Differential Pressure as a Measure

of Particulate Matter Emissions

from Diesel Engines

STEVEN E. MISCHLER* AND

JON C. VOLKWEIN

National Institute for Occupational Safety and Health,

Pittsburgh Research Laboratory, 626 Cochrans Mill Road,

Pittsburgh, Pennsylvania 15236

A diesel particulate matter analyzer capable of direct,

real-time measurement of engine exhaust particulate is

necessary to effectively institute source control technology

currently being used on diesel equipment and to ensure

that the control measures are working. To investigate the

potential of a differential pressure monitor to measure

diesel particulate matter in undiluted exhaust, samples were

collected from three different diesel enginessKubota,

Isuzu, and Deutzsrunning under 12 different RPM and load

scenarios. These measurements were compared to

elemental carbon concentrations in the sampled exhaust

as determined by using the NIOSH 5040 analytical method.

Elemental carbon is used as a surrogate measurement

for diesel particulate matter. The results of the two data

sets were then compared using a linear regression analysis.

The coefficient of determination (or R2

) was calculated

to be 0.98, 0.94, and 0.74 for the Kubota, Deutz, and Isuzu

engines, respectively. R2 values of this magnitude

indicate that this method can be successful in estimating

elemental carbon emissions in the engines tested. In

addition, for replicate samples, the coefficient of variation

ranged from 7.1% to 10.2% with an average of 8.5%.

These data indicate that this method could prove useful

to mechanics as they work to maintain engines and DPM

control technologies.

Introduction

In an effort to reduce the exposure of the underground metal/

nonmetal mining community to diesel particulate matter

(DPM), the Mine Safety and Health Administration (MSHA)

published an underground metal/nonmetal rule that reduced

the allowable concentration of DPM to an interim limit of

400 μg/m3 and eventually to a final limit of 160 μg/m3 (1).

Under this rule, DPM is defined as total carbon (TC), based

on the National Institute for Occupational Safety and Health

(NIOSH) Method 5040 analysis (2), or 1.3 times the elemental

carbon (EC) results from the same test (DPM ) TC ) 1.3 ×

EC). Currently, MSHA is not mandating a specific course

that mine operators need to follow to reach these limits, and

thus operators are able to implement their own approach to

achieve these concentrations.

The two main avenues available to reduce DPM concentrations in a mine environment are to either reduce the

emissions of DPM at the source or reduce the ambient

concentrations by increasing the ventilation rates. Because

altering ventilation rates and patterns tends to be expensive

and difficult, most mine officials will attempt to control DPM

emissions at the source. Several options are currently

available that will reduce emissions of DPM at the source,

including engine design, engine derating, fuel formulations,

fuel additives, diesel oxidation catalytic converters, and diesel

particulate filters (3). The effectiveness of the source control

technologies in decreasing DPM emissions has been demonstrated in mines in both Canada and the United States (4,

5).

One problem discussed by these researchers is the lack

of a portable, near-real time, commercially available DPM

analyzer for undiluted exhaust (5-7). A method capable of

quantifying engine exhaust particulate, directly from the

tailpipe, is necessary in order to effectively institute source

control technology and to ensure that the control measures

are working. Some instruments are commercially available

that can estimate DPM in diluted and undiluted exhaust.

These include a tapered element oscillating microbalance

(TEOM), a nephelometer, an aetholometer, a photoacoustic

instrument, and a smoke meter (6). While each of these

measurement systems has certain merit, their usefulness in

a mine environment may not be practical due to the need

for expensive and complicated equipment. In addition, the

use of a dilution tunnel during DPM measurement studies

introduces the potential for the creation of particulate matter

artifacts and the coagulation of DPM particles (8).

In this paper the results of testing a differential pressure

(∆P) monitor developed to measure DPM in undiluted diesel

exhaust are reported. Previous research on the ∆P monitor

for ambient particulate sampling (9, 10) demonstrated that

the method was useful for measuring aerosols of uniform

composition and that small mass loadings of DPM onto filters

results in a rapid increase in pressure across the filter. These

concepts suggested the use of the ∆P monitor for a direct

diesel engine exhaust monitor. In addition, Birch and Cary

(11) established the use of EC as a marker for exposure to

DPM. Because EC constitutes a large portion of the particulate

mass, it can be quantified at low levels, and its only significant

source in many workplaces is the diesel engine (11). More

recent studies in both mining and other industries have

corroborated the use of EC as a surrogate for DPM (12-14),

and MSHA included the use of EC in the underground metal/

nonmetal DPM rule, as discussed above.

