EPA/600/R-97/146
March 1998
Environmental Technology
Verification Report
Field Portable X-ray
Fluorescence Analyzer
Metorex X-MET 920-P and 940
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.
vmmcxnsm musum
ENVIRONMENTAL TECHNOLOGY VERIFICATION PROGRAM
VERIFICATION
TECHNOLOGY TYPE: FIELD PORTABLE X-RAY FLUORESCENCE ANALYZER
APPLICATION; MEASUREMENT OF METALS IN SOIL
T! JCHNOLOGY NAME; X-MET 920-P AND X-MET 940
('()MPANY: METOREX, IMC,
PRINCETON CROSSROADS CORPORATE CENTER
P.O. BOX 3540
PRINCETON, NJ
PHONE: (609) 406-9000
The U S Fnvuonmental 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
Tet hiiology Verification (ETV) Program is to further environmental protection by substantially accelerating the
acceptance and tine 01 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 summarae? the results of a demonstration, of the Metorex X-MET 920-P and 940 analyzers,
PROGRAM OPERATION
The I1 PA, in partnership with recognized testing organizations, objectively and systematically evaluates the
pcifoimance of innovative technologies. Together, with the full participation of the technology developer, they
de\ elop plans, conduct tests, collect and. analyze data, and report findings. The evaluations are conducted according
to «i ngoious demonstration plan and established protocols for quality assurance. The EPA's National Exposure
Roseau h Laboratory, which conducts demonstrations of field characterization and monitoring technologies,
sek cted PRC Environmental Management, Inc., as the testing organization for the performance verification of field
portable X-ray fluorescence (FPXRF) analyzers.
DEMONSTRATION
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
tndept ndcntly assess the accuracy and comparability of each instrument.
1 be 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, cad.iiii.uni, and antimony were
Sttcondarv analytes. The demonstration sites were located in Iowa (the RV Hopkins site) and Washington (the
ASARCO site). These sites were chosen because they exhibit a wide range of concentrations for .most of the target
metals and are located in different climatological regions of the United States; combined, they exhibit three distinct
.soil types: sand, clay, loam. The conditions at, these sites are representative of those environments under which
the technology would be expected to operate. Details of the demonstration, including a data summary and
EPA-VS-SCM-09 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, Reid
Portable X-ray Fluorescence Analyzer, Metorex X-MET 920-P and 940." The EPA document number for this
report is EPA/600/R-97/146.
The EPA Method 6200 was and validated the derived from this demonstration. This method may
be used to support the general application of FPXRF for environmental analysis.
TECHNOLOGY
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 by an energy proportional
response in an X-ray detector. Energy dispersion affords a highly efficient, full-spectrum measurement which
enables the use of low intensity excitation sources (such as radioisotopes) 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 decisions to be on-site and reduces the number of
that to be submitted for laboratory analysis. In the operation of these instruments, the user be aware that
FPXRF analyzers do not respond well to chromium and that detection limits may be 5 to 1C) times than
conventional laboratory methods. As with all field collection programs, a portion of the samples should be
to a laboratory for confirmatory analyses,
Metorex considers the X-MET 920-P and 940 to have equivalent performance characteristics. Advances in
electronics have led to the redesign of the 920-P into a smaller and lighter version, the X-M.ET 940, At the time
of the demonstration, the 920-P was commercially available while the 940 was tested as a prototype.
These are designed to produce quantitative on the concentration of in soils, sludges, and
other solids. Each instrument consists of a battery-operated electronics unit and a solid-state probe system (SSPS)
The SSPS houses two excitation sources and a lithium-drifted (Si[LiJ) detector (cooled by liquid nitrogen) foi
elemental excitation and detection. The SSPS allows for in situ analysis or the measurement of samples in cupv
Hither instrument can be operated and calibrated site-specific calibration samples or through the use of
fundamental (FP) calibration software. During this deinonstration, the FP software was used and fine-
tuned with the use of one site-specific to improve data comparability. During this demonstration, the
instruments were configured to report arsenic, barium, cadmium, copper, chromium, iron, lead, nickel, and zinc
At the time of the demonstration, each instrument cost about $55,000; either could be leased for $6,000 per month.
OF
These findings do not distinguish between the two analyzers. The original study design intended to test the
prototype X-MET 940; however, near the end of the data collection at the ASARCO site, a data acquisition problem
occurred which prevented any additional use of this analyzer. The demonstration was resumed using an X-MET
920-P. It should also be noted that the first 920-P unit also encountered difficulties with data acquisition and was
replaced by the developer. Although both instruments encountered problems, Metorex technical support was
responsive to the needs of the demonstration,
1'he performance characteristics of the X-MET 920-P and 940 include the following:
* limits: Precision-based detection limits were determined by collecting 10 replicate measurements
on site-specific soil samples with concentrations 2 to 5 times the expected MDLs. The results were 120
milligrams per kilogram (mg/kg) or less for arsenic, barium, cadmium, copper, lead, nickel, and zinc. The
measured value for chromium was 210 mg/kg. A value for iron was not determined due to insufficient samples
in the required concentration range,
* Throughput: Average throughput was 10 to 12 analyses per hour using a live count of 240 seconds. This rate
only represents the analysis time since different personnel were to prepare the samples.
EPA-W-SCM-09 The accompanying notice is an integral part of this verification statement March 1998
iv
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» Drift: Tht*. us a measurement ol the analyzer's variability in quontitatmg a known conccitttation ol a standard
over time. No data, was produced for either analyzer to assess drift.
* These instruments produced results for 1,192 of the 1,260 for a completeness of 94,6
percent, slightly below the demonstration objective of 95 percent. Software and mechanical problems reduced
completeness.
» Lithium carbonate blanks were analyzed periodically throughout the demonstration.
Concentrations of copper and iron were detected in all the blanks.
* Precision; The goal of the demonstration was to achieve relative deviations (RSD) less than 20
percent at analyte concentrations of 5 to 10 times the method detection limits. The RSD values for all analytes
were less than 8 percent, except chromium and nickel which had RSD values of 23 and 25 percent, respectively.
Values for iron and cadmium were not reported due to insufficient data,
» Accuracy: Intramethod accuracy was assessed using site-specific PE soil samples and soil SRMs, The results
show that 28 of 38 (73.6 percent) of the analytes in the site-specific PEs were within the quantitative acceptance
range of 80 -120 percent. The barium and cadmium concentrations were underestimated in all PE samples, and
one of the three measured values for chromium was overestimated. The soil SRM data showed that. 19 of 30
or 63 percent of the analytes were in the acceptable range,
* Comparability: This demonstration showed these instruments produced data that exhibited a I0g,0-log1(, 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 0,94 for arsenic, 0.93 for copper, 0,94 for iead, 0.86 for
zinc, 0.6? for chromium, and 0,43 for barium. Values for cadmium, nickel, and iron were not reported due to
insufficient data.
» quality levels; on precision and comparability to the reference methods, these instruments
produced definitive level for arsenic, lead, copper, and zinc and of qualitative screening level for
chromium and barium. Values for cadmium, nickel, and iron could not be assigned without adequate precision
or comparability data,
1 he results of this demonstration show that either the Metorex X-MET 920-P or X-MET 940 can provide useful,
cost-effective data for environmental problem-solving and. decision-making. Undoubtedly, instruments will
he employed in a variety of applications, ranging from serving as a complement to data in a fixed
analytical laboratory to generating data that will 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,
r*
'A. < f */. \? v.
lMi> ,1, h
Director ^
National Exposure Research Laboratory
< Jffiee of Research and Development
NOTICE: EPA verifications are based on an evaluation of technology performance under specific, predetermined criteria, and the
appropriate quality assurance procedures, EPA mak.es no expressed or implied warranties as to the performance of the technology
aid does not certify that a technology will always, under circumstances other than those tested, operate at the levels verified, The
end user is solely responsible for complying with any and all applicable Federal, State, and Local requirements.
EPA-VS-SCM-09 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 Superfund 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 ORD's Environmental Sciences
Division in Las Vegas, Nevada.
Gary J. Foley, Ph.D.
Director
National Exposure Research Laboratory
Office of Research and Development
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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 these 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: RV Hopkins and the ASARCO Tacoma Smelter. 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 samples analyzed during this demonstration represented 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 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 can reduce the number of samples that need to be submitted for confirmatory analysis. Of
the seven commercially available analyzers tested, 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 part of the RV Hopkins
sample analyses. This environmental technology verification report (ETVR) presents information
regarding the X-MET 920-P and X-MET 940 analyzers. Separate have been published for the
other analyzers demonstrated.
Some operational downtime was experienced by the X-MET 920-P during periods of heavy rainfall,
resulting in hardware problems which limited the use of the X-MET 940 at the RV Hopkins site. Some
data loss occurred at both sites. Although the cause of this data loss cannot be determined, it is likely
that with additional experience and familiarity with the analyzers, the operator could have reduced this
data loss.
During operations, quantitative data was provided by both analyzers on a real-time basis. The X-MET
920-P and 940 analyzers reported arsenic, chromium, copper, lead, zinc, iron, nickel, cadmium, and
barium. These analyzers used total source count times of 240 live-seconds for this demonstration. These
count times resulted in a sample throughput averaging between 10 and 12 samples per hour.
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The X-MET 920-P and 940 analyzers provided definitive level data (equivalent to reference data) for
copper, arsenic, lead, and zinc. The analyzers produced required qualitative screening level data for
chromium and barium. No assignment of data quality could be made for nickel, iron, or cadmium due to
a lack of sufficient data needed to calculate precision, accuracy, or the coefficient of determination.
The analyzers generally exhibited a precision similar to that of the reference methods. They exhibited
precision values ranging between 3 and 25 percent relative standard deviation at 5 times the method
detection limit (MDL) for all of the reported analytes. The analyzers'quantitative results were based on
a calibration using the fundamental parameters method. The field-based MDLs were generally 2 to 3
times higher than the precision-based MDLs. Except for nickel, the field-based MDLs were higher than
the developer's published MDLs. This difference was most likely due to count-time differences and to
the differences in the developer's definition or the demonstration's definition of MDLs.
This demonstration found that the X-MET 920-P and 940 analyzers were generally simple to operate in
the field. The operator required no specialized experience or training. Ownership and operation of this
analyzer may require specific licensing by state nuclear regulatory agencies. There are special radiation
safety training requirements and costs associated with this type of license. These analyzers can provide
rapid, real-time analysis of the metals content of soil samples at hazardous waste sites. Either 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. The X-MET 920-P and 940 analyzers were found to be
effective tools for field-based analysis of metals contamination in soil.
VIM
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Table of Contents
Section Page
Notice ii
Verification Statement iii
Foreword vi
Abstract vii
List of Figures xi
List of Tables xii
List of Abbreviations and Acronyms xiii
Acknowledgments xv
1 Executive Summary
2 Introduction 3
Demonstration Background, Purpose, and Objectives 3
Reference Methods 4
SiteSelection 5
Predemonstration Sampling 7
Experimental Design 8
Qualitative Factors 10
Quantitative Factors 10
Evaluation of Analyzer Performance 13
Deviations from the Demonstration Plan 19
Sample Homogenization 21
3 Reference Laboratory Results 23
Reference Laboratory Methods 23
Reference Laboratory Quality Control 24
Quality Control Review of Reference LaboratoryData 25
Reference Laboratory Sample Receipt, Handling, and Storage Procedures.. 25
Sample Holding Times 26
Initial and Continuing Calibrations 26
Detection Limits 26
Method Blank Samples 27
Laboratory Control Samples 27
Predigestion Matrix Spike Samples 27
Postdigestion Matrix Spike Samples 28
Predigestion Laboratory Duplicate Samples 29
Postdigestion Laboratory Duplicate Samples 29
Performance Evaluation Samples 29
Standard Reference Material Samples 30
Data Review, Validation, and Reporting 30
ix
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Section
Quality Assessment of Reference Laboratory Data 31
Precision 31
Accuracy 32
Representativeness 34
Completeness 34
Comparability 37
Use of Qualified Data for Statistical Analysis 38
4 X-MET 920-P and 940 Analyzers 41
Theory of FPXRF Analysis 41
Background 42
Operational Characteristics 44
Equipment and Accessories 44
Operation of the Analyzers 47
Background of the Technology Operator 47
Training 47
Reliability 48
Health and Safety 50
cost 51
Performance Factors 52
Detection Limits 52
Throughput 54
Drift 54
Intramethod Assessment 54
Blanks 54
Completeness 55
Precision 55
Accuracy 57
Comparability 61
Intermethod Assessment 62
5 Applications Assessment and Considerations 71
General Operational Guidance 75
6 References 77
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List of Figures
Figure Page
2-1 Sample Preparation and Analysis 9
2-2 Linear and Log-log Data Plots-X-MET 920-P and 940 Analyzers 14
3-1 Pre- and Postdigestion Duplicate Samples 32
3-2 Reference Method PE and CRM Results 35
3-3 Reference Method SRM Results 39
4-1 Principle of Source Excited X-ray Fluorescence 42
4-2 Analyzer Comparison Data 50
4-3 Critical Zone for the Determination of a Field-based Method Detection Limit
for copper 53
4-4 Precision vs. Concentration-X-MET 920-P and 940 Analyzers 56
4-5 Site-specific PE Sample Results-X-MET 920-P and 940 Analyzers 59
4-6 SRM Results-X-MET 920-P and 940 Analyzers 60
4-7 PE and CRM Results-X-MET 920-P and 940 Analyzers 64
4-8 Sample Preparation Effect on Lead Results -7-0
XI
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List of Tables
Table
2-1 Performance and Comparability Variables Evaluated 11
2-2 Criteria for Characterizing Data Quality 16
3-1 Reference Laboratory Quality Control Parameters 24
3-2 SW-846 Method 6010A LRLs for Target Analytes 27
3-3 Reference Laboratory Accuracy Data for Target Analytes 33
3-4 SRM Performance Data for Target Analytes 37
3-5 Leach Percent Recoveries for Select NIST SRMs 38
4-I X-MET 920-P Instrument Specifications 44
4-2 X-MET 940 Instrument Specifications 45
4-3 Instrument and Field Operation Costs 52
4-4 Method Detection Limits-X-MET 920-P and 940 Analyzers 53
4-5 Precision Summary-X-MET 920-P and 940 Analyzers 56
4-6 Accuracy Summary for Site-Specific PE and SRM Results-X-MET 920-P and
940 Analyzers 58
4-7 PE and CRM Results-X-MET 920-P and 940 Analyzers 63
4-8 Regression Parameters by Primary Variable-X-MET 920-P and 940 Analyzers — 65
4-9 Regression Parameters for the Sample Preparation Variable and Soil Texture-
X-MET 920-P and 940 Analyzers 66
4-10 Regression Parameters for the Sample Preparation Variable and Site Name-
X-MET 920-P and 940 Analyzers 68
4-11 Summary of Data Quality Level Parameters 70
5-1 Summary of Test Results and Operational Features 72
5-2 Effects of Data Correction on FPXRF Data Comparability to Reference Data
for All In Situ-Prepared Samples 74
XII
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List of Abbreviations and Acronyms
a
P
ACES
Am241
CCB
ccv
Cd109
Cl
CLP
cm
cm2
cm3
CRM
EPA
ERA
ETVR
eV
Fe55
FP
FPXRF
ICAL
ICP-AES
ICS
ICV
IDW
keV
LCS
LED
IOQio
LRL
MCA
mCi
MDL
mg/kg
mL
mm
MMTP
mrem/hr
MRI
NDD
NERL-ESD
alpha
beta
automated contaminant evaluation software
americium-241
continuing calibration blank
continuing calibration verification
cadmium-109
confidence interval
Contract Laboratory Program
centimeter
centimeter squared
cubic centimeter
certified reference material
Environmental Protection Agency
Environmental Resource Associates
environmental technology verification report
electron volt
iron-55
fundamental parameters
field portable X-ray fluorescence
initial calibration
inductively coupled plasma-atomic emission spectroscopy
interference check standard
initial calibration verification
investigation-derived waste
kiloelectron volt
laboratory control samples
light-emitting diode
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
Norton Disk Doctor
National Exposure Research Laboratory-Environmental Sciences Division
XIII
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MIST National institute of Standards and Technology
o s w Office of Solid Waste
PAL performance acceptance limit
PARCC precision, accuracy, representativeness, completeness, and comparability
PC personal computer
PE performance evaluation
ppm part per million
PRC PRC Environmental Management, Inc.
psi pounds per square inch
QA quality assurance
QAPP quality assurance project plan
QC quality control
r correlation coefficient
r2 coefficient of determination
RCRA Resource Conservation and Recovery Act
RPD relative percent difference
RSD relative standard deviation
RTC Resource Technology Corporation
SD standard deviation
Si(Li) silicon lithium
SITE Super-fund Innovative Technology Evaluation
SOP standard operating procedure
SRM standard reference material
SSPS solid state probe system
TC toxicity characteristic
USGS United States Geological Survey
XPCS X-MET PC System
XRF 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 Famam, and Alan Byrnes (PRC Environmental
Management, Inc.); for analyzer operation, Nate Meyer (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), Shirley Wasson (National Risk Management Research Laboratory), Brian Schumacher
(National Exposure Research Laboratory), and Bill Engelmann (National Exposure 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 James R Pasmore,
Metorex, Inc.
