NIST Technical Note 1802

Characterizing the Response of 

Direct-Reading Particulate Detectors 

for the Fire Overhaul Environment

Rodney A. Bryant

Paul S. Greenberg 
NIST Technical Note 1802

Characterizing the Response of 

Direct-Reading Particulate Detectors 

for the Fire Overhaul Environment

Rodney A. Bryant

Fire Research Division

Engineering Laboratory

Paul S. Greenberg

Glenn Research Center

National Aeronautics and Space Administration

June 2013

U.S. Department of Commerce 

Cameron F. Kerry, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1802

Natl. Inst. Stand. Technol. Tech. Note 1802, 46 pages (June 2013) 

CODEN: NTNOEF

iii

EXECUTIVE SUMMARY

The overhaul of a fire scene is a stage of firefighting where respiratory protection is often 

disregarded due to the perception of low risk for respiratory injury. The operation resembles a 

structural demolition, with the added presence of smoldering debris. Limited information exists 

on what respiratory threats remain. Hand-held direct-reading particulate detectors have long 

been used to monitor respiratory threats for industrial hygiene applications. Therefore, it may be 

possible to adapt this technology to meet the needs of the firefighter. 

Even though direct-reading particulate detectors have been used for quite some time, there is not 

a consensus standard for evaluating worker exposure as a function of the device output. There is 

a diversity of particles present in the fire overhaul environment and their characteristics

significantly differ depending on the scenario. Optical direct-reading instruments are sensitive to 

the physical characteristics of the particles, which presents many challenges for accurately 

detecting particulates during fire overhaul. 

The goal of the present work was to conduct a preliminary characterization of hand-held 

direct-reading particulate detectors when exposed to the types of particulates anticipated in the 

fire overhaul environment, and to transfer the knowledge gained into recommendations that will 

provide the foundation for future research, standards development, and testing protocols specific 

to the needs of the firefighter community.

Four optical dust monitors, representative of those currently available on the market, were 

simultaneously exposed to smoke surrogates and smoke from burning materials to characterize 

their response. The response from the dust monitors was linear over the range of particle mass 

concentrations for current exposure threshold limits. However, the sensitivity varied with 

respect to the aerosol for some of the detectors. When exposed to smoke from burning materials, 

the response of the dust monitors did not adequately reflect the hazard present. Some burning 

materials produced very high particle number concentration, which could be a significant 

respiratory hazard. The dust monitors’ output was in terms of mass concentration and did not 

always indicate the presence of the hazard. 

Key recommendations for performance evaluation metrics and testing protocols include:

1. Evaluating dust monitors to confirm a linear response of over the range of the current 

respiratory threshold limits, 0 mg/m3 to 10 mg/m3. 

2. Using a condensation particle counter to generate a reference measurement of particle 

occurrence that is independent of particle characteristics such as size, shape, and 

refractive index. 

3. Using the cone calorimeter and standard test method ASTM E 1354-11a to generate real 

smoke particles. 

Key recommendations for improvements in direct-reading detector technology include:

1) A dual device, one that can measure mass concentration and number concentration 

simultaneously, would be ideal. When small particles dominate, mass concentration is 

low. Measurements of number concentration can still reflect the presence of a hazard. 
iv

2) Adding an internal or external dilution system to optical particle counters to allow more 

of the devices to operate in high particle concentration environments like fire overhaul.

Key recommendations for future research include:

1. Investigating the use of aerosolized mineral oil to calibrate dust monitors for use in the 

fire overhaul environment.

2. Identifying a comprehensive list of flammable materials found in buildings beyond the 

list considered here to create aerosols that characterize the full range of refractive index 

that may be encountered during overhaul. 

Keywords: fire overhaul, respiratory protection, particulate detection, aerosol detection, fire 

safety, first responder, firefighter
v

ACKNOWLEDGMENTS

The Science and Technology Directorate of the U.S. Department of Homeland Security (DHS) 

sponsored the production of this material under Interagency Agreement HSHQDC-09-X-00476 

with the National Institute of Standards and Technology (NIST). The authors wish to thank the 

following NIST staff and student interns for their valuable contributions: Mr. Marco Fernandez 

(NIST Technician) for fabricating the experimental equipment as well as conducting 

experiments; Mr. Matthew Sakofs (Summer Undergraduate Research Fellowship Student, Univ. 

of Maryland) for data analysis; Mr. Olatunde Sanni (Student Intern, Univ. of Maryland) for 

conducting experiments and data analysis; and Mr. John Shields (NIST Technician) for 

conducting the cone calorimeter experiments.
vi

Table of Contents

EXECUTIVE SUMMARY ...........................................................................................................iii

ACKNOWLEDGMENTS .............................................................................................................. v

INTRODUCTION .......................................................................................................................... 1

BACKGROUND ............................................................................................................................ 2

Aerosols ...................................................................................................................................... 2

Concentration and Exposure ................................................................................................... 2

Distributions............................................................................................................................ 2

The Respirable Fraction.......................................................................................................... 4

Physical Properties.................................................................................................................. 5

Aerosol Measuring Instruments.................................................................................................. 6

Optical Dust Monitors............................................................................................................. 7

Optical Particle Counters........................................................................................................ 8

EXPERIMENTAL METHODS.................................................................................................... 14

Apparatus.................................................................................................................................. 14

RESULTS AND DISCUSSION................................................................................................... 18

Response to smoke surrogates.................................................................................................. 18

Response to Burning Materials................................................................................................. 19

Cedar..................................................................................................................................... 20

Plywood ................................................................................................................................ 22

Oriented Strand Board (OSB)............................................................................................... 24

Pine ....................................................................................................................................... 26

Gypsum Board (Drywall) ..................................................................................................... 28

Cotton Upholstery................................................................................................................. 30

Polyurethane Foam ............................................................................................................... 32

CONCLUSIONS........................................................................................................................... 36

RECOMMENDATIONS.............................................................................................................. 36

Future Work.............................................................................................................................. 37

Metrics and Testing Protocols .................................................................................................. 37

Improvements for Real-Time Detector Technology................................................................. 38

REFERENCES ............................................................................................................................. 39
1

INTRODUCTION

The overhaul of a fire scene is a stage of firefighting where respiratory protection is often 

disregarded due to a perception of low risk and the desire to remove the heavy and cumbersome 

self-contained breathing apparatus. The overhaul operation occurs after the immediately visible 

fire has been extinguished and involves searching for and exposing hidden pockets of fire to 

ensure that the fire is completely extinguished. The operation resembles a structural demolition, 

with the added presence of smoldering debris. Limited information exists on what respiratory 

threats are present during this stage of firefighting. The need for alternatives for respiratory 

protection that are fitted to the task and the environment has been voiced by the firefighter 

community [1] and studies have recommended that some level of respiratory protection should 

be implemented. [2,3] Choosing the appropriate respiratory protection for individual events can 

only be accomplished with real-time information about the exposure hazards. Hand-held 

direct-reading particulate detectors have been used in other environmental monitoring 

applications, and it may be possible to adapt the technology to meet the needs of the firefighter. 

Direct-reading particulate detectors are considered a compliment to traditional filter-based 

gravimetric methods for exposure monitoring of work environments. Even though these 

instruments have been used for quite some time, there is not a consensus standard for evaluating 

worker exposure using direct-reading particulate detectors. Only in recent years has a best 

practice guide been developed for optical particle counters for workplace exposure. [4,5] Optical 

direct-reading instruments are sensitive to the physical characteristics of the particles. 

Depending on the measurement techniques, some characteristics can be exploited while others 

introduce measurement error. It is recommended to calibrate the instruments for the specific 

aerosol and that the physical characteristics of the aerosol remain stable during the exposure 

assessment. Unfortunately, there is a diversity of particulates present in the fire overhaul 

environment and their characteristics differ depending of the scenario. Therefore this presents 

many challenges for accurately detecting particulates during fire overhaul. 

To explore the potential for using hand-held direct-reading particulate detectors to aid 

firefighters during overhaul, the Department of Homeland Security sponsored a workshop on 

Real-Time Particulate Monitoring. The workshop was held at the National Institute of Standards 

and Technology (NIST) in May 2007. [6] Members of the fire service, particulate detector 

manufacturers, public health professionals, aerosol researchers, fire science researchers, and 

standards organizations convened to discuss the need for better technology to assess the level of 

respiratory protection required for work by firefighters in the overhaul environment. The 

consensus among workshop participants was that additional research is needed to better 

understand the health effects of particulates on firefighters, to better characterize the particulates 

present during overhaul, and to better characterize the response of particulate detectors to the 

overhaul environment. 

