{"title":"Bioaerosol data distribution: probability and implications for sampling in evaluating problematic buildings.","authors":"R Christopher Spicer, Harry J Gangloff","doi":"10.1080/10473220301411","DOIUrl":null,"url":null,"abstract":"<p><p>Airborne fungal contamination in the indoor environment is a substantial contributor to indoor air quality (IAQ) problems, yet there are no set numerical standards by which to evaluate air sampling data. Intuitively appealing is the operational model that the indoor air should not be significantly different from the outdoor air, but determining what is \"significant\" as well as where to sample and how many samples to collect to determine significance have not been firmly established. The purpose of this study was to determine the number of samples and their locations necessary to determine significant differences in airborne fungi between the ambient and indoor environments. Sampling results from several hundred air samples for culturable fungi from various sites were used to derive a probability of detection in the outdoor air for problematic or \"marker\" fungal species. Under the assumption that indoor fungal growth results in an increase in the probability of detection for a given fungal species, mathematical probability dictates the number of samples necessary in the indoor (target zone) and in the outdoor (reference zone) air to demonstrate significance. Ultimately, it is the sparse distribution of the problematic species that drives the number of required samples to demonstrate a significant difference, which varies depending upon the level of significance desired. Therefore, the number of samples in each zone can be adjusted to reach a target difference in detection frequency, or an investigator can assess a sampling scheme to identify the differences in detection frequency that show significance.</p>","PeriodicalId":8182,"journal":{"name":"Applied occupational and environmental hygiene","volume":"18 8","pages":"584-90"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10473220301411","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied occupational and environmental hygiene","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10473220301411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Airborne fungal contamination in the indoor environment is a substantial contributor to indoor air quality (IAQ) problems, yet there are no set numerical standards by which to evaluate air sampling data. Intuitively appealing is the operational model that the indoor air should not be significantly different from the outdoor air, but determining what is "significant" as well as where to sample and how many samples to collect to determine significance have not been firmly established. The purpose of this study was to determine the number of samples and their locations necessary to determine significant differences in airborne fungi between the ambient and indoor environments. Sampling results from several hundred air samples for culturable fungi from various sites were used to derive a probability of detection in the outdoor air for problematic or "marker" fungal species. Under the assumption that indoor fungal growth results in an increase in the probability of detection for a given fungal species, mathematical probability dictates the number of samples necessary in the indoor (target zone) and in the outdoor (reference zone) air to demonstrate significance. Ultimately, it is the sparse distribution of the problematic species that drives the number of required samples to demonstrate a significant difference, which varies depending upon the level of significance desired. Therefore, the number of samples in each zone can be adjusted to reach a target difference in detection frequency, or an investigator can assess a sampling scheme to identify the differences in detection frequency that show significance.