hydrosol with the liquid being volatile. Initial droplets containing 99.9 percent (v/v) of a volatile solvent (e.g., water) and 0.1 percent agent would significantly reduce the amount of agent that could be transported by the aircraft.

Notwithstanding the above, for some specific applications particles that would be released in sizes smaller than 1 μm AD should be considered.

Particles with sizes greater than 10 μm AD have traditionally been excluded from consideration in bioaerosol ambient sampling because deposition of those particles in the alveolar region of the human respiratory system is considerably less effective than deposition of particles smaller than 10 μm AD,⁵ and also because naturally occurring background interferents such as plant pollens and other debris contain a relatively high fraction of particles larger than 10 μm AD, which may influence some detector systems. However, for some ambient environment sampling applications, there is justification for considering the detection of bioaerosol particles with sizes larger than 10 μm AD. From the respiratory point of view, a particle size as large as 100 μm can be inspired into the oral cavity or nasal passages with an efficiency of 50 percent.⁶ In addition, the methods used in the preparation and release of a bioaerosol can lead to a considerable fraction of aerosol particles with sizes greater than 10 μm AD. For example, if a lyophilized powder were not finely ground or if either a solid or liquid agent were released in an unsophisticated manner (low energy input to the aerosolization process), most of the organisms could be associated with sizes larger than 10 μm AD.

Figure 4.1 shows the size distributions of Bacillus subtilis spores that were aerosolized by a very simple process. The spores, in the form of a lyophilized, unground powder, were placed in an envelope that was processed through a mail-sorting machine, where the envelope was subjected to sudden pressure forces applied by high-speed rollers and belts. On a particle number basis, only 4 percent of the particles are associated with sizes larger than 7.1 μm AD; however, because the number of spores in a particle varies approximately with the cube of particle size, about 65 percent of the spores are associated with sizes larger than 7.1 μm AD (volume distribution in Figure 4.1). Current speciation detectors generally respond to the number of cells, and because the number of cells in similar particles increases with the cube of diameter, it may be desirable to collect the larger particles, assuming the absence of significant changes in other effects such as increasing background interferents.

For sampling of aerosols in building environments, either from ductwork or from the occupied environment, particles with sizes outside the nominal range 1 to 10 μm AD should be considered.

The upper size for occupied environment sampling applications should be selected after considering several factors such as the potential methods for aerosolization of threat bioagents, the loss of particles during transport from the site of aerosolization to the sampling location, the effectiveness of filters and air conditioning components in duct sampling applications, and a realistic assessment of being able to collect a sample of the aerosol particles and transport it to the detector. In general terms, aerosol particles with sizes much larger than about 30 μm are difficult to efficiently sample and transport using this sampling apparatus, so this size could be considered as a nominal upper limit for many applications.

Interior sampling scenarios include sampling from ductwork and from occupied open areas such as subway stations, airports, arenas, and office buildings. An aerosol concentrator may not be required for

Front Matter (R1-R12)

Executive Summary (1-6)

1 Background and Overview (7-12)

2 Scenarios, Defensive Concepts, and Detection Architectures (13-22)

3 Indoor and Outdoor Bioaerosol Backgrounds and Sampling Strategies (23-45)

4 Bioaerosol Samplings Systems for Near-Real-Time Detection (46-70)

5 Point and Standoff Detection Technologies (71-83)

6 Nucleic Acid Sequence-Based Identification for Detect-to-Warn Applications (84-104)

7 Structure-Based Identification for Detect-to-Warn Applications (105-129)

8 Chemistry-Based Identification for Detect-to-Warn Applications (130-148)

9 Function-Based Detection (149-154)

10 Design Considerations for Detect-to-Warn Defensive Architectures (155-177)

11 Summary of Conlusions and a Path Forward (178-186)

Appendix A: Biographical Sketches of Committee Members (187-193)

Appendix B Acronyms and Abbreviations (194-196)