v>EPA
United States
Environmental Protection
Agency
Office of Air Quality
Planning end Standards
Research Triangle Park. NC 27711
EPA-454/R-95-010
January 1994
Air
SURFACE COAL MINE
EMISSION FACTOR
FIELD STUDY
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EPA-454/R-95-010
SURFACE COAL MINE
EMISSION FACTOR
FIELD STUDY
Emission Factor And Inventory Group
Emissions, Monitoring, And Analysis Division
Office Of Air Quality Planning And Standards
U. S. Environmental Protection Agency
Research Triangle Park, NC 27711
January 1994
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This report has been reviewed by the Office Of Air Quality Planning And Standards, U. S.
Environmental Protection Agency, and has been approved for publication. Any mention of trade
names or commercial products is not intended to constitute endorsement or recommendation for use.
EPA-454/R-95-010
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FOREWORD
Section 234 of the Clean Air Act was included in the Amendments signed into
law on November 15,1990. A partial citation of this requirement is as follows:
"the Administrator shall analyze the accuracy of such model and emission
factors and make revisions as may be necessary to eliminate any
significant over-prediction of air quality effect of fugitive particulate
emissions from such sources."
The group responsible for emission factors (methods) and requirements of
Section 234 is the Emissions, Monitoring and Analysis Division with the Office of Air
Quality Planning and Standards. Upon passage this organization initiated steps to
reexamine the available data base, evaluate the emission and dispersion (diffusion
and deposition simulation) models most vulnerable to possible biases and tendencies
to over-estimate emissions and their impacts upon air quality. This report and related
reports described herein, focuses on the emission factors and estimation models.
Separate reports focus on the dispersion modeling aspects of this effort.
Emission estimation for the simple processes is an approximate science, at
best. There are frequently significant variabilities due to the complex nature of the
processes modeled. Although emissions of "dust" from a mining operation might be
viewed at first observation as basic and fundamental, this is in reality far from the
case. To develop an emission estimation model, you must examine and analyze each
physical force and phenomenon which affects emissions. In mining operations, these
variables may include the physical characteristics of the soil and surfaces,
meteorological conditions, size, weight, load, number of tires and speed of moving
vehicles and so on. What at casual glance might appear to be a simple process, is in
fact, very complex and sensitive to many uncertainties.
The first steps taken in preparation for responding to Section 234 were to
review the existing emissions models and data bases, explore the availability of
resources from private and government sources, determine the extent of a cooperative
spirit within the affected industry toward needed studies, and design studies to
address potential weaknesses in a prioritized manner. References 2 through 4
describe the steps and results of these review and design efforts. Also, several
meetings were held with representatives of the Wyoming mining industry, the
Wyoming State agency (Wyoming is the area most affected by Section 234) and the
Environmental Protection Agency Regional Office. Both industry and State
representation were very helpful and forthcoming with input, comments, and often with
on-site support during the conduct of the efforts described in this report.
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The net result of these efforts conducted on emission estimation modeling tend
to be in general agreement with previous work, though resulting net emission
estimates for a typical mine will tend to be a little lower. However, many uncertainties
and questions remain. A fully implemented plan to study the variables to the full
satisfaction of concerns would likely cost a minimum, of 5 to 10 times the resources
that were available for the efforts reported here. Even with substantial additional
funding, a high level of uncertainty would remain due to limitations of estimation
technology and inherent variabilities in a physical process such as this.
Related to the numerical values of emission factors are assumptions that must
be made to use them. These assumptions involve the intertwining of numerous
technical, policy and regulatory issues. These studies have served to sensitize the
regulators and regulated communities to these relationships and to ignite a renewed
spirit of common sense implementation of these programs. Additional efforts are
underway to further refine the emission factors presented here for incorporation into
AP-42.
James H. Southerland
Emission Factor and Inventory Group
Emissions, Monitoring, and Analysis Division
IV
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CONTENTS
Preface ••••' jjj
Figures .
Tables IX
1. Introduction *
2. Air Sampling Methodology Jj
2.1 General air sampling equipment and techniques 7
2.2 Particulate sample handling and analysis 11
2.3 Emission factor calculation procedures . 15
2.4 Source material sample collection and analysis 18
2.5 Control application intensity 21
3. Test Results 23
4. Special Studies 37
4.1 Measured particle size distributions 38
4.2 Variability in input parameters for current emission
factor models 38
4.3 Expanded ambient air quality measurements 38
4.4 Saturation sampling results 44
4.5 Source activity monitoring results 52
5. Reexamination of Current Emission Factors 57
5.1 Overview of currently available emission factors 57
5.2 Haul truck travel 58
5.3 Light- and medium-duty traffic 78
5.4 Scrapers 80
5.5 Revision of haul truck emission factor 81
6. References 8^
Appendices
A. Ambient air quality monitoring results A-1
B. Combined data sets B'1
C. Time histories of road surface material moisture content C-1
D. Comparison of emission factors D'1
E. Log of Comments E~1
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FIGURES
Number
1 Location of test sites 6
2 Upwind and downwind sampling arrays 8
3 Cyclone preseparator • 10
4 Cyclone preseparator/cascade impactor combination used during
"sizing" tests 12
5 Cordero monitoring sites, HV-1, HV-2, and HV-3 45
6 Saturation sampler locations, 1 through 6 48
7 Frequency distributions of number of haul trucks in use during the
day and evening shifts • 54
8 Frequency distributions of number of scrapers in use during day and
evening shifts • 55
9 Frequency distribution of pieces of major equipment in use over either
shift . 56
10 Photocopy of Table 8.24-2 of AP-42 59
11 Photocopy of Table 8.24-4 of AP-42 60
12 WDEQ emission factors for surface mining activities 61
13 Box plots of emission factors and independent variables in the old and
new haul truck data sets (27 and 34 data points, respectively) 65
14 Comparison of measured emission factors against PM-10 Section 8.24
haul road estimates for the old data sets 67
15 Comparison of measured emission factors against PM-10 Section 8.24
haul road estimates for the new data sets 68
16 Comparison of measured emission factors against TSP Section 8.24
haul road estimates for the old data sets 69
17 Comparison of measured emission factors against TSP Section 8.24
haul road estimates for the new data sets . 70
18 Comparison of measured emission factors against PM-10 Section 11.2.1
estimates for the old data sets 71
19 Comparison of measured emission factors against PM-10 Section 11.2.1
estimates for the new data sets 72
20 Comparison of measured emission factors against TSP Section 11.2.1
estimates for the old data sets 73
21 Comparison of measured emission factors against TSP Section 11.2.1
estimates for the new data sets 74
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FIGURES (Continued)
Number
22 Comparison of measured emission factors against Wyoming suspended
haul road estimates for the old data sets 75
23 Comparison of measured emission factors against Wyoming suspended
haul road estimates for the new data sets 76
24 Correlation matrix of log-transformed emission factors and independent
variables 82
25 Cumulative frequency of the ratio of the quasi-independent estimate
to the measured PM10 emission factor 85
VII
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TABLES
Number
1 Sampler deployment 9
2 Quality assurance procedures for sampling media 14
3 Quality assurance procedures for sampling equipment 14
4 Criteria for suspending or terminating a test • • 15
5 Exposure profiling test site parameters 24
6 Summary of Phase I testing at Cordero 26
7 Concentration measurements 27
8 Plume sampling data 28
9 Unpaved road emission factors for observed traffic mixes . 33
10 Comparison of geometric mean emission factors (Ib/VMT) for
uncontrolled and controlled tests of haul truck emissions ,35
11 Particle sizing data 39
12 Surface material samples collected during field exercise 40
13 Summary statistics for road surface material samples collected 43
14 Monitored TSP concentrations 46
15 Monitored PM-10 concentrations . . . • • 47
16 Saturation sampler results 49
17 Summary statistics for daily number of haul trucks in use by material
and shift • • 53
18 Summary of emission factor equations for SCMs 62
19 Results from light-duty vehicle emission test comparisons 79
20 Results from scraper emission test comparisons 80
21 Recommended emission factor models 83
22 Results of cross-validation study 84
VIII
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ACKNOWLEDGEMENT
This test report was prepared by Midwest Research Institute (MRI) under
contract No. 68-D2-0159. Midwest Research Institute acknowledges the Cordero
Mining Co. for its cooperation in providing access to the study site.
Mr. Dennis Shipman was the U. S. EPA work assignment manager for the
project.
IX
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SECTION 1
INTRODUCTION
This report describes a study of airborne particulate matter released from the
activities conducted at open pit coal mines in the western United States. The study
focuses on one facility in Wyoming and provides information to evaluate emission
factors of relevance to new air quality programs nationwide. This program is a direct
result of the Clean Air Act Amendments (CAAA) of 1990 which require examination of
how the ambient air quality near mines is projected.
At present, ambient particulate matter (PM) impacts from surface coal mining
operations are assessed in the following ways:
1. The mine's operating plan is reviewed to identify major PM emission
sources, such as blasting, overburden removal, haul trucks, etc. The
plan is also reviewed to determine source activity rates—such as tons of
coal mined or tons of overburden removed per year—over the effective
life of the mine.
2. An emission factor (mass emitted per unit source activity) is proposed for
each major source. Factors for surface coal mines are usually selected
from Section 8.24 of AP-421 or from a list used by a state agency, such
as that prepared by the Wyoming Department of Environmental Quality
(WDEQ).
3. Values from items 1 and 2 above are combined to estimate annual and
worst-case-day PM emission rates from the mine.
4. Dispersion models (such as the Industrial Source Complex [ISC] model)
are then used to simulate the atmospheric transport of the estimated
emissions. Resulting ambient air concentration estimates are compared
against National Ambient Air Quality Standards (NAAQSs) or Prevention
of Significant Deterioration (PSD) increments.
The principal pollutant of interest is particulate matter (PM), with special
emphasis placed on PM-10—particulate matter no greater than 10 |j,mA (microns in
aerodynamic diameter). PM-10 is the basis for the current NAAQSs and thus
represents the size range of the greatest regulatory interest. Nevertheless, formal
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establishment of PM-10 as the standard basis is relatively recent, and almost all
historical surface coal mine field measurements reflect a particulate size other than
PM-10. Of these, the most important is TSP, or total suspended particulate, as
measured by the standard high-volume (hi-vol) air sampler.
The field study described in this report was undertaken to address certain
issues identified in the CAAA of 1990 relative to potential overestimation of the air
quality impacts of western surface coal mining. Specifically, the principal objective of
this study was to compare field measurements against available emission factors for
surface coal mines and to revise the factors as necessary.
This report presents the results from one phase of a field program undertaken
in response to Section 234 of the CAAA. As part of the overall planning process,
however, several other studies have already been completed.
An initial study2 thoroughly reviewed emission factors either currently used for
or potentially applicable to inventorying particulate matter emissions at surface coal
mines. A companion report3 describing emission factors available for coal processing
operations, was also prepared. For each anthropogenic emission source, an emission
factor was suggested. Overall, the recommendations followed the guidelines
presented in Section 8.24 of AP-42; the most notable exception applied to general
light- to medium-duty traffic. For this source, independent test data allowed an
objective evaluation and selection based on the performance of available emission
models.
The report concluded that additional source testing was necessary to address
major shortcoming in the data base:
• Although mines in the east account for half of the coal surface mined in
the United States, particulate emission sources at those mines have not
been well characterized. Applicability to eastern mines notwithstanding,
it is unknown how well most of the AP-42 SCM factors perform in a
general sense. Essentially all available test data were used in
developing the Section 8.24 factors. Thus, there are no independent
data against which calculated emission factors can be objectively
compared. The lack of independent test data presents a limitation on the
use of the surface coal mining factors in both eastern and western
mines.
• Because most surface coal mining field measurements were made
during the late 1970s and early 1980s, data generally reflect a particle
size range other than PM-10. The PM-10 emission factors presented in
AP-42 Section 8.24 are actually scaled based on size data presented for
the generic emission factors presented in Section 11.2.
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A second planning program3 recommended an "integrated" approach to field
measurements in response to Section 234. In general terms, the recommended
approach combined extensive long-term air quality and meteorological monitoring with
intensive short-term, source-directed testing. The long-term data collection is
necessary to answer the following questions:
Does the current emission factor/dispersion modeling methodology result
in systematic overprediction of air concentrations?
If so, what is the degree of overprediction?
How well do dispersion model results match measurements in time and
space?
No matter how sophisticated, long-term monitoring of ambient "far-field"
concentrations alone cannot answer questions such as the following:
What portion or portions of the methodology are most responsible for
overprediction?
Can the identified portions be modified so that systematic overprediction
is effectively removed?
What fraction of material emitted at the bottom of the pit eventually
escapes?
To answer these types of questions, the plan recommended that the long-term
ambient program be supplemented with the intensive short-term source/ambient
monitoring. Quantitative examination of separate steps in the emission factor/
dispersion model methodology is necessary to answer the "how" and"why" questions.
Furthermore, the particulate tracer studies would provide a quantitative basis for
development of a pit retention algorithm.
As a practical matter, it became necessary to revise the integrated approach.
Under the revised approach,4 most source-directed measurements would be con-
ducted first, with full-scale long-term ambient monitoring taking place in the year
following.
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This report describes the first phase of the field measurements, and the results
obtained. Testing occurred during the fall of 1992 at the Cordero Mine in Wyoming's
Powder River Basin. Also presented are details of the sampling methods, data
analysis, and quality assurance procedures followed in the study. The report
compares test results with the historical data base to assess how well currently
available emission factors perform. In addition, results from special studies conducted
to support the later full-scale ambient monitoring program are summarized.
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SECTION 2
AIR SAMPLING METHODOLOGY
This study was primarily directed toward characterizing paniculate emissions
from "line" sources (e.g., haul roads, scrapers, etc.) at surface mining sites. The
original review2 of emission factors placed the highest priorities on obtaining new field
data for haul truck traffic.
Six test sites were identified at the Cordero mine. Figure 1 presents a recent
aerial photograph of the Cordero mine and indicates the location of the following test
sites:
Site Description
1 North-south portion of permanent coal haul road from
north pit.
1B Northwest-southeast portion of permanent haul road
from north pit. At same grade as site 1 but
approximately 800 ft from start of ramp down to coal
load-out area.
2 Southwest-northeast portion of permanent coal haul
road from south pit.
3 North-south portion of permanent coal haul road from
south pit.
4 Overburden haul route at east end of north pit area.
' 5 North-south scraper route between topsoil removal
and stockpile area at west end of north pit.
Testing did not occur at site 3, however, as the coincidence of westerly winds and
haul road traffic from the south pit was infrequent while MRI crew members were on-
site.
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1 - Test site locations
D - Other set-up locations
A - Rain gauge location
Figure 1. Location of test sites.
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2.1 GENERAL AIR SAMPLING EQUIPMENT AND TECHNIQUES
The exposure profiling technique used for the tests in this study is based on the
isokinetic profiling concept that is used in conventional source testing. The passage of
airborne pollutant immediately downwind of the source was measured directly by
means of simultaneous multipoint sampling over the effective cross section of the
open dust source plume. This technique used a mass-balance calculation scheme
similar to EPA Method 5 stack testing, rather than requiring indirect calculation through
the application of a generalized atmospheric dispersion model.
The equipment deployment for a typical test is shown in Figure 2 and Table 1.
(Slightly variant deployment schemes were used when testing took place in less
accessible locations such as "Site 5" in Figure 1.) The primary air sampling device in
this study was a high-volume (hi-vol) air sampler fitted with a cyclone preseparator
(Figure 3). The cyclone exhibits an effective 50% cutoff diameter (D50) of
approximately 10 (imA when operated at a nominal flow rate of 40 acfm (68 m3/h).
Besides the samplers fitted with the cyclone preseparator to sample PM-10
emissions, two other types of samplers were used in the upwind (background) and
downwind arrays. Standard hi-vols were placed at two heights near one of the
downwind arrays to sample TSP emissions. Those measurements provide a valuable
link to past surface coal mine emission factor studies, in that all of those studies
focused on TSP as the basis of the former PM NAAQS. Volumetric flow controllers
kept the flow rates of each cyclone and the 3-m hi-vol at a nominal value of 40 acfm;
the flow rate of the 1-m hi-vol was maintained at 40 scfm by means of a variac.
PM-10 reference method samplers (Wedding and Associates' PM-10 Critical Flow
High-Volume Samplers) were also used, with one located alongside the upwind array
and another next to a downwind array.
Throughout each test, wind speed was monitored at the downwind sampling
site by directional warm-wire anemometers (Kurz Model 465) at up to three heights.
(In a few cases, equipment malfunction required use of a backup wind speed system
consisting of manual records of Biram vane anemometer readings.) Horizontal wind
direction was monitored by a wind vane at a single height. Wind speed and direction
were scanned using a Fluke HYDRA data logger, and 5-min averages were stored on
a computer file. The vertical profile of horizontal wind speed was determined by fitting
the measurements to a logarithmic profile.
Because of the importance of incoming solar radiation on the control
performance of watering of unpaved travel surfaces, a mechanical pyranograph
(Weathertronics Model 3010) was deployed in the immediate vicinity of watered travel
surfaces. This device provided a continuous record of the intensity of direct and
scattered solar radiation during the test period. Four rain gauges were also positioned
throughout the mine; their locations are shown in Figure 1.
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TABLE 1. SAMPLER DEPLOYMENT
Upwind/
downwind
U
U
D (one to three
arrays)
D
D
D
D
No. of
instruments
1
1
4 per array
2
1
1
3
Measurement
height(s)
(m)
1.9
1.9
1,3, 5, 7
1,3
3
3
1, .3, 5
Type of
sampler or
instrument
Cyclone
Wedding
PM-10
Sampler
Cyclone
Hi-Vol
Wedding
PM-10
Sampler
Wind vane
Warm wire
anemometer
Parameter
measured
PM-10
PM-10
PM-10
TSP
PM-10
Wind direction
Wind
velocity
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Back-up Filter
Holder
Scale - Inches
Figure 3. Cyclone preseparator.
MRt.M-JFt9712.SS
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In addition to the general emission testing program, a number of additional
studies were conducted as part of the overall field sampling effort. One study,
requested by the Source Receptor Branch (SRB) of the Office of Air Quality Planning
and Standards (OAQPS), measured near-source particle size distributions. SRB
requested that data be collected to support the development of a new deposition
algorithm. The combination of a cyclone preseparator and a cascade impactor (C/CI)
(Figure 4) provides a means to isokinetically collect and aerodynamically size total
paniculate (TP). Each sizing test deployed C/CI combinations at 1- and 3-m heights
at a nominal distance of 5 m from the roadway; wind data were collected in the same
manner as for emission tests.
The cyclone preseparators remove coarse particles which otherwise would tend
to bounce off the glass fiber impaction substrates, causing fine particle measurement
bias. To further reduce particle bounce problems, each substrate was sprayed with
stopcock grease solution to provide a sticky impaction surface. Each impactor
consisted of five impaction stages (cutoffs for 50% collection are 10.2, 4.2, 2.1, 1.4,
and 0.73 |imA at 20 acfm, the flow rate used in this special phase of the program).
Variacs were used to control the flow rates.
2.2 PARTICULATE SAMPLE HANDLING AND ANALYSIS
The sampling and analysis procedures followed in this field testing program
were subject to certain QA guidelines. These guidelines will be discussed in
conjunction with the activities to which they apply. These procedures met or
exceeded the requirements specified in the reports entitled "Quality Assurance
Handbook for Air Pollution Measurement Systems, Volume II—Ambient Air Specific
Methods" (EPA 600/4-77-027a) and "Ambient Monitoring Guidelines for Prevention of
Significant Deterioration" (EPA 450/2-78-019).
As part of the QA program for this study, routine audits of sampling and
analysis procedures were performed. The purpose of the audits was to demonstrate
that measurements were made within acceptable control conditions for particulate
source sampling and to assess the source testing data for precision and accuracy.
Examples of items audited include gravimetric analysis, flow rate calibration, data
processing, and emission factor calculation. The mandatory use of specially designed
reporting forms for sampling and analysis data obtained in the field and laboratory
aided in the auditing procedure. Further details on specific sampling and analysis
procedures are provided in the following sections.
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5 STAGE CASCADE
IMMCTOK
Figure 4. Cyclone preseparator/cascade impactor combination used
during "sizing" tests.
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2.2.1 Preparation of Sample Collection Media
Particulate samples were collected on Whatman Grade AH glass fiber filters,
with the exception of the Wedding PM-10 samplers which require quartz filters. Prior
to the initial weighing, the filters were equilibrated for 24 h at constant temperature
and humidity in a special weighing room. During weighing, the balance was checked
at frequent intervals with standard (Class S) weights to ensure accuracy. The filters
remained in the same controlled environment for another 24 h, after which a second
analyst reweighed them as a precision check. Ten percent of the filters used in the
field served as blanks. The QA guidelines pertaining to preparation of sample
collection media are presented in Table 2.
