United States
Environmental Protection
Agency
Office of Research and
Development
Washington, D.C. 20460
EPA/6007R-87i'145
March 1998
vvEPA Environmental Technology
Verification Report
Field Portable X-ray
Fluorescence Analyzer
Spectrace TN 9000 and
TN Pb Field Portable X-ray
Fluorescence Analyzers
SUPERFUND INNOVATIVE
TECHNOLOGY EVALUATION
Environmental Technology
Verification Program
057CMB98
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March 1998
Environmental Technology
Verification Report
Field Portable X-ray
Fluorescence Analyzer
Spectrace TN 9000 and TN Pb Field
Portable X-ray Fluorescence Analyzers
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. 20460
ss
ENVIRONMENTAL TECHNOLOGY
VERIFICATION PROGRAM
ENVIRONMENTAL TECHNOLOGY VERIFICATION PROGRAM
VERIFICATION STATEMENT
TECHNOLOGY TYPE: FIELD PORTABLE X-RAY FLUORESCENCE ANALYZER
APPLICATION: MEASUREMENT OF METALS IN SOIL
TECHNOLOGY NAME: TN 9000
COMPANY:
ADDRESS:
PHONE:
TNSPECTRACE
2555 N. INTERSTATE HWY 35
P.O. BOX 800
ROUND ROCK, TX 78680-0800
(512)388-9100
The U.S. Environmental Protection Agency (EPA) has created a program to facilitate the deployment of innovative
technologies through performance verification and information dissemination. The goal of the Environmental
Technology Verification (ETV) Program is to further environmental protection by substantially accelerating the
acceptance and use of improved and more cost-effective technologies. The ETV Program is intended to assist and
inform those involved in the design, distribution, permitting, and purchase of environmental technologies. This
document summarizes the results of a demonstration of the Spectrace TN 9000 Analyzer.
PROGRAM OPERATION
The EPA, in partnership with recognized testing organizations, objectively and systematically evaluates the
performance of innovative technologies. Together, with the full participation of the technology developer, they
develop plans, conduct tests, collect and analyze data, and report findings. The evaluations are conducted according
to a rigorous demonstration plan and established protocols for quality assurance. The EPA's National Exposure
Research Laboratory, which conducts demonstrations of field characterization and monitoring technologies,
selected PRC Environmental Management, Inc., as the testing organization for the performance verification of field
portable X-ray fluorescence (FPXRF) analyzers.
DEMONSTRATION DESCRIPTION
In April 1995, the performance of seven FPXRF analyzers was determined under field conditions. Each analyzer
was independently evaluated by comparing field analysis results to those obtained using approved reference
methods. Standard reference materials (SRM) and performance evaluation (PE) samples also were used to
independently assess the accuracy and comparability of each instrument.
The demonstration was designed to detect and measure a series of inorganic analytes in soil. The primary target
analytes were arsenic, barium, chromium, copper, lead, and zinc; nickel, iron, cadmium, and antimony were
secondary analytes. The demonstration sites were located in Iowa (the RV Hopkins site) and Washington (the
AS ARCO 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, and 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-03
The accompanying notice is an integral part of this verification statement
iii
March 1998
-------
discussion of results, may be found in the report entitled "Environmental Technology Verification Report, Field
Portable X-ray Fluorescence Analyzer, Spectrace TN 9000 and TN Pb Field Portable X-ray Fluorescence
Analyzers." The EPA document number for this report is EPA/600/R-97/145.
The EPA Method 6200 was tested and validated using the data derived from this demonstration. This method may
be used to support the general application of FPXRF for environmental analysis.
TECHNOLOGY DESCRIPTION
This analyzer operates on the principle of energy dispersive X-ray fluorescence spectroscopy where the
characteristic 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. FPXRF instruments are designed to provide rapid analysis of metals in soil. This information allows
investigation and remediation decisions to be made on-site and reduces the number of samples that need to be
submitted for laboratory analysis. In the operation of these instruments, the user must be aware that FPXRF
analyzers do not respond well to chromium and that detection limits may be 5 to 10 times greater than conventional
laboratory methods. As with all field analysis programs, a portion of the samples should be sent to a laboratory for
confirmatory analyses.
The TN 9000 is a commercially available instrument that can use up to three radioactive sources and a mercuric
iodide semiconductor detector for the analysis of metals in soil. It is field portable, weighing less than 20 pounds,
and can be battery powered for up to 8 hours. For this demonstration, the TN 9000's Soils Application software
was configured to report concentrations for chromium, iron, nickel, copper, zinc, arsenic, lead, cadmium, antimony,
and barium. Contaminant concentrations are computed using a fundamental parameters (FP) calibrated algorithm
included in the analyzer's operations software. The FP calibration does not require site-specific calibration
standards. The TN 9000 can conduct in situ measurements or measure samples in cups. At the time of testing, the
TN 9000 cost about $58,000, or it could be leased for $6,000 per month or $3,500 for 2 weeks.
VERIFICATION OF PERFORMANCE
The performance characteristics of the TN 9000 include the following:
• Detection limits: Precision-based detection limits were determined by collecting 10 replicate measurements
on site-specific soil samples with metals concentrations 2 to 5 times the expected MDLs. The results were 100
milligrams per kilogram (mg/kg) or less for all of the reported analytes except chromium, which was determined
to be 200 mg/kg using the Fe55 source and 500 mg/kg using the Cd109 source which was used for the other
reported analytes. Values for iron and cadmium were not reported due to an insufficient number of samples in
the required concentration range.
• Throughput: Average throughput was 8.5 to 10.5 analyses per hour using a source count time of 220 seconds.
This rate only represents the analysis time, since different personnel were used to prepare the samples.
• Drift: This was evaluated using the results of a daily analysis of an SRM which contained quantifiable levels
of arsenic, barium, copper, lead, and zinc. Over the 18 days of the demonstration, the RSD values for the mean
recovery of barium, copper, lead, and zinc were all less than 8 percent. The corresponding value for arsenic was
18.2 percent.
• Completeness: The TN 9000 produced results for 1,259 of the 1,260 samples analyzed, resulting in a
completeness of 99.9 percent. The remaining sample was lost due to operator error in transferring the data.
• Blank results: During the demonstration, 37 blank samples were analyzed. None of the reported primary
analytes were detected above the method detection limits. However, iron frequently reported a value above the
MDL after analyzing samples with an iron concentration of greater than 20,000 mg/kg.
• Precision: The goal of the demonstration was to achieve relative standard deviations (RSD) less than 20
percent at analyte concentrations of 5 to 10 times the method detection limit. The RSD values for antimony,
EPA-VS-SCM-03
The accompanying notice is an integral part of this verification statement
iv
March 1998
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arsenic, barium, copper, lead, and zinc were less than 8 percent. The corresponding value for chromium was 22
percent. Values for cadmium, iron, and nickel were not reported because too few samples containing the
required concentrations were measured.
• Accuracy: Intramethod accuracy was assessed using site-specific soil PE samples and soil SRMs. The data
showed that 37 of 41 or 90.2 percent of the PE sample analytes had recoveries within the quantitative acceptance
range of 80-120 percent. A corresponding 22 of 24 or 91.7 percent of the SRM analytes were within the 80 -
120 percent recovery range. The TN 9000 gave more accurate results when the reference sample closely
matched the matrix used to set the fundamental parameters calibration for the analyzer.
• Comparability: This demonstration showed that the TN 9000 produced data that exhibited a Iogi0-logi0 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 0.95 for arsenic, 0.95 for copper, 0.96 for lead, 0.93 for
zinc, 0.79 for barium, and 0.78 for chromium. Results for iron, nickel, cadmium, and antimony were not
reported due to limited sample data. ,
• Data quality levels: Using the demonstration derived precision RSD results and the coefficient of determination
as the primary qualifiers, the TN 9000 produced definitive level data for arsenic, copper, lead, and zinc;
quantitative level data for barium; and data of qualitative screening level for chromium. Values for iron, nickel,
cadmium, and antimony could not be assigned without adequate precision or comparability data.
The results of the demonstration show that the Spectrace TN 9000 can provide useful, cost-effective data for
environmental problem-solving and decision-making. Undoubtedly, it will be employed in a variety of applications,
ranging from serving as a complement to data generated in a fixed analytical laboratory to generating data that will
stand alone in the decision-making process. As with any technology selection, the user must determine what is
appropriate for the application and the project data quality objectives.
GaryJ.:
Director
National Exposure Research Laboratory
Office 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 makes no expressed or implied warranties as to the performance of the technology
and 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-03
The accompanying notice is an integral part of this verification statement
V
March 1998
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
Washington, D.C. 20460
ENVIRONMENTAL TECHNOLOGY
VERIFICATION PROGRAM
ENVIRONMENTAL TECHNOLOGY VERIFICATION PROGRAM
VERIFICATION STATEMENT
TECHNOLOGY TYPE: FIELD PORTABLE X-RAY FLUORESCENCE ANALYZER
APPLICATION: MEASUREMENT OF METALS IN SOIL
TECHNOLOGY NAME: TNPb ANALYZER
TN SPECTRACE
2555 N. INTERSTATE HWY 35
P.O. BOX 800
ROUND ROCK, TX 78680-0800
(512) 388-9100
COMPANY:
ADDRESS:
PHONE:
The U.S. Environmental Protection Agency (EPA) has created a program to facilitate the deployment of innovative
technologies through performance verification and information dissemination. The goal of the Environmental
Technology Verification (ETV) Program is to further environmental protection by substantially accelerating the
acceptance and use of improved and more cost-effective technologies. The ETV Program is intended to assist and
inform those involved in the design, distribution, permitting, and purchase of environmental technologies. This
document summarizes the results of a demonstration of the Spectrace TN Pb Analyzer.
PROGRAM OPERATION
The EPA, in partnership with recognized testing organizations, objectively and systematically evaluates the
performance of innovative technologies. Together, with the full participation of the technology developer, they
develop plans, conduct tests, collect and analyze data, and report findings. The evaluations are conducted according
to a rigorous demonstration plan and established protocols for quality assurance. The EPA's National Exposure
Research Laboratory, which conducts demonstrations of field characterization and monitoring technologies,
selected PRC Environmental Management, Inc., as the testing organization for the performance verification of field
portable X-ray fluorescence (FPXRF) analyzers.
DEMONSTRATION DESCRIPTION
In April 1995, the performance of seven FPXRF analyzers was determined under field conditions. Each analyzer
was independently evaluated by comparing field analysis results to those obtained using approved reference
methods. Standard reference materials (SRM) and performance evaluation (PE) samples also were used to
independently assess the accuracy and comparability of each instrument.
The demonstration was designed to detect and measure a series of inorganic analytes in soil. The primary target
analytes were arsenic, barium, chromium, copper, lead, and zinc; nickel, iron, cadmium, and antimony were
secondary analytes. The demonstration sites were located in Iowa (the RV Hopkins site) and Washington (the
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, and 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-04
The accompanying notice is an integral part of this verification statement
vi
March 1998
-------
discussion of results, may be found in the report entitled "Environmental Technology Verification Report, Field
Portable X-ray Fluorescence Analyzer, Spectrace TN 9000 and TN Pb Field Portable X-ray Fluorescence
Analyzers." The EPA document number for this report is EPA/600/R-97/145.
The EPA Method 6200 was tested and validated using the data derived from this demonstration. This method may
be used to support the general application of FPXRF for environmental analysis.
TECHNOLOGY DESCRIPTION
This analyzer operates 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 the 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 remediation decisions to be made on-site and reduces the number of samples
that need to be submitted for laboratory analysis. In the operation of these instruments, the user must be aware that
FPXRF analyzers do not respond well to chromium and that detection limits may be 5 to 10 times greater than
conventional laboratory methods. As with all field collection programs, a portion of the samples should be sent
to a laboratory for confirmatory analyses.
The TN Pb Analyzer was specifically designed to analyze for lead in a variety of matrices. It is field portable,
weighing less than 20 pounds, and can be battery powered up to 8 hours. It uses a single radioactive source
(cadmium-109) and a mercuric iodide semiconductor detector to analyze metals in soils using relatively short count
times (60 seconds). The single radioactive source limits the number of analytes which can be detected. In this
demonstration, the TN Pb Analyzer's Soils Application software was configured to report concentrations for
arsenic, chromium, copper, lead, and zinc. Contaminant concentrations are computed using a fundamental
parameters (FP) calibrated algorithm included in the analyzer's operations software. The FP method does not
require site-specific calibration samples; however, such samples can be used to customize the calibration to a
particular matrix. The TN Pb Analyzer can conduct in situ measurements or measure samples in cups. At the time
of the demonstration, the cost of the TN Pb Analyzer was about $39,500, or it could be leased for $5,500 per month
or $3,000 for 2 weeks.
VERIFICATION OF PERFORMANCE
The performance characteristics of the TN Pb Analyzer include the following:
• Detection limits: Precision-based detection limits were determined by collecting 10 replicate measurements
on site-specific soil samples with metals concentrations 2 to 5 times the expected MDLs. The results were 115
milligrams per kilogram (mg/kg) or less for arsenic, copper, lead, and zinc. Chromium was determined to be
460 mg/kg.
• Throughput: Average throughput was between 20 and 25 analyses per hour using a count time of 60 seconds.
This rate only represents the analysis time since different personnel were used to prepare the samples.
• Drift: Based on a daily analysis of an SRM, which contained quantifiable levels of arsenic, copper, lead, and
zinc, the drift RSD values for the mean recovery of these analytes were less than 8 percent.
• Completeness: The TN Pb Analyzer produced results for all of the 1,260 samples for a completeness of 100
percent.
• Blank results: During the demonstration,, a total of 20 SiO2 blank samples was analyzed. None of the reported
analytes were detected above the method detection limits.
• Precision: The goal of the demonstration was to achieve relative standard deviations (RSD) of less than 20
percent at analyte concentrations of 5 to 10 times the method detection limits. The RSD values for arsenic,
copper, lead, and zinc were less than 10 percent. A value for chromium was not determined due to a lack of
sufficient samples in the required concentration range.
EPA-VS-SCM-04
The accompanying notice is an integral part of this verification statement
vii
March 1998
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• Accuracy: Intramethod accuracy was assessed using site-specific PE soil samples and soil SRMs. The data
showed that 20 of 28 results (71.4 percent) of the PE sample analytes had recoveries within the 80 - 120 percent
quantitative acceptance range and that 100 percent (16 of 16) of the SRM analytes were within this range.
Results were more accurate when the sample closely matched the matrix used to set the fundamental parameters
calibration.
• Comparability: This demonstration showed that the TN Pb Analyzer produced data that exhibited a Iog10-log10
linear correlation to the reference data. The coefficient of determination (r2) which is a measure of the degree
of correlation between the reference and field data was 0.95 for arsenic, 0.94 for copper, 0.95 for lead, and 0.92
for zinc. The value for chromium, 0.55, was derived primarily from clay soil at the RV Hopkins site.
« Data qualify levels: Using the demonstration derived precision RSD results and the coefficient of
determination as the primary qualifiers, the TN Pb Analyzer produced definitive level data for arsenic, copper,
lead, and zinc. A value for chromium could not be assigned without adequate precision data.
The results of the demonstration show that the Spectrace TN Pb Analyzer can provide useful, cost-effective data
for environmental problem-solving and decision-making. Undoubtedly, it will be employed in a variety of
applications, ranging from serving as a complement to data generated in a fixed analytical laboratory to generating
data that will stand alone in the decision-making process. As with any technology selection, the user must
determine what is appropriate for the application and the project data quality objectives.
GaryJ.EoJ£yJPh.D.
Director
National Exposure Research Laboratory
Office 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 makes no expressed or implied warranties as to the performance of the technology
and 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-04
The accompanying notice is an integral part of this verification statement
viii
March 1998
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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
IX
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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 EPXRF analyzers perform in comparison to a standard reference method, (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 the location of 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 were represented by three
distinct soil textures: sand, clay, and loam. The reference methods used to evaluate the comparability of
data were EPA SW-846 Methods 3050A and 6010A, "Acid Digestion of Sediments, Sludges, and Soils"
and "Inductively Coupled Plasma-Atomic Emission Spectroscopy," respectively.
The FPXRF analyzers tested in this demonstration were designed to provide rapid, real-time analysis of
metals concentrations in soil samples. This information 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 TN 9000 and TN Pb Analyzers. Separate ETVRs have
been published for the other analyzers demonstrated.
Quantitative data were provided by both of the Spectrace analyzers on a real-time basis. The TN Pb
Analyzer reported fewer target analytes than the TN 9000 and used shorter count times. The shorter
count times resulted in a nearly two- to threefold increase in sample throughput. Both the TN 9000 and
the TN Pb analyzers provided definitive level (equivalent to reference data) quality data for arsenic, lead,
zinc, and copper; the TN 9000 produced quantitative screening level (not equivalent to reference data,
but correctable with confirmatory analysis) quality data for barium. TN 9000 data for chromium was
classified as qualitative, based primarily on the results of the precision determination. Data quality levels
for the remaining analytes could not be assigned due to the lack of precision or correlation data. The TN
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Pb Analyzer did not report nickel, iron, barium, cadmium, or antimony and did not report a precision
value for chromium in the concentration range specified to determine the data quality level. These
results were obtained using a factory-set fundamental parameters calibration. Sample homogenization
was the single most important factor influencing data comparability.
This study showed that the two analyzers produced data that exhibited a Iog10-log10 linear correlation.
Through regression analysis of Iog10 transformed data, the analyzers' data can be corrected to be even
more comparable to reference data. Correction of the in situ-prepared data resulted in up to an eightfold
increase in average relative accuracy for both the TN Pb and TN 9000 analyzers. Unless a user has
regulatory approval, confirmatory (reference) sampling and data correction is recommended when using
these analyzers for site characterization or remediation monitoring.
This demonstration found that both TN Spectrace FPXRF analyzers were generally simple to operate in
the field. The operators 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. Generally, either the
developer's listed or field-based method detection limits were 5 times or more higher than the reference
method detection limits. The TN 9000 and TN Pb Analyzer were both effective tools for field-based
analysis of metals contamination in soil and may allow investigation or remediation decisions to be made
more efficiently on-site which may reduce the number of samples that need to be submitted for
confirmatory analysis.
XI
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Table of Contents
Section
Page
Notice ii
Verification Statements jjj
Foreword ix
Abstract x
List of Figures xv
List of Tables xvi
List of Abbreviations and Acronyms r. xvii
Acknowledgments xix
1 Executive Summary 1
2 Introduction 3
Demonstration Background, Purpose, and Objectives 3
Reference Methods 4
Site Selection 5
Predemonstration Sampling 7
Experimental Design 8
Qualitative Factors 10
Quantitative Factors 10
Evaluation of Analyzer Performance 13
Deviations from the Demonstration Plan 20
Sample Homogenization 21
3 Reference Laboratory Results 23
Reference Laboratory Methods 23
Reference Laboratory Quality Control 24
Quality Control Review of Reference Laboratory Data 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
xii
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Section Pace
Data Review, Validation, and Reporting 30
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 TN Pb Analyzer 41
Theory of FPXRF Analysis 41
Background 42
Operational Characteristics 43
Equipment and Accessories 43
Operation of the Analyzer 44
Background of the Technology Operator 44
Training 45
Reliability 45
Health and Safety 46
Cost 46
Performance Factors 47
Detection Limits 47
Throughput 49
Drift 49
Intramethod Assessment 49
Blanks 49
Completeness 50
Precision 50
Accuracy 51
Comparability 54
Intermethod Assessment 56
5 TN 9000 Analyzer 64
Theory of FPXRF Analysis 64
Background 65
Operational Characteristics 66
Equipment and Accessories 66
Operation of the Analyzer 68
Background of the Technology Operator 68
Training 68
Reliability 68
Health and Safety 69
Cost 69
Performance Factors 70
Detection Limits 70
Throughput 72
Drift 73
XIII
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Section
Intramethod Assessment
Blanks
Completeness ..
Precision
Accuracy
Comparability ...
Intermethod Assessment
Applications Assessment and Considerations
General Operational Guidance
73
74
74
74
75
81
81
92
97
References 99
XIV
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List of Figures
Figure
Page
2-1 Sample Preparation and Analysis 9
2-2 Linear and Log-log Data Plots 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 Critical Zone for the Determination of a Field-based Method Detection Limit
for Copper 48
4-3 Drift Summary—TN Pb Analyzer 50
4-4 Precision vs. Concentration—TN Pb Analyzer 51
4-5 SRM Results—TN Pb Analyzer 54
4-6 Site-specific PE Sample Results—TN Pb Analyzer 55
4-7 PE and CRM Results—TN Pb Analyzer 57
4-8 Sample Preparation Effect on Lead and Arsenic,Results—TN Pb Analyzer 61
5-1 Principal of Source Excited X-ray Fluorescence 65
5-2 Critical Zone for the Determination of a Field-based Method Detection Limit
for Copper 72
5-3 Drift Summary—TN 9000 Analyzer 73
5-4 Precision vs. Concentration for Lead and Copper—TN 9000 Analyzer 76
5-5 Site-specific PE Sample Results—TN 9000 Analyzer 79
5-6 SRM Results—TN 9000 Analyzer 80
5-7 PE and CRM Results—TN 9000 Analyzer 83
5-8 Sample Preparation Effect on Arsenic and Lead Results—TN 9000 Analyzer 87
xv
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List of Tables
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
4-1 Analyzer Instrument Specifications—TN Pb Analyzer 44
4-2 Instrument and Field Operation Costs 47
4-3 Method Detection Limits—TN Pb Analyzer 48
4-4 Precision Summary—TN Pb Analyzer 50
4-5 Accuracy Summary for Site-Specific PE and SRM Results—TN Pb Analyzer 53
4-6 Accuracy Summary for PE and CRM Results—TN Pb Analyzer 56
4-7 Regression Parameters by Primary Variable—TN Pb Analyzer 58
4-8 Regression Parameters for the Sample Preparation Variable and Soil Texture—
TN Pb Analyzer 59
4-9 Regression Parameters for the Sample Preparation Variable and Site Name—
TN Pb Analyzer 62
4-10 Summary of Data Quality Level Parameters 63
5-1 Analyzer Instrument Specifications—TN 9000 Analyzer 67
5-2 Instrument and Field Operation Costs 70
5-3 Method Detection Limits—TN 9000 Analyzer 71
5-4 Precision Summary—TN 9000 Analyzer 75
5-5 Accuracy Summary for Site-Specific PE and SRM Results—TN 9000 Analyzer 78
5-6 PE and CRM Results—TN 9000 Analyzer 82
5-7 Regression Parameters by Primary Variable—TN 9000 Analyzer 86
5-8 Regression Parameters by Sample Preparation Variable and Soil Texture—
TN 9000 Analyzer 88
5-9 Regression Parameters by Sample Preparation Variable and Site Name—
TN 9000 Analyzer 90
5-10 Summary of Data Quality Level Parameters 91
6-1 Summary of Test Results and Operational Features—TN Pb Analyzer 93
6-2 Summary of Test Results and Operational Features—TN 9000 Analyzer 94
6-3 Effects of Data Correction on FPXRF Comparability to Reference Data
for All In Situ-Prepared Samples—TN Pb Analyzer 95
6-4 Effects of Data Correction on FPXRF Comparability to Reference Data
for All In Situ-Prepared Samples—TN 9000 Analyzer 96
XVI
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List of Abbreviations and Acronyms
a
P
ug
um
AC
Am241
CCB
CCV
Qd109
Cl
CLP
cm
cm2
cm3
CRM
EPA
ERA
eV
Fe55
FP
FPXRF
Hgl2
ICAL
ICB
ICP-AES
ICS
ICV
IDL
IDW
ETVR
keV
LCS
LRL
MCA
mCi
MDL
mg/kg
mL
mm
mrem/hr
MMTP
alpha
beta
microgram
micrometer
alternating current
americium-241
continuing calibration blank
continuing calibration verification
cadmium-109
confidence interval
Contract Laboratory Program
centimeter
square centimeter
cubic centimeter
certified reference material
Environmental Protection Agency
Environmental Resource Associates
electron volt
iron-55
fundamental parameter
field portable X-ray fluorescence
mercuric iodide
initial calibration
initial calibration blank
inductively coupled plasma-atomic emission spectroscopy
interference check standard
initial calibration verification
instrument detection limit
investigation-derived waste
environmental technology verification report
kiloelectron volt
laboratory control samples
base 10 logarithm
lower reporting limit
multichannel analyzer
millicurie
method detection limit
milligram per kilogram
milliliter
millimeter
millirems per hour
Monitoring and Measurement Technologies Program
XVII
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MRI Midwest Research Institute
NERL-ESD National Exposure Research Laboratory—
Environmental Sciences Division
NIST National Institute of Standards and Technology
NRMRL National Risk Management Research Laboratory
OSW Office of Solid Waste
PAL performance acceptance limit
PARCC precision, accuracy, representativeness, completeness, and
comparability
PC personal computer
PE performance evaluation
PI prediction interval
ppm part per million
PRO PRC Environmental Management, Inc.
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
SiO2 silicon dioxide
SITE Superfund Innovative Technology Evaluation
SOP standard operating procedure
SRM standard reference material
TC toxicity characteristic
USGS United States Geological Survey
XRF X-ray fluorescence
xviii
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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 Bollinger, and Ed Hubert (PRC Environmental Management, Inc.); for
technical and peer review, Paula Hirtz, David Farnam, and Alan Byrnes (PRC Environmental
Management, Inc.); for analyzer operation, Bryce Smith, TN 9000, and Robert Beilfuss, TN Pb Analyzer
(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 Todd Rhea, Peter Berry, and Raj Natarajan, TN Spectrace.
XIX
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Section 1
Executive Summary
In April 1995, the U.S. Environmental Protection Agency (EPA) sponsored a demonstration of field
portable X-ray fluorescence (FPXRF) analyzers. The primary objectives of this demonstration were to
evaluate these analyzers for: (1) their analytical performance relative to standard analytical methods,
(2) the influence of sample matrix variations (texture, moisture, heterogeneity, and chemical
composition) on performance, (3) the logistical and economic resources needed to operate these
technologies in the field, and (4) to test and validate an SW-846 draft method for FPXRF analysis.
