EPA/600/R-97/147
March 1998
Environmental Technology
Verification Report
Field Portable X-ray
Fluorescence Analyzer
Scitec MAP Spectrum Analyzer
U.S. Environmental Protection Agency
Office of Research and Development
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Notice
The information in this document has been funded wholly or in part by the U.S. Environmental
Protection Agency (EPA) under Contract No. 68-CO-0047 to PRC Environmental Management, Inc.
This work supports the Superfund Innovative Technology Evaluation Program administered by the
National Risk Management Research Laboratory, Cincinnati, Ohio. This demonstration was conducted
under the Monitoring and Measurement Technologies Program which is managed by the National
Exposure Research Laboratory-Environmental Sciences Division, Las Vegas, Nevada. It has been
subjected to the Agency's peer and administrative review, and has been approved for publication as an
EPA document. Mention of corporation names, trade names, or commercial products does not constitute
endorsement or recommendation for use of specific products.
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
Washington, D.C.
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ENVIRONMENTAL TECHNOLOGY
VERIFICATION STATEMENT
TECHNOLOGY TYPE: FIELD PORTABLE X-EAY FLUORESCENCE ANALYZER
APPLICATION; OF IN SOIL
TECHNOLOGY NAME: MAP SPECTRUM ANALYZER
COMPANY: SCITEC CORPORATION
415 N. QUAY
WA
The U.S. Environmental Protection Agency (EPA) has created a program to facilitate the deployment of innovative
technologies through performance verification and information dissemination. The goal of the Environmental
Technology Verification (ETV) Program is to further environmental protection by substantially accelerating the
acceptance and use of improved and more cost-effective technologies. The ETV Program is intended to assist and
inform those involved in. the design, distribution, permitting, and purchase of environmental technologies. This
document summarizes the results of a demonstration of the Scitec MAP Spectrum Analyzer,
PROGRAM OPERATION
The EPA, In partnership with recognized testing organizations, objectively and systematically evaluates the
performance of innovative technologies. Together, with the full participation of the technology developer, they
develop plans, conduct tests, collect and analyze data, and report findings. The evaluations are conducted according
to a rigorous demonstration plan and established protocols for quality assurance. The EPA's National Exposure
Research Laboratory, which conducts demonstrations of field characterization and monitoring technologies,
selected PRC Environmental Management, Inc., as the testing organization for the performance verification of field
portable X-ray fluorescence (FPXRF) analyzers.
DESCRIPTION
In April 1995, the performance of seven FPXRF analyzers was determined under field conditions. Each analyzer
was independently evaluated by comparing field analysis results to those obtained using approved reference
methods. Standard reference materials (SRM) and performance evaluation (PE) samples also were used to
independently assess the accuracy and comparability of each instrument.
The demonstration was designed to detect and measure a series of inorganic analytes in soil. The primary target
analytes were arsenic, barium, chromium, copper, lead, and zinc; nickel, iron, cadmium, and antimony were
secondary analytes. The demonstration sites were located in Iowa (the RV Hopkins site) and Washington (the
4SARCO site). These sites were chosen they exhibit a wide range of concentrations for most of the
metals and are located in different ellmatological regions of the United States; combined, they exhibit three distinct
soil types: sand, clay, and loam. The conditions at these are representative of those environments under which
the technology would be expected to operate. Details of the demonstration, including a data summary and
EPA-W-SCM-07 The accompanying notice is an integral part of this verification statement March 1998
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discussion of results, may be found in the report entitled "Environmental Technology Verification Report, Field
Portable X-ray Fluorescence Analyzer, Scitec MAP Spectrum. Analyzer," The EPA document number for this
report is EPA/600/R-97/147,
The EPA SW-846 Method 6200 was and the this demonstration. This
method be used to support: the application of FPXRF for analysis,
TECHNOLOGY DESCRIPTION
These analyzers operate on the principle of energy dispersive X-ray fluorescence spectroscopy where the
characteristic energy components of the excited X-ray spectrum are analyzed directly as an energy proportional
response In an X-ray detector. Energy dispersion affords a highly efficient, full-spectrum which
enables the use of low intensity excitation sources (such as radloisotopes) and compact battery-powered, field-
portable electronics. The FPXRF Instruments are designed to provide rapid analysis of metals in soil. This
information allows investigation and remediation decisions to be en-site and reduces the number of samples
that need to be submitted for laboratory analysis. In the operation of these instruments, the user must be aware that
PPXKF analyzers do not respond well to chromium and that detection limits may be 5 to 10 times greater than
conventional laboratory methods. As with all field collection programs, a portion of the samples should be sent
to a laboratory for confirmatory analyses.
The MAP Spectrum Analyzer was originally designed to detect lead on painted surfaces using a cobalt-57 excitation
source. It is now marketed for detecting and other metals in. soil, especially when equipped with a cadmium-
109 source. Two other sources, americum-24! and cobalt-57, are also available. The MAP Spectrum Analyzer was
empirically calibrated by the developer prior to the demonstration using site-specific calibration The
instrument designed to be portable, is composed of two parts, the scanner which weighs 3,5 and an 11 •
pound control console. In this demonstration, the MAP Spectrum Analyzer configured to four of the
primary target analytes: arsenic, copper, lead, and zinc. It was operated only in the in situ mode. At the time of
the demonstration, the cost of the MAP Spectrum Analyzer with the cadmium-109 source was $32,000, or it could
he for $4,675 per month,
OF
The performance characteristics of the MAP Spectrum Analyzer include the following;
» Detection limits; Precision-based detection limits were determined by collecting 10 replicate measurements
on site-specific soil with metals concentrations 2 to 5 the expected MDLs. Results from
25 milligrams per kilogram (mg/kg) for zinc to 525 mg/kg for copper. Corresponding Yalues were 225 mg/kg
for arsenic and 165 mg/kg for
» Throughput: Average throughput was 9-12 samples per hour a live count time of 240 seconds. This
rate only represents the analysis time since different personnel were used to prepare the samples,
* Drift; Based on a periodic analysis of a calibration check sample, drift was the greatest for copper least
for zinc. The drift values for the recovery of copper varied from -25 to +35 percent; arsenic was ±1 s«
percent; was -15 to +25 percent; and zinc ±5 percent,
» The MAP Spectrum Analyzer produced results for 628 of the 630 in situ samples for u
completeness of 99.7 percent, above the demonstration objectiYe of 95 percent.
* Blank results: Three of the four reported analytes were not detected above the field-based method detection
limits in the blanks. Anomalous readings were reported for copper but were considered to be an artifact of the
blank measurement process,
» Precision: The of the demonstration was to achieve relative deviations (RSD) of less 20
percent at concentrations of 5 to 10 times the method detection limits. The RSD for arsenic, lead,
and were 9 percent RSD. Copper had an RSD of less 15 percent.
EPA-W-SCM-07 The accompanying notice is an integral part of this verification statement March 1998
iv
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• Accuracy: Accuracy was assessed by using site specitic soil Pb samples and soil SRMs The data showed
that 5 of 1? results (29.4 percent) of the analytes in these samples had recoveries within a quantitative
of 80 -120 percent. This analyzer showed the accuracy for with 50 percent of
the within the 80 -120 percent recovery range. The instrument underestimated arsenic and copper in
the site-specific PE samples, especially at low concentrations. Recovery values for zinc were inconsistent but
overall were underestimated.
» This demonstration showed that the MAP Spectrum Analyzer produced that exhibited
a Iog)0-log10 linear correlation to the reference data. The coefficient of determination (r2) which is a measure
of the degree of correlation between the reference and field data was 0,85 for lead, 0,80 for copper, 0,76 for
arsenic, and 0.67 for zinc.
* quality levels: Using the demonstration derived precision RSD results and the coefficient of determination
as the primary qualifiers, the MAP Spectrum Analyzer produced definitive level data for lead; of
quantitative screening level for copper and arsenic; and data of qualitative screening level for zinc.
The results of the demonstration show that the Scitec MAP Spectrum Analyzer can provide useful, cost-effective
data for environmental problem-solving and decision-making. Undoubtedly, it will be employed in a variety of
applications, ranging from serving as a complement to data generated in a fixed analytical laboratory to generating
data that will stand alone in the decision-making process. As with any technology selection, the user must
determine what is appropriate for the application and the project data quality objectives.
Gary J,
Director
National Exposure Research Laboratory
{tffice of Research and Development
1 NOTICE: EPA verifications are based on an evaluation of technology performance under specific, predetermined criteria and the
i Appropriate quality assurance procedures, EPA no expressed or implied warranties as to the performance of the technology
find does not certify that a technology will always, under circumstances other than those tested, operate at the levels verified. The
I i'nd ow i«i solelv responsible for complying with any and all applicable Federal, State, and lx>cal requirements
EPA-VS-SCM-07 The accompanying notice is an integral part of this verification statement March 1998
V
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Foreword
The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the Nation's land,
air, and water resources. Under a mandate of national environmental laws, the Agency strives to formulate
and implement actions leading to a compatible balance between human activities and the ability of natural
systems to support and nurture life. To meet this mandate, the EPA's Office of Research and Development
(ORD) provides data and science support that can be used to solve environmental problems and to build the
scientific knowledge base needed to manage our ecological resources wisely, to understand how pollutants
affect our health, and to prevent or reduce environmental risks.
The National Exposure Research Laboratory (NERL) is the Agency's center for the investigation of technical
and management approaches for identifying and quantifying risks to human health and the environment.
Goals of the Laboratory's research program are to develop and evaluate technologies for the characterization
and monitoring of air, soil, and water; support regulatory and policy decisions; and provide the science
support needed to ensure effective implementation of environmental regulations and strategies.
The EPA's Super-fund Innovative Technology Evaluation (SITE) Program evaluates technologies for the
characterization and remediation of contaminated Superfund and Resource Conservation and Recovery Act
(RCRA) corrective action sites. The SITE Program was created to provide reliable cost and performance
data to speed the acceptance of innovative characterization and monitoring technologies.
Effective measurement and monitoring technologies are needed to assess the degree of contamination at a
site, to provide data which can be used to determine the risk to public health or the environment, to supply
the necessary cost and performance data to select the most appropriate technology, and to monitor the
success or failure of a remediation process. One component of the SITE Program, the Monitoring and
Measurement Technologies Program, demonstrates and evaluates innovative technologies to meet these
needs.
Candidate technologies can originate from within the federal government or from the private sector. Through
the SITE Program, developers are given the opportunity to conduct a rigorous demonstration of their
technology's performance under realistic field conditions. By completing the evaluation and distributing the
results, the Agency establishes a baseline for acceptance and use of these technologies. The Monitoring and
Measurement Technologies Program is managed by ORDs Environmental Sciences Division in Las Vegas,
Nevada.
Gary J. Foley, Ph.D.
Director
National Exposure Research Laboratory
Office of Research and Development
VI
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Abstract
In April 1995, the U.S. Environmental Protection Agency (EPA) conducted a demonstration of field portable
X-ray fluorescence (FPXRF) analyzers. The primary objectives of this demonstration were (1) to determine
how well FPXRF analyzers perform in comparison to standard reference methods, (2) to identify the effects
of sample matrix variations on the performance of FPXRF, (3) to determine the logistical and economic
resources needed to operate FPXRF analyzers, and (4) to test and validate an SW-846 draft method for
FPXRF analysis. The demonstration design was subjected to extensive review and comment by the EPA's
National Exposure Research Laboratory, EPA Regional and Headquarters Superfund technical staff, the
EPA's Office of Solid Waste-Methods Section, and the technology developers.
Two sites were used for this demonstration: the RV Hopkins site and the ASARCO Tacoma Smelter site
(ASARCO). RV Hopkins is an active steel drum recycling facility and a former battery recycling operation.
It is located in Davenport, Iowa. The ASARCO site is a former copper and lead smelter and is located in
Tacoma, Washington. The test samples analyzed during this demonstration were evenly distributed between
three distinct soil textures: sand, loam, and clay. The reference methods used to evaluate the comparability
of data were EPA SW-846 Methods 3050A and 6010A, "Acid Digestion of Sediments, Sludges, and Soils"
and "Inductively Coupled Plasma-Atomic Emission Spectroscopy," respectively.
The FPXRF analyzers tested in this demonstration were designed to provide rapid, real-time analysis of
metals concentrations in soil samples. This information allows investigation and remediation decisions to be
made on-site more efficiently and can reduce the number of samples that need to be submitted for
confirmatory analysis. Of the seven commercially available analyzers evaluated, one is manufactured by
Niton Corporation (the XL Spectrum Analyzer); two are manufactured by TN Spectrace (the TN 9000 and
TN Pb Analyzer); two are manufactured by Metorex Inc. (the X-MET 920-P Analyzer and the X-MET 920-
MP Analyzer); one is manufactured by HNU Systems, Inc. (the SEFA-P Analyzer); and one is manufactured
by Scitec Corporation (the MAP Spectrum Analyzer). The X-MET 940, a prototype FPXRF analyzer
developed by Metorex, was given special consideration and replaced the X-MET 920-P for a portion of the
demonstration. This environmental technology verification report (ETVR) presents information regarding
the performance of the Scitec MAP Spectrum Analyzer. Separate ETVRs have been published for the other
analyzers demonstrated.
Quantitative data were provided by the MAP Spectrum Analyzer on a real-time basis. This FPXRF analyzer
was configured to report arsenic, copper, lead, and zinc. The analyzer used a count time of 240 live-seconds,
which resulted in a throughput of 9 to 12 samples per hour. The analyzer used one radioactive source,
cadmium-109 coupled to a solid-state silicon detector. The MAP Spectrum Analyzer provided definitive
level data (equivalent to reference data) for lead; quantitative screening level data (not equivalent to
reference data, but correctable by collecting confirmatory samples) for copper and arsenic; and qualitative
screening level data (identifies presence or absence only) for zinc. The analyzer exhibited precision at 5 to
10 times the method detection limits of less than 15 percent relative standard deviation (RSD) for all four of
the reported analytes. The analyzer generally exhibited a precision similar to the reference method.
VII
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The analyzer's quantitative results were based on an empirical calibration using site-specific calibration
samples. Field-based method detection limits (MDL) for this analyzer were slightly lower than the precision-
based MDLs for arsenic, copper, and lead, but much higher for zinc. Data correction had limited effect on
the analyzer's average relative bias and accuracy. Except for copper, the precision-based and field-based
MDLs were below the developer's projected MDL of 250mg/kg. The site variable did not affect data
comparability. The soil variable showed a slight trend of poorer comparability in loam soils. This study
showed that the MAP Spectrum Analyzer produced data that exhibited logio-logio linear correlation for all
four of the reported analytes.
This demonstration found that the MAP Spectrum Analyzer was simple to operate in the field. This FPXRF
analyzer is used only in the in situ mode which means it analyzed samples in minimally disturbed soil. The
operator required no specialized training or experience to operate the analyzer. Ownership and operation of
this instrument may require specific licensing by state nuclear regulatory agencies. There are special
radiation safety training requirements and costs associated with this type of licensing.
The MAP Spectrum Analyzer can provide rapid, real-time analysis of the metals content of soil samples at
hazardous waste sites. The analyzer can quickly distinguish contaminated areas from noncontaminated areas,
allowing investigation and remediation decisions to be made more efficiently on-site which may reduce the
number of samples that need to be submitted for confirmatory analysis.
VIM
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Table of Contents
Section "age
Notice ii
Verification Statement iii
Foreword vi
Abstract vii
List of Figures xi
List of Tables x
List of Abbreviations and Acronyms xm
Acknowledgments xv
1 Executive Summary 1
2 Introduction 3
Demonstration Background, Purpose, and Objectives 3
Reference Methods 4
Site Selection 5
Predemonstration Sampling 7
Experimental Design 8
Qualitative Factors 10
Quantitative Factors 10
Evaluation of Analyzer Performance 12
Deviations from the Demonstration Plan 19
Sample Homogenization 20
3 Reference Laboratory Results 22
Reference Laboratory Methods 22
Reference Laboratory Quality Control 23
Quality Control Review of Reference Laboratory Data 24
Reference Laboratory Sample Receipt, Handling, and Storage Procedures 24
Sample Holding Times 25
Initial and Continuing Calibrations 25
Detection Limits 25
Method Blank Samples 26
Laboratory Control Samples 26
Predigestion Matrix Spike Samples 26
Postdigestion Matrix Spike Samples 27
Predigestion Laboratory Duplicate Samples 28
Postdigestion Laboratory Duplicate Samples 28
Performance Evaluation Samples 28
Standard Reference Material Samples 29
Data Review, Validation, and Reporting 29
ix
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Quality of Reference Laboratory Data .,,.,,,.,,,,,,,,,,,,,,,,.,,,,,, 30
Precision [[[ 30
Accuracy [[[ 31
Representativeness [[[ 33
[[[ 33
Comparability [[[ 36
Use of Data for Analysis ..................................... 37
4 MAP Analyzer[[[ 40
Theory of FPXRF .,,.,.,.,,,..,,»..,,,..,,,.....,,,.,,,,,.,,,.,,.,, 40
[[[ 41
,,,.,...,,,,.,.,,.,.,,,,,.,,.,..,,,,,.,,.,,.,,,,. 42
Equipment and Accessories ,.,..,..,...,,,,,,,..,,...,,,,,..,.,..,,,,.,,. 42
Operation of the Analyzer ,.,,,,,.,.,,,,.....,,,.,,.,.,..,,.,,.,..,,..,,,, 44
Background of the Technology Operator.,,,,.,,,.,,.,,,.,.,,,,,.,...,,,.,,.. 45
Training [[[ 45
[[[ 45
and [[[ 47
[[[ .... 47
[[[ 49
Limits [[[ 49
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List of Figures
2-1 Sample Preparation and Analysis ............................................. 9
2-2 Linear and Log-log Data Plots ,,,,,,,,,,,,,,,.,..,,,,,,,.,.,.,,,.,...,,,.,,,. 14
3-1 Pre- and Postdigestion Duplicate Samples ..................................... 31
3-2 Reference Method PE and CRM Results ,,,.,,,,,.,.,,.,,...,.,,,,,,..,.,,,,,,, 34
3-3 Reference Method SRM Results .,,,.,,,,,....,,,,..,.,.,,,.,,,....,.,.,,..., 38
4-1 Principle of Source Excited X-ray Fluorescence ,,,,,.,..,,,.,,,,..,..,,,,,,,..,, 41
4-2 Critical Zone for the Determination of a Field-based Method Detection Limit for Zinc ..... 49
4-3 Drift Summary ,...,,,,.,.,.,.,,..,,,,.,.,.,.,.,,.,,,,,.,...,,,,»,...,.,,, 51
XI
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List of Tables
2-1 Performance and Comparability Variables Evaluated ,,.,,.,,,,,,,,,,,,,,,,......,, 11
2-2 Criteria for Characterizing Data Quality ..,.,,,, 18
3-1 Reference Laboratory Quality Control Parameters ............................... 23
3-2 SW-846 Method 6010A LRLs for Target Anaiytes ,.....,.,,,.,,,,.,,,,.,...,..,,. 26
3-3 Reference Laboratory Accuracy Data for Target Anaiytes ,,,,,,,.,.,.,,,,,,..,,,.., 32
3-4 SRM Performance Data for Target Anaiytes ,,,,,,,..,,..,,,,,.,,.,,,,,,,.,,,,.. 36
3-5 Leach Percent Recoveries for Select MIST SRMs .,.,.,,.,,.,...,,...,,,,,,..,.,, 37
4-1 Analyzer instrument Specifications .,.,,,,...,.,.,,,.,,,,,,..,,,..,..,..,..,,, 43
4-2 Instrument and Field Operation Costs ......................................... 48
4-3 Method Detection Limits [[[ 49
4-4 Precision Summary [[[ 52
4-5 Accuracy Summary of Site-Specific PE and SRM Results ,,,.,,,,....,.,.,,,,,.,,,, 54
4-6 Regression Parameters by Primary Variable .................................... 55
4-7 Regression Parameters for the Sample Preparation Variable by Soil Texture ..... 57
4-8 Regression Parameters for the Sample Preparation Variable by ...... 58
4-9 Summary of Data Quality Level Parameters .................................... 58
5-1 Summary of Test Results and Operational Features .............................. 60
5-2 Effects of Data Correction on FPXRF Comparability to for AH
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List of Abbreviations and Acronyms
a
P
Am241
CCB
CCV
Cd10s
Cl
CLP
cm
cm2
cm3
Co57
CRM
DC
EPA
ERA
ETVR
e¥
FPXRF
ICAL
ICB
ICP-AES
ICS
ICV
IDL
!DW
keV
LCD
LCS
log,0
LRL
MCA
mCi
MDL
mg/kg
ml
mm
MMTP
mrem/hr
MR!
alpha
beta
americium-241
continuing calibration blank
continuing calibration verification
cadmium-l 09
confidence interval
Contract Laboratory Program
centimeter
centimeter squared
cubic centimeter
cobalt-57
certified reference material
direct current
Environmental Protection Agency
Environmental Resource Associates
environmental technology verification report
electron volt
field portable X-ray fluorescence
initial calibration
initial calibration blank
inductively coupled plasma-atomic emission spectroscopy
interference check standard
initial calibration verification
instrument detection limit
investigation-derived waste
kiloelectron volt
liquid crystal display
laboratory control samples
base 10 logarithm
lower reporting limit
multichannel analyzer
millicurie
method detection limit
milligram per kilogram
milliliter
millimeter
Monitoring and Measurement Technologies Program
millirems per hour
Midwest Research Institute
National Exposure Research Laboratory-Environmental Sciences Division
XIII
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N1ST
OSW
PAL
PARCC
PC
PE
PI
ppm
PRC
QA
QAPP
QC
r
p
RCRA
RPD
RSD
RTC
SD
SITE
SOP
SRM
SSCS
TC
USGS
XRF
National Institute of Standards and Technology
Office of Solid Waste
performance acceptance limit
precision, accuracy, representativeness, completeness, and comparability
personal computer
performance evaluation
prediction interval
part per million
PRC Environmental Management, Inc.
quality assurance
quality assurance project plan
quality control
correlation coefficient
coefficient of determination
Resource Conservation and Recovery Act
relative percent difference
relative standard deviation
Resource Technology Corporation
standard deviation
Superfund Innovative Technology Evaluation
standard operating procedure
standard reference material
site-specific calibration sample
toxicity characteristic
United States Geological Survey
X-ray fluorescence
XIV
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Acknowledgments
The U.S. Environmental Protection Agency (EPA) wishes to acknowledge the support of all those who
helped plan and conduct this demonstration, interpret data, and prepare this report. In particular, for
demonstration site access and relevant background information, Tom Aldridge (ASARCO) and Harold Abdo
(RV Hopkins); for turnkey implementation of this demonstration, Eric Hess, Patrick Splichal, and Harry Ellis
(PRC Environmental Management, Inc.); for editorial and publication support, Suzanne Ladish, Anne
Witebsky, Karen Bellinger, and Ed Hubert (PRC Environmental Management, Inc.); for technical and peer
review, Paula Hirtz, David Farnam, and Alan Byrnes (PRC Environmental Management, Inc.); for analyzer
operation, Frank Bryant (PRC Environmental Management, Inc.); for sample preparation, Scott Schulte,
Keith Brown, and Curt Enos (PRC Environmental Management, Inc.); for EPA project management, Stephen
Billets, National Exposure Research Laboratory-Environmental Sciences Division; and for peer review, Sam
Goforth (independent consultant), John Wallace (Wallace Technologies), and Shirley Wasson (National Risk
Management Research Laboratory). In addition, we gratefully acknowledge the participation of Oliver
Fordham, EPA Office of Solid Waste; Piper Peterson, EPA Region 10; Brian Mitchell, EPA Region 7; and
Kevin Dorow, Scitec Corporation.
xv
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Section 1
Executive Summary
In April 1995, the U.S. Environmental Protection Agency (EPA) sponsored a demonstration of field
portable X-ray fluorescence (FPXRF) analyzers. The primary objectives of this demonstration were to
evaluate these analyzers for: (1) their analytical performance relative to standard analytical methods, (2)
the influence of sample matrix variations (texture, moisture, heterogeneity, and chemical composition) on
performance, (3) the logistical and economic resources needed to operate these technologies in the field,
and (4) to test and validate an SW-846 draft method for FPXRF analysis. Secondary objectives for this
demonstration were to evaluate FPXRF analyzers for their reliability, ruggedness, cost, range of
usefulness, and ease of operation.