2.2.2 Pretest Procedures/Evaluation of Sampling Conditions
Weather forecasts, on-site meteorological measurements, and discussions with
mine personnel formed the basis to select which, if any, emission sources and sites
would be most acceptable for testing on any given day. Equipment was moved to the
most promising site, and deployment was initiated.
Once the source testing equipment was set up, if meteorological conditions
were favorable, filters were inserted and air sampling commenced. Information
recorded on specially designed reporting forms included:
1. Air samplers—Start/stop times, wind speed profiles, sampler flow rates,
and wind direction measured from a perpendicular to the roadway
centerline (5-min average). See Table 3 for QA procedures.
2. Traffic data—Truck ID, speed measured by radar gun, travel direction,
number of axles and tires, whether the truck is loaded or empty, and
what material is being hauled.
3. General meteorology—Wind speed, wind direction, temperature,
barometric pressure, and solar radiation.
Sampling time was long enough to provide sufficient particulate mass and to
average over several cycles of the fluctuation in the emission rate (e.g., vehicle
passes on the road). Occasionally sampling could be interrupted because of the
occurrence of unacceptable meteorological conditions and then restarted when
suitable conditions returned. Table 4 presents criteria used for suspending or
terminating a source test.
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TABLE 2. QUALITY ASSURANCE PROCEDURES FOR SAMPLING MEDIA
Activity
QA check/requirement
Preparation
Inspect and imprint glass fiber media with identification
numbers.
Conditioning
Equilibrate media for 24 h in clean controlled room
with relative humidity of 45% (variation of less than
±5% RH) and with temperature of 23°C (variation of
less than ±1°C).
Weighing
Weigh hi-vol filters to nearest 0.1 mg.
Auditing of weights
Independently verify final weights of 10% of filters (at
least four from each batch). Reweigh batch if weights
of any hi-vol filters deviate by more than ±2.0 mg. For
tare weights, conduct a 100% audit. Reweigh tare
weight of any filters that deviate by more than
±1.0 mg. Follow same procedures for impactor
substrates used for sizing tests. Audit limits for
impactor substrates are 1.0 and 0.5 mg for final and
tare weights, respectively.
Correction for handling effects
Weigh and handle at least one blank for each 1 to 10
filters of each type used to test.
Calibration of balance
Balance to be calibrated once per year by certified
manufacturer's representative. Check prior to each
use with laboratory Class S weights.
TABLE 3. QUALITY ASSURANCE PROCEDURES FOR
SAMPLING EQUIPMENT
Activity
QA check/requirement2
Maintenance
All samplers
Check motors, gaskets, timers, and flow measuring devices
at each plant prior to testing.
Operation
Timing
Start and stop all downwind samplers during time span not
exceeding 1 min.
Isokinetic sampling
(cyclones)
Adjust sampling intake orientation whenever mean wind
direction dictates.
Change the cyclone intake nozzle whenever the mean wind
speed approaching the sampler falls outside of the
suggested bounds for that nozzle. This technique allocates
no nozzle for wind speeds ranging from 0 to 10 mph, and
unique nozzles for four wind speed ranges above 10 mph.
Prevention of static mode
deposition
Cover sampler inlets prior to and immediately after
sampling.
"Mean" denotes a 3- to 15-min average.
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TABLE 4. CRITERIA FOR SUSPENDING OR TERMINATING A TEST
A test may be suspended or terminated if :a
1. Rainfall ensues during equipment setup or when sampling is in progress.
2. Mean wind speed during sampling falls below 1.8 (4 mph) for more than 20% of the
sampling time.
3. The angle between mean wind direction and the perpendicular to the path of the
moving point source during sampling exceeds 45 degrees for two consecutive
averaging periods.
4. Daylight or available artificial lighting is insufficient for safe equipment operation.
5. Wind speeds are high enough that safe equipment operation is not possible.
6. Source condition deviates from predetermined criteria (e.g., occurrence of truck spill,
accidental water splashing prior to uncontrolled testing, or high winds result in high
upwind concentrations that cannot be effectively distinguished from downwind
concentrations). ^
a "Mean" denotes a 3- to 15-min average.
2.2.3 Sample Handling and Analysis
To prevent particulate losses, the exposed media were carefully transferred at
the end of each run to protective containers for transportation. The interior surfaces of
cyclone preseparators were washed with distilled water; particulate matter that
collected on the interior surfaces of cyclone preseparators during sizing tests was
rinsed into separate sample jars which were then capped and taped shut. In the field
laboratory, exposed filters were placed in individual glassine envelopes and then into
numbered file folders. When exposed filters and the associated blanks were returned
to the MRI laboratory, they were equilibrated under the same conditions as the initial
weighing. After reweighing, 10% were audited to check weighing accuracy.
To determine the sample weight of particulate collected on the interior surfaces
of samplers, the entire wash solution was passed through a 47-mm (1.8-in) Buchner-
type funnel holding a glass fiber filter under suction. This water was passed through
the Buchner funnel ensuring collection of all suspended material on the 47-mm filter,
which was then dried in an oven at 100°C for 24 h. After drying, the filters were
conditioned at constant temperature and humidity for 24 h.
All wash filters were weighed with a 100% audit of tared and a 10% audit of
exposed filters. Blank values were determined by washing "clean" (unexposed)
cyclone preseparators in the field and following the above procedures.
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2.3 EMISSION FACTOR CALCULATION PROCEDURES
To calculate emission rates from exposure profiling test data, a conservation of
mass approach is used. The passage of airborne particulate (i.e., the quantity of
emissions per unit of source activity) is obtained by spatial integration of distributed
measurements of exposure (mass/area) over the effective cross section of the plume.
Exposure is the point value of the flux (mass/area-time) of airborne particulate
integrated over the time of measurement, or equivalently, the net particulate mass
passing through a unit area normal to the mean wind direction during the test. The
steps in the calculation procedure for line sources are described below.
2.3.1 Particulate Concentrations
The concentration of particulate matter measured by a sampler is given by:
C = 103 JL
Qt
where: C = particulate concentration ((ig/m3)
m = particulate sample weight (mg)
Q = sampler flow rate (m3/min)
t = duration of sampling (min)
To be consistent with the NAAQSs, all concentrations and flow rates are
expressed in standard conditions (25°C and 101 kPa or 77°F and 29.92 inHg).
The isokinetic flow ratio (IFR) is the ratio of a directional sampler's intake air
speed to the mean wind speed approaching the sampler. It is given by:
IFR- Q
aU
where: Q = sampler flow rate (m3/min)
a = intake area of sampler (rrr)
U = mean wind speed at height of sampler (m/min)
This ratio is of interest in the sampling of total particulate, since isokinetic
sampling ensures that particles of all sizes are sampled without bias. Note, however,
that because the primary interest in this program is directed to PM-10 emissions,
sampling under moderately nonisokinetic conditions poses no difficulty. It is readily
agreed that 10 [im (aerodynamic diameter) and smaller particles have weak inertia!
characteristics at normal wind speeds and therefore are relatively unaffected by
anisokinesis.
16
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Exposure represents the net passage of mass through a unit area normal to the
direction of plume transport (wind direction) and is calculated by:
E(h) = 1CT7 x CUt
where: E(h) = particulate exposure (mg/cm2) at height h (m)
C = net concentration (|ig/m )
U = approaching wind speed (m/s)
t = duration of sampling (s)
Exposure values vary over the spatial extent of the plume. If exposure is
integrated over the plume effective cross section, then the quantity obtained
represents the total passage of airborne particulate matter due to the source.
For a line source, a one-dimensional integration is used:
A1=JoHEdh
where: A1 = integrated exposure (m-mg/cm2)
E = particulate exposure (mg/cm2)
h = vertical distance coordinate (m)
H = effective extent of plume above ground (m)
The effective height of the plume is found by linear extrapolation of the
uppermost net concentrations to a value of zero.
Because exposures are measured at discrete heights of the plume, a numerical
integration is necessary to determine A1. The exposure must equal zero at the
vertical extremes of the profile (i.e., at the ground where the wind velocity equals zero
and at the effective height of the plume where the net concentration equals zero).
However, the maximum exposure usually occurs below a height of 1 m, so that there
is a sharp decay in exposure near the ground. To account for this sharp decay, the
value of exposure at the ground level is set equal to the value at a height of 1 m. The
integration is then performed using Simpson's rule. Because Simpson's rule requires
an odd number of equally spaced points, additional points are obtained (if needed) by
linear extrapolation.
2.3.2 Particulate Emission Factors
The PM-10 emission factor for particulate generated by vehicular traffic on a
straight road segment expressed in grams of emissions per vehicle-kilometer traveled
(VKT) is given by:
where: e = particulate emission factor (g/VKT)
A1 = integrated exposure (m-mg/cm )
17
-------
e = 104 A1
N
N = number of vehicle passes (dimensionless)
TSP emission factors eTSp were found by scaling the PM-10 emission factor e
by the geometric mean ratio of TSP to PM-10 exposure at the 1- and 3-m heights:
ETSP(1) ETSP(3)
e-rqp = 6
TSP ^ E(1) E(3)
where: eTSp = TSP emission factor (g/VKT)
E(h) = PM-10 exposure (mg/cm2) at height h (m)
ETSp(n) = Tsp exposure (mg/cm2) at height h (m)
2.4 SOURCE MATERIAL SAMPLE COLLECTION AND ANALYSIS
A sample of loose aggregate was collected from the travel surface for each
source emission test to characterize the emitting material. Each sample was analyzed
for silt and moisture contents, as described below. Furthermore, for tests of emissions
from watered surfaces, frequent grab samples were collected for moisture analysis.
The following describes the procedures used to collect unpaved road surface
samples.
1. Ensure that the site offers an unobstructed view of traffic and that
sampling personnel are visible to drivers. If the road is heavily traveled,
use one person to "spot" traffic and ensure that the person collecting the
surface sample (increment) gets safely out of the way.
2. Using string or other suitable markers, mark a 0.3-m (1-ft) width across
the road. (WARNING: Do not mark the collection area with a chalk line
or in any other method likely to introduce fine material into the sampled
3. With a whisk broom and dustpan, remove the loose surface material
from the hard road base. Do not abrade the base during sweeping.
Sweeping should be performed slowly so that fine surface material is not
injected into the air. NOTE: Collect material only from the portion of the
road over which the wheels and carriages routinely travel (i.e., not from
berms or any "mounds" along the road centerline).
18
-------
4. Periodically deposit the swept material into a clean, labeled container of
suitable size (such as a metal or plastic 19-L [5-gal] bucket) with a
sealable polyethylene liner. Increments may be mixed within this
container.
5. Record the required information on the sample collection sheet.
Additional "grab" samples were collected during tests of watered surfaces
to determine how moisture content varied during the test period.
Samples were comprised of approximately 10 increments taken at
randomly selected spots across the road.
Upon return to MRPs main laboratories, samples underwent moisture and silt
content determination. Moisture content was determined by weight loss upon oven
drying. Silt content refers to the fraction of material smaller than 200 mesh, as
determined by mechanical dry sieving.
The following steps describe the moisture content determination procedure:
1. Preheat the oven to the desired temperature of approximately 80°C
(180°F) or 110°C (230°F). Record oven temperature.
2. Record the make, capacity, and smallest division of the scale.
3. Weigh the empty laboratory sample containers which will be placed in
the oven to determine their tare weight. Record the tare weight(s).
Check zero before each weighing.
4. Weigh the laboratory sample(s) in the container(s). For materials with
high moisture content, ensure that any standing moisture is included in
the laboratory sample container. Record the combined weight(s). Check
zero before each weighing.
5. Place sample in oven and dry overnight. Samples likely to contain
significant amounts of coal were dried for only 1.5 h at 80°C. Samples
unlikely to contain coal (e.g., overburden haul roads or scraper routes)
were dried overnight (nominally 24 h) at 110°C.
6. Remove sample container from oven and (a) weigh immediately if
uncovered, being careful of the hot container; or (b) place the tight-fitting
lid on the container and let cool before weighing. Record the combined
sample and container weight(s). Check zero reading on the balance
before weighing.
7. Calculate the moisture as the initial weight of the sample and container
minus the oven-dried weight of the sample and container divided by the
19
-------
initial weight of the sample alone. Record the value. The 1.5-h samples
may be redried at 110°C (230°F) overnight for comparison purposes.
8. Calculate the sample weight to be used in the silt analysis as the
oven-dried weight of the sample and container minus the weight of the
container. Record the value.
Silt analysis was then performed on the oven-dried sample using the following
procedure:
1. Select the appropriate 20-cm (8-in) diameter, 5-cm (2-in) deep sieve
sizes. Recommended U.S. Standard Series sizes are 3/8 in, No. 4,
No. 40, No. 100, No. 140, No. 200, and a pan. Comparable Tyler Series
sizes can also be utilized. The No. 20 and the No. 200 are mandatory.
The others can be varied if the recommended sieves are not available or
if buildup on one particulate sieve during sieving indicates that an
intermediate sieve should be inserted.
2. Obtain a mechanical sieving device such as vibratory shaker or a
Roto-Tap without the tapping function.
3. Clean the sieves with compressed air and/or a soft brush. Material
lodged in the sieve openings or adhering to the sides of the sieve should
be removed (if possible) without handling the screen roughly.
4. Obtain a scale (capacity of at least 1,600 g or 3.5 Ib) and record make,
capacity, smallest division, date of last calibration, and accuracy.
5. Weigh the sieves and pan to determine tare weights. Check the zero
before every weighing. Record weights.
6. After nesting the sieves in decreasing order with pan at the bottom,
dump dried laboratory sample (preferably immediately after moisture
analysis) into the top sieve. The sample should weigh between - 400
and 1,600 g (0.9 to 3.5 Ib). This amount will vary for finely textured
materials; 100 to 300 g may be sufficient with 90% of the sample
passing a No. 8 (2.36-mm) sieve. Brush fine material adhering to the
sides of the container into the top sieve, and cover the top sieve with a
special lid normally purchased with the pan.
7. Place nested sieves into the mechanical sieving device and sieve for
10 min. Remove pan containing minus No. 200 and weigh. Repeat the
sieving in 10-min intervals until the difference between two successive
pan sample weighings (where the tare weight of the pan has been
subtracted) is less than 3.0%. Do not sieve longer than 40 min.
20
-------
8. Weigh each sieve and its contents and record the weight. Check the
zero reading on the balance before every weighing.
9. Collect the laboratory sample and place the sample in a separate
container if further analysis is expected.
10. Calculate the percent of mass less than the 200 mesh screen (75 urn).
This is the silt content.
2.5 CONTROL APPLICATION INTENSITY
To measure the application intensity of water for dust control, MRI placed tared
sampling pans at various locations on the test road strips prior to application. Special
attention was paid to the problem associated with the suppressant splashing off the
bottom of the pan. To reduce this potential source of error, an absorbent material was
used to line the bottom of the pan. Once the control agent was applied, the sample
pans were reweighed.
The application intensity measured by each pan is given by:
mf - mt
a =
p A
where: a = application intensity (volume/area)
mf = final weight of the pan and solution (mass)
mt = tare weight of the pan and absorbent material (mass)
p = weight density of solution (mass/volume)
A = area of the pan (area)
The individual application intensities obtained were examined to determine any
significant spatial variations and to obtain an average value.
21
-------
22
-------
TEST RESULTS
SECTION 3
A total of 36 PM-10 emission tests, distributed over various sources and five
test sites, was performed. In keeping with priorities established in the earlier emission
factor review, a majority of the field effort was devoted to emissions from haul truck
traffic. For haul roads especially, testing spanned a large range of wind speeds. In
addition, a fairly broad spectrum of haul road dust control was tested, ranging from
essentially unimproved overburden haul routes to extremely well controlled coal haul
roads. The permanent north and south pit coal haul roads were well compacted and
had been treated with surfactants for several years. These roads are further
controlled by regular watering. TSP emission tests were run concurrently with 22 of
the PM-10 tests. In addition, three PM-10 and three TSP light-duty captive traffic tests
on permanent coal haul roads were completed. These tests were performed to
quantify the importance of light-duty versus haul truck traffic on the roads. Finally, two
tests of scraper travel were also conducted.
Table 5 presents the site parameters for these tests, including the date and
location of the testing and general meteorology. Runs with an "X" suffix identify sizing
tests conducted to provide the Source Receptor Branch (SRB) at EPA with additional
information about the particle size distribution of haul road emissions. These tests are
discussed as "Special Studies" in the next section.
Table 6 presents a summary of 1992 field testing at the Cordero mine.
Table 7 presents the concentration measurements collected during the field
program. Table 8 lists, for each run, the individual PM-10 exposure values at each
sampling location. TSP exposure values are also listed. These exposure values were
integrated over the plume area in order to determine emission factors, which are given
in Table 9. Table 9 also presents road surface and vehicle parameters.
Approximately one-half of all haul truck emission tests were controlled by use of
water/surfactant. Appendix C contains relatively detailed time histories of the water
application as well as surface moisture content. Table 10 compares results for
different haul road test sites. As one can see by comparing the two sets of mean
emission factors, water/surfactant afforded control efficiencies ranging from about 40%
to 70% from PM-10 and from about 30% to 60% for TSP. These findings are in good
agreement with the commonly applied control efficiency value of 50% for watering.
Section 5 of this report discusses further the emission factors developed during
this study.
23
-------
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TABLE 6. SUMMARY OF PHASE I TESTING AT CORDERO
Site IDa
PM-10 emission tests:
Uncontrolled
Controlled
Total
1
4
4
Coal haul roads
1B
8b
12
20
2
2b
2
4
Overburden
haul road
4
3
3
6
Total
17
17
34
TSP emission tests:
Uncontrolled 2b 4b 2b 2 10
Controlled 8 2 2 12
Total 2 12 4 4 22
Emission tests of captive
light-duty vehicles
Uncontrolled 2b'c 1b>c 3C
Particle sizing tests:
Uncontrolled
Controlled
Total
2
2
2
2
1
1
3
2
5
Refer to Figure 1 for test site locations. Not included in the table are the two
uncontrolled scraper tests (BB-46, -47) conducted at site 5.
Permanent coal haul roads had been treated with surfactant for several years and
were very well compacted with little loose surface material. "Controlled" and
"uncontrolled" in the sense used here means that testing took place with or without
supplemental watering of the surface.
c Each entry reflects one PM-10 and one TSP test.
"Moisture tracking" refers to collection of grab samples from watered road every 10
to 30 min to better define average moisture contents for controlled roads.
26
-------
TABLE 7. CONCENTRATION MEASUREMENTS
upwind PH.,
Concentration
Ci.'9/m')
Run
882
BB3
BB6
BB7
BBS
BB10
8811
BB12
BB13
BB14
8815
8816
BB17
BB18
BB19
BB20
SB21
BB22
BB23
BB25
BB26
BB27
B829
BB31
8B33
BB34
BB35
BB36
8838
8B39
8B40
8841
BB42
8843
BB44
BB46
BB47
BB45
BB48
1.9 m
Cyclone
38.34
38.34
14.96
14.96
14.96
9.40
9.40
7.45
7.45
6.73
6.73
6.73
79.54
79.54
79.54
79.54
3.64
3.64
17.10
8.78
8.78
8.78
2.87
2.87
9.28
9.28
9.28
19.11
262.6
262.6
10.65
10.65
10.65
6.65
14.96
9.45;
9.45'
3.94'
3.94'
1.9 m
Uedding
36.34
36.34
9.86
9.86
9.86
9.44
9.44
17.27
17.27
6.14
6.14
6.14
53.59
53.59
53.59
53.59
11.19
11.19
31.91
14.14
14.14
14.14
5.38
5.38
5.38
17.81
194.6
194.6
13.07
13.07
13.07
12.98
Downwind PH..
1 m
Cyc I one
2447
3698
858.7
977.3
1030
319.4
337.1
199.9
161.2
336.9
608.5
936.1
209.3
608.4
340.2
749.8
198.8
350.4
109.8
78.76
368.1
538
3920
1563
322.9
1012
658.7
1679
453.6
488.5
150.0
502.6
673.0
38.05
241.5
1564'
1484"
101.9
400.9
3 m
Cyc I one
1218
1906
477.3
795.0
395.0
174.3
138.8
239.2
171.9
226.0
358.4
607.2
203.2
559.5
487.2
510.8
129.8
418.3
126.5
90.52
406.9
205.7
2275
1905
200.1
413.0
453.0
2304
502.5
401.7
124.3
378.1
539.3
36.65
63.61
729.1
939.2
70.75
258.3
. Concentration (yg/m5)
' 5 m
Cyclone
980.9
1277
429.2
451.9
439.7
123.8
75.80
117.2
76.81
183.0
312.3
506.7
148.4
403.6
342.9
304.0
73.95
265.9
128.7
71.48
117.9
251.6
1681
1367
131.7
321.7
254.1
2130
383.2
284.6
121.7
353.8
513.2
47.59
24.67
275.2"
568.9"
39.27
47.16
7 m
Cyclone
462.7
657.5
223.8
251.8
269.9
60.34
63.13
38.45
62.20
83.55
127.7
263.4
146.5
310.1
279.7
301.4
61.74
246.9
85.24
29.58
53.22
126.0
842.0
673.2
100.3
233.0
171.3
1165
331.9
249.9
95.14
169.2
414.6
34.86
57.12
0
11.81
3 m
Wedding
1354
473.5
542.1
183.3
199.2
195.0'
195.0'
197.7°
197.7"
143.2'
143. 2*.