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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 necessary 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, data quality, and ease of operation.
This demonstration was intended to provide users 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) assist in the planning,
execution, and reporting on a demonstration of FPXRF analyzers. This demonstration was conducted
under the EPA's Super-fund 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, 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 Niton
Corporation (the Niton XL Spectrum 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 part of the RV Hopkins sample analyses. The X-MET 920-P and 940 are
essentially the same instruments, only their physical characteristics differ. This report presents data on
the X-MET 920-P and 940 analyzers. 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 analytes likely to produce interferences for the PPXRP 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 the target analytes were
determined by each instrument.
To demonstrate the analyzers, hazardous waste sites in Iowa (the RV Hopkins site) and in the State
of Washington (the ASARCO site) were selected. These sites were selected 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 two analyzers were simple to operate in the field. However,
unexpected software and hardware problems caused data loss and project downtime. The developer had
provided a training course for the operator that was similar to that provided to a purchaser of the
instrument. The training encompassed enough PPXRP theory and hands-on analyzer use to allow the
operator to manipulate the data collection software and to adjust instrument parameters such as count
times and target analytes. Some of the downtime and data loss encountered could have been avoided
with increased operator experience; however, Metorex provided accessible and timely field support. The
analyzers were portable and capable of operating continuously over a 12-hour work day with appropriate
battery changes. The almost continuous rain encountered at the ASARCO site caused an operational
downtime for the X-MET 920-P Analyzer. Moisture entered the probe causing a source controlling
electronics malfunction, as well as fogging of the source detector window.
The analyzers reported results for lead, arsenic, zinc, copper, nickel, iron, cadmium, barium, and
chromium. Antimony was the only target analyte not reported by the analyzers. The analyzers used two
radioactive sources coupled with a silicon-drifted lithium (Si[Li]), semiconductor, crystal diode detector.
The type and strength of the radioactive sources allowed each instrument to produce reliable data at
count times as short as 100 live-seconds. The count times used in this demonstration resulted in a sample
throughput averaging 10 to 12 samples per hour.
The X-MET 920-P and 940 analyzers produced data meeting definitive level criteria (equivalent to
reference data) for arsenic, copper, lead, and zinc. The analyzers produced qualitative screening level
data for chromium and barium. Assignment of data quality levels for cadmium, nickel, and iron could
not be made due to insufficient data.
The analyzers exhibited a precision similar to the reference methods. The chromium data generally
showed the lowest precision of the primary analytes. Site and soil texture did not appear to affect data
comparability. This conclusion is based on data associated with lead and zinc, the two target analytes
evenly distributed, over a wide range of concentrations, at all sites and in all soil textures. The use of
fundamental parameters (PP) calibration seemed to minimize any impact of inter-element interferences.
Based on performance, this demonstration found both the Metorex 920-P and 940 to be effective
tools for characterizing the concentration of metals in soil samples. As with all of the PPXRP 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 X-MET 920-P and 940 analyzers. These two analyzers, as designed by Metorex,
are identical with the exception of their size and weight. The design of the 940 is based on advances in
microelectronics which allows the data collection, interpretation, and storage unit to be more compact
and lighter. At the time of the demonstration, the X-MET 920-P was commercially available, while the
X-MET 940 was a prototype unit. This ETVR presents information relative to the X-MET 920-P and
940 analyzers. The X-MET 920-P and 940 analyzers use a Si(Li) detector and any combination of three
radioactive sources: cadmium-109 (Cd109), americium-241 (Am241), and iron-55 (Fe"). The analyzers
used in this demonstration were equipped with the Cd109 and Am241 sources only. The analyzers can be
operated in an in situ or intrusive mode. The in situ mode is commonly called "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 probe is placed in direct contact with the soil surface, and a measurement is taken.
In the intrusive mode of operation, a soil sample is physically collected, dried or sieved, and placed into a
sample cup. The sample cup is placed into an analysis chamber on the probe and a measurement is taken,
This section provides general information about the demonstration such as the purpose, objectives,
and design. Section 3 presents and discusses the quality of the data produced by the reference methods
against which the analyzer was evaluated. Section 4 discusses the X-MET 920-P and 940 analyzers'
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
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 how well FPXRF analyzers identify and quantify concentrations of metals in soils. The primary
objectives were to evaluate FPXRF analyzers in the following areas: (1) their accuracy and precision
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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 needed to operate these analyzers; 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, and range of usefulness, data quality, and ease of operation. The performances of the
FPXRF analyzers were not compared against each other. Instead, the performance of each analyzer was
independently and individually compared to that of conventional analytical methods commonly used in
regulatory enforcement or compliance activities. In addition, each analyzer's performance was assessed
relative to measurements 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 Method 6010A
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 was 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 this
demonstration, the term "reference" was substituted for "confirmatory" since the data were used as a
baseline for comparison. In accordance with Federal Acquisition Regulations, MRI was awarded a
subcontract to analyze soil samples using the reference methods. The award was made based on MRI's
costs, ability to meet the demonstration's quality assurance project plan (QAPP), requirements, and its
position as the only commercial laboratory identified that could perform all the analyses in the required
timeframe.
Method 3 050A is the standard acid extraction procedure 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 6010A is the standard method used to analyze Method 3050A extracts. Both of these
methods are described in 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 have 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 uses of the data. Data quality
parameters usually include five indicators of data quality referred to as the PARCC parameters:
precision, accuracy, representativeness, completeness, and comparability. In addition, method detection
limits (MDLs) 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 methods was acceptable.
The following paragraphs provide definitions of each of the PARCC parameters.
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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 differences (RPD) is used to provide this estimate
of random errors between duplicate samples.
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 estimated 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
at its optimum capability to provide results that represented 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 analytical
methods, 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.
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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,
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, PRC and EPA determined that the RV Hopkins and
ASARCO sites met most of the site-selection criteria, and therefore, would be the sites used 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 the 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 have targeted four general areas of the site as acceptable candidates for this
demonstration: 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-site: granular and slag. The composition of the granular fill
material ranges from sand to silt with demolition debris and slag debris mixed throughout. The 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 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. No slag was analyzed as part of this demonstration.
The RV Hopkins site is located in the west end of Davenport, Iowa. The facility occupies
approximately 6.68 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 1800's. Aerial surveys beginning in 1929
show that the rock quarry occupied the majority of the site initially, gradually decreasing until it was
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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
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 comer of the site.
Areas investigated on each 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 1,000 mg/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
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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.
Experimental Design
The experimental design for this demonstration was developed to meet the primary and secondary
objectives stated above, and was approved by all demonstration participants prior to the start of the
demonstration. The design is detailed in the demonstration plan and is summarized below.
Approximately 100 soil samples 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 FPXRFs and by the reference methods.
The X-MET 920-P and 940 analyzers can be operated in either the in situ or intrusive mode. The
two modes of FPXRF analysis involve slightly different measurement and sampling procedures (Figure
2-1). Each procedure was designed to reflect common applications of FPXRF analyzers. 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. Each analyzer then took one in situ measurement in the middle of each
sample area. This data point represented FPXRF in situ measurements for unprepared soils (in situ-
unprepared). Replicate measurements were taken at 4 percent of these locations to assess analyzer
precision. All replicate measurements were taken from the same spot in the sampling area.
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. If the sodium fluorescein salt was not evenly distributed, the
homogenization and checking process were repeated until the sodium fluorescein was evenly distributed.
This monitoring process assumed that thorough distribution of sodium fluorescein was indicative of good
sample homogenization. The effectiveness of this homogenization is discussed later in this section.
The homogenized sample was then spread out inside a 1-inch-deep petri dish. Each FPXRF analyzer
took one measurement from this homogenized material. This represented the homogenized sample
analysis for the in situ analyzers (in situ-prepared). This approximated 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 soils as the unprepared precision measurements.
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COLLECT
10RSJUCATE
MEASUREMENTS
WITHOUT
MOVING PROffi
CONDUCT
INTRU9VE INSTRUMENT
MEASUREMENTS
(NO PREPARATION)
SFUT AND PACKAGE
TW030GRAMAUOUOTS
"OR REmSNCC METHOD
ANALYSIS
LAGB.3M FOR BOTH
3CB1/601CA ANALY3S
Figure 2-1. Sample Preparation and Analysis: This flowchart depicts the handling procedures for each
sample taken during the demonstration.
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Following the in situ-prepared procedure, the sample material was passed through a No. 10 mesh
sieve (2-mm openings) and approximately 10 grams of this material was placed in a sample cup for
analysis in an intrusive mode. The same sample cup was used for each FPXRF analyzer. Replicate
measurements were collected from 4 percent of these samples to assess analyzer precision. These
replicate measurements were made on the same soils as thein situ-prepared precision measurements,
These data represented FPXRF intrusive mode measurements on soils with no sample preparation
(intrusive-unprepared). Sample material from this preparation step was collected and submitted to the
reference laboratory for analysis.
Following the intrusive-unprepared procedure, a portion of the soil sample was dried in a convection
oven at 110 °C for 1 hour and ground with a mortar and pestle until it passed through a No. 40 stainless-
steel sieve (0.425mm openings). These samples were then analyzed in the intrusive mode. Four percent
of these samples underwent replicate measurements to evaluate analyzer precision. These replicate
measurements were performed on the same soils as in the intrusive-unprepared precision measurements.
This data represented FPXRF intrusive measurements on prepared soils (intrusive-prepared).
These preparation procedures allowed the evaluation of the effects of sample preparation on FPXRF
comparability to reference data.
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
learning required to operate a given FPXRF analyzer. To assess this factor, PRC operators were trained
by the developers on how to operate their respective FPXRF 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 FPXRF 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 can be no qualitative estimate of the "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.
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The data developed by the FPXRF analyzers were 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 X-MET 920-P and 940 reported all of these analytes
except antimony.
Evaluations of analyzer data comparability involved examining the effects of each site, soil texture,
and sample preparation technique on performance and comparability (Table 2-1). Two sites were
sampled for this demonstration and, therefore, 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). Four sample preparation steps were used: (!)'« situ-
unprepared, (2) in situ-prepared, (3) intrusive-unprepared, and (4) intrusive-prepared. 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, and the ASARCO data was divided into sand and loam soil
textures; then each soil texture was subdivided by the four soil preparations. These variables allowed the
examination of particle size and homogenization effects on data comparability. These effects were
believed to have the greatest potential impact on data comparability.
Table 2-1. Performance and Comparability Variables Evaluated
Site Name (315) | Soil Texture (315)
ASARCO (215)
RV Hopkins (100)
Sand (100)
Loam (115)
Clay (100)
Preparation Step [1,260]
in situ-unprepared [IOO]
in situ-prepared [IOO]
intrusive-unprepared [IOO]
intrusive-prepared [IOO]
in situ-unprepared [115]
in situ-prepared [115]
intrusive-unprepared [115]
intrusive-prepared [115]
in situ-unprepared [IOO]
in situ-prepared [IOO]
intrusive-unprepared [100]
intrusive-prepared [IOO]
Notes: () Total number of sample points.
[] Total number of measurements 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 MDLs; 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 'frue" 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.
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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 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 the common
microwave drying times used in the field. These split samples were then 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 the
best results for specific target analytes. The developers, however, selected count times that produce 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 longer and
shorter than those set by the developers. This allowed the evaluation of the effects of count times on
analyzer performance. Since sample throughput can be affected by adjusting count times, operators used
only the developer-specified count times throughout the demonstration.
An important health and safety issue during the demonstration was the effectiveness of radioactivity
shielding of each FPXRF analyzer. Occasional radiation readings were quantitatively made with a
gamma ray detector near each analyzer to assess the potential for exposure to radiation.
A compilation of the costs associated with the use of 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 initialpoint-and-
shoot preparation, heterogeneity was minimized by sample homogenization. This effect was
evaluated through the sample preparation data.
• Particle Size: The effect of particle size was evaluated with the two intrusive sample preparations.
Theoretically, precision and accuracy should increase as particle size decreases and becomes more
uniform.
• 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. This effect
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was partially examined in the comparison of analyzer performance between intrusive-unprepared
and intrusive-prepared analyses. This step in sample preparation involved drying and grinding.
. 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 the 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 help determine the data quality of
each PPXRP 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 pairt-
test were the statistical tools used to assess comparability and data quality.
A principal goal of this demonstration was the comparison of PPXRP data and the reference
laboratory data. EPA SW-846 Methods 3050A/6010A were selected as the reference methods because
they represent the regulatory standard against which PPXRP is generally compared. In comparing the
PPXRP 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 samples being
measured, there are also major differences. These differences, or "perspectives," allow the user to
characterize the same sample in slightly different ways. Both have a role in site characterization and
remediation. It is important to consider these differences and the measurement error intrinsic to each
method when comparing the PPXRP method against a reference method.
The reference methods chosen for this analysis 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, which
represents the material from most surfaces, and clay and carbonate minerals. 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, PPXRP analyzers generally produce X-ray excitation in an area of approximately
3 cm2 to a depth of approximately 2.5 centimeters. 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 PPXRP 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 PPXRP 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
PPXRP analyzers and the reference methods is not necessarily due to error in the PPXRP method but
rather to the inherent differences in the nature of the analytical methods.
The comparison of PPXRP data and the reference data employs linear regression as the primary
statistical tool. Linear regression analysis intrinsically contains assumptions and conditions that must be
valid for the data set. Three of the most important assumptions are: (1) the linearity of the relationship,
13
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(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 related 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 dataand reference data are, in fact, related linearly and
that this assumption is correct.
Linear Data Plot-Lead
2 4 8 8 10
Thousands
Reference Data {mg/kg}
Linear Data Plot-Copper
4 8
Thousands
Reference Data (mg/kg)
10
Log-Log Data Plot-Lead
10000
Log-Log Data Plot-Copper
10000
1000
s
es
a
CL
u.
100 r
10 100 1000
Reference Data (mg/kg)
10000
10 100 1000 10QQO
Reference Data (mg/kg)
Figure 2-2, Linear and Log-log Plots-X-MET 920-P and 940 Analyzers: These graphs
illustrate the linear relationship the X-MET 920-P and 940 analyzer data and the reference
data. The linear data plots the concentration of this relationship with
increased scatter at higher concentrations. The log-log eliminate this concentration effect.
