compounds, when the target is to predict the concentration of gas phase based on the particle-associated phase. When converted to particle-bound fraction values, the results, for selected compounds, are superior to those obtained by Junge-Pankow model by several orders of magnitude, in terms of the prediction error.
机译:在富含碳的有机化合物的热燃烧过程中,会发生多环芳烃向周围空气的排放。先前对多环芳烃的大气分布研究表明,实验值与Junge-Pankow理论吸附模型之间的相关性较低,表明应使用其他方法来描述分配现象。本文评估了多元分段线性M5’模型树模型对气体颗粒分配问题的适用性。将在24个背景,城市和工业场所收集的129个环境空气样品获得的与颗粒相关的分数的实验值与使用M5’和Junge-Pankow模型获得的预测结果进行了比较。当目标是根据颗粒相关相预测气相浓度时,提出的M5方法和学习的模型能够对某些低分子量化合物实现良好的相关性(相关系数> 0.9)。当转换为颗粒结合的分数值时,就预测误差而言,所选化合物的结果比Junge-Pankow模型获得的结果好几个数量级。

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