Secondary objectives for this demonstration were to evaluate FPXRF analyzers for their reliability,
ruggedness, cost, range of usefulness, data quality, and ease of operation.
This demonstration was intended to provide users a reference measure of performance and 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 Superfund Innovative Technology Evaluation (SITE) Program and managed by the
National Exposure Research Laboratory-Environmental Sciences Division (NERL-ESD) under the
Monitoring and Measurement Technologies Program (MMTP), Las Vegas, Nevada.
The FPXRF analyzers, tested in this demonstration, were designed to provide rapid, real-time
analysis of metals concentrations in soil samples. This information will allow investigation and
remediation decisions to be made on-site more efficiently, and it should reduce the number of samples
that need to be submitted for confirmatory analysis. Of the seven commercially available analyzers
evaluated, one is manufactured by HNU Systems, Inc. (the SEFA-P Analyzer); two are manufactured by
TN Spectrace (the TN 9000 and TN Pb Analyzer); one is manufactured by Niton Corporation (the Niton
XL Spectrum Analyzer); one is manufactured by Scitec Corporation (the MAP Spectrum Analyzer); and
two are manufactured by Metorex Inc. (the X-MET 920-P Analyzer and the X-MET 920-MP 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 demonstration. This environmental technology evaluation
report (ETVR) presents information relative to the TN Spectrace TN 9000 and TN Pb analyzers.
Separate ETVRs will be published for the other analyzers that were demonstrated.
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 FPXRF analyzers. The primary analytes for these
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comparisons were arsenic, barium, chromium, copper, lead, and zinc; nickel, iron, cadmium, and
antimony were secondary analytes. Because of design considerations, not all of these analytes were
determined by each instrument.
To demonstrate the analyzers, hazardous waste sites in Iowa (the RV Hopkins site) and in
Washington (the ASARCO site) were selected. The sites were chosen because they exhibit a wide range
of concentrations for most of the target analytes, are located in different climatological regions of the
United States, and combined they exhibit three distinct soil textures: sand, clay, and loam.
This demonstration found that the TN 9000 and TN Pb analyzers were simple to operate in the field.
The developer provided a training course for the technology operators similar to that provided to a
purchaser of the equipment. The training encompassed enough FPXRF theory and hands-on analyzer use
to allow the operators to manipulate the data collection software, calibrate the analyzer, and adjust
instrument parameters such as count times and target analytes. The training and subsequent technical
support, required during the demonstration, provided additional guidance on calibration procedures and
data usability. Based on this experience, more training would have helped in the successful application
of this analyzer. The analyzers did not experience an operational failure resulting in project down time
or data loss during the analysis of more than 1,260 soil samples. The analyzers were field portable, and
could operate on battery power or on alternating current. The analyzers required an auxiliary computer
to process and store data. Downloading data to both paper and electronic format was accomplished
without difficulty.
The TN Pb Analyzer reports results for fewer analytes than the TN 9000. Of the target analytes for
this demonstration, the TN Pb Analyzer reported lead, arsenic, copper, chromium, and zinc. Iron, nickel,
cadmium and antimony were not reported by the TN Pb Analyzer. The TN 9000 reported all of the target
analytes for this demonstration. The TN Pb Analyzer uses a single radioactive source and the TN 9000
can use up to three unique radioactive sources. The TN Pb Analyzer used only one source and a shorter
count time resulted in a two- to threefold increase in sample throughput for the TN Pb Analyzer relative
to the TN 9000.
The TN Pb Analyzer produced data meeting definitive level (equivalent to reference data) criteria for
lead, zinc, arsenic, and copper. The TN 9000 provided definitive level quality data for arsenic, copper,
lead, and zinc; and data of quantitative screening level quality for barium. This analyzer produced
qualitative screening level data for chromium. Data quality levels could not be assigned for chromium
with the TN Pb Analyzer and for nickel, iron, cadmium, and antimony for the TN 9000 due to a lack of
adequate precision or correlation data.
Both analyzers exhibited precision similar to the reference methods at the 5 to 10 times the precision-
based method detection limit (MDL) concentration level. As expected, the chromium data generally
showed the poorest precision of the primary analytes. Of the four sample preparation steps evaluated, the
initial sample homogenization had the greatest impact on data comparability. Site and soil texture did
not appear to affect data comparability.
Based on the performance of both TN Spectrace analyzers, this demonstration found them to be
effective tools for characterizing the concentration of metals in soil samples. As with all FPXRF
analyzers, unless a user has regulatory approval, confirmatory (reference) sampling and data correction is
recommended when using these analyzers for site characterization and remediation monitoring.
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Section 2
Introduction
This environmental technology evaluation report (ETVR) presents information on the demonstration
of both the Spectrace TN Pb and the TN 9000 analyzers. These analyzers were developed to perform
elemental analyses (metals quantitation) in the petroleum and petrochemical industry, the mining and
minerals industry, and the environmental field. These analyzers use a mercuric iodide (HgI2) detector
with radioactive sources to quantitate metals concentrations. Both analyzers can be operated in either an
in situ or intrusive mode. The in situ mode is commonly called a "point-and-shoot" mode. In this mode
of operation, the point of measurement on the soil surface is cleared of loose debris and organic matter,
the analyzer's probe is then placed 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 then placed
into a sample cup. The 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 both analyzers were evaluated. Section 4 discusses the TN Pb Analyzer, its capabilities,
reliability, throughput, accuracy, precision, comparability to reference methods, and other performance
factors. Section 5 provides the same information for the TN 9000. Section 6 discusses the potential
applications of both analyzers, presents a method for data correction, and suggests a framework for a
standard operating procedure (SOP). Section 7 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 the performance of FPXRF analyzers to identify and quantify metals in soils. The primary
objectives were to evaluate FPXRF analyzers in the following areas: (1) their accuracy and precision
relative to conventional analytical methods; (2) the influence of sample matrix variations (texture,
moisture, heterogeneity, and chemical composition) on their performances; (3) the logistical and
economic resources needed to operate these analyzers; and (4) to test and validate an SW-846 draft
method for FPXRF analysis.
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Secondary objectives for this demonstration were to evaluate FPXRF analyzers for their reliability,
ruggedness, cost, 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 obtained 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" is substituted for "confirmatory" since the data was used as a
baseline for comparison. MRI was awarded the subcontract to analyze soil samples using the reference
methods in accordance with Federal Acquisition Regulations. The award was made based on MRI's
costs, ability to meet the demonstration's quality assurance project plan (QAPP), requirements, and as
the only commercial laboratory identified that could perform all the analyses in the required timeframe.
Method 3050A is the standard acid extraction method used for determining metals concentrations in
soil samples. It is not a total digestion method, and potentially it 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 had to provide a known level of
data quality. For all measurement and monitoring activities conducted by the EPA, the Agency requires
that data quality parameters be established based on the end 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.
Precision refers to the degree of mutual agreement between replicate measurements and provides an
estimate of random error. Precision is often expressed in terms of relative standard deviation (RSD)
between replicate samples. The term relative percent difference (RPD) is used to provide this estimate of
random between duplicate samples.
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Accuracy refers to the difference between a sample result and the reference or true value. Bias, a
measure of the departure from perfect accuracy, can be calculated from the reference or true value.
Accuracy and bias for the reference laboratory were assessed by evaluating calibration standard linearity,
method blank results and the percent recoveries of matrix spike samples, laboratory control samples
(LCS), standard reference materials (SRMs), and PE samples.
Representativeness refers to the degree to which data accurately and precisely measures the
conditions and characteristics of the parameter of interest. Representativeness for the reference
laboratory was ensured by executing consistent sample collection procedures including sample locations,
sampling procedures, storage, packaging, shipping, equipment decontamination, and proper laboratory
sample handling procedures. Representativeness was ensured by using the appropriate reference method
at its optimum capability to provide results that represent the most accurate and precise measurement it is
capable of achieving. The combination of the existing method requirements supplemented by the
demonstration QAPP provided the guidance to assure optimum performance of the method.
Representativeness was assessed by evaluating calibration standards, method blank samples, duplicate
samples, and PE samples.
Completeness refers to the amount of data collected from a measurement process compared to the
amount that was expected to be obtained. For the reference data, completeness referred to the proportion
of valid, acceptable data generated.
Comparability refers to the confidence with which one data set can be compared to another. Data
generated from the reference methods should provide comparable data to any other laboratory performing
analysis of the same samples with the same analytical methods. Comparability for the reference methods
was achieved through the use of standard operating procedures (SOPs), EPA-published guidance, and the
demonstration QAPP. QC samples that were used to evaluate comparability include: calibration
standards, method blank samples, matrix spike samples, replicate samples, LCSs, SRMs, and PE samples.
Site Selection
PRC conducted a search for suitable demonstration sites between September and November 1994.
The following criteria were used to select appropriate sites:
• The site owner had to agree to allow access for the demonstration.
• The site had to have soil contaminated with some or all of the target heavy metals. (Slag, ash, and
other deposits of mineralized metals would not be assessed during the demonstration.)
• The site had to be accessible to two-wheel drive vehicles.
• The site had to exhibit one or more of the following soil textures: sand, clay, or loam.
• The site had to exhibit surface soil contamination.
• The sites had to be situated in different climatological environments.
PRC contacted NERL-ESD, regional EPA offices, state environmental agencies, metals fabrication,
and smelting contacts to create an initial list of potential demonstration sites. PRC received considerable
assistance from the EPA RCRA and Superfund branches in Regions 4, 6,7, 8, 9, and 10. PRC also
contacted the Montana Department of Health and Environment, the Nevada Bureau of Mines and
Geology, the Oklahoma Department of Environmental Quality, the Arizona Department of
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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 acceptable for the
demonstration.
The ASARCO site consists of 67 acres of land adjacent to Commencement Bay. The site is marked
by steep slopes leading into the bay, a slag fill that was used to extend the original shoreline, a cooling
water pond, and various buildings associated with the smelting process. Partial facility demolition was
conducted in 1987. Most of the buildings were demolished between 1993 and 1994. The only buildings
remaining are the Fine Ore Building, the Administrative Building, and a Maintenance Garage.
Past soil sampling results targeted four general areas of the site 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 the 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 6.7 acres in a heavy industrial/commercial zoned area. Industrial activities in the area of
the RV Hopkins property included the manufacture of railroad locomotive engines during the mid-1800's.
The RV Hopkins property was a rock quarry during the late 1800's. Aerial surveys beginning in 1929
show that the rock quarry occupied the majority of the site initially, gradually decreasing until it was
completely filled by 1982. It was reported that the site was used to dispose of demolition debris,
automotive, and scrap metal. The site also has been used by a company that recycled lead acid batteries.
RV Hopkins began operating as a drum reconditioner in 1951 across the street from its current
location. In 1964, the site owner reportedly covered the former quarry area of the site with foundry sand.
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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
Pennsylvania!! age.
The RV Hopkins property is composed of five buildings: the office and warehouse, a warehouse used
to store drums of hazardous waste and a waste pile, a manufacturing building, a drum reclamation
furnace, and a cutting shed. The office and the warehouse are located on the southwest corner of the site.
Areas investigated on 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. Those
sampling events had the following objectives:
• To provide data on, or verify, the extent of surface contamination at each site and to locate
optimum sampling areas for the demonstration.
• To allow the developers to analyze samples from the demonstration sites in advance of the
demonstration, and if necessary, refine and recalibrate their technologies and revise their operating
instructions.
• To evaluate samples for the presence of any unanticipated matrix effects or interferences that might
occur during the demonstration.
• To check the quality assurance (QA) and QC procedures of the reference laboratory.
One hundred soil samples were analyzed on each site by the FPXRF analyzers during the
Predemonstration sampling activities. The samples represented a wide range in the concentration of
metals and soil textures. Thirty-nine samples were submitted for reference method analysis using EPA
SW-846 Methods 3050A/6010A. Twenty-nine of these samples were split and sent to the developers.
Nine field duplicates were collected and submitted for reference method analysis to assess proposed
sample homogenization procedures. One purchased PE sample also was submitted to the reference
laboratory to provide an initial check of its accuracy.
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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 (XKF) analyzers.
Experimental Design
The experimental design of this demonstration was developed to meet the primary and secondary
objectives stated above, and was approved by all participants prior to the start of the demonstration. The
design is detailed in the demonstration plan 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 for all sample preparation steps and
by the reference methods.
The two TN Spectrace analyzers demonstrated can be operated in either an in situ or intrusive mode.
During the demonstration, these two modes of FPXRF analysis required 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 square was cleared of all vegetation, debris, and
gravel larger than 2 mm in diameter. The FPXRF in situ analyzers took one measurement in each sample
area. This data represented FPXRF in situ technology measurements for unprepared soils (in situ-
unprepared). Replicate measurements were taken at 4 percent of these locations to assess analyzer
precision.
After the in site-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 2 minutes,
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 throughout the sample, 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.
Following the in situ-prepared analysis, 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 by
the FPXRF analyzers 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.
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These replicate measurements were made on the same soils as the in wYw-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 step, 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 (0.425-mm openings). The sample was then analyzed by the FPXRF 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).
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 level of
training 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 (Sections 4 and 5).
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 quantitative estimate of "operator effect."
Quantitative Factors
Many factors in this demonstration could be quantified by various means. Examples of quantitative
factors evaluated during this demonstration include analyzer performance near regulatory action levels,
the effects of sample preparation, effects of microwave sample drying, count times, health and safety
considerations, costs, and interferences.
The data developed by the 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 TN 9000 Analyzer reported all 10 of these analytes.
The TN Pb Analyzer reported arsenic, chromium, lead, zinc, and copper.
10
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Evaluations of analyzer data comparability involved examining the effects of each site, soil texture,
and sample preparation technique (Table 2-1). Two sites were sampled for this demonstration, 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 (clay, sand,
and loam). Four sample preparation steps were used: (1) in ,«ta-unprepared, (2) in s/ta-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. This approach allowed the examination of particle size and
homogenization effects on data comparability. These effects were seen to have the greatest impact on
data comparability.
Table 2-1. Performance and Comparability Variables Evaluated
Variables
Site Name (31 5)
ASARCO (215)
RV Hopkins (100)
Soil Texture (31 5)
Sand (100)
Loam (11 5)
Clay (100)
Preparation Step [1,260]
in situ-unprepared [1 00]
in situ-prepared [100]
intrusive-unprepared [100]
intrusive-prepared [100]
in situ-unprepared [115]
in situ-prepared [115]
intrusive-unprepared [115]
intrusive-prepared [115]
in situ-unprepared [1 00]
in situ-prepared [100]
intrusive-unprepared [1 00]
intrusive-prepared [1 00]
Notes: ( ) Total number of sample points.
t ] 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 "true" concentrations in each sample. Where duplicate samples existed,
concentrations for the duplicates were averaged and the average concentration was considered to
represent the true value for the sample pair. This procedure was specified in the demonstration plan. If
one or both samples in a duplicate pair exhibited a nondetect for a particular target analyte, that pair of
data was not used in the statistical evaluation of that analyte. The reference methods reported measurable
concentrations of target analytes in all of the samples analyzed.
In addition to the quantitative factors discussed above, the common FPXRF sample preparation
technique of microwave drying of samples was evaluated. Sample temperatures during this procedure
can be high enough to melt some mineral fractions in the sample or combust organic matter. Several
metals that present environmental hazards can volatilize at elevated temperatures. Arsenic sublimes at
11
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188 °C, within the potential temperature range achieved during microwave drying of samples. To assess
this effect, 10 percent of the homogenized, crushed, oven-dried, and sieved samples were split and heated
in a microwave oven on high for 3 minutes. This time was chosen to approximate common microwave
drying times used in the field. Splits of these samples were submitted for reference analysis. The
reference data for these samples were compared to the corresponding reference data produced from the
convection oven-dried samples. These data showed the effects of the microwave drying variable on
analyte concentration. This was considered a minor variable and it was only evaluated for the reference
laboratory in an attempt to identify any potential effect on data comparability.
Another quantitative variable evaluated was the count time used to acquire data. During the formal
sample quantitation and precision measurement phase of the demonstration, the count times were set by
the developers and remained constant throughout the demonstration. Count times can be tailored to
produce the best results for specific target analytes. The developers, however, selected count times that
produced the best compromise of results for the entire suite of target analytes. To allow a preliminary
assessment of the effect of count times, select soil samples were analyzed in replicate using count times
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. Quantitative radiation readings were made with a gamma ray
detector near each analyzer to assess the potential for exposure to radiation.
A compilation of the 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 the user in estimating the total cost associated with a field use of these instruments.
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 sita-unprepared measurements, heterogeneity was partially controlled by
restricting measurements within a 4-by-4-inch area. For measurements after the initial point-and-
shoot preparation, heterogeneity was minimized by sample homogenization. This effect was
evaluated through the sample preparation data.
• Particle Size: 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
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.
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• 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 these analyzers 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 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
FPXRF analyzer's output. The data comparability and quality determination was primarily based on a
comparison of the analyzer's data and the reference data. Linear regression and a matched pairs t-test
were the statistical tools used to assess comparability and data quality.
A principal goal of this demonstration was the comparison of FPXRF data and the reference
laboratory data. EPA SW-846 Methods 3050A/6010A were selected as the reference methods because
they represent the regulatory standard against which FPXRF is generally compared. In comparing the
FPXRF data and reference data, it is important to recognize that, while similar these methods are not
identical. These differences 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 procedure when comparing the FPXRF method against a
reference analytical method.
The reference method, chosen for this demonstration, employs a wet chemical analysis and partial
digestion of approximately 1 to 2 grams of sample (approximately 0.25 cubic centimeters (cm3)
depending on sample bulk density). The digestion process extracts the most acid-soluble portion of the
sample. Since the digestion is not complete, the less acid-soluble components are not digested and are
not included in the analysis. These components may include the coarser-grained quartz, feldspar, lithic
components, and certain metal complexes. In contrast, FPXRF analyzers generally produce X-ray
excitation in an area of approximately 3 cm2 to a depth of approximately 2.5 centimeters (cm). This
equates to a sample volume of approximately 7.5 cm3. X-rays returning to the detector are derived from
all matrix material including the larger-grained quartz, feldspar, lithic minerals, metal complexes, and
organics. Because the FPXRF method analyzes all material, it represents a total analysis in contrast to
the reference methods, which may represent a select or partial analysis. This difference can result in
FPXRF concentrations that are higher than corresponding reference data when metals are contained
within nonacid soluble complexes or constituents. It is important to note that if metals are contained in
nonacid soluble complexes, a difference between the FPXRF analyzers and the reference methods is not
necessarily due to error in the FPXRF method but rather to the inherent differences in the nature of the
analytical methods.
The comparison of FPXRF 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 each data set. The most important assumptions are: (1) the linearity of the relationship, (2) the
confidence interval and constant error variance, and (3) an insignificant measurement error for the
independent variable (reference data).
13
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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 data and reference data are, in fact, related linearly and
that this assumption is correct.
Linear Data Plot
10
8
6
4
2
0
-h Arsenic I
2468
Thousands
Reference Data (mg/kg)
10
Linear Data Plot-Lead
! 4 6
Thousands
Reference Data (mg/kg)
Log Transformed Data Plot
'10000
1000
100
10000
Log-Log Data Plot-Lead
J5 10
10 100 1000 10000
Reference Data (mg/kg)
1
i
£
Z
1000
100
10
10 100 1000
Reference Data (mg/kg)
10000
Figure 2-2. Linear and Log-log Data Plots: These graphs illustrate the linear relationship
between the FPXRF data and the reference data. The linear data plots illustrate the concentration
dependence of this relationship with increased scatter at higher concentrations. The log-log plots
eliminate this concentration dependence 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
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.
14
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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 (Iog10) 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 Iog10 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 varied depending on whether the error is caused
by the reference methods or the FPXRF method. If the error is random or if the error for the reference
methods is small compared to the total regression error, then conventional regression analysis can be
performed and the error becomes a part of the random error term of the regression model. This error
(based on the Iog10 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 Iog10 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
this interpretation, the reference data does allow unambiguous resolution of data quality.
The comparison of reference data to the FPXRF data is referred to as an intermethod comparison.
All reference and QA/QC data were generated using an EPA-approved definitive level analytical method.
If the data obtained by an analyzer were statistically similar to the reference methods, the analyzer was
considered capable of producing definitive level data. As the statistical significance of the comparability
decreased, an analyzer was considered to produce data of a correspondingly lower quality. Table 2-2
defines the criteria that determined the analyzer's level of data quality (EPA 1993).
Data from this demonstration were used to place analyzer data into one of three data quality levels as
follows: (1) definitive, (2) quantitative screening, and (3) qualitative screening. The first two data
quality levels are defined in EPA guidance (1993). The qualitative screening level criteria were defined
in the demonstration plan (PRC 1995) to further differentiate the screening level data as defined by the
EPA.
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Table 2-2. Criteria for Characterizing Data Quality
Data Quality Level
Definitive Level
Statistical Parameter3
r2 = 0.85 to 1.0. The precision (BSD) must be less than or equal to 10
percent and the inferential statistics indicate that the two data sets are
statistically identical.
Quantitative Screening
Level
r2 = 0.70 to 1.0. The precision (RSD) must be less than 20 percent,
and the inferential statistics indicate the data sets are statistically
different.
Qualitative Screening
r2 = 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 later in the "Intermethod Comparison"
subsection in Section 4.
b
The regression parameters apply to either raw or Iog10 transformed data sets. The precision
criteria apply to only the raw data.
r2 Coefficient of determination.
RSD Relative standard deviation.
Definitive level data are considered the highest level of quality. These data are usually generated by
using rigorous, well-defined, analytical methods, such as approved EPA or ASTM methods. The data is
analyte-specific with full confirmation of analyte identity and concentration. In addition, either
analytical or total measurement error must be determined. Definitive level 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 be confirmed using analytical methods and QA/QC procedures and criteria associated
with definitive data. The quality of unconfirmed screening data cannot be determined.
Qualitative screening level data indicates the presence or absence of contaminants in a sample
matrix, but does not provide reliable concentration estimates. The data may be compound-specific or
specific to classes of contaminants. Generally, confirmatory sampling is not required if an analyzer's
operation is verified with one or more check samples.
At the time of this demonstration, approved EPA methods for FPXRF did not exist. As part of this
project, 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.
The comparability data set for each of these analyzers consisted of 1,260 matched pairs of FPXRF
and reference method data. This data set was analyzed as a whole and then subdivided and analyzed with
respect to each of the variables listed in Table 2-1. This nesting of variables allowed the independent
assessment of the influence of each variable on comparability.
16
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To obtain an adequate data set to evaluate the performance of these analyzers, a total of 315 soil
samples was analyzed by the reference method. These samples were also analyzed by the FPXRF
analyzers for each of the four sample preparation steps. This produced 1,260 data values for each
analyzer. 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 both sites and all soil textures. The effects of sample preparation variables were evaluated for all
of the reported 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 Iog10 transformed data), then the influence was considered to be insignificant.
However, if the correlation worsened, the cause was examined and an explanation proposed. If the
correlation improved, resulting in an improved r2 and reduced standard error of the estimate, then the
impact of the variable was considered significant. For example, if the r2 and standard error of the
estimate for a given target analyte improved when the data set was divided into the four sample
preparation steps, the sample preparation variable was determined to be significant. Once this was
determined, the variables of site and soil texture were evaluated for each of the four sample preparations
steps. If the site or soil texture variable improved the regression parameters for a given soil preparation,
then that variable was also considered significant.
After the significant variables were identified, the impact of analyte concentration was examined.
This was accomplished by dividing each variable's Iogi0 transformed data 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 these data sets. If this did not result in improved r2 values and reduced
standard errors of the estimate, the relationship between the analyzer's Iogi0 transformed data and the
Iog10 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 Iogi0 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 (Iog10 transformed FPXRF data), and the independent variable (Iog10 transformed
reference data). The r2 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
(2-1)
where
b is the y-intercept of the regression line, m is the slope of the regression line,
and Y and X are the loglo transformed dependent and independent variables, respectively
Values for r vary from a value of 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 (log,0 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 was considered to
have met the first requirement for definitive level data classification (Table 2-2). The second criteria,
precision RSD was then examined and required to be equal to or less than 10 percent RSD to return the
definitive data quality level. If both these criteria are satisfied, then certain inferential statistical
parameters are evaluated. For example, the regression line's y-intercept and slope may be 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 (Iogj0 FPXRF=log10 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). If there is no significant difference between
these values, then a final assignment to the definitive data quality level is made. These criteria were used
in turn to assign a data quality level for each analyte.
Slope Test for Significant Differences
r, m - 1
(2-2)
where
m is the slope of the regression line, SE is the standard error of the slope,
and Z is the normal deviate test statistic.
18
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Y-intercept Test for Significant Differences
z - *-°
(2-3)
where
b is the y-intercept of the regression line, SE is the standard error of the slope,
and Z is the normal deviate test statistic.
The matched pairs t-test was also used to evaluate whether the two sets of Iog10 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
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 (PRC 1995), 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
Iog10 transformed reference data.
If the r2 was between 0.70 and 1, the precision (RSD) less than 20 percent, and the slope or intercept
were not statistically equivalent to their ideal values, the analyzer was considered to produce quantitative
screening level data quality (Table 2-2). In this case, the linear regression is usually sufficiently
significant so that bias could be identified and corrected. Therefore, quantitative screening data 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 RSD of greater than 20 percent. An analyzer producing data at this level is considered capable
of detecting the presence or lack of contamination, above its detection limit, with at least a 90 percent
accuracy rate, but 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
result represents the lower MDL for the analyzers.
In a second approach, MDLs were determined by analysis of the low concentration outliers on the
Iog10 transformed FPXRF and Iog10 transformed reference method data cross plots. These cross plots for
all analytes characteristically exhibited a region below the MDL where the linearity of the relationship
disintegrated. Above the MDL, the FPXRF concentrations increased linearly with increasing reference
method values. Effectively, the linear correlation between the two methods abruptly changes to no
correlation below the MDL. The value of the MDL was assigned by determining the concentration
where the linear relationship disintegrates and is reported at two SDs above this concentration. This
MDL represented a field- or performance-based value.
