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Regressing gas/particle partitioning data for polycyclic aromatic hydrocarbons

机译：多环芳烃的回归气体/颗粒分配数据

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Polycyclic aromatic hydrocarbons (PAHs) were measured in the rural atmosphere of Southern Ontario, Canada from October 2001 to November 2002. Sixty seven pairs of gaseous and particle-bound concentrations of PAHs were determined concurrently in a forest and a clearing. The gas/particle partitioning behavior of the PAHs was investigated by fitting the original Junge-Pankow equation to the fraction in the particle phase phi for each set of measured data, either allowing the slope m to deviate from -1 (two-parameter model) or not (one-parameter model). This fitting procedure was judged more robust than linear logarithmic regressions involving the gas/particle partition coefficient, because the latter is sensitive to the applied blank correction, tends to ignore a significant amount of analytical information, and gives undue weight to more uncertain data points. The experimental data fit was good for both nonlinear models, and discrepancies between experimental data and models and between models are mostly related to sampling/experimental artifacts. In particular, samples taken close to the freezing point appear to suffer from blow-off artifacts. Applying slopes m different from -1 appears only justified if it can be assured that a second parameter indeed provides a better fit and that this better fit is not due to experimental, analytical, or statistical artifacts. The magnitude of the differences in the model fitting parameters between sampling events is consistent with the reported variability in the nature and concentration of atmospheric particles. Statistical tests on the regression results indicate that the gas/particle partitioning was not significantly different between the forest and the clearing.
机译：2001年10月至2002年11月，在加拿大安大略省南部的乡村大气中测量了多环芳烃（PAHs）。同时在森林和一片荒地中同时测定了67对气态和颗粒结合的PAHs浓度。通过将原始的Junge-Pankow方程拟合到每组测量数据的粒子相phi中的分数来研究PAH的气体/粒子分配行为，或者允许斜率m偏离-1（两参数模型）是否（单参数模型）。该拟合程序被认为比涉及气体/颗粒分配系数的线性对数回归更为稳健，因为后者对所应用的空白校正很敏感，往往会忽略大量分析信息，并给更多不确定的数据点以不适当的权重。实验数据拟合对于两个非线性模型都是好的，实验数据与模型之间以及模型之间的差异主要与采样/实验伪像有关。尤其是，接近冰点的样品似乎会出现吹散伪影。仅当可以确保第二个参数确实提供了更好的拟合度并且该更好的拟合度不是由于实验，分析或统计伪像时，才应用与-1不同的斜率m似乎是合理的。采样事件之间的模型拟合参数差异的大小与所报告的大气颗粒的性质和浓度的变化一致。对回归结果的统计测试表明，森林和砍伐之间的气体/颗粒分配没有显着差异。

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