This research evaluates the relationship between near realtime ∆P measurements and the amount of EC found on a

filter collected from undiluted diesel exhaust. The use of ∆P

to track EC emissions can be employed to monitor the

deterioration of an engine or the diesel emission control

technologies over time. The convenience and affordability

of this type of instrument, consisting only of a small pump

and a filter probe, would allow it to be used extensively in

mines by mine mechanics to determine when servicing is

necessary or if a particular engine is exceeding normal

operating emission levels. With this information, these

mechanics could more efficiently work to maintain the diesel

engines and emission control technologies and, thus, reduce

DPM emissions, resulting in lower exposure levels for the

underground metal/nonmetal community. In addition, as

regulations begin to appear that concern diesel emissions to

the ambient air, a form of this technology could be useful

to mechanics working with on-road diesel vehicles and

nonmine equipment to assist them in reducing emissions

from these sources.

* Corresponding author telephone: (412)386-6588; fax: (412)386-

4917; e-mail: smischler@cdc.gov.

10.1021/es0491230 Not subject to U.S. Copyright. Publ. xxxx Am. Chem. Soc. VOL. xx, NO. xx, xxxx / ENVIRON. SCI. & TECHNOL. 9 A

Published on Web 00/00/0000 PAGE EST: 6.1
Experimental Section

The ∆Pmonitor approach is based on measuring the increase

in differential pressure that develops across a glass fiber filter

collecting DPM at a flow rate of 0.250 L/min. An incremental

increase in DPM upon the filter should correspond to a

proportional increase in the differential pressure across that

filter.

Description of ∆P Monitor. The basic components of

the ∆P monitor consist of (1) a 5.08 cm (2 in.) long, 0.96 cm

(0.38 in.) diameter stainless steel inlet probe; (2) a 0.96 cm

(0.38 in.) diameter glass fiber collection filter with a custom

holder: (3) a flow-controlled sampling pump with a flow

capacity of 0.250 L of air/min; (4) a pressure transducer

capable of measuring to 0.1 in. of water; (5) exhaust cooling

coil; and (6) a pump protective filter. Figure 1 is a schematic

of this sampling apparatus. For this investigation, an SKC

pocket pump with integral pressure transducer (SKC Inc.,

Eighty-Four, PA) was used to collect the sample onto the

filter.

Sampling Procedure. The sampling procedure for the

∆P monitor was initiated by introducing the sample probe

into the exhaust stream of the diesel engine. The probe tip

was secured approximately 5 cm (2 in.) into the exhaust pipe

by a fabricated clamp attached to the exhaust pipe. The clamp

maintained the probe tip at a constant angle to ensure that

the exhaust velocity pressure would not interfere with the

differential pressure measurements. The diameter of the

exhaust pipe must be sufficient so that the combination of

exhaust velocity pressure and static pressure are less than

the clean filter backpressure, which in this investigation was

approximately 4 in. by water gauge.

The clamp, along with the design of the sample probe

which had a 90° bend after the filter, allowed the researchers

to place and hold the probe in the exhaust stream without

being affected by the extremely hot exhaust stream. Keeping

the filter in the hot exhaust stream avoided problems with

condensation. The temperature of the exhaust stream at the

sampling point reached as high as 800 °F, and was measured

using a type K thermocouple. It is also important to note

that, because the particles of interest were less than 1 μm in

aerodynamic diameter, losses due to particle inertia are

considered small; thus, isokenetic sampling was not necessary

(5).

The sampling pump was started exactly as the probe

entered the exhaust stream. After 20 s, the initial ∆P

measurement in inches of water was recorded. Preliminary

tests demonstrated that it took approximately 20 s for the

pressure increase to stabilize after the probe entered the

exhaust stream. The final ∆P measurement was recorded 60

s later, and the pump was immediately stopped. The probe

was quickly removed from the exhaust stream and allowed

to cool. Because EC was being collected during the initial 20

s stabilization period, prior to the start of the ∆P measurement, the total sampling time was 60 s for ∆P and 80 s for

EC. Once the probes had cooled sufficiently, the glass fiber

filter was removed and sent to the laboratory for Method

5040 analysis. The analysis technique is described later in

this section.

