Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning
Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning
复制标题
使用监督机器学习通过单颗粒烟灰光度计对氧化铁气溶胶进行分类
DOI：
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发表时间：
2019
影响因子：
3.8
通讯作者：
K. Lamb
中科院分区：
文献类型：
--
作者：
K. Lamb
抽象的。单颗粒烟尘光度计（SP2）使用激光诱导白炽来检测单个颗粒的气溶胶。SP2经过改进，在其窄带和宽带白炽探测器之间提供更大的光谱对比度，此前已用于表征耐火炭黑（rBC）和光吸收金属气溶胶，包括氧化铁（FeOx）。然而，单粒子不能从它们的白炽峰高（粒子质量的函数）和色比（黑体温度的测量）单独明确地识别。机器学习为改善这些气溶胶的分类提供了一种有前途的方法。在这里，我们探讨了使用监督机器学习算法对用改进的SP2获得的单粒子信号进行分类的优点和局限性。不同的气溶胶，白炽在SP2（富勒烯烟灰，矿物粉尘，火山灰，粉煤灰，Fe 2 O3和Fe 3 O 4）的实验室样品被用来训练一个随机森林算法。然后将训练好的算法应用于实验室样品和大气气溶胶的测试数据集。该方法通过提供颗粒可能属于特定气溶胶类别（rBC、FeOx等）的分数或条件概率，提供了用于对白炽气溶胶进行分类的系统方法。考虑到其观察到的单粒子特征。我们考虑两种替代方法，以确定气溶胶在混合人口的基础上，他们的单颗粒SP2响应：一个特定的类标签为每个采样的物种，和一个更广泛的类（rBC，人为FeOx，灰尘样）的颗粒与类似的SP2响应。将基于将训练的随机森林算法应用于包括每个类别的示例的测试数据集的单粒子特征的最可能的粒子类别（具有最高平均概率的粒子类别）的预测与那些粒子的真实类别进行比较，以估计泛化性能。虽然特定类别方法对rBC和Fe 3 O 4表现良好（≥ 99%的这些气溶胶被正确识别），但其对其他气溶胶类型的分类明显较差（只有47%-66%的其他颗粒被正确识别）。使用更广泛的类的方法，我们发现在实验室中测量的FeOx样品的分类准确率为99%。该方法允许将FeOx分类为人为的或灰尘样的气溶胶，有效球直径为170至>1200纳米。类尘气溶胶和rBC被误认为是人为FeOx的情况很小，对于更广泛的类别情况，<3%的类尘气溶胶和<0.1%的rBC被误认为是FeOx。当应用这种方法在博尔德，CO采取的大气观测，一个明确的模式与FeOx一致，观察到不同于粉尘状气溶胶。
Abstract. Single particle soot photometers (SP2) use laser-induced incandescence to detect aerosols on a single particle basis. SP2s that have been modified to provide greater spectral contrast between their narrow and broad-band incandescent detectors have previously been used to characterize both refractory black carbon (rBC) and light-absorbing metallic aerosols, including iron oxides (FeOx). However, single particles cannot be unambiguously identified from their incandescent peak height (a function of particle mass) and color ratio (a measure of blackbody temperature) alone. Machine learning offers a promising approach for improving the classification of these aerosols. Here we explore the advantages and limitations of classifying single particle signals obtained with a modified SP2 using a supervised machine learning algorithm. Laboratory samples of different aerosols that incandesce in the SP2 (fullerene soot, mineral dust, volcanic ash, coal fly ash, Fe2O3, and Fe3O4) were used to train a random forest algorithm. The trained algorithm was then applied to test data sets of laboratory samples and atmospheric aerosols. This method provides a systematic approach for classifying incandescent aerosols by providing a score, or conditional probability, that a particle is likely to belong to a particular aerosol class (rBC, FeOx, etc.) given its observed single particle features. We consider two alternative approaches for identifying aerosols in mixed populations based on their single particle SP2 response: one with specific class labels for each species sampled, and one with three broader classes (rBC, anthropogenic FeOx, and dust-like) for particles with similar SP2 responses. Predictions of the most likely particle class (the one with the highest mean probability) based on applying the trained random forest algorithm to the single particle features for test data sets comprising examples of each class are compared with the true class for those particles to estimate generalization performance. While the specific class approach performed well for rBC and Fe3O4 (≥99 % of these aerosols are correctly identified), its classification of other aerosol types is significantly worse (only 47 %–66 % of other particles are correctly identified). Using the broader class approach, we find a classification accuracy of 99 % for FeOx samples measured in the laboratory. The method allows for classification of FeOx as anthropogenic or dust-like for aerosols with effective spherical diameters from 170 to >1200 nm. The misidentification of both dust-like aerosols and rBC as anthropogenic FeOx is small, with <3 % of the dust-like aerosols and <0.1 % of rBC misidentified as FeOx for the broader class case. When applying this method to atmospheric observations taken in Boulder, CO, a clear mode consistent with FeOx was observed, distinct from dust-like aerosols.
影响因子：
3.8
作者：
N. Robinson;J. Allan;J. A. Huffman;P. Kaye;Virginia Foot;M. Gallagher
通讯作者：
N. Robinson;J. Allan;J. A. Huffman;P. Kaye;Virginia Foot;M. Gallagher