Toward Developing a New Occupational Exposure Metric

Approach for Characterization of Diesel Aerosols

Emanuele G. Cauda,1 Arthur L. Miller,1

1

U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control

and Prevention, National Institute for Occupational Safety and Health, Office of Mining Safety and

Health Research, Pittsburgh, Pennsylvania, USA

Bon Ki Ku,2

2

U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control

and Prevention, National Institute for Occupational Safety and Health, Division of Applied Research

and Technology, Cincinnati, Ohio, USA

and Teresa L. Barone3

3

Emissions and Catalysts Research Center, Oak Ridge National Laboratory, Knoxville, Tennessee, USA

The extensive use of diesel-powered equipment in mines makes

the exposure to diesel aerosols a serious occupational issue. The exposure metric currently used in U.S. underground noncoal mines

is based on the measurement of total carbon (TC) and elemental

carbon (EC) mass concentration in the air. Recent toxicological

evidence suggests that the measurement of mass concentration is

not sufficient to correlate ultrafine aerosol exposure with health

effects. This urges the evaluation of alternative measurements. In

this study, the current exposure metric and two additional metrics,

the surface area and the total number concentration, were evaluated by conducting simultaneous measurements of diesel ultrafine

aerosols in a laboratory setting. The results showed that the surface area and total number concentration of the particles per unit

of mass varied substantially with the engine operating condition.

The specific surface area (SSA) and specific number concentration

(SNC) normalized with TC varied two and five times, respectively.

This implies that miners, whose exposure is measured only as TC,

might be exposed to an unknown variable number concentration

of diesel particles and commensurate particle surface area. Taken

separately, mass, surface area, and number concentration did not

completely characterize the aerosols. A comprehensive assessment

of diesel aerosol exposure should include all of these elements, but

the use of laboratory instruments in underground mines is generally impracticable. The article proposes a new approach to solve

this problem. Using SSA and SNC calculated from field-type measurements, the evaluation of additional physical properties can be

obtained by using the proposed approach.

The mention of any company or product does not constitute an 

endorsement by the National Institute for Occupational Safety and 

Health (NIOSH). The findings and conclusions in this article are those 

of the authors and do not necessarily represent the views of NIOSH. 

The authors would like to thank Dr. Ryan Lebouf at Division of 

Respiratory Disease Studies (NIOSH) for performing BET analysis of 

the samples. 

Address correspondence to Emanuele G. Cauda, U.S. Department 

of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety 

and Health, Office of Mining Safety and Health Research, 626 Cochrans 

Mill Road, Pittsburgh, PA 15236, USA. E-mail: ecauda@cdc.gov 

INTRODUCTION

The relationship between aerosol exposure and health effects 

in occupational environments has been extensively investigated 

and reviewed. Crucial steps for studying this relationship include evaluating occupational aerosols via measurement and 

characterization techniques. One of the goals of the National 

Institute for Occupational Safety and Health (NIOSH) is to 

reduce work-related illness by generating and promoting new 

scientific knowledge. Whereas through the first part of the 20th 

century, particle number was the primary metric for determining 

health-related aerosol exposure risk (Walton and Vincent 1998), 

in the last 50 years risk has been mostly characterized in terms of 

mass concentration of the particles (Maynard and Jensen 2001; 

Maynard and Aitken 2007). 

The use of aerosol mass concentration as a metric has recently been questioned and challenged, in particular because of 

the rapid increase of the nanotechnology sector and the related 

exposure of the workers to ultrafine or nanoscale particles (mean 

diameter smaller than 0.1 μm) (Balbus et al. 2007). Among 

aerosol and nanotoxicology scientists, there is still considerable 

uncertainty over which method should be employed to characterize ultrafine and nanoaerosol exposures, but there is gathering 

consensus that relying only on mass concentration measurements will clearly not be appropriate (Maynard and Kuempel 

2005; Oberdorster et al. 2005; Wittmaack 2007; Sager et al. 
2008; Pauluhn 2009; Fubini et al. 2010; Schulte et al. 2010). To 

address this issue, a recent publication presented an overview 

of four different metrics (number, length, surface area, mass) to 

evaluate aerosol exposure with reference to various classes or attributes of nanostructured particles (Maynard and Aitken 2007). 

Based on that work, it appears that particle surface area may be 

an appropriate metric for characterizing nanoscale materials. 

This approach is also supported by data suggesting a correlation 

between particle surface area and inflammatory response for different materials (Karakoti et al. 2006; Sager et al. 2008; Sager 

and Castranova 2009), and other data showing strong toxicitybased evidence that surface area is an appropriate exposure 

metric for low solubility particles (Tran et al. 2000; Brown et al. 

2001.; Monteiller et al. 2007; Sager et al. 2008). 

While measurement of ultrafine aerosol particle number concentration requires an understanding of instrument range of operability, accuracy, and calibration, the study of the aerosol surface area leads to more complex challenges, herein described. 

The Brunauer–Emmett–Teller (BET) method (Brunauer et al. 

1938), the standard method for measuring the total exposed surface area of particles, is based on the adsorption of gas molecules 

on the surface of the particles. This method, which is generally 

used to characterize bulk material like powders, is only available 

as off-line analysis and requires a large mass of sample. 