Scatter is relatively constant over the entire plot.
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 relatively uniform over the entire
concentration range). The change in 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
14
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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
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 logio transformed data.
The third assumption, requiring an insignificant measurement error in the reference data, was not true
for all analytes. The consequences of measurement error vary 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 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 log,, 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. Based on
these findings, the reference data does allow unambiguous resolution of data quality determination.
The comparison of the reference data to the FPXRF data represents 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 assign analyzer data into one of three data quality levels
as follows: (1) definitive, (2) quantitative screening, and (3) qualitative screening. The first two data
15
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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.
Table 2-2. Criteria for Characterizing Data Quality
Data Quality Level
Definitive Level
Statistical Parameter3
? = 0.85 to 1 .0. The precision (RSD) must be less than or equal to
10 percent and inferential statistics indicate the two data sets are statistically
similar.
Quantitative
Screening, Level
r* = 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
?= less than 0.70. The precision (RSD) is greater than 20 percent. The data
must have less than a 10 percent false negative rate.
Notes: The statistical tests and parameters are discussed in Section 4, I'ntermethod Comparison."
The regression parameters apply to either raw or logio transformed data sets. The precision
criteria apply to only the raw data.
r* Coefficient of determination.
RSD Relative standard deviation.
Definitive level data are considered the highest level of quality. These data are usually generated
from rigorous analytical methods such as those approved by the EPA or ASTM. The data is
analyte-specific with confirmation of analyte identity and concentration. In addition, either analytical or
total measurement error must be determined. Definitive data may be generated in the field, as long as the
QA/QC requirements are satisfied.
Quantitative screening data provide unconfirmed analyte identification and quantification, although
the quantification may be relatively imprecise. It is commonly recommended that at least 10 percent of
the screening data must 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, 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, approved EPA methods for FPXRF did not exist. As part of this
study, PRC prepared draft Method 6200 "Field Portable X-Ray Fluorescence Spectrometry for the
Determination of Elemental Concentrations in Soil and Sediment." The draft method has been submitted
for inclusion in Update 4 of SW-846 scheduled for approval in FY-97. For purposes of this
demonstration, the absence of a current 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 the data produced by the reference methods, as well as
analyzer-specific criteria such as precision as defined in Table 2-2.
The comparability data set for each analyzer consisted of 1,260 matched pairs of reference method
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 potential influence of each variable on comparability.
16
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To obtain an adequate data set to evaluate the performance of the analyzers, a total of 315 soil
samples was analyzed by the reference laboratory. These samples were analyzed by the Metorex
analyzers for each of the four sample preparation steps. This produced an equivalent set of 1,260 data
values, 630 in each mode in situ or intrusive. 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 the 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 a higher r2
value 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. Then, linear regression
analysis was conducted on the three data sets. If this did not result in improved r2 values and reduced
standard errors of the estimate, 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
ranges of values. Therefore, this statistical analysis 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), and r2. In
linear regression analysis, the r provides a measure of the degree or strength of the correlation between
17
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the dependent variable (log,, transformed FPXRF data), and the independent variable (log,, transformed
reference data). Ther* 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 = mX + b-
where
h is the y -intercept of the regression line, m is the slope of the regression line,
and Y and X are the tog]0 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 FPXRF
and reference data. As the r2 departs from 1.0 and approaches zero, there is more unexplained variation,
due to such influences as lack of perfect 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 was required to be equal or less than 10 percent RSD to retain the
definitive data quality level. If both these criteria were not satisfied, certain inferential statistical
parameters were then evaluated. First, the regression line's y-intercept and slope were 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). This procedure was used to assign an overall
data quality level for each analyte.
Slope Test for Significant Differences
Z = —-!?!_____
where
m is the slope of the regression line, SE is the standard error of the slope,
Z is the normal deviate test statistic,
The matched pairs t-test was also used to evaluate whether the two sets of logio transformed data
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
18
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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
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.
Y-intercept Test for Significant Differences (2-3)
z . _LL°_
where
b is the y-intercept of the regression tine, 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 less than 20 percent RSD, and the slope or
intercept were not statistically equivalent to the ideal values, the analyzer was considered to produce
quantitative screening level data quality (Table 2-2). 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 for a percentage of the samples would provide a basis for determining a correction factor.
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 they had a
precision greater than 20 percent RSD. 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 is not considered suitable for reporting of concentrations.
MDLs for the analyzers were determined in two ways. One approach followed 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
resultant represented the precision-based MDL for the analyzers.
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 FFXRF concentrations increased linearly with increasing reference
method values. Effectively, the linear correlation between the two methods abruptly changes to no
correlation below the MDL. The value of the MDL was assigned by determining the concentration
where the linear relationship disintegrates and reporting a value at two SDs above this concentration.
This MDL represented a field- or performance-based MDL.
Deviations from the Demonstration Plan
Seven deviations were made from the demonstration plan during the on-site activities. The first dealt
with determining the moisture content of 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 was
collected immediately adjacent to the original sample location and was used for determining moisture
19
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content. This was done to conserve sample volume needed 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 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 and the
intrusive-prepared samples 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 indicated 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 of microwaving. This interval is within common microwave drying
times used in the field.
An analysis of the microwaved samples showed that this 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 and drying altering the reference methods'extract on 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 RPD s 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 metals concentration. 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 between 24 and 240 hours 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
20
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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 PPXRP analysis of 10 to 12 SRM or PE samples. Each analyzer measured a total of 28
SRM or PE samples. Instead, 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 was used for comparative purposes for the reference methods as were PE data for the PPXRP
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 PPXRP 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 sample homogenization. Approximately onequarter
teaspoon of dry sodium fluorescein powder was added to each sample prior to homogenization. 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 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, including the secondary analytes in the RPD calculation which
produced a mean RPD total (error) and a 95 percent confidence interval of 9.3 ± 1.6.
Total Measurement Error = \[(Sample Homogenization Error)* + (Laboratory Error)2] (2-4)
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
21
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interval around the mean RPDs (laboratory error) of predigestion duplicate analyses, was 9.3 ±2.9 for
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 = ^{(Total Measurement Error)2 - (Laboratory Error)2}
Although the possibility for poorly homogenized samples exists under any routine, at the scale of
analysis used by this demonstration, the samples were considered to be completely homogenized.
22
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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
S W-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 S W-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.
23
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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 governecc 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 6010 AQC 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 Method ' " . . ...
Parameter' • Frequency •• Requirement . QAPP Requirement . ^,
• ' ' . . . " . , .. j^#;";;-'
Initial Calibration
Verification (iCV)
Standard
Continuing Calibration
Verification (CCV)
Standard
Initial and Continuing
Calibration (ICB)
and
Interference Check
fiCS)
High Calibration
Check
Method Blanks
Laboratory Control
Samples
Predigestion Matrix
Spike Samples
Postdigestion Matrix
Sptko
With initial
calibration
After analysis of every 1 0
and at the end
of analytical run
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 initial
calibration
With each batch of
samples of a similar
matrix
With each batch of
samples of a similar
matrix
With each batch of
samples of a similar
matrix
With each batch of
samples of a similar
matrix
±10 percent of true value
±1 0 percent of value
±3 deviations of
the background
mean
±20 percent of true value
±5 percent of value
No QC requirement
specified
No QC requirement
specified
80 - 120 percent recovery
75 - 1 25 percent recovery
j 1 0 percent of true value
•t10 ppicent of true value
No target anai|rtes at
concentrations greater than
2 times the lower reporting
limit (LRL)
*20 percent of true value
±10 percent of true value
No target analytes at
concentrations greater Shan
2 times the LRL
80 - 1 20 percent recovery
80 - 1 20 percent recovery
80 - 120 percent recovery
24
-------
Table 3-1.
Reference Method
Parameter Frequency Requirement QAPP Requirement
Performance Evaluation
Samples
Predigestion Laboratory
Duplicate Samples
Postdlgestion
Laboratory Duplicate
Samples
As submitted during
demonstration
With each batch of
of a similar
matrix
With each of
of a similar
matrix
No QC requirement
specified
20 percent relative
percent difference (RPD)b
No QC requirement
specified
80-120 percent recovery
within performance
acceptance limits (PAL)
20 percent RPPC
10 percent RPDC
Notes: Quality control parameters were on the raw reference data,
RPD control limits only to original and laboratory duplicate sample results that were greater
than 10 times the instrument detection limit (IDL).
RPD control only pertain to original and laboratory sample results that were greater
than or eqyai to 10 times 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.
25
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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 Holding 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 thelCAL. 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 laboratoryLRLs 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.
26
-------
Table 3-2. 801OA LRLs for Target
Analytes
Analyte LRL (mg/k§) Anatyte | LRL{mg/kg)
Antimony
Arsenic*
Barium*
Cadmium
Chromium*
[_ 6-4
10.6
5,0
U- °'80
2,0
Copper*
Iron
Lead*
Nickel
Zinc*
1.2
600«
8,4
3.0
2,0
Notes:
LRL due to background
interference,
* Primary anaiyte.
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
anaiyte 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 anaiyte 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.
27
-------
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
28
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postdigestion spike recoveries for target analytes met the QA/QC requirements of the QAPP 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 forinorganics 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"
-------
to the reference laboratory for analysis by EPA SW-846 Methods 3050A/6010A. The reference
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, XRF, 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 (NIST), 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-defmed 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/010A
as were the ERA PE or RTC CRM samples. Comparison of EPA SW-846 Methods 3050A/010A 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.
30
-------
* B - the reported value was obtained from a reading that was less than the LRL but greater
than 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 performed by ICP-AES (Method 6010),
Qyality 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.
Precision
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.
31
-------
40
1 30
i
*
H 20
is
ff
I 10
1
0
Predigestion Duplicate Samples
i
f
t
i
C
__J
t
!
1 [
! !
3
f
I l
I 1
3 i
I
3 t
1 I
1
L— -^
i
3 !
3
* I
!
i
i
3""""'
3
3
i i
i
3
C
t
,,,,„,!
I
t
1
i I
! I
1
f
t
1
C
1
3 C
* !
i
]
i
!
Antirnjny Arsenic Bariym Chromium Cadmium Capper Iron Lead Mctel Zinc
Analyte
(WO)
-* M
-------
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 PAL 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.
3-3. Reference Laboratory Accuracy Data for Target Analyfes
1
Analyte • r
Antimony £
Arsenic , £
Barium c
Cadmium _i £
Chromium 1 £
Coppef £
|fon V
b'ad £
Nickel c
Zinc f
•Percent' Within
i - . 'Acceptance Range
» 100
$ 100
1 100
I 100
> 100 j
> 89
100
i 87.5
I 100
78
Mean
Percent
Recovery
104
106
105
84
91
123
98
86
95
120
Range of
Percent
Recovery
83 125
90 160
83- 139
63-93
77 101
90 - 332
79-113
35-108
79-107
7§ - 309
SDof
Percent
Recovery
15
22
L „ , ™ „„ »H
r 21
10
8 1
^_
12
22
10
72
Concentration
Range (mg/kg)
50 - 4,955
r 25 - 397
19-586
1 .2 - 432
L 11-187
144-4,792
6,481 -
52-5,194
13-13,279
76 - 3,021
Notes: n Number of with detectable analyle concentrations,
SD
rug/kg Milligrams per kilogram,
LCS percent recoveries for all the primary analytes were acceptable in 21 of the 23 sample batches.
Lead recovery was unacceptable in one sample batch and lead results for each sample in that batch were
rejected.
Copper recovery was unacceptable in another sample batch, and copper results for each sample in
this batch also were rejected. Percent recoveries of the remaining primary analytes in each of these two
batches were acceptable. In all, 136 of 138 LCS results or 98.5 percent fell within the control limits.
33
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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.
34
-------
10000
150
50
Antimony
IRafarsnct Data OTrueValus •Percent Recovery
500
JS 400
•£• 300
•S
| 200
I 100
0
Arsenic
I Reference Data DTrue Value
H
200
100
120
80
40
1% Recovery
800
800 —.
400 —
150
200 —•
Barium
IReferenca Data DTrue Value
50
1% Recovery
O O O O O O fit
o o o o o m
us ••>• « at i~ H
(fijj/fiuu)
-1"
r
i
J
-
Cadmium
jference Data QTry« V»lus •% B»eo¥»r
"^ * m OB -»
o o o o
o
Percent Ftecovery
Chromium
•Reference Data DTrue Value
•Percent Recovery
120
40
Figyre 3-2, Reference Method PE and CRM Results: These graphs illustrate the relationship
the reference and the true values for the PE or CRM samples. The gray bars represent
the percent recovery for the reference data. Each set of three bars (black, white, and gray) represents a
PE or CRM sample, on this high percentage of acceptable results for the ERA and CRM
PE samples, the accuracy of the laboratory method was considered acceptable.
35
-------
100000
Copper
•Reference Data DTrua Value
400
1% Recovery
Iron
•Reference Data DTrue Value HPercent Recovery
10000
100
10
I
J
Lead
•[Reference Data DTrua Value
• Percent Recovery
125
100 I"
§
75 |
-------
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 the SRMs. 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 SRMs 2709,2710, 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.
Table 3-4. Data for Target Analytes
Percent Within • | Mean j Range of [ SO of
Acceptance Percent Percent Percent Concentration
. Range . Recovery . Recovery I Recovery I Range (mg/kg)
1 Afttffnony
f SHPiW"*
Bai i,.n
1/Hfjn tii im
rhi< fiium
linn
lend
N,oM
/lf!r.___
5
11
8
10
10
7
\f
IG
16
0
72
12
50
0
88
14
82
19
75
22
84
41
80
45
82
62
83
67
81
15 37
87- 108
21 -89
^
43-95
14 6?
33 94
?3 84
37-99
25 91
32-93
9
10
21
15
18
17
25
17
17
14
3,8-171
18-828
414-1,300
2.4 - 72
38 - 509
35 -
-
19-5,532
14 - 299
81 -
Notes:
n
SD
Number of SRM with analyte concentrations.
Standard deviation.
per kilogram.
37
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Table 3-5, teach Percent Recoveries for S0!ect NIST SRPs
Analyte
NIST SRM 2709
I Reference
Published j Laboratory
Result'1 i Result
NIST SRM 2710
| Reference
Published I • Laboratory
Resylf I Result
NIST SRM 2711
Reference
Published Laboratory
Resylf Result
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
_
-
41
_
61
92
86
69
89
94
_
106
37
_
_
r— ~gg
r~~~84~~
87
76
78
21
94
51
92 _j
49
92
80
92
71
85 "^
_
87
45
84
-
92
78
96
69
88
_
86 _j
28
96
43
88
76
95
78
89
20
91
25
. 87
_ 4i_
90
88
90
70
85
Notes: Published results found in an addendum to SRM certificates for NIST SRMs 2709, 2710, and
2711.
NIST National Institute of Standards and Technology,
SRM Standard reference materials,
- Analyte not present 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.
38
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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/601OA, 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.
aoo
Antimony
IRgferenceOata OTrue Value
• Hsrcsnt Recovery
800
,600
400
c
§200
S
1
M
120
100
Arsenw
Ifteferencs Data DTrueV 20
o
o
S3
60 ec
c
fl>
o
I
40
Barium
I Reference Data DTrus Value
1% Recovery
Cadmium
I Reference Data O True Value
HI % Recovery
3-3. These graphs the relationship the
and the true for the SRM samples. The gray represent the percent
recovery for the data. Each set of three bars (black, white, and gray) a single
SRM sample.
