Fluorescence spectra of atmospheric aerosol particles measured using one or two excitation...

Fluorescence spectra of atmospheric aerosol

particles measured using one or two excitation

wavelengths: Comparison of classification

schemes employing different emission and

scattering results

Yong-Le Pan1*

, Steven C. Hill1, Ronald G. Pinnick

1,

Hermes Huang2, Jerold R. Bottiger

3, and Richard K. Chang

4

1US Army Research Laboratory, Adelphi, MD 20783, USA 2Real-Time Analyzers, Inc., Middletown, CT 06457, USA

3US Army Edgewood Chemical and Biological Command, Aberdeen Proving Ground, MD 21010, USA 4Department of Applied Physics, Yale University, New Haven, CT 06520, USA

*[email protected]

Abstract: An improved Dual-wavelength-excitation Particle Fluorescence

Spectrometer (DPFS) has been reported. It measures two fluorescence

spectra excited sequentially by lasers at 263 nm and 351 nm, from single

atmospheric aerosol particles in the 1-10 µm diameter size range. Here we

investigate the different levels of discrimination capability obtained when

different numbers of excitation and fluorescence-emission wavelengths are

used for analysis. We a) use the DPFS to measure fluorescence spectra of

Bacillus subtilis and other aerosol particles, and a 25-hour sample of

atmospheric aerosol at an urban site in Maryland, USA; b) analyze the data

using six different algorithms that employ different levels of detail of the

measured data; and c) show that when more of the data measured by the

DPFS is used, the ability to discriminate among particle types is

significantly increased.

©2010 Optical Society of America

OCIS codes: (280.1415) Biological sensing and sensors; (010.1100) Aerosol detection;

(300.2530) Fluorescence, laser-induced; (120.6200) Spectrometers and spectroscopic

instrumentation.

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1. Introduction

There is an increasing interest in characterizing biological and other organic carbon

containing aerosols. Atmospheric aerosol contains complex internal and external mixtures of

organic and inorganic compounds. A large fraction of the organic component of atmospheric

aerosol consists of Primary Biological Aerosol (PBA). PBA indicates airborne particles that

include bacteria, bacterial spores, fungal spores, pollens, viruses, algae, and parts of plants,

fungi, or animals that may have been directly injected into the atmosphere by, e.g., wind

ablation. PBA occur over a large size range, varying in diameter from a few 10’s of nm (e.g.,

small viruses) to 100 µm (aggregates, or some pollens). PBA may be generated from what

may sometimes be thought of as biological waste. For example, rotting wood may contain

fungal hypae (parts of the body of the fungus). Frass, the excrement produced by insects as

they eat plants, etc., may consist largely of bacteria and fungi. PBA aerosol particles may be

mixtures of biological and nonbiological material. PBA is important in: a) transmission of


diseases of humans, other animals and plants; b) generating allergies; and c) acting as cloud

condensation nuclei. PBA tend to absorb more light than most inorganic aerosols, especially

at shorter wavelengths, and may therefore affect the heating and cooling of the atmosphere.

PBA has been reported to comprise 15 to 80 percent of the atmospheric aerosol [1–3],

depending on geographic site and season. Elbert et al. [3] have estimated that the global

average fungal-spore mass concentration in the lower atmosphere is 1 µg/m3, and that 50

Tg/yr of fungal spores are emitted each year. There is an enormous range of molecules and

combinations of molecules in PBA and other organic-carbon aerosol [4–11].

The intrinsic Laser-Induced-Fluorescence (LIF) of aerosol particles has been used for

detection and classification of PBA and other organic-chemical aerosol particles. The first

attempts to exploit the LIF of single airborne particles for their classification began in the

mid-1990s. Pioneering efforts concentrated on measurement of the undispersed fluorescence

within one to three bands [12–17]. Various sensors for airborne particles that measure

fluorescence in one to three bands have been commercially available for years (see review by

DeFreez [18]). Almost concurrent with the early developments, more capable LIF detectors

that measure the dispersed fluorescence spectrum in many channels [19–30] were developed

in an attempt to increase the potential for discrimination and provide information on particle

composition. Also, LIF-based sensors have been developed with dual-wavelength excitation

with the capability to detect emission in two or four bands along with elastic scattering

[25,31–35]. One LIF sensor measures the time-dependent fluorescence in four bands [35].

Preliminary reports of a sensor capable of dual-wavelength excitation and measurement of

fluorescence spectra for each excitation wavelength have appeared [36,37].

The number of different types of fluorescent molecules occurring in PBA and other

organic-carbon aerosol is enormous. The great majority of the fluorophors of the fluorescent

molecules in PBA include one or more aromatic rings, which are commonly heterocyclic and

substituted. Some fluorophores, e.g., tryptophan, tyrosine, nicotinamide adenine

dinucleotides, and flavins, occur in all living cells, but not necessarily in all PBA. Many

common fluorophors in PBA do not occur in all cells, e.g.: chlorophylls in plants; lignins,

lignans, sinapyl alcohols and many others in woody plants; and compounds such as ferullic

acid (with a fluorescence spectrum similar to NADH) in cellulose. Other fluorophors from

secondary metabolites of plants and fungi [38] can occur in PBA and may occur in a smaller

number of species, e.g., fluorescein is made by Pseudomonas aeruginosa and P. fluorescens;

coumarin and its derivatives such as umbelliferone are found in several plant families.

Although studies of the genetic material from microorganisms in atmospheric aerosol are in

their infancy [4,44], such studies are expected to eventually illustrate in more detail the

immense diversity of microorganisms transported in air. A diversity in genomic material is

likely related to a diversity in secondary metabolites. Many non-biological compounds found

in the atmosphere, including a very significant fraction of polycyclic aromatic hydrocarbons

(PAHs) and their oxidation products, are highly fluorescent [39]. Although PAHs can occur

primarily in combustion-generated particles smaller than 1 µm in diameter, these small

particles can agglomerate to particles larger than 1 µm diameter [40], and may agglomerate

with nonfluorescent particles [41]. Humic materials and humic-like substances (HULIS) are

other fluorescent materials that occur in atmospheric aerosol [42,43]. HULIS can be formed

from biomass burning, anthropogenic sources, and marine sources, and may arise as

secondary organic aerosols from lignin pyrolysis products, etc.

Given the extra complexity and cost of multiple excitation and/or multiple emission LIF

systems, an important question is: What additional discrimination capability is added when

more excitation and/or emission wavelengths are used for analysis of aerosols? The question

is open ended because there are: a) so many different discrimination problems (different target

aerosols, different ambient background aerosols containing a highly diverse array of

microoranisms, different excitation and emission wavelengths, etc.), and b) so many different

fluorescent molecules in atmospheric aerosols.


