Revised Assessment of 

Detection and Quantitation 

Approaches 

October 2004 
Engineering and Analysis Division


Office of Science and Technology


Office of Water (4303T)


U.S. Environmental Protection Agency


1200 Pennsylvania Avenue, NW


Washington, DC 20460


EPA-821-B-04-005 


October 2004 
Disclaimer 

This document has been reviewed and approved for publication by the Engineering and Analysis 

Division, Office of Science and Technology. Neither the United States Government nor any of its 

employees, contractors, or their employees make any warranty, expressed or implied, or assumes any 

legal liability or responsibility for any third party’s use of or the results of such use of any information, 

apparatus, product, or process discussed in this report, or represents that its use by such party would not 

infringe on privately owned rights. 
Table of Contents 

FOREWORD ............ ............ ............ ............ ............ ........... i 

CHAPTER 1: INTRODUCTION ............ ............ ............ ............ .... 1-1 

1.1 Background 

1.2 Clause 6 Settlement Agreement Requirements 

1.3 EPA’s Approach to Conducting this Assessment 

1.4 Peer Review of the Agency’s Assessment 

1.5 Proposal and Request for Public Comments 

1.6 Terminology used in this Document 

CHAPTER 2: OVERVIEW AND HISTORY OF DETECTION AND QUANTITATION LIMIT 

APPROACHES ............ ............ ............ ............ ...... 2-1 

2.1 Currie’s Call for Standardization 

2.2 Development of the MDL and ML as Practical Embodiments of Currie’s Proposal 

2.3 Other Detection and Quantitation Approaches 

CHAPTER 3: ISSUES PERTAINING TO DETECTION AND QUANTITATION ........... 3-1 

3.1 Analytical Chemistry Approaches to Detection and Quantitation 

3.2 CWA Regulatory Issues Affecting Detection and Quantitation 

3.3 Statistical Issues 

CHAPTER 4: EVALUATION CRITERIA ............ ............ ............ ........ 4-1 

4.1 Criterion 1 

4.2 Criterion 2 

4.3 Criterion 3 

4.4 Criterion 4 

4.5 Criterion 5 

4.6 Criterion 6 

4.7 Consensus Principles 

CHAPTER 5: ASSESSMENT ............ ............ ............ ............ ....... 5-1 

5.1 Detection Limit Approaches 

5.2 Quantitation Limit Approaches 

CHAPTER 6 FINDINGS AND NEXT STEPS ............ ............ ............ ..... 6-1 

APPENDIX A:	 LITERATURE SEARCH REGARDING DETECTION AND QUANTITATION 

LIMIT APPROACHES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 

APPENDIX B: COMPUTATION OF DETECTION AND QUANTITATION LIMITS . . . . . . . .B-1 

APPENDIX C: EXAMPLE CALCULATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1 
Foreword 

EPA has assessed current procedures for determining the sensitivity of test methods and their 

application to Clean Water Act (CWA) Programs. The assessment was required by a settlement 

agreement with the Alliance of Automobile Manufacturers, et al. We announced the availability of our 

preliminary assessment for public comment on March 12, 2003. This assessment discussed statistical, 

chemical, and regulatory issues related to detection and quantitation and different approaches to 

detection and quantitation. The Agency has revised the preliminary assessment Document to incorporate 

public comment on that assessment. 

In a related action on March 12, 2002, we proposed to revise EPA’s method detection limit 

(MDL) definition and procedure, and codify our minimum level (ML) procedure. The MDL and ML, 

respectively and in order of increasing magnitude, are the EPA’s embodiment of a detection and a 

quantitation limit. 

In this revised assessment, we have: 

• Explained why and how we conducted this assessment (Chapter 1), 

• Identified relevant concepts to include in the assessment (Chapter 2 of this document), 

•	 Identified issues that may be relevant to the assessment from an analytical chemistry, statistical, 

or regulatory perspective (Chapter 3), 

•	 Used six criteria to evaluate the ability of each procedure or concept to support activities under 

the Clean Water Act (Chapter 4), 

• Assessed how well each concept meets the evaluation criteria (Chapter 5), 

• Summarized our findings and discussed next steps (Chapter 6), and 

•	 With real-world data and several different procedures, calculated and compared detection and 

quantitation limits, and evaluated the theoretical and practical limitations of each concept 

(Appendices). 

Public comment on the preliminary assessment and the proposed regulatory revisions expressed 

many divergent views that conflicted with the proposed revisions. Commenters noted that: (1) the MDL 

does not adequately address analytical variability or systematic error (bias); (2) the MDL does not always 

achieve a one percent (1%) false positive rate; (3) EPA should provide better guidance on the intended 

use of the MDL and ML in compliance reporting; and (4) the MDL and ML are not appropriate for all 

applications in CWA programs. Several commenters expressed support for two alternatives to the MDL 

and ML that were submitted by a laboratory association and the U.S. Geological Survey, respectively. 

Although none of the alternative procedures recommended by commenters fully satisfied EPA’s needs 

under the CWA, several procedures contain steps, such as blank correction, that EPA believes warrant 

further consideration. There was no agreement among commenters as to which of the competing 

alternatives or revisions to adopt. Commenters suggested that we work together to discuss mutual 

concerns and possible solutions rather than proceed with the proposed revisions. We agree and recognize 

that these concerns provide a strong starting point for a continued dialog with stakeholders. 

Based on this new information, it is clear that there is a broad interest in improving current 

procedures and uses, but no consensus for a specific procedure or procedures has emerged among the 

laboratory, industry, regulatory or regulated communities. In addition, EPA sees merit in alternative 

procedures suggested by commenters; however, none of these completely satisfy EPA’s needs. Thus, we 

believe that it is appropriate to withdraw the March 2003 proposed revisions, take final action on the 

2003 assessment to complete the terms of the settlement agreement, and obtain additional stakeholder 

i 
input. In a Federal Register notice published on September 15, 2004 [69 FR 55547], we announced that 

a neutral party is exploring the feasibility of a process by which a broad group of stakeholders would 

work together to define and address concerns about the way detection and quantitation limits are 

calculated and used to support CWA programs. This stakeholder process would include stakeholders 

representing constituencies such as citizens, environmental organizations, permit writers, regulators and 

regulated industries. We trust that this stakeholder process will address the wide variety of views held by 

stakeholders and lead to recommendations for possible improvements to current EPA procedures and/or 

use of alternative procedures. 

To facilitate open discussion and consideration of issues, we have made every effort to ensure 

that this Revised Assessment Document does not prejudge the result of a future stakeholder process. We 

look forward to further stakeholder participation in this process. 

ii 
Chapter 1 

Introduction 

1.1 Background 

On June 8, 1999 (64 FR 30417), EPA promulgated (i.e., published in a final rule) Method 

1631B: Mercury in Water by Oxidation, Purge and Trap, and Cold Vapor Atomic Fluorescence Spectrometry (the "method") for use in EPA's Clean Water Act programs. The method was developed 

specifically to measure mercury at ambient water quality criteria levels and includes a method detection 

limit (MDL; see 40 CFR part 136, Appendix B) of 0.2 nanograms per liter (ng/L). 

Following promulgation, a lawsuit was filed challenging EPA on the validity of the method. The 

basis of the challenge included several specific aspects of Method 1631 as well as the general procedures 

used to establish the MDL and minimum level of quantitation (ML) published in the method. In order to 

settle the lawsuit, EPA entered into a settlement agreement (the "Settlement Agreement") with the 

Alliance of Automobile Manufacturers, Inc., the Chemical Manufacturers Association, and the Utility 

Water Act Group (collectively the “Petitioners”) and the American Forest and Paper Association 

(“Intervenor”) on October 19, 2000. Under Clause 6 of the Settlement Agreement, EPA agreed to 

perform an assessment of detection and quantitation limit concepts. The complete text of Clause 6 is 

provided in Exhibit 1-1 of this chapter. A summary of Clause 6 is provided in Section 1.2. The summary 

is followed by a description of EPA’s approach to the assessment, including the material and data 

evaluated (Section 1.3), the use of an independent peer review to evaluate the Agency's assessment 

(Section 1.4), and EPA’s March 2003 publication of and request for comment on the February 2003 

assessment, and a related proposal concerning potential changes to detection and quantitation limit 

procedures approved for use under the Clean Water Act (Section 1.5). A brief discussion of the 

terminology used in this document is provided in Section 1.6. 

1.2 Clause 6 Settlement Agreement Requirements 

Clause 6 of the Settlement Agreement is titled Reassessment of Method Detection Limit and 

Minimum Level Procedures. Clause 6 consists of five subclauses, a - b and d - f. (There is no subclause 

c.) 

1.2.1 Clause 6a 

Clause 6a broadly defines the scope of the assessment and provides a schedule for completing 

the initial phase. Specifically, Clause 6a requires EPA to: 

•
 Sign and forward to the Office of Federal Register (OFR) a notice inviting public comment on a 

reassessment of existing EPA procedures for determining the detection and quantitation limits of 

contaminants in aqueous samples. 

• Forward the notice to the OFR on or before February 28, 2003. 

• Provide a period of at least 120 days for public comment on the notice. 

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•
 At a minimum, include the MDL procedure published at 40 CFR part 136, Appendix B, and the ML 

procedure described in Section 17.8 of Method 1631B, in the reassessment of detection and 

quantitation limits. 

• Invite comment on one or more alternative procedures for determining and describing test sensitivity. 

Clause 6a also provides EPA with the option of proposing modifications to the existing procedures. 

1.2.2 Clause 6b 

Clause 6b requires that EPA obtain a peer review of its reassessment, and describes six specific 

topics that must be included in the charge to the peer reviewers. Specifically, Clause 6b requires EPA to: 

•
 Submit the reassessment of existing procedures (including any proposed modifications thereof) and 

any evaluation of alternatives for peer review by experts in the field of analytical chemistry and the 

statistical aspects of analytical data interpretation. 

• Conduct the peer review in accordance with EPA’s peer review policies. 

•
 Prepare a charge to the peer review panel that requests the peer reviewers to consider: 

� Criteria for selection and appropriate use of statistical models 

� Methodology for parameter estimation 

� Statistical tolerance and prediction 

� Criteria for design of detection and quantitation studies, including selection of concentration 

levels (“spiking levels”) 

� Interlaboratory variability, and 

� Incorporation of elements of probability design. 

1.2.3 Clause 6d 

Clause 6d requires EPA to provide the Petitioners and Intervenor (the “litigants”) with an 

opportunity for review of the Agency’s assessment concurrent with the Clause 6b peer review. 

1.2.4 Clause 6e 

Clause 6e requires EPA to provide the litigants with: 

•
 An opportunity to meet periodically (i.e., every six months) to discuss the Agency’s progress during 

development of the assessment, 

• A plan for performing the assessment on or before the second of these meetings, and 

• Copies of relevant documents, where appropriate, in advance of these meetings. 

1.2.5 Clause 6f 

Clause 6f establishes a schedule and requirements concerning final action on the notice described 

in Clause 6a. Specifically: 

•
 On or before September 30, 2004 (since amended to November 1, 2004), EPA is to sign and forward 

to the OFR a notice taking final action on the notice described in Clause 6a, and 

•
 Coincident with publication of this notice of final action, EPA is to provide the litigants with an 

opportunity to meet and discuss the implications of the final notice and/or the need for any 

subsequent EPA action in light of the final notice. 

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Exhibit 1-1. Full Text of Clause 6 of the Settlement Agreement 

6. Reassessment of Method Detection Limit and Minimum Level Procedures 

a.	 On or before February 28, 2003, EPA shall sign and forward to the Office of the Federal Register for prompt 

publication a notice inviting public comment on a reassessment of the existing Agency procedures for determination 

of sensitivity of analytic test methods for aqueous samples, specifically, EPA procedures for determining the 

detection limits and levels of quantitation of contaminants in aqueous samples, including, at a minimum, the 

“Definition and Procedure for Determination of the Method Detection Limit” published at 40 C.F.R. Part 136, 

Appendix B, as well as the “minimum level” procedures, which is described in section 17.8 of Method 1631B. The 

notice shall invite comment on EPA’s evaluation of one or more alternative procedures for determining and 

describing test sensitivity. The notice also may propose modifications to the existing procedures. The notice shall 

invite public comment for a period of no less than one hundred twenty (120) days. 

b.	 Prior to publishing the notice inviting public comment on EPA procedures for determining test sensitivity, EPA shall 

submit its reassessment of existing procedures (including any proposed modifications thereof) and its evaluation of 

alternatives for peer review by experts in the field of analytical chemistry and the statistical aspects of analytical 

data interpretation. In its charge to the peer review panel, EPA shall request that the peer review consider: criteria 

for selection and appropriate use of statistical models; methodology for parameter estimation; statistical tolerance 

and prediction; criteria for design of detection and quantitation studies, including selection of concentration levels 

(“spiking levels”); interlaboratory variability; and incorporation of elements of probability design. EPA (or its 

authorized representative) shall conduct the peer review in accordance with EPA’s current peer review policies in 

the January 1998 Science Policy Council Handbook (EPA 100-B-98-00) [sic], including any subsequently-developed 

EPA peer review documents that may revise or amend that Handbook. 

[Note - the correct document number for the Science Policy Council Handbook is EPA 100-B-98-001] 

[c. Note - there is no clause "6.c" in the Settlement Agreement] 

d.	 During the peer review period, EPA shall also provide an opportunity for concurrent review and comment by the 

Petitioners and Intervenor. 

e.	 In the development of the reassessment/assessment of alternatives, EPA shall provide the Petitioners and 

Intervenor with a periodic opportunity to meet (i.e., every six (6) months) on the Agency’s progress. EPA shall 

prepare and present the Petitioners and Intervenor with the Agency’s “plan” for conducting the 

reassessment/assessment of alternatives on or before the second such periodic meeting. Where appropriate, EPA 

shall provide the Petitioners and Intervenor with copies of relevant documents in advance of such meetings. 

f.	 On or before September 30, 2004 (Note: since amended to November 1, 2004), EPA shall sign and forward to the 

Office of the Federal Register for prompt publication a notice taking final action on the notice described in 

subparagraph 6.a. Coincident with publication of the notice of final action, EPA shall provide Petitioners and 

Intervenor an opportunity to meet to discuss the implications of the final notice and/or the need for any subsequent 

EPA action in light of the final notice. 

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1.3 EPA’s Approach to Conducting this Assessment 

This document details the Agency’s assessment of methodology for the determination of method 

sensitivity, specifically: detection and quantitation limits. This assessment is being conducted in 

accordance with a plan summarized in Section 1.3.1 and is based, in part, on an assessment of the data 

described in Section 1.3.2. 

1.3.1 Study Plan 

EPA developed a technical approach for 1) conducting the assessment, and 2) complying with all 

applicable requirements of the Settlement Agreement. The approach was documented in a draft study 

plan that has since formed the general framework for the assessment described in this Assessment 

Document. EPA also conducted a literature search to identify and review issues and concepts that should 

be considered when developing the plan. A summary of this literature review is provided in Appendix A 

to this Assessment Document. 

The study plan described roles and responsibilities for implementing the plan, provided a 

background discussion of detection and quantitation limit concepts, including the MDL and ML, and 

outlined a series of 11 events associated with the Agency’s assessment of detection and quantitation limit 

approaches. The relationship between those planned events and this Assessment Document is 

summarized in Exhibit 1-2 at the end of this chapter. 

Although the Settlement Agreement did not require that EPA seek formal peer review on its draft 

plan, the Agency chose to conduct a peer review of the draft plan. The peer review was initiated in 

December 2001, conducted in accordance with EPA’s peer-review policies, and performed by two 

statisticians and two chemists. EPA reviewed the comments and recommendations offered by these 

reviewers, and where appropriate, revised the plan to reflect the peer-review comments. EPA also 

reviewed, and where appropriate, revised the plan to reflect comments provided by the petitioners 

following their concurrent review. 

1.3.2 Material and Data used in the Assessment 

In order to perform the assessment described in this document, EPA sought to collect 

documentation describing existing detection and quantitation limit concepts and procedures and data that 

could be used to evaluate these concepts and procedures. 

Documentation concerning the existing concepts and procedures was obtained by performing a 

literature search as described in Appendix A to this Assessment Document, and where appropriate, by 

purchasing copies of documents describing concepts or procedures from the organizations that published 

them. 

In performing this assessment, EPA hoped to identify a substantial amount of data containing 

results of direct relevance to the determination of detection and low-level measurement capability. That 

is, measurement results in the low concentration region. To date, EPA has been able to identify only six 

data sets that were of use in fully evaluating variability in the range of analytical detection and 

quantitation. Three of the six were developed by EPA for the express purpose of studying the 

relationship between measurement variation and concentration across a wide variety of measurement 

techniques and analytes. EPA refers to these data sets as “EPA’s ICP/MS Study of Variability as a 

Function of Concentration,” “EPA’s Multi-technique Variability Study” (also referred to as the “Episode 

1 - 4	
6000 study”), and “EPA’s GC/MS Threshold Study” (also referred to as “the Episode 6184 study”). In 

all three cases, replicate measurement results from each combination of analyte and measurement 

technique were produced by a single laboratory over a wide range and large number of concentrations. 

The fourth data set was developed by the American Automobile Manufacturer’s Association (AAMA) 

for the purpose of estimating one particular kind of quantitation value. That quantitation value is called 

an alternative minimum level (AML; see Gibbons et al., 1997). In the AAMA study, replicate results 

were measured at a limited number of concentrations by multiple laboratories using EPA Method 245.2 

(cold vapor atomic absorption; CVAA) for mercury and EPA Method 200.7 (inductively coupled 

plasma/atomic emission spectroscopy; ICP/AES) for twelve other metals. The final two data sets were 

jointly gathered by EPA and the Electric Power Research Institute (EPRI) to support interlaboratory 

validation of EPA Methods 1631 and 1638. 

The studies from which these six data sets were obtained are summarized in sections 1.3.2.1 -

1.3.2.6 below. Additional information about these studies can be found in Appendices B and C to this 

Assessment Document. 

In March 2003, EPA published an Assessment Document dated February 2003, and requested 

comments on the assessment and additional data to support continued evaluation of detection and 

quantitation limits. Three stakeholders commenting on the assessment also offered to provide EPA with 

data that would substantiate their views or aid EPA in further evaluating detection and quantitation 

procedures. These data are further described in Sections 1.3.2.7 - 1.3.2.8 and Section 1.3.3 below. 

Although the petitioners offered specific suggestions for other data sets that they believed should 

be considered in this assessment, EPA found that these data sets did not include a sufficient number of 

results in the region of detection and quantitation to yield information for the assessment, overlapped 

with data already used in the assessment, or exhibited signs of significant contamination that made the 

data inappropriate for inclusion in the assessment. These data, and EPA’s decisions regarding the data, 

are discussed in Section 1.3.3 below. 

1.3.2.1 EPA’s ICP/MS Study of Variability as a Function of Concentration 

The objective of the ICP/MS study was to characterize variability as a function of concentration 

using EPA's draft Method 1638 for determination of nine metals by inductively coupled plasma with 

mass spectroscopy (ICP/MS). The nine metals were silver, cadmium, copper, nickel, lead, antimony, 

selenium, thallium, and zinc. The ICP/MS instrument used in this study averages triplicate scans to 

produce a single measurement of each element at each concentration. Such averaging is typical of 

ICP/MS design and use. 

In preparation for the study, the ICP/MS was calibrated using triplicate scans averaged to 

produce a single measurement of 100, 1,000, 5,000, 10,000, and 25,000 nanograms per liter (ng/L) for 

each element. Originally, the instrument was calibrated using unweighted least squares estimates under 

the assumption of linearity. Subsequently, the analytical results were adjusted with weighted least 

squares estimates. Weighted least squares estimates are based on the knowledge that variability 

(expressed as the standard deviation) increases with increasing analyte concentration. 

1 - 5	
Although the instrumentation has the capability to provide intensity results for each of the three 

scans at each concentration, averaging the three scans to produce a single measurement is the normal 

operating mode, and the average was used to produce the measurements in this study. Draft Method 

1638 specifies the use of average response factors rather than least squares estimation of a linear 

calibration, although it does allow for the use of such procedures. 

All nine metals were spiked into reagent water to produce solutions at concentrations of: 0, 10, 

20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, and 25,000 ng/L. Each solution was divided into 

seven replicate aliquots for subsequent analysis. The aliquots were analyzed beginning with the blank 

(zero concentration) followed by analyses from the highest to the lowest concentration. This sequence 

was chosen to minimize carry-over effects and to allow the analyst to stop at the concentration that 

returned zero results. Carry-over is caused by residual sample remaining in the inlet system of the 

instrument, in this case, the ICP/MS. Carry-over can occur when analysis of a high-concentration sample 

is followed by analysis of a relatively low-concentration sample, as could occur if the replicates were 

analyzed in random order. Use of the highest to lowest analytical sequence ensured that each successive 

concentration analyzed was close enough to the previous concentration that any effects of carryover 

would be negligible and, therefore, would not compromise study results. (A more in-depth discussion of 

the randomized design and the effects of carry-over issues is provided in Chapter 3, Section 3.3.8.2). 

Results at multiple mass-to-charge ratios, or m/z's, were reported for each metal, although draft 

Method 1638 specifies only one m/z for eight of the nine metals. For lead, m/z's 206, 207, and 208 are 

specified. Only data associated with m/z's specified in draft Method 1638 were used in the ICP/MS 

study. 

1.3.2.2 EPA’s Multi-technique Variability Study (the “Episode 6000 Study”) 

In 1997 and 1998, EPA conducted a study of variability vs. concentration for a number of 

analytical methods. Five laboratories were employed for the analyses; each analyte and method 

combination was tested by one of these laboratories. Details of the study design are described in EPA’s 

Study Plan for Characterizing Variability as a Function of Concentration for a Variety of Analytical 

Techniques (July 1998). Based on the sampling episode number assigned to the study by the EPA 

Sample Control Center, the study and results have become known as the Episode 6000 study and data. 

The analytes and analytical techniques studied were: 

• Total suspended solids (TSS) by gravimetry 

• Metals by graphite furnace atomic absorption spectroscopy (GFAA) 

• Metals by inductively-coupled plasma atomic emission spectrometry (ICP/AES) 

• Hardness by ethylene diamine tetraacetic acid (EDTA) titration 

• Phosphorus by colorimetry 

• Ammonia by ion-selective electrode 

•
 Volatile organic compounds by purge-and-trap capillary column gas chromatography with a 

photoionization detector (GC/PID) and electrolytic conductivity detector (GC/ELCD) in series 

• Volatile organic compounds by gas chromatography with a mass spectrometer (GC/MS) 

• Available cyanide by flow-injection/ligand exchange/amperometric detection 

• Metals by inductively-coupled plasma spectrometry with a mass spectrometer (ICP/MS) 

1 - 6	
In this study, an initial (range finding) MDL was determined for each combination of analyte and 

analytical technique using minor modifications to the MDL procedure at 40 CFR part 136. Specifically. 

the modifications made the optional iterative step 7 of the MDL procedure mandatory and required the 

spike concentration to be no more than a factor of three times the determined MDL (instead of a factor of 

five times). During the study, however, two of the laboratories found that the reduction in the allowable 

spike range necessitated an unreasonably large number of iterations. In continuing the study, EPA 

returned to the spike-to-MDL ratio of five published in the 40 CFR part 136, Appendix B procedure. 

After determining the initial MDL, each laboratory analyzed 7 replicate samples spiked at 

concentrations that were 100, 50, 20, 10, 7.5, 5.0, 3.5, 2.0, 1.5, 1.0, 0.75, 0.50, 0.35, 0.20, 0.15, and 0.10 

times the initial MDL. In a few instances, laboratories analyzed more than 7 replicates. As often as 

possible, the replicate analyses at each concentration level were produced using the same calibration that 

was used in determining the initial MDL. Where laboratory reports indicated that multiple calibrations 

were conducted, each result was associated with its calibration in the data analysis. 

Spiked aqueous solutions were analyzed in order from the highest concentration (100 times the 

MDL) to the concentration at which 3 or more non-detects (zeros) were encountered among the 7 

replicates, or the lowest concentration specified (0.1 times the MDL), whichever occurred first. This 

analysis order (1) minimized carryover that could occur in some methods if a low-concentration sample 

had followed a high-concentration sample (as may happen when samples are analyzed in random order), 

and (2) prevented collection of a large number of zeros if the signal disappeared. 

For methods that do not produce a signal for a blank, the signal will disappear somewhere below 

the MDL, i.e., a zero will be reported. Laboratories were instructed that when three nondetects (out of 

seven measurements) were reported, it was not necessary to move to the next lower concentration, 

because it would be of no practical value to have laboratories measure seven zeros, move to a lower 

level, measure seven zeros, etc. 

A variant of the iterative procedure for determining the MDL was used for organic compounds 

determined by chromatographic methods. Methods for organics normally list many (15 to 100) analytes, 

and the response for each analyte is different. Therefore, to determine an MDL for each analyte, the 

concentration of the spike would need to be inversely proportional to the response. Making a spiking 

solution with 15 to 100 different concentrations is cumbersome and error prone. The approach used in 

the study was to run seven replicates at decreasing concentrations until signal extinction, then select the 

concentration(s) appropriate for the determining the MDL for each analyte according to the MDL 

procedure. In some cases, the laboratories selected the concentrations, in others cases, EPA did. This 

approach was generally applied for organics analysis. However, laboratories also had the option of using 

some combination of the monotonically decreasing concentrations described above and a few selected 

concentrations to achieve the desired spiking levels. 

1.3.2.3 EPA’s GC/MS Threshold Study (the “Episode 6184 Study”) 

Data from the Episode 6184 study of variability vs. concentration were used to evaluate the 

effect of GC/MS thresholds on the ability to identify semivolatile organic compounds at low 

concentrations. Details of the design of this study are described in EPA’s Study Plan for Characterizing 

Error as a Function of Concentration for Determination of Semivolatiles by Gas Chromatography/Mass 

Spectrometry (December 1998). Data were generated for 82 semivolatile organic compounds using EPA 

Method 1625C (semivolatile organic compounds by GC/MS). MDLs were not determined for these 

compounds. Instead, solutions of the analytes were prepared and analyzed at concentrations of 50.0, 

1 - 7	
20.0, 10.0, 7.50, 5.00, 3.50, 2.00, 1.50, 1.00, 0.75, 0.50, 0.35, 0.20, 0.15, 0.10, 0.075 and 0.050 ng/�L (or 

�g/mL). Each solution was injected into the GC/MS in triplicate with the mass spectrometer threshold 

set to zero, and again in triplicate with the mass spectrometer threshold set to a level typical of that used 

in routine environmental analyses. As with the ICP/MS study and the Episode 6000 study, and for the 

same reasons described in Section 1.3.2.1, samples were analyzed in order from the highest to the lowest 

concentration. 

1.3.2.4 AAMA Metals Study of Methods 200.7 and 245.2 

The American Automobile Manufacturers Association conducted an interlaboratory study of 

EPA Method 200.7 (metals by ICP/AES) and Method 245.2 (mercury by CVAA). The study was 

designed to estimate a quantitation value based on a concept termed the alternative minimum level 

(AML) that had been described in the literature (Gibbons et al., 1997). Nine laboratories participated in 

the study, and each reported data for the following 13 metals: aluminum, arsenic, cadmium, chromium, 

copper, lead, manganese, mercury, molybdenum, nickel, selenium, silver and zinc. Study samples were 

analyzed by EPA Method 200.7 for 12 of the metals. Mercury was determined by EPA Method 245.2. 

As part of the study design, the nine laboratories were randomized prior to the start of the study. 

Five sample matrices (including reagent water) were studied, including four wastewater matrices that are 

representative of the automotive industry. Starting from a blank, or unspiked sample, all target analytes 

were spiked at four concentrations to yield a total of five concentrations per matrix. Concentrations 

ranged from 0.01 to 10 �g/L for mercury and selenium on the low end, and from 2.0 and 1000 �g/L for 

mercury and selenium on the high end. In addition, the concentrations were matrix-dependent. The same 

concentration ranges for each metal by matrix combination were used for all five weeks of the study. 

Matrix A (reagent water) was analyzed in all nine laboratories, and three laboratories analyzed 

each of the other four matrices. All analyses were repeated weekly over a five-week period. As a result, 

a total of 6,825 observations were obtained, which includes 2,925 observations for matrix A (9 labs × 13 

metals × 5 spike concentrations × 5 weeks), and 975 observations (3 labs × 13 metals × 5 spike 

concentrations × 5 weeks) for each of the other four matrices (6,825 = 2,925 + (975 × 4)). There were 

two missing values for chromium in matrix A from laboratories 1 and 9. 

1.3.2.5 Method 1631 Interlaboratory Validation Study 

The Method 1631 interlaboratory validation study was conducted by EPA to evaluate 

performance of the method and to gather data to evaluate existing performance specifications, including 

detection and quantitation limits. To accommodate stakeholder interests and expand the scope of the 

study, the Electric Power Research Institute (EPRI) funded the distribution of additional samples to study 

participants. 

This jointly funded study involved an international community of twelve participating 

laboratories and one referee laboratory. Each participating laboratory analyzed four different matrices, 

each containing mercury at a concentration selected to allow for characterization of method performance 

across the measurement range of the method. Each of the 12 participating laboratories was provided with 

13 sample pairs (a total of 26 blind samples). These included 1 filtered effluent pair, 1 unfiltered effluent 

pair, 4 filtered freshwater pairs, 1 filtered marine water pair, 1 unfiltered marine water pair, and 5 spiked 

reagent water pairs. All 12 laboratories received and analyzed the same sample pairs (a total of 312 

analyses). To measure the recovery and precision of the analytical system, and to monitor matrix 

interferences, the laboratories were instructed to analyze matrix spike and matrix spike duplicate samples 

1 - 8	
on specified field samples for each filtered and unfiltered matrix, spiked at 1-5 times the background 

concentration of mercury determined by analysis of an unspiked aliquot of the sample. The laboratories 

were instructed to perform all other QC tests described in Method 1631, including the analysis of blanks, 

and to conduct MDL studies in reagent water following the procedure at 40 CFR part 136. 

1.3.2.6 Method 1638 Interlaboratory Validation Study 

The Method 1638 interlaboratory validation study was conducted by EPA to evaluate 

performance of the method and to gather data that would allow revision of existing performance 

specifications, including detection and quantitation limits. To accommodate stakeholder interests and 

expand the scope of the study, the Electric Power Research Institute funded the distribution of additional 

samples to study participants. 

A total of eight laboratories (and a referee laboratory) participated in the study. The study was 

designed so that each participating laboratory would analyze sample pairs of each matrix of interest at 

concentrations that would span the analytical range of the method. Each laboratory was provided with 11 

sample pairs (a total of 22 blind samples). These included 1 filtered effluent pair, 1 unfiltered effluent 

pair, 4 filtered freshwater pairs, and 5 spiked reagent water pairs. All eight laboratories received and 

analyzed the same sample pairs (a total of 176 analyses). To measure the recovery and precision of the 

analysis, and to monitor matrix interferences, the laboratories were instructed to analyze a matrix spike 

and matrix spike duplicate of specified field samples in each filtered and unfiltered matrix, spiked at 1-5 

times the background concentration of the analytes determined by analysis of an unspiked aliquot of the 

sample. The laboratories were instructed to perform all other QC tests described in Method 1638, 

including the analysis of blanks, and to conduct MDL studies in reagent water following the procedure at 

40 CFR part 136. 

1.3.2.7 American Council of Independent Laboratories Data 

The American Council of Independent Laboratories (ACIL) is a trade association representing 

independent, commercial scientific and engineering firms. Its members are professional services firms 

engaged in testing, product certification, consulting, and research and development. On behalf of its 

membership, ACIL submitted comments on EPA’s proposal. To substantiate their comments, ACIL 

provided EPA with data summary tables consisting of blank analyses used to calculate detection limits. 

The data provided were performed by a single laboratory using Method 200.7 for five analytes. Because 

only blank sample analyses were available, not all detection and quantitation limit procedures could be 

assessed using the data. However, comparisons of the detection limit procedures submitted by ACIL and 

the US Geological Survey were performed based on these data and are discussed in Appendix C. In 

addition, because blanks were analyzed approximately two to three times per week, a comparison of 

long-term to short-term variability was also performed using these blank data. ACIL also submitted an 

alternative procedure for estimation of a detection limit, which is summarized in sect. 2.3.3 of this 

document. 

1.3.2.8 U.S. Geological Survey Method Detection Limit Data 

To assist EPA’s assessment of their long-term MDL (LT-MDL) procedure, the US Geological 

Survey (USGS) provided data from blank sample analyses. These data represented a combination of 78 

metals, methods and matrices, and were analyzed approximately twice per month. Unlike the blank data 

provided by ACIL, these blanks were collected in the field, and, therefore, include more sources of 

variability. As with the ACIL data, it was not possible to assess all detection and quantitation limit 

1 - 9	
procedures using the blank data set because some procedures require use of samples spiked at one or 

more concentrations. The LT-MDL procedure is summarized in sect 2.3.4 of this document. 

USGS also submitted spiked sample results along with the blank data. These spikes were limited 

to a single concentration, and did not sufficiently characterize the region of interest to allow for full 

evaluation of detection and quantitation levels. 

1.3.3 Data Considered but not Used in this Assessment 

The Petitioners and Intervenor to the Settlement Agreement suggested ten specific data sets that 

EPA should consider in its assessment of detection and quantitation limits. EPA evaluated each of these 

data sets to determine if the design of the study, including the concentrations targeted in the study, would 

provide sufficient data for evaluating measurement variability in the region of interest (i.e., at 

concentrations below, at, and above the region of detection and quantitation). If such data were 

available, EPA further evaluated the data set to ensure that it was of sufficient quality to support the 

Agency’s assessment. Four of the ten data sets met these requirements and were used in EPA’s 

assessment. Table 1 identifies each of the data sets suggested by the petitioners along with a brief 

rationale for using or excluding the data from this assessment, additional discussion is in Appendix B. 

After EPA published the February 2003 Assessment Document for comment, ACIL submitted 

data as described in Section 1.3.2.7 above, and three commenters offered to provide EPA with additional 

data that would enhance EPA’s assessment. EPA requested the data offered by each of these 

organizations, but received a response from only two of the three (Laucks Testing Laboratories and 

USGS). After evaluating the data, EPA determined that the data from Laucks Testing Laboratories was 

not useful because it was incomplete. The Laucks data unfortunately did not include the data from 

extraction to detection which is needed to compare detection and quantitation approaches. Most of the 

data sent by USGS was useful and is described in Section 1.3.2.8. 

Dataset Source 

and Year 

Table 1. 

Analytes and 

technology EPA ’s Use of Data sets 

Data Sets Suggested by Petitioners and Commenters 

AAMA 

1996-1997 

AAMA 

1996-1997 

EPA/EPRI 

1997-1998 

EPA/EPRI 

1997-1998 

AC IL 

2002-2003 

USGS 

2002-2003 

EPRI 

1987 

EPRI 

1990 

Metals by ICP/AES 

(200.7) 

Mercury by CVAA 

(245.2) 

Mercury by CVAF 

(1631) 

Metals by ICP/MS 

(1638) 

Metals by ICP/AES 

(200.7) 

Metals by ICP/MS 

and GFAA 

Metals by GFAA 

(EPA 200) 

Metals by ICP/AES 

(EPA 200.7) 

Use d in th is ass ess me nt an d de scrib ed in Sec tion 1 .3.2.4 

Use d in th is ass ess me nt an d de scrib ed in Sec tion 1 .3.2.4 

Use d in th is ass ess me nt an d de scrib ed in Sec tion 1 .3.2.5 

Use d in th is ass ess me nt an d de scrib ed in Sec tion 1 .3.2.6 

Use d in th is ass ess me nt an d de scrib ed in Sec tion 1 .3.2.7 

Use d in th is ass ess me nt an d de scrib ed in Sec tion 1 .3.2.8 

No t use d in th is ass ess me nt be cau se o f insuffic ient low -leve l data 

No t use d in th is ass ess me nt be cau se o f insuffic ient low -leve l data 

1 - 10	
Dataset Source 

and Year 

Analytes and 

technology EPA ’s Use of Data sets 

EPRI 

1994 

Al, Be, Tl by GFAA 

(EPA 200) 

Not used in this assessment because of overlap with EPA's Episode 6000 Study, 

which provides data on the same analytes but covers a larger number of 

concentrations in the region of interest 

AAMA 

1996-1997 

PCBs by GC/ECD 

(608.2) 

Not used in this assessment because of overlap with EPA's Episode 6000 Study, 

which provides data on the same analytes but covers a larger number of 

concentrations in the region of interest 

EPRI 

1996 

Cd, As, Cr by GFAA 

(EPA 200) 

Not used in this assessment because of overlap with EPA's Episode 6000 Study, 

which provides data on the same analytes but covers a larger number of 

concentrations in the region of interest 

MMA 

2000-2001 

Aroclors 1016 and 

1260 by GC/ECD 

No t used in th is as ses sm en t be cau se the maxim um nu mbe r of r ep licate s (5 ) in 

the d atas et, is les s tha n the min imu m n um ber r equ ired (7 ) to ca lculate an M DL . 

Samples spiked with low levels of Aroclors exhibited average recoveries >500%, 

across 10 laboratories. 

Laucks Testing 

Laboratory 

2003 

Mercury, 

2,4-Din itrop henol, 

Hexachlorocyclopen tadie ne, 

4-Nitrophenol (OSW 

8270) 

Not used in this assessment because the dataset was incomplete. Data included 

only calibration data and not the extraction to detection data nee ded to com pare 

det/q uan t proc edu res. 

1.4 Peer Review of the Agency’s Assessment 

In August 2002, EPA conducted a formal peer review of the Agency’s assessment. This peer 

review, which satisfied requirements in Clause 6b of the Settlement Agreement, was conducted in 

accordance with EPA’s peer review policies described in the Science Policy Council Handbook (EPA 

100-B-00-001). The review was performed by two experts in the field of analytical chemistry and two 

experts in the statistical aspects of analytical data interpretation. Each reviewer was provided with a 

draft version of this Assessment Document, which documented the Agency’s approach to the assessment 

and the Agency’s preliminary findings and conclusions. Reviewers also were provided with copies of all 

data evaluated in the assessment, statistical programs used to analyze the data, and copies of the detection 

and quantitation concepts and procedures evaluated by EPA. In accordance with the Agency’s peer 

review policies, the reviewers were provided with a written ‘charge’ intended to ensure the evaluation 

would meet EPA needs. 

In its charge to the peer reviewers, EPA requested a written evaluation of whether the assessment 

approach described by EPA is valid and conceptually sound. Reviewers also were asked to consider and 

address eight specific questions pertaining to the adequacy of the concepts and issues considered, the 

evaluation criteria developed by EPA, EPA’s assessment and conclusions, the data used to perform the 

assessment, suggested improvements to the procedures discussed, and EPA’s consideration of 

interlaboratory vs. intralaboratory issues. Comments from peer reviewers were generally supportive of 

EPA’s assessment and its presentation of the assessment in the Assessment Document. Where 

appropriate, EPA revised that Assessment Document to reflect specific suggestions and comments 

offered by the peer reviewers. The revised version of the Assessment Document, reflecting peer 

reviewer comments, was completed in February 2003, and made available through a public notice on 

March 12, 2003 (see section 1.5 below). Copies of all materials associated with the peer review, 

1 - 11	
including the peer review charge, the materials provided to the peer reviewers for review, complete 

copies of the peer reviewers’ comments, and detailed EPA responses to each of the comments were 

provided in the public docket supporting the Agency’s March 2003 assessment. 

1.5 Proposal and Request for Public Comments 

On February 28, 2003, the EPA Administrator signed two notices for publication in the Federal 

Register. These notices fulfilled EPA’s obligations under Clause 6(a) of the Settlement Agreement and 

were published in the Federal Register on March 12, 2003. 

The first of these notices announced the availability of EPA’s assessment of detection and 

quantitation procedures that are applied to analytical methods used under the Clean Water Act. It also 

announced that results of the assessment could be found in the “Technical Support Document for the 

Assessment of Detection and Quantitation Concepts” (EPA 821-R-03-005, February 2003), requested 

public review and comment on the assessment. The full text of this notice was published at 68 FR 

11791, March 12, 2003. 

The second notice requested comment on proposed revisions to the detection and quantitation 

definitions and procedures at 40 CFR part 136. The proposed changes were based on the assessment and 

on stakeholder comments received over the years. The full text of this notice was published at 68 FR 

11770, March 12, 2003. 

1.5.1 Summary of Changes Proposed in March 2003 

EPA proposed a number of technical and editorial changes to the definitions specified at 40 CFR 

136.2 and to the procedure specified at 40 CFR 136, Appendix B. A detailed description of those 

changes can be found in the March 12, 2003 public notice (68 FR 11770). Briefly, those proposed 

changes included: 

•
 A revised definition of the term “detection limit” at 40 CFR 136.2(f) to explicitly equate the term 

with the “method detection limit” specified in 40 CFR 136, Appendix B; and a revised definition 

of the term “method detection limit” included in Appendix B to provide technical clarifications 

and more clearly equate the term with the “critical value” described by Currie (1968, 1995) and 

the Limit of Detection described by the American Chemical Society (Keith et al., 1980; 

McDougal et al., 1983). Those concepts are further described in Chapter 2 of this assessment 

document. 

•
 An expanded Scope and Application discussion in the codified MDL procedure to recognize that 

there are a variety of purposes and analytical methods for which the MDL procedure may be 

employed. The proposed revisions provided examples of four common uses of the MDL 

procedure (i.e., demonstrating laboratory capability with a particular method; monitoring trends 

in laboratory performance; characterizing method sensitivity in a particular matrix; and 

establishing an MDL for a new or revised method for nationwide use.) The proposed revisions 

also clarified that the procedure may not be applicable to certain test methods such as those used 

to measure pH or temperature. 

•
 Proposed modifications to the considerations for estimating the detection limit in Step 1 of the 

codified MDL procedure and to the specifications for establishing the test concentration range in 

Step 3 of the codified procedure. 

•
 Proposed deletion of the optional procedure for calculating a 95% confidence interval estimate 

for the MDL. 

1 - 12	
•
 Proposed changes to the iterative procedure to mandate its use when determining an MDL for a 

new or revised method or when developing a matrix-specific MDL, but allow it to remain 

optional when determining an MDL for other purposes, such as verifying lab performance. 

• Proposed addition of a new procedural section to address the treatment of suspected outliers. 

•
 Proposed deletion of the discussion of analysis and use of blanks included in Section 4(a) of the 

codified procedure. 

•
 Proposed changes to the optional pre-test described in Section 4(b) of the procedure to improve 

the utility of results from this test. 

•
 Editorial changes to the codified version of the MDL. Examples of these editorial changes 

include addition of a summary section, clarifications, reorganization of steps, simplified 

presentation of calculations, and deletion of the reporting section. 

• Proposed addition of a definition of the ML at 40 CFR 136.2 

• Proposed addition of a procedure (including a definition) of the ML to 40 CFR 136, Appendix B 

•
 Explicit allowance of alternative detection and quantitation procedures, provided that the 

resulting detection and quantitation limits meet the sensitivity needs for the specific application. 

The objective of this proposed allowance was to provide greater flexibility in establishing or 

improving the sensitivity of methods for use under CWA and facilitate approval of analytical 

methods from other agencies or organizations that utilize alternate detection and quantitation 

concepts. 

In addition to requesting comment on the assessment and the proposed revisions, EPA also 

specifically requested comment on several aspects of the proposal, including alternative actions that 

could have been taken. With respect to the ML, for example, EPA explicitly sought comment on the 

proposed addition of the ML definition to 40 CFR 136.2 and procedure to 40 CFR 136, Appendix B vs 

an alternative option of not incorporating the definition at 40 CFR 136.2, but instead continuing to 

specify the ML on a method-by-method basis. EPA encouraged commenters to support their views with 

data or information that would assist the Agency in making a final decision. 

1.5.2 Impact of Comments on the Assessment 

EPA provided a 120-day period following publication of the notices for submission of comments 

(from the date of publication of the notices to July 10, 2004). In response to requests from stakeholders, 

EPA re-opened this comment period on July 16, for an additional 30 days (68 FR 41988). 

During the comment periods, EPA received comments from 126 individuals or organizations 

representing the diversity of the stakeholder community on this issue. They included 23 laboratories, 31 

water treatment plants, 3 federal agencies, 11 state and county agencies, 23 industrial firms, 3 instrument 

manufacturers, 19 trade organizations, 4 consultants, 8 individuals, and the law firm representing the 

petitioners. Comments offered by these groups addressed more than 25 different issues. A complete 

summary of the comments and EPA’s responses to those comments can be found in Appendix B to this 

Assessment Document. These comments are discussed at various locations throughout this document, 

and include discussion of: 

•
 Additional detection and quantitation limit procedures suggested by commenters. (Chapters 2, 3, 

and 5) 

•
 Public comments received on chemical, regulatory, and statistical issues, along with EPA’s 

consideration of these issues in light of the comments received. (Chapter 3) 

•
 Comments received on each of the evaluation criteria used in EPA’s assessment and EPA’s 

response to those comments. (Chapter 4) 

1 - 13	
•
 Potential process for additional stakeholder involvement on the evaluation of detection and 

quantitation limit procedures (Chapter 6) 

Appendix C contains a detailed analysis of the detection and quantitation limit procedures evaluated 

through computation of limits using the data described in Section 1.3.2. This analysis has been revised to 

reflect new data and comments on the original version of the assessment, which was published as 

Appendix C to the February 2003 version of EPA’s Assessment Document. 

1.6 Terminology used in this Document 

We use the term "quantitation" in this document because of its common usage among analytical 

chemists, even though we recognize that the term "quantification" (i.e., the act of quantifying) is the term 

listed in most dictionaries. Also, when referring to detection and quantitation, we use the words 

"approach" or "concept" to refer, generically, to the procedures used to establish detection and 

quantitation limits or the theories on which those procedures are based. We use the word "limit" rather 

than "level" to indicate that the detection and quantitation concepts are directed at the lowest 

concentration or amount at which an analyte is determined to be present (detection) or may be measured 

(quantitation). In choosing the word ‘limit’ we do not mean to imply any sense of permanence. We 

recognize that measurement capabilities generally improve over time, and that detection or quantitation 

‘limits’ established today may be superseded by future developments in analytical chemistry. 

Although the Settlement Agreement refers to the word “sensitivity” to describe detection and 

quantitation limits, we have avoided such use of the term “sensitivity” in this document because the term 

is widely used by analytical chemists to describe something other than detection and quantitation 

capabilities. Traditionally, analytical chemists have referred to the term “sensitivity” as meaning 

instrument signal units per concentration units, such as is given for a calibration slope or a response 

factor. For example, in ion selective potentiometry, the sensitivity is 59 millivolts per decade change in 

concentration for monovalent species and half that for divalent species. Sensitivity is a performance 

characteristic, but it differs from detection limits. For example, one might compare the sensitivity of 

instruments. Obtaining a sensitivity of 10,000 counts per ppb indicates a properly functioning Sciex 250, 

while a Perkin-Elmer 6000’s sensitivity would be 100,000 counts per ppb. Another performance 

characteristic of sensitivity is that it may vary in an expected pattern as with mass to charge ratio in mass 

spectrometry or atomic number for x-ray fluorescence spectrometry. 

1 - 14	
Exhibit 1-2. Relationship of Assessment Document to 

Assessment of Detection and Quantitation Limit Approaches 

Event 1, Develop a detailed plan for responding to Clause 6 the Settlement Agreement: This event was completed in April 

2002 when the draft plan was revised to reflect peer review and Litigant comments. 

Event 2, Identify and explore issues to be considered: The Settlement Agreement identified six specific issues that should be 

considered during the assessment of detection and quantitation limit concepts, and subjected to formal peer review. During 

development of the technical approach, EPA identified a number of other issues that should be considered during the 

assessment. EPA listed and described each of these issues in the study plan and noted that identification of issues is likely to 

be a dynamic process, in that as a suite of issues is identified and discussed, other issues may surface. Finally, EPA stated 

its intent to prepare an “issue paper” that fully explained and discussed each of the identified issues. Chapter 3 of this 

Assessment Document serves the function of the issue paper described in the plan. 

Event 3, Develop criteria against which concepts can be evaluated: After fully considering all relevant issues, EPA developed 

a suite of criteria that could be used to evaluate the suitability of various detection and quantitation procedures for use in CWA 

programs. Chapter 4 of this Assessment Document provides and describes the criteria selected by EPA after its 

consideration of all pertinent issues. 

Event 4, Evaluate existing procedures for establishing detection and quantitation levels : EPA evaluated existing detection 

and quantitation limit concepts used or advanced 1) by voluntary consensus standards bodies (VCSBs), 2) in the published 

literature, 3) by EPA. As per the terms of the Settlement Agreement, the MDL and ML were explicitly targeted for inclusion. 

EPA committed to evaluating concepts published by ASTM International and ISO and to consider approaches and procedures 

offered by other organizations such as the American Chemical Society (ACS) and the International Union of Pure and Applied 

Chemistry (IUPAC), as well as other approaches that have been adopted by EPA for use in other programs or that were 

identified during EPA’s review of the published literature. Chapter 2 describes the concepts that EPA evaluated in the 

assessment. Where appropriate, these approaches also are discussed in context to the issues that are identified and 

discussed in Chapter 3. Chapter 5 presents the results of EPA’s assessment of each approach against the evaluation criteria 

established in Chapter 4. Appendices B and C of this document present additional details of EPA's assessment of each 

approach, using the data described in Chapter 1, Section 1.3. 

Event 5, Develop and evaluate alternative procedures: EPA planned to develop and evaluate alternative procedures and 

modifications to existing procedures only if the Agency’s assessment of existing procedures suggested that modifications or 

alternatives to the existing procedures were needed. EPA noted that its primary objective in developing such alternatives (or 

modifications) would be to address deficiencies noted in Event 4 and improve the performance of the procedures that best 

meet the criteria established in Event 3. In accordance with the plan and with EPA’s findings during the assessment, this 

Assessment Document includes suggested modifications to the existing MDL and ML procedures. 

Event 6, Conduct peer review of the Agency’s assessment: EPA documented results of the Agency’s assessment in a draft 

Assessment Document that was completed in August, 2002. EPA conducted a formal peer review of the assessment in 

accordance with the Agency’s peer-review policies and guidance. The peer review was performed by two experts in the field 

of analytical chemistry and two experts in the statistical aspects of analytical data interpretation. 

Events 7 - 11, Actions taken following peer review. After considering peer review comments, EPA revised its assessment and 

the draft Assessment Document to reflect peer review comments. In March 2003, EPA published two FR notices that met the 

terms of Settlement Agreement Clause 6a. Comments were received on those notices over a 4 month period ending in 

August 2003. EPA evaluated all comments received, and revised its assessment as appropriate to reflect these comments. 

This document details this revised assessment. 

1 - 15	
Chapter 2


Overview and History of


Detection and Quantitation Limit Approaches


It is not possible to measure the concentration of a substance in water all the way down to zero. 

As an analogy, consider the following example: imagine measuring an object less than 16th of an inch in 

length with a ruler marked in 1/16th-inch increments. How well can the length of the object be measured 

using only the ruler? Similar issues arise as chemists try to measure ever smaller concentrations of 

substances in water. In response to the challenges associated with measuring low concentrations, 

chemists have defined numerical values that provide points of reference for reporting and using 

measurement results. These values are usually referred to as detection and quantitation limits. This 

chapter provides an overview of detection and quantitation approaches and procedures in analytical 

chemistry and their use in Clean Water Act applications. 

2.1 Currie’s Call for Standardization 

Since 1968, most of the literature regarding detection and quantitation has referenced the work of 

Dr. Lloyd Currie, recently retired from the National Institutes of Science and Technology (NIST, 

formerly the National Bureau of Standards). In 1968, Currie published a paper in which he reviewed the 

then current state of the art regarding detection and quantitation, presented a three-tiered concept, and 

demonstrated his concept with operational equations for a single laboratory. In his paper, Currie 

reviewed eight existing definitions for the concept of detection, and reported that when these eight 

operational definitions were applied to the same data, they resulted in numerical values that differed by 

nearly three orders of magnitude. These results made it impossible to compare the detection capabilities 

of measurement methods using available publications. Currie proposed standardizing the terminology 

using theoretical definitions that he called the critical value, the detection limit, and the determination 

limit. (In 1995, writing on behalf of International Union of Pure and Applied Chemistry (IUPAC), Currie 

used the term “quantification limit” instead of his original term “determination limit.” Substantial 

agreement with the International Organization for Standardization (also known as "ISO") on the meaning 

and language of detection and quantitation was achieved later, although some “subtle differences in 

perspective” remain [Currie, 2000]). His purpose for these definitions was to create a system in which 

the standard documentation of any measurement method would include a statement of capabilities that 

were directly comparable to any other method for measuring the same substance. 

Currie used terms from statistical decision 

theory as the basis for his three-tiered system. In 1968 

and 1995, Currie defined the critical value as the 

measured value at which there is a small chance that 

the concentration in the sample is zero. Consequently, 

any measured result greater than or equal to the critical 

value is considered evidence that the sample contains 

the substance of interest. Currie was careful to 

emphasize that the decision as to whether the substance 

has been detected is made by comparing the 

measurement result to the critical value. Figure 2-1 

shows a critical value selected such that measurements 

greater than the critical value have less than a 1% 

Figure 2-1 

2 - 1	
chance of being associated with a sample that does not contain the substance of interest. The area under 

the curve to the right of the critical value represents the probability that a measured value will exceed the 

critical value. The area under the curve to the left of the critical value represents the (much greater) 

probability of observing a value that is less than the critical value when the true concentration is zero. 

Currie (1968 and 1995) used the term detection limit to refer to a true concentration that has a 

high probability of generating measured values greater than the critical value. That is, measurements on 

samples that contain concentrations equal to the 

detection limit have a high probability of exceeding the 

critical value and are, therefore, unlikely to result in a 

decision that the substance is not detected in the 

sample. In Currie’s concept, the critical value and the 

detection limit are related and functionally dependent, 

but it is clear that the detection decision is made on the 

basis of comparing sample by sample measurements to 

the critical value. While Currie’s terminology is 

consistent with standard statistical decision theory, it is 

in all likelihood responsible for a great deal of 

confusion among chemists and others who may 

associate the term ‘limit’ with some sort of decision 

point. Currie (1995) states: “The single, most important 

application of the detection limit is for planning. It Figure 2-2 allows one to judge whether the CMP (Chemical 

Measurement Process) under consideration is adequate for the detection requirements.” Figure 2-2 

shows a detection limit selected such that 99% of the measurements on a sample containing this 

concentration are expected to be above the critical value. The bell-shaped curve centered at the detection 

limit illustrates how likely various measurement responses are when the concentration of the substance in 

a sample is equal to the detection limit. That is, the figure shows the probability density of values 

measured in a sample with a true concentration equal to the detection limit. The area under the curve to 

the left of the critical value is equal to 1% of the total area, while the area to the right is equal to 99%. 

Currie (1968, 1995) defined the determination limit, later renamed the quantification limit, as 

(quoting Currie, 1995) “performance characteristics that mark the ability of a CMP to adequately 

‘quantify’ an analyte.” Quantification limits “serve as benchmarks that indicate whether the CMP can 

adequately meet the measurement needs. The ability to quantify is generally expressed in terms of the 

signal or analyte (true) value that will produce estimates having a specified relative standard deviation 

(RSD) commonly 10%.” This translates into a quantification limit equal to a multiplier of 10 times the 

standard deviation (a measure of measurement variability) at the limit. The multiplier of 10 (equal to the 

inverse of the 10% RSD) is arbitrary, but has been used widely. IUPAC selected 10 as a “default value” 

(Currie, 1995), implying other values are possible. In papers published in 1980 and 1983, the American 

Chemical Society’s Committee on Environmental Improvement also recommended the use of a multiplier 

of 10 for determining quantitation limits (see MacDougall, et al., 1980 and Keith, et al., 1983). 

Measured concentrations greater than the quantitation limit are considered to be reliable by chemists, 

although from a statistical perspective, any measured value, along with knowledge of the precision of the 

measurement, is useful. 

Currie’s goal of having method developers publish directly comparable descriptions of detection 

and quantitation capability remains elusive more than thirty years after publication of his first paper on 

this topic. Even if Currie’s three-tiered concept were used, the treatment of related issues causes 

2 - 2	
difficulty in comparing methods. Some of these issues include interlaboratory variability, selection of 

appropriate statistical models, design of detection and quantitation capability studies, and statistical 

prediction and tolerance. These and other issues are discussed in Chapter 3 of this document. 

2.2	 Development of the MDL and ML as Practical Embodiments of 

Currie’s Proposal 

In 1981, staff at EPA’s Environmental Monitoring and Support Laboratory in Cincinnati, Ohio, 

published a procedure for determining what they referred to as a method detection limit (MDL) (Glaser 

et al., 1981). The MDL functions as a practical, general purpose version of Currie’s critical value. The 

MDL was subsequently promulgated for use in CWA programs on October 26, 1984 (49 FR 43234) at 40 

CFR part 136, Appendix B. Prior to formal development of the MDL in 1981, the EPA Office of Water 

had included the term “minimum level” (ML) or “minimum level of quantitation” in some methods for 

analysis of organic pollutants. These methods were proposed on December 3, 1979 and subsequently 

promulgated on October 26, 1984, along with the MDL. Additional information about the MDL and ML 

is provided below in Sections 2.2.1 and 2.2.2. 

2.2.1 Method Detection Limit 

Conscious of the definitions provided by Currie and others, Glaser et al. (1981) stated “[t]he 

fundamental difference between our approach to detection limit and former efforts is the emphasis on the 

operational characteristics of the definition. [The] MDL is considered operationally meaningful only 

when the method is truly in the detection mode, i.e., [the] analyte (the substance of interest) must be 

present.” Expanding on this reasoning, Glaser et al. (1981) developed MDL estimates for methods that 

produce a result of zero for blanks, such as EPA Methods 624 and 625 for determination of organic 

pollutants by gas chromatography/mass spectrometry (GC/MS). Blank variability exists, whether or not 

it can be detected by measurement processes. Failure to detect this variability may be attributed to 

insufficient sensitivity of the measurement process or, as is the case with some measurement processes, 

thresholds that are built into equipment which censor measurements below certain levels. Currie’s 

critical value is dependent on the ability to estimate measurement variability of blank samples. In cases 

where the substance is not detected in direct measurements on blanks, an alternative approach to 

estimating blank variability must be used. One option is to estimate measurement variability at 

concentrations that represent the lowest possible levels where a signal can be detected. This is the basic 

approach of the MDL, which provides a general purpose, straightforward, operational procedure for 

estimating a quantity analogous to the Currie critical value when measurement processes applied to blank 

samples do not produce detectable signals. More complex statistical procedures for estimating blank 

variability are possible and may be preferable from a rigorous statistical perspective, but the MDL has 

been found to be satisfactory by chemists in a wide range of applications. 

In 1984, the MDL became a regulatory option for wastewater discharge permits authorized under 

the Clean Water Act. To determine the MDL, at least seven replicate samples with a concentration of the 

pollutant of interest near the estimated detection capabilities of the method are analyzed. The standard 

deviation among the replicate measurements is determined and multiplied by the t-distribution for n-1 

degrees of freedom (in the case of 7 replicates, the multiplier is 3.143, which is the value for 6 degrees of 

freedom). The decision to base the MDL on a minimum of seven replicates reflected a consensus among 

EPA chemists and statisticians that a requirement of seven replicates is not overly burdensome for 

laboratories and that laboratories could reasonably be expected to perform the analyses in a single batch. 

2 - 3	
Both the MDL concept and the specific definition at part 136 have been used within EPA by the 

Office of Ground Water and Drinking Water (OGWDW), the Office of Solid Waste (OSW), the Office 

of Emergency and Remedial Response (OERR), and others. The MDL also has been used outside of 

EPA in Standard Methods for the Examination of Water and Wastewater, published jointly by the 

American Public Health Association (APHA), the American Water Works Association (AWWA), and 

the Water Environment Federation (WEF), and in methods published by the ASTM International, and 

elsewhere. 

Some members of the regulated industry and others have criticized the MDL because: 

• There are some inconsistencies between the definition and the procedure 

• It does not account explicitly for false negatives 

• It does not always yield a 1% false positive rate 

• It does not sufficiently account for blank bias 

• A prediction or tolerance limit adjustment is not provided 

• It does not account for interlaboratory and temporal intralaboratory variability, and 

• It allows discretion in the use of the optional iterative procedures 

These issues are discussed later in this document. 

2.2.2 Minimum Level of Quantitation 

The minimum level of quantitation (ML) was originally proposed on December 5, 1979 (44 FR 

69463) in footnotes to Table 2 of EPA Method 624 and to Tables 4 and 5 of EPA Method 625. The ML 

was defined as the "level at which the entire analytical system must give recognizable mass spectra and 

acceptable calibration points" (in the footnote to Table 2 in Method 624) and as the "level at which the 

entire analytical system must give mass spectral confirmation" (in the footnote s to Tables 4 and 5 in 

EPA Method 625). 

Between 1980 and 1984, EPA also developed Methods 1624 and 1625 and promulgated these 

methods along with the final versions of EPA Methods 624 and 625 on October 26, 1984 (49 FR 43234). 

The definitions of the ML in the promulgated versions of EPA Methods 1624 and 1625 were the "level at 

which the analytical system shall give recognizable mass spectra (background corrected) and acceptable 

calibration points" (in footnote 2 to Table 2 in Method 1624) and as the "level at which the entire 

GC/MS system must give recognizable mass spectra (background corrected) and acceptable calibration 

points" (in footnotes 2 to Tables 3 and 4 in Method 1625). 

As EPA developed additional methods over the next decade, the definition of the ML was 

generalized to "the lowest level at which the entire analytical system must give a recognizable signal and 

acceptable calibration point for the analyte" (see, e.g., Section 24.2 of EPA Method 1613 at 40 CFR part 

136, Appendix A). In generating actual numerical values for MLs, the lowest calibration point was 

estimated from method development studies and included in the methods, although a specific calculation 

algorithm was not used. EPA methods that include the ML generally specify the number of calibration 

standards to be used and the concentrations of those standards. As a result, laboratories using those 

methods calibrate their analytical systems with a multi-point calibration (i.e., calibrate using a series of 

standards at different concentrations over the range of the instrument) that includes a standard at the 

lowest calibration point listed in the method (i.e., the ML). 

2 - 4	
In response to a need to establish a compliance evaluation threshold when the water qualitybased permit limit is below the detection limit of the most sensitive analytical method published at 40 

CFR part 136, EPA refined the definition of the ML in 1994 as 10 times the same standard deviation 

used to calculate the MDL1

. Because the MDL is commonly determined as 3.14 times the standard 

deviation of seven replicate measurements, the ML was commonly calculated as 3.18 times the MDL. 

(The figure of 3.18 was derived by dividing 10 by 3.14; if more than 7 replicates were used to determine 

the MDL, both the MDL and the ML multipliers are adjusted accordingly, based on values from the tdistribution.) This calculation makes the ML analogous to Currie’s quantification limit and the 

American Chemical Society’s limit of quantitation (LOQ), which is defined as ten times the standard 

deviation of replicate or low concentration measurements (MacDougall, et al., 1980 and Keith, et al., 

1983). 

To simplify implementation of the ML, the definition also was expanded to state that the 

calculated ML is rounded to the whole number nearest to (1, 2, or 5), times 10n

, where n is an integer. 

The reason for this simplification is that calibration of an analytical system at some exact number (e.g., 

6.27) is difficult and prone to error, whereas rounding to the whole number nearest to (1, 2, or 5) x 10n 

provides a practicable value. The most recent definition of the ML is "the lowest level at which the 

entire analytical system must give a recognizable signal and acceptable calibration point for the analyte. 

It is equivalent to the concentration of the lowest calibration standard, assuming that all methodspecified sample weights, volumes, and cleanup procedures have been employed. The ML is calculated 

by multiplying the MDL by 3.18 and rounding the result to the number nearest to (1, 2, or 5) x 10n

, where 

n is an integer," and this definition was contained in the version of EPA Method 1631 that was 

promulgated on June 8, 1999 (64 FR 30417) (see Section 17.8 of EPA Method 1631 Revision B). 

The ML will generally be somewhat lower than Currie’s quantitation limit, even when similar 

sample sizes and estimation procedures are used. This is because the standard deviation used to calculate 

the ML will generally be smaller than the standard deviation at the lowest concentration at which the 

relative standard deviation is 10%. This is due to the fact that, in almost all cases, standard deviation is 

non-decreasing with increasing concentration, e.g., it generally tends to increase as concentration 

increases. 

Some members of the regulated industry and others have criticized the ML because it: 

� Does not account for interlaboratory and temporal intralaboratory variability, and 

� Is based on a multiple of the estimated standard deviation which is assumed to be constant in the 

region of detection and quantitation, rather than a fitted model as suggested by the regulated industry. 

These concerns are discussed later in this document. 

1

The refined definition of the ML first appeared in EPA's 1994 draft National Guidance for the Permitting, 

Monitoring, and Enforcement of Water Quality-based Effluent Limitations Set Below Analytical Detection/ 

Quantitation Levels”. The draft guidance was very controversial and never finalized. However, the refined 

definition of the ML has remained in use for newer analytical methods. 

2 - 5 
2.3 Other Detection and Quantitation Approaches 

To expand somewhat on Currie (1968), standardizing the operational definitions of detection and 

quantitation would benefit society by making it easier to compare and select measurement methods based 

on low-level measurement capability and requirements in particular applications. Unfortunately, in spite 

of agreement on general principles and definitions advanced by Currie and his supporters, consensus on 

procedures that would result in comparable detection and quantitation estimates has been elusive. 

Sections 2.3.1 - 2.3.3, which are by no means an exhaustive list of the various approaches advanced to 

date, highlight approaches that have been most widely advanced for environmental applications. 

2.3.1 EPA Approaches 

Over the years, a number of detection and quantitation limit approaches have been developed, 

suggested, or used by EPA in responding to differing program mandates. In part, this situation reflects 

actual differences in the mandates, and in part, it reflects the fact that no concept advanced to date has 

emerged as a clear ‘winner’ that meets all needs for all situations. Approaches that have been used or 

suggested by EPA include the: 

• MDL and ML (described in Sections 2.2.1 and 2.2.2) 

• Instrument detection limit (IDL) 

• Practical quantitation limit (PQL) 

• Estimate quantitation limit (EQL) 

• Contract-required detection limit (CRDL) and contract-required quantitation limit (CRQL) 

Instrument Detection Limit: EPA methods for analysis of metals have historically included an instrument 

detection limit, or IDL. Functionally, the IDL is similar to the MDL except that the IDL includes 

temporal variability (it is determined on 3 non-consecutive days) and does not include all sample 

processing steps (the IDL characterizes the detection capabilities of the instrument as opposed to the 

method). Because IDLs do not reflect the entire measurement process and, for the most part, have been 

used only for measurement of metals, EPA did not consider the IDL as a potential alternate to the MDL 

when conducting the assessment described in this Assessment Document. 

Practical Quantitation Limit: The practical quantitation limit, or PQL, was established in the 1980s by 

EPA’s drinking water program as the lowest concentration at which reliable measurements can be made. 

The PQL is defined as "the lowest concentration of an analyte that can be reliably measured within 

specified limits of precision and accuracy during routine laboratory operation conditions" (52 FR 

25690, July 8, 1987). The PQL is a means of integrating information on the performance of approved 

analytical methods into the development of a drinking water regulation. The PQL incorporates the 

following: 

• Quantitation, 

• Precision and bias, 

• Normal operations of a laboratory, and 

•
 The programmatic need to have a sufficient number of laboratories available to conduct compliance 

monitoring analyses of drinking water samples. 

EPA uses two main approaches to determine a PQL for an analyte under the Safe Drinking Water 

Act (SDWA). One approach is to use the data from Water Supply (WS) studies (e.g., laboratory 

performance evaluation studies conducted by the Agency as part of the certification process for drinking 

2 - 6	
water laboratories). The PQL is established at the concentration at which at least 75% of the laboratories 

in the study, or the subset representing EPA Regional laboratories and state laboratories, obtain results 

within some predetermined percentage of the true value of the test samples (e.g., ±30%). This approach 

is used in most cases when WS data are available to calculate a PQL. The WS data approach was used to 

determine the PQLs for Phase V inorganic chemicals such as antimony, beryllium, cyanide, nickel and 

thallium (July 17, 1992; 57 FR 31776), as well as many other contaminants regulated under the SDWA. 

