EPA-650/4-74-005-e
November 1975 Environmental Monitoring Series
GUIDELINES FOR DEVELOPMENT
OF A QUALITY ASSURANCE PROGRAM:
VOLUME V - DETERMINATION
OF SULFUR DIOXIDE EMISSIONS
FROM STATIONARY SOURCES
U.S. Environmental Protection Agency
Office of Research and Development
Washington, 0. C. 20460
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EPA-650/4-74-005-e
GUIDELINES FOR DEVELOPMENT
OF A QUALITY ASSURANCE PROGRAM
VOLUME V - DETERMINATION
OF SULFUR DIOXIDE EMISSIONS
FROM STATIONARY SOURCES
by
J.W. Buchanan and D.E. Wagoner
Research Triangle Institute
Research Triangle Park, North Carolina 27709
Contract No. 68-02-1234
ROAP No. 26BGC
Program Element No. 1HA327
EPA Project Officer: Steven M. Bromberg
Environmental Monitoring and Support Laboratory
Office of Monitoring and Technical Support
Research Triangle Park, North Carolina 27711
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
Washington, D.C. 20460
November 1975
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EPA REVIEW NOTICE
This report has been reviewed by the U.S. Environmental Protection
Agency and approved for publication. Approval does not signify that
the contents necessarily reflect the views and policies of the Environ-
mental Protection Agency, nor does mention of trade names or commer-
cial products constitute endorsement or recommendation for use.
RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environ-
mental Protection Agency, have been grouped into series. These broad
categories were established to facilitate further development and applica-
tion of environmental technology. Elimination of traditional grouping was
consciously planned to foster technology transfer and maximum interface
in related fields. These series are:
1. ENVIRONMENTAL HEALTH EFFECTS RESEARCH
2 . ENVIRONMENTAL PROTECTION TECHNOLOGY
3. ECOLOGICAL RESEARCH
4. ENVIRONMENTAL MONITORING
5. SOCIOECONOMIC ENVIRONMENTAL STUDIES
6. SCIENTIFIC AND TECHNICAL ASSESSMENT REPORTS
9. MISCELLANEOUS
This report has been assigned to the ENVIRONMENTAL MONITORING
series. This series describes research conducted to develop new or
improved methods and instrumentation for the identification and quantifica-
tion of environmental pollutants at the lowest conceivably significant
concentrations. It also includes studies to determine the ambient concentra-
tions of pollutants in the environment and/or the variance of pollutants
as a function of time or meteorological factors.
This document is available to the public for sale through the National
Technical Information Service, Springfield, Virginia 22161.
Publication No. EPA-650/4-74-005-e
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TABLE OF CONTENTS
SECTION PAGE
I INTRODUCTION 1
II OPERATIONS MANUAL 4
2.0 GENERAL 4
2.1 EQUIPMENT SELECTION 8
2.2 PRESAMPLING PREPARATION 13
2.3 ON-SITE MEASUREMENTS 22
2.4 POSTSAMPLING OPERATIONS (BASE LABORATORY) 28
III MANUAL FOR FIELD TEAM SUPERVISOR 35
3.0 GENERAL 35
3.1 ASSESSMENT OF DATA QUALITY (INTRATEAM) 37
3.2 SUGGESTED PERFORMANCE CRITERIA 39
3.3 COLLECTION AND ANALYSIS OF INFORMATION 40
TO IDENTIFY TROUBLE
IV MANUAL FOR MANAGER OF GROUPS OF FIELD TEAMS 49
4.0 GENERAL 49
4.1 FUNCTIONAL ANALYSIS OF THE TEST METHOD 53
4.2 ACTION OPTIONS 64
4.3 PROCEDURES FOR PERFORMING A QUALITY AUDIT 70
4.4 DATA QUALITY ASSESSMENT 73
V REFERENCES 85
APPENDIX A METHOD 6 87
APPENDIX B ILLUSTRATED AUDIT PROCEDURES AND CALCULATIONS 95
APPENDIX C GLOSSARY OF SYMBOLS 99
APPENDIX D GLOSSARY OF TERMS 101
ill
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LIST OF ILLUSTRATIONS
FIGURE NO. PAGE
1 Operational flow chart of the measurement process 5
2 Dry gas meter sample calibration data 17
3 On-site sampling data sheet 25
4 Sample analysis data sheet 34
5 Sample control chart for standardized barium 46
perchlorate solution
6 Sample control chart for range of duplicate 47
measurements
7 Summary of data quality assurance program 5<:
8 Added cost versus data quality (CV) for selected 68
action options
9 Added cost versus data quality (CV. ) for selection 69
action options
10 Example illustrating p < 0.10 and satisfactory data 77
quality
11 Example illustrating p > 0.10 and unsatisfactory 77
data quality
12 Flow chart of the audit level selection process 80
13 Average cost versus audit level (n) 83
6-1 S09 sampling train 88
XV
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LIST OF TABLES
TABLE NO. PAGE
1 Suggested performance criteria 39
2 Assumed means and coefficients of variation of 58
variables influencing emission rate determinations
for SC>2
3 Variance analysis for Vm 60
4 Variance analysis for Cso 60
5 Variance analysis for Qs 61
6 Variance analysis for reproducibility of ER 61
7 Assumed within-laboratory, between-laboratory, 66
and laboratory bias for action options
8 Computation of mean difference, 3, and standard 78
deviation of differences, sj
9 Sample plan constants, k for P {not detecting a 79
lot with proportion p outside limits L and U}<0.1
V
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SECTION I INTRODUCTION
This document presents guidelines for developing a quality assur-
ance program for Method 6—Determination of Sulfur Dioxide Emissions
from Stationary Sources. This method was initially published by the
Environmental Protection Agency in the Federal Register, December 23,
1971, and a later version is reproduced as appendix A of this report
for convenience of reference.
This document is divided into four sections:
Section I, Introduction. The introduction lists the overall
objectives of a quality assurance program and delineates the program
components necessary to accomplish the given objectives.
Section II, Operations Manual. This operations manual sets
forth recommended operating procedures to assure the collection of data
of high quality, and instructions for performing quality control checks
designed to give an indication or warning that invalid data or data of
poor quality are being collected, allowing for corrective action to be
taken before future measurements are made.
Section III, Manual for Field Team Supervisor. This manual contains
directions for assessing data quality on an intrateam basis and for col-
lecting the information necessary to detect and/or identify trouble.
Section IV, Manual for Manager of Groups of Field Teams. This manual
presents information relative to the test method (a functional analysis)
to identify the important operations, variables, and factors; a methodology
for comparing action options for improving data quality and selecting the
preferred action; and statistical properties of and procedures for carrying
out a quality audit for an independent assessment of data quality.
The objectives of this quality assurance program for Method 6 are to:
1. Minimize systematic errors (biases) and control precision
within acceptable limits in the measurement process,
2. Provide routine indications for operating purposes of
satisfactory performance of personnel and/or equipment,
3. Provide for prompt detection and correction of conditions
that contribute to the collection of poor quality data, and
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4. Collect and supply information necessary to describe the
quality of the data.
To accomplish the above objectives, a quality assurance program must
contain the following components:
1. Recommended operating procedures,
2. Routine training of personnel and evaluation of performance
of personnel and equipment,
3. Routine monitoring of the variables and parameters that may
have a significant effect on data quality,
4. Development of statements and evidence to qualify data and
detect defects, and
5. Action strategies to increase the level of precision/accuracy
in the reported data.
Component (2) above will be treated in the final report of this con-
tract.
Implementation of a properly designed quality assurance program should
enable measurement teams to achieve and maintain an acceptable level of pre-
cision and accuracy in their sulfur dioxide emissions measurements. It
will also allow a team to report an estimate of the precision of its measure-
ments for each source emissions test.
Variability in emission data derived from multiple tests conducted
at different times includes components of variation from:
1. Process conditions,
2. Equipment and personnel variation in field procedures, and
3. Equipment and personnel variation in the laboratory.
In many instances time variations in source output may be the most
significant factor in the total variability. The error resulting from
this component of variation is minimized by knowing the time characteristics
of the source output and sampling proportionally. The sampling period
should span at least one complete output cycle when possible. If the cycle
is too long, either the sample collection should be made during a portion of
the cycle average, or multiple samples should be collected and averaged.
Quality assurance guidelines for Method 6 as presented here are de-
signed to insure the collection of data of acceptable quality by prevention,
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detection, and quantification of equipment and personnel variations in
both the field and the laboratory through:
1. Recommended operating procedures as a preventive measure,
2. Quality control checks for rapid detection of undesirable
performance, and
3. A quality audit to independently verify the quality of the
data.
The scope of this document has been purposely limited to that of a
field and laboratory document. Additional background information will be
contained in the final report under this contract.
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SECTION II OPERATIONS MANUAL
2.0 GENERAL
This manual sets forth recommended procedures for determination of
sulfur dioxide emissions from stationary sources according to Method 6.
(Method 6 is reproduced from the Federal Register, and is included as
appendix A of this document.) Quality control procedures and checks
designed to give an indication or warning that invalid or poor quality
data are being collected are written as part of the operating procedures
and are to be performed by the operator on a routine basis. Results from
certain strategic quality control checks will be used by the supervisor
for the assessment of data quality.
The sequence of operations to be performed for each field test is
given in figure 1. Each operation or step in the method is identified
by a block. Quality checkpoints in the measurement process, for which
appropriate quality control limits are assigned, are represented by blocks
enclosed by heavy lines. Other quality checkpoints involve go/no-go
checks and/or subjective judgments by the test team members with proper
guidelines for decisionmaking spelled out in the procedures.
The precision/accuracy of data obtained from this method depends upon
equipment performance and the proficiency and conscientiousness with which
the operator performs his various tasks. From equipment checks through
on-site measurements, calculations, and data reporting, this method is
susceptible to a variety of errors. Detailed instructions are given for
minimizing or controlling equipment error, and procedures are recommended
to minimize operator error. Before using this document, the operator
should study Method 6 as reproduced in appendix A in detail.
It is assumed that all apparatus satisfies the reference method
specifications and that the manufacturer's recommendations will be followed
when using a particular piece of equipment.
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PRESAMPLING PREPARATION
1. SELECT THE EQUIPMENT APPROPRIATE
FOR THE PROCESS (SOURCE) TO BE
TESTED. CHECK THE EQUIPMENT FOR
PROPER OPERATION.
2. CALIBRATE EQUIPMENT WHEN FIRST
PURCHASED AND WHEN DAMAGED OR
ERRATIC BEHAVIOR IS OBSERVED
3. PACK EQUIPMENT IN A MANNER TO
PRECLUDE BREAKAGE OR DAMAGE
DURING HANDLING AND SHIPMENT
ON-SITE S02 MEASUREMENT
4.
TRANSPORT EQUIPMENT FROM FLOOR
LEVEL TO THE SAMPLING SITE BY
THE BEST MEANS AVAILABLE.
ASSEMBLE THE EQUIPMENT ON-SITE
AND PERFORM AN OPERATIONAL CHECK
(EVALUATION OF THE SYSTEM)
DETERMINE THE TRAVERSE POINT
(SAMPLE POINT) ACCORDING TO
METHOD 1.
EQUIPMENT SELECTION
AND
CHECK
EQUIPMENT
CALIBRATION
PACKAGE EQUIPMENT
FOR SHIPMENT
TRANSPORT EQUIPMENT
TO TEST SITE
ASSEMBLE/CHECK
EQUIPMENT
ON-SITE
DETERMINE TRAVERSE
POINT (METHOD 1)
7.
DETERMINE THE INSIDE AREA OF STACK
BY (1) MEASURING THE DIAMETER, OR
(2) MEASURING THE CIRCUMFERENCE
AND CORRECTING FOR WALL THICKNESS.
DETERMINE INSIDE AREA
OF STACK
Figure 1. Operational flow chart of the measurement process.
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10.
11.
12.
13.
PERFORM THE VELOCITY TRAVERSE OF
THE STACK GAS USING THE QUALITY
ASSURANCE DOCUMENT FOR METHOD 2.
PERFORM VELOCITY
TRAVERSE (METHOD 2)
DETERMINE THE MOISTURE CONTENT OF
THE STACK GAS USING THE QUALITY
ASSURANCE DOCUMENT FOR METHOD 4.
DETERMINE THE MOLECULAR WEIGHT OF
THE STACK GAS (WET BASIS) USING THE
QUALITY ASSURANCE DOCUMENT FOR METHOD
3 AND THE RESULTS OF STEP 9 ABOVE.
DETERMINE MOISTURE
CONTENT (METHOD 4)
DETERMINE MOLECULAR
WEIGHT (METHOD 3)
DETERMINE THE VOLUMETRIC FLOW RATE
OF THE SOURCE USING THE QUALITY
ASSURANCE DOCUMENT FOR METHOD 2.
DETERMINE VOLUMETRIC
FLOW RATE (METHOD 2)
PREPARE ABSORBING REAGENTS AND
PIPETTE 15 ml OF 80 PERCENT
ISOPROPANOL (BUBBLER) AND 15 Ml
OF 3 PERCENT H (IMPINGERS).
SET UP SAMPLING TRAIN AND LEAK-
CHECK SYSTEM.
PREPARE ABSORBING REAGENTS.
ADD TO COLLECTION
SYSTEM
SET UP TRAIN.
LEAK-CHECK TOTAL SYSTEM.
14. PERFORM SAMPLE COLLECTION (PRO-
PORTIONAL) ACCORDING TO THE PRO-
CEDURE GIVEN IN SUBSECTION 2.3.3-
COLLECT SAMPLE
Figure 1. Operational flow chart of the measurement process (continued)
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POSTSAMPLING OPERATIONS
15. PERFORM LEAK CHECK AT END OF TEST.
16. QUANTITATIVELY RECOVER IMPINGER
SOLUTIONS.
17.
VISUALLY INSPECT EQUIPMENT FOR
DAMAGE AFTER ALL MEASUREMENTS HAVE
BEEN MADE AND RECORDED.
18.
PACK EQUIPMENT AND SAMPLES FOR SHIP-
MENT BACK TO THE BASE LABORATORY.
19. PERFORM POST CALIBRATION AND
ANALYSIS OF SAMPLES FOR SULFUR
DIOXIDE.
20. PERFORM CALCULATIONS UTILIZING
ALL FIELD AND CALIBRATION DATA.
21. FORWARD DATA WITH PERTINENT
REMARKS CONCERNING QUALITY CHECKS
FOR FURTHER INTERNAL REVIEW OR TO
USER.
LEAK-CHECK
SAMPLING TRAIN
TRANSFER COLLECTED SAMPLES
TO SHIPPING CONTAINERS
INSPECT EQUIPMENT
FOR DAMAGE
PACK EQUIPMENT AND SAMPLES
FOR SHIPMENT
PERFORM POST-CALIBRATION
ANALYSIS
PERFORM CALCULATIONS
REPORT DATA
Figure 1. Operations flow chart of the measurement process (continued)
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2.1 EQUIPMENT SELECTION
A schematic of an assembled sulfur dioxide sampling train with all
components identified is shown in figure 6-1 of appendix A. Specifications,
criteria, and/or design features as applicable are given in this section
to aid in the selection of equipment to assure the collection of data of
consistent quality. Procedures and, where applicable, limits for acceptance
checks are given. The descriptive title, identification number, if applica-
ble, and the results of the acceptance check are recorded in the receiving
record file, dated, and signed by the individual performing the check. Also,
if a calibration is required as part of the acceptance check, the data are
recorded in the calibration log book.
2.1.1 Sampling Probe.
2.1.1.1 Design Characteristics. The sampling probe should be of borosilicate
(Pyrex) glass of 5-6 mm inside diameter encased in a stainless steel sheath
and equipped with a heating system capable of maintaining a gas temperature
at 21 175° C (350° F) at the exit end of the probe during sampling (ref. 1).
Stack gases at high temperatures should be cooled to less than 375° C (700° F).
The sampling tip of the probe should have retaining ridges on both sides of
the particulate filter to hold the filter in place. For stack gas tempera-
tures in excess of 425° C (800° F), a probe fabricated from quartz can be
used. The main objective is for the probe material to be nonreactive with
the gas constituents; hence, not introducing a bias into the analytical method.
2.1.1.2 Acceptance Check„ Upon receiving a new probe, it should be
visually checked for specification; i.e., is it the length and composition
ordered? The probe should be checked for cracks or breaks and leak-checked
on a sampling train as described in subsection 2.2.2.2. Also, the probe
heating system should be calibrated according to subsection 2.2.2.2. Any
probe not satisfying the acceptance check should be repaired, if possible,
or rejected.
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2.1.2 Midget Bubbler/Impingers.
2.1.2.1 Design Characteristics. One midget bubbler (25 m£) per train
is required, with glass wool packed in the top to prevent carryover of
sulfuric acid mist. The porosity of the bubbler should be type A (145-175
microns).
Three midget impingers (25 m&) with orifices calibrated to deliver
2.5-3.0 1pm at 2.2 mmHg vacuum are required per sampling train. Connections
between midget bubbler and impinger should be of inert materials (plastic
or rubber tubing is not desirable because of absorption and desorption of
gaseous species) (ref. 2).
2.1.2.2 Acceptance Check. Each bubbler/impinger is checked visually for
damage, such as breaks or cracks; and manufacturing flaws, such as poorly
shaped connections.
2.1.3 Vacuum Pump.
2.1.3.1 Design Characteristics. The vacuum pump should be capable of main-
taining a flow rate of approximately 3 to 5 &/min for pump inlet vacuums
up to 500 mm of Hg with the pump outlet near standard pressure, i.e., 760 mm
of Hg. The pump must be leak-free when running and pulling a vacuum (inlet
plugged) of 380 mm of Hg. Two types of vacuum pumps are commonly used. They
are a modified sliding fiber vane pump and a diaphragm pump. For safety
reasons, the pump should be equipped with a three-wire electrical cord.
2.1.3.2 Acceptance Check. Install a vacuum gage or, preferably, a mercury
manometer in the pump inlet line. Plug the inlet line and run the pump until
the vacuum gage reads 380 mm of Hg. Vacuum, then clamp the pump outlet line
and turn off the pump. The vacuum reading should not change noticeably in
5 minutes.
2.1.4 Dry Gas Meter.
2.1.4.1 Design Characteristics. The dry gas meter must be capable of
measuring total volume with an accuracy of + 2 percent. It should be rated
at about 3 £/minute.