In this paper we investigate the increase in discrimination capability obtained when more

excitation and/or emission wavelengths are employed. The data are taken using a Dual-

wavelength-excitation single-Particle Fluorescence Spectrometer or DPFS, which can

measure the fluorescence spectra and elastic scattering excited at both 263 nm and 351 nm

laser wavelengths for single atmospheric aerosol particles (in the 1-10µm size range) as they

flow through a sampling cell. We use the DPFS to measure fluorescence spectra of: a) several

types of test particles, including a preparation of Bacillus subtilis (a spore forming bacterium);

and b) of atmospheric aerosol measured during a 25-hour period at an urban site in Maryland,

USA. Then we analyze the data using six different algorithms that employ successively more

features of the measured data. Each algorithm determines the fraction of atmospheric particles

that are similar to the preparation of B. subtilis spores. We find that when both spectra and

elastic scattering signals measured by the DPFS are used, only 0.06% of the atmospheric

particles measured have fluorescence spectra and scattering signals consistent with (i.e.,

within two standard deviations at each wavelength) the specific preparation of B. subtilis

studied. We employ B. subtilis, not because anyone is concerned specifically about the

occurrence of B. subtilis in the atmosphere, but because it contains some fluorophors known

to occur in a significant fraction of PBA, it is frequently used as simulant for bacterial agents,

and it is relatively innocuous.

We also describe improvements in the DPFS system which provide for higher-quality

measurements of single-particle fluorescence spectra and elastic scattering. This work

demonstrates the increased capability for discrimination provided by the DPFS and similar

fluorescence-based approaches for measurement and classification of atmospheric aerosol,

particularly for differentiation of certain biological aerosol particles from the natural

background.

2. Methods

2.1 Experimental: Improved DPFS aerosol sampling system

The DPFS system for the rapid measurement of dual-wavelength excited fluorescence spectra

and elastic scattering from single atmospheric aerosol particles flowing through an optical cell

is illustrated in Fig. 1. The system employs two pulsed UV-lasers (wavelengths 263-nm and

351-nm), fired sequentially, to excite the fluorescence, and a single 32-anode PMT for the

detection of fluorescence spectra. The basic scheme of this system has been briefly described

previously [36,37]. Briefly, the key improvements over those systems are: i) the use of

infrared (IR) diode trigger lasers instead of visible trigger lasers so that the visible

fluorescence can be measured over a larger wavelength range; ii) positioning the lasers so that

the two spectra are recorded for optimal alignment (the particle is at approximately the same

distance from the focal point of the collection optics for each spectrum); and iii) an improved

combination of optical filters.

In order to reliably sample micron-sized atmospheric particles at reasonable rates, a virtual

impactor concentrator was used upstream from the inlet of the DPFS. The concentrator (MSP

model 4220) samples air at a rate of 330 L/min and provides concentrated aerosol particles

(over the 2-10µm size range) in the minor outlet flow at a rate of about 1 L/min. This minority

outlet flow is fed to the inlet nozzle assembly of the DPFS (Fig. 1B) for aerodynamic focusing

which results in further particle concentration. The nozzle assembly consists of two concentric

nozzles: an inner nozzle that forms the aerosol jet (having diameter around 400 µm) with

particles moving at about 10 m/sec speed), and an outer nozzle for a clean air sheath flow.

This nozzle focuses the aerosol into a jet that remains collimated, laminar, and cylindrical for

a distance of about 1cm from the nozzle [30]. The nozzle assembly is mounted in a small

optical chamber (see Fig. 1A - a cubical airtight cell, 5 cm on each side), where single

particles are interrogated with lasers. The chamber is aspirated through an outlet tube

concentrically aligned with the inlet nozzle assembly. A piston pump (KNF Neuberger, UN


86) draws air through the nozzles and chamber. A uniform flow is achieved by inserting a

critical orifice between the outlet tube and the piston pump, which reduces pressure

fluctuations caused by the pump.

Fig. 1. Dual-wavelength Excitation Single Particle Fluorescence Spectrometer (DPFS). (A) top

view schematic of DPFS, (B) aerosol sampling system, and (C) expanded view of optics for

measuring sequentially two fluorescence spectra excited by two UV laser pulses (263 nm and

351 nm) from single aerosol particles.

Particles flowing near the center of the aerosol jet and 5 mm below the nozzle tip are

detected (from their scattering) by two continuous wave (CW), crossed IR diode-laser beams

(785- and 830-nm, 10 mW), which are positioned perpendicular to the aerosol jet. The

intersection of these diode laser beams forms an approximately 150-µm × 150-µm region that

defines the “trigger volume” within the aerosol jet (see detail in Fig. 1A and 1C). Once

particles within the trigger volume are detected by the PMTs (one PMT is equipped with an

interference filter that passes 785-nm light, and the other PMT equipped to pass 830-nm

light), and the signals exceed a preset threshold, a logic AND gate generates a 500-ns TTL

pulse to initiate the measurement protocol.


Fig. 2. Timing protocol for the Dual-wavelength excitation single Particle Fluorescence

Spectrometer (DPFS).

The detailed time sequence of events is shown in Fig. 2. First, the two diode lasers are

turned off coincident with the AND gate TTL pulse (turn-off time is about 1 µs) so that

scattering from them will not affect subsequent measurements. Second, 5 µs after the TTL

pulse is sent, the first probe laser, a 263 nm laser pulse (10-ns, 0.030-mJ, 1-mm diameter,

forth harmonic of a Q-switched Nd:YLF laser, Photonics Industries DC- 150-263) is fired to

excite fluorescence in the targeted particle (after 5 µs the particle will have traveled about 50

µm below the trigger volume). The emitted fluorescence is collected by a large-aperture (NA

= 0.4) Schwarzschild reflective objective (Newport 50105) and focused onto a spot centered

about 1 mm below the middle of the input slit of a spectrograph (Jobin Yvon, CP-140). The

dispersed spectrum is recorded by a 32-anode PMT (Hamamatsu H7260). The overall spectral

resolution is about 16.5 nm per anode of the multi-anode PMT. Any residual scattering or

fluorescence induced by the two IR lasers and electronic noise generated by the turn-off

process are mostly eliminated by taking data 5-µs later. A long-pass liquid filter (dimethyl-

formamide diluted with water in a 1-cm thick cell) is placed in front of the spectrograph slit to

block nearly all the elastic scattering from the 263-nm laser; this filter efficiently transmits the

fluorescence with wavelengths longer than 280 nm. The ratio of DMF to water is adjusted so

that the magnitude of the elastic scattering leaking through the filter can be used to estimate

particle size, but not so large as to saturate the detector for particles within the 1-10 µm size

range. Third, the fluorescence spectrum and elastic scattering intensity for each particle

measured by the 32-anode PMT are captured and analyzed by a custom-designed readout and

processing electronics interface (PhotoniQ OEM, Vtech). This board is triggered 50 ns earlier

than the UV laser, and first reads the background charge from the PMT at each anode, and

then reads the signal charge that accumulates in 200 ns during the fluorescence emission

window. The absolute fluorescence intensity at each anode is obtained by subtracting the

background charge from the signal charge. The entire reading, subtracting, analog-digital

conversion (ADC), spectral analysis, and data saving process for the spectrum takes 11.6 µs.