This demonstration was intended to provide users with a reference measure of performance and to
act as a guide for the application of this technology. In this demonstration, the reference methods for
evaluating the comparability of data were SW-846 Methods 3050A and 6010A, "Acid Digestion of
Sediments, Sludges, and Soils" and "Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-
AES)," respectively.
The EPA requested that PRC Environmental Management, Inc. (PRC) plan, implement, and report on
a demonstration of FPXRF analyzers. This demonstration was conducted under the EPA's Superfund
Innovative Technology Evaluation (SITE) Program and managed by the National Exposure Research
Laboratory-Environmental Sciences Division (NERL-ESD) under the Monitoring and Measurement
Technologies Program (MMTP), Las Vegas, Nevada.
The FPXRF analyzers tested in this demonstration were designed to provide rapid, real-time analysis
of metals concentrations in soil samples. This information will allow investigation and remediation
decisions to be made on-site more efficiently, and it should reduce the number of samples that need to be
submitted for confirmatory analysis. Of the seven commercially available analyzers evaluated, one is
manufactured by Niton Corporation (the Niton XL Spectrum Analyzer); two are manufactured by
Metorex Inc. (the X-MET 920-P Analyzer and the X-MET 920-MP Analyzer); two are manufactured by
TN Spectrace (the TN 9000 and the TN Pb Analyzer); one is manufactured by HNU Systems, Inc. (the
SEFA-P Analyzer); and one is manufactured by Scitec Corporation (the MAP Spectrum Analyzer). The
X-MET 940, a prototype FPXRF analyzer developed by Metorex, was given special consideration and
replaced the X-MET 920-P for a portion of the demonstration. This environmental technology
verification report (ETVR) presents information regarding the Scitec MAP Spectrum Analyzer. Separate
ETVRs will be published for the other analyzers that were demonstrated.
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The target analytes for this demonstration were selected from the Resource Conservation and
Recovery Act's (RCRA) Toxicity Characteristic (TC) list, analytes known to have a high aquatic toxicity
and likely to produce interferences for the FPXRF analyzers. The primary analytes for these
comparisons were arsenic, barium, chromium, copper, lead, and zinc; nickel, iron, cadmium, and
antimony were secondary analytes. Because of design considerations, not all of these analytes were
determined by each instrument. For this demonstration, the MAP Spectrum Analyzer was configured to
report lead, copper, arsenic, and zinc.
To demonstrate these analyzers, hazardous waste sites in Iowa (the RV Hopkins site) and in
Washington (the ASARCO site) were selected. The sites were chosen because they exhibit a wide range
of concentrations for most of the target analytes, are located in different climatological regions of the
United States, and combined they exhibit three distinct soil textures: sand, loam, and clay.
This demonstration found that the MAP Spectrum Analyzer was simple to operate in the field. It was
designed to be used in the in situ mode; that is to analyze samples in minimally disturbed soil. The
developer provided a training course for the technology operator which was similar to that provided to a
purchaser of the equipment. The training encompassed enough FPXRF theory and hands-on use to allow
the operator to manipulate the data collection software, calibrate the analyzer, and adjust instrument
parameters such as count times and target analytes. In addition, the developer provided radiation safety
training, required for the use of this analyzer. A license was obtained from the State of Kansas, which
has reciprocal licensing agreements with States of Iowa and Washington. The Scitec technical staff
provided accessible and timely field support. The analyzer itself was portable and was operated
continuously more than a 10 to 12-hour work day with appropriate battery changes. The rainy weather
conditions encountered during the demonstration caused no operational downtime for the analyzer.
The analyzer used one radioactive source, cadmium-109, coupled to a solid-state silicon detector.
The count times used in this demonstration (240 live-seconds) resulted in a sample throughput of 9 - 12
samples per hour. The MAP Spectrum Analyzer produced data meeting definitive level criteria
(equivalent to reference data) for lead; data meeting quantitative screening level criteria (not equivalent
to reference data, but correctable with confirmatory sample analysis) for copper and arsenic; and data
meeting qualitative screening level criteria (identifies the presence or absence of contamination) for zinc.
The analyzer generally exhibited precision similar to that of the reference methods. Field-based
method detection limits (MDL) for this analyzer were lower than the precision-based values for arsenic,
copper, and lead, but much higher for zinc. Most of the precision-based and field-based MDLs were
below the developer's projected MDL of 250 mg/kg. The site variable did not appear to affect data
comparability. The soil variable showed a slight trend of poorer comparability in loam soils. Data
correction had limited effect on the analyzer's average relative bias and accuracy.
Based on the performance of the analyzer, this demonstration found the MAP Spectrum Analyzer to
be an effective tool for characterizing the concentration of target metals in soil samples. As with all
FPXRF analyzers, unless a user has regulatory approval, confirmatory (reference) sampling and data
correction is recommended when using this technology for site characterization or remediation
monitoring.
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Section 2
Introduction
This environmental technology verification report (ETVR) presents information from the
demonstration of the MAP Spectrum Analyzer. This analyzer was developed by Scitec Corporation to
perform elemental analyses (metals quantitation) in the field, most commonly lead in soil and paint. This
analyzer uses a solid-state silicon detector and a cadmium-109 (Cd109) source to detect metals in the test
sample. The analyzer is designed to operate in the in situ mode; this is commonly referred to as"point-
and-shoot." In this mode of operation, the point of measurement on the soil surface is cleared of loose
debris and organic matter, the analyzer's probe is then placed directly on the soil surface, and a
measurement is taken.
This section provides general information about the demonstration including the purpose, objectives,
and design. Section 3 presents and discusses the quality of data produced by the reference methods
against which the analyzer was evaluated. Section 4 discusses the MAP Spectrum Analyzer's
capabilities, reliability, throughput, accuracy, precision, comparability to reference methods, and other
evaluation factors. Section 5 discusses the potential applications of the analyzer, presents a method for
data correction, and suggests a framework for a standard operating procedure (SOP). Section 6 lists the
references cited in this ETVR.
Demonstration Background, Purpose, and Objectives
The demonstration was conducted under the Monitoring and Measurement Technologies Program
(MMTP), a component of the SITE Program. MMTP is managed by NERL-ESD, Las Vegas, Nevada.
The goal of the MMTP is to identify and demonstrate new, innovative, and commercially available
technologies that can sample, identify, quantify, or monitor changes in contaminants at hazardous waste
sites. This includes those technologies that can be used to determine the physical characteristics of a site
more economically, efficiently, and safely than conventional technologies. The SITE Program is
administered by the National Risk Management Research Laboratory, Cincinnati, Ohio.
The purpose of this demonstration was to provide the information needed to fairly and thoroughly
evaluate the performance of FPXRP analyzers to identify and quantify concentrations of metals in soils.
The primary objectives were to evaluate FPXRP analyzers in the following areas: (1) their accuracy and
precision relative to conventional analytical methods; (2) the influence of sample matrix variations
(texture, moisture, heterogeneity, and chemical composition) on their performances; (3) the logistical and
economic resources necessary to operate these analyzers; and (4) to test and validate an SW-846 draft
method for FPXRP analysis.
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Secondary objectives for this demonstration were to evaluate FPXRF analyzers for their reliability,
ruggedness, cost, range of usefulness, and ease of operation. The performance of each analyzer was not
compared against another. Instead, the performance of each analyzer was independently and individually
compared to the performance of standard analytical methods commonly used in regulatory enforcement
or compliance activities. In addition, each analyzer's performance was assessed relative to measurement
of standard reference materials (SRM), performance evaluation (PE) samples, and other quality control
(QC) samples.
A special request was made by Mr. Oliver Fordham, the demonstration's technical advisor, EPA
Office of Solid Waste (OSW), for Midwest Research Institute (MRI) to analyze some of the soil samples
to validate the performance of draft Method 3052 "Microwave Assisted Acid Digestion of Ash and Other
Siliceous Wastes." Thirty percent of the soil samples were extracted using draft Method 3052 and then
analyzed by Method 6010A. The data generated from the draft Method 3052 and Method6010A
analysis were not used for comparative purposes to the FPXRF data in this demonstration.
Reference Methods
To assess the performance of each analyzer, FPXRF data were compared to reference data. The
reference methods used for this assessment were EPA SW-846 Methods 3050A/6010A, which are
considered the standards for metals analysis in soil for environmental applications. For purposes of these
discussions, the term "reference" was substituted for "confirmatory" since the data were used as a
baseline for comparison. MRI was awarded the subcontract to analyze soil samples using the reference
methods in accordance with Federal Acquisition Regulations. The award was made based on MRFs
costs, ability to meet the demonstration's quality assurance project plan (QAPP) requirements, and as the
only commercial laboratory identified that could perform all the sample analyses in the required
timeframe.
Method 3050A is the standard acid extraction procedure used for determining metals concentrations
in soil samples. It is not a total digestion method, and it potentially does not extract all the metals in a
soil sample. Method 601OA is the standard method used to analyze Method 3050A extracts (Section 3).
High quality, well documented reference laboratory results were essential for meeting the objectives
of the demonstration. For an accurate assessment, the reference methods had to provide a known level of
data quality. For all measurement and monitoring activities conducted by the EPA, the Agency requires
that data quality parameters be established based on the end use of the data. Data quality parameters
include five indicators often referred to as the PARCC parameters: precision, accuracy,
representativeness, completeness, and comparability. In addition, method detection limits (MDL) are
often used to assess data quality.
Reference methods were evaluated using the PARCC parameters to establish the quality of data
generated and to ensure that the comparison of FPXRF analyzers to reference data was acceptable. The
following narrative provides definitions of each of the PARCC parameters.
Precision refers to the degree of mutual agreement between replicate measurements and provides an
estimate of random error. Precision is often expressed in terms of relative standard deviation (RSD)
between replicate samples. The term relative percent difference (RPD) is used to provide this estimate of
random error between duplicate samples.
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Accuracy refers to the difference between a sample result and the reference or true value. Bias, a
measure of the departure from perfect accuracy, can be calculated from the reference or true value.
Accuracy and bias for the reference laboratory were assessed by evaluating calibration standard linearity,
method blank results and the percent recoveries of matrix spike samples, laboratory control samples
(LCS), standard reference materials (SRMs), and PE samples.
Representativeness refers to the degree to which data accurately and precisely measures the
conditions and characteristics of the parameter of interest. Representativeness for the reference
laboratory was ensured by executing consistent sample collection procedures including sample locations,
sampling procedures, storage, packaging, shipping, equipment decontamination, and proper laboratory
sample handling procedures. Representativeness was ensured by using the appropriate reference method
to provide results that produced the most accurate and precise measurement it was capable of achieving.
The combination of the existing method requirements supplemented by the demonstration QAPP
provided the guidance to assure optimum performance of the method. Representativeness was assessed
by evaluating calibration standards, method blank samples, duplicate samples, and PE samples.
Completeness refers to the amount of data collected from a measurement process compared to the
amount that was expected to be obtained. For the reference data, completeness referred to the proportion
of valid, acceptable data generated.
Comparability refers to the confidence with which one data set can be compared to another. Data
generated from the reference methods should provide comparable data to any other laboratory performing
analysis of the same samples with the same analytical methods. Comparability for the reference methods
was achieved through the use of standard operating procedures (SOPs), EPA-published guidance, and the
demonstration QAPP. QC samples that were used to evaluate comparability include: calibration
standards, method blank samples, matrix spike samples, replicate samples, LCSs, SRMs, and PE samples.
Site Selection
PRC conducted a search for suitable demonstration sites between September and November 1994.
The following criteria were used to select appropriate sites:
. The site owner had to agree to allow access for the demonstration.
• The site had to have soil contaminated with some or all of the target heavy metals. (Slag, ash,
and other deposits of mineralized metals would not be assessed during the demonstration.)
. The site had to be accessible to two-wheel drive vehicles.
• The site had to exhibit one or more of the following soil textures: sand, clay, or loam.
. The site had to exhibit surface soil contamination.
. The sites had to be situated in different climatological environments.
PRC contacted NERL-ESD, regional EPA offices, state environmental agencies, metals fabrication,
and smelting contacts to create an initial list of potential demonstration sites. PRC received considerable
assistance from the EPA RCRA and Superfund Branches in Regions 4,6,7,8,9, and 10. PRC also
contacted the Montana Department of Health and Environment, the Nevada Bureau of Mines and
Geology, the Oklahoma Department of Environmental Quality, the Arizona Department of
Environmental Quality, the Missouri Department of Natural Resources, the Arizona Bureau of Geology,
and the New Mexico Bureau of Mines and Mineral Resources. PRC surveyed its offices in Kansas City,
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Kansas; Atlanta, Georgia; Denver, Colorado; Dallas, Texas; Albuquerque, New Mexico; Helena,
Montana; Chicago, Illinois; Seattle, Washington; and San Francisco, California, for information
regarding potential sites. These PRC offices have existing RCRA, Superfund, or Navy environmental
contracts that allow access to regional, state, and federal site information. PRC also used the Record of
Decision Scan database (Morgan and others 1993) to search for appropriate sites.
PRC screened 46 potential sites based on the site-selection criteria with the assistance of the various
contacts listed above. Based on this screening effort, PRC and EPA determined that the RV Hopkins and
ASARCO sites met most of the site-selection criteria, and therefore, would be the acceptable for the
demonstration.
The ASARCO site consists of 67 acres of land adjacent to Commencement Bay. The site is marked
by steep slopes leading into the bay, a slag fill that was used to extend the original shoreline, a cooling
water pond, and various buildings associated with the smelting process. Partial facility demolition was
conducted in 1987. Most of the buildings were demolished between 1993 and 1994. The only buildings
remaining are the Fine Ore Building, the Administrative Building, and a Maintenance Garage.
Past soil sampling results targeted four general areas of the site: the plant administration area, the
former cooling pond, the 1987 demolition area, and certain off-site residential areas adjacent to the
smelter stack. Previous sampling has shown surficial soils to be more contaminated than subsurface
soils. Arsenic, copper, and lead are the predominant contaminants in the local soils. The highest arsenic
concentrations were found in the soils around the former arsenic kitchen, along with cadmium and
mercury. The soils around the former cooling pond contained the highest copper concentrations and high
levels of silver, selenium, barium, and chromium. Lead concentrations are highest northeast of the
arsenic plant.
Much of the smelter site is covered with artificial fill material of varying thickness and composition.
Two general types of fill are found on the site: a granular fill and a massive slag fill. The composition of
the granular fill material ranges from sand to silt with demolition debris and slag debris mixed
throughout. The massive slag fill is a solid, fractured media restricted to the plant site. The surface soil
in the plant administration area has a layer of slag particles on top, ranging from 1 to 3 inches thick.
Surficial material in the parking lot area and southwest of the stack is mostly of glacial origin and is
composed of various mixtures of sand, gravel, and cobbles. The soils around the former cooling pond are
fine-grained lacustrine silts and clays. Alluvium upgradient of the former cooling pond has been almost
entirely covered with granular fill material. Generally, soils in the arsenic kitchen and stack hill areas are
sand mixed with gravel or sandy clay mixed with cobbles.
The RV Hopkins site is located in the west end of Davenport, Iowa. The facility occupies
approximately 6.7 acres in a heavy industrial/commercial zoned area. Industrial activities in the area of
the RV Hopkins property included the manufacture of railroad locomotive engines during the mid-1800's.
The RV Hopkins property was a rock quarry during the late 1800s. Aerial surveys beginning in 1929
show that the rock quarry occupied the majority of the site initially, gradually decreasing until it was
completely filled by 1982. It was reported that the site was used to dispose of demolition debris,
automotive, and scrap metal. The site also has been used by a company that recycled lead acid batteries.
RV Hopkins began operating as a drum reconditioner in 1951 across the street from its current
location. In 1964, the site owner reportedly covered the former quarry area of the site with foundry sand.
No foundry sand was analyzed as part of this demonstration. RV Hopkins receives between 400 and 600
drums per day for reconditioning, accepting only drums that meet the definition of "empty" according to
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40 Code of Federal Regulations 261.7. Most of the drums received at the facility come from the paint,
oil, and chemical industries. The surrounding area is reported to be underlain by Devonian-aged
Wapsipinicon Limestone, and gray-green shale, lime mud, and sand stringers dating back to the
Pennsylvanian age.
The RV Hopkins property is composed of five buildings: the office and warehouse, a warehouse used
to store drums of hazardous waste and a waste pile, a manufacturing building, a drum reclamation
furnace, and a cutting shed. The office and the warehouse are located on the southwest corner of the site.
Areas investigated on this site include the furnace area, the old and new baghouses, the former drum
storage area on the north end of the facility, the former landfill, and a drainage ditch. Major
contaminants include barium, lead, chromium, and zinc, as well as lesser concentrations of other metals,
such as copper and nickel, pesticides, and volatile organic compounds.
Based on historical data, the most concentrated contaminants in the furnace area are chromium, lead,
and zinc. The highest concentrations of these elements are at the furnace entrance, as opposed to the
furnace exit. The concentrations of lead are higher in the old baghouse than in the new, while the new
baghouse exhibits a higher concentration of chromium, as well as high iron, lead, and barium
concentrations. The former landfill has concentrations of barium, chromium, lead, nickel, and zinc
greater than l,000mg/kg. Lead is the most prevalent contaminant in the former drum storage area with
lesser concentrations of barium, chromium, and zinc.
Predemonstration Sampling
Predemonstration sampling was conducted at both sites between December 5 and 14, 1994. These
sampling events had the following objectives:
• To provide data on, or verify, the extent of surface contamination at each site and to locate
optimum sampling areas for the demonstration.
• To allow the developers to analyze samples from the demonstration sites in advance of the
demonstration, and if necessary, refine and recalibrate their technologies and revise their
operating instructions.
• To evaluate samples for the presence of any unanticipated matrix effects or interferences that
might occur during the demonstration.
• To check the quality assurance (QA) and QC procedures of the reference laboratory.
One hundred soil samples were analyzed on each site by the FPXRF analyzers during the
Predemonstration sampling activities. The samples represented a wide range in the concentration of
metals and soil textures. Thirty-nine samples were submitted for reference method analysis using EPA
SW-846 Methods 3050A/6010A. Twenty-nine of these samples were split and sent to the developers.
Nine field duplicates were collected and submitted for reference method analysis to assess proposed
sample homogenization procedures. One purchased PE sample also was submitted to the reference
laboratory to provide an initial check of its accuracy.
