Department of Civil and Environmental Engineering, Environmental Engineering Program, University of Southern California, Los Angeles, California 90089-2531, USA.
Environ Sci Technol. 2010 Apr 1;44(7):2474-81. doi: 10.1021/es9018095.
Two easily available multivariate source apportionment models, Unmix and positive matrix factorization (PMF), often produce nearly the same source apportionment. However, this paper gives two examples in which this is not the case: a simulated air pollution data set of 8 species and 200 samples and a water quality data set of 32 PCB congeners and 106 sediment core samples from Sheboygan River Inner Harbor, WI. In the first case, a basic form of PMF fails primarily because the source compositions do not have any species with zero or near zero concentrations. Unmix produces source compositions and contributions that are much closer to the true values. A version of PMF with an adjustable parameter also gives good results. In the second case, each model found 5 sources for the Sheboygan PCB sediment data. PMF determined sources compositions were consistent with the original 50/50% Aroclor 1248/1254 mixture, a previously determined prominent dechlorination profile (processes H' + M), and three other partially dechlorinated profiles. The Unmix determined source compositions were not as successful as the Unmix results depended heavily on just three data points. Source apportionment results favor Unmix when edges in the data are well-defined and PMF when several zeros are present in the loading and score matrices. Since both models are seen to have potential weaknesses, both should be applied in all cases. If the two methods do not produce similar results the methods given in the paper can be used to select the model result most likely to be closest to the truth.
两种易于使用的多元源解析模型，Unmix 和正矩阵因子分解 (PMF)，通常会产生几乎相同的源解析结果。然而，本文给出了两个并非如此的例子：一个 8 种物质和 200 个样本的模拟空气污染数据集，以及一个 Sheboygan 河内港的 32 种 PCB 同系物和 106 个沉积物芯样本的水质数据集。在第一个例子中，基本形式的 PMF 主要因为源成分没有任何零浓度或接近零浓度的物质而失败。Unmix 产生的源成分和贡献更接近真实值。具有可调参数的 PMF 版本也能给出很好的结果。在第二个例子中，每个模型都为 Sheboygan PCB 沉积物数据找到了 5 个来源。PMF 确定的源成分与原始的 50/50% Aroclor 1248/1254 混合物、先前确定的明显脱氯谱（H' + M 过程）以及其他三个部分脱氯谱一致。Unmix 确定的源成分不如 PMF 成功，因为 Unmix 的结果严重依赖于仅仅三个数据点。当数据中的边缘定义明确时，源解析结果更倾向于 Unmix，而当加载和得分矩阵中存在多个零时，更倾向于 PMF。由于两种模型都被认为存在潜在的弱点，因此在所有情况下都应该应用这两种方法。如果两种方法没有产生相似的结果，则可以使用本文中给出的方法来选择最有可能接近真相的模型结果。