In the absence of WS data, the second approach that EPA uses is the multiplier method. In this 

approach, the PQL is calculated by multiplying the EPA-derived MDL by a factor between 5 and 10. 

The exact multiplier varies and sometimes depends on the degree of concern about the specific 

contaminant (i.e., based on a human health risk assessment for consumption of drinking water). 

Application of the PQL has been traditionally limited to drinking water. Furthermore, the PQL 

may not be related to the lowest quantitation limit because 1) the PQL is associated with the analyte and 

may have been determined irrespective of a specific analytical method (e.g., using data from a variety of 

methods approved for that analyte at 40 CFR part 141), 2) the performance evaluation (PE) samples 

from which it is derived contain pollutant concentrations that may be well above the true limit of 

quantitation, 3) the multiplier used to calculate a PQL when PE data are not available is somewhat 

dependent on concerns about risks from human exposure to contaminants in drinking water, and 4) the 

resulting PQLs may be too high for purposes other than the Safe Drinking Water Act (e.g., other EPA 

programs). In addition, because EPA has privatized the performance evaluation program for drinking 

water laboratory certification, it is not yet clear that appropriate data will be available in the future. 

Based on these facts, EPA did not conduct an assessment of the PQL for CWA applications. 

In the late 1980s, EPA’s Office of Solid Waste (OSW) adopted a different version of the PQL as 

a quantitation limit. No procedure for establishing the limits was given; instead values were extrapolated 

from the Contract Laboratory Program CRQLs (see below). Since 1994, OSW has actively removed the 

term "PQL" from its revised methods, replacing it with the term "estimated quantitation limit" (EQL). 

The term PQL and the original numerical values remain in a few older OSW guidance documents. 

Lowest Concentration Minimum Reporting Level (LCMRL) and Minimum Reporting Level (MRL): 

Recognizing the potential for improvements over the PQL approach, and mindful that confidence in 

quantitation depends on measurement precision as well as accuracy, EPA’s Office of Ground Water and 

Drinking Water has recently developed a standardized procedure for the determination of the “Lowest 

Concentration Minimum Reporting Level (LCMRL)” and a companion procedure for laboratories to 

establish their ability to quantify analytes at a “Minimum Reporting Level” (MRL). 

The Lowest Concentration Minimum Reporting Level (LCMRL) is defined as the lowest true 

concentration for which the future recovery is predicted to fall, with high confidence (99%), between 50 

and 150% recovery. A result below the LCMRL is an estimated value that does not satisfy these data 

quality objectives. However, it may be appropriate to report “estimated” data (i.e., below the LCMRL), 

depending upon the objectives of the study being conducted. The proposed LCMRL procedure is an 

iterative process that uses results from three or more different concentrations, of at least five to seven 

replicate reagent water samples at each concentration. The average recovery, standard deviation, number 

of replicates, and Student’s t value are used to calculate a prediction interval of results that takes into 

account accuracy and precision at the level tested. For a concentration level to pass criteria, the 

prediction interval of results must be contained within the boundaries of a predefined quality control 

interval. 

2 - 7	
The Agency also has developed a procedure for use in the drinking water program which permits 

laboratories to confirm that they are capable of meeting a required MRL during their initial 

demonstration of capability. The MRL validation procedure will involve the analysis of one set of at 

least seven replicate reagent water samples spiked at the required MRL. To be validated at the MRL, the 

calculated prediction interval of results must be contained within the predefined quality control interval. 

The Agency anticipates using standardized LCMRL/MRL procedures to support the monitoring 

required under the Safe Drinking Water Act for unregulated contaminants. Requirements for this 

monitoring are expected to be proposed in the Federal Register late in 2004. This proposal will include a 

full description of the LCMRL/MRL procedures. 

Estimated Quantitation Limit: EPA's Office of Solid Waste has defined the EQL as: 

"The lowest concentration that can be reliably achieved within specified limits of 

precision and accuracy during routine laboratory operating conditions. The EQL is 

generally 5 to 10 times the MDL. However, it may be nominally chosen within these 

guidelines to simplify data reporting. For many analytes the EQL analyte concentration 

is selected as the lowest non-zero standard in the calibration curve. Sample EQLs are 

highly matrix dependent. The EQLs in SW-846 are provided for guidance and may not 

always be achievable." (see SW-846, Chapter 1). 

As noted in most newer SW-846 methods, the EQLs are provided for guidance and may not 

always be achievable. Because the EQL is not rigorously defined and is guidance, because the EQL may 

be based on the MDL, and because the EQL can be the lowest calibration point and would, therefore, 

overlap the ML, EPA did not consider the EQL further in its assessment of detection and quantitation 

approaches. 

Contract-Required Detection and Quantitation Limits: EPA’s Superfund program has adopted the use of 

contractually-required limits that are based on consensus among analytical chemists about levels that can 

realistically be achieved in commercial laboratories using a contractually-specified method. Laboratories 

that participate in the Superfund Contract Laboratory Program (CLP) are required to demonstrate that 

they can achieve the specified CRDLs and CRQLs. The CRDLs are consensus values that apply to the 

analyses of metals using CLP methods. The CRQLs apply to organic analytes and are based on the 

concentration of the lowest non-zero calibration standard specified in the CLP methods, in a fashion 

analogous to the original derivation of the ML. Because few CWA applications involve the use of the 

CLP methods, EPA did not consider the CRDL or the CRQL as viable alternatives to the MDL and ML 

when conducting the assessment described in this document. 

2.3.2 Industry-supported Approaches 

The regulated community has demonstrated an interest in detection limit approaches since EPA 

first promulgated the MDL and ML for use in CWA programs in 1984 (49 FR 43234). As part of that 

rule, EPA promulgated Methods 601 through 613, 624, 625, 1624, and 1625 for organic compounds at 40 

CFR part 136, Appendix A and EPA Method 200.7 for metals by inductively coupled plasma 

spectrometry (ICP) at 40 CFR part 136, Appendix C. EPA also promulgated the MDL procedure at 40 

CFR part 136, Appendix B. The Virginia Electric Power Company (VEPCO) brought suit against EPA, 

challenging the Agency's use of the MDL in the promulgated methods. In a settlement, EPA agreed that 

2 - 8	
the MDL would be applicable only to the 600-series organic methods, as these methods already 

contained MDL values; i.e., it would not be applicable to EPA Method 200.7. The settlement agreement 

did not preclude future use of the MDL by EPA or the right of VEPCO to bring suit in such future use. 

After the VEPCO settlement, the regulated community, mainly through efforts of the Electric 

Power Research Institute (EPRI), remained involved in detection and quantitation approaches to be used 

under EPA's CWA programs. The first approaches that industry advanced were the compliance 

monitoring detection level (CMDL) and compliance monitoring quantitation level (CMQL) (Maddalone, 

et al., 1993). The CMDL/CMQL were variants of EPA's MDL/ML that attempted to adjust for 

interlaboratory variability. 

The regulated community continued its efforts to develop alternative detection and quantitation 

approaches with development of the "alternate minimum level" (AML) in the mid-1990s (Gibbons et al., 

1997). The AML is based on statistical modeling of standard deviation versus concentration, which 

requires large amounts of data. 

Most recently, the regulated community has funded development of the interlaboratory detection 

estimate (IDE) and interlaboratory quantitation estimate (IQE). The IDE and IQE have been balloted and 

approved by ASTM's Committee D-19 for water as Standard Practices D-6091 and D-6512, respectively. 

These approaches take into account nearly all sources of variability to arrive at detection and quantitation 

limits that are higher, on average, than the limits produced by other approaches (see Appendix C of this 

Assessment Document). Because the regulated community has shifted support from the CMDL/CMQL 

?Why? and the AML to the IDE and IQE, and because EPA is not aware of other organizations that 

currently advocate the earlier approaches, EPA did not consider industry approaches other than the 

IDE/IQE in its assessment of possible alternatives to the MDL and ML. 

As with all other approaches advocated to date, the IDE and IQE have fallen short of being ideal 

approaches for detection and quantitation for all organizations and applications. To date, EPA is not 

aware of a demonstrated implementation of the IDE or IQE in the development of an analytical method. 

Specific concerns that have been raised about the IDE and IQE are that: 

• They contain an allowance for false negatives that may be inappropriate, 

•
 The IDE and IQE are based on the use of prediction and/or tolerance intervals, which in some cases 

may yield conservative (high) estimates, 

•
 The IDE and IQE require a large amount of data in order to be able to model variability versus 

concentration, including data generated in multiple laboratories, and 

•
 The complexity and expense the statistical procedures involved in calculating an IDE and IQE could 

be a barrier to innovation and method development. 

These concerns are discussed in detail later in this document. 

In December 2002, the Inter-Industry Analytical Group (IIAG) submitted a proposal to EPA that 

recommends (1) a sensitivity test intended to “replace the MDL as a test of whether an individual 

laboratory is performing adequately,” and (2) an interlaboratory validation study design intended to 

characterize precision and accuracy of methods used for regulatory compliance. 

IIAG’s proposed sensitivity test includes the provision that EPA first determine the lowest 

calibration point of a method, prescribe a dilution of that calibration point as the spike level (e.g., at onehalf or two-thirds the lowest calibration point), specify a required number of replicates, and set a quality 

2 - 9	
control acceptance criterion. IIAG asserts that this test would provide all laboratories with a single spike 

level and an “unambiguous pass or no-pass test.” EPA solicited comment on approaches that might be 

considered appropriate for such determinations (i.e., the lowest calibration point of a method, an 

appropriate dilution, a number of replicates, and an acceptance criterion for standard deviation between 

measurements of the replicates). IIAG’s proposed “full range” validation study is intended to determine 

precision and bias across the entire working range of an analytical method (i.e., from a blank to the upper 

end of the working range) and would account for variability between laboratories. IIAG recommends 

that results of such a study be used to establish an interlaboratory method detection level. 

At the time IIAG’s submitted the sensitivity test and full range validation study, EPA did not 

have the opportunity to evaluate IIAG’s proposal against the criteria discussed in Chapter 4, but included 

the complete text of the recommendations in the regulatory record supporting the February 2003 

Assessment Document. EPA is including an assessment of this proposal in Chapter 5 of this document. 

2.3.3	 Approaches Advocated by the Laboratory Community and Voluntary Consensus 

Standards Bodies 

In 1980 (MacDougall et al., 1980) and 1983 (Keith et al., 1983), the American Chemical 

Society's Committee on Environmental Improvement (CEI) advanced approaches for the Limit of 

Detection (LOD) and Limit of Quantitation (LOQ). The ACS LOD is defined as the lowest 

concentration level that can be determined to be statistically different from a blank. The recommended 

value for the LOD is three times the standard deviation of replicate measurements of a blank or low-level 

sample. The LOD is roughly equivalent to the MDL in numerical terms and conceptually equivalent to 

Currie’s critical value. 

The ACS LOQ is defined as the level above which quantitative results may be obtained with a 

specified degree of confidence. The recommended value for the LOQ is 10 times the standard deviation 

of replicate measurements of blanks or low-level samples. Because the LOD and LOQ are still used by 

the analytical community, they have been included in EPA's reassessment of detection and quantitation 

approaches. 

In the mid-1980s, the ACS CEI introduced the concept of the Reliable Detection Limit (RDL) 

and the Reliable Quantitation Limit (RQL). The RDL and RQL were attempts at simplification of the 

LOD and LOQ. Both the RDL and the RQL involved applying a multiplier to the standard deviation 

derived from replicate measurements of a low-level sample. Neither concept received acceptance by the 

analytical community. Because the RDL and RQL are no longer being advanced by ACS, they were not 

considered for evaluation in EPA's assessment of detection and quantitation approaches. 

In 1999 (Currie, 1999a and 1999b), IUPAC and ISO reached substantial agreement on the 

terminology and approaches documented by Currie (1995), although “subtle differences in perspective” 

of the organizations remain (Currie, 2000). IUPAC and ISO have not, to date, published methods that 

include limits reflecting these standards. Similarly, although ASTM International adopted the IDE in 

1997 and the IQE in 2000, ASTM International has not included any IDE or IQE values in methods 

approved through the ASTM ballot process. On the other hand, ISO and ASTM International have 

published methods that employ the MDL. Because IUPAC and ISO have approved the critical value, 

detection limit, and quantification limit, and because ASTM International has approved through ballot 

the IDE and IQE, EPA has included these approaches in its assessment of detection and quantitation 

approaches. 

2 - 10	
At the ACS Annual Meeting held in August, 2002, CEI members discussed the issue of detection 

and quantitation, with the objective of determining if the LOD and LOQ approaches should be re-visited. 

At that meeting, several members suggested that the committee consider adopting a sample-specific 

detection limit approach in which the ratio of instrument signal to background noise is used to estimate a 

detection limit for each analyte in each sample analyzed. EPA did not include the signal-to-noise ratio 

concept in this assessment because its application is limited to specific types of measurement techniques, 

such as gas chromatography/mass spectrometry. Limitations of this concept for use in general 

environmental chemistry are best illustrated by the fact that it would not apply to any of the techniques 

traditionally used to determine the "conventional pollutants" cited in the Clean Water Act (the only 

pollutants cited by name in the Act), i.e., biochemical oxygen demand (BOD), total suspended solids 

(TSS), fecal coliforms, and pH. 

During the comment period on the February 2003 assessment document, the American Council 

of Independent Laboratories (ACIL) submitted a procedure that was developed to address bias that may 

arise in the estimation of detection limits under certain conditions. The ACIL procedure separates 

estimation of a detection limit into two cases: analyses that always produce a numeric result, even socalled “blank” samples (i.e., zero analyte added), and analyses that do not always produce a numeric 

result, i.e. blank samples appear to produce no signal. We will call these Case I and Case II. Analysis of 

samples for metals with inductively coupled plasma optical emission spectroscopy (ICP-OES) is an 

example of ACIL Case I; analysis of samples for PCBs with gas chromatography and mass spectrometry 

(GC/MS) is an example of ACIL Case II. 

For Case I analyses, ACIL suggests making use of the numeric results obtained from the analysis 

of blank samples which laboratories routinely run as a quality control measure. ACIL provided a 

detailed set of instructions for conducting the analyses and doing the MDL calculation. Differences 

between the ACIL Case I calculation and the EPA MDL calculation include: (1) use of blanks rather than 

low-level spikes to estimate standard deviation, (2) the calculation of both a critical level and a long-term 

MDL, where the MDL is based on adding the mean of the blank results to 2 times the product of the 

standard deviation and t-statistic, (3) a bias offset correction that adds the mean of the blank results to the 

calculated critical level and MDL, (4) recommends the use of results from a minimum of 20 analyses, 

and (5) analysis over the course of a year from routine daily operations (rather than on one day). ACIL’s 

Case I procedure is similar, but not identical, to the USGS procedure that is described in Section 3.3.4 of 

this document. The ACIL Case I procedure has no explicit limits on the amount of contamination 

allowed in the blanks before a laboratory is considered to be “out of spec.” 

For Case II, blanks cannot be used to estimate the standard deviation because they do not provide 

a response. Thus, Case 2 recommends an iteration of multiple low level spikes somewhat similar to the 

requirements in the EPA MDL procedure. However, the calculation of an MDL from the results of these 

spiking experiments differs significantly from the EPA MDL procedure. The procedure also specifies a 

sensitivity check for which some of the details are not as explicit compared to the Case I part of the ACIL 

detection limit procedure. 

2.3.4 Approaches Advocated by Other U. S. Government Agencies and Other Governments 

Within the U.S., EPA found that other Federal agencies tend to rely on the detection and 

quantitation limit approaches described above or on variants of those procedures. For example, the USGS 

National Water Quality Laboratory (NWQL) began using the EPA MDL procedure in 1992. USGS 

2 - 11	
NWQL has since developed a variant of the MDL called the long-term MDL (LT-MDL) that has been in 

routine use since 1999. The LT-MDL determination ideally employs at least 24 spiked samples prepared 

and analyzed by multiple analysts on multiple instruments over a 6- to 12-month period at a frequency of 

about two samples per month. 

Unlike EPA programs that rely on hundreds of commercial, Federal, State, and local laboratories 

for sample analysis, the samples collected for USGS water programs are analyzed by the USGS National 

Water Quality Laboratory in Denver, Colorado. As described by USGS, the long-term MDL is based on 

many of the same fundamental assumptions as the MDL, namely: 

1. Normal data distribution, 

2. Constant standard deviation from the spike concentration down to zero, and 

3. 
 Best-case detection condition (because LT-MDLs typically are determined by spiking the analyte 

in a clean matrix, e.g., reagent water). 

The three primary differences between the EPA MDL and the USGS LT-MDL procedures are 

the (1) larger minimum number (24) of spike samples, (2) longer time period, and (3) mixing of 

instruments and analysts in the determination of the LT-MDL. Because the MDL and LT-MDL 

approaches otherwise are so similar, EPA did not evaluate the long-term MDL approach in the February 

2003 assessment. Instead, EPA considered the underlying differences between the two approaches 

(namely the effects of temporal, instrument, and analyst variability) in its assessment of issues (see 

Chapter 3). 

In the LT-MDL procedure the low-level spike used for each analyte and instrument is 

recalibrated at least once a year or when an anomaly occurs. USGS has enhanced the LT-MDL 

procedure by using their large volume of uncensored blind laboratory blank data, which also is collected 

yearly, as a reality-check on the spike-based LT-MDL. In cases where the standard deviation used to 

calculate an LT-MDL based on blind blank data is significantly different (especially when greater) than 

the standard deviation used to calculate the spike-based LT-MDL, the blank data are used to calculate the 

LT-MDL. Blind blank data also are used to evaluate whether the calculated LT-MDL requires an off-set 

correction for blank bias, i.e, LT-MDL = (s * Student t) + median or mean blank concentration. This 

offset is similar, but not identical, to the ACIL Case I procedure described in Sect. 2.3.3 of this 

document. The LT-MDL offset correction compensates for a blank distribution that is not centered on 

zero (as assumed by the EPA MDL formula). 

The NWQL has found that this blank bias correction to the LT-MDL is especially important for 

blank-limited analytes, including some metals, total organic carbon, phenol, and nutrients. The NWQL 

also uses a data reporting convention that incorporates a higher reporting level (called the laboratory 

reporting level; LRL) that is set at two or more times the LT-MDL. However, this convention also 

includes reporting of data between the LT-MDL and LRL. 

Outside the U.S., EPA found that the European Union (EU) relies on the terminology and 

conventions developed by Currie, IUPAC, and others (Eurachem, 2000). The EU advocates reporting all 

results along with an estimate of the uncertainty associated with each value. In its discussion of the 

issue, the EU indicates that use of the term ‘limit of detection’ only implies a level at which detection 

becomes problematic and is not associated with any specific definition. Instead, the EU focuses its 

attention on ways to estimate uncertainty, basing its approach on the ISO Guide to the Expression of 

Uncertainty in Measurement (1993). However, the EU also notes that the use of uncertainty estimates in 

2 - 12	
compliance statements and the expression and use of uncertainty at low levels may require additional 

guidance. The United Kingdom’s Valid Analytical Measurement Programme (VAM) has adopted a 

similar approach that is based on both the ISO and the Eurachem guidance (Barwick and Ellison, 2000). 

Because these approaches are focused on estimating uncertainty rather than at establishing or defining 

limits for detection and quantitation, EPA did not consider the European approaches in this assessment. 

2 - 13	
Chapter 3 

Issues Pertaining to Detection and Quantitation 

As part of the Settlement Agreement concerning EPA's reassessment of detection and quantitation 

limit approaches, EPA considered several specific issues pertaining to these approaches. These issues 

included: 

• Criteria for selection and appropriate use of statistical models, 

• Methodology for parameter estimation, 

• Statistical tolerance and prediction, 

• Criteria for design of detection and quantitation studies, including selection of concentration levels 

(“spiking levels”), 

• Interlaboratory variability, and 

• Incorporation of elements of probability design. 

In developing the plan for conducting this assessment, EPA identified other issues that should be 

considered. With the exception of the first issue, these issues are discussed in this chapter and include: 

• Concepts of the lower limit of measurement (discussed in chapter 2), 

• The need for approaches that can support CWA programs, including: 

- method performance verification at a laboratory, 

- method development and promulgation, 

- National Pollutant Discharge Elimination System (NPDES) applications, 

- non-regulatory studies and monitoring, 

- descriptive versus prescriptive uses of lower limits to measurement, and 

- use of a pair of related detection and quantitation procedures in all OW applications 

• Censoring of measurement results, 

• Sources of variability (including, but not limited to interlaboratory variability), 

• False positives and false negatives, 

• Measurement quality over the life of a method, 

• Matrix effects, 

• Background contamination, 

• Outliers, 

• Instrument non-response, 

• Accepting the procedures of voluntary consensus standards bodies (VCSBs), 

• National versus local standards for measurement, 

• Ease of use (i.e., ability of study managers, bench chemists, and statisticians to do what is required by 

a detection or quantitation limit procedure), 

• Cost to implement the procedures, and 

• Laboratory-specific applications. 

These issues are organized into three subsections that follow. Section 3.1 discusses the issues 

that are primarily driven by analytical chemistry concerns, Section 3.2 discusses the issues that are 

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 primarily driven by CWA regulatory considerations, and Section 3.3 discusses issues that are primarily 

driven by statistical concerns. Table 3-1, at the end of this chapter, provides a summary of the issues 

discussed in Sections 3.1 - 3.3. 

3.1 Analytical Chemistry Approaches to Detection and Quantitation 

This section explains the key analytical chemistry issues involved in the development of detection 

and quantitation limits. These include: (1) nonzero sample blanks, (2) instrument censoring, (3) matrix 

effects, (4) analyte recovery, and (5) temporal variability of the measurement system. 

3.1.1 Nonzero Sample Blanks 

Analytical chemists rarely state that a sample contains zero concentration of a substance of 

interest. Even when the sample is created in a laboratory for the purpose of containing as little substance 

of interest as possible (a blank), analytical chemists recognize that some small residual amount of the 

substance may be present and contribute to the measurement result. The inability of a laboratory to 

reduce the concentration of a substance in the blank is often the limiting factor in attempts to make 

measurements at ever lower levels. 

A classic example of this potential problem was published by Patterson in the late 1960s and 

1970s (e.g., Patterson and Settle, 1976). Patterson demonstrated that the majority of concentrations of 

lead reported in the literature for such diverse matrices as urban dust, open ocean waters, and biological 

tissues were in error by several orders of magnitude. The source of the "gross positive errors" (or 

“positive bias” from blanks) was contamination introduced during sample collection, handling, and 

analysis. Interlaboratory studies of the day designed to determine consensus values for reference 

materials were, in fact, determining the consensus values for background contamination across 

laboratories. Patterson recognized the value in running blank samples (samples thought not to contain the 

substance of interest) to demonstrate that the sample collection, handling, and analysis processes were not 

introducing contamination. Patterson subsequently developed the techniques for "evaluating and 

controlling the extent and sources of industrial lead contamination introduced during sample collecting, 

handling, and analysis" that form the basis of the "clean techniques" used for metals analysis today, and 

that are incorporated in several EPA analytical methods, including EPA Method 1631 for measurement of 

trace-level mercury. 

The most common analytes for which contamination problems are encountered in environmental 

measurements are metals, primarily zinc because of its ubiquity in the environment. With the exception 

of some volatile organic compounds, such as methylene chloride and acetone, that are used as solvents in 

laboratories, contamination in the measurement of organic compounds is less of a problem than 

contamination of samples for metals analyses. Therefore, for determination of metals, a blank is usually 

included or compensated in the calibration whereas, for organics, except for the solvents, the 

concentration in the blank is generally assumed to be zero and there is no compensation of the calibration. 

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Measurement methods designed to determine substances at very low concentrations may include 

requirements for the preparation and analysis of a variety of blanks that are designed to identify the extent 

and the sources of contamination. Analysts understand that "blank" does not necessarily mean zero 

concentration, and through careful control and evaluation, it is possible to make measurements for which 

the blank contribution is sufficiently small to be considered negligible. 

In the February 2003 version of this document, EPA noted that useful detection and quantitation 

limit approaches should address the potential contribution of the blank through both the design of the 

study that generates the detection and quantitation limit estimates and the evaluation of the study results. 

Stakeholders commenting on EPA’s 2003 assessment of these approaches added that, for many blank 

analyses, there is a measurable response (blank bias) that can be attributed to reagents, sample vessels, 

and other contamination sources, and that the MDL procedure failed to take these blank responses into 

account. Several commenters suggested that the mean of the blank results should be added to the formula 

used to calculate the MDL. The American Council of Independent Laboratories (ACIL) submitted a 

procedure to use blanks rather than spikes to estimate a detection limit. The USGS submitted a long term 

MDL procedure that uses either the mean or median of blank results as a lower bound reality check on the 

MDL whenever an MDL computed from the low level spiking experiments is sufficiently less than the 

blank results. 

Following a careful evaluation of these comments and further consideration of this issue, EPA 

recognizes that, under certain conditions, it may be appropriate to account for blanks in establishing 

detection and quantitation limits with certain limitations. For example, a procedure to handle blanks 

should account for negative results, and should limit and control sample and laboratory contamination. 

Negative blanks are possible and can be caused by a blank-subtracted calibration in which the result for 

the calibration blank is greater than the results for the blanks used to establish the MDL. If such negative 

blanks were to result in a negative mean blank, adding the mean blank result to the formula could result in 

an unattainably low MDL. Conversely to eliminate unnecessarily high MDLs, laboratories also would 

need to ensure that the results of blank samples are not excessive. The laboratory would need to use 

"clean" and other techniques to control contamination to the lowest possible levels and/or use a second or 

higher order calibration function to preclude high results for a calibration blank from exerting undue 

influence on the sample results. In addition to working out some of the details of necessary bounds on 

blank correction and contamination, differences between the procedures submitted by USGS and ACIL 

need to be evaluated. 

3.1.2 Analytical Instrument Thresholds: Data Censoring 

Certain analytical instruments typically employ "thresholds" to eliminate spurious or background 

signals so that analysts can be relieved of the burden of removing or compensating these small signals. 

As a result, the instrument itself may return a response of zero to a blank (a "non-response"). As an 

example, gas chromatograph/mass spectrometer (GC/MS) instruments often contain thresholds below 

which no instrument signal is reported. With no instrument signal reported, no measurement result can 

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be reported, and the instrument will report zero to indicate the lack of a signal. To understand how 

instrument thresholds are used, it may be helpful to think of background static heard on a citizen-band 

(CB) radio or a walkie-talkie. The static is present, but it has no meaning. Turning the "squelch" knob to 

filter out the static also may make it impossible to hear the caller. In the context of detection, increasing 

the instrument threshold may cause the instrument to miss a substance of interest at a low level. 

In 1997, EPA conducted a study of 82 semivolatile (acid and base/neutral) organic compounds 

measured by EPA Method 1625 in order to observe the performance of a GC/MS instrument both with 

and without application of an instrument threshold (sometimes known as the "Episode 6184 study"); see 

Chapter 1, Section 1.3.2.3). In the study, solutions at up to 17 concentration levels were analyzed with 

the threshold on (i.e., low-level signals are automatically eliminated) and with the threshold off (i.e., there 

is no suppression of signals). Samples were analyzed at decreasing concentrations, including a blank 

concentration level, with triplicate determinations at each concentration. With the threshold turned on, all 

of the measurements made on the blank were reported as zero. This is not surprising, given the purpose 

of the instrument threshold. Without the threshold off, only 27 of 230 measurements on the blank (11%) 

were reported as zero, and no negative results were reported. 

Instrument thresholds have a direct and indirect impact on estimating detection and quantitation 

limits. The main direct impact is that it is not possible to estimate the standard deviation of measurements 

at zero. However, by definition, the standard deviation at zero is required to calculate the Currie critical 

value (CRV; Lc). The EPA MDL procedure was constructed to deal with this problem by providing a 

way to estimate a standard deviation at a low concentration, and including instructions for determination 

of a concentration as close to zero as is possible that will generate a measurement. 

To calculate an MDL using the 40 CFR 136, Appendix B, MDL procedure, it is necessary to find 

the lowest concentration at which the analytical system will return results. Many laboratories have run 

repeated measurements in order to find this concentration. The challenge of finding this lowest 

concentration manifested itself in EPA's variability versus concentration (Episode 6000) studies. 

Technologies for determination of organic, conventional pollutants, and metal analytes were evaluated in 

the Episode 6000 studies. The MDL procedure suggests iteration until the calculated MDL is within a 

factor of five of the spike level. For the Episode 6000 studies, EPA instructed laboratories to use a factor 

of three instead of five in an attempt to more narrowly define the lowest spike level at which 

measurements could be made. This change to a factor of three also was suggested by one of the peer 

reviewers charged with evaluating EPA’s 2003 assessment of detection and quantitation limits, who 

noted: 

"However, the use of as much as five times the critical level for the spike concentrations 

could be problematic. The inflation of the MDL by using a spike at the critical level is 

only 25% for a method with a high-level CV of 20% (this and other calculations here are 

done with the Rocke and Lorenzato 1995 variance function assuming a sample size of 7). 

A spike concentration of 3 times the critical level inflates the MDL to a value 140% 

higher, which even there may be tolerable. Use of a value 5 times the critical level gives 

an inflation of over 280%. ..." 

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Following some theoretical example calculations that are not reproduced here, the peer reviewer's 

comment continues with: 

"Thus, I would recommend that the procedure be altered to use concentrations that are 

no more than 3 times the detection limit, and perhaps to permit concentrations lower then 

the critical level, including possibly blanks" (Rocke, 2002). 

The reviewer's calculations suggest that the MDL may be strongly inflated for a spike level of 

five times the MDL, but only moderately inflated at a spike level of three times the MDL. However, 

during the Episode 6000 studies, several laboratories asked for relief from the factor of three requirement 

because a large number of iterations were required to meet it. In response, EPA reinstated the factor of 

five for these laboratories. If the reviewer's example calculations are correct and a practical procedure for 

determining the MDL using the factor of three were implemented, it could exacerbate the concern from 

the regulated community that MDL values are too low. 

Several stakeholders commenting on EPA’s 2003 assessment suggested that approaches to 

detection and quantitation should address methods that do not always produce an instrument response, e.g. 

so-called blanks never produce a response because of electronic censoring by the instrument, and that 

EPA’s approaches do not do so. These stakeholders prefer that the MDL not be applied to methods for 

which an identifiable analyte signal cannot be established using method blanks, where pattern recognition 

is required (e.g., Method 608 for PCBs as Aroclors), or where the method requires more than one signal 

for an analyte to be positively identified (e.g., the use of multiple ions in GC/MS methods). EPA 

recognizes that additional guidance needs to be developed for these methods. One commenter, the 

American Council of Independent Laboratories, submitted a draft set of procedures designed that partially 

addressed methods that do not produce an instrument response at zero concentration. EPA evaluated the 

ACIL procedure, which involves a complex set of iterative spiking experiments, and found that it needs 

further refinement. EPA agrees that this issue warrants further examination. 

3.1.3 Matrix Effects 

"Sample matrix" is a term used to describe all of the substances, other than the substance(s) of 

interest, present in an environmental sample. In the case of a wastewater sample, this would include the 

water itself, as well as any other dissolved or suspended materials. 

"Matrix effect" is a term used to describe a situation in which a substance or combination of 

substances in the sample (other than the substance[s] of interest) influence the results of the measurement. 

Positive interferences may inflate the results for the substance or make it difficult to distinguish one 

substance from another. However, unless the positive bias from the matrix is consistent and predictable, 

the measurement result may be unreliable. Negative interferences may suppress the results for the 

substance to the point that the results cannot be distinguished from background instrument noise. 

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In some cases, finding a matrix effect indicates that the analyst should select a more appropriate 

method. For example, a colorimetric method for the measurement of sulfide may be a poor choice for the 

analysis of a sample that is very cloudy or darkly colored. In other cases, characteristics of the sample 

such as its pH may destroy the substance of interest, effectively preventing analysis for that substance. 

Nearly all of the newer analytical methods approved at 40 CFR part 136 describe the preparation 

and analysis of quality control samples that are designed to indicate the presence of matrix effects (e.g., 

matrix spike and/or matrix spike duplicate samples). Many of these methods also contain techniques for 

addressing matrix effects. Further, EPA has developed guidance documents that amplify the discussions 

in those methods (e.g., Guidance on Evaluation, Resolution, and Documentation of Analytical Problems 

Associated with Compliance Monitoring, June 1993, EPA 821-B-93-001). For determination of mercury 

by EPA Method 1631 that is the subject of the Settlement Agreement, additional guidance on resolving 

matrix interferences to achieve specified detection and quantitation limits is provided in EPA’s Guidance 

for Implementation and Use of EPA Method 1631 for the Determination of Low-Level Mercury (March 

2001, EPA 821-R-01-023). Following the techniques in the methods and guidance will usually reduce 

adverse effects of the sample matrix on detection/quantitation limits and measurement results. 

3.1.3.1 Allowance for Matrix Effects in Detection and Quantitation Limits 

Some stakeholders have suggested that detection and quantitation limits should be determined in 

“real-world” matrices, rather than in reference matrices intended to simulate method performance in a 

particular medium (water, soil, biosolids, tissue). Some commenters on EPA’s 2003 assessment believe 

that any form of a detection limit study should require demonstration of the lowest level of detection 

achievable in an interference-free matrix and should relate this to what is actually detectable in a highly 

complex matrix. One commenter stated that EPA should provide an objective set of procedures that a 

permittee can follow to avoid liability when faced with an MDL or ML it legitimately cannot achieve 

because of the unique nature of its effluent. EPA notes that permittee liability was not a goal or purpose of 

our assessment of detection and quantitation approaches and issues. Although EPA recognizes stakeholder 

concerns about the matrix effects, there are several problems associated with the approach suggested by 

some commenters. These problems include: 

• Many “real-world” matrices contain the target pollutant at levels well above the detection or 

quantitation levels, making it impossible to characterize what can and cannot be detected at low 

levels. Diluting the sample to dilute the target pollutant concentration is an option. However, this 

also has the potential to dilute any interferences that might be present, thereby defeating the 

purpose of using the real-world matrix. 

• Use of a reference matrix to establish detection and quantitation limits allows the results to be 

reproduced (i.e., confirmed) by an independent party; such a confirmation may not be possible 

with many real world matrices that may be subject to seasonal, diurnal, or other types of 

variability. 

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• Few environmental analyses are conducted on actual samples of reagent water or other reference 

matrices and there may be matrix-specific limitations to the sensitivity of any given analytical 

method. From a practical standpoint, it would be very impractical to evaluate method sensitivity 

in every possible matrix to which a method might be applied, or to establish a subset of all 

possible matrices that would satisfy the concerns of every regulated discharger. 

• The cost of determining detection and quantitation limits in every possible matrix would be 

prohibitive. 

Because of these difficulties a reference matrix (or reference matrices) is an appropriate and 

practical first choice to establish detection and quantitation limits. And the procedures for defining these 

limits should allow for evaluation of data collected in the specific matrices of concern. Laboratories or 

data users are most able to determine which matrices might be considered to be “highly complex” based on 

the matrices that are typically analyzed in a given laboratory. EPA’s detection and quantitation procedures 

do not preclude laboratories from determining MDLs in matrices other than reagent or “blank” matrices, 

and the Agency encourages laboratories and others to determine matrix-specific MDLs when all efforts to 

resolve matrix interferences have been exhausted. The existing procedure at 40 CFR 136, Appendix B, 

includes a discussion regarding determination of matrix-specific MDLs for this reason. Laboratories 

usually are very capable of eliminating or compensating matrix interferences if tasked to do so. However, 

given the degree of concern about this issue it is appropriate for all parties to continue to search for 

additional solutions to the “real world” matrices issue. 

3.1.3.2 Repository of Reference Matrices 

Two of the four peer reviewers charged with evaluating EPA’s assessment of detection and 

quantitation limit approaches suggested that EPA create a repository of reference matrices, similar to those 

developed by NIST, and that these reference matrices be used to challenge a test method and to establish 

detection and quantitation limits (Cooke, 2002 and Wait, 2002). EPA has considered such a repository 

from time to time and again in response to this suggestion, but has been unable to resolve all of the issues 

surrounding such a repository. Some of these issues are: 

C The stability of aqueous samples,

C The holding times necessary to assure stability,

C The argument that no matrix from a given industrial discharge in an industrial category or subcategory

reflects the characteristics of another discharge in that or other industrial categories or subcategories, 

C The cost of maintaining such a repository, and 

C The potential conflict with NIST and with non-governmental organizations that provide reference 

matrices. 

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Given these issues, it is appropriate to leave the development and maintenance of standard 

reference materials (SRMs) and certified reference materials (CRMs) to NIST and the commercial 

marketplace. These reference materials are a useful means of challenging a test method and EPA has 

suggested in recent methods that reference matrices be analyzed, when available, as an additional QC 

measure. For example, when EPA developed an appendix to Method 1631 for application to matrices 

other than water, EPA specified use of a quality control sample (QCS) with the statement that "many 

certified reference materials (CRMs) are available for total mercury in plants, animals, fish, sediments, 

soils, and sludge" and the requirement that "recovery and precision for at least one QCS per batch of 

samples must meet the performance specifications provided by the supplier." 

Although SRMs and CRMs could be useful in establishing detection and quantitation limits, 

practical considerations are likely to preclude their use for this purpose in most situations. This is because 

the materials would need to contain the analytes of interest at levels that are near the detection limit (e.g., 

within 1 to 5 times the concentration of a determined MDL). Such concentrations are unlikely to occur in 

an SRM produced by NIST or a CRM produced by a vendor, and diluting the CRM/SRM would diminish 

matrix effects, as indicated in Section 3.1.3.1. 

As an alternative to using standard reference materials, EPA commonly tests its analytical methods 

on a variety of real-world matrices, and allows for this variability in the QC acceptance criteria for the 

matrix spike (MS) and matrix spike duplicate (MSD) samples. For example, EPA published performance 

data in Table 3 of EPA Method 1631B for reagent water, fresh water, unfiltered and filtered marine water, 

and unfiltered and filtered secondary effluent, and allowed for the variability among these matrices in the 

QC acceptance criteria for the MS/MSD in the method. ASTM Committee D 19 allows this approach in 

development of QC acceptance criteria for methods (see Section 6.5.1.1 of ASTM D 5847: Standard 

Practice for Writing Quality Control Specifications for Standard Test Methods for Water Analysis.) 

3.1.4 Recovery of Analytes from the Sample Matrix 

In the preceding two sections, we discussed bias (errors) from blank contamination and matrix 

effects. Errors from recovery effects (“recovery bias”) are discussed in this section. Recoveries are a 

measure of the amount (usually expressed as a percentage) of analyte that is recovered from the sample 

matrix and measured by the analytical system. Chemists sometimes use the phrase “accuracy of the 

method” when listing the percent recovery of an analyte. A goal of analytical chemistry is to achieve 

recoveries as close as possible to 100%. When this is not achieved, recovery correction may be used. The 

purpose of recovery correction is to adjust the measured concentration for the amount by which the 

measured concentration differs from the true concentration (if known). Recovery “factors” are initially 

determined by analysis of a sample containing a known (spiked) amount of the analyte. These factors are 

applied to measurements of samples with an unknown amount of the analyte in the same or a similar 

matrix. To illustrate the potential need for recovery correction, consider analytes, such as organic bases 

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(e.g., benzidine) and acids (e.g., phenols) in a water sample, that are either not totally (100%) recovered in 

the extraction process, or are adsorbed on the surface of a GC column at very low (nanogram) levels. As a 

result, the measured concentration of these analytes is always less than the true concentration in the water 

sample. These incomplete recoveries have led some developers of detection and quantitation limit 

approaches to believe that these limits should be recovery corrected (i.e., that the detection or quantitation 

limit should be adjusted inversely proportional to the recovery). For example, if an analyte is recovered at 

50%, the detection and/or quantitation limit should be doubled, and the amounts measured in unknown 

samples also should be doubled to allow for recovery correction. 

Several stakeholders have stated that understanding this “recovery bias” is particularly important 

when reporting results near the limit of detection, and is critical when reporting quantifiable results. These 

stakeholders believe that even if recovery bias is not controlled at the detection level, the approach to 

determining detection and quantitation limits should compensate for it. 

Few of the traditional approaches to establishing detection and quantitation limits include 

procedures for recovery correction. For example, the issue was not addressed by Currie in his original 

proposal of a critical value or quantitation limit. Similarly, neither EPA's MDL and ML nor the American 

Chemical Society’s LOD and LOQ, all of which are based on the approaches advanced by Currie, include 

a mechanism for recovery correction. When Currie introduced his critical value, he defined it as "the 

minimum significant value of an estimated net signal or concentration, applied as a discriminator against 

background noise" (Currie, 1995). Because the critical value is defined as a measured concentration rather 

than a true concentration, a recovery correction is not included. 

The use of recovery correction, however, has been included in several of the most recently 

developed approaches for detection and quantitation. For example, the minimum detectable value (MDV) 

adopted by ISO and IUPAC, and the interlaboratory detection estimate (IDE) and interlaboratory 

quantitation estimate (IQE) adopted by ASTM include procedures for recovery correction. The IQE also 

contains a further correction that we have termed a "bias" correction. 

In the ISO minimum detectable value approach, recovery is treated as a linear function versus 

concentration, and an extrapolation is used to estimate the recovery at zero concentration. This projection 

of the regression line to zero concentration can lead to errors because, depending on the intercept (in 

concentration units), the recovery at zero concentration can be positive, zero, or negative, resulting in an 

inflated minimum detectable value, an minimum detectable value very close to zero, or a negative 

minimum detectable value. For further details, see the section titled "Negative detection limits for the 

ISO/IUPAC MDV" in Appendix C to this Assessment Document, and the data in Table 2 of that appendix. 

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The IDE and IQE fit recovery versus concentration in a way analogous to the fitting in the 

minimum detectable value. The difference is that an unweighted model is used in the minimum detectable 

value, whereas the linear model in the IDE and IQE is weighted as determined by the model of standard 

deviation versus concentration that is used in calculating the IDE and IQE. (If this model is the constant 

model, the weighting is the same as for the minimum detectable value.) The IQE, but not the IDE, 

includes an additional correction for the bias associated with an estimate of the true standard deviation at 

each concentration as compared to the measured standard deviation at each concentration. In this context 

(a "bias" correction to the IQE), "bias" means the amount by which the estimated sample standard 

deviation differs from the true population standard deviation. This use should not be confused with a 

common use of "bias" in analytical chemistry measurements to denote the deviation of a result from the 

true value (usually expressed as percent.) 

Recovery correction may be appropriate if (1) when developing method detection and quantitation 

limits, the recovery is consistent across laboratories, matrices, and conditions, and (2) the relative 

variability (as relative standard deviation) remains constant as the recovery decreases. These two 

conditions are rarely observed. The first requirement (consistent recovery) would need to be tested under a 

variety of conditions because, if the recovery varies among laboratories, matrices, and analytical 

conditions, then a detection and/or quantitation limit would need to be developed for each of these 

conditions. EPA's experience is that poor recovery is rarely consistent; i.e., if one laboratory measures a 

recovery of 40%, another laboratory may measure 20%, or 60%, but not exactly 40%. 

Although some stakeholders disagree, EPA believes that the normal condition in environmental 

analytical measurements is that variability (as standard deviation) between sample results remains 

approximately constant as the recovery decreases ( i.e., the relative precision [as RSD] is poorer at low 

recovery). For example, if the RSD is 10% at 100% recovery, the RSD may be 50% at 50% recovery, and 

may be 100% at 10% recovery. For examples of the effect of poor recovery on precision, see the quality 

control (QC) acceptance criteria for the semivolatile organic compounds in Table 8 of EPA Method 1625 

(see 40 CFR part 136, Appendix A). This increase in relative variability is not the result of measurements 

being made at lower levels, as is the normal case, but is a result of variability in the extraction 

(partitioning) process. One stakeholder commenting on EPA’s 2003 assessment stated that EPA’s 

statement that variability remains approximately constant as recovery decreases may not hold true in all 

cases, and recommends that, if recovery falls within a specified level (e.g., less than 70%), detection limits 

should be adjusted accordingly. EPA acknowledges that there may be instances in which this general 

condition does not hold true. 

EPA has traditionally viewed recovery correction with great caution, and has preferred to require 

that laboratories analyze quality control samples to demonstrate that analytes are recovered within an 

acceptable level. For example, EPA's Office of Water methods require that laboratories prepare and 

analyze both a reference matrix and a sample matrix that have been spiked with the analytes of interest, 

and that these analytes be recovered within method-specified acceptance criteria. If the recovery criteria 

are met, then samples analyzed in the batch are considered to be reliable within the overall level of error 

associated with the method, and results are reported without correcting for the recovery. Measurements of 

dioxins/furans, PCBs, and pesticides can be made to very low (femtograms per liter) concentrations, with 

no decrease in recovery compared to recoveries observed at much, much greater (microgram per liter) 

concentrations. (One microgram is equivalent to one million femtograms). The ability to measure 

dioxins/furans, PCBs or pesticides down to these low concentrations demonstrates that recoveries for these 

compounds do not decrease with decreasing concentration. There also are chemicals, such as the 

nitrophenols and benzidine, that are not recovered reliably at sub microgram per liter levels. But these 

instances are known and recognized in the instructions for conducting these measurements. 

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MDLs are established and listed in methods based on the determined (measured) concentration 

(not the spike concentration), and laboratories and others that are required to verify MDLs, verify based on 

the determined concentration. If EPA estimated and listed MDLs in methods based on the spike (true) 

concentration, logic would require that the true (recovery corrected) concentration be used for regulatory 

compliance with the result that all results, not just the MDL, would be greater than nonrecovery corrected 

results. 

Recovery-correction techniques are employed in some Agency methods. Most notably are those 

methods that employ isotope dilution techniques, in which a stable, isotopically labeled analog of each 

target analyte is spiked into each sample. Because of their structural similarity to the analytes of interest, 

the labeled analogs are assumed to behave exactly like their unlabeled analogs (the target analytes). 

Because the recovery of the labeled analog will be similar to that of the target analyte, the technique allows 

for recovery correction of each target analyte and is particularly useful in highly complex matrices. In 

these methods, recovery correction techniques are specified as part of the procedures for calculating and 

reporting results and are dependent on the one-to-one relationship of the target analyte and the labeled 

analog. Inclusion of an additional procedure for recovery-correction in determining detection or 

quantitation limits for such methods could result in double-counting of analytical bias. 

Another procedure for dealing with bias (errors) from blank contamination, matrix effects, or 

errors from recovery effects (“recovery bias”), is to assure that the detection or quantitation limits which is 

determined meet the data users data quality needs for both precision and accuracy, without any correction. 

As described previously in chapter 2, EPA’s drinking water program is developing an approach to setting 

quantitation levels called the minimum reporting level or MRL. The MRL addresses these issues by 

setting a data quality objective for minimum and maximum permitted inaccuracy arising from these 

effects. 

The issue of bias in determination of detection or quantitation limits be it from blanks, matrices or 

other than 100% recovery of an analyte is a longstanding issue. All parties should continue to 

collaboratively work to develop other solutions or approaches to mitigate bias effects. 

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3.1.5 Temporal Variability of Analytical Measurements 

As with most other areas of technology, instruments continue to improve. Instrument 

manufacturers and laboratories are increasing data processing power, speed of analysis, and the reduction 

of chemical or electronic "noise." Any of these instrument improvements can be expected to improve the 

measurement of concentrations of environmental pollutants. This process can be illustrated for a variety of 

EPA methods. A case in point is EPA Method 1613 for determination of polychlorinated 

dibenzo-p-dioxins and polychlorinated dibenzofurans. Development of this method began in 1988. At the 

time, commercially available high resolution mass spectrometer systems were able to achieve a detection 

limit of approximately 4 pg/L and a ML of 10 pg/L. By the time that EPA proposed the method in 1991, 

the Canadian government published its own version that included a quantitation limit 5 pg/L. By the time 

EPA promulgated Method 1613 in 1997, many laboratories performing the analysis had replaced or 

supplemented their old instruments with newer models. As a result, many laboratories performing 

analyses using Method 1613 routinely measure sample results at levels 10 times lower than those analyzed 

routinely only 10 years earlier. 

Although there is no such thing as a “perfect” measurement, the idea that “practice makes perfect” 

(i.e., analytical results get better with practice) applies to the quality of measurements made with a given 

method over time. We can demonstrate this using simple techniques like laboratory control charts. The 

improvements are a result of experience, as well as improvements in equipment over time. EPA expects 

changes in performance when new staff are trained. For this reason, many EPA methods specify that "start 

up tests" be repeated each time new staff arrive. It is not unusual to see slight increases in measurement 

variability as new staff are trained followed by a decrease back to normal level after analysts become 

sufficiently experienced with the analytical method. . 

The use of quality control (QC) charts as a means of tracking method and laboratory performance 

as a function of time is described in EPA's Handbook for Analytical Quality Control in Water and 

Wastewater Laboratories (referenced in the 40 CFR part 136, Appendix A methods). Although these 

charts are instructive in tracking improvement or stability, they have two significant drawbacks: (1) they 

do not establish an absolute limit within which an analysis must be operated and (2) continued 

improvement can lead to unusually stringent limits that, eventually, will not be met. As long as absolute 

QC acceptance criteria (limits), such as those found in EPA methods, are established and as long as there is 

a recognition that stringent limits may be an artifact of improvement beyond what is routinely achievable, 

QC charts can be instructive in identifying statistically significant losses of, or improvements in, analyte 

responses in the region of interest. ASTM Committee D 19 adopted the philosophy of establishing 

absolute limits for analytical methods in approving Standard Practice D 5847. 

Stakeholders commenting on EPA’s 2003 assessment of procedures for characterizing the 

detection and quantitation limits of analytical methods expressed concern that EPA’s MDL and ML are 

determined using a single batch of samples representing a “snapshot” in time, and do not account for the 

temporal variability that can occur in a laboratory from day to day (e.g., due to use of multiple analysts and 

instrumentation, changing laboratory conditions). Although the codified version of the MDL does not 

preclude laboratories from incorporating temporal variability into the procedure (e.g., it allows the use of 

more than 7 replicates and does not require that the replicates be analyzed in a single batch), many users 

understand the MDL to be a single batch procedure. EPA encourages, where appropriate, use of data 

gathered over an extended period of time to calculate an MDL because measurement capabilities tend to 

improve and laboratory conditions tend to vary. Detection and quantitation limit calculations can be 

supported by procedures that allow laboratories to affordably characterize such changes and 

improvements. 

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3.2 CWA Regulatory Issues Affecting Detection and Quantitation 

Section 3.2.1 provides a brief overview of Clean Water Act activities that involve chemical 

measurements and are, therefore, directly impacted by detection and quantitation limit approaches. 

Specific issues to be considered in the context of these CWA applications and EPA’s regulatory 

obligations are discussed in Sections 3.2.2 - 3.2.6. 

3.2.1 Detection and Quantitation Limit Applications Under CWA 

The Clean Water Act directs EPA, States, and local governments to conduct a variety of data 

gathering, permitting, and compliance monitoring and enforcement activities. Many of these activities 

depend directly on environmental measurements and, therefore, are affected, both directly and indirectly, 

by detection and quantitation limit approaches. Stakeholders commenting on EPA’s assessment of 

detection and quantitation procedures stated that, because of the differing technical demands and 

regulatory and laboratory uses of detection and quantitation levels, the procedures for determining these 

values should be based on sound science. These stakeholders urged EPA to consider the implications of 

each technical decision it makes regarding determination of detection and quantitation values on the 

practical implementation of its regulations. 

Several commenters believe that EPA, permit holders, and laboratories would be better served if 

different approaches to detection and quantitation were taken for each use. Commenters specifically cite 

uses as a start-up test in a single laboratory, as a value characterizing a given analytical method, as a test 

for approving a method modification or alternate test procedure (ATP), compliance monitoring, and as a 

permit compliance level. The Inter Industry Analytical Group, in particular, has recommended the 

following 3-part approach: 

• A sensitivity test (as a test of start-up proficiency), 

• A long-term MDL approach ( for laboratory reporting), 

• Full-range validation study (such as the ASTM IDE/IQE) for validation of new methods and for 

setting quantitation levels that will be used as permit compliance levels. 

3.2.1.1 Method Development and Promulgation 

Section 304(h) of the Clean Water Act (CWA; the "Act") requires EPA to promulgate test 

procedures (analytical methods) to be used for data gathering to support certification, permitting, and 

monitoring under the Act. These methods are promulgated at 40 CFR part 136, and include methods 

developed by EPA as well as those developed by other organizations, such as the publishers of Standard 

Methods for the Examination of Water and Wastewater, as well as AOAC-International, ASTM 

International, the U.S. Geological Survey, instrument manufacturers, and others. Upon request by a 

laboratory, permittee, instrument manufacturer, or other interested party, EPA also considers alternate 

testing procedures (ATPs). If EPA deems these ATPs to be acceptable for use, they may be published at 

40 CFR part 136. A primary objective in promulgating methods developed by EPA and by other 

organizations is to provide the regulatory community, permittees, and laboratories with multiple options so 

that they may choose the method that yields the best performance at the lowest cost for the application. 

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In recent years, EPA has focused on developing methods for promulgation at 40 CFR part 136 

where no other methods are available that meet an immediate or anticipated regulatory need. The National 

Technology Transfer and Advancement Act of 1995 (NTTAA) encourages government agencies to 

consider methods published by voluntary consensus standards bodies (VCSBs), such as Standard Methods 

and ASTM International, when VCSB methods are available. EPA accepts that many of these methods 

have been through a sufficient level of testing, peer review, and scientific acceptance to warrant proposal if 

they meet EPA's regulatory needs. When an individual laboratory, permittee, or other organization 

submits a request for approval of an alternate test procedure, however, EPA generally requires that the 

procedure be subjected to a level of testing that demonstrates that the method provides sensitivity, 

accuracy, and other measures of performance comparable to an approved method. 

The lack of widespread consensus on detection limits has led organizations that develop methods 

to use different approaches, and many organizations have changed approaches over the years. Some 

stakeholders, who commented on the 2003 assessment, believe that method-specific detection and 

quantitation limits should account for interlaboratory variability, and therefore should be based on 

interlaboratory data. Other stakeholders believe that such a requirement would be overly restrictive and 

burdensome, resulting in fewer approved methods and technologies. The result is that a number of 

different approaches for detection and quantitation are embodied in the methods approved at 40 CFR part 

136. The vast majority of the approved methods include the MDL which, as noted in Section 2.2.1, has 

been used by several EPA Offices, Standard Methods, AOAC, ASTM, and others. Other approaches 

embodied in the methods at 40 CFR part 136 include, but are not limited to: 

1) a method “range” that is usually not defined, but is often interpreted as the lower end of the 

range in which pollutants either can be identified or quantified, 

2) an “instrument detection limit” that has been defined by a variety of procedures, but is intended 

to capture instrument sensitivity only, 

3) an "estimated detection limit" that may be based on best professional judgement, single 

laboratory data, or some other source of information, 

4) a "practical quantitation limit," that has typically been determined according to one of the 

scenarios described in Section 2.3.1, and 

5) "sensitivity" that is an undefined concept similar in result to the MDL. 

A solution to this lack of consensus would be to require that all methods promulgated at 40 CFR 

part 136 contain uniform approaches for detection and quantitation. However, taking such action would be 

disingenuous and confound methods promulgation because: 

• To date, no single detection and quantitation limit approach has emerged to meet the needs of all 

organizations for all applications. 

• If EPA’s were to select an approach that differs from those of other organizations, those organizations 

would be required to conform their method to accommodate the EPA approach. Doing so would mean 

that these organizations would have to invest additional laboratory resources to develop detection and 

quantitation limits that conform to OW definitions. 

• If outside organizations decided against conforming their approaches to that of EPA, fewer methods 

would be promulgated at 40 CFR part 136. This would result in fewer options for the regulatory, 

permittee, and laboratory communities. 

• If EPA selected an approach that has burdensome procedures for developing detection and quantitation 

limits, it could discourage development of innovative technology or method modifications. 

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Given these issues and EPA’s desire to encourage the development of improved measurement 

techniques, and provide the stakeholder community with a variety of measurement options whenever 

possible, it would be counter-productive to allow method developers the choice of only one detection or 

quantitation limit approach, or to only promulgate those methods that contain this single approach. 

However, there are real benefits to standardization, all new methods developed by EPA for promulgation 

at 40 CFR part 136 should reflect such standardization, and EPA should strongly encourage outside 

organizations to include these standardized approaches in their methods. However; there was no clear 

consensus as to what this standardized approach should be. Industry advanced IDE/IQE procedures but 

others did not necessarily support them. 

3.2.1.2 Verification of Laboratory Performance 

Just as sensitivity is important for evaluating method performance, it is important to verify that a 

laboratory using a method can achieve acceptable levels of sensitivity for making measurements. Such 

demonstrations can take many forms and should be viewed in the context of the decision to be made. The 

analytical methods published at 40 CFR part 136 are designed for monitoring compliance with CWA 

permits. Most pollutants in permits have a numeric limit, and compliance with this limit is determined by 

laboratory analysis of samples from the waste stream or water body regulated by the limit. The laboratory 

that conducts such analyses must be able to demonstrate that its detection or quantitation limits are low 

enough to assure reliable measurements. 

Thus, even where a method describes the sensitivity measured or estimated by the developer or the 

organization that published the method, some means are needed to demonstrate that a given laboratory can 

achieve sufficient sensitivity to satisfy the regulatory decision (e.g., monitoring compliance). 

The EPA MDL procedure provides a means for verifying laboratory performance and has long 

been used in this fashion by EPA and various other Federal and State agencies as a measure of method 

sensitivity. Other procedures may be employed, including analysis of reference materials containing the 

analytes of interest at concentrations that are at or below the regulatory limits of interest, spiked samples 

that are similarly prepared (e.g., matrix spikes), or laboratory performance evaluation samples such as 

those used in laboratory accreditation studies. Several commenters on EPA’s 2003 assessment 

recommended that a simple “sensitivity” test (e.g., determination of analyte recovery in a sample 

containing a low-spike concentration of the analyte) be used to evaluate or establish laboratory 

performance. Although at least two commenters submitted some ideas for conducting such a test, none 

were sufficiently or clearly detailed. However; EPA is open to consideration of approaches to verify lab 

performance. 

The IDE and IQE were advanced by the regulated industry and subsequently approved by ASTM 

International as a means of characterizing the performance of a method in laboratories that participate in an 

interlaboratory study. These approaches were developed to establish detection and quantitation limits that 

could be met by any laboratory that participated in the study. However; the IDE/IQE cannot be used to 

verify individual laboratory performance. 

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Developers of the IDE/IQE have recognized that an analogous approach is desirable for singlelaboratory application and have begun work on a within-laboratory detection estimate (WDE), to be 

followed by a within-laboratory quantitation estimate (WQE). As with the IDE/IQE, these approaches are 

intended to capture a wide range of sources of variability such as temporal variability, and include a 

prediction or tolerance limit (or both), but will not include interlaboratory variability. EPA would 

consider such single laboratory approaches if and when they are adopted by a voluntary consensus 

standards body, such as ASTM International. EPA will explore approaches for lab performance 

verification through the stakeholder process. 

3.2.1.3 National Pollutant Discharge Elimination System 

The National Pollutant Discharge Elimination System (NPDES) serves as a means by which EPA, 

States, and Tribes control point source releases of pollutants into the nation’s waters. Under this system, 

individual facilities are issued NPDES permits that provide effluent limitations that restrict the quantities, 

discharge rates, and concentrations of pollutants that may be legally discharged. Typically, these 

limitations are based on technology-based standards. If, however, these technology-based limits are not 

adequate to protect the water quality standard designated for the facility's receiving water, stricter controls 

are warranted. In such cases, NPDES permits must contain “water quality-based” controls. 

Development and Implementation of Technology-based Controls (Effluent Guidelines) 

EPA promulgates national effluent limitations guidelines and standards under the authority of 

Clean Water Act Sections 301, 304, 306, 307, 308, and 501. The regulations allow the discharge of 

pollutants from normal industrial processes when the discharges have been treated using various levels of 

available and affordable treatment technologies. Functionally, these industry-specific guidelines establish 

standards for the quality of wastewater discharges to waters of the United States. They are generally 

stated in the form of concentration-based limits for selected substances that are not to be exceeded. For 

example, the maximum oil concentration in wastewater separated from oil pumped out of an offshore well 

and discharged on any single day shall not exceed 42 milligrams per liter (mg/L). This form is called a 

numeric effluent guideline limit or numeric limit. 

Development and Implementation of Water Quality-based Controls 

States designate water-quality standards for various bodies of water within their boundaries. Each 

standard consists of a designated use, criteria to support that designated use, and an anti-degradation 

policy. Examples of designated uses include public water supply, recreation, and propagation of fish and 

wildlife. A discharge that causes, has reasonable potential to cause, or contribute to an excursion of an 

applicable water quality standard requires a water-quality based limit. Such a water-quality based limit 

shall be established at levels that derive from and comply with applicable water-quality standards and 

must be consistent with the assumptions and requirements of any available waste load allocation for the 

discharge, approved by EPA pursuant to 40 CFR 130.7. 

A special case occurs when the water quality-based effluent limit is less than the detection limit of 

the most sensitive analytical method. This case is addressed in Section 3.2.3 below, on compliance 

evaluation thresholds. 

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Permit Compliance Monitoring 

Under Clean Water Act Sections 318, 402, and 405, NPDES permits are issued to owners of 

facilities that discharge wastewater to waters of the United States (e.g., coastal areas, lakes, rivers, 

streams, certain wetlands, etc.). Specific discharge limits are established either for individual facilities or 

for classes of facilities. Individual permits are established for industries with many site-specific issues 

that determine the substances discharged, such as the pharmaceutical industry in which the specific drugs 

produced could influence the water quality. NPDES permits generally specify the use of measurement 

methods promulgated at 40 CFR part 136 under the Clean Water Act Section 304(h) for purposes of 

compliance monitoring and other reports submitted under NPDES permits. 

Detection plays a role in compliance monitoring because of concerns with measurement results at 

the low end of any analytical method. All measurement results are variable. At the low end of most 

measurement methods, there comes a point at which a particular measurement result is unacceptably likely 

(a policy decision) to have come from a sample in which the substance of interest is absent (zero 

concentration). Such a measurement result would be below the critical value defined by Currie (1995) 

and in common usage, would be called below detection. In practice, the reporting limit may be set equal 

to a critical value, detection limit, or quantitation limit. Assuming that the reporting limit is a detection 

limit of 1 mg/L of oil and grease, the measurement result would be reported as “less than 1 mg/L of oil 

and grease.” 

Stakeholders are particularly concerned with the use of the detection and quantitation limits for 

compliance purposes (e.g., judging whether a discharger is in compliance or whether a waterbody 

complies with its water-quality standards), for which a high level of reporting consistency and confidence 

in the data is required. Several commenters on EPA’s assessment stated that procedures used to determine 

these limits should provide the certainty required to make regulatory decisions. 

Several commenters suggested that there should be a single compliance benchmark for detection 

of each analyte that is independent of laboratory or method capabilities; laboratories used for compliance 

reporting would be required to demonstrate that they can detect at or below this level. These commenters 

note that such an approach would be particularly useful and appropriate for analytes with water quality (or 

other) standards set below current technological capabilities. 

3.2.1.4 Non-Regulatory Studies and Monitoring 

EPA conducts a variety of non-regulatory studies and monitoring activities to support its Clean 

Water Act programs. These activities range from long term surveys, such as the Great Lakes Water 

Quality Surveys that are conducted each spring and summer to monitor trends in water quality against 

established baselines, to short-term studies that are used to establish baselines, model pollutant cycles, and 

guide direction for future study and policy. Examples of such studies include the National Study of 

Chemical Residues in Fish that was conducted in the late 1980s (a follow-up to that study is currently 

underway), and the Lake Michigan Mass Balance Study conducted in the early 1990s. 

When designing a study or monitoring program, EPA uses information about detection and 

quantitation limits, along with information on the risks associated with the pollutant(s) of interest and the 

cost of measurement, to select an appropriate method for measuring the pollutant. Accepting all 

positively valued measurement results and selecting a measurement method with a detection limit lower 

than the level of concern for the substance being measured would provide some assurance that 

measurement results associated with that concentration would be positively valued. Selecting a 

3 - 17	
measurement method with a quantitation limit lower than the level of concern for the substance being 

measured would generate measurement results that are easier to explain to the data user and the general 

public. 

3.2.2 Descriptive versus Prescriptive Uses of Lower Limits to Measurement 

The literature on detection and quantitation generally assumes that these procedures are 

descriptive, as opposed to prescriptive. In other words, detection and quantitation studies are described as 

characterizing the current performance of a laboratory or laboratories using a method to measure a 

substance rather than specifying specific performance benchmarks that a laboratory must meet to 

demonstrate and maintain proficiency. Two possible reasons for this treatment are: (1) the intended 

audience includes laboratory staff and measurement methods developers who wish to make new methods 

useable by as many laboratories as possible, and (2) the author may have an institutional reason for not 

attempting to control variability and thus lower detection and quantitation limits. On the other hand, the 

technology-based and water quality-based effluent limitations programs administered by EPA’s Office of 

Water have an institutional goal of protecting human health and the environment. Providing this 

protection requires that the Agency be able to measure pollutants at ever lower concentrations. 

Establishing stringent standards and a compliance scheme for laboratories is one way to more rapidly 

develop the ability to measure at these concentrations. A prescriptive strategy concerning detection and 

quantitation limits would be to: 

• Determine the detection and quantitation limits at multiple laboratories. 

•
 Establish a detection limit and a quantitation limit for the method that is based on some 

performance of these laboratories. These limits could be established as the limits reported by the 

mean or median laboratory, or by some other criterion, such as the pooled value of the limits 

achieved by all laboratories or the limits that are met by a certain percentage of the laboratories. 

•
 Use the established detection and quantitation limits as performance standards that must be 

demonstrated by laboratories that practice the method. 

Such an approach is consistent with other performance standards included in EPA methods, such as 

standards for instrument calibration, recovery of spiked reference and matrix samples, etc. 

The use of such an approach would help ensure that prescriptive detection and quantitation limits 

(i.e., performance standards) reflect the capabilities of multiple laboratories, rather than a single state-ofthe-art research laboratory. Of course, it is possible that even when multiple laboratories are used to 

establish performance standards for detection and quantitation, some laboratories initially may not be able 

to achieve these standards. However, most laboratories facing this problem would try to improve and 

achieve these standards by investing in staff training, improved equipment, a stronger quality assurance 

program, or clean room practices and higher quality maintenance and operations. 

There is a risk that some members of the laboratory community will not be able to meet the 

standard, either because they are not willing to invest the resources necessary to do so or for other reasons. 

That risk should be considered when using a prescriptive approach to detection and quantitation (i.e., 

establishing limits that act as performance standards). Conversely, the risk of using a descriptive 

approach is that it can result in detection and quantitation limits that reflect a broad community of 

laboratories, including those that have made little if any effort to control contamination and variability at 

these low levels, thus raising detection and quantitation limits to a level that is higher than desired for 

protection of public health and the environment. 

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3.2.3 Compliance Evaluation Thresholds 

When technology-based effluent limitations are developed, the limits typically are at or above the 

quantitative measurement capabilities (e.g., the ML) of one or more analytical methods that are available 

to support compliance monitoring. Therefore, it is possible to monitor and evaluate permit compliance at 

concentrations with an accepted degree of measurement certainty. 

A situation that arises frequently in addressing water quality-based limits is that permit limits may 

be set below the detection or quantitation limit of the most sensitive, approved analytical method. This is 

particularly true for pollutants that are toxic in extremely low concentrations or that bioaccumulate. A 

recommended approach for these situations is to include in the permit, the appropriate permit limit derived 

from the water quality model and the waste load allocation for the parameter of concern, regardless of the 

proximity of the limit to the analytical detection level, along with an indication of the specific analytical 

method that should be used for monitoring (See Technical Support Document for Water Quality-based 

Toxics Control, EPA/505/2-90-001, March 1991). Both the MDL and ML have been used as reporting 

limits or compliance evaluation thresholds in NPDES permits. EPA promulgated regulations for NPDES 

permits for dischargers to the Great Lakes basin that require the use of the ML for compliance assessment 

purposes (See Appendix F, Procedure 8, Part B of 40 CFR 132). EPA has recommended for most 

permitting situations that the compliance level be defined in the permit as the ML (See Technical Support 

Document for Water Quality-based Toxics Control, EPA/505/2-90-001, March 1991). Outside of the 

Great Lakes basin, it is important to note, however, that states that implement the NPDES permits 

program have not always followed EPA’s guidance. The inconsistent use of the MDL and ML as 

reporting limits or compliance evaluation thresholds in NPDES limits suggest that EPA should 

develop additional implementation guidance. 