1.9
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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 used for determining moisture
content. This was done to conserve sample volume for the reference laboratory. The moisture content
sample was not put through the homogenizing and sieving steps prior to drying.
The second deviation dealt with the sample drying procedures for moisture content determination.
The demonstration plan required that the moisture content samples 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. It was found (through visual observation) that the samples were
completely dry after only 3 minutes. This interval is still 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' extraction efficiency for target analytes. For the
evaluation of the effects of microwave drying, there were 736 matched pairs of data where both element
measurements were positive. Of these pairs, 471 exhibited RPDs less than 10 percent. This 10 percent
level is within the acceptable precision limits for the reference laboratory as defined in the demonstration
QAPP. Pairs exhibiting RPDs greater than 10 percent totaled 265. RPDs greater than 10 percent may
have causes other than analysis-induced error. Of these 265, 96 pairs indicated an increase in metals
concentration with microwaving, and 169 pairs indicated a reduction in 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
20
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measurements and saved up to 3 days of analysis time. The final precision determinations for this
demonstration were based on 48 sets of 10 replicate measurements for each analyzer.
The sixth deviation involved method blanks. Method blanks were to be analyzed each day and were
to consist of a lithium carbonate that had been used in all sample preparation steps. Each analyzer had its
own method blank samples, provided by the developer. Therefore, at the ASARCO site, each analyzer
used its own method blank samples. However, at the RV Hopkins site, each analyzer used lithium
carbonate method blanks that were prepared in the field, in addition to its own method blank samples.
Both types of method blank analysis never identified method-induced contamination.
The seventh deviation involved assessing the accuracy of each analyzer. Accuracy was to be
assessed through FPXRF analysis of 10 to 12 SRM or PE samples. Each analyzer measured a total of 28
SRM or PE samples. In addition, PE samples were used to evaluate the accuracy of the reference
methods, and SRMs were used to evaluate the accuracy of the analyzers. This is because the PE
concentrations are based on acid extractable concentrations while SRM concentrations represent total
metals concentration. SRM data was used for comparative purposes for the reference methods as were
PE data for the FPXRF data.
Sample Homogenization
A key quality issue in this demonstration was ensuring that environmental samples analyzed by the
reference laboratory and by each of the FPXRF analyzers were splits from a homogenous 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 one-quarter
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 evaluation 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. The RPDs for the field duplicate samples reflect the total error for the homogenization process
and the analytical method combined (Equation 2-4). When total error for the reference laboratory was
determined, 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 produced a
mean RPD total (error) and a 95 percent confidence interval of 9.3 ± 1.6.
21
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Total Measurement Error = V'[(Sample Homogenization Error)2 + (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
interval around the mean RPDs (laboratory error) of predigestion duplicate analyses, was 9.3 ± 2.9 for all
of the target analytes.
To determine the error introduced by the sample homogenization alone, the error estimate for the
reference methods was subtracted from the total error (Equation 2-5). Based on the data presented
above, the laboratory-induced error was less than or approximately equal to the total error. This indicates
that the sample homogenization (preparation) process contributed little or no error to the overall sample
analysis process.
Sample Homogenization Error = V'[(Total Measurement Error)2 - (Laboratory Error)2]
(2-5)
Although the possibility for poorly homogenized samples exists under any homogenization routine,
at the scale of analysis used by this demonstration, the samples were considered to be completely
homogenized.
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
SW-846 Method 3050A. This is an acid digestion procedure where 1 to 2 grams of soil are digested on a
hot plate with nitric acid, followed by hydrogen peroxide, and then refluxed with hydrochloric acid. One
gram of soil was used for extraction of the demonstration samples. The final digestion volume was 100
milliliters (mL). The soil sample extracts were analyzed by Method 6010A.
Method 6010A provides analysis of metals using Inductively Coupled Plasma-Atomic Emission
Spectroscopy (ICP-AES). This method requires that a plasma be produced by applying a radio-frequency
field to a quartz tube wrapped by a coil or solenoid through which argon gas is flowing. The radio-
frequency field creates a changing magnetic field in the flowing gas inside the coil, inducing a circulating
eddy current on the argon gas that, in rum, heats it. Plasma is initiated by an ignition source and quickly
stabilizes with a core temperature of 9,000 - 10,000 degrees Kelvin.
Soil sample extracts are nebulized, and the aerosol is injected into the plasma. Individual analytes
introduced into the plasma absorb energy and are excited to higher energy states. These higher energy
states have short lifetimes and the individual elements quickly fall back to their ground energy state by
releasing a photon. The energy of the emitted photon is defined by the wavelength of electromagnetic
radiation produced. Since many electronic transitions are possible for each individual element, several
discrete emissions at different wavelengths are observed. Method 6010A provides one recommended
wavelength to monitor for each analyte. Due to complex spectra with similar wavelengths from different
elements in environmental samples, Method 6010A requires that interference corrections be applied for
quantification of individual analytes.
Normal turnaround times for the analysis of soil samples by EPA SW-846 Methods 3050A/6010A
range from 21 to 90 days depending on the complexity of the soil samples and the amount of QC
documentation required. Faster turnaround times of 1 - 14 days can be obtained, but at additional cost.
Costs for the analysis of soil samples by EPA SW-846 Methods 3050A/6010A range from $150 to
$350 per sample depending on turnaround times and the amount of QC documentation required. A
sample turnaround of 28 days, a cost of $150 per sample, and a CLP documentation report for QC were
chosen for this demonstration.
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 governed by the QC criteria in its SOPs,
Method 6010A, and the demonstration QAPP. Table 3-1 provides QAPP QC requirements that were
monitored and evaluated for the target analytes. Method 6010A QC guidelines also are included in Table
3-1. Due to the complex spectra derived from the analysis of the demonstration samples, the QAPP QC
requirements were applied only to the primary analytes. The QAPP QC requirements also were
monitored and evaluated for the secondary analytes and other analytes reported by the reference
laboratory. However, corrective actions were not required for the secondary analytes.
Table 3-1. Reference Laboratory Quality Control Parameters"
I Reference Method
Parameter Frequency Requirement QAPP Requirement
Initial Calibration
Verification (ICV)
Standard
Continuing Calibration
Verification (CCV)
Standard
Initial and Continuing
Calibration Blanks (ICB)
and (CCB)
Interference Check
Standard (ICS)
High Level Calibration
Check Standard
Method Blanks
Laboratory Control
Samples
Predigestion Matrix
Spike Samples
Postdigestlon Matrix
Spike Samples
With each initial
calibration
After analysis of every 10
samples and at the end
of analytical run
With each continuing
calibration, after analysis
of every 10 samples, 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 true value
±3 standard deviations of
the analyzer background
mean
±20 percent of true value
±5 percent of true value
No QC requirement
specified
No QC requirement
specified
80-120 percent recovery
75-125 percent recovery
±10 percent of true value
±1 0 percent of true value
No target analytes at
concentrations greater than
2 times the lower reporting
limit (LRL)
±20 percent of true value
±1 0 percent of true value
No target analytes at
concentrations greater than
2 times the LRL
80-120 percent recovery
80-120 percent recovery
80-120 percent recovery
24
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Table 3-1. Continued
Reference Method
Parameter Frequency Requirement QAPP Requirement
Performance Evaluation
Samples
Predigestion Laboratory
Duplicate Samples
Postdigestion
Laboratory Duplicate
Samples
As submitted during
demonstration
With each batch of
samples of a similar
matrix
With each batch of
samples 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 RPDC
1 0 percent RPDC
Notes: Quality control parameters were evaluated on the raw reference data.
RPD control limits only pertain to original and laboratory duplicate sample results that were greater
than 10 times the instrument detection limit (IDL).
RPD control limits only pertain to original and laboratory duplicate sample results that were greater
than or equal 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 the ICAL. The control limits for the ICS and high level calibration check
standard were ±20 percent recovery and ±10 percent of the true value, respectively. All ICALs, ICVs,
and ICSs met the respective QC requirements for all target analytes.
Continuing calibration verification (CCV) standards and continuing calibration blanks (CCB) were
analyzed following the analysis of every 10 samples and at the end of an analytical run. Analysis of the
ICS was also required after every group of 20 sample analyses. These QC samples were analyzed to
check the validity of the ICAL. The control limits for the CCVs were ±10 percent of the true value. The
control limits for CCBs were no target analyte detected at concentrations greater than 2 times the LRL.
All CCVs, CCBs, and ICSs met the QAPP requirements for the target analytes with the exception of one
CCV where the barium recovery was outside the control limit. Since barium was a primary analyte, the
sample batch associated with this CCV was reanalyzed and the resultant barium recovery met the QC
criteria.
Detection Limits
The reference laboratory LRLs for the target analytes are listed in Table 3-2. These LRLs were
generated through the use of an MDL study of a clean soil matrix. This clean soil matrix was also used
for method blank samples and LCSs during the analysis of demonstration samples. The MDL study
involved seven analyses of the clean soil matrix spiked with low concentrations of the target analytes.
The mean and standard deviation of the response for each target analyte was calculated. The LRL was
defined as the mean plus three times the standard deviation of the response for each target analyte
included in the method detection limit study. All LRLs listed in Table 3-2 were met and maintained
throughout the analysis of the demonstration samples.
The reference laboratory reported soil sample results in units of milligram per kilogram wet weight.
All reference laboratory results referred to in this report are wet-weight sample results.
26
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Table 3-2. SW-846 Method 601OA LRLs for Target Analytes
Analyte
Antimony
Arsenic*
Barium*
Cadmium
Chromium*
LRL (mg/kg) Analyte
6.4
10.6
5.0
0.80
2.0
Copper*
Iron
Lead*
Nickel
Zinc*
LRL (mg/kg)
1.2
600a
8.4
3.0
2.0
Notes:
LRL elevated due to background interference.
* Primary analyte.
mg/kg Milligrams per kilogram.
Method Blank Samples
Method blanks were prepared using a clean soil matrix and acid digestion reagents used in the
extraction procedure. A minimum of one method blank sample was analyzed for each of the 23 batches
of demonstration samples submitted for reference laboratory analysis. All method blanks provided
results for target analytes at concentrations less than 2 times the levels shown in Table 3-2.
Laboratory Control Samples
All LCSs met the QAPP QC requirements for all primary and secondary analytes except those
discussed below.
The primary analytes copper and lead were observed outside the QC limits in one of the 23 batches
of samples analyzed. Reanalysis of the affected batches was not performed by the reference laboratory.
These data were qualified by the reference laboratory. Copper and lead data for all samples included in
the affected batches were rejected and not used for demonstration statistical comparisons.
Concentrations of secondary analytes antimony, nickel, and cadmium were observed outside the QC
limits in the LCSs. Antimony LCS recoveries were continually outside the control limits, while nickel
and cadmium LCS recoveries were only occasionally outside QC limits. Antimony was a problem
analyte and appeared to be affected by acid digestion, which can cause recoveries to fall outside control
limits. Antimony recoveries ranged from 70 to. 80 percent. Since secondary analytes were not subject to
the corrective actions listed in the demonstration QAPP, no reanalysis was performed based on the LCS
results of the secondary target analytes. These values were qualified by the reference laboratory. All
other secondary analyte LCS recoveries fell within the QAPP control limits.
Predigestion Matrix Spike Samples
One predigestion matrix spike sample and duplicate were prepared by the reference laboratory for
each batch of demonstration samples submitted for analysis. The predigestion matrix spike duplicate
sample was not required by the QAPP, but it is a routine sample prepared by the reference laboratory.
This duplicate sample can provide data that indicates if out-of-control recoveries are due to matrix
interferences or laboratory errors.
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
27
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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
postdigestion spike recoveries for target analytes met the QA/QC requirements of the QAPP and were
considered acceptable.
28
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Predigestion Laboratory Duplicate Samples
Predigestion laboratory duplicate RPD results were within the control limit of 20 percent for analyte
concentrations greater than 10 times the LRL except for the following instances. RPDs for primary
analytes barium, arsenic, lead, chromium, and copper were observed above the control limit in five
predigestion laboratory duplicate samples. These samples were reanalyzed according to the corrective
actions listed in the QAPP. The reanalysis produced acceptable RPD results for these primary analytes.
RPD results for the secondary analytes antimony, nickel, and cadmium were observed outside the
control limit for a number of sample batches. No corrective action was taken for secondary analytes that
exceeded the RPD control limit.
Postdigestion Laboratory Duplicate Samples
All primary analyte postdigestion laboratory duplicate RPD results were less than the 10 percent
control limit for analyte concentrations greater than 10 times the LRL.
The RPDs for secondary analytes antimony and iron were observed above the 10 percent control
limit in two sample batches. No corrective action was taken for secondary target analytes that exceeded
the RPD control limit.
Performance Evaluation Samples
PE samples were purchased from Environmental Resource Associates (ERA). The PE samples are
Priority PollutnT™/Contract Laboratory Program (CLP) QC standards for inorganics in soil. This type
of sample is used by the EPA to verify accuracy and laboratory performance. Trace metal values are
certified by interlaboratory round robin analyses. ERA lists performance acceptance limits (PAL) for
each analyte that represent a 95 percent confidence interval (CI) around the certified value. PALs are
generated by peer laboratories in ERA's InterLaB™ program using the same samples that the reference
laboratory analyzed and the same analytical methods. The reported value for each analyte in the PE
sample must fall within the PAL range for the accuracy to be acceptable. Four PE samples were
submitted "double blind" (the reference laboratory was not notified that the samples were QC samples or
of the certified values for each element) to the reference laboratory for analysis by EPA SW-846
Methods 3050A/6010A. Reference laboratory results for all target analytes are discussed later in this
section.
Four certified reference materials (CRM) purchased from Resource Technology Corporation (RTC)
also were used as PE samples to verify the accuracy and performance of the reference laboratory. These
four CRMs 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.
29
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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-defined control limits of 80 - 120 percent recovery was calculated for
each primary and secondary analyte.
Analyses of SRMs were not intended to assess the accuracy of EPA SW-846 Methods 3050A/6010A
as were the ERA PE or RTC CRM samples. Comparison of EPA SW-846 Methods 3050A/6010A acid
leach data to SRM data cannot be used to establish method validity (Kane and others 1993). This is
because SRM values are acquired by analyzing the samples by methods other than the ICP-AES method.
In addition, these other methods use sample preparation techniques different from those for EPA SW-846
Methods 3050A/6010A. This is one reason no PALs are published with the SRM certified values.
Therefore, the SRMs were not considered an absolute test of the reference laboratory's accuracy for EPA
SW-846 Methods 3050A/6010A.
The SRM sample results were not used to assess method accuracy or to validate the reference
methods. This was due to the fact that the reported analyte concentrations for SRMs represent total
analyte concentrations. The reference methods are not an analysis of total metals; rather they target the
teachable concentrations of metals. This is consistent with the NIST guidance against using SRMs to
assess performance on leaching based analytical methods (Kane and others 1993).
Data Review, Validation, and Reporting
Demonstration data were internally reviewed and validated by the reference laboratory. Validation
involved the identification and qualification of data affected by QC procedures or samples that did not
meet the QC requirements of the QAPP. Validated sample results were reported using both hard copy
and electronic disk deliverable formats. QC summary reports were supplied with the hard copy results.
This qualified data was identified and discussed in the QC summary reports provided by the reference
laboratory.
Demonstration data reported by the reference laboratory contained three types of data qualifiers: C,
Q, and M. Type C qualifiers included the following:
• U - the analyte was analyzed for but not detected.
• 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:
30
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• 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).
Quality Assessment of Reference Laboratory Data
An assessment of the reference laboratory data was performed using the PARCC parameters
discussed in Section 2. PARCC parameters are used as indicators of data quality and were evaluated
using the review of reference laboratory data discussed above. The following sections discuss the data
quality for each PARCC parameter. This quality assessment was based on raw reference data and the
raw PE sample data.
The quality assessment was limited to an evaluation of the primary analytes. Secondary and other
analytes reported by the reference laboratory were not required to meet the QC requirements specified in
the QAPP. Discussion of the secondary analytes is presented in the precision, accuracy, and
comparability sections for informational purposes only.
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
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40
| 30
| 20
I 10
0
Predigestion Duplicate Samples
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Antimony Arsenic Barium Chromium Cadmium Copper Iron Lead Mcksl Zinc
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40
1- 30
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5 20
i 1°
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Postdigestion Duplicate Samples
-
-
-
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Antimony Arsenic Barium Chromium Cadrrium Copper Iron Lead Mckel Zinc
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Figure 3-1. Pre- and Postdigestion Duplicate Samples: The top graph illustrates the
reference laboratory's performance on analyzing predigestion duplicate samples. Twenty
percent RPD represents the prediaestion duplicate control limits defined in the demonstration
QAPP. Two points were deleted from this top graph: barium at 65 percent RPD and copper at
138 percent RPD. The bottom graph illustrates the reference laboratory's performance on
analyzing postdigestion duplicate samples. Ten percent RPD represents the Postdigestion
duplicate control limits defined in the demonstration QAPP.
Accuracy
Accuracy for the reference laboratory data was assessed through evaluations of the PE samples
(including the CRMs), LCSs, method blank sample results, and pre- and postdigestion matrix spike
samples. PE samples were used to assess the absolute accuracy of the reference laboratory method as a
whole, while LCSs, method blanks, and pre- and postdigestion matrix spike samples were used to assess
the accuracy of each batch of demonstration samples.
32
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A total of eight PE and CRM samples was analyzed by the reference laboratory. These included four
ERA PE samples and four RTC CRM samples. One of the ERA PE samples was submitted to the
reference laboratory in duplicate, thereby producing nine results to validate accuracy. The accuracy data
for all primary and secondary analytes are presented in Table 3-3 and displayed in Figure 3-2. Accuracy
was assessed over a wide-concentration range for all 10 analytes with concentrations for most analytes
spanning one or more orders of magnitude.
Reference laboratory results for all target analytes in the ERA PE samples fell within the PALs. In
the case of the RTC CRM PE samples, reference laboratory results for copper in one CRM and zinc in
two CRMs fell outside the published acceptance limits. One of the two out-of-range zinc results was
only slightly above the upper acceptance limit (811 versus 774 mg/kg). The other out-of-range zinc
result and the out-of-range copper result were about three times higher than the certified value and
occurred in the same CRM. These two high results skewed the mean percent recovery for copper and
zinc shown in Table 3-3. Figure 3-2 shows that the remaining percent recoveries for copper and zinc
were all near 100 percent.
Table 3-3 shows that a total of 83 results was obtained for the 10 target analytes. Eighty of the 83
results or 96.4 percent fell within the PALs. Only 3 out of 83 times did the reference method results fall
outside PALs. This occurred once for copper and twice for zinc. Based on this high percentage ;of
acceptable results for the ERA and CRM PE samples, the accuracy of the reference methods was
considered acceptable.
Table 3-3. Reference Laboratory Accuracy Data for Target Analytes
Analyte
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
n
6
R
9
9
9
9
7
R
9
9
Percent Within I
Acceptance Rangp
100
100
100
100
100
89
100
87.5
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
79 - 309
SDof
Percent
Recovery
15
22
21
10
8
79
12
22
10
72
Concentration
Range (mg/kg)
50 - 4,955
25 - 397
19-586
1.2-432
11-187
144 - 4,792
6,481 - 28,664
52-5,194
13-13,279
76 - 3,021
Notes: n Number of samples with detectable analyte concentrations.
SD Standard deviation.
mg/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.
Completeness
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
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10000
1000
100
150
Antimony
IReferenoe Data DTrue Value BPercent Recovery
500
400
300
200
100
0
200
160 £•
co
120
80
40
Arsenic
IRef erence Data OTrue Value B% Recovery
800
600
400
200-
150
Barium
{Reference Data OTrue Value m% Recovery
Concentratfon (mg/kg)
B-» i\3 co *. Oi
0 O O 0 O
3) 0 O O O 0 O
1
JJ
r
~
f
—
I
i
;
-
Cadmium
aference Data OTrue Value ffl% Recover
"* * en co -L
o o o g
o
Percent Recovery
8
200
150
100
50
0
Chromium
IReferenoe Data OTrue Value
IPercent Recovery
120
100
80
60
40
Figure 3-2. Reference Method PE and CRM Results: These graphs illustrate the relationship
between the reference data 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
single PE or CRM sample. Based on this high percentage of acceptable results for the ERA and CRM
PE samples, the accuracy of the reference laboratory method was considered acceptable.
35
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100000
10000
1000
100
10
400
300 £«
200 rf
100 jp
Copper
•Reference Data QTrua Value
1% Recovery .
10000
,g 1000-
125
100
Lead
IReferenca Data CJTrue Value
I Percent Recovery
100000
E « 25
•=• -S "°
tg 8
CB =
£ -§
g H 15
5
ml
t
r
t
Iron
-
\e\i
100 £•
-------
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 (PRO 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. SRM Performance Data for Target Analytes
Analyte
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
n
5
11
8
10
10
17
7
17
16
1fi
Percent Within
Acceptance
Range
0
72
12
50
0
88
14
82
19
75
Mean
Percent
Recovery
22
84
41
80
45
82
62
83
67
81
Range of
Percent
Recovery
15-37
67-106
21 -89
43-95
14-67
33 - 94
23-84
37-99
25-91
32-93
SDof
Percent
Recovery
9
10
21
15
16
17
25
17
17
14
Concentration 1
Range (mg/kg) |
3.8-171
18-626
414-1,300
2.4 - 72
36 - 509
35 - 2,950
28,900 - 94,000
19-5,532
14-299
81 - 6,952
Notes: n Number of SRM samples with detectable analyte concentrations.
SD Standard deviation.
mg/kg Milligrams per kilogram.
37
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Table 3-5. Leach Percent Recoveries for Select NIST SRMs
Analyle
NIST SRM 2709
| Reference
Published Laboratory
Result3 Result
NIST SRM 2710
| Reference
Published Laboratory
Result3 Result
NIST SRM 2711
Reference
Published Laboratory
Result3 Result
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
—
41
—
61
92
86
69
89
94
106
37
-
-
85
84
87
76
78
21
94
51
92
49
92
80
92
71
85
87
45
84
—
92
78
96
69
88
•^^••^^•^Mi
86
28
96
43
88
76
95
78
89
•^•^•^•i
20
91
25
87
49
90
66
90
70
85
Notes:
Published results found in an addendum to SRM certificates for NIST SRMs 2709, 2710, and
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/6010A, the
most widely used approved methods for determining metal concentrations in soil samples. The
comparison of FPXRF and the reference methods had to take into account certain limitations of both
methods, including matrix effects. For these reasons, qualified reference data were used for statistical
analysis.
The QC review and QA audit of the reference data indicated more than 98 percent of the data either
met the demonstration QAPP objectives or was QC coded for reasons not limiting its use in the data
evaluation. Less than 2 percent of the data were rejected based on QAPP criteria. Rejected data were
not used for statistical analysis. The reference data were considered as good as or better than other
laboratory analyses of samples performed using the same extraction and analytical methods. The
reference data met the definitive data quality criteria and was of sufficient quality to support regulatory
activities. The reference data were found to be acceptable for comparative purposes with the FPXRF
data.
200
Antimony
I Reference Data Q True Value H Percent Recovery
800
120
Arsenic
I Reference Data D True Value
i Percent Recovery
10000
o
1000
100
10
100
80
60
40
20
0
80
,60
40
20
100
"I
60 DC
•£
40 g
20
Barium
•Reference Data O True Value
•% Recovery
Cadmium
a Reference Data QTrue Value Bl% Recovery
Figure 3-3. Reference Method SRM Results: These graphs illustrate the relationship between the
reference data and the true values for the SRM samples. The gray bars represent the percent
recovery for the reference data. Each set of three bars (black, white, and gray) represents a single
SRM sample.
39
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E 400
§ 200
o
J
I
f
ri
I
i
Chromium
.
1 IL-
i,l
60 ST
40 £
c
20 .?
n
• Reference Data D True Value • % Recovery
I
I
ili
100
80
60
40
fl)
20 0-
Iron
I Reference Data d True Value
1% Recovery
400
100
Nickel
I Reference Data CD True Value
1% Recovery
10000
Copper
I Reference Data d True Value
I Rsrcent Recovery
10000
1000-
100
Lead
I Reference Data d True Value m %Recovery
10000
100
1000
I
§
100-
Zinc
I Reference Data O True Value
13% Recovery
Figure 3-3 (Continued). Reference Method SRM Results: These graphs illustrate the relationship
between the reference data and the true values for the SRM samples. The gray bars represent the
percent recovery for the reference data. Each set of three bars (black, white, and gray) represents a
single SRM sample.
40
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Section 4
TN Pb Analyzer
This section provides information on the Spectrace TN Pb Analyzer, including the theory of FPXRF,
operational characteristics, performance factors, a data quality assessment, and a comparison of results
with those of the reference laboratory.
Theory of FPXRF Analysis
FPXRF analyzers operate on the principle of energy dispersive XRF spectrometry. This is a
nondestructive qualitative and quantitative analytical technique that can be used to determine the metals
composition in a test sample. By exposing a sample to an X-ray source having an excitation energy close
to, but greater than, the binding energy of the inner shell electrons of the target element, electrons are
displaced. The electron vacancies that result are filled by electrons cascading in from the outer shells.
Electrons in these outer shells have higher potential energy states than inner shell electrons, and to fill the
vacancies, they give off energy as they cascade into the inner shell vacancies (Figure 4-1). This release
of energy results in an emission of X-rays that is characteristic to 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 intensity of X-rays emitted by a sample, it is possible to identify and
quantify the elemental composition of the sample. A qualitative analysis can be made by identifying the
characteristic X-rays produced by the sample. The intensity of each characteristic X-ray is proportional
to the concentration of the target and can be used to quantitate each 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 KB-line is produced by a
vacancy in the K shell filled by an M shell electron. The K« transition is between 7 and 10 times more
probable than the KB transition. The Ka-line is approximately 10 times more intense than the K6-line for
a given element, making the Ka-line analysis the preferred choice for quantitation purposes. Unlike the
41
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K-lines, the L-lines (La and LB) for an analyte are of nearly equal intensity. The choice of which one to
use for analysis depends on the presence of interfering lines from other analytes.
