Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers

Recent progress in online detection methods of bioaerosols

The aerosol transmission of coronavirus disease in 2019, along with the spread of other respiratory diseases, caused significant loss of life and property; it impressed upon us the importance of real-time bioaerosol detection. The complexity, diversity, and large spatiotemporal variability of bioaerosols and their external/internal mixing with abiotic components pose challenges for effective online bioaerosol monitoring. Traditional methods focus on directly capturing bioaerosols before subsequent time-consuming laboratory analysis such as culture-based methods, preventing the high-resolution time-based characteristics necessary for an online approach. Through a comprehensive literature assessment, this review highlights and discusses the most commonly used real-time bioaerosol monitoring techniques and the associated commercially available monitors. Methods applied in online bioaerosol monitoring, including adenosine triphosphate bioluminescence, laser/light-induced fluorescence spectroscopy, Raman spectroscopy, and bioaerosol mass spectrometry are summarized. The working principles, characteristics, sensitivities, and efficiencies of these real-time detection methods are compared to understand their responses to known particle types and to contrast their differences. Approaches developed to analyze the substantial data sets obtained by these instruments and to overcome the limitations of current real-time bioaerosol monitoring technologies are also introduced. Finally, an outlook is proposed for future instrumentation indicating a need for highly revolutionized bioaerosol detection technologies.

Airborne Prokaryotic, Fungal and Eukaryotic Communities of an Urban Environment in the UK

Bioaerosols often contain human pathogens and allergens affecting public health. However, relatively little attention has been given to bioaerosols compared with non-biological aerosols. In this study, we aimed to identify bioaerosol compositions in Manchester, UK by applying high throughput sequencing methods and to find potential sources. Samples were collected at Manchester Air Quality Super Site at the Firs Environmental Research Station in November 2019 and in February 2020. Total DNA has been extracted and sequenced targeting the 16S rRNA gene of prokaryotes, ITS region of fungal DNA and 18S rRNA gene of eukaryotes. We found marine environment-associated bacteria and archaea were relatively more abundant in the February 2020 samples compared with the November 2019 samples, consistent with the North West marine origin based on wind back-trajectory analysis. In contrast, an OTU belonging to Methylobacterium, which includes many species resistant to heavy metals, was relatively more abundant in November 2019 when there were higher metal concentrations. Fungal taxa that fruit all year were relatively more abundant in the February 2020 samples while autumn fruiting species generally had higher relative abundance in the November 2019 samples. There were higher relative abundances of land plants and algae in the February 2020 samples based on 18S rRNA gene sequencing. One of the OTUs belonging to the coniferous yew genus Taxus was more abundant in the February 2020 samples agreeing with the usual pollen season of yews in the UK which is from mid-January until late April. The result from this study suggests a potential application of bioaerosol profiling for tracing the source of atmospheric particles.

Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier

The share of non-exhaust particles, including tire wear particles (TWP), within the airborne dust and particularly within PM10 has increased in recent years due to a significant reduction of other particles including exhaust road traffic emissions. However, the quantification of TWP is a demanding task due to the non-specificity of tracers, and the fact that they are commonly contained in analytically challenging low concentrations (e.g. Zn, styrene, 1,3-butadiene, vinylcyclohexene). This difficulty is amplified by the chemical and morpho-textural heterogeneity of TWP resulting from the interaction between the tires and the road surface. In contrast to bulk techniques, automated single particle SEM/EDX analysis can benefit from the ubiquitous heterogeneity of environmental TWP as a diagnostic criterion for their identification and quantification. For this purpose, we follow a machine-learning (ML) approach that makes use of an extensive number (67) of morphological, textural (backscatter-signal based) and chemical descriptors to differentiate environmental particles into the following classes: TWP, metals, minerals and biogenic/organic. We present a ML-based model developed to classify airborne samples (trained by >100,000 environmental particles including 6841 TWP), and its application within a one-year monitoring campaign at two Swiss sites. In this study, the mass concentrations of TWP in the airborne fractions PM80-10, PM10-2.5 and PM2.5-1 were determined. Furthermore, the particle size distribution and shape characteristics of 5621 TWP were evaluated. A cut through a TWP by means of FIB-SEM evidences that the mineral and metal particles typically found in TWP are not only present on the particle surface but also throughout the complete TWP volume. At the urban background site, the annual average mass fraction of TWP and micro-rubber in PM10 was 1.8% (0.28 μg/m³) and 0.9%, respectively. At the urban kerbside site, the corresponding values were 6 times higher amounting to 10.5% (2.24 μg/m³) for TWP, and 5.0% for micro-rubber.