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2.1.4.2 Acceptance check. Check new dry gas meters visually for damage
and perform a calibration according to subsection 2.2.2.7. Reject the dry
gas meter if it is damaged, behaves erratically, or cannot be adjusted to
agree to within + 2 percent of the wet test meter over the flow rate range
of interest.
2.1.5 Rotameter.
2.1.5.1 Design Characteristics. A rotameter or its equivalent (range of
0-5 £/min) is used to monitor and control sample flow rate.
2.1.5.2 Acceptance Check. A calibration curve is to be supplied by the
manufacturer. The rotameter is checked against the calibrated dry gas
meter with which it is to be used. If the rotameter is not within + 5 per-
cent of the manufacturer's calibration curve, recalibrate and construct a
new calibration curve (this procedure will also correct for local pressures
that differ from standard pressure at which the manufacturer's calibration
curve was developed).
2.1.6 Needle Valve.
2.1.6.1 Design Characteristics. A metering valve with convenient sized
fittings is required in the sampling train to adjust the sample flow rate.
It is recommended that the needle valve be placed on the vacuum side of the
pump.
2.1.7 Drying Tube.
2.1.7.1 Design Characteristics. A drying tube packed with 6- to 16-mesh
indicating-type silica gel, or equivalent, to dry the sample. A simple
solution to this is utilizing polyethylene drying tubes and 3/8-in. i.d. tub-
ing. Glass wool should be packed in each end of the tube to hold the
silica gel and protect the sampling system. Plastic tubing can be utilized
in connections past the collection system without the possibility of
affecting the sample concentration. The drying tube should have a minimum
capacity of 30-50 g of silica gel.
2.1.7.2 Acceptance Check. Visually check the drying tube for proper size
and damage.
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2.1.8 Thermometers.
2.1.8.1 Design Characteristics. Dial-type thermometers with ranges of -
to 50° c are suitable for monitoring the inlet and outlet temperatures of the
dry gas meter (only one is required if the average temperature is measured).
2.1.8.2 Acceptance Check. Dial-type thermometers are easily damaged. Each
new thermometer is checked visually for damage, such as a dented or bent
stem. Each thermometer should read within + 3° C of the true value when
checked in an ice water bath. Damaged thermometers that cannot be calibrated
are rejected.
2.1.9 Barometer.
2.1.9.1 Design Characteristics. A barometer, usually an aneroid barometer,
should be capable of measuring atmospheric pressure to within 2.5 mm of Hg
(0.1 in. of Hg). An alternative is to obtain the uncorrected barometric
pressure from a nearby weather station.
2.1.9.2 Acceptance Check. Check the field barometer against a mercury-in-
glass barometer or equivalent. Adjust the field barometer to agree with the
mercury barometer, if they differ by more than + 5 mm of Hg (0.2 in. of Hg) .
Reject the barometer if it cannot be adjusted to agree with the reference
barometer.
2.1.10 Stack Gas Velocity Measuring System.
See the Quality Assurance Document of this series for Method 2 - Determi-
nation of Stack Gas Velocity and Volumetric Flow Rate (type-S pitot tube)
for a discussion of this system (ref. 5).
2.1.11 Stack Gas Temperature Measuring System.
This system is treated as a subsystem of the velocity measuring system
and is discussed in the document referenced in subsection 2.1.10.
2.1.12 Stack Gas Pressure Measuring System.
This system is treated as a subsystem of the velocity measuring system
and is discussed in the document referenced in subsection 2.1.10.
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2.1.13 Sample Recovery Apparatus.
2.1.13.1 Design Characteristics. Sample recovery apparatus is described
below.
2.1.13.1.1 Glass wash bottles. Two or more glass wash bottles are needed
for quantitative recovery of collected samples.
2.1.13.1.2 Polyethylene storage bottles. One 125-m£ polyethylene bottle
is required for each collected sample, plus one polyethylene container to
retain a blank for each absorbing solution used in testing.
2.1.13.2 Acceptance Check. Visually check wash bottles and/or storage
bottles for damage.
2.1.14 Analysis Glassware.
2.1.14.1 Design Characteristics. Analysis glassware is described below.
2.1.14.1.1 Pipettes. Several volumetric pipettes (class A), including 5-,
10-, 20-, and 25-m£ should be available for the analysis.
2.1.14.1.2 Volumetric flasks. Volumetric flasks (class A), are required and
should include 50-, 100-, and 1000-m£ sizes.
2.1.14.1.3 Burettes. A 5- or lO-mfc microburette is required for samples
of low concentrations and a 50-mJi standard burette for all other titra-
tions.
2.1.14.1.4 Erlenmeyer flasks. Several 125-m2, Erlenmeyer flasks are
required for titration of vessels.
2.1.14.2 Acceptance Check. Check all glassware for cracks, breaks, and
manufacturing flaws.
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2.2 PRESAMPLING PREPARATION
2.2.1 Preliminary Site Visit (Optional).
The primary objective of preliminary site visit is to gather
information to design and implement an efficient source test. Prior
preparation will result in the prevention of unwarranted loss of time,
unnecessary expenses, and injury to test and/or plant personnel. A test
plan developed from a thorough set of parameters will result in more pre-
cise and accurate results. Test experience and a complete set of sampling
equipment may allow dropping the preliminary site visit.
2.2.1.1 Process (Background Data on Process and Controls). It is recommend-
ed that the testers, before a preliminary site visit is made or before
performing tests, become familiar with the operation of the plant. Data
from similar operations that have been tested should be noted for
further consideration of the ifnal analytical results.
2.2.1.2 Sampling Site Preparedness. Each facility tested should provide an
individual who understands the plant process and who has the authority to
make decisions concerning plant operation to work with the team. This would
include decisions concerning whether the plant would be operated at normal
load conditions or at a rated capacity. If the source is cyclic in nature,
information must be made available as to the timing of the sequence and the
duration of the cycle. This individual will supervise installation of ports,
sampling platform and electrical power. If the above installations are already
in existence, they must be examined for their suitability in obtaining a
valid test and to insure that all facilities meet minimum safety standards. If
ports have to be installed, specify 75- or 100-mm (3- or 4-in.) ports with
plugs. Port locations should be based upon Method 1 of the Federal Register
(ref. 1). Port locations must be based upon existing technical know-
ledge and sound judgment. An electrical service should be available at
the sampling area with 115-volt and 20-ampere service.
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2.2.1.3 Stack Gas Conditions. The following should be determined on the
initial site survey, either by measurement or estimation:
T = Average stack gas temperature
avg
P = The static pressure (positive or negative)
s
AP = The average velocity head
% H_0 - Moisture content
M = Gas constituent concentration .
s
The above parameters can be roughly determined using an inclined manometer
with a 0- to 125-mm range, a type-S pitot tube, manual thermometer or thermo-
couple attached to the pitot tube with potentiometric readout device. The
moisture content (approximate) can be determined with a wet bulb-dry bulb
technique (acid gases >_ 10 ppm SO., will give high results) or by condensation
(Method 4), and the gaseous constituents by hand-held indicator kits. Nomo-
graphs are useful in checking and/or estimating required preliminary data
(ref. 4).
2.2.1.4 Method and Equipment for Transporting Apparatus to Test Site.
The preliminary site visit (or correspondence) should include a logical plan
between plant personnel and tester on how the equipment can best be trans-
ported to the sampling site. A presampling area must be designated in which
absorbing solution can be prepared and can be added to the collection system.
In addition to the above, it is recommended, when permitted, that pictures
be taken of the hoisting area and sampling area (ports and sampling platform)
so that any further discussion will be clarified.
2.2.2 Apparatus Check and Calibration.
2.2.2.1 Sampling Train. The design specifications of the SO train used by
the EPA is given in appendix A of this document (fig. 6-1). Commercial models
of this system are available. Each individual or fabricated train must be in
compliance with the specifications in the reference method.
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2.2.2.2 Probe (Filter). Clean the probe internally by brushing, first using
tap water, then distilled, deionized water followed by acetone, and allow it
to dry in the air. In extreme cases, the glass liner can be cleaned with
stronger reagents. In either case, the objective is to leave the glass
liner chemically inert. The probe's heating system should be checked to
see that it is operating properly. The probe temperature can be profiled
with a remote reading thermometer or bv a thermocouple with readout device.
The probe should be sealed on the filter side and checked for leaks at a
vacuum of 380 mm of Hg (15 in. of Hg). The probe must be leak-free under
these conditions. The glass liner should be sealed inside the metal sheath
to prevent diluent air from entering the source (most stacks are under a
negative pressure).
2.2.2.3 Midget Bubbler, Midget Impinger, and Glass Connections. All glass-
ware should be cleaned with detergent and tap water, then distilled, deionized
water. All glassware should be visually inspected for cracks or breakage and
be repaired or discarded.
2.2.2.4 Drying Tubes. Drying tubes should be packed with silica gel and
sealed at both ends.
2.2.2.5 Valve and Rotameter. The flow control valve and rotameter should
be cleaned prior to each field trip or on any instance of erratic behavior.
Follow the maintenance procedure as recommended by the manufacturer.
2.2.2.6 Pump. The vacuum pump should be serviced as recommended by the
manufacturer every 3 months, or upon erratic operation.
2.2.2.7 Dry Gas Meter. The dry gas meter should be calibrated versus a
calibrated wet test meter.
1. The wet test meter should be rated at about 3 Jl/rev with a + 1
percent accuracy. The wet test meter should be operated as
directed by the manufacturer's instructions.
2. Connect the components as shown in figure 6-1 (appendix A).
Charge the bubbler and two impingers with 15 m£ of water each.
Plug the inlet of the bubbler and leak check the system at 250
mm of Hg vacuum. Carefully release the vacuum and turn off the
15
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pump. This leak test will check the total system (excluding
probe) for leaks.
3. Connect the outlet of the wet test meter to the inlet of
the midget bubbler. Run the pump for 15 minutes with the rotam-
eter set at approximately 1.4 £/min to allow the pump to
warm up and to allow the interior surface of the wet test
meter to be wetted.
4. Fill in the following data sheet (figure 2) and calculate y = ratio
of accuracy of wet test meter to dry gas meter:
V (P + D )(t, + 273) ,..
y „ w m m d '_ . (1)
V.P (t + 273)
d m w '
The pressure drop across the wet test meter, D , should be less than 25 mm
of IkO and can be ignored. Equation (1) then becomes
V (t + 273)
Y = — ' (2)
V, (t + 273)
d w
Consider the dry test meter to be in calibration if y is equal to 1.0 + 0.02.
This data can also be used to check the rotameter calibration curve (+ 5
percent) which is furnished by the manufacturer.
2.2.2.8 Pitot Tube. When used exclusively for proportional sampling,
it is not necessary to calibrate the pitot tube. However, if the pitot
tube is used to perform a velocity traverse, it must be calibrated. The
pitot tube should be calibrated according to the procedures given in the
quality assurance document of this series for Method 2 (ref. 5). During
calibration, the pitot tube should be strapped to the sampling probe in
the same configuration that it will be used in the field. Also, the sam-
ple flow rate commonly used in the field should be maintained during cali-
bration.
2.2.3 Reagents and Equipment.
2.2.3.1 Sampling. The bubbler solution which consists of 80 percent
16
-------
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17
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isopropanol is prepared by mixing 80 m£ of reagent-grade isopropanol with 20
of distilled, deionized water. The midget impinger absorbing reagent (hydro-
gen peroxide, 3 percent) is prepared by diluting 100 m£ of 30 percent hydro-
gen peroxide to 1 A with distilled, deionized water. All reagents are to
be prepared fresh daily. All reagents must be ACS reagent grade. Solutions
containing isopropanol should be kept in sealed containers to prevent evapora
tion.
2.2.3.2 Sample Recovery. Distilled, deionized water will be required on
site for quantitative transfer of impinger solutions to storage containers.
This water and reagent grade isopropanol will be used to clean the midget
bubbler after testing and prior to taking of another sample.
2.2.3.3 Source Sampling Tools and Equipment. The need for specific tools
and equipment will vary from test to test. A listing of the most frequently
used tools and equipment is given below.
1. Equipment transportation
a. Lightweight hand truck that can be used to transport
cases and be converted to a four-wheel cart for sup-
porting the meter box control unit.
b. A 13-mm (0.5-in.) continuous filament nylon rope with
large boat snap and snatch block for raising and lower-
ing equipment on stacks and roofs.
c. Tarpaulin or plastic to protect equipment in case of
rain. Sash cord 6 mm (0.25 in.) for securing equipment
and tarpaulin.
d. A canvas bucket is useful for transporting small items
up and down the stack.
2. Safety Equipment
a. Safety harness with nylon and steel lanyards, large
throat snap hooks for use with lanyards for hooking
over guard rails or safety line on stack.
b. A fail-safe climbing hook for use with climbing harness
when climbing ladders having a safety cable.
c. Hard hats with chin straps and winter liners. Gas masks,
safety glasses and/or safety goggles.
18
-------
d. Protective clothing including the following:
appropriate suits, rain, heat, and cold,
gloves (both asbestos and cloth) and steel-toed
shoes.
e. Steel cable 5 mm (0.1875 in.) with thimbles, cable
clips and turn buckles. These are required for
installing a safety line or securing equipment
to the stack structure.
3. Tools and Spare Parts
a. Electrical and power equipment
1. Circular saw
2. Variable voltage transformer
3. Variable speed electrical drill and bits
4. Ammeter-voltmeter-ohmmeter (VOM)
5. Extension cords - light (No. 14 Avg) 2 x 25
6. 2 3-wire electrical adapters
7. 3-wire electrical triple taps
8. Fuses
9. Electrical wire
b. Tools
1. Tool boxes (one large, one small)
2. Screwdrivers
one set flat blade
one set philips
3. C-clamps—2 each; 150 mm (6 in.), 75 mm (3 in.)
c. Wrenches
1. Open end set—6 mm to 25 mm (0.25 to 1 in.)
2. Adjustables—150 mm (6 in.), 300 mm (12 in.)
3. One chain wrench
4. One 300 mm (12 in.) pipe wrench
5. One Allen wrench set
19
-------
d. Miscellaneous
1. Silicone sealer
2. Silicone vacuum grease
3. Pump oil
4. Manometers (gage oil)
5. Anti-seize compound
6. Pipe fittings
7. Dry cell batteries
8. Flashlight
9. Valves
10. Thermometers (dial), 1 m (36 in.) and a remote
reading thermometer
11. Vacuum gage
12. SS tubing - 6, 10, and 125 mm (0.25, 0.375, and
0.5 in.) short lengths
13. Heavy-duty wire (telephone type)
14. Adjustable packing gland
2.2.3.4 Data Recording. Pack one large briefcase with at least the
following:
1. Data sheets or data notebook
2. Carbon paper
3. Slide rule or electronic calculator
4. Psychometric charts
5. Combustion nomographs (ref. 4)
6. Pencils, pens
7. Calibration data, y> c > etc.
2.2.4 Package Equipment for Shipment.
This aspect of any source testing method in terms of logistics, time of
sampling and quality of data is very dependent upon the careful packing of
equipment with regard to (1) accessibility in the field, (2) care of movement
on site, and (3) optimum functioning of measurement devices in the field.
Equipment should be packed under the assumption that it will receive severe
treatment during shipping and field operation. One major consideration in
20
-------
shipping cases is the construction materials. Durable containers are the
most cost effective.
2.2.4.1 Probe. Pack the probe in a case protected by polyethylene or other
suitable packing material. The inlet and outlet should be sealed and protect-
ed from breakage. An ideal container is a wooden case or equivalent lined
with foam material in which separate compartments are cut to hold individual
devices. This case can also contain a pitot tube for velocity determina-
tions. The case should have handles or eye hooks that can withstand hoisting
and be rigid enough to prevent bending or twisting of the devices during
shipping and handling.
2.2.4.2 Midget Bubblers. Impingers, Connectors and Assorted Glassware. All
bubblers, impingers, and glassware should be packed in a rigid container
and protected by polyethylene or other suitable packing material. Indivi-
dual compartments for all glassware will help to organize and protect each
individual piece.
2.2.4.3 Rotameter, Drying Tubes and Volumetric Glassware. A sturdy case
lined with foam material can contain the rotameter, drying tubes, and assort-
ed volumetric glassware.
2.2.4.4 Pump. The vacuum pump should be packed in a shipping container
unless its housing is sufficient for travel. Additional pump oil should
be packed with the pump if oil is required for its operation. It is advisable
to always carry a spare pump in case of pump failure.
2.2.4.5 Wash Bottles and Storage Containers. Storage containers and miscel-
laneous glassware should be packed in a rigid foam-lined container. The
storage requirement for polyethylene bottles is not as stringent as with
glass bottles.
21
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2.3 ON-SITE MEASUREMENTS
The on-site measurement activities include transporting the equipment to
the test site, unpacking and assembling the equipment, confirming duct mea-
surements and traverse points (if volumetric flow rate is to be determined),
velocity traverse, determination of molecular weight and stack gas moisture
content, sampling for sulfur dioxide, and data recording.
2.3.1 Transport of Equipment to the Sampling Site.
The most efficient means of transporting or moving the equipment from
floor level to the sampling site as decided during the preliminary site
visit (or by prior correspondence) should be used to place the equipment on-
site. Care should also be exercised to prevent damage to the test equipment
or injury to test personnel during the moving phase. A "laboratory"-type
area should be designated for preparation of the absorbing reagents, charging
of the bubbler and impingers, and sample recovery.
2.3.2 Preliminary Measurements and Setup.
The reference method outlines the determination of the concentration
of sulfur dioxide in the gas stream. The volumetric flow rate must be
determined using Reference Methods 1, 2, 3, and 4 if the mass emission rate
is to be determined (ref. 5).
Fill in the test identification required on the sample data sheet of
figure 3, or on a similar form.
2.3.3 Sampling.
The on-site sampling includes preparation and/or addition of the absorb-
ing reagents to the midget bubbler and impingers, setup of the sampling
train, connection to the electrical service, preparation of the probe
(leak check of entire sampling train and addition of particulate filter),
insertion of the probe into stack, sealing the port, checking temperature
of probe, sampling and recording of the data. A final leak check of the
entire sampling train must always be performed.