From a technical standpoint, a one-sided limit that addresses false positives only, such as Currie's 

critical value or EPA's MDL, is the most appropriate approach for producing a compliance evaluation 

threshold for the situation in which the WQBEL is less than a detection limit in the most sensitive 

analytical method because the one-sided limit allows measurement to the lowest possible level while 

protecting a discharger from the risk of a false violation. For example, consider the situation in which 

2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) is to be evaluated against the ambient water quality criterion 

of 13 parts-per-quintillion (ppqt). The most sensitive analytical method approved at 40 CFR part 136 is 

EPA Method 1613, with an MDL of 4 parts-per-quadrillion (ppq) and an ML of 10 ppq. The MDL is 

more than 300 times greater than the ambient criterion. Therefore, if dioxin is detected in the receiving 

water as a result of a discharge (i.e., the measurement result is greater than the MDL of 4 ppq), there has 

been an exceedance of the ambient criterion. Use of the ML as a compliance evaluation threshold is 

appropriate because it is the point at which the measurement could be considered reliable. 

3.2.4 National versus Local Standards for Measurement 

In accordance with the Settlement Agreement, EPA is re-examining the approaches of detection 

and quantitation used with methods approved for use at 40 CFR part 136. The Clean Water Act 

authorizes States and local governments to implement permits, with the requirement that they be at least as 

protective (stringent) as the national standards established by EPA. EPA recognizes that some States have 

implemented approaches to detection and quantitation that are either specific to that State, result in lower 

numeric limits in discharge permits, or both. Given that State and local governments use different 

approaches, a change by EPA with regard to this assessment of detection and quantitation procedures may 

have an unanticipated impact on those States and local governments. 

3 - 19	
3.2.5 Cost and Implementation Issues 

Detection and quantitation limit procedures are typically employed by organizations that develop 

methods and by laboratories that use the methods. Method developers typically include governmental 

organizations such as EPA, NOAA, USGS, and DOE, or voluntary consensus standards bodies (VCSBs) 

such as the American Public Health Association (APHA), ASTM International, AOAC-International, and 

ISO/IUPAC. Method developers also may include manufacturers of instruments or supplies used in 

testing. Users of methods generally are the laboratories performing tests to assess and assure product 

quality, to support regulatory compliance monitoring, or to support scientific studies. 

Method development requires a more diverse set of skills than method use because such 

development generally demands an understanding of quality systems, statistics, and analytical 

technologies. Personnel working for the method developer generally include a project manager, 

measurement analysts, who are experienced in several measurement technologies or very experienced in a 

specific, complex technology, and at least one statistician. Operating laboratories typically will not have a 

statistician, and the breadth and dept of the analyst experience may be less than in a method development 

laboratory, because an operating laboratory is focused on obtaining reliable results in the analysis of a 

given sample using a well-tested measurement technology. 

3.2.5.1 Implementation of a Detection/Quantitation Limit Procedure by a Method Developer 

The basic resources available to the method developer are time, money, and the technical skills of 

the developers’s staff. The fundamental decision for implementing a detection or quantitation procedure 

is whether that procedure is intended to characterize the performance of the method at a well-performing 

laboratory or the performance of the method across a group of laboratories. If the procedure is intended to 

characterize the performance of the method across a group of laboratories, it is also necessary to decide if 

there will be some way to compare the performance of individual laboratories to the group performance 

standard. There are serious time, cost, and skill issues with each of these decisions. Ordering these 

decisions from the least resource intensive to the most, they are characterizing the performance of the 

method: (1) at a well-performing laboratory, (2) at a group of laboratories, or (3) at a group of laboratories 

with comparisons of individual laboratories. Other costs for the method developer could include 

planning, data management, reference laboratory services, and whether laboratories are willing to 

volunteer for the study or if their services must be purchased. 

An independent decision is whether to assume a simple model for measurement variability and 

limit the number of test concentrations, iterate assuming a simple model, or to design a study of the 

relationship between measurement variation and the concentrations of the substances measured by the 

method. This decision will greatly influence the number of samples measured in the study and the 

required skill of the personnel who design, conduct and interpret the results of the study. If the 

laboratories do not volunteer for the study, then the direct cost for measuring these samples or blanks 

ranges from a few dollars per sample to more than $1,000 per sample for some analytes. Until the 

relationship between measurement results and standard concentrations becomes well known, such studies 

will require the active participation of professional statisticians in design, implementation, and analysis. 

3.2.5.2 Implementation of a Detection/Quantitation Limit Procedure by a Laboratory 

A laboratory may implement detection or quantitation procedures for its own quality control 

purposes, because of regulatory requirements, or to participate in a round robin study for a VCSB or some 

other organization. When participating in the study of another organization, the laboratory may 

3 - 20	
voluntarily accept some cost of the study for marketing purposes, professional development, or to 

benchmark the performance of the laboratory. 

In each case, a detection or quantitation limit approach will be of little utility if it is not capable of 

being implemented by the laboratory. An advantage of straightforward approaches such as the EPA 

MDL, the ACS limit of detection, and the ISO/IUPAC critical value is that they can, in principle, be 

understood by analysts expected to use the approach. Likewise, the procedures described for 

implementing the MDL approach are straightforward and have been implemented by thousands of 

laboratories. In contrast, the ASTM IDE and IQE procedures are highly complex and, as a consequence, 

are beyond the capability of most environmental testing laboratories. 

Highly complex procedures are usually more costly to implement than simple procedures. As 

noted in Section 3.1.5, method performance generally improves over time. This means that a detection 

and quantitation limit approach should be supported by procedures that will allow individual laboratories 

and other organizations to affordably characterize such improvement. Mandating interlaboratory studies 

using complex detection and quantitation procedures means that laboratories lacking statistical support 

staff, and seeking to develop new techniques or modify existing techniques to achieve improved 

measurement sensitivity would have to rely on, and perhaps even pay, other laboratories to demonstrate 

the sensitivity of their procedures. This limitation has the effect of hindering method development and 

improvement. 

3.2.6	 Use of a pair of related detection and quantitation procedures in all Clean Water Act 

applications. 

In Section 3.2.1, we discussed several different applications for detection and quantitation limits 

under the Clean Water Act. To review, these applications are: 

• Method development and promulgation, 

• Method performance verification at a laboratory, 

• Technology-based effluent guidelines development, 

• Water quality-based effluent limits development, 

• Permit compliance monitoring, and 

• Non-regulatory studies and monitoring. 

In the 2003 assessment, EPA argued that although EPA could develop a separate detection and 

quantitation approach for each of these applications and attempt to define and evaluate each of these 

approaches in our re-examination of detection and quantitation approaches, the resulting matrix of 

applications and approaches would cause confusion for stakeholders, such as regulators, permittees, and 

the laboratory community. To minimize this confusion, EPA suggested that a single pair of related 

detection and quantitation procedures could meet the needs of all CWA applications. Some commenters 

disagreed with this approach and recommended that at least two distinct procedures should be used, one 

for method development and one for verifying laboratory performance. 

3.2.7 Accepting the Procedures of Voluntary Consensus Standards Bodies 

In February 1996, Congress enacted Public Law 104-113 (15 USC 3701), the National 

Technology Transfer and Advancement Act (NTTAA). This act directs "federal agencies to focus upon 

increasing their use of (voluntary consensus) standards whenever possible, thus reducing federal 

procurement and operating costs." The Act gives Federal agencies discretion to use other standards 

3 - 21	
except where the use of voluntary consensus standards would be "inconsistent with applicable law or 

otherwise impractical." 

The NTTAA is implemented by Federal agencies based on the policies described in Circular 

A-119 from the Office of Management and Budget (OMB). The current version of this OMB circular was 

published in the Federal Register on February 19, 1998 (63 FR 8546). Neither the NTTAA nor Circular 

A-119 require that agencies replace existing government standards with standards from a voluntary 

consensus standard body (VCSB). If EPA already has standards in place for detection and quantitation 

approaches, EPA is not obligated by NTTAA to replace these with VCSB standards. Although some 

stakeholders commenting on EPA’s 2003 assessment encouraged EPA to allow use of alternative 

procedures for determining detection and quantitation levels, commenters in general did not support 

eliminating continued use of the MDL or ML. 

Circular A-119 also discusses the effect of the policy on the regulatory authorities and 

responsibilities of Federal agencies. The circular states that: 

"This policy does not preempt or restrict agencies' authorities and responsibilities to 

make regulatory decisions authorized by statute. Such regulatory authorities and 

responsibilities include determining the level of acceptable risk; setting the level of 

protection; and balancing risk, cost, and availability of technology in establishing 

regulatory standards. However, to determine whether established regulatory limits or 

targets have been met, agencies should use voluntary consensus standards for test 

methods, sampling procedures, or protocols." 

Thus, EPA is responsible for establishing the levels of risk and protection, not only for the regulatory 

limits applied to discharges, but also to the risks of decision errors (e.g., false negatives or false positives) 

in the detection and quantitation approaches applicable under the Clean Water Act. 

Finally, Circular A-119 describes two types of technical standards: performance standards and 

prescriptive standards. A performance standard is defined as: 

"a standard ... that states requirements in terms of required results with criteria for 

verifying compliance but without stating the methods for achieving required results." In 

contrast, a prescriptive standard is one "which may specify design requirements, such as 

materials to be used, how a requirement is to be achieved, or how an item is to be 

fabricated or constructed." 

Neither the NTTAA nor Circular A-119 direct agencies to favor performance standards over 

prescriptive standards, or vice versa. EPA believes that the current MDL procedure is a prescriptive 

standard, in that it specifies both the design of the MDL study and how the requirement to establish 

method sensitivity be achieved. There is some obvious flexibility or opportunity for judgement in 

employing the MDL procedure, and much of the historical debate over the utility of the MDL procedure 

would suggest that it may not be prescriptive enough. The alternative detection and quantitation 

approaches evaluated in this document, including the approaches submitted by commenters on the 2003 

assessment, also are prescriptive, not performance, standards. 

To effect a performance-based approach to estimating detection and quantitation limits, an option 

that EPA may consider is to allow method developers, laboratories, and others the choice of any one of a 

variety of approaches to establishing these limits, including the existing MDL procedure or a VCSB 

3 - 22	
standard. Thus, establishing method sensitivity could be considered a performance standard under 

NTTAA and Circular A-119, rather than a prescriptive standard. That these different approaches 

(prescriptive standards) yield different answers would be immaterial if EPA evaluates the answers relative 

to a specific decision, i.e. the benchmark becomes a performance rather than a prescriptive standard. That 

evaluation should not be divorced from knowledge of the decision to be made (e.g., the regulatory limit 

for a given pollutant). 

3.2.8 Alternative Procedures 

One of the peer reviewers who evaluated a draft version of the February 2003 assessment 

document noted that: 

"EPA has stated in the TSD that one primary procedure is needed for clarity and to avoid 

confusion among stakeholders. If alternate procedures are needed, the EPA Clean Air Act 

system of reference and equivalent methods has worked well, and could be a model for 

EPA to follow under the Clean Water Act." (Cooke, 2002) 

EPA currently assesses and has approved at 40 CFR Part 136 methods that employ an alternative 

procedure for establishing method sensitivity. This approval process includes an overall evaluation of the 

suitability of the method in entirety and thus includes the detection or quantitation approach used to 

establish the performance specifications listed in the method. 

The peer reviewer is referring to the system of reference methods used under the Clean Air Act. 

This system is similar to the existing "alternate test procedure" (ATP) program for analytical methods 

currently used within the Office of Water. The difference between the ATP program and the case of the 

procedures for establishing detection and quantitation limits is that in an ATP program, the goal is clear 

and agreed upon (i.e. is a method appropriate for CWA applications), whereas there remain fundamental 

theoretical issues surrounding the relative merits of the various detection and quantitation approaches that 

are the subject of this document. 

For example, when a test procedure is developed for use in the Clean Air Act or Clean Water Act 

programs, the reference method is designed to measure Analyte X, in Matrix Y, at some concentration 

related to a regulatory need (e.g., a compliance limit) or environmental study. Alternative procedures may 

be capable of making measurements of Analyte X in Matrix Y, at the level of concern, using completely 

different instrumentation. Thus, the demonstration of equivalency between the reference method and a 

possible alternative method is judged using a metric that consists of Analyte X, Matrix Y, and the level of 

concern, as well as other aspects of method performance. 

In contrast, the primary differences between the EPA MDL/ML approaches and potential 

alternatives such as the ASTM IDE and IQE are related to the philosophical differences of how detection 

and quantitation limits should be derived and applied. These differences are described at length in this 

chapter and the rest of the Assessment Document. Therefore, EPA does not believe that a variant of 

existing ATP programs is likely to be an effective model for assessing other detection and quantitation 

procedures. 

A stakeholder commenting on EPA’s 2003 assessment recommended that EPA adopt alternative 

procedures in Appendix B of 40 CFR 136 as site-specific alternatives to the MDL and ML when such an 

alternative is determined to be necessary by a discharger and/or regulatory agency (e.g., in special cases 

when more scientifically rigorous procedures are needed). As noted previously, EPA has reviewed and 

3 - 23	
approved at 40 CFR Part 136 methods that employ an alternative procedure for establishing method 

sensitivity as part of an overall evaluation of the suitability of the method. EPA has done so without need 

of any revisions to appendix B at 40 CFR part 136. 

3.3 Statistical Issues 

This section provides a brief explanation of the key statistical issues involved in the development 

of detection and quantitation limits. 

3.3.1 Sources of Variability 

Various known and unknown sources of variability will influence measurements made by a 

laboratory using a specific method. These sources may include random measurement error, differences in 

analysts, variations between different equipment manufacturers and models, variations in analytical 

standards, routine fluctuations in equipment performance, and variations in facility conditions (e.g., 

varying levels of background contributions). 

There are several ways in which some of these sources of variability can be controlled. One is a 

strong quality assurance (QA) program that includes use of: 1) trained and qualified staff, 2) properly 

maintained equipment, 3) standards that are fresh and properly prepared and stored, 4) written standard 

operating procedures and methods for all sample handling, analysis, and data reduction/reporting 

activities, 5) procedures for monitoring ongoing laboratory performance, and 6) quality control (QC) 

samples and QC acceptance criteria to ensure that the laboratory systems are in control. The EPA 

methods promulgated at 40 CFR part 136 require the use of qualified staff, appropriately cleaned and 

calibrated equipment, and properly prepared standards. Many of these methods also provide detailed 

steps for performing all sample handling and analysis activities, and detailed requirements for analysis of 

specific quality control samples with corresponding QC acceptance criteria. 

Even when prescribed EPA method requirements and guidance are used, however, it is not 

possible to completely eliminate all variability that can occur within or between laboratories. Even with 

procedures in place to control quality and reduce variability, it should be recognized that some 

laboratories, analysts, and instruments may achieve lower detection and quantitation limits than others. 

Ultimately, some laboratories may not be capable of meeting low-level measurement requirements without 

some effort to improve operations. 

Many of these sources of variability are considered in establishing detection and quantitation 

limits for analytical methods used under EPA's Clean Water Act programs because these detection and 

quantitation limits are first established in single-laboratory studies, then evaluated or verified in multiple 

laboratories, and, where necessary, further evaluated in an interlaboratory study. These studies include 

evaluation of method performance characteristics, including detection and quantitation capabilities, in 

multiple laboratories using multiple matrices, analysts, instrumentation, reporting activities, standards, and 

reagents. Although detection and quantitation are not evaluated in the various matrices used in these 

studies, EPA’s MDL procedure includes instructions for determination matrix-specific MDLs. 

Some stakeholders commenting on EPA’s assessment of approaches to detection and quantitation 

believe that accounting for these sources of variability when determining detection and quantitation limits 

is necessary because relative variability increases as the lower sensitivity limits of a method are 

approached. Some stakeholders believe, for example, that a methodology for detection and quantitation 

has to address the variability that occurs across laboratories (interlaboratory variability) using the same 

3 - 24	
analytical method. Other stakeholders believe, however, that interlaboratory variability is not an issue 

because detection and quantitation decisions are made in a single laboratory. Some stakeholders believe 

that procedures should address the long-term variability that can occur within a single laboratory over 

time. As discussed in section 3.1.5 of this chapter, EPA encourages, where appropriate, gathering data to 

address temporal variability. EPA acknowledges that interlaboratory variability is very important during 

the methods development process and should be incorporated, as appropriate, during the process. EPA 

also recognizes that within lab variability should be considered when establishing laboratory performance. 

Over the years, stakeholders have noted that the variability that can result from application of 

analytical methods to different matrices also should be addressed by procedures for determining methodspecific detection and quantitation limits. However, it has been EPA's experience that matrix effects 

typically can be overcome using various sample processing procedures. In EPA's interlaboratory 

validation studies of the 600-series wastewater methods, the recoveries of some organic analytes from 

real-world matrices were closer to 100% than were recoveries from a reagent water matrix. This effect is 

thought to be attributable to dissolved solids in the real-world matrix that, in effect, "salt out" the organic 

compounds. EPA does not believe it is appropriate or feasible to aggressively pursue matrix effects in 

establishing detection and quantitation limits (i.e., EPA has not attempted to find worst-case matrices in 

order to maximally exacerbate matrix effects). Instead, EPA considers the type of matrices that would be 

regulated under the Clean Water Act (e.g., the effluents that are discharged from properly designed and 

operated secondary treatment plants). Further discussion of matrix effects can be found in Section 3.1.3. 

Because detection and quantitation limits focus exclusively on the capabilities of the measurement 

process, a source of variability that is not considered in any of the detection and quantitation limits is the 

variability that is associated with sample collection. If the sample is not representative of the population 

from which it was collected, then the variability associated with measurements made in the region of 

detection or quantitation may be immaterial. For example, EPA’s Technology Innovation Office 

conducted a study to characterize the effects of sampling variability on measured results. In that study, 

results from seven discrete samples collected within a two-foot distance of one another were evaluated. 

Each sample was analyzed for the explosive TNT on-site using a colorimetric test kit, and in a laboratory 

using EPA SW-846 Method 8330 (high-performance liquid chromatography). Analysis of the results 

from these measurements indicated that 95% of the total variability was due to sampling location and only 

5% was due to differences between the analytical methods. Put another way, differences in sampling 

location caused 19 times more uncertainty in the data results than did the choice of analytical method, 

over a distance of only 2 feet (Crumbling, 2002). While this result may not be typical, and EPA does not 

mean to diminish the importance of understanding measurement error in the region of detection and 

quantitation, EPA believes it is important to understand it in the context of the overall sampling and 

analysis error. 

3.3.2 False Positives and False Negatives 

3.3.2.1 False Positives and False Negatives in Making Detection Decisions 

In this section, we discuss the impact of detection, quantitation, and reporting levels on false 

positive measurement results and false negative measurement results. The definitions of false positives 

and false negatives are directly related to the concepts of critical value and detection limit used by Currie 

(1995). These terms were adapted from statistical decision theory to establish the framework for decision 

making with regard to detection of analytes. The critical value (Lc), as defined by Currie, is the point at 

which the detection decision is made. That is, measured values that are less than the critical value are 

judged to be not statistically different from blanks ("not detected"). Measured values that are no less than 

3 - 25	
the critical value are judged to be statistically different from the blanks ("detected"). Denoting measured 

values that are less than the critical value as non-detects constitutes censoring and is discussed in more 

detail in Section 3.3.5. 

The critical value is defined such that when the analyte is not present in a sample, there is a small 

possibility that a measurement will exceed the critical value. A measurement that indicates the critical 

value has been exceeded is, therefore, the result of one of two circumstances: (i) the analyte is present in 

the sample; or (ii) the analyte is not present in the sample and, by chance, the measurement has exceeded 

the critical value. The occurrence of (ii) is defined in statistics as Type I error (“false positive”). A 

measurement that is less than the critical value occurs when: (iii) the analyte is not present in the sample; 

or (iv) the analyte is contained in the sample at the hypothesized concentration but the measurement 

procedure fails to indicate its presence. The occurrence of (iv) is defined as the Type II error ("false 

negative"). 

Decision 

The f mar le situations: 

State of the Sample 

Concentration=C, 

where C>0 

Analyte Present 

Concentration=0 

Analyte Not Present 

ollowing table sum izes possib

Concentration=C, Correct (i) Type I error (ii) 

where C>0 

Concentration =0 Type II error (iv) Correct (iii) 

Calculating the probability of a Type I error only requires assumptions regarding the distribution 

of observations under the hypothesis that the concentration is equal to zero. In the terminology of 

statistical decision theory, Concentration = C, where C>0 corresponds to a true value is referred to as the 

?Alternative Hypothesis” (see, e.g. Introduction to Mathematical Statistics, by Hogg and Craig, 5th 

edition, [1995]). When C is hypothesized, assumptions need to be made about the distribution of 

observations at Concentration=C for the probability of Type II error to be evaluated. 

In analytical chemistry, the probability of Type I error is often called the “false positive” rate and 

the probability of Type II error is often called the “false negative” rate. The statistical alternative 

hypothesis should be specified before introducing the false negative rate. An error common to some 

published discussions of false negative rates and detection and quantitation concepts is to state that use of 

Currie's detection limit as a reporting limit or action level will somehow ?control” the rate of false 

negatives. This is both incorrect and counter-productive, because a single level cannot control false 

negative rates. 

Currie introduced the idea of a Detection Limit, Ld, in place of a statistical alternative. The 

Detection Limit is not a part of the detection decision process (i.e., is the concentration in the sample 

statistically different from the blank?). The Detection Limit is defined such that when the true 

concentration of an analyte is equal to the Detection Limit, there is a small probability that a measured 

value will be less than the Critical Value (detection decision-making level in this case), and thereby result 

in the false negative decision. 

3 - 26	
One of the peer reviewers of EPA’s 2003 Technical Support Document (the TSD) stated: 

“Also, to reemphasize, the single most problematic issue when developing a detection 

limit is correction for false negatives. I took from the TSD (in §3.3.6) an implicit 

emphasis on LC-type values such as the MDL [when correctly calculated, as in (1)], as 

motivated by an underlying sort of practical/environmental conservatism that essentially 

removes false negatives from the estimator’s development. I am willing to accept this 

interpretation. I suspect the fray will continue, however, since there seems to be a fair 

amount of confusion on the issue in the analytical chemistry literature. The bottom line 

from my reading of the TSD is that, in effect, we are calculating an LC, but using 

terminology that makes some readers think it’s an LD. I can accept the argument that 

false negative errors are not the critical issue here, and hence that the approach is 

reasonable (once correct calculations are undertaken). But, the Agency should put forth 

an effort to overcome this confusion in terminology. (I expect they will ask me how, and 

in reply I’d suggest emphasizing that an LC calculation is a form of decision limit, not a 

detection limit. But here I suspect many users will still confuse the terms, or reverse 

their meaning, or not see the difference, or who knows what else? I don’t know how 

winnable this battle is...) “ (Piegorsch, 2002) 

To illustrate the intent of Currie's detection limit, consider a case where the detection decisionmaking level is set equal to Currie's critical value, and a sample is spiked at a true concentration equal to 

Currie's detection limit. Given a large number of measurements on this sample, about 99% of the 

measurement results will be reported as being measured above the detection decision-making level, and 

1% of the measurement results will be reported as being measured below this level. Knowledge of the 

lowest true concentration that will routinely produce acceptable results (e.g., Currie's detection limit) can 

be used to determine if the measurement method meets the needs of a study. For instance, a study 

concerned with a wastewater treatment technology that is not expected to be effective at concentrations 

below 10 mg/L may call for a relatively inexpensive measurement method capable of detecting the analyte 

at 10 mg/L, rather than a more expensive measurement method capable of measuring a hundred times 

lower. 

3.3.2.2: Effect of Bias on Rates of False Positives 

The presence of bias in a method can have a strong effect on the rate of false positives associated 

with detection limit estimates. For example, in defining the critical level, Currie assumed that blank 

results follow a Normal distribution centered about zero (0). However, for some methods and analytes, 

this assumption may not hold due to factors that can and should be controlled, such as calibration errors 

and high background contamination. In many cases, bias can lead to either under- or over-estimation of 

detection limits. In cases such as these, not taking bias into account when determining detection and 

quantitation limits (using the mean or median of the results, for example) may influence false positive 

rates. 

3.3.3 Use of Multiple Replicates 

Existing detection/quantitation procedures are based on estimating the standard deviation of blank 

or spiked replicates. Statistical estimates tend to be less variable when the number of replicates increases. 

Some commenters on EPA’s 2003 assessment believed that use of only seven replicates over a short 

period of time results in a substantial underestimation of the MDL. However EPA’s MDL procedure does 

not limit the maximum number of samples that the laboratory may use to estimate the MDL; the procedure 

3 - 27	
simply sets a minimum number of seven replicates. Laboratories may choose to improve their estimates 

of the standard deviation that is used to calculate the MDL by analyzing more than seven replicates. 

3.3.4 Statistical Prediction and Tolerance 

To define a critical value, a detection limit, or a quantitation limit, different descriptive 

terminology is used to distinguish differences in the numeric value of the limit. The following example 

uses a critical value, but the questions motivating detection and quantitation limit decisions may be 

phrased in a similar fashion. 

In setting a critical value, do we want a critical value that tells us how likely it is that: 

• A measurement result was produced by measuring a blank sample, 

• The next measurement result will be produced by measuring a blank sample, or 

• The next [pick any number] of measurement results will be produced by measuring a blank sample? 

In statistical terms, these three objectives may be addressed, respectively, by application of methodology 

for determining: 

• Percentiles; 

• Prediction intervals; and 

• Tolerance intervals. 

Percentiles are fairly straight forward to interpret, i.e., they specify the percentage of a distribution 

that falls below a given percentile value. Prediction and tolerance intervals are, in effect, confidence 

intervals on percentiles and can be somewhat more difficult to understand and apply. There are many 

excellent textbook and literature references that present the theory and application of prediction and 

tolerance intervals such as Hahn and Meeker, Statistical Intervals, 1991, and Pratt and Gibbons, Concepts 

of Non-parametric Theory, 1981. Hahn and Meeker describe at length the different statistical intervals 

including their properties, applications, and methodology for constructing the intervals. Pratt and Gibbons 

have an excellent discussion of tolerance intervals that is general in application due to the non-parametric 

perspective, i.e., no distributional assumptions are required for the results to be valid. 

One of the peer reviewers of EPA's 2003 assessment stated: 

“Tolerance intervals are inappropriate for environmental monitoring. The main issues 

here are 1) is the true concentration greater than some specified safe action level, with 

sufficient confidence, and 2) what interval of possible concentrations is consistent with 

one or a series of measurements, with a specified degree of confidence? Both are 

statements about a given sample or series of samples, and not about the hypothetical 

variability of future estimates. Suppose that one has a sample of 10 observations with 

mean concentration of 1 ppb and standard deviation of 0.5 ppb. Then the estimated 99% 

critical level is (2.326)(0.5) = 1.2 ppb. One may choose to use a t-score instead of a 

normal score so that the chance that a future observation will exceed this level is in fact 

99%. In this case, the critical level estimate would be (3.250)(0.5) = 1.6 ppb. This does 

actually correspond to a prediction interval for future observations from a zero 

concentration sample. 

3 - 28	
“If one asked instead for a 95% confidence interval for the .99 percentage point of the 

true distribution of measurements (assuming normality) when the true quantity is zero, 

this can be calculated approximately using a chi-squared distribution and covers the 

interval (0.9 ppb, 2.4 ppb). It does not, however, make sense to use 2.4 ppb as a 

threshold, since the chance of a future observation exceeding 2.4 ppb when the true mean 

concentration is 0 is about .0005, far smaller than the intended false-positive limit of 

.01.” (Rocke, 2002) 

Another of the peer reviewers of this assessment stated: 

"the operational definition as taken from pp. 5-2/5-3 of 

MDL = t 0.99 (df) S 

does not correspond to a confidence statement that I can interpret.... This should be 

replaced, although I agree that a number of statistical quantities could be used; this is 

where the “fray” seems to be most boisterous. ay, the TSD, and I, should be 

more careful in the use of statistical terminology. We both refer often to confidence 

“intervals,” when in fact the quantity of interest is a confidence limit — or tolerance 

limit, etc. — on some underlying parametric quantity.)... 

"If we accept the TSD’s argument on p. 3-25 that the practical value of tolerance limits is 

limited, then the MDL should be viewed as a prediction limit. if so, it must contain 

an additional term as per Gibbons (1994, p. 98): 

"One caveat: I think the prediction limit argument is acceptable, if the use of 

tolerance limits rather than prediction limits is in fact desired, then Gibbons’ (1994, p. 

99) presentation or an equivalent approach should be used instead to correct the MDL 

calculation." (Piegorsch, 2002) 

Similarly, Hahn and Meeker describe situations in which the various intervals or limits are 

appropriate to use. s noted by the peer reviewer, the terms ?intervals” and ?limits” are sometimes used 

interchangeably). They also give examples of the sort of applications that are suitable for each type of 

limit although the decision to use a particular type of limit in a given application is not determined strictly 

by theoretical considerations. is also a matter of judgment. 

Prediction intervals contain results of future samples from a previously sampled population with a 

specified level of confidence. its are not estimators of parameters such as means or 

percentiles. or example, a prediction interval may be constructed to contain future sampling results 

expressed as a mean or standard deviation of a future sample or all of a certain number of individual 

future sampling results. 

(By the w

And

although 

(A

It

Prediction lim

F

3 - 29	
While the theoretical construct underlying Currie’s critical level is clear and straightforward, EPA 

recognizes that estimating this level from limited data is less straightforward and the choice of an 

appropriate statistical methodology involves policy judgements that might legitimately differ for different 

uses of the MDL. 

3.3.4.1 Tolerance Intervals 

Tolerance intervals contain a specified proportion of a population of measured values with a given 

statistical confidence level. For example, we say that a proportion, P, of a population is contained within 

the intervals (L1, L2) with (1-�)100% confidence. Random variables that are the lower and upper ends of 

the interval, L1 and L2 , respectively, are referred to as tolerance bounds. A tolerance bound is therefore 

the endpoint of an interval of random length that is determined on the basis of having a specified 

probability of 1-� that its coverage of the population is at least equal to a specified value P. The quantity 

1-� is referred to as the confidence level for the interval and P is the minimum proportion of the 

population contained in the interval. Tolerance bounds are not estimators of values such as a mean or a 

percentile but rather bounds that are always guaranteed to contain the desired value at some level of 

statistical confidence. Pratt and Gibbons discuss this and other properties that affect the utility of 

tolerance intervals and create difficulties in their interpretation and application. 

In effect, the determination of what, if any, interval to use is a policy decision. The choice should 

consider how easy it is to estimate the interval you want under the conditions that exist. As Pratt and 

Gibbons point out, the interpretation of tolerance intervals (and analogously, prediction intervals) can be 

problematic, especially when issues of sample size and the choice of confidence level come into play. 

Pratt and Gibbons cite examples where the interplay of sample size and high percentile and confidence 

levels make tolerance intervals useless. 

3.3.4.2 Use of Tolerance and Prediction in Setting Detection and Quantitation Limits 

Statistical intervals can be, and have been by a number of authors, adapted for use in setting 

detection and quantitation limits. The basic approach requires a functional definition of detection or 

quantitation that includes a statistical term or terms. An interval could then be constructed about the 

statistical term which could be used to assess the detection or quantitation limit, or make an adjustment to 

a calculated value that would result in the detection or quantitation limit. For example, most detection 

limit estimators are functionally dependent on an estimate of standard deviation of measurement error. A 

statistical interval could be constructed about the standard deviation and the length of the interval could be 

used to assess the detection limit. The end points of the interval could be used as the basis for an 

adjustment (upward or downward) in the calculated limit. 

The error rates in ASTM’s IDE Standard Practice are based on statistical tolerance intervals (i.e., 

the nominal Type 1 error rate is 5% (5%=100%-95%), and the nominal Type 2 error rate is 10% (10% = 

100%-90%)). Several stakeholders have commented that the use of a tolerance interval approach can 

protect, at a 99% level of confidence, against false positives and false negatives, and that tolerance 

intervals become increasingly important with a decreasing sample size. For example, if the sample 

standard deviation is determined with 7 measurements and all sources of variance are properly represented 

in the 7 measurements, then there is approximately a 5% chance that the true population standard 

deviation will be more than two times the sample standard deviation. For a typical ICP determination of 

20 or more elements this means that at least one is likely to have a calculated MDL two times lower than 

it should be. Obviously the false positive rate for this element will be large. 

3 - 30	
The use of prediction and/or tolerance limits in setting detection and quantitation limits should be 

evaluated in the context of the specific application and policy considerations. In practice, the effect of 

adjustment of detection and quantitation limits by use of prediction and tolerance intervals can be quite 

large, depending on the amount of data available and the choices of percentiles and confidence levels. 

3.3.5 Censoring Measurement Results 

Measurement results are often reported as less than some detection, quantitation, or reporting limit 

(see Section 3.2.1.3, Permit Compliance Monitoring) without providing a single best estimate for the 

numeric result. For example, if a direct reading of the measurement results indicates a concentration of 3 

mg/L and the reporting limit for the substance is 5 mg/L, the laboratory may only report that the 

measurement result is less than 5 mg/L. Statisticians call this suppression of results that are less than a 

specified amount “censoring.” Reasons for the practice of censoring relate directly to issues surrounding 

the development of detection and quantitation limits (i.e., the premise that measurement results below 

certain low levels may not be useable for certain purposes). 

Some data users prefer to use the actual measurement results (even if they are negative values), 

rather than to censor the results at a reporting or detection limit, because censoring data at such a limit 

loses information about low-level measurements and can introduce bias into the data set. If all low values 

are eliminated, then the average (mean) of the remaining data would have a positive bias. In other words, 

while negative or extremely low values may be considered problematic by some, they are of value to 

statisticians and modelers, because they convey useful information about the distribution of results. 

Some programs, such as EPA's Superfund Contract Laboratory Program, require laboratories to 

report measurement results in conjunction with a qualifier that the result is below a specified detection, 

quantitation, or reporting level. In the example provided in the first paragraph of this section, the 

laboratory might report both a measured value of 3 mg/L and a reporting limit of 5 mg/L. Under certain 

assumptions, measurement results below the specified reporting level could then be used to calculate 

averages and statistical estimates that would be superior to estimates calculated using censored data. 

Although the Superfund approach provides the greatest degree of flexibility for data users, it 

should be used with care. First, data users who choose to use values reported below a detection or 

quantitation limit need to have a firm understanding of the limitations of those data. Second, and as noted 

in Section 3.2.1.3, Permit Compliance Monitoring, reporting data below a detection or quantitation limit 

can lead to misinterpretation. 

One of the peer reviewers that evaluated EPA’s 2003 assessment of detection and quantitation 

limit approaches noted that European Union (EU) has adopted another variant for reporting or censoring 

data. 

"In this case, the EU has adopted EPA Method 1613B (for analysis of dioxins and furans) 

as well as EPA’s MDL approach. However, the EU has further specified that the MDL be 

used as an Upper Bound reporting limit where all non-detects are found in the analysis of 

human or animal foodstuff. This forces laboratories to achieve levels available with 

modern instrumentation, otherwise, the Upper Bound reporting level is above the 

regulatory compliance level, and the data (or foodstuffs) are rejected" (Cooke, 2002). 

3 - 31	
EPA agrees that this approach, which yields a ?worst-case” (or highest possible) estimate of the 

pollutant concentration, can serve as an incentive to the analytical and regulated community to pursue 

measurements at the lowest levels which analytical methods are capable of achieving. However, EPA also 

cautions that this approach effectively censors measurements made below the MDL and could yield an 

overestimate of the concentration of the analyte of concern. 

Several stakeholders have requested that EPA provide specific guidance and procedures regarding 

data censoring and reporting, particularly when data are reported for compliance evaluation. EPA notes 

that the decision to censor data is a data reporting and data use policy issue, not a laboratory issue. This 

holds without regard to what detection or quantitation limit approach is used. The EU approach reflects a 

similar point of view, in that it relies on the MDL as a detection approach, but also establishes this limit as 

the reporting level for non-detects to better encourage development of lower MDLs. However, EPA also 

recognizes that laboratory methodologies and data reporting and use policies are interrelated. 

3.3.6 Outliers 

Outliers are extreme or aberrant measurement values that, on inspection, do not follow the 

characteristics of a set of data. Outliers may be generated by a number of causes, such as errors in 

following an analytical procedure, errors in recording results, or the result of extreme random variation in 

a properly operating process. For example, if a new measurement method is being tested but the 

laboratory fails to follow the procedure correctly when analyzing some samples, the associated 

measurement results may stand out as outliers. A graphic example is provided in Figure 3-1, which shows 

measured concentrations of aluminum versus spike concentrations for analytical results obtained using 

EPA Method 1620. At a spike concentration of 250 �g/L, one of the measured values is approximately 

750 �g/L. This result visually stands out from the rest of the values, and may be an outlier. 

3 - 32	
Figure 3-1 

Stakeholders commenting on EPA’s assessment of detection and quantitation procedures 

generally believed that outliers should be identified and removed from data used to determine detection 

and quantitation limits. Commenters added that, although it would be helpful to have specific instructions 

for identifying outliers, application of the instructions should be optional (i.e., to the discretion of the data 

user). 

A common process for identifying potential outliers is to apply one or more statistical procedures 

for identifying values far from the mean (average) of the data. An example of such a procedure is 

described in ASTM Practice D-2777. 

Because extreme values can be expected to occur on occasion, it may not be appropriate to 

exclude them from the results used to develop detection or quantitation values. As recommended in the 

ASTM procedure, the first step is to contact the laboratory to try to determine and resolve the cause. A 

review of the analyst's records associated with the measurement may establish whether the extreme value 

was caused by failure to follow the method or by some rare event associated with the method. If the 

method under study was not followed, or there is a known or suspected analytical error, it is appropriate to 

exclude the measurement result from the detection or quantitation analysis. If the measurement result is a 

rare event associated with the method under study it may also be appropriate to exclude the measurement 

result from the results in the study. EPA believes that results that are associated with spurious errors that 

cannot be corrected will invalidate the measurement and should not be incorporated into the MDL 

3 - 33

determination. 

3.3.7 Detection and Quantitation Studies 

3.3.7.1 Study Design 

The issues associated with the design of detection and quantitation studies include: 

•	 how effectively a selection of spike concentrations can be used to correctly determine which 

model type should be used to model variability, 

•	 the extent to which the distance between spike concentrations can impact estimates of detection 

and quantitation limits, 

•	 how to reduce the influence of uncontrollable factors in the measurement process (probability 

design), 

• how complete to make the design factors in terms of the physical measurement process, and 

• how flexible to make the design factors in terms of the physical measurement process. 

Spike Concentrations and Modeling 

If a model under consideration cannot be described by the number of spike concentrations in the 

design, then it is not possible to tell if the model is appropriate. To take the simplest example, it is not 

possible to describe the slope of a line associated with linearly increasing variation from a single spike 

concentration. Two well-spaced spike concentrations would allow you to estimate a slope, but would 

provide no idea of the variability of the estimate. Three well-spaced spike concentrations represent the 

minimum requirement for estimating the linear relationship and the variability of that relationship. 

Clayton et al. (1987) describe the relationship between the spread of the spike concentrations, the 

number of spike concentrations, and the number of replicate measurements with regard to estimated 

variability when a linear model is used. While the specific equation used in this paper does not apply to 

all models, it indicates principles that do apply. Increasing the number of replicate measurements and 

reducing the spread of the spike concentrations are all expected to reduce estimated variability along with 

the associated detection and quantitation limits. However, one of the components of variability associated 

with detection and quantitation is that associated with estimating the calibration relationship. To account 

for this source of variation, it may be appropriate to cover the entire calibration range. On the other hand, 

many replicates at a high concentration may improperly weight the data in favor of high detection and 

quantitation estimates. 

It is also important to note that modeling of variability introduces modeling error, and direct 

measurements of the variance in the region of interest may provide a more appropriate estimate of 

variability, especially where the change in variance over this region is small. 

3 - 34

Probability Design 

The process known as randomization is an important statistical consideration in the design and 

interpretation of experimental studies. Randomization involves the allocation of experimental units to 

factors and treatments under study according to a design determined by probability. Randomization 

avoids bias and systematic errors that can occur in studies where randomization is not used. 

Randomization is discussed in classic texts such as Statistics for Experimenters, by Box, Hunter, and 

Hunter (1978). 

In studies of measurement methods, randomization can be used in the process of creating spike 

concentration solutions and the ordering of analyses. However, randomization has practical drawbacks, 

particularly with regard to studies designed to establish detection or quantitation limits. For example, 

consider a simple study involving the analyses of samples spiked at five concentrations of the analyte of 

interest, with five replicate samples analyzed at each concentration. A total of 25 analyses are required 

for the study, and the analyses of the samples can be organized in a 5 by 5 matrix. A random number is 

assigned to each block in the matrix as a means of randomizing the order of the replicates at each 

concentration. 

By virtue of this randomized design, a sample with a high concentration of the analyte of interest 

may end up being analyzed immediately prior to a sample with a very low concentration of the analyte. 

Unfortunately, this can lead to problems that result from the "carry-over" of analyte within the 

instrumentation from one analysis to the next. When carry-over occurs, the apparent concentration of the 

low-concentration sample can be inflated because some of the high-concentration sample l may be carried 

into the low-concentration sample 2. In the context of a study designed to establish ?how low you can go” 

(i.e., establishing a detection limit), carry-over of the analyte into a low-concentration sample may 

compromise the results by inflating the result for low-concentration sample 2, but not inflating the results 

for other low-concentration samples because the randomized design did not cause them to be analyzed 

immediately following a high-concentration sample. 

Analysts are aware of the potential for carry-over and generally take steps during routine analyses 

to minimize the chance that it will occur. Examples of steps that can minimize carry-over problems 

include analyzing ?cleaner” samples before ?dirtier” samples, and interspersing ?blanks” between samples 

when possible or practical. Obviously, the intentional segregation of low and high concentration samples 

defeats the purpose of the randomized design. Interspersing blanks between the samples can be effective, 

as well as blocking similar concentrations together and randomizing blocks. But in order to ensure that 

the blanks do not have other effects on the results, blanks would be needed between each sample or block 

analysis, and this would greatly increase the cost of the study (e.g., 25 samples and 24 blanks would be 

required in case of pure randomization). Although this was done for the Episode 6000 study, this 

approach would not be practical in most cases. Therefore, despite the statistical benefits, in practice, 

randomization of the sample analysis sequence can be difficult to apply in detection and quantitation limit 

studies. 

In the Agency’s studies of variability as a function of concentration discussed in Sections 1.3.2.1 -

1.3.2.3 of this document, EPA chose to use a non-random design to avoid carry-over problems and to limit 

the potential difficulties with measurements at very low concentrations. For example, if there was no 

instrument response at concentration X, then it would be unlikely that there would be a response at a 

concentration of X/2. In the non-random design, EPA permitted the analyst to stop analyses of everlower concentrations, whereas a randomized design would have required that all the samples be analyzed, 

even when there was no instrumental response for many of those samples. 

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One of the peer reviewers evaluating EPA’s 2003 assessment commented that the effects of carryover could have been mitigated by studying variability around the calibration line rather than the mean of 

the replicates. However, carry-over affects subsequent samples differently. The effect of the carry-over 

cannot be mitigated, regardless of whether variability is studied around the calibration line or the mean of 

the replicates, unless the amount of carry-over is known and can be subtracted from the affected (lowconcentration) sample. This subtraction has limitations because of error accumulation and because the 

amount of carry-over cannot be determined precisely without extensive studies at multiple concentrations. 

Completeness 

The physical measurement process can be studied using rough approximations or it can be studied 

more rigorously. A rough approximation could use the available components of a method as applied to 

convenient samples. A more rigorous study would use a complete, specific, and well-defined 

measurement method with all sample processing steps. The appropriate level of study will probably 

depend on the purpose of the study. 

Measurement procedures (methods) may be more or less strictly designed. Variability in what is 

allowed in the procedures may add to variability in the measurement results. To the extent that 

permutations of a method’s procedures are not expected to be used in a particular detection or quantitation 

study, EPA recommends that this information be included in the report on the study results. While there 

may be physical/chemical reasons for extrapolating the results of a variability study on one set of 

procedures to permutations of those procedures, there is no statistical basis for making such an 

extrapolation. Statistical theory by itself is only able to describe conditions that have been observed. On 

the other hand, a knowledge of the underlying physics of the measurement process can guide the 

completeness of the modeling process when statistical procedures fail. For example, the Rocke and 

Lorenzato model in the linear or log-log domain may be the best general characterization of a physical 

measurement process. Therefore, this model can be applied to data to produce a complete answer when 

statistical procedures fail to deduce the "correct" model. 

3.3.7.2 Criteria for the Selection and Appropriate Use of Statistical Models 

Detection and quantitation limits may be based on statistical models of the relationship between 

measurement variation and the concentration of a substance in the sample. Results are produced by 

adding varying known amounts of the substance to the sample (“spiking”), making replicate measurements 

at each concentration, and modeling the variability of the results as a function of concentration. This 

section summarizes the history of modeling variability versus concentration, considers criteria for 

selecting models, and discusses current practices with regard to available data. 

3.3.7.2.1 Short History of Modeling Measurement Results 

Over time, a number of different models have been used to estimate measurement variation. 

Currie (1968) modeled variation in radiochemical measurement methods using a procedure associated 

with counting large numbers of distinct objects which are appropriately modeled with the Poisson 

distribution. However, he relied on large sample sizes and standard normal distributions to describe all 

other types of measurement methods. Hubaux and Vos (1970) developed a procedure based on an 

estimated calibration relationship that uses smaller sample sizes to estimate Currie’s detection and 

quantitation limits. Again, measurement results were assumed to follow standard normal distributions, but 

it was also assumed that measurement variation was constant throughout the range of interest. Similarly, 

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Glaser et al. (1981) suggested that measurement variation increases linearly with concentration, but they 

did not provide estimators under this theory because they believed that measurement variation is usually 

approximately constant in the range of detection. Glaser et al. (1981) did suggest that, when appropriate 

data were available, a linear regression analysis of the relationship over the analytical range be performed. 

Clayton et al. (1987) discussed transforming the measurement results (using logarithms or square root 

functions). Gibbons et al. (1991) suggested that measurement variability may be proportional to 

concentration. Rocke and Lorenzato (1995) proposed a model motivated by physical characteristics of 

measurement processes, in which measurement variability is approximately constant at low 

concentrations, but changes in a continuous mathematical manner to a relationship where variability 

increases as concentration increases. 

Figure 3-2 illustrates the fundamental analytical measurement models in linear and logarithmic 

domains. The models are applicable to nearly all analytical measurements; we will not deal with the 

exceptions because they represent a small percentage of cases. As can be seen from the top two graphs, 

response is a linear function of concentration in both the linear and log domains. The middle two graphs 

and the bottom two graphs are those most pertinent to the discussion of detection and quantitation. 

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Figure 3-2 

3.3.7.2.2 Detection Limits Using Variability at Low Concentrations 

The middle two graphs in Figure 3-2 show variability versus concentration and show the model 

postulated by Rocke and Lorenzato. The flat (constant) portion of the graph in the linear domain is 

difficult to see because it occurs near the origin, but it can be seen easily in the log domain. Most 

detection approaches (e.g., Currie's critical value and detection limit; EPA's MDL; the ACS LOD) are 

constructed assuming that the flat (constant) region of the variability versus concentration relationship 

holds true, although the graph is rarely displayed (a horizontal line would be singularly uninteresting). 

Detection approaches such as Currie's critical value, detection limit, LOD, and MDL are constructed by 

multiplying the standard deviation in the flat region by some constant. 

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Contention and differences of opinion occur in determining how to arrive at an "appropriate" 

standard deviation and what to do with the standard deviation when you have it. Currie's critical value 

and EPA's MDL use a multiple of the standard deviation in a similar manner (a t-statistic adjusted for the 

number of replicates used for Currie's critical value; 3.14 for 7 replicates in EPA's MDL). The IDE uses 

an additional upward adjustment based on a statistical tolerance limit calculation. 

3.3.7.2.3	 Quantitation Limits Using Standard Deviation Multiples and Models of Standard 

Deviation versus Concentration and RSD versus Concentration 

Both the limit of quantitation (LOQ) advanced by Currie and the American Chemical Society's 

Committee on Environmental Improvement and EPA's minimum level of quantitation (ML) result from 

multiplication of the standard deviation by a factor of 10, again assuming a flat portion of the variability 

versus concentration graph. This factor of 10 is directed at achieving a relative standard deviation (RSD) 

of 10 percent. An advantage of this approach is that a quantitation limit is produced, regardless of what 

the RSD turns out to be. 

For example, it is known that the determination of 2,4-dinitrophenol by EPA Method 625 

produces highly variable results and that 10 percent RSD cannot be achieved at any concentration level for 

this compound. Multiplying the standard deviation of replicate measurements of low-level samples results 

in a quantitation limit that is considerably higher than the quantitation limits for other compounds 

measured by Method 625. The RSD at this quantitation limit could be 30, 50, or 70 percent. Limiting the 

RSD associated with the quantitation limit to some arbitrary value (e.g., 30%, as with the ASTM IQE) 

could prohibit the use of EPA Method 625 for determination of 2,4-dinitrophenol. If 2,4-dinitrophenol 

were present at a high concentration in a discharge, it would not be reported. Although it could be argued 

that a more precise method should be used for determination of 2,4-dinitrophenol, determination of 

pollutants by a large suite of different methods would be quite costly with little meaningful benefit. 

Increasing precision (i.e., decreasing measurement error) would be critical only if the concentration at 

issue was near enough to a compliance limit that measurement error could influence the compliance 

determination. On the other hand, having widely varying RSDs for different analytes within the same 

method may be confusing to permitting and enforcement authorities who may not appreciate the subtleties 

of reporting violations in light of the underlying RSDs. 

Another means of arriving at a limiting RSD is to graph RSD versus concentration, as shown in 

the bottom two graphs of Figure 3-2. This approach is used by the ASTM IQE. It has the advantage that a 

model is fit to data, rather than using a point estimate such as the Currie and ACS LOD or the EPA ML. 

However, this approach requires considerably more data than are necessary for approaches based on point 

estimates. In addition, how a model is selected can play a major role in the outcome. 

3.3.7.2.4 Criteria for Selecting Models 

Both statistical and graphical procedures have been proposed for selecting between models for 

predicting measurement results based on spike concentrations. 

Statistical Criteria 

While statistical criteria are available for choosing between models of similar types, the currently 

available criteria are not satisfactory for choosing between the wide variety of models considered for the 

relationship between measurement variation and spike concentration, based on EPA's studies. More 

technically, statistical criteria include using: (1) the simplest model to obtain statistical significance, (2) 

3 - 39

the model with the smallest estimated variability, and (3) the model with the smallest likelihood ratio. 

Given the wide variety of models considered for detection and quantitation, there are problems associated 

with each of these procedures. Data that obviously do not follow the model may produce statistically 

significant results, variability may be estimated with weights that make the various estimates 

incomparable, and the likelihood function may not be comparable between models. 

Graphical Criteria 

Graphical criteria may be susceptible to some subjectivity in their application, but they are 

currently the best available method for choosing between models. At the most basic level, the primary 

graphical criteria is for the form of the model to be suggested by the available data. To consider the 

quality of the graphical analysis, it is useful to see if some small number of data are overly influential in 

determining if a model does or does not fit. Given the ability of the human eye to discern deviations from 

a straight line rather than from a curved line, a useful technique is to plot the data so that they will indicate 

a straight line if they follow the model of interest. 

Both graphical and statistical criteria will be strongly affected by the number and choice of spike 

concentrations used to fit the different models. Too few spike concentrations will lessen the statistical 

power of significance tests for slope and curvature from which decisions on the type of model will be 

made. In addition, the amount of subjectivity with which decisions are made using graphs increases when 

fewer concentration levels are used. For example, the judgement of whether a residual plot depicts 

“random scatter” is essentially impossible when only five concentration levels are used (i.e., the residual 

plot will include only five points). The number of results from which standard deviations are calculated 

will also have an effect on how models are selected. This set of results may include analysis of multiple 

replicates at a single laboratory or analysis of one or more replicates from multiple laboratories. If data 

are obtained from too few laboratories or replicates, the standard deviation estimates will be less reliable, 

which could lead to incorrect model selection based on statistical or graphical criteria. 

3.3.7.2.5 Assessment of Current Models 

EPA plotted variability versus concentration data to evaluate the extent to which real data from 

measurement methods used under the Clean Water Act would conform to a number of different models. 

For details of how data sets were selected and how data were collected within the data sets, see Appendix 

B, Characterizing Measurement Variability as a Function of Analyte Concentration for a Variety of 

Analytical Techniques, of the February 2003 Technical Support Document (EPA-821-R-03-005, February 

2003). Four sets of composite scatter plots for all combinations of analytical technique, analyte, and study 

were produced. These sets include: 

1. Measurement versus Spike Concentration, 

2. Log Measurement versus Log Spike Concentration, 

3. Observed Standard Deviation versus Spike Concentration, 

4. Log Standard Deviation versus Log Spike Concentration, and 

5. Relative Standard Deviation (RSD) versus Log Spike Concentration. 

There are hundreds of scatter plots in each set, sorted by the source, measurement technique, and 

study. The first set of scatter plots can be used to evaluate how well measurement results match the 

spiked concentration. If the assumed straight line model is true, then the relationship outlined by the 

plotted data will be approximately linear. These relationships are plotted using log-log plots so that small 

deviations from the straight line can be visualized easily. All the graphs are contained in attachments to 

3 - 40

Appendix B of the Technical Support Document (EPA-821-R-03-005, February 2003). 

The plot of observed standard deviations versus spike concentrations can be used to evaluate the 

reasonableness of the constant variation and/or linearly increasing variability models (Currie, 1968, 

Hubaux and Vos, 1970, and Glaser et al., 1981). If the constant model for standard deviation is true, there 

would be no apparent relationship between the standard deviation and spike concentration. If the straightline model for standard deviation is true, plots are expected to indicate an approximately linear 

relationship. Analogously, the standard deviation/spike concentration versus spike concentration is 

expected to show a straight-line relationship when variability is proportional to the spike concentration 

(Gibbons et al., 1991). The log-log plots of standard deviation versus spike concentration are expected to 

indicate if log or square root transformations may be appropriate (Clayton et al., 1987) or to display a 

shape that approximates a "hockey stick" when it is appropriate to use the model proposed by Rocke and 

Lorenzato (1995). With the Rocke and Lorenzato model, variability near zero will be approximately 

constant, but will increase proportionally with concentrations in the higher concentration range. 

Because the large number of resulting plots makes it difficult to draw general conclusions, for the 

most part, conclusions must be considered on a case-by-case basis. 

3.3.7.3 Methodology for Parameter Estimation 

Along with various approaches of detection and quantitation and models for measurement, a 

number of specific procedures have been suggested for estimating model parameters. Maximum 

likelihood and least squares are two generally applicable statistical methods that can be used in estimating 

model parameters. There are advantages and disadvantages to both that must be weighed in particular 

cases. A standard statistical practice for evaluating the quality of an estimation procedure is to calculate 

the precision and bias, usually best understood by examining a plot of residuals from a fit to a function. 

All else being equal, the estimation procedure resulting in the greatest precision and least bias is preferred. 

In some cases, precision and bias can be calculated based on the assumptions behind the estimation 

procedure. In other cases, it is either necessary or convenient to estimate precision and bias using 

simulations. From a general theoretical perspective, the maximum likelihood estimation methodology is 

preferable because it generates estimates that are generally best with regard to properties of precision and 

bias (especially for larger sample sizes), while also being approximately normally distributed. 

Unfortunately, maximum likelihood methodology sometimes can be problematic because the method 

requires the solution of complex equations. Least squares estimation is generally more tractable, and thus 

is more generally applicable, although the estimates that result may not be as desirable from a theoretical 

statistical perspective. 

What can sometimes be overlooked in considering estimation and model fitting is that direct 

measurement of variation of the blank or low level concentration may be the most cost-effective and least 

difficult method to implement especially where variability does not change much over the region of 

interest. 

3 - 41

Chapter 4 

Evaluation Criteria 

This chapter presents the criteria developed by EPA as a means for evaluating and selecting 

acceptable detection and quantitation limit approaches for use in Clean Water Act (CWA) programs. 

These criteria reflect EPA’s careful consideration of the issues identified and discussed in Chapter 3, 

including EPA’s needs under CWA programs. A total of six criteria were established, and are discussed in 

Sections 4.1 - 4.6. The six evaluation criteria are: 

Criterion 1: The detection and quantitation limit approaches should be scientifically valid.

Criterion 2: The approach should address demonstrated expectations of laboratory and method

performance, including routine variability.

Criterion 3: The approach should be supported by a practical and affordable procedure that a

single laboratory can use to evaluate method performance.

Criterion 4: The detection level approach should identify the signal or estimated concentration at

which there is 99% confidence that the substance is actually present when the analytical method is

performed by experienced staff in well-operated laboratories.

Criterion 5: The quantitation limit approach should identify the concentration that gives a

recognizable signal that is consistent with the capabilities of the method when a method is

performed by experienced staff in well-operated laboratories.

Criterion 6: Detection and quantitation approaches should be applicable to the variety of decisions

made under the Clean Water Act (CWA), and should support state and local obligations to

implement measurement requirements that area at least as stringent as those set by the Federal

government.

Section 4.7 presents additional principles recommended by stakeholders commenting on EPA’s 

assessment. 

4.1 Criterion 1 

Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

The concept of scientific validity is widely accepted but loosely defined. For the purposes of this 

evaluation, a detection/quantitation approach or methodology will be considered scientifically valid if it 

meets the following conditions: 

• It can be (and has been) tested, 

• It has been subjected to peer review and publication, 

• The error rate associated with the approach or methodology is either known or can be estimated, 

• Standards exist and can be maintained to control its operation (i.e., it is supported by well-defined 

procedures for use), and 

• It has attracted (i.e., achieved) widespread acceptance within a relevant scientific community. 

4 - 1
While EPA acknowledges that other measures could be established to demonstrate scientific 

validity, EPA has adopted the conditions cited because they reflect those discussed by the U.S. Supreme 

Court as pertaining to assessments of scientific validity when considering the admissibility of expert 

scientific testimony2

. These conditions also are directly relevant to EPA’s needs. 

Some stakeholders supported the use of objective criteria for determining scientific validity, but 

questioned the appropriateness of using criteria that were designed for courts and juries to support 

scientific decisions made by scientific experts. EPA carefully reviewed the Court’s conditions for 

demonstrating the scientific validity of an expert’s reasoning or methodology, and believes that these 

conditions are appropriate for demonstrating the scientific validity of any scientific approach or 

methodology, including those that might be used to establish detection and quantitation limits under 

CWA. EPA further believes these criteria are consistent with the EPA Science Policy Council’s 

assessment factors for evaluating the quality of scientific and technical information (EPA 100/B-03/001, 

June 2003), including the extent to which technical information and data are peer reviewed and 

appropriately tested. However, EPA is willing to consider alternative or supplemental criteria for 

evaluating scientific validity as it moves forward with the stakeholder process. 

Stakeholders agree that detection and quantitation levels should be based on sound scientific 

principles, and note that low-cost and/or simple approaches should not be selected if inaccurate or 

unmeasurable limits may result. Stakeholders also noted that some of the conditions listed above (e.g., the 

condition that an approach or methodology should have attracted widespread acceptance within a relevant 

scientific community) have the potential for favoring concepts already adopted and required by regulatory 

agencies. EPA agrees that this is a valid concern, and therefore, will consider the overall validity and 

practicality of new approaches. 

4.2 Criterion 2 

Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

As discussed in Chapter 3 of this Assessment Document, the detection and quantitation limit(s) 

for an analyte in an analytical method can be established from a single-laboratory study, multiple singlelaboratory studies, or an interlaboratory study. 

Early methods developed by EPA under Clean Water Act programs, and nearly all methods 

developed by EPA under Safe Drinking Water Act programs, were developed by an EPA research 

laboratory in Cincinnati, Ohio with specialized experience in the analytical chemistry of drinking water. 

This laboratory also established method detection and quantitation limits which, in many instances, 

initially could not be achieved in other laboratories. Over time, however, the difficulty in achieving these 

limits was overcome as analysts gained experience with the use of these new methods. 

Stakeholders have suggested that detection and quantitation limits be developed using data from 

multiple laboratories in order to account for the routine inter- and intra-laboratory variability that can 

occur over time. Although compliance measurements are made in single laboratories, EPA agrees that 

detection and quantitation limits in methods that will be widely used by many laboratories should consider 

2

Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993) and Kumho Tire Co. v. 

Carmichael, 526 U.S. 137 (1999) 

4 - 2 
these sources of variability. For this reason, after the development in a single laboratory of a new or 

modified analytical method with an initial estimate of detection and quantitation limits, EPA's Office of 

Science and Technology evaluates and verifies these limits in multi-laboratory studies. 

Voluntary consensus standards bodies (VCSBs) such as ASTM International have historically 

used interlaboratory studies to establish method performance. Over the past 5 to 10 years, ASTM 

International has been developing interlaboratory and single-laboratory approaches for detection and 

quantitation. Single-laboratory studies at a specialized research laboratory may produce detection and 

quantitation limits that are lower than those produced by studies that gather data from many laboratories 

that may or may not be experienced with the method. EPA believes that a realistic expectation of method 

and laboratory performance likely lies somewhere in between that provided by a specialized singlelaboratory study and that provided by an interlaboratory study with no pre-qualification requirements. 

Estimates of detection and quantitation limits should consider the inherent variability of the measurement 

process, but not be based on the lowest common denominator, e.g., data from inexperienced or unqualified 

analysts and laboratories. 

EPA expects that laboratories must meet some minimum standards of performance and experience 

with a method, and sets performance criteria in methods. Examples of such criteria include measures to 

demonstrate that a laboratory is producing accurate results at a concentration of interest (i.e., analysis of 

reference standards or spiked samples), measures to demonstrate that results are not biased by 

contamination (i.e., analysis of blanks), and measures to demonstrate that the laboratory can detect 

pollutants at low concentrations (i.e., at the method detection limit). It is likely that laboratory 

performance will improve (and variability will be lower) when laboratories are required to meet specified 

performance criteria in order to report results. 

A further consideration concerning routine variability of laboratory performance is the means for 

rejection of outliers to more accurately estimate routine variability. True outliers can occur in laboratory 

data, and some means of resolving outlier issues should be included. Statistical procedures are available 

for the identification of candidate outlier values. Once a candidate outlier has been identified, evaluation 

of the value from a QA/QC perspective (e.g., some procedural error or quality control error has occurred) 

should be the basis of exclusion of the value from a data set. In cases where no cause for the outlier has 

been identified, it may reasonable to reject an outlier on statistical grounds, but every effort should be 

made to justify the exclusion on technical grounds. 

In examining each approach against this criterion, EPA will evaluate whether the approach can be 

used to provide realistic expectation of laboratory performance. As part of this assessment, EPA will 

examine the sources of variability captured by the approach, and the degree to which the statistics that 

underlie the approach realistically reflect these sources of variability. 

4.3 Criterion 3 

Criterion 3:	 The approach should be supported by a practical and affordable procedure that a single 

laboratory can use to evaluate method performance. 

Any approach or procedure for determining detection and quantitation limits at a single laboratory 

should be simple, with detailed instructions, and cost-effective to implement (i.e., it should be reliable and 

?laboratory-friendly”). Laboratories that use detection or quantitation procedures range from large 

laboratories and laboratory chains with a wide range of technical capabilities, to small laboratories 

operated by one or a few people with limited statistical skills. While this range of laboratory capability 

4 - 3

places a premium on simplicity and ease, EPA agrees with stakeholders that data reliability and quality are 

also important. A suitable approach or procedure for detection and quantitation incorporates the right 

balance between the need for valid data and the need for the procedure to be simple and inexpensive to 

perform. EPA also believes that if a procedure is complicated, it will be prone to error in use. Similarly, 

if a procedure requires investment of extensive resources that cannot be billed to the client, laboratories 

will have a disincentive to use the procedure. Therefore, if EPA wishes to encourage development and 

use of innovative techniques that improve measurement performance or lower measurement costs, the 

Agency should consider practicality and affordability as significant, if not equal, considerations to 

scientific validity. 

After evaluating each of the issues discussed in Chapter 3 of this document, EPA concluded that 

successful implementation of CWA programs depends on the ability of laboratories to easily and 

affordably: 

•	 demonstrate that a method works in a particular matrix at the levels of concern (i.e., demonstrate the 

absence of matrix effects), 

•	 characterize improvements in measurement capabilities in terms of detection and quantitation 

capabilities, and 

• characterize the detection and quantitation capabilities of new methods. 

A matrix effect is an interference in a measurement that is caused by substances or materials in 

the sample other than the analyte of interest that are not removed using the procedures in the method or 

other commonly applied procedures. In the context of detection and quantitation, matrix effects may 

manifest themselves by precluding measurements at levels as low as could be measured were the 

interference not present. From a practical perspective, it is not possible to test the detection and 

quantitation capability of an analytical method in every possible matrix in which it may be used. At a 

minimum, it is unlikely that EPA or any other organization or laboratory could possibly identify and 

obtain samples of every matrix to which the method might be applied, and even if such a feat were 

possible, the cost and logistics of doing so would be prohibitive. 

The situation for characterizing matrix effects on detection and quantitation is similar to the 

situation for characterizing matrix effects on measurement performance at higher concentration levels. In 

the latter case, EPA typically uses one or more spiked real-world or reference matrices (e.g., reagent 

water, sand, diatomaceous earth) to establish QC acceptance criteria that verify performance of the 

method at mid-to-high concentrations. Each analytical method includes QC acceptance criteria for such 

real-world and reference matrix spikes, along with a suite of quality control requirements designed to 

verify that failures are attributable to the matrix rather than to an analytical system that is out of control. 

EPA would prefer to utilize detection/quantitation concepts that allow for similar characterization of 

detection/quantitation capabilities in representative matrices and that are supported by simple, costeffective procedures that would allow individual laboratories to evaluate the effects of specific matrices 

on these capabilities on an as needed basis. Because methods approved at 40 CFR part 136 already 

contain a suite of quality control procedures and QC acceptance criteria that control laboratory 

performance, EPA believes that it is not necessary to verify detection and quantitation limits in each and 

every batch of each and every matrix analyzed. Rather, such testing can be done on an as-needed basis 

when it is suspected that matrix interferences may preclude reliable measurements at low levels. 

4 - 4

Another consideration influencing the need for simplicity and practicality is that measurement 

capabilities generally improve over time. As is discussed in Section 3.1 of this document, and as has been 

noted by stakeholders, this is attributable to a variety of factors, including: 

• increased staff experience with a given technique, 

• technological upgrades or improvements in the instrumentation used for analysis, and 

•	 development of new instrumentation or techniques that improves detection/quantitation, precision, or 

bias. 

In each case, the improvements may not be observed across the entire laboratory community. In the case 

of increased staff experience, for example, it is obvious that a laboratory that specializes in one type of 

analysis, such as low-level mercury measurements, will develop greater experience with these analyses 

than a laboratory that rarely performs these measurements. Likewise, it is easy to see how one or a few 

laboratories that concentrate their business on a particular type of analysis might be willing to invest 

significant resources in new or upgraded equipment to improve performance, whereas laboratories that 

rarely perform such analyses would not find such upgrades to be cost-effective. 

Improvements in measurement capability, including the development of new methods, may create 

a dynamic decision-making process, in that measurements at lower levels may allow EPA and States to 

identify and measure previously undetected pollutants. Such improvements offer a means for monitoring 

and controlling (i.e., regulating) the discharge of previously unregulated, but harmful, pollutants. 

Therefore, it is in the best interest of the environment for EPA to encourage the development and use of 

improved environmental analysis procedures and equipment by providing practical and affordable 

procedures for evaluating method performance. 