39
-------
600
Jr. 400
c
,2
5
1 200
o
c
o
O
80
SO £•
>
o
o
S)
40 OC
20
Chromium
• Reference Data DTrue Value
• % Recovery
10QGQr
r
c
SB
O
c
o
-------
Section 4
X-MET 920-P and 940 Analyzers
This section provides information on the Metorex's X-MET 920-P and 940 Analyzers including
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 shells.
Electrons in these 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
fixed wavelengths 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 each characteristic X-rays
emitted is proportional to the concentration of the source 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 (B), which indicates the next outermost
shell from which electrons fell to fill the vacancy and produce the X-ray. For example, a ka-line is
produced by a vacancy in the K shell filled by an L shell electron, whereas a Ks-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 approximately 10 times more intense than the &-line for
41
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a given element, rnaMng the Ka-llne analysis the preferred choice for quantitation purposes. Unlike the
K-lines, the L-Iines (L, and LB) for an analyte are of nearly intensity. The choice of which one to
use for analysis depends on the presence of interfering lines from other analytes.
ixetttion X-ray from the
FPXBF Source
X
X
X
An excited •tectron is dtoptecad, crtathg an X,
electron vacancy, X
An €tut*r electron she! electron caacadea to the inner ttectron ifwi to
fl tha vacancy. As tNa ttectron cascades, 8 f«teas*s energy in the
fofffiof an K-ray,
Characteristic X-ray
Figyr* 4-1. Principle of Soyrct Excited X-ray Fluorescence; This the dynamics
of source excited 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 is 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, theK-
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 thesource. For example, when using a Cd109 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
Metorex is an international supplier of advanced equipment for metal detection, materials testing,
and chemical analysis. It offers a wide range of products from field portable and laboratory-grade metals
and alloy analyzers to in-line (process) analyzers. It has more than 20 years of experience in developing
X-ray analyzer technologies.
Metorex developed the X-MET 920 line as a modular system, which users can configure for their
specific analytical problem. This line includes the X-MET 920-P and 940 analyzers.
42
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The basic configuration of the analyzers includes: a disk operating system-based personal computer
(PC) with Metorex electronics added in the form of a board that plugs directly into the computer
expansion slot identified as the X-MET PC System (XPCS), Metorex proprietary XRF software, and the
Metorex XRF analysis probe. The data acquisition, storage, and processing systems are housed in the
XPCS module independent of the analysis probe which contains excitation sources as well as the X-ray
detector. The X-MET 920 line uses IBM compatible computers so that the analyzers can be configured
with desktop, laptop, or portable computers. The XPCS contains a 2,048 multichannel analyzer (MCA)
that is used to collect the spectra. The MCA portion of the technology is contained on a single electronic
board that is plugged into one of the expansion slots of the XPCS. Menudriven software guides the user
through routine analysis and calibration, and is used to generate and display the data.
With the X-MET 920 line, Metorex offers several analytical probes. The probes contain the
excitation sources and either a silicon (drifted with lithium) Si(Li) detector or a gas-filled proportional
counter detector.
Metorex developed the X-MET 920-P and X-MET 940 to perform elemental analysis in the
petroleum and petrochemical industry, the mining and minerals industry, and the environmental field.
The X-MET 920-P and 940 are field portable technologies that can be operated in the in situ, or intrusive
mode. At the time of this demonstration, the X-MET 920-P was commercially available; the X-MET 940
was a prototype. The X-MET 920-P and 940 are Metorex's "portable" versions on the X-MET 920 line.
The X-MET 940 is a smaller, lighter version of the X-MET 920-P. Other than the difference in the size
and weight of the XPCS, these instruments are identical in function. Both analyzers use energy
dispersive XRF spectroscopy to determine the elemental composition of soils and other solid waste
materials as well as liquids and slurries. The X-MET 920-P and 940 can identify and quantify the
concentrations of 70 elements, 32 of which can be identified and quantified simultaneously. Metorex
offers three excitation sources, Fe55, Cd109, and Am241, and two detectors (Si(Li) and gas-filled
proportional counter) in its various probes. For this demonstration, both analyzers used the "solid state
probe system" (SSPS) equipped with the Cd109 and Am241 sources and a Si(Li) detector. The SSPS is
designed to house two excitation sources. The choice of sources is determined by the user depending on
the target analytes.
For in situ analyses using either analyzer, the probe is pointed downward and placed flat against the
soil surface to allow the probe window to come into contact with the soil surface. For intrusive analyses,
the probe is pointed upward and a protective sample cover is attached over the probe window. The soil
samples are placed in sample cups and the sample cups are placed inside the protective sample cover for
analysis. In either mode, sample measurement is initiated by depressing a button located underneath the
handle at the window end of the probe. This exposes the sample to primary radiation from the two
excitation sources, sequentially: fluorescent and backscattered radiation reenters the probe through the
source-detector window and is counted by the Si(Li) detector, which passes on the electronic pulses to
the MCA for processing.
The X-MET 920-P and 940 can be operated and calibrated using either the X-MET software or
Automated Contaminant Evaluation Software (ACES). The X-MET software is used to analyze samples
using an empirical calibration, The ACES software is used to analyze samples with a fundamental
parameters (FP) calibration. During this demonstration, the ACES software was used to run the X-MET
920-P and 940. The X-MET 920-P was used to analyze all samples from the ASARCO site and about
half of the samples from the RV Hopkins site. The X-MET 940 was used to analyze the in situ samples
at the RV Hopkins site. Equipment failure precluded its use for all the RV Hopkins samples.
-------
Operational Characteristics
This section discusses equipment and accessories, operation of the analyzers in the field, description
of the operator training requirements, reliability of the analyzers, health and safety concerns, and
representative operating costs.
Equipment and Accessories
The X-MET 920-P and 940 come with all of the accessories necessary for both in situ and intrusive
operation. Metal, foam-padded carrying cases are provided for storage of the XPCS units and the
analysis probe. Specifications for the X-MET 920-P and the 940 used during this demonstration are
provided in Tables 4-1 and 4-2, respectively.
Table 4-1. X-MET Instrument Specifications
Characteristic
Resolution
Sources
Detector
Analyzer Size
Probe Weight
Computer Size
Computer Weight
Computer Configuration
Power Source
Operational Checks
Intrusive Operation
Contact: Jim Pasmore
1900 HE Division
Bend, OR 97701
1-800-229-9209
(FAX)
Specification !
17QeV(Mn-Ka)
20 mCi Ctl109 and 30 mCi Am241 (Fe55 also
Si(Li) - Liquid nitrogen cooled
(gas-filled proportional available)
2Gcmx 10 cm x 25 cm
4,1 kilograms (with liquid nitrogen)
33 cm x 45 cm x 9 cm
5.5 kg (without external battery); 1 1 .5 kg (with external battery)
Central processing unit 386SX 25 megahertz with a math
coprocessor; 2 MB RAM; 80 MB hard disk; VGA graphics;
color screen; 3,5-inch floppy disk; two serial ports and a
printer port; 2,048 channel MCA
120V (AC), internal or external
Pure lead check sample
Attachment of protective sample cover
Three main components comprise the analytical system of each instrument: the computer, electronics
unit, and probe. With the X-MET 920-P, the PC and 2,048-channel MCA are packaged together in a
weatherproof plastic enclosure. The X-MET 920-P features a handle which swings to the back of the
unit to hold it at an angle for use on the ground or on a table. The keyboard is covered with a form-fitted
soft rubber cover to prevent water from entering the unit. All ports are fitted with watertight caps. The
bottom of the unit is fitted with grooves that accept an external battery pack for use in the field. The unit
also contains an internal battery that is capable of powering the system for a short time. The X-MET
920-P has been given a National Electrical Manufacturers Association No. 4 enclosure rating. The
external battery for the X-MET 920-P slides onto the bottom of the unit. A short power cord that is
44
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permanently attached to the battery case plugs into the side of the X-MET 920-P. The battery is the same
width and depth as the X-MET 920-P and is about 5 cm (2 inches) in height. The external battery weighs
6 kilograms (kg) (13 pounds). During the demonstration, the battery lasted about 5 to 7 hours when fully
charged. To fully charge, the external battery is plugged into the combination AC adaptor and battery
charger overnight. The combination AC adaptor and battery charger can be used to simultaneously
power the X-MET 920-P and charge a battery.
Table 4-2, X-MET i40 Instrument Specifications
Characteristic Specification
Resolution
Sources
Detector
Analyzer Size
Probe Weight
Computer
Computer Weight
Computer Configuration
Power Source
Operational Checks
Intrusive Operation
Contact; Jim Pasmore
1§00 NE Division Street
OR 97701
1-800-229-9209
(FAX)
170eV(Mn-Ka)
20 mCi Cd106 and 30 mCI Am241 (Fe55 also available)
Si(Li) - Liquid nitrogen cooled
(gas-filled proportional available)
20cmx 10 cm x 25 cm
4,1 kilograms (with liquid nitrogen)
38 cm x 36 cm x 5 cm
4,7 kg (without external battery); 6,4 kg (with external
battery pack)
Central processing unit 386SX 25 megahertz with a math
coprocessor; 4 MB RAM; 10 MB flash disk; CGA graphics;
a monochrome screen; connector for an external
keyboard; serial, printer, and external floppy disk drive
ports; 2,048 channel MCA
120V (AC), internal or external battery pack
Pure lead check
Attachment of protective cover
The X-MET 940, which was used to analyze a portion of the samples at the RV Hopkins site, is a
smaller, lighter version of the X-MET 920-P. It also features a handle which swings to the back of the
unit to hold it at an angle for use on the ground or on a table. The keyboard on this unit was a prototype
version of what will be found on production models. One feature of its layout was the addition of several
keys to the right of the screen. These keys, which are separate from the rest of the keyboard, are used to
operate some of the basic functions in taking measurements. All ports are fitted with watertight caps.
The unit contained an internal battery that is capable of powering the unit for up to 2.5 hours. A nylon
carrying bag with shoulder straps was provided for analyses in the field. Metorex provided a prototype
external battery for the X-MET 940. In the production model, Metorex plans to design the battery to
slide onto the bottom of the unit as with the X-MET 920-P. The prototype model, however, did not have
this feature. A short power cord, which is permanently attached to the battery, plugs into the side of the
X-MET 940. The battery is the same width and depth as the X-MET 940 and is about 3.8 cm (1.5
inches) in height. The prototype external battery weighed about 2 pounds. During the demonstration,
45
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each battery lasted at least 2.5 hours when fully charged. To fully charge, the prototype external battery
needed to be plugged into the X-MET 940 while the unit was plugged into an AC outlet. The prototype
battery required about 8 hours to fully charge.
The high resolution probe used with both analyzers is the solid state probe system (SSPS); it is a
hand-held, compact unit that contains aSi(Li) detector. The detector achieves a manganese Ka X-ray
resolution of 170 eV. The detector is cooled by a 0.5-liter liquid nitrogen dewar built into the probe,
which allows for 8 - 12 hours of field use. A dewar is similar to a Thermos except that it is used to
store super-cooled liquids such as liquid nitrogen. This probe can be used as a surface probe to perform
in situ analyses by pointing downward at the soil surface, or with the attachment of a protective sample
cover, the probe can analyze soil samples intrusively from a sample cup. The protective sample cover is
easily removed or mounted on the probe with two hex-key screws. The probe dewar can be filled with
liquid nitrogen before each measurement session or can remain connected to the "mother" dewar (30-liter
capacity). During this demonstration, PRC supplied the 30-liter dewar; however, a dewar can be
purchased or leased from Metorex. Metorex provided a funnel assembly to fill the probe dewar from the
mother dewar. Metorex provided a leather glove and safety glasses for protection from liquid nitrogen
when it disconnected the funnel assembly from the probe dewar. Approximately 10 minutes is required
to fill the probe dewar with liquid nitrogen and an additional 45 minutes to allow the Si(Li) detector to
cool and stabilize each day prior to use.
The probe used during the demonstration contained the dual radioactive source configuration of a 20-
millicurie (mCi) Cd109 and 30 mCi Am241 source. This is the most common configuration of sources for
environmental work. The probe can only contain a total of two sources. These radioactive sources are in
the form of an 8-mm-diameter by 5-mm-thick capsule. Both sources in the probe used during the
demonstration were installed in February 1995. The sources are pneumatically driven and shifted into
the measurement position using pressure produced from liquid nitrogen off-gassing in the probe's dewar.
This off-gassing creates a pressure of 10 pounds per square inch (psi). The probe is equipped with an
interlock mechanism to prevent operator exposure to the radioactive sources. When not in the
measurement position, the radioactive sources are retracted into a tungsten shield.
One side of the probe contains a window, which displays a red color when the Cd109 source is in the
measurement position and a green color when it is retracted into its tungsten shield. The other side of the
probe contains a similar window that indicates the position of the Am241 source. The probe contains two
buttons. One button, which is located underneath the handle at the window end of the probe, begins a
measurement. The other button, a reset button located beneath the other end of the handle, causes the
exposed source to be retracted into its tungsten shield. The probe's sample window is environmentally
sealed by a 25-mm-diameter window of clear Kapton™ film.
The probe is connected to the analyzers with a coiled, flexible cord that is about 5 or 6 feet long.
When fully extended, the cord reaches about 15 feet. The cord is permanently attached to the probe.
Metorex provided a cardboard box with a fitted, protective foam insert for storing and shipping the
probe. Wipe test results for the probe's sources were shipped with the analyzers. Semiannual wipe tests
are required to monitor for source leakage.
Metorex brought pure element standards to the training sessions at the ASARCO site in Tacoma,
Washington, and to the RV Hopkins site in Davenport, Iowa. To perform either an empirical calibration
or a FP calibration, a pure element standard for each target analyte is required. These pure element
standards are in the shape of a coin about the size of a $0.50 piece. They are large enough to completely
cover the probe's Kapton™ window. Other equipment and supplies that are helpful when using the
46
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X-MET 920-P or 940, which is not supplied by the developer, include protective gloves, paper towels,
and a permanent marking pen.
Operation of the Analyzers
Three steps were involved in operating the analyzers: (1) performing the calibration, (2) taking
measurements, and (3) managing data. The X-MET 920-P and 940 can be calibrated empirically or
through the use of FPs. An FP calibration was performed using the ACES software. There were three
steps in calibrating the analyzers using the ACES software.
In the first step, the operator inputs concentration data from one fully-characterized sample, which is
referred to as the standard sample. Metorex recommends that the standard sample be collected at the site
of interest. However, an NIST or similar standard could be used if a site-specific sample has not been
analyzed. The more accurate the data for the standard sample, the better the results At both sites,
Metorex helped the operator decide which concentrations to use for these analytes. The same computer
screen which contains the standard sample data will also display the abbreviated chemical names of the
target analytes, the source to be used to excite each target analyte, and the peak(Ka, La, LB, etc.) to be
used for quantification. This step requires some knowledge of XRF theory to properly select the
appropriate source and peak. This step of choosing the source and peak is no longer necessary in the
latest ACES software Version 2.0.
In the second step, the operator corrects for any X-ray spectral overlaps for the target analytes. This
is done by acquiring spectra from pure element standards for all of the target analytes. Once the spectra
of pure elements are acquired, the software prompts the operator to create overlap tables. This requires
highlighting the target analytes on a periodic table and pressing a key to begin the calculation of the
overlap tables.
The final step involves analyzing the standard sample to generate response factors for each target
analyte. Metorex suggested a count time of 200 seconds for each source to acquire these spectra. Once
the three steps were completed, the analyzer is calibrated and ready to use. The operator noted that
setting the correction factors were a trial-and-error process that would become simpler with experience.
Background of the Technology Operator
The operator chosen for analyzing soil samples using the X-MET 920-P and 940 has been a PRC
employee for more than 4 years. He holds a bachelor's degree in biology and a minor in chemistry and a
master's degree in environmental science. While at PRC, he has worked on projects involving site
investigation and hazardous waste management. Prior to working for PRC, he spent 1 year working as an
environmental scientist for the Site Investigation Section of the Indiana Department of Environmental
Management, where he conducted preliminary assessments and site investigations of potential hazardous
waste sites for the Superfund program.