2.3.3.1 Preparation and/or Addition of Absorbing Reagents to Collection
System. If on-site preparation of absorbing reagent is necessary, follow
directions as given in section 2.2.3.1 of this document. Pipette 15 m£ of
22
-------
3 percent hydrogen peroxide into each of the first two midget impingers.
The final midget impinger is left dry. Glass wool must be placed at the top
of the midget bubbler to serve as a filter to prevent sulfuric acid (H?SO.)
mist carryover into the midget impingers; i.e., biasing the SO- results high.
2.3.3.2 Assembling Sampling Train. Assemble the sampling train as shown
in figure 6-1 of appendix A and perform the following:
1. Leak check the sampling system by plugging the probe inlet and pull-
ing 250 mm of Hg (10 in. of Hg) vacuum. Record the leak rate on
the data sheet of figure 3.
Caution: When releasing the vacuum after a leak test, release the
vacuum slowly to prevent loss of reagent from the impinger, thus
saturating the silica gel in the drying tube.
A leakage rate not in excess of 1 percent of the sampling rate
is acceptable. In practical circumstances, the system should be
leak-free at this vacuum level. When the system is leak free
(to pass test), turn on the probe heater.
Note: A crossover system over the pump as used in the sampling train
of EPA Method 8 is useful in leak testing. In the design of Method
8, the needle valve should be placed between the main valve and
vacuum gauge. The rotameter should be positioned between the pump
and the dry gas meter.
2. Place a loosely packed filter of glass or quartz wool in the end
of the probe.
3. Attach the pitot lines of the type-S pitot tube to a differential
pressure gauge.
4. Prior to taking of the sample, perform a preliminary velocity traverse
of the stack to get a high, low and medium pressure of AP (mm of HO).
1/2
5. Take the square root of the high value (AP) and assign a rota-
meter setting of 3 £/min to this value. If the stack gas velocity
is constant or nearly so, set the rotameter to an approximate flow
rate of 2 Jl/min.
6. During the test the needle value is varied to provide a relative
change in flow (rotameter setting) as a function of the stack gas
1/2
velocity; i.e., as a function of (AP , not of AP (velocity pres-
sure head). From an initial velocity traverse, a table can be pre-
1/2
pared ratioing the (AP) values to the scale of the rotameter;
23
-------
thereby, the operator can readily set the flow rate during the
sampling process. To reduce calibration in the field, a table of
1/2
AP, (AP) and rotameter settings can be prepared prior to the
field test for a ready reference.
2.3.3.3 Sampling (Proportional). Sampling must be proportional so that the
resultant calculated concentration of SO is an accurate representation of
what actually exited the stack during the sampling period; that is, if the
stack gas velocity is constant, the sample flow rate is kept constant, and
if the stack gas velocity varies with time or position, the sample flow rate
must be adjusted proportionally. For a discussion of proportional sampling,
consult chapter 8 or reference 6.
1. Place crushed ice around the impinpers. Add salt, if necessary, to
keep the stack gas tenperature down as it leaves the last impinger.
2. When the probe temperature is up to 175° C (350° F) , insert the
probe into the stack to the centroid of the cross section if the
2
cross sectional area is less than 5.0 m , or at a point no closer
to the walls than 1.0 m (3 ft) if the cross sectional area is
5.0m or more.
3. The minimum acceptable sampling time is 20 minutes and minimum
sampling volume is 21.2 I corrected to standard conditions. The
total sample volume at meter conditions should be on the order of
28 £ (1 ft3).
4, Perform a final leak check at a vacuum 25 to 50 mm of Hg greater
than the highest vacuum recorded during the test period and record
the leak rate on the data sheet of figure 3.
5. Remove the probe iiom the stack and disconnect it from the train.
Drain the ice bath and purge the remaining part of the train by draw-
ing "clean" ambient air through the system for 15 minutes. An appro-
priate scrubber can be used to insure that the air is free from parti-
culate sulfate and SO (ref. 7). This purging process is to remove
dissolved S0_ from the midget bubbler into the midget impingers,
If SO has been collected, the impinger absorbing solution shou.
be acidic. Check the first impinger with pH indicating paper.
24
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Plant: Name
Stack No.
Team Supervisor
TEST IDENTIFICATION
Location
Date
Operators
Dry Gas Meter No.
Probe No.
Pi tot Tube No.
EQUIPMENT IDENTIFICATION
Sample Bottle No.
Type Filter
C_
PRELIMINARY MEASUREMENTS
Barometric Pressure, ?„
Probe Temperature, T ,
Stack Area, AS>
iP : Low
of HG
. High
(mm of Hg)
DRY GAS METER DATA
Volume Readings (t)
Initial Final
Volume. Vm* (t)
*Vm is the gas volume through the dry gas meter at the meter temperature and pressure.
LEAK TEST RESULTS
Run No.
1
2
3
4
5
6
Run No.
1
2
3
4
5
6
Initial
Leak Rate U/min)
Initial
Vacuum (I
Ho)
Final
Leak Rate U/min)
Final
Vacuum (mm Hg)
SAMPLING DATA
Sampling Time
(m/h)
Meter Volume
(O
iP
Rotameter Temperature
Setting (*C)
Figure 3. On-site sampling data sheet.
25
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If the absorbing reagent is not acidic and it is known that SO
is present in the stack gas, the sampling train and reagent should
be checked and the run repeated.
The particulate filter should be changed at the end of each
individual test. It has been suggested that particulate buildup
on the probe filter may result in a loss of S09 due to reactions
with the particulate matter (ref. 13).
2.3.4 Sample Recovery.
The reference method requires a transfer of the contents of the impingers
and connection washings to a suitable storage container. This transfer
should be done in a "laboratory-type area" to prevent contamination of the
test sample.
2.3.4.1 Impinger Solution (H^Op . Disconnect the impingers after completion
of the purge. Discard the contents of the midget bubbler into an appropriate
container. Transfer the contents of the midget impingers into a labeled
polyethylene sample bottle. Rinse the three midget impingers and the connect-
ing tubes with distilled water and add these washings to the same sample
bottle. The total rinse volume should be _£ 10 m£.
2.3.5 Sample Logistics (Data) and Packing of Equipment.
The above procedures are followed until the required number of tests
are completed. If the glassware (bubbler, impingers and connectors) are used
in the next test, they should be rinsed with distilled water (impingers,
connectors, and bubbler) and then with isopropanol (bubbler only). A new
drying tube should be inserted into the sampling train. The following is
recommended at the completion of the test:
1. Check all sample containers for proper labeling (time and date
of test,, location of test, number of test and any pertinent documen-
tation) .
2. All data recorded during the field test should be recorded in dupli-
cate by carbon paper or by using data sheets and a field laboratory
notebook. One set of data should be mailed to the base laboratory
and the other hand-carried. This is a recommendation that can
prevent a very costly and embarrassing mistake.
26
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3. All sample containers and sampling equipment should be examined
for damage, noted in log book, and properly packed for shipment
to the base laboratory. All shipping containers should be proper-
ly labeled to prevent loss of samples or equipment.
27
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2.4 POSTSAMPLING OPERATIONS (BASE LABORATORY)
2.4.1 Apparatus Check.
2.A.1.1 Type-S Pitot Tube. The type-S pitot tube is checked according to
the Quality Assurance Document for Method 2 (Determination of Stack Gas
Velocity and Volumetric Flow Rate) (ref. 5).
2.4.1.2 Dry Test Meter (Sampling Train). A postcheck (a postcheck for
one test can serve as the presampling check for the next field test)
should be made of the entire sampling train to check for proper operation
of the probe, pump, dry gas meter, rotameter, valve(s), thermometer, and
vacuum gage. Set up the sampling train. Leak-check the vacuum system at
250 mm of Hg vacuum. Determine y as previously instructed in subsection
2.2.2.7. This is a check on the system for future testing and gives
confidence in the data from the previous field test. This is a recommended
procedures to improve data quality and to prevent field sampling under
assumed conditions.
2.4.2 Analysis (Base Laboratory).
The requirements for a precise and accurate analysis are an experienced
analyst and familiarity with the analytical method. Calibrations is of
the utmost importance and neglect in this area cannot be accepted. The
analytical method is based on the insolubility of barium sulfate (BaSO.)
and the formation of a colored complex between barium ions and thorin
indicator, [l-(0-arsonophenyzlazo)-2-naphthal-3, 6-disulfonic acid, disodium
salt]. Aliquots from the impinger solution are analyzed by titration with
barium perchlorate to the pink-orange endpoint.
2.4.2.1 Reagent (Standardization and Analysis). The following reagents
are required for the analysis of the SO samples:
1. Water-deionized, distilled
2. Isopropanol
3. Thorin indicator - l-(0-arsonophenylazo)-2-napthal-3, 6-disulfonic
acid, disodium salt (or equivalent). Dissolve 0.20 g in 100 m£
distilled water.
28
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4. Barium perchlorate [Ba(C10,)2 . 3H20]. Dissolve 1.95 g in 200 ml
distilled water and dilute to 1 £ with isopropanol. Standardize
with sulfuric acid (H2SO,).
5. Sulfuric standard (0.01 N). Standardized against 0.01 N NaOH
which has previously been standardized against potassium acid
phthalate [C6H,(COOH) Cook primary standard].
2.A.2.2 Standardization of Sodium Hydroxide. Dry the potassium acid phtha-
late for 1 to 2 hours at 110° C and cool. Accurately weigh approximately
0.25 g in 250 mi of distilled, deionized water (preferably freshly boiled
and cooled). Add two drops of phenolphthalein indicator and titrate with
0.1N sodium hydroxide (NaOH) to the first pink color that persists for 30
seconds. The base (NaOH, 0.1N) can be purchased commercially or prepared
from reagent grade NaOH. Standard textbooks of analytical chemistry give
instructions on preparation of NaOH solutions of any desired normality. The
fresh 0.01N NaOH is prepared by pipetting 50 mi of the standardized solution
into a 500-m£ volumetric flask and diluting to the mark with distilled,
deionized water. The final concentration of the base will be the standard-
ized value divided by 10.
2.4.2.3 Standardization of Sulfuric Acid vs. 0.01N NaOH. The 0.01N sulfuric
acid is standardized by pipetting 25 mi of the H-SO solution into a 250-m£
Erlenmeyer flask that contains 25 mi of water. A blank should be prepared
that contains 50 mi of distilled, deionized water. Add two drops of
phenolphthalein indicator to the standard sample and to the blank and titrate
with the above standardized 0.01N NaOH until the first permanent pink color
that lasts for 30 seconds. All standards should be done in triplicate.
At the completion of the titrations, calculate the normality, with correc-
tion for the blanks.
2.4.2.4 Standardization of Barium Perchlorate (0.01N). Pipette 25 mi of
sulfuric acid standard (0.01N) into a 125 mi Erlenmeyer flask. Add 100 mi
of reagent-grade isopropanol and two to four drops of thorin indicator and
titrate to a pink endpoint using 0.01N barium perchlorate. Run a blank
29
-------
acid, disodium salt (or equivalent). Dissolve 0.20 g in 100 m£
distilled water.
4. Barium perchlorate [Ba(C10^>2 * 3H-0]. Dissolve 1.95 g in 200 mi
distilled water and dilute to 1 £ with isopropanol. Standardize
with sulfuric acid (H SO ) .
5. Sulfuric standard (0.01 N). Standardized against 0.01 N NaOH which
has previously been standardized against potassium acid phthalate
[C,H.(COOH) Cook primary standard],
o 4-
2.4.2.2 Standardization of Sodium Hydroxide. Dry the potassium acid phtha-
late for 1 to 2 hours at 110° C and cool. Accurately weigh approximately
0.25 g in 250 m£ of distilled, deionized water (preferably freshly boiled
and cooled). Add two drops of phenolphthalein indicator and titrate with
0.1N sodium hydroxide (NaOH) to the first pink color that persists for 30
seconds. The base (NaOH, 0.1N) can be purchased commercially or prepared
from reagent grade NaOH. Standard textbooks of analytical chemistry give
instructions on preparation of NaOH solutions of any desired normality. The
fresh 0.01N NaOH is prepared by pipetting 50 m£ of the standardized solution
into a 500-m£ volumetric flask and diluting to the mark with distilled,
deionized water. The final concentration of the base will be the standardized
value divided by 10.
2.4.2.3 Standardization of Sulfuric Acid vs. 0.01N NaOH. The 0.01N sulfuric
acid is standardized by pipetting 25 m£ of the H?SO solution into a 250-m£
Erlenmeyer flask that contains 25 ml of water. A blank should be prepared
that contains 50 m£ of distilled, deionized water. Add two drops of
phenolphthalein indicator to the standard sample and to the blank and titrate
with the above standardized 0.01N NaOH until the first permanent pink color
that lasts for 30 seconds. All standards should be done in triplicate.
At the completion of the titrations, calculate the normality, with correc-
tion for the blanks.
2.4.2.4 Standardization of Barium Perchlorate (0.01N). Pipette 25 m£ of
sulfuric acid standard (0.01N) into a 125 m£ Erlenmeyer flask. Add 100 m£
of reagent-grade isopropanol and two to four drops of thorin indicator and
titrate to a pink endpoint using 0.01N barium perchlorate. Run a blank
30
-------
which contains 25 mJl of deionized, distilled water and 100 mA of isopropanol.
Standardizations should be done in triplicate. All thorin titrations should
be done against a white background. This will facilitate the detection of
the pink endpoint (orange-pink color). The analyst unfamiliar with this
titration should carry out titrations on aliquots at low, medium, and high
concentrations. Pipette various aliquots of 0.01N H-SO, and add four times
this volume of 100 percent isopropanol and titrate with barium perchlorate
to become familiar with the endpoint. The presence of particulate matter can
make the detection of this endpoint quite difficult (ref. 8). The normality
of the BaCIO, is calculated as:
4
NBaC104 = \2S04 X v°lum*H2So4/Volume BaCl04
2.4.2.5 Sample Analysis. Transfer the contents of the sample bottle to a
50-m£ volumetric flask (V n ). Dilute to the mark with deionized, distilled
soln
water. Pipette a 10-m£ aliquot (V ) of this solution into a 125-m& Erlenmeyer
3.
flask and add 40 m£ of isopropanol. Add two to four drops of thorin indicator
and titrate to a pink endpoint (orange-pink) using standardized 0.01N barium
perchlorate (the volume used in titration is recorded as V , m£). Run a
blank with each series of samples from the absorbing solution used in the
field (the volume of titrant used for blank is recorded as V
, ,
As a check on the analysis, new aliquots should be taken from at least
two of the six samples and analyzed. If either new analysis differs more
than 10 percent from the original analysis, all samples should be reanalyzed
until two or more analyses from each sample agree within 10 percent. The
average of these values of V should be averaged and used in subsequent
calculations.
2.4.3 Calculations.
Calculation errors due to procedural or mathematical mistakes can be
a large component of total system error. Therefore, it is recommended that
each set of calculations be repeated or spot-checked, preferably by a team
member other than the one that performed the original calculations. If a
difference greater than typical roundoff error is detected, the calculations
should be checked step by step until the source of error is found and
31
-------
corrected. A computer program is advantageous in reducing calculation
errors. A standardized computer program could be developed to treat all raw
field data. If a computer program is used, the original data entry should be
s
checked and if differences are observed, a new computer run made.
2.4.3.1 Sample Volume. Calculate the sample volume at standard conditions
(25° C and 760 mm of Hg) by
/ o v \ V P
/ K j m m
V mm of Hg / T
V -0.39211 - ^= - 1 " m (2)
m . , V mm of Hg / T
std e m
where V = Volume of gas sample through the dry gas meter
std
corrected to standard conditions, Si.
V = Volume of gas sample through the dry gas meter at
meter conditions, £.
T = Average dry gas meter temperature, ° K.
m
P = Barometric pressure at the dry gas meter, mm of Hg.
Compute V to three significant digits and record the value on the sample
std
analysis data sheet of figure 4.
2.4.3.2 Sample Concentration. Calculate the concentration of sulfur
dioxide on a dry basis at standard conditions for a given sample by
o2 (N (v - v J) (V T /v
_ g£ _ V \ t tb// V soln
a
S02 (g-eq)m
where C _ = Concentration of sulfur dioxide at standard conditions
2 3
on a dry basis, g/m .
32 = Conversion factor, including the number of grams per
gram equivalent of sulfur dioxide (32 g/g-eq) , 1000 m£/£,
1000 £/m3, g £2/(g-eq)m3/mJL
N = Normality of barium perchlorate titrant, g-eq/£.
V = Volume of barium perchlorate titrant used for the sample,
V , = Volume of barium perchlorate titrant used for the blank,
tb
mJL
32
-------
V , = Total solution volume of sulfur dioxide, 50 mS,.
soln
V = Volume of sample aliquot titrated, m£.
3.
Record the calculated concentration on the sample analysis data sheet of
figure 5.
2.4.3.3 Emission Rate. To calculate the emission rate, first calculate the
volumetric flow rate at standard conditions by
1/2
(4)
Q = 2.378 x 10 (1 - B ) C_ (/AP) A
s wo P avg s
P
s
(T ) M
s avg s J
where Q = Volumetric flow rate, dry basis, standard conditions,
S 3
m /hr.
B = Proportion by volume of water vapor in the stack gas
wo
(from Method 4), dimensionless.
C = Pitot tube calibration coefficient (Method 2) , dimension-
less.
avg = Average of the square roots of velocity pressure head
1/2
measurements, (mm of H_0)
2
A = Cross-sectional area of stack, m .
P = Absolute stack gas pressure, mm of Hg.
S
M = Molecular weight of stack gas (wet basis) (Methods 3 and
S
4) g/g-mole.
(T ) = Average stack gas temperature, °K.
The emission rate (ER) is given by
ER = C x Q (5)
where ER = Emission rate, g/hr.
3
C = SO concentration, g/m .
bU~ Z
Q = Volumetric flow rate, m /hr.
S
Record the values of Q and ER on the sample analysis data sheet of
S
figure 4.
33
-------
SAMPLE VOLUME
Vm = *• Tm = °K» Pm = m of Hg
'V P
y _ n onoi ">• (mm
mst(j — mmofHgV Tm
S02 CONCENTRATION
_(g-eq)/£ Vt ml, Vtb ml
'spin--'- -I- Va^^m1'
N("V+ - V..VV , V "\ ,
CSQ =32 U ^bAsoln a) __ ^g/m3 (see eq. (3), p.