Excitation X-ray from the
FFXF^ Source
\
\
An excKed electron is displaced, creating an
electron vacancy.
N.
An outer electron she! electron cascades to the Inner electron shell to
(> the vacancy. As this electron cascades, ft releases energy In the
form of an X-ray.
Characteristic X-ray
Figure 4-1. Principle of Source Excited X-ray Fluorescence: This figure illustrates 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, also known as the
absorption edge energy, represents the amount of energy an electron has to absorb before it is displaced.
The absorption edge energy is somewhat greater than the corresponding line energy. Actually, the K-
absorption edge energy is approximately the sum of the K-, L-, and M-line energies of the particular
element, and the L- absorption edge energy is approximately the sum of the L- and M-line energies.
FPXRF analytical methods are more sensitive to analytes with absorption edge energies close to, but less
than, the excitation energy of the source. For example, when using 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
Since 1988, TN Spectrace has produced field portable and laboratory-grade XRF technologies for a
broad range of applications. The TN Pb Analyzer was released in 1993 specifically for analyzing lead in
a variety of matrices such as soil, paint, paint chips, surface dust, and air filters. Using the "Soils
Application" software supplied with the analyzer, it can also identify and quantify arsenic, chromium,
iron, copper, zinc, and manganese in soils.
The TN Pb Analyzer uses an HgI2 semiconductor detector that achieves a manganese-Ka X-ray
resolution of approximately 300 eV. The detector is operated at a subambient temperature using a low
power thermoelectric (Peltier) cooler in the measurement probe.
42
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To perform either an in situ or intrusive analysis, a sample is positioned in front of the plastic film
probe window and the sample measurement sequence is initiated. This exposes the sample to primary
radiation from the source. Fluorescent and backscattered X-rays from the sample re-enter the analyzer
through the window and are counted in the high resolution HgI2 detector. When analyzing intrusive
samples, the probe is placed upright in a stand and the sample, which is contained in a thin-windowed
plastic cup, is placed over the probe measurement window beneath a swing-down safety shield.
Analyte concentrations are computed using a fundamental parameter (FP) calibrated algorithm that is
part of the TN Pb Analyzer's software package. The TN Pb Analyzer uses FPs to calibrate its detector.
The FPs are based on the physics of the excitation of target analytes and the emission of X-rays. The FP
method does not require site-specific calibration samples; however, site specific samples can be used to
customize the calibration to a particular site or matrix. The software package supports multiple XRF
calibrations. Each application requires a complete analysis configuration, including target analytes to be
measured, interfering target analytes in the sample, and a set of FP calibration coefficients.
Operational Characteristics
This section discusses equipment and accessories, operation of the analyzer in the field, background
of the operator, training, reliability of the analyzer, health and safety concerns, and representative
operating costs.
Equipment and Accessories
The TN Pb Analyzer comes with all the accessories needed for both in situ and intrusive operation
(Table 4-1). A hard-shell carrying case containing the equipment protected by foam inserts is provided
for transportation and storage.
Two main components make up the analytical system: a probe and an electronics unit. The probe
contains the radioisotope source, Cd109, for sample excitation and the HgI2 detector for analyte detection,
identification, and quantitation. The source is encapsulated and housed in a metal turret with additional
lead shielding inside the probe. The source exposes the sample to excitation radiation through a sealed 1-
inch-diameter Mylar™ window in the face of the probe. The X-ray-induced fluorescence from the
sample passes back through the window and is intercepted by the HgI2 detector. This signal is then
transferred to the electronics unit, which identifies and measures the energy of each X-ray and builds a
spectrum of analyte peaks on a 2,048-channel multichannel analyzer (MCA). This spectrum contains the
peak lines for all the metals present in the sample.
Spectral data is communicated from the probe to the electronics unit through a flexible cable of 6,12,
or 20 feet in length. The standard cable length is 6 feet. X-ray emission peaks are integrated and metal
concentrations in milligrams per kilogram or percentage values are calculated. The electronics unit will
store and display both numerical results and spectra from a measurement. A maximum of 600 sets of
numerical results and 100 spectra can be stored before downloading to a personal computer (PC) using
an RS-232 cable.
The electronics unit can be operated from a battery or from an alternating current (AC) electric line
using a plug-in adaptor unit. The TN Pb Analyzer is supplied with two nickel-cadmium batteries and a
battery charger. The batteries last approximately 8 hours and require a minimum of 14 hours to fully
recharge. For in situ analysis, the developer provided a water-resistant carrying case and a strap for easy
43
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portability on-site. The carrying case has a flap on top that can be closed to protect the unit from adverse
weather conditions.
Table 4-1. Analyzer Instrument Specifications—TN Pb Analyzer
I Characteristic
Resolution
Source
Detector
Probe Size
Probe Weight
Probe Operating Temperature
Electronics Unit Size
Electronics Unit Weight
Electronics Unit Operating
Temperature
Electronics Unit Storage Capacity
Power Source
Operational Checks
Intrusive Operation
Computer Interface Operation
Contact: Raj Natarajan
2555 N. Interstate Hwy. 35
Round Rock, TX 78664
(800) 736-0801
(512) 388-9200 (FAX)
Specification
< 300 eV (Manganese-Ka)
30 millicuries (mCi) Cd109 (with shim inserts)
Hgl2-Peltier cooled
12.7 cm x 7.6 cm x 21 .6 cm
1 .9 kilograms
0 to 49 °C
32 cm x 30 cm x 10 cm
6.7 Kilograms
0 to 49 °C
600 sets of numerical results and 100 spectra
120V or 220V (alternating current) or internal batteries
3 NIST SRMs, silicon dioxide (SiO2) and Teflon® blanks,
pure element check sample kit
Uniblock probe stand
RS 232 serial input/output cable, operators manual,
application and results software, and training video
Other equipment and supplies that are helpful when using the TN Pb Analyzer, which is not supplied
by the developer, include a PC for downloading the FPXRF data, protective gloves, paper towels, and a
permanent marking pen.
Operation of the Analyzer
For this demonstration, the TN Pb Analyzer was operated on battery power only. The in situ analysis
was performed with the analyzer in the carrying case. The probe was pointed at the soil surface and
analysis was started by pressing a trigger on the back of the probe. For intrusive analysis, the probe was
placed in the "uniblock" pointed upward with the safety shield attached. The "uniblock" is a free
standing support for the probe. All intrusive analyses at both sites were performed by setting the
analyzer on a table top located indoors. At the ASARCO site, the room was not heated or cooled so
analysis occurred at ambient temperatures. At the RV Hopkins site, the area where the analyzers were
operated was maintained at 25 °C.
Background of the Technology Operator
The PRC operator chosen for analyzing soil samples using the TN Pb Analyzer has a bachelor's
degree in environmental science. Prior to conducting this work, this operator worked for a year and a
44
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half in a pharmaceutical laboratory as an analytical chemist and a half year as an environmental scientist.
The operator received approximately 8 hours of training by the developer prior to the start of the
demonstration. The training covered the theoretical background of XRF technology and specific
operation of the TN Pb Analyzer.
Training
The training included step-by-step instructions on how to set up and use the TN Pb Analyzer. These
instructions covered connecting the nickel-cadmium battery, attaching the probe to the electronics unit,
setting up the "Soils Applications" software, operating the keyboard and analyzer software, modifying
the count times for the Cd109 source, setting the probe in the "uniblock" and attaching the safety shield for
intrusive analysis, downloading results to a PC, and performing instrument maintenance, for example,
replacing the probe window.
The TN Pb Analyzer was calibrated prior to the training using an FP algorithm and fine tuned with
site-specific soil samples supplied from the predemonstration activities. Part of the training included a
discussion of QC requirements, such as the analysis of a pure iron energy calibration check, a silicon
dioxide (SiO2) blank, and at least one NIST SRM. Possible interferences that could be encountered and
recommended procedures for preparing both in situ and intrusive soil samples for analysis were
discussed in detail. At the conclusion of the training, the developer was confident that the operator was
ready to operate the TN Pb Analyzer. The developer accompanied the PRC operator during the first
morning at the ASARCO site and observed the operator analyzing soil samples. No problems were
encountered and the developer left the site.
Reliability
A reliability check of the TN Pb Analyzer was carried out by a daily measurement of a reference
sample. The reliability check involved a 50-second measurement of a pure iron sample. This
measurement verified (1) fluorescent element sensitivity; (2) spectrometer energy resolution; and
(3) spectrometer energy calibration. To be acceptable, the measured relative X-ray intensity of iron had
to be greater than 0.95 and the equivalent intensity of manganese and cobalt had to be less than 0.006.
Relative intensity refers to the measured value relative to that obtained at the time of the initial
instrument calibration. If the intensity conditions were not met, then the iron sample was reanalyzed. No
energy calibrations were required during the demonstration based on the iron sample results.
During the demonstration, there were frequent light to moderate rains while the analyzer was
performing the in situ measurements. The developer recommended that samples analyzed by the TN Pb
Analyzer have less than 20 percent moisture content by weight. The samples collected during this
demonstration contained up to 30 percent moisture content by weight. This increased moisture content
did not appear to reduce the analyzer's data comparability. During the ASARCO site sampling, there
was a period of heavy rain for approximately 1.5 hours. After the rain, it was common for the soil
surface to be saturated. This did not pose an operational problem for the analyzer in the in situ mode. At
the ASARCO and the RV Hopkins sites, the temperatures ranged approximately from 5 to 16 °C and
from 6 to 22 °C, respectively. Despite the less than ideal weather conditions, there were no mechanical
or electronic problems experienced with the TN Pb Analyzer during the course of the demonstration.
The only maintenance required was the replacement of the probe window cover once due to
contamination and damage from small pebbles. The replacement of the probe window cover took
approximately 2 to 3 minutes. A spare probe window was included with the analyzer.
45
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Health and Safety
The potential for exposure to radiation from the excitation source is the greatest health and safety
consideration while using the analyzer. Radiation was monitored with a radiation survey meter.
Background radiation at the two sites was between 0.006 and 0.012 millirems per hour (mrem/hr).
Radiation exposure 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 the analyzer was analyzing a sample. Radiation exposure was also monitored
at a point on the probe where the operator's hand was located during analysis to provide a realistic value
of operator exposure. The TN Pb Analyzer is sold under a general license, meaning that the analyzer is
designed and constructed in such a way that anybody operating it, as instructed by the developer, will not
be exposed to harmful radiation levels set by 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, radiation values of 0.40 - 0.45 mrem/hr at the probe face and 0.05
to 0.06 mrem/hr at the probe handle were obtained for the TN Pb Analyzer with the Cd109 source
exposed. While collecting intrusive measurements with the TN Pb Analyzer, radiation values of 0.50 -
0.60 mrem/hr directly above the protective cover and 0.05 to 0.06 mrem/hr 1.0 foot from the protective
cover were obtained with the Cd109 source exposed. All measured radiation values were less than the
occupational level of 2.0 mrem/hr. The operator noted, however, there was no safety feature on the
analyzer that would prevent a person from accidental exposure by pushing the trigger on the rear of the
probe to start an analysis while the probe was pointed at the operator or another person.
Cost
At the time of the demonstration, the cost of a new TN Pb Analyzer was $39,500. This included all
of the equipment necessary to operate the analyzer. The analyzer is warranted for a full year with an
optional extended warranty. The TN Pb Analyzer can be rented from the developer for $5,000 per month
or $3,000 for 2 weeks. Additional field packs can be purchased for $200 and external batteries, charger,
and adapter for $750. A 12-month or 24-month extended warranty can be purchased for $2,750 or
$4,750, respectively. Periodic maintenance includes replacement and disposal of the Cd109 source every
2 years at a cost of $3,500 - $3,800. For optimum performance, the Cd109 source must be "deshimmed"
every 6-10 months at a cost of $ 1,500. Deshimming is the process of removing shielding around the
source to keep emissions nearly constant. Because the TN Pb Analyzer contains a radioisotope, a wipe
test must be conducted once every 6 months at a cost of $40.
The developer offers a training course at its offices or on-site. The cost of a 2-day training course at
the developer's office is only the cost of travel per student. The cost of an on-site course is $1,000 per
day, plus travel expenses for the developer's instructor. Costs associated with the operator vary
depending on the technical knowledge and experience of the operator.
The primary cost benefit of field analysis is the quick access to analytical data. This allows the
process dependent on the testing to move efficiently onto the next phase. Costs associated with field
analysis are very dependent on the scope of the project. Since most of the mobilization costs are fixed,
analyzing a large number of samples lowers the per sample cost. This is a key advantage that field
analysis has over a conventional laboratory. Furthermore, more samples are usually taken for field
46
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analysis since questions raised in the preliminary findings may be resolved completely without the need
to return for another sample collection event.
A representative list of costs associated with the operation of the TN Pb Analyzer is presented in
Table 4-2. Also included in this table is the measured throughput and the per sample charge of the
reference laboratory. Given the special requirements of this demonstration, it was not considered fair or
reasonable to report a per sample cost for the field analysis. However, some estimate can be derived
from the data provided in the table.
Table 4-2. Instrument and Field Operation Costs
TN Pb Analyzer
Replacement Source
Operator Training (Vendor Provided)
(On-site Training)
Radiation Safety License (State of Kansas)
Periodic Source Maintenance
Field Packs for Batteries and Charger
Amount
$ 39,500
5,000
3,500
—
1,000
500
1,500
750
Purchase Price
Per Month Lease
ForCd109
—
Per day
—
"Deshimming"
required every
6-10 months
—
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
20-25
150
(Varies, depending
on sample load)
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 on site-
specific soil samples with metals concentrations 2 to 5 times the expected MDL value. These data were
obtained during the precision study. Based on these results, a standard deviation was calculated and the
MDLs were reported at 3 times the standard deviation for each analyte. All the precision-based MDLs
were calculated for soil samples that had been dried, ground, and placed in a sample cup, the intrusive
mode of sample preparation. The precision-based MDLs for the TN Pb Analyzer are shown in Table 4-3.
Another method of determining MDLs involved the direct comparison of the FPXRF data and the
reference data. When these sets of data were plotted against each other, the resultant plots were linear.
47
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As the plotted line approached zero for either method, there was a point at which the FPXRF data
appeared to correspond with the same reading of the reference data. Figure 4-2 shows an example data
plot for copper for the TN Pb Analyzer to illustrate this effect. By determining the mean values of this
data point, it was possible to determine a field or performance-based MDL for the analyzer. For the TN
Pb Analyzer these field-based MDLs are shown in Table 4-3.
Table 4-3. Method Detection Limits—TN Pb Analyzer
Developer-based
Analyte MDLa (mg/kg)
Arsenic
Chromium
Copper
Lead
Zinc
46
330
80
25
64
Precision-based Field-based
MDL (mg/kg) MDL (mg/kg)
50
460
115
40
95
77
2,400
216
44
168
Notes: Corrected to reflect 60-second count time for the Cd109
source.
Mg/kg Milligrams per kilogram.
100000
10000
1000
100
10
i i i i mil i i i ii ml i i 111 in
10 100 1000 10000 100000
Reference Data (mg/kg)
Figure 4-2. Critical Zone for the Determination of a Field-
based Method Detection Limit for Copper: Between 100 and
300 mg/kg for the reference data, the linear relationship
between the two data sets changes. This point of change
identified the point at which field-based MDLs for the analyzer
were determined.
48
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Throughput
The TN Pb Analyzer used a Cd109 source count time of 60 seconds. With the additional "dead" time
of the system and the time required to label each sample and store data between sample measurements,
the time required to analyze one soil sample was 2 to 2.5 minutes. This resulted in a throughput of 20 -
25 samples per hour. The minimum number of samples analyzed in a 10-hour day, during the
demonstration, was 195 samples. This was for in situ measurements in the field at the ASARCO site
where the operator sometimes had to traverse distances of up to 0.5 miles between samples. The
maximum number of samples analyzed in a 12-hour day was 330 samples for intrusive measurements at
the ASARCO site.
This throughput took into account the time necessary to analyze three QC samples, one SiO2 blank,
one pure iron sample calibration check, and one NIST SRM. These QC sample analyses are
recommended by the developer. 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 m situ homogenized samples and the intrusive samples. Homogenization required an
average of approximately 5 minutes per sample (in «fw-prepared), 20 minutes per sample were required
for No. 10 sieving (intrusive-unprepared), and 10 minutes per sample were required for grinding and
sieving (intrusive-prepared). Approximately 30 minutes were spent daily downloading the data to a PC
and printing out a hard copy.
Drift
Drift is a measure of an analyzer's variability in quantitating a known amount of a standard over
time. For the TN Pb Analyzer, drift was evaluated by reviewing results from the analysis of NIST SRM
2710. This SRM contained quantifiable levels of arsenic, copper, lead, zinc, and iron. It was analyzed
four times by the TN Pb Analyzer during the demonstration. This data was reduced to RSDs for the
target analytes and the percent drift from the mean recovery of the true value. The percent drift from the
mean recovery for each day is shown in Figure 4-3. The RSD values for all analytes were less than 8
percent, and the mean percent recoveries were between 90 and 100 percent. The RSD values for copper,
lead, zinc, and iron were all less than 3 percent and 8 percent for arsenic. These low RSD values and
high percent recoveries indicate that for the analytes found in the SRM, the TN Pb Analyzer displayed
little drift during the demonstration. The minimal drift that did occur was less than the 10 percent limit
noted in the demonstration QAPP.
Intramethod Assessment
Intramethod measures of the analyzer's performance include results on analyzer blanks,
completeness, precision, accuracy, and comparability. The following paragraphs discuss these
characteristics.
Blanks
Analyzer blanks for the TN Pb Analyzer consisted of SiO2 blocks. These blanks were routinely
analyzed at the beginning and end of each day or at the beginning and in the middle of the day. They
were used to monitor for contamination by material such as residual soil left on the face of the probe. A
total of 20 SiO2 blanks was analyzed during the demonstration. None of the target analytes were detected
in any of the 20 blanks.
49
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10
5
£
§
•£ o
0)
s
* -5
-10
A
g
D
Bonn
D u B
D D D
D
rsenic Copper Lead Zinc Iron
Analyte
Figure 4-3. Drift Summary—TN Pb Analyzer: This graph illustrates the drift experienced by
the analyzer at the two demonstration sites.
Completeness
A total of 315 soil samples was analyzed four times (four preparation steps) resulting in 1,260 sample
results. The TN Pb Analyzer produced results for all 1,260 samples for a completeness of 100 percent,
above the demonstration objective of 95 percent.
Precision
Precision was expressed in terms of the percent RSD between replicate measurements. The precision
data for the target analytes reported by the TN Pb Analyzer are shown in Table 4-4. The results reported
at 5 to 10 times the MDL reflects the precision generally referred to in analytical methods, such as
SW-846.
Table 4-4. Precision Summary—TN Pb Analyzer
Mean % RSD Values by Concentration Range
5 -10 Times
Analyte MDLa (mg/kg)
Arsenic
Chromium
Copper
Iron
Lead
Zinc
4.11 (16)
ND
9.11 (8)
ND
5.93(12)
7.48(16)
50 - 500
(mg/kg)
16.47(8)
ND
18.00(24)
ND
8.93 (12)
13.42 (24)
500-1,000
(mg/kg)
3.47(12)
21.73(12)
5.82 (4)
ND
5.02 (8)
7.12(16)
>1,000 (mg/kg)
2.30 (8)
24.62 (4)
2.60 (12)
2.18 (48)
2.52 (20)
ND
Notes: The MDLs referred to in this column are the precision-based
MDLs shown in Table 4-3.
mg/kg Milligrams per kilogram.
ND No data.
() Number of samples, each consisting of 10 replicate analyses.
50
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The TN Pb Analyzer performed 10 replicate measurements on 12 soil samples that had analyte
concentrations ranging from less than 50 mg/kg to greater than 10,000 mg/kg. Each of the 12 soil
samples underwent the four different sample preparation steps described previously in Section 2.0.
Therefore, there was a total of 48 precision points for the TN Pb Analyzer. The replicate measurements
were taken using the source count times discussed in the previous section of this report. For each analyte
in each precision sample, a mean concentration, SD, and RSD were calculated.
In this demonstration, the analyzer's precision or RSD for a given analyte had to be less than or equal
to 20 percent to be considered quantitative screening level data and less than or equal to 10 percent to be
considered definitive level data. With the exception of chromium, the analyzer's precision data, reflected
in the 5 to 10 times MDL range, were below the 10 percent RSD required for definitive level data quality
classification. Chromium data was not represented in this range. The lower precision for chromium was
expected because chromium is a problematic analyte for FPXRF analysis, especially at 60-second count
times.
Figure 4-4 shows an asymptotic relationship between concentration and precision. In this figure,
precision shows little improvement at concentrations greater than 250 ppm; however, at concentrations
below 250 ppm, precision is highly concentration dependent. Although only lead is shown in this figure,
this trend was true for all of the reported analytes. These samples were purposely chosen to span a large
concentration range to test the effect of analyte concentration on precision.
40
30
10
0 2 4 6
Thousands
Lead Concentration (mg/kg)
Figure 4-4. Precision vs. Concentration—TN Pb Analyzer: This graph illustrates the analyzer's
precision as a function of analyte concentration.
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 TN Pb Analyzer by using site-
specific PE samples and SRMs. Accuracy was evaluated by comparing percent recoveries for each target
analyte reported by the TN Pb Analyzer. The TN Pb Analyzer analyzed six site-specific PE samples and
14 SRMs. The operator knew the samples were PE samples or SRMs, but did not know the true
51
-------
concentration or the acceptance range. These PE samples and SRMs were analyzed the same way as all
other samples.
The six site-specific PE samples included three from each of the two demonstration sites. These
samples were collected during the predemonstration activities and were 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 soil, four stream or river
sediment, two ash, and one sludge SRM. The SRMs were obtained from MIST, USGS, Commission of
European Communities-Community Bureau of Reference, National Research Council-Canada, and the
South African Bureau of Standards. The SRMs contained known certified concentrations of certain
target analytes reported in this demonstration.
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 4-5 summarizes the accuracy data for the target
analytes for the TN Pb Analyzer. Figures 4-5 and 4-6 show the true value, the measured value, and
percent recovery for the individual SRMs and PEs, respectively. No figure was presented for chromium
because only one sample produced a detectable concentration of chromium. True value results from the
site-specific PEs and SRMs with concentrations less than the precision-based MDLs listed in Table 4-3
were also excluded from the accuracy assessment.
Overall, the TN Pb Analyzer produced 20 out of 28 results or 71.4 percent within the 80 - 120
percent recovery acceptance range for all analytes in the six site-specific PE samples. Seven of the eight
results falling outside of the acceptance range were below the lower limit of 80 percent recovery. Only
the 129 percent recovery for chromium in one sample was above the upper limit of 120 percent recovery.
For all six site-specific PEs, only three out of 28 percent recoveries were above 100 percent. Table 4-5
also shows that the mean percent recoveries for all six analytes in the PEs were less than 100 percent.
This indicates that, in general, the TN Pb Analyzer was producing results that were biased slightly low.
Table 4-5 summarizes the accuracy data for the SRMs. A more detailed analysis of the SRM data is
presented in Figure 4-5. A graph is not presented for chromium because no samples produced a
detectable chromium concentration. The iron concentrations in the SRMs were in the tens of thousands
of milligrams per kilogram which is in a concentration range where the TN Pb Analyzer should perform
well. Some analytes such as copper, lead, and zinc had concentrations spanning 1 or more orders of
magnitude in the SRMs. Overall, the TN Pb Analyzer produced 31 out of 42 results within the 80 - 120
percent recovery acceptance range for an accuracy of 73.8 percent. Of the 11 results that fell outside of
the acceptance range, six results were low and five were high. This nearly equal ratio of high results to
low in addition to the mean percent recoveries shown in Table 4-5 indicates that the TN Pb Analyzer was
not showing a high or low bias for copper, iron, lead, and zinc. The TN Pb Analyzer did appear to show
a slightly low bias for arsenic concentrations. Except for chromium, the TN Pb Analyzer produced
percent recoveries ranging from 38 percent for copper in one sediment SRM to 151 percent for zinc in
the one sludge SRM.
A more detailed analysis of the SRM data showed that there was a matrix effect on the TN Pb
Analyzer's accuracy. The TN Pb Analyzer produced 16 out of 16 results or 100 percent within the
acceptance range for all target analytes hi the seven soil SRMs. This demonstrated that the TN Pb
Analyzer was more accurate when analyzing SRMs that closely matched the matrix used to set the
fundamental parameters (FP) of the analyzer. The TN Pb Analyzer showed the lowest comparability to
the one sludge SRM by overestimating all analyte concentrations by a factor of 1.3 to 1.5. The overall
52
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accuracy was 60 percent for the four sediment SRMs and 75 percent for the two ash SRMs.
Specifically, two sediment, one ash, and the one sludge SRM accounted for all 11 results that fell outside
of the acceptance ranges. This indicates that SRMs of a different matrix (sediment, ash, or sludge) than
that of soil may not serve as adequate accuracy checks when the FP calibration is based on soil SRMs.
The TN Pb Analyzer was the least accurate for chromium when assessing the site-specific PEs and
SRMs. This was expected for two reasons. First, two of the three samples shown in Table 4-6 had
concentrations less than 2 times the precision-based MDL for chromium, which may have negatively
affected the results. Second, the developer did not design this analyzer for chromium and was not certain
what the TN Pb Analyzer's capabilities for chromium would be. The overall accuracy for the remaining
five analytes for the PEs and SRMs combined was similar, ranging from 71 percent for zinc to 83 percent
for iron. The TN Pb Analyzer was expected to perform well for iron given that the iron concentrations in
the PEs and SRMs were well above MDLs yet in a linear range for the TN Pb Analyzer.
Table 4-5. Accuracy Summary for Site-Specific PE and SRM Results—TN Pb Analyzer
Percent Within
Analyte n Acceptance Range
Mean
Percent
Recovery
Range of SD of
Percent Percent
Recovery Recovery
Site-Specific Performance Evaluation Samples
Arsenic
Chromium
Copper
Iron
Lead
Zinc
3
?