175.8
99. 323
99.32'
205.8'
205.8'
191.5
522.3
197.7
92.36
56.92
Downwind TSP
Concentration
iugtm't
1 m
17480
2372
1528
1769
884.9
2138
1072
1893
829.2
1274
787.9
723.6
1515
13280
2368
3364
2383
11730
1725
1988
4190
440.8
923.7
868.9
1802
3 m
12720
2149
2461
1067
1074
973.6
1802
836.4
2020
832.9
1272
804.3
600.9
1193
9803
1804
2661
2039
11490
1074
1743
3513
336.6
328.5
452.3
875.8
' Dc3* 3 m Uedding operated during
1.2 m height.
1 1.5 m height.
4.5 m height.
2 runs with the same filter.
MR1-MNR9712-55
27
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TABLE 8. PLUME SAMPLING DATA
Run
BB2
BB3
BB6
BB7
BBS
BB10
BB11
BB12
BB13
Sanplint
Height
(m)
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
PM,0
3 Sampling Rate
(mVhr)
61.86
61 .49
61.28
61 .42
61.55
62.08
61.74
62.08
60.86
61.27
61.15
61.45
61.16
60.67
60.47
60.67
60.55
60.81
60.74
61.01
59.69
60.08
59.79
59.99
59.91
59.30
59.23
59.36
60.60
60.81
60.52
60.72
60.25
60.54
60.01
(cfnO
36.41
36.19
36.07
36.15
36.23
36.54
36.34
36.54
35.82
36.06
35.99
36.17
36.00
35.71
35.59
35.71
35.64
35.79
35.75
35.91
35.13
35.36
35.19
35.31
35.26
34.90
34.86
34.94
35.67
35.79
35.62
35.74
35.46
35.63
35.32
IFR
1.04
0.88
0.82
0.78
1.03
0.88
0.83
0.79
1.48
1.31
1.24
1.19
1.49
1.31
1.22
1.19
1.83
1.45
1.31
1.24
1.52
1.27
1.18
1.13
1.52
1.26
1.18
1.12
1.04
0.87
0.82
0.78
1.02
0.87
0.82
Net PH10 TSP
Exposure Sampling Rate
(rug/cm2)
(mVhr) (cfm)
2.376 67.96 40
1.370 61.08 35.95
1.170
0.5500
3.007
1.807
1.282
0.6686
1.058
0.6605 60.33 35.51
0.6251
0.3263
1.436
1.325
0.7845
0.4401
0.5362 67.96 40
0.2565 60.21 35.44
0.3158
0.2010
1.102 67.96 40
0.7028 59.23 34.86
0.6628
0.2448
1.182
0.5567
0.3072
0.2600
0.3608 67.96 40
0.5153 59.80 35.20
0.2620
0.07730
0.2992
0.3776
0.1704
Net TSP
Exposure
-------
TABLE 8 (Continued)
Run
BB14
BB15
BB16
B17
BB18
BB19
BB20
BB21
BB22
Sampling
Height
(m)
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
PM,0
Sampling Rate
(mVhr)
59.63
62.13
62.40
62.13
62.47
61.55
62.01
61.86
62.08
61.61
61.81
61.66
61.86
61.54
62.08
61.72
62.00
61.78
61.35
61.08
61.28
61.35
61.59
61.52
61.66
61.76
61.18
60.91
61.25
60.76
61.21
60.94
61.15
60.69
60.20
(cfm)
35.10
36.57
36.73
36.57
36.77
36.23
36.50
36.41
36.54
36.26
36.38
36.29
36.41
36.22
36.54
36.33
36.49
36.36
36.11
35.95
36.07
36.11
36.25
36.21
36.29
36.35
36.01
35.85
36.05
35.76
36.03
35.87
35.99
35.72
35.43
IFR
0.77
1.14
0.97
0.90
0.87
0.97
0.83
0.78
0.75
1.22
0.94
0.85
0.81
1.37
1.00
1.21
1.13
1.40
1.00
1.20
1.12
1.65
1.16
1.36
1.27
1.73
1.18
1.02
0.95
1.39
1.55
1.43
.1.87
2.12
2.47
Net PHIO TSP
Exposure Sampling Rate
(mg/crn8)
(mVhr) (cfm)
0.1405
0.5934 67.96 40
0.4651 61.67 36.30
0.4002
0.1820
1.006 67.96 40
0.6876 61.35 36.11
0.6377
0.2629
1.223
1.016
0.9330
0.5086
0.2544 67.96 40
0.3337 61.13 35.98
0.2095
0.2187
0.9479
1.187
0.9049
0.6915
0.2654 67.96 40
0.5969 60.99 35.90
0.4404
0.3622
0.5238
0.4895
0.2919
0.3126
0.2871 67.96 40
0.2251 60.37 35.53
0.1356
0.1175
0.1473 67.96 40
0.1820 60.04 35.34
Net TSP
Exposure
-------
TABLE 8 (Continued)
PH,0
Sampling Sampling Rate
"w
-------
TABLE 8 (Continued)
Run
BB36
BB38
BB39
BBAO
8B41
BB42
BB43
BB44
Sampling
Height
(m)
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
1
3
5
7
PM,o
Sampling Rate
CmVhr)
61.37
61.10
61.23
60.50
59.52
59.72
59.92
60.99
61.38
61.25
61.32
61.16
60.60
60.47
60.60
62.08
61.64
61.05
61.52
61.84
62.10
61.81
62.17
62.03
61.52
61.38
61*. 52
61.10
60.03
60.67
61.32
60.82
59.82
60.45
61.11
(Cfm)
36.12
35.96
36.04
35.61
35.03
35.15
35.27
35.90
36.13
36.05
36.09
36.00
35.67
35.59
35.67
36.54
36.28
35.93
36.21
36.40
36.55
36.38
36.59
36.51
36.21
36.13
36.21
35.96
35.33
35.71
36.09
35.80
35.21
35.58
35.97
IFR
1.36
1.27
1.36
2.45
1.96
1.82
1.96
1.12
1.04
0.95
1.22
1.12
1.02
0.95
1.21
1.25
1.42
1.31
1.26
1.27
1.46
1.36
1.31
1.17
1.09
1.02
0.97
1.16
1.19
1.09
1.04
1.42
1.02
1.07
1.18
Net PM,0 TSP
Exposure Sampling Rate
(mg/cm2)
(mVhr) (cfm)
0.8566 61.18 36.01
0.5284
0.3743
0.7418 67.96 40
1.250 59.67 35.12
1.252
0.7149
0.4731 67.96 40
0.7306 60.40 35.55
0.3992
0.2415
0.4966
0.3780
0.06511
(O)1
0.3030 67.96 40
0.2913 61.23 36.04
0.3048
0.2417
1.284
1.137
1.139
0.5495
1 .224 67.96 40
1.154 61.18 36.01
1.177
0.9870
0.04402 67.96 40
0.05451 60.60 35.67
0.08226
0.06029
0.4189 67.96 40
0.1235 60.47 35.59
0.02775
0.1294
Net TSP
Exposure
(mg/cm2)
3.919
5.233
6.273
3.621
2.471
4.297
4.436
7.723
7.649
0.6086
0.5995
1.680
0.7959
31
MRI-M\R9712-55
-------
TABLE 8 (Continued)
Run
BB46
BB47
BB45
BB48
BB100X
IB102X
IB111X
BB112X
BB121X
Sampling
Height
1.5
3.0
4.5
1.5
3.0
4.5
1
3
5
7
1
3
5
7
1
3
1
3
1
3
1
3
1
3
PH,o
Sampling
(ma/hr)
60.11
60.26
59.41
60.11
58.56
59.35
61.69
60.60
61.32
61.93
61.69
60.67
61.32
61.98
29.63
29.63
33.98
33.98
30.73
30.73
30.38
30.38
33.98
33.98
Rate
(cfm)
35.38
35.47
34.97
35.38
34.47
34.93
36.31
35.67
36.09
36.45
36.31
35.71
36.09
36.48
17.44
17.44
20.00
20.00
18.09
18.09
17.88
17.88
20.00
20.00
IFR
1.15
1.07
1.15
2.00
1.85
1.73
1.22
1.13
1.16
1.50
1.56
1.20
1.10
1.42
1.42
1.28
1.20
0.94
0.66
0.71
0.98
0.70
1.05
0.92
Net PH10 TSP
Exposure . Sampling Rate
(mg/cm2)
(m'/hr) {cfm}
3.460
1.741
0.6744
0.8510
0.5827
0.3685
0.1530 67.96 40
0.1334 61.05 35.93
0.07769
CO)'
0.3212 67.96 40
0.2631 61.13 35.98
0.04924
0.009500
67.96 40
62.06 36.53
67.96 40
Net TSP
Exposure
(dig/cm8)
1.352
0.8958
1.455
0.9020
* Downwind concentration is less than upwind concentration.
MFUW.RB7t2.SS
32
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34
-------
TABLE 10 COMPARISON OF GEOMETRIC MEAN EMISSION
FACTORS (Ib/VMT) FOR UNCONTROLLED AND CONTROLLED
TESTS OF HAUL TRUCK EMISSIONS
Uncontrolled
Site
1
1B
2
4
Overall
PM-10
6.1
3.6
7.3
13
5.5
TSP
42
14
46
72
31
Controlled
PM-10
2.2
2.4
5.8
2.6
TSP
10
17
57
15
35
-------
36
-------
SECTION 4
SPECIAL STUDIES
In addition to the general emission testing program, a number of additional
studies were conducted as part of the overall field sampling effort. Many of the
studies addressed issues expected during a subsequent field program focusing on
ambient air measurements. These included studies to:
1. Determine particle size distributions of emissions from travel sources.
2. Characterize variability in input variables for currently available emission
factor models.
3. Obtain an expanded ambient air quality data set for the period of the
field exercise.
4 Gain practical experience with "saturation samplers" in anticipation of
their use during a second phase of field activities emphasizing air quality
monitoring.
5. Collect source activity information for days when air quality sampling took
place (i.e., items 3 and 4 above).
Each of these special studies is discussed in greater detail below.
Besides collecting additional field samples, two other special studies were of
interest in this program. Both require the incorporation of test results from this
program into the historical emission factor data base. With this expanded data base,
studies were undertaken to:
6. Evaluate how well currently available emission factors perform for
surface coal mines.
7. Perform statistical analyses with a view to reformulating the emission
factor models.
Examination of the expanded data base is presented in Section 5.
37
-------
4.1 MEASURED PARTICLE SIZE DISTRIBUTIONS
During the planning stages for the field program, the Source Receptor Branch
(SRB) requested that some particle size distribution data be collected to support the
development of a new deposition algorithm. A total of four coal and one overburden
haul road sizing tests were conducted during the course of the field exercise; these
tests were shown with an "X" suffix in Table 5 and used cyclone/cascade irnpactor
(C/CI) combinations discussed in Section 2.1. Each "X" test deployed C/CI combina-
tions at 1- and 3-m heights at a nominal distance of 5 m from the roadway.
Table 11 presents the particle size distribution data developed during the fiefd
program. In general, the data follow the expected patterns of being coarser (a) at
lower heights and (b) at higher wind speeds.
4.2 VARIABILITY IN INPUT PARAMETERS FOR CURRENT EMISSION FACTOR
MODELS
Table 12 presents the analysis results for all the material and moisture "grab"
samples collected during the field exercise. In addition to samples collected in
conjunction with specific exposure profiling and particle sizing tests, the table includes
three sets of "moisture tracking" samples. "Moisture tracking" refers to a series of
grab samples taken over several watering cycles of haul truck routes to better quantify
average moisture levels. The principal reason for taking these samples was the
anticipation that the expanded data base might yield a relationship between moisture
content and emission factor.
Summary statistics for the material sample parameters are presented in
Table 13. What is most important to note in Table 13 is the overall variability in the
material parameters, especially silt loading and moisture content. One can certainly
expect considerable variability in these parameters because of the regular main-
tenance (grading and laying of new scoria as needed) and watering programs in place
at the mine. Because of the variability in the parameters that serve as input to surface
coal mine emission factors, the ambient monitoring field exercise being planned as a
follow-up to this study provides for a series of roadway surface sampling to better
track emission trends.
4.3 EXPANDED AMBIENT AIR QUALITY MEASUREMENTS
Representatives of the Wyoming Mining Association, the Wyoming Department
of Environmental Quality, OAQPS, and MRI met in Casper on August 31, 1992, to
review the goals of the testing program. That meeting resulted in a decision to
conduct additional air sampling with Cordero's high-volume sampling network to
approach a 1-day-in-2 frequency rather than the normal 1-day-in-6.
38
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-------
Run
Pre BB-2,3
Post BB-2,3
Pre BB-10,11
Post BB-10,11
During BB-111X
During BB-111X
After BB-111X
During BB-112X
Post BB-112X
10/21/92 at 1320
10/21/92 at 1352
10/21/92 at 1411
10/21/92 at 1444
10/21/92 at 1503
10/21/92 at 1547
1B Pre BB-6,7
Post BB-6,7
Post BB-8
Pre 88-12,13
Post BB-12,13
During BB-14
Post BB-16
Pre BB-17
During BB-17
During BB-17
During BB-18,19
During BB-20
Post BB-20
During BB-21
During BB-22
Post B8-22
During BB-23
Post BB-23
During BB-25
Post BB-25
Silt X
10.1
11.3
2.25
3.91
2.36
0.67
2.11
0.98
2.27
5.15'
5.15'
5.15'
5.15'
5.15'
5.15'
2.6
4.53
3.03
1.65
2.82
3.32
2.05
3.3
1.51
1.43
1.25
3.29
4.49
1.76
1.7
3.19
1.48
2.31
2.07
5.56
90 min
moisture X
1.02
0.74
0.66
0.33
1.94
4.53
2.78
3.22
1.95
5.54
0.53
0.41
0.58
0.55
0.27
0.65
0.37
0.88
1.62
0.85
0.78
0.65
0.3
1.4
1.9
0.87
S.35
1.72
4.49
3.14
Overnight
moisture %
1.24
0.92
1.52
0.82
7.8
2.8
2.1
3.1
2
2.38
4.92
3.12
3.55
4.38
6.16
1.38
1
1.01
1.18
0.99
1.77
1.39
1.94
2.32
1.13
1.45
1.46
1.33
2
2.5
1.27
6.2
1.99
4.85
3.14
Water
added*
no
no
no
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
no
no
no
no
no
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Total
surface
loading
(g/m1)
1017
1612
432.1
577.4
55. 8
141.7
105.9
113.5
108
520.7
359.1
496.3
423
1154
400.4
165. 4
263.3
344.4
107
155.9
235.1
169.7
146.4
220.4
109.4
138.6
497.4
349.1
988.9
750.2
125.9
45.86
77.42
86.34
34.57
Surface
silt
loading
(g/m2)
102.72
182.16
9.72
22.58
1.32
0.95
2.23
1.11
2.45
26.82
18.49
25.56
21.78
59.43
20.62
4.30
11.93
10.44
1.77
4.40
7.81
3.48
4.83
3.33
1.56
1.73
16.36
15.67
17.40
12.75
4.02
0.68
1.79
1.79
1.92
40
MHI-MR9712-55
-------
TABLE 12 (Continued)
Site Run
During BB-26
During BB-26,27
During SB -27
During 88-38,39
During BB-38,39
Post BB-48
2 Pre BB-33
Post BB-34
During BB-35
During BB-35
After BB-35
During BB-43
During 88-43
During 8B-44
10/21/92 at 1315
10/21/92 at 1330
10/21/92 at 1347
10/21/92 at 1411
10/21/92 at 1429
10/21/92 at 1453
10/21/92 at 1506
10/21/92 at 1525
10/21/92 at 1549
10/21/92 at 1608
4 Post BB-31
During BB-36
During BB-40
During BB-40,41
During BB-41
During BB-41,42
Post BB-42
Pre BB-121X
10/23/92 at 1420
10/23/92 at 1443
Silt X
1.9
2.99
2.45
1.69
1.44
1.95
3.02
4.88
3.56
4.28
3.28
2.32
1.23
1.82
2.5<
2.5'
2.5C
2.5e
2.5C
2.5C
2.5C
2.5*
2.5'
2.5C
19.2
12.9
4.07
5.5
5.1
8.84
10.12
5.93
10.5'
10.5'
90 min
moisture X
6.94
1.7
1.8
1.86
5.44
3.49
0.43
6.95
2.73
3.65
4.7
2.68
2.35
4.87
2.63
4.48
4.25
3.83
2.03
6.93
Overnight
moisture X
6.94
1.85
1.92
8.2
12.3
2.1
1.5
0.91
3.7
2
1.9
5.65
3.65
0.68
6.95
2.9
3.77
4.87
2.96
2.35
5.13
2.78
4.78
5.08
3.78
5
6.8
4.6
6.6
3.9
4.8
5.06
2.93
7.68
Water
added*
yes
yes
yes
yes
yes
no
no
no
yes
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
no
yes
yes
yes
yes
yes
no
yes
yes
Total
surface
loading
(g/ml)
53.63
100.3
129
79.21
368.1
40.86
165.7
287.7
437.4
376.4
239.8
185.2
243.9
99.25
128.5
201,3
141.2
200.9
121.9
138.6
130.5
111.1
111.3
581.7
356.1
1901
505.2
295.1
1129
571
1179
92.87
68.82
82.77
Surface
silt
loading
-------
TABLE 12 (Continued)
Site
Run
— — _
10/23/92 at 1502
10/23/92 at 1520
10/23/92 at 1539
10/23/92 at 1558
10/23/92 at 1623
10/26/92 at 1431
10/26/92 at 1451
10/26/92 at 1511
10/26/92 at 1546
10/26/92 at 1606
10/26/92 at 1616
Overburden pile
During BB-46
j>uring 88-47
^ S!i^^^S3SIS33
JPS tet uh**>u,&_
Silt Z
10 5' '
IV »J
10.5'
10.5'
10 5'
* V fj
10.5'
5.59*
5.59'
5.59*
5.59*
5.59*
5.59'
12.5
12.7
14
"
. 90 mi'n Overnight
moisture X moisti.r" t
5.38 6.79
C f
5-5 8.21
2TC
•TS 4.97
2.29 •» 05
•" j.92
*-1 5.33
7.46
7.17
6.74
7.91
9.31
8.1
9.73
4.88
5.11
Water
added*
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
no
no
=====
Total
surface
loading
Cg/fn )
342.6
332.5
508
466.1
177.5
519.4
605.5
530.9
442.7
651
445.8
4180
6388
^ ^-_
Surface
silt
loading
(g/m2)
35.97
34.91
53.34
48.94
18.64
29.03
33.85
29.68
24.75
36.39
24.92
530.86
_894.32
42
MRI-M\R9712-5S
-------
TABLE 13. SUMMARY STATISTICS FOR ROAD SURFACE
MATERIAL SAMPLES COLLECTED3
90 min
Silt % moisture %
Site 1
Mean
Std. dev.
No. of cases
Site 1 B
Mean
Std. dev.
No. of cases
Site 2
Mean
Std. dev.
No. of cases
Site 4
Mean
Std. dev.
No. of cases
Site 5
Mean
Std. dev.
No. of cases
4.11
3.72
10
2.53
1.09
26
2.99
1.16
9
8.78
4.66
10
13.4
N/A
2
2.27
1.75
10
1.65
1.71
24
3.74
1.65
13
4.10
1.73
8
_ "
' —
0
Overnight
moisture %
3.12
1.99
15
2.83
2.72
26
3.42
1.74
18
6.05
1.76
21
5.00
N/A
2
Total
surface
loading
(9/m2)
501
444
15
222
225
26
217
131
18
534
424
21
5,280
N/A
2
5.1 -
Surface
silt
loading
(g/m2)
35.4
60.2
10
5.48
5.21
26
8.04
5.66
9
64.3
71.6
10
713
N/A
2
Refer to Figure 1 for test site locations.
