Building flexible biological particle detection algorithms for emerging real-time instrumentation

为新兴实时仪器构建灵活的生物颗粒检测算法

• 批准号: 2278799
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2278799
• 关键词: Building; flexible; biological; particle; detection

Abstract: Primary Biological Aerosol Particles (PBAP) are understood to play key roles in the atmosphere as condensation nuclei (CCN), ice nuclei (IN), and in the formation of both volatile and non-volatile secondary organic aerosols (SOA) by gas/particle partitioning. Equally, their role in public health and national defense is frequently shown to be of paramount importance to society, with the ongoing Covid-19 virus being just one example. The development of emerging real-time autonomous detection techniques such as ultra violet light induced fluorescence (UV-LIF) represent significant breakthroughs in early warning detection systems, in addition to furthering our understanding of these fundamental atmospheric interactions and processes. Yet, despite these advances, substantial challenges still remain. As of yet, the algorithms required to correctly compute and classify such vast amounts of data over multiple instruments does not exist, and, at the time of this proposal, is mostly limited to individual exploratory studies testing a handful of methods without rigorous review or direct comparison with other instruments. Thus, there is a current lack of standardised, robust algorithms which can differentiate between large numbers of variables (fungi,viruses, bacteria, pollen etc) and weakly fluorescent non-biological particles which continues to limit the potential of UV-LIF instruments. This proposal shall explore current detection methods used within the industry whilst also highlighting the limitations of each and how this research proposes to address them. This research shall evaluate and build upon existing algorithms in order to improve classification capabilities of UV-LIF instrumentation and develop strategies for mapping information from high resolution to low resolution instruments to build more robust detection networks. Implications of this research include detection systems which are more reliable and applicable over broader sets of variables and over a variety of UV-LIF instruments.

摘要：一次生物气溶胶颗粒（PBAP）在大气中作为凝结核（CCN）、冰核（IN）以及通过气/粒分配形成挥发性和非挥发性二次有机气溶胶（SOA）中起着关键作用。同样，他们在公共卫生和国防中的作用经常被证明对社会至关重要，持续的新冠病毒只是一个例子。新兴的实时自主探测技术，如紫外线诱导荧光（UV-LIF）的发展代表了预警探测系统的重大突破，除了进一步加深我们对这些基本的大气相互作用和过程的理解。然而，尽管取得了这些进展，仍然存在重大挑战。到目前为止，在多个仪器上正确计算和分类如此大量的数据所需的算法还不存在，并且在本提案提出时，主要限于测试少数方法的个别探索性研究，而没有严格的审查或与其他仪器的直接比较。因此，目前缺乏标准化的、鲁棒的算法，该算法可以区分大量变量（真菌、病毒、细菌、花粉等）和弱荧光非生物颗粒，这继续限制UV-LIF仪器的潜力。该提案将探讨行业内使用的当前检测方法，同时强调每种方法的局限性以及本研究如何解决这些问题。这项研究将评估和建立在现有的算法，以提高UV-LIF仪器的分类能力，并制定从高分辨率到低分辨率仪器的映射信息的策略，以建立更强大的检测网络。这项研究的意义包括检测系统，更可靠，适用于更广泛的变量集和各种UV-LIF仪器。