Several real-time techniques for estimating the surface area 

have been evaluated, all of them using principles different from 

gas adsorption. Monitors using the diffusion charging technique 

have been found to be capable of measuring the active surface 

area of ultrafine particles (Ntziachristos et al. 2004; Jung and 

Kittelson 2005; Ku and Maynard 2005). Active surface area 

(Fuchs 1963) is defined as the surface of a particle that is involved in interactions with the surrounding gas. The diffusion 

charging technique is also the basis for a recently developed 

monitor (Fissan et al. 2007; Wilson et al. 2007), which has the 

response proportional to the ICRP lung deposition model for 

the alveolar and tracheobronchial regions of the lung (Fissan 

et al. 2007). This monitor indicates the aerosol surface area deposited in different regions of the lung for particles up to 400 nm 

(Asbach et al. 2009). The specific response of the diffusion 

chargers to a single particle has been found to be affected by the 

size of the particle (Ku and Maynard 2005; Ku 2010). The size 

distribution of the aerosol obtained from an electro-mobility 

analyzer can also be processed to estimate the surface area of 

the particles (Woo et al. 2001; Ku and Maynard 2005; Lall 

and Friedlander 2006; Wang and Friedlander 2007). The size 

distribution measurement, and thus the estimated surface area, 

is affected by the morphology and aggregation of the particles 

(Lall and Friedlander 2006) but these are typically not known 

with precision in occupational environments without in-depth 

analysis. Studies that compared the results of these methods 

for measuring the surface area of ultrafine and nanoaerosols 

have been performed (Ku 2010) and recently concluded 

(Ku and Kulkarni 2012). 

The toxicological studies on occupational ultrafine particles 

generally do not consider how the aerosols are monitored and 

measured in the field. Since the proponents of using surface area 

as a metric for nanoparticles exposure have so far not provided 

indication on which type of surface area is the most appropriate, 

it is advisable to use as many different measurement methods 

as possible. The feasibility of the measurements on site needs 

to be considered too: some monitors can be easily employed in 

field studies (diffusion chargers), while the complexity, size, and 

lack of robustness of electro-mobility spectrometers complicate 

their use in environments different than laboratories. 

In underground mines, diesel-powered equipment emissions 

are the main contributor to ultrafine aerosol, generally referred 

to as diesel particulate matter (DPM). The International Agency 

for Research on Cancer (IARC) has labeled diesel exhaust as 

a human carcinogen (Benbrahim-Tallaa et al. 2012) while the 

National Institute for Occupational Safety and Health (NIOSH) 

(NIOSH and CDC 1988) has identified DPM as a potential occupational carcinogen. Deployment of contemporary (cleaner) 

diesel engines along with exhaust aftertreatment technologies 

such as diesel particulate filter (DPF) systems, disposable filter 

elements (DFEs), diesel oxidation catalysts (DOCs), and the 

use of alternative fuels such as biodiesel, are commonly considered the most effective strategies and technologies for curtailing 

DPM at its source. 

Recent studies have shown that these strategies can effectively limit the DPM aerosol mass concentration to the permissible exposure level (Bugarski et al. 2006, 2009). The same 

studies, which focused the impact of uncatalyzed DPFs on 

emissions, showed how the average size of the aerosol measured in the mine atmosphere by an electro-mobility spectrometer shifts toward smaller particles (Bugarski et al. 2006, 

2009): likely the post-DPF aerosol measured was mainly 

formed by nucleation of semivolatile compounds with a different chemical composition from the original emitted DPM, 

but no definitive data were presented. From a size distribution perspective, the DPM consists of two distinctive modes 

(Kittelson 1998): an accumulation mode with a median around 

100 nm and a nucleation mode characterized by a peak below 

30 nm. While the accumulation mode is the predominant mode 

present for exhaust without aftertreatment technologies, high 

number concentrations related to nucleation mode have been 

recorded on treated exhausts. 

Currently, the concentration of DPM in underground US noncoal mines is measured by using two surrogates, elemental carbon (EC) and total carbon (TC) carried out via the NIOSH 5040 

method. The TC is the carbonaceous portion of the DPM total 

mass and the EC is the nonorganic portion of TC. The DPM total 

mass is generally not measured because its results might be affected by nondiesel PM sources present in the mines. Currently, 

the DPM permissible exposure level in underground noncoal 

mines is set to a level of 160 3 μg/m TC and the measurement of 

EC is carried out regularly (71 Fed. Reg. 28924 2006). 
From an occupational exposure perspective, the variability 

in the DPM urges the assessment in underground mines to 

be reconsidered. Research is needed to prove if the current 

metric alone, based on the mass concentration of DPM, is capable of completely characterizing the exposure to this evolving 

aerosol from a physical perspective. The complexity of the DPM 

aerosols suggests that a comprehensive assessment should not 

be limited to the measurement of a single physical property. 

From a source characterization standpoint, the surface area of 

DPM nanoparticles emitted by a diesel engine was recently 

identified as a physical parameter appropriate for evaluating biological response (Ramachandran et al. 2005; Alfoldy, et al. 

2009; Giechaskiel et al. 2009; Hesterberg et al. 2010; Swanson 

et al. 2010). The variability in number concentration of the emitted DPM particles under different engine configurations and operating conditions calls for the evaluation of that property too. 

This study is a part of a comprehensive effort to evaluate the 

exposure assessment in relation to the evolution of DPM aerosol 

in underground mines, mainly due to the use of aftertreatment 

technologies and other abatement strategies. As a first step of 

this effort, the current metric (mass-based) was employed simultaneously with two additional physical metrics—surface area 

and number concentration—to measure the aerosols emitted by 

an untreated diesel engine exhaust under three different engine 

conditions. The tests were carried out in a laboratory exposure 

chamber, which provides controllable and relatively stable testing conditions. An array of particle measurement systems and 

methodologies was used to fully analyze the properties of DPM 

aerosols. 