The goal of the present work is to conduct a preliminary characterization of hand-held 

direct-reading particulate detectors when exposed to the types of particulates anticipated in the 

fire overhaul environment. The characterization was conducted using the current state-of-the-art 

hand-held devices. The knowledge gained by characterizing the response of the detectors for 

this unique environment provides the foundation for future research, standards development, and

testing protocols specific to the needs of the firefighter community.
2

BACKGROUND

Aerosols

Concentration and Exposure

Respiratory exposure to an aerosol (a suspension of solid or liquid particles in a gas) is a 

combination of two factors: i) the concentration of the aerosol itself, and ii) the length of the time 

of exposure. This combination is expressed in equation 1:

Total Dose = (Aerosol concentration) x (Time of Exposure) (1) 

The concentration of an aerosol can be expressed in several ways. For example, the 

concentration can be specified as the number of particles that are found in some specified 

volume (e.g. per cubic foot, per cubic meter, etc.). Although the total number of particles in the 

volume is one way of expressing concentration, other properties of the particles can also be used. 

For example, each particle in the volume has a certain amount of mass. From the perspective of 

respiratory health, the total mass of all the particles together in a specified volume is traditionally 

the most common way of characterizing the concentration of an aerosol. This concentration

shall be referred to as CM(V), where the subscript M denotes the total amount of mass 

contributed by all the particles contained in the volume (V). Specifically:

CM (V) = Aerosol mass concentration = Total mass / unit volume (2)

The standard for concentration used here was adopted by the American Conference of 

Governmental Industrial Hygienists, Inc. (ACGIH). [7,8] Specifically, this standard specifies a 

maximum permissible exposure, expressed as the total amount of mass of particles contained in 

one cubic meter. This concentration defines the allowable, time-average mass concentration that 

can be encountered in a continuous eight hour period. In this standard, the total mass is 

expressed in units of grams. This exposure limit is referred to as the TLV, or Threshold Limit 

Value. ACGIH guidelines for TLV are 3 milligrams per cubic meter for respirable particulates 

and 10 milligrams per cubic meter for inhalable particulates. Particulates deposited in the gasexchange or alveolar region of the respiratory system are defined as respirable, while those that 

enter the nose or mouth and can deposit anywhere in the respiratory tract are defined as 

inhalable. 

Distributions

Sources of respirable particles associated with fire overhaul and emergency response situations 

do not produce particles of a single size (monodisperse). Rather, they result in a broad, or 

polydisperse Particle Size Distribution (PSD). Referring to Figure 1, there are two important 

characteristics that describe a PSD. These two readily apparent features describe where the peak 

in the PSD occurs, and the width of the distribution. Since conventional limits in respiratory 

exposure concern the mass of the associated aerosols, Figure 1 shows the mass-weighted PSD. 

Instead of graphically indicating the relative number of each particle size present in the 

distribution, this representation shows the amount of mass contributed by each different particle 

size. Figure 1 shows the Mass Median Diameter (MMD). The median connotes that the PSD 

contains equal amounts of mass above this value as it does below. For this particular PSD, the 
3

MMD does not coincide exactly with the peak of the distribution, although its location can be 

associated with the location of the peak. This will be discussed in more detail shortly.

Figure 1 Sample mass-weighted particle size distribution (PSD).

The width of the PSD is customarily described by the symbol σg, and is defined mathematically 

as the geometric standard deviation (GSD). Setting aside the precise mathematical expression, 

the GSD describes the region of the PSD that contains roughly 70 % of the total number of 

particles. In this plot, σg also describes the region of the PSD that contains roughly 70 % of the 

total mass of this aerosol.

Characterizing the performance of particle measuring instruments requires a set of test aerosols 

with precisely specified and reproducible PSDs. Data collected on a very large number of 

aerosols produced from a broad variety of sources reveals that many individual aerosols can be 

accurately described by a lognormal PSD. The exact mathematical expression for a lognormal 

PSD is as follows:

(ln ) 2 (ln )

(ln ln )

exp 2 ln 2

2

p

g

p

g

d d N d CMDdN















− − = π σ σ (3) 

where:

dp = particle diameter

σg = geometric standard deviation

dN = number of particles of diameter dp

N = total number of particles

CMD = Count Median Diameter

Particle diameter (μm)
4

The Count Median Diameter (CMD) that appears in equation 3 is analogous to the Mass Median 

Diameter. In this case, the PSD contains the same number of particles above this value as it does 

below. For lognormal PSDs, the CMD and MMD are simply related:

MMD = CMD exp(3ln2σg) (4)

The PSD shown in Figure 1 is a lognormal distribution. Note that when the x-axis (or particle 

size axis) is plotted on a logarithmic scale, the PSD appears symmetric. Note also that the MMD 

does not coincide with the peak of the PSD, but is slightly offset. The peak in the number 

distribution is called the Mode Diameter, and is equal to: CMD exp(-ln2σg).

For most aerosols of general interest, the CMD is in the range of approximately 0.2 μm to

4.0 μm, and σg is in the range of 1.2 to 3.0. Specific values of interest for the testing purposes 

discussed here will be addressed when describing the characteristics of individual instruments 

and how they respond.

The Respirable Fraction

To this point, the PSD has been taken to describe the ambient aerosol, or the aerosol as it occurs 

naturally in the environment. In actuality, the TLV standard does not describe the ambient 

aerosol, but rather the portion of an ambient aerosol that is deposited in the gas-exchange region 

of the lung (also referred to as the alveolar region). Much attention has been devoted to the 

amount and size of particles that penetrate to various levels within the respiratory tract as a 

whole. The Respirable Fraction describes the fraction of particles of a given size [in an ambient 

aerosol] that penetrate into the respirable region of the lung. This relationship is shown 

graphically in Figure 2. [8,9]

In general, laboratory reference instruments are designed to the best degree possible to make 

unbiased measurements. Specifically, the intent is to accurately sample and measure ambient 

aerosols. The situation is somewhat different, however, for instruments intended for industrial 

hygiene or environmental health applications. In this case, the desire is to characterize human 

exposure, or more specifically, the Respirable Fraction. For instruments intended for this type of 

application, the input sampling probes are intentionally designed to replicate the behavior 

illustrated graphically in Figure 2. In this fashion, the instrument provides a measure of the 

Respirable Fraction ostensively deposited in the alveolar region of the lung. As will be 

discussed, this penetration function is an important factor when modeling the expected response 

of various aerosol measuring instruments.
5

Figure 2 Standard convention for the Respirable Fraction.

This consideration is illustrated in Figure 3. Here is shown the PSD of an aerosol with a MMD 

of 4.2 μm and a σg of 2.0. The solid line illustrates the mass distribution versus particle size for 

the naturally occurring, or ambient aerosol. In contrast, the dotted line is the respirable fraction 

of this same aerosol. In this example, the respirable fraction contains roughly 54 % as much 

mass as the ambient aerosol, a significant difference.

Figure 3 Comparison of mass-weighted PSDs for ambient aerosol and Respirable Fraction.

Physical Properties

Particle size will affect the accuracy of an instrument, but other properties may also affect the 

accuracy of a given instrument in measuring a given aerosol. As will be discussed, there are 

several types of commercially available instruments for this purpose. Since these devices are 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 10 100

Aspiration Efficiency

Aerodynamic diameter (μm)

Inhalable

Respirable

Aerodynamic diameter (μm)

MMD = 4.2 μm

σg = 2.0
6

based on a variety of different measurement techniques, their accuracies are affected in different 

ways depending on particle properties.

In addition to size, the other main properties that may influence the measurement are:

- Shape

- Density

- Optical refractive index

Variations in these properties will affect the readings from different types of devices. It should 

be noted that the environment of interest here, fire overhaul, will generally contain a mixture of 

particles. Therefore, it is important to consider how an individual instrument will respond to a 

combination of particle properties that are present simultaneously. Again it is emphasized that 

current standards for respiratory exposure are not based on the number of particles in the aerosol 

or their specific size, but rather on the total respirable mass.