Training
The operator received 2 days of training by the developer at the start of the demonstration.
Approximately a half day of the training was dedicated to the theoretical background of XRF and the
remaining 1.5 days were spent on specific operation and hands-on training for the X-MET 920-P. The
hands-on training covered test measurements using the pure element samples, empirical calibrations, FP
calibrations, the analysis of various standards and soil samples, and the steps for saving spectra and
47
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quantitative results. The developer indicated that the standard training course is 3 days. The developer
stated that the PRC operator received more one-on-one instruction in 2 days than users normally receive
in a standard 3-day course. The course is designed for users with experience ranging from little or no
scientific background to scientists with many years of experience. Metorex tailors its training course to
match the level of user experience and the primary intended use of its equipment.
Two Metorex representatives stayed at the ASARCO site throughout the first day of in situ analysis
and assisted the PRC operator that evening with calculating concentrations and downloading the spectra
and results files to a floppy disk. They departed the next morning. One Metorex representative came to
the RV Hopkins site for 2 days prior to the start of the field work to review the procedures with the
operator.
Reliability
More than 1,800 individual measurements were collected with the X-MET 920-P and 940 analyzers
using the high-resolution probe and the Si(Li) detector. This included the measurement of soil samples
using the four sample preparation steps (1,260 measurements), 10 replicate measurements on 48 samples
for a precision assessment (480 measurements), the measurement of QC samples such as blanks, PE
samples, and SRMs, and multiple measurements of the same samples while varying count times. While
collecting these measurements over a period of 20 working days, several operational problems were
encountered. These problems can be divided into two categories: (1) mechanical, or (2) electronic or
software. These problems are discussed below.
The first mechanical problem was encountered with the probe of the X-MET 920-P at the ASARCO
site. During the third day of field work at this site, it rained much of the day and the probe got wet.
Throughout the morning, one of the two pressure release valves on the probe dewar kept frosting up.
Eventually, the probe stopped functioning. Thinking that the pressure in the dewar was getting low, the
operator refilled the probe dewar with liquid nitrogen. After waiting for the probe to stabilize, the
operator again began taking measurements. Within a few minutes, the pressure release valve began
releasing nitrogen gas because the valve could not close completely due to the frost. The measurement
stopped after the LED warning light came on. The operator refilled the probe dewar with liquid nitrogen,
but the problem occurred once more. A call was then made to Metorex technical support for assistance.
It was suggested that the operator cover the pressure release valves with a plastic bag to prevent
condensation from the humid environment. The operator did this and was able to continue taking
measurements. Metorex also suggested that, in the evening, the operator use a hair dryer to dry the
pressure release valves.
On the morning of the fourth day of field work at the ASARCO site, a small brass nut fell out of the
probe dewar when the operator opened the dewar for filling. The operator again called Metorex for
assistance. The operator was instructed to remove the pressure release valves with a wrench and replace
the small nut. When the operator removed the valves, he saw that a nut was missing. The operator put
the nut on the end of the piston and replaced the pressure release valve. This was an easy procedure that
took about 10 minutes. It is possible that this missing nut caused some of the problems encountered
during the afternoon of the third day of field work.
The next problem occurred at the second sample location on the fourth day, when the data
acquisition process stopped. After trying several troubleshooting measures, it was concluded that the
dewar was holding pressure but there was a problem with the source logic within the software. The
operator informed Metorex that there was some moisture under theKapton™ window and on theSi(Li)
-------
detector that had accumulated from the day before while measurements were being taken in a light rain.
Metorex informed the operator that there was a photosensor beneath the window that controls source
logic, which in turn affects the switching of sources during a measurement. If water droplets
accumulated on the photosensor, the computer software system would be unable to tell which source was
exposed and the probe would stop functioning. Metorex suggested placing the probe next to a heater to
evaporate the moisture from under the Kapton™ window.
To remedy this problem, the probe was placed next to a portable heater. After several hours,
moisture accumulated on the underneath side of the window but could not escape. It was decided to cut
the Kapton™ window out of the probe to let the condensation escape. The following morning, the fifth
day of field work at the ASARCO site, the inside of the probe was dry. The operator then placed a sheet
of Mylar XRF film over the window since the Kapton™ window had been removed. This solved the
problem and the probe functioned properly for the remainder of the demonstration. These problems
resulted in one day of downtime and prevented the PRC operator from collecting data for 67 in situ
samples at the ASARCO site.
While using the prototype model of the X-MET 940 for three days at the RV Hopkins site, the PRC
operator encountered three problems. The first minor problem was that the number "1" key on the X-
MET 940' s keypad did not work. Metorex had anticipated keypad problems with its prototype model and
had brought an external keypad to use with the X-MET 940. The operator connected the external keypad
to the X-MET 940 and the problem was solved.
A software problem occurred during the late afternoon on the third day of using the X-MET 940.
During an analysis, the measurement stopped after the probe switched from source A, the Cd109 source, to
source B, the Am241 source. The following message appeared on the screen: "Unable to proceed with
this operation. Disk write error. Please press enter." The operator started the measurement again, but
the same message appeared. He repeated the measurement two additional times, but the same message
appeared. The PRC operator then tried to calculate concentrations from spectra acquired up to that point.
The same message appeared on the screen. The operator ran Norton Disk Doctor (NDD) on the X-MET
940 but was unable to correct the problem. The PRC operator called Metorex, but Metorex was unable
to recommend a fix for the problem over the phone. The developer instructed the operator to return the
X-MET 940 to Metorex. After Metorex received the X-MET 940 and tried to run the ACES software, it
diagnosed that the X-MET 940' s hard drive was full. Metorex informed the PRC operator that each time
the X-MET 940 is shut off, a file is created that stores information about the X-MET 940 and the probe.
After three days of using the X-MET 940, these self-creating files had used up all of the memory on the
hard drive. Metorex said it would change the error message from "Disk write error" to something like
'hard drive full." Metorex also said that it would inform future technology users of this self-creating file
and how to remove it. No data was lost as a result of this problem because the PRC operator did not save
any spectra or results files on the hard drive.
When the X-MET 940 was returned to Metorex, the operator resumed RV Hopkins sample analyses
with the X-MET 920-P. When the RV Hopkins calibrations were set up on the X-MET 920-P, correction
factors for zinc and nickel produced erroneous data for these analytes: All of the RV Hopkins samples
analyzed by the X-MET 920-P were biased high for nickel and zinc by approximately 600 mg/kg and 800
mg/kg, respectively. This bias is shown in Figure 4-2. The cause of this error could not be established.
This problem caused the loss of zinc and nickel data for samples 380 - 399 for the in situ-prepared
analysis, and all of the intrusive zinc and nickel data at the RV Hopkins site. This data loss was not
considered in the discussion of analyzer data completeness, since this did not reflect an analyzer-induced
data loss.
49
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When the operator began acquiring spectra with the X-MET 920-P at the RV Hopkins site, several
error messages appeared. One of the messages read: "Sector not found reading drive C." NDD was used
to diagnose the problem and encountered several bad sectors and marked them so they would not be
used. Similar problems were encountered during the following 2 days. Eventually, enough bad sectors
were encountered on the X-MET 920-P 's hard drive that they could not be fixed with NDD.
Metorex sent a new X-MET 920-P XPCS unit, the PRC operator removed the MCA card from the
nonfunctional X-MET 920-P, and placed it in the new X-MET 920-P. This was a relatively easy process
that took about 30 minutes. The operator then copied the software and all files containing the ASARCO
calibration onto the new X-MET 920-P. This was an easy process that took about 30 minutes. The
operator then finished analyzing all the samples without any problems.
1.5 2 2.5 3
Log of Reference Method Data {mg/kg}
ft)3'5
s
a
S 3
2.5
D
n
Log of Reference Method Data (mg/kg)
Figure 4-2. Analyzer Data: These graphs illustrate the shift In analyzer that
occurred when the X-MET 940 replaced with the X-MET 920-P at number 380 during
the in situ-prepared analysis. The nickel data appears to have generally increased by approximately
600 mg/kg and the analyzer's zinc clusters around 1,000 mg/kg over a wide range of reference
concentrations,
Health and Safety
The potential for exposure to radiation from the excitation source was the largest health and safety
consideration while using the analyzers. The X-MET 920-P and the 940 are sold under a general license,
meaning that the analyzers are designed and constructed in such a way that while in use, per the
instruction manual, an operator would not accumulate a radiation dose higher than that from naturally
occurring radiation. One objective of the demonstration was to evaluate radiation exposure to operators
from the analyzers. Radiation was monitored with a gamma-ray detector radiation survey meter.
Background radiation at the two sites was between 0.006 and 0.012 millirems per hour (mrem/hr).
Radiation was monitored in the in situ and intrusive modes 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. Although
50
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the X-MET 920-P and the 940 are sold under a general license, meaning that they have met all safety
requirements according to the Nuclear Regulatory Commission, many states still recommend that
radiation from survey instruments be below a certain level. For example, in the State of Kansas, the
permissible occupational exposure 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 (probe pointing down), maximum radiation values of 0.15 - 0.20
mrem/hr and 1.2 to 1.5 mrem/hr at the probe face were obtained with theCd10* source and Am241 source
exposed, respectively. The radiation values dropped to 0.020 - 0.030 mrem/hr at the probe handle when
either of the two sources was exposed. While collecting intrusive measurements (probe pointing up),
radiation values of 0.15mrem/hr at the side of the protective sample cover and 0.015- 0.020 mrem/hr
above the protective sample cover were obtained with the Cd109 source exposed. With theAm241
exposed, radiation values of 2.3 to 2.5 mrem/hr at the side of the protective sample cover and 0.015 -
0.020 mrem/hr above the protective sample cover were obtained. Background radiation levels were
obtained at the trigger under the handle with either source exposed. All radiation values at the probe
handle were below the occupational level of 2.0 mrem/hr.
Transferring liquid nitrogen from an external dewar to the internal dewar of the SSPS used for both
the X-MET 920-P and 940 was another health and safety consideration. Due to the extremely low
temperature of liquid nitrogen, the operator must take care to avoid contact during the filling operation.
Safety goggles and gloves must be worn during this process. It is also recommended that a laboratory
coat be worn when filling the SSPS with liquid nitrogen.
cost
The primary cost benefit of field analysis is the quick access to analytical data. This allows the
process depending on the test results to move efficiently to 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 usually 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 finding may be resolved completely without the need to
return for another sample collection event.
A representative list of costs associated with the Metorex 920-P is presented in Table 4-3. 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 fair to report a per sample
cost for the field analysis. However, some estimate can be derived from the data in this table.
Since it was tested as a prototype, no-cost data was available for the X-MET 940. At the time of the
demonstration, the X-MET 920-P and SSPS with two radioisotope sources cost $52,470. This includes
the X-MET and ACES software, eight pure element standards, a liquid nitrogen dewar, and 3 days of
training for two people at Metorex. Travel and accommodation costs for the training are not included.
Spare batteries are available for $425 and spare battery chargers are available for $340. Periodic
maintenance includes replacement and disposal of the Cd109 source every 2 years at a cost of $4,500 with
an additional $500 disposal fee.
The X-MET 920-P can be rented from Metorex. There is a 1-month minimum rental. The cost is 10
percent of the purchase price per month, and all shipping costs. Users have a choice of training options.
The first is a 3-day class offered at Metorex's facility at $685 per person plus travel and lodging
51
-------
expenses. On-site training classes are also available, Metorex must be contacted for details regarding
the on-site training classes.
Table 4-3. instrument and Field Operation Costs
Item Amount
X-MET 920-P
Replacement Source
Operator Training (Vendor Provided)
Radiation Safety License
$
5,500
5,000
685
500
Purchase Price
Per Month Lease
For Cd109
—
(State of Kansas)
Field Operation Costs ' .
Supplies and Consumables (Sample cups,
window film, sieves, standards)
Field Chemist (Labor Charge)
Per diem
Travel
Sample Throughput
Cost of Reference Laboratory Analysis
300 - 500
100-150
80-120
200 - 500
10-12
150
(Varies, depending
on sample
Per day
Per day
Per traveler
Samples per hour
Per sample
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 of site-
specific soil samples with metals concentrations 2 to 5 times the expected MDLs. These data were
obtained during the measurement of instrument precision. Based on this precision data, a standard
deviation was calculated and the MDLs were defined as 3 times the standard deviation for each target
analyte. All the precision-based MDLs were calculated for soil samples that had been dried, ground, and
placed in a sample cup, the highest degree of sample preparation. The precision-based MDLs for theX-
MET 920-P and 940 are shown in Table 4-4.
The precision-based MDLs were obtained using a 140-second count time for the CdIW source and a
100-second count time for the Am144 source. Table 4-4 also lists MDLs reported by the developer. The
developer's MDLs were acquired using a200-second count time for each source. The counting statistics
for FPXRF analysis indicate that it would take a fourfold increase in count time to increase the precision
and therefore reduce MDLs by 50 percent.
Another method of determining MDLs involves the direct comparison of the log,, transformed
FPXRF data and the log,, transformed 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 4-3 illustrates this effect for copper. This point was determined by
observation and was somewhat subjective; however, a sensitivity analysis showed that even a 25 percent
52
-------
error in identifying this point resulted in up to 10 percent changes in MDL calculation. By determining
the mean values of this FPXRF data and subsequently two standard deviations (SD) around this mean, it
was possible to determine a field or performance-based MDL for the analyzer. For the X-MET 920-P
and 940 analyzers, these field-based MDLs are also shown in Table 4-4.
Table 4-4. Method Detection Limits—X-MET 920-P
and 940 Analyzers
Developer-based Precision-based i Field-based
Analyte MDL (mg/kg) | MDL(mg/kg) j MDL {mg/kg)
Arsenic
Barium
Chromium
Cadmium
Copper
Iron
Lead
Nickel
Zinc
75
20
200
25
80
85
60
80
75
55
30
210
25
75
Not determined
45
120
70
90
1,320
470
55
210
Not determined
45
200
120
Note: mg/kg Milligrams per kilogram
1
&
CL
1
X
100000
,
1000
100
10
100
1000 10000
Reference Data (mg/kg)
Figure 4-3, Critical Zone for the Determination of a Field-
Method Detection Limit for Copper; 100
200 mg/kg for the reference data, the linear relationship
the two changes. This point of
the point at which field-based MDLs for the
were determined.
Because iron was generally at of thousands of milligrams per kilogram, reasonable field-
detection limits could not be calculated. For the other elements, the precision-based MDLs were
53
-------
very similar to the developerMDLs. The field-based MDLs for most of the analytes were 1.5 to 3 times
higher than the developer's or precision-based MDLs. The field-based and precision-based MDL for
lead were the same.
Antimony, a secondary analyte for this demonstration, was not reported by this analyzer, although it
is in the analyzer's range and can be determined. At the time of the demonstration, a standard with a
certified value for antimony was unavailable. Antimony can be analyzed by the X-MET 920-P and the
940 when they are equipped with an Am241 source.
Throughput
Both analyzers used a total source live-second count time of 240 seconds. With the additional
"dead" time of the detector and the time required to label each sample and store data between sample
measurements, the time required to analyze one soil sample was 5 to 6 minutes. This resulted in a
throughput of approximately 10 to 12 samples per hour in the intrusive mode. The throughput for the in
situ samples was 8 to 10 measurements per hour which includes the time required to walk to the next
sample location.
