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Number concentrations of fine and ultrafine particles containing metals

机译：含金属的细颗粒和超细颗粒的数量浓度

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Typical classification schemes for large data sets of single-particle mass spectra involve statistical or neural network analysis. In this work, a new approach is evaluated in which particle spectra are pre-selected on the basis of an above threshold signal intensity at a specified m/z (mass to charge ratio). This provides a simple way to identify candidate particles that may contain the specific chemical component associated with that m/z. Once selected, the candidate particle spectra are then classified by the fast adaptive resonance algorithm, ART 2-a, to confirm the presence of the targeted component in the particle and to study the intra-particle associations with other chemical components. This approach is used to characterize metals in a 75,000 particle data set obtained in Baltimore, Maryland. Particles containing a specific metal are identified and then used to determine the size distribution, number concentration, time/wind dependencies and intra-particle correlations with other metals. Four representative elements are considered in this study: vanadium, iron, arsenic and lead. Number concentrations of ambient particles containing these elements can exceed 10,000 particles cm~(-3) at the measurement site. Vanadium, a primary marker for fuel oil combustion, is observed from all wind directions during this time period. Iron and lead are observed from the east-northeast. Most particles from this direction that contain iron also contain lead and most particles that contain lead also contain iron, suggesting a common emission source for the two. Arsenic and lead are observed from the south-southeast. Particles from this direction contain either arsenic or lead but rarely both, suggesting different sources for each element.
机译：大数据集的单颗粒质谱的典型分类方案涉及统计或神经网络分析。在这项工作中，评估了一种新方法，其中基于高于阈值的信号强度以指定的m / z（质荷比）预先选择粒子光谱。这提供了一种简单的方法来识别可能包含与该m / z相关的特定化学成分的候选粒子。选择后，然后通过快速自适应共振算法ART 2-a对候选粒子光谱进行分类，以确认目标成分在粒子中的存在并研究粒子内与其他化学成分的关联。该方法用于表征在马里兰州巴尔的摩获得的75,000个粒子数据集中的金属。识别包含特定金属的粒子，然后将其用于确定尺寸分布，数量浓度，时间/风依赖性以及与其他金属的粒子内相关性。本研究考虑了四个代表性元素：钒，铁，砷和铅。包含这些元素的环境颗粒的数量浓度在测量点可能超过10,000 cm〜（-3）。在这段时间内，从所有风向都可以观察到钒，它是燃料燃烧的主要标志。从东北向观察到铁和铅。从这个方向来看，大多数包含铁的颗粒也包含铅，并且大多数包含铅的颗粒也包含铁，这暗示了两者的共同排放源。从东南偏南观察到砷和铅。从这个方向来看，粒子含有砷或铅，但很少同时含有砷和铅，这表明每种元素的来源不同。

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