Bioaerosol sampling from various building sites, some of which were subjected to water damage and microbial growth, provided the opportunity to evaluate current recommendations for interpreting bioaerosol sampling data. Data from the ambient environment, a control building, and areas known to have microbial contamination were used as source data for random simulations. The simulations generated two comparison zones from microbial data from the same environment as a test model to identify the failure rate for Spearman's rank correlation. The results of the simulation indicated a failure rate approaching 60%, depending on the number of samples assigned to each zone by the simulation. The simulations indicated that nonparametric statistical treatment of bioaerosol data as currently recommended for building assessment purposes has limitations. An inordinately high Type II error (failure to reject a null hypothesis which is actually not true) is especially apparent when there are small numbers of samples. For example, in applying this methodology to clearance air sampling, a work zone subjected to removal of all moldy materials and a thorough particulate cleaning would still have a significant chance of failure solely due to the variability of the data, if individual samples are evaluated to identify "localized" contamination. This is significant with regard to the number of samples collected and the interpretation of individual samples in rendering evaluations of microbial contamination.

Culturable airborne fungal spore sampling at five building sites during 2002-2003 provided a bank of outdoor data (102 samples total) to evaluate differences in levels of individual species of airborne fungi during the morning and afternoon hours. A minimum of 15 (outdoor) air samples was collected at each site, and data were segregated into morning (before noon) and afternoon subsets. Significant differences in airborne levels for all detected fungal types between the morning and afternoon subsets were determined for each site, using a direct calculation of probability. Significance was defined by differences in frequency of detection above the combined median (p=0.90 or greater) for the respective fungal type. The levels of various species of fungi in the outdoor air varied significantly between morning and afternoon data sets at all five sites, with no pattern by species, time of day, or location. Levels of Penicillium, Aspergillus, or other fungal species associated with problematic buildings if detected outdoors, can be significantly greater in the morning or afternoon (or exhibit no significant difference) on any given day. The data does not indicate laboratory analysis as a major contributor to the variability exhibited in bioaerosols, and underscores the necessity of collecting sufficient number of samples in the outdoor air in both the morning and afternoon to prevent bias when comparing a suspect indoor environment to outdoor conditions.

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A number of interpretive descriptors have been proposed for bioaerosol data due to the lack of health-based numerical standards, but very few have been verified as to their ability to describe a suspect indoor environment. Culturable and nonculturable (spore trap) sampling using the bootstrap version of Monte Carlo simulation (BMC) at several sites during 2003-2006 served as a source of indoor and outdoor data to test various criteria with regard to their variability in characterizing an indoor or outdoor environment. The purpose was to gain some insight for the reliability of some of the interpretive criteria in use as well as to demonstrate the utility of BMC methods as a generalized technique for validation of various interpretive criteria for bioaerosols. The ratio of nonphylloplane (NP) fungi (total of Aspergillus and Penicillium) to phylloplane (P) fungi (total of Cladosporium, Alternaria, and Epicoccum), or NP/P, is a descriptor that has been used to identify "dominance" of nonphylloplane fungi (NP/P > 1.0), assumed to be indicative of a problematic indoor environment. However, BMC analysis of spore trap and culturable bioaerosol data using the NP/P ratio identified frequent dominance by nonphylloplane fungi in outdoor air. Similarly, the NP/P descriptor indicated dominance of nonphylloplane fungi in buildings with visible mold growth and/or known water intrusion with a frequency often in the range of 0.5 Fixed numerical criteria for spore trap data of 900 and 1300 spores/m(3) for total spores and 750 Aspergillus/Penicillium spores/m(3) exhibited similar variability, as did ratios of nonphylloplane to total fungi, phylloplane to total fungi, and indoor/outdoor ratios for total fungal spores. Analysis of bioaerosol data by BMC indicates that numerical levels or descriptors based on dominance of certain fungi are unreliable as criteria for characterizing a given environment. The utility of BMC analysis lies in its generalized application to test mathematically the validity of any given descriptor or criterion for bioaerosols, which can be an important tool in quantifying the uncertainty in interpreting bioaerosol data.

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.