Jrt .
mstd
VOLUMETRIC FLOW RATE
n ' --- dimensionless, B1(n - ' -- dimension less
p wo
Pbar ^i^j^L mm of Hg, (^H ^^_^_ or _^1^_=__ (mm of
(see eq. (4), p. 31)
EMISSION RATE (ER)
ER = C$0 x Qs = g/hr (see eq. (5), p. 32)
Figure 4. Sample analysis data sheet.
AS - ' m , PS ' mm of Hg,
M . _ g/g-mole
s
5 , r- T Ps I1/2 3
= *.754 x 105 (1 - B...J CJAP) ,..„ A_ I /T N M I = mj/hr
34
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SECTION III MANUAL FOR FIELD TEAM SUPERVISOR
3.0 GENERAL
The term "supervisor" as used in this document applies to the indivi-
dual in charge of a field team. He is directly responsible for the valid-
ity and the quality of the field data collected by his team. He may be
a member of an organization which performs source sampling under contract
to government or industry, a government agency performing source sampling,
or an industry performing its own source sampling activities.
It is the responsibility of the supervisor to identify sources of un-
certainty or error in the measurement process for specified situations and,
if possible, eliminate or minimize them by applying appropriate quality
control procedures to assure that the data collected are of acceptable qual-
ity. Specific actions and operations required of the supervisor for a
viable quality assurance program are summarized in the following listing.
1. Monitor/Control Data Quality
a. Direct the field team in performing field tests according to
the procedures given in the Operations Manual.
b. Perform or qualify results of the quality control checks
(i.e., assure that checks are valid).
c. Perform necessary calculations and compare quality control
checks to suggested performance criteria.
d. Make corrections or alter operations when suggested perfor-
mance criteria are exceeded.
e. Forward qualified data for additional internal review or to
user.
2. Routine Operation
a. Obtain from team members immediate reports of suspicious
data or malfunctions. Initiate corrective action or, if
necessary, specify special checks to determine the trouble;
then take corrective action. Document the corrective action
taken.
b. Examine the team's log books periodically for completeness
and adherence to operating procedures.
35
-------
c. Approve data sheets, calibration checks, etc., for filing.
3. Evaluation of Operations
a. Evaluate available alternative(s) for accomplishing a given
objective in light of experience and needs.
b. Evaluate operator training/instructional needs for specific
operations.
Consistent with the realization of the objectives of a quality assurance
prugram as given in section I, this section provides the supervisor with
brief guidelines and directions for:
1. Collection of information necessary for assessing data quality
on an intrateam basis.
2. The use of performance criteria to aid in the collection of data
of acceptable precision and accuracy.
3. Isolation, evaluation, and monitoring of major components of
system error.
The above three topics will be discussed in the order that they appear
in this manual. In subsection 3.1 a method of assessing data quality on an
intrateam basis is given. This method involves calculating a sample standard
deviation using the six replicate runs required in a field test and calcu-
lating 90 percent confidence limits for the average of the six replicates.
Subsection 3.2 presents suggested criteria for judging equipment per-
formance and frequency of calibration.
Directions for collection and analysis of information to identify
trouble and subsequently control data quality within acceptable limits are
given in the third subsection.
36
-------
3.1 ASSESSMENT OF DATA QUALITY (INTRATEAM)
Sulfur dioxide concentration, C *, for a particular field test is
the average of six replicates. Intrateam assessment of data quality as
discussed herein provides for an estimate of the precision of the measure-
ments. Precision in this case refers to replicability, i.e., the variability
among replicates and is expressed as a coefficient of variation. This pre-
cision statement combines variability due to process changes and random measure-
ment errors. This technique does not provide the information necessary for
estimating measurement bias (see subsection 4.1.2 for a discussion of bias)
which could occur, for example, from an error due to sampling train leaks,
insufficiently heated sampling prcbe, or failure to sample proportionally.
However, if the operating procedures given in the Operations Manual are
followed, the bias should be small in most cases. An independent quality
audit which would make po:.-:;. ible a bias estimate is suggested and discussed
in section IV, the Management Manual.
3.1.1 Calculating Precision of Field Data.
Each field test is comprised of at least three sample runs. Using
the sample runs as replicates, a standard deviation can be calculated.
This calculated standard deviation is a combined measure of the measurement
and process variabilities. The standard deviation is calculated by
T 6
2
I _
1 /?
tc - r \ «- j. / £
( SC> (i) C£
2
5
(6)
where s i C j - standard deviation for the 6 runs
t oL/rt
C (i) = SO concentration for the i run
C = mean S0? concentration for the 6 runs
oUr, ^
and 5 = number of runs minus 1, or the number of degrees of freedom.
^Throughout this document, CQn is used to mean a single SO- determination
oU „
and the notation. C is used to
S02
is the average of six replicates.
„
and the notation. C is used to represent the result of a field test, and
37
-------
3.1.2 .Reporting Data Quality.
It is recommended that the average measured salfur dioxide concentration,
C , be reported with 90 percent confidence limits. Assuming the C_rt is
C°2 S02
normally distributed (this is usually a valid assumption since sample means
tend to be normally distributed even for nonnormal parent distributions)
and using s{C } as calculated in 3.1.1 above to estimate the standard
bU2
deviation, exact confidence limits can be calculated for the true C n value
so2
using the Student t-distribution with r - 1 = 5 degrees of freedom. This
assumes no bias in the average values. The average measure value with 90
percent confidence limits is reported as
- 3
where C = the average of 6 replicates, g/m
_ bU2 '
s{C } = estimated standard deviation of C based on 6
2 3 2
replicates, g/m
2.02 = 95 — percentile of the Student t-distribution with
5 degrees of freedom which yields a 90 percent confi-
dence interval.
— 33
For example, if for a given field test C = 2 g/m and 2 g/m s(C _ }
bU,~ O
3
was calculated to be 0.08 g/m , the reported value with 90 percent confi-
dence limits would be
2.0 g/m3 + (2.02) (0.08 g/m3)
or the true sulfur dioxide concentration, C , would be assumed to be
bU2
in the interval
1.84 g/m3 < CSQ (t) < 2.16 g/m3
The utility of the above statement follows from the fact that if this
procedure for computing confidence limits is followed, the 90 percent of
the time the true C value will be contained within the given limits
2
(assuming that C is not biased) .
38
-------
3.2 SUGGESTED PERFORMANCE CRITERIA
Data assessment as discussed in the previous subsection is based on
the premise that all variables are controlled within a given level, there-
by guarding against large undetected biases in the measurement process.
These levels of suggested performance criteria are the values given in the
Operations Manual for determining when equipment and/or personnel variability
is out of control. Criteria for judging performance are summarized in
table 1.
Table 1. Suggested performance criteria
1. Suggested Criteria for Field Equipment Performance:
(a) Dry Gas Meter:
(b) Barometer:
(c) Thermometers:
(d) Stack Temperature
Measuring System:
(e) Sampling Train Leakage:
(f) Probe Heating System:
0.98 £ Y 1 1-°2
j^ 5 mmHg
+ 1° C
+ 3° C
< 1 percent of sampling flow
rate at 250 mmHg
Uniform heating of probe, with
a minimum temperature of 175° C
at exit end and at a flow rate
of 2 £/min at room temperature
2. Suggested Criteria for Analytical Procedure:
(a) Duplicate Samples:
(b)
(c) Reference Gas:
Standard 0.01N Barium
Perchlorate:
<_ 5 percent of mean
<_ 0.0005N of mean
< 17 percent of reference value
39
-------
3.3 COLLECTION AND ANALYSIS OF INFORMATION TO IDENTIFY TROUBLE
In a quality assurance program, one of the most effective means of
preventing trouble is to respond immediately to indications of suspicious
data or equipment malfunctions. There are certain visual and operational
checks that can be performed while the measurements are being made to
help insure the collection of data of good quality. These checks are
written as part of the routine operating procedures in section II. In
order to effectively apply preventive maintenance procedures to the
measurement process, the supervisor must know the important variables in
the process, know how to monitor the critical variables, and know how to
interpret the data obtained from monitoring operations. These subjects are
discussed in the following subsections:
3.3.1 Identification of Important Variables .
Determination of the sulfur dioxide concentration requires a sequence
of operations and measurements that yields as an end result a number that
serves to represent the average sulfur dioxide emission rate for that field
test. There is no way of knowing the accuracy ; i.e., the agreement between
the measured and the true value, for a given field test. However, a knowledge
of the important variables and their characteristics allows for the application
of quality control procedures to control the effect of each variable at a given
level during the field test, thus providing a certain degree of confidence in
the validity of the final result.
A functional analysis of this method of measuring the sulfur dioxide
emission rate of a stationary source was made to try to identify important
components of system error. Also, results of a collaborative study of
Method 6 (ref. 3) showed an averaga within-laboratory coefficient of variation
of 4.0 percent and a between-laboratory coefficient of variation of 5.8 per-
cent. These results were used as an estimate of overall system error, while the
individual error components are estimated using professional judgment in a
manner such that their combined variability is consistent with overall system
error.
40
-------
Variability in emissions data derived from multiple restrictions
includes components of variation from:
(1) Process conditions,
(2) Equipment and personnel in field procedures, and
(3) Equipment and personnel in the laboratory.
In many instances time variations in source output may be the most
significant factor in the total variability. In order to judge the rela-
tive magnitudes of measurement variability and process output variability,
process parameters should be monitored throughout the test. Process informa-
tion is also required if the sulfur dioxide emission rate is to be given as
a function of fuel input. The exact process data to be obtained are dependent
upon the process being tested. In general, all factors which have a bearing
on the emissions should be recorded at approximately 15-minute intervals.
It is important to realize that the largest measurement errors can re-
sult from poor technique such as an insufficient purge of the sampling train
after sample collection, failure to maintain the probe at a given temperature,
or failure to adequately leak,check the sampling train. Such deviations from
recommended procedures generally cannot be evaluated or corrected. It is im-
portant to detect and eliminate such occurrences while the test is in progress.
Collaborative test results (ref. 3) indicate that over 70 percent of the total
variability in the method occurs during sample collection, leaving the analysis
phase responsible for less than 30 percent.
Sources of variation involving equipment include:
(1) The dry gas meter,
(2) The sampling probe heater,
(3) Sampling train leaks,
(4) Impinger solution temperature, and
(5) Vacuum pump malfunctions.
These sources of variation are controlled either by a calibration or calibra-
tion check before each field test or by special checks immediately before,
during, and immediately after the field test.
Assuming good technique, i.e., neglecting the possibility of gross
errors due to sampling and/or analysis mistakes, the major error sources of
the measurement process are discussed and the effect of each source on the
41
-------
measured mass emission rate is estimated in a functional analysis (sub-
section 4.1). The relationship of a particular variable or error source
to the final measured value should be obtained from this subsection, and
a summary of the important parameters is given. Specific subjects to be
discussed include:
(1) Equipment calibration,
(2) Sampling train leaks,
(3) Proportional sampling,
(4) Sample recovery,
(5) Reagent standardization,
(6) Sample blanks, and
(7) Calculations.
3.3.1.1 Equipment Calibration. Equipment calibration is the crux of any
quality assurance program. It is important that the calibration procedure
be carried out correctly, that the calibration standards are properly main-
tained, and that the frequency of calibration is adequate.
The quality assurance document of this series for Method 2 (ref. 5)
should be adhered to for calibration of apparatus used in determining the
volumetric flow rate of the source being tested.
The dry gas meter, including its temperature measuring device, is cali-
brated to achieve an acceptable level of accuracy in the sample volume at
standard conditions. An error in the sample volume is directly reflected in
the SO^ concentration measurement and subsequently in the mass emissions rate
measurement (see equations (3) and (5) of section II).
3.3.1.2 Sampling Train Leaks. Sampling train leaks result in measured
sample volumes larger than the true sample volume. Leaks also introduce an
error in the collected S0« that cannot be corrected in most cases. Sampling
train leaks must be kept sufficiently small so that the resulting error will
not significantly increase total system variability.
42
-------
3.3.1.3 Proportional Sampling. Proportional sampling implies that the
sample gas velocity inside the sampling probe be maintained at a fixed
fraction of the stack gas velocity throughout sample collection. Further-
more, collecting the sample at one point in the stack assumes a fixed spatial
velocity profile in the stack; i.e., if the stack gas velocity increases
by 10 percent at one point in the stack, it increases by 10 percent in all
other points in the stack. Such an assumption is not always true. To more
closely approximate true proportional sampling, it is suggested that pro-
portional sampling at a point be carried out for each samplej i.e, a sample
is collected at one point in the stack with the ratio of sampling and stack
gas velocities maintained constant. Each of the 6 samples should be col-
lected at a different cross-sectional point in the stack to average out errors
from a variable stack gas velocity profile.
3.3.1.4 Sample Recovery. Sample recovery, including purging of the sampling
train and rinsing of the impingers and connecting tubes to quantitatively re-
cover the sample,can be critical to the precision and accuracy of the measure-
ment.
3.3.1.5 Reagent Standardization. An error in the standardized barium
perchlorate solution (subsec. 2.4.2.4) is directly reflected in the S07 con-
centration and mass emission rate (see equations (3) and (5) of section II).
3.3.1.6 Sample Blanks. The use of blanks is important in correcting the
field sample for contaminants contained in the reagents and not part of the
collected sample. Also, the routine measurement of blanks should preclude
errors due to the use-of contaminated reagents.
3.3.1.7 Calculations. Calculations for this method are known to be a
major source of error. Some calculations involve several terms and should
only be attempted (for the final report) at a desk or work table and
preferably with the aid of a calculator or at least a good slide rule. A
computer program using raw data as an input is highly recommended for
making the final calculations.
As a check, it is recommended that all calculations be independently
repeated from raw data.
43
-------
3.3.2 How to Monitor Important Variables .
In general, if the procedures outlined in the Operations Manual
are followed, the major sources of measurement variability will be in
control. It is felt, however, that the supervisor should visually check
certain parameters and operations periodically while measurements are being
made to insure good operator technique and the proper use of equipment.
The parameters and operations to check are the same as those recommended for
the auditor as listed in subsection 4.3.2.
Results of the calibration checks for the dry gas meter, rotameter,
thermometer, and pitot tube should be checked before each field test.
Any item of equipment not satisfying the suggested performance criteria
of table 1 should be calibrated or replaced.
Also, actual involvement in or observance of such on-site operation
as (1) sampling train leak check before and after sample collection, (2) purg-
ing the sampling train after sample collection, and (3) sample recovery should
serve as a means of monitoring these important operations.
There appears to be a need for actual field data on several of the
parameters or variables involved in this measurement method in order to
better judge their influence on measurement variability. One of the most
effective means of identifying and quantifying important sources of vari-
ability is through the use of quality control charts. Quality control charts
will provide a basis for action with regard to the measurement process;
namely, whether the process is satisfactory and should be left alone, or is
out of control (or approaching control limits) and action should be taken to
find and eliminate the causes of excess variability. The quality control
charts can be evaluated after a period of time to determine the range of
variation that can be expected for each variable charted under normal operating
conditions.
For Method 6, two control charts are recommended, as follows:
1. A range chart for the BaCIO, normality determinations, and
2. A range chart for duplicate determinations of SO samples.
It is good practice to note directly on control charts the reason for
out-of-control conditions, if determined, and the corrective actions taken.
44
-------
It is also good practice to maintain control charts in large size; e.g.,
8% x 11 inches or larger and to keep them posted on a wall for viewing
by all concerned, rather than having them filed in a notebook.
3.3.2.1 Pitot Tube Calibration Coefficient. A sample control chart
for pitot tube calibration checks is given in the quality assurance document
of this series for Method 2, Determination of Stack Gas Velocity and Volumetric
Flow Rate (type-S pitot tube), page 38 (ref. 5).
3.3.2.2 Control Chart for Normality of Standardized Barium Perchlorate
Solution. For a given batch of standardized barium perchlorate, it is assumed
that later checks should not deviate more than + 5 percent from the original
value. Using + 5 percent as the action limits for checking and preparing new
reagents, a control chart for the standardized barium perchlorate solution
can be constructed. For this example, an original standardized value of 0.0100N
is used and the control chart would be as shown in figure 5.
Each time the barium perchlorate solution is restandardized, that
value is plotted on the control chart and connected to the previous, plotted
value by a straight line.
It is recommended that any time the most current standardization
value deviates more than + 5 percent from the original or the previous value,
all reagents used in standardizing the barium perchlorate solution should be
checked and/or restandardized. If no problems are detected in these reagents,
then a new batch of barium perchlorate should be prepared and standardized.
It is also recommended that the latest standardization value be used
in subsequent sample analyses.
3.3.2.3 Control Chart for Duplicate Samples. The within-laboratory co-
efficient of variation (CV) for the Method 6 analytical phase is estimated
as 1.6 percent (ref. 3). A. control chart for the range, R, can be con-
structed as in figure 6. The range computed as the difference for duplicate
analyses (to be made on two out of the six SO- samples taken per field test)
is calculated as
%d =
CS02(oj " CS02(d)
CS02(o) + CS02(d)
45
x 100 (8)
-------
0.0005
^. 0.0004
1 g 0.0003
E «
3? 0.0002
z o
0.0001
_ACJION_LIMIJ_
WARNING LIMIT
Duplicate number
Date
Analyst
problem and
corrective
action
1
2
3
4
5
6
7
8
9
10
-^
Figure 5. Sample control chart for standardized barium
perchlorate solution.
46
-------
%d
t
60
5.0
4.0
3.0
2.0
1.0
0
Duplicate number
Date
Analyst
problem and
corrective
action
UCL* =5.9
WL = 4.5
•
-
R** = 1.8
•
k.
1
2
3
4
5
6
7
8
9
10
* UCL = D4R = 3.267 x 1.8 = 5.9
** —
R =
-------
where C , ^ = original determination of S02 concentration
2
C ,,, = duplicate determination of SO™ concentration
The magnitude of % d (the number obtained, ignoring the sign),
symbolized as | % d |, is plotted each time a duplicate measurement is made.
The analysis procedure, reagents, and apparatus should be checked and
appropriate action taken any time one of the following criteria is exceeded:
1. One point falls outside the upper control limit.
2. Two consecutive points fall between the warning limit and
upper control limit.