Fourth, a second probe laser, a 351 nm laser pulse (10-ns, 0.025-mJ, 1-mm diameter, third

harmonic of a Q-switched Nd:YLF laser, Photonics Industries DC- 150-351) is fired to excite

fluorescence in the same particle 12 µs after firing the 263 nm laser pulse (17 µs after the


AND gate TTL pulse, during which time the particle has moved about 170 µm below the

trigger volume). The fluorescence excited by the 351-nm laser pulse is focused onto a spot

centered about 1 mm above the middle of the input slit. A long-pass filter with cutoff at 380

nm covers the upper half of the slit to block nearly all the elastic scattering from the 351-nm

laser, but without attenuating the fluorescence from 280 nm to 380 nm excited by 263 nm

laser that was previously focused onto the lower part of the slit. The center of the two

interrogation zones for the two UV-laser probe pulses and the optical axis of the collecting

objective are centered at the middle of the slit. The corresponding 351-nm laser induced

fluorescence spectrum and elastic scattering is measured by the same 32-anode PMT and are

again captured by the PhotoniQ board.

The two IR diode lasers for triggering are turned on again, once the data reading process

performed by the PhotoniQ board is completed. The system does not accept any trigger from

the AND gate until 5 µs after the recording window for the second probe laser; this ensures

the board has enough time to finish the acquisition and data transfer process for the 351-nm

excited fluorescence spectrum and is ready to take the next 263-nm excited fluorescence

spectrum again for the next trigger event. This dead time is also sufficient to avoid false

triggering induced by scattering, fluorescence, or electronic noise that might arise from the

turn-on of the two IR lasers. The entire process for obtaining two fluorescence spectra excited

by two sequentially-fired laser pulses (at 263 nm and 351 nm) illuminating a single aerosol

particle takes about 25 µs. Therefore, the maximum frame rate allowed by the electronics is

on the order of 40,000 per second. However, our requirement that there be only a small

probability of sampling multiple particles at once limits the actual useable frame rate to some

number much smaller than that - around 4,000/s. In the work reported here the peak sample

rates are less than 500/s, well below this rate.

Because the depth-of-field of the collection optics (a reflective objective with numerical

aperture 0.4) is less than 10 µm, we try to limit the trigger region and the two interrogation

regions to be within 150 x 150 µm horizontally (perpendicular to the direction of flow), which

is defined by the crossed diode laser beams. The particles move about 120 µm vertically

between the times the two probe lasers fire. This distance is set by the shortest time separation

(12 µs) in which the electronics is able to record two spectra and by the speed of the particles

(10 m/s). To keep the two interrogation regions as close as possible to the focal point of the

Schwarzschild reflective objective, the first interrogation region (with illumination by the

263-nm laser) is positioned about 60 µm above the focal point of the lens, and the second

interrogation region (with illumination by the 351-nm laser) is positioned about 60 µm below

the focal point of the lens. The small depth-of-field of the collection optics is a main reason

for the need to use the double-nozzle assembly to form a laminar, cylindrical, aerosol jet that

remains somewhat uniform over the 170-µm distance from the trigger region to the second

interrogation region. Uniform trajectories are needed so that targeted particles can be more

reliably probed in the interrogation regions of the two sequentially-pulsed UV probe lasers.

Even with the aerodynamically focused particle stream, optical alignment is challenging, and

variability of particle position during interrogation by the probe laser still causes large signal

differences.

The DPFS does suffer from some background noise resulting from the relatively small

optical cell, and stray light from laser beams passing through the cell windows. To largely

eliminate these unwanted background signals, before taking data on test or atmospheric

aerosol particles, 1000 “background” spectra were routinely recorded in a “free-running”

mode with the DPFS triggered by a pulse delay generator (SRS3500) at 100 Hz, with no

particles present. Spectra for both probe lasers (263-nm and 351-nm) were taken under the

same experimental conditions as data in the normal “particle-triggering” mode. The DPFS

spectra recorded in the normal mode were then corrected by subtracting the average of the

1000 “background” spectra. Corrections were also made for including the scattering from


particles that have negligible fluorescence, but these are better described below, in Section

3.5.

2.2 Test particles and test site for atmospheric measurements

Various solutions/slurries of non-biological and biological samples were aerosolized:

polystyrene latex spheres (Duke Scientific); kaolin, a common clay mineral occurring in

atmospheric aerosol in arid regions and having very low intrinsic fluorescence (Particle

Information Services); tryptophan (Fluka); riboflavin and albumin (Sigma Chem.); Bacillus

subtilis spores that were not extensively washed to remove culture media (prepared by

Dugway Proving Ground); and MS2, a bacterial phage in a preparation that was not

extensively washed to remove culture media (prepared by the Armed Forces Institute of

Pathology).

An inkjet aerosol generator (IJAG [45]) is used to obtain test aerosols. A solution or

suspension of the material being aerosolized is placed into the IJAG cartridge. The IJAG

generates fairly uniform liquid droplets of approximately 50-µm diameter; smaller satellite

droplets are mostly removed by a secondary airflow. The droplets pass through a heated

drying column forming the residual dried aerosol particles. For aerosolizing the uniformly-

sized PSL calibration spheres, the suspension placed into the IJAG is diluted sufficiently so

that the probability of two PSL spheres occurring in the same droplet is very low. For

aerosolizing other test materials, the test material is dissolved and/or suspended in water and

placed into the IJAG, and the dried particle size depends on the concentration of the material

dissolved and/or suspended.

The test site for measuring atmospheric aerosol particles is in Adelphi, MD, USA (39° N

latitude, elevation 75 m). This site, located in the Baltimore-Washington metroplex, was also

used for a previous study [46]. Ambient air is drawn through a 15 cm-diameter, 10 m-long

metal duct protruding through the 25 m high roof of our laboratory (see Fig. 1B). This duct is

aspirated by a large squirrel-cage fan attached to a chemical-hood. Part of the air passing

through this duct is fed to the virtual impactor concentrator inlet with the minority flow fed to

the DPFS.