Aerosol Measuring Instruments 

Traditionally, particulate exposure limits have been quoted as mass concentration (mg/m3

). The 

mass concentration of aerosolized matter is most reliably determined by passing a known volume 

of gas through a filter and determining the increase in mass of the filter due to the amount of 

particulates deposited on the filter. Determining particulate mass concentration by accurately 

weighing the filter before and after sampling is simple, accurate and widely used. However, 

analysis requires a substantial amount of time since it requires the use of a sensitive 

microbalance, typically at a location different from the sample location. Direct-reading 

instruments can provide almost real-time results, within seconds to minutes depending on the 

sampling time and the nature of the instrument. The class of direct-reading instruments

considered here are optical instruments that measure particle mass, size, or occurrence indirectly 

from light scattering. In terms of mass response, these instruments tend to be less accurate than 

gravimetric filter measurements, but their rapid delivery of results allows one to measure 

environments that are changing, and to correlate the change with the measurements.

The detection and characterization of aerosolized particles can be accomplished by exploiting 

their optical properties. Because the particles' optical properties differ from that of the 

surrounding air, incident light is both scattered and absorbed by the particles. In principle, both 

the scattering and absorption effects can be utilized for particle measurements. However, 

because many particles of interest absorb very weakly, optical scattering provides a more 

practical basis for measurement instruments. Two properties of the light scattered by particles 

are generally used to measure their size and concentration: i) the total amount of light that is 

scattered, and ii) the geometry, or pattern exhibited by the scattered light. Both properties carry 

information regarding the size and the concentration of the aerosol. The recent advent of 

extremely compact, low cost, and reliable solid-state laser sources (laser diodes) and optical 

detectors (PIN (semiconductor) diodes) has led to the availability of a wide array of portable 

field devices to measure the size and concentration of aerosol particles. 
7

The basic construction of an optical particle detector is shown in Figure 4. An aerosol sample is 

first drawn into the instrument, at a specific volume flow rate, by way of an internal pump. The 

flow is continuous and typically set at several liters per minute. The sampled aerosol stream is 

then illuminated by a light source located within the instrument. Suitable optics are used to 

focus the light from the source onto the sample. One or more detectors are then used to collect 

the light scattered by the particles. The scattered light provides information about both the size 

and number of particles. Depending on the sophistication of the instrument, a number of 

detectors may be employed, to increase its accuracy or range. The output of the detector(s) is 

then processed by an internal computer, which computes the concentration of the sample, and in 

some cases, by particle size. This information is then logged into the instrument's memory. In 

many devices, the logged information includes the volume of the sample that is collected and the 

time of collection.

Figure 4. Schematic of the basic construction of a hand-held particle detector.

Optical Dust Monitors

One class of instrument is the aerosol or dust monitor, typically reporting concentration as 

mass/volume. It is designed to measure the total mass of the aerosolized particles, since mass is 

often used as a measure for evaluating exposure. In a dust monitor, the light scattered from a 

sample of particles is collected. The total mass of the sample is proportional to how many 

particles the sample contains, so a dust monitor measures mass by adding up the scattered light 

contributed by all of the particles together. Thinking more about how the mass of a group of 

spheres is determined, it is possible to understand that the total mass in the sample not only 

depends on how many spheres (particles) there are, but it depends on how big they are as well 

(the mass of each sphere being proportional to the cube of its diameter). In reality, the amount of 

light scattered by a particle is only approximately proportional to the cube of its diameter, so this 
8

aspect affects the ability of a dust monitor to measure mass accurately. Commercially available 

instruments are capable of measuring mass concentration over the range of 0.001 mg/m3 to 

400 mg/m3 over a particle size range of 0.1 μm to 10 μm. Most instruments are calibrated by 

comparing the instrument response to a standard dust, such as the ISO Test Dust, with standard 

gravimetric measurements. [10] Because the light scatter depends on the physical and optical 

properties of the aerosol, the calibration does not guarantee that the instrument will respond 

accurately to other aerosols. Therefore, when the properties of the aerosol are unknown, which 

is likely the case for field measurements, filter sample gravimetric measurements are 

recommended by manufacturers for direct comparison to calibrate the instrument to the specific 

aerosols in the environment.

Optical Particle Counters

Optical particle counters (OPCs) are one class of instruments that use light to measure aerosol 

particles. Specifically, OPCs measure the amount of light that is scattered by each individual 

particle. It is important to note that this approach essentially measures the particle’s size. 

Because the interest is the respirable mass of an aerosol, using an OPC requires assumptions to 

be made about the density of the particles in question. Since the mass of a particle is 

proportional to its density, any error made in the incoming assumption of particle density will 

result in a proportional error in the measurement of the total mass of the aerosol. In the fire 

overhaul environment, it is seldom known what specific particle materials are present, and many 

materials are likely to be present simultaneously. This presents a fundamental challenge in using 

an OPC to measure total respirable mass.

The basic concept of an OPC is straightforward. The amount of light that a particle scatters is 

related to its size. An OPC measures this scattered light, and compares the result to a preestablished calibration table. This table is typically compiled by the manufacturer using 

reference particles of known size and optical refractive index. As will be seen, it is important to 

emphasize that an OPC measures individual particles one at a time. It should also be noted that 

OPCs are calibrated using spherical particles. The amount of light scattered by non-spherically 

shaped particles is a complex phenomena, beyond the level of detail presented here.

An example of an OPC calibration table is shown in Figure 5. In this case, the values of 

scattered power were not obtained experimentally, but computed directly using a physical model 

for optical scattering. The geometry of the collection optics considered in this calculation is 

typical for some types of OPCs.
9

Figure 5 Scattered power versus particle size for a typical OPC.

A few features are readily apparent from Figure 5. For both curves, the amount of scattered 

power clearly varies as a function of the particle size. The two curves depict two different 

particle materials. Each material possesses an inherent optical refractive index, denoted as n. 

The refractive index of a given particle fundamentally affects the manner in which light is 

scattered. The upper, or blue, curve corresponds to a value of refractive index typical for 

pyrolysis products, essentially oily hydrocarbon droplets. The lower, or green, curve 

corresponds to carbonaceous soot characteristic of flaming combustion. For this case, the 

refractive index n = 1.6 + 0.66i. The second term (0.66i) is a mathematical way of expressing 

the fact that these particles scatter light, but absorb a portion of the light as well.

The oscillatory behavior of the upper blue curve in Figure 5 results from the complexities of light 

scattering from particles. Small changes in particle size greatly affect the angular distribution of 

the scattered light (in this case, the portion of the scattered light that reaches the optical detector). 

These rapid fluctuations introduce uncertainty into the OPC measurement; since by virtue of the 

calibration table, a given value of scattered power does not uniquely coincide with a single value 

of particle diameter. However, most OPCs display their measurements in terms of bins, e.g. the 

number of particles occurring in the bin from (1 to 5) μm, the bin from (5 to 10) μm, etc. Since 

many particles are grouped together in a bin, the effect of these oscillations tends to average out 

for the bin as a whole.

As seen in the lower curve, these oscillations are damped for particles that also absorb as well as 

scatter light. The more important consequence here is the observed difference in the amount of 

scattered light for these two types of particles. Depending on the refractive index of the particles 

that are used to calibrate the OPC, the instrument will tend to over or under predict the actual 

particle size when the refractive index differs as shown in Figure 5. In this particular case, the 

magnitude of this error at 10 μm is a factor of 2 to 3. Since this represents an error in the 

measurement of diameter, the associated error in the measurement of mass is 23 to 33 (since the 
10

mass of a particle is proportional to the cube of its diameter). In some cases, an OPC may be 

calibrated using several different types of particles, and the associated calibration tables are user 

selectable. However, such a feature may not be useful if the composition of the particles in a 

given environment is not known, or if several types of particles are present simultaneously.

Within the class of OPCs are instruments called Condensation Particle Counters (CPC). This 

class of instruments is capable of extending the lower size limit down to (10 to 20) nm. These 

devices amplify the light scatter from the particle by condensing a vapor around it and growing 

the particle to a several micrometer (>> 1 μm) sized droplet, similar to the process that forms 

clouds in the atmosphere. The micrometer sized droplets are easily detected but the 

measurement is independent of the particle size, shape, and refractive index. Therefore the 

instrument gives a measure of particle occurrence or counts only.

Figure 6 Cumulative mass distribution.

An important consideration for all instruments is the range of particle diameters they can 

measure. As will be seen, this feature can significantly affect their accuracy for certain aerosols, 

particularly in the determination of mass concentration. To illustrate this point, it is useful to 

refer to the cumulative mass distribution shown in Figure 6.

Figure 6 is best appreciated in comparison to the distributions shown in Figure 1 and Figure 3. 