Because of the diameter of the exhaust pipe and physical

sampling obstructions, it was necessary to sample the

replicates in sequence. All replicate samples were collected

one after the other within a 10-min period. The replicate

samples were all collected under steady-state conditions (15),

thus the diesel engine emission rate should have remained

constant. A minimum of three ∆P measurements were

collected for each test.

Test Engines. ∆P tests were completed on three different

engines: (1) Kubota V1200-B, (2) Isuzu C240, and (3) Deutz

912W. A summary of engine parameters is presented in Table

1. There were no throttle controls on the Kubota engine,

thus it ran under constant 1800 rpm. The constant throttle

on the Kubota limited the number of modes that could be

tested for this engine. Engine conditions were varied using

an electric generator by placing a percentage of the rated

generator power output on a resistance heater. This engine

was only tested under four load scenarios.

For both the Isuzu and Deutz engines, it was possible to

change the engine throttle as well as the torque, thus they

were tested using the 8-Mode test protocol developed by

MSHA (15). An eddy-current dynamometer was used to place

the necessary loads on the Isuzu and Deutz engines. This

system produces braking torque using the principle of eddy

currents induced in a rotating magnetic disk, immersed in

a magnetic field. The load on the engine was varied by

increasing or decreasing the strength of the magnetic field.

The operating conditions for each engine at each mode are

presented in Table 2.

Elemental Carbon Analysis. NIOSH Method 5040 analyzes for organic carbon (OC) and EC in two different stages

using a Sunset Laboratory, Inc. (Hillsboro, NC) carbon

analyzer. In the first stage, OC is measured by ramping up

the oven temperature over four different temperature steps

TABLE 1. Engine Parameters

parameter Kubota V1200-B Isuzu C240 Deutz 912W

combustion chamber type spherical swirl swirl

turbocharging no no no

engine geometry vertical/in-line vertical/in-line vertical/in-line

no. of cylinders 4 4 6

injection system direct indirect indirect

injection pressure 360 bar 117 bar 115 bar

rated power 14.2 bhp at 1800 rpm 56 bhp at 3000 rpm 94 bhp at 2300 rpm

rated torque 2.5 lb-ft at 1800 rpm 108 lb-ft at 2000 rpm 192 lb-ft 2200 rpm

FIGURE 1. Schematic of sampling apparatus.

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that are programmed into the instrument while pure helium

(He) flows over the sample. The temperature steps are

approximately 200, 450, 650, and 870 °C. The evolved OC is

oxidized to carbon dioxide (CO2) and subsequently reduced

to methane (CH4), which is measured using a flame ionization

detector (FID). Because no oxygen is present to oxidize the

EC, it remains on the filter.

In the second stage, the oven temperature is reduced to

about 600 °C and then raised to around 900 °C while a mixture

of He and oxygen (O2) flows over the sample, allowing EC to

react with O2 to evolve CO2. The EC is then measured in the

same way as the OC. A heat-treated quartz fiber filter is used

as a blank, and a sugar standard is run each day as an OC

reference.

Data Analysis. The ∆P results were calculated by subtracting the initial differential pressure from the final

differential pressure. This yields the increase in pressure due

to the filter loading that occurred in a one-minute period.

For each of the replicate sets of ∆P samples, a mean, standard

deviation, and upper and lower 95% confidence limits were

calculated.

Prior to comparing the ∆P data between each test, it was

first necessary to apply a temperature correction. A temperature correction was necessary because the sample flow

was being controlled at the sample pump. Since the temperature of the sampled air at the pump was near ambient

while the air temperature at the sampling point was at exhaust

gas temperatureswhich ranged between 100 and 800 °F

depending on the engine operating conditionsthe 250 mL/

min flow rate at the pump would be proportionally greater

at the higher temperatures occurring at the filter. A higher

volumetric flow rate through the filter would thus result in

a proportional increase in differential pressure across the

filter. The proportional increase in air volume was calculated

using a form of Charles law shown below:

The percentage increase in air volume was then used to

standardize the differential pressure increase measured

during each engine scenario. The use of Charles law to

standardize the pressure differential is appropriate in this

application because the sample probe is open to the

atmosphere; thus, the pressure in front of the filter should

remain constant.