5
6
6
6
100
0
100
67
67
67
89
65
92
87
87
82
87-92
0-129
83-107
70-98
70-101
70-90
2.5
NA
8.9
12
12
7.0
Concentration 1
Range (mg/kg) |
424 - 22,444
939 - 3,800
300-7,132
27,320 - 70,500
292-14,663
164-4,205
Soil Standard Reference Materials
Arsenic
Copper
Iron
Lead
Zinc
3
1
3
5
4
100
100
100
100
100
89
92
94
101
101
85-97
92
89-99
87-116
83-118
6.3
NA
4.7
12
15
105-626
2,950
28,900 - 35,000
101 -5,532
350 - 6,952
Sediment Standard Reference Materials
Arsenic
Chromium
Copper
Iron
Lead
Zinc
1
1
4
1
4
4
0
0
75
100
50
75
44
0
85
99
100
97
44
0
38-106
99
75-131
81 - 126
NA
NA
32
NA
23
21
211
509
99 - 452
41,100
161 -5,200
264 - 2,200
Ash & Sludge Standard Reference Materials
Arsenic
Copper
Iron
Lead
Zinc
?
1
?
3
3
50
0
100
67
33
87
141
86
106
109
73-101
141
85-86
88-133
68-151
NA
NA
NA
23
41
136-145
696
77,800 - 94,000
68 - 286
210-2,122
Notes: n Number of samples with detectable analyte concentrations.
SD Standard deviation.
mg/kg Milligrams per kilogram.
NA Not applicable. Standard deviation not calculated for two or fewer results.
53
-------
120
Arsenic
I Measured Value n True Value
I Percent Recovery
10000
1000
100
Copper
•Measured Value dTrue Value
120
10000
1000
I
8
Iron
I Measured Value O True Value
10
160
£•
120 I
80 §
S.
40
IFtercentRecovery
Lead
• Measured Value d True Value
! Percent Recovery
10000
1000
100
I
I
10
Zinc
•Measured Value d True Value
I Parcent Recovery
Figure 4-5. SRM Results-TN Pb Analyzer: These graphs illustrate the relationship between the
analyzer's data (measured values) and the true values for the SRMs. The gray bars represent the
percent recovery for the analyzer. Each set of three bars (black, white, and gray) represents a single
SRM sample.
Comparability
Ihtramethod comparability for the TN Pb Analyzer was assessed through the analysis of four ERA
PEs and four CRM PEs. This was done to present potential 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 PE samples had certified
analyte values determined by EPA SW-846 Methods 3050A/6010A. Therefore, since these methods do
not necessarily determine total metals concentrations in a soil, it was expected that the analyzer would
overestimate analyte concentrations relative to PALs. The ability of the TN Pb Analyzer to produce
results within the PALs and the percent recovery for each of the analytes was used to evaluate the TN
54
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Pb Analyzer's intramethod comparability. True value analyte concentrations in the ERA and CRM PEs
that were below the precision-based MDLs in Table 4-3 were excluded from the intramethod
comparability assessment.
450
160
Arsenic
I Measured Value OTrue Value
40
El Percent Recovery
1000000
250
8
I
100000
10000
1000
Iron
• Measured Value OTrue Value
•Percent Recovery
10000
Copper
I Measured Value O True Value
E3 Percent Recovery
1000000
200
50
Lead
•Measured Value d True Value
100000
150
Zinc
•Measured Value dTrue Value
(•Percent Recovery
Figure 4-6. Site-specific PE Sample Results—TN Pb Analyzer: These graphs illustrate the
relationship between the analyzer's data (measured values) and the true values for the site-specific
PE samples. The gray bars represent the percent recovery for the analyzer. Each set of three bars
(black, white, and gray) represents a single site-specific PE sample.
The TN Pb Analyzer performance data for all target analytes for the eight CRMs and PEs are
summarized in Table 4-6. 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 chromium. For the ERA PEs, the TN Pb Analyzer produced 12 out of 18 results or 66.7 percent
55
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within the acceptance range. For the CRMs, the TN Pb Analyzer produced 8 out of 17 results or 47.0
percent within the acceptance range. With the ERA and CRM PEs combined, the TN Pb Analyzer
produced 20 out of 35 results or 57.1 percent within the acceptance range. Based on the data presented
in Table 4-7, the TN Pb Analyzer's results were more comparable to the ERA PEs than the CRMs. The
better comparability to the ERA PEs versus the CRMs was unexpected because the ERA PEs had lower
analyte concentrations than the CRMs. With the exception of iron, the analyte concentrations in the ERA
PEs were all less than 350 mg/kg, which is less than 5 times the MDL for most of the analytes.
Table 4-6. Accuracy Summary for PE and CRM Results—TN Pb Analyzer
it
Percent Within
Analyte n Acceptance Range
Arsenic
Copper
Iron
Lead
Zinc
Arsenic
Chromium
Copper
Iron
Lead
Zinc
4
3
4
4
3
1
1
4
3
4
4
Mean
Percent
Recovery
Range of
Percent
Recovery
SDof
Percent
Recovery
Concentration ,
Range (mg/kg)
ERA Performance Evaluation Samples
100
67
0
75
100
117
129
203
137
103
89-127
110-142
174-248
102-180
96-104
19
17
36
32
6.3
65 - 349
144-196
7,130-10,400
52 - 208
101 -259
Certified Reference Materials
100
0
50
33
50
50
104
121
77
96
84
92
104
121
45-129
52-159
65-112
50-133
NA
NA
37
56
22
34
397
161,500
279 - 4,792
6,481 -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. Standard deviation not calculated for two or fewer results.
The TN Pb Analyzer produced only two out of 18 percent recoveries that were less than 100 percent
for the ERA PEs. All mean percent recoveries for the analytes in the ERA PEs were greater than 100
percent. This indicates that the TN Pb Analyzer was overestimating the results compared to the certified
values. This is consistent with the fact that FPXRF is a total metals technique whereas EPA SW-846
Methods 3050A/6010A used to certify the results in the ERA PEs are not.
Intermethod Assessment
The comparison of the analyzer's results to the reference method 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 analyzer and that produced by the reference laboratory. If
the Iog10 transformed FPXRF data were statistically equivalent to the Iog10 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
56
-------
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 were classified as qualitative screening level quality.
450
160
Arsenic
I Measured Value OTrue Value
ircent Recovery
10000
E, 1000
§
100
10
\
160
120
tr
80
40
Copper
I Measured Value d True Value
I Percent Recovery
1000000
350
-200
-150 EC
- 100
S.
Iron
•Measured Value D True Value
m Percent Recovery
Figure 4-7. PE and CRM Results—TN Pb Analyzer: These graphs illustrate the relationship
between the analyzer's data (measured values) and the true values for the PE and CRM samples.
The gray bars represent the percent recovery for the analyzer. Each set of three bars (black, white,
and gray) represents a single PE or CRM sample.
The TN Pb Analyzer was configured to report concentrations for five of the six primary analytes, and
one of the secondary analytes. The primary analytes it reported were arsenic, chromium, copper, lead,
and zinc. Iron was the only secondary analyte reported by this analyzer; however, since appropriate
precision and detection limit data could not be obtained, no data quality level could be assigned for iron.
The regression analysis on the entire Iog10 transformed data set indicated that arsenic, copper, lead,
and zinc all exhibited r2 values of 0.90 or greater. In all of these cases, the slopes and y-intercepts were
not significantly different from their ideal values of 1 and 0, respectively. This leads to the conclusion
that all these analytes can be measured at the definitive data quality level.
Additional data evaluation involved the assessment of the potential influence of the variables site,
soil texture, and sample preparation on the regression analysis of the Iog10 transformed data. Analysis
indicated no apparent impact of the site variable on the regression. The sample preparation variable
exhibited the greatest influence on the regression analysis (Table 4-8). In all cases, the greatest shift in
the r2 was exhibited between the in ^to-unprepared and the in site-prepared samples. This is consistent
with the fact that the homogenization step increased the possibility that the analyzer and the reference
57
-------
Table 4-7. Regression Parameters" by Primary Variable—TN Pb Analyzer
Arsenic ••JHH^^B Chromium
n ! r*
815
806
8
357
449
8
211
200
204
201
0.952
0.958
ND
0.966
0.950
ND
0.884
0.973
0.984
0.981
Std. Err. Y-lnt. Slope" ^^^fjj|jjjjj^^j n f2
0.15
0.14
ND
0.14
0.14
ND
0.22
0.11
0.08
0.09
0.20
0.18
ND
0.16
0.20
ND
0.45
0.11
0.10
0.16
0.95
0.95
ND
0.95
0.95
ND
0.83
0.97
0.99
0.98
All Data
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
In Situ-Prepared
Intrusive-Unprepared
Intrusive-Prepared
136
5
131
2
3
131
28
35
40
33
0.548
0.017
0.585
ND
ND
0.585
0.237
0.564
0.671
0.807
Std. Err. Y-lnt. Slope"
0.16
0.07
0.15
ND
ND
0.15
0.17
0.15
0.15
0.12
2.31
3.10
2.03
ND
ND
2.03
2.71
2.59
2.13
1.16
0.39
0.03
0.49
ND
ND
0.49
0.27
0.29
0.46
0.77
957
0.94
Copper
Std. Err. | Y-lnt. Slope
0.17 0.48 0.89
Variable
All Data
0.950
0.14
0.30
0.92
746
0.961
0.14
0.19
0.98
ASARCO Site
780
0.943
0.15
0.22
0.95
145
0.516
0.15
1.44
0.48
RV Hopkins Site
385
0.964
0.11
0.43
0.87
366
0.948
0.13
0.14
0.98
Sand Soil
347
0.951
0.14
0.21
0.93
443
0.951
0.13
0.42
0.92
Loam Soil
430
0.943
0.14
0.25
0.95
145
0.516
0.15
1.44
0.48
Clay Soil
385
0.964
0.11
0.43
0.87
246
0.866
0.26
0.67
0.82
In Situ-Unprepared
296
0.849
0.23
0.48
0.83
251
0.942
0.17
0.59
0.85
In Situ-Prepared
300
0.960
0.12
0.36
0.89
242
0.967
0.13
0.38
0.92
Intrusive-Unprepared
298
0.978
0.09
0.23
0.94
225
0.975
0.11
0.21
0.99
Intrusive-Prepared
293
0.976
0.10
0.21
0.96
1079
732
347
322
411
347
283
279
270
250
Variable
0.923
0.914
0.941
0.947
0.873
0.941
0.825
0.948
0.946
0.962
0.12
0.13
0.11
0.12
0.12
0.11
0.18
0.10
0.10
0.09
0.42
0.43
0.41
0.33
0.56
0.41
0.63
0.46
0.36
0.25
0.90
0.89
0.90
0.91
0.86
0.90
0.81
0.88
0.93
0.97
All Data
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
In Situ-Prepared
Intrusive-Unprepared
Intrusive-Prepared
Notes:
Y-lnt.
Std. Err.
n
Regression parameters based on Iog10 transformed data. Since the FPXRF data was
regressed as the dependent variable, the regression parameters cannot be used to correct
the FPXRF data. See Section 6.
Slope values determined with FPXRF data plotted on y-axis and the reference data plotted
on the x-axis.
Y-lntercept.
Standard error.
Number of data points.
ND Analytes not present in significant quantities to provide meaningful regression.
58
-------
Table 4-8. Regression Parameters" for the Sample Preparation Variable and Soil Texture —
TN Pb Analyzer
Arsenic
Std. Err. Y-lnt. I Slope"
Barium
Std. Err. Y-lnt. Slope"
In Situ-Unprepared
93
114
4
0.920
0.876
ND
0.20
0.21
ND
0.34
0.38
ND
0.86
0.87
ND
In Situ-Prepared
89
109
4
0.981
0.973
ND
0.10
0.10
ND
0.09
0.17
ND
0.97
0.96
ND
Intrusive-Unprepared
89
113
3
0.987
0.987
ND
0.08
0.07
ND
0.09
0.13
ND
0.98
0.99
ND
Intrusive-Prepared
88
113
3
0.981
0.984
ND
0.10
0.08
ND
0.15
0.17
ND
0.98
0.99
ND
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
3
3
28
ND
ND
0.237
ND
ND
0.17
ND
ND
2.71
ND
ND
0.27
In Situ-Prepared
3
3
31
ND
ND
0.600
ND
ND
0.14
ND
ND
2.18
ND
ND
0.43
Intrusive-Unprepared
3
3
39
ND
ND
0.703
ND
ND
0.14
ND
ND
1.94
ND
ND
0.53
Intrusive-Prepared
3
3
33
ND
ND
0.807
ND
ND
0.12
ND
ND
1.16
ND
ND
0.77
Chromium
Std. Err. Y-lnt. Slope"
In Situ-Unprepared
Soil Texture
In Situ-Unprepared
89
0.911
0.16
0.35
0.88
Sand Soil
85
0.871
0.21
0.35
0.84
112
0.818
0.27
0.58
0.87
Loam Soil
110
0.834
0.23
0.49
0.84
42
0.619
0.15
1.40
0.53
Clay Soil
99
0.845
0.22
0.74
0.77
In Situ-Prepared
Soil Texture
In Situ-Prepared
90 0.958
0.12
0.09
0.99
Sand Soil
89 0.959
0.13
0.23
0.93
113
0.957
0.12
0.54
0.87
Loam Soil
111
0.956
0.12
0.39
0.90
45
0.524
0.11
1.58
0.38
Clay Soil
99
0.976
0.09
0.45
0.85
Intrusive-Unprepared
Soil Texture
Intrusive-Unprepared
96 0.962
0.11
0.10
1.00
Sand Soil
88 0.980
0.09
0.14
0.97
114
0.982
0.08
0.36
0.93
Loam Soil
109
0.983
0.08
0.12
1.00
35
0.487
0.14
1.51
0.44
Clay Soil
100
0.986
0.07
0.37
0.89
Intrusive-Prepared
Soil Texture
Intrusive-Prepared
93
0.969
0.10
0.01
1.04
Sand Soil
89
0.966
0.12
0.18
0.97
113
0.980
0.09
0.32
0.95
Loam Soil
105
0.985
0.07
0.09
1.03
22
0.470
0.16
0.24
1.05
Clay Soil
98
0.988
0.07
0.29
0.92
59
-------
Table 4-8. Continued
m^m^^mmi^^^^H^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^i
n j r2 Std.Err. Y-lnt. Slope" ^^^^^^^| n r2 Std. Err.
| In Situ-Unprepared
87
101
96
0.910
0.720
0.844
0.14
0.17
0.19
0.60
0.78
0.61
0.78
0.75
0.85
In Situ-Prepared
84
107
88
0.966
0.899
0.971
0.10
0.10
0.08
0.33
0.66
0.44
0.92
0.81
0.88
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Y-lnt.
Slope"
Intrusive-Unprepared
77
106
86
0.972
0.910
0.970
0.09
0.10
0.08
0.21
0.49
0.36
0.97
0.89
0.92
Intrusive-Prepared
76
96
79
0.947
0.960
0.983
0.13
0.07
0.06
0.22
0.29
0.17
0.97
0.97
0.99
Notes: Regression parameters based on Iog10 transformed data. These parameters were calculated for
FPXRF data as the dependent variable, and thus, cannot be used to correct FPXRF data. See
Section 6.
Slope values determined with FPXRF data plotted on the y-axis and the reference data plotted on
the x-axis.
n Number of usable matched pairs of data points.
Y-lnt. Y-Intercept.
Std. Err. Standard Error.
ND Analyte not present in significant quantities to provide meaningful regression.
methods were analyzing a similar sample. The effect of sample preparation on analysis is illustrated in
Figure 4-8. Prior to the initial sample homogenization, only copper met the definitive level criteria.
However, its r2 was at the low end of the acceptability range. The initial sample homogenization step
accounted for between 40 and 99 percent of the total increase in the r2 resulting from all sample
preparation steps. This sample preparation pushed lead and arsenic into the definitive level data category
and elevated copper's r2 to the upper end of the acceptability range. The initial sample preparation step
improved the regression-based data quality for the zinc analysis; however, the t-test indicated the two
data sets were different so the analyzer produced quantitative screening level data for zinc through all
sample preparation steps. Since the analyzer's chromium precision was not measured at the required 5 to
10 times MDL, no data quality level could be assigned.
The influence of the site and soil texture variables was assessed for lead and zinc, the only two
analytes relatively evenly distributed between both sites and all soil textures, within each of the four
sample preparation steps (Tables 4-8 and 4-9). Little influence on the correlation was evident. Zinc
appeared to show slightly poorer correlation for the loam soils. Copper appeared to show a site or soil
effect, exhibiting much higher comparability for the ASARCO site and soils. However, this was
probably an artifact of the low copper concentration at the RV Hopkins site, less than 250 mg/kg. This
concentration (250 mg/kg) is near the field-based MDL for this analyzer.
The effect of contaminant concentration on comparability was also examined. The data sets for the
primary analytes were divided into the following concentration ranges: 0 -100 mg/kg, 100 - 1,000
mg/kg, and greater than 1,000 mg/kg as described in the demonstration plan. Regression analysis for
each target analyte and for each sample preparation step was performed on logjo transformed data sets
sorted by these concentration ranges. No consistent improvement was observed in either the r2 or the
60
-------
100000
I
° 10000
1000
100
10
In situ-unprepared--Lead
10 100 1000 10000 100000
Reference Data (mg/kg)
100000
1
° 10000
In situ-prepared-Lead
1000
100
10
10 100 1000 10000 100000
Reference Data (mg/kg)
100000
I
° 10000
J- 1000
S 100
z
10
Intrusive-unprepared-Lead
100000
° 10000
Intrusive-prepared-Lead
"8
10 100 1000 10000
Reference Data (mg/kg)
100000
1000
100
10
10 100 1000 10000
Reference Data (mg/kg)
100000
100000
1j 10000
1
2 1000
In situ-unprepared-Arsenic
In situ-prepared-Arsenic
"S
100
10
100000
I
° 10000
1000
100
10
10 100 1000 10000 100000
Reference Data (mg/kg)
10 100 1000 10000 100000
Reference Data (mg/kg)
Figure 4-8. Sample Preparation Effect on Lead and Arsenic Results—TN Pb Analyzer: These
graphs illustrate the effect of sample preparation on the comparability between the analyzer and the
reference data.
standard error for any of the concentration-sorted data sets. This indicates that the correlation is
independent of concentration for these ranges, and that the regression analyses associated with the entire
Iogi0 transformed data set are representative of the relationship between the analyzer's data and the
reference data. The regression parameters based on the logw transformed data were better, in all cases
for the data in the 0 - 2,000 mg/kg concentration range. Lead exhibited the greatest concentration range
effect; this analyte did not meet definitive level data quality criteria in the greater than 2,000 mg/kg
range. Identification of the exact cause of this concentration effect is beyond the scope of this project.
Possible causes include changes in reference method accuracy at higher concentrations due to analyte
interferences, and shifts in FPXRF performance at higher concentrations due to detector characteristics,
or inherent characteristics of the FP calibration. Whatever the cause, this apparent concentration effect
has a minor effect on overall data quality.
61
-------
Table 4-9. Regression Parameters8 for the Sample Preparation Variable and Site Name—
TN Pb Analyzer
Arsenic ^^^^^^^^H Barium
n j r* 1 Std. Err. j Y-lnt. | Slope" ^^^^^^^^| n r2 ! Std. Err. Y-lnt. 1 Slope"
In Situ-Unprepared
207
4
0.898
ND
0.21
ND
0.36
ND
0.87
ND
In Situ-Prepared
199
3
0.975
ND
0.11
ND
0.11
ND
0.97
ND
Intrusive-Unprepared
202
3
0.986
ND
0.08
ND
0.10
ND
0.99
ND
Intrusive-Prepared
201
3
0.981
ND
0.09
ND
0.16
ND
0.98
ND
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
In Situ-Unprepared
6
28
ND
0.237
ND
0.17
ND
2.71
ND
0.27
In Situ-Prepared
6
31
ND
0.600
ND
0.14
ND
2.18
ND
0.43
Intrusive-Unprepared
5
39
ND
0.703
ND
0.14
ND
1.94
ND
0.53
Intrusive-Prepared
6
33
ND
0.807
ND
0.12
ND
1.16
ND
0.77
Chromium ^^^^^^^^H Lead
n I r* [ Std. Err. I Y-lnt. | Slope" ^^^^^^^^| n | r2 | Std. Err.
In Situ-Unprepared
200
42
0.891
0.619
0.23
0.15
0.24
1.40
0.95
0.53
In Situ-Prepared
202
45
0.963
0.524
0.13
0.11
0.22
1.58
0.96
0.38
Intrusive-Unprepared
210
35
0.978
0.487
0.10
0.14
0.19
1.51
0.98
0.44
Intrusive-Prepared
203
22
0.981
0.470
0.09
0.16
0.07
0.24
1.03
1.05
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Y-lnt. Slope"
In Situ-Unprepared
196
99
0.839
0.845
0.229
0.217
0.440
0.743
0.838
0.765
In Situ-Prepared
200
99
0.953
0.976
0.129
0.087
0.292
0.449
0.918
0.848
Intrusive-Unprepared
198
99
0.978
0.987
0.091
0.066
0.126
0.388
0.988
0.881
Intrusive-Prepared
194
98
0.974
0.988
0.100
0.067
0.120
0.293
1.004
0.923
^^HHIii^^^HJJI^^^^^^IH^I^^Ii^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^l
BBHEyH
Std. Err.
Y-lnt. [ Slope" ^^^^^^^^^| n | r2
In Situ-Unprepared
188
96
0.831
0.844
0.163
0.188
0.663
0.613
0.776
0.849
In Situ-Prepared
192
88
0.932
0.971
0.109
0.078
0.470
0.436
0.875
0.879
Site Name
ASARCO
RV Hopkins
Site Name
ASARCO
RV Hopkins
Std. Err. I Y-lnt. I Slope"
Intrusive-Unprepared
183
86
0.935
0.970
0.112
0.081
0.339
0.360
0.937
0.917
Intrusive-Prepared
172
79
0.950
0.983
0.102
0.062
0.262
0.170
0.970
0.990
Notes: Regression parameters based on log,0 transformed data. These parameters were calculated for FPXRF data
as the dependent variable, and thus, cannot be used to correct FPXRF data. See Section 6.
b
Slope values determined with FPXRF data plotted on y-axis and the reference data plotted on the x-axis.
n Number of usable matched pairs of data points.
Y-lnt. Y-lntercept.
Std. Err. Standard Error.
ND Analyte not present in significant quantities to provide meaningful regression.
62
-------
To examine the potential effect of count times on analyzer comparability, a subset of 26 intrusive-
prepared samples from the RV Hopkins site was analyzed using doubled count times. This increase in
count times increased the r2 for both chromium and copper 0.02 and 0.14 units, respectively. None of the
other target analytes exhibited a count time effect (r2 values did not change) at the count times evaluated.
Another way to examine the comparability between the two methods involves measuring the average
relative bias and accuracy between the FPXRF data and the reference data. The average relative bias
indicates the average factor by which the two data sets differ. Concentration effects can affect bias. For
example, it is possible for an analyzer to greatly underestimate low concentrations but greatly
overestimate high concentrations had a relative bias of zero. To eliminate this concentration effect, the
data can be corrected by a regression approach (see Section 6), or only narrow concentration ranges can
be analyzed, or average relative accuracy can be examined. The average relative accuracy is the average
factor by which each individual analyzer measurement differs from the corresponding reference
measurement.
A final decision regarding the assignment of data quality levels derived from this demonstration
involves an assessment of both r2 and the precision RSD. Using the criteria presented in Table 2-2, a
summary of the TN Spectrace Pb Analyzer's data quality performance measures from this demonstration
is provided in Table 4-10.
Table 4-10. Summary of Data Quality Level Parameters
Target Pb Analyzer
Analytes Analytes
Arsenic
Barium
Chromium
Copper
Lead
Zinc
Nickel
Iron
Cadmium
Antimony
Arsenic
Not Reported
Chromium
Copper
Lead
Zinc
Not Reported
Iron
Not Reported
Not Reported
Precision
Mean %
5-10X
mg/kg)
RSD
MDL
4.1
—
Not Determined
9.1
5.9
7.5
—
Not Determined
—
—
i
Method Detection Coefficient of
Limits (mg/kg) Determination
(Precision-based) (r2 All Data)
50
—
460
115
40
95
—
Not Determined
—
—
0.95
—
0.55
0.94
0.95
0.92
—
Not Determined
—
—
Data Quality
Level
Definitive
—
Insufficient Data
Definitive
Definitive
Definitive
—
Insufficient Data
—
—
63
-------
Section 5
TN 9000 Analyzer
This section provides information on the Spectrace TN 9000 Analyzer, including the theory of
FPXRF, operational characteristics, performance factors, a data quality assessment, and a comparison of
results with those of the reference laboratory.
Theory of FPXRF Analysis
FPXRF analyzers operate on the principle of energy dispersive XRF spectrometry. This is a
nondestructive qualitative and quantitative analytical technique that can be used to determine the metals
composition in a test sample. By exposing a sample to an X-ray source having an excitation energy close
to, but greater than, the binding energy of the inner shell electrons of the target element in a sample,
electrons are displaced. The electron vacancies that result are filled by electrons cascading in from the
outer shells. Electrons in these outer shells have higher potential energy states than inner shell electrons,
and to fill the vacancies, they give off energy as they cascade into the inner shell vacancies (Figure 5-1).
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
specific wavelengths called "characteristic" X-rays. The energy of the X-ray is measured in electron
volts (eV). By measuring the position and intensity of X-rays emitted by a sample, it is possible to
identify and quantify the elemental composition of a sample. A qualitative analysis can be made by
identifying the characteristic X-rays produced by the sample. The intensity of the characteristic X-rays
emitted is proportional to the concentration of a given metal and can be used to quantitate each element.
Three electron shells are generally involved in the emission of characteristic X-rays during FPXRF
analysis: the K, L, and M shells. A typical emission pattern, also called an emission spectrum, for a
given element has multiple peaks generated from the emission X-rays by the K, L, or M shell electrons.
