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

摘要：初级生物气溶胶粒子(PBAP)在大气中起着凝结核(CCN)和冰核(IN)的作用，并通过气体/颗粒分配形成挥发性和非挥发性次级有机气溶胶(SOA)。同样，它们在公共卫生和国防中的作用经常被证明是社会最重要的，正在进行的新冠肺炎病毒只是一个例子。紫外光诱导荧光(UV-LIF)等实时自主探测技术的发展代表着早期预警探测系统的重大突破，同时也加深了我们对这些基本大气相互作用和过程的理解。然而，尽管取得了这些进展，但仍然存在重大挑战。到目前为止，还不存在在多个工具上正确计算和分类如此大量数据所需的算法，而且在本提案提出时，主要限于测试少数方法的个别探索性研究，而不是严格审查或与其他工具进行直接比较。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。该提案将探讨目前在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建议如何解决这些局限性。这项研究将在现有算法的基础上进行评估，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战略，以建立更强大的检测网络。这项研究的意义包括在更广泛的变量集和各种UV-LIF仪器上更可靠和更适用的检测系统。

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.