The EC results were obtained from the Method 5040

laboratory analysis in the units of μg of EC/cm2 of filter. These

data were used to calculate the EC concentration (mg/m3)

of the exhaust stream for each sample by dividing the EC

mass (μg) on the filter by the sample volume (m3) collected

over the 80 s sampling period and then correcting for units

(μg to mg). For each of the replicate sets of samples, a mean,

standard deviation, and upper and lower 95% confidence

limits were calculated.

To evaluate the relationship between ∆P and EC, a linear

regression analysis was used where EC was the dependent

variable and∆Pwas the independent variable. The coefficient

of determination (or R 2) was computed for each engine type.

R 2 is a summary measure of the goodness-of-fit of the model

or the proportion of variation in the dependent variable (EC)

that can be explained by the independent variable (∆P). This

relationship was also evaluated graphically using an XY

scatterplot with EC concentration (in mg/m3) on the Y-axis

and ∆P on the X-axis.

An analysis of variance was used to evaluate the fit of the

regression line by using the F statistic. The F statistic is the

ratio of the deviation between the predicted values for

elemental carbon and the mean to the deviations about the

regression line. If the F statistic results in a p value of less

TABLE 2. Summary of Engine Operating Conditions

mode Kubota V1200-B Isuzu C240 Deutz 912W

1 rpm 1800 3000 2300

load (%) 80 100 100

2 rpm 1800 3000 2300

load (%) 50 75 75

3 rpm 1800 3000 2300

load (%) 20 50 50

4 rpm 1800 3000 2300

load (%) no load 10 10

5 rpm 2000 1550

load (%) 100 100

6 rpm 2000 1550

load (%) 75 75

7 rpm 2000 1550

load (%) 50 50

8 rpm 842 675

load (%) no load no load

9 rpm 1550

load (%) 90

TABLE 3. Statistical Summary of Results with Isuzu Engine

∆P (in. of water/min)

test mean SD lower 95% CI upper 95% CI coeff of variation (%)

low idle/no load 0.69 0.14 0.35 1.03 19.9

intermediate speed (2000 rpm)/50% torque (20 hp) 2.72 0.47 1.56 3.88 17.2

intermediate speed (2000 rpm)/75% torque (29.7 hp) 4.25 0.17 3.83 4.66 3.9

rated speed (3000 rpm)/10% torque (5.7 hp) 1.88 0.04 1.78 1.99 2.1

rated speed (3000 rpm)/50% torque (25.4 hp) 2.76 0.13 2.43 3.09 4.8

rated speed (3000 rpm)/75% torque (38.4 hp) 3.94 0.27 3.27 4.61 6.8

rated speed (3000 rpm)/100% torque (50.6 hp) 4.58 0.08 3.88 5.27 1.7

EC concentration (mg/m3)

test mean SD lower 95% CI upper 95% CI coeff of variation (%)

low idle/no load 6.42 0.61 4.87 7.94 9.6

intermediate speed (2000 rpm)/50% torque (20 hp) 16.77 1.69 12.56 20.98 10.1

intermediate speed (2000 rpm)/75% torque (29.7 hp) 41.02 2.19 35.57 46.47 5.3

rated speed (3000 rpm)/10% torque (5.7 hp) 9.72 1.05 7.10 12.33 10.8

rated speed (3000 rpm)/50% torque (25.4 hp) 15.07 1.50 11.33 18.80 10.0

rated speed (3000 rpm)/75% torque (38.4 hp) 19.79 0.18 19.34 20.24 0.9

rated speed (3000 rpm)/100% torque (50.6 hp) 33.16 2.28 12.64 53.69 6.6

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than 0.05, then this indicates that the linear model is

appropriate for describing the relationship between ∆P and

EC.

The slope and intercept of the regression line were also

calculated as an additional comparison between the data

sets. The slope represents the rate of vertical change versus

the rate of horizontal change along the regression line.

The 95% confidence limit for the population mean for

each of the tests was calculated to indicate the precision in

the method. The equation used to calculate the confidence

limits is

where t is the student’s t distribution, S is the standard

deviation, and n is the sample size.