2.3 Preliminary analysis – check for validity of spectra

After data collection is complete, and before the analyses described later are done, a computer

program is used to confirm that each channel of each spectrum is within the correct range and

that each pair of spectra are in the correct order.

Confirm that the charge is within range on each channel. Because of nonlinearities which

distort the spectral profile when the intensity is near saturation, spectra within any channel

(anode of the 32-anode PMT) having a charge in pico-Coulombs greater than 400 (on a 0-600

pC scale) were discarded. The charge is accumulated during the 200 ns integration period of

the PhotoniQ board.

Confirm that spectra are from the same particle. The order of firing of the two lasers for

any particle is 263-nm then 351 nm. Occasionally, typically or always because of range errors

(which occur when a channel exceeds the 400 pC limit) the spectra become out of order in the

data file. To verify whether the two spectra are from the same particle, the program looks for

scattering from the first (excitation 263-nm) wavelength in a spectrum, and then the scattering

from the second (excitation 351-nm) wavelength in the following spectrum. If the two elastic

scattering peaks are not in the correct order the spectra are discarded.


3. Measurement and analysis of test and atmospheric particles

3.1 Polystyrene latex particles of known sizes

Fig. 3. Averaged scattering response (263-nm excitation) of the DPFS to monodispersed PSL

particles. The blue line is the linear fit result (see text). The error bars show standard

deviations.

Because the DPFS measures light scattered by particles and not their size directly, we

performed a crude calibration to convert the light scattering signal to particle size. For this

calibration we used standard polystyrene latex (PSL) particles aerosolized by an inkjet aerosol

generator (IJAG [45]). For size calibration, in order to produce particles that include only one

primary PSL spherical particle, the suspension was made sufficiently dilute that only about

one in five IJAG droplets contains a single PSL particle on average. Figure 3 shows the elastic

scattering responses (from the 263-nm laser pulse) for polystyrene latex particles with

nominal sizes 1 µm, 2 µm, 3 µm, 5.6 µm, and 8µm (obtained from Duke Scientific). The

particles are are specified by the manufactured to have a standard deviation in size of less than

5%.

Fig. 4. Histogram of (a) light scattering particle size distribution (263 nm) measured by the

DPFS, and (b) the aerodynamic paricle size distribution measured by the TSI-APS 3200 for

Polystrene (PSL) particles with nominal sizes 1 µm, 2 µm, 3 µm, 5.6 µm, and 8 µm.


The square root of the scattering intensity in Fig. 3 is plotted versus particle size because

particles in this size range (relative to the wavelenghth of the incident light) are known to

have total scattering cross sections that are roughly proportional to size. The best least squares

fit for the data was found to be

1.445 3.113y d= + (1)

where y is the square root of scattering intensity and d is the particle diameter in µm. Although

we do not measure total scattering, but only the light scattered at angles between 60 to 120

degrees, the fit to the data appears useful. However, use of this relation for extrapolation of

size to submicron particles is not valid. Using the relation to estimate sizes of absorbing or

nonspherical particles or particles having different refractive index, or inhomogeneities, will

produce size estimates with larger errors than suggested in Fig. 3.

Figure 4(a) shows histograms of light scattering size (determined from the relation above)

for polystyrene latex particles with nominal sizes 1 µm, 2 µm, 3 µm, 5.6 µm, and 8 µm. For

comparison, the histograms of aerodynamic paricle size histograms measured by the TSI-APS

3200 particle sizer are presented in Fig. 4(b). The light scattering particle size histograms

show reasonable agreement with the aerodynamic size histograms, but exhibit a larger

uncertainty, especially in the smaller size range. Better precision in the light scattering

measurement might result if a near forward scattering measurements were used, rather than

the scattering in the 60-120 degree angular range used here.

3.2 Tryptophan test particles: elastic scattering and fluorescence

Fig. 5. Scatter plot of total fluorescence from uniform tryptophan particles excited by 263-nm

and 351-nm probe lasers vs. light scattering particle size. Light scattering size is determined

from the 263 nm elastic scattering. Tryptophan test particles generated by the IJAG typically

have an approximate 30% dispersion in particle size. The calculated particle size generated by

the IJAG is about 4 µm.

The response of the DPFS in terms of elastic scattering and total fluorescence is illustrated in

Fig. 5. Homogenous tryptophan particles were generated with the IJAG. Given the


concentration of tryptophan in water, and the typical breakup size for droplets in the IJAG, the

dominant particle size should be about 4-µm diameter. Particles generated with the IJAG

typically have about 30% standard deviation in size, but there also tend to be some particles

much smaller in size.

Elastic scattering at both excitation wavelengths and spectra excited by both lasers were

measured as described in section 2. The 263-nm-excited total fluorescence and the 351-nm-

excited total fluorescence were obtained by summing the channels having fluorescence. The

upper plot in Fig. 5 shows the scatter plot of the elastic scattering at 351-nm vs. the elastic at

263-nm. The lower plot shows the fluorescence excited by 263-nm and by 351-nm vs. the

elastic at 263 nm. Each dot represents one particle. The light-scattering particle size was

estimated using Eq. (1).

Part of the scatter in the data in Fig. 5 is attributable to dispersion in size for particles

generated by the IJAG, and part arises from the variability in the response of the DPFS. The

variability in the elastic scattering at one wavelength can best be ascertained from Fig. 4

which shows the scattering results for quite uniformly sized PSL spheres. The upper curve in

Fig. 5 illustrates that even at a single scattering intensity at 263 nm, the scattering at 351 nm

can vary by a factor of 5. This relatively large variation arises because the elastic scattering

has such a strong angular dependence, and it is being collected at varying positions relative to

the focal point of the reflecting lens. A second reason for the variation is the differences in the

intensity of the illumination beam.

The fluorescence excited by either the 263-nm or the 351-nm beam is bunched more

tightly (than the 351-nm elastic scattering) at any given light-scattering-size primarily because

the angular dependence of the fluorescence is much smaller than the angular dependence of

elastic scattering. Consequently, the effect of particle position (with respect to the collection

optics) is far smaller in the case of fluorescence.

Because tryptophan strongly absorbs 263-nm light, the actual particle size determined

from the 263-nm scattering is expected to be larger than the “light scattering” size. This effect

of absorption on scattering size should be less significant with 351-nm light, which is

absorbed less by tryptophan.

