Secondary Organic Aerosol Formation by Partitioning and Heterogeneous Reactions: Parameterization, Characterization, and Predictive Model

通过分配和非均相反应形成二次有机气溶胶：参数化、表征和预测模型

• 批准号: 0757443
• 负责人: Myoseon Jang
• 金额: $ 15.5万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-24 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757443&HistoricalAwards=false
• 关键词: Secondary; Organic; Aerosol; Formation; Partitioning

The goals of this project are (1) to better understand secondary organic aerosol (SOA) formation by investigating partitioning and heterogeneous chemistry in SOA, and (2) to develop a model for predicting SOA formation that includes gas phase kinetics, partitioning, and the effects of heterogeneous chemistry in acidic SOA. Experiments will be carried out in chambers and the gas phase species and products will be analyzed with a wide variety of chemical and physical techniques. Data will be obtained on SOA formation from various precursor gases, including effects of relative humidity, temperature, and aerosol seed composition. An initial model of heterogeneous SOA formation will be developed for medium and strongly acidic aerosols; it will then be extrapolated to weakly acidic or neutral conditions, which more realistically reflect the conditions of ambient air. This project will lead to insights into sources of global SOA that are not explained by conventional partitioning theory. Undergraduates and graduate students will participate in this project and learn about experimental techniques and modeling. This training will help them establish a strong foundation for successful careers in atmospheric environmental chemistry. The project will also support the research efforts of a team of scientists with diverse backgrounds.

该项目的目标是(1)通过研究二次有机气溶胶(SOA)中的分配和非均相化学来更好地理解SOA的形成，以及(2)开发一个预测SOA形成的模型，该模型包括气相动力学、分配以及酸性SOA中非均相化学的影响。实验将在实验室中进行，并将使用各种化学和物理技术对气相物种和产品进行分析。将获得各种前体气体形成SOA的数据，包括相对湿度、温度和气溶胶种子成分的影响。对于中酸性和强酸性气溶胶，将建立一个非均相的SOA形成的初始模型；然后将其外推到弱酸性或中性条件，从而更真实地反映环境空气的状况。该项目将导致对传统划分理论无法解释的全球SOA来源的洞察。本科生和研究生将参与这个项目，并学习实验技术和建模。这一培训将帮助他们为在大气环境化学领域取得成功奠定坚实的基础。该项目还将支持一个具有不同背景的科学家团队的研究工作。