Results and Discussion

Summary statistics of the results for each engine are presented

in Tables 3-5. The results from the linear regression analysis

of the ∆P and EC data sets are presented in Table 6. Figures

2-4 present this relationship graphically for the Kubota,

Isuzu, and Deutz engines, respectively.

The emission concentrations at intermediate speed with

100% load for both the Deutz and Isuzu engines resulted in

the pump pressure limits being exceeded during sample

collection. As a result, both the ∆P and EC measurements

were compromised, resulting in these samples being voided

and removed from the data set. For the Deutz engine, a

sample was collected at the engine setting of intermediate

speed and 90% load to replace the voided samples. Results

for this test are included in the data set.

The R 2 values computed using the ∆P data and the EC

data indicate the degree to which ∆P can be used to estimate

EC in undiluted diesel exhaust. The R 2 values computed for

the Kubota, Deutz, and Isuzu engines were 0.98, 0.94, and

0.74, respectively, and are statistically significant at p<0.0001.

The coefficient of variation of the ∆P data was 7.1%, 10.2%,

and 8.1% for the Kubota, Deutz, and Isuzu engines, respectively.

TheFstatistic and the resultingpvalues for each regression

model are presented in Table 6. The resulting p values are

all highly statistically significant (p < 0.0001), indicating that

the linear model is appropriate for describing the relationship

between ∆P and EC.

The Isuzu engine R 2 value was 0.74, which is lower than

those for the other two engines. Also the slope of the data

for this engine is flatter than the other two engines tested.

There are several possible explanations for the cause of these

differences. The Isuzu engine was new and had less than 50

h of running time prior to these emissions tests. It is usually

recommended by engine manufacturers to allow 100 h of

runtime prior to emissions testing. Both the Kubota and the

Deutz had over 100 h of running time prior to these tests.

In addition, the sampling setup for the Isuzu engine was

different from the other two engines in that there was a

muffler installed on the exhaust pipe, and the exhaust pipe

was positioned vertically. Neither the Kubota nor the Deutz

engines had a muffler installed on the exhaust pipe, and

both exhaust pipes were horizontal.

The data from these tests indicate a statistically significant

linear relationship between the ∆P reading and the amount

of EC depositing on the collection filter. This relationship

identifies a potential method to achieve a relative measure

of engine EC emissions. A possible scenario for using the ∆P

method would be for a mine mechanic to take a baseline ∆P

measurement on a well-maintained engine. This vehicle

TABLE 4. Statistical Summary of Results with Kubota Engine

∆P (in. of water/min)

test mean SD

lower

95% CI

upper

95% CI

coeff of

variation (%)

20% load 1.50 0.09 1.36 1.65 6.0

50% load 3.35 0.14 3.12 3.57 4.2

80% load 5.67 0.45 4.95 6.39 8.0

no load 0.95 0.10 0.79 1.11 10.4

EC concentration (mg/m3)

test mean SD

lower

95% CI

upper

95% CI

coeff of

variation (%)

20% load 32.91 2.78 28.47 37.34 8.5

50% load 54.64 3.84 48.53 60.74 7.0

80% load 92.55 3.45 87.06 98.04 3.7

no load 19.71 0.93 18.24 21.19 4.7

sample mean ( tn-1,0.975(S/xn) (2)

TABLE 5. Statistical Summary of Results with Deutz Engine

∆P (in. of water/min)

test mean SD lower 95% CI upper 95% CI coeff of variation (%)

rated speed (2300 rpm), 100% load (82 hp) 4.89 0.20 4.40 5.39 4.1

rated speed (2300 rpm), 75% load (62 hp) 2.42 0.14 2.06 2.77 5.9

rated speed (2300 rpm), 50% load (42 hp) 1.84 0.06 1.69 1.98 3.1

rated speed (2300), 10% load (8 hp) 0.95 0.10 0.06 1.84 10.4

intermediate speed (1550 rpm), 75% load (47 hp) 3.00 0.25 2.39 3.62 8.2

intermediate speed (1550 rpm), 50% load (32 hp) 1.49 0.20 1 2 13.5

low idle (675)/no load 0.25 0.09 0.04 0.46 33.6

intermediate speed (1550 rpm), 90% load (57 hp) 5.31 0.48 4.12 6.49 9.0

EC concentration (mg/m3)