In evaluating this criterion, EPA will favor affordable and easy-to-use approaches and procedures 

that allow analysts to 1) determine matrix-specific variations when necessary, based on realistic data, and 

2) demonstrate lower detection and quantitation limits associated with improvements in measurement 

capabilities. Procedures for establishing the detection capabilities of new methods or associated with 

improved measurement capabilities should be practical enough to encourage such development. However, 

EPA recognizes that some uses for detection and quantitation limits may require a more comprehensive 

approach involving multiple laboratories. These procedures should specify the nature, minimum number, 

and concentration levels of the samples to be used, and the corrective action to be taken if the resulting 

detection or quantitation limit is inconsistent with the data from which it is derived. 

4.4 Criterion 4 

Criterion 4:	 The detection level approach should estimate the theoretical concentration at which there 

is 99% confidence that the substance is actually present when the analytical method is 

performed by experienced staff in a well-operated laboratory. 

Any approach to establishing levels at which detection decisions are made should be capable of 

providing regulators, the regulated community, and data users with a high level of confidence that a 

pollutant reported by a well-operated laboratory as being present really is present. Historically, 

approaches to making detection decisions have set the criterion for detection at 99 percent confidence 

(i.e., with 99% confidence that the analyte concentration is greater than zero). This criterion results in the 

probability of a false positive i.e., that a pollutant will be stated as being present when it actually is not 

(this is a Type I error), of one percent. The procedure also should be capable of generating a detection 

level when the substance of interest is not present in a blank and/or when instrument thresholds are used 

4 - 5

in routine operation. A well-operated laboratory is a laboratory that routinely monitors performance 

through QC analyses, control charts, and other measures to rapidly identify and correct deteriorating or 

poor performance, and with analysts experienced with method sample preparation, analysis, and detection 

procedures. 

In evaluating this criterion, EPA will favor approaches and procedures that reflect routine 

analytical conditions in a well-operated laboratory. 

4.5 Criterion 5 

Criterion 5:	 The quantitation limit approach should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of the method when a method is 

performed by experienced staff in well-operated laboratories. 

Measurement capabilities among laboratories vary depending on a number of factors, including, 

but not limited to, instrumentation, training, and experience. Similarly, measurement capabilities among 

different analytical methods vary depending on a number of factors, including the techniques and 

instrumentation employed and the clarity of the method itself. In evaluating different approaches to 

estimating quantitation limits, EPA will give preference to those approaches that strike a reasonable 

balance between using either state-of-the art laboratories or a highly varied community of laboratories to 

establish quantitation limits. 

Historical approaches to recognizing laboratory capabilities in establishing detection and 

quantitation limits have varied between two extremes of establishing the limit in a state-of-the-art research 

laboratory to reflect the lowest possible limit that can be achieved, and establishing the limit based on 

statistical tolerance intervals calculated from a large number of laboratories with varying levels of 

experience, instrumentation and competence. Generally, use of the former has been employed to serve as 

a goal or performance standard to be met by other laboratories, whereas use of the latter treats the limit, 

not as a performance standard that needs to be met by each laboratory, but rather as a characterization of 

the performance of the capabilities of a population of laboratories at the time of method development. 

Historical approaches to recognizing method capabilities also have varied between those that 

allow the error expressed as relative standard deviation, or RSD among low-level measurements to vary, 

depending on the capabilities of the method, and those that fix this error (RSD) at a specific level. 

Initially, Criterion 5 stated that the “quantitation limit should identify a concentration at which 

the reliability of the measured result is consistent with the capabilities of the method when a method is 

performed by experienced staff in a well-operated laboratory.” Reviewers from within EPA questioned 

the criterion’s implication that measurements below a quantitation limit could be considered unreliable. A 

similar concern was expressed by one of the peer reviewers charged with evaluating EPA’s assessment 

and an earlier draft of this Assessment Document. This reviewer noted that: 

“almost all implementations of limits of quantitation have nothing to do with whether the 

measurements are actually quantitative,” and that “any level at which the instrument can 

be read, and at which there is a reliably estimated standard deviation is a level at which 

quantitation is possible” (Rocke, 2002) 

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The peer reviewer suggested that Criterion 5 might be rewritten as: 

“the quantitation limit should identify a concentration at which the instrument yields a 

measurable signal at least 99% of the time, and which is no smaller than the detection 

level. Such a quantitation limit will often be the same as the detection level.” 

EPA agrees that this is a valid perspective, in that if the pollutant is identified and the analytical system 

produces a result (i.e., a measurable or recognizable signal), quantitation occurs. Although this 

interpretation of a quantitation limit has validity, implementation of such an approach would require that 

all values generated by an analytical system be reported, along with an estimate of the uncertainty 

associated with each value (e.g., the "reliably estimated standard deviation" mentioned by the peer 

reviewer). As noted in Section 2.3.4, several organizations, including the European Union, are developing 

procedures for estimating the uncertainty associated with measured results. If successful, such an 

approach would eliminate many of the data censoring concerns discussed in Section 3.3.5. Given the 

difficulty in achieving consensus on an appropriate means of establishing a quantitation limit, however, 

EPA believes that it would also be difficult to obtain consensus on an appropriate means for estimating the 

uncertainty associated with each result measured on each environmental sample. In addition, analytical 

chemists have used and perceive that they understand a quantitation limit to mean the lowest 

concentration at which an analyte can be identified and quantified with some degree of certainty. This 

understanding necessarily involves use of the sound judgment of a qualified analytical chemist. 

Therefore, EPA will continue to monitor developments on this subject, and if appropriate, reevaluate this issue if and when it becomes practical and widely accepted by the laboratory, regulatory, and 

regulated communities. In the meantime, EPA believes that the traditional approach of defining a 

quantitation limit at some level above the detection limit provides a data user with a reasonable degree of 

confidence in the measured value without requiring that laboratories develop and report individual 

estimates of uncertainty. Criterion 5 reflects this belief. 

In evaluating the approaches, EPA will give preference to those approaches that strike a 

reasonable balance between using either state-of-the art laboratories or a highly varied community of 

laboratories to establish quantitation limits. 

4.6 Criterion 6 

Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of decisions 

made under the Clean Water Act, and should support State and local obligations to 

implement measurement requirements that are at least as stringent as those set by the 

Federal government. 

The Clean Water Act requires EPA to conduct, implement, and oversee a variety of data gathering 

programs. As noted in Section 3.2 of this Assessment Document, these programs include, but are not 

limited to: 

• Survey programs to establish baselines and monitor changes in ambient water quality, 

• Screening studies to identify emerging concerns and establish the need for more in-depth assessment, 

•	 Effluent guideline studies to establish technology-based standards for the control of pollutants in 

wastewater discharges, 

4 - 7

•	 Toxicity and environmental assessment studies to establish water quality-based standards for the 

control of pollutants in wastewater, and 

•	 Risk assessment studies designed to characterize and evaluate human health and environmental risks 

associated with various water body uses. 

In addition, EPA needs to evaluate detection limit or quantitation capabilities for methods 

approved at 40 CFR part 136 for the following applications: 

� Ambient and effluent permitting and compliance monitoring under NPDES and the pretreatment 

program and under State and local programs, 

� Quality control in analytical laboratories, and 

� Method development, promulgation, and modification. 

In theory, EPA could evaluate each of these applications independently and identify a detection and 

quantitation limit approach that is best suited to each application, as recommended by some stakeholders 

commenting on EPA’s assessment. In the 2003 assessment, EPA stated that this would increase 

confusion, record keeping burdens, and laboratory testing burdens. EPA also stated that data generated 

under a single procedure can be used for development of detection and quantitation limits that are 

applicable to more than a single use. For example, the data used to determine the capabilities of multiple 

laboratories using a given method may also be used to develop method-specific detection and quantitation 

limits. For these reasons, EPA recommended the adoption of a single pair of related detection and 

quantitation procedures used to address all or most Clean Water Act applications. Some stakeholders 

recommend the use of different approaches for different CWA applications. For example, these 

stakeholders would prefer a more rigorous approach to determining detection and quantitation limits for 

method development than for verifying laboratory performance. They would like to include a procedure 

that is based on a multilaboratory approach rather than a single laboratory approach to define detection 

and quantitation capabilities of analytical methods. EPA recognizes that the complexity and statistical 

rigor appropriate for a detection and quantitation approach for method development and validation would 

be greater than that needed for demonstrating laboratory proficiency. EPA plans to seek additional 

stakeholder input on whether different approaches are needed for different CWA purposes (see Chapter 

6). 

Although EPA prefers to identify a manageable set of detection and quantitation limit approaches to 

meet CWA needs, EPA believes that any reasonable approach advanced by other organizations should be 

acceptable for use provided it meets the needs of the specific application for which it would be used. 

Allowing use of detection and quantitation approaches developed by other organizations provides the 

stakeholder community with increased measurement options that may help reduce measurement costs or 

improve measurement performance for specific situations. This approach also is consistent with the intent 

of the National Technology Transfer and Advancement Act. 

The Clean Water Act authorizes State or local governments to implement specific aspects of the Act, 

with the provision that they do so in a way that is at least as protective (i.e., stringent) as the national 

standards put forth by EPA. Therefore, this criterion is intended to ensure that any detection and 

quantitation limit approach adopted by the Office of Water is sufficiently clear and defined to ensure 

consistency with approaches adopted by State or local governments. 

Finally, it is important to differentiate between detection and quantitation limit approaches and 

compliance evaluation thresholds. Detection and quantitation limit approaches pertain to measurement 

process thresholds. In contrast, compliance evaluation thresholds are used to support wastewater 

4 - 8

discharge limits established in National Pollutant Discharge Elimination System (NPDES) or pretreatment 

program permits. Such limits are usually expressed as either a maximum concentration of pollutant 

allowed in the discharge or a maximum mass of pollutant allowed to be discharged in a specific time 

period. 

Ideally, and in most cases, analytical methods are available to allow for detection and quantitation of 

pollutants at concentrations that are lower than the discharge levels needed to protect or restore the quality 

of the receiving water. When such measurement capability does not exist (e.g., analytical methods are not 

available that can reliably measure at levels necessary to protect receiving water), permitting authorities 

must decide how to evaluate and report pollutant concentrations at these levels. Historically, EPA has 

recommended that in such cases, the permitting authority include the water quality-based limit in the 

permit, but establish the compliance evaluation threshold at the quantitation limit of the most sensitive 

available method. 

In examining each approach against this criterion EPA will consider 1) the applicability of various 

detection/quantitation approaches to the variety of data gathering decisions that must be made under the 

CWA, including those that do and those that do not involve compliance monitoring, and 2) the ability of 

the approaches to support State and local obligations for implementing the CWA. As discussed in 

Chapter 6, EPA believes that additional discussion about this criterion is appropriate based on negative 

comments from stakeholders regarding the use of a single pair of detection and quantitation limit 

approaches to meet all CWA needs. 

Consensus Principles 

Some stakeholders commenting on EPA’s assessment of approaches to detection and quantitation 

expressed their support of a set of “consensus principles” submitted by 36 signatories representing 

industry and laboratory communities. EPA agrees with certain consensus principles such as the principle 

that detection and quantitation levels should be based on sound scientific principles, and that low-cost 

and/or simple approaches should not be used if invalid data will result (see Criterion 1 above). As another 

example, EPA incorporated routine variability, the rate of false positives, precision, and matrix effects in 

several criteria, and considered these aspects in its assessment of detection and quantitation concepts. 

Some of these consensus principles are included in the criteria discussed in this chapter. Other consensus 

principles have clarified or highlighted existing aspects of approaches to detection and quantitation and 

provide a framework for additional consideration. 

For ease of consideration, the consensus principles recommended by commenters have been separated 

by EPA into technical and policy considerations and include: 

Technical Considerations 

•	 Detection and quantitation levels must be based on sound scientific principles. Low-cost and/or 

simple approaches must not be selected if inaccurate compliance determinations or unmeasurable 

permit limits may result. 

• The definition of “quantitation” must account for both precision and bias. 

• Detection limit procedures must take into account the variability and bias of method blank results. 

•	 False positives (Type I errors), false negatives (Type II errors), and precision must all be addressed by 

detection concepts and reporting of analytical results for regulatory purposes. 

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4.7 
•	 Precision, bias, and qualitative identification (where appropriate) must all be addressed by the 

definition and concepts of quantitation and by the reporting of analytical results for regulatory 

purposes. 

•	 Detection limit procedures must include procedures for ongoing demonstration of sensitivity, 

preferably incorporated into the routine analytical quality control as a check against false negatives. 

•	 Detection and quantitation levels must take into account routine inter- and intra-laboratory variability 

within a laboratory over time. 

•	 In its procedures for establishing detection and quantitation levels, EPA must develop guidance on 

how to account for the effects of various matrices. 

Policy Considerations 

•	 The LC, LD, and LQ are three distinct points, each of which has unique criteria that must be satisfied. 

For consistency with international standards, EPA must adopt the definitions of LC (critical value), LD 

(detection limit), and LQ (quantification limit) of IUPAC (International Union of Pure and Applied 

Chemistry) that are being adopted by international standards organizations (e.g., the International 

Organization of Standardization (ISO)). 

•	 The definitions of and procedures for determining detection and quantitation levels must take into 

account that quantitation levels are used as regulatory compliance levels in NPDES permits. 

•	 EPA should specify consensus standard procedures for establishing significant figures and for 

rounding data. 

•	 EPA must strive for consistency across all EPA offices (the Office of Water, Office of Research and 

Development, Office of Ground Water and Drinking Water, and Office of Solid Waste and 

Emergency Response) in defining and applying detection and quantitation levels. 

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Chapter 5 

Assessment 

This chapter summarizes EPA’s assessment of various detection and quantitation limit approaches 

against the evaluation criteria established in Chapter 4. Assessments of detection limit approaches are 

presented in Section 5.1 and include an assessment of the: 

• EPA method detection limit (MDL; Section 5.1.1), 

• ASTM International interlaboratory detection estimate (IDE; Section 5.1.2), 

• American Chemical Society (ACS) limit of detection (LOD; Section 5.1.3), 

•	 International Organization for Standardization/International Union of Pure and Applied Chemistry 

(ISO/IUPAC) critical value (CRV; Section 5.1.4), 

• ISO/IUPAC minimum detectable value (MDV; Section 5.1.5), 

• American Council of Independent Laboratories (ACIL) Critical Value ( ACIL LC; Section 5.1.6), 

•	 United States Geological Survey (USGS) Long-term Detection Limit (USGS LT-MDL; Section 

5.1.7), and 

•	 Inter-industry Analytical Group (IIAG) Sensitivity Test and Full-Range Validation Study (Section 

5.1.8). 

Assessments of quantitation limit approaches are presented in Section 5.2 and include an assessment of 

the: 

• EPA minimum level of quantitation (ML; Section 5.2.1), 

• ASTM International interlaboratory quantitation estimate (IQE; Section 5.2.2), 

• ACS limit of quantitation (LOQ; Section 5.2.3), and 

• ISO/IUPAC LOQ (section 5.2.4). 

A brief summary of the evaluation is presented in Tables 5-1 (detection limit approaches) and 5-2 

(quantitation limit approaches). 

EPA’s 2003 assessment of detection and quantitation limit approaches focused on approaches 

developed or published by ASTM International, the American Chemical Society (ACS), ISO/IUPAC, and 

EPA. Stakeholder commenting on the initial assessment suggested that EPA should include additional 

approaches in the next assessment. In addition to the initial four approaches, EPA has included three 

additional approaches in this Revised Assessment document. These approaches are: the long-term MDL 

developed by USGS, a new detection limit procedure developed by the American Council of Independent 

Laboratories (ACIL), and a paired approach involving a sensitivity test and full-range validation study 

submitted by the Petitioners (the Inter-industry Analytical Group). Several commenters advocated these 

as approaches that more realistically reflect measurement variability. These additional approaches are 

discussed and assessed in Sections 5.1.6 - 5.1.8 of this chapter. 

Some stakeholders commenting on EPA’s 2003 assessment believed that the evaluation criteria 

used by EPA were written to favor the MDL and ML over other approaches to detection and quantitation. 

EPA disagrees. The criteria were written to reflect EPA’s needs for detection and quantitation approaches 

under the CWA, and it is not necessary that an acceptable approach meet all of these criteria under all 

conditions. Because the MDL and ML were developed to address EPA’s needs, it should not be 

surprising that the MDL and ML procedures generally meet the criteria EPA set out to assess detection 

and quantitation procedures. EPA has frankly assessed the MDL and ML against these criteria and notes 

5 - 1

that the MDL and ML procedures do not meet all of these criteria under all operating conditions (see 

Sections 5.1.1 and 5.2.1 below). Due to the variability and unpredictability inherent in measurement 

science, it is unlikely that any procedure would meet all of EPA’s criteria under all conditions. However, 

EPA is open to further discussions with stakeholders about the appropriateness of the evaluation criteria 

described in Chapter 4, in particular, the issue of whether EPA should adopt different approaches for 

different applications, as discussed in Chapter 6. 

5.1 Detection Limit Approaches 

Sections 5.1.1 through 5.1.8 describe EPA’s assessment of eight detection limit approaches. Each 

discussion is divided into two major subsections. The first subsection describes the approach and, where 

applicable, the procedure that supports the approach. The second subsection details EPA’s assessment of 

the approach based on the five criteria established in Chapter 4 for evaluating detection limit approaches. 

Note:	 Of the six assessment criteria in Chapter 4 four (Nos. 1, 2,3 and 6) pertain to both detection and 

quantitation limit approaches. One criterion (No. 4) pertains only to detection limit approaches, 

and one criterion (No. 5) pertains only to quantitation limit approaches. Therefore, the following 

discussion of each detection and quantitation limit approach applies only the five applicable 

criteria. 

5.1.1 Evaluation of the MDL 

Section 5.1.1.1 is an overview of the MDL approach and the procedures used to implement the 

approach. Section 5.1.1.2 describes EPA’s assessment of the MDL against the five evaluation criteria that 

apply to detection limit approaches.(i.e., Criteria 1-4, and Criterion 6). 

5.1.1.1 Description of the MDL Approach and Procedure 

As promulgated at 40 CFR part 136, Appendix B, the MDL is defined as: 

“the minimum concentration of a substance that can be measured and reported with 99% 

confidence that the analyte concentration is greater than zero and is determined from 

analysis of a sample in a given matrix containing the analyte.” 

A six-step procedure is given in Appendix B, with an optional seventh step to verify the 

reasonableness of the MDL determined in the first six steps. The procedure is intended for use by 

experienced analytical chemists. A brief summary of the MDL procedure is as follows: 

1.	 The analyst makes an estimate of the detection limit based on one of four options: the instrument 

signal to noise ratio; three times the standard deviation of replicate blank measurements; a break in the 

slope of an instrument calibration curve; or known instrument limitations. 

2.	 The analyst prepares a volume of reagent water that is as free of the target analyte as possible (if the 

MDL is to be determined in reagent water). 

3.	 The analyst prepares a sufficient volume of spiked reagent water (or of an alternate matrix) to yield 

seven replicate aliquots that have a concentration of the target analyte that is at least equal to or in the 

same concentration range as the estimated detection limit (it is recommended that the concentration of 

the replicate aliquots be between 1 and 5 times the estimated detection limit). 

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4. All of the replicate aliquots are processed through the entire analytical method. 

5. The variance (S2) and standard deviation (S) of the replicate measurements are determined, as follows: 

where: 

Xi

 = the analytical results in the final method reporting units obtained from the n sample aliquots and 

� refers to the sum of the X values from i=l to n, and 

i =1 to n 

6. The MDL is then determined by multiplying the standard deviation (S) by the Student’s t-statistic at a 

99% percentile for n-1 degrees of freedom. f seven replicates are used, the Student’s t-value is 3.143. 

This information is used to calculate the MDL as follows: 

where: 

MDL = the method detection limit 

t(n-1,1-� = .99) = the Student's t-value appropriate for a 99% confidence level with n-1 degrees of 

freedom, and 

S = the standard deviation of the replicate analyses. 

A 95% confidence interval for the determined MDL may be calculated from percentiles of the chi 

square over degrees of freedom distribution (�2

/df). 

7. The optional iterative procedure to verify the reasonableness of the MDL involves spiking the matrix 

at the MDL that was determined in Step 6, and analyzing another seven replicates spiked at this level. 

The F-ratio of the variances (S2

) is determined and compared with the F-ratio found in the table, 

I

5 - 3

which is 3.05. If S2 

A/S2 B>3.05, the analyst is instructed to respike at the most recently calculated 

MDL and process the samples through the procedure starting with Step 4. If 2 

A/S2 B<=3.05, then the 

pooled standard deviation is determined (S2 

A is the larger of the two variances). The pooled standard 

deviation is then used to calculate the final MDL as follows: 

where 2.681 is equal to t(12, 1-� =.99 ). 

The 95% confidence interval around the final MDL may be determined using the chi squared 

distribution. 

The MDL procedure given at 40 CFR part 136, Appendix B is described as being applicable to 1) 

a wide variety of sample types, ranging from reagent water containing the analyte of interest to wastewater 

containing the analyte of interest, and 2) a broad variety of physical and chemical measurements. 

5.1.1.2 ssment of the MDL Against the Evaluation Criteria 

The following five subsections discuss the MDL approach and procedure in the context of the five 

evaluation criteria that concern detection limit approaches (i.e., Criteria 1-4, and Criterion 6). 

5.1.1.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

For the purposes of evaluating scientific validity, EPA is using the conditions discussed by the 

Supreme Court in Daubert v. Merrell Dow Pharmaceuticals (1993) and Kumho Tire Co. v. Carmichael, 

(1999) (see Chapter 4, Criterion 1). 

Condition 1: t can be (and has been) tested. The MDL procedure meets this condition. er the years, 

as stakeholders have sought to improve upon or identify alternative procedures, the MDL has been the 

subject of a number of studies and comparisons, including this assessment. As a result, the MDL is one of 

the most widely tested detection limit procedure in the history of detection approaches. (See Appendix A 

for a list of literature references concerning the MDL and other detection limits.) 

Critics of the MDL have noted that the detection limit produced with the MDL procedure can vary 

depending on the spike level used. It is true that an initial MDL may be calculated using any spike level, 

regardless of how high. hough a high initial spike level will result in an initially high MDL, the selfcorrection check in the MDL procedure requires the final spike level to be within a certain range of the 

reported (i.e. final) MDL. Specifically, Step 1 of the MDL procedure focuses the spiking level on the 

lowest concentration at which measurements can be made, and the factor of 5 requirement in Steps 3 and 

4 assure that the determined MDL will be at or near this concentration. Therefore, the requirements 

included in Steps 1, 3 and 4 guard against an artificially high MDL being produced due to the choice of a 

high initial spike level. also recognizes the concern that the iterative procedure in step 7, which 

provides a reality check on the results obtained in steps 1 - 6 is optional. sider whether 

additional guidance on this aspect of the procedure is needed. 

S

Asse

I Ov

Alt

EPA 

EPA will con

5 - 4

In preparation for the assessment of detection and quantitation approaches, EPA tested the MDL 

procedure with 10 different techniques, at decreasing spike concentrations, to evaluate this concern and 

determine how well the procedure characterized the region of interest. Results of the study suggest that, 

although the calculated MDL could vary depending on the spike level used, the MDL procedure is capable 

of reasonably estimating the lowest level at which measurements can be made when the factor of 5 

requirement is met. 

One of the stakeholders commenting on EPA’s 2003 assessment suggested that the MDL failed to 

meet this condition because EPA should have tested it in “real world” matrices. EPA does not agree with 

this suggestion for several reasons. First, it is not practical or possible to test detection limits in every real 

world matrix, and there is no consensus as to which real world matrix would represent an appropriate real 

world matrix for testing. Second, many real world matrices contain the target pollutant at levels well 

above the detection or quantitation limit, making it impossible to characterize what can and cannot be 

detected at low levels. In theory, the sample could be diluted to dilute the target pollutant, but in practice 

sample dilution would also likely dilute any interferences that might be present, thereby defeating the 

purpose of using a real world matrix. The current EPA approach, which exhaustively tests the MDL 

procedure in a reference matrix using multiple techniques and ten different concentrations that span the 

entire region of interest, is more than adequate to constitute “testing” of the MDL procedure. On the other 

hand, where data suggests that matrix interferences may significantly affect achievable quantitation and 

detection limits, this should be considered by a permit writer on a case by case basis. 

Condition 2: It has been subjected to peer review and publication. The MDL meets this condition. Prior 

to promulgation by EPA, the MDL approach and supporting procedure was published by Glaser et al. in a 

peer-reviewed journal (Glaser, et al., 1981). The MDL procedure has been included at 40 CFR part 136, 

appendix B since 1984. Values resulting from this procedure have been included, published, and tested in 

many analytical methods since promulgation, including methods published by EPA and other Federal 

agencies, and by consensus standards organizations and trade associations such as ASTM International, 

and APHA, AWWA, and WEF. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. The error 

rate is specified by �, with a suggested value of 0.01(1%). Therefore, the MDL meets this condition. In 

addition, the Step 7 of the MDL procedure suggests calculating a 95% confidence interval for the 

determined MDL, providing additional estimation about the uncertainty (i.e., error) of the MDL 

determined using the procedure. 

The US Geological Survey (USGS) provided a dataset of spiked and blank sample data that EPA 

used to evaluate the error rate associated with the MDL. (Error rates associated with the ACIL and USGS 

detection limit procedures also were evaluated and are discussed in Sections 5.1.6 and 5.1.7.) Although 

the sample size was insufficient to conclusively demonstrate the error rate of the MDL, the results suggest 

the actual error rate is close to the intended 1%. In this case, the observed mean error rate was 2.9%. 

Readers are referred to Appendix B for a discussion of two factors affecting this estimate - relatively small 

sample size and some added long-term variability. 

In the 2003 assessment, EPA suggested deleting the procedure for calculating the 95% confidence 

interval because it appeared to be rarely, if ever, used. No commenters specifically agreed with this 

suggestion, but several commenters responded that it should be retained. One commenter, arguing in 

favor of the procedure, stated that “It has long been recognized that a 95% confidence level is appropriate 

to establish standards and other regulatory requirements.” Considering these comments, EPA now 

believes there is no compelling reason to remove this procedure. 

5 - 5

Condition 4: Standards exist and can be maintained to control its operation. The MDL approach is 

supported by a clearly defined, published procedure to control its operation. The procedure gives the 

steps to be followed and instructs the analyst to use the entire measurement process. Hundreds, if not 

thousands, of laboratories have successfully implemented the MDL procedure since its promulgation in 

1984. EPA has found that when laboratories are required to perform MDL studies as part of an 

interlaboratory study, the results reported by the laboratories are generally consistent. EPA has observed 

similar consistency in use of the MDL by laboratories required to perform the procedure to demonstrate 

proficiency with a method. Therefore, the MDL meets this condition. 

Notwithstanding the preceding, the MDL procedure would be improved with additional guidance, 

particularly with respect to initial spike levels, handling outliers, the optional reasonableness step (Step 7), 

and multi-analyte test methods. The MDL procedure does not contain a discussion of outliers. It may be 

helpful to clarify that 1) results should be discarded only if the results are associated with a known error 

that occurred during analysis (e.g., the replicate was spiked twice) or through a statistically accepted 

analysis of outliers, and 2) that laboratories should not simply select the best seven results of a dataset. 

The optional step involves iterative testing to verify that the determined MDL is reasonable; EPA has 

observed that few organizations bother to perform this step. EPA also has observed that when a method 

involves a large number of analytes, it can be difficult to get all analytes to pass the iterative test in the 

same run. The MDL procedure would benefit from guidance on how and when to address each of these 

issues. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The MDL 

meets this condition. The MDL has been used experimentally since 1980 and in a regulatory context since 

1984. The MDL procedure is the most widely used and, therefore, the most widely tested detection limit 

procedure in the history of detection approaches. Within EPA, the MDL has been used by the Office of 

Research and Development, Office of Science and Technology, Office of Ground Water and Drinking 

Water, Office of Solid Waste, Office of Emergency and Remedial Response, and other offices. The MDL 

also has been used outside of EPA in methods published by ASTM International, in Standard Methods for 

the Examination of Water and Wastewater– jointly published by the American Public Health Association 

(APHA), the American Water Works Association (AWWA), and the Water Environment Federation 

(WEF), and in methods elsewhere. Although the MDL has been criticized, it is the most widely used 

approach of detection within the environmental chemistry community. 

Stakeholders commenting on EPA’s 2003 assessment of detection and quantitation procedures 

noted that the extent to which the MDL has been used is a result of EPA’s approval and inclusion of the 

procedure in 40 CFR part 136, and does not necessarily demonstrate that the MDL procedure produces an 

accurate assessment of detection. EPA agrees that the extent of use could be attributed, in part, to 

promulgation of the procedure at 40 CFR part 136. For this reason, EPA has not relied on widespread use 

of the MDL as a sole or over-riding argument for its continued use. Rather, EPA views widespread use of 

the MDL as one of many factors to be considered when evaluating which concept or concepts best meet 

the Agency’s needs under the Clean Water Act. For example, EPA agrees that the ability of a procedure 

to produce an accurate assessment of detection capabilities is an important consideration, and addresses 

this issue repeatedly throughout the assessment. In this chapter, for example, the ability of a procedure to 

produce an accurate assessment of detection capabilities is addressed in 

• Criterion 1, condition 3, which concerns error rate, 

• Criterion 1, condition 4, which concerns use of standards to control operation of the procedure, 

•	 Criterion 2, which addresses the ability of the procedure to realistically reflect laboratory and 

method performance, and 

5 - 6

•	 Criterion 4, which addresses the ability of the approach to identify the concentration at which 

users can be confident a substance reported as present is really present. 

5.1.1.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The MDL procedure is designed to demonstrate laboratory performance with a given analytical 

method, and can be applied to a broad variety of physical and chemical methods. The procedure also 

recognizes the importance of analyst experience and explicitly directs the analyst to employ all sample 

processing and computation steps given in the analytical method when determining the MDL. 

When the MDL procedure is followed as intended (i.e., all sample processing and analysis steps 

of the method that are applied to routine analyses are included in determination of an MDL), the 

demonstrated MDL will include some of the routine variability associated with the laboratory and the 

method. 

Stakeholders commenting on EPA’s assessment stated that, because the MDL procedure is 

performed in a single laboratory, on the same day, by the same analyst, in a single matrix, using a 

minimum of 7 replicates, the procedure does not account for all sources of variability. These commenters 

believe that the procedure does not address inter- or intralaboratory, long-term, concentration range, 

analyte/method, or matrix variability. EPA notes that the MDL procedure does not include the 

restrictions noted by these stakeholders (e.g., users are not restricted to use of only seven replicates; to 

analysis of all replicates on the same day; or to determination of MDLs only in reagent water). The MDL 

procedure includes, for example, instructions for determining a matrix-specific MDL and specifies that the 

procedure requires a complete, specific, and well-defined analytical method. However, EPA also 

recognizes that in practice the MDL procedure may be performed in the manner described by these 

comments and that doing so will limit the amount of routine variability reflected in the results. 

The MDL procedure provides users with the flexibility needed for multiple applications. For 

example, if a laboratory desires to evaluate its performance using a single method to analyze a particularly 

difficult matrix over a period of time (e.g., one year), the MDL procedure allows such an evaluation. 

However in some cases, the MDL procedure might benefit with specific provisions for including sources 

of variability that may not be addressed when following the minimum requirements of the MDL 

procedure. 

Stakeholders commenting on EPA’s assessment directed most of their concern at the lack of longterm variability in the MDL procedure. These commenters pointed to the American Council of 

Independent Laboratories (ACIL) procedures for calculating the critical level and long term-MDL (LTMDL) and to the US Geological Survey’s (USGS) procedures for generating their LT-MDL. These 

procedures include the collection of blanks over a long period of time to include this source of variability. 

The commenters stated that the lack of long-term variability leads to underestimates of Currie’s critical 

value (Lc), and one commenter included sets of blanks collected over 3 months to demonstrate this effect. 

EPA assessed the effect of long-term variability on calculated limits by simulating multiple 7-

replicate subsets from the full dataset offered by the commenter, and compared these short-term critical 

levels to the critical level calculated using the full data set. Although the range of days from which the 

sets of 7 replicates were simulated varied from between one week to greater than 3 weeks, a graphical 

5 - 7

analysis of the data did not reveal any effect of time on the resulting Lc. The total number of blanks also 

did not seem to have an effect on the percentage of short-term Lc results that exceeded the overall Lc. 

Details of this assessment are provided in Appendix C, along with possible reasons why expected 

differences were not observed. 

As noted in Section 3.3.3 of this RAD, a larger number of replicates will yield better estimates for 

standard deviations, and therefore, better estimates of Currie’s Lc and EPA’s analogous MDL. However 

the analysis performed in Appendix C demonstrates that MDLs estimating Lc based on 7 replicates are not 

biased low. These values are merely less precise than those based on a larger number of replicates. As 

noted previously, the current MDL procedure does not restrict laboratories to using 7 replicates (to the 

contrary, the procedure specifies a minimum of 7 replicates), nor does it restrict laboratories to performing 

the replicates on a single day. Laboratories that wish to perform more tests or to conduct their tests over a 

longer period of time should be encouraged to do so. 

Due to the variability inherent in measurement science, instrumentation, and the humans 

conducting analyses, laboratories may routinely obtain detection limits that are lower or higher than those 

obtained in another laboratory . Thus, when an MDL is determined during method development, it is 

important to determine that MDL in more than one laboratory to ensure the MDL published in the method 

reflects demonstrated expectations of method performance in a community of laboratories. It is not 

necessary for this community to include the entire universe of all possible laboratories that might desire to 

practice the method. Rather, during the stages of method development and validation, this community 

only should include well-operated laboratories with analysts who are experienced with the techniques 

used in the method, and have some familiarity conducting all of the steps in the new method before 

generating MDLs that will be published with the new method. 

In recent years, EPA's Office of Science and Technology has used single-laboratory studies to 

develop an initial estimate of the MDL for a new or modified method, and has verified these MDLs in 

interlaboratory studies or by conducting additional single-laboratory studies in other laboratories. For 

example, when EPA initially drafted Method 1631 for measurement of mercury, EPA estimated the MDL 

to be 0.05 ng/L based on results produced by a contract research laboratory. Additional single-laboratory 

MDL studies conducted in other laboratories suggested that the MDL should be raised to 0.2 ng/L to 

better reflect existing capabilities of the measurement community. During EPA’s interlaboratory study, 

each laboratory was asked to conduct an MDL study. Every laboratory in the interlaboratory study met 

the MDL of 0.2 ng/L (laboratory MDLs ranged from 0.04 to 0.18 ng/L), the value published in the 

promulgated version of Method 1631. 

The MDL procedure addresses demonstrated expectations of laboratory and method performance, 

including routine variability, and users should not be restricted to the minimum requirements of the MDL 

procedure. If the MDL procedure is employed for method development purposes, it should be performed 

in multiple laboratories to ensure that it adequately demonstrates expectations in a community of qualified 

laboratories. 

5.1.1.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance. 

The MDL procedure is among the most practical and affordable procedures that have been 

suggested for determining detection limits because of the reasonable number of minimum replicates 

(seven) and the relative ease with which the spiking experiments can be designed and the resulting data 

5 - 8

analyzed. The MDL is designed for use by a single laboratory, and can be performed by a single analyst 

using a single instrument. And the MDL procedure also allows MDLs from several analysts or 

instruments within a laboratory, or between laboratories to be pooled and provide an estimate of the range 

of MDLs that might be routinely expected. 

Use of the optional iterative procedure would increase the number of analyses by at least seven 

each time the procedure is implemented. If the procedure is implemented two times in reagent water, a 

minimum of 14 analyses are required. If the procedure is implemented two times in an alternative matrix, 

EPA estimates that 17-20 analyses may be required, given the possible need to determine the background 

concentration of the analyte in the alternative matrix. In any of these scenarios, the entire MDL 

determination can be performed in a single analytical batch (most EPA methods specify batch sizes of 20 

samples). 

5.1.1.2.4 Criterion 4:	 The detection level approach should estimate the theoretical concentration at 

which there is 99% confidence that the substance is actually present when the 

analytical method is performed by experienced staff in a well-operated 

laboratory. 

The MDL meets this condition as described under Section 5.1.1.2.1, Condition 3 of this document 

in many cases. However, EPA recognizes that there are cases where this does not hold, and that users of 

the MDL procedure see this as a significant problem. EPA sees merit in blank correction procedures 

developed by ACIL and USGS to address these cases. In future stakeholder consultations, EPA plans to 

discuss these and other alternative solutions to this problem. 

5.1.1.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government. 

The MDL meets this criterion. The MDL has been applied to a variety of decisions under the 

CWA since 1984. In addition, many States and others have adopted the MDL in their own programs. 

5.1.2 Evaluation of the ASTM International Interlaboratory Detection Estimate (IDE) 

The interlaboratory detection estimate (IDE) was published in 1997 by ASTM International as 

standard D6091. The IDE was developed with support from members of the regulated industry to provide 

a comprehensive detection limit procedure that addressed the concerns of the regulated industry, 

statisticians, and analysts involved in ASTM Committee D19 on water. 

A brief summary of the procedure is given in Section 5.1.2.1, and Section 5.1.2.2 presents EPA’s 

assessment of the IDE against the five criteria established for evaluating detection limit approaches (i.e., 

Criteria 1-4, and Criterion 6). 

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5.1.2.1 Description of the IDE Approach and Procedure 

ASTM Designation D 6091 is the Standard Practice for 99 %/95 % Interlaboratory Detection 

Estimate (IDE) for Analytical Methods with Negligible Calibration Error. As stated in the practice: 

"The IDE is computed to be the lowest concentration at which there is 90 % confidence 

that a single measurement from a laboratory selected from the population of qualified 

laboratories represented in an interlaboratory study will have a true detection probability 

of at least 95 % and a true nondetection probability of at least 99 % (when measuring a 

blank sample)." 

The IDE is determined and verified using a procedure containing 5 major steps with 

approximately 53 substeps and conditions. The full text of the IDE procedure is available from ASTM 

International. The five major steps and their functions are given in Section 6 of the IDE procedure and are 

as follows: 

1. Overview of the procedure. 

2.	 IDE Study Plan, Design, and Protocol - in this section, the task manager (study supervisor) chooses 

the analyte, matrix, and analytical method. Details are given for range finding; the concentrations to 

be used in the study; the study protocol (ASTM Practice D 2777 is suggested); the allowable sources 

of variation; and the number of laboratories, analysts, and days over which the study will be 

conducted. 

3.	 Conduct the IDE Study, Screen the Data, and Choose a Model - after the study data are collected and 

screened according to ASTM Practice D 2777, interlaboratory standard deviation (ILSD) versus 

concentration data are tabulated and one of three models is fit to the data. The first attempt is at 

fitting a constant model. If the attempt fails, a straight-line model is attempted. If the straight-line 

model fails, an exponential model is fitted. After fitting, the model is evaluated for reasonableness 

and lack of fit. If the model fails, the study supervisor determines if a subset of the data should be 

analyzed or if more data are needed. 

4.	 Compute the IDE - the IDE is computed using the ILSD model selected in Step 3 to estimate the 

interlaboratory standard deviation at a true concentration of zero and at the IDE, using a mean 

recovery model to transform measured and true concentrations. The IDE is computed as a one-sided 

90 % confidence upper statistical tolerance limit. 

5.	 Nontrivial Amount of Censored Data - this section addresses the effect of "non-detects" or "less-than." 

Suggestions are given to see if uncensored data can be obtained from the laboratories or if the study 

needs to be augmented with additional data. Suggestions are given for fitting a model to data that 

contain less than 10 % non-detects or less-than to produce an IDE. 

5.1.2.2 Assessment of the IDE Against the Evaluation Criteria 

The following five subsections discuss the IDE approach and procedure in the context of the five 

evaluation criteria that concern detection limit approaches. 

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5.1.2.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. The Electric Power Research Institute provided input into 

the design of EPA Method 1631 and 1638 Validation Studies for the purpose of calculating IDEs and 

IQEs. EPRI also calculated IDEs and IQEs based on these data. These two datasets include a total of ten 

metal analytes and therefore do not cover a wide range of analytical techniques and methods. Other than 

these two datasets, EPA is not aware of any organization, including ASTM International, that has 

conducted a study to test the procedure as written (i.e., designed and implemented an interlaboratory study 

that involves estimating an initial IDE [IDE0] and multilaboratory analyses of multiple concentrations of 

each matrix of interest surrounding IDE0). Developers of the approach performed limited testing of the 

approach on 1) simulated data sets and 2) real-world data sets generated for other purposes. However, 

these real-world data sets are of limited value for testing the IDE because the concentration ranges 

associated with the data are above the low-level region of interest. As part of this reassessment, EPA 

tested a variant of the IDE procedure on single-laboratory data sets designed for characterization of an 

analytical method in the region of detection. Despite the lack of comprehensive testing, the procedure can 

be tested, and therefore meets part of this condition. Specifically, the IDE meets the condition that it can 

be tested, but it only partially meets the condition that it has been tested. 

Condition 2: It has been subjected to peer review and publication. Although the IDE has not been 

published in the peer-reviewed scientific literature, the IDE has undergone extensive review and ballot by 

members of ASTM Committee D 19, many of whom are qualified peer reviewers. Therefore, although the 

IDE does not meet this condition in the sense of formal peer review and publication, it meets the intent of 

this condition (i.e., submission to scrutiny of the scientific community). In addition, the IDE was 

reviewed by four peer reviewers as part of EPA’s assessment of detection and quantitation limit 

approaches. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. In theory, 

expert statisticians could estimate the error rate of the IDE. However, the IDE procedure is extremely 

complex from an analytical chemistry and statistical perspective. As a result, it is unlikely that the error 

rate could be estimated by the typical users of the analytical method to which it would be applied, or even 

by the typical developers of an analytical method. Moreover, EPA found the model selection procedure to 

be highly subjective, a situation likely to yield different IDEs from the same data set, depending on the 

staff involved in performing the calculations. In practice, such conditions make it impossible to estimate 

the actual error associated with the IDE. Therefore, the IDE does not meet this condition. 

One of the four peer reviewers charged with evaluating EPA’s assessment of detection and 

quantitation limit approaches concurred with EPA’s assessment of the IDE, specifically stating, “I agree 

that the IDE procedure as outlined is so complex as to make simple determination of error rates 

associated with it untenable.” (Piegorsch, 2002) 

One stakeholder, however, stated that concerns about the complexity and subjectivity in the IDE 

(and IQE) procedures were unimportant, in part, because IDEs calculated using different models were 

generally very close, and in part because “user-friendly software is available that will automatically 

perform the IDE and IQE calculations.” To consider the merit of this comment, EPA calculated singlelaboratory variants of the IDE using each of the four major model types using the Episode 6000 data set, 

and true interlaboratory IDEs for each model type using the Method 1631 and 1638 interlaboratory study 

data sets. Results of these calculations, along with the RSDs between the different IDE values obtained 

for each analyte, are presented in Appendix B. Based on the calculated RSDs, there is a large amount of 

variability between the single-laboratory variants of the IDEs calculated using the different models. 

Generally, the IDEs calculated using the constant model were much greater than those calculated using the 

5 - 11

other models. The hybrid model generally yielded the lowest IDEs, and the IDEs calculated using the 

hybrid and exponential models were quite similar for some analytes, but quite different for others. While 

one might hope that the variability between models would decrease if interlaboratory variability were 

included in the calculations (as designed), EPA found this was not the case. To the contrary, RSDs 

between the IDEs calculated from the interlaboratory datasets suggest that variability between model 

estimates appears to increase when the additional variability between laboratories is included. 

To evaluate the commenters’ statement that the complexity and subjectivity of the procedures was 

not important because the calculations can be automatically performed using “user-friendly software,” 

EPA evaluated the two software packages offered by the commenter. One package was a DOS-based 

program called “QCalc” and the other was an Excel spreadsheet that calculates IDEs based on Excel 

functions, macros, and the Solver add-in function. EPA calculated single laboratory variants of the IDE 

for a random subset of 20 analytes from the Episode 6000 study using 1) the QCalc package, 2) the Excel 

spreadsheet, and 3) the suite of SAS programs EPA has been using to calculate IDEs as part of this 

assessment. To ensure that differences between results were due to the programs themselves, the same 

data were used for each program. Results of this comparison are provided in Appendix B to this Revised 

Assessment Document. 

One immediate problem was that comparisons could not be made between IDEs calculated using 

QCalc and the other software packages for all of the models because the QCalc package only performs the 

IDE calculation using two of the models (exponential and hybrid). The ASTM IDE procedure suggests 

that one of three models be used (constant, linear, and exponential). No explanation was provided as to 

why the software was limited to two models instead of three, or why one of the two models (i.e., the 

hybrid model) used in the software was not one of the three models recommended by ASTM. (The hybrid 

model used in QCalc is recommended by ASTM for calculation of an IQE but not for an IDE.) 

Although similarities were generally observed among the various software packages when the 

same model type was applied to the same set of data, EPA did observe strong differences in the values 

calculated using the hybrid model across the various software programs. The Excel values generated 

using the hybrid model were slightly higher than those determined using EPA’s programs and 

approximately twice as high as those determined using QCalc. Possible explanations for these differences 

are given in Appendix C. 

Perhaps the most significant problem with the assumption that use of the automated software 

packages alleviates the complexity and subjectivity in the IDE procedure is that the various packages do 

not always select the same model for the same set of data. ASTM’s IDE procedure (D 6091) specifies that 

the fitting to the constant model should be attempted first. If this fitting fails, a straight-line model should 

be attempted, and if that fails, the exponential model should be fitted and evaluated for reasonableness and 

lack of fit. EPA’s SAS programs were coded to preferentially select the constant, linear, and exponential 

models for the IDE, according to this scheme. However, QCalc and Excel packages each follow a 

different scheme. As a result, the EPA and QCalc programs selected the same model type to calculate the 

IDE for only 1 of the 20 analytes, the Excel and QCalc programs selected the same model type for only 6 

of the 20 analytes, and the Excel and EPA programs selected the same model type for only 1 of the 20 

analytes. Details and possible explanations for these underlying differences can be found in Appendix C. 

Based on these differences in selecting and fitting models, it does not appear that the two 

available software programs remove all complexity and subjectivity from the IDE calculation. Instead, 

they appear to introduce new issues by using steps not included in the ASTM procedures. The results 

support EPA’s conclusion that such conditions make it impossible to estimate the actual error associated 

with the IDE, and that the IDE, as currently constructed, does not meet this condition 3. 

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Condition 4: Standards exist and can be maintained to control its operation. The IDE approach and 

procedure is supported by a published procedure (standard) to control its operation. The procedure gives 

the steps to be followed in determining the IDE and instructs the study supervisor how to gather the data 

and compute an IDE. 

There are several "gray areas" in the published procedure. The most significant of which is in the 

description of model selection. The procedure provides insufficient guidance on use of residual plots to 

evaluate and select models and, as a result, selection of the model may be very subjective, especially if the 

number of concentrations is low. The problems noted in preceding Condition 3 concerning the use of 

different model selection strategies among three different programs (the QCalc and the Excel software 

packages provided by a commenter and EPA’s SAS programs) is a direct reflection of the subjective 

nature of model selection likely to result from the lack of guidance in the procedure. The discussion of 

what model to use after rejecting the exponential and linear model is also very vague. The Rocke and 

Lorenzato (hybrid) model is mentioned, as well as models with more than one coefficient. Much of the 

data evaluated by EPA have tended to suggest the exponential model, based on the statistical tests 

discussed. However, those data have almost always shown residual “patterns” when using this model, 

which would then lead to consideration of other models. In addition, fitting the constant model is never 

discussed in detail. Most likely, this is done by simply calculating a mean (weighted if necessary) of the 

variances from the different concentrations; however, such calculations are never explicitly stated. 

The IDE standard gives procedures that are inconsistent with procedures in the IQE standard, even 

though the two approaches should be consistent for a given analyte with a given method. For example, the 

exponential model figures prominently in the IDE procedure, where it is one of the three main models 

discussed. The Rocke and Lorenzato model is not discussed in the IDE procedure, but it figures 

prominently in the IQE procedure. In theory, a single model should support the definition of both the 

detection and quantitation limits for a given analyte by a given method. As another example, the IDE 

procedure includes a multiplier to account for bias in estimating the true standard deviation with the 

sample standard deviation, but the IQE does not. 

Although the IDE is supported by a published procedure, EPA found that the procedure will not 

adequately control its operation because of the degree of subjectivity involved in implementing the 

procedure and inconsistencies with its IQE counterpart. Therefore, the IDE does not meet this condition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The IDE was 

published by ASTM, International in 1997. ASTM, International is a voluntary consensus standards 

organization that constitutes part of the relevant scientific community, however, seven years after 

publication no new or revised ASTM standard has included detection limits using the IDE approach. EPA 

is not aware of an IDE that has been published in the open literature or in an analytical method. Thus, the 

IDE partially meets this criterion. 

5.1.2.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The IDE procedure, D6091, is designed to reflect expectations of interlaboratory performance, 

including routine variability. The procedure contains extensive instructions for dealing with unusual 

conditions, including sources of variability and outliers. However, EPA studies of a single-laboratory 

variant of the procedure suggested that the procedure may not always work as intended. For example, 

model selection based upon hypothesis tests (as described in Section 6.3.3.2 of D6091) almost always 

indicated that the exponential model should be used, even when the data seemed to be show constant or 

approximately linear error, while examination of residual plot indicated “systematic behavior” (i.e., non5 - 13

random deviations from the model) for the exponential and linear models. Information about singlelaboratory (or within-laboratory) variability is very important because assessments of laboratory 

performance is based on the variability (uncertainty) of the dat produced at that laboratory. Compliance 

measurements are made in a single laboratory and the results are reported with the uncertainty (variability) 

associated with that dataset. 

Another concern with the IDE procedure is that use of the non-mandatory appendices in ASTM D 

6512 to determine the fit of a model may produce results that differ from those that would be obtained by 

using the default procedures for testing model fit that are built into off-the-shelf statistical software, such 

as those used in the Excel spreadsheets discussed in Section 5.1.2.2.1. Such observations, along with the 

concerns described in Section 5.1.2.2.1, condition 4, lead EPA to believe that, while the IDE approach 

addresses demonstrated expectations of laboratory and method performance, the IDE procedure does not 

adequately do so. Therefore, the IDE only partially meets this criterion. 

5.1.2.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance. 

The IDE procedure is designed for use by an ASTM International study supervisor or task 

manager and not as a procedure that a single laboratory can use to evaluate method performance. EPA is 

aware that ASTM Committee D 19 is developing a Within-laboratory Detection Estimate (WDE), but the 

WDE is presently only in the formative stages. The WDE may meet this criterion, but the IDE does not. 

Regarding cost, the IDE procedure would be the most costly of the procedures that EPA has 

evaluated because of the time it would take to understand and implement the procedure, and requirements 

for: 1) estimation of IDE0, 2) interlaboratory data, 3) extensive statistical intervention in determining the 

correct model, and 4) possible reanalyses if the resulting IDE does not meet the criteria in the procedure. 

5.1.2.2.4 Criterion 4:	 The detection level approach should estimate the theoretical concentration at 

which there is 99% confidence that the substance is actually present when the 

analytical method is performed by experienced staff in a well-operated 

laboratory. 

By definition, the IDE is designed to achieve "a true detection probability of at least 95 % and a 

true nondetection probability of at least 99 %." Although the 99% probability of a "true nondetection" is 

equivalent to the 99% confidence that the substance is actually present given in Criterion 4, ASTM 

International also included the simultaneous requirement for a 95% probability of a "true detection." The 

developers are using the IDE as a means to control the rates of both false positive and false negative 

results, in essence, making the IDE analogous by definition and formulaic construction to the detection 

limit (DL) defined by Currie (1968). The IDE accomplishes this goal by using a tolerance limit that 

increases the IDE well above the point at which the detection decision would be made. For a discussion 

of this issue, see Sections 3.3.6 (false positives and false negatives) and 3.3.7 (prediction and tolerance 

intervals) in Chapter 3 of this document. 

As noted in Section 2.1 of Chapter 2 of this document, Currie (1968) used the term detection limit 

(subsequently termed the minimum detectable value) to refer to a true concentration that has a high 

probability of generating measured values greater than the critical value. That is, measurements on 

samples that contain concentrations equal to the detection limit have a high probability of exceeding the 

5 - 14

critical value and are, therefore, unlikely to result in a decision that the substance is not detected in the 

sample. However, the detection decision is made on the basis of comparing sample measurements to the 

critical value. With regard to his definition of the "detection limit," Currie (1995) states “The single, most 

important application of the detection limit is for planning.” 

When the allowance for false negatives and the prediction and tolerance limits are taken into 

account, the resulting IDE is raised to the point at which the probability of a false positive is less than .01 

by several orders of magnitude. This protection against false positive results is excessive and would yield 

numerical values of little practical value for making the detection decision. 

Although there is an estimate of Currie’s Lc included in the IDE procedure, it is unclear where 

the detection decision is made (it really should be an ICE/IDE procedure). If one focuses on the IDE and 

not the Lc estimate, this criterion not met. Therefore, it is not clear whether the IDE would meet this 

criterion (No. 4). 

5.1.2.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government. 

EPA's comparison of detection limits produced by various detection limit approaches shows that 

the median IDE is considerably higher than ACS, ISO/IUPAC, and EPA detection limits. Although the 

IDE could be applied to some decisions to be made under the CWA, it may not be appropriate for all uses. 

The IDE is an implementation of Currie detection level or minimum detectable value, and may in practice 

yield results higher than these levels. At best, the IDE only partially meets this criterion. 

5.1.3 Evaluation of the ACS Limit of Detection 

The limit of detection (LOD) was developed by the Committee on Environmental Improvement 

(CEI) of the American Chemical Society (ACS). ACS is a professional society for chemists and other 

scientists and the publisher of a number of scientific journals. It is not a voluntary consensus standards 

body (VCSB), nor does it develop or publish analytical methods. In 1978, the ACS/CEI began addressing 

concerns about the lack of useful standards for interlaboratory comparisons. In 1980, the Committee 

published its "Guidelines for Data Acquisition and Data Quality Evaluation in Environmental Chemistry" 

(MacDougall, et al., 1980), which included the approaches of the LOD and the limit of quantitation 

(LOQ). 

5 - 15

5.1.3.1 Description of the ACS LOD 

The 1980 "Guidelines" define the LOD as: 

"... the lowest concentration of an analyte that the analytical process can reliably detect. 

... The LOD in most instrumental methods is based on the relationship between the gross 

, the field blank Sb, and the variability in the field blank �b analyte signal S ." t

and construct the formal relations using the equation: 

where Kd is a constant. ACS recommended a minimal value of 3 for Kd. Thus, the LOD is 3� above the 

gross blank signal, Sb. In the 1980 publication, the ACS stated that at Kd = 3, there is a 7% risk of false 

negatives and false positives. Given that the LOD is 3� above the blank, however, EPA believes that the 

risk of false positives is somewhat less than 1%. 

In 1983, the ACS Committee published "Principles of Environmental Analysis" (Keith et al., 

1983). That publication occurred after the 1981 paper on the Method Detection Limit (MDL), and 

ACS/CEI stated that the LOD is numerically equivalent to the MDL as Sb approaches zero. However, 

neither the 1980 nor 1983 ACS publications provide a specific procedure for estimating the LOD, nor do 

they provide a minimum number of observations needed to estimate the gross blank signal or the 

variability term �b. 

5.1.3.2 Assessment of the LOD Against the Evaluation Criteria 

The following five subsections discuss the LOD approach and procedure in the context of the five 

evaluation criteria that concern detection limit approaches (i.e., Criteria 1-4, and Criterion 6). 

5.1.3.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. Testing of the ACS LOD is hampered by the lack of a 

supporting procedure for establishing an LOD, and a conceptual dependence on the variability associated 

with measuring blanks. For example, there is no detailed instructions, similar to those in the IDE and the 

MDL procedures, to govern the minimum number of analyses needed to characterize the variability of a 

blank sample. Because many environmental chemistry techniques yield a zero, or possibly even negative, 

value when a blank sample is analyzed, and because the LOD approach is based on the standard deviation 

of these results, directly testing the LOD in such techniques will yield a zero or negative value. One 

solution for testing is to rely on ACS’ 1983 statement that the LOD is conceptually equivalent to the MDL 

as the blank signal approaches zero, and employ the MDL procedure as a means for indirectly testing the 

LOD approach. EPA believes that use of the MDL procedure is a viable means for testing the approach; 

therefore, the LOD meets this condition. 

Condition 2: It has been subjected to peer review and publication. The LOD meets this condition because 

the LOD definition was published in the peer-reviewed journal Analytical Chemistry in 1980 and 1983. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. The error 

rates can be estimated, so the LOD meets this condition. The error rate for both false positives and false 

negatives is stated to be 7 % in the 1980 Analytical Chemistry article. However, EPA believes that, 

because the LOD is stated to be 3 times the standard deviation of replicate measurements of a blank, the 

5 - 16

false positive rate is overstated and is actually somewhat less than 1 % whereas the false negative rate 

depends on the true concentration in the sample. 

Condition 4: Standards exist and can be maintained to control its operation. The LOD does not meet this 

condition, because it lacks a clearly defined procedure for estimating the important terms required to 

derive it. Although it may be possible to derive LOD values from data used to derive EPA MDL values, 

there is no procedure giving explicit instructions on the use of replicate blanks, replicate spiked samples, 

or a minimum recommendation for the number of replicates. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Because 

ACS does not develop and publish analytical methods, it is difficult to determine the degree of acceptance 

of the LOD. EPA has not specifically investigated the numbers of papers published in ACS journals that 

include LOD values, and EPA's literature search for detection and quantitation approaches did not uncover 

a large number of citations that promote the LOD in particular. However, ACS LOD values have 

appeared in the technical literature. Given that ACS is a relevant scientific community, and that use of the 

LOD has appeared in the technical literature, the LOD meets this condition. 

5.1.3.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The LOD approach is designed to address realistic expectations of laboratory and method 

performance, including routine variability, and thus appears to meet this criterion. Unfortunately, ACS 

has not published a procedure to implement the approach. In other words, the LOD addresses 

demonstrated expectations of laboratory and method performance in theory, but in practice, provides no 

direct means for performing these demonstrations. Therefore, EPA believes the ACS LOD only partially 

meets this criterion. 

5.1.3.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance. 

The ACS LOD approach does not meet this criterion, because it is not supported by a clearly 

defined procedure for establishing the LOD. 

5.1.3.2.4 Criterion 4:	 The detection level approach should estimate the theoretical concentration at 

which there is 99% confidence that the substance is actually present when the 

analytical method is performed by experienced staff in a well-operated 

laboratory. 

The 1983 publication associated the LOD with the "99% confidence level when the difference (St 

- Sb) > 3�." Therefore, the LOD meets this criterion. 

5.1.3.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government. 

In the absence of a procedure for determining LOD values, the ACS LOD does not meet this 

criterion because it cannot be used in a regulatory context unless it is assumed to be functionally 

equivalent to the MDL (i.e., use the MDL procedure to establish an LOD). 

5 - 17

5.1.4 Evaluation of the IUPAC/ISO Critical Value (CRV) 

The critical value (CRV) was developed by the International Union of Pure and Applied 

Chemistry (IUPAC) and the International Organization for Standardization (ISO). IUPAC and ISO are 

professional societies for chemists and other scientists. ISO develops and publishes analytical methods 

through its Task Groups. In 1995, Lloyd Currie of the National Institute for Standards and Technology 

(NIST; formerly the National Bureau of Standards) published a signature discussion of IUPAC approaches 

for detection and quantitation (Pure and Appl. Chem. 67:10, 1699-1722). Although refined during the 

intervening years (see Currie, L.A., J. Radiochem. And Nuclear Chem. 245:1, 145-156, 2000), the CRV 

approach remains basically as described in 1995. 

5.1.4.1 Description of the ISO/IUPAC Critical Value (CRV) Approach and Procedure 

The 1995 article states that the critical value (Lc) is: 

"... the minimum significant value of an estimated net signal or concentration, applied as 

a discriminator against background noise. This corresponds to a 1-sided significance 

test.” 

For a normal distribution with known variance, Lc reduces to: 

Lc = z(1-�)

�0 

where: 

1-� is the false positive error rate, recommended at 5 % (� = 0.05), and 

�0 is the standard deviation at zero concentration 

If �0 is estimated by s0 (replicate measurements of a blank), z(1-�) is replaced by the Student’s tvalue. For 7 replicates (6 degrees of freedom), the Student’s t-value is 1.943, where � = 0.05. 

5.1.4.2 Assessment of the CRV Against the Evaluation Criteria 

The following five subsections discuss the CRV approach and procedure in the context of the five 

evaluation criteria that concern detection limit approaches (i.e., Criteria 1-4, and Criterion 6). 

5.1.4.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. The lack of a supporting procedure for establishing the 

CRV, coupled with its conceptual dependence on the variability of blank measurements makes testing of 

the approach difficult. For example, if blank measurements fail to produce a response, it is impossible to 

calculate a CRV because the standard deviation of multiple zero results is zero. One solution for testing 

the approach is to assume that the CRV is about equivalent to the MDL as the blank signal approaches 

zero, and use a slightly modified version of the MDL procedure to test the CRV approach. The slight 

modification involves selecting a Student’s t-value based on � = 0.05 instead of � = 0.01, for n-1 degrees 

of freedom. EPA believes this is a reasonable assumption, and therefore, that the MDL procedure is a 

viable means for testing the CRV approach. Therefore, the CRV meets this condition. 

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Condition 2: It has been subjected to peer review and publication. The IUPAC/ISO definitions meet this 

criterion. Moreover, it is likely that these definitions have received greater peer review than any of the 

other approaches. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. The error 

rate is specified by �, with a suggested value of 0.05 (5%). Therefore, the CRV meets this condition. 

Condition 4: Standards exist and can be maintained to control its operation. The CRV is defined in the 

various publications by Currie. However, EPA’s search of the literature and the ISO web site found no 

standard for control of the approach. Therefore, the CRV does not meet this condition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Because 

IUPAC and ISO are international bodies, it is difficult to determine the degree of acceptance of the CRV 

in the U.S. and the world community. EPA has not counted the number of papers in published journals 

that include CRV values, but EPA's literature search for detection and quantitation approaches did not 

produce many citations that promote the CRV in particular. Therefore, it is difficult to determine if the 

CRV meets this condition. 

5.1.4.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The CRV approach is designed to account for the variability of measurements of the blank in the 

context of a “chemical measurement process” (method). Unfortunately, neither ISO, IUPAC, nor Currie 

have published a procedure to implement the approach. As a result, the CRV addresses realistic 

expectations of laboratory and method performance in theory, but in practice, provides no direct means for 

demonstrating this performance. Therefore, the CRV partially meets this criterion. 

5.1.4.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance 

The CRV approach is not supported by a clearly defined procedure for establishing a CRV. 

Therefore, the CRV does not meet this criterion. 

5.1.4.2.4 Criterion 4:	 The detection level approach should estimate the theoretical concentration at 

which there is 99% confidence that the substance is actually present when the 

analytical method is performed by experienced staff in a well-operated 

laboratory. 

CRV suggests � = 0.05, resulting in 1-� of 0.95 or 95 % probability of detection . Therefore, the 

CRV does not meet this criterion. 

5.1.4.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government. 

In the absence of a procedure for establishing CRVs, the CRV approach does not meet this 

criterion because it cannot be used in a regulatory context. 

5 - 19

5.1.5 Evaluation of the IUPAC/ISO Detection Limit 

The detection limit or minimum detectable value (MDV) was developed by IUPAC/ISO and 

published in the same papers as the CRV (Section 5.1.4) 

5.1.5.1 Description of the IUPAC/ISO Detection Limit Procedure 

The 1995 publications define the minimum detectable value (detection limit) as follows: 

"The Minimum Detectable Value (MDV) ... [is] ... the net signal (or concentration) of that 

value (LD) for which the false negative error is �, given LC (or �).” (see the CRV for LC) 

For a normal distribution with known variance, LD reduces to: 

LD = z(1-�) �D + Lc 

where:


z is the score variable


1-� is the false negative error rate, recommended at 5 % (� = 0.05), and


�D is the standard deviation at the detection limit


Earlier publications refer to the minimum detectable value as the detection limit. To avoid 

confusion in terminology and to help distinguish the ISO/IUPAC approach from the MDL, LOD, and 

CRV, the ISO/IUPAC detection limit in this assessment will be referred to as the Minimum Detectable 

Value, abbreviated as MDV. 

5.1.5.2 Assessment of the ISO/IUPAC MDV Against the Evaluation Criteria 

The following five subsections discuss the ISO/IUPAC MDV approach and procedure in the 

context of the five evaluation criteria that concern detection limit approaches (i.e., Criteria 1-4, and 

Criterion 6). 

5.1.5.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. The lack of a supporting procedure for establishing the 

MDV makes testing of the approach difficult. However, the MDV probably can be tested using data 

similar to those used to generate MDL values. Therefore, the MDV meets this condition. 

Condition 2: It has been subjected to peer review and publication. The IUPAC/ISO definitions meet this 

condition; moreover, it is likely that this definition has received greater peer review than any of the other 

approaches. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. The error 

rates are specified by � and �, both with suggested values of 0.05 (5 %). Therefore, the error rate is 

known. 

Condition 4: Standards exist and can be maintained to control its operation. The MDV is defined in the 

various publications by Currie. However, EPA’s search of the literature and the ISO web site found no 

standard for control of the approach. Therefore, the MDV does not meet this criterion. 

5 - 20

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Because 

IUPAC and ISO are international bodies, it is difficult to determine the degree of acceptance of the MDV 

in the U.S. and the world community. EPA has not specifically investigated the number of papers in 

published journals that include MDV values, but EPA's literature search for detection and quantitation 

approaches did not uncover a large number of citations that promote the MDV in particular. Therefore, it 

is difficult to determine if the CRV meets this criterion. 

5.1.5.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The MDV approach is designed to account for the variability of measurements of the blank in the 

context of a “chemical measurement process” in the sense that it is used in concert with a critical value 

that is based on blank measurement variability. The MDV is the true concentration that is used in the 

planning of method evaluation and development. The actual detection decision is made at the critical 

value (CRV) which is determined from measured values. The approach of a true concentration MDV and 

its associated allowance for false negatives is of little practical value in making the actual detection 

decision. Therefore, the MDV does not meet this criterion. The allowance for false negatives in a 

regulatory context is discussed in greater detail in Chapter 3. 

5.1.5.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance 

The MDV approach is not supported by a clearly defined procedure for establishing MDV values. 

Therefore, the MDV does not meet this criterion. 

5.1.5.2.4 Criterion 4:	 The detection level approach should estimate the theoretical concentration at 

which there is 99% confidence that the substance is actually present when the 

analytical method is performed by experienced staff in a well-operated 

laboratory. 

The allowance for false negatives reduces the probability of false positives to a value smaller 

than 1% by several orders of magnitude. . This protection against false positive results is excessive and 

would yield numerical values of little practical value for making the detection decision. Perhaps more 

importantly, as noted by Currie (1995) and discussed in Section 5.1.2.2.4 of this document, the detection 

decision is made on the basis of comparing sample measurements to the critical value. Therefore, the 

MDV does not meet this criterion. 

5.1.5.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government 

In the absence of a procedure for establishing MDV values, the MDV approach does not meet to 

meet this criterion because it cannot be used in a regulatory context. 

5.1.6 Evaluation of the American Council of Independent Laboratories (ACIL) Critical Value 

During the comment period on the February 2003 assessment document, the American Council of 

Independent Laboratories (ACIL) submitted a procedure that was developed to address errors, which are 

referred to as “bias”, that may arise under certain conditions when estimating detection limits. The ACIL 

5 - 21

procedure separates estimation of the detection limit into two cases; cases where analyses always produce 

a numeric result (i.e., even so-called “blank” samples produce a signal), and cases where tests do not 

always produce a numeric result (i.e., blank samples appear to produce no signal). Blanks that do not 

produce a signal may do so either because they really are blanks, or the instrument is suppressing the 

signal. For convenience, EPA refers to these as Case I and Case II, respectively. Analysis of metals with 

inductively coupled plasma optical emission spectroscopy (ICP-OES) is an example of ACIL Case I, and 

analysis of organic pollutants with gas chromatography/ mass spectrometry is an example of ACIL Case 

II. Although the ACIL procedure appears to be a work-in-progress, it has some interesting approaches for 

the use of blanks, and is similar in some respects to the USGS LT-MDL procedure. 