Additionally, three samples representing low, medium, and high concentrations were collected at
each site. These samples were dried, ground, and then analyzed by six independent laboratories before
the demonstration began to create site-specific PE samples. These samples were analyzed with
laboratory-grade X-ray fluorescence XRF analyzers.
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Experimental Design
The experimental design for this demonstration was developed to meet the primary and secondary
objectives stated above, and was approved by all participants prior to the start of the demonstration. The
design is detailed in the demonstration plan (PRC 1995) and is summarized below.
Approximately 100 soil sample measurements were collected from each of three target soil textures:
clay, loam, and sand. This variety of soil textures allowed the examination of the effect of soil texture on
data comparability. Splits of these samples were analyzed by all of the FPXRFs and by the reference
methods.
The MAP Spectrum Analyzer is designed to operate in the in situ mode. The sampling and analysis
procedure was designed to test the common application of FPXRF analyzers. The sampling procedure
used is illustrated in Figure 2-1. Since the MAP Spectrum Analyzer operates in ths'n situ mode only,
the discussion of the experimental design will be limited to in situ sample preparation and analysis.
For in situ analysis, an area 4 inches by 4 inches square was cleared of all vegetation, debris, and
gravel larger than 2 millimeters (mm) in diameter. The analyzer took one in situ measurement in each
sample area. These data represented FPXRF in situ measurements for unprepared soilsfin situ-
unprepared). Replicate measurements were taken at 4 percent of these locations to assess analyzer
precision. Figure 2-1 depicts the sample analysis chain for in situ analyses. The MAP Spectrum
Analyzer only reported in situ-unprepared and in situ-prepared samples.
After the in situ-unprepared analysis was complete at a given location, the soil within the 4-inch by
4-inch square was removed to a depth of 1 inch and homogenized in a plastic bag. This produced a soil
sample of approximately 375 grams or 250 cubic centimeters (cm3). Sample homogenization was
monitored by adding 1 to 2 grams of sodium fluorescein salt (which fluoresces when exposed to
ultraviolet light) to the sample homogenization bag. During the predemonstration, it was determined that
sodium fluorescein did not affect the FPXRF or reference method analysis. Sample homogenization took
place by kneading the sample and sodium fluorescein salt in a plastic bag for 2 minutes. After this
period, the sample preparation technician examined the sample under ultraviolet light to assess the
distribution of sodium fluorescein throughout the sample. If the sodium fluorescein salt was not evenly
distributed, the homogenization and checking process were repeated until the sodium fluorescein was
evenly distributed throughout the sample. This monitoring process assumed that thorough distribution of
sodium fluorescein was indicative of good sample homogenization. The effectiveness of this
homogenization procedure is discussed later in this section.
The homogenized sample was then spread out inside a 1-inch-deep petri dish. The FPXRF analyzer
then took one measurement of this homogenized material. This represented the homogenized sample
analysis for thein situ analyzers (in situ-prepared). This process represents the common practice of
sample homogenization in a plastic bag and subsequent sample measurement through the bag. Replicate
measurements were also collected from 4 percent of these samples to assess analyzer precision. These
replicate measurements were made on the same soil samples that were used for the unprepared precision
determination.
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Figure 2-1. Sample and This the handling for
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Qualitative Factors
There are a number of factors important to data collection that are difficult to quantify and must be
evaluated qualitatively. These are considered qualitative factors. One such factor was the amount of
training required to operate a given FPXRP analyzer. To assess this factor, PRC operators were trained
by the developers on how to operate their respective FPXRP analyzers. All operators met or exceeded
the developers' minimum requirements for education and previous experience. Demonstration
procedures were designed to simulate routine field conditions as closely as possible. The developers
trained the operators using their respective operator training manuals. Based on this training and field
experience, the operators prepared a subjective evaluation assessing the training and technology
operation during the demonstration (Section 4).
Many analytical methods exhibit significant "operator effects," in which individual differences in
sample preparation or operator technique result in a significant effect on the numerical results. To reduce
the possible influence of operator effects, a single operator was used to operate each FPXRP analyzer.
While this reduced some potential error from the evaluation, it did not allow the analyzers to be
evaluated for their susceptibility to operator-induced error. A single operator was used to analyze all of
the samples at both sites during this demonstration. Sample preparation variation effects were minimized
in the field by using the same personnel to prepare samples. To eliminate the influence of operator
effects on the reference method analysis, only one reference laboratory was used to analyze the samples.
Based on this design, there is no quantitative estimate of "operator" effect.
Quantitative Factors
Many factors in this demonstration could be quantified by various means. Examples of quantitative
factors evaluated during this demonstration include analyzer performance near regulatory action levels,
the effects of sample preparation, effects of microwave sample drying, count times, health and safety
considerations, costs, and interferences.
The data developed by the FPXRP analyzers were to be compared to reference data for the following
primary analytes: arsenic, barium chromium, copper, lead, and zinc; and for the following secondary
analytes: nickel, iron, cadmium, and antimony. The specific analytes determined by the MAP Spectrum
Analyzer were arsenic, copper, lead, and zinc.
Evaluations of analyzer data comparability involved examining the effects of each site, soil texture,
and sample preparation technique (Table 2-1). Two sites were sampled for this demonstration. Thus,
two site variables were examined (RV Hopkins and ASARCO sites). These sites produced samples from
three distinct soil textures and, therefore, three soil variables were examined (clays, sands, and loams).
The demonstration plan identified four sample preparation steps: (1) in situ-unprepared, (2) in situ-
prepared, (3) intrusive-unprepared, and (4) intrusive-prepared (samples generated in steps 3 and 4 were
not analyzed by the MAP Spectrum Analyzer). These variables were nested as follows: each site was
divided into RV Hopkins and ASARCO data sets; the RV Hopkins data represented the clay soil texture,
while the ASARCO data were divided into sand and loam soil textures; then each soil texture was
subdivided by the soil preparations. This design allowed for the examination of particle size and
homogenization effects on data comparability. These effects were believed to have the greatest impact
on data comparability.
10
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Table 2-1. Performance
Variables
Site Name (315) SoW Texture (3t$J I, Preparation Step [630]
ASARCO(215)
RV Hopkins |100)
Loam (115)
Clay (100)
in [100]
in [100]
in [115]
in situ-prepared {115}
in [100]
in situ-prepared [100]
Notes:
{ ) Total number of points,
{ ] Total number of taken.
Of greatest interest to users is analyzer performance near action levels. For this reason, samples were
approximately distributed as follows: 25 percent in the 0 - 100 mg/kg range, 50 percent in the 100- 1,000
mg/kg range, and 25 percent in the greater than 1,000 mg/kg range. The lower range tested analyzer
performance near the middle range tested analyzer performance in the range of many action
levels for inorganic contaminants; and the higher range tested analyzer performance on grossly
contaminated soils. All samples collected for the demonstration were split between the FPXRF analyzers
and reference laboratory for analysis. Metal concentrations measured using the reference methods were
considered to represent the "true" concentrations in each sample. Where duplicate samples existed,
concentrations for the duplicates were averaged and the average concentration was considered to
represent the true value for the sample pair. This was specified in the demonstration plan. If one or both
samples in a duplicate pair exhibited a nondetect for a particular target analyte, that pair of data was not
used in the statistical evaluation of that analyte. The reference methods reported measurable
concentrations of target analytes in all of the samples analyzed.
In addition to the quantitative factors discussed above, the common FPXRF sample preparation
technique of microwave drying of samples was evaluated. Sample temperatures during this procedure
can be high enough to melt some mineral fractions in the sample or to combust organic matter. Several
metals that present environmental hazards can volatilize at elevated temperatures. Arsenic sublimes at
188*C, within the potential temperature range achieved during microwave drying of samples. To assess
this effect, 10 percent of the homogenized, crushed, oven-dried, and sieved samples were split and heated
in a microwave oven on high for 3 minutes. This time was chosen to approximate common microwave
drying times used in the field. These samples were submitted for reference analysis. The reference data
for these samples were compared to the corresponding reference data produced from the convection
oven-dried sample. These data showed the effects of the microwave drying variable on analyte
concentration. This was a minor variable and it was only evaluated for the reference laboratory in an
attempt to identify any potential effect on data comparability.
Another quantitative variable evaluated was the count time used to acquire data. During the formal
sample quantitation and precision measurement phase of the demonstration, the count times were set by
the developers and remained constant throughout the demonstration. Count times can be tailored to
produce the best results for specific target analytes. The developers, however, selected count times that
produced the best compromise of results for the entire suite of target analytes. To allow a preliminary
assessment of the effect of count times, select soil samples were analyzed in replicate using count times
11
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longer and shorter than those set by the developers. This allowed the evaluation of the effects of count
times on analyzer performance.
An important health and safety issue during the demonstration was the effectiveness of radioactivity
shielding of each FPXRF analyzer. Quantitative radiation readings were made with a gamma ray
detector near each analyzer to assess the potential for exposure to radiation.
A compilation of the cost of using each FPXRF analyzer was another important evaluation factor.
Cost includes analyzer purchase or rental, expendable supplies, such as liquid nitrogen and sample cups,
and nonexpendable costs, such as labor, licensing agreements for the radioactive sources, operator
training costs, and disposal of investigation-derived waste (IDW). This information is provided to assist
a user in developing a project cost analysis.
Factors that could have affected the quantitative evaluations included interference effects and matrix
effects. Some of these effects and the procedures used to evaluate their influence during this
demonstration are summarized below:
. Heterogeneity: For in situ-unprepared measurements, heterogeneity was partially controlled by
restricting measurements within a 4-by-4-inch area. For measurements after the initial point-and-
shoot preparation, heterogeneity was minimized by sample homogenization. This effect was
evaluated through the sample preparation data.
• Particle Size: Since no intrusive samples were analyzed, the effect of particle size was not
determined for this analyzer.
• Moisture Content: It has been suggested that major shifts in sample moisture content can affect a
sample's relative fluorescence. This effect could not be evaluated as thoroughly as planned
because of the small difference in sample moisture content observed at the two sites.
• Overlapping Spectra of Elements: Interferences result from overlapping spectra of metals that emit
X-rays with similar energy levels. The reference method analysis provided data on the
concentration of potential interferants in each sample.
Evaluation of Analyzer Performance
Metals concentrations measured by each analyzer were compared to the corresponding reference
laboratory data and to other QA/QC sample results. These comparisons were conducted independently
for each target analyte. These measurements were used to determine an analyzer's accuracy, data quality
level, method precision, and comparability to reference methods. PE samples and SRM samples were
used to assess analyzer accuracy. Relative standard deviations (RSD) on replicate measurements were
used to determine analyzer precision. These data were also used to determine the data quality of each
FPXRF analyzer's output. The data comparability and quality determination was primarily based on a
comparison of the analyzer's data and the reference data. Linear regression and a matched pairs t-test
were the statistical tools used to assess comparability and data quality.
A principal goal of this demonstration was the comparison of FPXRF data and the reference data.
EPA SW-846 Methods3050A/6010A were selected as the reference methods because they represent the
regulatory standard against which FPXRF is generally compared. In comparing the FPXRF data and
reference data, it is important to recognize that, while similar, the process by which the data are obtained
is not identical. While there is significant overlap in the nature of the analysis, there are also major
differences. These differences, or "perspectives,"allow the user to characterize the same sample in
12
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slightly different ways. Both have a role in site characterization and remediation monitoring. It is
important to consider these differences and the measurement error intrinsic to each method when
comparing the FPXRF method against a reference method.
The reference methods involve wet chemical analysis and partial digestion of approximately 1 to 2
grams of sample (approximately 0.25 cubic centimeters (cm3), depending on sample bulk density). The
digestion process extracts the most acid-soluble portion of the sample. Since the digestion is not
complete, the less acid-soluble components are not digested and are not included in the analysis. These
components may include the coarser-grained quartz, feldspar, lithic components, and certain metal
complexes. In contrast, FPXRF analyzers generally produce X-ray excitation in an area of approximately
3 cm2 to a depth of approximately 2.5 centimeters (cm). This equates to a sample volume of
approximately 7.5 cm3. X-rays returning to the detector are derived from all matrix material including
the larger-grained quartz, feldspar, lithic minerals, metal complexes, and organics. Because the FPXRF
method analyzes all material, it represents a total analysis in contrast to the reference methods, which
represent a select or partial analysis. This difference can result in FPXRF concentrations that are higher
than corresponding reference data when metals are contained within nonacid soluble complexes or
constituents. It is important to note that if metals are contained in nonacid soluble complexes, a
difference between the FPXRF analyzers and the reference methods is not necessarily due to error in the
FPXRF method but rather to the inherent differences in the two types of analytical methods.
The comparison of FPXRF data and the reference data used linear regression as the primary
statistical tool. Linear regression analysis intrinsically contains assumptions and conditions that must be
valid for each data set. Three important assumptions to consider include: (1) the linearity of the
relationship, (2) the confidence interval and constant error variance, and (3) an insignificant
measurement error for the independent variable (reference data).
The first assumption requires that the independent variable (reference data) and the dependent
variable (FPXRF data) are linearly related and are not described by some curvilinear or more complex
relationship. This linearity condition applies to either the raw data or mathematical transformations of
the raw data. Figure 2-2 illustrates that FPXRF data and reference data are, in fact, related linearly and
that this assumption is correct.
The second assumption requires that the error be normally distributed, the sum to equal zero, be
independent, and exhibit a constant error variance for the data set. Figure 2-2 illustrates that for raw
data, this assumption is not correct (at higher concentrations the scatter around the regression line
increases), but that for the logarithmic transformation (shown as a log-log plot) of the data, this
assumption is valid (the scatter around the regression line is uniform over the entire concentration range).
The error distribution (scatter) evident in the untransformed data results in the disproportionate influence
of large data values compared with small data values on the regression analysis.
The use of least squares linear regression has certain limitations. Least squares regression provides a
linear equation, which minimizes the squares of the differences between the dependent variable and the
regression line. For data sets produced in this demonstration, the variance was proportional to the
magnitude of the measurements. That is, a measurement of 100 parts per million (ppm) may exhibit a 10
percent variance of 10 ppm, while a 1,000 ppm measurement exhibits a 10 percent variance of 100 ppm.
For data sets with a large range in values, the largest measurements in a data set exert disproportionate
influence on the regression analysis because the least squares regression must account for the variance
associated with the higher valued measurements. This can result in an equation that has minimized error
for high values, but almost neglects error for low values because their influence in minimizing dependent
13
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variable error is small or negligible. In some cases, the resulting equations, biased by high-value data,
may lead to inappropriate conclusions concerning data quality. The range of the data examined for the
analyzers spanned between 1 and 5 orders of magnitude (e.g., 10 - 100,000 ppm) for the target analytes.
This wide range in values and the associated wide range in variance (influenced by concentration)
created the potential for this problem to occur in the demonstration data set. To provide a correlation that
was equally influenced by both high and low values, logarithms (logio) of the dependent and independent
variables were used, thus, scaling the concentration measurements and providing equal weight in the least
squares regression analysis to both small and large values (Figure 2-2). All statistical evaluations were
carried out on log,, transformed data.
Linear
246
Thousands
Reference Datajmo/kg}
10000
Log-Log Plot-Lead
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03
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1000 -
100
100
Reference Data (mg/kg)
10000
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Figure 2-2, Linear and Log-log Data Plots: These graphs the linear the
MAP Spectrum Analyzer's and the The the concentration
of this relationship with at higher concentrations. The log-log plots
eliminate this concentration effect. Scatter is relatively constant over the plot,
The third assumption, requiring an insignificant measurement error in the reference data, was not
true for all analytes. The consequences of measurement error varied depending on whether the error is
caused by the reference methods or the FPXRF method. If the error is random or if the error for the
reference methods is small compared to the total regression error, then conventional regression analysis
can be performed and the error becomes a part of the random error term of the regression model. This
14
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error (based on the logio transformed data) is shown in the regression summary tables in Section 4 as the
"standard error." In this case, deviations from perfect comparability can be tied to an analyzer's
performance. If the error for the reference methods is large compared to the total error for the correlation
of the FPXRF and the reference data, then deviations from perfect comparability might be due in part to
measurement error in the reference methods.
It is a reasonable assumption that any measurement errors in either the reference or FPXRF methods
are independent of each other. This assumption applies to either the raw data or the logio transformed
data. Given this assumption, the total regression error is approximately the sum of the measurement error
associated with the reference methods and the measurement error associated with the FPXRF method.
The reference methods' precision is a measure of independent variable error, and the mean square error
expressed in the regression analysis is a relative measure of the total regression error that was determined
during the regression analysis. Precision data for the reference methods, obtained from RPD analyses on
the duplicate samples from each site, for each analyte, indicated the error for the reference methods was
less than 10 percent of the total regression error for the target analytes. Subsequently, 90 percent of the
total measurement error can be attributed to measurement error associated with the analyzers.
The comparison of the reference data to the FPXRF data is referred to as the intermethod
comparison. All reference and QA/QC data were generated using an EPA-approved definitive level
analytical method. If the data obtained by an analyzer were statistically similar to the reference methods,
the analyzer was considered capable of producing definitive level data. As the statistical significance of
the comparability decreased, an analyzer was considered to produce data of a correspondingly lower
quality. Table 2-2 defines the criteria that determined the analyzer's level of data quality (EPA 1993).
Data from this demonstration were used to place analyzer data into one of three data quality levels as
follows: (1) definitive, (2) quantitative screening, and (3) qualitative screening. The first two data
quality levels are defined in EPA guidance (1993). The qualitative screening level criteria were defined
in the demonstration plan (PRC 1995) to further differentiate the screening level data as defined by the
EPA.
Definitive level data are considered the highest level of quality. These data are usually generated by
using well-defined, rigorous analytical methods. The data is analyte-specific with full confirmation of
analyte identity and concentration. In addition, either analytical or total measurement error must be
determined. Data may be generated in the field, as long as the QA/QC requirements are satisfied.
Quantitative screening data provides confirmed analyte identification and quantification, although
the quantification may be relatively imprecise. It is commonly recommended that at least 10 percent of
the screening data be confirmed using analytical methods and QA/QC procedures and criteria associated
with definitive data. The quality of unconfirmed screening data cannot be determined.
Qualitative screening level data indicates the presence or absence of contaminants in a sample
matrix, but does not provide reliable concentration estimates. The data may be compound-specific or
specific to classes of contaminants. Generally, confirmatory sampling is not required if an analyzer's
operation is verified with one or more check samples.
At the time of this demonstration, an approved EPA method for FPXRF did not exist. As part of this
demonstration, PRC prepared a draft Method 6200 "Field Portable X-Ray Fluorescence Spectrometry for
the Determination of Elemental Concentrations in Soil and Sediment." The draft method has been
subsequently submitted for inclusion in Update 4 of SW-846 scheduled for approval in FY-97. For the
15
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purposes of this demonstration, the lack of an EPA-approved final method did not preclude the analyzers'
data from being considered definitive. The main criterion for data quality level determination was based
on the comparability of each analyzer's data to that produced by the reference methods, as well as
analyzer-specific criteria such as precision.
The comparability data set for the MAP Spectrum Analyzer consisted of 630 matched pairs of
FPXRF and reference data for each target analyte. This data set was analyzed as a whole and then
subdivided and analyzed with respect to each of the variables listed in Table 2-1. This nesting of
variables allowed the independent assessment of the influence of each variable on comparability.
For the performance evaluation of this analyzer, a total of 315 soil samples was analyzed by the
reference methods. These samples were analyzed by the MAP Spectrum Analyzer for the in situ
preparation steps and produced 630 data points. Seventy of the 315 samples submitted to the reference
laboratory were split and submitted as field duplicates to assess the sample homogenization process.
Thirty-three of the 315 samples were also split and microwave-dried, then submitted for reference
method analysis to assess the effect of microwave drying. Of the 315 samples submitted for reference
method analysis, 215 were collected from the ASARCO site and 100 were collected from the RV
Hopkins site. Approximately twice as many samples were collected at the ASARCO site because two of
the target soil textures (sands and loams) were found there. Only one target soil texture (clay) was found
at the RV Hopkins site.
Evaluation of the influence of the site and soil variables was limited to an examination of the lead
and zinc data. These were the only primary analytes that exhibited a wide distribution of concentrations
across all sites and soil textures. The effects of sample preparation variables were evaluated for all target
analytes. If the evaluation of the influence of a given variable did not result in a better correlation, as
exhibited by a higher coefficient of determination (r2) and smaller standard error of the estimate (using
logio transformed data), then the influence was considered to be insignificant. However, if the
correlation worsened, the cause was examined and explained. If the correlation improved, resulting in an
improved r2 and reduced standard error of the estimate, then the impact of the variable was considered
significant. For example, if the r2 and standard error of the estimate for a given target analyte improved
when the data set was divided into the four sample preparation steps, the sample preparation variable was
determined to be significant. Once this was determined, the variables of site and soil texture were
evaluated for each of the four sample preparations steps. If the site or soil texture variable improved the
regression parameters for a given soil preparation, then that variable was also considered significant.
After the significant variables were identified, the impact of analyte concentration was examined.
This was accomplished by dividing each variable's logio transformed data set into three concentration
ranges: 0-100 mg/kg; 100 - 1,000 mg/kg; and greater than 1,000 mg/kg. A linear regression analysis
was then conducted on these data sets. If this did not result in improved r2 values and reduced standard
errors of the estimate, then the relationship between the analyzer's logio transformed data and the logio
transformed reference data was considered linear over the entire range of concentrations encountered
during the demonstration. This would mean that there was no concentration effect.