In those cases the graphs of the PSD are presented, which describes the amount of mass 

contributed by a particle of a specific size. In contrast, the cumulative distribution seen in Figure 

6 expresses the amount of mass contained from the smallest possible size (nominally a diameter 

of zero), up to a given diameter dp. When dp is equal to the largest size in the distribution, the 

cumulative distribution equals 100 percent by definition, as all the mass of all size particles has 

been accounted. In Figure 6, this occurs at approximately 10 μm. The yellow shaded area 

represents the value of dp for which 90 percent of the mass in the distribution is accounted. In 

Particle diameter, dp (μm)
11

this example, this occurs at a value of approximately 7 μm. More simply stated, 90 percent of 

the mass of the total distribution is contributed by particles 7 μm and smaller.

Figure 7 Diameters for 90 % cumulative mass for a range of respirable aerosols.

Illustrating the relationship of the cumulative mass and the particle size range afforded by a 

given measuring instrument is aided by Figure 7. The horizontal axis of the graph corresponds 

to the CMD of the distribution, and ranges from 0.2 μm to 4.0 μm. These values are reasonably 

typical for the aerosols under consideration. The vertical axis of the graph corresponds to the 

90 % cumulative diameter. For the aerosol shown in Figure 6, for example, this diameter was 

roughly 7 μm as discussed above. A family of curves is shown for values of σg ranging from 1.2 

to 3.0. This range of values is also typical for the types of aerosols of interest here. For any 

combination of CMD and σg throughout their respective ranges, Figure 7 indicates the particle 

diameter below which 90 % of the mass of the total distribution is found. It should be noted that 

the PSDs used to calculate the values seen in Figure 7 were first weighted with the curve shown 

in Figure 2. Specifically, Figure 7 shows the 90 % mass cumulative distribution for the 

respirable fraction of each aerosol under consideration.

Two extreme cases shown in Figure 7 help to illustrate how the accuracy of a given instrument is 

influenced by the size range of particles it can measure. Consider the case of CMD = 0.2 μm, 

and σg = 1.2. This point corresponds to the left-most end of the lowest curve in the graph. It can 

be seen for this distribution that 90 % of the total mass is contributed by particles smaller than

roughly 0.2 μm. Consider the opposite extreme, where CMD = 4.0 μm and σg = 3.0. In this 

case, 90 % of the mass for this aerosol is contributed by particles roughly 8 μm and below.

For many handheld OPCs, the particle size upper limit is on the order of 4 μm to 5 μm. If 90 % 

of the total mass is contributed by particles as large as 8 μm as in the second example, such an 

CMD (μm)

90 % cumulative mass diameter (μm)
12

instrument would under report the total mass, since it is unable to measure the largest sizes that 

contribute to the cumulative mass.

In more practical terms, handheld OPCs are often more limited in this application by the smallest 

size particles that they can measure. This lower size limit is typically on the order of 0.3 μm to 

0.5 μm. For the first example that was considered (CMD = 0.2 μm, and σg = 1.2), this represents 

an extremely significant consideration. Since 90 % of the cumulative mass occurs below this 

size, such instruments would grossly under report the total mass. In the extreme case, they 

would literally report nothing at all. In summary, Figure 7 illustrates that the range of particle 

sizes that a given instrument can detect significantly affects its accuracy depending upon the 

specific aerosol being measured.

An additional consideration in the use of OPCs concerns what are referred to as coincidence 

errors. While the above discussion has centered on the measurement of the mass of an aerosol, 

coincidence errors are related to the number of particles in the distribution. Functionally, an 

OPC measures one particle at a time. It is important to realize, however, that both the incoming 

beam of light that illuminates the particles, and the region of space that is viewed by the optical 

detector are not infinitesimally small points. Rather, the incident beam of light illuminates a 

certain volume of space (on the order of a cubic millimeter, typically). Similarly, the detector 

that measures the scattered light is sensitive to particles that reside in a certain spatial region. 

The overlap between these regions is referred to as the Optical Sample Volume (OSV). 

Specifically, a particle residing within the OSV scatters light from the illuminating beam, and 

this light is measured by the optical detector.

Since an OPC is used to measure the size of each particle in the aerosol individually, it is 

important that no more than one particle occupies the OSV at a time. If the number of particles 

in a given volume of aerosol is very, very low, there is a high probability that this will be true. 

However, if the number concentration is continuously increased, eventually there will be a large 

probability of more than one particle being in the OSV at the same time. When this occurs, the 

detector collects the scattered light from more than one particle. Referring to the example 

calibration table shown in Figure 5, it can be seen that the OPC erroneously interprets this event 

as arising from a single particle that is larger than the individual particles themselves. For this 

reason, an important performance characteristic for an OPC concerns the largest number 

concentration that it can safely measure without a significant probability for coincidence errors. 

This situation can be appreciated by referring to Figure 8:

The horizontal axes of Figure 8 depict the same range for CMD and σg as in Figure 7 above. The 

total mass concentration is fixed at a value of 3 mg/m3

; corresponding to the ACGIH exposure 

standard previously discussed. Similar to the calculation relating to Figure 7, the various 

distributions were weighted to provide only the respirable fraction. The vertical axis illustrates 

the number concentration corresponding to the values of CMD and σg for a given aerosol.
13

Figure 8 Aerosol number concentration for a fixed mass concentration of 3 mg/m3.

From Figure 8, it can be seen that the number density associated with these various aerosols 

varies significantly. Specifically, the lowest number density (CMD = 4.0 μm, σg = 1.4) and the 

highest (CMD = 0.2 μm, σg = 1.4) differ by almost four orders of magnitude. Of greater concern 

is that the highest number density case exceeds the value where many OPCs can operate without

risk of coincidence errors. This figure illustrates potential difficulties in using OPCs when the 

properties of the PSD are not known in advance with any certainty. This situation can be further 

complicated if several materials, each with different PSDs, are present simultaneously.

In summary, several considerations have been identified that potentially affect the accuracy of 

OPCs in measuring aerosol mass exposure. These considerations must be reflected in the testing 

protocol used to evaluate the performance of individual instruments. A matrix of test aerosols 

and conditions must be specified to address the following factors that have been identified in the 

preceding discussion:

1. Since OPCs nominally measure particle size, uncertainty in the particle density gives rise to a 

proportional uncertainty in determining the measured mass exposure. Individual instruments 

may include provisions for inputting the particle density, while in other cases a default value 

is assumed. This aspect must be further investigated and quantified for aerosols or 

combinations of aerosols for which the density is not well known.

2. The measurement of particle size is affected by the particle refractive index. Some 

instruments are calibrated for a number of particle materials, where in other cases a default 

value is assumed. For this reason, a range of aerosols with differing values of refractive 

index must be examined. These tests must also address combinations of materials where the 
14

individual refractive indices may not be well known, or where the relative amounts of the 

individual materials may be unknown.

3. The range of particle sizes that a specific OPC can measure can significantly affect the 

accuracy in determining mass concentration. Test aerosols and combinations of aerosols can 

be used to address this consideration by virtue of having an appropriately wide range of 

PSDs.

4. Coincidence errors arise when the number density of the aerosol becomes sufficiently large. 

For some OPCs, this can occur at number concentrations that are less than the corresponding 

allowable mass exposures. It is important that the concentration of test aerosols with a range 

of PSDs be varied to determine when this performance limit is reached for individual 

instruments.

EXPERIMENTAL METHODS

Apparatus

A major challenge in sampling particulates is ensuring that the instrumentation is not over 

exposed or saturated with too many particles. Saturation can lead to the coincidence errors

mentioned previously. Frequent saturation can lead to long term measurement error from optics 

degradation due to particulates depositing on the optics. This situation requires cleaning, 

maintenance, and possible recalibration. Therefore, sampling from environments with high 

loadings of particulates requires dilution of the sample stream. 

In this study, particulate sampling occurs very near the source and within a closed system. The 

dilution that would normally occur in an open system is absent and therefore the particle loading 

is often beyond the limits of some of the instrumentation. A custom sampling system with 

multiple levels of dilution was designed for this study. Because OPCs, in general, have lower 

concentration limits than dust monitors, multiple stages of dilution were required in order to 

measure particle number concentration and particle mass concentration simultaneously.

