Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets
Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets
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DOI：
10.5194/acp-15-8077-2015
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发表时间：
2015-07
影响因子：
6.3
通讯作者：
K. Wyche;P. Monks;K. Smallbone;J. Hamilton;M. Alfarra;A. Rickard;G. Mcfiggans;M. Jenkin;W. Bloss;Annette C Ryan;C. Hewitt;A. MacKenzie
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作者：
K. Wyche;P. Monks;K. Smallbone;J. Hamilton;M. Alfarra;A. Rickard;G. Mcfiggans;M. Jenkin;W. Bloss;Annette C Ryan;C. Hewitt;A. MacKenzie
抽象的。高度非线性的动力系统，如大气化学中的动力系统，需要采用分层方法进行实验和建模，以便最终确定和实现对完全开放系统的基本过程的理解。大气模拟室包括复杂性的中间，在经典的实验室实验和完整的环境系统之间。因此，它们可以生成大量难以解释的数据。在这里，我们描述和实施化学计量学降维方法的反卷积和解释复杂的气相和颗粒相组成的光谱。该方法包括主成分分析（PCA），层次聚类分析（HCA）和正最小二乘判别分析（PLS-DA）。这些方法是，第一次，适用于同时获得的气相和颗粒相组成的数据，从一系列全面的环境模拟室实验集中在生物挥发性有机化合物（BVOC）的光氧化和相关的二次有机气溶胶（SOA）的形成。我们主要研究了SOA的生物前体异戊二烯、α-蒎烯、柠檬烯、月桂烯、芳樟醇和β-香芹烯。化学计量学分析用于根据控制化学和形成的产物对氧化系统和所得SOA进行分类。结果表明，“模型”生物氧化系统可以成功地分离和分类，根据其氧化产物。此外，在气相和颗粒相获得的结果的整体视图显示了不同的SOA形成化学，在气相中开始，继续管理各种BVOC SOA组合物之间的差异。所获得的结果被用来描述在氧化的气相基质的上下文中的颗粒组合物。该技术的扩展，它纳入统计模型的数据，从人为（即甲苯）氧化和“更现实”的植物围隔系统，表明这样的合奏化学计量映射有可能被用于分类更复杂的光谱来源不明。更具体地说，除了从无花果和桦树实验中的围隔数据表明，异戊二烯和单萜排放源，分别可以映射到统计模型结构和它们的位置向量可以提供洞察它们的生物来源和控制氧化化学。扩展的方法来分析环境空气的潜力进行了讨论，使用从零维箱模型结合从主化学机制（MCMv3.2）获得的机械数据得到的结果。这种扩展到分析环境空气将证明是一个强大的资产，协助确定SOA的来源和阐明所涉及的基本化学机制。
Abstract. Highly non-linear dynamical systems, such as those found in atmospheric chemistry, necessitate hierarchical approaches to both experiment and modelling in order to ultimately identify and achieve fundamental process-understanding in the full open system. Atmospheric simulation chambers comprise an intermediate in complexity, between a classical laboratory experiment and the full, ambient system. As such, they can generate large volumes of difficult-to-interpret data. Here we describe and implement a chemometric dimension reduction methodology for the deconvolution and interpretation of complex gas- and particle-phase composition spectra. The methodology comprises principal component analysis (PCA), hierarchical cluster analysis (HCA) and positive least-squares discriminant analysis (PLS-DA). These methods are, for the first time, applied to simultaneous gas- and particle-phase composition data obtained from a comprehensive series of environmental simulation chamber experiments focused on biogenic volatile organic compound (BVOC) photooxidation and associated secondary organic aerosol (SOA) formation. We primarily investigated the biogenic SOA precursors isoprene, α-pinene, limonene, myrcene, linalool and β-caryophyllene. The chemometric analysis is used to classify the oxidation systems and resultant SOA according to the controlling chemistry and the products formed. Results show that "model" biogenic oxidative systems can be successfully separated and classified according to their oxidation products. Furthermore, a holistic view of results obtained across both the gas- and particle-phases shows the different SOA formation chemistry, initiating in the gas-phase, proceeding to govern the differences between the various BVOC SOA compositions. The results obtained are used to describe the particle composition in the context of the oxidised gas-phase matrix. An extension of the technique, which incorporates into the statistical models data from anthropogenic (i.e. toluene) oxidation and "more realistic" plant mesocosm systems, demonstrates that such an ensemble of chemometric mapping has the potential to be used for the classification of more complex spectra of unknown origin. More specifically, the addition of mesocosm data from fig and birch tree experiments shows that isoprene and monoterpene emitting sources, respectively, can be mapped onto the statistical model structure and their positional vectors can provide insight into their biological sources and controlling oxidative chemistry. The potential to extend the methodology to the analysis of ambient air is discussed using results obtained from a zero-dimensional box model incorporating mechanistic data obtained from the Master Chemical Mechanism (MCMv3.2). Such an extension to analysing ambient air would prove a powerful asset in assisting with the identification of SOA sources and the elucidation of the underlying chemical mechanisms involved.