Numerous statistical tests have been designed to evaluate the significance of differences between two
populations. In comparing the performance of the FPXRF analyzers against the reference methods, the
linear regression comparison and the paired t-test were considered the optimal statistical tests. The
paired t-test provides a classic test for comparing two populations, but is limited to analysis of the
average or mean difference between those populations. Linear regression analysis provides information
not only about how the two populations compare on average, but also about how they compare over
16
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ranges of values. Therefore, this statistical analysis technique provides information about the structure of
the relationship; that is, whether the methods differ at high or low concentrations or both. It also
indicates whether the FPXRF data is biased or shifted relative to the reference data.
Linear regression provides an equation that represents a line (Equation 2-1). Five linear regression
parameters were considered when assessing the level of data quality produced by the FPXRF analyzers.
This assessment was made on the logio transformed data sets. The five parameters were the y-intercept,
the slope of the regression line, standard error of the estimate, the correlation coefficient (r), andr2. In
linear regression analysis, the r provides a measure of the degree or strength of the correlation between
the dependent variable (logio transformed FPXRF data), and the independent variable (logio transformed
reference data). Ther2 provides a measure of the fraction of total variation which is accounted for by the
regression relation (Havlick and Grain 1988). That is, it is a measure of the scatter about a regression
line and, thus, is a measure of the strength of the linear association.
Y = m x + b (2-1)
where
b is the y-intercept of the regression line, m is the slope of the regression line,
and Y and X are the log,, transformed dependent and independent variables, respectively
Values for r vary from 1 to -1, with either extreme indicating a perfect positive or negative
correlation between the independent and dependent variables. A positive correlation coefficient indicates
that as the independent variable increases, the dependent variable also increases. A negative correlation
coefficient indicates an inverse relationship, as the independent variable increases the dependent variable
decreases. An r2 of 1.0 indicates that the linear equation explains all the variation between the data sets.
As the r2 departs from 1.0 and approaches zero, there is more unexplained variation, due to such
influences as lack of association with the dependent variable (logio transformed FPXRF data), or the
influence of other independent variables.
If the regression correlation exhibited an r2 between 0.85 and 1.0, the FPXRF data were considered to
have met the first requirement for definitive level data classification (Table 2-2). The second criteria,
precision was then examined and required to be equal to or less than 10 percent RSD to retain the
definitive data quality level. If both these criteria are not satisfied, certain inferential statistical
parameters were evaluated. First, the regression line's y-intercept and slope are examined. A slope of 1.0
and a y-intercept of 0.0 would mean that the results of the FPXRF analyzer matched those of the
reference laboratory (logio FPXRF=logio reference). Theoretically, the more the slope and y-intercept
differ from the values of 1.0 and 0.0, respectively, the less accurate the FPXRF analyzer. However, a
slope or y-intercept can differ slightly from these values without that difference being statistically
significant. To determine whether such differences were statistically significant, the Z test statistics for
parallelism and for a common intercept was used at the 95 percent confidence level for the comparison
(Equations 2-2 and 2-3) (Kleinbaum and Kupper 1978). These criteria were used to assign data quality
levels for each analyte.
The matched pairs t-test was also used to evaluate whether the two sets of logio transformed data sets
were significantly different. The paired t-test compares data sets, which are composed of matched pairs
of data. The significance of the relationship between two matched-pairs sets of data can be determined by
comparing the calculated t-statistic with the critical t-value determined from a standard t-distribution
table at the desired level of significance and degrees of freedom. To meet definitive level data quality
requirements, both the slope and y-intercept had to be statistically the same as their ideal values, as
17
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defined in the demonstration plan, and the data had to be statistically similar as measured by the t-test.
Logio transformed data meeting these criteria were considered statistically equivalent to the logio
transformed reference data.
2-2, for Characterizing Data Quality
Data QuaIity LeveI
Definitive Level
Statistical Parameter*
r2 = 0,85 to 1,0. The precision (RSD) must be than or to 10
and the inferential statistics must indicate that the two are statistically
similar.
Quantitative
Screening Level
r2 = 0.70 to 1.0, The precision (RSD) must be less than 20 percent, but the
inferential statistics indicate that the data sets are statistically different
Qualitative
Screening
r = than 0,70, The precision (RSD) is greater than 20 percent. The data
must have than a 10 percent false negative rate.
Notes;
FIK> statistical and parameters are discussed later in the "Intermetiiod
Assessment" subsection in Section 4,
b
The regression parameters apply to either raw or Iog10 transformed sets. The
precision criteria apply to only the raw data.
r2 Coefficient of determination,
RSD Relative standard deviation.
Slope Test for Significant Differences
(2-2)
where
m is the slope of the regression line, SE is the standard error of the slope,
and Z is the normal deviate test statistic,
Y-intercept Test for Significant Differences
b - 0
(2-3)
o
where
b is the y intercept of the regression line, SE is the standard error of the slope,
and Z is the normal deviate test statistic,
If the r2 was between 0.70 and 1, the precision was between 10 and 20 percent RSD, and the slope or
intercept were not statistically equivalent, then the analyzer was considered to produce quantitative
screening level data quality. However, the linear regression was deemed sufficient so that bias could be
identified and corrected. Results in this case could be mathematically corrected if 10 - 20 percent of the
samples are sent to a reference laboratory. Reference laboratory analysis results from these samples
would provide a basis for determining a correction factor.
18
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Data placed in the qualitative screening level category exhibit r2 values less than 0.70. These data
either were not statistically similar to the reference data based on inferential statistics or had a precision
RSD greater than 20 percent. An analyzer producing data at this level is considered capable of detecting
the presence or lack of contamination, above its detection limit, with at least a 90 percent accuracy rate,
but it is not considered suitable for reporting of concentrations.
MDLs for the analyzers were determined in two ways. One approach followed a standard SW-846
protocol. In this approach, standard deviations (SD) from precision measurements for samples exhibiting
contamination 5 to 10 times the estimated detection levels of the analyzers were multiplied by 3. The
result represented the precision-based MDL for the analyzer.
In a second approach, MDLs were determined by analysis of the low concentration outliers on the
logio transformed FPXRF and logio transformed reference method data cross plots. These cross plots for
all analytes characteristically exhibited a region below the MDL where the linearity of the relationship
disintegrated. Above the MDL, the FPXRF concentrations increased linearly with increasing reference
method values. Effectively, the linear correlation between the two methods abruptly changes to no
correlation at a point below the MDL. The value of the MDL was assigned by determining the point
where the linear relationship disintegrates and assigning the MDL at two SDS above this concentration.
Deviations from the Demonstration Plan
Seven deviations were made from the demonstration plan during on-site activities. The first dealt
with the determination of the moisture content of the samples. The demonstration plan stated that a
portion of the original sample would be used for determining moisture content. Instead, a small portion
of soil immediately adjacent to the original sample location was used for determining moisture content.
This was done to conserve sample volume for the reference laboratory. The moisture content sample was
not put through the homogenizing and sieving steps prior to drying.
The second deviation dealt with the sample drying procedures for moisture content determination.
The demonstration plan required that the moisture content samples would be dried in a convection oven
at 150 °C for 2 hours. Through visual observation, it was found that the samples were completely dried
in 1 hour with samples heated to only 110 "C. Therefore, to conserve time, and to reduce the potential
volatilization of metals from the samples, the samples for moisture content determination were dried in a
convection oven at 110 °C for 1 hour.
The third deviation involved an assessment of analyzer drift due to changes in temperature. The
demonstration plan required that at each site, each analyzer would measure the same SRM or PE sample
at 2-hour intervals during at least one day of field operation. However, since ambient air temperature did
not fluctuate more than 20 °F on any day throughout the demonstration, potential analyzer drift due to
changes in temperature was not assessed.
The fourth deviation involved the drying of samples with a microwave. Instead of microwaving the
samples on high for 5 minutes, as described in the demonstration plan, the samples were microwaved on
high for only 3 minutes. This modification was made because the plastic weigh boats, which contained
the samples, were melting and burning when left in the microwave for 5 minutes. In addition, many of
the samples were melting to form a slag. PRC found (through visual observation) that the samples were
completely dry after only 3 minutes. This interval is within common microwave drying times used in the
field.
19
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An analysis of the microwaved samples showed that the drying process had a significant impact on
the analytical results. The mean RPD for the microwaved and nonmicrowaved raw data were
significantly different at a 95 percent confidence level. This suggests that the microwave drying process
somehow increases error and sample concentration variability. This difference may be due to the
extreme heat that altered the reference methods' extraction efficiency for target analytes. For the
evaluation of the effects of microwave drying, there were 736 matched pairs of data where both element
measurements were positive. Of these pairs, 471 exhibited RPDs less than 10 percent. This 10 percent
level is within the acceptable precision limits for the reference laboratory as defined in the demonstration
QAPP. Pairs exhibiting RPDs greater than 10 percent totaled 265. RPDs greater than 10 percent may
have causes other than analysis-induced error. Of these 265,96 pairs indicated an increase in metals
concentration with microwaving, and 169 pairs indicated a reduction in the concentration of metals. The
RPDs for the microwaved samples were 2 to 3 times worse than the RPDs from the field duplicates. This
further supports the hypothesis that microwave drying increases variability.
The fifth deviation involved reducing the percentage of analyzer precision measuring points. The
demonstration plan called for 10 percent of the samples to be used for assessment of analyzer precision,
Due to the time required to complete analysis of an analyzer precision sample, only 4 percent of the
samples were used to assess analyzer precision. This reduction in samples was approved by the EPA
technical advisor and the PRC field demonstration team leader. This eliminated 720 precision
measurements and saved up to 3 days of analysis time. The final precision determinations for this
demonstration were based on 48 sets of 10 replicate measurements for each analyzer.
The sixth deviation involved method blanks. Method blanks were to be analyzed each day and were
to consist of a lithium carbonate that had been used in all sample preparation steps. Each analyzer had its
own method blank samples, provided by the developer. Therefore, at the ASARCO site, each analyzer
used its own method blank samples. However, at the RV Hopkins site, each analyzer used lithium
carbonate method blanks that were prepared in the field, in addition to its own method blank samples.
Both types of method blank analysis never identified method-induced contamination.
The seventh deviation involved assessing the accuracy of each analyzer. Accuracy was to be
assessed through FPXRF analysis of 10 to 12 SRM or PE samples. Each analyzer measured a total of 28
SRM or PE samples. In addition, PE samples were used to evaluate the accuracy of the reference
methods, and SRMs were used to evaluate the accuracy of the analyzers. This is because the PE
concentrations are based on acid extractable concentrations while SRM concentrations represent total
metals concentration. SRM data were used for comparative purposes for the reference methods as were
PE data for the FPXRF data.
Sample Homogenization
A key quality issue in this demonstration was ensuring that environmental samples analyzed by the
reference laboratory and by each of the FPXRF analyzers were splits from a homogenized sample. To
address this issue, sample preparation technicians exercised particular care throughout the field work to
ensure that samples were thoroughly homogenized before they were split for analysis. Homogenization
was conducted by kneading the soil in a plastic bag for a minimum of 2 minutes. If after this time the
samples did not appear to be well homogenized, they were kneaded for an additional 2 minutes. This
continued until the samples appeared to be well homogenized.
Sodium fluorescein was used as an indicator of complete sample homogenization. Approximately
one-quarter teaspoon of dry sodium fluorescein salt was added to each sample prior to homogenization.
20
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After the homogenization was completed, the sample was examined under an ultraviolet light to assess
the distribution of sodium fluorescein throughout the sample. If the fluorescent dye was evenly dispersed
in the sample, homogenization was considered complete. If the dye was not evenly distributed, the
mixing was continued and checked until the dye was evenly distributed throughout the sample.
To evaluate the homogenization process used in this demonstration, 70 field duplicate sample pairs
were analyzed by the reference laboratory. Sample homogenization was critical to this demonstration; it
assured that the samples measured by the analyzers were as close as possible to samples analyzed by the
reference laboratory. This was essential to the primary objectives of this demonstration, the evaluation
of comparability between analyzer results and those of the reference methods.
The homogenization process was evaluated by determining the RPD between paired field duplicate
samples. TheRPDs for the field duplicate samples reflect the total error for the homogenization process
and the analytical method combined (Equation 2-4). When total error from the reference laboratory was
determined for the entire data set, the resultant mean RPD total (error) and 95 percent confidence interval
was 9.7 ± 1.4, for all metals reported. When only the primary analytes were considered, the RPD total
(error) and 95 percent confidence interval was 7.6 ± 1.2.
Total Measurement Error = ^Sample Homogenization Error)2 + (Laboratory Error)2] * " *
Using internal QA/QC data from 27 analyses, it was possible to determine the reference laboratory's
method error. The reference analytical method precision, as measured by the 95 percent confidence
interval around the meanRPDs (laboratory error) of predigestion duplicate analyses, was 9.3 ± 2.9 for all
of the target analytes.
To determine the error introduced by the sample homogenization alone, the error estimate for the
reference methods was subtracted from the total error (Equation 2-5). Based on the data presented
above, the laboratory-induced error was less than or approximately equal to the total error. This
indicates that the sample homogenization (preparation) process contributed little or no error to the overall
sample analysis process.
Sample Homogenization Error = y [(Total Measurement Error)2 - (Laboratory Error)2] i " I
Although the possibility for poorly homogenized samples exists under any homogenization routine,
at the scale of analysis used by this demonstration, the samples were considered to be completely
homogenized.
21
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Section 3
Reference Laboratory Results
All soil samples collected from the ASARCO and RV Hopkins sites were submitted to the reference
laboratory for trace metals analysis. The results are discussed in this section.
Reference Laboratory Methods
Samples collected during this demonstration were homogenized and split for extraction using EPA
SW-846 Method 3050A. This is an acid digestion procedure where 1 to 2 grams of soil are digested on a
hot plate with nitric acid, followed by hydrogen peroxide, and then refluxed with hydrochloric acid. One
gram of soil was used for extraction of the demonstration samples. The final digestion volume was 100
milliliters (mL). The soil sample extracts were analyzed by Method 6010A.
Method 6010A provides analysis of metals using Inductively Coupled Plasma-Atomic Emission
Spectroscopy (ICP-AES). This method requires that a plasma be produced by applying a radio-frequency
field to a quartz tube wrapped by a coil or solenoid through which argon gas is flowing. The radio-
frequency field creates a changing magnetic field in the flowing gas inside the coil, inducing a circulating
eddy current on the argon gas that, in turn, heats it. Plasma is initiated by an ignition source and quickly
stabilizes with a core temperature of 9,000 - 10,000 degrees Kelvin.
Soil sample extracts are nebulized, and the aerosol is injected into the plasma. Individual analytes
introduced into the plasma absorb energy and are excited to higher energy states. These higher energy
states have short lifetimes and the individual elements quickly fall back to their ground energy state by
releasing a photon. The energy of the emitted photon is defined by the wavelength of electromagnetic
radiation produced. Since many electronic transitions are possible for each individual element, several
discrete emissions at different wavelengths are observed. Method 6010A provides one recommended
wavelength to monitor for each analyte. Due to complex spectra with similar wavelengths from different
elements in environmental samples, Method 6010A requires that interference corrections be applied for
quantification of individual analytes.
Normal turnaround times for the analysis of soil samples by EPA SW-846 Methods 3050A/6010A
range from 21 to 90 days depending on the complexity of the soil samples and the amount of QC
documentation required. Faster turnaround times of 1- 14 days can be obtained, but at additional cost.
Costs for the analysis of soil samples by EPA SW-846 Methods 3050A/6010A range from $150 to
$350 per sample depending on turnaround times and the amount of QC documentation required. A
sample turnaround of 28 days, a cost of $150 per sample, and a CLP documentation report for QC were
chosen for this demonstration.
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Reference Laboratory Quality Control
The reference laboratory, Midwest Research Institute (Kansas City, MO), holds certifications for
performing target analyte list metals analysis with the U.S. Army Corps of Engineers-Missouri River
Division, the State of California, and the State of Utah. These certifications include on-site laboratory
audits, data package review audits, and the analysis of PE samples supplied by the certifying agency. PE
samples are supplied at least once per year from each of the certifying agencies. The reference
laboratory's results for the PE samples are compared to true value results and certifying agency
acceptance limits for the PE samples. Continuation of these certifications hinges upon acceptable results
for the audits and the PE samples.
The analysis of soil samples by the reference laboratory was governed by the QC criteria in its SOPS,
Method 6010A, and the demonstration QAPP. Table 3-1 provides QAPP QC requirements that were
monitored and evaluated for the target analytes. Method 6010A QC guidelines also are included in Table
3-1. Due to the complex spectra derived from the analysis of the demonstration samples, the QAPP QC
requirements were applied only to the primary analytes. The QAPP QC requirements also were
monitored and evaluated for the secondary analytes and other analytes reported by the reference
laboratory. However, corrective actions were not required for the secondary analytes.
Table 3-1. Reference Laboratory Quality Control Parameters*
Reference Method
Parameter Frequency Requirement QAPP Requirement
Initial Calibration
Verification (ICV)
Standard
Continuing Calibration
Verification (CCV)
Standard
Initial and Continuing
(ICB)
and
Interference Check
(ICS)
High Level Calibration
Check
Laboratory Control
Samples
Predigestlon
With each initial
calibration
After analysis of every 10
samples and at the end
«£f^SME2l!HE________,
With continuing
calibration, after analysis
of every 10 and
at the end of analytical
run
With every initial
calibration and after
analysis of 20 samples
With every
calibration
With each batch of
of a similar
matrix
With of
of a similar
matrix
With batch of
of a similar
matrix
With each batch of
of a similar
matrix
±1 0 percent of true value
±1 0 percent of true vaiye
±3 standard deviations of
the analyzer background
mean
±20 percent of true
±5 of value
No QC requirement
specified
No QC requirement
specified
80 - 1 20 percent recovery
75-125 percent
±1 0 percent of trye value
±1 0 percent of trye value
No target analytes at
concentrations greater than
2 times the lower reporting
limit (LRL)
±20 percent of true value
±10 percent of true
No at
concentrations greater than
2 the LRL
80-120 recovery
80-120 percent recovery
80-120 percent recovery
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Table 3-1. Continued!
Reference Method
Parameter Frequency Requirement QAPP Requirement
Performance Evaluation
Samples
Predigestion laboratory
Duplicate Samples
Postdigestion
Laboratory Duplicate
Samples
As submitted during
demonstration
With of
of a similar
matrix
With of
of a similar
matrix
No QC requirement
specified
20
percent (RPD)b
No QC reqyirement
specified
80-120 percent recovery
within performance
limits (PAL)
20 RPD*
10 percent RPDC
Notes: Quality control parameters were evaluated on the raw data,
RPD control only pertain to original and laboratory sample that were
than 10 times the instrument detection limit (IDL),
RPD control only pertain to original and laboratory duplicate results that were greater
or equal to 10 the LRL,
PRC performed three on-site audits of the reference laboratory during the analysis of pre-
demonstration and demonstration samples. These audits were conducted to observe and evaluate the
procedures used by the reference laboratory and to ensure that these procedures adhered to the QAPP QC
requirements. Audit findings revealed that the reference laboratory followed the QAPP QC require-
ments. It was determined that the reference laboratory had problems meeting two of the QAPP QC
requirements: method blank results and the high level calibration check standard's percent recovery. Due
to these problems, these two QAPP QC requirements were widened. The QC requirement for method
blank sample results was changed from no target analytes at concentrations greater than the lower
reporting limit (LRL) to two times the LRL. The QC requirement for the high level calibration standard
percent recovery was changed from ±5 to ±10 percent of the true value. These changes were approved
by the EPA and did not affect the results of the demonstration.
The reference laboratory internally reviewed its data before releasing it. PRC conducted a QC
review on the data based on the QAPP QC requirements and corrective actions listed in the demon-
stration plan.
Quality Control Review of Reference Laboratory Data
The QC data review focused upon the compliance of the data with the QC requirements specified in
the demonstration QAPP. The following sections discuss results from the QC review of the reference
laboratory data. All QC data evaluations were based on raw data.
Reference Laboratory Sample Receipt, Handling, and Storage Procedures
Demonstration samples were divided into batches of no more than 20 samples per batch prior to
delivery to the reference laboratory. A total of 23 batches containing 315 samples and 70 field duplicate
samples was submitted to the reference laboratory. The samples were shipped in sealed coolers at
ambient temperature under a chain of custody.
Upon receipt of the demonstration samples, the reference laboratory assigned each sample a unique
number and logged each into its laboratory tracking system. The samples were then transferred to the
reference laboratory's sample storage refrigerators to await sample extraction.
24
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Samples were transferred to the extraction section of laboratory under an internal chain of custody.
Upon completion of extraction, the remaining samples were returned to the sample storage refrigerators
Soil sample extracts were refrigerated in the extraction laboratory while awaiting sample analysis.
Sample Ho/ding Times
The maximum allowable holding time from the date of sample collection to the date of extraction and
analysis using EPA SW-846 Methods 3050A/6010A is 180 days. Maximum holding times were not
exceeded for any samples during this demonstration.
