Environ. Sci. Technol. 2007, 41, 5783-5788

Real-Time Estimation of Elemental

Carbon Emitted from a Diesel

Engine

ARTHUR L . MILLER,* , †

MATTHEW C . HABJAN, †

* Corresponding author phone: (509) 354-802; fax: (509) 354­

8099; e-mail: ALMiller@cdc.gov. † National Institute for Occupational Safety and Health.

AND

KIHONG PARK ‡

‡ Gwangju Institute of Science and Technology.

National Institute for Occupational Safety and Health,

Spokane, Washington, and Gwangju Institute of Science and

Technology, Gwangju, Korea

New Mining Safety and Health Administration (MSHA)

regulations limit the mass concentration of airborne diesel

particulate matter (DPM) or, more specifically, the

concentration of elemental carbon (EC), in underground

mines. The mine operators are responding by introducing

a variety of controls to reduce DPM in the mines,

potentially including the evaluation of new maintenance

procedures to reduce underground mine vehicle emissions.

There is currently a lack of an inexpensive and dependable

method to directly measure the DPM concentration

emitted from the vehicle tailpipe. To that end, this work

demonstrated a simple field portable method for estimating

the mass concentration of elemental carbon exiting the

tailpipe of a diesel engine using a direct reading photometer.

Simultaneous measurements of tailpipe exhaust were

made with a Thermo Electron Personal DataRAM 1200

photometer (particulate mass concentration based on light

scattering) and by analyzing PM2.5 and PM1.0 samples

collected on quartz fiber filters using the National Institute

of Occupational Safety and Health (NIOSH) method 5040

(mass concentration of EC via thermal-optical method).

Results indicate surprisingly good correlation (R2 ) 0.97)

of the two methods when the data are adjusted for relative

humidity (RH) and corrected using an empirically generated

calibration factor. Although preliminary, it may be possible

to implement this method in maintenance shops to monitor

emission trends and to compare emissions of various

vehicles in a fleet. Such data will be useful for fleet planning

to meet new air quality standards.

1. Introduction

New Mining Safety and Health Administration (MSHA)

regulations limit the mass concentration of airborne diesel

particulate matter (DPM) or, more specifically, the concentration of elemental carbon (EC), in underground mines (1).

Mine operators have responded by introducing a variety of

controls to reduce DPM in the mines (2-4). One control

usedby someoperators is toimplementmore rigorous vehicle

maintenance procedures. Although methods have been

developed to evaluate a variety of engine emissions for

maintenance purposes (5-7), there is still a need for an

inexpensive and dependable method to directly measure the

EC concentration at the vehicle tailpipe.

When detailed measurement of size distribution of the

engine-emitted particulate matter is required, instruments

such as the scanning mobility particle sizer (SMPS) or the

real-time engine emissions particle sizer (EEPS) instrument

(made by TSIInc1) may be used.(The use of company names

in this document does not imply NIOSH endorsement of

any company or product). Such instruments are unfortunately relatively expensive, usually require dilution of the

exhaust aerosol, and are generally not well-suited for use in

anundergrounddieselmaintenance shopdue to theirunique

operating requirements such as accurate control and measurement of dilution air. In addition, although particulate

mass can be estimated from the data using assumptions for

shape and density, they do not differentiate the fraction of

DPM that is EC.

There are a variety of instruments available to estimate

the carbon concentration in aerosols. These include the

Magee Scientific Aethalometer, the Thermo Electron MAAP,

the Photoelectric Aerosol Sensor (PAS 2000) from EcoChem

Analytics, and the Series 5400 Ambient Carbon Particulate

Monitorfrom Rupprecht & Patashnick (8). The Aethalometer

has been shown to be effective in estimating the EC in DPM.

Itdetermines carbonmass concentrationinsampledaerosols

by measuring the optical light absorption of black carbon

(BC) at specified wavelengths (9) and gives results that

correlate very well with EC concentrations evaluated by the

NIOSH Method 5040 (10).