43
-------
To the extent practical, MRI duplicated Cordero's standard sampling
procedures, using media supplied by Inter-Mountain Laboratories (IML). Supplemental
high-volume ambient air sampling began as soon as possible after the receipt of filters
from IML A total of nine additional 24-h ambient air sampling periods were realized,
beyond the normal 1-day-in-6 frequency for the Cordero network. This essentially
doubled the amount of high-volume data available for the period of the field exercise.
Figure 5 shows the locations of the Cordero monitoring stations. Equipment at
each of the three sites was as follows:
' Site Air quality monitors
HV1 Standard hi-vol (TSP)
HV2 and HV2A Two colocated standard hi-vols and two colocated Wedding
PM-10 samplers
HV3 Standard hi-vol and Wedding PM-10 sampler
Tables 14 and 15 present the TSP and PM-10 concentrations, respectively,
measured during the 1992 field exercise. Appendix A presents copies of monitoring
results supplied by IML.
4.4 SATURATION SAMPLING RESULTS
In addition to operating Cordero's monitors on a nominal 1-day-in-2 frequency,
MRI also conducted 10 days of "saturation" sampling at the six locations shown in
Figure 6. Saturation samples refer to small, battery-powered devices originally
developed to "saturate" an area with PM-10 monitors. Sampling locations were
chosen to be downwind of the major north pit sources under typical wind conditions.
The 10 days provided practical experience and insight into using saturation samplers
during a second phase of field activities directed toward air quality measurements,
Table 16 presents the saturation sampling results. As can be seen from the
table, MRI encountered several difficulties with the saturation sampling, including
negative sample weights on the filters that showed visible discoloring. Negative
weights were found on both the quartz and fiber film media recommended by EPA and
its equipment contractor (ManTech). Other specific findings are as follows:
1. Contrary to some fears expressed to MRI, the battery packs can reliably
provide 24 h of sampling time.
44
-------
MRI-M\R9712-55
Figure 5. Cordero monitoring sites, HV-1, HV-2, and HV-3.
45
-------
TABLE 14. MONITORED TSP CONCENTRATIONS
Monitoring station3
Date
09/03/92
09/09/92
09/1 5/92
09/21/92
09/27/92
09/11/92
09/13/92
09/17/92
09/23/92
09/29/92
10/01/92
10/03/92
10/07/92
10/09/92
10/13/92
10/15/92
10/17/92
10/21/92
10/23/92
10/27/92
HV1
37
21
37
.
f
35
25
45
.
.
t
t
8
12
30
17
39
23
25
34
HV2b
t
(
.
25
42
47
49
47
50
50
29
16
31
36
21
63
51
57
33
HV2ab
46
44
55
43
32
59
34
63
54
55
62
34
m
t
48
33
77
60
58
33
HV3
26
56
30
46
18
39
B
65
27
27
m
25
11
.
30
16
34
18
23
•
a Refer to Figure 5 for monitoring locations.
Colocated monitors.
46
-------
TABLE 15. MONITORED PM-10 CONCENTRATIONS
Monitoring station3
Date
09/03/92
09/09/92
09/15/92
09/21/92
09/27/92
09/11/92
09/13/92
09/17/92
09/23/92
09/29/92
10/01/92
10/03/92
10/07/92
10/09/92
10/13/92
10/15/92
10/17/92
10/21/92
10/23/92
10/27/92
a Refer to Figure 5
D P1 /-i Insofar! mr\nitn
HV2b
12
13
20
11
9
19
10
13
20
17
14
5
11
,
12
24
18
22
10
for station locations.
re
HV2ab
11
13
21
10
8
19
10
13
20
17
„
13
5
11
13
11
25
18
22
11
HV3C
.
.
•
•
.
•
.
13
16
16
6
9
15
.
20
11
16
16
PM-10 sampler added to station HV3 during September 1993.
47
-------
(1 la - 3000 it)
Sol*
Figure 6. Saturation sampler locations, 1 through 6.
48
MRI-M\R9712-SS
-------
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2. Sampling cartridges and media appeared to develop considerable static
charge which complicated filter handling activities. It is not clear as to
how important low relative humidity at the mine is to this phenomenon.
3. Filters appeared to be only slightly discolored after 10 to 12 h of
exposure. Taken together with the negative catches, saturation samplers
are probably best suited to operation in areas with higher expected
concentrations (i.e., "near-field" measurements within the immediate
vicinity of PM sources).
4.5 SOURCE ACTIVITY MONITORING RESULTS
In many atmospheric dispersion modeling programs, source activity is often
estimated by apportioning annual production equally throughout the year. -8 The
ability to provide better resolution in source activity levels is important in meeting the
field program's second phase goal of model evaluation.
The August 31 meeting in Casper resulted in a decision to collect relatively
detailed records about major source activity levels on the days that the Cordero high-
volume network was operated. This was later extended to those days when saturation
sampling occurred. The following information was obtained for each shift during
approximately 15 days of source activity monitoring:
1. The location of each shovel in operation.
2. Number and size of haul trucks assigned to each shovel and route taken.
3. Number and general location of scrapers in use at the mine site.
Table 17 presents summary statistics for the number of trucks assigned per
shift to haul either overburden or coal during the 20 days that the Cordero air
monitoring or saturation sampler network was operated. Figure 7 presents a bar
graph of the number of haul trucks in use per either shift. Figure 8 presents the same
type of information for the mine's two scrapers and Figure 9 summarizes the number
of equipment in use during any one shift.
Both the table and the figures show that values considerably different form
long-term averages are quite likely. As noted earlier, better definition of source activity
levels is a major objective of the second phase of field studies.
52
-------
TABLE 17. SUMMARY STATISTICS FOR DAILY NUMBER
OF HAUL TRUCKS IN USE BY MATERIAL AND SHIFT
Material3
Pit
North
North
South
South
Shift
Day
Evening
Day
Evening
Coal
2.8 ± 1.6
2.4 ±1.9
0.55 ± 1 .4
0.35 ±1.1
Overburden
2.5 ± 1.6
2.4 ±1,7
0.75 + 1.3
0.60 ±1.4
a Entries represent mean + standard deviation of number
of trucks used.
53
-------
BAR GRAPH OF NO. OF HAUL TRUCKS IN USE (DAY SHIFT)
N = 19
VALUE COUNT PERCENT
0. 0 .00
1. 0 .00
2. 0 .00
3. 0 .00
4. 0 .00
5. 5 26.32 ^^•MMHMHMBM
6. 5 26.32 MMHHBMHMMHHHMBHI
7. 2 10.53
8. 3 15.79
9. 2 10.53
10. 1 5.26
11. 1 5.26
BAR GRAPH OF NO. OF HAUL TRUCKS IN USE (EVENING SHIFT)
N = 18
VALUE COUNT PERCENT
0. 0 .00
1. 0 .00
2. 0 .00
3. 0 .00
4. 0 .00
5. 5 27.78 ^•^••••MMMMMMMH"
6. 5 27.78 §««••••••••••••••••
7. 4 22.22 MMMBiMBMBBBHMB
8. 2 11.11
9. 2 11.11
Figure 7. Frequency distributions of number of haul trucks in use
during the day and evening shifts.
54
MRI-MVH9712-55
-------
BAR GRAPH OF SCRAPERS IN USE (DAY SHIFT)
N = 19
VALUE COUNT PERCENT
0. 3 15.79 •
1. 10 52.63
2. 6 31.58
BAR GRAPH OF SCRAPERS IN USE (EVENING SHIFT)
N = 18
VALUE COUNT PERCENT
0. 2 11.11 •
1. 10 55.56
2. 6 33.33
Figure 8. Frequency distributions of number of scrapers in use during
day and evening shifts.
MRI-MXR9712-55
55
-------
BAR GRAPH OF NO. OF HAUL TRUCKS IN USE (EITHER SHIFT)
VALUE
0.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
N =
37
COUNT PERCENT
0
0
0
0
0
10
10
6
5
4
1
1
.00
.00
.00
.00
.00
27.03
27.03
16.22
13.51
10.81
2.70
2.70
BAR GRAPH OF SCRAPERS IN USE (EITHER SHIFT)
VALUE
0.
1.
2.
N =
37
COUNT PERCENT
5 13.51
20 54.05
12 32.43
Figure 9. Frequency distribution of pieces of major equipment in use
over either shift.
56
MRI-M\R9712-55
-------
SECTION 5
REEXAMINATION OF CURRENT EMISSION FACTORS
The field study described in this report is a direct result of 1990 CAAA's
requirement to reexamine the accuracy of factors for fugitive particulate emissions at
surface coal mines. This section provides further discussion of the emission factors
presented in Section 3, focusing on how well available models predict observed
emissions.
As discussed in Section 3, the field tests conducted during the field exercise fall
into the following three broad classes:
Source Runs
Haul truck travel BB-2 through -43 in Table 9
Light-duty vehicle travel BB-44, -45, -48 in Table 9
Scraper travel BB-46, -47 in Table 9
Primary emphasis was placed upon haul truck traffic because the earlier emission
factor review2 stressed the need for new data for that source.
The performance of available emission factor models for each of these source
categories is discussed below.
5.1 OVERVIEW OF CURRENTLY AVAILABLE EMISSION FACTORS
An objective of this field program was to compare field measurements against
emission factors available for surface coal mines. In addition, the new and existing
emission factor data bases have been compared in an effort to determine whether the
two may be combined for the purpose of developing revised models.
Section 8.24 of EPA Publication AP-421—"Compilation of Air Pollutant Emission
Factors"—presents numerous predictive equations and single-valued emission factors
for use at western surface coal mines. The predicted equations in the AP-42 section
are based upon results from a series of field measurements9 conducted during the
late
57
-------
1970s and early 1980s. Figures 10 and 11 reproduce AP-42 Tables 8.24-2 and
8.24-4, respectively, and together the two tables represent official EPA guidance on
estimating particulate emissions from surface coal mines.
Figure 12 presents TSP emission factors recommended by the WDEQ for
surface mining activities.8 The WDEQ factor for haul roads is based on an old (1978)
version of the AP-42 "generic" unpaved road emission factor. As such, the WDEQ
factor is not based on a separate surface mine field measurement data set. In
addition, WDEQ includes a correction term in each factor to account for fallout of large
particles.
At least two points about the evaluation should be noted. First, the only
available factors for the PM-10 size range are based on a mean ratio of a factor for
different size fractions. The particle sizing data from the field program that resulted in
Figure 10 did not lend themselves to developing an "after-the-fact" factor once the
PM-10 size range was promulgated as the basis for NAAQS in 1987. Thus, it was
important to obtain independent TSP and PM-10 emission test data in order to assess
the adequacy of the available PM-10 emission factors.
Second, there have been great changes in what constitutes'"typical" conditions
at surface coal mines since the testing was conducted 10 or more years ago. For
example, since the time that the older data base was developed,
1. "Typical" haul trucks have greatly increased capacity.
2. There is far less general, light-duty traffic.
3. Permanent haul routes have been controlled for many years.
An earlier report2 presented the history of field testing at surface coal mines
and traced the development of the emission factors currently contained in Section 8.24
of AP-42. That report summarized candidate emission factors for different fugitive
dust sources at surface coal mines; that summary is presented as Table 18. Note
that, throughout this section, emission factors and other variables are described in
English units—such as pounds and miles—rather than metric (SI) units—such as
kilograms and kilometers.
5.2 HAUL TRUCK TRAVEL
Emissions from haul truck traffic were by far of the most interest in the present
study. This level of interest is warranted because prior studies show that haul roads
typically account for a very high portion of emissions from surface coal mining. •
Furthermore, as noted in the preceding discussion, haul trucks now have far greater
capacity and permanent coal haul routes are typically highly controlled, as compared
to the time when the older data base was assembled.
58
-------
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-------
TABLE 8.24-4. UNCONTROLLED PARTICULATE EMISSION FACTORS FOR
OPEN DUST SOURCES AT WESTERN SURFACE COAL MINES
Source
Dull lint
Topaoil raauval by
scraper
Overburden
replacement
Truck leadi&t by
pover shovel
(batch drep)c
Train leading (batch
or continuous drop)
lottos) due? truck
unloadini
(batch drop)1
End due? truck
ualoadini
(batch drop)'
Scraper ualeadint
(batch drop)e
Vind eroiioe of
exposed areas
Haterial Hine
location'
Overburden Any
Coal V
Topaoil Any
IV
Overburden Any
Overburden V
Coal Any
III
Overburden V
Coal IV
II!
II
I
Any
Coal V
Topaoil IV
Ueded land, Any
atripped over-
burden, frsded
overburden
TSP
aeUaeion
factor*
1.3
0.59
0.22
0.10
o.oss
0.029
0.44
0.22
0.012
0.0060
0.037
0.01B
0.02S
0.014
0.0002
0.0001
0.002
0.001
0.027
0.0)4
0.005
0.002
0.020
0.010
0.0)4
0.0070
0.066
0.033
0.007
0.004
0.04
0.02
0.38
OtK
.63
Emission
Units Factor
Ratio*
Ib/bolr
kt/bole
Ib/bolr
ks/hole
Ib/T
k»/Ht
Ib/T
kt/Ht
Ib/T
k*/Mt -
Ib/T
kt/ttf
Ib/T
kf /Kg
lb/T
kt/IK
lb/T
kj/T
lb/T
k»/H|
lb/T
«*/«»
lb/T
kfVHg
lb/T
lb/T
kl/Bt
lb/T
kt/»t
lb/T
k»/ttt
T
Nl
(bectarejjyr)
,'
t
E
E
E
E
D
0
C
•c
c
c
D
D
D
C
E
E
E
E
E
E
E
E
D
D
D
D
E
E
C
C
C
£
Xoenn auecrali 1 through V refer to apecific Bint location! for.whirfi the
correapoadiai eaiiaiiea factora vere developed (Reference 4). Tablen *.2*-'
aid 1.24-5 preaut characteriatica of each of tbeac aiino. We texlt for
correct, uae of ta*ee "mi»e specific" eaiaaion factor!. The other factors
(fro* Reference 5 except for overburden drillint fro* Keferencr 1) tan be
applied to any w.atem surface coal mine.
Total suspended paniculate (TSP) denotes what ii Bcasured by a standard nifh
volume sassier (see Section 11.2).
Predictive esdsiiea factor equation*, which fenerally provide Bore accurate
•stiiutei of aoissiou, *re preaented in Chapter 11.
8.24-8
EMISSION FACTORS
9/88
Figure 11. Photocopy of Table 8.24-4 of AP-42.
MRI.«.ft9712.SS
60
-------
STArr or UTOHIIK:
*?' * y.i.i jyLT0?.. H";! T ' YJL SWTJUJ! »sioH_,FACTQia
JOR iiiH.ii'ji; *''rctviit^ j«imnrv, 197*
(r*rlfciil*te •!» 30 we. at«l ••Mllar, n« fallout fnnettn* required)
Coot rol
Cunt r.ol Technique F.I f If letter
.
0. OA Ih/y.t' (I ) * 0.75
. n.OIII./t(3l.3^W)lb/mi(3}
365
, x 0.62 itippreaunt MX
Acce«« RoaJe 7C • O.BUlt p«vlnr. or en«nl lil
J6S b, JtBhIll»tlon o( t.n*»
z 0.62 with chip ind aeil •nrfncB 701
3. Haul ta*J «rp«Ir and
C0f»tr-n -ton
Ci ... *32 lh«/hr<2) v.t.rln, 501
[]2 lWlir<2) u.t.rln, SOI
Coal-Truek/Sliovel 0.001 Ib/ton(t) » 0.70
Co* 1-From end Lontlvf JO.CKI1 Ib/ton « 0.70
Ur»ftlw»-»ontcnd Lo«iJ«r 'O.OOJ Ik/ton
O.ni7 lb/ten(l) x 0.73 *. coil-water »pr«y*
'0.017 Ik/ ten
h. co»l-
30 Ih/bl.Mtd) ' 0.75
35 lb/bl*»t{}} « 0.?5
2. Fro- Reference 3 t- O.I1»(S/30) f 365-«)lbi/VHT
wh«r« • • ellt content of tout! aurface Mterlel(Z)
S » vclitcl* apeed In etph
.actor vlioitld be *iiiared for *p*eJ« 1*»* tluo 3D "fib
road mate
Fre* Reference 4 Z - AIICl.'V cen/acre/yr Soil Type A 1. tao/eere/yr
where A - portion of loaaei which become euependeJ Rocky, Ccaveily 0.0?$ 31
I * soil erotllbllltr Somly 0.010 134
K - i-irfaci rc-nlmo.. fietor Fine 0.041 31
C - cllMtlc factor CUy Loa- 0.023 *'
L'- tmiheltared flald width factor K _ vnrl«» fretm 0 5 to 1 O- t 0 1* nartully tt»e.l
- 0.7 for 1000' 4 1.0 for 20OO* and fe.cer c . Tnble 3.u el'„{„*'„<:'. or C - 0.3*5 (.-') t (r-S)^
V - -rotative cover factor (u.e » - 1.0) vhe(> u . mwt^ vtn
Reference 1 of 10 to 20 tlMi «or« {leader vt. altov«l) were not reaeonnblc. tliua the selection of 0.003 Ilia/ton.
i. Given tl
fl* t IIM 11
7. Cttlaut* only - not •«*lured, lie eorrectlofl )• twdt for Z luapcrwicd mater 1*1 •• darn !• not available.
8. 1.2 u Ih/ecre/hour wliere u la wind ..peed In »/•*(. Factor IncJudea B0<<>e tint activity aroimj ami mi ptlet.
9. Ttom Reference 3 I - 0.03<»/l.S)C» tnv 1 ronMntaI. Inc.. May. 1976.
(3 ' 41 "Cott-pllatlon of Air Foltutant Cat*»Ion Factor! (StipplaMnca 1-8)", Key, 197B.
(4) l-^K-« .. ., "Evaluation mt Fwflttv* Dwat CailBfl««Ma Cr«* HlKlot". by FIDCa CMvlra-Mental, Inc.. Aarll. 1976.
(3) C. Cowherd end K.V. OeWrlka. "»a«al,>f.M«it af Fugitive Dwat blaatM Factara far iNdwttrfal Source*", Taper llo.
78-33.A. Annual He*tl»g Air PallwtlM Ca«tral AaMciatian. MatMtaei. T««aa (Jw«e. 1«71).
Figure 12. WDEQ emission factors for surface mining activities
(as taken from Reference 8).
61
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63
-------
Figure 13 compares a variety of measurements from the current study (i.e., the
"new" data set) to the study6 that led to the factors in Figure 10. Throughout the
remainder of this report, the "old" data set refers to those measurements supporting
the AP-42 emission factors (i.e., those presented in Reference 9). The PM-10 and
TSP data sets for haul trucks are given in Appendix B. Figure 13 contains several
"box and whisker" plots which are convenient means comparing different samples.
Each box contains 50% of the observations in a data set. (For example, half of the
old data set's PM-10 emission factors lie between approximately 1 and 4 Ib/VMT.)
The median is represented by the tick mark within the box. The whiskers on each box
typically span the range of values observed, except if any values are "outside" certain
limits. In that case, outliers are shown as either "0" or "*" and the whiskers denote the
range of the values that are not outliers. Finally, parentheses denote approximate
95% confidence intervals for the median.
Keeping the explanation of box plots in mind, one can see from Figure 13 that
the measured PM-10 emission factors do not differ substantially from one data set to
another. TSP emission factors show greater differentiation from one set to the other.
As a group, emission factor values from the new set tend to be slightly greater than
emission factors in the older set.
Figure 13 also shows that there are very substantial differences between the
"source conditions" in the two data sets. In keeping with the trend toward larger haul
trucks, average vehicle weights observed in the current program are two to three
times greater than in those tests supporting the AP-42 Section 8.24 emission factors.
Silt loading and silt content values do not differ as dramatically as do the
vehicle weights; nevertheless, the means in the old and new data sets differ at the 5%
level of significance. Furthermore, the mean vehicle speed in the new data set is
significantly (at 5% level) lower than in the old data set.
Figure 13 shows that even though the two sets of emission factors are
somewhat comparable, similarity in source conditions is not the reason. The new data
set has heavier haul trucks; all other things being equal, one would expect the new
emission factors to be greater than the old factors. On the other hand, the new data
set contains "cleaner" road surfaces and slower-moving vehicles; all other things being
equal, one would now expect the new factors to be lower than the old factors.
To better understand how well available emission factors predict measured
emission levels, a series of comparisons was undertaken. The old and new
measured emissions were compared against values estimated from three different
emission factors:
64
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65
-------
1. Haul truck factor in AP-42 Section 8.24:
e
= 0.0031 (W)3'5
= 0.0067 (w)3'4 (L)0-2
10
eTSP
wheree10, eTSP = PM-10 or TSP emission factor (Ib/VMT)
w = mean number of wheels
2.