Initial and Continuing Calibrations
Prior to sample analysis, initial calibrations (ICAL) were performed. ICALs for Method 6010A
consist of the analysis of three concentrations of each target analyte and a calibration blank. The low
concentration standard is the concentration used to verify the LRL of the method. The remaining
standards are used to define the linear range of the ICP-AES. The ICAL is used to establish calibration
curves for each target analyte. Method 6010A requires an initial calibration verification (ICV) standard
to be analyzed with each ICAL. The method control limit for the ICV is ±10 percent. An interference
check sample (ICS) and a high level calibration check standard is required to be analyzed with every
ICAL to assess the accuracy of the ICAL. The control limits for the ICS and high level calibration check
standard were ±20 percent recovery and ±10 percent of the true value, respectively. All ICALs, ICVs,
and ICSs met the respective QC requirements for all target analytes.
Continuing calibration verification (CCV) standards and continuing calibration blanks (CCB) were
analyzed following the analysis of every 10 samples and at the end of an analytical run. Analysis of the
ICS was also required after every group of 20 sample analyses. These QC samples were analyzed to
check the validity of the ICAL. The control limits for the CCVs were ±10 percent of the true value. The
control limits for CCBs were no target analyte detected at concentrations greater than 2 times the LRL.
All CCVs, CCBs, and ICSs met the QAPP requirements for the target analytes with the exception of one
CCV where the barium recovery was outside the control limit. Since barium was a primary analyte, the
sample batch associated with this CCV was reanalyzed and the resultant barium recovery met the QC
criteria.
Detection Limits
The reference laboratory LRLs for the target analytes are listed in Table 3-2. These LRLs were
generated through the use of an MDL study of a clean soil matrix. This clean soil matrix was also used
for method blank samples and LCSs during the analysis of demonstration samples. The MDL study
involved seven analyses of the clean soil matrix spiked with low concentrations of the target analytes.
The mean and standard deviation of the response for each target analyte was calculated. The LRL was
defined as the mean plus three times the standard deviation of the response for each target analyte
included in the method detection limit study. All LRLs listed in Table 3-2 were met and maintained
throughout the analysis of the demonstration samples.
The reference laboratory reported soil sample results in units of milligram per kilogram wet weight.
All reference laboratory results referred to in this report are wet-weight sample results.
25
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3-2, 601OA for
Anatftes
Notes;
LRL due to
interference,
* Primary analyte.
mg/kg Milligrams per kilogram.
Method Blank Samples
Method blanks were prepared using a clean soil matrix and acid digestion reagents used in the
extraction procedure. A minimum of one method blank sample was analyzed for each of the 23 batches
of demonstration samples submitted for reference laboratory analysis. All method blanks provided
results for target analytes at concentrations less than 2 times the levels shown in Table 3-2.
Laboratory Control Samples
All LCSs met the QAPP QC requirements for all primary and secondary analytes except those
discussed below.
The primary analytes copper and lead were observed outside the QC limits in one of the 23 batches
of samples analyzed. Reanalysis of the affected batches was not performed by the reference laboratory.
These data were qualified by the reference laboratory. Copper and lead data for all samples included in
the affected batches were rejected and not used for demonstration statistical comparisons.
Concentrations of secondary analytes antimony, nickel, and cadmium were observed outside the QC
limits in the LCSs. Antimony LCS recoveries were continually outside the control limits, while nickel
and cadmium LCS recoveries were only occasionally outside QC limits. Antimony was a problem
analyte and appeared to be affected by acid digestion, which can cause recoveries to fall outside control
limits. Antimony recoveries ranged from 70 to 80 percent. Since secondary analytes were not subject to
the corrective actions listed in the demonstration QAPP, no reanalysis was performed based on the LCS
results of the secondary target analytes. These values were qualified by the reference laboratory. All
other secondary analyte LCS recoveries fell within the QAPP control limits.
Predigestion Matrix Spike Samples
One predigestion matrix spike sample and duplicate were prepared by the reference laboratory for
each batch of demonstration samples submitted for analysis. The predigestion matrix spike duplicate
sample was not required by the QAPP, but it is a routine sample prepared by the reference laboratory.
This duplicate sample can provide data that indicates if out-of-control recoveries are due to matrix
interferences or laboratory errors.
26
-------
Predigestion spike recovery results for the primary analytes arsenic, barium, chromium, copper, lead,
and zinc were outside control limits for at least 1 of the 23 sample batches analyzed by the reference
method. These control limit problems were due to either matrix effects or initial spiking concentrations
below native analyte concentrations.
Barium, copper, and lead predigestion matrix spike recovery results were outside control limits in
sample batches 2,3, and 5. In all of these cases, the unacceptable recoveries were caused by spiking
concentrations that were much lower than native concentrations of the analytes. These samples were re-
prepared, spiked with higher concentrations of analytes, reextracted, and reanalyzed. Following this
procedure, the spike recoveries fell within control limits upon reanalysis.
One predigestion matrix spike recovery was outside control limits for arsenic. The predigestion
matrix spike duplicate sample also was outside of control limits. This sample exhibited an acceptable
RPD for the recovery of arsenic in the predigestion matrix spike and duplicate. A matrix interference
may have been responsible for the low recovery. This sample was not reanalyzed.
Chromium predigestion matrix spike recoveries were outside control limits in 7 of the 23 batches of
samples analyzed. Five of these seven failures exhibited recoveries ranging from 67 to 78 percent, close
to the low end of the control limits. These recoveries were similar in the predigestion matrix spike
duplicate samples prepared and analyzed in the same batch. This indicates that these five failures were
due to matrix interferences. The predigestion matrix spike duplicate samples prepared and analyzed
along with the remaining two failures did not agree with the recoveries of the postdigestion matrix spike
samples, indicating that these two failures may be due to laboratory error, possibly inaccuracies in
sample spiking. These seven predigestion matrix spike samples were not reanalyzed.
The zinc predigestion matrix spike recovery data were outside control limits for four batches of
samples analyzed. In three of the spike recovery pairs, recoveries ranged from 70 to 76 percent, close to
the lower end of the control limits. The fourth recovery was much less than the lower end of the control
limits. All of the predigestion matrix spike duplicate samples provided recoveries that agreed with the
recoveries for the predigestion matrix spike sample recoveries indicating that the low recoveries were
due to matrix effects. These predigestion matrix spikes and associated samples were not reanalyzed.
The secondary analytes, cadmium, iron, and nickel, had predigestion spike recoveries outside control
limits. Cadmium spike recoveries were outside control limits six times. These recoveries ranged from
71 to 85 percent. Iron spike recoveries were outside of control limits once. Nickel spike recoveries were
outside control limits four times. These recoveries ranged from 74 to 83 percent. Antimony spike
recoveries were always outside control limits. No corrective action was taken for these secondary target
analytes.
Demonstration sample results for all target analytes that did not meet the control limits for
predigestion matrix spike recovery were qualified by the reference laboratory.
Postdigestion Matrix Spike Samples
All postdigestion matrix spike results were within the control limit of 80 - 120 percent recovery for
the primary analytes.
Secondary analytes, antimony, and iron were observed outside the control limits. However, no
corrective action was taken for secondary analytes as stated in the demonstration QAPP. All
27
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postdigestion spike recoveries for target analytes met the QA/QC requirements of theQAPP and were
considered acceptable.
Predigestion Laboratory Duplicate Samples
Predigestion laboratory duplicate RPD results were within the control limit of 20 percent for analyte
concentrations greater than 10 times the LRL except for the following instances. RPDs for primary
analytes barium, arsenic, lead, chromium, and copper were observed above the control limit in five
predigestion laboratory duplicate samples. These samples were reanalyzed according to the corrective
actions listed in the QAPP. The reanalysis produced acceptable RPD results for these primary analytes.
RPD results for the secondary analytes antimony, nickel, and cadmium were observed outside the
control limit for a number of sample batches. No corrective action was taken for secondary analytes that
exceeded the RPD control limit.
Postdigestion Laboratory Duplicate Samples
All primary analyte postdigestion laboratory duplicate RPD results were less than the 10 percent
control limit for analyte concentrations greater than 10 times the LRL.
The RPDs for secondary analytes antimony and iron were observed above the 10 percent control
limit in two sample batches. No corrective action was taken for secondary target analytes that exceeded
the RPD control limit.
Performance Evaluation Samples
PE samples were purchased from Environmental Resource Associates (ERA). The PE samples are
Priority PollutnT™/Contract Laboratory Program (CLP) QC standards for inorganics in soil. This type
of sample is used by the EPA to verify accuracy and laboratory performance. Trace metal values are
certified by interlaboratory round robin analyses. ERA lists performance acceptance limits (PAL) for
each analyte that represent a 95 percent confidence interval (CI) around the certified value. PALS are
generated by peer laboratories in ERA's InterLaB™ program using the same samples that the reference
laboratory analyzed and the same analytical methods. The reported value for each analyte in the PE
sample must fall within the PAL range for the accuracy to be acceptable. Four PE samples were
submitted "double blind" (the reference laboratory was not notified that the samples were QC samples or
of the certified values for each element) to the reference laboratory for analysis by EPA SW-846
Methods 3050A/6010A. Reference laboratory results for all target analytes are discussed later in this
section.
Four certified reference materials (CRM) purchased from Resource Technology Corporation (RTC)
also were used as PE samples to verify the accuracy and performance of the reference laboratory. These
fourCRMs were actual samples from contaminated sites. They consisted of two soils, one sludge, and
one ash CRM. Metal values in the CRMs are certified by round robin analyses of at least 20 laboratories
according to the requirements specified by the EPA Cooperative Research and Development Agreement.
The certified reference values were determined by EPA SW-846 Methods 3050A/6010A. RTC provides
a 95 percent PAL around each reference value in which measurements should fall 19 of 20 times. The
reported value from the reference laboratory for each analyte must fall within this PAL for the accuracy
to be considered acceptable. As with the four PE samples, the four CRMs were submitted "double blind"
28
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to the reference laboratory for analysis by EPA SW-846 Methods 3050A/6010A. Thereference
laboratory results for the target analytes are discussed later in the Accuracy subsection.
Standard Reference Material Samples
As stated in the demonstration plan (PRC 1995), PE samples also consisted of SRMs. The SRMs
consisted of solid matrices such as soil, ash, and sludge. Certified analyte concentrations for SRMs are
determined on an analyte by analyte basis by multiple analytical methods including but not limited to
ICP-AES, flame atomic absorption spectroscopy, ICP-mass spectrometry, XRP, instrumental neutron
activation analysis, hydride generation atomic absorption spectroscopy, and polarography. These
certified values represent total analyte concentrations and complete extraction. This is different from the
PE samples, CRM samples, and the reference methods, which use acid extraction that allows quantitation
of only acid extractable analyte concentrations.
The reference laboratory analyzed 14 SRMs supplied by the National Institute of Standards and
Technology (NISI), U.S. Geological Survey (USGS), National Research Council Canada, South African
Bureau of Standards, and Commission of the European Communities. The percentage of analyses of
SRMs that were within the QAPP-defined control limits of 80- 120 percent recovery was calculated for
each primary and secondary analyte.
Analyses of SRMs were not intended to assess the accuracy of EPA SW-846 Methods 3050A/6010A
as were the ERA PE or RTC CRM samples. Comparison of EPA SW-846 Methods 3050A/6010A acid
leach data to SRM data cannot be used to establish method validity (Kane and others 1993). This is
because SRM values are acquired by analyzing the samples by methods other than the ICP-AES method.
In addition, these other methods use sample preparation techniques different from those for EPA SW-846
Methods 3050A/6010A. This is one reason no PALS are published with the SRM certified values.
Therefore, the SRMs were not considered an absolute test of the reference laboratory's accuracy for EPA
SW-846 Methods 3050A/6010A.
The SRM sample results were not used to assess method accuracy or to validate the reference
methods. This was due to the fact that the reported analyte concentrations for SRMs represent total
analyte concentrations. The reference methods are not an analysis of total metals; rather they target the
teachable concentrations of metals. This is consistent with the NIST guidance against using SRMs to
assess performance on leaching based analytical methods (Kane and others 1993).
Data Review, Validation, and Reporting
Demonstration data were internally reviewed and validated by the reference laboratory. Validation
involved the identification and qualification of data affected by QC procedures or samples that did not
meet the QC requirements of the QAPP. Validated sample results were reported using both hard copy
and electronic disk deliverable formats. QC summary reports were supplied with the hard copy results.
This qualified data was identified and discussed in the QC summary reports provided by the reference
laboratory. '
Demonstration data reported by the reference laboratory contained three types of data qualifiers: C,
Q, and M. Type C qualifiers included the following:
• U - the analyte was analyzed for but not detected.
29
-------
» B - the reported value was obtained from a was less the LRL but
or equal to the IDL,
Type Q qualifiers included the following;
• N - spiked sample recovery was not within control limits.
« * - duplicate analysis was not within control limits.
Type M qualifiers include the following;
« P - analysis by ICP-AES (Method 6010)
Quality Assessment of Reference Laboratory Data
An assessment of the reference laboratory data was performed using the PARCC parameters
discussed in Section 2. PARCC parameters are used as indicators of data quality and were evaluated
using the review of reference laboratory data discussed above. The following sections discuss the data
quality for each PARCC parameter. This quality assessment was based on raw reference data and the
raw PE sample data.
The quality assessment was limited to an evaluation of the primary analytes. Secondary and other
analytes reported by the reference laboratory were not required to meet the QC requirements specified in
the QAPP. Discussion of the secondary analytes is presented in the precision, accuracy, and
comparability sections for informational purposes only.
Precis/on
Precision for the reference laboratory data was assessed through an evaluation of the RPD produced
from the analysis of predigestion laboratory duplicate samples and postdigestion laboratory duplicate
samples. Predigestion laboratory duplicate samples provide an indication of the method precision, while
postdigestion laboratory duplicate samples provide an indication of instrument performance. Figure 3-1
provides a graphical summary of the reference method precision data.
The predigestion duplicate RPDs for the primary and secondary analytes fell within the 20 percent
control limit, specified in the QAPP, for 17 out of 23 batches of demonstration samples. The six results
that exceeded the control limit involved only 11 of the 230 samples evaluated for predigestion duplicate
precision (Figure 3-1). This equates to 95 percent of the predigestion duplicate data meeting the QAPP
control limits. Six of the analytes exceeding control limits had RPDs less than 30 percent. Three of the
analytes exceeding control limits had RPDs between 30 and 40 percent. Two of the analytes exceeding
control limits had RPDs greater than 60 percent. These data points are not shown in Figure 3-1. Those
instances where the control limits were exceeded are possibly due to nonhomogeneity of the sample or
simply to chance, as would be expected with a normal distribution of precision analyses.
The postdigestion duplicate RPDs for the primary and secondary analytes fell within the 10 percent
control limit, specified in the QAPP, for 21 out of 23 batches of demonstration samples. The two results
that exceeded the control limit involved only 3 of the 230 samples evaluated for postdigestion duplicate
precision in the 23 sample batches (Figure 3-1). This equates to 99 percent of the postdigestion duplicate
data meeting the QAPP control limits. The RPDs for the three results that exceeded the control limit
ranged from 11 to 14 percent.
30
-------
Predigestion Duplicate Samples
40
1
1
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B on
5
0
1
t®
"I 10
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Antimony Arsenic Barium Chramum Qrtrfum topper Iron lodd Nctel Zinc
Anaiyte
3-1. Pro- Postdigestion The top graph the
laboratory's performance on Twenty
RPD the control limits in the demonstration
QAPP, Two from this top barium at 85 RPD and copper at
138 RPD, The bottom the laboratory's on
Ten RPD the
control limits in the demonstration QAPP.
Accuracy
Accuracy for the reference laboratory data was assessed through evaluations of the PE samples
(including the CRMs), LCSs, method blank sample results, and pre- and postdigestion matrix spike
samples. PE samples were used to assess the absolute accuracy of the reference laboratory method as a
whole, while LCSs, method blanks, and pre- and postdigestion matrix spike samples were used to assess
the accuracy of each batch of demonstration samples.
A total of eight PE and CRM samples was analyzed by the reference laboratory. These included four
ERA PE samples and four RTC CRM samples. One of the ERA PE samples was submitted to the
31
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reference laboratory in duplicate, thereby producing nine results to validate accuracy. The accuracy data
for all primary and secondary analytes are presented in Table 3-3 and displayed in Figure 3-2. Accuracy
was assessed over a wide-concentration range for all 10 analytes with concentrations for most analytes
spanning one or more orders of magnitude.
Reference laboratory results for all target analytes in the ERA PE samples fell within the PALs. In
the case of the RTC CRM PE samples, reference laboratory results for copper in one CRM and zinc in
two CRMs fell outside the published acceptance limits. One of the two out-of-range zinc results was
only slightly above the upper acceptance limit (811 versus 774 mg/kg). The other out-of-range zinc
result and the out-of-range copper result were about three times higher than the certified value and
occurred in the same CRM. These two high results skewed the mean percent recovery for copper and
zinc shown in Table 3-3. Figure 3-2 shows that the remaining percent recoveries for copper and zinc
were all near 100 percent.
Table 3-3 shows that a total of 83 results was obtained for the 10 target analytes. Eighty of the 83
results or 96.4 percent fell within the PALs. Only 3 out of 83 times did the reference method results fall
outside PALs. This occurred once for copper and twice for zinc. Based on this high percentage of
acceptable results for the ERA and CRM PE samples, the accuracy of the reference methods was
considered acceptable.
Table 3-3. Reference Laboratory Accuracy Data for Target Anaiyies
Anafyte
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
n
Q
6
Q
i
9
9
7
8
9
9
Percent Within
Acceptance Range
100
100
100
100
100
89
100
87.5
100
78
Mean
Percent
Recovery
104
106
105
84
91
123
98
86
95
120
Rang© of
Percent
Recovery
83-125
r^90^160
83-139
63-93
77-101
90 - 332
^___1
35-108
79-107
79 - 309
SO of
Percent
Recovery
15
22
21
10
8
79
12
22
10
u_I£_^
Concentration
Range (mg/kg)
50 -
25 - 397
19-588
1,2-432
11-187
144-4,792
-
, 52-5,194
13-13,279
78 -
Notes: n Number of samples with detectable anaiyte concentrations.
SD Standard deviation,
mg/kg Milligrams per kilogram,
LCS percent recoveries for all the primary analytes were In 21 of the 23 batches.
Lead recovery was unacceptable In one sample batch and lead results for each in that batch were
rejected.
Copper recovery unacceptable in batch, and for in
this were rejected. Percent recoveries of the remaining primary in of two
batches In all, 136 of 138 LCS results or 98,5 fell within the control limits.
32
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Method blank samples for all 23 batches of demonstration samples provided results of less than 2
times the LRL for all primary analytes. This method blank control limit was a deviation from the QAPP,
which had originally set the control limit at no target analytes at concentrations greater than the LRL.
This control limit was widened at the request of the reference laboratory. A number of batches were
providing method blank results for target analytes at concentrations greater than the LRL, but less than 2
times the LRL. This alteration was allowed because even at 2 times the LRL, positive results for the
method blank samples were still significantly lower than the MDLs for each of the FPXRF analyzers.
The results from the method blank samples did not affect the accuracy of the reference data as it was to
be used in the demonstration statistical evaluation of FPXRF analyzers.
The percent recovery for the predigestion matrix spike samples fell outside of the 80 - 120 percent
control limit specified in the QAPP in several of the 23 batches of demonstration samples. The
predigestion matrix spike sample results indicate that the accuracy of specific target analytes in samples
from the affected batches may be suspect. These results were qualified by the reference laboratory.
These data were not excluded from use for the demonstration statistical comparison. A discussion of the
use of this qualified data is included in the "Use of Qualified Data for Statistical Analysis" subsection.
The RPD for the postdigestion matrix spike samples fell within the 80 - 120 percent control limit
specified in the QAPP for all 23 batches of demonstration samples.
The QA review of the reference laboratory data indicated that the absolute accuracy of the method
was acceptable. Based on professional judgement, it was determined that the small percentage of outliers
did not justify rejection of any demonstration sample results from the reference laboratory. The accuracy
assessment also indicated that most of the batch summary data were acceptable. Two batches were
affected by LCS outliers, and some data were qualified due to predigestion matrix spike recovery
outliers. This data was rejected or qualified. Rejected data was not used. Qualified data were used as
discussed below.
Representativeness
Representativeness of the analytical data was evaluated through laboratory audits performed during
the course of sample analysis and by QC sample analyses, including method blank samples, laboratory
duplicate samples, and CRM and PE samples. These QC samples were determined to provide acceptable
results. From these evaluations, it was determined that representativeness of the reference data was
acceptable.
Comp/eteness
Results were obtained for all soil samples extracted and analyzed by EPA SW-846 Methods
3050A/6010A. Some results were rejected or qualified. Rejected results were deemed incomplete.
Qualified results were usable for certain purposes and were deemed as complete.
To calculate completeness, the number of nonrejected results was determined. This number was
divided by the total number of results expected, and then multiplied by 100 to express completeness as a
percentage. A total of 385 samples was submitted for analysis. Six primary analytes were reported,
resulting in an expected 2,310 results. Forty of these were rejected, resulting in 2,270 complete results.
Reference laboratory completeness was determined to be 98.3 percent, which exceeded the objective for
this demonstration of 95 percent. The reference laboratory's completeness was, therefore, considered
acceptable.
33
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10000
1000 —
100
150
-J50
Antimony
BReference Data DTrue Value •Percent Recovery
500 r— •
400 —
300 —
fi 200
c
c
6'} S
% Recovery
200
I 100
50
6
I if!
