Verifying Interpretive Criteria for Bioaerosol Data Using (Bootstrap) Monte Carlo Techniques

Abstract

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.

Keywords:

• bioaerosol
• bootstrap
• criteria
• Monte Carlo

Notes

^A Outdoor air sampling at Building G collected on 3 separate days.

^B Indoor air from Building G, Zone 1, assumed to be “clean” based on visual inspection.

^A Outdoor air sampling at Building G collected on 3 separate days.

^B Indoor air from Building G, Zone 1, assumed to be “clean” based on visual inspection.

^A Each outdoor air sampling at Buildings L and RM were conducted on separate days.

^B Indoor air from Buildings SM and ENT, Zone 2, assumed to be “clean” based on visual inspection.

^A Outdoor air sampling at Buildings L and RM collected on separate days.

^B Indoor air from Buildings SM and ENT assumed to be “clean” based on visual inspection.

^A Each outdoor air sampling at Buildings L and RM were collected on separate days.

^B Indoor air from Building ENT, Zone 2, assumed to be “clean” based on visual inspection.

^A Each outdoor air sampling at Buildings L and RM were collected on separate days.

^B Indoor air from Building ENT, Zone 2, assumed to be “clean” based on visual inspection.