Giventheneed to measure ECgenerated by diesel engines

in underground maintenance shops,ratherthan using some

combination of the instruments mentioned above, we chose

to evaluate direct-reading photometers. Photometers are

simple and inexpensive devices that give readings that have

been shown to correlate well with total particulate mass

concentration measurements. The main aim of our work

was to evaluate the effectiveness and accuracy of the

photometers for measuring EC at the engine tailpipe. This

wasdoneby takingmeasurementsofundiluteddiesel exhaust

using three identical direct-reading photometers while

simultaneously collecting three filter samples of the exhaust

for analysis using the NIOSH Method 5040. Using the NIOSH

method as a reference, the data were subsequently analyzed

to determine how well the photometer readings approximated the EC measurements. This approach is similar to a

study conducted in Australia, testing multiple instruments

against filter samples, including the instrument used in this

study (11). That study was conducted on diluted exhaust,

however, while our work was designed as a preliminary

feasibility study regarding the use of photometers to monitor

EC emissions directly at the tailpipe, i.e., using undiluted

exhaust. Based on the preliminary results presented here,

we are planning further testing of our method on a variety

of different engines in the future.

2. Materials and Methods

2.1. Direct-Reading Photometers. Portable, direct-reading

dust monitors are often called nephelometers, aerosol

photometers, or light scattering aerosol monitors. They

operate by illuminating aerosol passing through a defined

volume and detecting the scattered light (12). Such devices

have been shown to yield measurements which correlate

well to gravimetrically measured mass concentrations of

airborne particles (13, 14). However, when measuring a

polydisperse aerosol containing ultrafine particles not detectable by photometric techniques, a calibration factor may

be used to account for the undetectable portion. Best data

correlations are achieved when such calibration factors
account for variations in particle size and refractive index,

which is influenced by chemical composition. It is therefore

recommended that such devices be calibrated against a

standard measurement method for the aerosol of interest

under the same test conditions (12).

There are many different photometers, each with their

own unique operating parameters and calibration scheme

(12, 13). The photometer chosen for this evaluation is the

Thermo Electron Personal DataRAM 1200 (PDR). We chose

the PDR because of its relatively low cost and ease of use.

The PDR is a small lightweight instrument designed to be

attached to a workers belt and is often operated as a passive

sampler.It can also be used as an active sampler as was done

for our experiments. For active sampling, an auxiliary pump

was used to draw air over the sensor and a cyclone (BGI

model GK2.05) was attached to remove larger particles, i.e.,

those larger than 5.5 μm. The PDR gives best sensitivity in

the respirable range, is auto ranging between 0.001 and 400

mg/m3, can sample as fast as 1 Hz, and has built-in data

logging capability. It is rezeroed by simply resetting the zero

reference and can operate approximately 70 h at an average

aerosol concentration of 5.5 mg/m3 before the zero reference

begins to drift due to contaminated optics (13). Rezeroing

is obtained by running a clean flow of air through the PDR

using a high efficiency particulate (HEPA) filter, while the

PDR recalibrates itself by resetting the zero reference. It also

is field-cleaned by clearing the optics with a compressed air

jet. The PDR is factory calibrated using SAE Fine (ISO Fine)

dust, provided by Powder Technology Inc. which conforms

to ISO 12103 Pt1 (15).

2.2. NIOSH Standard Method 5040. The mass concentration of the EC and OC was determined using the NIOSH

Method 5040, although only the EC data are reported here.

The NIOSH Method 5040 is often used when assessing the

EC content of diesel-laden aerosols and is considered a

reference method (16). In this method, the particulate is

collected on quartz-fiber filter media and analyzed using a

thermal-opticaltransmittance technique (17).This approach

is somewhat similar to the interagency monitoring of

protected visual environments (IMPROVE) thermal optical

reflectance method but with different temperature and

charring correction protocols (18). Note that the NIOSH

method is a thermal-optical transmittance method (i.e., it

uses the optical transmittance to correct the charring), while

the similar IMPROVE method is called a thermal-optical

reflectance method and uses reflectance of the optical signal

to correct for the charring (19).

For analysisby theNIOSHmethod, sampleswere collected

at a flow rate of 1.94 Lpm onto prefired quartz-fiber filters

mounted in 37 mm plastic filter cassettes. Stable flow was

achieved by drawing exhaust emissions through critical

orifices using a vacuum pump. For each test condition, filter

samples were collected in triplicate to ensure repeatability

ofresults, and sampling timeswere 20mininlengthto ensure

adequate filterloading. The filterloadings for alltests ranged

from 27 to 180 μg/cm2 for EC. The EC data presented in this

report are based on the average of the three samples taken

at each condition. The samples were sent to a NIOSHcontracted lab (DataChem Labs in Salt LakeCity), along with

media blanks and field blanks as required. The samples were

analyzed by the thermal optical method (20) and timeweighted average values of EC and OC in units of mg/m3

were derived from the analytical results.