The aim of this study was to explore an approach based 

on field-feasible measurements of the three metrics in order to 

generate a more complete characterization of the DPM ultrafine 

aerosol. Since the complexity of the surface area measurement 

is of particular interest, special focus was placed on measuring 

the aerosol surface area using different techniques. 

METHODS

Aerosol Generation

A stable source of DPM aerosol was provided using a Marple 

aerosol chamber (Figure S1–S2 in the online supplemental information) (Marple and Rubow 1983). The chamber is hexagonal 

in cross section, 2.44-m high with an inside diameter of 1.19 m. 

Aerosol is introduced at the top of the chamber, is thoroughly 

mixed by an air jet, and flows through a 10-cm-thick honeycomb structure in which turbulence is reduced and low-velocity 

flow exits and passes through the test section. Past work has 

shown the sampling zone of the chambers to be very uniform 

with relative standard deviation between samples typically less 

than 0.05 (Marple and Rubow 1983). 

The emissions of a Kubota diesel engine connected to a 

10-kW electrical generator (constant speed 1800 rpm) were used 

as a source of DPM aerosol. Diesel electrical generators are very 

common in underground mines for power generation. The exhaust of the diesel engine was not retrofitted with any control 

technology: this configuration is still the common practice for 

most diesel engines in underground noncoal mines. Previous 

studies on the engine showed a DPM aerosol very rich in EC 

and a very low concentration in volatile compounds (Noll and 

Birch 2008). The engine was run on ultra-low sulfur, road grade 

diesel fuel. Three resistive load conditions were employed (5, 

30, and 80%) to mimic typical working conditions for the engine. The 5% load represents the idle condition whereas the 

30% load characterizes a light working condition. A working 

condition under heavy load is represented by the 80% load. 

During tests, a portion of the exhaust emitted by the engine was 

mixed with particle-free fresh air just before the introduction in 

the Marple chamber. The target was to obtain two mass concentration levels of DPM in the chamber, “low” and “high.” Past 

studies on the chamber showed that the dilution ratio was approximately 200 and 500 for high and low mass concentration, 

respectively. The low mass concentration level was designed to 

target a TC concentration below or close to the current permissible exposure limit (160 μg/m3

) in underground noncoal mines, 

whereas the high mass concentration level targeted TC levels 

that would be out of compliance in an underground mine (approx. 500 μg/m3

). A tapered element oscillating microbalance, 

total mass concentration (TEOM 1400a) (Thermo Scientific, 

Franklin, MA, USA), was used to monitor the DPM aerosol 

mass concentration in real time. Tests were started when the 

aerosol concentration in the chamber was steady as measured 

by the TEOM. The mass concentration was maintained for 4 h 

to allow the sampling of a representative portion of particles for 

the real-time monitors and off-line analyses. 

Real-Time Measurement

Several real-time monitors were employed in parallel to measure the properties of the aerosol present in the chamber under 

different testing conditions. Each monitor extracted the aerosol 

from the Marple chamber via a conductive tube (6-mm OD). A 

correction for diffusion losses was not considered. The numberweighted particle size distribution of the aerosol was measured 

with a model 3936 Scanning Mobility Particle Sizer (SMPS) 

spectrometer (TSI, Shoreview, MN, USA). The concentration 

of particles from 14.6 to 661 nm was measured with a scanning 

time of 120 s. The SMPS consisted of a 3080 differential mobility analyzer (DMA) and a 3776 condensation particle counter 

(CPC), with sample and sheath airflow rates of 0.3 and 3 lpm, 

respectively. The density was assumed to be constant 1.8 g/cc 

and the diffusion correction was applied. The SMPS data for 

both number and surface area were interpreted by using the 

idealized aggregates (IA) method (Lall and Friedlander 2006), 

which was previously used for DPM particles emitted by diesel 

generators (Chung et al. 2008). 

The number concentration of the particles was also monitored 

by a portable CPC (3007, TSI, Shoreview, MN, USA). For some 

engine conditions, the aerosol concentration in the chamber 
exceeded the maximum limit of the portable CPC (which is 

about 105 particles/cm3

). Therefore, a T-valve with a HEPA 

(high-efficiency particulate arrestor) filter in the clean line, or 

a dilutor consisting of an HEPA filter cartridge with a single 

orifice drilled through the end cap (Evans et al. 2010), was used 

as a dilution system, and the dilution ratio was calculated during 

preliminary testing. 

The mass concentration of the DPM aerosol was monitored 

in real time by a TEOM 1400 (Thermo Scientific, Franklin, MA, 

USA) preceded by a 10-mm Dorr-Oliver cyclone and a DPM 

cassette impactor (SKC, Eighty Four, PA, USA). The cyclone 

and impactor preclassified the aerosol entering the TEOM, allowing only particles with an average aerodynamic diameter 

smaller than 0.8 μm to reach the analyzer (Noll et al. 2005). 

A nanoparticle surface area monitor (NSAM, TSI model 

3550) was used to measure aerosol surface area concentrations 

that would deposit in the alveolar region of the lung. This monitor has been found to provide reliable results even at concentrations above 106 #/cm3 (Asbach et al. 2009). The monitor is 

equipped with a 1-μm cyclone preselector. 