The sample analysis time did not include the time required for sample handling and preparation, or
for data downloading, printing, and documentation. Considerable time was spent preparing the in situ
homogenized samples and the intrusive samples. Homogenization required an average of approximately
5 minutes per sample (in situ-prepared), 20 minutes per sample was required for No. 10 sieving
(intrusive-unprepared), and 10 minutes per sample was required for grinding and sieving (intrusive-
prepared). The operator noted that it took about 10 - 15 minutes to fill the probe dewar with liquid
nitrogen and 45 minutes for the probe to cool and stabilize. This time was used to calculate
concentrations from spectra collected the previous day and to perform data management tasks such as
printing a hard copy of the data. On average, it took about 1 hour after arriving at the site before the
operator could start taking measurements.
Drift
Drift is a measurement of an analyzer's variability in quantitating a known amount of a standard over
time. Normally drift is evaluated by reviewing results from the periodic analysis of an SRM or other
check samples. No data was produced by either analyzer to assess drift.
Intramethod Assessment
Intramethod assessment measures of the analyzer's performance include results on analyzer blanks,
completeness, intramethod precision, intramethod accuracy, and intramethod comparability. The
following paragraphs discuss these characteristics.
Blanks
Analyzer blanks consisted of pure lithium carbonate. The blanks were placed directly in a sample
cup after all four sample preparation steps like the dried and ground soil samples. The blanks were used
to monitor for contamination of the probe by material such as residual soil left on the probe face. Four
blanks were analyzed at the ASARCO site using the ASARCO site-specific FP calibration model. Four
blanks also were analyzed at the RV Hopkins site using the RV Hopkins site-specific FP calibration
model. Iron was detected in all blanks at levels ranging from 237 to 304 mg/kg. These concentrations
-------
were above the MDL listed by the developer; however, they were not of concern as most of the soil
samples contained iron concentrations exceeding 20,000 mg/kg. Copper also was detected in all blanks
at levels ranging from 85 to 103 mg/kg. These levels were slightly above the developer and precision-
based MDLs, but were below the field-basedMDLs.
Completeness
The analyzers produced data for 1,192 out of the 1,260 samples for a completeness of 94.6 percent,
slightly below the demonstration objective of 95 percent. Data was not obtained for 68 samples at the
ASARCO site. The mechanical problems with the probe while using the X-MET 920-P caused the loss
of data for 67 in situ-unprepared samples, most of which were in the loam textured soil. The remaining
missing data point was for one in situ-prepared ASARCO soil sample. This omission was caused by an
oversight of the operator.
Precision
Precision was expressed in terms of the percent RSD between replicate measurements. The precision
data for the target analytes detectable by the analyzers are shown in Table 4-5. The precision data
reflected in the 5 to 10 times the MDL range reflects the precision generally referred to in analytical
methods manuals such as SW-846.
These analyzers 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 four sample preparation steps. Therefore, there was a total of 48
precision points for the analyzers. 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. The percent RSD is defined as the SD divided by the mean
concentration times 100.
In this demonstration, the analyzer's precision 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 results, reflected by the precision data in the 5 to 10 times MDL
range, were all below the 10 percent RSD required for definitive level data quality classification with the
exception of chromium and nickel. Cadmium and iron did not have sufficient data to allow data quality
conclusions based on precision. Table 4-5 shows that chromium and nickel precision was slightly greater
than 20 percent, placing the chromium and nickel results in the qualitative level data quality
classification based solely on precision. The lower precision for chromium was expected because
chromium is a problematic analyte in FPXRF analysis. The lower precision for nickel may be an artifact
of the low concentrations of nickel in the soil samples and the influence of iron interference in the
samples.
There was no observable effect of sample preparation on precision. This was expected because the
method used to assess precision during this demonstration assessed analyzer precision and not total
method precision. These samples were purposely chosen to span a large concentration range to test the
effect of analyte concentration on precision. As the concentration of the target analyte increased, the
precision improved. Figure 4-4 shows an asymptotic relationship between concentration and precision.
In this figure, precision shows little improvement at concentrations greater than 400 mg/kg; however, at
concentrations below 400 mg/kg, precision is highly concentration dependent. Although lead is shown in
Figure 4-4, a similar trend was exhibited by the other target analytes. Table 4-5 shows that the RSD
55
-------
values were less than 10 percent for all analytes except chromium and nickel at concentrations greater
than 500 mg/kg.
Table 4-5. Precision Summary—X-MET 92Q-P and §40 Analyzers
Analyte
Mean % BSD Values by Concentration Range
5-10 Times | 50-500 i 500-1,000 >1,000
MDLa (mg/kg) (mg/kg) I (mg/kg) (mg/kg)
Arsenic
Barium
Chromium
Copper
Iron
Lead
Cadmium
Nickel
Zinc
3.36 (8)
3,38 (24)
22,72 (4)
7,80 (8)
ND
4.80(12)
ND
24.92(16)
4,26(16)
10,48(12)
3,16(32)
35.21 (4)
11,35(28)
ND
11.19(24)
24.95 (48)"
27.35 (8)
9,18(24)
3.04 (4)
2,34(12)
(8)
3.95 (8)
ND
3,62 (8)
ND
(8)
4,12(20)
1.28(4)
2,46 (4)
(4)
2.70(12)
1,43(48)
1,87 (16)
ND
ND
2.65 (4)
Notes; The MDLs referred to in this column are the precision-based MDLs
shown in Table 4-4,
u
This value may be biased high because the cadmium concentration
in the soil samples was near the defection limit.
mg/kg Milligrams per kilogram.
ND No data.
(} Number of samples, including the four sample preparation steps,
each consisting of 10 replicate analyses. Numbers do not always
add up to 48 because some samples had analyte concentrations
below the analyzer's MDL
<#*»*>«,.
a
CO
cc
T3
CO
05
e,
-------
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 analyzers using site-specific PE samples and SRMs.
Accuracy was evaluated through a comparison of percent recoveries for each primary and secondary
analyte reported by the analyzers. The analyzers measured six site-specific PE samples and 14 SRMs.
The operator knew the samples were PE samples or SRMs, but did not know the true concentration or the
acceptance range. These PE samples and SRMs were analyzed in the same way as all other samples.
The six site-specific PE samples consisted of three from each of the two demonstration sites that
were collected during thepredemonstration activities and sent to six independent laboratories for analysis
by laboratory-grade XRF analyzers. The mean measurement for each analyte was used as the true value
concentration. The 14 SRMs included seven stream or river sediments, two ash, and one sludge SRM.
The SRMs were obtained from NIST, USGS, Commission of European Communities, National Research
Council-Canada, and the South African Bureau of Standards. The SRMs contained known certified
concentrations of certain target analytes.
These PEs and SRMs did not have published acceptance ranges, As specified in the demonstration
plan, an acceptance range of 80 - 120 percent recovery of the true value was used to evaluate accuracy
for the six site-specific PEs and 14 SRMs. Table 4-6 summarizes the accuracy data for the target
analytes in these samples. Figures 4-5 and 4-6 show the true value, the measured value, and percent
recovery for the individual site-specific PEs and SRMs, respectively. Although nickel was reported by
the X-MET 920-P, no nickel data is presented or discussed in this accuracy assessment. There was an
error in the nickel calibration that caused this data to be unusable. No figure is presented for the
cadmium data for the site-specific PEs or for the chromium data for the SRMs because there were two or
less detects in both cases.
Based on the 80 - 120 percent recovery acceptance range, the analyzers' accuracy varied from 0
percent for barium and cadmium to 100 percent for arsenic, copper, iron, and lead in the site-specific
PEs. Overall, the analyzers produced 28 out of 38 results or 73.7 percent within the 80 - 120 percent
recovery acceptance range for all analytes in the six PEs. Nine out of the 10 results falling outside of the
acceptance range were below the lower limit of 80 percent recovery. Only a 140 percent recovery for
chromium in one PE sample was above the upper limit of 120 percent recovery. Table 4-6 shows that the
mean percent recoveries for seven of the eight analytes in the PEs were less than 100 percent. The mean
percent recoveries and range of percent recoveries indicate that, in general, the analyzers were producing
results that were biased slightly low for iron, lead, and zinc, and substantially low for barium. The
analyzers were underestimating barium concentrations by a factor of 2 to 3. This underestimation of
barium is somewhat difficult to explain because the site-specific PE samples were used to assist in the
calibration of both analyzers and, therefore, the barium results should have been similar to the true value.
The cadmium data for the site-specific PEs were inconclusive with only two data points and one of those
samples having a true cadmium value of 27.9 mg/kg, which was very close to the precision-based MDL
of 25 mg/kg. The one chromium result above the acceptance range was for a sample with a chromium
concentration near the precision-based MDL and below the field-based MDL. The one zinc result that
fell below the 80 percent acceptance range was only slightly below with a 78 percent recovery.
57
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Table 4-6. Accuracy Summary for Site-Specific PE and Results—X-MET and 940
Analyzers
Analyte
Percent I [
Within | Mean | Range of SO of
Acceptance Percent Percent Percent
Range Recovery Recovery Recovery
Concentration
Range (mg/kg)
Site-Specific Performance Evaluation Samples
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Zinc
4
6
2
3
5
6
6
6
100
0
0
07
100
100 ~~l
__JM___j
83
95
40
64
117
99
86
85
88
89-103
21 -57
57-72
92-140
91 - 109
81 -91
80-92
78-94
8.7
13
NA j
24
7,9
3,3
4,6
6,9 ~~1
92 - 22,444
792 - 7,240
27,9 - 353
247 -
300-7,132
-
292-14,663
184-4,205
Soil Standard Reference Materials
Arsenic
Barium
Cadmium
Copper
Iron
Lead
Zinc
3
5
3
4
3
5
7
33
20
100
25
100
80
l~~86
50
70
100
162
97
^_|
111
0-91
47-117
81 -115
100-210
93-J03_j
Uj*ll94_ j
96-161
58
29
18 ^
53
5.8
21
23
105-826
707 -
21,8-71.8
!_ 78 - 2,950
- 35,000
101 -5,532
81 -
Sediment Standard Reference Materials
Arsenic
Barium
Chromium
Copper
Iron
Lead
Zinc
l] 0
3
1 1
4
1
4
4
67
0
25
too
25
75
L_71
34
22
83
94
72
81
71
63-103
22
10-175
[ NA ]
20
NA
211
335-414
509
^73 99 - 452
94 J_ NA j 41,100
61-81 j 9.2
46-102
25
161 -8,200
264 -
Ash and Sludge Standard Reference Materials
Arsenic
Barium
Copper
Iron
Lead
Zinc
2
2
3
2
r _
3
0
0
87
100
33
100
128
59
120
84
46
106
126-131
55-83
116-125
84
0-84
99-112
NA
136-145
NA^II 709-1,500
4.2~^T_ 113-698
NA Jj7~~77,800 -
53
6,7
._ 68 " 286
210-2,122
Notes:
n Number of with detectable analyte concentrations.
SD Standard deviation.
m§/kg Milligrams per kilogram.
NA Not applicable, standard deviation not calculated for than three results.
58
-------
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40
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rti
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o
o
too
o
o
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t Rtcoveiy
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10
120
100
120
(O
CC
Si
40
tore
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sy'i%d Vaiut OTme Valpa
40
tfti Recovery
Figure 4-5. PE Results—X-MET i20-P and §40 Analyzers: These
graphs the the analyzers* values) the true
for the site-specific PE The gray the percent recovery for the
analyzers. Each set of three (black, white, and gray) a site-specific PE
sample.
59
-------
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B Peroan! Recova^y
Figure 4-6. SRM Results—X-MET 920-P and 940 Analyzers: These graphs illustrate the
relationship between the analyzers' (measured values) and the true values for the SRMs,
The gray represent the recovery for the analyzers. Each set of three bars (black,
white, and gray) represents a single SRM sample.
60
-------
Table 4-6 provides a summary of the accuracy data for the SRMs. A more detailed analysis of the
SRM data is presented in Figure 4-6. The analyzers'accuracy for the SRMs varied from 0 percent for
arsenic and chromium in the sediment SRMs and arsenic and barium in the ash and sludge SRMs to 100
percent for iron in all SRMs, cadmium in the soil SRMs, and zinc in the ash and sludge SRMs. The iron
concentrations were in the tens of thousands of milligrams per kilogram, which is in a concentration
range that the analyzers should perform well. Some analytes such as copper, lead, and zinc had
concentrations spanning 1 or more orders of magnitude in all SRMs. Overall, for all analytes in all
SRMs, the analyzers produced 35 out of 63 results within the 80 - 120 percent recovery acceptance range
for an accuracy of 55.5 percent.
A more detailed analysis of the SRM data showed that there was a matrix effect on the analyzers
accuracy. The analyzers produced 19 out of 30 results or 63.3 percent within the acceptance range for all
target analytes in the seven soil SRMs; eight out of 18 or 44.4 percent within the acceptance range for all
target analytes in the four sediment SRMs; and eight out of 15 results or 53.3 percent within the
acceptance range for all target analytes in the ash and sludge SRMs. This demonstrated that the
analyzers were more accurate when measuring SRMs of a soil matrix, than sediment, sludge, or ash.
This was expected because the soil SRMs more closely matched the matrix of the site-specific soil
samples used to calibrate the analyzers.
Although, in general, the analyzers displayed greater accuracy for the soil SRMs, the trend was not
consistent among analytes. The accuracy for arsenic and lead was greatest for the soil SRMs. The
accuracy for copper and zinc was greatest for the ash and sludge SRMs. Barium accuracy was greatest
for the sediment SRMs. Iron accuracy was 100 percent for all SRMs.
The X-MET 920-P and 940 displayed similar or greater accuracy for all analytes except barium and
cadmium for the site-specificPEs as compared to the soil SRMs. These results were expected for three.
reasons. First, site-specific PEs were used to calibrate the X-MET 920-P. Second, the analytical
technique (laboratory-grade XRF) used to determine the true analyte concentrations in the site-specific
PEs was similar to the FPXRF technique. As described in Section 3, varying analytical techniques were
used to determine the total analyte concentrations in the SRMs. Third, the analyte concentrations were
often higher in the site-specific PEs versus the soil SRMs. This data supported the developer's
contention that a site-specific PE sample is a better assessment of accuracy than an SRM for these
analyzers.
Comparability
Intramethod comparability for the analyzers was assessed through the analysis of four ERA PEs and
four CRMPEs. This was done to present users additional information on data comparability relative to
different commercially available QC samples. The eight PEs were analyzed in the same way as all other
samples. As described in Section 3, these eight PEs had certified analyte values determined by Methods
3050A/6010A. Therefore, since these methods do not necessarily determine total metals concentrations
in soil, it was expected that the analyzer would overestimate analyte concentrations relative to PALs.
The ability of the analyzers to produce results within the PALS and the percent recovery for each of the
analytes was used to evaluate intramethod comparability. As with the site-specific PEs and SRMs, the
arsenic, cadmium, copper, iron, lead, and zinc data generated from the ASARCO calibration application
and the chromium data generated from the RV Hopkins calibration application were used for the
comparability assessment. A combination of both applications' data was used in an assessment of the
barium data. No nickel data is presented or discussed because of the problem with the nickel calibration.
61
-------
The analyzers'performance data for all target analytes for the eight CRMs andPEs are summarized
in Table 4-7. The measured values, true values, and percent recoveries for all detectable analytes are
shown in Figure 4-7. No figure is shown for chromium because there was only one detect. For the ERA
PEs, the analyzers produced 15 out of 28 results or 53.6 percent within the acceptance range. For the
CRM PEs, the analyzers produced 13 out of 21 results or 61.9 percent within the acceptance range.