test mean SD lower 95% CI upper 95% CI coeff of variation (%)

rated speed (2300 rpm), 100% load (82 hp) 99.43 5.73 85.19 113.66 5.8

rated speed (2300 rpm), 75% load (62 hp) 34.46 2.12 29.19 39.74 6.2

rated speed (2300 rpm), 50% load (42 hp) 24.34 0.74 22.51 26.17 3.0

rated speed (2300 rpm), 10% load (8 hp) 18.32 0.14 17.05 19.59 0.8

intermediate speed (1550 rpm), 75% load (47 hp) 50.12 11.77 20.88 79.36 23.5

intermediate speed (1550 rpm), 50% load (32 hp) 29.42 0.65 27.80 31.05 2.2

low idle (675 rpm)/no load 3.68 0.07 3.52 3.85 1.8

intermediate speed (1550 rpm), 90% load (57 hp) 115.52 3.27 107.41 123.64 2.8

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could then be tested routinely and once ∆P exceeded a preset

number, identified through emission tests, the vehicle would

be sent back to the shop for maintenance. Currently, routine

testing of gaseous emissions from diesel engines is common

in mines. The ∆P test could be added to this set of tests to

help ensure that the equipment is properly maintained and

that the DPM emissions are controlled. In the same way, the

∆P method could be used to ensure the efficiency of DPM

emission control devices, such as diesel particulate filters

(DPFs). When DPFs are new, the DPM emissions will be

negligible and will not be detected by the ∆P method.

TABLE 6. Correlation of ∆P and EC Data

statistic Kubota V1200-B Isuzu C240 Deutz 912W

R2 0.98 0.74 0.94

slope 14.8 7.7 21.6

y intercept 7.5 -2.8 -7.6

n 16 20 23

F statistic 876.64 50.66 328.93

df 1, 14 1,18 1, 21

F statistic p value p < 0.0001 p < 0.0001 p < 0.0001

FIGURE 2. Scatterplot of differential pressure vs EC data sets for the Kubota engine.

FIGURE 3. Scatterplot of differential pressure vs EC data sets for the Isuzu engine.

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However, as the DPFs age, leaks can occur. Once a leak occurs,

the DPM emissions will increase, and this increase will be

detected during a routine test using the ∆P method, allowing

the DPFs to be exchanged when necessary. The effect of

better maintenance of engines and control technology will

be a decrease in both the emissions of DPM from the diesel

equipment and the ambient concentrations of DPM in the

underground metal/nonmetal mine environment. In addition, further work on this method, involving measurements

on a larger number and variety of diesel engines, will refine

this technique and enable it to be applicable for DPM

emission measurements on a wide range of nonmining diesel

vehicles and equipment. The need to perform these measurements will be important as regulations for diesel emissions to ambient air become more stringent. The convenience, ease of use, affordability, and almost real-time nature

of the data indicate that the ∆P method could prove useful

to mine operators and mechanics as they work to maintain

engines and DPM control technologies.

There is not sufficient data presented here to develop a

calibration curve for using the ∆P method to calculate an EC

concentration in the exhaust plume. The development of

these calibration curves need to be addressed in future work

if an EC concentration measurement is desired. During this

future work it will be necessary to establish guidelines for

engine operating conditions during the ∆P method tests,

such as running the engine at a torque/stall or a high idle

condition. Changes in these operating conditions create

differences in the characteristics of the emitted DPM, such

as increasing or decreasing the amount of organic carbon

absorbed to the EC. A change in the absorbed organic carbon

could affect the filter backpressure characteristics (16) and

the resulting ∆P measurement. Characteristics of the emitted

DPM also change depending on the engine type. These engine

differences will also need to be addressed if a single calibration

curve is to be established.

Acknowledgments

The authors thank Jon Hummer, Larry Patts, Shawn Vanderslice, and Jim Noll for their help with the field measurements

and 5040 analysis, and Linda McWilliams for her assistance

with the statistical analysis. The authors also acknowledge

the help of MSHA Tridelphia for making their dynamometer

available for our measurements.

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FIGURE 4. Scatterplot of differential pressure vs EC data sets for the Deutz engine.

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Received for review June 10, 2004. Revised manuscript received November 30, 2004. Accepted December 3, 2004.

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