5.1.6.1 Description of the ACIL Approach and Procedure 

For Case I analyses, ACIL offers procedures for calculating a limit that approximates Currie’s 

critical value (LC) and procedures for calculating a limit that approximates Currie’s detection limit (LD). 

As discussed in Chapter 2 and noted again in Section 5.1.5 above, Currie’s LD was designed to account 

for the variability of measurements of the blank in the context of a “chemical measurement process” in the 

sense that it is used in concert with a critical value that is based on blank measurement variability. The LD 

is the true concentration that is used in the planning of method evaluation and development. The actual 

detection decision is made at the critical value (LC), which is determined from measured values. The 

approach of a true concentration LD and its associated allowance for false negatives is of little practical 

value in making the actual detection decision. For this reason, EPA focused its assessment of ACIL’s 

procedure on the ACIL version of Currie’s critical value rather than the ACIL version of LD. 

For Case II analyses, ACIL suggests a procedure that does not rely on the Currie LC and LD 

framework. Instead, the procedures involve picking an initial spike value, adjusting that level up or down 

based on whether the analyte was detected, and spiking seven replicates at the new level. 

A brief description of each procedure is provided below. 

ACIL’s Case I Critical Value (ACIL LC) 

As with EPA’s MDL, the ACIL LC is an attempt to approximate Currie’s critical value. Whereas 

EPA’s MDL is based on the standard deviation of blank samples spiked with low levels of the target 

analyte, ACIL’s Case I detection limit is based on the standard deviation of the blank samples run as part 

of the laboratories ongoing QC program. (Because some methods will not yield a result when blanks are 

analyzed, ACIL’s LC procedure is accompanied by a spiked sample approach that can be used with those 

methods.) 

Although ACIL does not formally define ACIL LC, a footnote 2 to the procedure describes it as 

“very similar to Currie’s critical level, LC (Anal. Chem. Vol. 40 No 3, March 

1968, p586). It is the level at which there is a given confidence that a result can 

be distinguished from the blank.” 

Key features of the ACIL Case I detection limit are as follows: 

•	 The procedure relies on the use of blanks (instead of low-level spikes) to estimate standard 

deviation. 

• When a sufficient number of blanks are used in the calculation, the mean blank result is added 

5 - 22

into the calculation to account for high bias exhibited in the blanks. 

•	 ACIL states that at least 7 blanks should be used, but recommends more (as many as 100). If the 

number of replicates is small, ACIL recommends using a tolerance interval calculation for 

estimating ACIL LC. Instead of defining exactly what constitutes a “small” number of replicates, 

ACIL loosely defines it as fewer than 20 or 30. The confidence level for the tolerance interval 

also is not specified. If the tolerance level approach is used, the mean blank result is not included 

in the calculation (unlike the calculation used when there are more than 20 to 30 results). 

•	 If multiple instruments are to be used for the same test and will have the same reporting limit, a 

minimum of 7 blank results from each instrument should be used, and the results should be 

combined to generate the standard deviation. 

•	 It is acceptable (and expected) that some results will have negative values, and these negative 

values should not be censored. Outlier removal is allowed, using a statistically accepted test, if 

appropriate cautions are taken to guard against excessive or inappropriate rejection of data. 

•	 ACIL provides a verification procedure that is based on comparing the variance of the blank 

results to results from a new set of blanks. 

• ACIL suggests reporting all results that meet or exceed the ACIL LC. 

The formula for ACIL LC is: 

Where is the mean of blank results 

s is the standard deviation of blank results, and 

n is the number of blank results 

ACIL’s Case II Detection Limit 

For Case II analyses, ACIL’s procedures involve picking an initial spike value, adjusting that level 

up or down based on whether the analyte was detected, and spiking seven replicates at the new level. 

Details of this procedure are as follows: 

•	 Unlike the procedures used for methods that yield numeric results, ACIL Case II procedures 

would use spiked samples to determine the detection limit for methods that do not always yield 

numeric results. 

• An initial spike value is chosen based on prior experience. (Detailed guidelines are not provided.) 

•	 One replicate at this level is analyzed; if the analyte is detected, a new sample should be prepared 

at 1⁄2 the initial spike value. If the analyte is not detected at the original level, a new sample 

should be prepared at 2x the initial spike value. This process is repeated to find the lowest level 

that can be detected 

•	 Once that level is identified, a minimum of 7 replicates spiked at the lowest level at which that 

analyte was detected are analyzed, and the replicates must be analyzed in three different batches. 

If the analyte is detected in all replicates, the Case II MDL is set to this spike value. If the analyte 

is not detected in all 7 replicates, at least 7 additional replicates are prepared and analyzed at 

twice this value. If the analyte is detected in all 7 replicates spiked at this higher concentration, 

the Case II MDL is set to this higher spike value. This process is repeated until the analyte is 

detected in all 7 replicates. 

5 - 23

•	 The ACIL procedure includes a verification step that consists of spiking the reference matrix at 1 

to 3 times the Case II MDL (or 1 to 4 times for multi-analyte methods) to verify that the analyte(s) 

can be detected. If not, the test is repeated at increasing spike levels until detection, and setting 

the Case II MDL to the level where the analyte(s) were first detected. 

• ACIL suggests reporting all results that meet or exceed the Case II MDL. 

5.1.6.2 Assessment of the ACIL LC against the Evaluation Criteria 

The following five subsections discuss the ACIL LC approach and procedure in the context of the 

five evaluation criteria that concern detection limit approaches (i.e., Criteria 1-4, and Criterion 6). 

5.1.6.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. Although ACIL had not conducted an exhaustive study to 

test the ACIL LC, ACIL did apply data generated from member laboratories to the procedure in order to 

calculate ACIL LC values. ACIL also compared those values with values produced by EPA’s MDL using 

the same procedure. The results of these tests are included in the public docket supporting this 

assessment. As part of its own assessment, EPA also tested the procedure using data obtained from the 

U.S. Geological Survey. In this testing, EPA generated ACIL LC values, compared those values with 

values produced by other procedures, and calculated error rates associated with each of the values. Given 

these studies, the ACIL LC meets this condition. 

Condition 2: It has been subjected to peer review and publication. The ACIL procedure was developed to 

support ACIL’s comments on EPA’s 2003, assessment, and it has been subjected to limited peer review 

within ACIL’s member community. Although ACIL references publication of the procedure on the ACIL 

website, EPA made repeated attempts to locate the procedure on the website over a period of several 

months, and was unable to locate it. Given the limited peer review beyond the member community, and 

the lack of publication in a publicly accessible medium, the ACIL procedure does not meet this criterion. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. The ACIL 

procedure meets this condition. According to the formula used for estimating ACIL LC, the error rate, is 

specified by �, with a suggested value of 0.01(1%). EPA was able to evaluate this error rate using a small 

set of data provided by the US Geological Survey. The data included spiked and blank sample results for 

18 pollutants, most of which were analyzed by multiple methods, yielding 75 unique analyte/method 

combinations. For each combination, 25 - 52 blanks were provided. EPA used these blanks to calculate 

the ACIL LC, and compared the results of individual blanks with the calculated ACIL LC. (Details of this 

assessment are provided in Appendix C.) In theory, no more than 1% of the blanks should have produced 

a result that exceeded the ACIL LC. Although the sample size was insufficient to conclusively 

demonstrate the error rate of the ACIL LC, the results suggest the actual error rate is close to the estimate 

of 1%. In this case, the observed mean error rate was 1.9%, and the highest error observed for any 

method/analyte combination was only 3.8%. Given the small sample size, failure of a single blank could 

(and did) result in a 3.8% failure rate, suggesting that this study may yield an error rate that is larger than 

that which would be observed in a larger study. Regardless, it is clear that the ACIL LC meets this 

condition because the estimated error rate is given as part of the procedure, and the actual error rate can be 

calculated through studies such as the one described above. 

5 - 24

Condition 4: Standards exist and can be maintained to control its operation. The ACIL LC is supported by 

a written procedure (standard) to control its operation. However, the procedure appears to be in draft 

form, is somewhat difficult to follow and interpret, and contains inconsistencies and ambiguities that are 

typical of a draft document. In particular, the instructions for Case II are not as clear or detailed as those 

for Case I. 

As an example of the inconsistencies, a footnote to the ACIL LC states that a tolerance interval 

will be a more reliable estimate of the ACIL LC if the number of blanks is small (i.e., fewer than 20 or 30). 

This implies that the tolerance interval calculation and preferred ACIL LC will converge as the number of 

blank results increases. However, this is not the case. The tolerance interval calculation will almost 

always yield a higher result than the preferred ACIL LC calculation. The only way that the tolerance 

interval calculation will result in an ACIL LC that is either lower or equal to the original ACIL LC is when 

blank contamination is high (unlike the preferred ACIL LC calculation, the tolerance interval calculation 

does not include the mean of the blanks). It is unclear why the reliability of one calculation compared to 

the other depends on the number of blank results. 

An example of the ambiguities in the procedure is that the alternative calculations, such as the 

tolerance interval calculation, are presented as suggestions instead of requirements. This could lead to 

confusion, as now written, if, as ACIL recommends that, the ACIL LC be used as a reporting limit. 

A different type of ambiguity in the procedure concerns the lack of sufficient detail to ensure 

consistent application. For example, it is not clear exactly when the tolerance interval calculation is to be 

used because the procedure defines small as 20 - 30 samples. When would 20 samples be sufficient and 

when would 30 samples be sufficient? Moreover, the tolerance interval calculation does not specify the 

confidence level used. In an example, both 99% and 95% are given as possibilities. In comparison, the 

critical value calculated in ASTM’s IDE sets the confidence level at 90%. Setting the confidence level at 

99% will yield an ACIL LC value between 11% and 37% higher than one calculated at 95%, based on the 

numbers of blank results for which the tolerance interval approach is suggested. 

Given these problems, the current ACIL procedure does not meet this condition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The ACIL 

LC was supported by a large number of commenters, most of whom came from the ACIL member 

community or the environmental laboratory community. Of note, however, is that supporters included 

instrument vendors, consultants, and several members of the industrial community, including the Interindustry Analytical Group which offered its own approach to detection and quantitation and which has 

been highly supportive of the ASTM IDE and IQE approaches. Therefore, EPA believes that the ACIL LC 

meets this condition. 

5.1.6.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The ACIL LC is designed to address realistic expectations of laboratory and method performance, 

including temporal variability, instrument variability, analyst variability, and high bias observed in blank 

results. Based on EPA’s analysis of the ACIL LC presented in Appendix C, EPA believes that the 

approach meets this criterion provided it is interpreted and applied consistently. (Concerns about the need 

for clarification of the procedure are described in Section 5.1.6.1, Condition 4). 

5 - 25

5.1.6.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance 

The ACIL LC meets this criterion. It is similar to the EPA MDL procedure, but it relies on the use 

of QC data generated during routine laboratory operations, thereby making it even more cost effective 

than the MDL. 

5.1.6.2.4 Criterion 4:	 The detection level approach should estimate the theoretical concentration at 

which there is 99% confidence that the substance is actually present when the 

analytical method is performed by experienced staff in a well-operated 

laboratory 

Footnote 2 to the ACIL procedure describes the ACIL LC as “very similar to Currie’s critical 

level, LC (Anal. Chem. Vol. 40 No 3, March 1968, p586). It is the level at which there is a given 

confidence that a result can be distinguished from the blank.” According to the formula used for 

estimating ACIL LC, the error rate is specified by �, with a suggested value of 0.01(1%). This alpha value 

means that, if the analyte is not present in the sample, it will be reported as present (i.e., a false positive) 

no more than 1% of the time. In lay terms, this suggests 99% confidence that, if a substance is reported as 

present, it really is present. Therefore, the ACIL LC meets this criterion. 

5.1.6.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government 

If EPA’s interpretation of the ACIL procedure is correct, the ACIL LC appears to meet this 

criterion. 

5.1.7 Evaluation of the USGS Long-term Detection Limit (USGS LT-MDL) 

The USGS National Water Quality Laboratory (NWQL) began using the EPA MDL procedure in 

1992. USGS NWQL has since developed a variant of the MDL called the long-term MDL (LT-MDL) that 

has been in routine use by the NWQL since 1999. The procedure for calculating the LT-MDL is 

described in Section 5.1.7.1 below. Section 5.1.7.2 describes EPA’s assessment of the LT-MDL against 

the five evaluation criteria that concern detection limit approaches (i.e., Criteria 1 - 4, and Criterion 6). 

5.1.7.1 Description of the USGS Approach and Procedure 

As described in the USGS Open-File Report 99-193, the LT-MDL is a modification of the EPA 

MDL designed to “capture greater method variability,” thereby leading to higher detection limits than 

those obtained using the EPA MDL procedure. As described by USGS, and noted in Chapter 2, the LTMDL is based on many of the same fundamental assumptions as the MDL, namely: 

1. Normal data distribution, 

2. Constant standard deviation from the spike concentration down to zero, and 

3. 	 Best-case detection condition (because LT-MDLs typically are determined by spiking the analyte in a 

clean matrix, e.g., reagent water). 

5 - 26

The LT-MDL is determined using low-level spikes of reagent water. three primary 

differences between the EPA MDL and the USGS LT-MDL procedures are: 

1. Larger minimum number (24) of spike samples, 

2. Longer time period, and 

3. Combining results from different instruments and analysts in the determination of the LT-MDL. 

The USGS Open File Report does not provide an example of the exact calculation used for the 

LT-MDL. A originally presumed that the standard deviation of the results from the 24 spiked sample 

analyses is multiplied by the Student’s t-value appropriate for 23 degrees of freedom (t=2.499). 

However, USGS comments submitted in response to EPA’s assessment of detection and 

quantitation approaches included a copy of a presentation from the USEPA Region 6 12th Annual Quality 

Assurance Conference, in Dallas, Texas in August 2002. provided significant additional 

information on the calculation of the LT-MDL. ifically, the LT-MDL uses “F-pseudosigma” (F�) 

place of S, the sample standard deviation, used in the EPA MDL calculation. pseudosigma is a nonparametric measure of variability that is based on the interquartile range of the data. may 

be calculated using either the mean or median of a set of long-term blanks, or from long-term spiked 

sample results, such that: 

where: 

M = mean or median of blank results 

n = number of spiked sample results, and 

F� = F-pseudosigma, a nonparametric estimate of variability calculated as: 

where: 

Q3 and Q1 = the 75th percentile and 25th percentile of spiked sample results, respectively. 

USGS believes that the use of F� provides an estimate that is more robust and not influenced by outliers. 

Like the EPA MDL, the LT-MDL is designed to limit the chance of a false positive result to �1%. 

However, the LT-MDL is designed to be used in conjunction with a “laboratory reporting level” (LRL) as 

part of an overall reporting scheme for the NWQL. As described by USGS, the LRL is set as a multiple of 

the LT-MDL. The multiplier varies, depending on the mean/median recovery of the analyte in the spiked 

samples used for the LT-MDL. If the mean or median recovery is 100%, then the multiplier is 2. 

mean or median recovery, the multiplier increases to 2.7, and at 50% recovery, the LRL multiplier 

increases to 4. n each of these cases, the multiplier is essentially equivalent to dividing twice the LTMDL by the mean recovery (i.e., 2.7 LT-MDL ~ 2 LT-MDL/75%). 

The LRL is designed to achieve a risk of �1% for both false negatives and false positives. 

reporting scheme used at the NWQL with the LT-MDL and LRL does not censor results at the LRL, and 

the laboratory reports all results between the LT-MDL and the LRL with a lab-specific flag. 

The 

EP

That presentation 

Spec in 

FThe LT-MDL

At 75% 

I

The 

5 - 27

The USGS presentation from the 2002 meeting describes how USGS enhanced the LT-MDL 

procedure by using their large volume of uncensored blind laboratory blank data as a reality-check on the 

LT-MDL derived from spiked reagent water samples. In cases where the standard deviation used to 

calculate an LT-MDL based on blind blank data is significantly different (especially when greater) from 

the standard deviation used to calculate the spike-based LT-MDL, the blank data are used to calculate the 

LT-MDL. Blind blank data also are used to evaluate whether the calculated LT-MDL requires an off-set 

correction for blank bias, i.e, [LT-MDL = (S x Student’s t) + median or mean blank concentration]. This 

offset is similar, but not identical, to the ACIL Case I procedure described in Section 2.3.3 of this 

document. The LT-MDL offset correction compensates for a blank distribution that is not centered at zero 

(an assumption in the EPA MDL procedure). 

The NWQL has found that this blank bias correction to the LT-MDL is especially important for 

blank-limited analytes, including some metals, total organic carbon, phenol, and nutrients. In practice, the 

NWQL recalculates the LT-MDL annually, and compares the results between years using Levene’s test of 

equal variance, which they have found to be less influenced by departures from normality than the F-test – 

an important consideration given that the LT-MDL is based on a non-parametric estimate of variability. 

5.1.7.2 Assessment of the USGS LT-MDL against the Evaluation Criteria 

The following five subsections discuss the USGS approach and procedure in the context of the 

five evaluation criteria that concern detection limit approaches (i.e., Criteria 1-4, and Criterion 6). 

5.1.7.2.1 Criterion 1:	 The detection and quantitation limit approaches should be scientifically 

valid. 

Condition 1: It can be (and has been) tested. The LT-MDL meets this condition. 

USGS has tested and used the LT-MDL since October 1998. Evaluation and use of the LT-MDL 

began with four methods in use by the NWQL for low-level volatiles by GC/Ms, trace metals by 

ICP/AES, Kjeldahl nitrogen, and phosphorus. According to the Open File Report, the LT-MDL was 

scheduled for testing in 17 additional methods, including semivolatile organics, organochlorine pesticides, 

organophosphorus pesticides, pesticides analyzed by HPLC, metals by ICP/MS, metals by GFAA, and ion 

chromatography. 

EPA used a combination of blank and spiked data submitted by USGS to compute the USGS LTMDL and compare it to the EPA MDL The blanks were analyzed by USGS over a period of one year and 

represented a combination of 78 analytes, methods, and matrices, while the spiked sample results 

represented 39 analytes, methods, and matrices. The analytes were all metals or wet chemistry parameters 

such as phosphorus and nitrate/nitrite. 

Condition 2: It has been subjected to peer review and publication. The LT-MDL does not appear to meet 

this condition. 

Information on the LT-MDL is relatively limited and EPA is not aware of additional USGS 

publications beyond Open File Report 99-193 and the August 2002 presentation. EPA did not identify 

any additional publications regarding the LT-MDL in its earlier literature search. The Open File Report 

itself does not provide any indication that it was subject to a peer-review process. 

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Condition 3: The error rate associated with the procedure is either known or can be estimated. The error 

rate is specified by �, with a value of 0.01(1%). Therefore, the LT-MDL may meet this condition. 

In its evaluation of USGS data submitted as comments (see Appendix C) EPA found that the 

mean percentage of blanks results that exceeded the detection limit estimate (LT-MDL) ranged from 3.7% 

to 4.4%, depending on whether the mean or median blank result was used to estimate LT-MDL. These 

rates exceeded that of the EPA MDL Therefore, although EPA’s evaluation found that the error rate for 

the LT-MDL exceeded the theoretical error rate designed into the procedure, the error rate can be 

estimated from actual data. 

Condition 4: Standards exist and can be maintained to control its operation. The LT-MDL may partially 

meet this condition, in that the NWQL may have formal procedures in place that more fully describe the 

LT-MDL. However, as noted above, the information in the Open File Report does not include an explicit 

formula for calculation of the LT-MDL, nor are other details of the overall procedure, such as the choice 

of spiking levels, provided in a clear and consistent fashion. The August 2002 presentation provides 

critical information about the use of F� that is not present the Open File Report. The LT-MDL could meet 

this criterion, if the procedure were clearly documented by USGS. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The LTMDL does not meet this condition 

EPA believes that the LT-MDL is only used at the NWQL. Several commenters, including ACIL, 

suggested that EPA examine the USGS LT-MDL more closely, specifically in regards to its inclusion of 

long-term variability. There is, however, no evidence in the comments that the concept has achieved a 

large following among laboratories or other agencies. 

5.1.7.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and 

method performance, including routine variability. 

EPA believes that the LT-MDL meets this criterion because it incorporates the variability of 

responses over a long time period, and where a laboratory has multiple instruments and analysts running 

the same analysis, it incorporates variability across instruments and analysts. 

5.1.7.2.3 Criterion 3:	 The approach should be supported by a practical and affordable 

procedure that a single laboratory can use to evaluate method 

performance 

The LT-MDL partially meets this criterion. However, the LT-MDL is not a detailed readily 

available “procedure”. Also, the LT-MDL requires data collected over a 12-month period. Given that 

many State regulatory programs require that laboratories provide an annual demonstration of capabilities, 

including demonstrating their detection limits, the use of the LT-MDL would have to be limited to those 

laboratories that already have a year’s worth of data available. Some other single-lab approach would 

have to be used for an initial demonstration of method performance. 

5.1.6.2.4 Criterion 4:	 The detection level approach should estimate the theoretical 

concentration at which there is 99% confidence that the substance is 

actually present when the analytical method is performed by experienced 

staff in a well-operated laboratory 

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According to the formula used for calculating the USGS LT-MDL, the error rate is specified by �, 

and the LT-MDL is designed with a value of 0.01(1%). Because the method uses a nonparametric 

estimate of S, it may not always yield a 1% false positive rate. EPA empirical analysis indicates false 

positive rates in the range of 3.7% to 4.4%. This compares favorably with the performance of other 

methods. Thus, the LT-MDL adequately meets this criterion at least in practice. 

5.1.7.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the 

variety of decisions made under the Clean Water Act, and should support 

State and local obligations to implement measurement requirements that 

are at least as stringent as those set by the Federal government 

The LT-MDL may meet this criterion. The LT-MDL is designed as part of a broader reporting 

scheme and it is unclear that EPA, States, and local authorities would be willing or able to use results 

reported according to that scheme in enforcement scenarios (e.g., “flagged” data). 

5.1.8	 Evaluation of the Inter-industry Analytical Group (IIAG) Full-Range Validation and 

Sensitivity Test 

In December 2002, the Inter-Industry Analytical Group (IIAG) submitted a proposal to EPA that 

recommends (1) a sensitivity test intended to “replace the MDL as a test of whether an individual 

laboratory is performing adequately,” and (2) an interlaboratory validation study design intended to 

characterize precision and accuracy of methods used for regulatory compliance. Although their approach 

was received too late for consideration prior to publication in the 2003 Assessment Document, EPA 

provided notice of the approach, requested public comment on it, and agreed to evaluate the IIAG 

approach in updating the 2003 assessment. Section 5.1.8.1 describes the IIAG approach, and Section 

5.1.8.2 describes EPA’s assessment of the IIAG approach against the applicable evaluation criteria. 

5.1.8.1 Description of the IIAG Approach and Procedure 

Full Range Validation 

IIAG has proposed that, EPA commit to performing interlaboratory method validation studies 

designed to produce a “full range” of data, including precision and accuracy, from the point of instrument 

detection to the upper end of the working range. IIAG has indicated that “such a full range validation will 

enable EPA to consider DL/QL options in light of data quality objectives without being constrained by a 

limited database.” IIAG suggests that, at a minimum, EPA should commit to performing such full range 

validation studies for all new methods that it develops and that all organizations submitting new methods 

for EPA approval should be required to provide the full range data as well. 

Sensitivity Test 

IIAG also has proposed that EPA consider the use of a “sensitivity test” instead of the MDL to 

demonstrate that a laboratory is capable of performing according to EPA expectations at the lower range 

of a test method. IIAG’s suggested process for developing this test is as follows: 

•	 EPA would first identify the lowest concentration at which the entire analytical system gives a 

recognizable signal and acceptable calibration point. 

•	 EPA would then select a simple dilution of that concentration, and develop QC criteria based on 

the test results from several laboratories performing the test at that dilution (in the same way that 

QC criteria are developed by EPA for initial precision and recovery demonstrations in methods 

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such as Method 1631). 

•	 Laboratories could then perform an “Initial Performance Demonstration” (IPD) of their capability 

to achieve the desired sensitivity by (1) analyzing several replicates of the same sample dilution 

(using the full method), (2) using the results to compute the standard deviation, and (3) confirming 

that the results fall within the QC criteria range. IIAG emphasizes that the dilution level would 

not be considered the detection level, but rather a performance level. 

IIAG further suggests that this multi-replicate IPD test would be verified on an ongoing basis. To 

minimize complexity, IIAG suggests that the ongoing test be conducted at the same spike level as their 

“Initial Performance Demonstration.” IIAG did not suggest a specific frequency for conducting these 

ongoing tests. 

Finally, IIAG suggests that EPA commit to using this IPD sensitivity test in lieu of the MDL, and 

that EPA express a willingness, subject to funding availability or a third party commitment, to perform 

testing as necessary to develop “sensitivity” QC criteria, and to modify the few existing Part 136 methods 

that require the MDL for IPD. 

Section 5.1.8.2 below discusses EPA’s evaluation of the scientific elements proposed IIAG 

approach. 

5.1.8.2 Assessment of the IIAG Approach against the Evaluation Criteria 

The following six subsections discuss the IIAG approach and procedure in the context of the six 

evaluation criteria. The first three criteria apply to both detection and quantitation limits, Criterion 4 

applies to detection limits only, Criterion 5 applies to quantitation limits only, and Criterion 6 applies to 

both. Because the IIAG full range validation and sensitivity test approach applies to both types of limits, 

all 6 criteria are discussed below. 

5.1.8.2.1 Criterion 1:	 The detection and quantitation limit approaches should be scientifically 

valid. 

Condition 1: It can be (and has been) tested. To EPA’s knowledge, the IIAG sensitivity test approach has 

not been tested by any organization, including IIAG. The IIAG approach is still a rough framework, and 

basic details, such as the number of replicates required and the actual spiking levels to be used, still need 

to be specified. Testing of the approach in its current framework is possible but would be very expensive, 

one might have to conduct tests with multiple spiking levels and with varying numbers of replicates, for 

example, to be sure that the tests will reflect the final sensitivity test procedure. If the procedure were 

refined to describe the exact steps and requirements, it could be tested more efficiently. 

IIAG’s full validation study approach can be and has been tested. For example, EPA conducted a 

full interlaboratory validation study of Method 1631 prior to promulgating the method at 40 CFR 136. 

That study, which involved 12 participating laboratories, yielded an overall mean percent recovery of 93 

and an overall relative standard deviation of 13 percent across all samples. 

IIAG has stated that “Although the full-range interlaboratory is aimed at characterizing a method’s 

ability to quantify rather than to detect a pollutant concentration, the study could be used to establish an 

interlaboratory detection level as well” and “The best solution for performing a full-range validation to 

establish detection and quantitation levels and precision and bias for promulgating nationwide standards 

and compliance levels is the ASTM IDE/IQE approach.” 

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The ASTM IDE and IQE are constructed by fitting a model to variability versus concentration 

data, rather than being derived from the standard deviation of replicate measurements of a single 

concentration level. As discussed in Section 5.1.2 and detailed in Appendix C, EPA used data from the 

Episode 6000 study to compare IDEs calculated using data from all 16 concentration levels reported to 

IDEs calculated using data from only 5 of the concentrations (i.e., at 5, 10, 20, 40, and 80 times the 

standard deviation of replicate measurements of a blank sample or the lowest level at which measurements 

could be made). Results of the comparison are summarized in Table 9 of Appendix B to the draft TSD. 

The results show that the median 16-point IDE is approximately 1.3 times greater than the median 5-point 

IDE, indicating that data resulting from measurements of concentration levels in the region of detection 

and quantitation in some cases may yield lower IDE’s than data from a wider range of concentration data. 

EPA refers readers of this document to Sections 5.1.2.2.1, 5.2.2.2.1, 5.2.2.2.2, and Appendix B 

for a discussion of additional reasons why EPA believes the ambiguities and inconsistencies in IDE/IQE 

procedures preclude these procedures from being the best solution for performing a full range study to 

estimate detection and quantitation limits. 

Condition 2: It has been subjected to peer review and publication. The IIAG procedure does not meet this 

criterion. EPA is not aware of any peer review or publication of the document in a peer reviewed journal. 

The IIAG document was submitted directly to EPA by the Petitioners, and EPA made the document 

available to the public for comment. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. At present 

the IIAG’s approach consists of a proposed framework rather than a detailed procedure. It lacks key 

specifics, such as how many replicates would be used in the IPD phase of the test, and what spiking levels 

would be used. IIAG suggests that EPA would select these levels, and suggests “probably 4 - 7" for the 

number of replicates. 

While IIAG suggests the dilution would be a simple dilution of the lowest calibration standard, 

offering “1/3 or 1⁄2, for example”, they also state that “It is not absolutely necessary to reduce the spike 

level below the lowest calibration point, however, and the sensitivity test could be performed with a spike 

at the lowest calibrations standard instead of at a dilution of it.” No guidelines are offered for which of 

these levels (or other levels) should be chosen, nor are guidelines offered for the number of replicates 

needed. 

Given the lack of detail, the current framework would be subject to different interpretations by 

different readers or users, and the error rate associated with the procedure would vary depending on how 

the procedure was implemented. Because the error rate is neither known, nor can it be estimated, the 

IIAG approach does not meet this condition. 

The IIAG procedure is a framework with interesting aspects for further consideration by the full 

scientific and regulatory community. EPA would be willing to work with IIAG and other stakeholders to 

identify the details needed to augment this framework to where it would meet this condition. 

Condition 4: Standards exist and can be maintained to control its operation. As previously noted, the 

IIAG approach consists of a proposed framework rather than a detailed procedure framework, and lacks 

key details that are needed to control its operation. Given the lack of detail, the current framework does 

not meet this condition. 

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Again this procedure is a framework with interesting aspects for further consideration by the full 

scientific and regulatory community. EPA would be willing to work with IIAG and other stakeholders to 

identify the details needed to augment this framework to where it would meet this condition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The IIAG 

procedure does not meet this condition. It was suggested by a limited group of the relevant scientific 

community (industry firms that comprise the “Inter-industry Analytical Group” and whose wastewater 

discharges are regulated under the Clean Water Act), and comments on the their approach were mixed. 

Excluding comments submitted by IIAG itself, EPA received comments from: 

• Three electric power producers whose discharges also are regulated under CWA, 

•	 Two publicly owned wastewater treatment systems which regulates industrial discharges to their 

system under CWA and whose own discharges are subject to regulation under CWA, 

•	 Two commercial environmental laboratories that utilize the methods and detection limit 

procedures approved at 40 CFR 136 to serve their client’s needs, 

• One trade council, and 

• One private citizen. 

All three electric power firms supported the IIAG approach. The two publicly owned treatment 

systems offered mixed reviews. One supported the sensitivity test and offered suggestions for further 

consideration; the other opposed the sensitivity test but offered limited support of the interlaboratory 

validation studies, suggesting that they be limited to the relatively small group of priority pollutants whose 

water quality based effluent limits are below the method reporting levels. Both of the environmental 

laboratories were opposed to the IIAG approach, and the trade council suggested that it should be used “as 

an alternative procedure for dischargers to implement... on a site-specific basis, at their discretion”, noting 

that “As an alternate method, facilities would be able to deal with this on a case-by-case basis and would 

not need to utilize numerous laboratories to develop the more elaborate detection limits and quantitation 

limits that the IIAG proposes”. 

Given these comments, it would appear that acceptance may be widespread within the industrial 

discharger community, but it is not widespread among the entire relevant scientific community. 

5.1.8.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and 

method performance, including routine variability. 

In principle, the IIAG sensitivity test meets this criterion because it is intended to provide realistic 

information about laboratory and method performance, both with an initial demonstration and with 

follow-up demonstrations that provide information concerning routine variability. However, and as 

previously noted, the procedure is not sufficiently detailed to allow laboratories to meet this criterion. To 

clearly meet this criterion, detailed specifications to allow for consistent implementation of the procedure 

throughout the laboratory community need to be developed. 

5.1.8.2.3 Criterion 3:	 The approach should be supported by a practical and affordable 

procedure that a single laboratory can use to evaluate method 

performance 

If the IIAG framework was developed into a detailed procedure, this sensitivity approach would 

meet this single laboratory criterion. This could complement the IIAG full range validation study, which 

does not meet this criterion because it is an interlaboratory procedure. The sensitivity test, once detailed, 

could be performed by a single laboratory and used to evaluate method performance. 

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5.1.8.2.4 Criterion 4:	 The detection level approach should estimate the theoretical 

concentration at which there is 99% confidence that the substance is 

actually present when the analytical method is performed by experienced 

staff in a well-operated laboratory 

Because the spiking level to be used in IIAG’s sensitivity test is not defined it is not possible to 

evaluate whether that test meets or does not meet this criterion. IIAG also suggests that a full-range 

validation study should be used to establish an interlaboratory detection limit, and recommends use of the 

ASTM IDE procedure as the best means of doing so. If this is the case, the full range validation study 

would fail this criterion for the reasons given in Section 5.1.2.2.4 regarding the IDE. 

5.1.8.2.6 Criterion 5:	 The quantitation limit should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of a method 

when a method is performed by experienced staff in well-operated 

laboratories. 

The IIAG’s proposed sensitivity test requirement is likely to meet this criterion once details 

regarding the procedure are specified. Depending on the spiking levels that are specified in the final 

procedure, however, it is very likely that the IIAG sensitivity test may not identify the lowest 

concentration at which the signal is recognizable when the method is performed by experienced staff in a 

well-operated laboratory. 

5.1.8.2.6 Criterion 6:	 Detection and quantitation approaches should be applicable to the 

variety of decisions made under the Clean Water Act, and should support 

State and local obligations to implement measurement requirements that 

are at least as stringent as those set by the Federal government 

IIAG’s suggested use of a full range validation study meets this criterion because such validation 

studies provide useful information about the performance of the method. As noted previously, EPA 

typically conducts interlaboratory validation studies at multiple concentrations ranges before 

promulgating a method for nationwide use at 40 CFR part 136. However, for the reasons discussed 

elsewhere in this document, EPA does not agree that data collected across the full range of the method 

should be used to establish detection or quantitation levels. 

In the absence of a detailed procedure that could be use to fully evaluate IIAG’s, it is difficult to 

determine if the IIAG sensitivity test meets this criterion. 

5.2 Quantitation Limit Approaches 

Sections 5.2.1 through 5.2.4 describe EPA’s assessment of four quantitation limit approaches. 

Each discussion is divided into two major subsections. The first subsection describes the approach and, 

where applicable, the procedure that supports the approach, and the second subsection details EPA’s 

assessment of the approach based on the five criteria established in Chapter 4 for evaluating quantitation 

limit approaches. These criteria are Nos. 1 -3, 5 and 6; No. 4 only is applicable to detection limits. 

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5.2.1 Assessment of the EPA Minimum level of Quantitation (ML) 

Section 5.2.2.1 provides an overview of the ML approach and the procedures used to implement 

the approach. Section 5.2.2.2 contains EPA’s assessment of the ML against the five evaluation criteria 

that concern quantitation limit approaches (i.e., Criteria 1-3, and Criteria 5 and 6). 

5.2.1.1 Description of the ML Approach and Procedures 

The definition of the ML includes a statement of the approach and the procedures used to 

establish the ML. This definition states that the ML is: 

“the lowest level at which the entire analytical system must give a recognizable signal 

and acceptable calibration point for the analyte. It is equivalent to the concentration of 

the lowest calibration standard, assuming that all method-specified sample weights, 

volumes, and clean up procedures have been employed. The ML is calculated by 

multiplying the MDL by 3.18 and rounding the results to the number nearest to (1, 2, or 

5) x 10n, where n is an integer.” 

The ML is designed to provide a practical embodiment of the quantification level proposed by 

Currie and adopted by IUPAC. It is functionally analogous to Currie’s “determination limit” (described in 

Chapter 2, Section 2.1) and the American Chemical Society’s Limit of Quantitation (LOQ). The LOQ is 

discussed in Section 5.2.3 of this chapter. Chapter 2 (Section 2.2.2) describes the ML approach in 

additional detail. 

The first part of the ML definition (i.e., the lowest level at which the system gives a recognizable 

signal and acceptable calibration point for the analyte) ties the quantification limit to the capabilities of 

the measurement system. The second part of the ML definition provides a procedural means for 

establishing the ML. 

The procedural component of the definition is designed to yield an ML value that equals 

approximately 10 times the standard deviation of replicate analyses used to determine the MDL. (The 

exact value corresponding to 10 times the standard deviation is rounded to avoid error that would arise 

from preparation of calibration standards at exact, “unrounded” concentrations.) The 3.18 multiplier is 

derived by dividing 10 by the value of the t-statistic for seven replicates. Laboratories that choose to 

perform MDL studies with more than the required minimum of seven replicates follow the instructions in 

appendix B of 40 CFR part 136 to select the t-statistic value for the number of replicates used. Therefore, 

the 3.18 multiplier for the ML calculation should be proportionally adjusted. Similarly, the Student’s tvalue is adjusted when a laboratory performs the optional iterative test described in Step 7 of the MDL 

procedure, or if outlier testing results in the use of less than seven replicates to establish the MDL. 

5.2.1.2 Assessment of the ML against the Evaluation Criteria 

The following five subsections discuss the ML approach and procedure in the context of the five 

evaluation criteria that concern quantitation limit approaches (i.e., Criteria 1-3, and Criteria 5 and 6). 

5.2.1.2.1 Criterion 1:	 The detection and quantitation limit approaches should be scientifically 

valid. 

Condition 1: It can be (and has been) tested. The ML meets this condition. The ML has been used 

experimentally since 1979 and in the regulatory context since 1984. The ML is tested each time a 

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laboratory calibrates an instrument because methods that include the ML require that it be included as the 

lowest non-zero standard in these calibrations. 

EPA also has tested the MDL and ML procedure with ten different techniques at decreasing spike 

concentrations to evaluate how well the MDL and ML procedures characterized the region of interest in 

preparation for this reassessment of detection and quantitation limit approaches. Results of the study 

suggest that (1) although the calculated MDL and ML could vary depending on the spike level used, the 

procedure was capable of reasonably estimating detection and quantitation limits when the full iterative 

MDL procedure was employed, and (2) the rounding process employed to determine the ML generally 

yielded consistent MLs even with slight variations in the calculated MDL. EPA recognizes that additional 

guidance may be necessary on the selection of the initial spiking level and uses of the iterative procedure. 

In other words, if the procedure for establishing an ML is properly implemented for a given 

method, it will yield an ML value that is consistent with the approach, and this ML value can be verified 

(tested) by a laboratory when it calibrates the instrument used to analyze samples by the method. 

One of the stakeholders commenting on EPA’s 2003 assessment suggested that the ML failed to 

meet this condition because EPA should have tested it in “real world” matrices. EPA does not agree with 

this suggestion for several reasons. First, it is not practical or possible to test detection limits in every real 

world matrix, and there is no consensus as to which real world matrix would represent an appropriate real 

world matrix for testing. Second, many real world matrices contain the target pollutant at levels well 

above the detection or quantitation limit, making it impossible to characterize what can and cannot be 

detected at low levels. In theory, the sample could be diluted to dilute the target pollutant, but in practice 

sample dilution would also likely dilute any interferences that might be present, thereby defeating the 

purpose of using a real world matrix. The current EPA approach, which exhaustively tests the ML 

procedure in a reference matrix using multiple techniques and ten different concentrations that span the 

entire region of interest, is more than adequate to constitute “testing” of the ML procedure. On the other 

hand, where data suggests that matrix interferences may significantly affect achievable quantitation and 

detection limits, this should be considered by a permit writer on a case by case basis. 

Condition 2: It has been subjected to peer review and publication. The ML has not been published in a 

peer reviewed journal. However, it was evaluated by four peer reviewers as part of EPA’s assessment of 

detection and quantitation limits. These reviewers noted that: 

“The MDL and ML concepts evaluated in Section 5.1.1 and 5.2.1, respectively, are shown 

in this evaluation to be technically sound and practical.” (Wait, 2002) 

“With respect to the limit of quantitation concept, the EPA ML is as good as any of the 

others given...” (Rocke, 2002) 

“The MDL and ML have stood the test of time and provide a proven methodology which 

meets evaluation criteria stated in the TSD.” (Cooke, 2002). 

In addition, the definition of the ML describes the approach and the procedures used to establish 

the ML. This definition is included in EPA Method 1631, which was extensively peer reviewed in 

accordance with EPA policies on peer review prior to publication and promulgation. Given that EPA’s 

policies on peer review are as stringent as or more stringent than those used by many published journals, 

the ML has met a high standard of scientific review and scrutiny, and therefore, meets the intent of this 

condition. 

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Condition 3: The error rate associated with the procedure is either known or can be estimated. If 

rounding is not considered, the error can be easily estimated. The calculation is still straightforward, but 

tedious, when the ML rounding procedures are employed. Given these caveats, the ML partially meets 

this condition. 

Condition 4: Standards exist and can be maintained to control its operation. The ML meets this criterion. 

Detailed procedures (i.e., standards) for establishing the ML are embodied in the definition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The ML 

meets this condition. The ML is analogous to the American Chemical Society’s LOQ and to the 

ISO/IUPAC quantification limit, which suggests widespread acceptance. 

5.2.1.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The ML procedure meets this criterion. It is designed to provide a means by which a laboratory 

can demonstrate performance with a method under routine laboratory operating conditions. All recently 

developed EPA CWA methods require that a laboratory calibrate its instrument prior to analyzing 

environmental samples. The ML is defined as the lowest non-zero standard in the laboratory’s calibration, 

and therefore, reflects realistic expectations of laboratory performance with a given method under routine 

laboratory conditions (i.e., under conditions of routine variability). 

The ML is based on the standard deviation of replicate analyses used to establish the MDL. As 

described in Section 5.1.1.2.2, these analyses are performed to characterize laboratory and method 

performance, including routine variability, at low concentrations. When a laboratory performs an MDL 

study with seven replicates and multiplies the results by 3.18, the laboratory has demonstrated that it can 

achieve expected levels of performance at the ML. 

Due to the variability inherent in measurement science, instrumentation, and the humans 

conducting analyses, laboratories may routinely obtain limits that are lower or higher than those obtained 

in another laboratory. Thus, when an ML is determined during method development, it is important to 

determine that ML in more than one laboratory to ensure the ML published in the method reflects 

demonstrated expectations of method performance in a community of laboratories. It is not necessary for 

this community to include the entire universe of all possible laboratories that might desire to practice the 

method. Rather, during the stages of method development and validation, this community only should 

include well-operated laboratories with analysts who are experienced with the techniques used in the 

method, and have some familiarity conducting all of the steps in the new method before generating MDLs 

that will be published with the new method. See Section 5.1.1.2.2 for additional discussion of this topic. 

5.2.1.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure 

that a single laboratory can use to evaluate method performance. 

The ML meets this criterion. It is designed for use by a single laboratory. The ML can be directly 

determined from the MDL, which is among the most affordable of procedures for determining detection 

limits (see discussion in Section 5.1.1.2.3 for additional details), by a simple multiplication of the MDL 

and a application of a rounding procedure. 

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5.2.1.2.4 Criterion 5:	 The quantitation limit approach should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of the method when 

a method is performed by experienced staff in well-operated laboratories. 

The ML meets this criterion. The ML can be verified in a laboratory each time it calibrates an 

instrument. This calibration depends on identification of a recognizable signal for the analyte. In 

addition, because EPA includes the ML as the low point in the calibration range, that concentration is 

within the capabilities of the method, as demonstrated by either multiple single-laboratory studies or a 

multi-laboratory study of the method. 

Notwithstanding the preceding, analysis of Episode 6000 data (see appendices) produced 

anomalous results from two methods (EPA 502.2 and 524.2) that employ instrument thresholds. For 17% 

of EPA 502.2 and 49 % of EPA 524.2 analytes the calculated ML was below the concentration at which 

all seven spiked replicates were detected, i.e. less than the lowest MDL spike. The Episode 6000 dataset is 

not reflective of a typical compliance measurement or method development study because the range of 

concentrations studied encompassed several orders of magnitude and included concentrations well below 

the MDL. This atypical range was employed to push the limits of the instrumentation and the theory 

underlying determination of the variability of measurements. 

In a qualified operating laboratory, or during a method development study, if MLs were calculated 

to be less than the concentration at which all seven spiked MDL replicates were detected, the laboratory 

would take corrective measures. When a method is developed for EPA’s CWA program, each laboratory 

in a multi-laboratory study would consult with EPA and take corrective measures, such as calibration 

adjustments so that reported MDLs are above the signal threshold. In these cases, the calculation of ML = 

3.18 * MDL always yields a value greater then the MDL and meets the criterion of “recognizable signal”. 

5.2.1.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and 

local obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government. 

The ML meets this criterion. It has been used in Clean Water Act programs since 1984. 

5.2.2 Assessment of the IQE 

The Interlaboratory Quantitation Estimate (IQE) was published by ASTM, International in 2000 

as standard D 6512. The IDE was developed with support from members of the regulated industry in an 

attempt to provide a comprehensive quantitation limit procedure that addresses the concerns of the 

regulated industry, statisticians, and analysts. A brief summary of the procedure for establishing an IQE is 

given in Section 5.2.2.1. Section 5.2.2.2 presents EPA’s assessment of the IQE against the five criteria 

established for evaluating quantitation limit approaches (i.e., Criteria 1-3, and Criteria 5 and 6). 

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5.2.2.1 Description of the IQE Approach and Procedure 

The ASTM Designation D 6512 is the Standard Practice Interlaboratory Quantitation Estimate. 

As stated in the practice: 

"IQEZ% is computed to be the lowest concentration for which a single measurement from a 

laboratory selected from the population of qualified laboratories represented in an 

interlaboratory study will have an estimated Z % relative standard deviation (Z % RSD, 

based on interlaboratory standard deviation), where Z is typically an integer multiple of 

10, such as 10, 20, or 30, but Z can be less than 10." 

The IQE is determined and verified using a procedure containing 5 major steps with 

approximately 46 substeps and conditions. The full text of the IQE procedure is available from ASTM 

International. The 5 major steps and their functions are given in Section 6 of the IQE procedure and are 

summarized below: 

1. Overview of the procedure. 

2.	 IQE Study Plan, Design, and Protocol - in this section, the task manager (study supervisor) chooses 

the analyte, matrix, and analytical method. Details are given for the appropriate range of study 

concentrations; the model of recovery vs. concentration; the study protocol (ASTM Practice D 2777 is 

suggested); the instructions to be given to the participating laboratories, including reporting 

requirements; the allowable sources of variation; and the number of laboratories, analysts, 

measurement systems, and days over which the study will be conducted. 

3.	 Conduct the IQE Study, Screen the Data, and Choose a Model - after the study data are collected and 

screened according to ASTM Practice D 2777, the interlaboratory standard deviation (ILSD) versus 

concentration data are tabulated and one of three models is fit to the data. The first attempt is at 

fitting a constant model. If the attempt fails, a straight-line model is attempted. If the straight-line 

model fails, a hybrid (Rocke/Lorenzato) model is fit. After fitting, the model is evaluated for 

reasonableness and lack of fit. If the model fails, the study supervisor determines if a subset of the 

data should be analyzed or if more data are needed. 

4.	 Compute the IQE - the IQE is computed using the ILSD model selected in Step 3 to estimate the 

relative standard deviation as a function of concentration. The first attempt is at 10% RSD (IQE10%). 

If this attempt fails, IQE20% is tried, then IQE30%. IQEs greater than 30% are not recommended. 

5.	 Nontrivial Amount of Censored Data - this section of the IQE procedure addresses the effect of "nondetects" or "less-than." Suggestions are given to see if uncensored data can be obtained from the 

laboratories or if the study needs to be augmented with additional data. Suggestions are given for 

fitting a model to data that contain less than 10% non-detects or less-than to produce an IQE. 

5.2.2.2 Assessment of the IQE Against the Evaluation Criteria 

The following five subsections discuss the IQE approach and procedure in the context of the five 

evaluation criteria that concern detection limit approaches (i.e., Criteria 1-3, and Criteria 5 and 6). 

5 - 39

5.2.2.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. The Electric Power Research Institute provided input into 

the design of EPA Method 1631 and 1638 Validation Studies for the purpose of calculating IDEs and 

IQEs. EPRI also calculated IDEs and IQEs based on these data. These two datasets include a total of ten 

metal analytes and therefore do not cover a wide range of analytical techniques and methods. Other than 

these two datasets, EPA is not aware of any organization, including ASTM, that has conducted a study to 

test the IQE procedure as written (i.e., designed and implemented an interlaboratory study involving 

multi-laboratory analysis of multiple concentrations of each matrix of interest). It has been tested by its 

developers using simulated data sets and on interlaboratory data sets that do not adequately characterize 

the low level region of interest. As part of this reassessment, EPA tested a variant of the IQE procedure 

on single-laboratory data sets that were designed to characterize an analytical method in the region of 

detection and quantitation. Despite the lack of comprehensive testing performed to date, the IQE 

procedure can be tested if sufficient resources are invested. 

Condition 2: It has been subjected to peer review and publication. Although the IQE has not been 

published in the peer-reviewed scientific literature, the IQE has undergone review and ballot by members 

of ASTM Committee D 19, many of whom are qualified peer reviewers. Thus, the IQE meets the intent of 

this condition (i.e., submission to scrutiny of the scientific community). In addition, the IQE was 

reviewed by four peer reviewers as part of EPA’s assessment of detection and quantitation limit 

approaches. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. In theory, 

an expert statistician could estimate the error rate of the IQE. However, the IQE procedure is extremely 

complex from an analytical chemistry and statistical perspective. As a result, it is unlikely that the error 

rate could be estimated by the staff of an environmental testing laboratory. Moreover, in attempting to 

follow the IQE procedure during this reassessment, EPA found the procedure to be subjective, particularly 

with respect to selection of an appropriate statistical model. The subjective nature of the procedure is 

likely to yield different IQEs from the same data set, depending on the staff involved in analyzing the data 

and performing the calculations. (The likelihood of this problem is illustrated in appendix B to this 

Assessment Document.) This subjective variability eliminates the ability to estimate the actual error 

associated with the IQE. Therefore, the IQE does not meet this condition. 

As discussed in Section 5.2.2.1, Condition 3, regarding the IDE, one stakeholder stated that 

concerns about the complexity and subjectivity of the IQE (and IDE) procedures were unimportant, in 

part, because IQEs calculated using different models were very close, and in part, because “user friendlysoftware that will automatically perform the IDE and IQE calculations. EPA obtained copies of such 

software from the commenter and used that software to evaluate the validity of this comment. As 

described at length in Section 5.2.2.1, EPA concluded that 1) the subset of models used varies among the 

software packages, 2) the software packages do not always apply the same model to the same data sets, 

and 3) even if the same model is used, there is a large amount of variability between the results produced 

when applying the different software packages to the same set of data. Based on these differences, EPA 

concluded that the available software programs do not remove all complexity and subjectivity from the 

IQE calculations. Instead, the software programs appear to introduce new issues by using steps not 

included in the ASTM procedures. 

Condition 4: Standards exist and can be maintained to control its operation. The IQE approach and 

procedure is supported by a published procedure (standard) to control its operation. The procedure gives 

the steps to be followed in determining the IQE and instructs the study supervisor how to gather the data 

and compute an IQE. 

5 - 40

There are several "gray areas" in the published procedure. The most significant gray area is in 

model selection. The procedure provides insufficient guidance on the use of residual plots as a basis for 

selecting models and as a result, selection of the model may be very subjective, especially if the number of 

concentrations is low. The discussion of what model to use after rejecting the hybrid and linear models 

also is very vague. The exponential model is mentioned, as well as models with more than one 

coefficient. In addition, fitting the “constant model” is never discussed in detail. Most likely, this is done 

by simply calculating a mean (weighted if necessary) of the variances from the different concentrations, 

however such a calculation is never explicitly stated. As discussed under Condition 4 of Section 5.1.2.2.1 

(scientific validity of the IDE procedure), there appear to be inconsistencies between the IDE and IQE 

that suggest conceptual conflicts between these two standards. 

Based on these findings (along with those discussed under Criterion 2 below), the procedure is not 

sufficient to control operation of the IQE because of the high degree of subjectivity involved in 

implementing the procedure, statistical errors in the procedure, and internal inconsistencies with the IDE. 

Therefore, the IQE does not meet this condition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The IQE was 

published by ASTM four years ago (2000). EPA has not found an IQE in the open literature or in an 

analytical method, including an ASTM method. 

5.2.2.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The IQE procedure is designed to reflect expectations of interlaboratory performance, including 

routine variability. The procedure contains extensive instructions for dealing with unusual conditions, 

including sources of variability and outliers. Based on studies of the single-laboratory variant of the 

procedure in which the model selection proved to be highly subjective, it is not clear that IQE procedure 

would demonstrate realistic expectations of laboratory and method performance. 

The IQE procedure suggests attempting to fit study results to a constant, linear, or hybrid model. 

If all of these fail, the procedure suggests trying a different model, such as the exponential model. (The 

exponential model figures more prominently in the IDE procedure, where it is one of the three main 

models discussed, replacing the Rocke and Lorenzato model.) Although the exponential model may be 

appropriate for the IDE (which is not tied to a fixed RSD), it yields unacceptable results when applied to 

the IQE procedure. Under the exponential model, relative variability (standard deviation divided by the 

true concentration) does not consistently decrease with increasing concentration (i.e., as concentration 

increases, relative variability decreases down to a specific percentage, and then begins to increase). This 

is not realistic of laboratory and method performance. In addition, the exponential model will often result 

in having two possible values each for IQE10%, IQE20%, and IQE30%. 

Another concern with the IQE procedure is that use of the non-mandatory appendices in ASTM D 

6512 to determine the fit of a model may produce results that differ from those that would be obtained 

using the default procedures for testing model fit that are built into off-the-shelf statistical software, such 

as the Excel files discussed in Condition 3. 

Given the subjectivity and confusion involved in selecting the model, EPA tried using the same 

data set to calculate a single-laboratory variant of the IQE with each of the available models and found 

that the calculated IQEs varied widely when different models were used. 

Based on the problems described above, EPA believes the IQE does not meet this criterion. 

5 - 41

5.2.2.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance. 

The IQE procedure is neither practical nor affordable in a single-laboratory context.. It is 

designed for use by an ASTM study supervisor or task manager and not as a procedure that a single 

laboratory can use to evaluate method performance. EPA is aware that ASTM Committee D 19 is 

contemplating development of a within-laboratory quantitation estimate (WQE), but the WQE has not 

been approved through an ASTM ballot, and therefore, it cannot be adequately evaluated at this time. The 

WQE may meet this criterion, but the IQE does not. 

Regarding affordability, EPA estimates that the cost of implementing IQE procedure would be 

more than twice the cost of EPA's present implementation of the ML. The increased cost stems from the 

additional low-level data required to assure that variability versus concentration is being characterized in 

the region of detection and quantitation, challenges involved in applying the statistical procedures in the 

IQE, and because of the anticipated reanalysis and rework required if either the procedure failed to 

produce an IQE or if the resulting IQE failed to meet the specifications in the IQE procedure. 

5.2.2.2.4 Criterion 5:	 The quantitation limit approach should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of the method when a 

method is performed by experienced staff in well-operated laboratories. 

If the IQE were developed in an interlaboratory study that met the requirements of D 6512, the 

calculated IQE would likely be achievable by experienced staff in a well-operated laboratory. Therefore, 

the IQE meets this criterion. 

However, similar to the discussion of criterion 5 for the ML (section 5.1.2.4) anomalous results 

occur. Analysis of episode 6000, analysis of Episode 6000 data (see appendices) produced anomalous 

results from two methods (EPA 502.2 and 524.2) that employ instrument thresholds. For 9% of EPA 

502.2 and 59 % of EPA 524.2 analytes the calculated single-lab IQE was below the concentration at which 

all seven spiked replicates were detected. These results indicate that an IQE study coordinator, after 

calculating IQE from multi-labs results, would have calculated IQEs below the instrument threshold. The 

IQE procedure is silent on what happens in this case. As previously noted, the Episode 6000 dataset is not 

reflective of a typical compliance measurement or method development study because the range of 

concentrations studied encompassed several orders of magnitude and included concentrations well below 

the detection limit. And this dataset was not developed according to the procedures in D 6512 (the IQE). 

5.2.2.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government 

There is no database of IQE values for CWA analytes that were calculated according to D 6512. 

These are the data with which one would compare existing CWA limits and thereby assess the effect of 

using IQEs as reporting and compliance limits in CWA programs. 

5.2.3 Assessment of the ACS Limit of Quantitation 

The Limit of Quantitation (LOQ) was developed by the Committee on Environmental 

Improvement of the American Chemical Society (ACS) and published in the same two papers as the LOD. 

5 - 42

5.2.3.1 Description of the ACS LOQ Approach and Procedure 

The 1983 "Principles" define the LOQ as: 

"... the level above which quantitative results may be obtained with a specified degree of 

confidence." 

The same relationship used to define the LOD is used for the LOQ: 

but the recommended minimal value for Kd be set at 10. Thus, the LOQ is 10� above the gross blank 

signal, Sb. According to the 1983 publication, the LOQ corresponds to an uncertainty of ±30% (10� ± 

3�). This uncertainty statement is based on � equal to 10% of the LOQ. 

Neither the 1980 nor 1983 ACS publications provide a specific procedure for estimating the LOQ, 

nor do they provide a minimum number of observations needed to estimate the gross blank signal or the 

variability term �b. 

5.2.3.2 Assessment of the ACS LOQ Against the Evaluation Criteria 

The following five subsections discuss the ACS LOQ approach and procedure in the context of 

the five evaluation criteria that concern detection limit approaches (i.e., Criteria 1-3, and Criteria 5 and 6). 

5.2.3.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. Testing of the LOQ is hampered by the lack of a supporting 

procedure for establishing an LOQ, and a conceptual dependence on the variability of blank 

measurements. If the blank measurements fail to produce a response, it is impossible to calculate an LOQ 

because the standard deviation of multiple zero-value results is zero. One solution for testing the 

approach is to assume that the LOQ is approximately equivalent to the ML as the blank signal approaches 

zero. If this is a reasonable assumption, the ML procedure is a viable means for testing the LOQ 

approach, and the LOQ would meet this condition. 

Condition 2: It has been subjected to peer review and publication. The ACS LOQ definition was 

published in the peer-reviewed journal Analytical Chemistry in 1980 and 1983. Therefore, the ACS LOQ 

meets this condition. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. The LOQ 

meets this condition. The definition of the LOQ specifically estimates the uncertainty associated with a 

concentration at the LOQ as ±30% based on 10% RSD (Kd = 10). Other choices may be made based on 

study requirements, policy judgments and/or specific results. 

Condition 4: Standards exist and can be maintained to control its operation. The ACS LOQ lacks a 

clearly defined procedure for estimating the important terms required to derive it. Although it may be 

possible to derive ACS LOQ values from data used to derive EPA MDL values, there is no discussion of 

using replicate blanks, replicate spiked samples, or a minimum recommendation for the number of 

replicates. Therefore, the ACS LOQ does not meet this condition. 

5 - 43

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Because the 

ACS does not develop and publish reference analytical methods, it is difficult to determine the degree of 

acceptance of the LOQ. EPA has not investigated the numbers of papers published in ACS journals that 

include LOQ values, but EPA's literature search for detection and quantitation approaches did not uncover 

a large number of citations that promote the LOQ in particular. 

5.2.3.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability 

The LOQ approach is designed to address realistic expectations of laboratory and method 

performance, including routine variability, and therefore, it appears to meet this criterion. Because the 

ACS has not published a procedure to implement the approach, in practice the LOQ provides no direct 

means for demonstrating this performance. The ACS LOQ, the only partially meets this criterion. 

5.2.3.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance. 

Because the ACS LOQ approach is not supported by a clearly defined procedure for establishing 

the LOQ, it does not meet this criterion. 

5.2.3.2.4 Criterion 5:	 The quantitation limit approach should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of the method when a 

method is performed by experienced staff in well-operated laboratories. 

Given the relationship of the ACS LOQ to the ML, EPA believes the LOQ meets this criterion for 

the reasons outlined in Section 5.2.1.2.4, which discusses EPA’s assessment of the ML against Criterion 4 

for evaluating quantitation limit approaches. 

5.2.3.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government. 

In the absence of a procedure for determining LOQ values, the ACS LOQ does not meet this 

criterion because it cannot be used in a regulatory context. The LOQ passes this criterion only if it is 

assumed to be approximately equivalent to the ML (i.e., the ML procedure is used to establish an LOQ). 

5.2.4 Assessment of the IUPAC/ISO Limit of Quantitation 

A similar LOQ approach was developed by IUPAC/ISO and published in the same papers as the 

CRV and MDV (see Sections 5.1.4 and 5.1.5). 

5.2.4.1 Description of the ISO/IUPAC LOQ Approach 

The 1995 "Recommendations" define the LOQ as: 

"... the ability of a CMP [chemical measurement process] to adequately ‘quantify’ an 

analyte. The ability to quantify is generally expressed in terms of the signal or analyte 

(true) value that will produce estimates having a specified relative standard deviation 

(RSD), commonly 10 %.” 

5 - 44

The relationship used to define the LOQ is: 

LQ = KQ × �Q 

The recommended value for KQ is 10. Thus, the LOQ is 10� above the blank signal, �Q. 

5.2.4.2 Assessment of the IUPAC/ISO LOQ Against the Evaluation Criteria 

The following five subsections discuss the IUPAC/ISO LOQ approach and procedure in the 

context of the five evaluation criteria that concern detection limit approaches (i.e., Criteria 1-3, and 

Criteria 5 and 6). 

5.2.4.2.1 Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Condition 1: It can be (and has been) tested. Testing of the IUPAC/ISO LOQ is hampered by the lack of 

a supporting procedure for establishing an LOQ, and a conceptual dependence on the variability of blank 

measurements. If the blank measurements fail to produce a response, it is impossible to calculate an LOQ 

because the standard deviation of zero is zero. One solution for testing the approach is to assume that the 

ISO/IUPAC LOQ is approximately equivalent to the ML as the blank signal approaches zero. If this is a 

reasonable assumption, the ML procedure is a viable means for testing the LOQ approach, and the 

ISO/IUPAC LOQ meets this condition. 

Condition 2: It has been subjected to peer review and publication. The IUPAC/ISO LOQ definition has 

been published by Currie in the peer-reviewed journals Pure and Appl. Chem. in 1995; in Anal. Chim. 

Acta in 1999, in Chemometrics and Intelligent Lab Systems in 1997; and in J. Radioanal. and Nuclear 

Chem. in 2000. Therefore, the IUPAC/ISO LOQ meets this condition. 

Condition 3: The error rate associated with the procedure is either known or can be estimated. EPA used 

data generated in the Episode 6000 study to estimate the error rate associated with the LOQ. The Episode 

6000 results show that the median error across all analytes and analytical techniques at 10� is 

approximately ±14% with approximately 95% confidence. 

Condition 4: Standards exist and can be maintained to control its operation. The IUPAC/ISO LOQ lacks 

a clearly defined procedure for estimating the important terms required to derive it. Although it may be 

possible to derive IUPAC/ISO LOQ values from data used to derive EPA MDL values, there is no 

discussion of using replicate blanks, replicate spiked samples, or a minimum recommendation for the 

number of replicates. Therefore, the IUPAC/ISO LOQ does not meet this condition. 

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Acceptance 

of this approach by the scientific community is currently not known. Acceptance would be indicated by 

use of the LOD in ISO methods. EPA's search for detection and quantitation approaches in the open 

technical literature did not uncover a large number of citations that reference the LOQ. Therefore, it is 

difficult to determine if the ISO/IUPAC LOQ meets this condition. 

5.4.2.2.2 Criterion 2:	 The approach should address realistic expectations of laboratory and method 

performance, including routine variability. 

The most recent publication on the IUPAC/ISO LOQ (J. Radioanal. and Nuclear Chem., op. cit.) 

provides insight into this issue through measurements of 14C by accelerator mass spectrometry. Therefore, 

the IUPAC/ISO LOQ passes this criterion for at least some measurement techniques. 

5 - 45

5.4.2.2.3 Criterion 3:	 The approach should be supported by a practical and affordable procedure that 

a single laboratory can use to evaluate method performance. 

The ISO/IUPAC LOQ approach is not supported by a clearly defined procedure for establishing 

the LOQ. Therefore, it does not meet this criterion. 

5.4.2.2.4 Criterion 5:	 The quantitation limit approach should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of the method when a 

method is performed by experienced staff in well-operated laboratories. 

Assuming a relationship of the IUPAC/ISO LOQ to the ML, the LOQ satisfies this criterion for 

the reasons outlined in Section 5.2.1.2.4, which discusses EPA’s assessment of the ML against Criterion 4 

for evaluating quantitation limit approaches. 

5.4.2.2.5 Criterion 6:	 Detection and quantitation approaches should be applicable to the variety of 

decisions made under the Clean Water Act, and should support State and local 

obligations to implement measurement requirements that are at least as 

stringent as those set by the Federal government 

In the absence of a procedure for determining LOQ values, the ISO/IUPAC LOQ does not meet to 

meet this criterion because it cannot be used in a regulatory context. The ISO/IUPAC LOQ passes only if 

the ML procedure is used to establish an LOQ. 

5 - 46

Chapter 6 

Findings and Next Steps 

What are EPA’s findings in this revised assessment? 

In this revised assessment of detection and quantitation approaches, the Agency has evaluated the 

codified MDL procedure, the ML procedure that EPA proposed to codify in 2003, and several alternative 

procedures. Some of these alternative procedures were submitted to EPA during the comment period on 

EPA’s 2003 assessment, which was detailed in the February 2003 Technical Support Document (EPA821-R-03-005). In today’s assessment, we have: 

• Identified relevant procedures to include in the assessment (Chapter 2); 

•	 Identified issues that may be relevant to the assessment from an analytical chemistry, statistical, or 

regulatory perspective (Chapter 3); 

•	 Used six criteria to evaluate the ability of each procedure to support activities under the Clean 

Water Act (Chapter 4); 

• Assessed how well each procedure meets the evaluation criteria (Chapter 5); 

•	 With real-world data and several different procedures, calculated and compared detection and 

quantitation limits using, and evaluated the theoretical and practical limitations of, each procedure 

(Appendices B and C). 

The assessment of the theoretical and practical applications of each procedure (Appendices B and 

C) suggests that different procedures produce different detection and quantitation limits. Observed 

differences are largely due to different sources of variability accounted for among the procedures. The 

overall assessment of each procedure against each of the evaluation criteria suggests that no single pair of 

detection and quantitation limit procedures perfectly meets EPA’s six evaluation criteria. Although the 

MDL and ML procedures are closest to meeting these criteria, as discussed under EPA’s next steps, we 

recognize that this is not the end of our consideration of future improvements to EPA procedures and/or 

adoption of specific alternative procedures. 

In response to stakeholders who suggested that EPA clarify or revise some steps in these 

procedures, we proposed modest revisions to the MDL procedure and proposed to codify an ML definition 

and procedure in conjunction with the 2003 assessment. We also proposed to codify an existing option 

that allows use of other detection and quantitation procedures to develop detection and quantitation limits. 

Public comment on both the 2003 assessment and the proposed revisions expressed many divergent views 

that conflicted with the proposed modifications to the procedures. Commenters suggested that we work 

with stakeholders to discuss mutual concerns and possible solutions rather than proceed with the proposed 

revisions. Some commenters submitted detailed, alternative procedures or regulatory revisions. However, 

there was no agreement among these commenters as to which of the competing alternatives or revisions to 

adopt, and none of them fully satisfied EPA’s needs under the CWA. We have therefore decided to 

withdraw the proposed revisions. 

6 - 1

What are EPA’s next steps? 

We believe that it is appropriate to withdraw the proposed revisions, take final action on the 2003 

assessment, and explore the feasibility of using a stakeholder process to facilitate a resolution of the 

technical and policy issues raised during the public comment period. It is in the best interest of all parties 

to solicit additional stakeholder input through consultations. In a Federal Register notice published on 

September 15, 2004 [69 FR 55547], we announced that a neutral party is studying the feasibility of a 

process by which a broad group of stakeholders would work together to define and address concerns about 

the way detection and quantitation limits are calculated and used to support CWA programs. This 

potential stakeholder process will expand the list of interested stakeholders to include state, tribal and local 

governments, environmental groups and other interested parties. We trust that this potential stakeholder 

process will address the wide variety of views held by stakeholders and may lead to recommendations for 

possible improvements to current EPA procedures and/or use of alternative procedures. 