L = surface silt loading (g/rrr)
The "generic" unpaved road emission factor in AP-42 Section 11.2.1:
e =k(5.9)UL -T. Ul Ui
where: e
k
s
S
W
w
PM-10 or TSP emission factor (Ib/VMT)
0.36 for PM-10, 0.80 for TSP)
surface silt content (%)
mean vehicle speed (mph)
mean vehicle weight (tons)
mean number of wheels per vehicle
3. The WDEQ-recommended haul truck factor:
e = 0.81 s .
where: e = TSP emission factor (Ib/VMT)
s = surface silt content (%)
S = mean vehicle speed (mph)
The WDEQ factor for haul roads is based on an old (1978) version of the
AP-42 "generic" unpaved road emission factor. As such, the WDEQ factor is not
based on a separate surface mine field measurement data set. Note that because the
emission factor is being compared against near-source measurements, the 0.62
correction term has not been applied.
The ability of various emission factor models to predict measured emission
factors is illustrated in Figures 14 through 23.
66
-------
2000
1000
500
200
100
50
8! 20
£ 10
0.5
0.2
Comparison of AP-42 Section 8.24 Estimate
Against Observed PM-10 Emission Factor
0 = "Old" data set supporting Section 8.24
X = "New" data set collected in this study
Summary Information on Predicted-to-Observed Ratio
Geometric Mean
Maximum
Minimum
No. of Cases
0.2
0.5
500 1000 2000
OBSERVED EMISSION FACTOR (Ib/VMT)
Figure 14. Comparison of measured emission factors against PM10
Section 8.24 haul road estimates for the old data sets.
Centerline represents perfect agreement, other two lines
are factor-of-three bounds.
MRI-MXR9712-55
67
-------
2000
1000
500
200
*"%
>—
1 100
£
*^
g so
8 20
S 10
o
1
0.5
0.2
Comparison of AP-42 Section 8.24 Estimate
Against Observed PM-10 Emission Factor
0 * "Old" data set supporting Section 8.24
X « "New" data set collected in this study
Summary Information on Predicted-to-Observed Ratio
Geometric Mean
• Maximum
Minimum
No. of Cases
0.2
0.5
50
2 5 10 20
OBSERVED EMISSION FACTOR (Ib/VHT)
100
200
500 1000 2000
Figure 15. Comparison of measured emission factors against PM10
Section 8.24 haul road estimates for the new data sets.
Centerline represents perfect agreement, other two lines
are factor-of-three bounds.
68
MRI-M\R9712-5S
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o
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K-
u
I
2000 i
1000 I
i
500 I-
i
i
200
100 i-
50 }•
I
i
I
20 |
10 I-
0.2
0.2
Comparison of AP-42 Section 8.24 Estimate
Against Observed TSP Emission Factor
0 = "Old" data set supporting Section 8.24
X = "New" data set collected in this study
0.5
Summary Information on Predicted-to-Observed Ratio
Geometric Mean 1.68
Maximum 34.8
Minimum 0.35
No. of Cases 27
2 5 10 20 50 100 200
OBSERVED EMISSION FACTOR (Ib/VMT)
500 1000 2000
Figure 16. Comparison of measured emission factors against TSP
Section 8.24 haul road estimates for the old data sets.
Centerline represents perfect agreement, other two lines
are factor-of-three bounds.
MRI-M\R9712-55
69
-------
2000
1000
Comparison of AP-42 Section 8.24 Estimate
Against Observed TSP Emission Factor
"Old" data set supporting Section 8.24
"New" data set collected in this study
Summary Information on Predicted-to-Observed Ratio
Geometric Mean
Maximum
Minimum
No. of Cases
1000 2000
OBSERVED EMISSION FACTOR (Ib/VMT)
Figure 17. Comparison of measured emission factors against TSP
Section 8.24 haul road estimates for the new data sets.
Centerline represents perfect agreement, other two lines
are factor-of-three bounds.
70
MR|.M\R9712-55
-------
Comparison of AP-42 Section 11.2.1 "Generic*
Model Against Observed PM-10 Emission Factor
0 = "Old" data set supporting Section 8.24
X = "New" data set collected in this study
Summary Information on Predicted-to-Observed Ratio
Geometric Mean 4.46
Maximum 176
Minimum 0.86
No. of Cases
0.2
2 5 10 20 50
OBSERVED EMISSION FACTOR (Ib/VMT)
100 200
500 1000 2000
Figure 18. Comparison of measured emission factors against PM10
Section 11.2.1 estimates for the old data sets. Centerline
represents perfect agreement, other two lines are factor-of-
three bounds.
MR|.M\RS712.55
71
-------
IT
"T
T
2000
1000
500
200
100
I
g 50
«<
U.
S
S 20
i
S 10
u
o
i
0.5
0.2
Comparison of AP-42 Section 11.2.1 "Generic"
Model Against Observed PM-10 Emission Factor
0 = "Old" data set supporting Section 8.24
X = "New" data set collected in this study
Geometric Mean
Maximum
Minimum
No. of Cases
Summary Information on Predicted-to-Observed Ratio
J_
_L
J_
_L
_L
J_
0.2
0.5
2 5 10 20 50
OBSERVED EMISSION FACTOR (Ib/VHT)
100
200
500 1000 2000
Figure 19. Comparison of measured emission factors against PM10
Section 11.2.1 estimates for the new data sets. Centerline
represents perfect agreement, other two lines are factor-of-
three bounds.
72
MHI-M\H9712 SS
-------
2000
1000
500
200
100
50
20
£ 10
o
0.5
0.2
Comparison of AP-42 Section 11.2.1 "Generic1
Model Against Observed TSP Emission Factor
0 = "Old" data set supporting Section 8.24
X = "New" data set collected in this study
Geometric Mean 3.11
Maximum 104
Minimum 0.81
No. of Cases 26
Summary Information on Predicted-to-Observed Ratio
_L
_L
0.2
0.5
2 5 10 20 50
OBSERVED EMISSION FACTOR (Ib/VMT)
100
200
500 1000 2000
Figure 20. Comparison of measured emission factors against TSP
Section 11.2.1 estimates for the old data sets. Centerline
represents perfect agreement, other two lines are factor-of-
three bounds.
73
-------
2000
1000
500
200
f+
1 100
g 50
5
u.
g
3
u
1
20
10
0.5
0.2
Comparison of AP-42 Section 11.2.1 "Generic"
Model Against Observed TSP Emission Factor
0 » "Old" data set supporting Section 8.24
X = "New" data set collected in this study
Summary Information on Predicted-to-Observed Ratio
Geometric Mean
Maximum
Minimum
No. of Cases
1.06
2.44
0.44
22
JL
_L
JL
_L
_L
J_
0.2
0.5
2 5 10 20 50
OBSERVED EMISSION FACTOR Ub/VMT>
100 200
500 1000 2000
Figure 21. Comparison of measured emission factors against TSP
Section 11.2.1 estimates for the new data sets. Centerline
represents perfect agreement, other two lines are factor-of-
three bounds.
74
MRI-M\H9712-55
-------
U
2000
1000
500
200
100
50
20
10
1
0.5
0.2
Comparison of Wyoming DEQ-recommended Model
Against Observed TSP Emission Factor
0 = "Old" data set supporting Section 8.24
X = "New" data set collected in this study
I L
0.2
Summary Information on Predicted-to-Observed Ratio
Geometric-Mean 0.54
Maximum 13.1
Minimum 0.11
No. of Cases 26
_L
0.5
_i_
2 5 10 20 50
OBSERVED EMISSION FACTOR (Ib/VMT)
J U
100 200
500 1000 2000
Figure 22. Comparison of measured emission factors against
Wyoming suspended haul road estimates for the old data
sets. Centerline represents perfect agreement, other two
lines are factor-of-three bounds.
MHI-M\R9712-55
75
-------
g
5
5
g
(A
t/>
is
u
i
g
2000
1000
500
200
100
50
20
10
1
0.5
0.2
Comparison of Wyoming DEQ-recommended Model
Against Observed TSP Emission Factor
0 s "old" data set supporting Section 8.24
X = "New" data set collected in this study
Summary Information on Predicted-to-Observed Ratio
Geometric Mean 0.11
Maximum 0.24
Minimum 0.04
Ho. of Cases 22
0.2 0.5
10 20
50 100 200
OBSERVED EMISSION FACTOR (Ib/VMT)
J_
500 1000 2000
Figure 23. Comparison of measured emission factors against
Wyoming suspended haul road estimates for the new data
sets. Centerline represents perfect agreement, other two
lines are factor-of-three bounds.
76
MRI-M\R9712-55
-------
Particle Emission factor Figure
size range Data set model No.
PM-10 old AP-42 Section 8.24 14
PM-10 new AP-42 Section 8.24 15
TSP old AP-42 Section 8.24 16
TSP new AP-42 Section 8.24 17
PM-10 old AP-42 Section 11.2.1 18
PM-10 new AP-42 Section 11.2.1 19
TSP old AP-42 Section 11.2.1 20
TSP new AP-42 Section 11.2.1 21
TSP old WDEQ factor3 22
TSP new WDEQ factor3 23
See Figure 12.
The performance of each model is discussed briefly below.
Haul road models in AP-42 Section 8.24 (Figures 14 through 17)—The
discussion will be largely limited to comparisons with the new data set because the
older data set was used to develop the Section 8.24 models. In general, these
models tend to underpredict the newer independent data set, with the degree of
underprediction becoming more pronounced for the higher observed emission factors.
For both PM-10 and TSP, the Section 8.24 models yield predicted emission factors in
a fairly tight band. This is especially true for PM-10—all predicted factors lie between
roughly 1 and 2 Ib/VMT, while observed factors span an additional order of magnitude.
The tight band of predicted values is due to the fact that the Section 8.24 model
includes only the mean number of wheels as a correction parameter, and that
parameter did not vary substantially in the new data set. In summary, the
Section 8.24 models in their present state substantially underestimated the newer set
of measured haul road emission factors. This may not be particularly surprising, given
that Figure 13 showed that some underlying source conditions are far different
between the two data sets.
"Generic" unpaved road model in AP-42 Section 11.2.1 (Figures 18 through
21)—Because none of the old data set was used to develop the generic unpaved road
models, Figures 18 through 21 all represent independent applications of the unpaved
road emission factor model. In general, the source conditions of the two data sets are
outside the range of source conditions in tests supporting the Section 11.2.1 models.
In each comparison, the Section 11.2.1 model tends to overpredict observed haul
truck emission factors. The degree of overprediction is less pronounced for the new
data set as compared to the old. For only the TSP emissions in the new data set (i.e.,
Figure 21) can the performance of the generic unpaved road emission factor be
considered reasonably acceptable.
77
-------
Wyoming DEQ haul road model (Figures 22 and 23)—As was the case earlier,
the comparisons in Figures 22 and 23 represent independent applications of the
WDEQ haul truck factor. As noted earlier, the WDEQ factor is essentially the same as
an old version of the AP-42 generic unpaved road equation. There is a trend to
underpredict observed emission factors, especially for the new data set. The
underprediction results even though the WDEQ's fallout correction factor has not been
applied; had the correction been applied, the already too low estimates would be
approximately 40% lower still.
In addition to the predictive equations discussed above, Table 16 presents two
other candidate TSP emission factors for haul trucks. The single-valued factor of
246.8 Ib/VMT (taken from Reference 10) represents extreme overprediction for each
data set considered. On the other hand, the expression based upon road waterings
per hour12 appears to predict the new data set fairly well, as shown below:
Ratio of predicted-to-observed TSP
emission factors
No. of
cases
Geometric
mean
0.715
1.12
0.915
Standard geometric
deviation
2.30
2.09
2.22
Uncontrolled 10
Controlled 12
Overall 22
However, when this factor is applied to tests in the historical data base, the
resulting comparisons are not as favorable. For the "old" data set of Reference 9
which supports AP-42 Section 8.24, the mean predicted-to-observed ratio is 1.77, with
a standard geometric deviation of 2.61. Only uncontrolled tests can be readily
compared because the test report does not contain the information necessary to
compute road watering frequency.
None of the available emission factor models appear fully capable of accurately
predicting independent haul truck emission data, as a whole. This is especially true
for the PM-10 size range. The issue is further discussed in Section 5.4 of this report.
5.3 LIGHT- AND MEDIUM-DUTY TRAFFIC
The review of emission factors2 recommended that the "generic" unpaved road
equation in AP-42 Section 11.2.1 replace the current factor for light- to medium-duty
vehicle emission factor in Section 8.24. That recommendation was based on a
comparison against independent emission data.
78
-------
This study provided new independent test data against which the recommended
factor could be evaluated. A total of three light-duty vehicle emission tests
(Runs BB-44, -45, and -48) were conducted with captive traffic during the present
study. (The old and new light- to medium-duty vehicle data sets are presented in
Appendix B.) Although the AP-42 Section 8.24 model can produce very accurate
estimates in many cases, the same model has been found to be capable of providing
a very unacceptable estimate. This is believed to be the result of the model's
dependence on the fourth power of moisture content (see Figure 10). For all three
light- to medium-duty vehicle tests conducted int he present study, the moisture
contents were outside the range in the supporting data set.
Table 19 compares the new test results to estimates obtained from the
recommended "generic" model.
TABLE 19. RESULTS FROM LIGHT-DUTY VEHICLE EMISSION
TEST COMPARISONS
Size
range
PM-10
PM-10
PM-10
TSP
TSP
TSP
Run
BB-44
BB-45
BB-48
Geometric mean
BB-44
BB-45
BB-48
Geometric mean
Measured
emission
factors
(Ib/VMT)
0.25
0.078
0.12
0.13
1.3
0.60
0.49
0.72
AP-42 Section
11.2.1
estimates
(Ib/VMT)
0.24
0.26
0.26
0.25
0.54
0.58
0.58
0.57
Predicted
to
observed
ratio
0.976
3.35
2.19
1.91
0.426
0.960
1.19
0.786
The generic unpaved road emission factor model in AP-42 Section 11.2.1
appears to produce acceptable predictions over a broad range of source conditions.
In fact, when the generic unpaved road emission factor is applied to the combined
light- and medium-duty data sets, the following mean ratios are obtained:
Predicted-to-observed ratio
Size range
PM-10
TSP
No. of cases
14
14
Geometric
mean ratio
1.08
0.839
Std. geometric
deviation
3.08
2.78
79
-------
The above comparisons indicate that the generic unpaved road emission factor
model can provide very acceptable estimates for light- to medium-duty traffic at
surface coal mines.
5.4 SCRAPERS
Two tests (Runs BB-46 and -47) were conducted of emissions from scrapers in
a travel mode. (The combined scraper data sets are presented in Appendix B.)
Measured emissions were compared to:
1. The scraper emission factor model given in AP-42 Section 8.24.
2. The generic unpaved road emission factor model in Section 11.2.1 of
AP-42.
Table 20 presents the results from these comparisons.
TABLE 20. RESULTS FROM SCRAPER EMISSION TEST COMPARISONS
Ratio of predicted to observed
Size range Run AP-42 Section 8.24 AP-42 Section 11-2.1
PM10 BB-46
BB-47
Geometric mean
0.374
0.624
0.482
0.897
1.57
1.18
Although Table 20 suggests that the generic unpaved road model might be preferable
to the current scraper expression in AP-42 Section 8.24, that conclusion does not
appear to be warranted upon further examination. When applied to the combined
scraper emission data sets, the generic unpaved road equation substantially
overpredicts observed emissions:
Predicted-to-observed ratio
Size No. of
range cases
Geometric
mean
2.43
1.50
Std. geometric
deviation
3.52
3.21
PM-10 15
TSP 13
Unlike haul trucks and haul routes, current scraper activities at surface coal
mines are not substantially different from the time when the old data set was
80
-------
assembled. Based on the limited data available for comparison, the Section 8.24
scraper model is recommended for emission estimation purposes. A recent update to
AP-42 Section 11.2 has placed a high priority on the need for additional emission field
testing of earthmoving activities.13
5.5 REVISION OF HAUL TRUCK EMISSION FACTOR
Section 5.2 found none of the currently available emission factor models to be
fully capable of accurately estimating independent emission data, especially for the
PM-10 size range. This finding may not come as a surprise because of the
differences in the data sets. Although emission factors are fairly comparable, there
are very pronounced differences in source conditions between the data sets. The new
haul truck data set is characterized by higher mean vehicle weights, slower speeds,
and lower silt loadings.
A primary purpose of any emission factor is to project what emission levels will
be in the future. For that reason it is important that emission factors reflect the source
conditions that can be expected in the future. Even though Section 5.2 found a few
combinations of measured and predicted emissions that agreed fairly well, the
favorable comparisons were certainly not the result of similarities in underlying source
conditions. For example, the generic unpaved road emission factor model performed
quite acceptably in estimating the new TSP emission data set. Nevertheless, this
does not occur because the new source conditions are similar to those in the
underlying data base. The new tests' mean vehicle weights can be twice as high as
the highest value in the supporting data base. Furthermore, even though all vehicle
weights are less than that maximum, the generic factor substantially overpredicts the
old data set.
To better estimate haul truck emissions in the future, this study developed a
new model from the test data. Although one would prefer to assemble a larger data
base, it was not believed that the differences between the two data sets allowed them
to be combined. By using only newer test data, it is believed that the resulting
emission factor would best match source conditions that can be expected at surface
coal mines in the future.
Stepwise multiple linear regression was used to develop a predictive model with
the final data set. The potential correction factors include:
- surface silt content, s
- surface moisture content, M
- silt loading, sL
- mean vehicle weight, W
- mean vehicle speed, S
- mean number of wheels, w
All variables were log-transformed in order to obtain a multiplicative model as in the
past. Figure 24 presents the correlation matrix of the log-transformed independent
and dependent variables. The most notable features of the correlation matrix are the
high degree of interdependence between silt content and the emission factors,
81
-------
PEARSON CORRELATION MATRIX
LEF10
LSILT
LSL
LH01ST
LTONS
LHPH
LWHLS
LEF10
1.00
0.76
0.71
-0.16
0.29
0.11
0.34
NUHBER OF OBSERVATIONS: 34
LEFTSP
LSILT
LSL
LHOIST
LTONS
LHPH
LWHLS
LEFTSP
1.00
0.82
0.74
-0.03
0.38
0.18
0.47
NUHBER OF OBSERVATIONS: 22
LSILT
1.00
0.85
0.05
0.35
0.04
0.43
LSILT
1.00
0.83
0.07
0.33
-0.02
0.51
LSL
1.00
0.04
0.25
0.24
0.25
LSL
1.00
-0.00
0.29
0.13
0.38
LMOIST
1.00
0.25
-0.40
-0.04
LHOIST
1.00
0.30
-0.43
0.09
LTONS
1.00
-0.46
0.75
LTONS
1.00
-0.39
0.76
LMPH LWHLS
1.00
-0.35
LMPH
1.00
LWHLS
1.00
-0.33 1.00
Figure 24. Correlation matrix of log-transformed emission factors
and independent variables.
82
MRI-M\R9712-55
-------
together with the low degree of interdependence between silt and moisture. This
suggests that silt and moisture contents may be effectively used to derive an emission
factor model. Stepwise regression of the data did indeed result in a PM-10 model of
that type.
Several points should be noted about the regression results. First, the
expression for PM-10 was considered first so that models comparable over the
different size ranges would result.
Second, during an exploratory phase, it was found that a TSP model based
solely on a stepwise regression of the data would result in a model involving silt,
weight, and speeds. In addition, the independent variables were found to be raised to
powers fairly comparable to those in the generic unpaved road model in AP-42
Section 11.2.1. This is in keeping with the results shown in Figure 21. Nevertheless,
to maintain similarity between the different emission factor models, the final TSP
model was derived to resemble the PM-10 model.
The following equation presents the final recommended emission factor models.
where e is emission factor in Ib/VMT, s is silt content (%), M is moisture content (%},
and k, a, and b are given in Table 21.
TABLE 21. RECOMMENDED EMISSION FACTOR MODELS
Size range
PM-10
TSP
Sample size
34
22
k
3.4
16
a
0.8
0.9
b
-0.2
-0.2
Multiple R2
0.611
0.660
83
-------
Both models "explain" roughly 60% of the variability in the emission factors.
Because of the differences in the old and new data sets, no attempt was made
to apply the revised factors to the older, independent data. However, another type of
validation study was undertaken to assess the predictive capability of the revised
emission model for PM-10. This employed a standard cross-validation (CV)
technique. Using this technique, each point in the underlying data base is excluded
one at a time, and the equation generated from the reduced data base is used to
estimate the missing value. This method generates "quasi-independent" comparisons.
By using a CV technique, "n" quasi-independent estimates are obtained from a
data base of "n" tests, and the overall validity of using stepwise regression to obtain a
model of the form
is evaluated. Summary information is shown in Table 22.