The smoke or aerosol sample was drawn from a mostly homogenous mixture of air and 

particulates. A schematic of the sample system, Figure 9, demonstrates that the sample was 

diluted twice before passing through an enclosure that held the dust monitors, and then it was 

diluted twice more before passing through a cell from which the particle counters would draw 

their sample. Stage 1 of the dilution occurred at the venturi pump which drew the sample from 

the source. A stream of filtered air was added just after the venturi pump for the second stage of 

dilution. The third and fourth stages of the dilution were achieved by partitioning a desired 

amount of the sample with laminar flow elements and passing the remainder through HEPA 

filters to remove the particulates before reintroducing it back into the sample stream. A 

photograph of the system is shown in Figure 10. The system was capable of achieving a 

dilution ratio of 5000:1. The typical range of dilution ratios used in this study is shown in Figure 

11 as the area filled with hash marks. 
15

Figure 9 Schematic of the sampling system demonstrating the multiple stages (1st, 2nd, 3rd, 

4th) of dilution.

Figure 10 Photograph of sampling system.

Dilution

Smoke Source

Dust Monitor

Enclosure

Particle Counter Sample Cell

Dilution

Filtered Air

Filtered Air

Smoke Sample

Filtered Air

Diluted Sample Venturi Pump

Filter

Flow Meter

Laminar Flow Element Laminar Flow Element

Filter

Diluted Sample

Diluted Sample

3rd

1st 2nd

4th

Rotameter

Dust Monitor

Enclosure

1st and 2nd

Stages of Dilution

3rd and 4th

Stages of Dilution

Particle Counter

Sample Cell
16

Figure 11 Range of dilution ratios used for this study.

When sampling combustion generated particulates, typically the gas sample will be heated to 

preserve the condition of the sampled particulates as they are transported through the sampling 

system and to the detector. For the objective of these experiments, simulating real smoke 

particles that are representative of the fire overhaul environment, heating the air sample is not 

necessary. When overhaul begins, the combustion products have been diluted significantly by 

the ambient air and they are significantly cooler than during the period of active fire. Therefore, 

by not heating the sample, the process is more reflective of the overhaul environment.

Measurements of particle count and particle size were conducted in order to provide reference 

information on particle concentration and size distribution. The samples for these measurements 

were drawn from the Particle Counter Sample Cell, where dilution was the highest. Particle 

concentration was measured using a CPC (Kanomax Model 3800), while the particle size 

distribution was measured using a particle size spectrometer with a range of 90 nm to 10,000 nm.

(Particle Measuring Systems, Model LAS-X II). Manufacturer quoted expanded uncertainty 

(95 % confidence interval) for the CPC was ±20 % of the measured particle concentration. The 

quoted expanded uncertainty for measurement of particle size was ±6 % for the particle 

spectrometer. The CPC provides a reference measurement that is independent of particle 

characteristics. It is a measure of particle occurrence. 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0.10 0.15 0.20 0.24

Total System Dilution

Laminar Flow Element Setpoint
17

Aerosols such as mineral oil (CMD=0.2 μm) and an aqueous glycol solution (theatrical fog, 

particle size range: 0.25 μm to 60 μm) were selected as surrogates for the particulates generated 

during overhaul. White (clear) mineral oil has been used to test smoke detectors and is a 

reasonable surrogate for the particulates generated during the material pyrolysis prior to flame 

ignition. A smoke detector tester (Gemini 501) was used to generate mineral oil droplets. 

Similarly, glycol fog served as a surrogate for particulates generated during pyrolysis. A 

theatrical fog machine (Rosco 1700) was used to aerosolize the aqueous glycol solution. Each 

aerosol was used to evaluate the response of the dust monitors under controlled experimental 

conditions.

Several materials were selected to represent the sources of particulates generated during a fire 

and firefighting activities in building, Table 1. Wood materials like cedar, plywood, oriented 

strand board (OSB), and pine were selected as representative building materials. Similarly,

gypsum board was selected as a representative building material. Cotton upholstery and 

polyurethane foam represent the material make-up of furniture items. Some of the wood 

materials may also be included in this category.

Table 1 Material dimensions and mass.

Material Length (cm) Width (cm) Thickness (cm) Mass (g)

Cedar 10.0 10.0 2.6 121.7

Plywood 10.1 10.1 1.5 106.4

OSB 10.1 10.1 1.2 80.6

White Pine 10.1 10.1 1.9 84.3

Gypsum Board 9.8 9.8 1.3 69.7

Cotton Upholstery* 10.0 10.0 0.3 12.3

PU Foam 10.0 10.0 2.5 6.6

* Folded sheet with layer thickness of 0.17 cm.

The material samples were cut into squares, approximately 10 cm x 10 cm, for burning in the 

cone calorimeter, a standard test apparatus used to determine the heat and smoke contribution to 

a fire from test materials. Sample conditioning and burning followed the procedures of ASTM E 

1354-11a. [11] Each sample was placed in the cone calorimeter (horizontal orientation) and 

exposed to a heat flux of 25 kW/m2 from the radiant cone to induce pyrolysis and subsequent 

burning of the sample. The samples were exposed until the fire and all visible radiation from the 

sample, i.e. glowing, had ceased. During sample exposure, the dilution system was used to 

collect aerosol samples from the exhaust of the cone calorimeter. 
18

RESULTS AND DISCUSSION

Response to smoke surrogates

Four optical dust monitors were simultaneously exposed to the smoke surrogates (aerosolized 

mineral oil and an aerosolized aqueous glycol solution) to characterize their response. The dust 

monitors were selected to be representative of those available on the market and are here

identified by the letters A, B, C, and D. Each dust monitor is a handheld device and was 

marketed as a personal exposure monitor. Instruments A, B, and C were active sampling 

devices, meaning they were equipped with an internal pump to continuously extract a sample of 

air from the surrounding environment. Instrument D was not equipped with an internal pump 

and therefore performed a passive sample of the surrounding environment. Each of the 

instruments inferred mass concentration from the laser light scattered off the particles in the 

sampling volume. Measurements of particle count concentration were conducted simultaneously 

using the CPC. These measurements were from a diluted sample in order to avoid coincidence 

errors for the CPC. Each dust monitor was operated using its default or factory setup; therefore 

measurements present here are for the factory calibration of each instrument. Manufacturer 

quoted relative expanded uncertainty estimates were ±15 %, ±5 %, ±10 %, and ±10 %, for 

instruments A, B, C, and D, respectively.

Figure 12 Dust monitor response to the number concentration (diluted) of smoke 

surrogates. 

Dust monitor mass concentration measurements are plotted with respect to CPC particle count 

concentration measurements in Figure 12. As expected, the response of the dust monitors was 

linear. Most important, the response was linear over the range of mass concentration defined by 

the ACGIH exposure limits for particulates, 3 mg/m3 for respirable particulates and 10 mg/m3 for 

inhalable particulates. The factory calibration for each dust monitor is referenced to a 

gravimetric measurement of the industry standard material, aerosolized fine test dust (ISO 

12103-1, A2), either by direct comparison to the gravimetric measurement or a reference 

instrument with direct comparison to the gravimetric measurement. For aerosolized mineral oil, 

instrument A had the greatest response to particle count concentration, followed by instruments 

C and B which had similar responses. Instrument D had the lowest response; a factor of 2 lower 

than instruments B and C and a factor of 4 lower than instrument A. Three of the instruments 

logged significantly different values for particle mass concentration for the aqueous glycol 

solution, while two responded with similar outputs. The variation in instrument response for the 

0

2

4

6

8

10

12

14

16

18

20

0 1000 2000 3000 4000 5000 6000

Dust Monitor Response, mg/m3

Particle Concentration (diluted), #/cm3

A (glycol)

B (glycol)

C (glycol)

D (glycol)

0

2

4

6

8

10

12

14

16

18

20

0 1000 2000 3000 4000 5000 6000

Dust Monitor Response, mg/m3

Particle Concentration (diluted), #/cm3

A (mineral oil)

B (mineral oil)

C (mineral oil)

D (mineral oil)
19

same aerosol demonstrates the large uncertainty associated with using these instruments in their 

factory configuration to detect an aerosol other than the calibrating aerosol. However, the linear 

response of the instruments demonstrates that they can be calibrated for the aerosol of interest.

