Inversion Algorithms for the Aerodynamic Aerosol Classifier with Applications

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在全球造成370万人过早死亡；这种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’），这会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（变暖和变冷）中起着重要作用，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）。

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.