The most commonly measured X-ray emissions are from the K and L shells; only elements with an
atomic number of 58 (cerium) or greater have measurable M shell emissions.
Each characteristic X-ray peak or line is defined with the letter K, L, or M, which signifies which
shell had the original vacancy and by a subscript alpha (a) or beta (6), which indicates the next outermost
shell from which electrons fell to fill the vacancy and produce the X-ray. For example, a K«-line is
produced by a vacancy in the K shell filled by an L shell electron, whereas a KB-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 K^-line is approximately 10 times more intense than the KB-line for
a given element, making the Ka-line analysis the preferred choice for quantitation purposes. Unlike the
64
-------
K-lines, the L-lines (La and L6) for an analyte are of nearly equal intensity. The choice of which one to
use for analysis depends on the presence of interfering lines from other analytes.
Excitation X-ray from the
FPXRF Source
\
\
An excited electron is displaced, creating an
electron vacancy.
N
An outer electron shell electron cascades to the inner electron shell to
fill the vacancy. As this electron cascades, it releases energy in the
form of an X-ray.
Characteristic X-ray
Figure 5-1. Principle of Source Excited X-ray Fluorescence: This figure illustrates 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, also known as the
absorption edge energy, represents the amount of energy an electron has to absorb before it is displaced.
The absorption edge energy is somewhat greater than the corresponding line energy. Actually, the K-
absorption edge energy is approximately the sum of the K-, L-, and M-line energies of the particular
element, and the L- absorption edge energy is approximately the sum of the L- and M-line energies.
FPXRF analytical methods are more sensitive to analytes with absorption edge energies close to, but less
than, the excitation energy of the source. For example, when using 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
Since 1988, the Spectrace has produced field portable and laboratory-grade XRF technologies for a
broad range of applications. The TN 9000 Analyzer was released in 1992 to address environmental
applications.
The TN 9000 Analyzer uses a HgI2 semiconductor detector that achieves a manganese Ka X-ray
resolution of approximately 300 eV. The detector is operated at a subambient temperature using a low
power thermoelectric (Peltier) cooler in the measurement probe.
65
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The TN 9000 Analyzer uses energy dispersive XRF spectrometry to determine elemental
composition of soils, sludges, aqueous solutions, oils, and other waste materials. It uses three radioactive
isotopes, iron-55 (Fe55), cadmium-109 (Cd109), and americium-241 (Am241), to produce excitation X-rays.
The TN 9000 Analyzer can identify and quantify target metals from sulfur through uranium on the
periodic chart of the elements. When more than one source is needed to detect a specific element, the
appropriate source is selected according to its excitation efficiency for the target element. Generally, the
source with the excitation energy closest to, but above, the absorption edge energy for a given metal is
selected for performing an analysis. Interferences sometimes affect this selection.
To analyze a sample with the TN 9000 Analyzer, the sample is positioned in front of a plastic film
probe measurement window and sample measurement sequence is initiated. The sample is exposed to
primary radiation from the source. Only one of the three sources is exposed at a time. If all three sources
are required for a sample's analysis, three source exposures are sequenced automatically. Fluorescent
and back-scattered X-rays from the sample re-enter through the window and are counted by the high
resolution HgI2 detector. The surface probe of the HgI2 detector provides for both in situ and intrusive
soil analysis. For intrusive analysis, the probe is placed upright in a stand, and the sample, contained in a
thin-windowed plastic cup, is placed over the probe measurement window beneath a swing-down safety
shield.
Analyte concentrations are computed using a fundamental parameters (FP) calibrated algorithm
included in the analyzer's software. The developer uses FPs to calibrate its FPXRF analyzer. The FPs
are based on the physics of X-ray excitation and emission. The menu-driven software in the TN 9000
Analyzer supports multiple XRF calibrations in a "Soil Applications" software package. Each
application contains a complete analysis configuration including target metals to be measured, interfering
target metals in the sample, and a set of FP calibration coefficients. The FP calibration does not require
site-specific calibration samples; however, these samples can be used to fine tune the calibration.
Operational Characteristics
This section discusses equipment and accessories, operation of the analyzer in the field, background
of operator training, reliability of the analyzer, health and safety concerns, and representative operating
costs.
Equipment and Accessories
The TN 9000 Analyzer comes with all the accessories needed for in situ and intrusive operation. A
hard-shell carrying case containing the equipment protected by foam inserts is provided for transportation
and storage. Specifications for the analyzer are provided in Table 5-1.
Two main components make up the analytical system: a probe and an electronics unit. The probe
contains three radioisotope sources: Fe55 (50 mCi), Cd109 (5 mCi), and Am241 (5 mCi) for sample
excitation and the HgI2 detector. The sources are encapsulated and housed in a metal turret with
additional lead shielding inside the probe. These sources can sequentially expose the sample to
excitation radiation through the sealed 1-inch-diameter polypropylene cover over the Mylar™ window in
the face of the probe. The source-induced fluorescence from the sample passes back through the window
and is intercepted by the HgI2 detector. The detector quantitates the energy of each characteristic
emission X-ray and builds a spectrum of analyte peaks on a 2,048-channel MCA, which is contained in
the electronics unit. The standard probe operating temperature is 0 - 49 °C, and the standard probe
storage temperature is -40 to 43 °C.
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Table 5-1. Analyzer Instrument Specifications—TN 9000 Analyzer
Characteristic
Resolution
Sources
Detector
Probe Size
Probe Weight
Probe Operating Temperature
Electronics Unit Size
Electronics Unit Weight
Electronics Unit Operating
Temperature
Electronics Unit Storage Capacity
Power Source
Operational Checks
Intrusive Operation
Computer Interface Operation
Contact: Raj Natarajan
2555 N. Interstate Hwy. 35
Round Rock, TX 78664
(800) 736-0801
(512) 388-9200 (FAX)
Specification
< 300 eV (Manganese-Ka)
5 mCi Cd109, 50 mCi Fe55, 5 mCi Am241
Hgl2-Peltier cooled
1 2.7 cm x 7.6 cm x 21 .6 cm
1 .9 kilograms
0 to 49 °C
32 cm x 30 cm x 1 0 cm
6.7 kilograms
0 to 49 °C
300 sets of numerical results and 120 spectra
120V or 220V (AC) or internal batteries
3 NIST SRMs, SiO2 and Teflon® blanks, pure element
check
Uniblock probe stand
RS 232 serial input/output cable, operators manual,
application and results software, and training video
Spectral data is communicated to the electronics unit through a flexible cable of 6,12, or 20 feet in
length. The standard cable length is 6 feet. X-ray emission peaks are integrated and concentrations in
ppm or percentage values are calculated. The electronics unit will store and display both numerical
results and spectra from a measurement. A maximum of 300 sets of numerical results and 120 spectra
can be stored before being downloaded to a PC using an RS-232 cable.
The electronics unit can be operated from a battery or from an alternating current electric line using a
plug-in adaptor unit. The TN 9000 Analyzer is supplied with two nickel-cadmium batteries and a battery
charger. The batteries last approximately 4 to 5 hours and require a minimum of 14 hours to fully
recharge. For this demonstration, the developer provided two additional batteries and chargers so that
analysis could continue for up to 12 hours per day. For in situ analysis, the developer provided a water-
resistant carrying case and a strap for easy portability on-site. The carrying case has a flap on top which
can be closed to protect the analyzer from the environment.
Other equipment and supplies that are helpful when using the TN 9000 Analyzer, which is not
supplied by the developer, include a PC to download data, protective gloves, paper towels, and a
permanent marking pen.
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Operation of the Analyzer
For this demonstration, the TN 9000 Analyzer was operated on battery power during the in situ
phases of the demonstration. The in situ analysis was performed with the analyzer in the carrying case.
The probe was placed in contact with the soil surface and analysis was started by pressing a trigger on
the back of the probe. For intrusive analysis, the probe was pointed upward with the safety shield
attached. All intrusive analyses at both sites were performed by setting the analyzer on a table top
located indoors. At the ASARCO site, the room was not heated or cooled so analysis occurred at
ambient temperatures which ranged from 5 to 16 °C. At the RV Hopkins site, the area used for the
analysis was maintained at approximately 25 °C.
Background of the Technology Operator
The PRC operator selected to analyze soil samples using the TN 9000 Analyzer has a bachelor's
degree in zoology, which included 30 hours of undergraduate chemistry, and a master's degree in
environmental engineering. This operator worked as a gas chromatography chemist in an environmental
analytical laboratory for 3 years and as an assistant chemist at a chemical company for 3 years prior to
accepting a position at PRC. His job at PRC, for the past year, has involved performing on-site analyses,
conducting site investigations, performing risk assessments, and evaluating remedial design systems.
Training
The operator viewed a 22-minute training video which described the analyzer, applications of the
analyzer, instructions on the analysis procedures for in situ and intrusive sample measurements, and
procedures for downloading data from the analyzer to a PC. The operator then received approximately 6
hours of training at the start of the demonstration by the developer. The training covered the theoretical
background of XRF and certain specific applications of the TN 9000 Analyzer as they would relate to
this demonstration.
The operator estimated that approximately 80 percent of the training was "hands-on." The training
included step-by-step instructions involving the daily setup and use of the TN 9000 Analyzer. The
developer had calibrated the TN 9000 Analyzer prior to the training using an FP algorithm based on
NIST soil SRMs. Part of the training included a discussion of QC requirements such as the analysis of a
pure iron energy calibration check, a SiO2 blank, and at least one NIST SRM; possible interferences; and
procedures for preparing both in situ and intrusive soil samples for analysis. At the conclusion of the
training, the developer was confident that the operator was ready to operate the TN 9000 Analyzer. The
developer accompanied the operator to the ASARCO site during the first morning and observed him
analyzing soil samples. No problems were encountered, and the developer left the site.
Reliability
A reliability check of the TN 9000 Analyzer was carried out by a daily measurement of a reference
sample. This check required a 50-second measurement of a pure iron sample. By this one measurement,
a verification was obtained of (1) fluorescent element sensitivity; (2) spectrometer energy resolution; and
(3) spectrometer energy calibration. To be acceptable, the measured relative X-ray intensity of iron had
to be greater than 0.95, and the equivalent intensity of manganese and cobalt had to be less than 0.006.
Relative intensity refers to the new value relative to that obtained at the time of the initial instrument
calibration. No energy recalibrations were required during the demonstration based on the pure iron
sample results.
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During the demonstration, there were frequent light to moderate rains while the analyzer was
collecting the in situ measurements. After this rain, it was common for the soil surface to be saturated.
The developer recommends that samples analyzed by the TN 9000 have less than 20 percent moisture
content by weight. The samples collected during this demonstration contained up to 30 percent moisture
content by weight. This increased moisture content did not reduce the analyzer's data comparability. At
the ASARCO and RV Hopkins sites, the temperatures ranged from 5 to 16 °C and from 6 to 22 °C,
respectively. Despite the less than ideal weather conditions, there were no mechanical or electronic
problems experienced with the TN 9000 Analyzer during the course of the demonstration.
Health and Safety
The potential for exposure to radiation from the excitation sources was the largest health and safety
consideration while using the analyzer. Radiation was monitored with a radiation survey meter.
Background radiation at the two sites was between 0.006 and 0.012 mrem/hr. Radiation exposure was
monitored in both the in situ and intrusive modes while the shutters of the analyzers were open to obtain
a worst-case scenario. The radiation was measured within 5 cm of the probe face while the analyzer was
analyzing a sample. Radiation exposure also was monitored at a point on the probe where the operator's
hand was located during analysis to provide a realistic value of operator exposure. The TN 9000
Analyzer is sold under a general license, meaning that the analyzer is designed and constructed in such a
way that anybody operating it, as per the instruction manual, will not be exposed to harmful radiation
levels set by 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 mrem/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), the following radiation values were
obtained at the probe face for the TN 9000 Analyzer: Cd109 source, 0.10 to 0.12 mrem/hr; Fe55 source,
0.025 - 0.035 mrem/hr; and Am241,0.50 - 0.60 mrem/hr. Radiation background levels were recorded at
the probe handle while the Fe55 and Cd109 sources were exposed, while 0.020 - 0.025 mrem/hr were
recorded when the Am241 source was exposed. While collecting intrusive measurements with the TN
9000 Analyzer, the following radiation values were obtained on top of the protective sample cover: Cd109
source, 0.09 to 0.10 mrem/hr; Fe55 source, 0.008 - 0.012 mrem/hr; and Am241 source, 0.08 to 0.10
mrem/hr. All measured radiation values were less than the permissible 2.0 mrem/hr. The operator noted
there was no safety feature on the analyzer that prevented a person from accidentally exposing someone
by pushing the button on the rear of the probe to start an analysis while the probe was pointed at the
operator or another person.
Cost
At the time of demonstration, the cost of a new TN 9000 analyzer was $58,000. This included all of
the equipment necessary for operation of the analyzer. The analyzer has a full-year warranty with an
optional extended warranty. The TN 9000 Analyzer can be rented through several companies for $6,000
per month or $3,500 for 2 weeks. Additional field packs can be purchased for $200 and external
batteries, charger, and adapter for $750. A 12-month or 24-month extended warranty can be purchased
for $2,750 or $4,750, respectively. Periodic maintenance includes replacement of the Cd109 source every
2 years at a cost of $3,500 - $3,800. The Fe55 source should be replaced every 4 to 5 years. The cost of
replacement of the Cd109 and Fe55 sources together is $6,800. The Am241 source has a half-life of 433
years and does not need to be replaced. Because the TN 9000 Analyzer contains a radioisotope, a wipe
test must be performed every 6 months at the cost of $60. The developer offers a training course at its
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offices or on-site. The cost of the on-site course is $1,000 per day, plus travel expenses. Operator costs
will vary depending on the technical knowledge of the operator.
The primary cost benefit of field analysis is the quick access to analytical data. This allows the
process dependent on the testing to move efficiently onto the next phase. Costs associated with field
analysis are very dependent on the scope of the project. Since most of the mobilization costs are fixed,
analyzing a large number of samples lowers the per sample cost. This is a key advantage that field
analysis has over a conventional laboratory. Furthermore, more samples are usually taken for field
analysis since questions raised in the preliminary findings may be resolved completely without the need
to return for another sample collection event.
A representative list of costs associated with the Spectrace TN 9000 is presented in Table 5-2. Also
included in this table is the measured throughput and the per sample charge of the reference laboratory.
Given the special requirements of this demonstration, it was not considered fair to report a per sample
cost for the field analysis. However, some estimate can be derived from the data provided in the table.
Table 5-2. Instrument and Field Operation Costs
TN 9000
Operator Training (Vendor Provided)
Radiation Safety License (State of Kansas)
Field Packs for Batteries and Charger
Amount
$ 58,000
6,000
—
500
750
Purchase Price
Per Month Lease
—
—
—
Replacement Sources
Cd109
Fe55
Am241
3,500
3,000
N/A
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
9.5-10.5
150
Every 2 years
Every 4 years
Lifetime use
I^^H^H
(Varies, depending
on sample load)
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 were determined using standard EPA SW-846 protocols. Ten replicate measurements were
collected on site-specific soil samples having metals concentrations 2 to 5 times the expected MDLs.
These data were obtained from the same samples used in the precision assessment. Based on these 10
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replicate measurements, a standard deviation on the replicate analysis was calculated. For the purpose of
this demonstration, these precision-based MDLs, presented in Table 5-3, are defined as 3 times the
standard deviation for each analyte. The precision-based MDLs were obtained using a 100-second count
time and the Cd109 source. All the precision-based MDLs were calculated for soil samples that had been
dried and ground in a sample cup.
Table 5-3. Method Detection Limits— TN 9000 Analyzer
Developer
Analyte MDL (rng/kg)
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
58
35
16
255
164
372
62
157
20
89
50
Precision-based
MDL (rng/kg)
55
60
60
ND
200
500
85
ND
45
100
80
Field-based
MDL (mg/kg)
68
100
975a
247
443
838
195
ND
46
286
165
Notes:
mg/kg
ND
This MDL may be an artifact of the reference data at
concentrations below 200 mg/kg, or it may be an
artifact of barium concentration determination by total
analysis method.
Chromium low based on Fe55 source.
Chromium high based on Cd109 source.
Milligrams per kilogram.
Not determined.
Table 5-3 also lists MDLs reported by the developer. The developer's MDLs were acquired using a
200-second count time for each source with a SiO2 blank free of any potential interferences but spiked
with the target analytes.
Because the developer's MDLs were based on 200-second count times, while the precision-based
MDLs were calculated based on the shorter count times, the developer's MDLs were corrected for
comparison purposes. According to XRF counting statistics, the precision-based MDLs will vary by the
square root of the count time. Therefore, the developer MDLs for elements reported by the Cd109 source
were multiplied by a factor of 1.4 (square root 2) and by a factor of 1.82 (square root 3.33) for the
elements reported by the Fe55 and Am241 sources. The developer MDLs listed in Table 5-3 have been
corrected by the factors listed above to account for count time differences.
Another method of determining MDLs involves the direct comparison of the analyzer data and the
reference method data. When these sets of data are plotted against each other, the resultant plots were
linear. As the line approached zero concentration, there was a point at which the analyzer data appeared
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to correspond to the reference data. Figure 5-2 illustrates this effect for copper. By determining the
concentration value of this data point, it was possible to determine field-based MDLs for the analyzer.
These field-based MDLs are shown in Table 5-3.
10 100 1000 10000 100000
Reference Method Data (mg/kg)
Figure 5-2. Critical Zone for the Determination of a Field-
based Method Detection Limit for Copper: Between 100 and
200 mg/kg for the reference data, the linear relationship
between the two data sets changes. This point of change
identified the point at which field-based MDLs for the analyzer
were determined.
Although the TN 9000 Analyzer reported results for 24 analytes, only the target analytes are shown
in Table 5-3. Cadmium was reported only at very low concentrations and a precision-based MDL could
not be determined. Iron was mostly found at concentrations in the tens of thousands of milligrams per
kilogram so that reasonable detection limits could not be calculated. The precision-based MDLs were
generally higher than the developer's detection limits, but usually within a factor of 2. The field-based
MDLs were generally higher than the precision-based MDLs. The differences between the developer's
MDLs and the precision- and field-based MDLs is probably due to increased matrix interferences
inherent in environmental soil samples.
Throughput
The TN 9000 Analyzer used a total source live-second count time of 220 seconds or 3.7 minutes.
With the additional "dead" time of the analyzer and the time required to label each sample and store data
between sample measurements, the time required to analyze one soil sample was between 5 and 6
minutes. At the beginning of the demonstration, the operator was able to analyze 8.5 in situ soil samples
per hour. As he gained more experience and became more efficient at operating the TN 9000 Analyzer,
he was able to analyze 9.5 in situ soil samples per hour. In the intrusive mode with the samples already
prepared, the throughput was increased to 9.5 to 10.5 samples per hour. The operator found he was
capable of analyzing an average of 100 soil samples in a 10-hour day. The maximum number of soil
samples analyzed was 128 in a 12-hour day. This throughput did not include the analysis of an average
of six QC samples, such as two SiO2 blanks, two pure iron sample calibration checks, and two NIST
SRMs. These QC analyses are recommended by the developer. Sample analysis time did not include the
time required for sample handling and preparation or for data downloading, printing, and documentation.
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Considerable time was spent preparing the in situ homogenized samples and the intrusive samples. The
sample homogenization process took approximately 5 minutes per sample, wet sieving took nearly 20
minutes per sample, and grinding and sieving took approximately 10 minutes per sample. Approximately
30 minutes were spent daily downloading data to a PC and obtaining a hard copy.
Drift
Drift is a measure of an analyzer's variability in quantitating a known amount of a standard over
time. For the TN 9000 Analyzer, drift was evaluated by reviewing results from the daily analysis of
NIST SRM 2710. This SRM contained quantifiable levels of arsenic, barium, copper, iron, lead, and
zinc. NIST SRM 2710 data was collected over 18 days, approximately 67 percent at the ASARCO site
and 33 percent at the RV Hopkins site. This data was reduced to RSDs for the target analytes, and the
percent drift from the mean recovery of the true value. The percent drift from the mean recovery for each
of the 18 days is shown in Figure 5-3. The RSD values for barium, copper, iron, lead, and zinc were all
less than 8 percent. The RSD for arsenic was much higher at 18.2 percent. This higher RSD for arsenic
is probably an artifact of interference from the much greater concentration of lead in the sample. The
developer has noted that in past analyses of NIST SRM 2710, the precision of the arsenic analysis in the
presence of 5,500 ppm lead was 18 percent in a 100-second measurement. The low RSD values indicate
that for the concentrations of analytes found in NIST SRM 2710, the TN 9000 Analyzer exhibited little
drift during the demonstration. With the exception of arsenic, the drift that did occur was less than the 10
percent RSD specified in the demonstration plan.
4O
4± 2°
Q
c 0
o>
0)
°- -20
-4O
_D
D
_n
\-B
@
td
ijUj
D
i
Ji
™
111
1 • I
D
Arsenic Barium Copper Lead Znc
Analyte
Figure 5-3. Drift Summary—TN 9000 Analyzer: This graph illustrates the analyzer's drift over
a period of 18 days. Each bar represents a single measurement on a single day. The same
sample was used throughout the demonstration.
Intramethod Assessment
Intramethod measures of the analyzer's performance included results on analyzer blanks,
completeness, precision, accuracy, and comparability. The following sections discuss these
characteristics.
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Blanks
Analyzer blanks for the TN 9000 Analyzer consisted of SiO2 blocks. These blanks were routinely
analyzed at the beginning and end of each day. They were used to monitor contamination due to such
factors as residual soil left on the face of the probe. A total of 37 SiO2 blanks was analyzed during the
demonstration. None of the primary analytes were detected in the 37 blanks. Iron was frequently
detected at concentrations ranging from 150 to 250 mg/kg. This small amount of iron is actually present
in the SiO2 matrix. These concentrations of iron would not have significantly affected the results of the
soil samples because iron concentrations in the soil samples were mostly greater than 20,000 mg/kg.
Completeness
A total of 315 soil samples was analyzed four times (four sample preparation steps) resulting in 1,260
sample results. The TN 9000 Analyzer produced results for 1,259 of the 1,260 samples for a
completeness of 99.9 percent, above the demonstration objective of 95 percent. The one missing sample
result was due to operator error and was not to an analyzer malfunction. The operator failed to analyze
one in sittt sample at the ASARCO site.
Precision
Precision refers to the degree of repeatability or agreement among individual measurements of the
same sample and provides an estimate of analyzer-induced or random error. Precision for this
demonstration was expressed in terms of the percent RSD between replicate measurements. The
precision data for the target analytes are shown in Table 5-4. The TN 9000 Analyzer performed 10
replicate measurements on 12 soil samples that had analyte concentrations ranging from less than 50
mg/kg to tens of thousands of milligrams per kilogram. Each of the 12 soil samples underwent the four
sample preparation steps providing 480 precision data points for each analyte. Since the replicate
analyses were taken without moving the probe or sample, the resulting measurements reflect analyzer
precision and not method precision, which would include sample preparation. The replicate
measurements were obtained using the source count times discussed previously. For each detectable
analyte in each precision sample, a mean concentration, standard deviation, and RSD were calculated.
In this demonstration, the 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 precision of the analyzer was defined by measurements in the 5 to 10 times the
expected MDL range. The analyzer's precision was below the 10 percent RSD required for definitive
level data classification for all target analytss except chromium (Table 5-4). Nickel, cadmium, and iron
did not have sufficient data to allow data quality conclusions based on precision. Table 5-4 shows that
chromium precision in this concentration range was greater than 20 percent placing the chromium data in
the qualitative screening level category. The decreased precision for chromium shown in Table 5-4 was
expected as chromium is a problematic analyte for FPXRF analysis. The average RSD values for nickel
and cadmium shown in Table 5-4 are biased high because of the low inherent nickel and cadmium
concentrations in the precision samples.
There was no significant sample preparation effect on precision. This was expected because the
method used to assess precision during this demonstration was primarily measuring analyzer precision,
not total method precision. There was a concentration effect on the precision data. The precision
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 increased
74
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(Figure 5-4). The largest increase in precision occurred at concentrations 2 to 3 times the detection limit
for that analyte. The precision continued to increase until 1,000 - 2,000 mg/kg, then stabilized above
analyte concentrations of 2,000 mg/kg.
Table 5-4. Precision Summary— TN 9000 Analyzer
Mean % RSD Values by Concentration Range
Analyte
5-10 Times
MDLa
50 - 500
(mg/kg)
500-1,000
(mg/kg)
>1,000
(mg/kg)
Antimony
Arsenic
Barium
Cadmium
Chromium0
Copper
Iron
Lead
Nickel
Zinc
6.54 (8)
5.33 (12)
4.02 (20)
ND
22.25(12)
7.03 (8)
ND
6.45(12)
ND
7.27(16)
12.52(16)
9.68 (8)
ND
29.84b (48)
38.95(12)
19.02(24)
ND
9.69(12)
30.85" (16)
13.59(24)
4.54 (4)
4.39(12)
3.70 (40)
ND
29.10(4)
6.21 (4)
ND
5.34 (2)
ND
7.27(16)
ND
2.87 (8)
2.67(8)
ND
ND
3.35(12)
1.78(48)
3.68 (20)
ND
ND
Notes: The MDLs referred to in this column are the precision-
based MDLs shown in Table 5-3.
b
These values may be biased high because the
concentration of these analytes in the soil samples was
near the detection limit.
Values calculated from chromium low results from Fe55
source.
mg/kg Milligrams per kilogram.
ND No data.
( ) Number of samples, including all four preparation steps,
each sample represents 10 replicate measurements.
Numbers do not always add up to 48 precision points
because some samples had analyte concentrations below
the analyzer's MDL
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 TN 9000 Analyzer by using site-
specific PE samples and SRMs. Accuracy was evaluated through a comparison of percent recoveries for
each target analyte. The TN 9000 Analyzer analyzed 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 site-specific PE samples and SRMs were analyzed in the same way as all other
samples.
75
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30
20
TJ
CO
10
2 4
Thousands
Lead Concentration (mg/kg)
0 2 4 6 8 10
Thousands
Copper Concentration (mg/kg)
Figure 5-4. Precision vs. Concentration for Lead and Copper—TN 9000 Analyzer: These
graphs illustrate the analyzer's precision as a function of analyte concentration.