3. Seven consecutive points fall above the R line.
When criterion 1 is exceeded, the six S02 samples for that field
test should be reanalyzed, after the cause of the excess variability has
been determined and corrected. Exceeding the second or third criteria
will usually indicate poor technique and the need for additional super-*-
vision/training.
48
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SECTION IV MANUAL FOR MANAGER OF GROUPS OF FIELD TEAMS
4.0 GENERAL
The guidelines for managing quality assurance programs for use with
Test Method 6 - Determination of Sulfur Dioxide Emissions from Stationary
Sources are given in this part of the field document. This information is
written for the manager of several teams for measuring source emissions and
for the appropriate EPA, State, or Federal Administrators of these programs.
It is emphasized that if the analyst carefully adheres to the operational
procedures and checks of section II, then the errors and/or variations in
the measured values should be consistent with the performance criteria as
suggested. Consequently, the auditing routines given in this section
provide a means of determining whether the stack sampling test teams of
several organizations, agencies, or companies are following the suggested
procedures. The audit function is primarily one of independently obtaining
measurements and performing calculations where this can be done. The purpose
of these guidelines is to:
1. Present information relative to the test method (a functional
analysis) to identify the important operations and factors.
2. Present a methodology for comparing action options for improving
the data quality and selecting the preferred action.
3. Present a data quality audit procedure for use in checking ad-
herence to test methods and validating that performance criteria are
being satisfied.
4. Present the statistical properties of the auditing procedure in
order that the appropriate plan of action may be selected to yield
an acceptable level of risk to be associated with the reported
results.
These four purposes will be discussed in the order stated in the sections
which follow. The first section will contain a functional analysis of the
test method with the objective of identifying the most important factors
which affect the quality of the reported data and of estimating the expected
variation and bias in the measurements resulting from equipment and operator
errors.
49
-------
Section 4.2 contains several actions for improving the quality of the
data; for example, by improved analysis techniques, instrumentation, and/or
training programs. Each action is analyzed with respect to its potential
improvement in the data quality as measured by its precision. These
results are then compared on a cost basis to indicate how to select the
preferred action. The cost estimates are used to illustrate the methodology.
The manager or supervisor should supply his own cost data and his own actions
for consideration. If it is decided not to conduct a data audit, sections
4.1 and 4.2 would still be appropriate as they contain a functional analysis
of the reference method and of alternative methods or actions.
There are no absolute standards with which to compare the routinely
derived measurements. Furthermore, the taking of completely independent
measurements at the same time that the routine data are being collected (e.g.,
by introducing two sampling probes into the stack and collecting two samples
simultaneously) is not considered practical due to the constrained environ-
mental and space conditions under which the data are being collected.
Hence, a .combination of an on-site system audit, including visual observa-
tion of adherence to operating procedures and a quantitative performance
quality audit check, is recommended as a dual means of independently checking
on the source emissions data.
The third section contains a description of a data quality audit pro-
cedure. The most important variables identified in section 4.1 are considered
in the audit. The procedure involves the random sampling of n stacks from
a lot size of N = 20 stacks (or from the stacks to be tested during a three-
month period, if less than 20) for which one firm is conducting the source
emissions tests. For each of the stacks selected, independent measurements
will be made of the indicated variables. These measurements will be used
in conjunction with the routinely collected data to estimate the quality of
the data being collected by the fie2d teams.
The data quality audit procedure is an independent check of data col-
lection and analysis techniques with respect to the important variables.
It provides a means of assessing data collected by several teams and/or firms
with the potential of identifying biases/excessive variation in the data
50
-------
collection procedures. A quality audit should not only provide an inde-
pendent quality check, but also identify the weak points in the measurement
process. Thus, the auditor, an individual chosen for his background
knowledge of the measurement process, will be able to guide field teams in
using improved techniques. In addition, the auditor is in a position to
identify procedures employed by some field teams which are improvements
over the current suggested ones, either in terms of data quality and/or
time and cost of performance. The auditor's role will thus be one of aiding
the quality control function for all field teams for which he is responsible,
utilizing the cross-fertilization of good measurement techniques to improve
the quality of the collected and reported data.
The statistical sampling and test procedure recommended is sampling;
by variables. This procedure is described in section 4.4. It makes maxi-
mum use of the data collected, and it is particularly adaptable to the
small lot size and consequently the small sample size applications. The
same sampling plans can be employed in the quality checks performed by a
team or firm in its own operations. The objectives of the sampling and
test procedure are to characterize data quality for the user and to identi.fy
potential sources of trouble in the data collection process for the purpose
of correcting the deficiencies in data quality.
Section 4.4.4 describes how the level of auditing, sample size n, may
be determined on the basis of relative cost data and prior information
about the data quality. This methodology is described in further detail in
the Final Report on the Contract. The cost data and prior information con-
cerning data quality are supplied to illustrate the procedure and these data
must be supplied by the manager of groups of field teams depending upon the
conditions particular to his responsibility.
Figure 7 provides an overall summary of the several aspects of the data
quality assurance program as described in these documents. The flow diagram
is subdivided into four areas by solid boundary lines. These areas corre-
spond to specific sections or subsections of the document as indicated in the
upper right hand corner of each area. The details are considered in these
respective sections of the document and will not be described here.
51
-------
Pollutant
Measurement
Method
Subsection 4.1
Functional
Analysis
Estimate Ranges
and Distributions
of Variables
Identify and Rank
Sources
Bias/Variation
Perform Overall
Assessment
Subsections 4.3 and 4.4
Subsection 4.2
Develop Standards
for Audit Procedure
Develop Standards
for Q. C.
Procedure
Satisfactory
Institute
QC Procedure
for Critical
Variables
Evaluate Action Options
for Improving Data
Quality
Quality Using
Audit Data
Continue to Use
Measurement Meth
as Specified
Modified
Measurement
Method
Continue to use
Measurement Method
as Specified
Figure 7. Summary of data quality assurance program.
52
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4.1 FUNCTIONAL ANALYSIS OF THE TEST METHOD
Test Method 6—Determination of Sulfur Dioxide Emissions from
Stationary Sources—is described in the Federal Register of December 31,
1971 and reproduced in appendix A of this document. This method is used
to determine the concentration of sulfur dioxide in stack gas. Results
from this method combined with the volumetric flow rate as measured by
Method 2 yield a sulfur dioxide emission rate for the source being tested.
A functional analysis of the measurement process is performed to
identify and, where possible, quantify important sources of variability.
Estimates of the error ranges associated with intermediate measurements
are made using published data if available, and engineering judgment if
data are not available. Extensive use is made of the results from a
collaborative test of the method (ref. 3) for overall variability and
for the division of variability due to the sample collection and analy-
sis phases of the process. A variance analysis is performed to show the
influence of the intermediate measurements on the measured sulfur dioxide
concentration and the sulfur dioxide emission rate..
The functional analysis is discussed in two parts. First, the
governing mathematical relationships are given, and estimated means and
coefficients of variation are given for each variable. A variance analy-
sis is then performed identifying the most critical variables. An
approximate model is given for estimating overall variability using only
the most critical variables.
Special symbols and definitions used in the functional analysis
include the following:
C = A single determination of S0_ concentration at standard
2 3
conditions, on a dry basis, g/m .
3
C = The average SO- concentration of six repetitions, g/m .
S02 L
CV{C } = Within-laboratory coefficient of variation (same laboratory,
personnel, equipment, and sample), percent.
CV, (C o } = Between-laboratory coefficient of variation (variation in
2
simultaneous determinations of C by different laboratories
at the same true value of C ), percent.
53
-------
CV {C } = Laboratory bias coefficient of variability in SO
L ^^9 *•
determinations due to changes in personnel, equipment, and,
procedural details), percent.
CV{C } t/6= Repeatability coefficient of variation for SO determina-
OUn L
tions based on six replicates, percent.
\CV2 [Ccn } + CV2 {Ccr. }/6 = Reproducibility coefficient of variation
L oU_ o(J~
for a field test result based on six replicates, percent.
= CV(R)
4.1.1 Variable Evaluation and Error Range Estimates.
The emission rate of sulfur dioxide is calculated from measured values
by the relationship
N(V - V . )(V . /V )T f P
ER = 280.1 - 5 - tb soln a m
m m W° P avg S 15V avg Msj
where ER = Sulfur dioxide emission rate, g/hr.
N = Normality of barium perchlorate titrate, g-eq/1.
V = Volume of barium perchlorate titrant used for the sample, m&.
V , = Volume of barium perchlorate titrant used for the blank, m£.
V . = Total solution volume of sulfur dioxide, 100 m£.
soln
V = Volume of sample aliquot titrated, mA.
a
T = Average dry gas meter temperature, °K.
V = Volume of gas sample through the dry gas meter at meter condi-
m
tions, £.
P = Barometric pressure at the dry gas meter, mm of Hg.
B = Proportion by volume of water vapor in the stack gas,
dimensionless .
(/AP) = Average of the square roots of the velocity pressure head
measurements, (mm of H«0) ' ,
2
A = Stack cross sectional area, m .
P = Absolute stack pressure, mm of Hg.
S
54
(8)
-------
(T ) • Average stack gas temperature, °K.
s avg
M - Stack gas molecular weight on a wet basis, g/g-mole.
Table 2 lists the variables in the equation with estimated coefficients
of variations and mean values to be used in the variance analysis in the
following subsection.
Note: Measurements generally made only once per field test have a zero
variability for within-laboratory determinations. This is indi-
cated in table 2 for the last seven variables.
The first four variables in table 2 are the only ones unique to this
method. The remaining variables are common to methods 2, 3, and 4 and are
discussed in the quality assurance documents of this series dealing with the
above methods. Estimates of the variance of the first four variables in
table 2 [(V - Vfc.)> (V .. /V ), N and V ] are discussed in the following
t tb soln a m
subsections.
4.1.1.1 Volume of Titrant (V - V . ). The difference in the volumes of
titrant used in the sample and the blank, symbolized by (V - V ), is a
direct measure of the quantity of S0? absorbed in the sample solution.
The component of error or variability of this term attributable to the analy-
sis phase of the measurement method; i.e. , the actual volume determinations
of V and V , and the ability to detect the endpoint in the titration should
be relatively small. (The variability of the analysis phase is only about
one third of the variability due to sample collection (ref. 3).) However,
the above term directly reflects the variability due to sample collection.
Any difference in the mass of S0_ in a given volume of stack gas and that
retained in the absorbing solution after sampling that volume of pas will
result in the same percent difference in the volume of titrant that the
sample would have required (had there been no error in sample collection)
and the volume actually required in the analysis.
Differences in the true mass of SO,, in a given volume of stack gas and
the measured value can result from:
1. Incomplete purging of the sampling train after sample collection
(ref. 9),
55
-------
2. Less than 100 percent collection efficiency of the absorbing
solution (ref. 3),
3. Loss of SO- due to reactions with particulate matter trapped by
the particulate filter (ref. 13),
4. Loss of sample during sample recovery.
There are no data available for estimating the variability of these
terms. The coefficients of variation of 3.9 and 5.5 (for within-laboratory
and between-laboratory variabilities) as given in table 2 were determined
by estimating the variances of all the other variables in table 2 then adding
what was needed to make the total variability agree with the results of
the collaborative test of the method (ref. 3).
4.1.1.2 Fraction of total solution volume titrated (V , /V ). The sample
•—so In—a—
volume is determined by transferring the contents of the sample bottle
(see subsection 2.4.2.5) into,a 100-m£ volumetric flask and diluting to the
mark. Errors due to the volumetric flask, incomplete transfer, and diluting
to the mark should be small in most cases. A pipette is used to measure the
aliquot, V , and (neglecting operator mistakes) should exhibit negligible
3.
variability. The estimated coefficients of variation of 0.5 and 1.0 percent
(for within-laboratory and between-laboratory, respectively) does not signifi-
cantly increase the total variability of the measurement method.
4.1.1.3 Normality of barium perchlorate titrant (N). By exercising proper
care the normality of the barium perchlorate titrant should be repeatable
to the fourth decimal place. Since the titrant is approximately 0.01 N the
fourth decimal place would be equivalent to 1.0 percent. Coefficients of
variation of 0.1 and 1.0 percent are used for this analysis as shown in
table 2. The small value of 0.1 is used for fixed within-laboratory repeat-
ability since the same batch of titrant is used for all samples from a given
field test. Hence, there should be little variability. However, the variabi-
lity between batches within a laboratory would be expected to be significantly
larger. Thus, a CV of 1.0 percent is assumed.
4.1.1.4 Sample volume (V )_. Variability in the measured sample gas volume
at meter conditions can result from:
56
-------
1. Calibration variability (of the dry gas meter),
2. Inprecision of the dry gas meter,
3. Sampling train leaks,
4. Meter reading errors.
The coefficients of variation given in table 2 of 0.50 and 1.25 percent
are estimates of what can be expected of a properly trained and motiva-
ted field team.
To simplify the variance analysis, the overall equation can be written
in terms of SO- concentration and volumetric flow rate. That is,
ER = C^ x Qs (9)
3
where the concentration, C , in g/m is given by
Cso . 32 _
2 m ,
std
and V , the sample gas volume, &, through the dry gas meter (corrected
std
to standard conditions) is v P
Vm = 0.3921 ~— ~ (11)
std m
Also, the volumetric flow rate, Q , in m /hr is given by
s
r ps
(T ) M
|_ s avg s
1/2
Q =8.754x10^(1-8 )C (v'AP) A /tp ° „ (12)
s wo p avg s ' f™ ^ MI
The variance analysis can now be performed in steps. That is, the coeffi-
cients of variation are determined for V , C_- , Q , the ER, in that order
mstd S°2 S
(tables 3, 4, 5, 6). An approximate model containing the most important
variables is developed. This model should give a reasonable estimate of
measurement variability under normal operating conditions.
57
-------
Table 2. Assumed means and coefficients of variations of variables
in influencing emissions rate determinations for S0?
Variable
percent
3.90
0.50
0.10
0.50
0.25
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Between-Laboratory
Coefficient of
Variation
CVb{X} percent
5.50
1.00
1.00
1.25
0.50
0.30
0.30
1.00
1.70
1.00
0.40
1.00
0.71
58
-------
We have defined that
o
and for values in table 2, CV {C } becomes
L, bU_
34.12 = CV2{C } + 17.1,
Li oUrt
then CV2{C_ } = 17.1
and CL {C } = 4.1 percent.
Li oU_
The reproducibility coefficient of variation then is taken as
CV(R) = cv£{cso } + cv2{cso
CV(R) = 4.5 percent
based on six (6) replicates.
59
-------
Table 3. Variance Analysis for Vt
std
Variable Assumed CVJj{*} (CV2{X» x
= Weighted CV2{X> (CV2{X»
m
1.56 (1.31)
1.56 (1.31)
m
m
0.09 (0.08)
0.25 (0.19)
0.09 (0.08)
0.25 (0.19)
m
std
CvAv } = 1.90 (1.58)
b ra
std
CVJV } = 1-38 (1.26)
b mstd
Table 4. Variance analysis for C
SO,
Variable Assumed
N
(Vt - \b>
b
1.0 (0.01)
30.25 (15.21)
x Weighting _
Factor
1
1
Weighted
cv2{x) (cv2{x»
1.0 (0.01)
30.25 (15.21)
1.00 (0.25)
1.00 (0.25)
m
std
1.90 (1.58)
1.90 (1.58)
CVCS02} = 3"-
Cvb{cso2} - 5'
60
-------
Table 5. Variance analysis for Q
Variable
1 - B
wo
C
P
A
P
8
(A)
avg
(T )
s avg
M
s
^s
Assumed CV^(X>
D
0.09
1.00
1.00
0.16
2.89
1.0
0.50
x Weighting
Coefficient
1.23
1.0
1.0
0.25
1.00
0.25
0.25
^
cvb
Weighted
CV*{X}
b
0.11
1.00
1.00
0.04
2.89
0.250
0.125
(Q } = 5.42
S
{Q } = 2.33%
s
*The weighting coefficient for 1 - B is 1/(1 - B ), and
wo wo
assuming B to be 0.10, this yields l/(.9) = 1.23.
wo
Table 6. Variance analysis for reproducibility of ER
TT . , -i A j ^Tr2riri Weighting Weighted
Variable Assumed CV IX} x ° ° = °
actor cv^{x}
SS02 CVL(SS02} = "-1 ! 17'!
CV2{Cgo }/6 = 2.8 1 2.8
Q 5.44 1 5.4
CV2{ER} = 25.3
ER
CV{ER} = 5.0%
61
-------
4.1.2 Interferences.
4.1.2.1 Cations from participates. The poor endpoint visibility of the
barium ion-thorin titration is mentioned in subsection 2.4.2.4. A detailed
field study (ref. 14) indicates that this problem is due to the presence
of interfering cations (sodium and potassium). The introduction of a neutral
pH* fine particulate filter in the sampling train will reduce the possibility
of poor endpoints due to the presence of sodium and potassium-containing
particulate matter.
4.1.2.2 Nitric oxide. Nitric oxide does not interfere with Method 6
results (ref. 13).
4.1.3 Bias Analysis.
The collaborative study of Method 6 (ref. 3) indicates that the method
exhibited a "significant negative bias" at higher SO- concentrations, i.e.,
3 z
from 0.48 to 0.80 g SCL/m . Another study (ref. 9) by the same organization
also indicates a negative bias at high concentrations, although the investi-
gators admit the possibility of sampling from a gas cylinder having a lower-
than-indicated concentration of sulfur dioxide. The collaborative study
(ref. 3) indicated that the negative bias was not due to the analytical
phase of Method 6, which appeared to be unbiased. A very recent series
of experiments conducted by Dr. Joseph Knoll at EPA (ref. 12) contradicts
the results of previous studies and indicates no negative bias at very high
S09 concentrations (up to 30,000 ppm). At such high concentrations it is
important to limit the amount of gas sampled, to avoid exhausting the
peroxide in the impinger tubes.
A good possibility for explaining low results, if one accepts the data
indicating no analytical phase bias, is failure to carry out the purging
procedure rigorously. A field evaluation of Method 6 (ref. 9) showed that
up to 14 percent of the S0_ may be retained in the bubblers if purging is not
carried out. A negligible percentage of SO™ remains in the bubblers if
purging is done thoroughly (refs. 9, 13).
Subsection 4.1.1.1 indicates other possible reasons for incomplete
sample collection, all of which would negatively bias the results. However,
*A glass fiber filter is not acceptable because of the natural alkalinity
of glass.