To facilitate open, frank and inclusive discussions, we have made every effort to ensure that this 

Revised Assessment Document does not prejudge the result of the potential stakeholder process. In 

particular, we recognize that the following stakeholder issues or suggestions provide a strong starting point 

for a continued dialogue with stakeholders. 

Assessment Evaluation Criteria Issues 

The February 2003 assessment identified and discussed six criteria the Agency used to evaluate 

several different approaches to detection and quantitation. The six evaluation criteria are: 

Criterion 1: The detection and quantitation limit approaches should be scientifically valid. 

Criterion 2: The approach should address demonstrated expectations of laboratory and method 

performance, including routine variability. 

Criterion 3: The approach should be supported by a practical and affordable procedure that a 

single laboratory can use to evaluate method performance. 

Criterion 4: The detection limit approach should identify the signal or estimated concentration at 

which there is 99% confidence that the substance is actually present (i.e., a one percent false 

positive rate) when the analytical method is performed by experienced staff in a well-operated 

laboratory. 

Criterion 5: The quantitation limit approach should identify the concentration that gives a 

recognizable signal that is consistent with the capabilities of the method when a method is 

performed by experienced staff in a well-operated laboratory. 

Criterion 6: Detection and quantitation approaches should be applicable to the variety of decisions 

made under the Clean Water Act, and should support state and local obligations to implement 

measurement requirements that are at least as stringent as those set by the Federal government. 

Stakeholders commented that these six criteria favored the MDL and ML procedures. Some 

stakeholders noted instances where criterion four fails for the MDL, i.e., does not represent the limit at 

which there is a 99% confidence that the observed signal is not a false positive. Stakeholders also 

disagreed with EPA’s reliance on only one detection and one quantitation procedure, the MDL and ML 

(see criterion six discussion at 4.6 in this document) Stakeholders suggested that different detection and 

6 - 2
quantitation procedures with different levels of rigor be developed and applied to the disparate uses of 

these limits in CWA programs. Uses of these limits include verification of laboratory performance, 

method validation, and as a guide for reasonable bounds on values to consider for permit limits. EPA 

recognizes that the complexity and statistical rigor appropriate for a detection and quantitation approach 

for method development and validation would be greater than that needed for demonstrating laboratory 

proficiency. Although EPA believes that the six evaluation criteria are suitable for purposes of this 

assessment, they need not be the only starting point for future stakeholder evaluations of revised or 

alternative detection and quantitation procedures. 

Technical and Policy Issues 

Some of the major comments on the MDL and ML procedures that influenced our decision to 

withdraw the proposed rule, and to seek additional stakeholder input, include: (1) the MDL does not 

adequately address analytical variability or systematic error (bias); (2) a need for better guidance on the 

intended use of these limits in CWA programs; (3) the need for different procedures for different CWA 

applications, such as method development, laboratory performance checks, and permit limits. Commenters 

also asked for clearer guidance on specific steps in the MDL procedure, such as selection of initial spike 

concentrations, and use of iterative and outlier procedures. 

The technical issues of analytical variability and bias attributable to blanks encompass a range of 

concerns. Stakeholders have suggested that detection and quantitation procedures should: 

• vary in the nature and extent of statistical rigor and performance verification checks depending on 

the end; 

• use of a calculated limit; 

• account for more sources of variability, such as the variability between and within laboratories; 

• require more then seven samples and collect samples over a long period of time; and 

• use routine blank samples collected over long periods of time to account for background signals 

and temporal variability (e.g., ACIL and USGS procedures). 

EPA believes these suggestions merit serious consideration, and plans to use the stakeholder 

process to consider ways to address them. 

Conclusion 

This Revised Assessment Document addresses comments and concerns received from stakeholders 

and peer reviewers. Based on this new information, EPA believes that discussion of alternatives or 

improvements to current detection and quantitation concepts or procedures and uses should continue. It is 

clear that there is a broad interest in improving current procedures and uses, but no consensus for a specific 

procedure or procedures has emerged among the laboratory, industry, regulatory or regulated communities. 

We look forward to further stakeholder participation in this process. 

6 - 3
Appendix A


Literature Search Regarding


Detection and Quantitation Limit Approaches


Introduction 

Beginning in 2001, DynCorp conducted a search of published literature to identify articles that 

discuss detection and quantitation limit approaches. This literature search effort was conducted under 

EPA Contract No. 68-C-01-091 to support an evaluation of detection and quantitation limit approaches by 

the EPA’s Office of Water. 

The principal goal of this literature search was to determine if any new detection or quantitation 

limit approaches had been published since an earlier search conducted for EPA by Science Applications 

International Corporation (SAIC) in 1997 and 1998. That search resulted in an annotated bibliography 

developed by SAIC and delivered to EPA in 1998. 

In August 2002, EPA included the literature search results in a draft Technical Support Document 

(TSD) that was submitted for formal peer review. As part of the charge to the peer reviewers, EPA asked 

them to identify any additional references. Following EPA's review of the suggested additional 

references, references relevant to the TSD were added. 

How the search was conducted 

This search was conducted using two major techniques: 

• a search of an on-line citation index (an index of articles cited by other authors), and 

• a general on-line search of literature. 

On-line citation index search 

Because the search was intended to identify detection and quantitation limit approaches and not 

specific numeric limits associated with a particular analytical method, DynCorp began by searching for 

references to the major approaches known to EPA. These included the Agency’s method detection limit 

(MDL) and any other terms that have been suggested to the Agency as alternative detection or 

quantitation limit approaches. In addition to searching for these approaches, DynCorp also searched the 

citation index to identify references to the original authors of these approaches and for any other authors 

who either cited the original approaches, the original papers underlying those approaches, or the authors 

of those approaches. DynCorp used a similar approach to find any papers that cited the references 

identified in the earlier literature search by SAIC. 

DynCorp staff evaluated the full title of each identified citation to determine its relevance to 

EPA’s objective. Where available electronically and at no additional cost, DynCorp staff also reviewed 

the abstract and/or full paper to further characterize relevance. All papers that were determined to be 

relevant, or even possibly relevant, were obtained in hardcopy or electronic format for evaluation by 

EPA. 

After reviewing all papers determined to be relevant to EPA’s objective, DynCorp examined all 

of the references cited in those papers to identify additional papers of interest. These, too, were obtained 

in hardcopy or electronic format for evaluation by EPA, except where noted below. 

A - 1 
General on-line literature search 

DynCorp performed an on-line direct search of published literature (e.g., a literature database of 

published articles, not a citation index) using general terms such as "detection limit," "quantitation limit," 

or "calibration." As expected, this approach returned a very large numbers of papers that mention these 

terms, even if the focus of the paper was on something far removed from the development or assessment 

of approaches about detection and quantitation, and proved to be of limited value in serving EPA’s 

objectives for the search. Therefore, DynCorp discontinued this effort and narrowed the on-line literature 

search to a search for additional, uncited works by authors of the approaches known to EPA or identified 

through the citation index approach. 

Papers determined to be relevant to EPA’s objective were obtained in electronic or hardcopy 

format for evaluation by EPA, except where noted below. 

How the results are presented 

DynCorp identified a total of 161 relevant publications using the approach described above. 

Thirty-three (33) of these publications were also identified in the earlier search by SAIC. Of the 128 

remaining publications, 35 were published since the SAIC search was completed. 

The peer reviewers suggested additional publications covering a variety of topics, including: 

quality control, analysis of mercury, and approaches to dealing with censored data. EPA reviewed the 

citations from the peer reviewers and determined that 20 directly addressed detection or quantitation 

approaches. In particular, EPA noted that the issue of censored data applies regardless of the specific 

detection or quantitation limit associated with the data, so those citations dealing with censored data were 

not included. 

Each of the 181 publications identified in the search is listed in Attachment 1, which provides the 

title, year of publication, authors, and source citation. The citations for the 33 papers identified in the 

earlier search by SAIC are included in the attachment, and can be identified by the phrase "annotated 

only" in parentheses after the title of the paper. 

The final column of the attached spreadsheet is labeled "Category." All of the citations identified 

in the SAIC literature search and the current search conducted by DynCorp were placed in one of the six 

following categories, based on the principal characteristic of the article: 

•
 Background - The citation discusses background information (including early works by Currie, 

Kaiser, and others). 

• Calibration concept - The citation primarily deals with calibration of analytical instrumentation 

•
 Critique - The major thrust of the citation is to critique one or more approaches, as opposed to 

introducing a new approach 

•
 Multi-laboratory approach - The citation describes an approach to developing detection and/or 

quantitation limits that relies on multi-laboratory measurements 

•
 Single-laboratory approach - The citation describes an approach to developing detection and/or 

quantitation limits that relies on single-laboratory measurements 

•
 Single-laboratory, multi-level approach - The citation describes an approach to developing detection 

and/or quantitation limits that relies on single-laboratory measurements but explicitly includes 

multiple concentrations. 

A - 2 
Although there is some degree of overlap between categories, and some papers could probably be 

classified in more than one category, each citation was classified into only one category for the purposes 

of this search. 

A seventh category called "Not found" was used for three papers that were identified in the 

literature search, but for which copies could not readily be obtained. One paper is from a German journal 

that was not available via interlibrary loan. A second article also was not available via interlibrary loan. 

The third citation is an abstract by Currie, from 1983. Given that the work of Currie is well-represented 

in the other citations and the fact that this citation appears to be only an abstract, additional efforts were 

not expended to obtain a copy. 

The 20 publications suggested by the peer reviewers were all included at the end of the list, under 

an eighth category called "Suggested by a peer reviewer." 

The references presented in the table were sorted by category and year of publication and are 

displayed with the most recent citations in each category first. 

Summary 

The principal goal of this literature search effort was to determine if any new detection or 

quantitation limit approaches had been published in the literature since the search by SAIC in 1997 -

1998. As anticipated, citations were identified that relate to the recent efforts of the International 

Organization for Standardization (ISO), the International Union of Pure and Applied Chemists (IUPAC), 

and the ASTM International. Additional articles critiquing various approaches were identified as well. 

However, no previously unknown detection or quantitation limit approaches were uncovered as a result of 

this effort. Likewise, the references suggested by the peer reviewers provided additional details and 

applications of existing detection and quantitation approaches, but did not suggest any approaches that 

had not already been identified. 

A - 3 
Results of the 2001 Literature Search 

Category 

Background 

Title Year Author Source 

Some Case Studies of Skewed (and other ab-normal) Data Distributions Arising 

in Low-Level Environmental Research 

2001 L.A. Currie Fresenius Journal of Analytical Chemistry 370: 705-718 

Legislative Limits Below Detection Capability 2000 S.L.R. Ellison, V.J. Barwick, A. Williams Accreditation Quality Assurance 5: 308-313 

International Recommendations Offered on Analytical Detection and 

Quantification Concepts and Nomenclature 

1999 L.A. Currie Analytica Chimica Acta 391: 103 

Detection and Quantitation Limits: Origins and Historical Overview 1999 L.A. Currie Analytica Chimica Acta 391: 127-134 

1996 ASMS Fall Workshop: Limits to Confirmation, Quantitation, and Detection 1997 R. Baldwin, R.A. Bethem, R.K. Boyd, W.L. 

Budde, T. Cairns, R.D. Gibbons, J.D. 

Henion, M.A. Kaiser, 

Journal of the American Society for Mass Spectrometry 8: 

1180-1190 

Measurement precision and 1/f Noise in Analytical Instruments 1996 Y. Hayashi, R. Matsuda, R.B. Poe Journal of Chromatography A 722: 157-167 

Fossil- and Bio-mass Combustion: C-14 for Source Identification, Chemical 

Tracer Development, and Model Validation 

1994 L.A. Currie, G.A. Klouda, D.B. Klinedinst, 

A.E. Sheffield, A.J.T. Jull, D.J. Donahue, 

M.V. Connolly 

Nuclear Instr. And Methods in Physics Res. B 92: 404-409 

Interlaboratory Comparison of Instruments Used for the Determination of 

Elements in Acid Digestates of Solids 

1994 D.E. Kimbrough, J. Wakakuwa Analyst 119: 383-388 

Throwaway Data 1994 L.H. Keith Environmental Science & Technology 28: 389A-390A 

EPA's Office of Water Surges Toward MDL Solution 1994 Larry Keith Radian 

In Pursuit of Accuracy: Nomenclature, Assumptions, and Standards 1992 L.A. Currie Pure & Applied Chemistry 64:455-472 

Interlaboratory Aspects of Detection Limits Used for Regulatory and Control 

Purposes 

1988 L.B. Rogers ACS Symposium Series 361:94-108 

Noise and Detection Limits in Signal-Integrating Analytical Methods 1988 H.C. Smit, H. Steigstra ACS Symposium Series 361:126-148 

Effects of Analytical Calibration Models on Detection Limit Estimates 1988 K.G. Owens, C.F. Bauer, C.L. Grantr ACS Symposium Series 361:194-207 

Real-World Limitations to Detection 1988 D. Kurtz, J. Taylor, L. Sturdivan, W. 

Crummett, C. Midkiff, R. Watters Jr, L. 

Wood, W. Hanneman, W. Horwitz 

ACS Symposium Series 361:288-316 

Detection Limits - A Systematic Approach to Detection Limits is Needed When 

Trace Determinations are to be Performed 

1986 S.A. Borman Analytical Chemistry 58: A986 

Chemometrics and Analytical Chemistry 1984 L.A. Currie Chemometrics 56: 115-146 

Quality Control in Water Analyses 1983 C. Kirchmer ES&T 17: 174A-181A 

Validation of Analytical Methods 1983 J.K. Taylor Analytical Chemistry 55: 600A-602A, 608A 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

A - 4 
Title 

Trace Analyses for Wastewaters - Author's response 

Zur Theorie der Eichfunktion bei der spektrochemischen Analyse 

The Reliability of Detection Limits in Analytical Chemistry 

A Review and Tutorial Discussion of Noise and Signal-to-Noise Ratios in 

Analytical Spectrometry - I. Fundamental Principles of Signal-to-Noise Ratios 

A Review and Tutorial Discussion of Noise and Sign-to-Noise Ratios in 

Analytical Spectrometry - II. Fundamental Principles of Signal-to-Noise Ratios 

Year 

1982


1982


1980


1978


1978


A Tutorial Review of Some Elementary Concepts in the Statistical Evaluation of 1978


Trace Element Measurements


Analysis of Lead in Polluted Coastal Seawater 

Multielement Analysis with an Inductively Coupled Plasma/Optical Emission 

System 

Interlaboratory Lead Analyses of Standardized Samples of Seawater 

Statistical and Mathematical Methods in Analytical Chemistry 

1976


1976


1974


1972


Studies of Flame and Plasma Torch Emission for Simultaneous Multi-Element 1972


Regression and Calibration with Nonconstant Error Variance 1990 

Author Source 

D. Foerst Envir. Sci. & Tech. 16: 430A - 431A 

V.H. Kaiser DK 535: 309-319 

J.D. Winefordner, J.L. Ward Analytical Letters 13: 1293-1297 

C.T.J. Alkemade, W. Snelleman, G.D. 

Boutilier, B.D. Pollard, J.D. Winefordner, 

T.L. Chester, N. Omenetto 

Spectrochimica Acta 33B: 383-399 

G.D. Boutilier, B.D. Pollard, J.D. 

Winefordner, T.L. Chester, N. Omenetto 

Spectrochimica Acta 33B: 401-415 

P.W.J.M. Boumans Spectrochimica Acta 33B: 625-634 

C. Patterson, D. Settle, B. Glover Marine Chemistry 4: 305-319 

R.M. Ajhar, P.D. Dalager, A.L. Davison American Laboratory 72-78 

P. Brewer, N. Frew, N. Cutshall, J.J. 

Wagner, R.A. Duce, P.R. Walsh, G.L. 

Hoffman, J.W.R. Dutton, W.F. Fitzgerald 

Marine Chemistry 2: 69-84 

L.A. Currie, J.J. Filliben, J.R. DeVoe Anal. Chem. 44: 497R-512R 

P.W.J.M. Boumans, F.J. De Boer Spectrochimica Acta 27B: 391-414 

Lloyd Currie Anal. Chem. 40: 586-593 

J. Ramirez-Munoz Talanta 13: 87-101 

J.B. Roos Analyst 87: 832-833 

S.D. Phillips, W.T. Estler, T. Doiron, K.R. 

Eberhardt, M.S. Levenson 

Journal of Research of the National Institute of Standards 

and Technology 106: 371-379 

R.D. Gibbons, D.K. Bhaumik Technometrics 43: 192-198 

K. Danzer, L.A. Currie Pure and Applied Chemistry 70: 993-1014 

R.W. Mee, K.R. Eberhardt Technometrics 38: 221-229 

K.R. Eberhardt, R.W. Mee Journal of Quality Technology 26: 21-29 

M. Davidian, P.D. Haaland Chemometrics and Intelligent Laboratory Systems 9: 231-

248 

A - 5	

Category 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Background 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Analysis- I. Preliminary Investigations 

Quantitative Determination: Application to Radiochemistry 

Qualitative and Quantitative Sensitivity in Flame Photometry 

The Limit of Detection of Analytical Methods 

A Careful Consideration of the Calibration Concept 

Weighted Random-Effects Regression Models with Application to 

Interlaboratory Calibration 

1968


1966


1962


2001


2001


Guidelines for Calibration in Analytical Chemistry-Part I. Fundamentals and 1998


Single Component Calibration (IUPAC recommendations 1998) 

A Comparison of Uncertainty Criteria for Calibration 

Constant-Width Calibration Intervals for Linear Regression 

1996


1994

Title Year Author Source 

Calibration with Randomly Changing Standard Curves 1989 D.F. Vecchia, H.K. Iyer, P.L. Chapman Technometrics 31: 83-90 

Linear Calibration When the Coefficient of Variation is Constant 1988 Y.C. Yao, D.F. Vecchia, H.K. Iyer Probability and Statistics: Essays in Honor of Franklin A. 

Graybill, 297-309 

Analytical Method Comparisons by Estimates of Precision and Lower Detection 

Limit 

1986 D.M. Holland, F.F. McElroy Environmental Science & Technology 20: 1157-1161 

Design Considerations for Calibration 1986 J.P. Buonaccorsi Technometrics 28: 149-155 

Multivariate Calibration when the Error Covariance Matrix is Structured 1985 T. Naes Technometrics 27: 301-311 

An Implementation of the Scheffé Approach to Calibration Using Spline 

Functions, Illustrated by a Pressure-Volume Calibration 

1982 J.A. Lechner, C.P. Reeve, C.H. 

Spiegelman 

Technometrics 24: 229-234 

Measuring and Maximizing Precision in Analyses Based on Use of Calibration 

Graphs 

1982 D.G. Mitchell, J.S. Garden Talanta 29: 921-929 

Calibration in Quantitative Analysis: Part2. Confidence Regions for the Sample 

Content in the Case of Linear Calibration Relations 

1981 J. Agterdenbos, F.J.M.J. Maessen, J. 

Balke 

Analytica Chimica Acta 132: 127-137 

Design Aspects of Scheffe’s Calibration Theory using Linear Splines 1980 C.H. Spiegelman, W.J. Studden Journal of Research of the National Bureau of Standards 85: 

295-304 

Nonconstant Variance Regression Techniques for Calibration-Curve-Based 

Analysis 

1980 J.S. Garden, D.G. Mitchell, W.N. Mills Anal. Chem. 52: 2310-2315 

Calibration in Quantitative Analysis 1979 J. Agterdenbos Analytica Chimica Acta 108: 315-323 

Calibration Curves with Nonuniform Variance 1979 L. Schwartz Analytical Chem. 51: 723-727 

Elimination of the Bias in the Course of Calibration 1978 L.J. Naszódi Technometrics 20: 201-205 

Optimal Designs for the Inverse Regression Method of Calibration 1973 M.A. Thomas, R.H. Myers Communications in Statistics 2: 419-433 

A Statistical Theory of Calibration 1973 H. Scheffé The Annals of Statistics 1: 1-37 

On the Problem of Calibration 1972 G.K. Shukla Technometrics 14: 547-553 

Statistical Processing of Calibration Data in Quantitative Analysis by Gas 

Chromatography 

1970 P. Bocek, J. Novak J. Chromatog. 51: 375-383 

Estimation of a Linear Function for a Calibration Line: Consideration of a Recent 

Proposal 

1969 J. Berkson Technometrics 11: 649-660 

A Note on Regression Methods in Calibration 1969 E.J. Williams Technometrics 11: 189-192 

Classical and Inverse Regression Methods of Calibration in Extrapolation 1969 R.G. Krutchkoff Technometrics 11: 605-608 

Optimal Experimental Designs for Estimating the Independent Variable in 

Regression 

1968 R.L. Ott, R.H. Myers Technometrics 10: 811-823 

Category 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

Calibration 

A - 6	
Title Year Author Source 

Classical and Inverse Regression Methods of Calibration 1967 R.G. Krutchkoff Technometrics 9: 425-439 

The Interpretation of Certain Regression Methods and their Use in Biological 

and Industrial Research 

1939 C. Eisenhart The Annals of Mathematical Statistics 10: 162-186 

The Three "Rs" for Relevant Detection, Reliable Quantitation and Respectable 

Reporting Limits 

2000 Ann Rosecrance Env. Testing & Anal. 9: 13,50 

Detection and Quantification Capabilities and the Evaluation of Low-Level Data: 

Some International Perspectives and Continuing Challenges 

2000 L.A. Currie Journal of Radioanalytical and Nuclear Chemistry 245: 145-

156 

Realistic Detection Limits from Confidence Bands 1999 J.R. Burdge, D.L. McTaggart, S.O. Farwell Journal of Chemical Education 76: 434-439 

Response to Comment of "An Alternative Minimum Level Definition for 

Analytical Quantification" 

1999 Henry Kahn, William Telliard, Chuck White Env. Sci. & Tech. 33: 1315 

Comment on "An Alternative Minimum Level Definition for Analytical 

Quantification" 

1999 H.G. Rigo Env. Sci & Tech. 33: 1311-1312 

Response to Comment on "An Alternative Minimum Level Definition for 

Analytical Quantification" 

1999 Robert Gibbons, David Coleman, Ray 

Maddalone 

Env. Sci. & Tech. 33: 1313-1314 

Comment on "An Alternative Minimum Level Definition for Analytical 

Quantification" 

1998 Henry Kahn, William Telliard, Chuck White Envir. Sci & Tech 32: 2346-2348 

Response to Comment on "An Alternative Minimum Level Definition for 

Analytical Quantification" 

1998 Robert Gibbons, David Coleman, Ray 

Maddalone 

Envir. Sci & Tech 32: 2349-2353 

A Discussion of Issues Raised by Lloyd Currie and a Cross Disciplinary View of 

Detection Limits and Estimating Parameters that are Often At or Near Zero 

1997 C.H. Spiegelman Chemometrics and Intelligent Laboratory Systems 37: 183-

188 

A Mock Trial for Critical Values (Detection Limits) 1997 C.H. Spiegelman, P. Tarlow STATS: The Magazine for Students of Statistics 20: 13-16 

Comment on "An Alternative Minimum Level Definition for Analytical 

Quantification" 

1997 David Kimbrough Envir. Sci. & Tech. 31: 3727-3728 

The Smallest Concentration 1997 R.F. Moran, E.N. Brown Clinical Chemistry 43: 856-857 

A Statistical Overview of Standard (IUPAC and ACS) and New Procedures for 

Determining the Limits of Detection and Quantification: 

Voltammetric and Stripping Techniques (Technical Report) 

1997 J. Mocak, A.M. Bond, S. Meitchell, G. 

Scollary 

Pure and Applied Chemistry 69: 297-328 

Response to Comment on "An Alternative Minimum Level Definition for 

Analytical Quantification" 

1997 R.D. Gibbons, D.E. Coleman, R.F. 

Maddalone 

Envir. Sci. & Tech 31: 3729-3731 

Some Conceptual and Statistical Issues in Analysis of Groundwater Monitoring 

Data 

1996 R.D. Gibbons Environmetrics 7: 185-199 

Some Statistical and Conceptual Issues in the Detection of Low Level 

Environmental Pollutants 

1995 Robert Gibbons Environ. & Ecol. Statistics 2: 125-167 

Application to 

Category 

Calibration 

Calibration 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

Critique 

A - 7	
Title Year 

Comment on "Method Detection Limits in Solid Waste Analysis" 1995 

Comment on "Method Detection Limits in Solid Waste Analysis" 1995 

"You Can't Compute With Less-Thans" 1994 

Limits of Detection 1994 

Conflicting Perspectives About Detection Limits and About the Censoring of 

Environmental Data 

1994 

Limit of Discrimination, Limit of Detection and Sensitivity in Analytical Systems 1994 

Discussion of: A Study of the Precision of Lead Measurements at 

Concentrations Near the Method Limit of Detection 

1994 

Limits of Detection Methodologies 1993 

Method Detection Limits in Solid Waste Analysis 1993 

Defining the Limits 1993 

A Study of the Precision of Lead Measurements at Concentrations Near the 

Method Limit of Detection 

1993 

Detection Limit Concepts: Foundations, Myths, and Utilization 1992 

Difficulties Related to Using Extreme Percentiles for Water Quality Regulations 1991 

A Simple Rule for Judging Compliance Using highly Censored Samples 1991 

Current Method for Setting Dioxin Limits in Water Requires Reexamination 1990 

Kaiser 3-Sigma Criterion - A Review of the Limit of Detection 1990 

MCL Noncompliance: Is the Laboratory at Fault? 1990 

Qualitative or Quantitative Characterization of Spectrographic Methods? The 

Detection and Determination Limits 

1990 

False Positives, Detection Limits, and Other Laboratory Imperfections: The 

Regulatory Implications 

1989 

Evaluation of Detection Limit Estimators 1988 

Chemometrics - Measurement Reliability 1988 

The Detection Limit 1988 

Author Source Category 

D.E. Coleman Environmental Science & Technology 9-280 Critique 

Janice Wakakuwa, David Kimbrough Envir. Sci. & Tech. 29: 281-282 Critique 

Ken Osborn, Ann Rosecrance East Bay Municipal Utility District, Core Laboratories Critique 

N. Cressie Chemometrics Intelligent Laboratory Systems 22: 161-163 Critique 

M.J.R. Clark, P.H. Whitfield Water Resources Bulletin 30: 1063-1079 Critique 

R. Ferrus, M.R. Egea Analytica Chimica Acta 287: 119-145 Critique 

B.R. Nott, R.R. Maddalone Water Environment Research 66: 853-854 Critique 

J. Lindstedt Plating and Surface Finishing 80: 81-86 Critique 

David Kimbrough, Janice Wakakuwa Enviro. Sci. & Tech 27: 2692-2699 Critique 

G. Stanko, W. Krochta, A. Stanley, T. 

Dawson, K. Hillig, R. Javick, R. Obrycki, B. 

Hughes, F. Saksa 

Environmental Lab 1: 16-20 Critique 

P.M. Berthouex Water Environment Research 65: 620-629 Critique 

D.A. Chambers, S.S. Dubose, E.L. 

Sensintaffar 

Health Phys. 63: 338-340 Critique 

P. M. Berthouex, Ian Hau Research Journal WPCF 63: 873-879 Critique 

P. M. Berthouex, Ian Hau Research Journal WPCF 63: 880-886 Critique 

J. LaKind, E. Rifkin Env. Sci. & Tech 24: 963-965 Critique 

L.S. Oresic, V. Grdinic Acta Pharmaceutica Jugoslavica 40: 21-61 Critique 

Steven Koorse AWWA p53-58 Critique 

Karol Florian Chemia Analityczna 35:129-139 Critique 

Steven Koorse Environmental Law Reporter 19: 10211-10222 Critique 

F.C. Garner, G.L. Robertson Chemometrics and Intelligent Laboratory Systems 3: 53-59 Critique 

K. Castaneda-Mendez Clinical Chemistry 34: 2494-2498 Critique 

P.S. Porter, R.C. Ward, H.F. Bell Environmental Science & Technology 22: 856-861 Critique 

29: 27

A - 8	
Title Year 

Estimation of Detection Limits for Environmental Analytical Procedures - A 1988

Tutorial 

Limits of Detection 

Clarification of the Limit of Detection in Chromatography 

Limit of Detection: A Closer Look at the IUPAC Definition 

Trace Analyses for Wastewaters 

A comparison of statistical and empirical detection limits 

Challenges in Regulatory Environmetrics 

Determining Quantitation Levels for Regulatory Purposes 

Defining Detection and Quantitation Levels 

1984 

1984 

1983 

1982 

1998 

1997 

1996 

1993 

Source Category 

ACS Symposium Series 361: 78-93 Critique 

Analytical Chemistry 56: 130A Critique 

Chromatographia 18: 503-511 Critique 

Analytical Chem. 55: 712-724 Critique 

Envir. Sci. & Tech. 16: 430A Critique 

Journal of AOAC International 81: 105-110 Multilab 

Chemometrics Intelligent Laboratory Systems 37: 43-53 Multilab 

P.F. Sanders, R.L. Lippincott, A. Eaton Journal American Water Works Association 88: 104-114 Multilab 

Raymond Maddalone, James Rice, Ben 

Edmondson, Babu Nott, Judith Scott 

Water Envir. & Tech. Jan.93: 41-44 Multilab 

Hadrich J et al. Deutsche Lebensmittel-Rundschau 1999, 95(10), 428-436 not found 

Goudey R et al. Novart FDN Sym 220; 144-157 not found 

Currie LA Abstracts of Papers of the American Chemical Society, 185 

(Mar), 63-PEST 

not found 

T. Georgian, K.E. Osborn Env. Testing & Analysis 10: 13-14 Single lab 

D.P. Hautman, D.J. Munch, C. Frebis, 

H.P. Wagner, B.V. Pepich 

Journal of Chromatography A 920: 221-229 Single lab 

S. Geib, J.W. Einax Fresenius Journal of Analytical Chemistry 370: 673-678 Single lab 

E. Desimoni, S. Mannino, B. Brunetti Accreditation Quality Assurance 6: 452-458 Single lab 

ISO ISO 11843-2 Single lab 

L.A. Currie Analytica Chimica Acta 391: 105-126 Single lab 

C. J. Obinger Childress, W. T. Foreman, 

B. F. Connor, and T. J. Maloney 

USGS Open-File Report 99-193, 19 pages. Single lab 

Author 

Cliff Kirchmer 

J.K. Taylor 

J.P. Foley, J.G. Dorsey 

Gary Long, J.D. Winefordner 

C.J. Kirchmer 

G.C.C. Su 

C.B. Davis 

Concept 2000-A Statistical Approach for Analytical Practice - Part I: Limits of 1999 

Detection, Identification, and Determination 

Statistics and Environmental Policy: udies from Long-Term 

Environmental Monitoring Data 

The Many Dimensions of Detection in Chemical Analysis 

A Practical Strategy for Determining and Verifying Detection Limits 

Case St 1999 

1983 

2001 

Review of the Methods of the US Environmental Protection Agency for Bromate 2001


Determination and Validation of Method 317.0 for Disinfection By-Product


Anions and Low -Level Bromate


Comparison of Detection Limits in Environmental Analysis - Is it Possible? An 2001


Approach on Quality Assurance in the Lower Working Range by Verification


On the Assessment of Compliance with Legal Limits, Part I: Signal and 

Concentration Domains 

2001 

Capability of Detection - Part 2 2000 

Nomenclature in Evaluation of Analytical Methods Including Detection and 

Quantitation Capabilities (IUPAC Recommendations 1995) 

1999 

New Reporting Procedures Based on Long-Term Method Detection Limits and 

Some Considerations for Interpretations of Water-Quality Data Provided by the 

U.S. Geological Survey National Water Quality Laboratory 

1999 

A - 9 
Title Year Author Source Category 

Analyses of Polychlorinated Biphenyls and Chlorinated Pesticides in Biota: 

Method and Quality Assurance 

1999 P. Cleemann, G.B. Paulsen Journal of AOAC International 82: 1175-1184 Single lab 

Detection Limits of Organic Contaminants in Drinking Water 1998 W.M. Draper, J.S. Dhoot, J.S. Dhaliwal, 

J.W. Remoy, S.K. Perera, F.J. Baumann 

Journal of the American Water Works Association 90: 82-90 Single lab 

Detection: International Update, and Some Emerging Di-lemmas Involving 

Calibration, the Blank, and Multiple Detection Decisions 

1997 L.A. Currie Chemometrics and Intelligent Laboratory Systems 37: 151-

181 

Single lab 

Regulations - From an Industry Perspective or Relationships Between Detection 

Limits, Quantitation Limits, and Significant Digits 

1997 D. Coleman, J. Chemometrics and Intelligent Laboratory Systems 37: 71-80 Single lab 

Capability of Detection - Part 1 1997 ISO ISO 11843-1 Single lab 

Determination of Site-Specific Effluent Detection Limits 1996 George Neserke, Harold Taylor Water Env. Res. 66: 115-119 Single lab 

Multivariate Detection Limits Estimators 1996 R. Boque, F.X. Rius Chemometrics and Intelligent Laboratory Systems 32: 11-23 Single lab 

Nomenclature in Evaluation of Analytical Methods including Detection and 

Quantification Capabilities 

1995 Lloyd Currie Pure & 3 Single lab 

Reporting Low-Level Analytical Data Third Draft (1995-11-08) -- New Project of 

Commission V.I., International Union of Pure and Applied Chemistry 

1995 William Horwitz IUPAC Single lab 

IUPAC Recommendations for Defining and Measuring Detection and 

Quantification Limits 

1994 LA Currie, W. Horwitz Analuses Magazine 22: 24-26 Single lab 

Recommendations for the Presentation of Results of Chemical Analysis 1994 L.A. Currie, G. Svehla Pure & Applied Chemistry Single lab 

Detarchi-A Program for Detection Limits with Specified Assurance Probabilities 

and Characteristic Curves of Detection 

1994 L. Sarabia, M.C. Ortiz TRAC-Trends in Analytical Chemistry 13: 1-6 Single lab 

Quality Control Level: An Alternative to Detection Levels 1994 D.E. Kimbrough, J. Wakakuwa Environmental Science & Technology 28: 338-345 Single lab 

Multivariate Decision and Detection Limits 1993 A. Singh Analytica Chimica Acta 277: 205-214 Single lab 

A Model of Measurement Precision at Low Concentrations 1993 P.M. Berthouex, D.R. Gan Water Environment Research 65: 759-763 Single lab 

Robust Procedure for Calibration and Calculation of the Detection Limit of 

Trimipramine by Adsorptive Stripping Voltametry at a Carbon Paste Electrode 

1993 M.C. Ortiz, J. Arcos, J.V. Jurarros, J. 

Lopez-Palacios, L.A. Sarabia 

Analytical Chemistry 65: 678-682 Single lab 

Nondetects, Detection Limits, and the Probability of Detection 1991 D. Lambert, B. Peterson, I. Terpenning JASA 86: 266-277 Single lab 

Detection Limits: For Linear Calibration Curves with Increasing Variance and 

Multiple Future Detection Decisions 

1991 R.D. Gibbons, F.H. Jarke, K.P. Stoub Waste Testing and Quality Assurance: TP 1075, D. 

Friedman, Ed., American Society for Testing and Materials, 

Philadelphia 3: 337-390 

Single lab 

Limits of Detection in Multivariate Calibration 1991 G. Bauer, W. Wegscheider, H.M. Ortner Fresenius Journal of Analytical Chemistry 340: 135-139 Single lab 

Estimating Detection Limits in Ultratrace Analysis. 

Estimated Detection Limits 

1991 C.L. Stevenson, J.D. Winefordner Applied Spectroscopy 45:1217-1224 Single lab 

Auses, N. Grams 

Appl. Chem. 67: 1699-172

66: 595-608 

ASTM S

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Detection Limits with Specified Assurance Probabilities 1987 C.A. Clayton, J.W. Hines, and P.D. Elkins Analytical Chemistry 59: 2506-2514 

Limit of Detection and Limit of Determination - Application of Different Statistical 

Approaches to an Illustrative Example of Residue Analysis 

1987 J. Vogelgesang Fresenius Zeitschrift Fur Analytsche Chemie 328: 213-220 

Determining the Lowest Limit of Reliable Assay Measurement 1983 L. Oppenheimer, T.P. Capizzi, R.M. 

Weppelman, H. Mehta 

Analytical Chemistry 55: 638-643 

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R.A. Libby, J.K. Taylor, G. Wentler 

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Stephan Quave, William Budde 

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Guidelines for Data Acquisition and Data Quality Evaluation in Environmental 

Chemistry 

1980 MacDougall, Daniel, et al. Anal. Chem. 52: 2242-2249 

Sensitivity and Limit of Detection in Quantitative Spectrometric Methods 1974 J.D. Ingle Jr. Journal of Chemical Education, 51, 100-105. 

Decision and Detection Limits for Linear Calibration Curves 1970 A. Hubaux, G. Vos Analytical Chemistry 42: 849-855 

Limits for Quantitative Detection and Quantitative Determination 1968 L.A. Currie Analytical Chemistry 40: 586-593 

A Statistical Method for Evaluation of Limiting Detectable Sample 

Concentrations 

1967 P.A. St. John, W.J. McCarthy, J.D. 

Winefordner 

Analytical Chem. 39: 1495-1597 

Initial Evaluation of Quantitative Performance of Chromatographic Methods 

Using Replicates at Multiple Concentrations 

2001 M.A. Castillo, R.C. Castells Journal of Chromatography A 921: 121-133 

Multivariate Detection Limits with Fixed Probabilities of Error 1999 R. Boque, M.S. Larrechi, F.X. Rius Chemometrics and Intelligent Laboratory Systems 45: 397-

408 

Evaluation of Approximate Methods for Calculating the Limit of Detection and 

Limit of Quantification 

1999 M.E. Zorn, R.D. Gibbons, W.C. Sonzogni Environmental Science & Technology 33: 2291-2295 

Limits of Detection, Identification and Determination: A Statistical Approach for 

Practitioners 

1998 J. Vogelgesang, J. Hadrich Accreditation Quality Assurance 3: 242-255 

Weighted Least-Squares Approach to Calculating Limits of Detection and 

Quantification by Modeling Variability as a Function of Concentration 

1997 M.E. Zorn,, R.D. Gibbons, W.C. Sonzogni Analytical Chemistry 69: 3069-3075 

Detection Limits in GC-MS Multivariate Analysis 1997 Boque R et al. Quimica Analytica, 16(2), 81-86 

An Alternative Minimum Level Definition for Analytical Quantification 1997 Robert Gibbons, David Coleman, 

Raymond Maddalone 

Environmental Science & Technology 31: 2071-2077 

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Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab 

Single lab -

multilevel 

Single lab -

multilevel 

Single lab -

multilevel 

Single lab -

multilevel 

Single lab -

multilevel 

Single lab -

multilevel 

Single lab -

multilevel 

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A Two-Component Model for Measurement Error in Analytical Chemistry 1995 David Rocke, Stefan Lorenzato Technometrics 37: 176-184 Single lab -

multilevel 

Practical Quantitation Limits 1992 R.D. Gibbons, N.E. Grams, F.H. Jarke, 

K.P. Stoub 

Chemometrics and Intelligent Laboratory Systems 12: 225-

235. 

Single lab -

multilevel 

Experimental Comparison of EPA and USATHAMA Detection and Quantitation 

Capability Estimators 

1991 C.L. Grant, A.D. Hewitt, T.F. Jenkins American Laboratory 23: 15-33 Single lab -

multilevel 

High Pressure Liquid Chromatography Determination of the Intermediates Side 

Reaction Products in FD&C Red No. 2 and FD&C Yellow No. 5: Statistical 

Analysis of Instrument Response 

1978 C.J. Bayley, E.A. Cox, J.A. Springer J. Assoc. Off. Anal. Chem 61: 1404-1414. Single lab -

multilevel 

Method Detection Limits: to Organic Environmental Chemistry Data 2002 Wade, T. L., J. L. Sericano, Y. Qian, G. 

Wolff, and G. Denoux 

Presentation at ACS Symposium, Boston, MA, August 2002 Suggested by a 

peer reviewer 

Detection limits 2002 van oet, H. Encyclopedia of Environmetrics, 1, El-Shaarawi, A. H. and 

Piegorsch, W. W. (eds.), 504-515. Chichester: John Wiley & 

Sons 

Suggested by a 

peer reviewer 

Statistical Approaches to Estimating Mean Water Quality Concentration with 

Detection Limits 

2002 Shumway, R., R. Azari, and M. Kayhanian Environmental Science and Technology 36: Suggested by a 

peer reviewer 

Detection Limit of Isotope Dilution Mass Spectrometry 2002 Yu, L. L., J. D. Fassett, and W. F. Guthrie Analytical Chemistry. 74: 3887-3891 Suggested by a 

peer reviewer 

ISO 17025 Requirements: Uncertainty for Dioxin Analysis in 

Food and Feed from Validation Data? 

2002 G. Eppe, and E. De Pauw Proceedings of the 22nd International Symposium on 

Halogenated Environmental Organic Pollutants and POPs, 

Barcelona, Spain, August 12-18, 2002, Vol. 59, pp. 403-

406, 2002 

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peer reviewer 

Are Target Dioxin Levels in Animal Feedingstuffs Achievable for Laboratories in 

Terms of Analytical Requirements? Interlaboratory study 

2002 G. Eppe, and E. De Pauw Proceedings of the 22nd International Symposium on 

Halogenated Environmental Organic Pollutants and POPs, 

Barcelona, Spain, August 12-18, 2002, Vol. 59, pp. 407-

410, 2002 

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Environmental Statistics with S-PLUS 2001 Millard, S. P., and Neerchal, N. K. Chapman & Hall/CRC, Boca Raton, FL Suggested by a 

peer reviewer 

Quantifying Uncertainty in Analytical Chemistry 2000 Ellison, S. L. R., M. Roslein, and A. 

Williams, eds. 

EURACHEM/CITAC Guide 2000 Suggested by a 

peer reviewer 

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Protocol for Uncertainty Evaluation from Validation Data 

2000 Barwick V.J., Ellison S.L.R. VAM Project 3.2.1 Suggested by a 

peer reviewer 

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Assessment 

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peer reviewer 

Application

der V

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Title Year Author Source Category

Foundations and future of detection and quantification 1996 Currie, L. A. Proceedings of Section 

on Statistics and the Environment, 1-8 

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peer reviewer 

Statistical Methods for Groundwater Monitoring 1994 Gibbons, R. D. John Wiley & Sons, New York Suggested by a 

peer reviewer 

Spatial chemostatistics 1994 Cressie, N. Environmental Statistics, Assessment, and Forecasting, 

Cothern, C. R. and Ross, N. P. (eds.), 131-146. Boca Raton, 

FL: Lewis Publishers 

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Donohoe, R. R 

Environmental Science & Technology 28: 8-90 Suggested by a 

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A new approach for accommodation of below detection limit data in trend 

analysis of water quality 

1994 Nagaraj, N. K., and Brunenmeister, S. L. Environmental Statistics, Assessment, and Forecasting, 

Cothern, C. R. and Ross, N. P. (eds.), 113-127. Boca Raton, 

FL: Lewis Publishers 

Suggested by a 

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Errors and detection limits 1992 Adams, M. J. In Methods of Environmental Data Analysis, Hewitt, C. N. 

(ed.), 181-212. Amsterdam: Elsevier Applied Science 

Suggested by a 

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peer reviewer 

Less than obvious: Statistical treatment of data below the detection limit 1990 Helsel, D. R Environmental Science & Technology 24: 66-1774 Suggested by a 

peer reviewer 

Environmental tests: Are they valid? 1990 Maynard, A. W. Chemical Technology 20: 151-155. Suggested by a 

peer reviewer 

Detection in Analytical Chemistry: Importance, Theory, and Practice 1988 Currie, L. A. American Chemical Society, New York Suggested by a 

peer 

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limits the American Statistical Association,

89

17

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Appendix B 

Computation of Detection and Quantitation Limits 

INTRODUCTION 

This appendix supports the Revised Assessment Document (RAD) for EPA's assessment of 

detection and quantitation approaches. It presumes that the reader has read chapters three through five 

of the RAD. 

We have compared detection and quantitation limits computed from data gathered by EPA or 

submitted to EPA by stakeholders commenting on EPA’s February 2003 (EPA-821-R-03-005) 

assessment. The comparison shows that, in general, detection limits derived from a single concentration 

level such as EPA’s MDL are, on average, approximately the same as detection limits derived from 

similar approaches such as the ACS LOD and LOQ and ISO/IUPAC CRV and MDV, and are 

approximately three times lower than a single-laboratory variant of ASTM’s IDE; and that all quantitation 

limit approaches, such as EPA's ML, the ACS and ISO/IUPAC LOQ, and a single-laboratory variant of 

ASTM's IQE, produce quantitation limits that are generally only slightly different. 

EPA's Approach to Establishing Detection and Quantitation Limits in Analytical Methods 

The Engineering and Analysis Division (EAD) within EPA's Office of Science and Technology 

develops analytical methods for use in EPA's Clean Water Act (CWA) programs. In developing these 

methods, EAD first conducts a single-laboratory study in which an MDL and ML are determined 

followed by multiple single-laboratory studies in which the MDL and ML are either verified or if 

necessary, revised. If resources, time, and applications of the method warrant, an interlaboratory study is 

conducted in which the MDL and ML are further verified or, if necessary, revised. 

To set an MDL, which is both conservative and achievable by qualified laboratories, we generally 

select the highest MDL from among the MDLs determined or verified by laboratories in the various 

studies. For example, EPA determined the MDL in Method 1631 (mercury by cold-vapor atomic 

fluorescence) as 0.05 ng/L in a single laboratory and revised this MDL to 0.2 ng/L based on multiple 

single-laboratory studies. All laboratories verified the MDL of 0.2 ng/L in an interlaboratory study. 

Unlike a single-lab MDL and ML computed in a laboratory quality-control setting, the interlaboratory 

MDL established during method development is set as a high-biased estimate of Currie’s Lc. Thus, the 

single-lab MDL and resulting ML, when scaled up with the interlaboratory MDL data, are very 

conservative. This interlaboratory scaling up protects against unrealistically low values, and responds to 

concerns that the MDL is a single-laboratory approach that produces unrealistically low MDLs. 

DETECTION AND QUANTITATION LIMITS ASSESSED 

EPA used several datasets to evaluate various approaches to determining detection and 

quantitation values. These data are described in the Data section of this Appendix. 

In the original Assessment Document (EPA, February 2003), four different detection and three 

different quantitation limits were evaluated and compared. The detection limits were the EPA method 

B - 1

detection limit (MDL), the International Standards Organization/International Union of Pure and Applied 

Chemistry (ISO/IUPAC) critical value (CRV) and minimum detectable value (MDV), and a singlelaboratory variant of the ASTM interlaboratory detection estimate (IDE). The quantitation limits were the 

EPA minimum level of quantitation (ML), the ISO limit of quantitation (LOQ), and a single-laboratory 

variant of the ASTM interlaboratory quantitation estimate (IQE). 

Several stakeholders commenting on EPA’s assessment of data expressed difficulty in replicating 

EPA’s calculations supporting these evaluations. Based on these comments, EPA reviewed the computer 

programs used to calculate the various limits, and compared results obtained using these programs to 

calculation results and software packages submitted by commenters. EPA concluded that many of the 

discrepancies between EPA and commenter calculations were due to differences in the datasets and 

software used (see Software Comparison, later in this appendix). As a result of this review, EPA did, 

however, find some discrepancies which have been resolved in this document. Revisions are listed below: 

� In calculating the single-laboratory IDE (SL-IDE) and single-laboratory IQE (SL-IQE) based on 

the Exponential model using the Episode 6000 and Method 1631 and 1638 validation study data, 

incorrect weights were used when modeling recovery. Because the majority of the SL-IDEs 

were calculated using this model, most of the SL-IDEs presented in Tables 2, 6, 7 and 8 have 

changed. Because the SL-IQEs were not calculated based on the exponential models, these 

values did not change. 

� When calculating MLs based on the Episode 6000 data, the resulting ML was incorrectly rounded 

up for many analytes. This has been corrected, and many of the calculated MLs in Tables 4 and 

5 have changed. 

� In the 2003 assessment, blank results were included in the calculations of the ISO/IUPAC CRV, 

MDV and LOQ. Upon further review, it was decided that it was invalid to use blank results 

included in the Episode 6000 study, because these blanks were used to assess carry-over, and 

would not be representative of routine blank analyses. Therefore, the ISO/IUPAC limits were 

re-calculated using the lowest spike concentration in place of blank results. 

� For two analytes in the Episode 6000 data (uranium and thallium by Method 200.8), incorrect 

formatting caused multiple spiking levels to be combined improperly. This affected the calculation 

of all limits for these analytes. This calculation has been fixed, and the limits have changed 

slightly for these two analytes. 

� After completion of the Original Assessment Document, a new version of the IDE procedure 

(D6091-03) was published by ASTM. This procedure included the use of a standard deviation 

bias correction factor which was not included in the prior version (D6091-97). Therefore, all 

IDEs calculated using the Episode 6000 and Methods 1631 and 1638 validation study data were 

re-calculated using this correction factor. For the majority of analytes, the resulting IDEs 

increased slightly (by approximately 4%). 

The effect of these changes on the analyses are discussed in the Results of Computations section 

of this Appendix. To better explain how calculations were run, Appendix C gives example calculations of 

the SL-IDE, SL-IQE, MDL and ML for one analyte. 

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Along with comments on EPA’s assessment, both the American Council of Independent 

Laboratories (ACIL) and USGS submitted data and procedures. ACIL submitted a procedure for 

calculating a critical level (CRV) and Long-Term MDL (LTMDL). USGS submitted its procedure for 

calculating a long-term MDL (USGS LT-MDL). Both the ACIL critical level and USGS LT-MDL are 

estimates of Currie's Lc, and are therefore comparable to the EPA MDL. Both the ACIL and USGS 

procedures, however, are based on results collected over a long period of time. The ACIL critical level is 

based on blank results, and the USGS LT-MDL is based on spiked results. The formula for the ACIL 

critical level is identical to that of EPA’s MDL, except that the mean of the blanks is added to the product 

of the standard deviation and t-statistic. The USGS procedure does not use a sample standard deviation, 

but instead uses a non-parametric estimate of variability that is based on the interquartile range. The 

USGS LT-MDL procedure also allows addition of the mean or median of blank results to the LT-MDL. 

ACIL also suggested a separate CRV procedure (ACIL “Case 2") for calculating estimates for 

those methods for which analysis of blank samples does not produce a signal. For these methods, ACIL 

suggested an iterative procedure that first determines the lowest level at which all 7 replicates are 

detected, and then estimates the CRV as the lowest of the observed results of 7 spikes. The analogue of 

Currie’s Ld is estimated as this lowest spike level. EPA finds merit in the idea of dividing the methods 

into two groups (depending on the presence or absence of a signal from analysis of blank samples) and in 

the idea of estimating the detection level of the instrument, and plans to further investigate the ACIL 

approach. However, the particular implementation of the ACIL Case 2 procedure has some conceptual 

problems that precluded it from evaluation at this time. These problems are described later in this 

Appendix (see “Episode 6000 Data”). 

EPA provides further discussion of these approaches and the Agency’s reasons for selecting 

them in Chapters 1 and 2 of the RAD. 

Commonality of Approaches 

The EPA, ACS, and ISO/IUPAC approaches are all multiples of the standard deviation of either 

replicate measurements of a blank or of the lowest spike concentration that produces positive (non-zero) 

results for all 7 replicates. Similarly, the ACIL and USGS approaches are based on multiples of a 

parametric or nonparametric estimate of variability of replicate measurements, with the difference that 

the given estimate includes greater sources of variability than those of the other single-concentration 

approaches. 

Other subtle distinctions are that (1) ISO/IUPAC suggest a false positive rate of 5 % (� = 0.05) 

for the CRV and MDV, whereas EPA specifies a false positive rate of 1 % (� = 0.01) for the MDL and 

(2) the EPA MDL was calculated by pooling data from two concentration levels after determining that 

the variabilities of the two concentration levels are not significantly different (as provided as an option in 

step 7 of the MDL procedure), thereby increasing the degrees of freedom to 12 from the 6 used in 

computation of the ISO/IUPAC CRV and ACS LOD. The consequence of distinction (1) is that an 

approach with a higher allowed false positive rate (� = 0.05) will produce a lower detection limit than an 

approach with a lower false positive rate (� = 0.01). The consequence of distinction (2) is that a 

detection limit resulting from pooling at two levels will be more stable and likely somewhat lower than a 

detection limit at a single level (given the same variability at each level) because the degrees of freedom 

are increased in the t statistic. 

B - 3

The ACS and ISO/IUPAC approaches specify replicate measurements of blank samples. In 

computing detection and quantitation limits from the Episode 6000 data, blank results were not used, as 

blanks analyzed in this study included carry-over effects, and were therefore not representative of routine 

blank results. Therefore, the lowest spike concentration (or, in the case of the MDL, two lowest spike 

concentrations) that produced a non-zero result was used for computation of all approaches. This 

simplification condensed the EPA MDL and the ACS LOD to a single approach subsequently termed the 

EPA/ACS DL. Similarly, the EPA ML and ACS LOQ were condensed to a single approach, termed the 

EPA/ACS QL. 

The remaining single-concentration approaches are the ISO/IUPAC CRV, MDV, and LOQ, the 

ACIL critical level and the USGS LT-MDL. The ISO/IUPAC CRV differs from the EPA/ACS DL 

because of its suggested use of a false positive rate of 5% (� = 0.05) versus use of a false positive rate of 

1% (� = .01) in the EPA/ACS DL. The ISO/IUPAC MDV also differs from the EPA/ACS DL because 

of (1) its suggested use of a false positive rate of 5% (� = 0.05), (2) its stated false negative rate of 5 % 

(�=0.05), and (3) recovery correction (estimated using a linear regression). Therefore, the ISO/IUPAC 

CRV and MDV were each treated separately (were not combined with the EPA or ACS approaches) 

from the other detection limit approaches in the data analysis. The ISO/IUPAC LOQ is also different 

from the other quantitation limit approaches and was treated separately from these approaches. The 

ACIL critical level differs from the EPA/ACS DL in its inclusion of long-term variability and the addition 

of the mean blank result to the limit. The USGS LT-MDL differs from the EPA/ACS DL in its inclusion 

of long-term variability, the addition of the median or mean blank result to the limit, and the use of a 

nonparametric estimate of variability in place of the sample standard deviation. Because of the lack of 

long-term variability and representative blank results in the Episode 6000 data, the ACIL critical level and 

USGS LT-MDL could not be calculated using the Episode 6000 data. Assessments of these approaches 

in comparison to the EPA/ACS DL were done using blank and spiked sample data that were submitted to 

the Agency by ACIL and USGS. 

The ASTM IDE and IQE were treated separately because they are constructed by fitting a 

model to variability versus concentration data, rather than being derived from the standard deviation of 

replicate measurements of a single concentration, (as are the EPA, ACS, ISO/IUPAC and ACIL 

approaches). Similar to some of the ISO/IUPAC approaches, the ASTM IDE and IQE include 

“protection” against false negatives and recovery correction (see section 3.3 of the Revised Assessment 

Document for a discussion on EPA’s concerns about false negative protection). The IQE, but not IDE, 

also includes an added correction for the bias associated with an estimate of the true standard deviation at 

each concentration. In the context of the IQE, the word "bias" means the amount by which the estimated 

sample standard deviation is low compared to the true population standard deviation, and should not be 

confused with common use of the word "bias" in an analytical measurement. 

Single-laboratory Variants of Interlaboratory Approaches 

Because the EPA, ACS, and ISO/IUPAC approaches are single-laboratory approaches, and the 

ASTM IDE and IQE are interlaboratory approaches, the ASTM approaches could not be computed using 

the single-laboratory data in the Episode 6000 studies. To solve this problem, single-laboratory variants of 

the IDE and IQE were used. These single-laboratory variants were termed the SL-IDE and SL-IQE for 

“single-laboratory IDE” and “single-laboratory IQE,” respectively. The SL-IDEs and SL-IQEs were 

constructed using the overall standard deviation within a single laboratory at each concentration rather 

than the overall standard deviation across all laboratories at each concentration. 

B - 4

Attempted Application to Interlaboratory Data 

EPA attempted to apply the various approaches to interlaboratory study data in response to a 

request by the Petitioners to the Settlement Agreement and so that detection and quantitation limits could 

be compared. However, because the EPA, ACS, and ISO/IUPAC approaches are single-laboratory 

approaches, whereas, the ASTM approaches are interlaboratory approaches, it was not possible to 

compute directly comparable detection and quantitation limits from the same data. 

It was possible, however, to compare the detection and quantitation limits produced by EPA and 

the Electric Power Research Institute (EPRI) from the EPA Method 1631 and EPA Method 1638 

interlaboratory study data. Although the resulting detection and quantitation limits are either singlelaboratory (EPA) or interlaboratory (ASTM), as appropriate to the particular approach, a comparison of 

the resulting limits can be informative. The EPRI detection and quantitation limits are presented in EPRI 

reports of the results of the Method 1631 and Method 1638 studies. 

DATA 

Datasets Evaluated 

The datasets used to evaluate the detection and quantitation approaches discussed above are 

described in this section. EPA computed EPA/ACS DLs and QLs; ISO/IUPAC CRVs, MDVs and 

LOQs; and single-laboratory variants of ASTM IDEs (SL-IDEs) and IQEs (SL-IQEs) using the Episode 

6000 data. EPA also computed IDEs and IQEs for the Method 1631 and 1638 interlaboratory study data. 

EPA computed ACIL’s critical level, USGS’s LT-MDL and EPA’s MDL based on a combination of 

blank and spiked data submitted by USGS, and performed an assessment of the effect of long-term 

variability based on blank data submitted by ACIL. 

EPA’s Variability versus Concentration Studies ("Episode 6000") 

In 1997 and 1998, EPA conducted a study of variability vs. concentration for a number of 

analytical methods. Six laboratories were employed for the analyses; each analyte and method 

combination was tested by one of these laboratories. For nearly all of the technologies, the studies were 

conducted by spiking reagent (i.e., blank, presumably "clean") water at 16 different concentrations per 

analyte, ranging from 100 times an initial estimate of the MDL to 0.1 times the initial estimate. A total of 

198 analytes were measured, generally with seven replicates analyzed at each concentration. Details of 

the study design are described in EPA’s Study Plan for Characterizing Variability as a Function of 

Concentration for a Variety of Analytical Techniques (July 1998), and in Appendix C of the February 

2003 Assessment document. Based on the sampling episode number assigned to the study by the EPA 

Sample Control Center, the study and results have become known as the Episode 6000 study and data. 

The analytes and analytical techniques studied were: 

� Total suspended solids (TSS) by gravimetry


� Metals by graphite furnace atomic absorption spectroscopy (GFAA)


� Metals by inductively-coupled plasma atomic emission spectrometry (ICP/AES)


� Hardness by ethylene diamine tetraacetic acid (EDTA) titration


� Phosphorus by colorimetry


B - 5

� Ammonia by ion-selective electrode


� Volatile organic compounds by purge-and-trap capillary column gas chromatography with a


photoionization detector (GC/PID) and electrolytic conductivity detector (GC/ELCD) in series 

� Volatile organic compounds by gas chromatography with a mass spectrometer (GC/MS) 

� Available cyanide by flow-injection/ligand exchange/amperometric detection 

� Metals by inductively-coupled plasma spectrometry with a mass spectrometer (ICP/MS) 

EPA's 2003 assessment of detection and quantitation examined a dataset populated with the results of 

this study, the object of which was to characterize analytical variability as a function of concentration over 

a wide range of concentrations, analytes, and analytical methods. Data from this study, including many 

tables and plots, were provided in the record supporting EPA's original assessment and discussed in 

EPA's "Technical Support Document for the Assessment of Detection and Quantitation Approaches," 

EPA 821-R-03-005, February 2003. The database developed contains a total of approximately 22,000 

data points. This study was conducted in contract laboratories. EPA performed a contract compliance 

review of these studies at the time the studies were conducted, but not a point-by-point review of each of 

the tens of thousands of data points. 

In the study, an initial (range finding) MDL was determined for each combination of analyte and 

analytical technique using a revised draft of the MDL procedure. The revised draft had three significant 

changes: (1) the definition was more closely conformed to the MDL procedure; (2) optional iterative step 

7 of the MDL procedure was made mandatory; and (3) the spike concentration to MDL was reduced 

from 5 to 3 in an attempt to narrow the resulting MDL. During data gathering, two laboratories 

complained that the reduction in spike to determined MDL ratio (from 5 to 3) caused a large number of 

iterations and stated that 5 was more reasonable. Subsequently, EPA returned to the spike to MDL ratio 

of 5 published in the 40 CFR 136, Appendix B procedure. 

After determining the initial MDL, each laboratory analyzed 7 replicates of samples spiked at 

concentrations of 100, 50, 20, 10, 7.5, 5.0, 3.5, 2.0, 1.5, 1.0, 0.75, 0.50, 0.35, 0.20, 0.15, and 0.10 times the 

initial MDL. In a few instances, laboratories analyzed more than 7 replicates. Results associated with 

the replicate analyses at each concentration level were obtained, as often as possible, using the same 

calibration that was used in determining the initial MDL. Where laboratory reports indicated that multiple 

calibrations were conducted, the association between each result and its calibration was used in the data 

analysis. 

Spiked aqueous solutions were analyzed in order from the highest concentration (100 times the MDL) 

to the concentration at which 3 or more non-detects (zeros) were encountered among the 7 replicates, or 

the lowest concentration specified (0.1 times the MDL), whichever occurred first. This analysis order (1) 

minimized carryover that could occur in some methods if a low-concentration sample had followed a highconcentration sample (as may happen when samples are analyzed in random order), and (2) prevented 

collection of a large number of zeros if the signal disappeared. 

A variant of the iterative MDL procedure was used for organic compounds determined by 

chromatographic methods. Methods for organics normally list many (15 to 100) analytes, and the 

response for each analyte is different. Therefore, to determine an MDL for each analyte, the 

concentration of the spike would need to be inversely proportional to the response. Making a spiking 

solution with 15 to 100 different concentrations is cumbersome and error prone. The approach used in the 

study was to run 7 replicates at decreasing concentrations until signal extinction, then select the 

B - 6

concentration(s) appropriate for the MDL for each analyte according to the MDL procedure. In some 

cases the laboratories selected the concentrations, in others cases, EPA did. This approach was generally 

applied for organics analysis. However, laboratories also had the option of using some combination of 

monotonically decreasing concentrations described above and a few selected concentrations to achieve 

the desired spiking levels. 

Some commenters on the 2003 assessment noted possible errors. EPA reviewed these comments 

and examined the individual data values and other aspects of the assessment that commenters thought 

were in error. Commenters commented most frequently on measurements of organic compounds by 

EPA Methods 502.2 (halogenated and aromatic volatiles by GC with photoionization and electrolytic 

conductivity detectors in series) and 524.2 (volatiles by GC/MS) that were included in the Episode 6000 

dataset. EPA performed a more comprehensive review of these data points, and found that the 

calculated recoveries of some of the compounds are higher or lower than would be expected for the 

analytical technologies employed. There also appear to be low background concentrations of some 

compounds in the reagent (blank) into which the analytes were spiked. Backgrounds are commonly 

observed in determinations of metals, radionuclides, and some volatiles. 

Without the raw data for the analyses in question, it is not possible to unequivocally determine the root 

cause(s) of the high or low recoveries and possible backgrounds. However, atypical recoveries may have 

been the result of (1) laboratories making measurements at levels as much as 50 times below the lowest 

level to which they would normally calibrate to establish MDLs and MLs at as low a level as could be 

measured, and (2) EPA's request that the laboratories use a single calibration (rather than multiple) to 

prevent discontinuities in the variability vs concentration trends that were the object of these studies. 

Another possible explanation for the low apparent recoveries is the setting of thresholds in the GC and 

GC/MS analyses. If a small constant area of a GC response peak is removed by thresholding, the relative 

amount of area that is removed will increase as the concentration is reduced, resulting in lower apparent 

recoveries at the lower concentrations. This effect would be consistent with observations in some of the 

data. 

As for possible backgrounds for volatiles or metals, these backgrounds likely were either present in 

the reagent (blank) water used by the laboratories for the MDL determinations, or by carry-over from 

one sample to another. To test for carry-over, some laboratories analyzed one or more blank sample 

between spike levels after verification of calibration. Instances in which multiple blanks were analyzed 

often show decreasing small concentrations for some of the analytes. However, these backgrounds 

resulting from carry-over mean that blank results should not be used to assess false positive rates of the 

different limits calculated using the Episode 6000 data. 

Interlaboratory Study Data 

EPA used data from two interlaboratory method validation studies to calculate IDEs and IQEs for a 

total of 10 metal analytes. These studies were conducted by EPA to evaluate performance of EPA 

Methods 1631 and 1638, and to gather data to evaluate existing performance specifications, including 

detection and quantitation limits. To expand the scope of the study, the Electric Power Research Institute 

(EPRI) funded the distribution of additional samples to study participants. Each study included multiple 

participant laboratories: twelve for Method 1631 and eight for Method 1638. 

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The two studies were designed so that each participating laboratory would analyze sample pairs of 

each matrix at concentrations that would span the analytical range of the method. Each laboratory was 

provided with multiple sample pairs, including samples measured in filtered effluent, unfiltered effluent, 

marine water, filtered freshwater, and spiked reagent water. Each laboratory analyzed reagent water 

sample pairs for each analyte at five different concentration levels. The results of the reagent water 

analyses were used to fit variability functions and calculate IDEs and IQEs. 

Data from these studies also are discussed in Chapter 1 of this document. 

Data Submitted by Stakeholders 

EPA also used datasets containing results from analysis of blank samples provided by two 

stakeholders. Blanks analyzed over a period of three months for five analytes using Method 200.7 were 

provided by the American Council of Independent Laboratories (ACIL), while blanks analyzed over a 

period of one year representing 78 analytes were provided by the US Geological Survey (USGS). In 

addition to these blank results, USGS sent results of the analyse of spiked samples representing 39 

analytes. Because spiked samples were analyzed only at a single concentration level, many of the 

different detection and quantitation limits, such as the SL-IDE and SL-IQE, cannot be calculated using 

these data. However, a comparison of the critical level suggested by the ACIL, the LT-MDL suggested 

by USGS, and the EPA MDL was performed using the blank and spiked results. 

The data submitted by ACIL and USGS also are discussed in Chapter 1 of this document. 

Datasets Not Evaluated 

The Petitioners and Intervenor to the Settlement Agreement provided the list of datasets shown in 

Table 1 and suggested that EPA evaluate detection/quantitation limit approaches using the datasets on the 

list. However, in reviewing the datasets suggested, EPA determined that many were developed for 

characterizing the behavior of an analyte or analytes across the analytical range of a method, rather than 

in the region of detection and quantitation. For example, any dataset developed prior to the advent of the 

IDE and IQE would be inappropriate because there could not have been an estimate of IDE0 or IQE0 

(i.e., an initial estimate of the given limit; see Section 6.2.2.1 of D6091 and D6512). This eliminates all 

datasets in Table 1 except the EPA/EPRI Method 1631, the EPA/EPRI Method 1638 dataset, and the 

MMA 2001-2 dataset. It is possible that some spike level in one or more of the datasets developed prior 

to the advent of the IDE and IQE would fortuitously meet the IDE/IQE criteria. But the IDE and IQE 

can be circular; i.e., once developed from a given dataset, there may be a spike level in the dataset that 

can be construed to meet the criteria. Datasets developed without following the IDE and IQE 

procedures, particularly without making an a priori estimate of IDE0 or IQE0, do not meet the 

requirements of the IDE and IQE procedures, regardless of whether the data in them can be construed to 

have met those requirements after the fact. 

In addition, these datasets do not lend themselves to the comparisons used in this report because the 

developers of these datasets did not apply the measurements needed to establish an MDL and ML. 

Therefore, MDLs and MLs could not be determined for comparisons (see the section titled "EPA's 

Approach to Establishing Detection and Quantitation Limits in Analytical Methods"). 