The six site-specific PE samples included three from each of the two demonstration sites. These
samples were collected during the predemonstration activities and sent to six commercial 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 soil, four stream or river sediment, two ash, and
one sludge SRM. The SRMs were obtained from MIST, USGS, Commission of European Communities-
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Bureau of Reference, 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 5-5 summarizes the site-specific PE and SRM accuracy
data for the target analytes for the TN 9000 Analyzer. Figures 5-5 and 5-6 show the true values, the
measured value, and percent recoveries for the individual site-specific PEs and SRMs, respectively. No
figures were presented for analytes that had less than three samples with detectable concentrations. True
value results from the site-specific PEs and SRMs with concentrations less than the precision-based
MDLs listed in Table 5-3 were excluded from the accuracy assessment.
The TN 9000 Analyzer was 100 percent accurate for all analytes in the site-specific PE samples with
the exception of chromium, nickel, and zinc. Overall, the TN 9000 Analyzer produced 37 out of 41
results within the 80 -120 percent recovery acceptance range for all analytes in the six site-specific PE
samples. This translates into a 90.2 percent accuracy for all analytes. Two of the four results were only
slightly outside the acceptance range, a nickel recovery of 125 percent in one sample and a zinc recovery
of 79.1 percent in one sample. The other two unacceptable results fell far outside the acceptance ranges
with a nondetect or 0 percent recovery for chromium in one sample and a zinc recovery of 58.3 percent in
one sample. The 58.3 percent recovery for zinc was for a PE sample that contained 164 mg/kg zinc
which is less than the field-based MDL and less than 2 times the precision-based MDL. With the
exception of chromium, the TN 9000 Analyzer produced mean percent recoveries near 100 percent for all
analytes (Table 5-5). These results were for analytes with concentrations spanning 1 or more orders of
magnitude in the site-specific PE samples.
A detailed analysis of the SRM data is presented in Figure 5-6. The TN 9000 Analyzer accuracy for
the SRMs varied from 0 percent for chromium (only one SRM concentration for chromium above the TN
9000 Analyzer's MDL) to 100 percent for antimony and iron in all SRMs. The iron concentrations in the
SRMs were in the tens of thousands of milligrams per kilogram so it was not surprising the TN 9000
Analyzer performed well for iron. Some analytes such as barium, copper, lead, and zinc had
concentrations spanning one or more orders of magnitude in the SRMs. Overall, the TN 9000 Analyzer
produced 38 out of 58 results within the 80 - 120 percent recovery range for an accuracy of 65.5 percent.
Of the 20 results that fell outside of the acceptance range, four results were low, and 16 were high. This
ratio of high results to low, in addition to the mean percent recoveries shown in Table 5-5, indicated that,
in general, the TN 9000 Analyzer overestimated analyte concentrations in the SRMs, especially for
barium. The lowest recovery produced by the TN 9000 Analyzer was 67 percent for copper in the
Canadian sediment SRM. The highest recovery was 198 percent for barium in one of the USGS soil
SRMs. The TN 9000 Analyzer results for all analytes were less than 2 times the reported SRM true
value for all SRMs.
A more detailed analysis of the SRM data showed that there was a matrix effect on the TN 9000
Analyzer accuracy. The TN 9000 Analyzer produced 22 out of 24 or 91.7 percent of the results within
the acceptance range for all target analytes in the soil SRMs; 10 out of 19 or 52.6 percent for the
sediment SRMs; and 6 out of 15 or 40 percent for the ash and sludge SRMs. The greater accuracy for the
soil SRMs is expected since it was using an FP calibration based on the NIST soil SRMs. Only barium
recovery in the two USGS soil SRMs was outside (above) the acceptance range. This demonstrates that
the TN 9000 Analyzer is more accurate when analyzing SRMs that closely match the matrix used to set
its FPs. The TN 9000 Analyzer performed the poorest on the one sludge SRM by overestimating all
analyte concentrations by a factor of 1.5 to 1.7. With the sludge SRM removed from the data, the TN
77
-------
9000 Analyzer had percent recoveries less than 140 percent for all analytes in all SRMs except for
barium in one USGS SRM.
Table 5-5. Accuracy Summary for Site-Specific PE and SRM Results—TN 9000 Analyzer
: Mean Range of SD of
[ Percent Within Percent Percent Percent Concentration
Analyte n i Acceptance Range Recovery Recovery Recovery Range (mg/kg)
Site-Specific Performance Evaluation Samples
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
4
3
6
1
2
5
6
6
2
6
100
100
100
100
50
100
100
100
50
67
96
94
100
110
41
96
97
98
115
85
85-105
87-101
94-110
110
0-82
85-120
87-105
91 - 103
105-125
58-103
8.82
7.07
5.95
NA
NA
14.5
6.43
4.34
NA
15.4
Soil Standard Reference Materials
Arsenic
Barium
Copper
Iron
Lead
Nickel
Zinc
3
5
2
3
5
1
5
100
60
100
100
100
100
100
101
130
88
98
100
99
98
89-115
98-198
80-96
95-102
80-115
99
93-112
13
40
NA
3.7
13
NA
8.1
51 - 2,253
424-19,584
792 - 7,240
353
939 - 3,800
300-7,132
27,320 - 70,500
292-14,663
312-444
164-3,490
105-626
707 - 2,240
131 - 2,950
28,900 - 35,000
101 -5,532
299
106-6,952
Sediment Standard Reference Materials
Antimony
Arsenic
Barium
Chromium
Copper
Iron
Lead
Zinc
1
1
3
1
4
1
4
4
100
0
33
0
50
100
75
50
100
68
125
178
100
99
104
94
100
68
107-139
178
67-139
99
82-138
74-122
NA
NA
16
NA
31
NA
25
18
171
211
335-414
509
99 - 452
41,100
161 -5,200
264 - 2,200
Ash and Sludge Standard Reference Materials
Arsenic
Barium
Copper
Iron
Lead
Nickel
Zinc
2
2
3
2
2
1
3
50
50
0
100
50
0
33
107
123
143
88
122
123
115
85-127
117-130
124-174
86-89
91 - 153
123
77-166
NA
NA
27
NA
NA
NA
46
136-145
709-1,500
113-696
77,800 - 94,000
72 - 286
247
210-2,122
Notes: n Number of samples with detectable analyte concentrations.
SD Standard deviation.
mg/kg Milligrams per kilogram.
NA Not applicable. Standard deviation not calculated for two or fewer results.
78
-------
10000
Antimony
•Measured Value OTrueVakjo
I Percent Recovery
100000
10000
1000
100
Arsenic
• Measured Value dTrue Value
I Percent Recovery
10000
10000
1000
100
10
125
100
75
50
Barium
•Measured Value DTrue Value . Si Rarcent Recovery
Copper
I Measured Value DTrue Value BDPsrcent Recovery
90
70
50
30
O
10
111!
120
100 £•
80 £
I
60 Jj
40
Iron
I Measured Value a True Value
I Percent Recovery
Lead
I Measured Value ClTrue Value
•Percent Recovery
10000
f
1000
100
10
hlllll
120
100
80
60
40
Zinc
I Measured Value DTrue Value El Percent Recovery
Figure 5-5. Site-specific PE Sample Results—TN 9000 Analyzer: These graphs
illustrate the relationship between the analyzer's data (measured values) and the true
values for site-specific PE samples. The gray bars represent the percent recovery
for the analyzer. Each set of three bars (black, white, and gray) represents a single
site-specific PE sample.
79
-------
800
160
£
Arsenic
IMeuuredValua nTrue Value •Percent Recovery
10000
1000
8
8
100
200
150
100 §
£
50
Barium
•Measured Value nTrue Value •Percent Recovery
10000
1000
100
10
hllll
zoo
I-
150 >
100
120
DC
100
50
i
8
i
o
Copper
I Measured Value OTrue Value
I Percent Recovery
Iron
I Measured Value nTrue Value
40
1 Percent Recovery
10000
i
Illl
160
120
80
40
S.
10000
1000
100
10
ID
175
150
125
100
75
50
Lead
I Measured Value n True Value
I Percent Recovery
Zinc
I Measured Value d True Value
I Percent Recovery
Figure 5-6. SRM Results—TN 9000 Analyzer: These graphs illustrate the relationship between
the analyzer's data (measured values) and the true values for the SRMs. The gray bars represents
the percent recovery for the analyzer. Each set of three bars (black, white, and gray) represents a
single SRM sample.
The TN 9000 Analyzer displayed almost identical accuracy for the soil SRMs and the site-specific
PEs (90.2 percent). This indicates that the matrix of the soil SRMs matched the matrix of the site-
specific samples well enough such that the FP calibration based on the soil SRMs produced results that
were over 90 percent accurate for site-specific samples. It also indicates that SRMs of a sediment, ash, or
sludge matrix are not as suitable of accuracy checks when the FP calibration is based on a soil matrix.
80
-------
Comparability
Intramethod comparability for the TN 9000 Analyzer was assessed through the analysis of four ERA
PEs and four CRM PEs. These eight samples were analyzed by the TN 9000 Analyzer in the same
manner as all other samples. As described in Section 3, these eight samples had certified analyte
concentrations determined by EPA SW-846 Methods 3050A/6010A. The ERA PEs had published PALs
based on a 95 percent confidence interval around each certified concentration. The GRMs had a 95
percent confidence interval around each certified concentration. The CRMs had a 95 percent prediction
interval (PI) associated with each certified value. The ability of the TN 9000 Analyzer to produce results
within the PALs or Pis and the percent recovery for each of the analytes was used to evaluate the TN
9000 Analyzer's intramethod comparability. True value analyte concentrations in the ERA and CRM
PEs that were below the precision-based,MDLs in Table 5-3 were excluded from the intramethod
comparability assessment.
The TN 9000 Analyzer performance data for all primary and secondary target analytes for the PE and
CRM samples are summarized in Table 5-6. The measured values, true values, and percent recoveries
for all detectable analytes are shown in Figure 5-7. No figure is shown for chromium and nickel because
there were only one and two reported certified concentrations, respectively, for these two analytes. For
the ERA PEs, the TN 9000 Analyzer produced 15 out of 29 results or 51.7 percent within the acceptance
range. For the CRMs, the TN 9000 Analyzer produced 17 out of 23 results or 73.9 percent within the
acceptance range. With the ERA and CRMs combined, the TN 9000 Analyzer produced 32 out of 52
results or 61.5 percent within the acceptance range. Based on the data presented in Table 5-6, the TN
9000 Analyzer's results were more comparable to the CRMs than the ERA PEs. Also, the mean percent
recovery was nearer 100 percent for all analytes in the CRMs versus the ERA PEs except for arsenic.
The better comparability to the CRMs versus the ERA PEs may have been an artifact of the low analyte
concentrations in the ERA PEs. With the exception of iron, the analyte concentrations in the ERA PEs
were all less than 350 mg/kg which is less than 5 times the precision-based MDL.
The TN 9000 Analyzer overestimated antimony concentrations in the ERA PEs and barium
concentrations hi the ERA PEs and CRMs. These results were expected because FPXRF techniques (or
total metals analytical methods) often produce antimony and barium results much higher than those
obtained from EPA SW-846 Methods 3050A/6010A (Kane and others 1993). The TN 9000 Analyzer
also produced results for iron and nickel in the ERA PEs that were much higher than the certified values.
Again, these are two analytes for which the acid leaching technique of Method 3050A will not achieve
100 percent recovery. Therefore, it was not surprising that the TN 9000 Analyzer's results were higher
for iron and nickel. For all analytes in the ERA PEs, only 2 out of 29 recoveries were less than 100
percent. This indicated that the TN 9000 Analyzer generally gave higher results for PEs that had values
certified by EPA SW-846 Methods 3050A/6010A, especially when the analyte concentrations were less
than 5 times the precision-based MDL.
Intermethod Assessment
The comparison of the TN 9000 Analyzer results to the reference method was performed using the
statistical procedures detailed in Section 2. The purpose of this statistical evaluation was to determine
the comparability between data produced by the analyzer and that produced by the reference laboratory.
If the Iogi0 transformed FPXRF data were statistically equivalent to the Iogj0 transformed reference data
and had acceptable precision (10 percent RSD or less), 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
81
-------
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 data.
The TN 9000 Analyzer was configured to report concentrations for all of the target analytes. The
developer recommends that reported concentrations less than three times their associated standard
deviations should not be considered valid data. This analyzer reported two values for chromium. The
chromium high values were based on sample analysis by the Cd109 source and the chromium low values
were based on sample analysis by the Fess source.
Table 5-6. PE and CRM Results—TN 9000 Analyzer
Percent Within j Mean Range of
: Acceptance | Percent Percent
Analyte n j Range j Recovery Recovery
SDof
Percent
Recovery
Concentration
Range (mg/kg)
ERA Performance Evaluation Samples
Antimony
Arsenic
Barium
Cadmium
Copper
Iron
Lead
Nickel
Zinc
3
4
4
2
4
4
4
1
3
100
100
0
0
75
0
75
0
67
311
101
762
172
131
195
112
169
114
270 - 344
72-120
446-1,064
156-188
113-174
168-240
72-146
169
107-121
38
21
272
NA
28
34
32
NA
9.7
56-99
65 - 349
111 -319
90-131
88-196
7,130-10,400
52 - 208
135
101 -259
Certified Reference Materials
Antimony
Arsenic
Barium
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Zinc
1
1
2
2
1
4
3
4
1
4
100
100
0
100
100
100
67
75
100
50
149
108
270
115
99
92
110
103
108
92
149
108
193-347
101 -129
99
61 -142
78-154
66-139
108
41 -130
NA
NA
NA
NA
NA
35
40
30
NA
38
4,955
397
342 - 586
362 - 432
161,500
279 - 4,792
6,481 -191,650
120-144,740
13,279
546-22,217
Notes: n Number of samples with detectable analyte concentrations.
SD Standard deviation.
mg/kg Milligrams per kilogram.
NA Not applicable. Standard deviation not calculated for two or fewer results.
82
-------
10000
8
Antimony
I Measured Value dims Value
400
300
200
100
BPercent Recovery
Arsenic
•Measured Value OTrue Value
•Percent Recovery
10000
650
200
10
Barium
• Measured Value dTrue Value
IPercent Recovery
Cadmium
• Measured Value OTrua Value BPercent Recovery
10000
E 1000
g
1
100
200
1000000
50
Copper
I Measured Value D True Value BPercent Recovery
100000
10000
1000
III
mil
250
200
150 EC
100
50
Iron
•Measured Value dTrue Value
I Percent Recovery
100000
10000
1000
100
,l
150
125
100
75
50
25
Lead
• Measured Value OTrueVakje Si Percent Recovery
Zinc
I Measured Value OTrue Value
B Percent Recovery
Figure 5-7. PE and CRM Results—TN 9000 Analyzer: These graphs illustrate the
relationship between the analyzer's data (measured values) and the true values for the
PE and CRM samples. The gray bars represent the percent recovery for the analyzer.
Each set of three bars (black, white, and gray) represents a single PE or CRM sample.
83
-------
The regression parameters for the six primary analytes are shown in Table 5-7. The regression
analysis of the entire Iog10 transformed data set showed that arsenic, copper, lead, and zinc had r2 values
at or above 0.92. In the cases of arsenic, lead, copper, and zinc, the slopes and y-intercepts were not
significantly different from 1.0 and 0.0, respectively. Barium and chromium had r2 values ranging from
0.79 to 0.67. Based on these data, the analyzer tended to overestimate barium and chromium
concentrations by a factor of greater than 10.0 relative to the reference method. The slope values in
Table 5-7 were determined by plotting the Iog10 transformed FPXRF data on the y-axis and the Iogi0
transformed reference data on the x-axis.
The next step in the data evaluation involved the assessment of the potential impact of the variables:
site, soil texture, and sample preparation step on the regression analysis of the Iogi0 transformed data
(Table 5-7). 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 analysis.
Generally, the largest shift in the r2 was exhibited between the in «Yw-unprepared and in situ-prepared
analyses (Figure 5-8). Sample homogenization accounted for between 80 and 100 percent of the total
increase in the r2 experienced across all sample preparation steps. This makes sense since the
homogenization step assured that the analyzer and the reference method were analyzing essentially the
same sample. Arsenic and copper data met definitive level data quality criteria prior to initial sample
homogenization. For lead, the initial sample homogenization (in situ-prepared) improved the
comparability between the two data sets to the point that the analyzer met the definitive level criteria.
The remaining primary target analytes, barium and chromium, never exceeded quantitative or qualitative
screening levels, respectively. The chromium data was considered qualitative screening level because
the precision was greater than 20 percent.
The impact of the site and soil texture variables was then assessed for each of the four sample
preparation steps (Tables 5-8 and 5-9). This evaluation was conducted for lead and zinc only. These
were the only primary analytes exhibiting relatively even concentration distribution between the site and
soil variables. Copper and barium tended to exhibit site and soil effects. However, a closer examination
of the data shows that the reported concentrations were approaching either instrument MDLs or a very
narrow range of concentrations. This held for the site and soil variables. No clear relationship was
observed for these variables and the comparability of the technology's data with the reference method
data. A minor trend was noticed for zinc. The loam soil always produced the poorest correlation;
however, these correlations still met the quantitative screening level criteria.
Within the four sample preparation steps, the effect of contaminant concentration was also examined.
The log,0 transformed data sets for the primary target analytes were sorted into the following
concentration 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 no consistent improvement in either the r2 or the
standard error for any of the concentration-based data sets. This indicates that there is no concentration
effect and that the regression analyses associated with the entire data set are most representative of the
relationship between the analyzer data and the reference data. After examining the analyzer and
reference data plots, a slight shift in the slope of the plot was noticed at approximately 2,000 mg/kg
(Figure 5-8). When the data was assessed in the 0 - 2,000 mg/kg and greater than 2,000 mg/kg
concentration ranges, a definite concentration effect was noticed. The regression parameters were
generally better for the data in the 0 - 2,000 mg/kg concentration ranges. Lead exhibited the greatest
effect, the comparison consistently produced lower r2 values in the greater than 2,000 mg/kg range.
Identification of the exact cause of the concentration effect is beyond the scope of this project. This
effect does not appear to strongly effect data quality, and it is less pronounced for the TN 9000 relative to
84
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the TN Pb Analyzer. Possible causes include changes in reference method accuracy at higher
concentrations due to analyte interferences, and shifts in FPXRF performance at higher concentrations
due to detector characteristics, or inherent characteristics of the FP calibration.
To examine the effect of count times on the analyzer's comparability, a subset of 26 samples from
the RV Hopkins site was reanalyzed using twice the original count times. This increased the r2 values for
both chromium and copper measurements from 0.09 to 0.23 units, respectively. Antimony, arsenic,
barium, cadmium, lead, nickel, iron, and zinc did not show as great an effect.
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 Spectrace TN 9000 data quality performance measures from this demonstration are
shown in Table 5-10.
85
-------
Table 5-7. Regression Parameters" by Primary Variable—TN 9000 Analyzer
816
Arsenic
Std. Err. | Y-Int. | Slope
Variable
Barium
Std. Err. | Y-lnt. Slope
0.949
0.15
0.16
0.96
All Data
1223
0.787
0.11
1.87
0.50
804
0.964
0.13
0.11
0.98
ASARCO Site
827
0.412
0.09
2.26
0.31
ND
ND
ND
ND
RV Hopkins Site
393
0.869
0.12
1.67
0.58
359
0.970
0.13
0.08
0.97
Sand Soil
385
0.078
0.06
2.62
0.12
445
0.962
0.12
0.14
0.97
Loam Soil
444
0.611
0.11
1.78
0.53
ND
ND
ND
ND
Clay Soil
393
0.869
0.12
1.67
0.58
204
0.909
0.20
0.34
0.88
In Situ-Unprepared
305
0.675
0.14
1.86
0.50
205
0.981
0.09
0.09
0.96
In Situ-Prepared
306
0.770
0.11
1.91
0.48
202
0.983
0.09
0.02
1.01
Intrusive-Unprepared
306
0.865
0.08
1.91
0.49
201
0.965
0.13
0.14
0.99
Intrusive-Prepared
306
0.853
0.09
1.86
0.52
••^^•••^•lililESIIII^^HHHH w_.:_ui. HH^^^^^B^EuHHli^^^^H
n f r* i Std. Err. I Y-lnt.
959
824
135
378
444
135
250
241
228
239
0.951
0.966
0.488
0.951
0.963
0.488
0.888
0.966
0.981
0.976
0.16
0.13
0.16
0.13
0.12
0.16
0.24
0.13
0.10
0.11
0.32
0.10
1.16
0.05
0.28
1.16
0.54
0.28
0.13
0.25
Slope" ^^^^fjjfilB^^^^ n I r" I Std. Err. I Y-lnt. Slope0
0.94
1.00
0.59
1.00
0.96
0.59
0.87
0.93
0.99
0.98
All Data
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
In Situ-Prepared
Intrusive-Unprepared
Intrusive-Prepared
1177
792
387
351
440
387
296
294
297
294
0.956
0.951
0.953
0.957
0.953
0.953
0.871
0.979
0.983
0.979
0.14
0.14
0.13
0.13
0.13
0.13
0.23
0.09
0.09
0.10
0.19
0.16
0.29
0.13
0.20
0.29
0.38
0.14
0.11
0.14
0.96
0.97
0.93
0.96
0.97
0.93
0.90
0.96
0.99
1.00
7!nr~ ••••••••••••••• nhromiiim H n\nA
™^^^™ ^^-^— ll~.4-l-l,» ^^^^—^^^^.-..--——,-5—--—^-^=^^^=—
n I r2 [ Std. Err. 1 Y-lnt. 1 Slope H^H^H^m|g|^H^H^ n
1062
734
341
323
413
341
281
266
265
265
0.926
0.918
0.934
0.945
0.888
0.934
0.824
0.959
0.957
0.955
0.13
0.13
0.13
0.13
0.12
0.13
0.20
0.09
0.10
0.11
0.24
0.27
0.18
0.21
0.34
0.18
0.49
0.24
0.07
0.11
0.96
0.94
1.00
0.94
0.93
1.00
0.87
0.94
1.02
1.03
All Data I 277
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
In Situ-Prepared
Intrusive-Unprepared
Intrusive-Prepared
Chromium (High) ••••••••••••
n I r2 I Std. Err. I Y-lnt. I Slope" ^^^^HHHHH§^^|
160
18
143
10
8
143
39
35
42
45
0.674
ND
0.692
ND
ND
0.692
0.617
0.735
0.631
0.768
0.17
ND
0.17
ND
ND
0.17
0.19
0.15
0.18
0.17
1.93
ND
1.53
ND
ND
1.53
1.96
1.93
1.98
1.77
0.43
ND
0.57
ND
ND
0.57
0.43
0.42
0.41
0.51
All Data
ASARCO Site
RV Hopkins Site
Sand Soil
Loam Soil
Clay Soil
In Situ-Unprepared
In Situ-Prepared
Intrusive-Unprepared
Intrusive-Prepared
93
184
40
53
184
100
77
47
49
r2
0.782
0.003
0.673
0.047
0.032
0.673
0.865
0.513
0.770
0.748
Std. Err.
0.15
0.08
0.17
0.07
0.09
0.17
0.12
0.27
0.14
0.17
Y-lnt.
1.85
2.43
1.60
2.67
2.30
1.60
8.43
1.91
1.79
1.50
Slope"
0.41
0.03
0.49
-0.13
0.12
0.49
1.31
0.36
0.43
0.56
Notes: " Regression parameters based on Iog,0 transformed data. These parameters were calculated for FPXRF
data as the dependent variable, and thus, cannot be used to correct the FPXRF data. See Section 6.
Slope values determined by plotting FPXRF data on the x-axis and the reference data on the y-axis.
Y-lnt. Y-lntercept.
Std. Err. Standard error.
n Number of data points.
86
-------
£
100000
10000
1000
100
In situ-unprepared--Arsenic
10
10 100 • 1000 10000 100000
Reference Data (mg/kg)
100000
10000
1000
8 100
In situ-prepared-Arsenic
10
10 100 1000 10000
Reference Data (mg/kg)
100000
100000
i
i 10000
1000
100
Intrusive-unprepared-Arsenic
10
100000
f) 10000
J2
g 1000
i 100
Intrusive-prepared-Arsenic
10
10 100 1000 10000 100000
Reference Data (mg/kg)
. 10 100 1000 10000 100000
Reference Data (mg/kg)
100000
)
> 10000
« 1000
o
8
CO
z
In situ-unprepared--Lead
100
10
100000
i
< 10000
1000
100
In situ-prepared-Lead
10
.*"
10 100 1000 10000
Reference Data (rrig/kg)
100000
10 100 1000 10000 100000
Reference Data (mg/kg)
100000
Intrusive-unprepared-Lead
Q>
10000
s
1000
1_1
i 100
10
a
100000
10000
1000
100
Intrusive-prepared—Lead
10
10 100 1000 10000 100000
Reference Data (mg/kg)
10 100 1000 10000 100000
Reference Data (mg/kg)
Figure 5-8. Sample Preparation Effect on Arsenic and Lead Results—TN 9000
Analyzer: These graphs illustrate the effect of sample preparation on the comparability
between the analyzer and the reference data.
87
-------
Table 5-8. Regression Parameters* by Sample Preparation Variable and Soil Texture
TN 9000 Analyzer
Arsenic
Std. Err. Y-lnt. | Slope1
Barium
Std. Err. Y-lnt. Slope"
In Situ-Unprepared
90
112
2
0.924
0.911
ND
0.19
0.18
ND
0.34
0.26
ND
0.86
0.93
ND
In Situ-Prepared
92
112
4
0.986
0.981
ND
0.09
0.08
ND
0.04
0.15
ND
0.97
0.95
ND
Intrusive-Unprepared
87
111
3
0.989
0.987
ND
0.08
0.07
ND
-0.05
0.06
ND
1.02
1.01
ND
Intrusive-Prepared
90
111
3
0.980
0.983
ND
0.11
0.08
ND
0.09
0.13
ND
0.99
1.00
ND
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
95
110
98
0.015
0.570
0.742
0.07
0.12
0.17
2.92
1.77
1.69
-0.06
0.51
0.57
In Situ-Prepared
97
112
98
0.078
0.509
0.855
0.08
0.15
0.11
2.55
1.73
1.81
0.15
0.55
0.52
Intrusive-Unprepared
99
111
98
0.304
0.698
0.929
0.04
0.09
0.08
2.51
1.82
1.72
0.18
0.52
0.57
Intrusive-Prepared
95
111
99
0.293
0.742
0.938
0.03
0.09
0.09
2.63
2.27
1.56
0.13
0.79
0.63
Copper ^^^^^^H Lead |
n i r2 [ Std. Err. | Y-lnt.