3.3. Biological and non-biological test aerosols

Spectra of aerosols made from some biological and nonbiological materials are displayed in

Fig. 6. Each spectrum is an average of 100 spectra from nominal 5-µm-diameter particles. All

spectra are normalized to have the same peak height as that of B. subtilis. The sharp peaks at

263 nm and 351 nm are the elastic scattering from the lasers, and the peak 527 nm is the

second order of the elastic scattering peak at 263 nm. The spectra for all three simulants (B.

subtilis, albumin, and the phage MS2) have a strong fluorescence peak around 340 nm, the

region of peak emission from the amino acid tryptophan. The spectral shoulders from 400 to

600 nm show some differences which may be attributable to fluorescent compounds of the

growth material. Reduced nicotinamide compounds such as nicotinamide adenine

dinucleotide (NADH) may contribute in the 400 to 600 nm range for B. subtilis. However,

albumin is a protein (obviously not purified extremely well or it would lack the shoulder at

410 nm), and MS2 is a bacterial phage, which should have no NADH if it were washed well.

We and others have found that the fluorescence spectra: a) from one species of bacterium

prepared under different growth conditions can exhibit larger differences between each other

than those seen in the examples shown in Fig. 6; and b) from different species of bacteria,

prepared under the same conditions, can have very similar spectra [24]. This dependence of

the LIF-spectra of bacteria on growth conditions and sample preparation, and the similarity of

many types of bacteria if separated from any additional materials, makes it appear impossible

to discriminate between the many, many different species of bacteria that can be in

bioaerosols based only on their LIF spectra. Differences in LIF spectra for atmospheric

bacteria may be more related to how the bacteria grew and were aerosolized, and on any other


materials they may be agglomerated with. On the other hand some other types of

microorganisms, such as fungal spores and plant pollens may have a much higher fraction of

species with distinctive spectra. Also, it may be that certain species of bacteria when growing

in their natural environment, do have distinctive signatures, either because of their own

secondary metabolites, or because of fluorophors in their microenvironment.

Fig. 6. Typical DPFS fluorescence spectra from aerosolized biological and nonbiological

particles excited by 263-nm and 351-nm probe lasers. Spectra are averages of 100 single-

particle spectra for nominal 5 µm particles generated with the IJAG.

Figure 6 illustrates that for the three particle types (B. subtilis, albumin, and the phage

MS2, each prepared in such a way that fluorescent impurities remain) improved

discrimination can be achieved by using the fluorescence spectra excited by the 351-nm laser

(see right side of Fig. 6) along with the 263-nm-excited spectra. The spectral differences are

clearly visible to the naked eye. Also, because different samples can have markedly different

ratios of fluorescence-to-elastic-scattering, it is also possible to use the relative strengths of

the fluorescence signals, and the ratios of fluorescence-to-scattering, to discriminate more

effectively between these particles.

3.4 Thresholds for elastic scattering and fluorescence

Using the measurement described above, we decided that for a particle to be considered

fluorescent it should satisfy two conditions.

First, the light detected in the fluorescence wavelength region must be at least 3 times the

average of background noise (i.e., the intensities recorded when the laser fires when no

particle is present). For the rest of this paper the threshold for total fluorescence (summed

over all channels used) whether the 263-nm excited or the 351-nm excited, is 4, which also

happens to be 1/100th of the maximum allowed charge in pC in any channel.

Second, the ratio of total fluorescence to elastic scattering must be greater than a certain

fraction (0.34 for 263-nm fluorescence / 263-nm elastic, and 0.26 for 351-nm fluorescence /

351-nm elastic). This second condition is required because the light scattered by large

nonfluorescent particles (e.g., a 10-µm kaolin particle) is large, and some of this elastic

scattering can generate a small fluorescence signal on the 32-anode PMT. The elastic

scattering may generate some fluorescence from windows or filters of the cell, which may

find its way into the spectrometer. Alternatively, a small fraction of the elastic scattering may

scatter within the cell and may somehow leak to the fluorescence channels of the multi-anode

PMT. These two criteria are used for the analyses for the rest of this paper.

3.5 Atmospheric aerosol

The DPFS has been used to measure the concentration, fluorescence spectra and elastic

scattering (from which particle size is estimated) of atmospheric aerosol particles, quasi-

continuously during September 2009. Typically there are around ten to a few hundred


particles detected per second within the 1-10 µm size range. About one to two million spectra

for each excitation wavelength are commonly recorded each day. We first determined, from

the DPFS measurements of atmospheric aerosol, the elastic scattering and total (integrated

over wavelength) fluorescence excited by the 263-nm and 351-nm probe lasers. A typical data

set for 1000 atmospheric aerosol particles (sampled between 5:00-5:04 PM on Sept. 17, 2009)

is displayed in Fig. 7. The fluorescence intensity is plotted on a log-scale to better illustrate

the large dynamic range of fluorescence intensity. In this data set, 285, or 28.5% of the

particles have fluorescence above the noise floor and satisfy the two conditions stated in

Section 2.3.

Fig. 7. Scatter plot of total fluorescence vs. light scattering particle size from single

atmospheric aerosol particles excited by 263-nm and 351-nm probe lasers.

The measurements indicate that atmospheric aerosol particles have ratios of fluorescence-

to-elastic-scattering that vary by more than three orders of magnitude over the entire 1-10 µm

size range. From Figs. 4 and 5 we might estimate that a factor of five (out of the factor of

1000) may be attributable to variations in elastic scattering because of position relative to the

collection optics. So, even without the spread due to the DPFS, the range of ratios appears to

be more than 200. This finding is consistent with the notion that atmospheric aerosol particles

are generally complex mixtures that: 1) can contain mostly inorganic carbon with little or no

fluorescent organic carbon (inorganic carbon has very weak fluorescence and consequently a

majority of particles have fluorescence signals that fall below the DPFS noise floor), 2) can

contain both organic and non-organic carbon in a wide range of mass mixing ratios, and 3)

can contain significant amounts of highly fluorescent organic carbon compounds.

The complexity of atmospheric aerosol motivates us to pose the question: To what degree

can particular aerosols of interest be discriminated from the background atmospheric aerosol

using DPFS or some similar fluorescence-based sensors? In the next sections we attempt to

partially answer this question. We are not suggesting that any of the example particle

materials (B. subtilis, kaolin, or pure albumin, riboflavin, or tryptophan) used in this paper are

important materials of particular interest, although tryptophan and flavins are some of the

primary fluorophors in PBA. The question we are attempting to address regarding the value of

different amounts of information regarding elastic scattering and LIF spectra is more general,

and we hope is of some use to researchers interested in various specific types of PBA and

other organic carbon containing aerosols, which may have LIF spectra that are more or less

distinctive. For the comparison of the algorithms below, we employ B. subtilis, not because

anyone cares specifically about B. subtilis in the atmosphere, but because it is relatively


innocuous, has a spectrum when well washed that is similar to other spectra of various other

well- washed bacteria, and contains some fluorophors that are known to occur in a significant

fraction of PBA.