The NIOSH Method 5040 utilizes a thermal-optical

technique toquantify theECandOC.During implementation

of the method, carbon volatilized below 850 °C in a helium

atmosphere is defined as OC, while carbon burned off at 850

°C in an atmosphere of oxygen and helium is defined as EC.

The optical component of the method entails evaluation of

the char, i.e., residue left on the filter due to pyrolysis of

some OC during the first heating cycle. This char is oxidized

at the beginning of the second heating cycle and its mass

apportioned asOC.Anopticaltechnique is used to determine

when this char is removed from the filter, which is why the

method is called “thermal-optical”.

2.3. Diesel Particulate Generation. The source of diesel

particulate for this work was an Onan-Cummins “QuietDiesel” genset powered by a three cylinder, 1.5 liter Isuzu

engine. This unitis capable of providing 10 kW of continuous

AC power at a fuel flow rate of about 4 kg/hr. To maintain

a steady 60 Hz of ACcurrentfrom the generator,the fuel flow

to the engine is controlled by an electronic governor actuator

that maintains a constant engine speed of 1800 rpm. The

load onthe engine is provided by loading the generator outlet

with a resistive load bank made by the Simplex Company.

Forthe experiments, the engine was run at three conditions:

no-load (0 kW), 2 kW, and 6 kW. The corresponding fuel flow

rates were 1.0, 1.4, and 2.3 kg/hr, respectively. The fuel used

for alltests was standard no. 2 diesel fuel with nominal sulfur

level of 350 ppm.It should be noted that the size distribution

of particles generated by this relatively small engine (Figure

1) varies somewhat from that of typical heavy duty engines,

i.e., under normal operating conditions it produces no

discerniblenucleimode.

FIGURE 1. Number-weighted particle size distribution (dilutioncorrected) for Isuzu engine and Caterpillar C-12 engine, both

measured with an SMPS at moderate loads.

Thisdoesnotobviate the application

of results to larger engines, however, since the photometer

is insensitive to the ultrafine particles.

The emissions sample is drawnfrom the tailpipe as shown

in Figure 2. The 3.8 cm diameter exhaust line is connected

to a 20 cm diameter vertical vent pipe. About 15 cm from the

muffler exit, a sample is drawn through a tee fitting in the

exhaust line at a flow rate of about 20 Lpm. The sample is

routed through a 2.4 m long, 1.3 cm diameterinsulated pipe,

then through a 6.1 m long, 1.3 cm diameter copper cooling

coil to the multiport sampler.

2.4. Sampling Logistics. The primary goal of this work is

to evaluate the PDR photometer in parallel with samples

taken using a standard method to directly evaluate the

accuracy of the instrument. To that end, simultaneous

measurements were made at the diesel tailpipe using both

approaches with the engine operating at three different

loads: 0, 2, and 6 kW.

During each test, three samples of EC for analysis were

collectedsimultaneously ontoprefiredquartz filtersmounted

in cassettes, using calibrated critical orifices to control flow

rate, set to 1.94 Lpm. Two types of such filter samples were

collected at each engine condition, each using a different

type of filter holder. One was a DPM cassette no. 225-317

with a submicron impactor made by SKC Inc., and the other

was a standard 37 mm filter cassette. It was found that the

difference between the two approaches was negligible, so
only the data from the standard 37 mm cassettes are

presented in the results section.

For all tests, three PDRs connected in parallel sampled

the exhaust emissions through calibrated critical orifices at

the same flow rate (1.94 Lpm) as for the three filter samples.

The orifices were located on the exhaust side of the PDRs to

allow the instruments to operate at atmospheric conditions.