I
120
too
80
'40
Chromium
IHefewre Data OTrue Value
lF«fr«nt Recovery
c
«
e
£
Flgyre 3-2. PE and These the
between the and the true values for the PE or CRM The gray
the for the Each set of (black, white, and gray) a
PE or CRM on this high of for the ERA and CRM
PE the accuracy of the laboratory method acceptable.
34
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100000
10000
•S 1000
(8
c
f»
100
10
400
300 £•
200
100
Copper
IRef erence Data DTrus Value
1% Raco¥ ery
Iron
IReferenee Data OTrue Value
120
100
*
>
o
c
a)
."40
I Percent Recovery
100001— —
I,' 1000
I
15
S 100}-
10
I!
r
100
Lead
IRef erence Data OTrue Value
{Percent Recovery
Mckel
IReference Data DTrua Value
J40
1% Recovery
100000
S'
L
HI
C^
S
1000
too
10
M
400
300 £•
ffi
200
c
*
o
100 f
Zinc
•Reference Data QTrup Va'iK*
Recovery
3-2 (Continued). PE These graphs illustrate the
relationship the and the true for the PE or CRM samples. The gray
the recovery for the set of (black, white, and gray)
a single PE or CRM sample, on this high of results for the
ERA and CRM PE the accuracy of the laboratory method was considered
acceptable.
35
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Comparability
Comparability of the reference data was controlled by following laboratory SOPS written for the
performance of sample analysis using EPA SW-846 Methods 3050A/6010A. QC criteria defined in the
SW-846 methods and the demonstration plan (PRC 1995) were followed to ensure that reference data
would provide comparable results to any laboratory reporting results for the same samples.
Reference results indicated that EPA SW-846 Methods 3050A/6010A did not provide comparable
results for some analytes in the SRM samples. SRM performance data for target analytes is summarized
in Table 3-4 and displayed in Figure 3-3. As with the PEs, the analyte concentrations spanned up to 3
orders of magnitude in theSRMs. The percentage of acceptable (80 - 120 percent recovery) SRM results
and mean percent recovery was less than 50 percent for the analytes antimony, barium, chromium, iron,
and nickel. The low recoveries for these five analytes reflect the lesser tendency for them to be acid-
extracted (Kane and others 1993).
Under contract to the EPA, multiple laboratories analyzed NIST SRMs2709, 2710, and 2711 by
EPA SW-846 Methods 3050A/6010A. A range, median value, and percent leach recovery based on the
median value for each detectable element were then published as an addendum to the SRM certificates.
These median values are not certified but provide a baseline for comparison to other laboratories
analyzing these SRMs by EPA SW-846 Methods 3050A/6010A. Table 3-5 presents the published
percent leach recovery for the 10 primary and secondary analytes and the reference laboratory's results
for these three NIST SRMs Table 3-5 shows that the results produced by the reference laboratory were
consistent with the published results indicating good comparability to other laboratories using the same
analytical methods on the same samples.
f>t format-ice for Target
'4
84
41
82
6?
15
07
?t
41
14
33
?3
'J 7
?b
J2
-3/
10«
89
f*b
67
»94
84
™ Ui|
CJ"|
93
q
10
?1
15
If!
17
2b
17
17
14
«_— —— — .j
JH
18
414
r
I' 4
3«
35-
28,900
19
14
81
171
626
1,300
- 72
S09
?,95CI
94,0(10
5,532
?09
B,9'»2
Notes:
n
SD
mg/kg
/'. ' 81
Number of SRM samples concentrations.
Standard deviation,
per kilogram.
36
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Table 3-5. Leach Recoveries for Select NIST SRMs
Analyte
NIST SRM 2709
1 Reference
Published Laboratory
Result8 I Result
NIST SRM 2710
•j Reference
Published Laboratory
Result" Result
NIST SRM 2711
j . Reference
Published j Laboratory
Result* i Result
Antimony
Arsenic
Barium
Cadmiym
Chromium
Copper
Iron
Lead
Nickel
Zinc
_
_
41
-
61
92
88
89
S9__J
94
_
108
37
-
-
85
84
87
76
78
21
l__ ^
51
92
49
92
80
92
71
85
-
87
45
84
_
92
78
96
69
88
_
88
28
96
43
88
76
^^____^
78
89
20
91
25
87
49
90
66
90
70
85
foynd in an addendum to SRM certificates for NIST SRMs 2709,2710, and
2711,
NIST Institute of Standards and Technology.
SRM materials,
- Analyte not above the method LRL.
The inability of EPA SW-846 Methods 3050A/6010A to achieve the predetermined 80- 120 percent
recovery requirement indicated that the methods used to determine the certified values for the SRM
samples were not comparable to EPA SW-846 Methods 3050A/6010A. Differences in the sample
extraction methods and the use of different analytical instruments and techniques for each method were
the major factors of this noncomparability. Because of these differences, it was not surprising that the
mean percent recovery was less than 100 percent for the target analytes. The lack of comparability of
EPA SW-846 Methods 3050A/6010A to the total metals content in the SRMs did not affect the quality of
the data generated by the reference laboratory.
The assessment of comparability for the reference data revealed that it should be comparable to other
laboratories performing analysis of the same samples using the same extraction and analytical methods,
but it may not be comparable to laboratories performing analysis of the same samples using different
extraction and analytical methods or by methods producing total analyte concentration data.
Use of Qualified Data for Statistical Analysis
As noted above, the reference laboratory results were reported and validated, qualified, or rejected
by approved QC procedures. Data were qualified for predigestion matrix spike recovery and pre- and
postdigestion laboratory duplicate RPD control limit outliers. None of the problems were considered
sufficiently serious to preclude the use of coded data. Appropriate corrective action identified in the
demonstration plan(PRC 1995) was instituted. The result of the corrective action indicated that the poor
percent recovery and RPD results were due to matrix effects. Since eliminating the matrix effects would
require additional analysis using a different determination method such as atomic absorption
spectrometry, or the method of standard addition, the matrix effects were noted and were not corrected.
37
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PARCC parameters for the reference laboratory data were determined to be acceptable. It was
expected that any laboratory performing analysis of these samples using EPA SW-846 Methods
3050A/6010A would experience comparable matrix effects. A primary objective of this demonstration
was to compare sample results from the FPXRF analyzers to EPA SW-846 Methods 3050A/6010A, the
most widely used approved methods for determining metal concentrations in soil samples. The
comparison of FPXRF and the reference methods had to take into account certain limitations of both
methods, including matrix effects. For these reasons, qualified reference data were used for statistical
analysis.
The QC review and QA audit of the reference data indicated more than 98 percent of the data either
met the demonstration QAPP objectives or was QC coded for reasons not limiting its use in the data
evaluation. Less than 2 percent of the data were rejected based on QAPP criteria. Rejected data were
not used for statistical analysis. The reference data were considered as good as or better than other
laboratory analyses of samples performed using the same extraction and analytical methods. The
reference data met the definitive data quality criteria and was of sufficient quality to support regulatory
activities. The reference data were found to be acceptable for comparative purposes with the FPXRF
data.
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between the the true values for the SRM samples. The gray the
percent recovery for the reference data. Each set of three bars (black, white, and gray) represents a
single SRM sample.
39
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Section 4
MAP Spectrum Analyzer
This section provides information on the Scitec's MAP Spectrum Analyzer including the theory of
FPXRF, operational characteristics, performance factors, a data quality assessment, and a comparison of
results with those of the reference laboratory.
Theory of FPXRF Analysis
FPXRF analyzers operate on the principle of energy dispersive XRF spectrometry. This is a
nondestructive qualitative and quantitative analytical technique that can be used to determine the metals
composition in a test sample. By exposing a sample to an X-ray source having an excitation energy close
to, but greater than, the binding energy of the inner shell electrons of the target element, electrons are
displaced. The electron vacancies that result are filled by electrons cascading in from outer electron
shells. Electrons in the outer shells have higher potential energy states than inner shell electrons, and to
fill the vacancies, the outer shell electrons give off energy as they cascade into the inner shell (Figure 4-
1). This release of energy results in an emission of X-rays that is characteristic of each element. This
emission of X-rays is termed XRF.
Because each element has a unique electron shell configuration, each will emit unique X-rays at set
energies called "characteristic" X-rays. The energy of the X-ray is measured in electron volts (eV). By
measuring the peak energies of X-rays emitted by a sample, it is possible to identify and quantify the
elemental composition of a sample. A qualitative analysis of the sample can be made by identifying the
characteristic X-rays produced by the sample. The intensity of characteristic X-rays emitted is
proportional to the concentration of a given element and can be used to quantitate each target element.
Three electron shells are generally involved in the emission of characteristic X-rays during FPXRF
analysis: the K, L, and M shells. A typical emission pattern, also called an emission spectrum, for a
given element has multiple peaks generated from the emission X-rays by the K, L, or M shell electrons.
The most commonly measured X-ray emissions are from the K and L shells; only elements with an
atomic number of 58 (cerium) or greater have measurable M shell emissions.
Each characteristic X-ray peak or line is defined with the letter K, L, or M, which signifies which
shell had the original vacancy and by a subscript alpha (a) or beta(6), which indicates the next outermost
shell from which electrons fell to fill the vacancy and produce the X-ray. For example, Ka-line is
produced by a vacancy in the K shell filled by an L shell electron, whereas Kn-line is produced by a
vacancy in the K shell filled by an M shell electron. The Ka transition is between 7 and 10 times more
probable than the KB transition. The Ka-line is also approximately 10 times more intense than the Ks-line
for a given element, making the k-line analysis the preferred choice for quantitation purposes. Unlike
40
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the K-lines, the L-lines (LB and L8) for an are of nearly Intensity, The choice of which one
to use for analysis on the of lines from
Exclafcn X-ray from the
FPXRF Soyrc*
N
\
An excited electron Is displaced, ewmthg an
electron vacancy.
X
An outer electron ehel electron cascade* to the "mmt ttectron shel to
il the vacancy. A» this ttectron cascade*, it releases energy In the
form of an X-ray.
Characteristic X-ray
Figure 4-1, of X-ray This figure the dynamics
of X-ray fluorescence,
An X-ray source can excite characteristic X-rays from an analyte only if its energy is greater than the
electron binding energies of the target analyte. The electron binding energy, also known as the
absorption edge energy, represents the amount of energy an electron has to absorb before it is displaced.
The absorption edge energy is somewhat greater than the corresponding line energy. Actually, the K-
absorption edge energy is approximately the sum of the K-, L-, and M-line energies of the particular
element, and the L- absorption edge energy is approximately the sum of the L- and M-line energies.
FPXRF analytical methods are more sensitive to analytes with absorption edge energies close to, but less
than, the excitation energy of the source. For example, when using aCd10* source, which has an
excitation energy of 22.1 kiloelectron volts (keV), an FPXRF analyzer would be more sensitive to
zirconium, which has a K-line absorption edge energy of 15.7 keV, than to chromium, which has a K-line
absorption edge energy of 5.41 keV.
Background
The MAP Spectrum Analyzer was originally developed by Scitec to detect lead in paint using a
cobalt-57 (Co57) excitation source. It is a lightweight, portable technology that collects in situ readings
by placing the scanner in direct contact with the surface to be measured. Scitec currently markets the
MAP Spectrum Analyzer as capable of detecting lead as well as other metals in soil, when equipped with
aCd109 source. The analyzer uses energy dispersive XRF spectroscopy to determine elemental
composition of paint, soil, and other solid materials. The specific analyzer demonstrated during this
evaluation was a third generation analyzer known as the MAP 3 Spectrum Analyzer. Since this
demonstration was completed, Scitec has produced a fourth generation analyzer known as the MAP 4
Spectrum Analyzer.
41
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Operational Characteristics
This section discusses equipment and accessories, operation of the analyzer in the field, description
of the operator, training, reliability of the analyzer, health and safety concerns, and representative
operating costs.
Equipment and Accessories
The primary components of the MAP Spectrum Analyzer are the control console and the ambient
scanner. The control console is connected to the ambient scanner with a lo-foot cable. The basic system
also includes a carry pack, rechargeable batteries, battery charger, operator's manual, site-specific
standard, and a shipping case. For this demonstration, the scanner, control console, battery charger, and
cords were contained in a carrying case. Additional equipment, such as the calibration check standards
and spare batteries, did not fit in the carrying case and were shipped in a separate box. Specifications for
the MAP Spectrum Analyzer used during this demonstration are provided in Table 4-1.
The equipment used in the demonstration included;
« One MAP 3 control console calibrated to detect arsenic, lead, copper, and zinc
» One MAP 3 scanner including a 55 millicuries (mCi) Cdm sealed source
» Six 12-volt direct current (DC) lead-gel batteries (the console requires two batteries leaving two
sets of two as spares)
« Two battery chargers
» One clip adapter for charging two batteries outside the console
» Two 10-foot cables for attaching the scanner to the console (one for use and one spare)
* One cable for connecting the console to a personal computer (PC)
» One computer port adapter
« AcuTransfer software
» One ring stand with clamps (the ring stand was used to hold the scanner in place to allow the
calibration check to be assayed and to hold the scanner on slopes in the field)
* Three calibration check standards in 4-ounce jars
» One painted wooden block check standard
« One carrying case
» Plastic wrap used to cover the face of the scanner
« One portable computer printer supplied by PRC
42
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4-1,
Resolution
Source
Detector
Temperature
Control
Control
Control Console Operating Temperature
Control Console
Control
Power
Operational Checks
Bill or Kevin
41 5 N, Quay
WA
____^^
1 ,5 keV
55 mCi Cd108 (Am 241 and Co57 also
silicon
33,7 cm
1 ,8 (kg) (3,5
-6 to 4 3 °C
1 9,3 cm x 20 cm x 7 8 cm
5 kg with haHones (11 pounds)
-8 to 43 °C
256
1 or 325
120-volt or 220-volt current, or 12-volt DC
Calibration per hour
The control console is a256-multichannel analyzer (MCA) contained in a high-impact plastic case.
It has a liquid crystal display (LCD) that can provide readouts of operation menus, measurement values,
calibration menus, count rates, time clock, analysis identification numbers, and a graphic spectrum
display. The keyboard is weatherproof and has a 14-key keypad. The two lead-gel type batteries
necessary to supply power to the control console are capable of 10 hours of continuous use without
recharging. Each lead-gel battery has an approximate useful life of 12- 18 months or 150 recharges.
The operator noted during this demonstration that the battery life ranged from 6.3 hours to 8.9 hours with
an average of 7.7 hours. The control console also has an output port for downloading data to a PC with
the use of the AcuTransfer software.
The ambient scanner is shaped like a pistol and contains the excitation source and the solid-state
silicon detector. It has a 0.5-mm-thick beryllium window and a 0.5-mm-thick aluminum front face plate.
The source shutter is constructed of tungsten and is designed to house one of three sources: cobalt-57
(Co57), cadmium-109 (Cd109), or americium-241 (Am241). The Cd109 source was used in this
demonstration. The Cd109 source was assayed as 55 mCi on September 24, 1993. Based on a half life of
462 days, the source was 24mCi at the beginning of the demonstration and was 23 mCi at the end. The
ambient scanner contained a solid-state silicon detector with a resolution of 1.5 keV at the Ka manganese
line. It has aBreech-lok™ connector that connects the scanner to the control console via a 10-foot cable.
Other equipment and supplies that are helpful when using the MAP Spectrum Analyzer, but are not
supplied by the developer, include paper towels, protective gloves, a marking pen, an umbrella to shield
the control console and scanner from rain, and lead foil to shield the operator from radiation during the
calibration checks.
43
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Operation of the Analyzer
Analysis with the MAP Spectrum Analyzer requires placing the scanner in direct contact with the
sampling medium and opening the shutter with a key. The shutter exposes the sample to X-rays from the
radioisotope source. Emission X-rays are then counted (measured) over an operator-specified period of
time (source exposure time) by a counting circuit. This data is recorded by the MCA and produces a
spectrum characteristic of the metals in the sample. The intensities for each target analyte are calculated
by software deconvolution of the characteristic spectra and converted to concentration values by means
of a calibration model. This model is derived empirically by measuring the intensities of the target
analytes in a set of calibration standards and fitting a linear function that relates these values to
concentration by a multiple regression procedure. The MAP Spectrum Analyzer measures a surface area
of about 20 mm in diameter.
An empirical calibration of the MAP Spectrum Analyzer was performed by the developer prior to the
demonstration using thepredemonstration soil samples as site-specific calibration standards (SSCS).
Calibration involved measuring the SSCSs and incorporating the data from the resultant spectra into a
mathematical function. This function, which is a component of proprietary software, is used to calculate
concentrations of the target analytes in the field samples.
Scitec states that to minimize enhancement or adsorption and spectral interference errors, calibration
standards should be collected from the specific site in question. The SSCSs should closely match the
matrix of the routine samples. Scitec recommends that characterization of the SSCSs be done by using a
total digestion procedure, rather than a partial extraction because X-ray fluorescence is most closely
related to a total extraction or digestion-type analysis. However, for this demonstration, the
concentration of analytes in the SSCSs was determined using EPA SW-846 Methods 3050A/6010A
because these were the methods used for the reference method analyses.
The in situ analysis with the MAP Spectrum Analyzer does not require that a sample be physically
removed from the ground. The probe is placed on the ground and the analysis mode is activated by
turning on a key. Acquisition time can be preset at any desired length: "Screen," "Test," or "Confirm" is
the most common. The measurement times for the three options are 15 seconds, 60 seconds, and 240
seconds, respectively. In this demonstration, the "confirm" assay with a 240-second count time was
used. These times are automatically corrected to account for the age of the source. Scitec points out that
the precision of the analysis will improve as the measurement time increases.
The operator found the MAP Spectrum Analyzer easy to use. The console has only 14 keys and
prompts the operator through the steps necessary to conduct each assay. The scanner and console are
relatively lightweight. The console is usually equipped with a shoulder strap to keep the hands free to
operate the scanner.
To operate the MAP Spectrum Analyzer, the operator set the console and scanner on the ground
which allowed hands-free operation. On slopes, the operator either stabilized the scanner by securing
the cord uphill or locking the scanner in the ring stand. The operator was able to maintain hands-free
operation for all samples except two, which were on very steep slopes.
The console produced readings in ppm on the LCD for arsenic, lead, copper, and zinc. The readings
appeared about 10 seconds after completing each assay and were automatically stored when the next
assay was started. The MAP Spectrum Analyzer was operated by battery power while used outdoors, but
it was connected to a battery charger while analyzing samples indoors. Downloading and printing data
44
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was accomplished using the AcuTransfer software provided. Downloading and printing required just a
few keystrokes on the console and computer keyboard.
QC procedures for the MAP Spectrum Analyzer included a calibration check sample. The
calibration check sample was used to assess the accuracy of the technology. The calibration check was
analyzed by placing the scanner in a specially built wooden jig with the scanner pointing up. A 4-ounce
sample jar covered with a plastic wrap was then placed upside down over the source and scanner. The
sample jar was filled with a soil sample collected during the predemonstration that was provided to
Scitec. Scitec instructed the operator to conduct five confirmatory assays of the calibration check
standard each morning and then one each hour during each day in the field. The five results each
morning were averaged and compared to the average values determined in the factory calibration check
performed by Scitec on this same soil sample. Scitec said that if the values differed by more than 250
ppm, the operator should contact the company. The operator found conducting calibration checks to also
be relatively easy.
Background of the Technology Operator
The operator was an environmental scientist with more than 9 years experience in the environmental
field. He earned a Master of Science degree from the University of Tulsa in 1986. He had worked at
PRC for more than 3 years prior to the demonstration. While at PRC, he has managed and worked on
many projects involving solid and hazardous waste and risk assessments.
Training
Training for safety and operation of the MAP Spectrum Analyzer was conducted by Scitec. The
operator attended radiation safety training in St. Louis, Missouri, on December 15, 1994, and attended
training on the operation of the MAP Spectrum Analyzer on April 3 and 4, 1995.
Radiation safety training was conducted at the AC Lead Testing and Training Center in St. Louis,
Missouri. The training was adequate to address the level of exposure expected from the MAP Spectrum
Analyzer. The operator received a certificate for the course.
The operator attended training to operate the MAP Spectrum Analyzer on April 3 and 4, 1995. This
training was conducted at the Scitec facility in Kennewick, Washington. Mr. Kevin Dorow of Scitec
conducted the training, which was sufficient to allow operation of the instrument under the conditions
expected during the demonstration.
The training included a description of the calibration procedure and a hands-on demonstration of the
process. The discussion of the AcuTransfer software included an extended session in the collection and
downloading of data. The second day of training was dedicated to field use where a number of analyses
were conducted and data were collected, as would be expected during the demonstration.
Reliability
Overall, 1,025 assays were conducted during this demonstration. This included 630 soil sample
assays, 240 precision measurements, 145 calibration check assays, 3 PE sample assays, 2 SRM assays,
and 5 blank assays. During the demonstration, there was frequent light to moderate rain while the
FPXRF analyzers were performing the in situ measurements. The temperatures fluctuated between 5 and
16 °C at the ASARCO site and 6 and 22°C at the RV Hopkins site. Despite the less than ideal weather
45
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conditions, no mechanical problems were experienced with the MAP Spectrum Analyzer. The only
maintenance necessary was to periodically wipe the plastic covering on the face of the scanner or replace
the plastic if it became too dirty to clean. The operator did encounter a few problems with the MAR
Spectrum Analyzer that are discussed below.