Fig. 8. Scatter plot of DPFS data for single atmospheric aerosol particles and various test

particles. Shown is the ratio of total fluorescence over visible fluorescence excited by 263 nm

versus total fluorescence excited by 351 nm and scattering particle size for atmospheric aerosol

particles (black circle), albumin (turquoise), B. subtilis (red), kaolin (blue), and riboflavin

(green). The results suggest that B. subtilis overlaps with only a small fraction of atmospheric

aerosol particles, and is well differentiated from kaolin and riboflavin.

Some typical DPFS results showing atmospheric fluorescence and elastic scattering, along

with some known biological materials and kaolin are displayed in Fig. 8. Shown is a scatter

plot of the ratio of total fluorescence to visible fluorescence excited by 263 nm versus total

fluorescence excited by 351 nm and scattering particle size. Data points representing single

particles are for B. subtilis, albumin, riboflavin, kaolin, and a sample of atmospheric aerosol

particles. We see that the data points for biological agent stimulant particles (here B. subtilis

and albumin) have only a small overlap with this sample of atmospheric particles; they have

essentially no overlap with kaolin and riboflavin.

4. Analysis methods: Algorithms investigated for discrimination

We constructed six detection algorithms which use varying levels of detailed data measured

by the DPFS. The two that use the fewest number of measured intensities (minimal capability

of the DPFS) are based only the elastic scattering at one wavelength (263-nm or 351-nm) and

the total (integrated) fluorescence excited by laser at that wavelength. The one that uses the

most measured intensities (more near the full capability of the DPFS) uses both elastic

scattering signals and both fluorescence spectra. Some key aspects of the algorithms are

summarized in Table 1.

Algorithm 1 discriminates biological particles based only on their 263-nm elastic

scattering (E263) and total fluorescence (F263-Tot) excited by light at 263-nm only. That is,

algorithm 1 requires that particles have: a) total 263-nm-excited fluorescence greater than 4;

and b) ratios of UV-fluorescence-to-elastic-scattering greater than 0.34.

Algorithm 2 discriminates biological particles based only on their 351-nm elastic

scattering (E351) and total fluorescence (F351-Tot) excited by light at 351-nm only. Thus,

algorithm 2 requires that particles have: a) total 351-nm-excited fluorescence greater than 4,

and b) ratios of UV-fluorescence-to-elastic-scattering greater than 0.26.


Table 1. The types of measured intensities used for each of the six algorithms investigated

here. All intensities are measured using the DPFS. The last column lists the percentage of

B. subtilis particles assigned to the target category. The elastic scattering listed here is not

used as a size threshold for any of the algorithms, but is used to normalizing the

fluorescence. The minimum particle sizes are determined by signals from the crossed-

beam diode lasers.

Algorithm 263-nm

elastic

E263

351-nm

elastic

E351

263-nm

total

F263-Tot

263-nm

2 bands

F263-bands

263-nm

spectrum

F263-Spec

351-nm

total

F351-Tot

351-nm

spectrum

F351-Spec

% B.

subtilis

assigned

1 Y Y 97.6

2 Y Y 85.3

3 Y Y 83.6

4 Y Y Y Y 76.1

5 Y Y 63.8

6 Y Y Y Y 52.4

Fig. 9. (a) Illustration of fluorescence spectra of B. subtilis measured by DPFS when the 263-

nm laser fires. All spectra have been normalized so that the amplitude of the 263-nm peak

[algorithms 1, 3-6] is 1.0. The blue line is the averaged fluorescence spectrum, determined

from 100 B. subtilis particles in the 1-10 mm size range [used in algorithms 5 and 6]. The bars

indicate two times the standard deviations of the measurements. (b) The spectral regions over

which the data is integrated over wavelength for use in different algorithms. The UV is in the

blue box, and the visible fluorescence regions are marked by the two red blocks. The elastic

scattering (one anode of the 32-anode PMT) is marked with purple.

Algorithm 3 discriminates biological particles similar to B. subtilis based on their elastic

scattering (263 nm) and two-band fluorescence (ultraviolet and visible) excited at 263 nm as

illustrated in Fig. 9. In this algorithm the UV (263 nm) excited fluorescence is divided into

ultraviolet (290-400 nm) and visible (400-600 nm) bands. For simplicity we list this algorithm

in terms of its ability to detect B. subtilis-like particles. To facilitate the description of this

algorithm consider the 263-nm-excited normalized fluorescence spectra of B. subtilis shown

in Fig. 9(a). The particles were selected in an attempt to create a distribution with sizes fairly

equally distributed in the 1-10 µm size range as shown in the inset of Fig. 9(a). Plotted is the

fluorescence intensity at each anode of the 32-anode PMT from B. subtilis aerosol particles


that are normalized by the elastic scattering. We can sum different channels (different regions

of the spectrum) to obtain three parameters for each spectrum: elastic scattering, total UV

fluorescence (integrated over 290 nm – 400 nm), and total visible fluorescence (integrated

over 400-nm to 505-nm and 540 nm to 600 nm). By summing 1000 such B. subtilis spectra we

can compute elastic scattering, UV fluorescence, and visible fluorescence parameters, ratios

of these parameters, their averages, and standard deviations about the average. The resulting

particle scattering parameters for 1-10 µm B. subtilis particles are: (a) the average ratio of UV

fluorescence to elastic scattering (F263-Band1 / E263 = 1.17 ± 0.39 (SD) and the average ratio of

visible fluorescence to elastic scattering F263-Band2 / E263 = 0.31 ± 0.15 (SD). For this algorithm,

a spectrum is considered B. subtilis-like if the two ratios are within 2 standard deviations of

the average.

Algorithm 4: Discrimination of biological particles having elastic scattering and two-band

fluorescence (ultraviolet and visible) excited by 263 nm and 351 nm that are B. subtilis-like.