Two portable DC2000CE diffusion charger monitors (DC1, 

DC2; EcoChem, USA) were used to measure DPM particle active surface area. Both instruments had a manufacturer-specified 

range of 0–2000 m2

/cm3 and sensitivity of 1 m2/cm3 μ μ . In the 

diffusion chargers, the sampled aerosol is exposed to a positive 

ion cloud before collection on a filter and charge measurement. 

Sampling for Off-Line Analysis

During each test, a portion of the aerosol present in the 

Marple chamber was collected on three 37-mm quartz fiber 

filters for off-line thermo-optical analysis using a commercial 

DPM sampler (225–317, SKC Inc., Eighty Four, PA, USA). The 

sampling system included a 10-mm Dorr-Oliver respirable cyclone and an impactor with a cut point of 0.8 μm. Stable flow 

was maintained using critical orifices connected in line with 

each sample. To simulate the collection of DPM samples in an 

underground environment for 8 h while completing a test in four 

hours, the flow rate was set at 3.4 lpm (twice the usual 1.7 lpm). 

The higher flow rate changed the sampler cut point, but the concentration of particles larger than 0.4 μm is very low and similar 

for the three engine conditions (Figure 1).

FIG. 1. Mean particle size distribution of DPM aerosols generated in the 

Marple chamber under three engine load conditions (5, 30, and 80%): (a) low 

mass concentration level; (b) high mass concentration level. (Color figure available online.) 

 For this reason, the 

change in the cut is not significant for this study. Each flow rate 

was checked with an electronic bubble meter (Gilibrator, Gilian 

Instrument Corp., Wayne, NJ, USA) prior to sampling in every 

phase of the study. Using a similar sampling system, three TX40 

filters (Pall, Ann Arbor, MI, USA) were used to collect samples 

for the BET analysis during the tests at high mass concentration 

level. 

Samples were collected electrostatically for transmission 

electron microscopy (TEM) analysis using a beta version of the 

ESPnano Model 100 particle sampler (ESPnano.com, Spokane, 

WA, USA) (Miller et al. 2010).The sampler drew aerosol from 

the Marple chamber at a flow rate of 0.11 lpm. Several times 

during each test, the sampling tube was purged for 85 s followed 

by a collection time of 10–50 s to extract a representative sample 

of aerosol. Particles were collected on a 400 mesh copper TEM 

grid coated with carbon film and were analyzed using a JEOL 

2010J Analytical TEM at 2.5 × 104 magnification. Images were 

acquired at random locations on the TEM grid, and a qualitative 

morphology comparison was made for particles generated at 5, 

30, and 80% load. 

Data Processing

For each test, the real-time mass concentration data obtained 

from the TEOM monitor were averaged for the entire length 

of the test. In addition, to increase the data population, five 

periods of stable concentration were identified for each testing 

condition and the average mass concentration for each period 

was calculated. DPM samples collected on quartz fiber filters 

were analyzed using the NIOSH Method 5040 to determine the 

TC and EC content (Birch 2002), and the average concentration 
of TC and EC aerosol present in the chamber was obtained using 

the total volume of air that passed through the filter during the 

test. The determination of TC from underground mine samples 

is generally obtained by correcting the organic carbon (OC) 

content via subtraction of the amount found in a secondary 

filter present in the DPM sampler (Noll and Birch 2008). This 

correction was applied on the samples collected in this study to 

correct the absorption of volatile nonparticle OC on the primary 

filter. 

The SMPS measurement of the number-weighted particle 

size distribution provided information on the geometric mean 

diameter (GMD), geometric standard deviation of the aerosol, 

and the total number concentration of the particles in the Marple 

chamber. The GMD was used to classify the aerosol produced 

by different engine operating conditions. The surface area of the 

DPM aerosols was calculated from the SMPS raw data by applying the IA method. The surface area of the particles collected on 

PVC filters was measured using the Brunauer–Emmett–Teller 

(BET) standard method (Brunauer et al. 1938). Total particle surface area was estimated from seven point adsorption isotherms 

collected at relative pressures in the range p/p0 = .05 to .35 using 

a Quadrasorb-SI analyzer (Quantachrome Instruments, Boynton 

Beach, FL, USA). Each filter was analyzed twice and the results 

were blank corrected for the background filter surface area. 

The active surface area concentration data (nm2 per unit of 

air volume—cm3

) of the aerosol obtained from the two diffusion 

chargers were averaged for the entire length of each test and for 

the five periods of stable TEOM condition. The same operation 

was carried out for the raw data provided by the NSAM monitor 

as for the surface area of the particles deposited in the alveolar 

region of the lung. 

The specific surface area (SSA) of the particles (m2

/g) was 

calculated using the average mass concentration of the aerosols 

(TEOM data) and the average surface area concentration from 

the real-time monitors (NSAM, DC1, DC2, and SMPS). The 

SSA was obtained for the entire length of each test and for the 

five periods of stable aerosol concentration. The SSA is often 

used to characterize solid particles and is a useful parameter for 

comparing and reconciling the surface area data at each of the 

two mass concentration levels (low and high). Two additional 

sets of SSA data were obtained using the TC and EC concentration measurements for each test. While the resulting SSA (TC) 

and SSA (EC) are generally not found in aerosol surface area 

studies, they do quantify the aerosol surface area (m2

) per unit 

of TC and EC (gram) for the DPM particles at each condition. 