After the third day of operation, the console "locked up" while the operator was reviewing data. The
console would not come out of the "recall" mode. When this happened, the operator turned off the
console, re-entered the data, and started the next assay. This appeared to correct the problem, no data
were lost and the unit functioned normally for the remainder of the demonstration.
The operator experienced problems when downloading data on four occasions during the
demonstration. On two attempts, the computer indicated data errors were detected. Both errors were
solved by reinitiation of the download sequence. The third downloading problem was a failure to
download all assay data without an indication of a problem on the computer screen. This could have
been a significant problem resulting in lost data except that the MAP Spectrum Analyzer console did not
return to the main menu as it should have. When the operator observed that the console did not return to
the main menu, he initialized a new download again with success.
The fourth downloading problem occurred late in the demonstration. The computer indicated data
errors in the transfer of assay data as occurred before. The operator retransmitted the data with apparent
success. However, when he attempted to print the assay data, the values for all four metals were zero.
The operator attempted to transfer data to the computer twice more with no success. The operator then
exited and reentered the AcuTransfer software, turned off the printer and renamed the file, and
attempted to download the data. Following this attempt, the computer indicated data errors were detected
in the assay transfer. The operator attempted to transfer the assay data again this time with success. The
source of the problem was not identified. The difficulties encountered in downloading resulted in about
40 minutes of lost time.
In the training provided by Scitec, the operator was instructed to call the manufacturer if the
calibration check standard varied from the factory check by more than 250 ppm. Both lead and copper
values varied by more than 250 ppm in the first set of five calibration check standards run in the
demonstration. As instructed, the operator called Scitec regarding the discrepancies. Scitec thought the
difference was due to heterogeneity in the calibration check standard used. Scitec told the operator to
continue operating the MAP Spectrum Analyzer and that it would send other calibration check standards
for the operator to run.
Data were sent two 4-ounce glass jars of AS ARC 0 soil samples and one painted wooden block to be
used as calibration check standards at the RV Hopkins site. The instructions were to use the wooden
block as the primary calibration check standard. Scitec said to conduct five assays of the wooden block
with the scanner placed upside down in the ring stand prior to any soil sample analysis. The developer
stated that the check standard assay would be equivalent to the factory check test. The new standards
meet the calibration requirements and data collection was resumed.
The most common operator error was forgetting to turn the key on the scanner to the "on" position
prior to starting an assay. This occurred nine times during the evaluation. Each time this occurred,
several minutes were lost restarting the test. However, the operator felt the safety considerations of the
key outweighed the inconvenience of forgetting to turn the key.
46
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The operator did not notice a low battery indication at any time during the demonstration. Each time
the batteries died, the operator was in the process of running an assay. Therefore, each of those assays
had to be restarted. The operator noted that if a low battery indicator had been observed, he would have
changed batteries between assays to prevent the need for reanalysis. The operations manual for the MAP
Spectrum Analyzer states that a "LowBatt" indicator will flash at the bottom right comer of the LCD
approximately 45 minutes prior to complete battery discharge. It is possible the operator simply failed to
notice the "Low Batt" indicator.
The operator observed that the MAP Spectrum Analyzer permitted identical sample numbers to be
entered. In this demonstration, this feature was not a problem because the operator kept careful notes
regarding sample numbers. However, if this were not the case, duplicate sample numbers could result in
confusion.
Health and Safety
Exposure to radiation from the excitation source was the largest health and safety consideration
while using the analyzer. Radiation was monitored with a radiation survey meter. Background radiation
at the two sites was between 0.006 and 0.012 millirems per hour (mrem/hr). Radiation was monitored
while the probe's source was exposed (during a measurement), obtaining a worst-case scenario. The
radiation was measured within 5 cm of the probe face while analyzing a sample. Radiation exposure also
was monitored at a point on the probe where the operator's hand was located during analysis to provide a
realistic value of operator exposure. The permissible occupational exposure in Kansas is 5,000 millirems
per year, which equates to approximately 2 to 3 mrem/hr assuming constant exposure for an entire work
year.
While taking in situ measurements in the field, a maximum radiation value of 1.2 mrem/hr at the
probe face was obtained with the Cd109 source exposed. The radiation values dropped off to 0.40
mrem/hr at the key and 0.05 mrem/hr at the handle of the scanner. While taking in situ measurements
indoors with the scanner pointed down at the sample, radiation values of 4.0 to 6.0 mrem/hr at the probe
face were obtained with the Cd109 exposed. The radiation values dropped off to 0.10 - 0.20 mrem/hr at 2
feet from the probe face and were 0.07 to 0.08 mrem/hr at the scanner handle. The operator placed a lead
shield and a row of bricks around the scanner while conducting the in situ measurements indoors. The
radiation behind the lead shield and bricks was measured at 0.03 to 0.05 mrem/hr with the Cd109 exposed.
With the exception of the radiation values right at the probe face, all radiation values were below the
permissible 2.0 mrem/hr.
A greater radiation hazard was experienced while measuring the calibration check sample. In this
mode, the scanner was pointed upward with the soil sample placed on top of the scanner. The scanner
was held motionless in a ring stand. While analyzing the calibration check standard, radiation values of
greater than 100 mrem/hr above the scanner and 10-20 mrern/hr at the side of the scanner were
encountered. At head height, these radiation values dropped to 0.20 mrem/hr at 1 foot from the scanner
and to 0.05 mrem/hr at 3 feet from the scanner.
Cost
At the time of this demonstration, the cost of a new MAP Spectrum Analyzer standard package was
$32,000 with a Cd109 source. The standard package includes the control console, the ambient scanner, a
Cd109 radioisotope source, auto source decay time correction, carry pack, rechargeable batteries, spectrum
display software, 256-kilobyte memory, battery charger, operator' s manual, shipping case, a 10-foot
47
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cable, and a lead-check standard. Periodic maintenance includes replacement and disposal of the Cd109
source every 2 years. A new Cd109 source costs $6,000 with a disposal cost of $75. A wipe test must be
performed every 6 months at a cost of $50. A replacement Cos7 source costs $3,695. The long half-life
of the Am241 source precludes the need for replacement. The MAP Spectrum Analyzer can be rented for
$4,675 per month plus a $4,225 deposit.
A basic radiation safety and operator training course is offered by Scitec for $245 per person plus
travel expenses. Costs to obtain the specific license for the MAP Spectrum Analyzer also were incurred
for this demonstration. It cost $500 to obtain the license for ownership and operation of a sealed
radioactive source in the State of Kansas. Since the demonstration sites were in Washington and Iowa,
reciprocal agreements were required from both states to operate the instrument in those states. The
reciprocal agreements cost $585 for Washington and $700 for Iowa. Operator training time may vary
depending on the technical knowledge of the operator. Scitec claims the MAP Spectrum Analyzer can be
used by individuals with no more than a high school education and a minimal amount of technical
training.
The primary cost benefit of field analysis is the quick access to analytical data. This allows the
process dependent on the testing to move efficiently onto the next stage. Costs associated with field
analysis are very dependent on the scope of the project. Since most of the mobilization costs are fixed,
analyzing a large number of samples lowers the per sample cost. This is a key advantage that field
analysis has over a conventional laboratory. Furthermore, more samples are usually taken for field
analysis since questions raised in the preliminary findings may be resolved completely without the need
to return for another sample collection event.
A representative list of costs associated with the MAP Spectrum Analyzer is presented in Table 4-2.
Also included in this table is the measured throughput and the per sample charge of the reference
laboratory. Given the special requirements of this demonstration, it was not considered reasonable to
report a per sample cost for the field analysis. However, some estimate can be derived from the data
provided in this table.
Table 4-2. Field Operation
Item ' • , . ' '
MAP
Operator Training (Vendor Prowided)
Radiation Safety (State of Kansas)
• • • • ^Af*KHii*t>: ''! ' ': •' . ; :.;
$
4,675
8,000
3,695
245
500
Price
Per Month
Tor Cd109
For CoST
—
_
Field Operation Costs • * , v •' . ",-•'. ••••••.'.„•'"' :"v':|
and cups,
window film,
Field Chemist (Labor Charge)
Per
Travel
Throughput
JlESlf^^
300 - 500
100-150
30-120
200 - 500
9-12
150
(Varies,
on
Per day
Per day
Per
per hour
>EHJS!2E^______
48
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Performance Factors
The following paragraphs describe performance factors, including detection limits, sample
throughput, and drift.
Detection Limits
MDLs, using SW-846 protocols, were determined by collecting 10 replicate measurements on site-
specific soil samples with metals concentrations 2 to 5 times the expected MDL value. These data were
obtained during the precision evaluation. Based on this precision data, a standard deviation was
calculated and the MDLs were defined as 3 times the SD for each target analyte. All the precision-based
MDLs were calculated for the measurements on the in situ-prepared soil samples. The precision-based
MDLs for the MAP Spectrum Analyzer are shown in Table 4-3. The precision-based MDLs for all
analytes were obtained using a 240-second count time for the Cd109 source.
Another method of determining MDLs involved the direct comparison of the FPXRF data and the
reference data. When these sets of data were plotted against each other the resultant plots were linear.
As the plotted line approached zero for either method, there was a point at which the FPXRF data
appeared to respond to the same reading for decreasing concentrations of the reference data. Figure 4-2
illustrates this effect for zinc. This point was determined by observation and was somewhat subjective;
however, an analysis showed that even a 25 percent error in identifying this point resulted in only a 10
percent change in MDL calculation. By determining the mean values of this FPXRF data and
subsequently two SDs around this mean, it was possible to determine a field or performance-based MDL
for the analyzer. For the MAP Spectrum Analyzer, these field-based MDLs also are shown in Table 4-3.
4-3.
' •'•'•,':'" '5'
Analyte >
Arsenic
Copper
Lead
/mo
• ' ; W^iitofi-tasiNi
\VM&fM0flC$ '.
225
525
185
25
• : ' FleW-to-ased " .
•M0L(mg/kg). •
150
270
160
180
Note: mg/kg Milligrams per kilogram.
wlOOGQ
Q 1000
I
a)
f
3
100
10
4-
10 100 1000
Reference Data (mg/kg)
10000
4-2. for the Determination
of a Limit for
Zinc; At approximately 180 mg/kg, the linear
the field and
changed. This point of to
the MDLs,
The developer stated a detection limit of 250 mg/kg should be achievable for all four primary
analytes. The field-based MDLs were close to or below 250 mg/kg. The precision-based MDLs showed
much more variation between analytes than did the field-based MDLs. The precision-based and field-
based MDLs were similar for arsenic and lead but different for copper and zinc. The high precision RSD
49
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for copper caused its precision-based MDL to be large. Likewise, the extremely good precision for zinc
caused its precision-based MDL to be very low. Given the nature of the detector in the MAP Spectrum
Analyzer and based on recommendations by the developer, the field-based MDLs for copper and zinc
appear more realistic than the precision-based MDLs.
Throughput
The MAP Spectrum Analyzer used a Cd109 source live count time of 240 seconds. With the
additional "dead" time of the detector and the time required to label each sample and store data in
between sample measurements, the time required to analyze one sample was between 5 and 7 minutes.
The average number of samples analyzed was 98 in an 1 1-hour day for a throughput of 8.9 samples per
hour throughout the demonstration. The minimum number of samples analyzed was on the first day at
ASARCO when 60 samples were analyzed in 9 hours for a throughput of 6.7 samples per hour. As the
operator became more familiar with the analyzer, the throughput increased. The maximum number of
samples analyzed was 140 in 12 hours at the ASARCO site for a throughput of 11.7 samples per hour.
This throughput was achieved while analyzing the in situ-prepared samples indoors.
This throughput included the time necessary to analyze the QC samples, which included five assays
of the calibration check, and the subsequent hourly analysis of the calibration check each day. The
throughput did not include the time required for sample handling and preparation or for data
downloading, printing, and documentation. Data handling required approximately 30 minutes each day.
Homogenization for the in situ-prepared samples required approximately 5 minutes per sample.
Drift
Drift is a measurement of an analyzer's variability in quantitating a known amount of a standard over
time. Drift was evaluated by reviewing results from the periodic analysis of the calibration check
sample. No developer claims were made concerning drift.
The calibration check was analyzed five times each morning and once per hour each day of analysis.
The drift summary is displayed for the four analytes in Figure 4-3. Each box on the figure represents the
mean performance for a given analyte for a given day. The drift values were standardized by taking the
mean for all calibration check sample measurements and then finding the percent difference between this
overall mean and the daily mean concentration. Figure 4-3 shows that the MAP Spectrum Analyzer
showed the most drift for copper and the least for zinc. These drift results mimic the reproducibility
displayed by the high and low precision-based MDLs for copper and zinc, respectively. The copper drift
varied from -25 to +35 percent, while the zinc drift remained between ±5 percent. Arsenic drift was
within ±15 percent, and the majority of the lead drift was within ±20 percent.
Intramethod Assessment
Intramethod assessment measures each analyzer' s performance on characteristics such as: analyzer
blanks, completeness of the data set, intramethod precision, and intramethod accuracy. The following
narrative discusses these characteristics.
50
-------
Aft
£
i 30
s
£
S
fe on
a
i
a
D
I_ o
-B §
e .........................
^
j 1 4 1
Arsenic Lead Copper Zinc
Analyte
Flgyre 4-3. Drift Summary: This figure the general drift of the In measuring a
check sample. Each point represents a different day's analysis of the sample, The daily
fluctuations for analyte are a direct representation of drift.
Blanks
Analysis blanks for the MAP Spectrum Analyzer were obtained by shooting ambient air with the
scanner. The blanks were used to monitor contamination of the scanner by material such as soil left on
the scanner face. Four blanks were analyzed during the demonstration, one using a 240-second count
time, and three using a 60-second count time. The results for all four blanks were similar. The blank
values for arsenic and lead were all below their precision and field-based MDLs. The zinc blank values
ranged from 176 to 179 mg/kg. These values were slightly below the field-based MDL for zinc but well
above the precision-based MDL for zinc of 25 mg/kg. The blank values for copper ranged from 477 to
673 mg/kg which are above the precision and field-based copper MDLs. These copper results were
surprising because the MAP Spectrum Analyzer gave copper values of 0 - 400 mg/kg for many of the soil
samples, so it is not believed that cross contamination caused the high blank results for copper. It may be
an artifact of using air as the blank matrix instead of a clean silica sand, which is more similar to a soil
matrix.
Comp/efeness
The MAP Spectrum Analyzer produced data for 628 out of the 630 samples for a completeness of
99.7 percent, which is above the demonstration objective of 95 percent. The two samples for which no
data were obtained were from the ASARCO site. In one case the operator failed to analyze the sample,
For the other sample, it appears a software malfunction in the downloading process caused the loss of
data. The lack of data was not caused by a mechanical or an electronic malfunction of the analyzer.
Prec/s/on
Precision was expressed in terms of the percent RSD between replicate measurements. The precision
data for the target analytes detectable by the analyzer are shown in Table 4-4. The precision data
reflected in the range of 5 to 10 times the MDL reflects the precision generally referred to in analytical
methods such as SW-846.
51
-------
Table 4-4. Precision Summary
Analyte
Arsenic
Copper
Lead
Zinc
Mean % RSD
5-10 Times
MDLa (mg/kg)
6.68 (2)
14,86(4)
8.54 (6)
0,84(18)
Values by Conce
SO - SOO
(mg/kg)
31.36(4}
ND
19,03(2)
0.77 (22)
ntratfon Range
500- 1,000
fmg/»*i)
(8)
(2)
8,71 (2)
L—JJiiS—
>f,iW:(pf*f|:!
8.88 (2)
14,88(4}
6.33 (10)
ND
Notes: The MDLs referred to in this column are the precision-
MDLs shown in Table 4-3.
mg/kg Milligrams per kilogram,
ND No data.
() Number of samples. Numbers do not always add up to 24
precision points because some had
concentrations below the analyzer's MDL.
The analyzer performed 10 replicate measurements on 12 soil samples that had analyte
concentrations ranging from less than 50 mg/kg to tens of thousands of milligrams per kilogram. Each of
the 12 soil samples underwent the two in situ sample preparation steps. Therefore, there was a total of 24
precision points for the analyzer. The replicate measurements were taken using the same source count
times used for regular sample analysis. For each detectable analyte in each precision sample, a mean
concentration, SD, and RSD were calculated.
In this demonstration, the analyzer's precision or RSD for a given analyte had to be less than or equal
to 20 percent to be considered quantitative screening level data and less than or equal to 10 percent to be
considered definitive level data. The analyzer's precision data, reflected by replicate determinations in
the 5 to 10 times MDL range, were below the 10 percent RSD required for definitive level data quality
classification for arsenic, lead, and zinc. Copper had an RSD between 10 and 20 percent, placing its
precision into the quantitative screening level data quality category.
The precision data in Table 4-4 shows there was an effect of concentration on the precision for
arsenic, copper, and lead. The precision samples were purposely chosen to span a large concentration
range to test the effect of analyte concentration on precision. As expected, the precision increased as
analyte concentration increased for arsenic, copper, and lead. The zinc precision changed little with
differing zinc concentrations and did not show the same trend as the other three analytes, possibly
because the zinc precision was so much better than the other three analytes that it was difficult to observe
changes. There was no observable effect of sample preparation on precision. This was expected because
the method used to assess precision during this demonstration was measuring analyzer precision, not total
method precision.
Accuracy
Accuracy refers to the degree to which a measured value for a sample agrees with a reference or true
value for the same sample.
Intramethod accuracy was assessed for the MAP Spectrum Analyzer by using three site-specific PE
samples and two NIST SRMs. There were 12 other SRMs and 3 other site-specific PEs included in this
demonstration, but there was not enough material for these other PEs and SRMs to fill a petri dish for
52
-------
Tabto 4-5. Accuracy Symmary of Site-Specific PE and SRM ResuKe
Arsenic •" Copper
True Meas. % True | Meas. . % Trye
Sample Cone. 1 Cone, Rec. • Cone. 1 Cone. Rec, Cone,
ASARCO
LowPE
ASARCO
Mod. PE
ASARCO
High PE
NIST SRM
2704
NiST
2710
419
837
22,444
23,4
626
227
54,2
497J 59.4
21.2751 94,8
56
799
Mean % Recovery
SO of % Recovery
% Within 80 to 120 Percent
Recovery Acceptance
NA
127.6
84.0
342
250
771
5,403
7,132
98,6
2,950
108
2,003
4,545
49
3,740
14,1
37,0
63,7
NA
126,8
Hii°4™
48,7
0,0
292
1,012
9,498
161
5,532
Lead Zinc
Meas, % True j Meas. %
Cone, Rec, Cone. 1 Cone, Rec.
0
983
10,049
0
4,175
0,0
95,2
105,8
NA
75,5
92.1
15.4
50,0
164
378
4,205
438
6,i52
180
210
4,499
175
i, _ „
386
109,8
55,6
107,0
39,9
5.5
63.6
44.7
40,0
Notes: % Rec, Percent recovery
PE Performance evaluation sample,
SRM Standard material,
NA Not applicable. Percent recovery not calculated concentration below the
method detection limit.
SD deviation,
For this demonstration, the M.AP Spectrum Analyzer was configured to report concentrations for
lead, arsenic, copper, and zinc. The regression analysis of the entire log,0 transformed set for
lead had an r2 value of 0,85, The corresponding value for copper 0,80,
while arsenic and zinc had r2 values of 0.76 and 0,67, respectively. The arsenic and copper comparability
was low for the whole set. The concentrations of at the RV site were
generally at or below the analyzer's precision-based MDLs.
The next step in the data evaluation involved the assessment of the potential impact of the variables:
site, soil texture, and sample preparation on the regression analysis (Table 4-6).
Based on this evaluation, there was no apparent impact of the site variable on the regression. The
soil variable showed a slight decreasing trend in comparability with the sand soils exhibiting the highest
comparability and the loam soils exhibiting the lowest comparability. The only exception to this soil
trend was for lead. Lead met definitive data quality criteria for the clay soil' s entire data set. This
increased comparability for the clay soil data may be due to the origin of the lead contamination. The
clay soils were only analyzed at the RV Hopkins site and the source of lead contamination at this site was
primarily paint waste. The paint matrix is what this analyzer was originally designed to analyze. The
slope values for the soil variable data indicated that the analyzer tended to underestimate lead and copper
concentrations and overestimate zinc concentrations. The slope values were determined by plotting the
FPXRF data on the x-axis (independent variable) and the reference method data on the y-axis (dependent
variable). The data were plotted in this fashion to get an indication of a correction factor to be applied to
the FPXRF data to get it to match the reference data.
54
-------
Table 4-6. by
449 0763 037 062
Site
Sand Soil
Loam Soil
Clay Soil
[0211 1 03
'013 j_"09?
1 43 0 73
In Situ-Unprepared
Inatu-Prepared
Notes: Regression parameters based on log.0 transformed Sirmo t>ic< f PXHF Jala wpre
usod as the dependent variable in calculating regfoswi»n |r
-------
Within the sample preparation steps, the effect of contaminant concentration was also examined.