With the ERA and CRM PEs combined, the analyzers produced 28 out of 47 results or 59.6 percent
within the acceptance range. Based on the data presented in Table 4-7, the analyzers'results were
slightly more comparable to the CRM PEs than the ERA PEs. The better comparability to the CRM PEs
was expected because the ERA PEs had lower analyte concentrations than the CRM PEs With the
exception of iron, the analyte concentrations in the ERAPEs were all less than 350mg/kg, which was
less than 5 times the MDL for most of the analytes. The arsenic and zinc data were more comparable for
the ERA than the CRM PEs, while the barium, copper, and iron data were more comparable for the CRM
than the ERAPEs. Lead and cadmium comparability were the same for the ERA and CRM PEs. The
chromium data were inconclusive.
The analyzers overestimated barium, copper, iron, lead, and zinc concentrations in the ERA PEs by a
factor of 1.3 to 2.0, shown by the mean percent recoveries in Table 4-7. The analyzers produced only six
out of 28 calculated percent recoveries for the ERA PEs less than 100 percent and four of these were for
cadmium. This is consistent with the fact that FPXRF method analysis is a total metal technique whereas
EPA SW-846 Methods 3050A/6010A used to certify the results in the ERA PEs are not. The poor
comparability for some analytes in the ERA PEs may be an artifact of analyte concentration. All copper
concentrations were less than 3 times the precision-based MDL and less than the field-based MDL. All
iron concentrations were 3 to 4 times less than the iron concentrations in the site-specific PEs used to
calibrate both analyzers. The one lead result outside of the acceptance range was for a sample with 52.4
mg/kg lead, which was slightly above the precision and field-based MDL of 45mg/kg. The one zinc
result outside of the acceptance range was for a sample with 101 mg/kg zinc, which was above the
precision-based MDL of 70mg/kg but less than the field-based MDL of 120 mg/kg.
As with the ERAPEs, the barium results in the CRMPEs were severely overestimated. The
comparability of the analyzers' results to the certified values in the CRM PEs was sample dependent.
Seven of the eight results outside of the acceptance limits were found for the two soil CRMPEs. This
was not expected since the matrix of these CRM PEs more closely matched the matrix of the site-specific
PEs than did the sludge or ash CRMPEs. It is possible that interferences were causing the poor
comparability in these two CRMs. One soil CRM PE contained nearly 20 percent iron which was greater
than the normal 2-10 percent iron found in most soil samples. The other soil CRM PE contained nearly
15 percent lead. The recoveries of arsenic, copper, lead, and zinc in these CRM PEs may have been
affected by the high concentrations of iron or lead. In both CRM PEs, it is possible that the FPs may not
have been able to compensate for the high concentrations of iron or lead.
Intermethod Assessment
The comparison of the X-MET 920-P and 940 results to those of the reference method's was
performed using the statistical methods detailed in Section 2. The purpose of this statistical evaluation
was to determine the comparability between data produced by the analyzers and that produced by the
reference laboratory. If the logio transformed FPXRF data were statistically equivalent to the logio
transformed reference data and had acceptable precision (10 percent RSD), the data met the definitive
level criteria. If the data did not meet the definitive level criteria but could be mathematically corrected
to be equivalent to the reference data, they met the quantitative screening level criteria. If the analyzer
did not meet the definitive level criteria, and the statistical evaluation could not identify a predictable
-------
bias in the data, but the analyzer identified the presence or absence of contamination with at least a 90
percent accuracy rate, the data was classified as qualitative screening level.
The regression analysis of the entire log,, transformed data set for the primary analytes showed that
arsenic, copper, and lead had revalues at or above 0.93.Barium, chromium, and zinc hadr2 values
ranging from 0.86 to 0.43. Based on a comparison of the raw data, the analyzers tended to overestimate
concentrations of all the target analytes except lead. Section 5 discusses the average relative bias of the
analyzers and how confirmatory analysis and data correction reduces FPXRF bias.
The next step in the data evaluation involved the assessment of the potential impact of the variables:
site, soil type, and sample preparation on the regression (Table 4-8). The effect of the site variable was
assessed for lead and zinc only. These were the only target analytes exhibiting a wide and similar
concentration distribution at each site. The evaluation of the soil variable is presented in Table 4-9.
4-7. PE and CRM Resu!t»—X-MET 92Q-P and 940 Analyzers
Analyte
Percent | | |
Within j Mean j Range of j SDof
Acceptance I Percent | Percent | Percent
Range | Recovery 1 Recovery Recovery
Concentration
Range (mg/kg)
ERA Performance Evaluation Samples
Arsenic
Barium
Cadmium.
Chromium
Copper
Iron
Lead
Zinc
4 l
4
4
0
4
4
4
4
100
25
100
^ NA
0
0
^J5__^
75
102
196
79
NA
202
177
134
[— 138
92-114
107-308
64-86
NA
178-247
153-232
92-212
127-160
9,16
83
10.3
NA
i_32
37
54
I 15.3 j
85 - 349
111 -319
47-131
NA
88-196
7,130-10,400
52 - 208
76 - 259
Certified Reference Materials
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Zinc
,,«
2
2
1
4
3
4
4
0
50 ~'
100
100
50
100
75
25
19
514
94
103
80
81
100
96
19
180-848
88-100
103
20-129
52-100
23-185
6.4-181
NA
397
NA J 342 - 586
NA
NA
49
25
87
78
362 - 432
161,500
279 -
8,481 -191,650
120-144,740
546-22,217
Notes:
n Number of samples with detectable analyte concentrations.
SD Standard deviation,
mg/kg Milligrams per kilogram.
NA Not applicable, analyte not present above LRL.
63
-------
450
120
Arsenic
•Measured Value DTtw Value
overy
10000
Btinuifi
IMsasureiJ Value a True Value
-»100
• FOrcent Recovery
500
400
s
' 300 -
,2
2 2QQ
o
§ 10)
O
120
100
so
-------
Table 4-8 Parameters" by Primary ¥ariable-X-MET 920-P and §40
" Baf fun
••V.|rO-:;:.W^H:Jttl».: ' Y-fttt
9^1' 0941 0?4 ' 0,11
7/3 QV>V aiJ~T"T?7~
/48 ' 0 •%S_J__0 ^_^ 0 Ob
)ai 09fa4 0 14 0 J1
VH H4M 0 1? | 021
, ib 09) Jt 031 ) 004
,!66 0 %/' " 0?1 Oil
M'4 0%4| 017 016
*>'»9 OOfWJ 020 ! 0?7
Slope
096
091
U_OJ5_
089
-
,£85_
0,95
1 03™"
094
095 '
All
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ Unprepared
In Situ Propaipd
Intrusive-Unprepared
Intrusive-Prepared
mm
891
748
172
388
380 "
398
253
309
_206j
'•r2 Sitt.tttv
O4~3 i P?3
[ 0 04 0 12
07/i 0 10
000 , 0 14
014 028
066 ! 016
on_j »i/
0 35 0 34
061 I 021
06? ' 019
. f-lnt, ,|
! 48 I 0 51'
?Q/ i 0 18
|_£_1S_4 0 34
? 38 Pi),J
1 S6 , 041
2 04 ' 0 J9 "
1 49 ^ 0 58
1 J1 ] 058
1 3b (; 01
j_ 1 52 0 «>6
' Chromium
Copper
*;jrv .••...r*.' ' .Std,Enr. 1 Y4nt.
1154r06/jl 0<8 1 172
771 QOOb" oii~~pF4'2
387 0 &bb ' 0 24 1 6?
378 0030 01*.' i 2/6
yv> ' oar>3 o u I 2 io
w/ tMi(j& o/; 1 6J
*\V u W5 0 M 1 W
V)5 0594' 0 19 17'
3031 {» /!<3 015 ' 169~
_J21j_^^ll_ ° 1& ' 1 66
Slope
049
00?
053
0,19
020
0~53
0.42
044
052
0.55
All Data
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepaietl
In Situ-Prepared
Intrusive Unprepdred
Intrusive Prepared
•B
1147
778
371
381
3%
""371
/34
29tt
303
307"
•••
093
09?
007
015
097
007
086
O9'i
0,as
094
Std.£rr.
0 19
t 0 12
"o r i
0 12
0 11
021
0/1
0 17
016
0 18
Y-4nt.-,
092
044
•
1 H7
r_LT
0 37
I 8?
1 1B
OBH
?)«!
088
''SOPS ';
0 75
090
0 17
088
002
0 1 7
063
077 "
0 76
079~
^^IHBH^H^BI^^^^^HHHJ^^^*J^^^H^^^^^H
H^si^^^^s^^^^^^giBiig^^^^^^^^^s
;.r. ,li' '..... .r* JStAErr, ¥-lnt.
1147 09454 01"^Z£jl_
'id 09Jfl| 014 006
yv ' owe o 18 1 03^
>'i 'V.fffj'" QI^~^~ 0^)6™"
1/8 0<)'>9' 0 14 ! -019
vi'*h ' 090& 018 039
?,"y QfC.fi 0"4 035
ai)6 09f>b 0 1? " 008
'203 09/11 0 t«> 0 10
307 OU68, 0 13 [ 0 (V
OH
.Slope
0,98
1.01
o'fis"
0,95
i!oe
0,85
!__>_
0,%
1,00
i;0r
Variable
All Data
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
In Silu-Prepared
Intrusive-Unprepared
Intrusive Prepared
BBBB
BMW
860 r 0860
747 0905
141 0/10
__t"___
358 0 844
141 ' 0710
^j-j— J^
253 0810
186~! 0960
__l£LL_L9r>ll
^____.
.StdLEnr.
018
0 14
021
01
-------
Table 4-9. Regression Parameters* for the Sample Preparation Variable and Soil Texture—
X-MET 920-P and 940 Analyzers
Arsenic
Std, Err. Y-Int I Slope
in Situ-Unprepared
95
50
71
0.933J_ 0,18 ^
r^77Po:21
0,074 |a53 n
I 0,43
jOL
0,05
0,81
0.85
0.88
In Situ-Prepared
95
114
66
0,989
0,989
0,089
0,08
0,06
0,53
0.16
0,15
-0.17
0.95
0,95
0.95
Intrusive-Unprepared
96
113
53
0.984
0,986
0.033
0,10
0.07
0,58
0.16
JD.11
0.39
0.95
0.96
0.58
Intrusive-Prepared
96
114
59
0.980
0.985
0,034
0,11
0,08
0.59
0,39
0.29
0,33
0.90
0.95
0,64
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
In Sity-Unprepared
85
48
97
0,037
0.678
0,774
0.08
0.12
0.11
2.28
1.36
""208
0.10
0.53
0.37
In Situ-Prepared
97
112
(|| J5J
0,004
0.053
h 0,798
0.33
0.36
_QM\
1.99
1.51
2.25
0,15
0.30
0.31
intrusive-Unprepared
91
_1JLLi
100
0.031
0.568
tO.636
0.06
0.14
2,34
'T.sio
LAiEJi^.
0,07
0.54
0,47
Intrusive-Prepared
98
110
97
0.104
0.423
0.784
0.06
0.11
0.09
2.23
1.38
2.24
0.14
0.33
0.35
Chromium
Std. Err. Y-lnt. | Slope
Copper I
Std. Err. Y-lnt I Slope
In Situ-Unprepared
94
49
92
0.049
0.005
0,715
0,08
0.14
0,25
2.77
2,28
1.27
-0.15
0,11
0,63
In Situ-Prepared
95
111
97
"0.035
0.028
0,618
0.14 ,
0.10
0.28
2.82
2.23
1.57
-0.25
0,11
0,52
Intrusive-Unprepared
94
14
100
0.046
0.082
0.752
013
010
oil?
283
^211
~1 81
-0.24
019
[OA8
Intrusive-Prepared
93
in
99
0,016
0,074
0.790
G,1QJ2,65
0.12
0,17
2.09
1,74
-0.11
0.21
0.53
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
94
50
87 ^
0.909
0.840
0.21 5"1
0.14
0.18
0.73
CU9 "
", 0.19 11.72
In Situ-Prepared
96
114
87
0.963,
0.985
0,053
0.10
0,07
0.22
0.50
0,40
1.82
Intrusive-Unprepared
96
0.972
1141 Q-990,
95] 0,034
0.09
0.06
0.34
aSsP
r~~o.i7 H.ie
Q.74
6.87
— — —
0.32
0.87
0.90
0,18
0.93
0.92
6.09
Intrusive-Prepared
97
14
_____
0.961
0,990
0.030
u__i-10
0,48
0.08 , 0.43
__°J1_J^£2_
0,91
0,93
"0Ji "
66
-------
Table 4-9. Continued
in Situ-Unprepared
88
49
99
0833
0,849
0 833 '
0,20
0.23
0,22
0.44
0.38
0,73
0.75
0,84
0,75
In Situ-Prepared
m
111
"100
0985
0988
0,931
0.08
0,08
~0.15
0.02
-0.19
0.50
0,97
1.05
0,81
Intrusive-Unprepared
97
113
99
0980
0973
0 951
0,10
0.12
0,15
-0.03
-0,40
-0.03
0.98
1,11
0,96
Intrusive-Prepared
98
111
98
0.970
0,975
0,968
0.12
0.11
0.11
0,01
"-o.isT
L°!L
1.00
1.10
0.92
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
in Situ-Unprepared
94
48
83
0.937
0.762
0.717
0,11
0.19
0.23
0.56
0.48
1.18
0,78
0.81
0,69
in Situ-Prepared
95
103
58
0.965
0.850
0,728
0,10
0.15
0.22
0.35
0.24
1.91
0.89
0.90
0,63
Intrusive-Unprepared
95
103
ND
0,977
0.913
ND
0.08
0,11
ND
0.22
0.28
ND
0,94
0,90
ND
Intrusive-Prepared
97
101
ND
0.973
L 0.882
ND
0,09
0,15
ND
0.39
0.30
ND
0.91
0.92
ND
Motes: Regression parameters based on Iogto transformed data. These calculated for
FPXRF as the dependent variable, and thus, cannot be to correct the FPXRF data.
Section 5,
b
Zinc results for Intrusive-unprepared and intrusive-prepared do not include from the RV
Hopkins site,
Y-lnt. Y-lntercept.
Std, Err. Standard Error.
n Number of points,
ND Analytes not present in significant quantities to provide meaningful regression.
Copper, arsenic, and cadmium did not exhibit a wide concentration distribution in the clay soil.
Therefore, the effect of soil type on the quantitation of these analytes was restricted to sand and loam
soil. Barium, chromium, and nickel did not exhibit a wide concentration distribution in either the sand or
loam soil. Therefore, the effect of soil type on the quantitation of these analytes was restricted to loam
soil only. This single soil type evaluation could not be used to assess the potential effect of soil type on
analyzer performance. Based on this evaluation, there was no apparent impact of either the site or soil
variables on the regression. The sample preparation variable exhibited the greatest impact on the
regression (Table 4-10). Generally, the largest shift in the r2 was exhibited between the in situ-
unprepared and in situ-prepared analyses (Figure 4-8); this step represents sample homogenization.
Generally, r2 values increased with increasing sample preparation. Except for zinc, sample
homogenization accounted for between 80 and 100 percent of the total increase in the r1 experienced
across all sample preparation steps. Zinc exhibited its greatest increase in comparability after the
intrusive-unprepared analysis. This sample preparation effect makes sense due to the fact that the
homogenization step assured that the analyzers and the reference methods were analyzing essentially the
same sample. The initial sample homogenization (in situ-prepared) improved the comparability for
67
-------
arsenic and zinc between the two logio transformed data sets to the point that the analyzers met the
definitive level criteria. Lead data produced by the analyzers met definitive level criteria after the final
sample preparation step. The analyzer data for copper met the definitive level quality criteria at the
initial sample preparation step. Increasing sample preparation increased comparability for barium;
however, the data for this analyte never met a higher level criteria. Chromium failed to meet quantitative
screening level criteria for the regression analysis and its precision data, slightly greater than 20 percent,
placed it in the qualitative screening level category.