TABLE 22. RESULTS OF CROSS-VALIDATION STUDY
Variable Minimum Maximum Mean Std. deviation
a
b
k
Exponent of silt
Exponent of moisture
Leading term
Ratio of quasi-
0.793
-0.323
3.32
0.345
0.878
-0.182
3.54
3.03
0.827
-0.223
3.43
0.991 a
0.021
0.026
1.0373
1.73a
independent estimate
to measured emission
factor
a Geometric mean/standard deviation.
Figure 25 presents the cumulative frequency distribution of the ratio of the
quasi-independent estimate obtained from the cross validation to the measured PM-10
emission factor. A little over 80% of the estimates are within a factor of 2 and
approximately 35% are within a factor of 1.5. The 95% confidence interval
corresponds to a factor of approximately 2.4. Results for the TSP model cross-
validation are similar, with 73% within a factor 2.
84
-------
CUMULATIVE FREQUENCY DISTRIBUTION
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
o
u
o
o
a
H-
a
c
>.
~z
(VI
H-
o
(_
o
(M
*•-
O
Note:
"Quasi-independent" comparisons
eliminate each data point (one at
a time) and use the remaining data
to estimate the missing value.
0.2
0.3 0.4 0.5
1.5
RATIO OF QUASI-INDEPENDENT ESTIMATE TO MEASURED PM-10 EMISSION FACTOR
Figure 25. Cumulative frequency of the ratio of the quasi-independent estimate
to the measured PM10 emission factor.
MHI-M\R9712-55
85
-------
Finally, the nonparametic runs test14 was used to examine the residuals (error in
prediction). This exercise found a tendency for the recommended emission factor
model to underestimate measured emissions at fairly high silt contents.
Readers should keep in mind that the recommended emission factor model tends to
overpredict for silt contents in excess of 12 to 15%. This tendency to overestimate
measured emissions is not considered a major limitation because silt contents that
high are relatively infrequent. All haul road silts used in Reference 8 were less than
10%. Appendix B indicates that one silt content in 9 exceeds 12%. Furthermore, silt
contents reported in Reference 7 are also well below 10%.
86
-------
SECTION 6
REFERENCES
1. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission
Factors (AP-42), Research Triangle Park, North Carolina, September 1985.
2. U.S. Environmental Protection Agency, Review of Surface Coal Mining
Emission Factors, EPA-454/R-95-007, July 1991.
3. U.S. Environmental Protection Agency, Development of a Plan for Surface Coal
Mine Study, EPA-454/R-95-008. October 1991.
4. U.S. Environmental Protection Agency, Surface Coal Mine Study Plan:
Phase I—Emission Factor Improvement, EPA-454/R-95-009, March 1992.
5. Baxter, T. E., D. D. Lane, C. Cowherd, Jr., and F. Pendleton, "Calibration of a
Cyclone for Monitoring Inhalable Particles," Journal of Environmental
Engineering, Vol. 112, No. 3, June 1986, pp. 468-478.
6. Davies, C. N., "The Entry of Aerosols in Sampling Heads and Tubes," British
Journal of Applied Physics, 2:921, 1968.
7. Cole, C. F., B. L. Murphy, J. S. Evans, and A. Garsd, Quantification of
Uncertainties in EPA's Fugitive Emissions and Modeling Methodologies at
Surface Coal Mines, TRC Environmental Consultants, February 1985.
8. Vardiman, S., and K. Winges, Powder River Basin Model Validation Analysis,
TRC Environmental Consultants, Englewood, CO, August 1991.
9. U.S. Environmental Protection Agency, Improved Emission Factors for Fugitive
Dust from Western Surface Coal Mining Sources, EPA-600/7-84-048, Two
Volumes, March 1984.
10. Ettinger, W. S., and R. E. McClure, Fugitive Dust Generation on a Southern
West Virginia Surface Coal Mine, APCA Specialty Conference on Fugitive Dust
Issues in the Coal Use Cycle, April 1983.
87
-------
11. Muleski, G. E., Unpaved Road Emission Impact, Report for Arizona Department
of Environmental Quality, March 1991.
12. Shearer, D. L, R. A. Doughterty, C. C. Easterbrook, Coal Mining Emission
Factor Development and Modeling Study, TRC Environmental Consultants,
July 1981.
13. Henk, B., and G. E., Muleski, Background Documentation for AP-42
Section 11.2.4, Heavy Construction Operations (Draft), EPA Contract No. 68-
DO-0123, April 1993.
14. Mostfeller, F., and J. W. Turkey, Data Analysis and Regression,
Addison-Wesley, Reading, Massachusetts.
88
-------
APPENDIX A
AMBIENT AIR QUALITY MONITORING RESULTS
This appendix presents air quality monitoring results reported by Inter-Mountain
Laboratories. The results are for the period September through October 1992.
Pages A-3 through A-15 contain the results for the days when Cordero personnel
operated the mine's sampling network and page A-16 presents results for days when
MRI personnel operated the network.
A-1
-------
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
SAMPLER ID
YEAR 1992
AREA
0080
SITE
888
PRO
J02
DATE
09/03/92
09/09/92
09/15/92
09/21/92
09/27/92
* low flow rate
** corrected flow
*** uncorrected flow
CONG
UG/M3
37
21
37
FILTER
NO.
36157
36161
36165
36170
36174
FLOW**
CMM
1.4659
1.5736
1.4857
INVALID :
INVALID :
FLOW***
CMM
1.5778
1.6756
1.6041
TIME
MIN
1425.0
1431.5
1432.1
NO TRANSDUCER
VOL
CM
2089
2253
2128
READING
GROSS
G
3.
3.
3.
7799
7383
7651
TARE
G
3.
3.
3.
7028
6919
6868
0
0
0
NET
G
.0771
.0464
.0783
TRANSDUCER FAILED
September
Quarter
Arithmetic
Mean
32
32
Geometric
Mean
31
31
A-3
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES,
CONSULTANT
SHERIDAN, WY.
INC.
SAMPLER ID
AREA
0080
SITE
BFM
PRO
HV2
YEAR 1992
CONG FILTER
DATE UG/M3 NO.
09/03/92 36158
09/09/92 36163
09/15/92 . 36167
09/21/92 36169
09/27/92 25 36172
* low flow rate
** corrected flow
*** uncorrected flow
FLOW** FLOW*** TIME VOL GROSS TARE
CMM CMM MIN CM G G
INVALID: RUNTIME NOT MIDNIGHT TO MIDNIGHT
INVALID: RAN ON WRONG DAY
INVALID: INSUFFICIENT RUNTIME
INVALID: START TIME 3:30 A.M.
1.6878 1.7861 1440.0 2430
NET
G
3.7401 3.6800 0.0601
September
Quarter
Arithmetic
Mean
25
25
Geometric
Mean
25
25
A-4
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
SAMPLER ID
YEAR 1992
AREA
0080
INTER-MODNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
SITE
BFM
PRO
HV2A
CONG
DATE UG/M3
09/03/92 46
09/09/92 44
09/15/92 55
09/21/92 43
09/27/92 32
* low flow rate
** corrected flow
FILTER
NO.
36159
36162
36166
36168
36173
FLOW**
CMM
1
1
1
1
1
.5041
.5982
.5700
.4226
.4231
FLOW*** TIME
CMM
1.6183
1.7011
1.6944
1.5217
1.5060
MIN
1420.4
1440.2
1439.8
1439.9
1440.2
VOL
CM
2136
2302
2261
2048
2050
GROSS
G
3
3
3
3
3
.7967
.7943
.8091
.7712
.7524
TARE
(3
3
3
3
3
3
.6991
.6932
.6847
.6832
.6868
NET
0.0976
0.1011
0. 1244
0.0880
0.0656
*** uncorrected flow
September
Quarter
Arithmetic
Mean
44
44
Geometric
Mean
43
43
A-5
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES, INC,
CONSULTANT
SHERIDAN, WY.
SAMPLER ID
YEAR 1992
AREA
0080
SITE
889
PRO
J02
DATE
09/03/92
09/09/92
09/15/92
09/21/92
09/27/92
CONG
UG/M3
26
56
30
46
18
FILTER
NO.
36156
36160
36164
36171
36175
FLOW**
CMM
1.5776
1.6256
1.5780
1.6083
1.6256
FLOW***
CMM
1.6971
1.7300
1.7027
1.7199
1.7199
TIME
MIN
1420.1
1414.9
1416.8
1415.0
1440.7
VOL
CM
2240
2300
2236
2276
2342
GROSS
TARE
NET
G G G
3.7485 3.6910 0.0575
3.8215 3.6937 0.1278
3.7527 3.6856 0.0671
3.7856 3.6802 0.1054
3.7180 3.6761 0.0419
* low flow rate
** corrected flow
*** uncorrected flow
September
Quarter
Arithmetic
Mean
35
35
Geometric
Mean
32
32
A-6
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
SAMPLER ID
YEAR 1992
AREA
0080
SITE
PM2
INTER-MOUNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
PRO
CONC
DATE UG/M3
09/03/92 12
09/09/92 13
09/15/92 20 •
09/21/92 11
OS/27/32 3
* low flow rate
** corrected flow
FILTER
NO.
35971
35973
35975
35985
35977
FLOW**
CMM
0.
0.
0.
0.
0.
9367
9468
9337
9421
9523
FLOW*** TIME
CMM MIN
1.0843
1.0727
1.0876
1.0779
1.0664
1428.
1433.
1433.
1433.
1432.
8
6
6
5
8
VOL
CM
1338
1357
1339
1351
1365
GROSS
4.5158
4.5125
4.4981
4.4821
4.5184
TARE
4.5000
4.4945
4.4710
4.4679
4.5066
NET
G
0.0158
0.0180
0.0271
0.0142
0.0118
*** uncorrected flow
September
Quarter
Arithmetic
Mean
13
13
Geometric
Mean
13
13
A-7
-------
CORDERO MINING COMPANY
CORDEKO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES,
CONSULTANT
SHERIDAN, WY.
INC.
SAMPLER ID
YEAR 1992
AREA
0080
SITE
PM2
PRO
A
CONC
DATS UG/M3
09/03/92 11
09/09/92 13
09/15/92 21 •
09/21/92 10
09/27/92 8
* low flow rate
«* corrected flow
FILTER
NO.
35972
35974
35976
35986
35978
FLOW**
CMM •
0.9611
0.9715
0.9582
0.9668
0.9648
FLOW***
CMM
1.1126
1.1007
1.1161
1.1061
1.0804
TIME
MIN
1444.
1444.
1448.
1448.
1449.
4
4
9
8
8
VOL
CM
1388
1403
1388
1401
1399
GROSS
4.
^4.
"4.
4.
4.
5120
5369
5100
4714
5426
TARE
4
4
4
4
4
.4963
.5182
.4814
.4570
.5308
NET
G
0.0157
0.0187
0.0286
0.0144
0.0118
»** uncorrected flow
September
Quarter
Arithmetic
Mean
13
13
Geometric
Mean
12
12
A-8
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
SAMPLER ID
YEAR 1992
AREA
0080
SITE
888
PRO
JO 2
FILTER FLOW**
NO. CMM
36177 INVALID:
37190 1.4975
36182 1.6209
36186 1.5036
36188 1.5265
* low flow rate
** corrected flow
*** uncorrected flow
DATE
10/03/92
10/09/92
10/15/92
10/21/92
10/27/92
CONG
UG/M3
12
17
23
34
FLOW*** TIME VOL
CMM MIN CM
POWER FAILURE
1.5797 1429.9 2141
1.6799 1430.8 2319
1.6131 1431.7 2153
1.6131 1431.9 2186
GROSS
G
TARE
G
NET
G
3.5528 3.5278 0.0250
3.7274 3.6891 0.0383
3.7433 3.6949 0.0484
3.7714 3.6963 0.0751
October
Quarter
Arithmetic
Mean
22
22
Geometric
Mean
20
20
A-9
-------
CORDERO MINING
CORDERO MINE
GILLETTE, WY.
COMPANY
INTER-MOUNTAIN LABORATORIES,
CONSULTANT
SHERIDAN, WY.
INC.
SAMPLER ID
AREA
0080
SITE
BFM
PRO
HV2
YEAR 1992
DATE
10/03/92
10/09/92
10/15/92
10/21/92
10/27/92
CONG
UG/M3 NO.
29 36179
31 37189
36180
36185
FILTER FLOW** FLOW*** TIME
21
51 ,
33 36189
* low flow rate
** corrected flow
*** uncorrected flow
CMM
CMM
MIN
1.5546 1.6787 1440.0
1.6090 1.6966 1440.0
1.7413 1.8040 1440.0
1.6655 1.7861 1440.0
1.6908 1.7861 1440.0
VOL
CM
2239
2317
2508
2398
2435
GROSS
G
3.7420
3.6071
3.7325
3.8194
3.7748
TARE
G
3.6775
3.5364
3.6805
3.6966
3.6953
NET
G
0.0645
0.0707
0.0520
0.1228
0.0795
October
Quarter
Arithmetic
Mean
33
33
Geometric
Mean
32
32
A-10
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
SAMPLER ID
YEAR 1992
AREA
0080
SITE
BFM
PRO
HV2A
CONC
FILTER FLOW** FLOW*** TIME
VOL
DATE
10/03/92
10/09/92
10/15/92
10/21/92
10/27/92
UG/M3
34
33
60
33
NO.
36178
37188
36181
36184
36190
* low flow rate
** corrected flow
*** uncorrected flow
CMM CMM MIN CM
1.3511 1.4589 1440.2 1946
INVALID: RUNTIME TOO LONG
1.5143 1.5688 1440.3 2181
1.4482 1.5531 1440.1 2086
1.4703 1.5531 1440.2 2118
GROSS TARE NET
G G G
3.7367 3.6713 0.0654
3.7567 3.6848 0.0719
3.8250 3.7001 0.1249
3.7728 3.7025 0.0703
October
Quarter
Arithmetic
Mean
40
40
Geometric
Mean
39
39
A-11
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
INTER-MOUNTAIN LABORATORIES,
CONSULTANT
SHERIDAN, WY.
INC.
SAMPLER ID
AREA
0080
SITE
889
PRO
J02
YEAR 1992
DATE
10/03/92
10/09/92
10/15/92
10/21/92
10/27/92
* low flow rate
** corrected flow
*** uncorrected flow
CONC
UG/M3
25
16
18
FILTER
NO.
36176
37191
36183
36187
36191
FLOW**
CMM
1.5931
INVALID
1.8265
1.6682
INVALID
FLOW*** TIME VOL GROSS TARE NET
CMM MIN CM G G G
1.7199 1416.0 2256 3.7180 3.6627 0.0553
: NO TRANSDUCER RECORDER READING
1.8919 1439.1 2629 3.7390 3.6977 0.0413
1.7887 1440.3 2403 3.7332 3.6901 0.0431
; MOTOR OR CLOCK FAILURE
October
Quarter
Arithmetic
Mean
20
20
Geometric
Mean
19
19
A-12
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
SAMPLER ID
YEAR 1992
AREA
0080
INTER-MOUNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
SITE
PM2
PRO
CONC
DATE UG/M3
10/03/92 14
10/09/92 11
10/15/92 12
10/21/92 18 .
10/27/92 10
* low flow rate
** corrected flow
*** uncorrected flow
FILTER
NO.
35980
37097
35984
37953
37956
FLOW**
CMM
0.9333
0.9557
0.9912
0.9397
0.9540
FLOW***
CMM
1.0882
1.0626
1.0638
1.0807
1.0645
TIME
MIN
1431.5
1431.3
1433.5
1433.0
1438.4
VOL
CM
1336
1368
1421
1347
1372
GROSS
G
4.5420
4.3432
4.5030
4.4209
4.4640
TARE
G
4.5240
4.3279
4.4866
4.3973
4.4500
NET
G
0.0180
0.0153
0.0164
0.0236
0.0140
October
Quarter
Arithmetic
Mean
13
13
Geometric
.Mean
13
13
A-13
-------
CORDERO MINING
CORDERO MINE
GILLETTE, WY.
COMPANY
SAMPLER ID
AREA
0080
SITE
PM2
INTER-MOUNTAIN LABORATORIES,
CONSULTANT
SHERIDAN, WY.
INC.
PRO
A
YEAR 1992
DATE
10/03/92
10/09/92
10/15/92
10/21/92
10/27/92
CONG
UG/M3
13
11
11
18
11
FILTER
NO.
35981
37096
35983
37954
37957
FLOW**
CMM
0.9455
0.9932
0.9982
0.9643
0.9790
FLOW***
CMM
1.1024
1.1043
1.0713
1.1090
1.0924
TIME
MIN
1449.7
1447.3
1450.5
1454.0
1450.2
VOL
CM
1371
1438
1448
1402
1420
GROSS
G
4.5214
4.3770
4.4941
4.4810
4.4823
TARE
G
4.5032
4.3610
4.4778
4.4563-
4.4671
NET
G
0.0182
0.0160
0.0163
0.0247
0.0152
* low flow rate
** corrected flow
*** uncorrected flow
October
Quarter
Arithmetic
Mean
13
13
Geometric
Mean
13
13
A-14
-------
CORDERO MINING COMPANY
CORDERO MINE
GILLETTE, WY.
SAMPLER ID
YEAR 1992
AREA
0080
INTER-MOUNTAIN LABORATORIES, INC.
CONSULTANT
SHERIDAN, WY.
SITE
PM3
PRO
DATE
10/03/92
10/09/92
10/15/92
10/21/92
10/27/92
CONC
UG/M3
16
9
11
16
FILTER
NO.
35979
37098
35982
37955
37958
FLOW**
CMM
0.9589
0.9820
INVALID
0.9655
0.9802
FLOW*** TIME VOL
CMM MIN CM
1.1176 1462.0 1402
1.0914 1463.5 1437
POWER FAILURE
1.1100 1459.6 1409
1.0933 1459.1 1430
GROSS TARE NET
G G G
4.4733 4.4512 0.0221
4.3398 4.3272 0.0126
4.4263
4.4586
4.4111
4.4357
0.0152
0.0229
* low flow rate
** corrected flow
*** uncorrected flow
October
Quarter
Arithmetic
Mean
13
13 .
Geometric
Mean
13
13
A-15
-------
ft, i*
Ifl 35
M Q.
r* m o rsi
O •£) CO —*\.
- co m m csi r
momocomin»-tcocr.
mtwf>o-«r'»<'*»^«^mfnf^ro'«r^r'r ^voooo^oo^ o^of^f mmmr-ir-r
KU oooooommm
*• os os er» os eft er* eo co as
coeococoeococococncn
CO CO CO CO O O
CO CO CO CO OS OS
r*j M CM (sj r»j csr
cococococococo
ra O cs ^
co co co r
o ~* in ^
r*- -a- P^ co in — «
f*j o\ r- co -T «D
o r- o os •-« o%
r- o ^H o r». n
—« r- \O CD O O O I
UJ UJ UJ
• i~4 —4 O O T 1-H —«
r in (
M oooofnoooo-jroooooomoooeiofoooociomo—«ooo_» — — -HO — oomo- en 01 ov o» o> <* ov
cr, <7- o> o> o% c* a, o^ c^ o a> C-. CT, os CT. ov CT> CT, ov o as o> ov CT> m w o m CTV o> en cr>
5 cftCto^e'om^c^o^o^mo^mo^cftc^os'o^o^c^cs'os'os'oCoCo^cn'^
SC OOOOOOOOOOOOOOOOCOOOOC3OOOOOOOOOO-
UlOOOO OOOO O O O O OOOO OOOO
COOOO
o s o o
(SJO^CSlCSt CS(OS(SlfSI
OOOO OOOO
csi os CM fst
OOOO
•~» T-l *~1 •*
o>r»4r*(|o«Nt(Niorii-m —4*-i^4(siosm«~«»-*^o
cst rn co T in
s; ooooooooo ocoococoocacaoo o o o o o o o o o o o
*r««.rN.r^-coco>llii>-fs-is-r-ca aocor-.rs'rs-rs- cococo
A-16
-------
APPENDIX B
COMBINED DATA SETS
This appendix presents both the data set supporting the emission factor
predictive equations currently contained in AP-42 Section 8.24 as well as the data set
developed during the study described in the main body of the report. Please note that
the PM-10 factors reported for the old data sets are based on log-normal interpolation
of particle size data presented in Reference 6 of the report.