The data sets for the analytes were sorted into the following concentrations ranges: 0-100 mg/kg, 100-
1,000 mg/kg, and greater than l,000mg/kg. The regression analysis for each target analyte and for each
sample preparation step was rerun on these concentration-sorted data sets. A review of these results
showed general improvement in the r2 and standard error for each target analyte with increasing
concentration. The 0-100 mg/kg concentration range showed the poorest comparability. This is most
likely due to this range generally occurring below the analyzer' sMDLs. The analyzer' s precision and
accuracy are lowest in this concentration range. Generally, the i^s improved between the 100 and 1,000
mg/kg and greater than 1,000 mg/kg ranges. This data indicated that there was a concentration effect on
comparability. This effect appears to be linked to the general proximity of a measurement to its
associated MDL. The further away from the MDL, the less effect concentration will have on quantitation
and comparability.
Another way to examine the comparability between the two methods involves measuring the average
relative bias and accuracy between the FPXRF data and the reference data. The average relative bias
indicates the average factor by which the two data sets differ. Concentration effects can affect bias. For
example, it is possible for an analyzer to greatly underestimate low concentrations but greatly
overestimate high concentrations and have a relative bias of zero. To eliminate this concentration effect,
the data can be corrected by a regression approach (see Section 5), or only narrow concentration ranges
can be analyzed, or average relative accuracy can be examined. The average relative accuracy is the
average factor by which each individual analyzer measurement differs from the corresponding reference
measurement.
A final decision regarding the assignment of data quality levels derived from this demonstration
involves an assessment of both r2 and the precision RSD. Using the criteria presented in Table 2-2, a
summary of the MAP Spectrum Analyzer's data quality performance in this demonstration is provided in
Table 4-9.
56
-------
Tablt 4-7. for the by Soil
Texture
Arsenic . • ''
Std. Err. I Y-Jnt. Slope
in Situ-Unprepared
89
110
32
0,895
0.799
[¥,164
0.28
0.28
f 0.59
-0.08
O301
1,02
0.98
0.92
1.31
In Situ-Prepared
92
111
23
i OJSS^
j)£!ij
0,009
0,18
0.18
0,68
-0.34
0.14
ziii!
1.08
0.96
L-ML.
Soil Texture
Soli
Loam Soil
Clay Soil
Soil Texture
Soil
Loam Soil
__Oa^So§__
in SItu-tJnprepared
48
59
78
0.882
0.731
0.762
0,30
0.36
0,27
-0.88
-1.41
0.37
In Situ-Prepared
44
57
81
0.945
0,915
0,923
0.21
0.15
0.18
-1.33
-1,24
-0.47
1.23
1,42
0.89
1.39
1,39
1.13
Copper
• Std. Err.'•• ('Y-hit. Slope'
In Situ-Unprepared
33
89
4
0,940
0,746
0.868
0.20
0.40 '
0.25
-2,04
' -0.86
10.78
1.50
1.19
-3,71
In Situ-Prepared
28
82
0.95S
0,927
_J£LMlL
0.13
,____
-2.31
•1.48
___^^IIL-Mi,
1.61
1.32
0.89
Soli Texture
Sand Soil
Loam Soil
__Jri!f!L§i2!L__
Soil Texture
Sand Soil
Loam Soil
_£la^So£_m_
In Situ-Unprepared
98
110
93
0743
0340
~6~750~
0 20 f 1 03
017 J 15'
0 17 _jl"07
062
036
"060
In Situ-Prepared
98
114
0738
" 0 550
023 J 087
^yjgj i 6«T
070
1056
Notes: Regression on Iog10 transformed Since the FPXRF
as the dependent in calculating parameters,
the most be to correct the FPXRf Section 5,
Y-lnt. Y-intercept,
Std. Err, error,
n Number of points.
57
-------
Table 4-8. Parameters'" for the Sample by
n r!;.,'.Stct Err; i Y-lnt. Slope".
In Sity-Unprepared
SteL Err.. Y-lnt/ Slope"
in
195
"a/
0861
0164
027
059
GQ8J
1 02]
Site
108
1.31
RVJjopWnsSite
78
0,807 0.34 -1.09
0.762! 0.27
I-!!
'o.'sY
In Situ-Prepared
Site Name
in
203
i 0.931 '
23 I !
019
•QU
1 64
1 03
03~f
ASARCO Site
102
RVHopMngSlte
81
0,931
0.923
0.19
0,18
IIHHB^^P^M
.'Y-lrtf. | Slope" ^^^^^^^H n . i Shj En. Y-lnt.^ Slopcr
In Situ-Unprepared
^^-^j—^^-^—-—
4 j, ' 0 ?5
-i 24T 1,29
10781 371
In Sity-Prepared
iTl [oil/' 0?i
-1 57j 1 35
Site Name
ASARCO Site
RV Hopkins Site
Site Name
ASARCO Site
RV Site
in Slty-Unprepared
209
»-JLl--_____^.
0.625
0.750
0.20
0.17
1.11
1.07
0.56
I 0.60
In Situ-Prepared
210
99
0.878
0.619
0.20
0.25
0.96
1.02
0.83
0.81
Y-lnt.
Std. Err,
on Iog10 transformed the FPXRF
were as the dependent variable in
the must be used to correct the FPXRF
Section. 5,
Slope values determined with FPXRF on the y-axis and the
reference data plotted on the x-axis.
Y-intereept.
Standard error,
n Number of data points.
Table 4-9. of
Ta:r§et •'
Analytes '
Arsenic
Barium
Chromium
Copper
Lerfil
Zinc
Nickwi
Iron
Cadmium
Anflrnony^
'/:;>'., : - ' ' ••" ' ! '•'
MAP Specify m "'
/".V'^ftn«l|ie« ' '•
Arsenic
Not Reported
Not
C
lead
Zinc
Nol Reported
Not Reported
Not
^NotReported^
•:-i*t eels!0rt '(mg/kg)
'• Miaw'%RSD
5'>10XMOL.
6.68
—
r™ ~~~~~»~~«
14.86
8.54
0,64
_
—
—
nieihod Oeiecttert-';
Limits (m^fcg) ' •''.•
(Preeislow-ba8*(IJ- '
225
—
_
525
165
25
_
_
—
,;;SyifMiB*«>f:;,;:
"'"OttermtHBlff «'" .'
:\f Ai'UfrtB^ ,;'
0.783
—
_
0.801
6.849
0.8i9
—
""""""
_
•.ttata Quality
,•:.•.. 4evel
Quantitative
—
_
Quantitative
Definitive
Qualitative
—
__
„„ „, _
58
-------
Section 5
Applications Assessment and Considerations
The MAP Spectrum Analyzer is designed to analyze for metals in soils, sludges, and other solids.
The analyzer uses an empirical site-specific calibration and quantitation procedure to maximize its
performance. This calibration accounts for common soil-related matrix interferences. This analyzer is
designed for field use in the in situ mode. The analyzer experienced no hardware failures during this
demonstration and the few software malfunctions resulted in little downtime and no lost data during the
1-month field demonstration. During this time, more than 630 samples were measured by the analyzer.
The training provided by the developer was sufficient to allow basic field operation. Limited developer
assistance was required to address the software problems encountered during the demonstration. The
developer provided accessible and timely field support. The use of this analyzer requires specific
radiation licensing, which adds some cost and training to the use of this analyzer.
Comparison of the analyzer' s logic transformed data to the logic transformed reference data indicated
that the analyzer could produce definitive level quality data for lead. This indicated that the analyzer's
data were statistically equivalent to the reference data for these analytes. For arsenic and copper, the
analyzer produced quantitative screening level data. In addition, this analyzer exhibited instrument
precision similar to the reference methods, indicating high measurement reproducibility. The analyzer
produced zinc data which met the qualitative screening level data quality criteria. A summary of key
operational features is listed in Table 5-1
The analyzer's probe uses one radioactive source allowing analysis of a limited number of metals in
soils. The analyzer used count times of 240 live-seconds. Longer count times generally increase
accuracy and lower the detection limits but decrease sample throughput. The throughput for the analyzer
was 9-12 samples per hour. There were no apparent effects of site or soil texture on performance for
any of the analytes; however, lead data did show its highest comparability for the RV Hopkins samples,
which were clay soils. This may be due to the fact that the lead in these soils was derived from paint
waste, a matrix for which this instrument was originally designed. This demonstration identified sample
preparation as the most important variable with regard to analyzer performance.
The analyzer can be applied only in an in situ mode. The data from this demonstration indicated that
when operated in the in situ-unprepared mode, the results did not show a strong correlation between
FPXRF and reference data. This may not be due to instrument error but rather to inherent spatial
variability of contamination, even within an area as small as the 4-inch by 4-inch grid sampled during this
demonstration. The greatest increase in correlation between the FPXRF data and reference data for the
analyzer was achieved after the initial sample preparation step (sample homogenization), which defined
the in situ-prepared sample set.
59
-------
Table 5-1. Summary of Test Results and Operational Features
Total weight than 15 pounds, battery life of 8 hours
Sample throughput of 9 to 12 per hour at 240 live-second count times
In situ measurements only
Rugged and retiableMPata cP^Pleten_ess_of 99.7 percent
Operation minima! training
Produces EPA quantitative data for arsenic and copper and EPA definitive level data
for
Empirical calibration is site-specific
Precision - Percent RSD than 15 percent at 5 to 10 times the MDL for all
anai_ytes^ __ ____________ _
Generally not susceptible to soil matrix effects
Can be on soils exhibiting up to 30 water saturation by weight
A source limits the number of elements that can be quantified
Empirical calibration requires well characterized site-specific samples
radiation when performing calibration checks with the scanner pointed upward
Produced EPA qualitative screening level for zinc
Based on this demonstration, the analyzer is well suited for the rapid real-time assessment of metals
contamination in soil samples. The ease of operation and minimal training requirements increases the
probability that a first-time user will produce reliable data. Although in most cases the analyzer
produced data statistically equivalent to the reference data, generally confirmatory analysis will be
required or requested for FPXRF analysis. If 10 - 20 percent of the samples measured by the analyzer are
submitted for reference method analysis, instrument bias relative to standard methods such as
3050A/6010A can be determined. This will only hold true if the analyzer and the reference laboratory
measure similar samples. This was accomplished in this demonstration by thorough sample
homogenization. Bias correction allows most FPXRF data to be corrected so that it more closely
matches the reference data. The demonstration showed that the analyzer exhibits a strong logio-logio
linear relationship with the reference data over a concentration range of 5 orders of magnitude. A
concentration effect on comparability was noted for this analyzer. Measurements near or below the
analyzer' sMDLs showed the poorest comparability. As concentrations rise above the MDLs, the data
comparability increases. This should be taken into consideration when evaluating the usability of field-
generated data. For optimum correlation and bias correction, samples with high, medium, and low
concentration ranges from a project should be submitted for reference method analysis.
The steps to correct FPXRF measurements to more closely match reference data are as follows:
1. Conduct sampling and FPXRF analysis.
2. Select 10-20 percent of the sampling locations for resampling. These locations can be evenly
distributed over the range of concentrations measured or they can focus on an action level
concentration range.
3. Resample the selected locations. Thoroughly homogenize the samples and have each sample
analyzed by FPXRF and a reference method.
4. Tabulate the resulting data with reference data in the y-axis column (dependent variable) and the
FPXRF data in the x-axis column (independent variable). Transform this data to the equivalent
logio value for each concentration.
60
-------
5. Conduct a linear regression analysis and determine the r2, y-intercept and slope of the
relationship. The r2 must be greater than 0,70 to proceed.
6. Place the regression parameters into Equation 5-1:
F(log{0 corrected FPXRF data) = rfope*(10g,0 FPXRF data) + Y-intercept (5-1)
7, Use the above equation with the Iog10 transformed FPXRF results from Step 4 above and
calculate the equivalent Iog10 corrected FPXRF data.
8. Take the anti-log,,, (10 v*w*~**>«*«***m(*r**i) of ^ equivalent logw corrected data
calculated in Step 7, These resulting values (in milligrams per kilogram) represent the corrected
FPXRF data,
To show the effect of correcting the FPXRF data, the change in average relative bias and accuracy
can be examined. The average relative bias between the FPXRF data and the reference data is a measure
of the degree to which the FPXRF over- or underestimates concentrations relative to the reference
methods. The relative bias is an average number for the entire data set and may not be representative of
individual measurements. An example of this can be seen in an analyzer's data where measurements are
underestimated at low concentrations but overestimated at high concentrations. On average, the relative
bias for this analyzer is zero; however, this bias is not representative for high or low concentration
measurements. To avoid this dilemma, three approaches can be taken: (1) the evaluation of average
relative bias can be focused on a narrow concentration range, (2) the analyzer's data can be corrected
using the regression approach described above, or (3) average relative accuracy can be calculated.
Average relative accuracy represents the percentage that an individual measurement is different from a
reference measurement. Table 5-2 shows the average relative bias and accuracy exhibited by the FPXRF,
before and after data correction using the eight-step approach previously discussed.
The average relative bias and accuracy for the analytes falling into the definitive level data quality
category are generally small. Alternately, analytes falling into the quantitative and qualitative screening
level data quality categories generally have larger average relative bias and accuracy.
In cases where the corrected average relative accuracy is worse than the raw average relative
accuracy, such as seen in Table 5-2 for arsenic, the eight-step FPXRF data correction approach presented
earlier may not be appropriate. If the data set in question is representative of the entire population of
data being characterized, then the raw FPXRF data merely needs to be multiplied by the raw average
relative accuracy factor for correction. However, the eight-step regression base approach should be used
anytime the performance of the analyzer is strongly concentration dependent or if the sample population
being used for data correction is not representative of the entire data population being characterized.
The Scitec MAP Spectrum Analyzer can provide rapid assessment of the distribution of metals
contamination at a hazardous waste site. This data can be used to characterize general site contamina-
tion, guide critical conventional sampling and analysis, and monitor removal actions. This demonstration
suggested that in some applications and for some analytes, the FPXRF data may be statistically similar to
the reference data. The development of Method 6200 will help in the acceptance of FPXRF data for all
definitive level applications and most quantitative screening level applications. The FPXRF data can be
produced and interpreted in the field on a daily or per sample basis. This real-time analysis allows the use
of contingency-based sampling for any application and greatly increases the potential for meeting project
objectives on a single mobilization.
61
-------
Table 5-2. Effects of Data Correction on FPXRF Comparability to Reference Data for All
In Situ-Prepared Samples
Average Average [ Average Relative Average Relative i Acceptable
Target j Relative Bias on Relative Bias on ! Accuracy on Accuracy on | Accuracy for
Analyte Raw Data" | Corrected Data" Raw Datac Corrected Data" PE Samples"
Arsenic
Copper
Lead
Zinc
1.06
0.71
0.93
1,56
1,13
1.29
1,06
1,23
2.19
__ __,
1,39
2.23
2.24
2,41
1.35
1,90
1,76
1.18
1,63
1,64
Notes: A measurement of average relative bias, measured as a (actor by which the FPXRF, on average,
over- or underestimates results relative to the reference methods. This measurement of bias is
based on raw (not Iog10 transformed) data. This average relative bias does not account for any
concentration effect on analyzer performance.
t»
A measurement of average relative bias on the FPXRF data after it has been corrected using the
eight-step regression approach.
A measurement of average relative accuracy at the 95 percent confidence interval, measured as a
factor by which the raw FPXRF, on average, over- or underestimates individual results relative to
the reference methods. This measurement of accuracy is based on raw (not log)0 transformed)
data. This average relative accuracy is independent of concentration effects.
d
A measurement of average relative accuracy at the 95 percent confidence interval, of the corrected
FPXRF data obtained using the eight-step regression approach,
A measurement of accuracy represents a factor and 95 percent confidence interval that define the
acceptable range of differences allowed between the reference method reported concentrations
and the true value concentrations in the PE samples. This bias is included only as & general
reference for assessing the improvement on comparability of FPXRF data and reference data after
FPXRF data correction.
The average relative is calculated as follows:
Average relative = ({£j[FPXRF|/Refereneej])/number of paired samples)-1
This value represents the percentage that the FPXRF over- or underestimates the reference data, on average,
for the entire data set. To convert this calculated value to a factor, 1,0 is added to the calculated average
relative bias. The above table presents the average relative bias as a factor.
The average relative accuracy is calculated as follows;
Average relative accuracy =SQRT (^{{FPXRFj/RefereneeJ-If/number of paired sample)
This value represents the percentage that an individual FPXRF measurement over- or underestimates the
reference data. The relative accuracy numbers in the table are calculated at the 95 percent confidence interval.
This is accomplished by adding two standard deviations to the above formula before the square root is taken.
To convert this calculated value to a factor, 1,0 is added to the calculated average relative accuracy. The above
table presents the average relative bias as a factor.
General Operational Guidance
The following paragraphs describe general operating considerations for FPXRF analysis. This
information is derived from SW-846 Method 6200 for FPXRF analysis.
General operation of FPXRF instruments will vary according to specific developer protocols. For all
environmental applications, confirmatory or reference sampling should be conducted so that FPXRF data
can be corrected. Before operating any FPXRF instrument, the developer's manual should be consulted.
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Most developers recommend that their instruments be allowed to warm up for 15 - 30 minutes before
analysis of samples. This will help alleviate drift or energy calibration problems.
Each FPXRF instrument should be operated according to the developer' s recommendations. There
are two modes in which FPXRF instruments can be operated: in situ and intrusive. The in situ mode
involves analysis of an undisturbed soil or sediment sample. Intrusive analysis involves collecting and
preparing a soil or sediment sample before analysis. Some FPXRF instruments can operate in both
modes of analysis, while others are designed to operate in only one mode. The two modes of analysis are
discussed below.
For in situ analysis, one requirement is that any large or nonrepresentative debris be removed from
the soil surface before analysis. This debris includes rocks, pebbles, leaves, vegetation, roots, and
concrete. Another requirement is that the soil surface be as smooth as possible so that the probe window
will have good contact with the surface. This may require some leveling of the surface with a stainless-
steel trowel. Most developers recommend that the soil be tamped down to increase soil density and
compactness. This step reduces the influence of soil density variability on the results. During the
demonstration, this modest amount of sample preparation was found to take less than 5 minutes per
sample location. The last requirement is that the soil or sediment not be saturated with water.
Developers state that their FPXRF instruments will perform adequately for soils with moisture contents
of 5 - 20 percent, but will not perform well for saturated soils, especially if ponded water exists on the
surface. Data from this demonstration did not see an effect on data quality from soil moisture content.
Source count times for in situ analysis usually range from 30 to 120 seconds, but source count times will
vary between instruments depending on required detection limits.
For intrusive analysis of surface soil or sediment, it is recommended that a sample be collected from
a 4- by 4-inch square that is 1 inch deep. This will produce a soil sample of approximately 375 grams or
250 cm3, which is enough soil to fill an 8-ounce jar. The sample should be homogenized and may be
dried and ground before analysis. The data from this demonstration indicated that sample preparation,
beyond homogenization, does not greatly improve data quality. Sample homogenization can be
conducted by kneading a soil sample in a plastic bag. One way to monitor homogenization is to add
sodium fluorescein salt to the sample. After the sample has been homogenized, it is examined under an
ultraviolet light to assess the distribution of sodium fluorescein throughout the sample. If the fluorescent
dye is evenly distributed in the sample, homogenization is considered complete; if the dye is not evenly
distributed, mixing should continue until the sample has been thoroughly homogenized, During the
demonstration, the homogenization procedure using the fluorescein dye required 3 to 5 minutes per
sample.
Once the soil or sediment sample has been homogenized, it can be dried. This can be accomplished
with a toaster oven or convection oven. A small portion of the sample (20 - 50 grams) is placed in a
suitable container for drying. The sample should be dried for 2 to 4 hours in the convection or toaster
oven at a temperature not greater than 150 *C. Microwave drying is not recommended. Field studies
have shown that microwave drying can increase variability between the FPXRF data and reference data.
High levels of metals in a sample can cause arcing in the microwave oven, and sometimes slag will form
in the sample.
The homogenized, dried sample material can also be ground with a mortar and pestle and passed
through a 60-mesh sieve to achieve a uniform particle size. Sample grinding should continue until at
least 90 percent of the original sample passes through the sieve. The grinding step normally averages 10
minutes per sample.
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After a sample is prepared, a portion of the sample should then be placed in a 31-mm polyethylene
sample cup (or equivalent) for analysis. The sample cup should be completely filled. The sample cup
should be covered with a 2.5-micrometer MylarTM (or equivalent) film for analysis. The rest of the soil
sample should be placed in ajar, labeled, and archived. All equipment, including the mortar, pestle, and
sieves, must be thoroughly cleaned so that the sample blanks are below the MDLs of the procedure.
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Section 6
References
Havlick, Larry L, and Ronald D. Grain. 1988. Practical Statistics for the Physical Sciences. American
Chemical Society. Washington, D.C.
Kane, J. S., S. A. Wilson, J. Lipinski, and L. Butler. 1993. "Leaching Procedures: A Brief Review of
Their Varied Uses and Their Application to Selected Standard Reference Materials." American
Environmental Laboratory. June. Pages 14-15.
Kleinbaum, D. G., and L. L. Kupper. 1978. Applied Regression Analysis and Other Multivariable
Methods. Wadsworth Publishing Company, Inc., Belmont, California.
Morgan, Lewis, & Bockius. 1993. RODScan,.
PRC Environmental Management, Inc. 1995. "Final Demonstration Plan for Field Portable X-ray
Fluorescence Analyzers."
U.S. Environmental Protection Agency. 1993. "Data Quality Objectives Process for Supetfund-Interim
Final Guidance." Office of Solid Waste and Emergency Response. Washington, D.C. EPA/540/R-
93/071.
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