Connected to the upstream side of the PDRs were 2.5 μm

BGI Model GK2.05 cyclones which provided a convenient

connection to the PDR, as well as ensuring that no large

particles entered the detection region. Since very little of the

DPM is greater than 2.5 μm it is likely that the system would

have worked withoutthe cyclone in place. Forthe tests where

the SKC filters were used, the PDRs were also equipped with

their own custom submicron impactor to provide similar

sample-size selection. This customized impactor was made

by dismantling an SKC filter cassette,removing the filter and

leaving the impactor, and reconnecting it to the PDR after

reassembly.Impactor seal integrity is assured with an O-ring.

For collecting parallel samples on multiple filters as well

as with the PDRs, we built a multiport sampler. It consisted

of a cylinder with an inlet and multiple outlet holes (Figure

2).

FIGURE 2. Experimental setup.

The cylinder was divided into two sections. The larger

inlet sectionprovided amixing baffle and a plenumchamber,

while the smaller top section provided the vacuum. A thick

plate separatedthe sections andwas fittedwithcriticalorifices

that act as flow meters forthe filter samples.The filters, PDRs,

and other instruments were connected to the multiport

sampler so that they measured the same aerosol at the same

time. A vacuum pump was used to maintain the vacuum for

the critical orifices, and a vacuum gauge monitored the

pressure to ensure that the orifices reached critical flow. The

temperature and relative humidity in the multiport sampler

were monitored, as well as the exhaust gas temperature,

which was measured by a thermocouple inserted into the

tailpipe.

The exhaust emissions underwent minimal treatment

before being sampled. Since our goal was simplicity, we

sampled the undiluted exhaust and only cooled and dried

itwith a simple condenser,which consisted of a coil of copper

tubing. Water collected in the coil and was emptied manually

between tests to prevent water build up in the coil, but a

water trap could easily be installed in such a setup.

3. Results and Discussion

3.1. Raw PDR Data. Figure 3 shows an example of real-time

PDRdata collected during one ofthe 0 kWexperimentswhich

is typical of the type of data collected at all loads during

these tests with the PDRs.

FIGURE 3. Raw PDR data from a single test (0 kW engine load).

Note that all three PDR’s are fairly

consistent (less than 15% difference between instruments)

and that the signal is somewhat sinusoidal. This trend

appeared in other instruments we used during the course of

this work, including the TSI DustTrak photometer and the

EcoChem particle-bound polycyclic aromatic hydrocarbon

(PAH) monitor, suggesting it is a real phenomenon. The

sinusoidal readings are possibly related to the variation in

soot production as a function of fluctuations in engine block

temperature via the cycling ofthe thermostatically controlled

cooling system. This highlights an advantage to using the

PDR for collecting such data under field conditions, since it

is easy to see fluctuations in the data which might indicate

unwanted changes in the test conditions.

3.2.Time-WeightedAverageData. For each test,this realtime data from each of the three PDR’s was averaged over

the duration of the test to get a time-weighted average for

each PDR. The average of those three numbers yielded a

single mass concentration value for each test. Likewise, the

data from the three filter samples collected for each test was

averaged. An example of such averaged, uncorrected data is

shown in Figure 4. Note that the data from the triplicate

samples show relatively little variation. The relative standard
deviation for the three averaged PDR measurements was

approximately 4% and for the three filter measurements

approximately 2%.

FIGURE 4. Averaged EC filter data over a 20 min test and PDR data

for the 2 kW engine load with standard deviation bars shown.

Time-weighted averages of raw data from four different

tests for each load are plotted in Figure 5 and demonstrate

that although there is variation in the data, some trends are

clear.

FIGURE 5. Averaged uncorrected PDR and EC filter data from multiple tests.

First note that since the NIOSH 5040 method is

considered a reference method, the filter data is assumed to

reflect the actual EC concentration in the exhaust aerosol.

The fact that the PDR data varies from the actual EC levels

is due to errors brought on mainly from two sources:

instrument calibration and relative humidity (RH) effects.

Also note that the test-to-test variation is highest for the low

load. This is believed to be a function of how long the engine

had been running, which affected engine block temperature

that was not monitored. Some tests were done as soon as the

tailpipe temperature stabilized after start up, whereas others

were done after the engine had been running at higher load.

At low load, the carbon emissions are highest which verifies

earlierwork using this engine (21).At high load,the emissions

are reduced, especially the measured EC. For this case, the

fuel consumption is greatest, and the measured relative

humidity ofthe emissions is thushighest,whichhelps explain

the higher PDR readings as the load increases.

