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说法，“据估计，城市和农村地区的户外空气污染在2012年每年在全球造成370万例过早死亡；这种死亡率是由于暴露于[或“气溶胶” [或“气溶胶”）的直径为10微米或更少的直径，这会导致心血管和呼吸道疾病，以及呼吸道疾病，以及Cancers。”加拿大医学协会估计，加拿大人在2008年因空气污染的影响而死亡，空气污染影响的年度经济成本为80亿加元。此外，颗粒物在全球气候强迫（变暖和冷却）以及气候变化的影响中起着重要作用。“包括改变生态系统，粮食生产和供水的破坏，损害基础设施和定居点，发病率和死亡率”（跨期间面板对气候变化）的影响。但是，如果没有工具来量化颗粒物排放及其特性，这些进步是不可能的。气溶胶研究中的一种有用的仪器是空气动态气溶胶分类器（AAC）。该赠款的目的是为AAC创建数据反转算法，以便通过与其他Aerosol Instruments的串联测量值来测量粒度分布和基本粒子特性，例如粒子质量和形态。这将允许AAC在许多应用中使用：量化大气污染，测量颗粒过滤效率，确定药物气溶胶的肺剂量，表征用于毒理学研究的纳米粒子等。****** **********的进一步目标是使用AAC，以及AAC和iNversion Algorithms，以了解该角色，以了解全球的全球状况。特别是，研究将重点介绍i）了解当二次有机物质对其凝结和ii）煤气颗粒如何在大气中崩溃，而ii）凝结材料和烟灰结构的变化如何改变烟灰的光学特性。这个过程对气候变化的政府间小组在气候模型中的巨大不确定性中得到了鲜为人知的理解。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。**