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The EPA 6000 dataset is comprehensive in coverage of analytes, analytical techniques, and a 

concentration range from 0.1 to 100 times the MDL, whereas the datasets suggested by Petitioners focus 

on metals, two Aroclors, and concentrations across the analytical range of the method. The range of data 

used for construction of an IDE or IQE is particularly important. As detailed in the discussion of the 

"Effect of number and spacing of concentrations for determination of the SL-IDE and SL-IQE" below, 

including data across the analytical range in calculation of an SL-IDE significantly raises the SL-IDE. 

After EPA published the February 2003 Assessment Document for comment, three commenters 

offered to provide EPA with additional data that would enhance EPA’s assessment. EPA requested the 

data offered by each of these organizations, but received a response from only two of the three (an 

analytical laboratory and USGS). After evaluating these data, EPA determined that the data from the 

analytical laboratory were not useful because they were limited to calibration data and did not include the 

data from extraction that is needed to compare detection/quantitation approaches. 

Michigan Manufacturers Association (MMA) Dataset 

In March of 2002, John Phillips of Ford Motor Company submitted a report of results from a study of 

two Aroclors (PCBs) by the Michigan Manufacturers Association (MMA) for EPA's consideration in 

evaluating detection and quantitation limit approaches. EPA did not use this dataset because of problems, 

such as the dataset was limited to a maximum number of five analytical results per spike level, which is 

inconsistent with the minimum number of seven analytical results per spike level required for determining 

an MDL, and other values that are determined using non-ASTM approaches. In comments on EPA's 

evaluation, Hunton and Williams (a law firm representing the Inter-industry Analytical Group), stated that 

EPA should not have excluded the MMA dataset from its assessment of detection and quantitation 

approaches. EPA notes, however, that because of the insufficient number of analytical results, 

comparison of various detection and quantitation approaches is not possible with this dataset, and has not 

included the dataset in this evaluation. In addition, MMA samples spiked with low levels of PCBs as 

Aroclors produced an average recovery on the order of 500% at the lowest spike concentration whereas 

PCBs are recovered at approximately 80% from water in this concentration range (see the recovery data 

in EPA Methods 608 and 1668A). A logical explanation for the 500% recoveries in the MMA study is 

that the samples were contaminated by the sample preparation laboratory, by many of the participant 

laboratories, or both. A single and simple test, which was not conducted in the MMA study, of an aliquot 

of the prepared water samples using a method, such as EPA Method 1668A, would have demonstrated 

that the samples were free from contamination and contained the stated spike concentrations at the time 

that the samples were prepared. 

COMPUTATIONS 

All computations were carried out using Statistical Analysis System (SAS) version 8.01. The 

equations for all approaches were programmed into the SAS software by a senior statistician, with 

assistance from senior analysts. There is some ambiguity in the IUPAC/ISO and ASTM detection and 

quantitation limit approaches and in interpretation of results from the ASTM approaches. Several 

formulas are given in the IUPAC/ISO documentation, but none are defined to be the official ISO/IUPAC 

detection and quantitation limit approaches. Therefore, calculations for the CRV, MDV, and LOQ were 

chosen because they were most representative of Lloyd Currie’s definitions of a critical value, detection 

B - 9

limit and quantitation limit. Ambiguity in results from the ASTM approaches is attributable to the 

subjective nature of interpreting residual plots for each analyte. To resolve this issue, IDE and IQE 

models were chosen using significance tests for slope and curvature. 

References used for the IUPAC/ISO approaches were those published by Currie in Pure and 

Applied Chemistry 67:10, 1699-1723 (1995) as updated by Analytica Chimica Acta 391 105-126 (1999). 

Where needed, the ASTM approaches were programmed as single-laboratory variants of the Practices D 

6091 (IDE) and D 6512 (IQE). EPA has included the SAS program code on the CD-ROM that supports 

this document. 

To assess stakeholder comments about calculations of the IDE and IQE that were performed and 

summarized in the original assessment document, EPA requested additional software packages offered by 

commenters who use the software to determine these limits. On April 20, 2004, EPA received copies of 

two software packages written for the purpose of determining the IDE and IQE from a representative of 

Ford Motor Company. The first of these is Qcalc (version 1.0), a DOS-based program. The second of 

these is an Excel spreadsheet which utilizes Excel functions, macros and an add-in function to determine 

IDEs and IQEs. These two programs were compared to the SAS programs used by EPA by calculating 

IDEs and IQEs based on a subset of the Episode 6000 dataset. The results of this comparison are 

described later in this Appendix (see section titled “Comparison of IDE/IQEs Calculated Using Different 

Software Packages”). 

Calculation of the ACIL CRV, USGS LTMDL, and EPA MDL was done using analytical results of 

blank and spiked samples submitted by USGS. Specific details of these calculations are described in the 

section titled “USGS Blank and Spiked Metals and Nutrient Data” later in this Appendix. 

RESULTS OF COMPUTATIONS 

Detection and quantitation limits are presented in a set of tables corresponding to the Episode 6000 

study, a single table corresponding to the Method 1631 and Method 1638 studies, and a single table 

summarizing limits calculated using data submitted by USGS. Within the Episode 6000 dataset, results for 

detection limits are compared followed by results for quantitation limits. Within the comparison of limits 

(detection or quantitation), the first table compares the actual limits followed by a table of percent 

differences between limits. 

Episode 6000 data 

Table 2 compares detection limits produced by four approaches (EPA/ACS DL; ISO/IUPAC CRV; 

ISO/IUPAC MDV; and ASTM SL-IDE) and Table 3 presents the percent difference between these 

approaches, using the formula given below: 

% difference = 

where: DL is the EPA/ACS DL for the given analyte, and 

Lim is the corresponding limit (CRV, MDV, or SL-IDE) being compared to the DL. 

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The median percent difference between the EPA/ACS DL and each of the other three limits was 

compared to 0% using two significance tests: the sign test and Wilcoxon rank-sum test. The sign test 

evaluates whether the given limit exceeds the EPA/ACS DL 50% of the time. The Wilcoxon rank-sum 

test is a more powerful test which, unlike the sign test, takes into account the magnitude of the difference 

between the two limits by ranking the percentage differences presented in Table 3. 

The ISO/IUPAC CRV was less than the corresponding EPA/ACS DL for 97% of the analytes and 

methods, with a median percent difference of -35.7%. The median percent difference of ISO/IUPAC 

CRV to EPA/ACS DL was significantly less than 0% based on both the sign and Wilcoxon tests with � = 

0.05 (p<0.0001 for both tests). The major reason for this difference is most likely the different Type I 

error rate for the two approaches (� = 0.01 for the EPA/ACS DL and � = 0.05 for the ISO/IUPAC 

CRV). 

The median percent difference between the ISO/IUPAC MDV and the EPA/ACS DL is 8.8% with 

the MDV exceeding the DL for 53% of the analytes. The median percent difference between the 

ISO/IUPAC MDV and EPA/ACS DL did not differ significantly from 0% based on the sign test 

(p=0.523) or the Wilcoxon rank-sum test (p=0.164) with � = 0.05. The likely reason that the two 

approaches do not yield significantly different results is that the correction for false negatives and 

recovery correction in the MDV (� = 0.05) are counteracted by the smaller Type I error rate for the 

EPA/ACS DL. 

The median percent difference between the ASTM SL-IDE and the EPA/ACS DL is 108.7%; i.e., 

the single-laboratory variant of the ASTM IDE is, on average, three times as large as that of the EPA and 

ACS approaches. The SL-IDE was greater than the corresponding EPA/ACS DL for 92% of the 

analytes and methods. The median ratio differed significantly from 1, based on both the sign and 

Wilcoxon tests with � = 0.05 (p<0.0001 for both tests). The median ratio and percent of SL-IDEs 

exceeding the corresponding EPA/ACS DL both increased slightly compared to the calculations 

presented in the original assessment document, due to the correction of the exponential model calculations 

for the SL-IDE and the use of the standard deviation bias correction. It is not surprising that the SL-IDE 

results were generally greater than the EPA/ACS DL, as the SL-IDE is an estimate of Currie’s LD, 

whereas the EPA/ACS DL is an estimate of Currie’s LC. In addition, the use of two tolerance interval 

limits in the IDE calculation likely also led to the large difference between the SL-IDE and EPA/ACS 

DLs. 

Table 4 compares quantitation limits produced by the three approaches (EPA/ACS QL; ISO LOQ; 

and ASTM SL-IQE) and Table 5 compares the percent difference between these approaches taking the 

EPA/ACS QL as reference. Similarly to the detection limit approaches, the median percent difference 

was compared to 0% using the sign and Wilcoxon tests. The median percent difference between the 

ISO/IUPAC LOQ and the EPA/ACS QL is -4.2%, and the median percent difference between the 

ASTM SL-IQE and the EPA/ACS QL is 19.6%. The ISO LOQ and ASTM SL-IQE are greater than 

the corresponding EPA/ACS QL for 47% and 62% of the analytes and methods, respectively. The 

median ratio between the LOQ and QL did not differ significantly from 0% based on the sign test 

(p=0.390), but did based on the Wilcoxon test (p=0.043) at �=0.05. The median ratio between the SLIQE and QL differed significantly from 0% based on both the sign test (p=0.001) and the Wilcoxon test 

(p<0.0001). 

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For the SL-IQE comparisons, this result is different from those presented in the original assessment 

document, due to the fixed rounding issue in the ML calculations (see discussion under Detection and 

Quantitation Limits Assessed). Because the EPA/ACS QL and the SL-IQE are both estimates of 

Currie’s LQ, the reason for this difference is not clear. One possible reason for this significant difference 

is that the SL-IQE does not assume that variability at the quantitation limit is equal to variability of the 

blank, whereas the EPA/ACS QL does. However, it is worth noting that the difference seems to be 

strongly affected by which model was used to calculate the SL-IQE. The median percent difference 

between the QL and SL-IQE is -7.7% when the hybrid model is used to calculate the SL-IQE compared 

to 67.9% and 179.6% for the linear and constant models, respectively. While use of the constant model 

assumes that the variability is constant between the blank and quantitation limit, this model type is 

generally chosen only when there are unusually high results at one or more of the lower spike levels for a 

given analyte. Therefore, the SL-IQEs calculated for these analytes are likely somewhat biased high. 

Although the Episode 6000 dataset is not ideal for evaluating the ACIL Case 2 iterative approach for 

those methods/instruments for which analysis of blank samples does not produce a signal, EPA estimated 

the ACIL Case 2 CRV using the lowest concentration level at which all 7 replicates were observed to 

test if the conceptual problem with ACIL’s implementation of Case 2 occurs in practice. EPA noticed 

that, because the estimate of Currie’s LC is based on measured values and the estimate of Currie LD is 

based on spike level, the estimate of LD could theoretically fall below LC for methods with recovery that 

systematically exceeds 100% or for data with some contamination. Looking at Episode 6000 data, EPA 

confirmed that this problem may occur in practice. In fact, it occurred for 35 of the 146 analytes (24%) 

measured using methods that do not always result in signals from analysis of blank samples. 

EPA/EPRI Method 1631 and 1638 Interlaboratory Method Validation Study Data 

Table 6 compares detection and quantitation limits computed from data generated in the Method 1631 

and Method 1638 interlaboratory studies. MDLs and MLs are those listed in EPA Methods 1631 and 

1638. EPA computed IDEs and IQEs for the purpose of preparing this assessment. IDEs and IQEs 

computed by EPRI are from the EPRI reports on the Method 1631 and Method 1638 interlaboratory 

studies. 

In reviewing these data, it must be recognized that the EPA MDLs and MLs are the result of 

selecting the highest MDL in EPA's single-laboratory studies or interlaboratory study, whereas the IDEs 

and IQEs are the result of a statistical process that includes recovery correction, correction for bias in the 

sample standard deviation (IQE only), allowance for prediction and tolerance intervals, interlaboratory 

variability, and model selection. The most significant reason for the instances of a large disparity between 

the EPA-determined IDEs/IQEs and the EPRI-determined IDEs/IQEs is model selection. EPA selected 

the model based on a strict application of the IDE and IQE procedures by a senior statistician. For those 

instances in which EPA and EPRI selected the same model, the IDEs and IQEs are nearly the same. 

Table 7 compares IDEs and IQEs resulting from the four main model types described in the ASTM 

IDE and IQE procedures. IDEs and IQEs resulting from the constant model were the highest for all 

analytes. IDEs and IQEs resulting from the other three models were almost equal for some analytes 

(lead, for example), and differed by more than an order of magnitude for others (mercury, for example). 

For two analytes, the IDE and IQE estimated using the linear model were negative. This was due to a 

negative intercept estimate in the precision model. The ASTM IDE and IQE procedures dictate that the 

linear model should not be used in this situation. 

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Table 7 also includes RSDs between the IDEs and IQEs calculated using the different model types. 

This was done based on commenter statements that the choice of model had only a minimal effect on the 

resulting IDE or IQE. This analysis is discussed later in this Appendix (see “Comparison of IDE and 

IQEs calculated using Different Models”). 

USGS Blank and Spiked Metals and Nutrient Data 

USGS supplied EPA with blank data collected over a period of one year for 78 metals and nutrient 

analytes and spiked data collected over a period of one year for 39 metals and nutrient analytes. These 

results were used to calculate both the USGS LT-MDL and ACIL critical level. The ACIL critical level 

was calculated using the blank results for the given analyte and method. The USGS LT-MDL was 

calculated based on the spike results for the given analyte and method. In addition, the LT-MDL was 

calculated in two ways: by adding the mean of the blank results for the given analyte and method, and by 

adding the median of the blank results for the given analyte and method. 

The EPA MDL also was calculated for each analyte/method using the spiked sample results provided 

by USGS. Because MDLs are typically calculated using fewer replicates than the 15 to 24 analyzed by 

USGS, EPA calculated the MDL by simulating different subsets of 7 replicates. Subsets were created by 

taking each set of 7 consecutive spiked results, i.e., the first 7 samples analyzed would be one subset, the 

2nd through 8th samples analyzed would be another subset, etc. This yielded a total of n-6 subsets, 

where n is the number of total samples for that analyte. The MDL was then determined by randomly 

choosing one of the n-6 subset MDLs. While the use of only seven replicates run consecutively in each 

subset minimized the effect of long-term variability, it is worth noting that the amount of temporal 

variability in each subset is still greater than that typically included in the EPA MDL (i.e., MDL datasets 

typically are generated in a single day); the time interval between the first and last replicate analyzed 

within a subset ranged from 30 to 48 days. Therefore, the calculated MDLs are likely somewhat higher 

than those that would be calculated using results generated over a single day. 

After calculation of these limits, the percentage of blank results included in the dataset that exceed 

each limit for each analyte was calculated. Because all limits were calculated at the 99% confidence 

level, it would be expected that the average percent of blanks exceeding each limit would be 

approximately 1% when the blank results follow a Normal distribution centered at 0. Limits based on 

each of the calculations are presented in Table 10. 

Generally, the percentage of blanks exceeding the ACIL critical level was lower than the percentage 

exceeding the other limits (see summary table following Table 10). The percentages of blanks exceeding 

the EPA MDL were slightly higher compared to the percentages exceeding the ACIL critical level, due to 

a small subset of analytes with notable blank bias. The USGS LT-MDL had higher rates of blank 

exceedance than either the ACIL or EPA limits, regardless of whether the mean or median was added to 

the limit. This suggests that the effect of blank bias was smaller than the effect of the method of 

estimating variability. USGS uses the nonparametric calculation to lessen the effect of outliers on the 

estimate of variability. Because those blanks that exceed a given limit are likely to be outliers themselves, 

this can lead to inflated exceedance rates. However, it is worth noting that, for the majority of analytes 

where blanks exceeded the calculated USGS limits, multiple blank results were greater than the 

associated limit. This suggests that some non-outlying blank results also are exceeding the USGS limits 

for some analytes. 

B - 13

DISCUSSION 

Negative detection limits for the ISO/IUPAC MDV 

The calculated ISO/IUPAC MDV was negative for 29 analytes in the Episode 6000 data. Negative 

MDVs are attributable to the use of a regression model to estimate recovery at each concentration. The 

standard errors and correlation of the regression parameters are included in the calculation of the MDV. 

Analytes for which the MDV was negative seemed to coincide with an unusually large standard error of 

the regression intercept, which generally occurred when the estimated intercept was strongly negative. 

The large standard error of the intercept was likely due to extrapolating the recovery model to zero 

concentration; the error around a regression line is greatest for concentrations furthest away from the 

mean spike level. The effect of this extrapolation also may be seen in the Episode 6000 data. No 

negative results were used in the MDV and LOQ calculations, yet the median recovery intercept for the 

analytes analyzed in the Episode 6000 dataset was equal to -0.11. The standard errors of the intercept 

and slope estimates were generally high (intercept median= 0.27, slope median=0.011), and therefore the 

estimated intercept and slope terms were frequently not significantly different from 0 and 1, respectively 

(intercept: not different from zero for 167 analytes/methods; slope not significantly different from 1 for 

106 analytes; both intercept and slope not significantly different for 79 analytes). Because the recovery 

model parameters are not significantly different from 0 or 1 for the majority of analytes, and both the 

estimated slope and the standard errors of the slope and intercept are included in the calculation of the 

MDV and LOQ, the inclusion of the recovery model estimates may bias the calculated limits, to the point 

that the resulting MDV can be negative. 

Effect of number and spacing of concentrations for determination of the SL-IDE and SLIQE 

Tests in the Episode 6000 studies were conducted at 16 concentration levels. The IDE procedure 

suggests using at least 5 concentration levels. Based on statistical theory we would expect the number 

and spacing of concentration levels to affect the outcome, with a larger number of concentrations 

producing a more reliable estimate. EPA used the Episode 6000 dataset to test this hypothesis. 

The IDE procedure suggests spike concentrations at 0.5, 1.0, 2, 4, and 8 times an initial estimate of 

the IDE (IDE0). IDE0 is estimated at 10 times the standard deviation of analytical results of blanks or 

replicates of the lowest level that can be measured. EPA’s Episode 6000 database contain results of 

analysis of at least 7 replicates at each of at least 16 concentration levels from 0.1 to 100 times the initial 

estimate of the MDL (a factor of 1000). Between 0.1 and 10 times the MDL, the data are spaced a 

factor of approximately 1.5 apart. Above 10 times the MDL, the data are spaced at 10, 20, 50 and 100 

times the MDL. The reason for the narrow spacing between 0.1 to 10 times the MDL was to attempt to 

allow more precise characterization of variability in the region of the MDL. 

The SL-IDEs and SL-IQEs in Tables 2 and 4, respectively, were computed and reported using all 16 

concentration levels because data were available at all of these levels. However, to determine the effect 

of the IDE procedure, a separate data analysis was performed. In this separate analysis, concentration 

levels were limited to a total of 5, and the 5 levels were selected to be as consistent as possible with the 

levels specified in the IDE procedure; i.e., at 5, 10, 20, 40, and 80 times the standard deviation of 

replicate measurements of a blank or the lowest level at which measurements could be made. The 

statement “lowest level at which measurements can be made” was interpreted to mean inclusion or 

B - 14

exclusion of results containing zeros and/or negative numbers. For purposes of this evaluation, 

concentrations that produced results containing a zero or negative number were excluded; i.e., the lowest 

concentration that contained no zeros or negative numbers was chosen as the concentration at which the 

standard deviation would be calculated for the purpose of estimating IDE0 and IQE0. Zeros and negative 

numbers were used in all of the other steps in calculating SL-IDEs and SL-IQEs. 

The SL-IDE was calculated after selecting the levels based on IDE0, and the results were compared 

to results produced when all 16 levels were included in calculating the SL-IDE. Results are summarized 

in Table 8. This table shows that the median percent difference between the 6-point IDE and the 16-point 

IDE is approximately -24.9% (where negative percent differences indicate that the 5-point IDE is less 

than the 16-point IDE). For those instances in which the same model was chosen (108 out of 198), the 

median percent difference was -35.6%, which was significantly different from 0% based on both the 

Wilcoxon rank-sum test and the sign test (p < 0.0001 for both tests). For those instances in which a 

different model was chosen (90 out of 198), the median percent difference was 1.3%, which was not 

significantly different from 0% based on either test (Wilcoxon: p=0.85; sign test: p>0.99). Because the 

choice of model can have a confounding effect on any differences between 16-point and 5-point SLIDEs, the focus should be on the instances in which the same model was chosen. For these instances, the 

results indicate that only data in the region of detection and quantitation should be used to establish a 

detection or quantitation limit. 

A similar comparison was performed between SL-IQEs (10%) calculated using all concentration 

levels to SL-IQEs (10%) calculated using only 5 concentration levels. Results of this comparisons are 

summarized in Table 9. While differences between the two calculations were not significant based on 

either the sign test (p=0.567) or the Wilcoxon test (p=0.345), the differences were larger than those 

between SL-IDEs, as seen by the larger median percent difference of -194.6%. Unlike the IDE 

comparison, a different model was used to calculate the 5-point SL-IQE than was used to calculate the 

16-point SL-IQE for most analytes. For these 145 analytes, the percent differences were large (median 

percent difference = 613.9%) but not systematically positive or negative (sign test: p=0.507, Wilcoxon: 

p=0.606). For the 50 analytes for which the same model was used to calculate the 5-point and 16-point 

SL-IQEs, the percent differences were strongly negative (median percent difference = -2,442.7%) and 

significantly less than 0 (sign test: p=0.015, Wilcoxon: p=0.0007). 

The reason for the use of 5 versus 16 concentration levels yielded significantly different results for the 

SL-IDE, but not for the SL-IQE, was likely due to the different model types that are recommended in the 

ASTM IDE and IQE procedures. Systematic differences in the calculated limit appear to occur when the 

same model type is applied to the 5-point and 16-point datasets. Because the exponential model is chosen 

based on the significance tests for most analytes in the IDE procedure, the model type used rarely differs 

between the two sets. There was less consistency in selecting models in the IQE procedure, and the 

choice of model differed between the 5-point and 16-point SL-IQE for approximately 75% of the 

analytes. Some of these differences, such as using the constant model instead of the hybrid model for the 

5-point SL-IQE calculation, appeared to result in higher SL-IQEs, while others, such as using the linear of 

hybrid model in place of the constant model for the 5-point calculation, appeared to yield lower SL-IQEs. 

Therefore, while differences in the selected model resulted in large percent differences, these differences 

were not consistently positive or negative. 

B - 15

Relative Standard Deviation at the ML and SL-IQE in the Episode 6000 Study 

The minimum level of quantitation (ML) is directed at the level at which 10% relative standard 

deviation (RSD) is attained. However, because the ML is not established at exactly 10% RSD, but is 

determined by multiplying the standard deviation that is obtained in determination of an MDL by 10 (as 

recommended by both ACS and Currie for ACS and ISO/IUPAC LOQs), the resulting RSD may not be 

10%. The Episode 6000 data provided the opportunity to determine the actual value of the RSD at the 

ML. For analytes that did not have a spike concentration at the ML, the RSD was determined by linear 

interpolation between spike levels. Results of the determination showed that the overall median RSD at 

the ML across all analytes in the Episode 6000 study was 9%, and the median RSD for the 10 analytical 

techniques ranged between 4 and 16 percent. For 29 analytes, no RSD could be calculated because 

signals were not generated for samples spiked at the ML. This was likely due to limitations with this 

dataset that are discussed earlier in this Appendix (see “EPA's Variability versus Concentration Studies”). 

For 114 of the 169 remaining analytes, the RSD fell between 5% and 15%. Among the analytes that fell 

outside this range, 28 had RSDs below 5% and 27 had RSDs greater than 15%. 

Because IQEs target a specified RSD, RSDs were also calculated based on the SL-IQEs calculated 

for the Episode 6000 data. Unlike the ML, the SL-IQE procedure does not contain a rounding step and, 

therefore, the calculated value never corresponded to one of the spike levels used in the study. For this 

reason, interpolation was required to calculate RSDs at the given SL-IQE value. The overall median 

RSD based on the SL-IQEs was 7%, with method-specific median RSDs ranging from 6% to 11%. No 

RSD could be calculated for 9 analytes because signals were not generated for samples spiked 

immediately above or below the SL-IQE. Similarly to the ML, this was likely due to issues with this 

dataset that are discussed earlier in this Appendix. 

Effect of Outliers on Detection/Quantitation Calculations 

The detection and quantitation limits based on the Episode 6000 dataset presented in Tables 2 through 

5 were calculated without removing any outlying results. This decision was made based on several 

reasons. There were generally only 7 results per spike level for each analyte, which is a very small 

dataset for which to apply outlier tests and removal. In addition, MDL and ML procedures do not include 

outlier removal steps and, therefore, removing outliers for any of the other procedures would hinder 

comparisons of the calculated limits themselves. However, based on stakeholder comments, an 

assessment of the effect of outlier removal procedures on the different detection and quantitation limits 

was added to this Appendix. 

Table 11 shows MDLs and SL-IDEs calculated after Grubbs outlier test (Grubbs F.E. “Procedures for 

Detecting Outlying Observations in Samples,” Technometrics, vol. 11 No. 1 1969) was applied to the 

data. Grubbs test was run at the 5% significance level, and a maximum of one result per spike level was 

removed based on the results of the test. The choice of outlier test and the associated significance level 

follows instructions in ASTM-D2777. However, a significance level of 1% is more appropriate for outlier 

removal tests, as a small sample size coupled with the significance level of 5% can lead to inappropriate 

removal of outliers. This is true especially for studies evaluating multiple concentrations. For example, in 

the Episode 6000 study, there were 16 concentrations and 149 of the 198 analytes considered had an 

outlier present at one or more concentrations based on application of Grubbs test with 5% significance 

level. 

B - 16

For each analyte, the percent difference of the SL-IDE or MDL calculated using all data compared to 

the SL-IDE or MDL (calculated using the data after outlier removal) was determined. Summary 

statistics of these ratios are presented in Table 11. Analytes without outliers are not included in the table 

or the analyses discussed in this section. 

Generally, SL-IDEs decreased slightly when outliers were removed. This is not surprising, as the 

removal of an outlying result decreases the variability at that spike level. The decrease in the SL-IDEs 

was not large, however, as the median percent difference comparing SL-IDEs calculated with and 

without outlier removal was 14.3%, where a positive percent difference indicates that the SL-IDE 

calculated without outlier removal was greater than the SL-IDE calculated after outlier removal. For a 

few analytes, removing outliers led to a change in the choice of model used to calculate the SL-IDE. In 

these cases, the presence of the outliers generally forced the constant model to be used; when outliers 

were removed, the exponential model was used. Therefore, the change in the calculated SL-IDE for 

these analytes was greater (median percent difference = 114.7%). 

Removal of outliers only changed the MDL results if outlier removal changed the choice of spike 

levels used to calculate the MDL, or occurred at one of the spike levels from which the original MDL 

was calculated. This occurred for 60 of the 149 analytes for which any outliers were removed. In these 

cases, the decrease in the MDL was slightly larger than the change in the SL-IDEs (median percent 

difference = 30.2%). 

For a small subset of analytes, either the SL-IDE or MDL increased after outlier removal. 

Generally, these increases were very small, and were likely due to increased tolerance factors or 

decreased mean recoveries for the SL-IDE, or to increased t-statistics for the MDL. 

SL-IQEs and MLs calculated with and without outlier removal are presented in Table 12. The effect 

of outlier removal on calculated SL-IQEs and MLs was generally similar to that on the SL-IDEs and 

MDLs. For the SL-IQE, the choice of model changed more frequently than for the SL-IDE (31 analytes 

compared to 8 for the SL-IDE). However, the median percent difference was almost equal to that for 

the SL-IDE (16.3%). The calculated ML changed based on outlier removal for only 31 analytes, 

compared to 60 for the MDL. This number was smaller than for the MDL because the ML rounding 

frequently overshadowed the effect of outliers. However, for the ML, the changes that did occur were 

greater (median percent difference = 66.7%). 

Evaluation of IDE/IQE Procedures 

Comparison of IDE and IQEs calculated using Different Models 

In the February 2003 Assessment Document, EPA expressed concern about the large amount of 

variability between calculated IDEs and IQEs resulting from the four different model types, and the 

subjectivity involved in selecting the most appropriate model. One stakeholder commented that this 

concern was not valid, and that IDEs calculated using different models were generally very close. To test 

this statement, EPA calculated SL-IDEs and SL-IQEs using each of the four major model types, and 

calculated RSDs between the different values for each analyte (“cross-model RSDs”). The resulting 

SL-IDEs are presented in Table 13. Median RSDs calculated for all analytes are presented at the bottom 

of the table. For several analytes, the calculated SL-IDE based on the linear model was negative due to 

the negative intercept of the fitted model. Because the ASTM procedure for calculating the IDE states 

B - 17

that the linear model should not be used in these instances, the SL-IDE based on the linear model was not 

included in these RSD calculations. 

There is a large amount of variability between RSDs calculated with these data using the different 

models. Generally, SL-IDEs calculated using the constant model were much greater than those 

calculated using the other models. The hybrid model yielded the lowest SL-IDEs, excluding cases where 

the linear model SL-IDE was negative. The SL-IDEs calculated using the hybrid and exponential models 

were quite similar for some analytes, but quite different for others. When examined separately by 

method, the variability between models was generally smaller for metals methods than organics methods. 

However, there was a large difference in cross-model RSDs between the two metals methods, (i.e., IDEs 

across models in Method 1620 had a median RSD of 27%, whereas IDEs across models in Method 200.8 

had a median RSD of 88%). 

RSDs between SL-IQEs calculated using the different models are included in Table 14. The 

variability between the different model estimates was similar to that of the SL-IDEs, with a median RSD 

of 136% between SL-IQEs (10%). Method-specific median cross-model RSDs among SL-IQEs (10%) 

ranged from 24% for Method 1620 to 166% for Method 524.2. 

To assess the effect of interlaboratory variability on the differences between estimates calculated 

from different models, cross-model RSDs were determined between the different IDEs and IQEs 

calculated based on the interlaboratory validation studies of Methods 1631 and 1638. These RSDs are 

presented in Table 7. Based on these data, the variability between model estimates appears to increase 

when the variability between laboratories is included. Cross-model RSDs between the IDEs calculated 

from the different model types ranged between 61% and 162%, with a median of 123%. These RSDs 

are greater than those calculated using the single-laboratory metals data in Episode 6000. Variability 

between IQEs was smaller than the variability between IDEs. Cross-model RSDs between IQEs ranged 

between 50% and 190%, with a median of 99%. 

Comparison of IDE/IQEs Calculated Using Different Software Packages 

A stakeholder commenting on EPA’s February 2003 data assessment stated that the Agency’s 

concerns about the complexity and subjectivity in the IDE and IQE procedures were unimportant due in 

part to the availability of software that will automatically perform the IDE and IQE calculations. EPA 

obtained two software packages from this stakeholder (see the section titled “Computations”) to aid in 

responding to this and other comments regarding the calculation of IDEs and IQEs in the February 2003 

TSD. 

EPA compared these two software programs using a random subset of 20 analytes from the Episode 

6000 dataset. To ensure that differences between results were due to the programs themselves, the same 

data were used for each program. Table 15 presents a comparison of the IDE and IQE10 (IQE at 10% 

RSD) results based on the two software packages, along with limits calculated using SAS programs (the 

latter limits match those presented in Tables 2 and 4). In addition, summary statistics of this comparison 

are presented in Table 16. Comparisons between IDEs and IQEs calculated using QCalc and the Excel 

software could not be done for all models, because QCalc only performs each calculation using two of the 

four models (exponential and hybrid for the IDE calculation, and linear and hybrid for the IQE 

calculation). 

B - 18

Generally, IDEs and IQEs calculated using SAS programs were very close to those determined using 

QCalc based on the same model type. The median ratio of the IDE or IQE calculated using SAS 

compared to the IDE or IQE calculated using QCalc equaled 0.99 or 1.00 for all model types. For two 

analytes (1,1,-dichloroethene and selenium by Method 1620) the hybrid IDEs and IQEs differed greatly 

between QCalc and the SAS programs. This appeared to be because the intercept term estimated by 

QCalc was negative for these analytes (resulting in negative IDEs and IQEs), whereas the intercept term 

estimated by SAS was approximately the positive absolute value of this estimate (resulting in positive 

IDEs and IQEs). 

IDEs and IQEs calculated using the Excel file were generally comparable to those calculated using 

the SAS programs and QCalc for the constant, linear, and exponential models. The differences between 

the values calculated using the Excel file and other packages, however, were much greater for the hybrid 

model. As seen by the median ratios, the estimated IDEs and IQEs determined based on the hybrid 

model using Excel were slightly higher than those determined using SAS, and approximately twice those 

determined using QCalc. Part of this difference is due to the negative values calculated by QCalc for two 

analytes. However, the calculated values differed greatly, as the resulting IQEs calculated by Excel 

using the hybrid model ranged from less than 0 to more than 6 times greater than that calculated using the 

SAS programs. These differences seem to be due to how the hybrid model is fit using Excel. The Solver 

add-in function used by Excel does not seem to follow the same Newton's Non-Linear Least Squares 

algorithm described in the ASTM procedures and followed by EPA's SAS programs and QCalc. 

In addition to differences in calculated limits based on the same model type, the different programs 

may yield different IDEs or IQEs based on which model type is indicated as most appropriate by a 

particular software package. QCalc and the Excel file both automatically suggest the same model type 

for the IDE and IQE. However, EPA often used a different model type for calculating the IDE and IQE. 

This was done because the ASTM IDE procedure lists constant, linear, and exponential as the three 

major model types to be considered, whereas the ASTM IQE procedure lists the constant, linear, and 

hybrid as the three major model types. Therefore, while the exponential model was used by EPA to 

calculate most IDEs, it was not used to calculate any of the IQEs. Because of this, while EPA and 

QCalc selected the same model type to calculate the IDE for only one analyte, the same model type was 

selected to calculate the IQE for 17 of the 20 analytes. 

The Excel file frequently chose a different model type than QCalc and the EPA SAS programs to 

calculate the IDE and IQE. The Excel file selected a different model type than QCalc for 14 of the 20 

analytes, and selected a different model than EPA's SAS program to calculate the IDE and IQE for 19 

and 17 analytes, respectively. The reason for this appears to be that the Excel file suggests that the 

appropriate decision be based on which model has the smallest sum of squared residuals. This is different 

from the statistical tests of slope and curvature used by QCalc and the SAS programs and also described 

in the ASTM procedures. While both QCalc and the Excel file also include graphs to aid in model 

selection, and could potentially yield more consistent model selection through these graphs, it is likely that 

many users would prefer the clearer answer provided by statistical tests or comparisons of sums of 

squared residuals. 

Based on these differences in selecting and fitting models, it does not appear that the two available 

software programs remove all complexity and subjectivity from the calculation of IDEs and IQEs. 

Instead, they appear to introduce new issues by using steps not included in the ASTM procedures. While 

QCalc appears to follow the ASTM procedures more closely than the Excel file, it does not perform 

B - 19

calculations for all model types and, therefore, may introduce greater subjectivity by only providing 

calculated limits based on inappropriate models. 

Effect Of Long-Term Variability 

Several stakeholders commenting on EPA’s assessment expressed concern about the lack of 

long-term variability included in the MDL procedure. Commenters state that the lack of long-term 

variability leads to underestimates of Currie's critical value (LC). In addition, ACIL included datasets 

containing results of blank samples analyzed over three months for 5 analytes to show this effect. These 

commenters pointed to the ACIL procedures for calculating the critical level (CRV) and long-term MDL 

(ACIL LT-MDL) and the USGS procedure for calculating the long-term MDL (LT-MDL), which include 

the collection of blanks over a long period of time. 

EPA assessed the effect of long-term variability on calculated limits by simulating multiple 7-replicate 

subsets from the full dataset, and comparing these short-term CRVs to the CRV calculated using the full 

dataset. Subsets were created by taking each set of 7 consecutive blanks, (i.e., the first 7 blanks analyzed 

would be one subset, the 2nd through 8th blanks analyzed would be another subset, etc.). This yielded a 

total of n-6 subsets, where n is the number of total blanks for that analyte. Because a blank will be used 

in as many as 7 subsets, the variability of the short-term CRVs was lower than what would be expected; 

however, the approach was used to yield the greatest number of simulated subsets. The CRV was then 

calculated for each subset: 

where CRVi, , and si are the critical value, the mean, and the standard deviation for the ith simulated 

subset of blank results, respectively. The overall CRV was calculated using the same formula, using the 

mean and standard deviation based on all blank results and a lower t-statistic based on the greater number 

of blank replicates. Table 17 shows the results of the comparison of calculated short-term and long-term 

CRVs for the five analytes. 

While the range of days from which sets of 7 replicates were simulated varied from between one 

week to greater than 3 weeks, graphical analyses did not show any effect of the number of days on the 

resulting CRV. The total number of blanks also did not seem to have an effect on the percentage of 

short-term CRVs that exceeded the overall CRV. The mean short-term CRV was generally very close 

to the overall CRV for each analyte. However, for three of the five analytes, the majority of the 

short-term CRVs exceeded were lower than the overall CRV, indicating that long-term variability did 

have an effect on the resulting limit. For the other analytes, the effect of any added variability was 

counteracted by the smaller t-statistic used in the calculation. These t-statistics ranged between 2.4 and 

2.5 between analytes, well below the 3.14 used when only 7 replicates are available. 

One possible reason for the number of short-term CRVs falling below the overall CRV was the 

presence of outliers. The ACIL procedure permits the use of an outlier procedure to remove outlying 

high or low blanks. EPA used Grubbs test and identified 3 blank results for silver, and 1 blank result each 

for barium and chromium, as outliers. After removal of these results, the overall and short-term CRVs 

were re-calculated for these 3 analytes. The results of these calculations are given in Table 18. 

B - 20

Because an outlying result is used in the calculation of the overall CRV (but only for a maximum of 7 

of the short-term CRVs), the effect of outlier removal was greater for the overall CRV than on the 

short-term CRVs. For all 3 analytes, the majority of the short-term CRVs were above the overall CRV, 

and the mean short-term CRV was slightly higher than the overall CRV. This was consistent with the 

results of cadmium and copper shown in Table 17, for which no outliers were detected. Because no 

information was available about why these results could have been outlying, it is not known if they were 

the result of a known error, or were in fact the result of the long-term variability included in the study. 

However, it appears that the effect of long-term variability is generally not large when compared to the 

effect of using more replicates on the t-statistic multiplier. 

As stated in Section 3.3.3, a greater number of replicates will yield improved estimates of standard 

deviation and, therefore, better estimates of Currie's LC. Based on this, although, EPA does not feel 

estimations of LC based on 7 replicates are biased low, these estimates may be less precise than those 

based on greater replicates. The large variability of the 7-replicate CRVs can be seen in the large ranges 

of short-term CRVs calculated with and without outlier removal. The use of the higher t-statistic also 

seems to counteract the added long-term variability. The ACIL procedure suggests 7-replicate CRVs are 

underestimates, and should therefore be multiplied by a factor of 2. The short-term CRV calculated in the 

ACIL procedure is based on blanks analyzed in a single batch and, therefore, are not comparable to the 

short-term CRVs simulated by EPA. However, such a multiplier is not necessary in calculating the MDL, 

even if long-term variability is not included in the analyses. 

SUMMARY 

Public comment on the February 2003 Assessment Document and the proposed regulatory revisions 

expressed many divergent views about the merits and usefulness of EPA’s 2003 assessment and 

proposed regulatory revisions. We recognize that there is a broad interest in improving current 

procedures and uses, but no consensus for a specific procedure or procedures has emerged among the 

laboratory, industry, regulatory or regulated communities. Thus, we have withdrawn the March 2003 

proposed revisions and, to meet the terms of the settlement agreement that is described in chapter 1, are 

taking final action on the 2003 Assessment Document in this Revised Assessment Document. This is not 

the end of our efforts to work together, as stakeholders have suggested, to discuss mutual concerns and 

possible solutions. We look forward to continued stakeholder participation in an ongoing dialog about the 

development and use of detection and quantitation limits in CWA programs. 

In this appendix, we have compared detection and quantitation limits computed from data gathered by 

EPA or submitted to EPA. This comparison shows that, in general, detection limits derived from a single 

concentration level such as EPA’s MDL are, on average, approximately the same as detection limits 

derived from similar approaches such as the ACS LOD and LOQ and ISO/IUPAC CRV and MDV, and 

are approximately three times lower than a single-laboratory variant of ASTM’s IDE; and that all 

quantitation limit approaches, such as EPA's ML, the ACS and ISO/IUPAC LOQ, and a single-laboratory 

variant of ASTM's IQE, produce quantitation limits that are generally only slightly different. In addition, 

the following are general statements about the datasets and/or analyses described in this appendix. 

1. Variability of Results 


Comparisons of detection and quantitation limits show high variability among the limits calculated


using the different approaches, even with data containing 7 replicates at 16 concentration levels (see


B - 21

the summary statistics at the end of Tables 3, 5, and 7). The net effect is that the systematic 

differences among detection and quantitation limits produced by the various approaches are 

overwhelmed by variability; i.e., there is a small systematic difference among the approaches but 

great variability in the detection and quantitation limits for a given analyte. This result is not surprising 

given the variability of data in the region of detection and quantitation. However, it is difficult to 

postulate a solution to the problem. Gathering more data in the region of detection and quantitation 

would appear to be a solution, but 91 data points were gathered for each analyte in the region 

between 0.1 and 10 times the MDL in the Episode 6000 studies, and it is unlikely that any organization 

could afford to gather even this amount of data for determination of a detection limit. Given the high 

degree of variability of the data, EPA's approach of conducting a single-laboratory study to gain a 

first estimate, followed by multiple single-laboratory studies to verify or revise the estimate, and an 

interlaboratory study, where warranted, to further verify and revise the estimate, is a reasonable 

means of establishing detection and quantitation limits because of the checks and balances that occur 

at each step. 

2. Regression Analysis 


Using a regression line to estimate a recovery correction at zero concentration causes great swings in


the resulting detection and quantitation limits such as the ISO/IUPAC MDV and LOQ. The


estimated regression parameters for the recovery models were often not significant, and the inclusion


of the estimated slope and the standard errors of the slope and intercept will, therefore, unnecessarily


bias the calculated MDV and LOQ, such that the calculated MDVs may be negative (see Discussion


section "Negative detection limits for the ISO/IUPAC MDV, and Table 2 for instances of negative


detection limits"). The estimated recovery model used in calculating the IDE and IQE is also strongly


affected by the chosen model of variability vs. concentration (see Tables 13 and 14). Even though a


linear regression is used to model recovery in each case, the weights used in the model are calculated


based on the variability model, and can vary greatly when the number of concentrations used is low. 


For the Episode 6000 data, the median RSD of the recovery slopes from the four different models


used in the IDE calculations for a given analyte and method was 5%. In addition, for 77 of the


analytes and methods (39%), at least one estimated recovery slope was greater than 1, and at least


one was less than 1. This suggests that the method could be considered to be high biased (and the


final IDE and IQE would be decreased by the recovery correction) and low biased (and the final IDE


and IQE increased) for the analyte, depending on the chosen precision model. For many analytes the


slopes were not significantly different from 1, suggesting that a recovery correction may not be


appropriate at all. This is in addition to the philosophical issue as to whether recovery correction is


warranted. If there is to be a correction for recovery, it may be better to use some average or


median value than a regression, or use a measured value near the region of interest.


3. IDE and IQE


Additional development of the ASTM IDE and IQE is needed before they can be used routinely, not


only because of the complexity of the procedures, but also because of the ambiguity in determining


that the correct model has been selected. While different software packages are available that


perform most of the calculations, there are many inconsistencies between these programs, and


between the programs and the ASTM procedures, that add another area of subjectivity to the


determination of IDE and IQEs. (For the consequences of model selection, compare the IDEs and


IQEs determined by EPA and EPRI in Table 6, and the IDEs and IQEs calculated from the different


B - 22

model types in Table 7. Some differ considerably as a result of model selection in application of the 

IDE and IQE procedures by different statisticians. In addition, the use of different software may lead 

to the selection of different models, as seen in Table 15.) 

4. Quantitation Limit Approaches


Quantitation limit approaches such as EPA's ML and the ACS and ISO/IUPAC LOQ that are


directed 10% RSD actually produce RSDs that are in the range of the 10% intended (see the


discussion in the Section titled "RSD at the ML in the Episode 6000 Study"). The median RSDs for


each method in the Episode 6000 dataset ranged from 6% to 16%, and 58% of the individual analyte


RSDs fell between 5% and 15%.


Commenters on our February 2003 Assessment Document suggested that procedures submitted by a 

laboratory association (ACIL) and the U.S. Geological Survey as alternatives to the MDL and ML should 

be considered. We agree, have evaluated these procedures in this Revised Assessment Document, and 

believe they provide a starting point for continued stakeholder discussions. 

Regarding these two procedures, we note the ACIL CRV generally yielded lower false positive rates 

than the USGS LT-MDL. This likely was due to the nonparametric estimate of variability used in the 

USGS procedure. False positive rates for the EPA MDL, which uses a parametric variability but does 

not include the mean blank result, were lower than the USGS LT-MDL, which does include the mean 

blank result. The ACIL procedure states that calculated CRVs are based on fewer replicates and/or 

short-term variability are biased low, and includes optional alternate calculations to use in these situations. 

However, comparison of CRVs calculated with full set of long-term blanks to those calculated with 

subsets of 7 blanks suggest that the absence of long-term variability is counteracted by the larger tstatistic used when the number of blank results is smaller. 

ACIL also included a separate procedure for methods for which analysis of blank samples does not 

always produce a signal. The idea of dividing methods into two groups has merit. However, the current 

ACIL procedure for these methods often generates estimates of Currie’s LC that are above the estimate 

of Currie’s LD when contamination is present. 

B - 23

TABLES 

Table 1. ts Suggested by Petitioners 

Dataset and year Analyte and technology 

Datase

AAMA 1996-7	

AAMA 1996-7 

AAMA 1996-7 

MMA 2000-1 

EPA/EPRI 1997-8 

EPA/EPRI 1997-8	

EPRI 1987	

EPRI 1990 

EPRI 1994 

EPRI 1996 

Metals by ICP/AES (200.7) 

Mercury by CVAA (245.2) 

PCBs by GC/ECD (608.2) 

PCB 1216 and 1260 by GC/ECD 

Mercury by CVAF (1631) 

Metals by ICP/MS (1638) 

Metals by GFAA (EPA 200) 

Metals by ICP/AES (EPA 200.7) 

Al, Be, Tl by GFAA (EPA 200) 

Cd, As, Cr by GFAA (EPA 200) 

B - 24

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

1,1,1,2-tetrachloroethane 502.2 ELCD 0.041 0.005 0.009 0.034 

1,1,1,2-tetrachloroethane 524.2 0.052 0.039 -0.047 0.244 

1,1,1-trichloroethane 502.2 ELCD 0.012 0.009 0.017 0.041 

1,1,1-trichloroethane 524.2 0.055 0.021 0.003 0.308 

1,1,2,2-tce+1,2,3-tcp 502.2 ELCD 0.064 0.047 0.086 0.179 

1,1,2,2-tetrachloroethane 524.2 0.132 0.131 0.128 0.436 

1,1,2-trichloroethane 502.2 ELCD 0.024 0.004 0.006 0.032 

1,1,2-trichloroethane 524.2 0.075 0.043 0.040 0.319 

1,1-dichloroethane 502.2 ELCD 0.010 0.007 0.014 0.083 

1,1-dichloroethane 524.2 0.033 0.020 0.016 0.229 

1,1-dichloroethene 502.2 ELCD 0.038 0.030 0.073 0.234 

1,1-dichloroethene 524.2 0.054 0.035 -0.037 0.335 

1,1-dichloropropanone 524.2 5.184 3.146 5.635 6.372 

1,1-dichloropropene 524.2 0.045 0.012 -0.030 0.287 

1,2,3-trichlorobenzene 502.2 ELCD 0.048 0.034 0.065 0.134 

1,2,3-trichlorobenzene 502.2 PID 0.057 0.042 0.088 0.115 

1,2,3-trichlorobenzene 524.2 0.070 0.040 0.031 0.275 

1,2,3-trichloropropane 524.2 7.328 0.046 0.033 1.263 

1,2,4-trichlorobenzene 502.2 ELCD 0.022 0.014 0.030 0.088 

1,2,4-trichlorobenzene 502.2 PID 0.070 0.038 0.080 0.124 

1,2,4-trichlorobenzene 524.2 0.053 0.050 0.052 0.224 

1,2,4-trimethylbenzene 502.2 PID 0.095 0.053 0.119 0.125 

1,2,4-trimethylbenzene 524.2 0.012 0.009 0.017 0.144 

1,2-dibromo-3-chloropropane 524.2 1.457 0.391 0.701 1.749 

1,2-dibromoethane 502.2 ELCD 0.096 0.007 0.013 0.164 

1,2-dibromoethane 524.2 0.127 0.117 0.170 0.326 

1,2-dichlorobenzene 502.2 ELCD 0.035 0.031 0.061 0.065 

1,2-dichlorobenzene 502.2 PID 0.033 0.024 0.054 0.148 

1,2-dichlorobenzene 524.2 0.030 0.023 -0.016 0.130 

1,2-dichloroethane 502.2 ELCD 0.017 0.003 0.005 0.042 

1,2-dichloroethane 524.2 0.039 0.024 0.013 0.258 

B - 25

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

1,2-dichloropropane 502.2 ELCD 0.023 0.014 0.029 0.043 

1,2-dichloropropane 524.2 0.056 0.030 0.026 0.247 

1,3,5-tmb+4-chlorotoluene 502.2 PID 0.067 0.045 0.100 0.114 

1,3,5-trimethylbenzene 524.2 0.011 0.008 0.008 0.135 

1,3-dichlorobenzene 502.2 ELCD 0.035 0.005 0.010 0.118 

1,3-dichlorobenzene 502.2 PID 0.093 0.077 0.170 0.126 

1,3-dichlorobenzene 524.2 0.023 0.016 -0.014 0.143 

1,3-dichloropropane 502.2 ELCD 0.016 0.008 0.015 0.047 

1,3-dichloropropane 524.2 0.038 0.024 -0.015 0.202 

1,4-dichlorobenzene 502.2 ELCD 0.026 0.005 0.009 0.061 

1,4-dichlorobenzene 524.2 0.023 0.017 -0.044 0.140 

1-chlorobutane 524.2 0.020 0.016 0.018 0.220 

2,2-dichloropropane 524.2 2.376 0.103 -0.159 0.691 

2-butanone 524.2 0.417 0.297 0.511 0.833 

2-chlorotoluene 502.2 ELCD 0.108 0.029 0.056 0.175 

2-chlorotoluene 502.2 PID 0.238 0.135 0.302 0.230 

2-chlorotoluene 524.2 0.016 0.009 0.002 0.136 

2-hexanone 524.2 1.316 0.148 0.231 0.902 

2-nitropropane 524.2 0.901 0.275 0.452 1.082 

4-chlorotoluene 502.2 ELCD 0.110 0.027 0.050 0.149 

4-chlorotoluene 524.2 0.010 0.008 0.007 0.123 

4-isopropyltoluene 524.2 0.010 0.008 0.003 0.117 

4-methyl-2-pentanone 524.2 0.812 0.480 0.733 1.195 

Acetone 524.2 0.859 0.440 0.804 2.120 

Acrylonitrile 524.2 0.863 0.444 0.653 1.333 

Allyl Chloride 524.2 0.032 0.026 0.005 0.229 

Aluminum 1620 29.555 15.043 28.666 206.975 

Aluminum 200.8 19.145 1.690 3.547 12.747 

Ammonia as Nitrogen 1 350.3 0.010 0.007 0.014 0.014 

Antimony 1620 1.552 0.801 1.754 4.260 

Antimony 200.8 0.178 0.003 0.007 0.019 

Arsenic 1620 1.065 0.917 1.375 1.410 

B - 26

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

Arsenic 200.8 0.226 0.137 0.272 0.366 

Barium 1620 1.702 1.337 1.831 1.837 

Barium 200.8 0.033 0.029 0.061 0.084 

Benzene 502.2 PID 0.030 0.029 0.067 0.079 

Benzene 524.2 0.014 0.014 0.026 0.125 

Beryllium 1620 0.528 0.339 0.408 0.448 

Beryllium 200.8 0.007 0.004 0.006 0.024 

Boron 1620 15.387 10.356 17.792 21.161 

Bromobenzene 502.2 ELCD 0.131 0.093 0.186 0.765 

Bromobenzene 502.2 PID 0.012 0.009 0.019 0.050 

Bromobenzene 524.2 0.044 0.036 -0.060 0.211 

Bromochloromethane 502.2 ELCD 0.013 0.012 0.024 0.482 

Bromochloromethane 524.2 0.125 0.113 0.159 0.345 

Bromodichloromethane 502.2 ELCD 0.004 0.003 0.005 0.075 

Bromodichloromethane 524.2 0.043 0.026 0.019 0.205 

Bromoform 502.2 ELCD 0.006 0.003 0.001 1.513 

Bromoform 524.2 0.123 0.065 0.031 0.400 

Bromomethane 502.2 ELCD 0.267 0.219 0.358 7.293 

Bromomethane 524.2 0.068 0.055 0.056 0.280 

Cadmium 1620 0.127 0.079 0.134 0.191 

Cadmium 200.8 0.004 0.007 0.012 0.022 

Calcium 1620 36.726 35.822 72.397 41.358 

Carbon Disulfide 524.2 0.027 0.015 -0.040 0.239 

Carbon Tetrachloride 524.2 0.038 0.027 -0.040 0.314 

Carbontet+1,1-dcp 502.2 ELCD 0.029 0.008 0.016 0.072 

Chloroace tonitrile 524.2 0.919 0.773 1.527 1.569 

Chlorobenzene 502.2 ELCD 0.011 0.010 0.022 0.460 

Chlorobenzene 502.2 PID 0.030 0.025 0.055 0.064 

Chlorobenzene 524.2 0.025 0.022 0.012 0.133 

Chloroethane 502.2 ELCD 0.108 0.008 0.009 2.598 

Chloroethane 524.2 0.066 0.041 0.038 0.395 

Chloroform 502.2 ELCD 0.043 0.006 0.009 0.032 

B - 27

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

Chloroform 524.2 0.036 0.027 0.021 0.225 

Chloromethane 502.2 ELCD 0.070 0.049 0.130 0.250 

Chloromethane 524.2 0.045 0.036 0.065 0.253 

Chromium 1620 0.310 0.254 0.386 0.496 

Chromium 200.8 0.073 0.062 0.125 0.408 

Cis-1,2-dce+2,2-dcp 502.2 ELCD 0.013 0.009 0.016 0.055 

Cis-1,2-dichloroethene 524.2 0.040 0.033 -0.023 0.234 

Cis-1,3-dichloropropene 502.2 ELCD 0.007 0.002 0.004 0.074 

Cis-1,3-dichloropropene 502.2 PID 0.057 0.048 0.099 0.082 

Cis-1,3-dichloropropene 524.2 0.038 0.024 -0.004 0.173 

Cobalt 1620 9.820 4.017 8.094 16.463 

Cobalt 200.8 0.001 0.001 -0.067 0.074 

Copper 1620 6.046 4.990 10.512 21.189 

Copper 200.8 0.037 0.027 0.053 0.798 

Dibromochloromethane 502.2 ELCD 0.009 0.006 0.011 0.436 

Dibromochloromethane 524.2 0.051 0.031 0.004 0.287 

Dibromomethane 502.2 ELCD 0.007 0.005 0.010 0.460 

Dibromomethane 524.2 0.102 0.082 0.112 0.388 

Dichlorodifluoromethane 502.2 ELCD 0.009 0.003 -0.020 0.240 

Dichlorodifluoromethane 524.2 0.083 0.054 0.037 0.560 

Diethyl Ether 524.2 0.120 0.114 0.163 0.376 

Ethyl Me thacryla te 524.2 0.045 0.031 0.013 0.273 

Ethylbenzene 502.2 PID 0.021 0.015 0.035 0.078 

Ethylbenzene 524.2 0.033 0.028 -0.024 0.198 

Hardness 1 130.2 0.828 0.554 1.152 2.258 

Hexachlorobutadiene 502.2 ELCD 0.043 0.010 0.021 0.094 

Hexachlorobutadiene 524.2 0.068 0.035 -0.031 0.308 

Hexachloroethane 524.2 0.056 0.049 0.038 0.288 

Hexchlobutadiene+naphthalene 502.2 PID 0.649 0.143 0.321 0.597 

Iron 1620 90.409 270.433 472.249 373.590 

Isopropylbenzene 502.2 PID 0.020 0.015 0.035 0.060 

Isopropylbenzene 524.2 0.011 0.010 0.010 0.120 

B - 28

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

Lead 1620 1.647 1.186 1.965 2.423 

Lead 200.8 0.655 0.061 0.120 0.204 

M+p Xylene 502.2 PID 0.090 0.012 0.026 0.121 

M+p Xylene 524.2 0.013 0.008 0.004 0.142 

Magnesium 1620 103.033 88.729 175.316 105.998 

Manganese 1620 6.856 1.081 2.591 6.808 

Manganese 200.8 0.031 0.030 0.049 0.109 

Mercury 200.8 0.004 0.003 -0.018 0.027 

Methacrylon itrile 524.2 0.356 0.228 0.362 0.718 

Methyl Iodide 524.2 0.025 0.023 -0.013 0.193 

Methyl Tert-butyl Ether 524.2 0.026 0.016 -0.033 0.225 

Methyla crylate 524.2 0.220 0.202 0.353 0.601 

Methylene Chloride 502.2 ELCD 0.128 1.835 4.917 2.841 

Methylene Chloride 524.2 0.082 0.072 0.093 0.314 

Methylm ethacryl ate 524.2 0.225 0.085 0.117 0.535 

Molybdenum 1620 2.455 1.714 3.787 3.034 

Molybdenum 200.8 0.004 0.003 0.000 0.271 

N-butylbenzene 502.2 PID 0.030 0.023 0.049 0.141 

N-butylbenzene 524.2 0.016 0.014 0.026 0.152 

N-propylbenzene 502.2 PID 0.040 0.022 0.049 0.092 

N-propylbenzene 524.2 0.038 0.026 -0.053 0.284 

Naphthalene 524.2 0.048 0.040 0.044 0.186 

Nickel 1620 20.219 13.262 25.697 25.560 

Nickel 200.8 0.146 0.058 0.107 0.083 

o-xylene 524.2 0.018 0.015 -0.032 0.198 

o-xylene+styrene 502.2 PID 0.059 0.037 0.082 0.116 

P-isoproptol+1,4-dcb 502.2 PID 0.073 0.056 0.123 0.159 

Pentachloroethane 524.2 0.553 0.019 -0.100 0.408 

Sec-butylbenzene 502.2 PID 0.055 0.032 0.075 0.081 

Sec-butylbenzene 524.2 0.014 0.011 -0.012 0.140 

Selenium 1620 0.849 0.619 1.493 1.975 

Selenium 200.8 0.192 0.156 0.302 0.416 

B - 29

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

Silver 1620 4.907 3.588 6.495 10.668 

Silver 200.8 0.004 0.002 0.004 0.012 

Sodium 1620 69.530 49.595 97.649 138.768 

Styrene 524.2 0.014 0.011 0.010 0.141 

Tert-butylbenzene 502.2 PID 0.029 0.020 0.047 0.074 

Tert-butylbenzene 524.2 0.022 0.012 0.023 0.186 

Tetrachloroethene 502.2 ELCD 0.018 0.014 0.029 0.061 

Tetrachloroethene 502.2 PID 0.062 0.040 0.094 0.156 

Tetrachloroethene 524.2 0.085 0.084 0.047 0.469 

Thallium 1620 0.512 0.651 1.406 1.153 

Thallium 200.8 0.000 0.000 0.001 0.001 

Thorium 200.8 0.001 0.001 -0.005 0.001 

Tin 1620 3.670 2.019 3.143 3.932 

Titanium 1620 4.777 4.453 8.050 5.376 

Toluene 502.2 PID 0.070 0.028 0.063 0.064 

Toluene 524.2 0.020 0.006 -0.004 0.146 

Total Phosphorus 1 365.2 0.006 0.005 0.009 0.013 

Total Suspended Solids 1 160.2 1.170 0.948 1.945 3.005 

Trans-1,2-dichloroethene 502.2 ELCD 0.041 0.041 0.090 0.081 

Trans-1,2-dichloroethene 524.2 0.038 0.032 -0.016 0.300 

Trans-1,3-dichloropropene 502.2 ELCD 0.012 0.003 0.005 0.098 

Trans-1,3-dichloropropene 502.2 PID 0.058 0.045 0.095 0.092 

Trans-1,3-dichloropropene 524.2 0.051 0.025 -0.007 0.223 

Trans-1,4-dichloro-2-butene 524.2 0.512 0.348 0.576 1.250 

Trichloroethene 502.2 ELCD 0.012 0.001 0.003 0.059 

Trichloroethene 502.2 PID 0.027 0.018 0.042 0.097 

Trichloroethene 524.2 0.061 0.058 0.056 0.332 

Trichlorofluoromethane 502.2 ELCD 0.108 0.249 0.612 2.079 

Trichlorofluoromethane 524.2 0.087 0.075 0.038 0.384 

Uranium 200.8 0.000 0.000 0.000 0.000 

Vanadium 1620 7.344 4.207 8.359 10.630 

Vanadium 200.8 0.555 0.512 0.994 0.864 

B - 30

Table 2. Comparison of Detection Limits (μg/L 

except where footnoted) for the Episode 6000 Dataset 

Analyte Method Procedure 

EPA/ 

ACS DL 

ISO 

CRV 

ISO 

MDV 

ASTM 

SL-IDE 

Vinyl Chloride 502.2 ELCD 0.270 0.039 0.077 3.672 

Vinyl Chloride 524.2 0.043 0.031 -0.007 0.365 

WAD Cyanide 1677 0.572 0.169 0.319 0.701 

Xylene (To tal) 524.2 0.009 0.005 0.007 0.128 

Yttrium 1620 1.923 1.370 2.518 3.247 

Zinc 1620 2.597 2.301 3.697 4.500 

Zinc 200.8 0.900 0.461 0.806 1.598 

1 Results reported as mg/L Note: ELCD or PID in the Proc edure c olumn indicate s the pho to-ioniza tion dete ctor (PID ) or electro lytic cond uctivity 

detector (ELCD ) in EPA Method 502.2 

Table 3. Percent Differences of 

Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset 

Analyte Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

1,1,1,2-tetrachloroethane 502.2 ELCD -159.2% -131.0% -20.3% 

1,1,1,2-tetrachloroethane 524.2 -28.9% -4142.5% 129.8% 

1,1,1-trichloroethane 502.2 ELCD -34.4% 32.1% 108.8% 

1,1,1-trichloroethane 524.2 -89.4% -177.7% 139.1% 

1,1,2,2-tce+1,2,3-tcp 502.2 ELCD -29.7% 29.9% 94.7% 

1,1,2,2-tetrachloroethane 524.2 -0.6% -3.4% 107.0% 

1,1,2-trichloroethane 502.2 ELCD -146.2% -116.9% 27.6% 

1,1,2-trichloroethane 524.2 -53.2% -60.4% 124.0% 

1,1-dichloroethane 502.2 ELCD -40.1% 31.0% 156.8% 

1,1-dichloroethane 524.2 -50.5% -70.3% 150.0% 

1,1-dichloroethene 502.2 ELCD -25.4% 61.8% 143.5% 

1,1-dichloroethene 524.2 -42.8% -1080.2% 144.1% 

1,1-dichloropropanone 524.2 -48.9% 8.3% 20.6% 

1,1-dichloropropene 524.2 -117.1% -1021.1% 146.2% 

1,2,3-trichlorobenzene 502.2 ELCD -34.9% 30.2% 94.9% 

1,2,3-trichlorobenzene 502.2 PID -29.4% 42.0% 67.0% 

1,2,3-trichlorobenzene 524.2 -53.5% -76.9% 119.2% 

1,2,3-trichloropropane 524.2 -197.5% -198.2% -141.2% 

B - 31

Table 3. Percent Differences of 


Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset


Analyte Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

1,2,4-trichlorobenzene 502.2 ELCD -39.7% 31.4% 121.2% 

1,2,4-trichlorobenzene 502.2 PID -59.9% 13.5% 55.5% 

1,2,4-trichlorobenzene 524.2 -5.0% -1.3% 123.6% 

1,2,4-trimethylbenzene 502.2 PID -55.5% 23.0% 28.0% 

1,2,4-trimethylbenzene 524.2 -25.8% 33.0% 168.6% 

1,2-dibromo-3-chloropropane 524.2 -115.4% -70.1% 18.2% 

1,2-dibromoethane 502.2 ELCD -172.1% -150.8% 52.9% 

1,2-dibromoethane 524.2 -8.6% 28.7% 87.8% 

1,2-dichlorobenzene 502.2 ELCD -12.4% 53.6% 59.5% 

1,2-dichlorobenzene 502.2 PID -30.7% 48.9% 127.6% 

1,2-dichlorobenzene 524.2 -28.0% -655.2% 125.1% 

1,2-dichloroethane 502.2 ELCD -140.1% -106.3% 83.9% 

1,2-dichloroethane 524.2 -48.6% -98.0% 147.5% 

1,2-dichloropropane 502.2 ELCD -45.0% 22.4% 61.1% 

1,2-dichloropropane 524.2 -59.7% -75.2% 125.7% 

1,3,5-tmb+4-chlorotoluene 502.2 PID -39.6% 39.4% 51.0% 

1,3,5-trimethylbenzene 524.2 -33.9% -28.8% 169.3% 

1,3-dichlorobenzene 502.2 ELCD -151.2% -112.2% 108.7% 

1,3-dichlorobenzene 502.2 PID -19.1% 58.3% 30.0% 

1,3-dichlorobenzene 524.2 -35.5% -754.8% 144.1% 

1,3-dichloropropane 502.2 ELCD -63.5% -2.1% 100.1% 

1,3-dichloropropane 524.2 -45.7% -457.8% 136.4% 

1,4-dichlorobenzene 502.2 ELCD -136.9% -94.1% 80.6% 

1,4-dichlorobenzene 524.2 -33.3% 654.4% 142.5% 

1-chlorobutane 524.2 -24.0% -11.7% 166.8% 

2,2-dichloropropane 524.2 -183.3% -228.6% -109.9% 

2-butanone 524.2 -33.5% 20.2% 66.6% 

2-chlorotoluene 502.2 ELCD -116.2% -64.0% 47.7% 

2-chlorotoluene 502.2 PID -55.5% 23.6% -3.6% 

2-chlorotoluene 524.2 -54.7% -165.4% 158.1% 

2-hexanone 524.2 -159.6% -140.3% -37.3% 

2-nitropropane 524.2 -106.6% -66.3% 18.2% 

B - 32

Table 3. Percent Differences of 


Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset


Analyte Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

4-chlorotoluene 502.2 ELCD -119.9% -74.8% 30.5% 

4-chlorotoluene 524.2 -21.8% -26.2% 170.8% 

4-isopropyltoluene 524.2 -18.2% -95.8% 169.2% 

4-methyl-2-pentanone 524.2 -51.4% -10.3% 38.1% 

Acetone 524.2 -64.5% -6.6% 84.7% 

Acrylonitrile 524.2 -64.0% -27.7% 42.9% 

Allyl Chloride 524.2 -19.8% -150.4% 150.6% 

Aluminum 1620 -65.1% -3.1% 150.0% 

Aluminum 200.8 -167.6% -137.5% -40.1% 

Ammonia as Nitrogen 350.3 -39.8% 30.4% 31.7% 

Antimony 1620 -63.8% 12.2% 93.2% 

Antimony 200.8 -193.1% -185.9% -161.5% 

Arsenic 1620 -14.9% 25.4% 27.9% 

Arsenic 200.8 -49.1% 18.7% 47.5% 

Barium 1620 -24.0% 7.3% 7.6% 

Barium 200.8 -12.2% 59.9% 87.9% 

Benzene 502.2 PID -2.5% 76.2% 89.5% 

Benzene 524.2 -1.9% 57.8% 158.7% 

Beryllium 1620 -43.8% -25.6% -16.5% 

Beryllium 200.8 -55.8% -16.7% 109.7% 

Boron 1620 -39.1% 14.5% 31.6% 

Bromobenzene 502.2 ELCD -33.8% 34.8% 141.6% 

Bromobenzene 502.2 PID -31.6% 44.4% 121.7% 

Bromobenzene 524.2 -18.1% 1274.8% 131.5% 

Bromochloromethane 502.2 ELCD -11.8% 55.9% 189.2% 

Bromochloromethane 524.2 -10.3% 23.8% 93.6% 

Bromodichloromethane 502.2 ELCD -35.9% 27.1% 178.8% 

Bromodichloromethane 524.2 -47.8% -76.2% 130.5% 

Bromoform 502.2 ELCD -64.7% -129.0% 198.4% 

Bromoform 524.2 -62.6% -120.5% 105.6% 

Bromomethane 502.2 ELCD -19.7% 29.2% 185.9% 

Bromomethane 524.2 -21.0% -19.6% 122.1% 

B - 33

Table 3. Percent Differences of 


Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset


Analyte Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

Cadmium 1620 -47.1% 5.5% 40.1% 

Cadmium 200.8 55.5% 99.6% 138.8% 

Calcium 1620 -2.5% 65.4% 11.9% 

Carbon Disulfide 524.2 -52.8% 990.7% 160.0% 

Carbon Tetrachloride 524.2 -33.8% 10302.8% 156.6% 

Carbontet+1,1-dcp 502.2 ELCD -110.8% -55.2% 85.6% 

Chloroace tonitrile 524.2 -17.3% 49.7% 52.3% 

Chlorobenzene 502.2 ELCD -11.3% 61.5% 190.3% 

Chlorobenzene 502.2 PID -19.2% 58.8% 71.4% 

Chlorobenzene 524.2 -12.7% -66.3% 137.5% 

Chloroethane 502.2 ELCD -171.0% -169.4% 184.1% 

Chloroethane 524.2 -47.0% -53.1% 142.6% 

Chloroform 502.2 ELCD -150.5% -129.4% -27.3% 

Chloroform 524.2 -29.2% -51.9% 144.5% 

Chloromethane 502.2 ELCD -34.7% 60.2% 112.8% 

Chloromethane 524.2 -21.8% 37.1% 139.8% 

Chromium 1620 -20.0% 21.9% 46.3% 

Chromium 200.8 -16.5% 52.5% 139.3% 

Cis-1,2-dce+2,2-dcp 502.2 ELCD -39.4% 21.8% 124.0% 

Cis-1,2-dichloroethene 524.2 -19.1% -760.6% 141.9% 

Cis-1,3-dichloropropene 502.2 ELCD -101.0% -61.1% 164.6% 

Cis-1,3-dichloropropene 502.2 PID -17.5% 54.1% 36.0% 

Cis-1,3-dichloropropene 524.2 -47.6% -251.5% 127.2% 

Cobalt 1620 -83.9% -19.3% 50.6% 

Cobalt 200.8 -23.4% 206.3% 194.5% 

Copper 1620 -19.1% 53.9% 111.2% 

Copper 200.8 -33.0% 35.3% 182.2% 

Dibromochloromethane 502.2 ELCD -46.7% 17.2% 191.8% 

Dibromochloromethane 524.2 -49.9% -168.4% 139.6% 

Dibromomethane 502.2 ELCD -21.1% 38.8% 194.4% 

Dibromomethane 524.2 -21.5% 9.2% 116.8% 

Dichlorodifluoromethane 502.2 ELCD -91.4% 511.1% 185.7% 

B - 34

Table 3. Percent Differences of 


Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset


Analyte Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

Dichlorodifluoromethane 524.2 -42.3% -76.4% 148.1% 

Diethyl Ether 524.2 -4.9% 30.4% 103.3% 

Ethyl Me thacryla te 524.2 -37.9% -108.3% 143.1% 

Ethylbenzene 502.2 PID -35.2% 46.8% 113.8% 

Ethylbenzene 524.2 -18.3% -1245.1% 142.3% 

Hardness 130.2 -39.6% 32.7% 92.6% 

Hexachlorobutadiene 502.2 ELCD -123.8% -69.6% 74.3% 

Hexachlorobutadiene 524.2 -63.3% -528.0% 127.6% 

Hexachloroethane 524.2 -12.4% -38.6% 134.9% 

Hexchlobutadiene+naphthalene 502.2 PID -127.7% -67.8% -8.4 % 

Iron 1620 99.8% 135.7% 122.1% 

Isopropylbenzene 502.2 PID -30.2% 53.0% 98.7% 

Isopropylbenzene 524.2 -8.3% -3.3% 167.1% 

Lead 1620 -32.6% 17.6% 38.1% 

Lead 200.8 -165.8% -138.0% -105.1% 

M+p Xylene 502.2 PID -154.5% -109.6% 28.6% 

M+p Xylene 524.2 -51.9% -100.0% 166.8% 

Magnesium 1620 -14.9% 51.9% 2.8 % 

Manganese 1620 -145.5% -90.3% -0.7% 

Manganese 200.8 -2.7% 45.4% 112.6% 

Mercury 200.8 -22.3% 331.3% 145.0% 

Methacrylon itrile 524.2 -43.7% 1.8% 67.4% 

Methyl Iodide 524.2 -7.9% -613.8% 153.7% 

Methyl Tert-butyl Ether 524.2 -45.4% 1591.3% 158.7% 

Methyla crylate 524.2 -8.6% 46.5% 92.9% 

Methylene Chloride 502.2 ELCD 173.9% 189.8% 182.7% 

Methylene Chloride 524.2 -13.4% 12.6% 117.2% 

Methylm ethacryl ate 524.2 -90.7% -63.2% 81.6% 

Molybdenum 1620 -35.5% 42.7% 21.1% 

Molybdenum 200.8 -25.1% -195.0% 194.5% 

N-butylbenzene 502.2 PID -26.9% 49.2% 130.0% 

N-butylbenzene 524.2 -11.7% 50.0% 162.5% 

B - 35

Table 3. Percent Differences of 


Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset


Analyte Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

N-propylbenzene 502.2 PID -58.0% 20.6% 77.9% 

N-propylbenzene 524.2 -38.7% 1215.0% 152.9% 

Naphthalene 524.2 -19.7% -8.6% 117.7% 

Nickel 1620 -41.6% 23.9% 23.3% 

Nickel 200.8 -86.4% -30.4% -55.2% 

o-xylene 524.2 -22.0% 735.4% 166.0% 

o-xylene+styrene 502.2 PID -46.2% 32.4% 65.1% 

P-isoproptol+1,4-dcb 502.2 PID -25.1% 51.8% 74.3% 

Pentachloroethane 524.2 -186.5% -288.7% -30.2% 

Sec-butylbenzene 502.2 PID -52.4% 29.7% 37.9% 

Sec-butylbenzene 524.2 -27.1% 2196.0% 163.9% 

Selenium 1620 -31.3% 55.0% 79.8% 

Selenium 200.8 -20.4% 44.8% 73.8% 

Silver 1620 -31.1% 27.9% 74.0% 

Silver 200.8 -77.6% -5.4% 102.6% 

Sodium 1620 -33.5% 33.6% 66.5% 

Styrene 524.2 -22.6% -31.1% 163.6% 

Tert-butylbenzene 502.2 PID -36.4% 48.6% 88.6% 

Tert-butylbenzene 524.2 -60.7% 5.5% 157.8% 

Tetrachloroethene 502.2 ELCD -26.2% 47.3% 109.0% 

Tetrachloroethene 502.2 PID -42.6% 41.5% 86.4% 

Tetrachloroethene 524.2 -0.3% -57.5% 138.9% 

Thallium 1620 24.0% 93.3% 77.0% 

Thallium 200.8 -18.1% 44.5% 67.0% 

Thorium 200.8 -17.9% 270.2% 50.1% 

Tin 1620 -58.1% -15.5% 6.9% 

Titanium 1620 -7.0% 51.0% 11.8% 

Toluene 502.2 PID -85.5% -11.0% -8.1% 

Toluene 524.2 -112.6% -290.2% 152.2% 

Total Phosphorus 365.2 -25.1% 44.5% 77.5% 

Total Suspended Solids 160.2 -21.0% 49.7% 87.9% 

Trans-1,2-dichloroethene 502.2 ELCD 1.2% 75.2% 66.8% 

B - 36

Table 3. Percent Differences of 


Detection Limits to the EPA/ACS DL for the Episode 6000 Dataset


Analyte 

Trans-1,2-dichloroethene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,4-dichloro-2-butene 