This algorithm is somewhat similar to sensors developed by Sivaprakasam et al, 2004 and

Kaye et al, 2005. In this case we use both elastic scattering channels and sum the fluorescence

into blue and visible bands. As with algorithm 3 above we tailor the algorithm for detection of

B. subtilis-like particles and use B. subtilis data to determine parameters for the algorithm.

Part of the algorithm (for 263-nm excitation) is the same as algorithm 3 above. To define the

remainder of the algorithm, fluorescence for 351nm excitation is integrated over the visible

(400-600 nm) band. The B. subtilis data have the ratio of visible fluorescence to elastic

scattering F351 / E351 = 1.03 (average) ± 0.62 (SD). This algorithm requires that this ratio (for

351-nm excitation) must be within 2 standard deviations of the average.

Algorithm 5 discriminates based on 263-nm elastic scattering and the 263-nm-excited

fluorescence spectrum to find particles similar to B. subtilis. The 263-nm-excited fluorescence

spectra of B. subtilis particles in Fig. 9(a) shows the fluorescence intensity at each anode of

the 32-anode PMT from B. subtilis aerosol particles that are normalized by the elastic

scattering. We use 1000 such B. subtilis spectra to calculate an average B. subtilis spectrum

and standard deviation about the average for each anode of the PMT (Table 2). For this

algorithm a particle’s normalized fluorescence spectrum must have signals in each anode of

the PMT within two standard deviations of the normalized average B. subtilis spectrum.

Algorithm 6 discriminates based on both 263-nm and 351-nm elastic scattering and both

263-nm- and 351-nm-excited fluorescence spectra to find particles similar to B. subtilis.

Discrimination of biological particles having fluorescence spectra (normalized by elastic

scattering) excited by both 263 nm and 351 nm that are B. subtilis-like. We use the

fluorescence spectra and relative intensity of fluorescence excited by both 263 nm and 351

nm. Again we consider 1000 normalized B. subtilis spectra and determine average B. subtilis

fluorescence spectra (normalized by the elastic scattering), and standard deviations about the

average, for each anode of the PMT (Table 2). For this algorithm a particle’s fluorescence

spectra (normalized by the elastic scattering intensity for each) at both excitation wavelengths

must have signals in each anode of the PMT within two standard deviations of the

corresponding normalized average B. subtilis spectrum.

There are many other algorithms for classifying bioaerosol particles besides the six

described here. Some, for example, may employ principal component analysis (PCA). We

chose to analyze the data here by simply comparing the ratios of fluorescence to scattering

intensities for certain integrated bands or spectral channels, partly because with these schemes

the electronics in the DPFS are fast enough to analyze each detected spectrum in 12 µs. We

are especially interested in these very fast algorithms partly because we are interested in real

time sorting of airborne particles [36]. There are of course other techniques that combine other

features such as laser-induced breakdown spectroscopy (LIBS) [49], multiple elastic

scattering measurements [31], or mass spectrometry [50] with LIF measurements. Those are

beyond the scope of this paper.


Table 2. Averages of B. subtilis aerosol particle normalized fluorescence spectra excited

by 263-nm in the 300-600 nm range (used in algorithms 5 and 6) and 351-nm in the 400–

700 nm range (used in algorithm 6). Given are average ratios and standard deviations

(SD) of fluorescence spectra normalized by elastic scattering at 263-nm.

263-nm Excited 351-nm Excited

Pixel

Center

wavelength

Average

fluorescence

Std Dev

Average

fluorescence

Std Dev

6 297.31 0.108 0.057

7 314.3 0.231 0.055

8 331.2 0.280 0.045

9 348 0.237 0.028

10 364.71 0.135 0.020

11 381.34 0.089 0.016

12 397.9 0.058 0.015 0.067 0.032

13 414.38 0.047 0.015 0.071 0.028

14 430.79 0.044 0.016 0.099 0.039

15 447.15 0.044 0.015 0.129 0.044

16 463.45 0.041 0.012 0.092 0.036

17 479.7 0.033 0.012 0.106 0.043

18 495.9 0.036 0.074 0.095 0.038

19 512.07 0.122 0.092 0.096 0.040

20 528.2 0.375 0.036 0.075 0.032

21 544.3 0.068 0.007 0.070 0.030

22 560.39 0.019 0.006 0.053 0.023

23 576.45 0.016 0.006 0.049 0.021

24 592.5 0.035 0.016

25 608.55 0.028 0.013

26 624.59 0.019 0.009

27 640.64 0.013 0.007

28 656.71 0.008 0.004

29 672.78 0.019 0.007

30 688.88

5. Discrimination of B. subtilis-like aerosol particles against atmospheric aerosol using

the six algorithms

To investigate the capability of the DPFS sensor to discriminate among different particle

types, we choose as a test problem the determination of the number of particles in an

atmospheric sample that are indistinguishable from one particular type of particle, the

preparation of B. subtilis spores used above in Figs. 6, 8, and 9. We analyzed an atmospheric

aerosol data ensemble collected at the Adelphi site during the time period from 5:00 PM on

Sept. 17, 2009 to 6:00 PM on Sept 18, 2009. Altogether, 1,419,127 particles were sampled

during this period. Of these, 351,943 particles, or 24.8%, had fluorescence intensity above the

noise floor.


Fig. 10. Particle count rates and percentages of particles in different categories as detected for

atmospheric aerosol by the DPFS and classified using the 6 algorithms. Particles were

measured during the time period Sept 15, 5 PM to Sept 16, 6 PM at Adelphi, MD. (A) Particle

count rates: total (black); fluorescent particles according to algorithm 1 (turquoise); fluorescent

particles according to algorithm 2 (orange); B. subtilis-like particles according to algorithms 3

(magenta); 4 (blue); 5 (green); and 6 (red). (B) Percentages of fluorescent particles according to

algorithm 1 (turquoise) and 2 (orange). Percentages of B. subtilis-like particles according to

algorithms 3 (magenta)); 4 (blue); 5 (green); and 6 (red).

Figures 10 and 11 demonstrate the capability of the DPFS to discriminate among particles.

The numbers and percentages of particles versus time that are assigned to be either biological

(for algorithms 1 and 2) or B. subtilis-like (for algorithms 3-6) are displayed in Fig. 10A and

10B. Figure 11 illustrates the average percentage for the total 25 hr period.

We term the fluorescent particles found by algorithms 1 and 2 “biological” because: a) in

many situations these particles will be primarily of biological origin, i.e., particles such as


fungal spores, pollens, bacteria, viruses, proteins, and cellulose; and b) many researchers

assume that they are primarily biological. Kanaani et al. [47], used a UV aerodynamic

particle sizer (UVAPS) to excite fungal spores at 355 nm and measure fluorescence at 420 nm

to 575 nm. They could detect the spores as biological particles. They did not think they could

differentiate in an ambient air sample the different species of fungi they examined. Huffman

et al. [48], used the UVAPS to measure fluorescent PBA for periods of months. They

summarized arguments for why the particles measured by the UVAPS are primarily PBA.