62
-------
there is no way to quantify the loss of sample due to these possibilities.
In view of the very recent work (ref. 12) mentioned earlier, and assuming
reasonable care in following the recommended collection and analysis pro-
cedure, results from Method 6 should be unbiased. Field personnel should
be aware of the possibility of introducing a negative bias due to any of
the reasons listed in section 4.1.1.1, or because of such problems as
sampling train leaks. No evidence is available indicating a positive bias
in the method.
63
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4.2 ACTION OPTIONS
Suppose it has been determined as a result of the functional analysis
and/or the reported data from the checking and auditing schemes, that the
data quality is not consistent with suggested standards or with the user
requirements. Poor data quality may result from (1) a lack of adherence to
the control procedures given in section II—Operations Manual, or (2) the
need for an improved method or instrumentation for taking the measurements.
It is assumed in this section that (2) applies, that is, the data quality
needs to be improved beyond that attainable by following the operational
procedures given for the reference method.
The selection of possible actions for improving the data quality can
best be made by those familiar with the measurement process. For each
action, the variance analysis can be performed to estimate the variance,
/
standard deviation, and coefficient of variation of the pertinent measure-
ment (s). In some cases it is difficult to estimate the reduction in specific
variances that are required to estimate the precisions of the pertinent
measurements. In such cases, an experimental study should be made of the
more promising actions based on preliminary estimates of precision/bias and
the costs of implementing each action.
In order to illustrate the methodology, three actions and appropriate
combinations thereof are suggested. Variance and cost estimates are made
for each action, resulting in estimates of the overall precision of each
action. The actions are as follows:
AO: Reference method
Al: Photometric endpoint detection (cost of $800/20 field tests)*
A2: Crew training workshop (cost of $1,000/20 field tests)
A3: Calculations by standard computer program (cost of $200/20 tests)
A4 (Al + A3): Improved endpoint detection plus calculations by computer
(total cost of $1,000/20 tests)
The costs given for each action are additional costs above that of the
reference method. The assumptions made concerning the reduction in the
variances (or improved precisions) are given in the following for each
action.
*Equipment costs are amortized over 5 years, and allowance is made for
the continuing cost of supplies and labor.
64
-------
AO: The collaborative study (ref. 3) estimates the precision of
Method 6 as follows:
CV = 4.00 percent
CV. =5.80 percent
b
CV =4.19 percent
JL
The same study indicates the method has a- negative bias at
3
higher sulfur dioxide concentrations (from about 0.5 g/m
to 0.8 g/m ). The magnitude is estimated at 5 to 10 percent
at this concentration level. However, the work of Dr. Knoll at
EPA indicates no significant bias, even at much higher concentra-
tions, of the order of 10,000 to 30,000 ppm. Another evaluation
of the method (ref. 9) reports "either a concentration bias in the
method or an inaccuracy in the concentration of the 707 ppm (SO )
cylinder."
The functional analysis (section 4.1) does not anticipate
a bias in Method 6, and provided the procedures (both field and
laboratory) stipulated in the method are rigorously adhered to,
unbiased results should be obtained (ref. 12).
Table 7 shows estimated values for CV, CV, , and CVT for each
b L
alternate strategy, as well as estimated costs. Actual costs must
be determined for each individual situation, and actual improve-
ments in CV, CV and CVT can be determined only by implementation
b L
of the various options.
Al: A major problem associated with Method 6 is the poor visibility
of the titration endpoint. Photometric endpoint detection would
greatly reduce imprecision due to visual estimation of endpoint
color and intensity. Unless the photometric technique became
highly standardized, however, there would remain differences in
technique among various laboratories, so that the improvement in
CV, is assumed not as great, and thus CV is virtually unchanged.
A2: From discussing this method with experienced field testers,
it is felt that the method requires an operator that understands
the system and its capability. Early detection of out-of-control
conditions by the operator can substantially improve data quality.
65
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Table 7. Assumed within-laboratory, between-laboratory,
and laboratory bias for action options
AO
Al
A2
A3
A4
Reference
Photometric End-Point
Detection
Crew Training
Workshop
Calculations by Standard
Computer Program
(Al + A3)
CV
CVR
0.8 CVR
0.8 CVR
1.0 CVR
0.78CVD
K
CVb
(CVR
0.9(CVb)R
0.8(CVb)R
0.89(CVb)R
0.80(CVb)R
cvL
(CVL)R
0.99(CVL)R
0.8(CVL)R
0.8(CVL)R
0.79(CVL)R
ADDED COST
PER 20
FIELD TESTS
0
$ 800
$1000
$ 200
$1000
CVD = 4.00, (CV.) = 5.80, (CV.)D = 4.19, taken directly from the collaborative
K D L K
test results.
It is assumed here that crew training could affect all sources of
variability, and, therefore, an improvement in all three measures
of variability is shown. A one-week course once a year, or special
OJT training, is estimated to cost approximately $1,000 per 20
field tests.'
A3: This recommended option serves a twofold purpose:
1. It eliminates human error (in the field) in calculation of the
SO concentration. There remains, of course, the possibility of
errors due to computer malfunction, keypunch error and the like.
2. It largely eliminates the illegal practice of discarding "bad"
runs and the reporting of only "acceptable" data by field
personnel, since the raw field data is submitted.
Another comparable option could be the use of "canned" programs writ-
ten for the various commercially available programmable calculators.
These could be made available by EPA, thus allowing local calculation
but standardizing the number of significant digits carried in each
step, the treatment of round-off and all other aspects of the calcu-
lation steps.
66
-------
Since one reason for laboratory bias, CV , could be improper
LI
calculation technique, A4 should in general reduce CV . This is
Li
a systematic error (bias). In addition, a small percentage (about
3 percent) of random calculation errors contribute to CV. If both
CVT and CV are reduced, then CV, should also be improved.
L b
Figure 8 shows the results in terms of cost and data quality. Data quali-
ty for this purpose is given as CV, the within-laboratory coefficient of varia-
tion. The figure then illustrates options for the individual laboratory to
consider. The manager of a number of teams would be more interested in how
CV, varies with cost, and this is given in figure 9. It must be emphasized
that figures 8 and 9 are given for illustrative purposes only and should not
in themselves be considered as basis for action by a laboratory or a group of
laboratories. Both the reductions in CV and CV, , as well as costs, are esti-
b
mates based on professional judgment. In particular, the values of CV and
CV, are based solely on judgment and there is no experimental evidence to
support these values. The figures illustrate that in principle it is possi-
ble to reduce the variability of Method 6 by a number of modifications of
the method, and that there is a cost associated with each modification.
Figures 8 and 9 also show "cost of reporting bad data" curves, which
assume that the cost increases as the data quality decreases. These function
curves must be determined for each specific situation according to the moni-
toring objectives of the laboratory or group of laboratories.
Once determined for a given situation, graphs such as figures 8 and 9
can be used to select an "optimal" monitoring strategy; i.e., one which gives
maximum increase in data quality for minimum cost.
In some instances a manager may need to know the total cost of attaining
a prescribed reduction in variability. Figures 8 and 9 can be used to find
the method which roost nearly meets the requirement. The cost of implementing
the method, plus the cost of reporting bad quality data when that method is
used, gives total cost.
It is, of course, possible to implement a combination of two or more
action options, with costs being additive and precision values being multi-
plicative (assumed independent). For example, if one chooses to implement
both Al and A2, the total cost would be $1,800 and the values of CV and CV.
b
would be 0.64 and 0.72, respectively.
67
-------
1
^_
(/>
UJ
L_
f
O
UJ
t— 1
u.
UJ
DC
|l|
UJ
O.
o
o
0
UJ
a
o
1100 -
1000 -
900 -
800 -
700 -
600 -
500 -
400 -
300 -
200 -
100 -
BE
COST C
POOR C
1
1
BEST ACTION
OPTIONS
CV (%)
Figure 8. Added cost versus data quality (CV) for selected action
options.
-------
oo
OL
o
o
1100 -
1000 -
900 -
800 -
700 -
600 -
500 -
400 -
2 300 -
Q
O
200 -
100 -
BEST ACTION
OPTIONS
COST OF REPORTING
POOR QUALITY DATA
1
1
1
2
1
3
cvb U)
A4
Figure 9 . Added cost versus data quality (CVb> for selection action
options.
69
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4.3 PROCEDURES FOR PERFORMING A QUALITY AUDIT
"Quality audit" as used here implies a comprehensive system of planned
and periodic audits to verify compliance with all aspects of the quality
assurance program. Results from the quality audit provide an independent
assessment of data quality. "Independent" in this case implies that the
auditor prepares a reference sample of S02 in air and has the field team
analyze the sample. The field team should not know the true SO- concentra-
tion. From these data, both bias and precision estimates can be made for
the analysis phase of the measurement process.
The auditor; i.e., the individual performing the audit, should have
extensive background experience in source sampling, specifically with the
characterization technique that he is auditing. He should be able to es-
tablish and maintain good rapport with field crews.
/
The functions of the auditor are summarized in the following list:
1. Observe procedures and techniques of the field team during on-site
measurements.
2. Have field team measure sample from a reference cylinder with
known S02 concentration.
3. Check/verify applicable records of equipment calibration checks
and quality control charts in the field team's home laboratory.
4. Compare the audit value with the field team's test value.
5. Inform the field team of the comparison results specifying any
area(s) that need special attention or improvement.
6. File the records and forward the comparison results with appro-
priate comments to the manager.
4.3.1 Frequency of Audit.
The optimum frequency of audit is a function of certain costs and
the desired level of confidence in the data quality assessment. A methodology
for determining the optimum frequency, using relevant costs, is presented
in the final report for this contract. Costs will vary among field teams and
types of field tests. Therefore, the most cost effective auditing level will
have to be derived using relevant local cost data according to the procedure
given in the final report on this contract.
70
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4.3.2 Collecting On-Site Information.
While on site, the auditor should observe the field team's overall
performance of the field test. Specific operations to observe should in-
clude, but not be limited to:
1. Setting up and leak-testing the sampling train;
2. Preparation and pipetting of absorbing solutions into bubblers
and impingers;
3. Sample collection;
4. Sample recovery and preparation for shipment.
The above observations can be used in combination to make an overall
evaluation of the team's proficiency in carrying out this portion of the
field test.
In addition to the above on-site observations, it is recommended that
the auditor have the capability for preparation of reference samples of SO
in air for analysis by the field team. (See ref. 3 for details of S0_-air
sample preparation.)
4.3.3 Collecting Home Laboratory Information.
The auditor must also observe the analytical phase of Method 6. Here
he should observe the following:
1. Sample aliquotting technique. This is particularly important, to
verify that standard analytical technique is being followed.
2. Titration technique, particularly endpoint detection.
3. Calculation procedure.
The analysis phase of Method 6 can be audited with standard sulfate
solutions, as discussed in reference 3.
4.3.3.1 Comparing Audit and Routine Values of SO . In field tests the audit
and routine (field team) values are compared by
d. - (so2). - (so2)aj (15)
where
d. = The difference in the audit and field test results for the j
3 3
audit, mg/m
(S09) . = Audit value of SO concentration for j audit, mg/m
^ aj i „
(SO ). = S0? concentration obtained by the field team, mg/m
71
-------
Record the value of d. in the quality audit log book.
4.3.4 Overall Evaluation of Field Team Performance.
In a summary-type statement, the field team should be evaluated on its
overall performance. Reporting the d value as previously computed is an ade-
quate representation of the objective information collected for the audit.
However, immeasurable errors can result from nonadherence to the prescribed
operating procedures and/or from poor technique in executing the procedures.
There error sources have to be estimated subjectively by the auditor. Using
the notes taken in the field, the team could be rated on a scale of 1 to 5
as follows:
5 - Excellent
4 - Above average
3 - Average
2 - Acceptable, but below average
1 - Unacceptable performance.
In conjunction with the numerical rating, the auditor should include justifi-
cation for the rating. This could be in the form of a list of the team's
strong and weak points.
72
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4.4 DATA QUALITY ASSESSMENT
Two aspects of data quality assessment are considered in this section.
The first considers a means of estimating the precision and accuracy of the
reported data; e.g., reporting the bias, if any, and the standard deviation
associated with the measurements. The second consideration is that of
testing the data quality against given standards, using sampling by variables.
For example, lower and upper limits, L and U, may be selected to include a
large percentage of the measurements. It is desired to control the percen-
tage of measurements outside these limits to less than 10 percent. If the data
quality is not consistent with the L and U limits, then action is taken to
correct the possible deficiency before future field tests are performed and
to correct the previous data when possible.
4.4.1 Est-tmating the Precision/Accuracy of the Reported Data.
Methods for estimating the precision (standard deviation) and accuracy
(bias) of the S0_ concentration were given in section 4.1. This section will
indicate how the audit data collected in accordance with the procedure described
in section 4.2 will be utilized to estimate the precision and accuracy of the
measures of interest. Similar techniques can also be used by a specific firm
or team to assess their own measurements. The differences between the field
team results and the audited results for the respective measurements are
dj = (so2). - (so2)a. .
Let the mean and standard deviation of the differences d., where j=l, ... n
be denoted by d, and s,, respectively. Thus
_ n
d = I d./n, (16)
J-l 3
and s
d
- 2 |1/2
I (d, - d)Z/(n - 1) (17)
J-l
J
Now d is an estimate of the bias in the measurements (i.e., relative to the
audited value). Assuming the audited data to be unbiased, the existence of
a bias in the field data can be checked by the appropriate t-test, i.e.,
73
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(18)
See ref. 15 for a discussion of the t-test.
If t is significantly large, say greater than the tabulated value of
t with n - 1 degrees of freedom, which is exceeded by chance only 5 percent
of the time, then the bias is considered to be real, and some check should
be made for a possible cause of the bias. If t is not significantly large,
then the bias should be considered zero, and the accuracy of the data is
acceptable.
The standard deviation s is a function of both the standard deviation
d
of the field measurements and of the audit measurements. Assuming the audit
values to be much more accurate than the field measurements, then s, is an
d
estimate of a{SO,,}, the population standard deviation for S0_ concentration
measurements. The standard deviation, s,, can be utilized to check the
d
reasonableness of the assumptions made in section 4.1 concerning a{SO_}*.
For example, the estimated standard deviation, s,, may be directly checked
a
against the assumed value, a{SCL}, by using the statistical test procedure
2 "H
2L. = -A
f 2
a{SO }
2
where X /f ^s the value of a random variable having the chi-square distribution
2
with f = n - 1 degrees of freedom. If x /f is larger than the tabulated value
exceeded only 5 percent of the time, then it would be concluded that the test
procedure is yielding more variable results due to faulty equipment or opera-
tional procedure.
The measured values should be reported along with the estimated biases,
standard deviations, the number of audits, n, and the total number of field
tests, N, sampled (n < N). Estimates; i.e., s and d which are significantly
d
different from the assumed population parameters, should be identified on the
data sheet.
2
The t-test and x -test described above and in further detail in the
*Values for a{SO,,} and a {SO } are found by multiplying the values of CV or
CV, by the assumed value of the mean concentration of SCL. This converts
the percentages into concentrations.
74
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final report on this contract, are used to check on the biases and standard
deviations separately. In order to check on the overall data quality as
measured by the percent of measurement deviations outside prescribed limits,
it is necessary to use the approach described in subsection 4.4.2 below.
4.4.2 Sampling by Variables.
Because the lot size (i.e., the number of field tests performed by a
team or laboratory during a particular time period, normally a calendar
quarter) is small, N = 20, and because the sample size is, consequently,
small (of the order of n = 3 to 8), it is important to consider a sampling
by variables approach to assess the data quality with respect to prescribed
limits. That is, it is desirable to make as much use of the data as possi-
ble. In the variables approach, the means and standard deviations of the
sample of n audits are used, in making a decision concerning the data quality.
Some background concerning the assumptions and the methodology is
repeated below for convenience. However, one is referred to one of a number
of publications having information on sampling by variables; e.g., see
refs. 16-21. The discussion below will be given in regard to the specific
problem in the variables approach, which has some unique features as com-
pared with the usual variable sampling plans. In the following discussion,
it is assumed that only S0~ measurements are audited as directed in section
4.3. The difference between the team-measured and audited value of S0?
is designated as d., and the mean difference over n audits by d is
d = 1/n I (SO ) . - (SO ) . (20)
j=l 2 3 2 aj
Theoretically, (SO ) and (S0«) should be measures of the same S09 concentra-
Z. Z. 3. Z.
tion and their difference should have a mean zero on the average. In addition,
this difference should have a standard deviation equal to that associated with
the measurements of S0_.
75
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Assuming three standard deviation limits, the values 3o - -12.0
3 * 3
mg/m and +12.0 mg/m define the respective lower and upper limits,
L and U, outside of which it is desired to control the proportion of
differences, d.. Following the method given in ref. 19, a. procedure
for applying the variables sampling plan is described below. Figures
10 and 11 illustrate examples of satisfactory and unsatisfactory data
quality with respect to the prescribed limits L and U.
The variables sampling plan requires the following information:
the sample mean difference, d, the standard deviation of these differences,
s,, and a constant, k, which is determined by the value of p, the propor-
tion of the differences outside the limits of L and U. For example, if it
is desired to control at 0.10 the probability of not detecting lots
with data qualities p equal to 0.10 (or 10 percent of the individual
differences outside L and U), and if the sample size n * 7, then the
value of k can be obtained from table 2 of ref. 19. The values of
d and s, are computed in the usual manner; see table 8 for formulas and
a specific example. Given the above information, the test procedure is
applied, and subsequent action is taken in accordance with the following
criteria:
1. If both of the following conditions are satisfied,
d - k s, > L = -12 mg/m
d + k sd 5 U = +12 mg/m
the individual differences are considered to be consistent
with the prescribed data quality limits, and no corrective
action is required.
2. If one or both of these inequalities are violated, possible
deficiencies exist in the measurement process as carried
out for that particular lot (group) of field tests. These
deficiencies should be identified and corrected before future
3
*12.0 mg/m assumes for calculation purposes an S02 concentration mean
of 100 mg/m3, with CV = 4.00%, so that 3a = 3 x 4.00 = 12.0 mg/m .
76
-------
P = P, + P, < 0.10
Figure 10. Example illustrating p < 0.10 and satisfactory
data quality.
p (percent of measured
differences outside
limits L and U) > 0.10
U
Figure 11. Example illustrating p > 0.10 and unsatisfactory
data quality.