It is important to remember that TC and EC are the only mass 

concentration measures currently used for determining DPM exposure in underground U.S. metal/nonmetal mines. Both SSA 

(TC) and SSA (EC) were calculated from the real-time data for 

the entire length of each test. 

An additional measurement of SSA (m2

/g) was obtained 

by dividing the total surface area of the DPM particles (BET 

method) with the measurement of the total mass of DPM present 

on the sampling filter. This value was obtained by measuring 

and averaging loaded and blank filter masses. The gravimetric 

method used for the BET data normalization can be compared 

to the SSA obtained from the real-time monitors and TEOM 

mass concentration data. 

The total number-weighted particle concentrations in the 

chamber under different conditions were also normalized with 

TEOM and TC and EC concentration data. The result, specific number concentration (SNC), provides an indication of the 

number of particles per gram of total mass, TC, and EC in the 

aerosol, respectively. 

RESULTS

Table 1 summarizes the six test conditions for this study. 

Three steady-state engine loads (5, 30, and 80%) were applied to 

the electrical generator.

TABLE 1
 

Sampling time and mass concentration of DPM aerosols generated in the Marple chamber during the study
 

Engine load 5% 30% 80% 5% 30% 80% 

Mass concentration level Low Low Low High High High 

TEOM average [μg/m3

] 67.5 70.2 118.2 223.1 462.3 494.0 

TEOM STDEV [μg/m3

] 9.9 11.2 24.8 35.6 48.3 65.8 

Sampling length [min] 240 240 120 240 240 240 

 The aerosol emitted by the engine under 

each engine load was directed into the chamber to generate two 

mass concentration levels: low and high. The table shows the 

average mass concentration measured by the real-time TEOM 

monitor and the relative standard deviation: the variance of the 

mass concentration was between 10 and 20% during each test. 

All tests were run for 240 min with the exception of the 80% 

engine load-low mass concentration test, which was run for only 

120 min because of engine malfunctioning. 

Figure 1 shows the SMPS average number-size distributions 

for the aerosols in the Marple chamber over three engine load 

conditions and two mass concentration levels. For each size 

bin, the standard deviation of the mean is provided. As the 

engine load increased, the number concentration distribution 

shifted to larger particles. The distributions were characterized 

only by the accumulation mode and there was no indication of 

nucleation mode: the GMD of the DPM aerosols varied from 
66 to 135 nm and the geometric standard deviation was below 

1.7 for all the conditions (Table 2). 

TABLE 2
 

Mean particle size and number concentration for DPM aerosols generated in the Marple chamber under different conditions
 

Engine load 5% 30% 80% 5% 30% 80% 

Concentration level Low Low Low High High High 

Geometric mean diameter [nm] 66.2 ± 1.1 86.6 ± 2.9 135.1 ± 4.9 66.9 ± 1.1 92.7 ± 3.6 129.6 ± 3.2 

Geometric standard deviation 1.59 1.59 1.66 1.56 1.63 1.66 

Total number concentration [104 #/cm3

] 23.3 ± 3.8 12.5 ± 2.4 8.1 ± 1.7 65.1 ± 8.2 69.3 ± 5.6 31.1 ± 3.9 

The geometric standard 

deviations were all in agreement with the DPM aerosol standard 

deviation proposed by Harris and Maricq (2001). There was no 

substantial variation in the GMD for aerosols generated by the 

same engine load when the chamber was operated under low and 

high mass concentration, which suggests that the dilution of the 

chamber did not alter the size characteristics of the aerosol. The 

total number concentration of the particles ranged from 8.1 ×

104 to 69.3 × 104 particles/cm3 and the variation within each 

test was between 10 and 15%. 

The morphology of particles present in the Marple chamber 

was qualitatively compared for engine loads 5, 30, and 80%. 

Although a systematic evaluation of particle morphology was 

not carried out, some images were consistent with the SMPS 

results. These showed increasing particle size with increasing 

engine load (Figure 2), which is a relationship reported earlier 

by Kittelson (1998) in a review of several diesel engine studies. 

The particles were present as solid nanoparticle aggregates. A 

preliminary estimation of the primary particle showed a diameter of 25 nm. This value is in line with the literature (Park 

et al. 2004; Mathis et al. 2005) and it was used to implement 

the IA method for the SMPS raw data by using the Aerosol 

Instrument Management (TSI, Shoreview, MN, USA) software 

tool. 

The SSA of the DPM aerosols was calculated by normalizing each instrument measurement with the TEOM mass concentration (Figure 3). The SSA data were plotted against the 

aerosol GMD characteristic of each engine operating condition. 

For each condition, the SSA values obtained from the SMPS 

data were higher than the SSA data obtained from the diffusion 

charger monitors and the NSAM. While the SSA provided by the 

BET analysis presented a very scattered trend, the replicate of 

each sample showed a low standard deviation. The high variance 

can be attributed to the uncertainties of the sampling process or 

most probably to the interaction of DPM particles and filter media, which limits the study of surface area with this protocol. 

Nevertheless, the SSA measured via the BET technique was between the values obtained using two types of real-time monitors. 

The plots of SSA normalized by TC and EC are provided in the 

supplemental information (Figure S3). 

Table 3 summarizes the surface area concentration and mass 

concentration of the aerosols measured by real-time monitors 

and off-line analysis. For each testing condition, the average 

and standard deviation of the data relative to the whole test 

were reported in the table. The data from DC1 for all high mass 

concentration tests and the data from DC2 for a single high mass 

concentration level at 80% engine load were excluded because 

of malfunctioning of the monitors. 