The CPC response is independent of the physical characteristics of the particulates. Figure 12

captures the response of the dust monitors to a change in the physical characteristics of the 

particulates. The aerosolized mineral oil and aqueous glycol solution are both liquid droplets, 

but with differences in size distribution and refractive index (mineral oil: n=1.47; aqueous glycol 

solution: 1.33 ≤ n ≤ 1.45). The response of the dust monitors to particle count concentration of 

the aqueous glycol solution is different when compared to that of the mineral oil. Instrument D 

again had the lowest response, and decreased by almost a factor of 2 when compared to mineral 

oil. Instrument B had the greatest response and increased by a factor of 5 when compared to 

mineral oil. The change in ranking of the dust monitor response between mineral oil and glycol 

follows: Mineral Oil – A; C; B; D; Glycol – B; C; A; D. This change in response for smoke 

surrogates or liquid droplet aerosols demonstrates the need for an aerosol or mixture of aerosols 

representative of the fire overhaul environment. However, there is a wide variety of aerosols in 

fire overhaul, so finding a single representative aerosol may prove difficult.

Response to Burning Materials

Material samples were burned in the cone calorimeter (see Figure 13) and the instruments were 

exposed to the products of the burning materials in order to characterize their response to real 

smoke samples. Using the sampling system described earlier, air samples were drawn from the 

exhaust of the cone calorimeter and diluted with filtered air before being simultaneously sampled 

by each of the instruments: four handheld dust monitors (labeled A through D), the CPC, and 

the particle size spectrometer. This simultaneous sampling allowed a time history of each

instrument’s response to be recorded and correlated to specific events during the burning 

process. A standard output of the cone calorimeter is the time history of the heat release rate for 

the burning material which could also be correlated to specific events. Events identified during 

the cone burns by visual observation were: ignition, flaming, and flame-out. Pyrolysis occurs 

just prior to ignition and an afterglow follows the flame-out. Time history plots of heat release 

rate, particle mass concentration, particle number concentration, and particle count median 

diameter (CMD) are presented in the following sections. Particle CMD was computed from the 

measured particle size distribution (PSD), assuming a lognormal distribution, equation 3.
20

Figure 13 Photo of glowing OSB sample in the cone calorimeter after the flame has

extinguished.

Cedar

Samples were cut from cedar planks and an example of the sample before burning is shown in 

the photo in Figure 14. Four samples were burned in the cone calorimeter to generate repeat 

experiments. The residual was a block of wood char also shown in Figure 14. 

Figure 14 Photo of cedar samples before and after being burned in the cone calorimeter.

Figure 15 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single cedar sample. Prior to ignition, during pyrolysis, the measured heat release rate was 

negligible. After ignition of the cedar sample, during flaming, the measured heat release 

transitioned through a sharp initial peak and then remained relatively steady for a period. Later it 

transitioned through another wider and lower magnitude peak. After that transition the flame 

extinguished (flame-out) and the heat release dropped during the period of afterglow. 
21

Figure 15 Time history measurements of heat release rate, particle mass concentration

(diluted), particle number concentration (diluted), and particle count median diameter for 

a burning cedar sample. Cone calorimeter incident heat flux was 25 kW/m2

.

0 500 1000 1500 2000

0

0.5

1

1.5

2

HRR, kW

 

0 500 1000 1500 2000

0

5

10

Mass Conc, mg/m

3

A

B

C

D

0 500 1000 1500 2000

0

2

4

6

x 104

No. Conc, #/cm

3

0 500 1000 1500 2000

100

120

140

160

180

Time, s

CMD, nm

Flaming

Pyrolysis

Ignition

Flame-out 

Afterglow
22

The handheld detectors measured two peak periods of particle mass concentration generated by 

the burning cedar sample. The first occurred during a period overlapping the pyrolysis and 

ignition of the sample. This also correlated with the first peak of heat release rate. The second 

peak in mass concentration was wider, much smaller in magnitude, and occurred near the second 

peak of heat release rate, just prior to the flame extinguishing. The measured mass concentration 

was negligible during the majority of the flaming period and during the period of afterglow.

The CPC measured the particle number concentration generated by the burning cedar sample. 

Measured particle number concentration was largest during the flaming period and between the 

two peaks of heat release rate. When the measured number concentration was large the 

measured mass concentration was low, if not negligible. Peaks in CMD correlated with peak 

mass concentration, while periods of lower CMD values correlated with periods of high number 

concentration. These observations suggest that a large fraction of the particles generated during 

the flaming period did not generate significant optical scattering in the dust monitors because 

they were either too small, of low refractive index, or both. 

Plywood

Samples were cut from plywood sheet and an example of the sample before burning is shown in 

the photo in Figure 16. Five samples were burned in the cone calorimeter to generate repeat 

experiments. The residual was a block of wood char also shown in Figure 16. 

Figure 16 Photo of plywood samples before and after being burned in the cone 

calorimeter.

Figure 17 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single plywood sample. Prior to ignition, during pyrolysis, the measured heat release rate was 

negligible. After ignition of the plywood sample, during flaming, the measured heat release 

increased sharply and went through a gradual peak. The heat release rate then remained 

relatively steady for a period. Later it transitioned through another gradual peak of greater width 

and magnitude. The heat release rate then dropped off and remained relatively steady for the 

remainder of the burn.
23

Figure 17 Time history measurements of heat release rate, mass concentration (diluted), 

number concentration (diluted), and count median diameter for a plywood sample. Cone 

calorimeter incident heat flux was 25 kW/m2.

0 200 400 600 800 1000 1200

0

1

2

3

4

5

HRR, kW

 p y

0 200 400 600 800 1000 1200

0

5

10

15

20

25

Mass Conc, mg/m

3

A

B

C

D

0 200 400 600 800 1000 1200

0

2

4

6

x 104

No. Conc, #/cm

3

0 200 400 600 800 1000 1200

120

140

160

180

200

Time, s

CMD, nm

Flaming

Pyrolysis

Ignition

Flame-out 

Afterglow
24

The handheld mass concentration detectors measured two peak periods that correlated with the 

peaks of heat release rate. The first occurred during a period overlapping the pyrolysis and 

ignition of the sample and was the minor of the two. The second peak was significantly greater 

than the first in magnitude but similar in width. It occurred during the second peak of heat 

release rate. With the exception of the peak regions, the measured mass concentration was 

generally very low. 

The CPC measured significant particle number concentration during the plywood sample burn. 

For three of the five samples burned, the peak number concentration was measured during the 

flaming period and between the peaks observed in the particle mass concentration trace. A 

secondary peak of number concentration was observed near the end of the flaming period during 

the second heat release rate peak. Similar to the cedar sample, when the measured number 

concentration was at its largest value the measured mass concentration was at its lowest. This 

observation again suggests that a large fraction of the particles generated during flaming did not 

generate sufficient optical scattering in the dust monitors. In general, the CMD went through 

two local peaks during the burn, with the second peak having the greatest magnitude. 

Oriented Strand Board (OSB)

Samples were cut from a sheet of OSB and an example of the sample before burning is shown in 

the photo in Figure 18 Four samples were burned in the cone calorimeter to generate repeat 

experiments. The residual was a block of wood char also shown in Figure 18. 

Figure 18 Photo of OSB samples before and after being burned in the cone calorimeter.

Figure 19 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single OSB sample. The general observations in the time history curves are almost identical to 

those of the plywood samples, with the exception of the particle number concentration trace. 

The difference is the absence of the peak number concentration during the flaming period and 

between the two heat release rate peaks. The remaining peak number concentration correlates 

with the peak mass concentration.
25

Figure 19 Time history measurements of heat release rate, mass concentration (diluted), 

number concentration (diluted), and count median diameter for an OSB sample. Cone 

calorimeter incident heat flux was 25 kW/m2.

0 100 200 300 400 500 600 700 800 900 1000

0

1

2

3

4

5

HRR, kW

 p 

0 100 200 300 400 500 600 700 800 900 1000

0

5

10

15

20

25

Mass Conc, mg/m

3

A

B

C

D

0 100 200 300 400 500 600 700 800 900 1000

0

1

2

3

4

x 104

No. Conc, #/cm

3

0 100 200 300 400 500 600 700 800 900 1000

120

140

160

180

200

Time, s

CMD, nm

Flaming

Pyrolysis

Ignition

Flame-out 

Afterglow
26

Pine

Samples were cut from planks of pine and an example of the sample before burning is shown in 

the photo in Figure 20. Four samples were burned in the cone calorimeter to generate repeat 

experiments. The residual was a block of wood char also shown in Figure 20. 

Figure 20 Photo of pine samples before and after being burned in the cone calorimeter.