B-1
-------
-------
HAUL TRUCKS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43 •
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
Run
J-6
J-9
J-10
J-11
J-12
J-20
J-21
K-1
K-6
K-7
K-8
K-9
K-10
K-11
K-12
K-13
K-26
L-1
L-3
L-4
P-1
P-2
P-3
P-4
P-5
P-6
P-7
P-8
P-9
BB-2
BB-3
BB-6
BB-7
BB-8
BB-10
BB-11
BB-12
BB-13
BB-14
BB-15
BB-16
BB-17
BB-18
BB-19
BB-20
BB-21
BB-22
BB-23
BB-25
BB-26
BB-27
BB-29
BB-31
BB-33
BB-34
BB-35
BB-36
BB-38
BB-39
BB-40
BB-41
BB-42
BB-43
- Road Surface Parameters -
Silt
Silt Moisture Loading
(%) (%) (g/m2)
7.9
9.4
9.4
8.2
14.2
11.6
.
7.7
2.2
2.8
3.1
4.7
7.7
8.9
11.8
1.8
.
13.0
13.8
18.0
4.7
4.7
4.1
2.0
3.1
2.8
2.4
7.7
1.6
10.7
10.7
3.6
3.6
3.8
3.1
3.1
2.2
2.2
3.3
2.1
2.1
2.1
1.3
1.3
3.9
1.8
1.7
1.9
3.8
2.5
2.7
19.2
19.2
3.0
4.9
3.7
12.9
1.6
1.4
4.8
6.5
9.5
1.8
5.4
3.4
2.2
4.2
6.8
8.5
.
2.2
7.9
0.9
1.7
1.5
2.0
2.0
2.3
2.7
.
7.7
4.9
5.1
0.4
0.4
0.3
0.3
0.0
2.9
1.5
15.3
20.1
1.1
1.1
1.2
1.2
1.0
1.2
1.2
1.1
1.1
1.8
1.4
1.4
1.8
1.3
1.5
1.4
2.0
2.5
4.1
4.0
4.4
1.9
3.8
3.8
1.5
0.9
2.5
5.0
10.3
12.3
5.7
5.0
4.4
4.7
- - Mean Vehicle Parameters - -
Speed Weight No. of
Cmph) (tons) Wheels
_ Ctt rl |-i -,*-. C> A*.
PM-10
Emi ss i on
Factor
(lb/VMT)
s'.s
12.2
6.7
33.4
38.3
'
60.1
7.8
10.1
10.2
22.1
22.3
25.8
34.2
1.2
,
58.5
75.9
253.8
23.0
23.0
23.8
4.0
4.1
13.7
11.0
52.4
7.0 .
142.0
142.0
8.1
8.1
11.2
16.2
16.2
3.1
3.1
7.8
3.5
3.5
3.2
1.7
1.7
16.0
17.4
12.8
1.2
1.9
2.0
3.1
68.4
68.4
5.0
14.0
13.2
245.0
3.3
5.3
18.4
41.4
84.9
3.7
19.3
19.3
20.0
15.0
16.8
15.0
32.9
34.8
34.2
36.0
29.2
36.0
30.0
36.0
31.7
31.7
26.1
20.0
20.0
26.7
26.1
31.1
31.7
31.1
31.7
31.1
29.2
31.1
NGW Data
36.4
36.4
22.7
22.4
21.2
27.5
27.5
22.6
22.6
22.9
21.3
22.1
24.6
23.0
22.8
24.3
22.8
24.3
22.6
19.2
21.8
19.5
18.8
20.8
29.2
28.6
28.0
19.3
22.0
21.8
21.2
22.3
22.0
30.4
65.0
60.0
60.0
99.0
125.0
110.0
63.0
89.0
24.0
65.0
74.0
69.0
73.0
95.0
64.0
84.0
95.0
107.0
86.0
79.0
42.0
94.0
55.0
47.0
25.0
61.0
47.0
58.0
Set -----.
131.0
131.0
200.0
200.0
220.0
160.0
160.0
155.0
155.0
92.0
183.0
178.0
169.0
184.0
192.0
175.0
218.0
161.0
181.0
207.0
183.0
244.0
283.0
271.0
153.0
170.0
173.0
286.0
141.0
137.0
271.0
267.0
275.0
164.0
8.0
7.7
9.9
9.5
10.0
9.3
6.1
7.4
4i9
6.3
6.7
6.6
6.5
7.3
6.6
6.8
8.8
9.3
8.3
8.5
7.2
9.7
7.6
7.1
5.6
7.6
7.5
8.7
5.5
5.5
5.8
5.7
5.6
5.5
5.5
5.8
5.8
5.2
5.7
5.6
5.5
6.0
5.7
5.7
5.8
5.5
5.7
5.7
5.7
5.8
5.9
6.1
5.4
6.1
5.4
6.0
5.3
5.3
6.1
5.9
5.9
5.5
4.6
14.1
9.4
4.9
2.9
3.1
1 .6
1.6
o'.B
2.0
1.5
1.5
2.0
0.3
1 .0
0 2
\J • £.
27.7
20.9
11.3
2.0
1 ?
1 • C.
0.'7
2.3
1 .2
1.'4
10.8
13.6
4.7
6.5
5.2
3.3
2.9
1.8
1.5
2.6
4.4
5.2
1.6
4.2
3.1
2.7
1.8
1.4
0.9
1.3
3.0
3.8
15.6
9.4
5.7
9.5
6.7
14.2
3.2
1 .7
2.6
5)7
13.0
0.8
B-3
-------
HAUL TRUCKS
Run
1 J-6
2 J-9
3 J-10
4 J-11
5 J-12
6 J-20
7 J-21
8 K-1
9 K-6
10 K-7
11 K-8
12 K-9
13 K-10
14 K-11
15 K-12
16 K-13
17 K-26
18 L-1
19 L-3
20 1-4
21 P-1
22 P-2
23 P-3
24 P-4
25 P-5
26 P-6
27 P-7
28 P-8
29 P-9
30 BB-2
34 BB-8
35 BB-10
37 BB-12
39 BB-14
40 BB-15
42 BB-17
44 BB-19
46 BB-21
47 BB-22
48 BB-23
49 BB-25
50 BB-26
53 BB-31
54 BB-33
55 BB-34
56 BB-35
57 BB-36
58 BB-38
60 BB-40
62 BB-42
63 BB-43
- Road Surface Parameters -
Silt
Silt Moisture Loading
(%) C/.) (g/m )
7.9
9.4
9.4
8.2
14.2 '
11.6
•
2'.2
2.8
3.1
4.7
7.7
8.9
11.8
1 .8
13 '.0
13.8
18.0
4.7
4.7
4.1
2~.Q
2'.8
2.4
7.7
1.6
10.7
3.8
3.1
7 ?
c, . c*
3.3
2.1
2.1
1.3
1.8
1.7
1.9
3.8
2.5
19.2
3.0
4.9
3.7
12.9
1.6
4.8
9.5
1.8
5.4
3.4
2.2
4.2
6.8
8.5
2*2
7*.9
0.9
1.7
1.5
2.0
2.0
2.3
2.7
7\7
4.9
5.1
0.4
0.4
0.3
0.3
0.0
2.9
1.5
15.3
20.1
1.1
1.0
1.2
1.1
1.8
1.4
1.8
1.5
2.0
2.5
4.1
4.0
4.4
3.8
1.5
0.9
2.5
5.0
10.3
5.7
4.4
4.7
3.8
12.2
6.7
33.4
38.3
6o!l
7.8
10.1
10.2
22.1
22.3
25.8
34.2
1.2
58'.5
75.9
253.8
23.0
23.0
23.8
4.0
4.1
13.7
11.0
52.4
7.0
142.0
11.2
16.2
3.1
7.8
3.5
3.2
1.7
17.4
12.8
1.2
1.9
2.0
68.4
5.0
14.0
13.2
245.0
3.3
18.4
84.9
3.7
- - Mean Vehicle Parameters - -
Speed Weight
(mph) (tons)
UlU Uat.0 ouL
19^3
20.0
15.0
16.8
15.0
32.9
34.8
34.2
36.0
29.2
36.0
30.0
36.0
31.7
31.7
26.1
20.0
20.0
26.7
26.1
31.1
31.7
31.1
31.7
31.1
29.2
31.1
- New Data Set
36.4
21.2
27.5
22.6
22.9
21.3
24.6
22.8
22.8
24.3
22.6
19.2
21.8
20.8
29.2
28.6
28.0
19.3
22.0
21.2
22.0
30.4
65.0
60.0
60.0
99.0
125.0
110.0
63.0
89.0
24.0
65.0
74.0
69.0
73.0
95.0
64.0
84.0
95.0
107.0
86.0
79.0
42.0
94.0
55.0
47.0
25.0
61.0
47.0
58.0
131.0
220.0
160.0
155.0
92.0
183.0
169.0
192.0
218.0
161.0
181.0
207.0
183.0
271.0
153.0
170.0
173.0
286.0
141.0
271.0
275.0
164.0
No. of
Wheels
B'.O
7.7
9.9
9.5
10.0
9.3
6.1
7.4
4.9
6.3
6.7
6.6
6.5
7.3
6.6
6.8
8.8
9.3
8.3
8.5
7.2
9.7
7.6
7.1
5.6
7.6
7.5
8.7
5.5
5.6
5.5
5.8
5.2
5.7
5.5
5.7
5.8
5.5
5.7
5.7
5.7
6.1
5.4
6.1
5.4
6.0
5.3
6.1
5.9
5.5
TSP
Emission
Factor
(Ib/VMT)
33 '.0
30.2
12.9
12.3
14.2
8.2
2.2
3.9
2.5
6.4
4.4
4.5
6.0
0.6
3.4
0.7
67.2
73.1
20.6
6.3
24.1
5.1
14.1
1.8
8.4
4.3
5.6
95.1
20.2
18.3
11.7
9.0
18.6
11.2
18.0
9.5
4.6
7.3
10.8
10.5
62.0
47.9
44.4
27.2
84.2
16.4
38.4
84.2
10.2
B-4
-------
SCRAPERS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Run
J-1
J-2
J-3
J-4
J-5
K-15
K-16
K-17
K-18
K-22
K-23
L-5
L-6
P-15
P-18 .
BB-46
BB-47
- Road
Silt
8.9
23.4
15.8
14.6
10.6
25 .'2
25.2
25.2
21.6
24.6
21.0
21.0
7.2
7.2
12.7
14.0
Surface Parameters -
Silt
Moisture Loading
(%) (g/m2)
5.7
2.3
4.1
1.5
0.9
6.'o
6.0
6.0
5.4
7.8
1.0
1.0
4.880
5.110
10.8
73.2
49.0
8.0
32.9
96.'s
96.8
96.8
65.0
78.2
50.0
50.0
531.0
894.0
- - Mean Vehicle Parameters - -
Speed Weight No. of
(mph) (tons) Wheels
19.3 50.0
19.3 53.0
24.2 54.0
20.0 36.0
18.0 70.0
28.0 46.0
30.0 64.0
23.0 57.0
25.0 66.0
31.7 45.0
28.0 54.0
21.1 53.0
20.0 50.0
16.2 42.0
10.0 64.0
15.5
18.0
63.0
65.0
4.1
4.0
4.1
4.0
4.0
4.0
4.0
4.1
4.0
4.0
4.0
4.0
4.0
4.0
4.0
4.060
4.0
PM-10
Emission
Factor
(Ib/VMT)
2.480
2.090
16.3
0.963
5.8
4.540
10.3
20.9
10.7
2.920
6.610
115.0
51.3
10.990
8.170
B-5
-------
SCRAPERS
1
2
3
4
5
6
7
8
9
10
11
12
13
H
15
- Road Surface Parameters -
<:; 1 1
Run
J-1
J-2
J-3
J-4
J-5
K-15
K-16
K-17
K-18
K-22
K-23
L-5
L-6
P-15
P-18
Silt
tt>
8.9
23.4
15.8
14.6
10.6
25 '.2
25.2
25.2
21.6
24.6
21.0
21.0
7.2
7.2
Moisture
C/.)
5.7
2.3
4.1
1.5
0.9
6io
6.0
6.0
5.4
7.8
i!o
1.0
Loading
(g/m3)
10.8
73.2
49.0
8.0
32.9
96! 8
96.8
96.8
65.0
78.2
50.0
50.0
- - Mean Vehicle Parameters - -
Speed Weight
(mph) (tons)
• Ulu uata set
19.3 50.0
19.3 53.0
24.2 54.0
20.0 36.0
18.0 70.0
28.0 46.0
30.0
23.0
25.0
31.7
28.0
21.1
20.0
16.2
10.0
64.0
57.0
66.0
45.0
54.0
53.0
50.0
42.0
64.0
No. of
Wheels
4.1
4.0
4.1
4.0
4.0
4.0
4.0
4.1
4.0
4.0
4.0
4.0
4.0
4.0
4.0
TSP
Emission
Factor
(Ib/VHT)
8.6
9.4
50.2
3.9
17.7
16.2
29.2
74.3
43.0
10.3
24.5
355.0
163.0
4'.0
B-6
-------
LIGHT AND MEDIUM DUTY
1
2
3
4
5
6
7
8
.9
10
11
12
13
14
15
,
Run
J-7
J-8
J-13
J-18
J-19
K-2
K-3
K-4
K-5
P-11
P-12
P-13
BB-44
BB-45
BB-48
- 'Road
Silt
(%)
3.0
3.0
10.1
8.8
8.2
4.9
4.9
5.3
5.3
5.5
5.5
5.5
1.820
1.950
1.950
Surface Parameters -
Moisture
(%)
3.6
3.6
1.0
1.1
0.9
1.6
1.6
1.7
1.7
0.9
0.9
0.9
0.680
2.1
2.1
Silt
Loading
(9/m2)
21.0
21.0
13.9
47.5
44.3
5.9
5.9
48.2
48.2
5.9
5.9
5.9
1.8
0.8
0.8
- - Mean Vehicle Parameters - -
Speed Weight No. of
-------
LIGHT AND HED1UH DUTY
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
- Road Surface Parameters -
Ci 1 1-
Run
J-7
J-8
J-13
J-18
J-19
K-2
K-3
K-4
K-5
P-11
P-12
P-13
BB-44
BB-45
BB-48
Silt
C/0
3.0
3.0
10.1
8.8
8.2
4.9
4.9
5.3
5.3
5.5
5.5
5.5
1.820
1.950
1.950
Moisture Loading
m (9/nO
3.6
3.6
1.0
1.1
0.9
1.6
1.6
1.7
1.7
0.9
0.9
0.9
0.680
2.1
2.1
21.0
21.0
13.9
47.5
44.3
5.9
5.9
48.2
48.2
5.9
5.9
5.9
1.8
0.8
0.8
- - Mean Vehicle Parameters - -
Speed Weight
(mph) (tons)
25.0
25.0
25.0
25.0
25.0
35.0
35.0
35.0
35.0
42.5
43.1
43.1
30.0
30.0
30.0
7.0
3.0
2.2
2.6
2.3
2.3
2.4
2.4
2.4
2.0
2.0
2.0
2.0
2.0
2.0
No. of
Wheels
4.2
4.0
4.0
4.0
4.1
4.0
4.0
4.0
4.0
4.0
4.0
4.0:
4.0
4.0
4.0
TSP
Emi'ssion
Factor
(Ib/VMT)
o!350
5.5
8.2
6.7
0.640
0.760
0.6
0.930
8.5
9.0
7.8
1.271
0.603
0.486
B-8
-------
APPENDIX C
TIME HISTORIES OF ROAD SURFACE
MATERIAL MOISTURE CONTENT
C-1
-------
-------
Page
C-4
C-5
C-6
C-7
Date
9/21/92
9/22/92
9/23/92
9/28/92
Site
.IB
IB
IB
IB
This appendix presents time histories of the moisture content in
the road surface material. Included are data collected not only
during emissions and particle sizing tests but also during the
"moisture tracking" sampling in October 1992:
Remarks
Collected during Runs BB-17 through -
20
Collected during Runs BB-21 and -22
Collected during Run BB-23
Collected during Runs BB-25 through -
27
Collected during Runs BB-33 and -34
(uncontrolled ) as well as watered Run
BB-35
Collected during Runs BB-38 and -39
Collected during Runs BB-40 through -
42
Collected during particle sizing tests
BB-111X and -112X.
Moisture tracking samples
Moisture tracking samples
Moisture tracking samples
Moisture tracking samples
C-8 10/10/92
C-9
C-10
C-ll
10/13/92
10/14/92
10/09/92
IB
4
C-12
C-13
C-14
C-15
10/21/92
10/21/92
10/23/92
10/26/92
1
2
4
4
Each time-history presents a graph showing road surface material
moisture content at the time the sample was collected. In
addition, the time of road waterings is also ^hown on the graph.
For most histories, other information is presented, including
a. photocopy of the pyranograph trace collected,
qualitatively showing incoming solar radiation at the
sampling location
b. summary information on the amount of water/surfactant
applied
c. general meteorological conditions (ambient air
temperature and wind speed) during the sampling period
C-3
-------
£ „
M
J
SURFACE MOISTl
_*
-* O M *• °> °° °
>n _ _ . r-
O *-, 1.25
feel 1
D *-
< <2 °-7£
| 1 0.
Dl — . u~
$ 8 0-2£
C0~ (
BB.17 BB-19
BB-18 BB-20
r ' J ""e"'"' &"~ °, •
k 12:00 13:00 14:00 15:00 16:00 17.00
i, -DtnotMWittrtrucicp^n tr\f*m TIMC
• LOCAL T IMt
-
5 ..„.«„ .....„« 4o.«n H^.nn 1A-OO 15-00 16:00 17:00 18.00
LOCALTIME
WATPRIWR INFORMATION
Time of water
truck passes
12:22
Number of
samples
9
Application intensity (gal/yd2)
Maximum
0.144
Minimum
0.001 1
Mean
0.0608
Std. deviation
0.0384
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-17
BB-18
BB-19
BB-20
Temperature (°F)
65
65
65
68
Mean wind speed (mph)
12.73
9.92
8.15
7.98
C-4
-------
ID
14
HI 12
CE
H 10
CO
i i
SURFACE
-b- 0)
2
BB-21 BB-22
-
JO" "1 ° -•--....
T "O
. T
4
11:00 . 12:00 13:00 14:00 15:00
- Denotes wtter tnx* pass LOCAL TIME
1.5
O *£ 1.25
£ 1 1
o --
g ^ 0.75
££ ^ 0.5
o -B °-25
w "" 0
f—
—
n
h:
=s-
— 1
be
»
^~^^^—
M1M
^-*
* In—
M
— -t--
-7 1
_, 1
' - •» i <
11:00 12:00 13:00 14:00
LOCAL TIME
15:00
WATERING INFORMATION
Time of water
truck passes
11:17
12:45
Number of
samples
6
3
Application intensity (gal/yd2)
Maximum
0.141
0.051
Minimum
0.013
0.015
Mean
0.0725
0.034
Std. deviation
0.0476
0.0180
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-21
BB-22
Temperature (°F)
78
82
Mean wind speed (mph)
8.11
4.54
C-5
-------
5?
**-**
LU
OL
?•
CO
0
HI
o
2:
cc.
CO
§
if
^•^ h*
D r-
21
D; --
n
CO
16
14
12
10
8
6
4
2
BB-23
— ^^^^^^^^
O
F- . .t .b. , .. . ,
1 11:00 12:00 13:00
^.Dtnotowtter truck pro LOCAL TIME
1,5
1.25
4
1
0.75
0.5
0.25
n
;'|il_-i"l^-M'/
hri=E-::=j^^r"!
ITr^^r^'t^r"'
i ~! 1 7tZ| •-*•— |— r"|
^^TrfeHiV-1
11:00 12:00 13:00
LOCAL TIME
WATERING INFORMATION
Time of water
truck passes
11:16
Number of
samples
4
Application intensity (gal/yd2)
Maximum
0.051
Minimum
0.033
Mean
0.041
Std. deviation
0.0093
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-23
Temperature (°F)
87
Mean wind speed (mph)
7.55
C-6
-------
'o' 14
ID
SURFACE MOIST
a M *. o> cx> o
i
1 5
O <-. 1.25
HC
Q ^
< ^ 0.75
o
D- -^ 0.5
o 5 °-25
W 0
BB-2S BB-26
BB-27
Q
A 1 v— o
' L .1.1.1 . , ..,,'.,
» 11:00 12:00 13:00 14:00 15:00
^- Denote* wrter truck pns LOCAL TIME
l-T-.f:-.. :lrc-4rr-i:.-n^tr~^ - . ;
r .^j^p^&^^^fef-^
:
11:00 12:00 13:00 14:00 15:00
LOCAL TIME
WATERING INFORMATION
Time of water
truck passes
11:04
12:44
Number of
samples
6
6
Application intensity (gal/yd2)
Maximum
0.211
0.310
Minimum
0.011
0.029
Mean
0.123
0.161
Std. deviation
0.0852
0.112
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-25
BB-26
BB-27
Temperature (°F)
60
66
69
Mean wind speed (mph)
18.17
13.51
12.05
C-7
-------
16
14
^o
5r 12
g
F~ *®
CO
O .