Independently from the unit used for the mass normalization (TEOM, TC, and EC) or the monitor employed, the SSA 

was found to decrease as engine load increased from 5 to 80%. 

For example, the SSA measured by the SMPS and normalized 

by the TC concentration varied from 80.7–115.3 m2

/g (5%) to 

51.2–57.4 m2

/g (80%), while for the NSAM data, the trend was 

from 17–31 m2

/g (5%) to 8–10 m2/g (80%). The SSA values 

obtained using the SMPS data were constantly higher compared 

to those determined by other monitors or techniques. No substantial difference was found for the SSA data obtained from the 

three diffusion charger monitors (DC1, DC2, and NSAM). The 

SSA measured by BET analysis presented the largest scatter, but 

the values were found to be within the range of data obtained 

from the real-time monitors. Finally, the SSA normalized using 

the TEOM data was consistently lower than the SSA normalized 

with the TC and EC: the SSATEOM < SSATC < SSAEC. 

The particle number concentrations measured by the SMPS 

and portable CPC were also normalized in terms of the mass 

concentration of the aerosol obtained as total mass (TEOM) 

and TC and EC concentration (Figure 4, Figure S4). For each 

testing condition, the two instruments provided within error the 

same SNC results. With increasing engine load, the SNC values 

dropped drastically during both low and high mass concentration 

level testing. Table 4 summarizes the SNC values obtained by 

the combination of data presented in Tables 2 and 3. For each 

gram of TC, the number of particles counted by the SMPS 

varied from 4124 × 1012–5925 × 1012 (5%) to 834 × 1012–896 

× 1012 (80%). Similar numbers were measured by the portable 

CPC. 

DISCUSSION

DPM was analyzed in an aerosol chamber under different 

mass concentration levels and engine operating conditions to 

simulate aerosols present in underground noncoal mines. Three 

substantially different size distributions were obtained (Figure 1). The GMD of the aerosols varied from 66 to 135 nm 

(Figure 1) and were comparable with values previously reported by Bugarski et al. (2009) for aerosols generated by 
untreated diesel exhaust in underground mines. Simultaneous 

measurement of mass, surface area, and number concentration were used to characterize the DPM aerosols. The average surface area concentration and number concentration values presented in this article compare well with the published 

data relative to diesel ultrafine particles or other nonengineered 

nanoparticles in occupational environments. 

FIG. 2. TEM photomicrographs of DPM generated in the Marple chamber under three engine load conditions: (a) 5%, (b) 30%, and (c) 80%. 

The number concentration of diesel particles measured during a campaign on 

untreated exhaust in a laboratory mine was (5.5–8.7) × 105 

particles/cm3 (Bugarski et al. 2009). The exposure of three occupational groups—bus drivers, parking garage attendants, and 

bus mechanics—to diesel particles was assessed with diffusion chargers and particle counters, with the surface area concentration varying between 60 and 150 μm2

/cm3 and number 
concentration between 0.25 × 105 and 1.5 × 105 particles/cm3 

(Ramachandran et al. 2005). The average number and surface 

area (DC) concentration of aerosols present in an automotive 

engine manufacturing center was (1–2) × 103 μm2

/cm3 and 5 ×

105

–106 particles/cm3 respectively (Heitbrink et al. 2009). 

As shown by Figures 3 and 4, a single metric does not define 

completely the DPM aerosols generated by an untreated exhaust. The DPM surface area, number concentration, and mass 

concentration are not related by defined univocal correlations. 

The surface area and number concentration per unit of mass 

(SSA and SNC) are not constant but they can vary up to twofold 

and fivefold, respectively. This implies that in mining environments the analysis of the exposure of miners to DPM will not be 

comprehensive if the monitoring is limited only to TC and EC. 

Unfortunately, only small instruments like diffusion chargers 

and portable counters can be employed in such an occupational 

environment on a regular basis. 

TABLE 3
 

Surface area and mass concentration of DPM aerosols measured with different instruments and techniques. Specific surface area
 

(SSA) concentration obtained using the total real-time mass concentration (TEOM) and the surrogates (EC, TC) generally
 

employed in mining environments to assess exposure to DPM
 

Engine load 5% 30% 80% 5% 30% 80% 

Concentration level Low Low Low High High High 

Surface area

SMPS 

NSAM 

DC1 

DC2 

2 μm /cm3 [102

] 

m2 μ /cm3 [102

] 

2 μm /cm3 [102

] 

2 μm /cm3 [102

] 11.0 ± 1.6 

44.5 ± 7.5 

9.7 ± 1.9 

7.4 ± 1.1 

40.3 ± 7.1 

6.4 ± 0.7 

5.8 ± 1.2 

9.9 ± 1.9

52.1 ± 11.2 

7.4 ± 2.3 

6.8 ± 1.6 

10.2 ± 2.4 

126.8 ± 19.1 

34.7 ± 5.5 

27.4 ± 2.1 

249.3 ± 23.1 

54.7 ± 4.6 

45.6 ± 4.1 

190.6 ± 22.5 

38.2 ± 4.5 

Mass Conc.