Table 4-10. Regression Parameters* for the Sample Preparation Variable Name-
and i40 Analyzers
In Slty-Unprtpared
145
71
0925
0074
0.19
0.53
0.39
0.05
0.83
0.88
In Situ-Prepared
208
8S
0990
0089
0.07
0.53
0,14
-0.17
0.95
0.95
Intrusive-Unprepared
209
53
0033
0.08
0.56
0,13
0.39
0.95
0.58
Intrusive-Prepared
210
59
0981
0034
0.09
0.59
0.35
0.33
0.92
0,64
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
In Situ-Unprepared
132
97
O.tBlj 0.10
0.774
0.11
2.09
2,08
0.19
0.37
In Sity-Prepared
213
75
0.005
0.798
0.38
0.08
2.00
2.25
0,10
0.31
Intrusiwe-Unprepared
206
100
0.219] 0.14
CX686J 0.19
1.85
1.82
0.30
0.47
Intrusive-Prepared
207
97
0.306
0.784
0,08
0.09
2.09
2.24
0.22
0.35
Chromium
Std. Err. Y-int. Slope
Copper
Std. Err. Y-lnt.1 Slope
In Situ-Unprepared
146
92
0038
,0715
0.12
0.25
2,84
1.27
-0.23
0.83
In Slty-Prepared
20 /
97
207
100
0001 0.13
>-^l^l^^^.
2.47
~" * ™— —•
1.57
-0,03
l,
0,52
Intrusive-Unprepared
U 003
0752
0.12
0.17
2.37
1.81
0.05
0.48
Intrysive-Prepared
204
99
0011
079Q
0,12
0.17
2.31
1.74
0.09
0.53
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
In
147
87
0888
0215
Situ-Unprepared
017
019
070
1 72
079
03?
In Situ-Prepared
209
87
0984
0053
008
022
__l
1 8">
089
016
Intrusive-Unprepared
210
«
0986
—
0034
008
0 17
035
1 90
oy->
009
Intrusive-Prepared
211
98
0984
0030
00«
022
044
200
09?
011
68
-------
Table 4-10.
In Situ-Unprepared
1,1?
0?0
0833,
030
073
084
075
In Situ-Prepared
0 983
00«
too
007
050
I 01
081
Site
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
in Situ-Unprepared
144
85
OSiQ1
0 686'
015
025
J 0,55
1 1.1?
078
071
In Situ-Prepared
200
83
0864!
071?"
0 17
0/3
O.P9
1.18
089
069
Intrusive-Unprepared
Site Name
Intrusive-Unprepared
J06 , 0977
»vj ' 0951
010
-0 17
003
1 03
096
ASARCO
RV Hopkins
196 ' 0957'
NO i Nt> ,
010
NO
0,23
NU
033
ND
Intrusive-Prepared
Site Name
Intrysive-Prepared
IS
Notes,
0
0 9RM
0 fl
011
0 1!
G28
1 05
092
ASARCO
RV Hopkins
199
ND
0927
ND
013
ND
0,48
ND
090
ND
Reyiesoion parameters based on Iog10 transformed data. Theso parameters ware calculated for
FPXRF data as the dependent variable, and thus, cannot be used to correct the FPXRF data. See
Section 5,
to
Zinc results for intrusive-unprepared and intrusive-prepared do not include samples from the RV
Hopkins site,
Y-lnt. Y-lntercept.
Std. Err, Standard Error,
n Number of points,
ND Analytes not present in significant quantities to provide meaningful regression.
Within the sample preparation steps, the effect of contaminant concentration was also examined.
The logio transformed data sets for the primary analytes were sorted into the following concentrations
ranges: 0-100 mg/kg, 100 - 1,000 mg/kg, and greater than 1,000 mg/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 with increasing
concentration. The 0-100 mg/kg concentration range showed the poorest comparability. This is most
likely due to this range generally occurring just above or below the analyzers field-based MDLs. The
analyzers' precision and accuracy are lowest in this concentration range. This proximity to MDLs was
also exhibited for most of the target analytes in the 100-l,000mg/kg range. Generally, the r2 values
improved between 5 and 30 percent between 100 and 1,000 mg/kg and greater than 1,000 mg/kg ranges.
This effect was minimized by examining the logio transformed data and the slight 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.
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 Metorex X-MET 920-P and 940 data quality performance in this demonstration is
provided in Table 4-11.
69
-------
In situ-unprepared
x
10 100 1000 10000 100000
Reference Data (mg/kg)
— 100000
In situ-prepared
i
D
£L
O
CM
O>
-
1000 -
100 -
100 1000
Reference Data (nng/kg}
1QQ00Q
, 100000
Intrusive-unprepared
10000 -
aj
1
a,
O
100000
Intrusive-prepared
O
OJ
I
10000 -
1000
10 100 1000 1GQ0G
Reference Data {mg/kg)
100CX»
100
1000
10000
Reference Data {mg/kg)
100000
Figure 4-8. Sample Preparation Effect on Lead Results; These log-log the
in comparability with changes in sample preparation step.
Table 4-11, Summary of Data Quality Level Parameters
Target
Analytes
Arsenic
Bariym
Chromium
Copper
Lead
Zinc
Nickel
Iron
Cadmiym
_AnWmorr^_
920-P/940
Analytes
Arsenic
Barium
Chromium
Copper
Lead
Zinc
Nickel
Iron
Cadmium
l_Not_Reported_
Precision (mg/kg)
Mean % BSD
S-1QXMDL
3.36
3.38
22,72
7.80
4.80
f 4.26 ,
24,92
Not Determined
Not Determined
u_____IL____,
Method Detection
Limits (mg/kg)
(Precision-based)
55
30
210
75
45
70
120 1
Not Determined
*~ 25
T:^_—^ • _j
Coefficient of
Determination
(rs AH Data)
0.94
0.43
0.67
0.93
0.94
0.86
Not Determined
Not Determined
Not Determinetl
____JIL___j
•
Data Quality
Level
Dofmitive
Oudlttativo
Qualitative
Definitive
Definitive
Definitive
Insufficient Oatd
Insutfic it nl Data
Insuffif tont Data
-
70
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Section 5
Applications Assessment and Considerations
The Metorex X-MET 920-P and 940 analyzers are designed to analyze for metals in soils. Developer
provided ACES software was used for calibration and quantitation to maximize instrument performance
and account for common soil-related matrix interferences. In addition, the calibrations can be fine tuned
using site-specific samples to further improve data comparability. These analyzers were designed for
field use and exhibited ruggedness through a variety of environmental operating conditions. The
analyzers experienced two failures resulting in downtime and lost data during the field demonstration.
During the demonstration, more than 1,260 samples were analyzed by these instruments. The training
provided by the developer was sufficient to allow basic field operation of the analyzers; however,
developer assistance was required to address software and equipment problems. The developer provided
highly accessible and timely field support.
The X-MET 940 was lighter and more compact than the X-MET 920-P. The data storage problems
associated with the X-MET 940's hard drive need to be resolved, allowing a greater number of samples to
be analyzed and reducing the potential for data loss or equipment downtime. A summary of the
operational features of both instruments is shown in Table 5-1.
Comparison of the logic transformed FPXRF data to the logio transformed reference data indicated
that the FPXRF and reference data are logio-logio linearly related. Based on this relationship, both
analyzers can produce definitive level data for lead, arsenic, copper, and zinc. This indicated that the
logio transformed FPXRF data were statistically equivalent to the logio transformed reference data for
these analytes. For the target analytes barium and chromium, the analyzers produced qualitative
screening level data. These analyzers, for the above elements, exhibited instrument precision similar to
the reference methods, indicating high measurement reproducibility.
Both analyzers can use up to two radioactive sources allowing analysis of a large number of metals in
soils. The analyzers generally use count times 100- 240 live-seconds. Longer count times and multiple
sources generally increase accuracy, the number of analytes and lower the detection limits but decrease
sample throughput.
There were no apparent effects of site or soil type on instrument performance. Both analyzers can be
applied in an in situ or intrusive mode. This demonstration identified sample preparation as the most
important variable with regard to comparability of the data from analyzers to the reference methods. The
data from this demonstration indicated that when operated in the in situ mode, the user most probably
would not be able to 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
71
-------
correlation between the FPXRF and reference data for the analyzers was achieved after the initial sample
preparation step (sample homogenization). Further sample preparation, such as sieving or drying and
sieving, in most cases did improve the comparability; however, relative to the demonstration-defined data
quality criteria, no increases in data quality level occurred. Based on the comparability study, the
analyzers produced field-based MDLs 2 to 3 times greater than the developer-supplied or precision-based
MDLs. This difference may have been due to differences in the definition of MDLs used by the
developer and the demonstration.
5-1, Summary of Test and Operational Features
«920-P: Computer weighs 28 pounds, probe weighs 9 pounds, life of 5 to 7 hours
«940; Computer weighs 14 pounds, probe weighs 9 pounds, life of 2 to 3 hours
Sample throughput of 10 to 12 per hour
Three
Up to two sources can be fitted on an analyzer's probe increasing the number of
Si(Li) or gas-filled proportional detectors available
Arsenic, lead, copper, zinc-definitive level data
Barium, chromium-qualitative screening level data
Fundamental parameters calibration can be fine tuned with site-specific (optional empirical
calibration
Precision—percent RSD values than 10 percent at 5 to 10 times the MDL for all
and Cr for those reported (Cd and Fe NR)
Can be on exhibiting up to 30 percent water saturation by weight
Can conduct in situ or intrusive measurements
Based on this demonstration, both analyzers are well suited for the rapid real-time assessment of
metals contamination in soil samples. This demonstration indicated that the use of these analyzers in a
rainy environment requires special handling to keep the probe dry. In addition, more extensive training
or operator experience would be needed to reduce the potential for data loss and downtime associated
with software problems. The addition of calibration check samples would also reduce the potential for
data loss. Although in several cases the analyzers produced data statistically equivalent to the reference
data, generally confirmatory analysis would be required or requested for FPXRF analysis. Ten to 20
percent of the samples measured by the analyzers should be submitted for reference method analysis;
then instrument bias relative to standard methods such as 3050A/6010A could be determined. This
would only hold true if the analyzers and the reference laboratory measure similar samples. This was
accomplished in this demonstration by thorough sample homogenization. Bias correction allows FPXRF
data to be corrected so that it approximates the reference data. The demonstration showed that both
analyzers exhibit strong logio-logio linear relationships with the reference data over a concentration range
of 5 orders of magnitude. For optimum correlation, samples in the high, medium, and low concentration
ranges should be submitted for reference method analysis.
-------
The steps to correct the FPXRP measurements to more closely match reference data are as follows:
1. Conduct sampling and FPXRP analysis.
2. Select 10-20 percent of the sampling locations for resampling. These resampling 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 FPXRP and a reference method.
4. Tabulate the resulting data with reference data in the x-axis column (dependent variable) and the
FPXRP data in the y-axis column (independent variable). Transform this data to the equivalent log,,
value for each concentration.
5. Conduct a linear regression analysis and determine the r2, y-intercept, and slope of the relationship.
The r2 should be greater than 0.70 to proceed.
6. Place the regression parameters into Equation 5-1:
Y (logic corrected FPXRF data) = slope* (login) FPXRF data) + Y-intercept (5-1)
7. Use the above equation with the logio transformed PPXRP results from Step 4 above and calculate
the equivalent logio corrected FPXRP data.
8. Take the anti-logio(10 [logio transformed corrected FPXRP data) of the equivalent logio corrected FPXRP data
calculated in Step 7. These resulting values (in milligrams per kilogram) represent the corrected
PPXRP data.
To show the effect of correcting the FPXRP data, the change in average relative bias and accuracy
can be examined. The average relative bias between the FPXRP data and the reference data is a measure
of the degree to which the FPXRP 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
an individual measurement. An example of this can be seen in the analyzers' 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 analyzers'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 over-
or underestimated relative to a reference measurement. Table 5-2 shows the average relative bias and
accuracy exhibited by the FPXRP, before and after data correction using the eight-step approach
previously discussed.
The average relative bias and accuracy for the analytes which fell into the definitive level data
quality category were generally small. The analytes falling into the quantitative and qualitative screening
level data quality categories had generally larger average relative bias and accuracy.
Once the FPXRP data is corrected using the regression approach presented earlier, both the average
relative bias and accuracy were reduced. These numbers are no longer strongly influenced by
concentration effect since the regression approach used to correct the data used logio transformed data.
The average relative bias and accuracy for the corrected data are similar to the acceptable average
relative bias between the reference data and PE samples (true values), as shown by the last column in
Table 5-2.
73
-------
Table 5-2, Effects of Data Correction on FPXRF Data Comparability to Reference Data for Ail In
Situ-Prepared Samples
Target Analyte
Average
Relative Bias
on Raw Data3
Average
Relative Bias
on Corrected
Data"
Average
Relative
Accuracy on
Raw Data0
Average
Relative
Accuracy on
Corrected
Data"
Acceptable
Relative
Accuracy
Based on PE
Samples"
Antimony
Arsenic
Barium
Chromium
Copper
Iron
Lead
Nickel
Zinc
4,60
1.07 ~~~1
3,21
8,15
2,18
,__^
1.92
3,91
1.87
1.19
„_____ _j
1,31
1.27
1.13
1,02
1.03
1.30
,_ _
10,40
2,4?
9.21
18,19
6.33
1.69
1,54
14.18
5.05
2.65
2.42
2.76
3,04
2,66
1.40
1.62
3.48
2.41
2.94 ± 0,56
1,76 ±0.28
1.36 ±0,06
1.55*0.15
1,18 ±0.47
1.54*0.14
1,63*0.23
1.56*0,14
1.64 ±0,12
Notes: A measurement of average relative bias, measured as a factor by which the FPXRF, on average,
over- or underestimates results relative to the reference methods. This measurement of bias is
based on raw (not iog,0 transformed) data. This average relative bias does not account for any
concentration effect on analyzer performance,
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 Iog10 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 SS percent confidence interval that define the
acceptable rang© of differences allowed between the reference method reported concentrations and
the true value concentrations In the PE samples. This bias is included only as a general reference
for the improvement on comparability of FPXRF data and reference data after FPXRF
correction.
The is calculated as follows:
Average relative = ({£j[FPXRFj/Referencej])/numb0r 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 The above table presents the average relative bias as a factor.
The average accuracy is calculated as follows:
Average relative accuracy =SQRT {£.,({FPXRF/Referencet3-1)Vnumber 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 relative
accuracy. The above table presents the average relative bias as a factor.
-------
Both analyzers can provide rapid assessment regarding the distribution of metals contamination in
soil at a hazardous waste site. This data can be used to characterize general site contamination, 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 approval of Method 6200 will help in the acceptance of FPXRF data. 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. Both analyzers are powerful tools for site characterization
and remediation. They provide a faster and less expensive means of analyzing metals contamination in
soil, relative to conventional approaches.
General Operational Guidance
The following paragraphs describe general operating considerations for FPXRF analysis. This
information is derived from SW-846 Method 6200.
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.
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 astainless-
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 soil 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 and 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, 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 when the sample is kneaded
75
-------
in a plastic bag is to add sodium fluorescein dye to the sample. After the moist 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.
After a sample is prepared, a portion should be placed in a 3 1-mm polyethylene sample cup (or
equivalent) for analysis. The sample cup should be completely filled and covered with a 2.5-micrometer
Mylar™ (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 the
method blanks are below the MDLs of the procedure.
76
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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 Superfund-Interim
Final Guidance." Office of Solid Waste and Emergency Response. Washington, D.C. EPA/540/R-
93/071.
77
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