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

摘要：原生生物气溶胶粒子（Primary Biological Aerosol Particles， PBAP）在大气中作为凝结核（CCN）和冰核（in）发挥着关键作用，并通过气体/粒子分配在挥发性和非挥发性二次有机气溶胶（SOA）的形成中发挥着关键作用。同样，他们在公共卫生和国防方面的作用经常被证明对社会至关重要，正在发生的Covid-19病毒只是一个例子。新兴的实时自主检测技术的发展，如紫外光诱导荧光（UV-LIF），除了进一步加深我们对这些基本大气相互作用和过程的理解外，还代表了预警检测系统的重大突破。然而，尽管取得了这些进展，实质性的挑战仍然存在。到目前为止，在多种仪器上正确计算和分类如此大量数据所需的算法还不存在，而且，在本提案提出时，主要限于单个探索性研究，测试少数几种方法，而没有严格的审查或与其他仪器的直接比较。因此，目前缺乏能够区分大量变量（真菌、病毒、细菌、花粉等）和弱荧光非生物颗粒的标准化、健壮的算法，这继续限制了UV-LIF仪器的潜力。本提案将探讨当前行业内使用的检测方法，同时也强调每种方法的局限性以及本研究如何提出解决这些问题的方法。本研究将对现有算法进行评估和改进，以提高UV-LIF仪器的分类能力，并制定从高分辨率仪器到低分辨率仪器的信息映射策略，以构建更强大的检测网络。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。

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.