Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers
Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers
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DOI：
10.3390/atmos11101039
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发表时间：
2020-10-01
期刊：
影响因子：
2.9
通讯作者：
Kaye, Paul
中科院分区：
文献类型：
--
作者：
Crawford, Ian;Topping, David;Kaye, Paul
我们展示了一项研究的结果，该研究评估了监督机器学习对单粒子紫外激光诱导荧光 (UV-LIF) 特征进行分类的效用，以使用多参数生物气溶胶光谱仪研究繁忙的多功能建筑中空气中初级生物气溶胶颗粒 (PBAP) 的浓度。首先，我们介绍并演示梯度增强集成决策树算法能够以高精度将实验室生成的 PBAP 样本准确地分类为广泛的分类类别。然后，我们开发了一个框架来评估分类精度和性能，使用 Hellinger 距离度量来比较产品参数概率密度函数相似性；该框架表明，关键训练课程在粒子荧光和形态方面有足够的不同，以利于分类。我们还证明了包含高级形态参数的实用性，以最大限度地减少类间合并并提高分类置信度，而仅依赖荧光光谱可能会导致错误归因。最后，我们将这些方法应用于大型多功能建筑内收集的环境数据，其中识别出环境细菌和真菌类类别，以显示与人类活动相对应的趋势；类真菌类别表现出一致的昼夜趋势，中午和每小时的峰值达到最大值，这与建筑物内的运动相关；类似细菌的气溶胶在开放时间内表现出复杂的、偶发的事件。当建筑物夜间和周末无人居住时，所有 PBAP 类别均降至较低的基线浓度。
We present results from a study evaluating the utility of supervised machine learning to classify single particle ultraviolet laser-induced fluorescence (UV-LIF) signatures to investigate airborne primary biological aerosol particle (PBAP) concentrations in a busy, multifunctional building using a Multiparameter Bioaerosol Spectrometer. First we introduce and demonstrate a gradient boosting ensemble decision tree algorithm's ability to accurately classify laboratory generated PBAP samples into broad taxonomic classes with a high level of accuracy. We then develop a framework to appraise the classification accuracy and performance using the Hellinger distance metric to compare product parameter probability density function similarity; this framework showed that key training classes were sufficiently different in terms of particle fluorescence and morphology to facilitate classification. We also demonstrate the utility of including advanced morphological parameters to minimise inter-class conflation and improve classification confidence, where relying on the fluorescent spectra alone would likely result in misattribution. Finally, we apply these methods to ambient data collected within a large multi-functional building where ambient bacterial- and fungal-like classes were identified to display trends corresponding to human activity; fungal-like classes displayed a consistent diurnal trend with a maximum at midday and hourly peaks correlating to movements within the building; bacteria-like aerosol displayed complex, episodic events during opening hours. All PBAP classes fell to low baseline concentrations when the building was unoccupied overnight and at weekends.