77
-------
Table 8. Computation of mean difference, d, and
standard deviation of differences, s^
General
d = (S02)
d,
1
d_
2
d.
3
d.
4
dc
5
d.
6
d7
Ed.
Formulas
.1 - U = +12 mg/m3
d
78
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Table 9. Sample plan constants, k for P (not detecting a log
with proportion p outside limits L and U} <. 0.1
Sample Size n p = 0.2 p = 0.1
3 3.039 4.258
5 1.976 2.742
7 1.721 2.334
10 1.595 2.112
12 1.550 2.045
Therefore, both conditions are violated and the lot of N = 20 measurements
is not consistent with the prescribed quality limits. The plan is designed
to aid in detecting lots with 10 percent or more defects (deviations falling
outside the designated limits L and U) with a risk of 0.10; that is, on the
avarage, 90 percent of the lots with 10 percent or more defects will be de-
tected by this sampling plan.
4.4.3 Cost Versus Audit Level.
The determination of the audit level (sample size n) to be used in
assessing the data quality, with reference to prescribed limits L and U
can be made either (1) on a statistical basis, by defining acceptable risks
for type I and type II errors, knowing or estimating the quality of the in-
coming data, and specifying the described level of confidence in the reported
data, or (2) on a cost basis, as described herein. In this section, cost
data associated with the audit procedure are estimated or assumed, for the
purpose of illustrating a method of approach and identifying which costs
should be considered.
A model of the audit process, associated costs, and assumptions made
in the determination of the audit level is provided in figure 12. It is
assumed that a collection of source emissions tests for N stacks is to be
made by a particular firm, and that n measurements (n _< N) are to be audited
at a cost, C = b + en, where b is a constant independent of n and c is
A
the cost per stack measurement audited. In order to make a specific deter-
mination of n, it is also necessary to make some assumptions about the
quality of the source emissions data from several firms. For example, it is
assumed in this analysis that 50 percent of the data lots are of good
79
-------
Collection of Source Emission
Tests (Lots of Size N)
50Z of Lots
< 10Z Defective
Acceptable
Quality
^^^
Audit n
Measurements
^
-*
C - b+cn - $600
Not Acceptable
Quality
\
Audit n
Measurements
/
s
*-
_ 507. of Lots
> 10Z Defective
Select Audit
Parameter n,
1
k
Yes
No
]
v is -
>\*S
Data
not t
Acce
Qua
y
Institut
Improve
(Correc
Pos
to be of
Data Declared
to be of
Acceptable
Quality
to
Expected Cost of
Treating Poor
Quality Data as
Good Quality Data
C,,, - $15,000
Expected Cost of
Falsely Inferring
Data are of Poor
Quality Cp|G -
$10,000
Expected Cost
Saving of Taking
Correct Action with
Respect to Poor
Quality Data
C,,,,, - $7,500
Figure 12. Flow chart of the audit level selection process.
80
-------
quality; i.e., one-half of the firms are adhering to good data quality as-
surance practice, and that 50 percent of the data lots are of poor quality.
Based on the analysis in section 4.1, good quality data is defined as that
which is consistent with the estimated precision/bias using the reference
method. Thus if the data quality limits L and U are taken to be the lower
and upper 3a limits, corresponding to limits used in a control chart, the
quality of data provided by firmly adhering to the recommended quality as-
surance procedures should contain at most about 0.3 percent defective mea-
surements (i.e., outside the limits defined by L and U). Herein, good
quality data is defined as that containing at most 10 percent defective
measurements. The definition of poor quality data is somewhat arbitrary; for
this illustration it is taken as 25 percent outside L and U.
In this audit procedure, the data are declared to be of acceptable
quality if both of the following inequalities are satisfied:
d + ks . < U
_ d
d - ks, > L ,
d
where d and s , are the mean and standard deviation of the data quality char-
acteristic (i.e., the difference of the field and audited measurements)
being checked. The data are not of desired quality if one or both inequali-
ties are violated, as described in section 4.3. The costs associated with
these actions are assumed to be as follows:
C = Audit cost = b + en. It is assumed that b is zero for this exam-
A
pie, and c is taken as $600/measurement.
C | = Cost of falsely inferring that the data are of poor quality, P,
P| G
given that the data are of good quality, G. This cost is assumed
to be one-half the cost of collecting emissions data for N = 20
stacks (i.e., 0.5 x $1,000 x 20 = $10,000). It would include the
costs of searching for an assignable cause of the inferred data
deficiency when none exists, of partial repetition of data collec-
tion, and of decisions resulting in the purchase of equipment to
reduce emission levels of specific pollutants, etc.
81
-------
C I = Cost of falsely stating that the data are of good quality, G,
given that they are of poor quality, P. This cost is assumed to
be $15,000 (= 0.75 x $1,000 x 20), and is associated with health
effects, litigation, etc.
C I = Cost savings resulting from correct identification of poor quality
data. This cost is taken to be $7,500; i.e., equal to one-half
of C-i., or equal to 0.375 x $1,000 x 20, the total cost of data
" I"
collection.
These costs are given in figure 13. The cost data are then used in
conjunction with the a priori information concerning the data quality, to
select an audit level n. Actually, the audit procedure requires the
selection of the limits L and U, n, and k. L and U are determined on the
basis of the analysis of section 4.1. The value of k is taken to be the
value associated with n in table 9 of section 4.4.2; i.e., the value
selected on a statistical basis to control the percentage of data outside
the limits L and U. Thus, it is only necessary to vary n and determine the
corresponding expected total cost E(TC) using the following cost model
E(TC = -CA - 0.5 Pp|G Cp|G + 0.5 Pp|p Cp|p - 0.5 PG|P C^ (22)
where the costs are as previously defined. The probabilities are defined
in a way similar to defining corresponding costs;
P^i = Probability that a lot of good quality data is falsely inferred
r\ G
to be of poor quality, due to the random variations in the
sample mean d and standard deviation, s,, in small samples of
a
size n.
P I = Probability that a lot of poor quality data is correctly identi-
fied as being of poor quality.
P I = Probability that a lot of poor quality data is incorrectly judged
"1^
to be of good quality, due to sampling variations of d and s.
These three probabilities are conditonal on the presumed lot quality
and are preceded by a factor of 0.5 in the total cost model, to correspond
to the assumed percentage of good (poor) quality data lots.
In order to complete the determination of n, it is necessary to calcu-
late each of the conditonal probabilities, using the assumptions stated
82
-------
$8000
234567
Audit Level (n)
|p - Proportion defective measurements in the "lot"
|p{Acc. lot with p} _< 0.1
10
Figure 13. Average cost vs audit level (n)
83
-------
for a series of values of n (and assoriated k, which is given in table 5).
The computational procedure is given in the final report of this contract.
These calculations were made for the ..ases n = 3, 5, 7, and 10 and for two
degrees of control on the quality of the data than can be tolerated; i.e.,
p = 0.2 and p = 0.1, the portion outside the limits L and U for which it
is desired to accept the data as good quality, with probability less than
or equal to 0.10. These computed probabilities are then used in conjunction
with the costs associated with each condition, applying equation (22) to
obtain the average cost versus sjmpie siae n for the two cases p = 0.1 and
0.2 The curves obtained from these results are given in figure 14. Tt can
be seen from these curves that the minimum cost is obtained by using n ~ 5
independent of p. However, it must bf. recognized that the costs used in
the example are for illustrative purples and may vary from one region to
another; thus, within the reasonable- uncertainty of the estimated costs, it
is suggested that p = 0.2 is more cost effective; this tends to permit data
of poorer quality to be accepted,
84
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'SECTION V REFERENCES
1. "Standards of Performance for New Stationary Sources," Federal
Register, Vol. 36, No. 247, December 23, 1971.
2. Driscoll, J., Becker, J., Herbert, R. , "Validation of Improved
Chemical Methods for Sulfur Oxide Measurements from Stationary
Sources," EPA-R2-72-105, National Environmental Research Center,
Research Triangle Park, North-Carolina 27709.
3. Hamil, Henry F. and Camann, David E., "Collaborative Study of Method
for the Determination of Sulfur Dioxide Emissions from Stationary
Sources," EPA Contract No. 68-02-0623, SwRI Project No. 01-3487-001,
National Environmental Research Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27709.
4. Smith, Walter S., and Grove, D. James, Stack Sampling Nomographs for
Field Estimations, Entropy Environmentalists, Inc., Research Triangle
Park, North Carolina 27709.
5. Smith, Franklin, Wagoner, D. E., and Nelson, A. C., "Determination
of Stack Gas Velocity and Volumetric Flow Rates," EPA Contract 68-
02-1234,1HA327, Research Triangle Institute, Research Triangle Park,
North Carolina, February 1974.
6. Brenchley, David L., Turley, C. David, and Yarmac, Raymond F.,
Industrial Source Sampling, Ann Arbor Sciences Publishers, Inc.,
1971.
7. "Selected Methods for the Measurement of Air Pollutants," Public
Health Service Publication No. 999-AP-ll, 1969, National Air Pollution
Control Administration, Research Triangle Park, North Carolina 27709.
8. Galeano, S. F., Tucker, T. W., and Duncan, L., "Determination of Sulfur
Oxides in the Flue Gas of the Pulping Process," JAPCA _22, 790 (1972).
9. Hamil, Henry F., "Laboratory and Field Evaluations of EPA Methods
2, 6, and 7," EPA Contract 68-02-0626, Southwest Research Institute,
San Antonio, Texas 78284.
85
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10. "Administrative and Technical Aspects of Source Sampling for
Participates," Publication No. APTD-1289, Environmental Protection
Agency, Research Triangle Park, North. Carolina, May 1971.
11. Grant, E. L. and Leavenworth, R. S., Statistical Quality Control,
4th ed., St. Louis: McGraw-Hill, 1972.
12. Private communication with Dr. Knoll.
13. Driscoll, John N., Flue Gas Monitoring Techniques, Ann Arbor Sciences
Publishers, Inc., 1974.
14. Driscoll, John N., _e_t aL, "Validation of Improved Chemical Methods
for Sulfur Oxides from Stationary Sources," Walden Research Corp.,
EPA Contract No. 68-02-009 (1972).
15. Cramer, H., The Elements of Probability Theory, John Wiley & Sons, 1955.
16. Statistical Research Group, Columbia University, C. Eisenhart, M. Hastay,
and W. A. Wallis, eds., Techniques of Statistical Analysis, New York:
McGraw-Hill, 1947.
17. Bowker, A. H. and Goode, H. P., Sampling Inspection Variables, New York:
McGraw-Hill, 1952.
18. Hald, A., Statistical Theory with Engineering Applications, New York:
John Wiley & Sons, 1952.
19. Owen, D. B., "Variables Sampling Plans Based on the Normal Distribution,"
Technometrics 9, No. 3 (August 1967).
20. Owen, D. B., "Summary of Recent Work on Variables Acceptance Sampling with
Emphasis on Non-normality," Technometrics 11 (1969):631-37.
21. Takogi, Kinji, "On Designing Unknown Sigma Sampling Plans Based on a Wide
Class on Non-Normal Distributions," Technometrics 14 (1972):699-78.
86
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APPENDIX A METHOD 6--DETERMINATION OF SULFUR
DIOXIDE EMISSIONS FROM
STATIONARY SOURCES
1. Principle and Applicability
1.1 Principle. A gas sample is extracted from the sampling point
in the stack. The acid mist (including sulfur trioxide) and the sulfur
dioxide are separated. The sulfur dioxide fraction is measured by the
barium-thorin titration method.
1.2 Applicability. This method is applicable for the determination
of sulfur dioxide emissions from stationary sources only when specified
by the test procedures for determining compliance with new source per-
formance standards. The minimum detectable limit of the method has been
3 73
determined to be 3.4 mg of SO /m (2.1 x 10 Ib/ft ). No upper limit has
been established.
2. Apparatus
2.1 Sampling. See figure 6-1.
2.1.1 Probe—Borosilicate glass, approximately 5 to 6 mm i.d., with
a heating system to prevent water condensation and equipped with a filter
(either in-stack or heated out-stack) to remove particulate matter includ-
ing sulfuric acid mist.
2.1.2 Bubbler and impingers—One midget bubbler, with medium coarse
glass frit and borosilicate or quartz glass wool packed in top (see figure
6-1) to prevent sulfuric acid mist carryover; and three midget impingers,
each with 30-ml capacity, or equivalent. The bubbler and midget impingers
shall be connected in series with leak free glass connectors. Silicone
grease may be used, if necessary, to prevent leakage.
2.1.3 Glass wool—Borosilicate or quartz.
2.1.4 'Stopcock grease—Acetone insoluble, heat stable silicone grease
may be used, if necessary.
2.1.5 Drying tube—Tube packed with 6- to 16-mesh indicating-type
silica gel, or equivalent, to dry the gas sample and to protect the meter
and pump.
2.1.6 Valve—Needle valve, to regulate sample gas flow rate.
87
-------
c
•H
CO
GO
fi
cx
a
CN
O
3
60
•rl
-------
2.1.7 Pump—Leak free, diaphragm pump, or equivalent, to pull gas
through the train.
2.1.8 Volume meter—Dry gas meter, sufficiently accurate to measure
the sample volume within 2 percent, calibrated over the range of flow
rates and conditions actually used during sampling and equipped with a
temperature gauge (dial thermometer, or equivalent).
2.1.9 Flow Meter—Potameter, or equivalent, to measure flow range
from 0 to 2 1 pm (0 to 5 cfh).
2.1.10 Pitot tube—Type S, or equivalent, attached to probe to
allow constant monitoring of the stack gas velocity so that the sampling
flow rate can be regulated proportional to the stack gas velocity. The
tips of the probe and pitot tube shall be adjacent to each other and the
free space between them shall be 1.9 cm (0.75 in.). The pitot tube must
also meet the criteria specified in Method 2 and calibrated according to
the procedure in the calibration section of that method.
The pitot tube shall be equipped with an inclined manometer, or
equivalent device, capable of measuring velocity head to within 10 per-
cent of the minimum measured value.
2.1.11 Temperature gauge—Dial thermometer, or equivalent, to
measure temperature of gas leaving impinger train to within 1° C (2° F).
2.1.12 Barometer—Mercury, aneroid, or other barometers capable of
measuring atmospheric pressure to within 2.5 mmHg (0.1 in. Hg). In many
cases, the barometric reading may be obtained from a nearby weather bureau
station, in which case the station value shall be requested and an adjust-
ment for elevation differences shall be applied at a rate of minus 2.5
mmHg (0.1 in. Hg) per 30 m (100 ft) elevation increase.
2.2 Sample recovery.
2.2.1 Wash bottles—Polyethylene or glass, 500 ml, two.
2.2.2 Storage bottles—Polyethylene, 100 ml, to store impinger
samples (one per sample).
2.3 Analysis.
2.3.1 Pipettes—Volumetric type, 5-ml size, 20-ml size (one per
sample), and 25-ml size.
2.3.2 Volumetric flasks—100-ml size (one per sample) and 1000 ml
sizes.
89
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2.3.3 Burettes—5-ml and 50-ml sizes.
2.3.A Erlenmeyer flasks—250-ml size (one for each sample,
blank, and standard).
2.3.5 Dropping bottle—125-ml size, to add indicator.
2.3.6 Graduated cylinder—100-ml size.
3. Reagents
Unless otherwise indicated, it is intended that all reagents conform
to the specifications established by the Committee on Analytical Reagents
of the American Chemical Society, where such specifications are available;
otherwise use best available grade.
3.1 Sampling.
3.1.1 Water-Deionized, distilled to conform to ASTM specification
D1193-72, Type 3.
3.1.2 Isopropanol, 80%-Mix 80 ml of isopropanol with 20 ml of de-
ionized, distilled water.
3.1.3 Hydrogen peroxide, 3%-Dilute 30% hydrogen peroxide 1:9 (v/v)
with deionized, distilled water (30 ml is needed per sample). Prepare
fresh daily.
3.2 Sample recovery.
3.2.1 Water-Deionized, distilled, as in 3.1.1.
3.2.2 Isopropanol, 80%—Mix 80 ml of isopropanol with 20 ml of de-
ionized, distilled water.
3.3 Analysis
3.3.1 Water—Deionized, distilled, as in 3.1.1.
3.3.2 Isopropanol, 100%.
3.3.3 Thorin indicator—l-(o-arsonophenylazo)-2-naphtol-3, 6-disul-
fonic acid, disodium salt, or equivalent. Dissolve 0.20 g in 100 ml of
deionized, distilled water.
3.3.4 Barium perchlorate solution, 0.01 N—Dissolve 1.95 g of barium
perchlorate trihydrate [Ba(C10 ) .3H 0] in 200 ml distilled water and dilute
to 1 liter with isopropanol. Bad™ may also be used. Standardize as in
section 5.2.
3.3.5 Sulfuric acid standard, 0.01 N—Purchase or standardize to
± 0.0002 N against 0.01 N NaOH which has previously been standardized
against potassium acid phthalate (primary standard grade).
90
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4. Procedure
4.1 Sampling.
4.1.1 Preparation of collection train. Measure 15 ml of 80%
isopropanol into the midget bubbler and 15 ml of 3% hydrogen peroxide
into each of the first two midget impingers. Leave the final midget
impinger dry. Assemble the train as shown in figure 6-1. Adjust
probe heater to operating temperature. Place crushed ice and water
around the impingers. Leak check the sampling train just prior to
use at the sampling site by plugging the probe inlet and pulling a
vacuum (capacity of pump) with the flow regulator valve wide open.
Observe the dry gas volume meter dial and time any apparent flow
using a stop watch. A leakage rate not in excess of 1% of the
sampling rate is acceptable. Close the flow regulator valve and
carefully release the probe inlet plug and turn off the pump.