3.3. Correlating the Data. The raw data from the

photometers were next plotted against the data from the

NIOSH method 5040 (Figure 6) and show thatthe correlation

has a linear trend for each load condition, but that the slope

varies. This is expected since both the size distribution and

theEC/OCratio change as the engine loadincreases, affecting

the scattering efficiency and the refractive index,respectively.

This confirms the importance of calibrating photometers to

the aerosol of choice as demonstrated in earlier works using

various types of dust(13). These correlations are also affected

by RH, which requires correcting the data using RH measurements made at each engine load condition.

Correction of the raw PDR data entails two steps. First,

the data must be corrected for RH effects, since the PDR

outputis affectedwhenRH>50% (22). Second,thehumiditycorrected data must be compared to a standard (in this case,

the filter data) and a calibration factor calculated to correct

the data to the standard. The PDR is factory-calibrated using

a reference dust (15), but also allows for user-based field

calibration. The calibration consists of entering a calibration

factor (via the menu-driven graphical user interface), which

is a multiplier used to correctthe signal intensity and thereby

adjust the instrument output. In this case, the calibration

factor accounts forthe differences between the diesel aerosol

and the aerosol used for factory calibration.

We first determined an appropriate multiplier to correct

for the RH levels measured at each engine load, using the

data in Table 1, published previously (22).

TABLE 1. Relative Humidity and its Corresponding Correction

Factors

relative humidity (%) 0 40 50 60 70 80 90 100

correction factor 1.0 1.0 0.93 0.83 0.71 0.56 0.40 0.22

Note that for each

set of tests at a specific load condition, RH levels were

approximately the same: �50% RH at 0 kW, �70% RH at 2

kW, and �90% RH at 6 kW.

We thencalculatedthe ratiooftheECvalues (the standard)

and the RH-corrected PDR data for all tests and averaged

them to get another such multiplier (i.e., a single calibration

factor for the exhaust aerosol from this engine). Using both

multipliers to adjust the PDR data gives the best possible

estimate of EC. A linearregression of the corrected PDR data

versus the standard method filter data is shown in Figure 7.

Note thatthe data correlatewell, as the correlationcoefficient

(R2) is near unity (0.97) and the regression slope (y) is slightly

below unity (i.e., 0.89).

The same process was repeated for estimating the

correlation between the total carbon (TC) (where TC ) EC

+ OC) and the PDR data, yielding a correlation coefficient

of 0.96 and a regression slope of 1.12 after correcting for RH.

The variationinthe regressionslopesderives fromdifferences

in the calibration factors for EC versus TC. This data is not

shown since our focus was on the EC fraction. The OC data

may have been slightly artificially high since we did not use
a “dynamic blank” to correct for potential condensation of

OC vapors on the filters during the collection process. The

difference in regression slope may also suggest that the

calibration factors reflect higher levels of OC on the smaller

particles as demonstrated in previous studies (23). More

research is recommended to better understand this phenomenon.

FIGURE 6. Averaged uncorrected PDR data versus standard method

(EC filter data) for three engine loading conditions.

FIGURE 7. Linear regression of corrected PDR data and EC filter

sample data.

It is important to note the limitations of this method. The

PDR will measure more than just the elemental carbon

particulates and anything affecting the relationship between

the elemental carbon and other particulates could affect the

calibration factor. Also the calibration factor will vary for

each engine and through use of different fuels or control

technologies such as diesel particulate filters. This outlines

the importance of verifying the results using a reference

method to ensure the best accuracy.

Results of this preliminary study show that, under

laboratory conditions, measurements made with the PDR

photometer canbe used to estimate ECemissions reasonably

well if the PDR data is properly adjusted for RH effects and

calibrated to a standard. Such measurements can be done

with no dilution of the exhaust emissions and by using a

simple condenser to cool and dry the sampled exhaust

enroute to the instrument. Photometers such as the PDR

may thus be a useful tool to evaluate trends in engine

emissions (particularly EC) over time and for comparing

relative levels of EC generated by different vehicles in a fleet.

More testing is necessary to evaluate the magnitude of dayto-day variations and thepracticality of sampling rawexhaust

under field conditions to determine the applicability of this

method for use in emissions-assisted maintenance programs

involving a variety of engine and vehicle types.

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