2 8
LU
< 6
o:
D 4
CO
2
0
1.5
O .— 1.25
0 -~
< ^ 0.75
fr C
Q; il o.s
o -i- 0>2S
CO ^ n
BB-33 BB-34 BB-3S
.0
C~^"—*~ •'"' ^ft
f *"-•— "— ...^^ i
, . , . , . . , . , . . 1 '
. 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00
A- Derates wrter truck pus LOCAL TIME
11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00
LOCAL TIME
WATPRINf? INFORMATION
Time of water
truck passes
16:42
Number of
samples
4
Application intensity (gal/yd2)
Maximum
0.082
Minimum
0.029
Mean
0.052
Std. deviation
0.023
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-33
BB-34
BB-35
Temperature (°F)
61
63
60
Mean wind speed (mph)
13.72
12.24
8.27
C-8
-------
SURFACE MOISTURE(%)
r>ro*.o>oooK)*.o
1.5
Z
O ~ 1.25
-------
10
14
£ 12
LET 12
rv
SURFACE MOISTUF
3 M *. O> OB O
1 S
O ,£. 1.25
1! ";
5 S 0.25
O "--
W 0
BB-40 BB-42
BB-41
1 °- o
b '•-. -°
G
» . i i | i i i i < •
A 13'00 14-00 15:00 16:00
^-D«octe»w»t«rtnxxp««t LOCAL TIME
Li I i — T--— H-,'
^* J - — ; ' J "f1"" ™* """ "p"™^'^ 1
f' • >'• • - '• • i- — - - .1 1 — - —-|
• i=B!M=sa3
- B=-^P^
, — i— i — f— — 1 U— , . , } ,
:4r i 1 1 -^-^
13:00 14:00 15:00 16:00
LOCAL TIME
WATERING INFORMATION
Time of water
truck passes
12:45
Number of
samples
4
Application intensity (gal/yd2)
Maximum
0.340
Minimum
0.062
Mean
0.157
Std. deviation
0.126
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-40
BB-41
BB-42
Temperature (°F)
45
45
44
Mean wind speed (mph)
12.24
11.88
11.63
C-10
-------
ID
14
^
. SURFACE MOISTUREI
•»» O) 00 O f
2
o
i
1 5
O ^ 1.25
|l 1
OL ^ 0.5
^ o, 0.25
w 0
BB-111X BB-112X
- Q
: • , , . "e~" f
l 10:00 11:00 12:00 13:00 14:00 15:00 16-QO 17:00
|.Dmtesw>ter truck IMS LOCAL TIME
<===-~- 3£^^5 =!=/ ^- -g — f =i- :^fe -^
fc — | r; — ,..j, —r ! 1 1 —
10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
LOCAL TIME
METEOROLOGICAL CONDITIONS DURING RUN
Run
BB-111X
BB-112X
Temperature (°F)
46
54
Mean wind speed (mph)
18.51
22.22
C-11
-------
SITE 1 - MOISTURE TRACKING
vP
?_..
111
o:
pi
to
0
LLl
LC
e:
CO
16
14
12
10
8
6
4
2
n
,o
,° 0--"""
: ^/-" '•© °'
«M M
• TT ' U •
A
.Dencte»wat«ft™*p«»
13:00 14:00 15:00 16:00
LOCAL TIME
g
i
o
CM
1.5
1.25
1
0.5
0.25
0
13:00 14:00 15:00
LOCAL TIME
16:00
C-12
-------
a:
o
LLl
o:
16
14
10
SITE 2 - MOISTURE TRACKING
- Denotes water truck pass
p
< 0''
13:00 14:00 15:00 16:00
LOCAL TIME
17-00
1.3
Q ,~ 1.25
§ E 1
Q -•
$ E °-75
o; ^ o.s
o 5 °'25
W 0
— 1
s
1 1
•E-
F"™
—
Li
5-
-f—
^—
t
H
irr
—
E£.
• r
— T~ '
gal
N=n
-r-
13:00 14:00 15:00 16:00 17:00
LOCAL TIME
C-13
-------
g
CO
O
LU
O
5
CO
SITE 4 - MOISTURE TRACKING (10/23/92)
16
14 -
12
10
8
. Denote. w«ter truck ptta
j
i
"©'
14:00 15:00 16:00 17:00
LOCAL TIME
1.5
Z
O *-. 1.25
|| ,
01 ^. °-5
J ^ 0-25
CO ^ o
P
iz
—
n
— 1
14:00 15:00 16:00 17:00
LOCAL TIME
C-14
-------
SITE 4 - MOISTURE TRACKING (10/26/92)
5"
5^_
LU
cc
Z3
CO
o
UJ
<
a:
D
CO
ID
14
12
10
8
6
4
2
0
-
•
-
•
_
O
(-, .0 °
°"G..Q..--'''
-
A A
•...•. T , T ,
- Denotes wcter truck pass
14:00 15:00 16:00 17:00
LOCAL TIME
2
O ~
r- c
^^ *^S
E *
^1
o:^
0 ™
CO
1.3
1.25
1
0.75
0.5
0.25
0
_
NO SOLAR RADIATION
DATA FOR THIS DAY
-
-
14:00 15:00 16:00 17:00
LOCAL TIME
C-15
-------
-------
APPENDIX D
COMPARISON OF EMISSION FACTORS
D-1
-------
-------
APPENDIX D
COMPARISON OF EMISSION FACTORS
This appendix presents a brief summary comparison between emission factors
currently contained in AP-42 Section 8.24 (Western Surface Coal Mining) and those
recommended based on results of the field study. Section 5 in the body of the report
presents similar comparisons, but in greater detail.
D.1 SUPPORTING DATA SETS
It is important to recall that the predictive emission factors were developed from
measurement-based data sets. These data sets contain not only the measured
emission factors but also indicators of "source conditions." These include variables
such as the texture and amount of material on a road surface as well as the speed and
weight of vehicles traveling over the road. For discussion purposes, the measurement
data set0'1 supporting the current AP-42 Section 8.24 emission factors has been
termed the "old" data set. Similarly, the data collected during the field study
discussed in the main body of the report constitute the "new" data set. (Appendix
B to the report presents these two data sets.)
It is also important to remember that the two data sets were collected at different
times. The old data set supporting the current version of Section 8.24 dates from
1980 and earlier. The new data set consists of measurements made in the fall of
1992. In the intervening 12 years, at least two major changes occured.
First, the basis for the National Ambient Air Quality Standards for particulate was
revised from total suspended particulate (TSP) to PM10 (particulate matter no greater
than 10 microns in aerodynamic diameter). The predictive emission factors for PM10
in Section 8.24 were scaled from factors for other particle size ranges. The present
study is the first coal mining study to (a) directly measure PM10 emission factors and
(b) to develop predictive emission factors specific to PM10. In addition, the present
study also interpolated PM10 emission factors for the old data set from information
presented in the test report.0"1
Second, the surface coal mining industry has changed substantially since the time that
old data base was developed. For example, "typical" haul truck capacities have
increased by a factor of two to three. Not surprisingly, average vehicle speeds on
haul routes are lower. Also, there is now less general, light-duty traffic on mine
roads. In addition, because many permanent haul routes have been treated for dust
control for many years, one can expect that physical properties of the permanent road
surfaces (i.e., the amount and texture of loose aggregate) to be different.
Because of these differences in "typical" source conditions, one has little reason to
D-3
-------
expect that a predictive emission factor developed from the old data set will
necessarily accurately predict measured emissions in the new data set.
D.2 HAUL TRUCKS
The geometric mean measured haul truck emission factors in the old and new data
sets are shown in Table D-1:
Table D-1
Geometric Mean Measured Haul Truck Emission Factors
Haul Truck Emission Factor (Ib/VMT)
Old Data Set
New Data Set
PM10
2.3
3.8
ISP
7.4
21
The emission factors are expressed in terms of pounds of particulate emitted per
vehicle-mile-traveled. The rest of this section discusses how well the different
predictive emission factors estimate measured values. Because the predictive
emission factors are expressed as equations of different independent variables
("correction factors"), one cannot simply tabulate the measured and predicted values
for comparison. Consequently, a different approach had to be undertaken. Two types
of comparisons were made; each is discussed below.
D.2.1
Predictive Emission Factors for Haul Trucks
The haul truck emission factors currently contained in AP-42 Section 8.24 are:
e10 = 0.0031 (w)3-5
eTSP = 0.0067 (w)3-4 (L}°-2
(D-1 a)
where
e10 ' eTSP =
w =
L =
PM10 or TSP emission factor, expressed in pounds emitted per
vehicle-mile-traveled (Ib/VMT)
mean number of wheels per vehicle traveling the road
surface silt loading, representing the mass per unit area (g/m2) of
loose material that passes a 200 mesh screen upon dry sieving
D-4
-------
The new emission factor equations presented in Section 5 of this report are:
e,0 = 3.4 (s/3)°-8 (M/2)-°-2 fD.2a)
eTSP = 16 (s/3)°-9 (M/2)'0-2 (D-2b)
where:
eio' %SP = pM10 or TSP emission factor, expressed in pounds emitted per
vehicle-mile-traveled (Ib/VMT)
s = surface material silt content, representing the mass fraction {%) of loose
road surface material that passes a 200 mesh screen upon dry sieving
M = surface material moisture content, representing the mass fraction
(%) of water in the road surface material, as determined by
weight loss upon drying
D.2.2 Comparison Between Mean Measured and Predicted Emission Factors
The first comparison investigated how well the predictive equations could predict
"typical" emission factors on the basis of "typical" correction parameters. This
approach compared the geometric mean measured emission factor against the value
predicted when the geometric mean value for each correction parameter is
substituted into the predictive equation. Tables D-2 and D-3 shows the results from
these comparisons, for PM10 and TSP, respectively.
Several features are evident in the first comparison:
1 . The current factors (Equations D-1 a and D-1 b) overpredictthe old measurement
data set. In other words, the current factors are not "centered" -- that is, the
current factors possess a slight degree of systematic overprediction of the
measured emission factors in the old data set used to develop the predictive
factors.
2. On the other hand, when the current factors were applied independently (i.e,
to data that were not used to develop the predictive emission factor), the
current emission factor equations severely underestimate measured emission
factors in the new data set.
3. When Equations D-2a and D-2b are applied independently, the new factors
overpredict measured emission factors in the old data set.
4. Agreement for the PM10 size fraction is better than that for the TSP size
D-5
-------
c
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"5
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D-7
-------
fraction in all comparisons.
Thus, this comparison indicates that the old factors possess some degree of
overprediction of the old emission data set and that they do not provide acceptable
estimates for emissions from haul trucks of type currently in use. Section 5 of the
report discusses this issue in more detail.
D.2.3 Comparison between Mean and Predicted "Production-Based" Emission
Factors
The second comparison attempts to at least partially correct for the most important
difference between source conditions in the old and new data sets. The old data set
consists of tests with haul trucks of approximately 100 ton capacity (roughly 85 to
120 tons) while haul trucks in the new data base are almost all 240 ton in capacity,
with a few 170-ton capacity vehicles.
To take the difference in haul truck capacity into account, the second comparison
considered emission factors referenced to "production" -- defined here as moving a
mass of material a certain distance -- rather than simply vehicle miles traveled (VMT).
To illustrate the approach, consider a hypothetical situation of moving a fixed amount
of coal {say, 10,000,000 tons per year) a fixed distance (say, 2 miles). As Table D-4
shows, the mean measured PM10 emission factors would lead one to estimate annual
PM10 emissions of 460 tons in the first case and 320 tons in the second.
Table D-4 Hypothetical Production Example
Case
100-ton capacity trucks
240-ton capacity trucks
Annual VMT
required for
Production9
400,000
170,000
Mean Measured
Emission Factor
(Ib/VMT)
2.3
3.8
Estimated
Annual
Emissions
(tons/yr)
460
320
(10,000,000 tons per year) by the capacity of the haul trucks.and multiplying
by twice the distance (2 miles) the material is to be transported. (Each mile of
transport involves two miles of travel -- one with the truck loaded, one with the
truck empty.)
The above example also illustrates how haul truck emission factors can be expressed
in terms of emissions due to "production". One can change the basis from pounds
D-8
-------
emitted from one mile of haul truck travel to pounds emitted to transport one ton of
material (coal or overburden) one mile. Thereafter, one can again consider how well
the predictive equations predict "typical" emission factors, except that emission
factors have been reexpressed in terms of production. Tables D-5 and D-6 shows the
results from these comparisons, for PM10 and TSP, respectively.
In Tables D-5 and D-6, measured production-based emission factors were found by
dividing by the geometric mean measured emission factor by the range of nominal
haul truck capacity for the data set -- 85 to 120 tons for the olde data set, 170 to
240 for the new. The predicted production-based emission factors were found by
dividing the predicted emission factor by the range of nominal truck capacity in the
data base underlying the emission factor equation. That is, predictions from Equation
D-1 a or D-1 b are divided by the range of 85 to 120 tons. Predictions from Equation
D-2a or D-2b are divided by the range of 170 to 240 tons.
The change in the basis of the emission factor does not affect any comparisons
between an emission factor and its supporting data base. Thus, for example, the
current Section 8.24 factors are still not "centered" and show slight overprediction
of measured emission factors in the older data set.
On the other hand, the change in basis partially "corrects" for some shortcomings of
the emission factors applied to independent (i.e.,-not used in the development of the
factors) data:
1. Although the current factors (Equations D-1 a and D-1b) still underestimate
measured emission factors in the new data set, the degree of underprediction
is substantially lower for the production-based emissions. Using the nominal
haul truck capacities of 100 and 240 ton respectively for the old and new data
sets, the recast PM10 factor is underpredicted by only 30%.
2. Similarly, while the new factors (Equations D-2a and D-2b) still overpredict
measured emission factors in the old data set, the degree of overestimation is
far less. When nominal capacities are used, the recast PM10 factor is only
overpredicted by 20%.
3. Agreement is still better for PM10 than for TSP.
In summary, the new haul truck emission factors (Equations D-2 and D-2b) will almost
always result in higher "Ib/VMT" values than the emission factors currently in Section
8.24. Recall, however, that the emission factors were developed from data sets with
very different source conditions. When emissions are considered on the basis of
production, however, the predictive emission factors do a far better job of estimating
measured emissions in the independent data set.
D-9
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-------
D.3 Light- and Medium-Duty Vehicular Traffic
A earlier review of emission factors0'2 recommended that the "generic" unpaved road
equation in AP-42 Section 11.2.1 replace the current factor for light- to medium-duty
traffic emission factor in Section 8.24. That recommendation was based on a
comparison against independent emission data. The present study reinforced the
earlier recommendation, based on not only "new" data but also reexamination of the
"old" data set supporting the current Section 8.24 expression.
The current Section 8.24 emission factor for liaht- to medium- duty traffic is
e10 = 2.23 (M)-4-3 (D-3a)
eTSP = 5.79 (M)-4-° (D-3b)
where
e10, eTSp = PM10 or TSP emission factor, expressed in terms of pounds
emitted per vehicle-mile-traveled (Ib/VMT)
M = surface material moisture content, representing the mass fraction
(%) of water in the road surface material, as determined by
weight loss upon drying
The "generic" unoaved road emission factor expression in AP-42 Section 11.2.1 is
e10 = 2.1 (s/12) (S/30) (W/3)°-7 (w/4)°-5 (D-4a)
eTSP = 4.7 (s/12) (S/30) (W/3)°-7 (w/4)°-5 (D-4b)
where:
BIO* STSP = PMio or TSP emission factor, expressed in pounds emitted per
vehicle-mile-traveled (Ib/VMT)
s = surface material silt content, representing the mass fraction (%)
of loose road surface material that passes a 200 mesh screen
upon dry sieving
S = average speed (mph) of vehicles traveling the road
W = average weight (tons) of vehicles traveling the road
w = mean number of wheels per vehicle traveling the road
The most important feature of the current Section 8.24 emission factors (Equations
D-3a and D-3b) is the highly nonlinear dependence upon moisture content. The range
D-12
-------
of moisture contents presented in Section 8.24 is quite small, only 0.9 through 1.7%.
These emission factors have been found to be capable of producing very unacceptable
estimates. The earlier review0'2 found that the current Section 8.24 factors for light-
to medium-duty vehicles could overpredict independentmeasured emission factors by
two orders of magnitude. The present study reinforced this finding; one measured
PM10 emission factor was overestimated by a factor of 40, while the TSP factor was
overestimated by a factor of 20.
The present study combined the old and new data sets and found that the generic
unpaved road emission factors (Equations D-4a and D-4b) capable of producing
acceptable estimates over a broad range of source conditions at surface coal mines.
Based on results from the the combined old and new data sets, the recommended
use of the generic emission factor in place of the Section 8.24 will reduce predicted
emissions approximately 20% for PM10 and 40% for TSP.
D-13
-------
REFERENCES
D-1. Axeitell, K. and C. Cowherd. Improved Emission Factors for Fugitive
Dust from Western Surface Coal Mining Sources. Two Volumes,. U.S.
Environmental Protection Agency. July 1981.
D-2. Muleski, G. E. Review of Surface Coal Mining Emission l-actors. U.S.
Environmental Protection Agency Contract No. 68-DO-0137, Assignment
10. July 1991.
D-14
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APPENDIX E
LOG OF COMMENTS
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References
1. Muleski, G. E. and B. Henk. Review and Update of Miscellaneous
Sources in Chapter 11, AP-42. Assignment 44, EPA Contract
68-DO-0123. April 1993.
2. Henk, B. and G. E. Muleski. Background Document for AP-42
Section 11.2.4, Heavy Construction Operations. Draft. Work Assignment
44, EPA Contract 68-DO-0123. April 1993.
3. U. S. Environmental Protection Agency. Control of Open Fugitive Dust
• Sources. EPA-450/3-88-008. Research Triangle Park, NC.
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This i« shown at tha bottom ot page 79 in January 17,
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especially in terms of their predictive accuracy wheix
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currently constitutes 'typical' operations -- indlcal
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References
1. Shearer, D. L, R. A. Dougherty, and C. C. Easterbrook. Coal Mining
Emission Factor Development and Modeling Study. TRC Environmental
Consultants, Inc. July 1981.
2. Vardiman, S. and K. Winges. Powder River Basin Model Validation
Analysis. TRC Environmental Consultants, Inc. August 1991.
3. Pyle, B. E. and J. D. McCain. Critical Review of Open Source
Particulate Emission Measurements -- Part II: Field Comparison. Work
Assignment 002, EPA Contract 68-02-3936. February 1986.
4. Brookman, E. T. Critical Review of Open Source Particulate Emission
Measurements - Current Procedures. TRC Environmental Consultants,
Inc. November 1983.
5. Brookman, E. T. Critical Review of Open Source Particulate Emission
Measurements - Part II: Field Comparison. TRC Environmental
Consultants, Inc. July 1984.
6. Cole, C. F., B. L. Murphy, J. S. Evans, and A. Garsd. Quantification of
Uncertainties in EPA's Fugitive Emissions and Modeling Methodologies
at Surface Coal Mines. TRC Environmental Consultants, Inc. February
1985. y
7. Muleski, G. E. Review of Surface Coal Mining Emission Factors. Work
Assignment 18, EPA Contract 68-DO-0137. July 1991.
8. Muleski, G. E., C. Cole and S. Vardiman. Development of a Plan for
Surface Coal Mine Study. Work Assignment 68, EPA Contract 68-DO-
0137. October 1991.
9. Henk, B. and G. E. Muleski. Background Document for AP-42 Section
11.2.4, Heavy Construction Operations. Draft. Work Assignment 44
EPA Contract 68-DO-0123. April 1993.
E-16
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TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
1. REPORT NO.
EPA-454/R-95-010
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Surface Coal Mine Emission Factor Study: Test Report
5. REPORT DATE
January 1994
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
G.E. Muleski, G. Garmen, C. Cowherd
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, MO 64110
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-D2-0159
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
Emission Factor and Inventory Group
Emissions, Monitoring, and Analysis Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
Final Report
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
Technical Representative: Dennis Shipman
16. ABSTRACT
This report presents the results of an emissions sampling program to measure airborne paniculate matter
released from the activities conducted at open pit coal mines in the western United States. The principal objective
of this study was to compare field measurements against available emission factors for surface coal mines and to
revise the factors as necessary. The field measurements were conducted during the fall of 1992 at the Cordero
surface coal mine in the Powder River Basin of Wyoming. A total of 36 PM-10 emission tests, distributed over
various sources and five test sites, was performed. The report presents the sampling methodology used, the
emission measurement results, the ambient monitoring results, the results of the reexamination of current emission
factors, and recommended emission factor models for haul truck travel, light-duty vehicle travel and scraper travel
on unpaved roads.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSAT1 Field/Group
Air Pollution
Surface Coal Mines
Fugitive Dust
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
165
20. SECURITY CLASS (Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE
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