Figure 21 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single pine sample. In general, the burning pine sample has two distinct peaks of heat release 

rate; the first being a very narrow peak occurring right after ignition, and the second being a 

more broad peak occurring just prior to the flame extinguishing. The magnitude of both peaks is 

very similar, but in general the first peak is the greatest. 

The handheld detectors measured two peak periods of particle mass concentration that correlated 

with the heat release rate peaks. The first peak was generally of larger magnitude, while both 

peaks had similar width. Measureable mass concentration, but significantly lower than the peak 

concentrations, was observed during the flaming period and between the two peaks. It was 

during this flaming period that the particle number concentration measured by the CPC was 

largest. Peaks in CMD correlated with peak mass concentration, while periods of low CMD 

values correlated with periods of high number concentration. These observations are similar to 

those of the cedar samples and suggest that a large fraction of the particles generated during this 

period of flaming did not result in in significant light scatter in the dust monitors. 
27

Figure 21 Time history measurements of heat release rate, mass concentration (diluted), 

number concentration (diluted), and count median diameter a for pine sample. Cone 

calorimeter incident heat flux was 25 kW/m2.

0 200 400 600 800 1000 1200 1400 1600

0

0.5

1

1.5

2

HRR, kW

 p 

0 200 400 600 800 1000 1200 1400 1600

0

2

4

6

8

Mass Conc, mg/m

3

A

B

C

D

0 200 400 600 800 1000 1200 1400 1600

0

2

4

6

8

x 104

No. Conc, #/cm

3

0 200 400 600 800 1000 1200 1400 1600

100

120

140

160

180

Time, s

CMD, nm

Flaming

Pyrolysis

Ignition

Flame-out 

Afterglow
28

Gypsum Board (Drywall)

Samples were cut from a sheet of gypsum board and an example of the sample before burning is 

shown in the photo in Figure 22. Four samples were burned in the cone calorimeter to generate 

repeat experiments. The residual was the charred paper covering and remaining gypsum shown 

in Figure 22. 

Figure 22 Photo of gypsum board samples before and after being burned in the cone 

calorimeter.

Figure 23 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single gypsum sample. The gypsum sample never ignited to form a flame. Its overall heat 

release rate was small compared to the other materials but it did generate a distinct peak. 

Distinct peaks were observed for the measured particle mass concentration and number 

concentration. Both occurred near the heat release rate peak. In general the peak mass 

concentration was to the left of the peak heat release rate while the peak number concentration 

was to the right. The CMD was relatively constant during the burn but a local maximum value 

was generally observed near the other peaks. 
29

Figure 23 Time history measurements of heat release rate, mass concentration (diluted), 

number concentration (diluted), and count median diameter for a gypsum board (drywall)

sample. Cone calorimeter incident heat flux was 25 kW/m2

.

0 50 100 150 200 250 300 350 400 450 500

0

0.05

0.1

0.15

0.2

HRR, kW

 

0 50 100 150 200 250 300 350 400 450 500

0

1

2

3

4

Mass Conc, mg/m

3

A

B

C

D

0 50 100 150 200 250 300 350 400 450 500

0

5000

10000

15000

No. Conc, #/cm

3

0 50 100 150 200 250 300 350 400 450 500

100

120

140

160

Time, s

CMD, nm

Pyrolysis

(no Ignition)

Igniter removed
30

Cotton Upholstery

Samples were cut from a sheet of cotton upholstery. The samples were cut to 20 cm x 10 cm and 

folded over once to have a final dimension of 10 cm x 10 cm. An example of the sample before 

burning is shown in the photo in Figure 24. Five samples were burned in the cone calorimeter to 

generate repeat experiments. The residual was the charred structure of the fabric shown in 

Figure 24. 

Figure 24 Photo of cotton upholstery samples before and after being burned in the cone 

calorimeter.

Figure 25 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single cotton upholstery sample. The flaming period for cotton samples is short compared to the 

wood products. A single peak transition of heat release rate was observed over this period. 

Distinct peaks were observed for the measured particle mass concentration and number 

concentration. Both correlated to the heat release rate peak, occurring near the heat release rate 

peak and slightly to the right or left. The CMD typically went through a local maximum near the 

other peaks and then was relatively constant during the remainder of the burn. 
31

Figure 25 Time history measurements of heat release rate, mass concentration (diluted), 

number concentration (diluted), and count median diameter for a cotton upholstery 

sample. Cone calorimeter incident heat flux was 25 kW/m2

.

0 100 200 300 400 500 600 700

0

1

2

3

4

5

HRR, kW

 U

0 100 200 300 400 500 600 700

0

0.5

1

1.5

2

Mass Conc, mg/m

3

A

B

C

D

0 100 200 300 400 500 600 700

0

5000

10000

15000

No. Conc, #/cm

3

0 100 200 300 400 500 600 700

100

120

140

160

Time, s

CMD, nm

Flaming

Pyrolysis

Ignition Flame-out 

Afterglow
32

Polyurethane Foam

Samples were cut from a block of foam and an example of the sample before burning is shown in 

the photo in Figure 26. Four samples were burned in the cone calorimeter to generate repeat 

experiments. The residual char left after the foam melted and burned is shown in Figure 26.

Figure 26 Photo of polyurethane foam samples before and after being burned in the cone 

calorimeter.

Figure 27 is a representative display for the time history of heat release rate, particle mass 

concentration, particle number concentration, and particle CMD, resulting from the burning of a 

single foam sample. Like cotton, the flaming period for the polyurethane foam samples was 

short compared to the wood products. Also similar to the cotton samples, single peak transitions 

of heat release rate, particle mass concentration and particle number concentration were observed 

over this period. The distinct peaks were all correlated, occurring very close together. The 

CMD typically went through a step transition from a lower to higher value and then was 

relatively constant during remainder of the burn. 
33

Figure 27 Time history measurements of heat release rate, mass concentration (diluted), 

number concentration (diluted), and count median diameter for a polyurethane foam 

sample. Cone calorimeter incident heat flux was 25 kW/m2.

0 50 100 150 200 250 300 350 400

0

1

2

3

4

5

HRR, kW

 p 

0 50 100 150 200 250 300 350 400

0

5

10

15

20

25

Mass Conc, mg/m

3

A

B

C

D

0 50 100 150 200 250 300 350 400

0

1

2

3

4

x 104

No. Conc, #/cm

3

0 50 100 150 200 250 300 350 400

100

150

200

Time, s

CMD, nm

Flaming

Pyrolysis

Ignition

Flame-out 

Afterglow
34

The time averaged particle mass concentration, particle number concentration, and particle CMD 

was computed for the duration of each burn (pyrolysis – ignition – flaming - flame-out -

afterglow) for each of the seven materials considered here. Figure 28 displays the sample 

average and standard deviation of the repeat experiments for each material. When all of the 

materials are viewed together, there is not a clear correlation between mass concentration and 

number concentration as observed with the surrogate smokes. The response of each handheld 

dust monitor appears to be unique for each material. However, the response appears to be 

grouped among types of materials, for example fabrics and paper, polymers and engineered 

materials, and untreated wood products. The ranking of the sensitivity of the dust monitors was 

generally the same for each material. Instrument A had the greatest sensitivity, followed by 

instruments B, C, and D, in decreasing order. The same ranking was observed with the 

aerosolized mineral oil. Also, as observed with the mineral oil, the sensitivities of instruments B 

and C were generally equal. This suggests that aerosolized mineral oil may be a good candidate 

as a surrogate smoke. This type of data can be used to begin to understand how to rescale the 

factory calibrations for smoke particulates.

Figure 28 Sample averages of time averaged particle mass concentration (diluted) and 

number concentration (diluted). Error bars represent one standard deviation of the 

sample of repeat measurements.

Moving from left to right in Figure 28, the measured particle mass concentration increases with 

increasing particle number concentration, except for the untreated woods, cedar and pine. For 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 5000 10000 15000 20000 25000 30000

Mass Concentration, mg/m3

Number Concentration, #/cm3

A

B

C

D

Cotton

Cedar

Plywood

OSB

PU Foam

Pine

Gypsum
35

these two materials the average measured mass concentrations were low when compared to the 

large values of average number concentrations generated. Figure 15 and Figure 21 show that for 

cedar and pine, respectively, there were long periods of very low to negligible measured mass 

concentration during periods of very high number concentration. These were also periods of 

lowest CMD. Not only were there long periods of particles going undetected by the dust 

monitors, these long periods are in included in the time averaged mass concentration, biasing it 

toward zero. 

