Comparative Testing and Evaluation of Nine Different Air Samplers: End-to-End Sampling Efficiencies as Specific Performance Measurements for Bioaerosol Applications

<div><p>Accurate exposure assessments are needed to evaluate health hazards caused by airborne microorganisms and require air samplers that efficiently capture representative samples. This highlights the need for samplers with well-defined performance characteristics. While generic aerosol performance measurements are fundamental to evaluate/compare samplers, the added complexity caused by the diversity of microorganisms, especially in combination with cultivation-based analysis methods, may render such measurements inadequate to assess suitability for bioaerosols. Specific performance measurements that take into account the end-to-end sampling process, targeted bioaerosol and analysis method could help guide selection of air samplers.</p> <p>Nine different samplers (impactors/impingers/cyclones/ electrostatic precipitators/filtration samplers) were subjected to comparative performance testing in this work. Their end-to-end cultivation-based biological sampling efficiencies (BSEs) and PCR-/microscopy-based physical sampling efficiencies (PSEs) relative to a reference sampler (BioSampler) were determined for gram-negative and gram-positive vegetative bacteria, bacterial spores, and viruses.</p> <p>Significant differences were revealed among the samplers and shown to depend on the bioaerosol's stress–sensitivity and particle size. Samplers employing dry collection had lower BSEs for stress-sensitive bioaerosols than wet collection methods, while nonfilter-based samplers showed reduced PSEs for 1 μm compared to 4 μm bioaerosols. Several samplers were shown to underestimate bioaerosol concentration levels relative to the BioSampler due to having lower sampling efficiencies, although they generally obtained samples that were more concentrated due to having higher concentration factors.</p> <p>Our work may help increase user awareness about important performance criteria for bioaerosol sampling, which could contribute to methodological harmonization/standardization and result in more reliable exposure assessments for airborne pathogens and other bioaerosols of interest.</p> <p>Copyright 2014 American Association for Aerosol Research</p> </div

MOESM1 of Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research

Additional file 1: Figure S1. Rarefaction curves with α-diversity measures: “Observed”, “Shannon”, and “Simpson” for subway air samples (N = 6) that were split and processed with the MetaSUB (N = 3) and Jiang (N = 3) or MetaSUB (N = 3) and Zymobiomics (N = 3) methods. Figure S2. Rarefaction curves with α-diversity measures: “Observed”, “Shannon”, and “Simpson” for the intermediate pellet (N = 6) and supernatant (N = 6) fractions from subway air samples (N = 6) processed separately with the MetaSUB method. Figure S3. Proportion of total DNA and 16S rRNA gene copy yield found in the supernatant fractions, referencing the total yield in the combined pellet and supernatant fractions, from subway air samples (N = 24) where the intermediate pellet and supernatant fractions were processed separately with the MetaSUB method. Figure S4. The 20 fungal species that were among the top 100 species from the random forest classification analysis of subway air samples (N = 3) that were split and processed with the MetaSUB (N = 3) and Jiang (N = 3) methods, where Z-score distributions were compared with linear models

MOESM2 of The subway microbiome: seasonal dynamics and direct comparison of air and surface bacterial communities

Additional file 1: Table S1. Type of environment, latitude and longitude for all sampled stations. Table S2. Overview of all samples included in the analyses. Table S3. PCR program for 16S rRNA gene amplicon sequencing. Table S4. The best-fit models of qPCR 16S rRNA gene copies for air samples and surface samples. Table S5. Top 20 phyla, families, and genera and species in surface samples collected on kiosks, benches, and railings. Table S6. Random forest classification models of samples collected from different surface types. Figure S1. The significant predictors of qPCR 16S rRNA gene copy yields in air samples. Figure S2. The significant predictors of qPCR 16S rRNA gene copy yields in surface samples. Figure S3. Quality profile of filtered reads. Figure S4. Rarefaction curves with observed diversity and Shannon’s Diversity Index. Figure S5. A) Relative abundances of the top 15 phyla across the three surface types and seasons. B) Heatmap of most abundant families. Figure S6. Top 20 most important genera in random forest classification analysis of samples collected in different seasons. Figure S7. Top 20 most important genera in random forest classification analysis of air and surface samples. Figure S8. Interaction effect between temperature (°C) and air/surface in the linear model of Shannon’s diversity index. Figure S9. PCoA plot of Bray Curtis dissimilarity distances with the only significant predictor (surface type) from the PERMANOVA model that included only surface-specific predictors