Some of these arguments also hold for why algorithm 1, which uses the total fluorescence

excited by 263 nm, primarily measures biological aerosols. Although PAHs and HULIS

(which are both common in the atmosphere) also fluoresce, the argument is that these

fluorophors tend to occur in smaller particles, ones that are too small to be detected by the

UVAPS as it is typically run. That argument would also apply to the DPFS. However, as

noted above, very small particles can agglomerate into larger particles [40,41]. Gabey et al.

[32], used two xenon light sources with peak emission around 280 nm and 370 nm (using a

WIBS-3) to obtain total UV-fluorescence, visible fluorescence and elastic scattering to

measure PBA in Indonesia. They argued that the great majority of the particles they measured

were fungal spores, and they noted that there were no significant burn sites near their

sampling points. A case where fluorescent particles may not be primarily biological is in the

smoke from biomass burning. At some distances downwind from a significant burn site, the

majority of the fluorescent particles having diameters greater than 1 µm will not be

“biological.”

Fig. 11. Percentages of atmospheric aerosol particles that cannot be distinguished from one

particular sample of B. subtilis using each of the six algorithms described in the text. The

atmospheric aerosol sample of 1,419,127 total aerosol particles was measured by the DPFS

during Sept 15, 5 PM to Sept 16, 6 PM, 2009, at Adelphi, MD.

The relative concentrations and percentages of particles that are: a) fluorescent, with an

assumption of likely being biological, for algorithms 1 and 2; or b) B. subtilis-like for

algorithms 3-6, are shown to differ greatly. They differ by about a factor of 250 for the overall

averages (Fig. 11) and by as much as three orders of magnitude for some time periods (e.g., 3

AM to 11 AM in Fig. 10). These six algorithms use increasingly more information available

from the DPFS as illustrated in Table 2.

For algorithms 1 and 2 the percentages of particles detected as fluorescent are 16.2% and

16.5% on average. It may be that fewer particles are biological, as discussed above. For

algorithms 3 to 6, which are constructed to detect B. subtilis against the natural atmospheric

background, the corresponding percentages of particles that are B. subtilis-like are: 2.49%,

1.43%, 0.46%, and 0.06%, as summarized in Fig. 11.

The very large differences in specificities obtained with the six algorithms (as seen in Figs.

10 and 11 above) illustrate the diagnostic power and potential versatility of the DPFS. These

six algorithms use successively more information measured by the DPFS regarding the


fluorescence intensities, fluorescence spectral shapes, and elastic scattering, and have an

ability to discriminate against an increasingly larger fraction of the atmospheric aerosol

background in the case of particles having known fluorescence spectra. As discussed in

Section 6.1, we envision concurrently running algorithms such as the six discussed here, along

with others, so that multiple particle types (or preparations) can be examined at one time with

different levels of specificity.

6. Discussion

6.1 The DPFS can analyze aerosols in many ways at once

LIF measurements of atmospheric and other environmental aerosols can be used for a variety

of objectives. For some applications, total PBA may be of primary interest. For such

applications algorithm 3-6 may be too selective, and algorithms 1 and 2 might be more

appropriate. For many applications, concurrent monitoring of multiple specific particle

preparations, as well as total PBA as determined by algorithms 1 and 2, may be what is

desired. For example, one may be interested in airborne allergens such as different types of

pollens, fungal spores, feces of cockroach and dust mites, and protein allergens from cats and

dogs. Or, for example, one may be interested in several different types of bacteria that have

been generated in different ways, and/or in one species of bacteria grown or generated under

several different conditions. For such applications, the versatility, diagnostic capability, and

concurrent monitoring capability of the DPFS could be especially useful. A separate algorithm

similar to algorithm 6 could be developed for each fluorescence spectral “signature”

associated with each type of allergenic particle, or bacterium, or for each preparation of

bacterial particle of the same type or species. These algorithms could be modified to be more

or less specific depending upon the specificity desired for each type. Given the capabilities of

modern computers it would be very feasible to search for many different target-particle LIF

spectra at once. At the same time as particles are being classified with these quite specific

algorithms and spectra, the particles could additionally be classified with other algorithms

(maybe ones more similar to algorithms 1-5) to obtain the total numbers of fluorescent

particles, or the number of particles having fluorescence within certain emission bands, etc.,

and possibly to include in these counts the number of bacterial particles of a particular species

that may have been prepared in ways different from any of those used for the development of

the highly specific algorithms.

Algorithm 6 was designed for one specific preparation of spores of one specific bacterium

(B. subtilis). Because this preparation was not highly washed it would contain other molecules

from the growth media or other sources. By constructing multiple algorithms similar to

algorithm 6, one for each preparation or source of interest, even for one species of bacteria, it

may be possible to distinguish among these different preparations, if the concentrations of

particles of the specific type(s) are sufficiently above the ambient background.

6.2 The DPFS can be especially sensitive because it is so selective

For specific applications where well defined spectrum is known, e.g., for a specific

preparation of bacteria or viruses, or for fungal spores grown under similar conditions, the

DPFS can be highly sensitive. In Fig. 10 there is a period of about seven hours where the

average particle count found by Algorithm 6 is less than 0.2 per minute. That background

count is so low that a small increase in the counts for that type of particle could be noticeable.

For example, if the count rose to 2 per minute for 5 minutes, that increase would be 10 times

the average rate, and would rise above the background for that type of particle. Whether or not

this high selectivity is considered a benefit or a problem depends on the objectives for the

measurements.


7. Summary

Atmospheric aerosol is known to contain complex external and internal mixtures of organic

and non-organic compounds. There is an enormous variability in the composition of particles

containing organic carbon, especially in the biological fraction of organic carbon aerosol.

Secondary plant and fungal metabolites probably play a large role in the diversity in the

fluorescence of bioaerosols. Here we show that using a fluorescence-based sensor that

measures the elastic scattering and UV-excited fluorescence spectra of particles at two

wavelengths, certain target particles (a specific preparation of B. subtilis bacterial spores in

the example here) can be largely discriminated against the natural atmospheric background.

These tests are only for a certain site during a particular 25 hour period, under specific

meteorological conditions. Further measurements will have to be carried out at sites with

different regional climates and atmospheric environments to more fully assess the diagnostic

power of the DPFS aerosol sensor. We envision the DPFS running multiple algorithms

concurrently, with one or more algorithm for each particle type of interest.

Acknowledgements

This research was supported by the Defense Threat Reduction Agency under the Physical

Science and Technology Basic Research Program, and by US Army Research Laboratory

mission funds.


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