Slope" ^^^^^^^| n
In Situ-Unprepared
94
113
44
0.918
0.852
0.494
0.15
0.25
0.18
0.34
0.45
1.21
0.88
0.92
0.61
In Situ-Prepared
95
113
33
0.973
0.979
0.592
0.09
0.09
0.11
-0.05
0.27
1.36
1.03
0.94
0.45
Intrusive-Unprepared
93
114
24
0.977
0.988
0.394
0.09
0.07
0.15
-0.11
0.26
0.82
1.07
0.96
0.75
Intrusive-Prepared
95
112
34
0.970
0.987
0.541
0.10
0.07
0.16
0.03
0.24
0.98
1.04
0.98
0.68
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
r2 Std. Err. Y-lnt. Slopeb |
In Situ-Unprepared
87
112
96
0.920
0.879
0.776
0.17
0.20
0.28
0.36
0.47
0.74
0.86
0.87
0.81
In Situ-Prepared
89
109
98
0.978
0.971
0.982
0.09
0.10
0.08
0.04
0.15
0.24
0.98
0.97
0.93
Intrusive-Unprepared
88
113
98
0.984
0.983
0.989
0.08
0.08
0.07
0.01
0.12
0.21
1.01
1.00
0.95
Intrusive-Prepared
87
109
99
0.972
0.974
0.988
0.11
0.10
0.07
0.11
0.12
0.17
1.00
1.02
0.98
88
-------
Table 5-8. Continued
7inrv •^^^•I^H Chromium (Low) 1
^^^^^^^^^^^^^^•••••^^^^^^^^^^^^^•B ^^^^^^^— ^— ^^--H
n 1 r2 Std. Err. Y-lnt. Slope" ^^^^^^^^H n | r2 Std. Err. Y-lnt. Slope" |
In Situ-Unprepared
80
106
92
81
101
84
79
101
86
77
106
81
0.950
0.753
0.861
0.11
0.17
0.20
0.40
0.70
0.38
0.85
0.78
0.97
In Situ-Prepared
0.968
0.923
0.976
0.10
0.10
0.07
0.23
0.37
0.18
0.94
0.90
0.97
Intrusive-Unprepared
0.963
0.935
0.979
0.11
0.10
0.08
0.00
0.21
0.06
1.03
0.98
1.03
Intrusive-Prepared
0.955
0.937
0.978
0.12
0.10
0.08
0.12
0.20
0.05
1.00
1.00
1.06
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
19
28
56
ND
ND
0.688
ND
ND
0.17
ND
ND
1.81
ND
ND
0.43
In Situ-Prepared
16
18
42
ND
ND
0.715
ND
ND
0.15
ND
ND
1.46
ND
ND
0.51
Intrusive-Unprepared
3
4
40
ND
ND
0.793
ND
ND
0.12
ND
ND
1.32
ND
ND
0.58
Intrusive-Prepared
3
3
43
ND
ND
0.842
ND
ND
0.13
ND
ND
0.76
ND
ND
0.80
Chromium (High)
Std. Err. Y-lnt. Slopef
Std. Err. Y-lnt. Slope"
In Situ-Unprepared
3
6
36
3
4
30
ND
ND
0.544
ND
ND
0.19
ND
ND
1.82
ND
ND
0.48
In Situ-Prepared
ND
ND
0.816
ND
ND
0.12
ND
ND
1.28
ND
ND
0.63
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Soil Texture
Sand Soil
Loam Soil
Clay Soil
Intrusive-Unprepared
3
3
37
ND
ND
0.779
ND
ND
0.14
• ND
ND
1.14
Intrusive-Prepared
3
3
39
ND
ND
0.794
ND
ND
0.16
ND
ND
1.44
ND
ND
0.69
ND
ND
0.62
Notes: * Regression parameters based on Iog10 transformed data. These parameters were calculated for
FPXRF data as the dependent variable, and thus, cannot be used to correct FPXRF data. See
Section 6.
" Slope values determined by plotting FPXRF data on the x-axis and the reference data on the y-
axis.
Y-lnt. Y-lntercept.
Std. Err. Standard Error.
n Number of data points.
ND Not detected. Analyte not present in significant quantities to provide meaningful regression.
89
-------
Table 5-9. Regression Parameters' by Sample Preparation Variable and Site Name
TN 9000 Analyzer
In Situ-Unprepared
Site Name
Barium
Std. Err. Y-lnt. I Slope-
In Situ-Unprepared
202
0.916
0.19
0.31
0.89
ASARCO Site
206 0.364
0.11
2.19 0.32
ND
ND
ND
ND
RV Hopkins Site
98
0.742
0.17
1.69
0.57
In Situ-Prepared
Site Name
In Situ-Prepared
204
0.982
0.09
0.08 0.97
ASARCO Site
207 0.477
0.11
2.10 0.38
ND
ND
ND
ND
RV Hopkins Site
98
0.855
0.11
1.81
0.52
Intrusive-Unprepared
Site Name
intrusive-Unprepared
198
0.986
0.08
0.00
1.02
ASARCO Site
207 0.600
0.06
2.29
0.30
3 ND
ND
ND
ND
RV Hopkins Site
98
0.929
0.08
1.72
0.57
Intrusive-Prepared
Site Name
Intrusive-Prepared
201
0.980
0.10
0.11
1.00
ASARCO Site
206
0.583
0.05
2.35
0.27
ND
ND
ND
ND
RV Hopkins Site
99
0.938
0.09
1.56
0.63
In Situ-Unprepared
Site Name
Std. Err. Y-lnt. Slope"
In Situ-Unprepared
203
0.912
0.21
0.19 0.97
ASARCO Site
200 0.885
0.20
0.38 0.88
44
0.494
0.18
1.21
0.61
RV Hopkins Site
96
0.776
0.28
0.74
0.81
In Situ-Prepared
Site Name
In Situ-Prepared
207
0.980
0.10
0.03 1.01
ASARCO Site
196 0.975
0.10
0.09 0.98
33
0.592
0.11
1.36
0.45
RV Hopkins Site
98
0.982
0.08
0.24
0.93
Intrusive-Unprepared
Site Name
Intrusive-Unprepared
208
0.984
0.09
0.04
1.02
ASARCO Site
200 0.980
0.09
0.05
1.02
24
0.394
0.15
0.82
0.75
RV Hopkins Site
98
0.989
0.07
0.21
0.95
intrusive-Prepared
Site Name
Intrusive-Prepared
208
0.982
0.09
0.11
1.02
ASARCO Site
197
0.970
0.11
0.12
1.01
34
0.541
0.16
0.98
0.68
RV Hopkins Site
99
0.988
0.07
0.17
0.98
Chromium (Low)
Std. Err. I Y-lnt. Slope"
186
93
183
84
181
86
184
81
In Situ-Unprepared
0.860
0.867
0.16
0.20
0.51
0.43
0.83
0.95
In Situ-Prepared
0.947
0.976
0.10
0.07
0.28
0.18
0.92
0.97
Intrusive-Unprepared
0.939
0.979
0.12
0.08
0.13
0.06
1.00
1.03
Intrusive-Prepared
0.939
0.978
0.12
0.08
0.17
0.05
1.00
1.06
Site Name
ASARCO Site
RV Hopkins Site
Site Name
ASARCO Site
RV Hopkins Site
Site Name
ASARCO Site
RV Hopkins Site
Site Name
ASARCO Site
RV Hopkins Site
^^^^^^^^^^^^^^^^^•^^^^^^^^^••••^^•^^^^••^^^•^^^•••I^^^^M
In Situ-Unprepared
47
56
ND
0.688
ND
0.17
ND
1.81
ND
0.43
In Situ-Prepared
34
42
ND
0.715
ND
0.15
ND
1.46
Intrusive-Unprepared
6
40
ND
0.793
ND
0.12
ND
1.32
ND
0.51
ND
0.58
Intrusive-Prepared
5
43
ND
0.842
ND
0.13
ND
0.76
ND
0.80
90
-------
Table 5-9. Continued
n r2 Std. Err. Y-lnt.
Chromium (High)
Slope" I^^^^^H n i
In Situ-Unprepared
9
36
ND
0.544
ND
0.19
ND
1.82
ND
0.48
In Situ-Prepared
7
30
ND
0.816
ND
0.12
ND
1.28
ND
0.63
Site Name
ASARCO Site
RV Hopkins Site
Site Name
ASARCO Site
RV Hopkins Site
Std. Err. Y-lnt.
Slope"
Intrusive-Unprepared
5
37
ND
0.779
ND
0.14
ND
1.14
ND
0.69
Intrusive-Prepared
6
39
ND
0.794
ND
0.16
ND
1.44
ND
0.62
Notes: Regression parameters based on Iog10 transformed data. These parameters were calculated for
FPXRF data as the dependent variable, and thus, cannot be used to correct FPXRF data. See
Section 6.
Slope values determined by plotting FPXRF data on the x-axis and the reference data on the y-
axis.
Y-lnt. Y-lntercept.
Std. Err. Standard Error.
n Number of data points.
ND Not detected. Analyte not present in significant quantities to provide meaningful regression.
Table 5-10. Summary of Data Quality Level Parameters
Target TN 9000
Analytes Analytes
Precision
(mg[kg)
Mean % RSD
5-10XMDL
Method Detection Coefficient of
Limits (mg/kg) Determination
(Precision-based) (r2 All Data)
Data Quality
Level
Arsenic
Barium
Chromium
Copper
Lead
Zinc
Nickel
Iron
Cadmium
Antimony
Arsenic
Barium
Chromium
Copper
Lead
Zinc
Nickel
Iron
Cadmium
Antimony
6.5
4.0
22.2
7.0
6.5
7.3
Not Determined
Not Determined
Not Determined
6.54
60
60
200
85
45
80
100
Not Determined
Not Determined
55
0.95
0.79
0.78
0.95
0.96
0.93
Not Determined
Not Determined
Not Determined
Not Determined
Definitive
Quantitative
Qualitative
Definitive
Definitive
Definitive
Insufficient Data
Insufficient Data
Insufficient Data
Insufficient Data
91
-------
Section 6
Applications Assessment and Considerations
Both TN Spectrace analyzers are designed to produce quantitative data on the concentration of
metals in soils, sludges, and other solids. The TN Spectrace "Soils Application" software used for
calibration and quantitation maximizes instrument performance and accounts for common soil-related
matrix interferences. The FP calibrations can be fine tuned with NIST soil SRMs to further improve data
comparability. These analyzers are designed for field use and exhibited ruggedness under a variety of
operating conditions. Neither analyzer experienced failures resulting in down time throughout the 1-
month field demonstration. During this time, more than 1,260 samples were analyzed by each
instrument. The short training video provided by the developer was sufficient to allow basic field
operation of either analyzer. The developer offered a training class in the use of the analyzers. This
training, coupled with on-line technical support, was sufficient to allow uninterrupted operation and no
data loss throughout the demonstration.
Comparison of the Iogi0 transformed TN Pb Analyzer and the logjo transformed reference data
indicated that the analyzer can provide quantitative screening level quality data for certain metals. Li
addition, a comparison showed that the FPXRF and reference data are Iog10-log10 linearly related. This
Iog10-log,0 linear correlation appears to hold more than 5 orders of magnitude. The relationship between
the analyzer data and the reference data would indicate that this analyzer could be used in most field
analytical applications. Analyzer bias could be corrected to more closely match the reference data. In
the case of copper, lead, zinc, and arsenic, the TN Pb Analyzer's Iogi0 transformed data was statistically
equivalent to the log,0 transformed reference data. This analyzer also exhibited analyzer precision
similar to the reference method, indicating a high degree of reproducibility.
The TN Pb Analyzer is generally operated with relatively short count times and has only one
radioactive source. The single radioactive source limits the number of analytes which can be detected.
The TN Pb Analyzer's "Soils Application" software can report concentrations for arsenic, chromium,
iron, copper, zinc, and manganese in soil samples. The shorter count times and the single radioactive
source combine to generally increase the sample throughput and detection limits but decrease the
analyzer accuracy. In a 10-hour day during the demonstration, 200 - 300 samples were analyzed. A
summary of this performance information is found in Table 6-1.
Analysis of the TN 9000 and reference data indicated that the TN 9000 produced definitive level
quality data for arsenic, copper, lead, and zinc. This indicates that the Iogi0 transformed TN 9000's data
was statistically equivalent to the Iog10 transformed reference data for these analytes. The TN 9000
produced quantitative screening level for barium. As with the TN Pb Analyzer, if 10 - 20 percent of the
samples analyzed by the TN 9000 were submitted for reference method analysis, bias in the TN 9000
data could be determined and the data could be corrected to more closely match reference data. In
92
-------
addition, this analyzer exhibited instrument precision similar to the reference method, indicating high
instrument reproducibility.
Table 6-1. Summary of Test Results and Operational Features-TN Pb Analyzer
Weighs less than 20 pounds, battery lifetime of 8 hours
Sample throughput — 20 to 25 samples per hour
Can conduct in situ measurements or measure prepared samples in cups
Achieved data completeness of 100 percent during the demonstration
One day training for operation
Measured radiation levels below occupational limits
Drift less than ±10 percent for all but one of the analytes monitored
Produces EPA definitive level quality for zinc, arsenic, copper, and lead
Data is strongly linearly related to Methods 3050A/6010A data
FP calibrations that can be fine tuned with site-specific samples
Percent RSD values less than 10 percent at 5 to 10 times the MDL for all analytes except chromium
Matrix-specific FP calibrations in "Soils Applications" software
Can be used on soils exhibiting more than 30 percent water saturation by weight
Single excitation source
The TN 9000 can use up to three radioactive sources allowing analysis of a large number of metals in
soils. The "Soils Application" software can report concentrations for potassium, calcium, titanium,
chromium, manganese, iron, cobalt, nickel, copper, zinc, arsenic, selenium, rubidium, strontium,
zirconium, molybdenum, mercury, lead, uranium, thorium, silver, cadmium, antimony, tin, and barium.
The TN 9000 generally uses longer count times, which are proportional to the number of sources used in
analysis. The longer count times and multiple sources generally increase accuracy and lower detection
limits but decrease sample throughput. Eighty to 100 samples were analyzed in a 10-hour day during the
demonstration. A summary of this performance information is found in Table 6-2.
For both analyzers, there was no apparent effect of site or soil type on performance. This
demonstration identified sample preparation as the most important variable with regard to analyzer
performance. The results from this demonstration indicated that when operated in the in situ mode, the
data did not show a strong correlation between FPXRF and reference data. This may not be due to
instrument error, but rather to inherent spacial variability of contamination, even within an area as small
as the 4-inch by 4-inch grid sampled during this demonstration. The greatest increase in correlation
between the FPXRF data and reference data for both analyzers was achieved after the initial sample
homogenization. Further sample preparation, such as sieving or drying and grinding, did not greatly
improve the comparability. However, this more involved sample preparation generally improved the
quality of chromium data. This was indicative of the general problematic nature and influence of particle
size of chromium determination by FPXRF.
Based on this demonstration, both of these analyzers are well suited for the rapid real-time
assessment of metals contamination in soil samples. Although in several cases the analyzers produced
data statistically equivalent to the reference data, confirmatory analysis is recommended for FPXRF
analysis as is indicated in SW-846 Method 6200. If 10 - 20 percent of the samples analyzed by either
analyzer are submitted for reference method analysis, instrument bias, relative to standard methods such
as Methods 3050A/6010A, can be corrected. The effects of data correction for both analyzers are
illustrated in Tables 6-3 and 6-4. These tables illustrate the effects of data correction on the in situ-
93
-------
prepared samples. Changes in average relative bias and accuracy are used to show the effects of data
correction. This will only hold true if the analyzers and the laboratory analyze similar samples. This was
accomplished in this demonstration by thorough sample homogenization. Bias correction allows
analyzer data to be corrected so that it approximates the Methods 3050A/6010A data. The demonstration
showed that these analyzers exhibit a strong linear relationship with the reference method data more than
a 5 orders of magnitude concentration range. For optimum correlation, samples with high, medium, and
low concentration ranges from a project must be submitted for reference method analysis.
Table 6-2. Summary of Test Results and Operational Features—TN 9000 Analyzer
Weighs less than 20 pounds, battery lifetime of 4 to 5 hours
Sample throughput — 8 to 10 samples per hour
Can conduct in situ measurements or measure prepared samples in cups
Data completeness of 99.9 percent during the demonstration
One day training for operator
AH measured radiation levels below occupational limits
Produces EPA quantitative screening level quality or better data for most analytes
Arsenic, copper, lead, zinc — definitive level
Barium — quantitative screening level
Chromium — qualitative level data
Data is linearly related to Methods 3050A/6010A data
FP calibrations that can be fine tuned with site-specific samples
Precision — percent RSD values less than 10 percent at 5 to 10 times the MDL for all analytes except
chromium
Accuracy of greater than 90 percent for all analytes in the site-specific PEs and soil SRMs
Three excitation sources allowing for analysis of more than 30 elements
Matrix-specific FP calibrations in "Soils Applications" software
Can be used on soils exhibiting more than 30 percent water saturation by weight
The steps for correcting the FPXRF measurements to more closely match the reference data are as
follows:
1. Conduct sampling and FPXRF 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 FPXRF and a reference method.
4. Tabulate the resulting data with reference data in the x-axis column (independent variable) and
the FPXRF data in the y-axis column (dependent variable). Transform this data to the equivalent
Iogj0 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 6-1:
Y (log,0 corrected FPXRF data) = slope *(log10 FPXRF data) + Y-intercept
(6-1)
94
-------
7. Use the above equation with the Iog10 transformed FPXRF results from Step 4 above and
calculate the equivalent Iog10 corrected FPXRF data.
8. Take the anti-log10 (10 [iog,0 transformed corrected FPXRF data]) of the equivalent Iog10 corrected FPXRF
data calculated in Step 7. These resulting values (in milligrams per kilogram) represent the
corrected FPXRF data.
Table 6-3. Effects of Data Correction on FPXRF Comparability to Reference Data for All In Situ-
Prepared Samples—TN Pb Analyzer
Target
Analyte
Average
Relative Bias
on Raw Data3
Average
Relative Bias on
Corrected Data"
Average
Relative Accuracy
on Raw Datac
Average
Relative
Accuracy on
Corrected Data"
Acceptable
Relative Accuracy
Based on
PE Samples6
Arsenic
Chromium
Copper
Iron
Lead
Zinc
1.07
8.78
1.52
1.47
1.17
1.50
1.00
1.63
1.07
1.02
1.05
1.03
1.30
18.58
2.29
1.55
1.48
1.65
1.28
2.29
1.63
1.19
1.34
1.27
1.76
1.55
1.18
1.54
1.63
1.64
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 Iog10 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.
A measurement of average relative accuracy at the 95 percent confidence interval, of the corrected
FPXRF data obtained using the eight-step regression approach.
A measurement of accuracy represents a factor and 95 percent confidence interval that define the
acceptable range of differences allowed between the reference method reported concentrations and
the true value concentrations in the PE samples. This bias is included only as a general reference for
assessing the improvement on comparability of FPXRF data and reference data after FPXRF data
correction.
The average relative bias is calculated as follows:
Average relative bias = ((£i[FPXRFj/Referencej])/number of paired samples)-!
This value represents the percentage that the FPXRF over- or underestimates the reference data, on average, for
the entire data set. To convert this calculated value to a factor, 1.0 is added to the calculated average relative
bias. The above table presents the average relative bias as a factor.
The average relative accuracy is calculated as follows:
Average relative accuracy =SQRT (Xi([FPXRF/Referencei]:1)2/number of paired sample)
This value represents the percentage that an individual FPXRF measurement over- or underestimates the
reference data. The relative accuracy numbers in the table are calculated at the 95 percent confidence interval.
This is accomplished by adding two standard deviations to the above formula before the square root is taken. To
convert this calculated value to a factor, 1.0 is added to the calculated average relative accuracy. The above table
presents the average relative bias as a factor.
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Table 6-4. Effects of Data Correction on FPXRF Comparability to Reference Data for All In Situ-
Prepared Samples—TN 9000 Analyzer
Target
Analyte
Average Average
Average Average Relative Average Relative Relative Accuracy
Relative Bias Relative Bias on Accuracy on Accuracy on Based on
on Raw Data" Corrected Datab Raw Datac Corrected Data" PE Samples0
Antimony
Arsenic
Barium
Chromium Lo
Chromium Hi
Copper
Iron
Lead
Nickel
Zinc
6.14
1.00
7.25
6.52
3.63
1.23
1.42
1.09
1.66
1.29
1.10
1.02
1.11
1.79
1.30
1.05
1.02
1.02
1.14
1.02
6.78
1.21
8.13
10.22
6.31
1.46
1.49
1.26
1.62
1.40
1.47
1.21
1.49
3.11
2.10
1.33
1.18
1.22
1.18
1.23
2.94
1.76
1.36
1.36
1.55
1.18
1.54
1.63
1.56
1.64
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 Iog10 transformed) data. This average relative bias does not account for any
concentration effect on analyzer performance.
b
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 95 percent confidence interval that define the
acceptable range of differences allowed between the reference method reported concentrations and
the true value concentrations in the PE samples. This bias is included only as a general reference
for assessing the improvement on comparability of FPXRF data and reference data after FPXRF
data correction.
The average relative bias is calculated as follows:
Average relative bias = ((£j[FPXRFj/Referencei])/number of paired samples)-1
This value represents the percentage that the FPXRF over- or underestimates the reference data, on average, for
the entire data set. To convert this calculated value to a factor, 1.0 is added to the calculated average relative
bias. The above table presents the average relative bias as a factor.
The average relative accuracy is calculated as follows:
Average relative accuracy =SQRT (Xi([FPXRF/Reference|]-1)2/number of paired sample)
This value represents the percentage that an individual FPXRF measurement over- or underestimates the
reference data. The relative accuracy numbers in the table are calculated at the 95 percent confidence interval.
This is accomplished by adding two standard deviations to the above formula before the square root is taken. To
convert this calculated value to a factor, 1.0 is added to the calculated average relative accuracy. The above
table presents the average relative bias as a factor.
To show the effect of correcting the FPXRF data, the change in average relative bias and accuracy
can be examined. The average relative bias between the FPXRF data and the reference data is a measure
of the degree to which the FPXRF over- or underestimates concentrations relative to the reference
methods. The relative bias is an average number for the entire data set and may not be representative of
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individual measurements. An example of this can be seen in an analyzer's data where measurements are
underestimated at low concentrations, but overestimated at high concentrations. On average, the relative
bias for this analyzer is zero; however, this bias is not representative for high or low concentration
measurements. To avoid this dilemma, three approaches can be taken: (1) the evaluation of average
relative bias can be focused on a narrow concentration range, (2) the analyzer's data can be corrected
using the regression approach described above, or (3) the 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. Tables 6-3 and 6-4 show the average relative bias
and accuracy exhibited by the Spectrace analyzers, before and after data correction using the eight-step
approach previously discussed.
The average relative bias and accuracy for the analytes falling into the definitive level data quality
category were generally small. However, the analytes falling into the quantitative and qualitative
screening level data quality categories had generally larger average relative bias and accuracy.
Once the FPXRF data is corrected using the regression approach presented earlier, both the average
relative bias and accuracy are greatly reduced. The average relative bias numbers are no longer strongly
influenced by a concentration effect since the regression approach used to correct the data used Iog10
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 Tables 6-3 and 6-4.
Based on the findings of this demonstration, both of these analyzers can provide rapid assessment of
the distribution of metals contamination 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 elements, the data may be
statistically similar to the reference data. The approval of SW-846 Method 6200 "Field Portable X-Ray
Fluorescence Spectrometry for the Determination of Elemental Concentrations in Soil and Sediment"
will speed the acceptance of this data for definitive level applications and most quantitative applications.
The analyzer 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. These analyzers are powerful tools for
site characterization and remediation. They provide a fast and accurate means of analyzing metals
contamination in soil.
General Operational Guidance
The following paragraphs describe general operating considerations for FPXRF analysis. This
information is derived from SW-846 Method 6200 for FPXRF analysis.
General operation of FPXRF instruments will vary according to specific developer protocols. For all
environmental applications, confirmatory or reference sampling should be conducted so that FPXRF data
can be corrected. Before operating any FPXRF instrument, the developer's manual should be consulted.
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 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
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modes of analysis, while others are designed to operate in only one mode. The two modes of analysis are
discussed below.
For in situ analysis, one requirement is that any large or nonrepresentative debris be removed from
the soil surface before analysis. This debris includes rocks, pebbles, leaves, vegetation, roots, and
concrete. Another requirement is that the soil surface be as smooth as possible so that the probe window
will have good contact with the surface. This may require some leveling of the surface with a stainless-
steel trowel. Most developers recommend that the soil be tamped down to increase soil density and
compactness. This step reduces the influence of soil density variability on the results. During the
demonstration, this modest amount of sample preparation was found to take less than 5 minutes per
sample location. The last requirement is that the soil or sediment not be saturated with water.
Developers state that their FPXRF instruments will perform adequately for soils with moisture contents
of 5 - 20 percent, but will not perform well for saturated soils, especially if ponded water exists on the
surface. Data from this demonstration did not see an effect on data quality from soil moisture content.
Source count times for in situ analysis usually range from 30 to 120 seconds, but source count times will
vary between instruments 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, and at a
maximum, 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 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 31-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 that
the method blanks are below the MDLs of the procedure.
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