Trichloroethene 

Trichloroethene 

Trichloroethene 

Trichlorofluoromethane 

Trichlorofluoromethane 

Uranium 

Vanadium 

Vanadium 

Vinyl Chloride 

Vinyl Chloride 

WAD Cyanide 

Xylene (To tal) 

Yttrium 

Zinc 

Method Procedure 

ISO CRV/ 

MDL 

ISO MDV/ 

MDL 

SL-IDE/ 

MDL 

524.2 -18.1% -495.6% 154.9% 

502.2 ELCD -117.4% -79.8% 157.3% 

502.2 PID -26.6% 47.3% 44.8% 

524.2 -69.2% -260.7% 125.8% 

524.2 -38.0% 11.8% 83.8% 

502.2 ELCD -156.0% -127.8% 133.2% 

502.2 PID -38.3% 42.8% 112.7% 

524.2 -4.9% -9.7% 137.6% 

502.2 ELCD 78.9% 140.1% 180.3% 

524.2 -15.3% -78.4% 125.9% 

200.8 -75.4% -32.9% 27.6% 

1620 -54.3% 12.9% 36.6% 

200.8 -8.0% 56.7% 43.6% 

502.2 ELCD -149.6% -111.4% 172.7% 

524.2 -32.6% -274.6% 157.7% 

1677 -108.6% -56.8% 20.2% 

524.2 -54.0% -20.8% 174.0% 

1620 -33.6% 26.8% 51.2% 

1620 -12.1% 34.9% 53.6% 

Zinc 200.8 -64.6% -11.0% 55.8% 

Note:
 ELCD or PID in the Proc edure c olumn indicate s the pho to-ioniza tion dete ctor (PID ) or electro lytic cond uctivity 

detector (ELCD ) in EPA Method 502.2 

B - 37

Summary Statistics for Table 3 

ISO CRV/ 

EPA/ACS DL 

% Difference 

ISO MDV/ 

EPA/ACS DL 

% Difference 

SL-IDE/ 

EPA/ACS DL 

% Difference 

Minimum -197.5% -4142.5% -161.5% 

25th p ercen tile -60.5% -76.4% 51.0% 

Median -35.7% 8.8% 108.7% 

75th p ercen tile -19.9% 44.5% 144.1% 

Maximum 173.9% 10302.8% 198.4% 

Median % 

Difference 

p-value for % 

difference=0 

CRV vs. -35.7% <0.0001 

MDV vs. 8.8% 0.164 

SL-IDE vs. 1087% <0.0001 

DL 

DL 

DL 

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure 

EPA/ 

ACS QL 

ISO/ 

IUPAC LOQ 

ASTM 

SL-IQE 

1,1,1,2-tetrachloroethane 502.2 ELCD 0.2 0.023 0.030 

1,1,1,2-tetrachloroethane 524.2 0.2 0.183 0.181 

1,1,1-trichloroethane 502.2 ELCD 0.05 0.044 0.830 

1,1,1-trichloroethane 524.2 0.2 0.102 0.240 

1,1,2,2-tce+1,2,3-tcp 502.2 ELCD 0.2 0.227 5.514 

1,1,2,2-tetrachloroethane 524.2 0.5 0.597 0.569 

1,1,2-trichloroethane 502.2 ELCD 0.1 0.018 0.060 

1,1,2-trichloroethane 524.2 0.2 0.212 0.290 

1,1-dichloroethane 502.2 ELCD 0.05 0.037 0.527 

1,1-dichloroethane 524.2 0.1 0.099 0.115 

1,1-dichloroethene 502.2 ELCD 0.1 0.191 3.796 

1,1-dichloroethene 524.2 0.2 0.159 0.129 

1,1-dichloropropanone 524.2 20 15.409 12.705 

1,1-dichloropropene 524.2 0.2 0.057 0.180 

1,2,3-trichlorobenzene 502.2 ELCD 0.2 0.168 0.851 

1,2,3-trichlorobenzene 502.2 PID 0.2 0.226 0.248 

B - 38

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure 

EPA/ 

ACS QL 

ISO/ 

IUPAC LOQ 

ASTM 

SL-IQE 

1,2,3-trichlorobenzene 524.2 0.2 0.192 0.216 

1,2,3-trichloropropane 524.2 20 0.268 11.316 

1,2,4-trichlorobenzene 502.2 ELCD 0.1 0.078 0.401 

1,2,4-trichlorobenzene 502.2 PID 0.2 0.208 0.439 

1,2,4-trichlorobenzene 524.2 0.2 0.231 0.141 

1,2,4-trimethylbenzene 502.2 PID 0.5 0.307 0.653 

1,2,4-trimethylbenzene 524.2 0.05 0.050 20.896 

1,2-dibromo-3-chloropropane 524.2 5 1.842 71.182 6 

1,2-dibromoethane 502.2 ELCD 0.5 0.037 0.592 

1,2-dibromoethane 524.2 0.5 0.560 0.417 

1,2-dichlorobenzene 502.2 ELCD 0.1 0.158 0.183 

1,2-dichlorobenzene 502.2 PID 0.1 0.139 0.346 

1,2-dichlorobenzene 524.2 0.1 0.101 0.085 

1,2-dichloroethane 502.2 ELCD 0.05 0.015 0.065 

1,2-dichloroethane 524.2 0.1 0.122 0.222 

1,2-dichloropropane 502.2 ELCD 0.1 0.075 0.102 

1,2-dichloropropane 524.2 0.2 0.148 0.196 

1,3,5-tmb+4-chlorotoluene 502.2 PID 0.2 0.259 0.189 

1,3,5-trimethylbenzene 524.2 0.05 0.044 23.744 

1,3-dichlorobenzene 502.2 ELCD 0.1 0.027 0.936 

1,3-dichlorobenzene 502.2 PID 0.2 0.438 0.465 

1,3-dichlorobenzene 524.2 0.1 0.080 0.076 

1,3-dichloropropane 502.2 ELCD 0.05 0.040 0.054 

1,3-dichloropropane 524.2 0.1 0.114 0.139 

1,4-dichlorobenzene 502.2 ELCD 0.1 0.025 0.101 

1,4-dichlorobenzene 524.2 0.1 0.069 0.078 

1-chlorobutane 524.2 0.05 0.082 29.943 

2,2-dichloropropane 524.2 10 0.572 38.009 

2-butanone 524.2 2 1.416 0.893 

2-chlorotoluene 502.2 ELCD 0.5 0.145 0.493 

2-chlorotoluene 502.2 PID 1 0.781 0.849 

2-chlorotoluene 524.2 0.05 0.046 0.053 

B - 39

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure 

EPA/ 

ACS QL 

ISO/ 

IUPAC LOQ 

ASTM 

SL-IQE 

2-hexanone 524.2 5 0.669 0.442 

2-nitropropane 524.2 2 1.280 0.590 

4-chlorotoluene 502.2 ELCD 0.5 0.132 0.142 1 

4-chlorotoluene 524.2 0.05 0.037 23.810 

4-isopropyltoluene 524.2 0.05 0.043 0.016 

4-methyl-2-pentanone 524.2 2 2.066 1.785 

Acetone 524.2 2 2.114 2.741 

Acrylonitrile 524.2 2 1.816 28.056 

Allyl Chloride 524.2 0.1 0.129 29.674 

Aluminum 1620 100 76.242 464.069 

Aluminum 200.8 50 9.418 29.684 

Ammonia as Nitrogen 2 350.3 0.05 0.037 0.035 

Antimony 1620 5 4.784 9.551 

Antimony 200.8 0.5 0.017 0.034 

Arsenic 1620 5 3.684 3.097 

Arsenic 200.8 1 0.720 0.798 

Barium 1620 5 4.722 4.118 

Barium 200.8 0.1 0.161 0.211 

Benzene 502.2 PID 0.1 0.173 0.182 

Benzene 524.2 0.05 0.075 0.044 

Beryllium 1620 2 1.055 0.980 

Beryllium 200.8 0.02 0.018 0.044 

Boron 1620 50 46.040 51.134 

Bromobenzene 502.2 ELCD 0.5 0.599 3.529 

Bromobenzene 502.2 PID 0.05 0.050 0.100 

Bromobenzene 524.2 0.2 0.167 0.140 

Bromochloromethane 502.2 ELCD 0.05 0.065 1.598 

Bromochloromethane 524.2 0.5 0.549 0.368 

Bromodichloromethane 502.2 ELCD 0.02 0.015 0.424 

Bromodichloromethane 524.2 0.2 0.135 0.128 

Bromoform 502.2 ELCD 0.02 0.018 3.393 

Bromoform 524.2 0.5 0.287 0.482 

B - 40

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Cobalt 200.8 0.005 

Copper 1620 20 

Copper 200.8 0.1 

Dibromochloromethane 502.2 ELCD 0.02 

Dibromochloromethane 524.2 0.2 

Analyte Method Procedure 

EPA/ 

ACS QL 

ISO/ 

IUPAC LOQ 

ASTM 

SL-IQE 

Bromomethane 502.2 ELCD 1 undefined 3 16.351 

Bromomethane 524.2 0.2 0.252 0.226 

Cadmium 1620 0.5 0.346 0.410 

Cadmium 200.8 0.02 0.046 0.063 

Calcium 1620 100 186.530 99.975 

Carbon Disulfide 524.2 0.1 0.077 0.101 

Carbon Tetrachloride 524.2 0.1 0.127 0.140 

Carbontet+1,1-dcp 502.2 ELCD 0.1 0.046 0.069 

Chloroace tonitrile 524.2 2 4.170 3.310 

Chlorobenzene 502.2 ELCD 0.05 0.058 1.766 

Chlorobenzene 502.2 PID 0.1 0.143 0.119 

Chlorobenzene 524.2 0.1 0.108 0.059 

Chloroethane 502.2 ELCD 0.5 0.053 5.826 

Chloroethane 524.2 0.2 0.185 0.255 

Chloroform 502.2 ELCD 0.2 0.029 0.025 

Chloroform 524.2 0.1 0.138 0.121 

Chloromethane 502.2 ELCD 0.2 0.342 1.734 

Chloromethane 524.2 0.2 0.181 0.141 

Chromium 1620 1 0.993 1.259 

Chromium 200.8 0.2 0.331 1.028 

Cis-1,2-dce+2,2-dcp 502.2 ELCD 0.05 0.045 0.039 

Cis-1,2-dichloroethene 524.2 0.1 0.154 0.144 

Cis-1,3-dichloropropene 502.2 ELCD 0.02 0.013 0.415 

Cis-1,3-dichloropropene 502.2 PID 0.2 0.254 0.017 1 

Cis-1,3-dichloropropene 524.2 0.1 0.117 0.141 

Cobalt 1620 50 20.916 40.837 

undefined 3 undefined 4	

27.513 47.509 

0.142 1.825 

0.030 1.252 

0.149 0.288 

Dibromomethane 502.2 ELCD 0.02 0.028 1.395 

B - 41

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure 

EPA/ 

ACS QL 

Dibromomethane 524.2 0.5 

Dichlorodifluoromethane 502.2 ELCD 0.02 

Dichlorodifluoromethane 524.2 0.2 

Diethyl Ether 524.2 0.5 

Ethyl Me thacryla te 524.2 0.2 

Ethylbenzene 502.2 PID 0.1 

Ethylbenzene 524.2 0.1 

Hardness 2 130.2 2 

Hexachlorobutadiene 502.2 ELCD 0.2 

Hexachlorobutadiene 524.2 0.2 

Hexachloroethane 524.2 0.2 

Hexchlobutadiene+naphthalene 502.2 PID 2 

Iron 1620 200 

Isopropylbenzene 502.2 PID 0.1 

Isopropylbenzene 524.2 0.05 

Lead 1620 5 

Lead 200.8 2 

M+p Xylene 502.2 PID 0.2 

M+p Xylene 524.2 0.05 

Magnesium 1620 500 

Manganese 1620 20 

Manganese 200.8 0.1 

Mercury 200.8 0.02 

Methacrylon itrile 524.2 1 

Methyl Iodide 524.2 0.1 

Methyl Tert-butyl Ether 524.2 0.1 

ISO/ 

IUPAC LOQ 

0.400 

0.012 

0.290 

0.563 

0.139 

0.089 

0.123 

2.973 

0.054 

0.160 

0.232 

0.834 

1490.589 

0.090 

0.056 

5.062 

0.318 

0.068 

0.042 

454.043 

7.948 

0.133 

0.056 

1.066 

0.108 

0.073 

0.966 

undefined 3 

0.354 

0.381 

9.752 

ASTM 

SL-IQE 

0.460 

1.091 5 

0.480 

0.404 

0.183 

0.157 

0.077 

5.465 

0.243 

0.228 

0.167 

1.542 

996.565 5 

0.129 

25.592 

5.698 

0.685 

0.222 

24.651 

267.199 

15.264 

0.245 

0.039 

19.062 

0.083 

0.122 

0.727 

6.033 

0.433 

20.773 

7.597 

Methyla crylate 524.2 1 

Methylene Chloride 502.2 ELCD 0.5 

Methylene Chloride 524.2 0.2 

Methylm ethacryl ate 524.2 1 

Molybdenum 1620 10 

Molybdenum 200.8 0.01 0.052 0.608 

B - 42

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure 

EPA/ 

ACS QL 

ISO/ 

IUPAC LOQ 

ASTM 

SL-IQE 

N-butylbenzene 502.2 PID 0.1 0.128 0.745 

N-butylbenzene 524.2 0.05 0.077 0.067 

N-propylbenzene 502.2 PID 0.2 0.128 0.186 

N-propylbenzene 524.2 0.1 0.110 29.878 

Naphthalene 524.2 0.2 0.184 0.108 

Nickel 1620 100 66.486 67.206 

Nickel 200.8 0.5 0.287 0.183 

o-xylene 524.2 0.05 0.062 0.040 

o-xylene+styrene 502.2 PID 0.2 0.210 0.181 

P-isoproptol+1,4-dcb 502.2 PID 0.2 0.318 0.456 

Pentachloroethane 524.2 2 0.086 0.551 

Sec-butylbenzene 502.2 PID 0.2 0.193 0.157 

Sec-butylbenzene 524.2 0.05 0.063 0.047 

Selenium 1620 2 3.859 5.235 

Selenium 200.8 0.5 0.805 1.045 

Silver 1620 20 16.734 25.842 

Silver 200.8 0.02 0.011 0.056 

Sodium 1620 200 251.546 337.755 

Styrene 524.2 0.05 0.054 0.041 

Tert-butylbenzene 502.2 PID 0.1 0.121 0.203 

Tert-butylbenzene 524.2 0.1 0.063 0.073 

Tetrachloroethene 502.2 ELCD 0.05 0.076 0.122 

Tetrachloroethene 502.2 PID 0.2 0.244 0.750 

Tetrachloroethene 524.2 0.2 0.378 30.554 6 

Thallium 1620 2 3.748 2.799 

Thallium 200.8 0.002 0.002 0.002 

Thorium 200.8 0.002 0.005 0.004 

Tin 1620 10 9.237 9.406 

Titanium 1620 20 20.807 14.236 

Toluene 502.2 PID 0.2 0.162 0.194 

Toluene 524.2 0.05 0.028 0.046 

Total Phosphorus 2 365.2 0.02 0.024 0.030 

B - 43

1 

2 

3 

4

5

6

Table 4. Comparison Quantitation Limits for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte 

Total Suspended Solids 2 

Trans-1,2-dichloroethene 

Trans-1,2-dichloroethene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,4-dichloro-2-butene 

Trichloroethene 

Trichloroethene 

Trichloroethene 

Trichlorofluoromethane 

Trichlorofluoromethane 

Uranium 

Vanadium 

Vanadium 

Vinyl Chloride 

Vinyl Chloride 

WAD Cyanide 

Xylene (To tal) 

Yttrium 

Zinc 

Zinc 200.8 2 2.147 7.024 

Method Procedure 

EPA/ 

ACS QL 

ISO/ 

IUPAC LOQ 

ASTM 

SL-IQE 

160.2 5 5.011 6.729 

502.2 ELCD 0.2 0.234 0.191 

524.2 0.1 0.141 0.153 

502.2 ELCD 0.05 0.016 0.729 

502.2 PID 0.2 0.244 0.175 

524.2 0.2 0.121 0.218 

524.2 2 1.803 30.108 

502.2 ELCD 0.05 0.008 3.169 

502.2 PID 0.1 0.108 0.401 

524.2 0.2 0.284 0.167 

502.2 ELCD 0.5 1.612 4.662 

524.2 0.2 0.279 42.490 6 

200.8 0.001 0.001 0.001 

1620 20 21.586 24.338 

200.8 2 2.627 1.933 

502.2 ELCD 1 0.264 8.234 

524.2 0.2 0.139 0.219 

1677 2 0.852 1.624 

524.2 0.02 0.027 23.520 

1620 5 6.571 8.962 

1620 10 9.575 10.452 

IQE 10% undefined, IQE 20% reported	

Results reported as mg/L	 No LOQ could be ca lculated due to a square ro ot of a negative number in the formula	

IQE 10%, IQE 20% and IQE 30% all neg ative based on chosen model (linear)	

IQE 10% and IQE 20% both negative, IQE 30% reported	

Hybrid model selected but did not converge; IQE 10% based on constant model instead	

Note: 
 ELCD or P ID in the Proce dure column indicates the photo-ioniza tion detector (PID) or electrolytic 

condu ctivity dete ctor (ELC D) in EP A Metho d 502.2 

B - 44

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte Method Procedure 

ISO 

LOQ/ML SL-IQE/ML 

1,1,1,2-tetrachloroethane 502.2 ELCD -158.7% -147.3% 

1,1,1,2-tetrachloroethane 524.2 -8.7% -9.8% 

1,1,1-trichloroethane 502.2 ELCD -11.8% 177.3% 

1,1,1-trichloroethane 524.2 -65.1% 18.0% 

1,1,2,2-tce+1,2,3-tcp 502.2 ELCD 12.8% 186.0% 

1,1,2,2-tetrachloroethane 524.2 17.6% 12.9% 

1,1,2-trichloroethane 502.2 ELCD -138.2% -49.6% 

1,1,2-trichloroethane 524.2 5.9% 36.6% 

1,1-dichloroethane 502.2 ELCD -28.6% 165.3% 

1,1-dichloroethane 524.2 -0.7% 13.7% 

1,1-dichloroethene 502.2 ELCD 62.5% 189.7% 

1,1-dichloroethene 524.2 -22.8% -43.3% 

1,1-dichloropropanone 524.2 -25.9% -44.6% 

1,1-dichloropropene 524.2 -111.1% -10.5% 

1,2,3-trichlorobenzene 502.2 ELCD -17.6% 123.9% 

1,2,3-trichlorobenzene 502.2 PID 12.2% 21.3% 

1,2,3-trichlorobenzene 524.2 -4.2% 7.7% 

1,2,3-trichloropropane 524.2 -194.7% -55.5% 

1,2,4-trichlorobenzene 502.2 ELCD -25.2% 120.2% 

1,2,4-trichlorobenzene 502.2 PID 3.8% 74.9% 

1,2,4-trichlorobenzene 524.2 14.5% -34.9% 

1,2,4-trimethylbenzene 502.2 PID -47.8% 26.5% 

1,2,4-trimethylbenzene 524.2 0.5% 199.0% 

1,2-dibromo-3-chloropropane 524.2 -92.3% 173.7% 

1,2-dibromoethane 502.2 ELCD -172.7% 16.9% 

1,2-dibromoethane 524.2 11.3% -18.1% 

1,2-dichlorobenzene 502.2 ELCD 45.1% 58.8% 

1,2-dichlorobenzene 502.2 PID 32.9% 110.2% 

1,2-dichlorobenzene 524.2 0.6% -16.5% 

1,2-dichloroethane 502.2 ELCD -108.1% 26.0% 

1,2-dichloroethane 524.2 19.7% 75.6% 

1,2-dichloropropane 502.2 ELCD -28.5% 2.3% 

B - 45

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte 

1,2-dichloropropane 

1,3,5-tmb+4-chlorotoluene 

1,3,5-trimethylbenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichloropropane 

1,3-dichloropropane 

1,4-dichlorobenzene 

1,4-dichlorobenzene 

1-chlorobutane 

2,2-dichloropropane 

2-butanone 

2-chlorotoluene 

2-chlorotoluene 

2-chlorotoluene 

2-hexanone 

2-nitropropane 

4-chlorotoluene 

4-chlorotoluene 

4-isopropyltoluene 

4-methyl-2-pentanone 

Acetone 

Acrylonitrile 

Allyl chloride 

Aluminum 

Aluminum 

Ammonia as nitrogen 

Antimony 

Antimony 

Arsenic 

Arsenic 

Method Procedure 

524.2 

502.2 PID 

524.2 

502.2 ELCD 

502.2 PID 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

524.2 

524.2 

524.2 

502.2 ELCD 

502.2 PID 

524.2 

524.2 

524.2 

502.2 ELCD 

524.2 

524.2 

524.2 

524.2 

524.2 

524.2 

1620 

200.8 ICP/MS 

350.3 

1620 

200.8 ICP/MS 

1620 

200.8 ICP/MS 

ISO 

LOQ/ML 

-29.8% 

25.7% 

-13.6% 

-114.2% 

74.5% 

-22.2% 

-22.9% 

13.0% 

-120.8% 

-37.2% 

48.8% 

-178.4% 

-34.2% 

-109.9% 

-24.6% 

-7.6% 

-152.8% 

-43.9% 

-116.3% 

-29.1% 

-15.4% 

3.2% 

5.5% 

-9.7% 

25.5% 

-27.0% 

-136.6% 

6.3% 

-167.5% 

-108.9% 

-111.5% 

199.2% 

-101.7% 

-11.3% 

31.3% 

173.4% 

198.7% 

129.1% 

-51.0% 

-30.9% -34.1% 

-4.4% 62.6% 

-186.6% -174.7% 

-30.3% -47.0% 

-32.5% -22.5% 

SL-IQE/ML 

-1.9% 

-5.5% 

199.2% 

161.4% 

79.7% 

-27.3% 

7.5% 

32.7% 

1.0% 

-24.2% 

199.3% 

116.7% 

-76.6% 

-1.4% 

-16.4% 

B - 46

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte Method Procedure 

ISO 

LOQ/ML SL-IQE/ML 

Barium 1620 -5.7% -19.3% 

Barium 200.8 ICP/MS 46.6% 71.5% 

Benzene 502.2 PID 53.7% 58.1% 

Benzene 524.2 40.5% -13.1% 

Beryllium 1620 -61.9% -68.5% 

Beryllium 200.8 ICP/MS -9.9% 75.0% 

Boron 1620 -8.2% 2.2% 

Bromobenzene 502.2 ELCD 18.0% 150.4% 

Bromobenzene 502.2 PID -0.9% 67.0% 

Bromobenzene 524.2 -18.1% -35.4% 

Bromochloromethane 502.2 ELCD 25.8% 187.9% 

Bromochloromethane 524.2 9.3% -30.3% 

Bromodichloromethane 502.2 ELCD -25.6% 182.0% 

Bromodichloromethane 524.2 -38.7% -43.9% 

Bromoform 502.2 ELCD -12.0% 197.7% 

Bromoform 524.2 -54.2% -3.7% 

Bromomethane 502.2 ELCD N/A 176.9% 

Bromomethane 524.2 23.2% 12.2% 

Cadmium 1620 -36.4% -19.7% 

Cadmium 200.8 ICP/MS 79.2% 103.6% 

Calcium 1620 60.4% -0.0% 

Carbon disulfide 524.2 -26.2% 1.3% 

Carbon tetrachloride 524.2 23.9% 33.4% 

Carbontet+1,1-dcp 502.2 ELCD -74.3% -37.3% 

Chloroace tonitrile 524.2 70.3% 49.3% 

Chlorobenzene 502.2 ELCD 15.7% 189.0% 

Chlorobenzene 502.2 PID 35.2% 17.4% 

Chlorobenzene 524.2 7.4% -50.8% 

Chloroethane 502.2 ELCD -161.8% 168.4% 

Chloroethane 524.2 -8.0% 24.2% 

Chloroform 502.2 ELCD -149.4% -155.3% 

Chloroform 524.2 31.7% 19.2% 

B - 47

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte Method Procedure 

Chloromethane 502.2 ELCD 

Chloromethane 524.2 

Chromium 1620 

Chromium 200.8 ICP/MS 

Cis-1,2-dce+2,2-dcp 502.2 ELCD 

Cis-1,2-dichloroethene 524.2 

Cis-1,3-dichloropropene 502.2 ELCD 

Cis-1,3-dichloropropene 502.2 PID 

Cis-1,3-dichloropropene 524.2 

Cobalt 1620 

Cobalt 200.8 ICP/MS 

Copper 1620 

Copper 200.8 ICP/MS 

Dibromochloromethane 502.2 ELCD 

Dibromochloromethane 524.2 

Dibromomethane 502.2 ELCD 

Dibromomethane 524.2 

Dichlorodifluoromethane 502.2 ELCD 

Dichlorodifluoromethane 524.2 

Diethyl ether 524.2 

Ethyl me thacryla te 524.2 

Ethylbenzene 502.2 PID 

Ethylbenzene 524.2 

Hardness 130.2 

Hexachlorobutadiene 502.2 ELCD 

Hexachlorobutadiene 524.2 

Hexachloroethane 524.2 

Hexchlobutadiene+naphthalene 502.2 PID 

Iron 1620 

Isopropylbenzene 502.2 PID 

Isopropylbenzene 524.2 

ISO 

LOQ/ML 

52.5% 

-9.8% 

-0.7% 

49.3% 

-9.5% 

42.4% 

-45.8% 

23.8% 

15.3% 

-82.0% 

N/A 

31.6% 

34.6% 

41.1% 

-29.0% 

34.7% 

-22.2% 

-53.2% 

36.7% 

11.9% 

-35.7% 

-11.5% 

20.4% 

39.1% 

-114.4% 

-22.3% 

14.8% 

-82.3% 

152.7% 

-10.4% 

11.9% 

SL-IQE/ML 

158.6% 

-34.9% 

22.9% 

134.9% 

-24.7% 

36.1% 

181.6% 

-168.7% 

34.2% 

-20.2% 

N/A 

81.5% 

179.2% 

193.7% 

36.0% 

194.3% 

-8.3% 

192.8% 

82.3% 

-21.3% 

-8.9% 

44.6% 

-25.4% 

92.8% 

19.4% 

13.3% 

-17.7% 

-25.9% 

133.1% 

25.3% 

199.2% 

Lead 1620 1.2% 13.1% 

B - 48

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte Method 

Lead 200.8 

M+p xylene 502.2 

M+p xylene 524.2 

Magnesium 1620 

Manganese 1620 

Manganese 200.8 

Mercury 200.8 

Methacrylon itrile 524.2 

Methyl iodide 524.2 

Methyl tert-butyl ether 524.2 

Methyla crylate 524.2 

Methylene chloride 502.2 ELCD 

Methylene chloride 524.2 

Methylm ethacryl ate 524.2 

Molybdenum 1620 

Molybdenum 200.8 ICP/MS 

N-butylbenzene 502.2 PID 

N-butylbenzene 524.2 

N-propylbenzene 502.2 PID 

N-propylbenzene 524.2 

Naphthalene 524.2 

Nickel 1620 

Nickel 200.8 ICP/MS 

O-xylene 524.2 

O-xylene+styrene 502.2 PID 

P-isoproptol+1,4-dcb 502.2 PID 

Pentachloroethane 524.2 

Sec-butylbenzene 502.2 PID 

Sec-butylbenzene 524.2 

Selenium 1620 

Selenium 200.8 ICP/MS 

Procedure 

ICP/MS 

PID 

ICP/MS 

ICP/MS 

ISO 

LOQ/ML SL-IQE/ML 

-145.2% -98.0% 

-98.3% 10.6% 

-17.7% 199.2% 

-9.6% -60.7% 

-86.2% -26.9% 

28.0% 84.1% 

94.2% 63.6% 

6.4% 180.1% 

7.3% -18.1% 

-31.2% 20.1% 

-3.5% -31.7% 

N/A 169.4% 

55.6% 73.6% 

-89.5% 181.6% 

-2.5% -27.3% 

135.3% 193.5% 

24.5% 152.7% 

42.2% 29.5% 

-44.1% -7.1% 

9.9% 198.7% 

-8.2% -59.5% 

-40.3% -39.2% 

-54.2% -92.9% 

21.3% -21.4% 

4.9% -10.0% 

45.6% 78.1% 

-183.5% -113.6% 

-3.4% -24.2% 

22.8% -5.2% 

63.5% 89.4% 

46.8% 70.6% 

Silver 1620 -17.8% 25.5% 

B - 49

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte Method Procedure 

ISO 

LOQ/ML SL-IQE/ML 

Silver 200.8 ICP/MS -59.2% 94.7% 

Sodium 1620 22.8% 51.2% 

Styrene 524.2 6.9% -20.6% 

Tert-butylbenzene 502.2 PID 19.2% 67.9% 

Tert-butylbenzene 524.2 -44.8% -30.6% 

Tetrachloroethene 502.2 ELCD 40.9% 83.5% 

Tetrachloroethene 502.2 PID 19.6% 115.8% 

Tetrachloroethene 524.2 61.5% 197.4% 

Thallium 1620 60.8% 33.3% 

Thallium 200.8 ICP/MS 3.8% 16.3% 

Thorium 200.8 ICP/MS 90.3% 74.9% 

Tin 1620 -7.9% -6.1% 

Titanium 1620 4.0% -33.7% 

Toluene 502.2 PID -21.1% -3.0% 

Toluene 524.2 -57.9% -9.1% 

Total phosphorus 365.2 17.2% 39.9% 

Total suspended solids 160.2 0.2% 29.5% 

Trans-1,2-dichloroethene 502.2 ELCD 15.7% -4.9% 

Trans-1,2-dichloroethene 524.2 33.7% 41.7% 

Trans-1,3-dichloropropene 502.2 ELCD -101.5% 174.3% 

Trans-1,3-dichloropropene 502.2 PID 19.8% -13.4% 

Trans-1,3-dichloropropene 524.2 -49.5% 8.7% 

Trans-1,4-dichloro-2-butene 524.2 -10.4% 175.1% 

Trichloroethene 502.2 ELCD -144.3% 193.8% 

Trichloroethene 502.2 PID 7.8% 120.2% 

Trichloroethene 524.2 34.6% -17.8% 

Trichlorofluoromethane 502.2 ELCD 105.3% 161.3% 

Trichlorofluoromethane 524.2 33.0% 198.1% 

Uranium 200.8 ICP/MS -33.2% 2.6% 

Vanadium 1620 7.6% 19.6% 

Vanadium 200.8 ICP/MS 27.1% -3.4% 

Vinyl chloride 502.2 ELCD -116.4% 156.7% 

B - 50

Table 5. Percent Differences of Quantitation Limits to the EPA/ACS QL 

for the Episode 6000 Dataset 

Analyte Method Procedure 

ISO 

LOQ/ML SL-IQE/ML 

Vinyl chloride 524.2 -35.7% 9.3% 

Wad cyanide 1677 WADCN -80.5% -20.8% 

Xylene (total) 524.2 28.4% 199.7% 

Yttrium 1620 27.2% 56.8% 

Zinc 1620 -4.3% 4.4% 

Zinc 200.8 ICP/MS 7.1% 111.4% 

Note:	 ELCD or P ID in the Proce dure colum n indicates the photo-ioniza tion detector (PID ) or electrolytic 

condu ctivity dete ctor (ELC D) in EP A Metho d 502.2 

Summary Statistics for Table 5 

ISO LOQ/QL SL-IQE/QL 

Minimum -194.7% -174.7% 

25th percen tile -35.0% -18.1% 

Median -4.2% 19.6% 

75th percen tile 23.0% 111.4% 

Maximum 152.7% 199.7% 

Comparison Sign Test 

p-value 

Wilcoxon 

p-value 

LOQ vs. QL 0.390 0.043 

SL-IQE vs. QL 0.0001 <0.0001 

Comparison # 

analytes 

Median % 

Difference 

Sign Test 

p-value 

Wilcoxon 

p-value 

SL-IQE vs. 

(constant model used for SL-IQE) 

32 179.6% <0.0001 <0.0001 

SL-IQE vs. 

(Linear model used for SL-IQE) 

65 67.9% <0.0001 <0.0001 

SL-IQE vs. 

(Hybrid model used for SL-IQE) 

100 -7.7% 0.533 0.160 

QL 

QL 

QL 

B - 51

--- ---

Table 6. Detection and Quantitation Limits for EPA Methods 1631 and 1638 

as Computed by EPA and by EPRI 

Ambient 

Detection limits 

MDL in 

(ng/L) 

Element1 WQC2 

Antimony 

Cadmium 

Copper 

Lead 

Mercury 

Nickel 

Selenium 

Silver 

Thallium 

14000 9.7 170 110 20 270 270 

370 25 160 150 100 540 380 

2400 87 800 770 200 3800 3000 

540 15 140 160 50 420 370 

12 0.2 0.81 0.43 0.5 0.55 1.6 

8200 330 230 130 1000 15000 330 

5000 450 810 600 1000 630 720 

320 29 440 100 5500 

1700 7.9 28 20 20 88 50 

Zinc 32000 140 1800 2100 500 21000 26100 

Quantitation limits 

IDE computed by 

ML in 

IQE computed by

Method EPA EPRI Method EPA EPRI 

1 Mercury determined by EPA Method 1631; all others by EPA Method 1638
 2 Lowest ambient water quality criterion (WQC) in the National Toxics Rule (40 CFR 131.36)


B - 52

Table 7. Comparison of IDEs and IQEs resulting from all model types for EPA Methods 1631 and 1638 

Calculated IDEs 

Analyte 

IDE, Based on Given Model RSD (%) 

Constant Linear Exponential Hybrid 

Antimony 2500 -80 1 170 100 148% 

Cadmium 1200 130 160 150 129% 

Copper 2700 1000 800 720 72% 

Lead 400 150 140 150 61% 

Mercury 8.3 0.058 0.81 0.52 162% 

Nickel 7000 -48 1 230 120 161% 

Selenium 4500 720 810 530 117% 

Silver 2500 710 440 650 89% 

Thallium 230 22 28 17 140% 

Zinc 10,000 1600 1800 1700 110% 

Calculated IQEs (10%) 

Analyte IQE, Based on Given Model RSD(%) 

Constant Linear Exponential Hybrid 

Antimony 5400 -570 1 380 270 145% 

Cadmium 2600 540 380 380 112% 

Copper 5900 3800 2100 2300 50% 

Lead 860 420 340 330 52% 

Mercury 18 0.55 2.1 1.6 150% 

Nickel 15,000 -160 1 500 270 190% 

Selenium 9600 7600 2200 630 3 86% 

Silver 5500 1500 4 1500 undefined 2 82% 

Thallium 500 88 67 47 124% 

Zinc 22,000 21,000 4800 6700 67% 

1

 Negative d ue to negative intercept estima te in precision m odel. 2 IDE or IQE did not converge to a single value for estimated models. 3

 IQE 10% undefined, IQE 20% reported 4

 IQE 10% negative, IQE 20% reported 

B - 53

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

Analyte Method Procedure SL-IDE 

(16) 

SL-IDE (5) 

Percent 

Difference 

1,1,1,2-tetrachloroethane 502.2 ELCD 0.034 0.011 -99.6% 

1,1,1,2-tetrachloroethane 524.2 0.244 0.170 -35.8% 

1,1,1-trichloroethane 502.2 ELCD 0.041 0.044 6.2% 

1,1,1-trichloroethane 524.2 0.308 0.035 -159.4% 

1,1,2,2-tce+1,2,3-tcp 502.2 ELCD 0.179 3.548 180.8% 

1,1,2,2-tetrachloroethane 524.2 0.436 0.538 20.8% 

1,1,2-trichloroethane 502.2 ELCD 0.032 0.013 -86.7% 

1,1,2-trichloroethane 524.2 0.319 0.229 -32.8% 

1,1-dichloroethane 502.2 ELCD 0.083 0.036 -78.8% 

1,1-dichloroethane 524.2 0.229 0.084 -92.7% 

1,1-dichloroethene 502.2 ELCD 0.234 0.120 -64.0% 

1,1-dichloroethene 524.2 0.335 0.080 -122.6% 

1,1-dichloropropanone 524.2 6.372 8.941 33.6% 

1,1-dichloropropene 524.2 0.287 4.435 175.7% 

1,2,3-trichlorobenzene 502.2 ELCD 0.134 0.169 23.1% 

1,2,3-trichlorobenzene 502.2 PID 0.115 0.069 -49.9% 

1,2,3-trichlorobenzene 524.2 0.275 0.150 -59.2% 

1,2,3-trichloropropane 524.2 1.263 16.238 171.1% 

1,2,4-trichlorobenzene 502.2 ELCD 0.088 0.100 13.1% 

1,2,4-trichlorobenzene 502.2 PID 0.124 0.075 -48.9% 

1,2,4-trichlorobenzene 524.2 0.224 0.115 -64.6% 

1,2,4-trimethylbenzene 502.2 PID 0.125 0.143 12.8% 

1,2,4-trimethylbenzene 524.2 0.144 0.059 -84.6% 

1,2-dibromo-3-chloropropane 524.2 1.749 0.432 -120.8% 

1,2-dibromoethane 502.2 ELCD 0.164 0.025 -147.8% 

1,2-dibromoethane 524.2 0.326 0.316 -3.1% 

1,2-dichlorobenzene 502.2 ELCD 0.065 0.057 -13.4% 

1,2-dichlorobenzene 502.2 PID 0.148 0.077 -62.5% 

1,2-dichlorobenzene 524.2 0.130 0.069 -61.3% 

1,2-dichloroethane 502.2 ELCD 0.042 0.026 -48.3% 

Exponential Hybrid 

(μg/L except where footnoted) 

SL-IDE 16 

Model 

SL-IDE 5 

Model 

Exponential Linear 

Exponential Exponential 

Exponential Exponential 

Exponential Hybrid 

Exponential Constant 

Exponential Exponential 

Exponential Linear 

Exponential Exponential 

Exponential Exponential 

Exponential Exponential 

Exponential Exponential 

Exponential Hybrid 

Exponential Exponential 

Exponential Consta nt 1 

Exponential Constant 

Exponential Exponential 

Exponential Exponential 

Exponential Consta nt 1 

Exponential Constant 

Exponential Exponential 

Exponential Exponential 

Exponential Constant 

Exponential Exponential 

Exponential


Exponential Exponential


Exponential Linear


Exponential Exponential


Exponential Exponential


Exponential Exponential


Linear 

B - 54

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

Analyte Method Procedure SL-IDE 

(16) 

SL-IDE (5) 

Percent 

Difference 

1,2-dichloroethane 524.2 0.258 0.211 -19.9% 

1,2-dichloropropane 502.2 ELCD 0.043 0.087 67.5% 

1,2-dichloropropane 524.2 0.247 0.221 -11.1% 

1,3,5-tmb+4-chlorotoluene 502.2 PID 0.114 0.141 21.4% Exponential Constant


Exponential Exponential


Exponential Constant


Exponential


Exponential Exponential


Exponential Exponential


Exponential Exponential


Exponential Linear


Exponential Exponential


Exponential Linear


Exponential


Exponential


Exponential Exponential


Exponential Constant


Exponential Exponential


Exponential Exponential


Exponential Constant


Exponential


Exponential Exponential


Exponential Exponential


Exponential Exponential


Exponential Constant


Exponential Exponential


Exponential Hybrid


Constant 

Hybrid 

Exponential 

Linear 

1,3,5-trimethylbenzene 524.2 0.135 0.049 -94.1% 

1,3-dichlorobenzene 502.2 ELCD 0.118 0.615 135.5% 

1,3-dichlorobenzene 502.2 PID 0.126 0.197 43.9% 

1,3-dichlorobenzene 524.2 0.143 0.038 -116.4% 

1,3-dichloropropane 502.2 ELCD 0.047 0.020 -81.3% 

1,3-dichloropropane 524.2 0.202 0.122 -49.2% 

1,4-dichlorobenzene 502.2 ELCD 0.061 0.040 -40.5% 

1,4-dichlorobenzene 524.2 0.140 0.051 -93.7% 

1-chlorobutane 524.2 0.220 0.061 -113.5% 

2,2-dichloropropane 524.2 0.691 0.122 -139.9% 

2-butanone 524.2 0.833 1.441 53.5% 

2-chlorotoluene 502.2 ELCD 0.175 0.117 -40.2% 

2-chlorotoluene 502.2 PID 0.230 0.409 56.2% 

2-chlorotoluene 524.2 0.136 0.039 -111.2% 

2-hexanone 524.2 0.902 0.904 0.3% 

2-nitropropane 524.2 1.082 9.354 158.5% 

4-chlorotoluene 502.2 ELCD 0.149 0.145 -3.2% 

4-chlorotoluene 524.2 0.123 0.038 -105.5% 

4-isopropyltoluene 524.2 0.117 0.038 -101.3% 

4-methyl-2-pentanone 524.2 1.195 1.088 -9.3% 

Acetone 524.2 2.120 30.183 173.8% 

Acrylonitrile 524.2 1.333 1.077 -21.3% 

Allyl Chloride 524.2 0.229 0.073 -103.6% 

Aluminum 1620 206.975 73.421 -95.3% 

Aluminum 200.8 12.747 22.654 56.0% 

Ammonia as Nitrogen 2 350.3 0.014 0.040 94.0% 

Constant


Exponential


Exponential


Constant 

Constant 

Constant 

(μg/L except where footnoted) 

SL-IDE 16 

Model 

SL-IDE 5 

Model 

Exponential Exponential 

Exponential Constant 

Exponential Exponential 

B - 55

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure SL-IDE 

(16) 

SL-IDE (5) 

SL-IDE 5 

Model 

6.467 41.2% Constant Linear 

Percent 

Difference 

SL-IDE 16 

Model 

Antimony 1620 4.260 

Antimony 200.8 0.019 0.304 176.5% Exponential Constant 

Arsenic 1620 1.410 2.268 46.6% Exponential Constant 

Arsenic 200.8 0.366 0.374 2.1% Exponential Exponential 

Barium 1620 1.837 1.624 -12.3% Constant Constant 

Barium 200.8 0.084 0.073 -13.7% Exponential Constant 

Benzene 502.2 PID 0.079 0.061 -25.0% Exponential Exponential 

Benzene 524.2 0.125 0.030 -122.6% Exponential Exponential 

Beryllium 1620 0.448 0.438 -2.2% Exponential Exponential 

Beryllium 200.8 0.024 0.017 -34.2% Exponential Constant 

Boron 1620 21.161 22.333 5.4% Exponential Exponential 

Bromobenzene 502.2 ELCD 0.765 0.348 -75.0% Linear Exponential 

Bromobenzene 502.2 PID 0.050 0.025 -65.4% Exponential Exponential 

Bromobenzene 524.2 0.211 0.165 -24.1% Exponential Exponential 

Bromochloromethane 502.2 ELCD 0.482 0.044 -166.9% Linear Exponential 

Bromochloromethane 524.2 0.345 0.507 38.1% Exponential Exponential 

Bromodichloromethane 502.2 ELCD 0.075 0.026 -95.5% Exponential Exponential 

Bromodichloromethane 524.2 0.205 0.088 -79.7% Exponential Exponential 

Bromoform 502.2 ELCD 1.513 0.025 -193.5% Constant Linear 

Bromoform 524.2 0.400 0.336 -17.4% Exponential Exponential 

Bromomethane 502.2 ELCD 7.293 0.760 -162.3% Constant Exponential 

Bromomethane 524.2 0.280 0.154 -57.8% Exponential Linear 

Cadmium 1620 0.191 0.211 9.8% Exponential Exponential 

Cadmium 200.8 0.022 0.016 -33.8% Exponential Constant 

Calcium 1620 41.358 53.375 25.4% Linear Constant 

Carbon Disulfide 524.2 0.239 0.087 -93.6% Exponential Linear 

Carbon Tetrachloride 524.2 0.314 0.174 -57.3% Exponential Linear 

Carbontet+1,1-dcp 502.2 ELCD 0.072 0.061 -15.5% Exponential Exponential 

Chloroace tonitrile 524.2 1.569 2.079 28.0% Exponential Exponential 

Chlorobenzene 502.2 ELCD 0.460 0.064 -151.5% Linear Exponential 

B - 56

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure SL-IDE SL-IDE (5) 

(16) 

Percent 

Difference 

SL-IDE 16 

Model 

Chlorobenzene 502.2 PID 0.059 -7.8% 

SL-IDE 5 

Model 

0.064 Exponential Exponential 

Chlorobenzene 524.2 0.133 0.034 -118.1% Exponential Exponential 

Chloroethane 502.2 ELCD 2.598 0.096 -185.7% Constant Linear 

Chloroethane 524.2 0.395 0.303 -26.3% Exponential Exponential 

Chloroform 502.2 ELCD 0.032 0.008 -117.3% Exponential Linear 

Chloroform 524.2 0.225 0.104 -73.4% Exponential Exponential 

Chloromethane 502.2 ELCD 0.250 0.520 70.3% Exponential Constant 

Chloromethane 524.2 0.253 0.150 -51.2% Exponential Exponential 

Chromium 1620 0.496 0.759 41.8% Exponential Constant 

Chromium 200.8 0.408 0.491 18.5% Linear Constant 

Cis-1,2-dce+2,2-dcp 502.2 ELCD 0.055 0.039 -35.0% Exponential Exponential 

Cis-1,2-dichloroethene 524.2 0.234 0.201 -15.2% Exponential Exponential 

Cis-1,3-dichloropropene 502.2 ELCD 0.074 0.024 -102.4% Exponential Exponential 

Cis-1,3-dichloropropene 502.2 PID 0.082 0.111 30.2% Exponential Exponential 

Cis-1,3-dichloropropene 524.2 0.173 0.119 -37.1% Exponential Exponential 

Cobalt 1620 16.463 12.267 -29.2% Exponential Exponential 

Cobalt 200.8 0.074 0.001 -195.2% Constant Exponential 

Copper 1620 21.189 15.897 -28.5% Constant Constant 

Copper 200.8 0.798 0.905 12.6% Constant Constant 

Dibromochloromethane 502.2 ELCD 0.436 0.394 -10.1% Linear Constant 

Dibromochloromethane 524.2 0.287 0.203 -34.3% Exponential Exponential 

Dibromomethane 502.2 ELCD 0.460 0.298 -42.8% Linear Constant 

Dibromomethane 524.2 0.388 0.439 12.5% Exponential Exponential 

Dichlorodifluoromethane 502.2 ELCD 0.240 1.225 134.5% Exponential Constant 

Dichlorodifluoromethane 524.2 0.560 0.591 5.4% Exponential Exponential 

Diethyl Ether 524.2 0.376 0.330 -12.9% Exponential Exponential 

Ethyl Me thacryla te 524.2 0.273 0.259 -5.2% Exponential Exponential 

Ethylbenzene 502.2 PID 0.078 0.050 -44.2% Exponential Exponential 

Ethylbenzene 524.2 0.198 0.107 -59.5% Exponential Exponential 

Hardness 2 130.2 2.258 4.886 73.6% Exponential Constant 

B - 57

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

Analyte Procedure SL-IDE 

(16) 

SL-IDE (5) 

Hexachlorobutadiene 502.2 ELCD 0.094 0.073 -24.8% 

Hexachlorobutadiene 524.2 0.308 0.237 -26.0% 

Hexachloroethane 524.2 0.288 0.260 -10.1% 

Hexchlobutadiene+naphthalene 502.2 PID 0.597 0.592 -1.0% Exponential Constant 

Iron 1620 373.590 1064.987 96.1% Linear Constant 

Isopropylbenzene 502.2 PID 0.060 0.041 -37.0% Exponential Exponential 

Isopropylbenzene 524.2 0.120 0.037 -104.7% Exponential Exponential 

Lead 1620 2.423 2.951 19.6% Exponential Constant 

Lead 200.8 0.204 2.872 173.5% Exponential Constant 

M+p Xylene 502.2 PID 0.121 0.119 -1.2% Exponential Constant 

M+p Xylene 524.2 0.142 0.031 -127.3% Exponential Exponential 

Magnesium 1620 105.998 184.221 53.9% Exponential Constant 

Manganese 1620 6.808 4.548 -39.8% Constant Constant 

Manganese 200.8 0.109 0.077 -34.7% Constant Constant 

Mercury 200.8 0.027 0.014 -63.8% Exponential Hybrid 

Methacrylon itrile 524.2 0.718 0.552 -26.2% Exponential Hybrid 

Methyl Iodide 524.2 0.193 0.109 -55.5% Exponential Exponential 

Methyl Tert-butyl Ether 524.2 0.225 0.173 -26.3% Exponential Exponential 

Methyla crylate 524.2 0.601 0.569 -5.5% Exponential Exponential 

Methylene Chloride 502.2 ELCD 2.841 -1.381 -578.5% Constant Constant 

Methylene Chloride 524.2 0.314 0.158 -66.1% Exponential Exponential 

Methylm ethacryl ate 524.2 0.535 0.382 -33.3% Exponential Linear 

Molybdenum 1620 3.034 6.028 66.1% Exponential Constant 

Molybdenum 200.8 0.271 0.006 -191.8% Constant Constant 

N-butylbenzene 502.2 PID 0.152 0.056 -93.0% Exponential Exponential 

N-butylbenzene 524.2 0.092 0.105 13.9% Exponential Constant 

N-propylbenzene 502.2 PID 25.560 41.908 48.5% Exponential Constant 

N-propylbenzene 524.2 0.083 0.070 -16.1% Exponential Constant 

Naphthalene 524.2 0.141 0.052 -91.4% Exponential Linear 

Nickel 1620 0.284 0.052 -137.6% Exponential Hybrid 

(μg/L except where footnoted) 

Method 

Percent 

Difference 

SL-IDE 16 

Model 

SL-IDE 5 

Model 

Exponential
 Linear

Exponential Exponential


Exponential Exponential


B - 58

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure SL-IDE 

(16) 

SL-IDE (5) 

Percent 

Difference 

SL-IDE 16 

Model 

SL-IDE 5 

Model 

Nickel 200.8 0.186 0.194 4.1% Exponential Exponential 

o-xylene 524.2 0.198 0.082 -82.9% Exponential Exponential 

o-xylene+styrene 502.2 PID 0.116 0.151 26.8% Exponential Constant 

P-isoproptol+1,4-dcb 502.2 PID 0.408 0.437 7.0% Exponential Linear 

Pentachloroethane 524.2 0.159 0.150 -5.8% Exponential Constant 

Sec-butylbenzene 502.2 PID 0.081 0.057 -35.3% Exponential Exponential 

Sec-butylbenzene 524.2 0.140 0.040 -111.6% Exponential Exponential 

Selenium 1620 1.975 1.801 -9.2% Exponential Exponential 

Selenium 200.8 0.416 0.342 -19.5% Exponential Exponential 

Silver 1620 10.668 11.589 8.3% Exponential Constant 

Silver 200.8 0.012 -0.084 269.8% Exponential Consta nt 1 

Sodium 1620 138.768 140.860 1.5% Exponential Exponential 

Styrene 524.2 0.141 0.048 -98.2% Exponential Exponential 

Tert-butylbenzene 502.2 PID 0.074 0.051 -35.9% Exponential Exponential 

Tert-butylbenzene 524.2 0.186 0.057 -106.6% Exponential Exponential 

Tetrachloroethene 502.2 ELCD 0.061 0.054 -11.0% Exponential Exponential 

Tetrachloroethene 502.2 PID 0.156 0.103 -40.6% Exponential Linear 

Tetrachloroethene 524.2 0.469 0.550 15.9% Exponential Linear 

Thallium 1620 1.153 1.249 8.0% Exponential Linear 

Thallium 200.8 0.001 0.000 -76.1% Exponential Exponential 

Thorium 200.8 0.001 0.000 -93.4% Exponential Constant 

Tin 1620 3.932 4.651 16.8% Exponential Exponential 

Titanium 1620 5.376 20.828 117.9% Exponential Constant 

Toluene 502.2 PID 0.064 0.064 -1.3% Exponential Constant 

Toluene 524.2 0.146 0.558 117.1% Exponential Consta nt 1 

Total Phosphorus 2 365.2 0.013 0.011 -18.1% Exponential Exponential 

Total Suspended Solids 2 160.2 3.005 2.370 -23.6% Exponential Exponential 

Trans-1,2-dichloroethene 502.2 ELCD 0.081 0.066 -21.7% Exponential Linear 

Trans-1,2-dichloroethene 524.2 0.300 0.075 -119.7% Exponential Hybrid 

Trans-1,3-dichloropropene 502.2 ELCD 0.098 0.033 -98.9% Exponential Exponential 

B - 59

Table 8. Comparison of 16-point and 5-point 

Single-laboratory IDEs (SL-IDEs) for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure SL-IDE 

(16) 

SL-IDE (5) 

Percent 

Difference 

SL-IDE 16 

Model 

SL-IDE 5 

Model 

Trans-1,3-dichloropropene 502.2 PID 0.092 0.116 22.7% Exponential Exponential 

Trans-1,3-dichloropropene 524.2 0.223 0.132 -51.1% Exponential Exponential 

Trans-1,4-dichloro-2-butene 524.2 1.250 1.448 14.7% Exponential Exponential 

Trichloroethene 502.2 ELCD 0.059 0.020 -99.6% 

Trichloroethene 502.2 PID 0.097 0.089 -8.5% 

Trichloroethene 524.2 0.332 0.344 3.6% 

Trichlorofluoromethane 502.2 ELCD 2.079 0.688 -100.5% 

Trichlorofluoromethane 524.2 0.384 0.384 0.1% 

Uranium 200.8 0.000 0.000 -70.8% 

Vanadium 1620 10.630 9.082 -15.7% 

Vanadium 200.8 0.864 1.023 16.9% 

Vinyl Chloride 502.2 ELCD 3.672 0.387 -161.9% 

Vinyl Chloride 524.2 0.365 0.188 -63.8% 

WAD Cyanide 1677 0.701 1.296 59.6% 

Xylene (To tal) 524.2 0.128 0.029 -126.9% 

Yttrium 1620 3.247 13.972 124.6% 

Zinc 1620 4.500 6.943 42.7% 

Zinc 200.8 1.598 5.245 106.6% 

Exponential Exponential 

Exponential Exponential 

Exponential Linear 

Constant 

Exponential Exponential 

Exponential Exponential 

Exponential Exponential 

Exponential Linear 

Constant 

Constant 

Exponential 

Linear 

Exponential Exponential 

Exponential Constant 

Exponential 

Exponential 

Linear 

Linear 

Constant 

Constant 

Constant 

Note:	 ELCD or PID in the Procedure column indicates the photo-ionization detector (PID) or electrolytic conductivity detector 

(ELCD ) in EPA Method 502.2 1

 Original model picked was Hybrid, but failed to converge 

2 Results reported as mg/L 

B - 60

Number of Analytes 

Minimum: 

25th percentile: 

Median: 

75th percentile: 

Maximum: 

SL-IDE (16) vs. SL-IDE (5) 

(all analytes) 

SL-IDE(16) vs. SL-IDE (5) 

(same model used) 

SL-IDE(16) vs. SL-IDE (5) 

(different models used) 

Summary Statistics for Table 8 

SL-IDE(16) vs. 

SL-IDE (5) 

(all analytes) 

SL-IDE(16) vs. SL-IDE (5) 

(same mod el used) 

198 108 

-578.5% -578.5% 

-79.5% -80.1% 

-24.9% -35.6% 

12.8% -9.3% 

269.8% 53.5% 

Number of 

analytes 

Median % 

Difference 

Sign Test pvalue 

198 -24.9% <0.0001 

108 -35.6% <0.0001 

90 1.3% >0.999 

B - 61


SL-IDE(16) vs. 

SL-IDE (5) 

(differe nt mo dels 

used) 

90 

-195.2% 

-72.2% 

1.3% 

55.5% 

269.8% 

Wilcoxon 

p-value 

<0.0001 

<0.0001 

0.847 
Table 9. Comparison of 16-point and 5-point 


Single-laboratory IQEs at 10% RSD (SL-IQEs 10%) for the Episode 6000 Dataset


Analyte 

1,1,1,2-tetrachloroethane 

1,1,1,2-tetrachloroethane 

1,1,1-trichloroethane 

1,1,1-trichloroethane 

1,1,2,2-tce+1,2,3-tcp 

1,1,2,2-tetrachloroethane 

1,1,2-trichloroethane 

1,1,2-trichloroethane 

1,1-dichloroethane 

1,1-dichloroethane 

1,1-dichloroethene 

1,1-dichloroethene 

1,1-dichloropropanone 

1,1-dichloropropene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichloropropane 

1,2,4-trichlorobenzene 

1,2,4-trichlorobenzene 

1,2,4-trichlorobenzene 

1,2,4-trimethylbenzene 

1,2,4-trimethylbenzene 

1,2-dibromo-3-chloropropane 

1,2-dibromoethane 

1,2-dibromoethane 

1,2-dichlorobenzene 

1,2-dichlorobenzene 

1,2-dichlorobenzene 

1,2-dichloroethane 

1,2-dichloroethane 

1,2-dichloropropane 

1,2-dichloropropane 

1,3,5-tmb+4-chlorotoluene 

1,3,5-trimethylbenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichloropropane 

1,3-dichloropropane 

1,4-dichlorobenzene 

1,4-dichlorobenzene 

1-chlorobutane 

2,2-dichloropropane 

2-butanone 

2-chlorotoluene 

Method 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

524.2 

502.2 

502.2 

524.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

524.2 

524.2 

502.2 

(μg/L except where footnoted) 

Procedure SLIQE10% 

(16) 

SLIQE10% 

(5) 

Percent 

Difference 

SL-IQE 

Model (16) 

SL-IQE 

Model (5) 

ELCD 0.030 0.048 45.7% Hybrid Linear 

0.181 0.320 55.3% Hybrid Linear 

ELCD 0.830 0.055 -175.2% Linear Hybrid 

0.240 0.081 -98.6% Hybrid Hybrid 

ELCD 5.514 6.984 23.5% Constant Constant 

0.569 0.942 49.4% Hybrid Linear 

ELCD 0.060 0.046 -26.2% Linear Linear 

0.290 0.344 17.1% Hybrid Linear 

ELCD 0.527 0.058 -160.5% Linear Hybrid 

0.115 0.099 -14.8% Hybrid Hybrid 

ELCD 3.796 0.305 -170.3% Linear Hybrid 

0.129 0.199 42.6% Hybrid Hybrid 

12.705 16.447 25.7% Linear Hybrid 

0.180 9.106 6 192.2% Hybrid Constant 

ELCD 0.851 0.341 -85.6% Linear Constant 

PID 0.248 0.246 -0.9% Hybrid Hybrid 

0.216 0.147 -38.1% Hybrid Linear 

11.316 33.343 6 98.6% Linear Constant 

ELCD 0.401 0.202 -65.9% Linear Constant 

PID 0.439 0.207 -72.0% Linear Hybrid 

0.141 3.760 185.6% Hybrid Constant 

PID 0.653 0.293 -76.2% Linear Constant 

20.896 0.119 -197.7% Constant Linear 

71.182 6 0.877 -195.1% Constant Hybrid 

ELCD 0.592 0.065 -160.2% Linear Linear 

0.417 0.579 32.5% Hybrid Linear 

ELCD 0.183 0.109 3 -50.9% Linear Linear 

PID 0.346 0.123 -94.7% Hybrid Hybrid 

0.085 0.117 32.3% Hybrid Linear 

ELCD 0.065 0.727 6 167.2% Hybrid Constant 

0.222 0.327 38.4% Hybrid Linear 

ELCD 0.102 0.178 54.1% Linear Constant 

0.196 0.219 10.9% Hybrid Linear 

PID 0.189 0.289 41.7% Hybrid Constant 

23.744 0.086 -198.6% Constant Linear 

ELCD 0.936 1.239 27.9% Linear Constant 

PID 0.465 0.404 -14.2% Linear Constant 

0.076 0.081 7.0% Hybrid Hybrid 

ELCD 0.054 0.448 157.0% Linear Constant 

0.139 0.154 10.0% Hybrid Hybrid 

ELCD 0.101 0.100 -1.3% Hybrid Linear 

0.078 0.068 -14.1% