Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol
Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol
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DOI：
10.5194/amt-8-4979-2015
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发表时间：
2015-01-01
影响因子：
3.8
通讯作者：
Gallagher, M. W.
中科院分区：
文献类型：
--
作者：
Crawford, I.;Ruske, S.;Gallagher, M. W.
本文将层次聚集聚类分析应用于多参数紫外光诱导荧光(UV-LIF)光谱仪数据，提出了一种改进的初级生物气溶胶粒子(PBAP)识别和定量方法。这项研究中使用的方法可以应用于台式计算机上超过1×10(6)点的数据集，从而允许数据集中的每个荧光粒子明确地聚集在一起。这降低了以前方法中使用的子采样和比较归因方法中发现的错误归因的可能性，提高了我们区分和量化PBAP元类的能力。我们使用已知粒子类型的实验室样本和环境数据集来评估几种分级凝聚聚类分析链接和数据归一化方法的性能。荧光和非荧光聚苯乙烯乳胶球使用宽带集成生物气溶胶光谱仪(WIBS-4)采样，其中光学尺寸、不对称因子和荧光测量作为分析包的输入。结果发现，带有z分数或极差归一化的Ward连锁法表现最好，分别正确归因于98%和98.1%的数据点。表现最好的方法被应用于BEACHON-ROMBAS(能量、气溶胶、碳、水、有机物和氮-落基山生物气溶胶研究的生物-水-大气相互作用)环境数据集，其中发现z分数和范围归一化方法产生类似的结果，每种方法产生代表真菌孢子和细菌气溶胶的集群，与先前的结果一致。将z-Score结果与以前的方法(WIBS分析程序，WASP)生成的集群进行比较，我们观察到WASP采用的亚采样和比较归属方法导致真菌孢子浓度被高估了1.5倍，细菌气溶胶浓度被低估了5倍。我们认为这可能是由于错误归因于错误的归属，这是由于WASP采用的亚采样和比较归属方法导致的错误归因于较差的质心定义和未能将颗粒分配到集群。与以前的方法相比，这里使用的方法允许分析整个荧光粒子群体，为每个粒子产生明确的集群属性，并提高集群质心清晰度和我们区分和量化PBAP元类的能力。
In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs) by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 x 10(6) points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in sub-sampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set.Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1% of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen-Rocky Mountain Biogenic Aerosol Study) ambient data set, where it was found that the z -score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z -score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the sub-sampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misattribution due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP. The methods used here allow for the entire fluorescent population of particles to be analysed, yielding an explicit cluster attribution for each particle and improving cluster centroid definition and our capacity to discriminate and quantify PBAP meta-classes compared to previous approaches.