The sample average of the CMD and GSD are listed in Table 2 for each of the materials, along 

with the sample average of measured mass concentration and number concentration. Comparing 

CMD and mass, the greater values of mass concentration correlate with larger measured CMD,

consistent with larger particles having larger mass. For example PU Foam had the largest mass 

concentration and the largest CMD on average. The CMD for the untreated wood materials were 

at the low end of the range for the materials tested, but the untreated wood materials generated 

the greatest number of particles on average. Since small particles carry less mass, this is more 

evidence that the conventional means of detecting and evaluating a respiratory hazard may not be

sufficient for environments that generate a mix of large and small particulates. Aerosol mass 

concentration is the current metric for exposure limits. Small particles are inhaled with more 

efficiency than large particles. Assuming that the particles generated during a fire are still 

present during overhaul, these results suggest that new metrics and tools for particle detection are 

necessary to limit exposures to small and potentially respirable particles.

Table 2 Sample mean ± standard deviation for repeat measurements of the time averaged 

particle CMD, GSD, mass concentration, and number concentration.

Materials CMD 

(nm) GSD (-) Mass Conc. (mg/m3

) No. Conc. 

(#/m3)

A B C D

Cotton 

Upholstery 131±2 1.30±0.01 0.20±0.05 0.16±0.04 0.18±0.12 0.06±0.02 1973±580

Cedar 134±2 1.30±0.01 0.89±0.20 0.64±0.15 0.52±0.13 0.29±0.09 21580±973

White 

Pine 135±10 1.30±0.02 1.16±0.44 0.78±0.46 0.59±0.35 0.34±0.18 24085±3319

Gypsum 

Board 135±12 1.30±0.06 0.75±0.10 0.59±0.08 0.40±0.05 0.23±0.03 3426±696

Plywood 157±3 1.30±0.01 3.60±0.12 1.79±0.05 1.71±0.04 0.95±0.03 14827±3911

OSB 157±3 1.30±0.01 2.90±0.29 1.34±0.14 1.30±0.14 0.74±0.08 10911±942

PU Foam 171±5 1.40±0.02 4.05±0.24 1.90±0.12 1.85±0.12 1.05±0.06 6757±508
36

CONCLUSIONS

The surrogate smokes investigated, induced linear responses from the dust monitors over the 

range of particle mass concentrations for which exposure threshold limits have been defined. 

However, for some of the detectors, the sensitivity varied with respect to the aerosol. Because 

there is a wide variety of aerosols in fire overhaul, finding a representative single surrogate 

aerosol may prove difficult. A mix of aerosols may be appropriate.

In order to try to simulate the types of particle present in the overhaul environment, seven 

materials were burned and the particulates generated during the burning were sampled. The 

response of each handheld dust monitor was unique for each material, however, the response 

across the devices were similar enough to categorize them with respect to types of materials. 

Three such categories defined here were fabrics and paper, polymers and engineered materials, 

and untreated wood products. 

Rankings of the sensitivity of the dust monitors were consistent across materials. Instrument A 

had the greatest sensitivity, followed by instruments B, C, and D, in decreasing order. The 

responses of instruments B and C were generally equal. This was consistent with the results for 

the aerosolized mineral oil. These similar observations, suggest that aerosolized mineral oil is a 

potential surrogate smoke that may be useful to understanding how to rescale the factory 

calibrations of dust monitors for the overhaul environment.

For the untreated wood products, cedar and pine, the periods of the highest particle number 

concentration correlated to the periods of the lowest particle mass concentration and particle size. 

The untreated wood products generated the largest average number concentration, but low 

average mass concentration. Particle mass concentration is the current metric for threshold

respiratory exposure limits. Since small particles are inhaled with more efficiency than large 

particles, the burning of untreated wood may create more of a respiratory hazard than the current 

metric would suggest. Assuming that all of the particles generated during the burning of 

untreated wood products are still present during overhaul, the results suggest that new metrics 

and tools for particle detection are necessary to limit respiratory exposures.

The cone calorimeter, a standard test apparatus used to determine the heat and smoke 

contribution to a fire from test materials, was used to generate smoke particulates from real 

materials. By following the standard test method, the apparatus provided consistent results for a 

specific material. Because the apparatus easily allows for particulate sampling of its exhaust and 

has an associated standard test method in place, it is a good candidate to use to further study the 

response of particulate detectors to burning materials.

RECOMMENDATIONS

The following list of recommendations is based on the knowledge gained from this work. It 

should provide a foundation for developing future research, standard testing protocols, and 

performance criteria that will allow the real-time particulate detector industry to improve and 

adapt the technology to the specific needs of firefighters and other first responders. 
37

Future Work

1. Aerosolized mineral oil was demonstrated as a potential surrogate smoke. A study 

should be conducted to investigate using aerosolized mineral oil to calibrate dust 

monitors for use in the fire overhaul environment.

2. A real-time measurement of particle mass or mass concentration that is traceable to a 

standard gravimetric method of analysis, but is not an optical scattering method should be 

included in future studies. A real-time measurement that is not an optical scattering 

measurement provides an independent confirmation of mass concentration.

3. Since OPCs nominally measure particle size, uncertainty in the particle density gives rise 

to a proportional uncertainty in determining the measured mass exposure. Individual 

instruments may include provisions for inputting the particle density, while in other cases 

a default value is assumed. This aspect must be further investigated and quantified for 

aerosols or combinations of aerosols for which the density is not well known.

4. The measurement of particle size by optical scattering can be affected by the particle 

refractive index. In similarity to the previous consideration, some instruments are 

calibrated for a number of particle materials, whereas in other cases a default value is 

assumed. For this reason, a range of aerosols with differing values of refractive index 

must be examined. These tests must also address combinations of materials where the 

individual refractive indices may not be well known, or where the relative amounts of the 

individual materials may be unknown. The list of materials should be expanded beyond 

the list considered here, but it should be consistent with flammable materials found in 

buildings.

5. A chamber to increase the mixing of the smoke generated at different times in the burn 

(or from different burning events) should be added to future investigations. Such a 

chamber would allow the smoke to mix and age much like the smoke in the fire overhaul 

environment.

6. Since large amounts of water are used to extinguish a building fire, humidity levels can 

be high in the overhaul environment. Therefore, humidity and/or aerosolized water 

droplets should be added to the aerosol sample stream to investigate the effects on the 

detector response.

Metrics and Testing Protocols

1. Evaluate dust monitors to confirm a linear response of over the range of the current 

respiratory threshold limits, 0 mg/m3 to 10 mg/m3

. 

2. Coincidence errors arise when the number density of the aerosol becomes sufficiently 

large. For some OPCs, this can occur at number concentrations that are less than the 

corresponding allowable mass exposures. It is important that the concentration of test 

aerosols with a range of PSDs be varied to determine when this performance limit is 

reached for individual instruments.

3. Simultaneous measurements of particle concentration should be performed with a CPC in 

order to have a reference measurement of particle occurrence that is independent of 

particle characteristics such as size, shape, and refractive index.

4. The range of particle sizes that a specific OPC can measure can significantly affect the 

accuracy in determining mass exposure. Test aerosols and combinations of aerosols must 

address this consideration by virtue of having an appropriately wide range of PSDs.
38

5. Scale the dust monitor responses with a single surrogate smoke such as aerosolized 

mineral oil or other appropriate droplet.

6. Recommend using the cone calorimeter and standard test method ASTM E 1354-11a to 

generate real smoke particles. Smoke samples should be mixed and aged to better reflect 

the operation of sampling smoke with a handheld device in the overhaul environment.

7. Calibrations performed against real smoke particulates should use a real-time 

measurement of particle mass or mass concentration that is traceable to a standard 

gravimetric method of analysis.

Improvements for Real-Time Detector Technology

1) A dual device that can measure mass concentration and number concentration

simultaneously would be ideal. When small particles dominate, mass concentration is 

low. Measurements of number concentration can still reflect the presence of a hazard. A 

good starting point is a particle counter that can convert its measurements to mass 

concentration with stated levels of accuracy.

2) The option to add an internal or external dilution system to optical particle counters could 

allow more of these devices to operate in high particle concentration environments like 

fire overhaul.

3) Including a standard list of calibration factors for different materials in the operation 

manuals of direct-reading particulate detectors would immediately show the user that 

care must be taken in interpreting the results while also offering a tool for better 

interpretation of the results. Aerosolized mineral oil could be one of the materials on the 

list.
39

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