Kinetics and mechanisms of atmospheric hydrocarbon oxidation and particle formation

每立方厘米的空气中含有数百到10万个直径小于1微米的细小颗粒。这些粒子可以吸收和散射阳光，也可以作为云滴形成的核心。因此，它们在气候中扮演着重要角色；我们对这些颗粒如何形成的不完全知识是全球气候模型中不确定性的一个主要原因。研究还表明，这些颗粒物对人类健康有重大的负面影响。细颗粒主要是由一种称为气粒转化的过程形成的，在该过程中，二氧化硫、芳香烃(来自石油产品)和萜烯(由植物排放)等气体发生反应，将它们转化为化合物，如硫酸和含氧有机化合物，形成固体和液体颗粒。尽管进行了大量研究，但对有机化合物的气相转化过程仍然只有部分了解。这既是由于这些过程的巨大复杂性，也是因为测量这些反应中形成的许多化合物的难度。本项目将开展的工作将是研究这些反应的一种新方法；其目的是增加重要的新的理解。通常，研究这类系统的第一步是分离和鉴定形成的各种化合物。取而代之的是，我们将获得整个化合物组装的高时间分辨率质谱图，并使用统计技术将其分解成不同的“成分”，每个成分在时间上以不同的方式变化。其想法是，这些统计方法允许从数据中提取最大信息。然后，我们将使用化学动力学的方法来表征组件的时间依赖性。这将把这些成分置于化学反应的背景下，这将有助于最终确定与每个成分相对应的化合物。

Every cubic centimeter of air contains hundreds to over a hundred thousand fine particles with diameters less than one micrometer. These particles can absorb and scatter sunlight and also act as the nuclei on which cloud droplets form. As a result, they play a major role in climate; our incomplete knowledge of how these particles form is a major cause of uncertainty in global climate models. It has also been demonstrated that these particles have a significant negative impact on human health. Fine particles are largely formed by a process called gas-to-particle conversion in which gases, such as sulfur dioxide, aromatic hydrocarbons (from petroleum products) and terpenes (emitted by plants) undergo reactions that convert them to compounds, such as sulfuric acid and oxygenated organic compounds, that form solid and liquid particles.The gas-to-particle conversion process for organic compounds is still only partially understood in spite of a great deal of research. This is due to both the great complexity of these processes and the difficulty of measuring many of the chemical compounds formed in these reactions. The work to be undertaken in this project will be a new approach towards studying these reactions; it is intended that this will add significant new understanding. Normally, the first step in studying systems like these is to isolate and identify the various chemical compounds that are formed. We will instead obtain high time resolution mass spectra of the entire assembly of compounds and use statistical techniques to decompose these into various "components" that each vary in time in a different manner. The idea is that these statistical methods permit the extraction of maximum information from the data. We will then use the methods of chemical kinetics to characterize the time dependencies of the components. This will place the components in a context of chemical reactivity, which will be useful in eventually identifying the chemical compounds that correspond to each component.