TEOM 

TC 

EC 

3 μg/m

3 μg/m

3 μg/m

67.5 ± 9.9 

55.2 ± 2.2 

31.2 ± 0.3 42.2 ± 0.3 

70.2 ± 11.2 

52.2 ± 2.6 

118.2 ± 24.8 

90.8 ± 6.5 

88.8 ± 8.2 

223.1 ± 35.6 

109.9 ± 6.4 

62.9 ± 3.7 

462.3 ± 48.3 

344.6 ± 9.8 

273.6 ± 2.8 

494.0 ± 65.8 

372.0 ± 23.9 

335.7 ± 6.4 

SMPS 

NSAM 

DC1 

DC2 

m2

/g 

m2

/g 

m2

/g 

m2

/g 

66.1 ± 14.7 

14.4 ± 3.6 

10.9 ± 2.3 

16.3 ± 3.4 

SSA (TEOM)

57.4 ± 13.6 4 4.1 ± 13.2 

9.1 ± 1.8 6.3 ± 2.4 

8.3 ± 2.1 5.7 ± 1.8 

14.1 ± 3.6 8.6 ± 2.7 

56.8 ± 12.4 

15.6 ± 3.5 

12.3 ± 2.2 

53.9 ± 7.5 

11.8 ± 1.6 

9.9 ± 1.4 

38.6 ± 6.8 

7.7 ± 1.4 

BET m2

/g 

SSA (gravimetric)

25.7 ± 11.5 42.5 ± 25.1 2 8.8 ± 13.4 

SMPS 

NSAM 

DC1 

DC2 

m2

/g 

m2

/g 

m2

/g 

m2

/g 

80.7 ± 13.0 

17.6 ± 3.6 

13.4 ± 2.1 

20.0 ± 3.1 

SSA (TC)

77.3 ± 14.1 5 7.4 ± 12.9 

12.3 ± 1.5 8.2 ± 2.7 

11.1 ± 2.3 7.5 ± 1.9 

18.9 ± 3.8 11.3 ± 2.8 

115.3 ± 18.6 

31.6 ± 5.4 

24.9 ± 2.4 

72.4 ± 7.1 

15.9 ± 1.4 

13.2 ± 1.3 

51.2 ± 6.9 

10.3 ± 1.4 

SMPS 

NSAM 

DC1 

DC2 

m2

/g 

m2

/g 

m2

/g 

m2

/g 

142.9 ± 23.9 

31.3 ± 6.9 

23.8 ± 3.5 

35.4 ± 5.3 

SSA (EC)

95.5 ± 16.7 5 8.7 ± 13.7 

15.2 ± 1.6 8.4 ± 2.8 

13.7 ± 2.8 7.7 ± 1.9 

23.4 ± 4.6 11.5 ± 2.9 

201.7 ± 32.5 

55.3 ± 9.4 

43.6 ± 4.2 

91.1 ± 8.5 

20.0 ± 1.7 

16.7 ± 1.5 

56.8 ± 6.8 

11.4 ± 1.4 

To solve this assessment problem a new approach is proposed. The plots in Figures 3 and 4 show how the SSA and 

SNC results characterize the different operating conditions of 

the diesel engine and thus define the DPM aerosol at each condition. This is possible because the DPM aerosols evaluated, 

characterized only by the accumulation mode, have a variable 

GMD but a steady geometric standard deviation as previously 

reported (Harris and Maricq 2001). By using the SSA—obtained 

from the NSAM monitor and normalized by the TC or EC—as 

an input data it is possible to quantify the geometric surface area 

and the number concentration without any information related to 

the operating engine condition (Figure 5a and b). The proposed 

approach can be described in steps. Simultaneous collection of 

just NSAM and TC (EC), data are recorded and used to calculate 

the SSA—the input data-point. The specific geometric surface 

area and number concentration are then retroactively retrieved 

by using the presented plots (Figure 5a and b) and finally the 
electro-mobility surface area and the number concentration obtained by using the TC (EC) values measured. In the same way, 

the SNC—from a portable CPC and normalized by TC or EC– 

can be used to quantify the electro-mobility and lung deposited 

surface area (Figure 5c and d). Even if not reported, the active 

surface area (DC) can be used alternatively as an input or output 

data in this approach. 

FIG. 3. Specific surface area (SSA) of DPM aerosols generated in the Marple 

chamber under different conditions. The SSA is based on the mass concentration 

obtained by the TEOM. (Color figure available online.) 

FIG. 4. Specific number concentration (SNC) of DPM aerosols generated in 

the Marple chamber under different conditions. The SNC is based on the mass 

concentration obtained by the TEOM. (Color figure available online.) 

TABLE 4
 

Specific number concentration (SNC) obtained using the mass concentration surrogates (EC, TC) generally employed in mining
 

environments to assess exposure to DPM
 

Engine load 5% 30% 80% 5% 30% 80% 

Concentration level Low Low Low High High High 

SNC (TEOM)—1014

SMPS #/g 33.7 ± 7.4 17.8 ± 4.4 6.9 ± 2.1 29.2 ± 5.9 15.0 ± 1.9 6.3 ± 1.1 

CPC #/g 38.5 ± 8.9 17.7 ± 3.9 8.0 ± 2.7 28.6 ± 5.9 11.7 ± 2.1 6.5 ± 1.1 

SNC (TC)—1014

SMPS #/g 41.2 ± 7.0 23.9 ± 4.7 8.9 ± 2.0 59.2 ± 8.2 20.1 ± 1.7 8.3 ± 1.2 

CPC #/g 47.1 ± 8.6 23.8 ± 3.8 10.4 ± 2.8 58.0 ± 8.4 15.8 ± 2.3 8.6 ± 1.1 

SNC (EC)—1014

SMPS #/g 70.2 ± 12.1 29.6 ± 567 9.2 ± 2.1 103.6 ± 14.4 25.3 ± 2.1 9.2 ± 1.2 

CPC #/g 83.4 ± 14.8 29.4 ± 4.5 10.7 ± 2.9 101.5 ± 14.7 19.8 ± 2.9 9.5 ± 1.1 

The application of the new approach to field data is based on 

the assumption that the plots in Figure 5 are valid for aerosols 

generated from diesel engines different than the one evaluated 

in this study. In this way, field collected SSA or SNC data can be 

used to quantify other properties not measured in the field. The 

validity of the assumption and proposed approach needs to be 

investigated and corroborated with further laboratory and field 

analysis in the future. While the nucleation mode is not considered in this approach, it is intuitive that any nucleation aerosol 