4.1.2 Sample collection. Record the initial dry gas meter
reading and barometric pressure. To begin sampling, position the
tip of the probe at the sampling point and start the pump. Adjust
the sample flow to a rate of approximately 1 1pm as indicated by the
rotameter. Sample at a rate that is proportional (within 20 percent
of the average) to the stack gas velocity throughout the run. Take
readings (dry gas meter, temperatures at dry gas meter and at impinger
outlet, rate meter, and velocity head) at least every five minutes
and when significant changes (20 percent variation in velocity head
readings) in stack conditions necessitate additional adjustments in
sample flow rate. Add more ice during the run to keep the temperature
of the gases leaving the last impinger at 20° C (68° F) or less. At
the conclusion of each run, turn off the pump and record the final
readings. Conduct a leak check as before. If excessive leakage rate.
is found void the test run. Remove the probe from the stack and dis-
connect it from the train. Drain the ice bath and purge the remaining
part of the train by drawing clean ambient air through the system for
15 minutes at the sampling rate. Note: Clean ambient air can be pro-
vided by passing air through a charcoal filter or through an extra
midget impinger with 15 ml 3% H 0<
91
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4.2 Sample recovery. Disconnect the impingers after purging.
Discard the contents of the midget bubbler. Pour the contents of
the midget impingers into a leak-free polyethylene bottle for ship-
ment. Rinse the three midget impingers and the connecting tubes
with deionized, distilled water and add the washings to the same
storage container. Mark the fluid level. Seal and identify the
sample container.
4.3 Sample analysis. Note level of liquid in container and
confirm whether or not any sample was lost during shipment by noting
this on analytical data sheet. (Note: Protect the 0.01 N barium
perchlorate solution from evaporation at all times.) Transfer the
contents of the storage container to a 100-ml volumetric flask and
dilute to exactly 100 ml with deionized, distilled water. Pipette
a 20-ml aliquot of this solution into a 250-ml Erlenmeyer flask,
add 80 ml of isopropanol, two to four drops of thorin indicator
and titrate to a pink endpoint using 0.01 N barium perchlorate.
Repeat and average the titration volumes. Run a blank with each
series of samples. Replicate titrations shall agree, within 1 per-
cent .
5. Calibration
5.1 Use methods and equipment as specified in Methods 2 and
5 and APTD-0576 to calibrate the ivtameter, pitot tube, dry gas
meter, barometer, thermometers, and probe heater.
5.2 Standardize the barium yerchlorate solution against 25 ml
of standard sulfuric acid to which 100 ml of isopropanol has been
added.
6. Calculations
Carry out calculations, retaining at least one extra decimal
figure beyond that of the acquired data. Round off figures after
final calculation.
6.1 Nomenclature.
C = Concentration of sulfur dioxide, dry basis corrected to
2
standard conditions, mg/dscm (Ib/dscf).
N = Normality of barium perchlorate titrant, milliequivalents/ml.
92
-------
P = Barometric pressure at the exit orifice of the dry gas
D 3i
meter, mmHg (in. Hg) .
P , = Standard absolute pressure, 760 mmHg (29.92 in. Hg).
std
T = Average dry gas meter absolute temperature, °K (°R).
m
T = Standard absolute temperature, 293° K (528° R).
S LQ
V = Volume of sample aliquot titrated, ml.
a
V * Dry gas volume as measured by the dry gas meter, dcm (dcf)
V , ,N= Dry gas volume measured by the dry gas meter,
m(std;
corrected to standard conditions, dscm (dscf).
V = Total volume of solution in which the sulfur dioxide
soln
sample is contained, 100 ml.
V = Volume of barium perchlorate titrant used for the
sample, ml (average of replicate titrations).
V = Volume of barium perchlorate titrant used for the
tb
blank , ml .
32.03 = Equivalent weight of sulfur dioxide.
6.2 Dry sample gas volume, corrected to standard conditions.
'v
_ r T r L_> i~ <_i | i utj.it IT t TO
m(std)
m
(6-1)
where:
o
K = 0.3857 K/mmHg for metric units.
o
= 17.65 R/in. Hg for English units.
6.3 Sulfur dioxide concentration.
/ V \
(V - V )N soln]
t tb »—- I
Cqn = K \ a / (6-2)
2 V
m(std)
93
-------
where :
K = 32.03 mg/meq. for metric units.
= 7.05 x 10~5 / / for English units.
7. References
7.1 Atmospheric Emissions from Sulfuric Acid Manufacturing
Processed, U.S. DHEW, PHS , Division of Air Pollution, Public Health
Service Publication No. 999-AP-13, Cincinnati, Ohio, 1965.
7.2 Corbett, P.F., The Determination of SO and SO in Flue
Gases, Journal of the Institute of Fuel, 24, 237-243, 1961.
7.3 Matty, R.E. and E.K. Diehl, Measuring Flue-Gas SO and
SO , Power 101:94-97, November 1957.
7.4 Patton, W.F. and J.A. Brink, Jr., New Equipment and
Techniques for Sampling Chemical Process Gases, J. Air Pollution
Control Association, 13, 162 (1963).
7.5 Rom, J.J., Maintenance, Calibration, and Operation of
Isokinetic Source-Sampling Equipment. Office of Air Programs,
Environmental Protection Agency, Research Triangle Park, N.C.,
March 1972. APTD-0576.
7.6 Hamil, H.F. and Camann, D.E., Collaborative Study of
Method for the Determination of Sulfur Dioxide Emissions From
Stationary Sources. Prepared for Methods Standardization Branch,
Quality Assurance and Environmental Monitoring Laboratory, National
Environmental Research Center, Environmental Protection Agency,
Research Triangle Park, N.C. 27709.
7.7 Annual Book of ASTM Standards. Part 23; Water, Atmospheric
Analysis, pp. 203-205. American Society for Testing Materials,
Philadelphia, Penna. (1972).
94
-------
APPENDIX B ILLUSTRATED AUDIT PROCEDURES
AND CALCULATIONS
A flow chart of the operations involved in an auditing program,
from first setting desired limits on the data quality to filing the
results, is given in the following pages. Assumed numbers are used and
a sample calculation of an audit is performed in the flow chart. Each
operation has references to the section in the text of the report where
it is discussed.
95
-------
MANAGER
5.
6.
7.
LIMITS FOR DATA QUALITY CAN BE SET BY WHAT
IS DESIRED OR FROM THE NATURAL VARIABILITY
OF THE METHOD WHEN USED BY TRAINED AND
COMPETENT PERSONNEL. FOR THIS EXAMPLE, IT
IS ASSUMED THAT a{S02) = 4.00 mg/m3
(subsec.4.1)*, AND USING ± 3 aiSOp}, THE
LIMITS ARE L = -12.0 mg/m3 AND U = +12.0 mg/m3.
FROM PRIOR KNOWLEDGE OF DATA QUALITY, ESTIMATE
THE PERCENTAGE OF FIELD MEASUREMENTS FALLING
OUTSIDE THE ABOVE LIMITS. IF NO INFORMATION
IS AVAILABLE, MAKE AN EDUCATED GUESS. IT IS
ASSUMED IN THIS EXAMPLE THAT 50 PERCENT OF THE
FIELD DATA ARE OUTSIDE THE LIMITS L AND U
(subsec. 4.4.3).
DETERMINE: (1) COST OF CONDUCTING AN AUDIT,
(2) COST OF FALSELY INFERRING THAT GOOD DATA
ARE BAD, (3) COST OF FALSElY INFERRING THAT
BAD DATA ARE GOOD, AND (4) COST SAVINGS FOR
CORRECTLY IDENTIFYING BAD DATA (subsec. 4.4.3).
DETERMINE THE AUDIT LEVEL EITHER BY (1) MINI-
MIZING AVERAGE COST USING EQUATION (22) OF
SUBSECTION 4.4.3, OR (2) ASSURING A DESIRED
LEVEL OF CONFIDENCE IN THE REPORTED DATA
THROUGH STATISTICS. FOR THIS EXAMPLE, THE
AUDIT LEVEL IS TAKEN AS n = 5 (fig. 14).
BY TEAMS, TYPES OF SOURCES, OR GEOGRAPHY,
GROUP FIELD TESTS INTO LOTS (GROUPS) OF ABOUT
20 THAT WILL BE PERFORMED IN A PERIOD OF ONE
CALENDAR QUARTER.
SELECT n OF THE N TESTS FOR AUDITING. COMPLETE
RANDOMIZATION MAY NOT BE POSSIBLE DUE TO AUDI-
TOR'S SCHEDULE. THE PRIMARY POINT IS THAT THE
FIELD TEAM SHOULD NOT KNOW IN ADVANCE THAT
THEIR TEST IS TO BE AUDITED.
ASSIGN OR SCHEDULE AN AUDITOR FOR EACH FIELD
TEST.
SET DESIRED
LOWER AND UPPER
LIMITS FOR DATA
QUALITY, L AND U
ESTIMATE AVERAGE
QUALITY OF FIELD
DATA IN TERMS OF
L AND U
DETERMINE OR
ASSUME RELEVANT
COSTS
DETERMINE AUDIT
LEVEL FROM
STATISTICS, OR
AVERAGE COST
GROUP FIELD TESTS
INTO LOT SIZES OF
ABOUT N = 20
RANDOMLY SELECT
n OF THE N TESTS
FOR AUDITING
ASSIGN/SCHEDULE
AUDITOR(S) FOR
THE n AUDITS
•}
Based on a 100 mg/m-3 sample mean and CV = 4.00%.
96
-------
AUDITOR
8. THE AUDITOR OBTAINS APPROPRIATE CALIBRATED 8
EQUIPMENT AND SUPPLIES FOR THE AUDIT
(subsec. 4.3).
9. OBSERVE THE FIELD TEAM'S PERFORMANCE OF THE 9
FIELD TEST (subsec. 4.3.2 AND 4.3.3) AND NOTE ANY
UNUSUAL CONDITIONS THAT OCCURRED DURING
THE TEST.
10. THE AUDITOR'S REPORT SHOULD INCLUDE (1) DATA 10
SHEET FILLED OUT BY THE FIELD TEAM,
(2) AUDITOR'S COMMENTS, (3) AUDIT DATA SHEET
WITH CALCULATIONS, AND (4) A SUMMARY OF THE
TEAM'S PERFORMANCE WITH A NUMERICAL RATING
(subsec. 4.3.4).
11. THE AUDITOR'S REPORT IS FORWARDED TO THE 11
MANAGER.
MANAGER
12. COLLECT THE AUDITOR'S REPORTS FROM THE r, 12
AUDITS OF THE LOT OF N STACKS. IN THIS
CASE n = 7 AND ASSUMED VALUES FOR THE
AUDITS ARE d] = -12, d2 = 6, ds = 0,
d4 = 20, ds = 17.4, de = 8.1, and dy = 0
(table 8).
13. CALCULATE ~3 AND sd ACCORDING TO THE SAMPLE IN 13
TABLE 4. RESULTS OF THIS SAMPLE CALCULATION
SHOW I = 5.6 AND sd = 11.6 (table 8, subsec.
4.4.2).
14. USE A t-TEST TO CHECK d FOR SIGNIFICANCE, FOR 14
THIS EXAMPLE t = (5.6 x /7)/4.00 = 3.70. THE
TABULATED t-VALUE FOR 6 DEGREES Of FREEDOM AT
THE 0.05 LEVEL IS 1.943; HENCE, d IS
SIGNIFICANTLY DIFFERENT FROM 0 AT THIS LEVEL.
ALSO, sd IS CHECKED AGAINST THE ASSUMED VALUE OF
4.00 mg/m3 BY A CHI-SQUARE TEST.
X2/f = sj-/a2{d} = (11.6)2/(4.00)2 = 8.4,
THE TABULATED VALUE OF x /6 AT THE 95 PER-
CENT LEVEL IS 1.64; HENCE, sd IS SIGNIFICANTLY
DIFFERENT FROM 4.00 mg/m3.
i
PREPARE EQUIPMENT
AND FORMS
REQUIRED IN AUDIT
OBSERVE ON-SITE
PERFORMANCE
OF TEST
I
PREPARE
AUDIT
REPORT
FORWARD
REPORT TO
MANAGER
COMBINE
RESULTS OF
n AUDITS
CALCULATE THE
MEAN, d, AND
STANDARD
DEVIATION. sd
I
_ TEST
d AND sd
97
-------
15. OBTAIN THE VALUE OF k FROM TABLE 6, FOR n = 7 15
AND p = 0.1. THIS VALUE 1^2.334, THEN
d + k sd = 32.7 mg/m3 AND d - k sd = -21.5 mg/m3
(subsec. 4.4.2).
16. COMPARE THE ABOVE CALCULATIONS WITH LIMITS 16
L AND U (subsec. 4.4.2). FOR THIS EXAMPLE
d" + k sd = 32.7 > U = 12.0 mg/m3
cf - k sd = -21.5 < L = -12.0 mg/m3
BOTH CONDITIONS ARE VIOLATED.
17. STUDY THE AUDIT AND FIELD DATA FOR SPECIFIC 17
AREAS OF VARIABILITY, SELECT THE MOST COST-
EFFECTIVE ACTION OPTION(S) THAT WILL RESULT
IN GOOD QUALITY DATA (subsec. 4.2). NOTIFY
THE FIELD TEAMS TO IMPLEMENT THE SELECTED
ACTION OPTION(S).
18. A COPY OF THE AUDITOR'S REPORT SHOULD BE SENT 18
TO THE RESPECTIVE FIELD TEAM. ALSO, THE DATA
ASSESSMENT RESULTS, i.e., CALCULATED VALUES OF
d, sd, AND COMPARISON WITH THE LIMITS L AND U
SHOULD BE FORWARDED TO EACH TEAM INVOLVED IN
THE N FIELD TESTS.
19. THE FIELD DATA WITH AUDIT RESULTS ATTACHED ARE 19
FILED. THE AUDIT DATA SHOULD REMAIN WITH THE
FIELD DATA FOR ANY FUTURE USES.
i.
CALCULATE
d + k sd
AND
d - k sd
COMPARE
(16) WITH
L AND U
MODIFY
MEASUREMENT
METHOD
INFORM
FIELD TEAMS
OF AUDIT
RESULTS
FILE AND
CIRCULATE OR
PUBLISH FIELD
DATA
98
-------
APPENDIX C GLOSSARY OF SYMBOLS
This is glossary of symbols as used in this document. Symbols used and
defined in the reference method (appendix A) are not repeated here.
SYMBOL DEFINITION
N Lot size, i.e., the number of field tests to be treated as
a group.
n Sample size for the quality audit (section IV).
CV{X} Assumed or known coefficient of variation (100 a /y ) .
X X
CV{X> Computed coefficient of variation (100 s /X) from a finite
.A.
sample of measurements.
a{X} Assumed standard deviation of the parameter X (population
standard deviation).
T{X} Computed bias of the parameter X for a finite sample
(sample bias) .
d. The difference in the audit value and the value of NCL
arrived at by the field crew for the j ^ audit.
d Mean difference between (S0n). and (SO.) . for n audits.
2 j 2 aj
s. Computed standard deviation of differences between (SCO. and
d 2 j
p Percent of measurements outside specified limits L and U.
k Constant used in sampling by variables (section IV).
P{Y} Probability of event Y occurring.
t, T^ Statistic used to determine if the sample bias, d, is
significantly different from zero (t-test) .
2 2
X /(n -1) Statistic used to determine if the sample variance, s , is
2
significantly different from the assumed variance, a , of
the parent distribution (chi-square test) .
99
-------
APPENDIX C GLOSSARY OF SYMBOLS (CONTINUED)
SYMBOL DEFINITION
L Lower quality limit used in sampling by variables.
U Upper quality limit used in sampling by variables.
CL Center line of a quality control chart.
LCL Lower control limit of a quality control chart.
UCL Upper control limit of a quality control chart.
SO Sulfur dioxide reported by the field team for field test.
(SO ) Sulfur dioxide concentration used in an audit check.
^. 3.
(SO ) Measured value of a calibration gas.
/ m
(SO ) Assayed or known value of a calibration gas.
100
-------
APPENDIX D
GLOSSARY OF TERMS
The following glossary lists and defines the statistical terms as used
in this document.
Accuracy
Bias
Lot
Measurement method
Measurement process
Population
Precision
Quality audit
Quality control
check
Sample
A measure of the error of a process expressed as a
comparison between the average of the measured values
and the true or accepted value. It is a function of
precision and bias.
The systematic or nonrandom component of measurement
error.
A specified number of objects to be treated as a
group; e.g., the number of field tests to be conducted
by an organization during a specified period of time
(usually a calendar quarter).
A set of procedures for making a measurement.
The process of making a measurement, including method,
personnel, equipment, and environmental conditions.
The totality of the set of items, units, or measure-
ments, real or conceptual, that is under considera-
tion.
The degree of variation among successive, independent
measurements (e.g., on a homogeneous material) under
controlled conditions, and usually expressed as a
standard deviation or as a coefficient of variation.
A management tool for independently assessing data
quality.
Checks made by the field crew on certain items of
equipment and procedures to assure data of good
quality.
Objects drawn, usually at random, from the lot for
checking or auditing purposes.
101
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1 REPORTNO |2
EPA-650/4-74-005e |
4. TITLE AND SUBTITLE
"Guidelines for Development of a Quality A
Program: Determination of Sulfur Dioxide
from Stationary Sources"
7 AUTHOR(S)
J. W. Buchanan, D. E. Wagoner
9 PERFORMING ORGANIZATION NAME AND ADDRESS
Research Triangle Institute
P.O. Box 12194
Research Triangle Park, NC 27709
12. SPONSORING AGENCY NAME AND ADDRESS
Office of Research & Development
U.S. Environmental Protection Agency
Washington, D. C. 20460
3. RECIPIENT'S ACCESSION NO.
5 REPORT DATE
ssurance November 1975
Emissions 6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT NO.
10. PROGRAM ELEMENT NO.
1HA327
1 1. CONTRACT/GRANT NO.
68-02-1234
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
15 SUPPLEMENTARY NOTES
16. ABSTRACT
Guidelines for the quality control of stack gas analysis for sulfur dioxide
emissions by the Federal reference method are presented. These include:
1. Good operating practices.
2. Directions on how to assess performance and to qualify data.
3. Directions on how to identify trouble and to improve data quality.
4. Directions to permit design of auditing activities.
The document is not a research report. It is designed for use by operating
personnel .
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
Quality assurance
Quality control
Air pollution
Stack gases
18 DISTRIBUTION STATEMENT
Unlimited
b. IDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
13H
14D
13B
21B
19. SECURITY CLASS (This Report) 21 NO. OF PAGES
UnrlascHfipH /&8
20 SECURITY CLASS (This page) 22 PRICE
Unclassified
EPA Form 2220-1 (9-73)
102
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