Inversion Algorithms for the Aerodynamic Aerosol Classifier with Applications

气动气溶胶分级器的反演算法及其应用

• 批准号: RGPIN-2015-05822
• 负责人: Olfert, Jason
• 金额: $ 2.48万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661182
• 关键词: Inversion; Algorithms; Aerodynamic; Aerosol; Classifier

According to the World Health Organization "outdoor air pollution in both cities and rural areas was estimated to cause 3.7 million premature deaths worldwide per year in 2012; this mortality is due to exposure to [suspended] particulate matter [or `aerosol'] of 10 microns or less in diameter, which cause cardiovascular and respiratory disease, and cancers." The Canadian Medical Association estimates 21,000 Canadians died from the effects of air pollution in 2008 and the annual economic cost of the effects of air pollution is $8 billion. Furthermore, particulate matter plays a major role in global climate forcing (both warming and cooling) and the impacts of climate change "include alteration of ecosystems, disruption of food production and water supply, damage to infrastructure and settlements, morbidity and mortality" (Intergovernmental Panel on Climate Change).******The negative effects of particulate matter can be reduced through technological advancements and regulations. However, these advancements are not possible without instruments to quantify particulate matter emissions and their properties. One useful instrument in aerosol research is the Aerodynamic Aerosol Classifier (AAC). The objective of this grant is to create data inversion algorithms for the AAC so that it can be used to measure particle size distributions and fundamental particle properties like particle mass and morphology through tandem measurements with other aerosol instruments. This will allow the AAC to be used in many applications including: quantifying atmospheric pollution, measuring particle filtration efficiency, determining lung dose of pharmaceutical aerosols, characterizing nanoparticles for toxicology studies, among others.******A further objective of this grant is to use the AAC, and the inversion algorithms, to understand the role soot has on global warming. In particular the research will focus on i) understanding how soot particles can collapse in the atmosphere when secondary organic material condenses on them and ii) how the condensed material, and changes in soot structure, changes the optical properties of the soot. This process is poorly understood and has been highlighted by the Intergovernmental Panel on Climate Change as a large uncertainty in climate models. Reducing uncertainty in climate models is essential for developing appropriate environmental policies and regulations.**

根据世界卫生组织的数据，“据估计，2012年全球城市和农村地区的室外空气污染每年导致370万人过早死亡；这些死亡是由于暴露在直径不超过10微米的[悬浮]颗粒物[或‘气溶胶’]中，这会导致心血管和呼吸系统疾病以及癌症。”加拿大医学会估计，2008年有2.1万加拿大人死于空气污染的影响，空气污染的影响每年造成的经济损失为80亿美元。此外，颗粒物在全球气候强迫(变暖和降温)中发挥着重要作用，气候变化的影响“包括生态系统的改变、粮食生产和供水的中断、对基础设施和住区的破坏、发病率和死亡率”(政府间气候变化专门委员会)。然而，如果没有量化颗粒物排放及其特性的工具，这些进步是不可能的。气溶胶研究中的一个有用仪器是空气动力学气溶胶分类器(AAC)。这笔赠款的目的是为AAC创建数据反演算法，以便它可以通过与其他气溶胶仪器的串联测量来测量颗粒尺寸分布和基本颗粒特性，如颗粒质量和形态。这将允许AAC用于许多应用，包括：量化大气污染、测量颗粒过滤效率、确定药物气雾剂的肺部剂量、表征用于毒理学研究的纳米颗粒等。*这笔赠款的另一个目标是使用AAC和反演算法，以了解烟尘对全球变暖的作用。特别是，研究的重点将是：一)了解二次有机物质凝聚时，烟尘颗粒如何在大气中坍塌；二)凝聚物质和烟尘结构的变化，如何改变烟尘的光学性质。人们对这一过程知之甚少，政府间气候变化专门委员会强调，这是气候模型中的一大不确定性。减少气候模型中的不确定性对于制定适当的环境政策和法规至关重要。