occurrence would produce an extremely high value of SSA and 

SNC input data. If enhanced and enriched with the addition of 

data from different engine configurations, the new metric approach would have the benefit to reduce the number of simultaneous measurements. The approach is completely independent 

from the engine operating condition that is generally difficult 

information to obtain during field studies. Furthermore, it can 

potentially provide average values aerosols emitted by diesel 

engine under more realistic transient duty cycles or aerosol generated by multiple exhausts. 

The approach described above is based on the measurements 

carried out by different monitors and measurements. For this 

reason, the measurements of the surface area employed in the 

new approach require some additional interpretation because 

each methodology is supposed to measure a different type 

of surface area. The surface area measured via off-line BET 
technique showed a very high scatter pattern and this presents 

an issue for comparing these data with the real-time technique 

results. The interaction of different DPM particles with the microfibers of the TX40 filters might have an influence in the 

absorption technique. In addition the possible degassing of the 

fibers in the filter media in the preparation for the BET analysis 

might have occurred. The three real-time techniques provided 

substantially different values of the DPM surface area concentration (Table 3). In theory, the surface area measured by the 

SMPS analyzer and corrected by the IA method represents the 

geometric surface area of the DPM aggregates. The assumption is that all the primary particles are equally exposed to the 

surrounding gas: the aggregates need to be linear-chain assumption with a low fractal dimension. Recently, Ku and Kulkarni 

(2012) showed that the geometric surface area of aggregates is 

underestimated by the IA method applied to mobility analysis 

when the chains have a fractal dimension of 2.37–2.63. Because 

no statistical morphology analysis was carried out in this study, 

the SMPS surface area can be considered only an estimation of 

geometric surface area. 

FIG. 5. The new metric approach presented with different SSA and SNC inputs data: (a) specific NSAM surface area (TC); (b) specific NSAM surface area 

(EC); (c) specific number concentration (TC); and (d) specific number concentration (EC). (Color figure available online.) 

By processing the recorded data, The 

NSAM signal for a single particle was found to be proportional with the mobility diameter to a power of 1.17. Studies 

on single NaCl particles (Jung and Kittelson 2005) and urban 

airborne nanoparticles (Ntziachristos et al. 2007) determined 

the NSAM proportionality as d1.13 and d1.258, respectively. Wilson et al. (2007) used the NSAM, previously known as EAD, 

for the measurement of urban particles from 3 to 500 nm and 

found the lung-deposited surface area and the NSAM signal to 

be proportional to d1.1–1.6 and d1.25, respectively. For this reason, 

the NSAM measured the lung (alveolar) deposit surface area 

of different DPM particles present in the aerosol chamber. For 

the two diffusion charger a proportionality of d1.34 (DC1) and 

d1.44 (DC2) was found. The values are consistent with previous 

studies: Ku and Maynard (Ku and Maynard 2005) found a proportionality of 1.5 for similar diffusion chargers when exposed 

to monodisperse silver nanoparticles for particle sizes from 80 
to 200 nm. From a theoretical perspective, the output of the DCs 

evaluated in this study agrees with the Fuchs surface area, which 

is proportional to d1.39 (Fuchs 1963). The surface area measured 

by the two DCs in Table 3 was therefore the active surface area 

of the DPM particles present in the exposure chamber. 

SUMMARY

The presence of DPM in underground mines leads to occupational exposure to ultrafine particles. While the mass concentration of an aerosol is commonly used as the only metric in 

occupational exposure assessment studies, surface and number 

concentration have received increasing attention for characterizing the exposure to particles below 100 nm. Because there is still 

no certainty on which metric better characterizes the aerosols in 

the interpretation of the toxicity data, the measurement of more 

than one metric should be carried out. In addition, the complexity of the surface area measurement for DPM suggests the use 

of different real-time and off-line methods. 

This study proved that a single metric is not sufficient to 

fully characterize DPM particles. The surface area and number 

concentrations of the particles per unit of mass, and particularly per unit of TC were not constant but varied from 2 to 

10 times, respectively, under different engine operating conditions. This finding implies that the mass content alone of TC 

or EC in underground samples does not provide complete information on worker exposure in this workplace. To solve this 

exposure assessment problem a new approach was proposed. 

The approach entails combining two field-based measurements 

and calculating the SSA to retroactively obtain other physical 

characteristics/metrics of the DPM aerosols. The SNC can be 

also used instead of the SSA. The new approach is independent 

from the engine operating conditions and it has the potential 

to be used for the assessment of aerosols generated by more 

realistic transient cycles and multiple untreated exhausts. 

Future works should challenge the new metric approach with 

different diesel engines and real underground mine aerosols. In 

addition, the evaluation of DPM particles emitted by diesel engines equipped with aftertreatment control technologies should 

be considered. 

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