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      Development and Validation of a Simple Bioaerosol Collection Filter System Using a Conventional Vacuum Cleaner for Sampling

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          Abstract

          Although numerous bioaerosol samplers for counting and identifying airborne microorganisms are available, the considerably high purchase and maintenance costs for the sampler often prevent broad monitoring campaigns for occupational or environmental surveillance of bioaerosols. We present here a newly developed simple adapter and filter system (TOP filter system) designed to collect bioaerosol particles from a defined air volume using conventional vacuum cleaners as air pumps. We characterized the physical properties of the system using air flow measurements and validated the biological performance. The culture-based detection capacities for airborne fungal species were compared to a standard impaction sampler (MAS-100 NT) under controlled conditions in a bioaerosol chamber (using Trichoderma spores as the test organism) as well as in the field. In the chamber, an overall equivalent detection capacity between all tested filters was recorded, although a significant underrepresentation of the TOP filter system for Trichoderma spores were seen in comparison to the MAS-100 NT. In a comparative field study ( n = 345), the system showed similar biological sampling efficiencies compared to the MAS-100 NT impactor, only the diversity of identified fungal communities was slightly lower on the filters. Thus, the system is suitable for large-scale environmental sampling operations where many samples have to be taken in parallel at a given time at distant locations. This system would allow endeavors such as antibiotics resistance monitoring or hygiene surveys in agricultural or occupational settings.

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          The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants.

          Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey (n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.
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            Indoor airborne bacterial communities are influenced by ventilation, occupancy, and outdoor air source

            Architects and engineers are beginning to consider a new dimension of indoor air: the structure and composition of airborne microbial communities. A first step in this emerging field is to understand the forces that shape the diversity of bioaerosols across space and time within the built environment. In an effort to elucidate the relative influences of three likely drivers of indoor bioaerosol diversity – variation in outdoor bioaerosols, ventilation strategy, and occupancy load – we conducted an intensive temporal study of indoor airborne bacterial communities in a high-traffic university building with a hybrid HVAC (mechanically and naturally ventilated) system. Indoor air communities closely tracked outdoor air communities, but human-associated bacterial genera were more than twice as abundant in indoor air compared with outdoor air. Ventilation had a demonstrated effect on indoor airborne bacterial community composition; changes in outdoor air communities were detected inside following a time lag associated with differing ventilation strategies relevant to modern building design. Our results indicate that both occupancy patterns and ventilation strategies are important for understanding airborne microbial community dynamics in the built environment.
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              Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere.

              Bacteria and fungi are ubiquitous throughout the Earth's lower atmosphere where they often represent an important component of atmospheric aerosols with the potential to impact human health and atmospheric dynamics. However, the diversity, composition, and spatiotemporal dynamics of these airborne microbes remain poorly understood. We performed a comprehensive analysis of airborne microbes across two aerosol size fractions at urban and rural sites in the Colorado Front Range over a 14-month period. Coarse (PM10-2.5) and fine (PM2.5) particulate matter samples were collected at weekly intervals with both bacterial and fungal diversity assessed via high-throughput sequencing. The diversity and composition of the airborne communities varied across the sites, between the two size fractions, and over time. Bacteria were the dominant type of bioaerosol in the collected air samples, while fungi and plants (pollen) made up the remainder, with the relative abundances of fungi peaking during the spring and summer months. As bacteria made up the majority of bioaerosol particles, we analyzed the bacterial communities in greater detail using a bacterial-specific 16S rRNA gene sequencing approach. Overall, bacterial taxonomic richness and the relative abundances of specific bacterial taxa exhibited significant patterns of seasonality. Likewise, airborne bacterial communities varied significantly between sites and across aerosol size fractions. Source-tracking analyses indicate that soils and leaves represented important sources of bacteria to the near-surface atmosphere across all locations with cow fecal bacteria also representing an important source of bioaerosols at the more rural sites during early fall and early spring. Together, these data suggest that a complex set of environmental factors, including changes in atmospheric conditions and shifts in the relative importance of available microbial sources, act to control the composition of microbial bioaerosols in rural and urban environments.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Aerosol Science and Engineering
                Aerosol Sci Eng
                Springer Science and Business Media LLC
                2510-375X
                2510-3768
                December 2021
                July 01 2021
                December 2021
                : 5
                : 4
                : 404-418
                Article
                10.1007/s41810-021-00110-9
                574dec10-ba25-40b7-a27b-e68410c8f3e7
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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