10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
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Systematic Characterization of the Wideband Integrated Bioaerosol Sensor (WIBS), Including Fluorescence Thresholding and Clustering Analysis Strategies
NICOLE SAVAGE, Christine Krentz, Tobias Könemann, Taewon Han, Gediminas Mainelis, Christopher Pöhlker, J. Alex Huffman, University of Denver, now at Aerosol Devices
Abstract Number: 1451 Working Group: Bioaerosols
Abstract There has been a recent steep increase in the frequency of published studies utilizing commercial instrumentation based on ultraviolet laser/light-induced fluorescence (UV-LIF) for detection of bioaerosols both outdoors and in the built environment. Significant work over several decades supported the development of the general technologies, but efforts to systematically characterize the operation of new commercial sensors has remained lacking. Specifically, there have been gaps in the understanding of how different classes of biological and non-biological particles can influence the detection ability of LIF-instrumentation.
We will present a summary of two recently published papers [1,2] that have systematically characterized the wideband integrated bioaerosol sensor (WIBS-4A) instrument and whose conclusions can be broadly applied regarding other commercial UV-LIF bioaerosol sensors. The laboratory study used 69 types of aerosol materials, including a representative list of pollen, fungal spores, and bacteria as well as the most important groups of non-biological materials reported to exhibit interfering fluorescent properties [1]. We will highlight the importance that particle size plays on observed fluorescence properties and will also discuss several particle analysis strategies, including the commonly used fluorescence threshold defined as the mean instrument background (forced trigger; FT) plus 3 standard deviations (σ) of the measurement. Changing the particle fluorescence threshold was shown to have a significant impact on fluorescence fraction and particle type classification. We conclude that raising the fluorescence threshold from FT + 3σ to FT + 9σ does little to reduce the relative fraction of biological material considered fluorescent, but can significantly reduce the interference from mineral dust and other non-biological aerosols. In particular, we examined several classes of highly fluorescent interfering particles, including examples of brown carbon, diesel soot, and cotton fibers, and we will discuss how these may impact WIBS analysis and data interpretation in various indoor and outdoor environments.
Results of the systematic laboratory study were analyzed using a hierarchical agglomerative clustering (HAC) algorithm applied to WIBS data in a similar way as recently used by the team at the University of Manchester (e.g. [3]). We systematically investigated 19 one-on-one matchups of individual particles types and varied input parameters in order to optimize clustering quality and also tested scenarios suggested in the first paper [1] regarding fluorescence thresholding strategies. Finally, we tested the clustering methods against several synthetic mixtures of laboratory particles in order to understand possible limits to the HAC strategy and to understand how the analysis techniques can best be applied to complex real-world data.
An overview of the main conclusions from these two papers will be presented. The studies were designed to propose analysis strategies that may be useful to the broader community of UV-LIF instrumentation users in order to promote deeper discussions about how best to continue improving UV-LIF instrumentation and analysis strategies.
References [1] Savage, N. J., Krentz, C. E., Könemann, T., Han, T. T., Mainelis, G., Pöhlker, C., and Huffman, J. A.: Systematic characterization and fluorescence threshold strategies for the wideband integrated bioaerosol sensor (WIBS) using size-resolved biological and interfering particles, Atmos. Meas. Tech., 10, 4279-4302, https://doi.org/10.5194/amt-10-4279-2017, 2017. [2] Savage, N. J. and Huffman, J. A.: The evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data by comparing Data preparation techniques, Atmos. Meas. Tech., In Preparation, 2018. [3] Ruske, S., Topping, D. O., Foot, V. E., Kaye, P. H., Stanley, W. R., Crawford, I., Morse, A. P., and Gallagher, M. W.: Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer, Atmos. Meas. Tech., 10, 695, 2017.