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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613769
• 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.

据世界卫生组织称，“2012年，城市和农村地区的室外空气污染估计每年导致全球370万人过早死亡;这一死亡率是由于暴露于直径10微米或更小的[悬浮]颗粒物[或'气溶胶']，导致心血管和呼吸系统疾病以及癌症。加拿大医学协会估计，2008年有21，000名加拿大人死于空气污染的影响，空气污染影响的年度经济成本为80亿美元。此外，颗粒物在全球气候强迫（升温和降温）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、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）。 颗粒物的负面影响可以通过技术进步和法规来减少。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。气溶胶研究中的一种有用的仪器是气动力气溶胶分类器（AAC）。这项资助的目的是为AAC创建数据反演算法，以便它可以用于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剂量，表征毒理学研究的纳米颗粒等。 这项资助的另一个目标是使用AAC和反演算法来了解煤烟对全球变暖的作用。特别是研究将集中在i）了解如何煤烟颗粒可以在大气中崩溃时，二次有机材料凝聚在他们和ii）如何凝聚的材料，并在煤烟结构的变化，改变煤烟的光学性质。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个很大的不确定性。减少气候模型的不确定性对于制定适当的环境政策和法规至关重要。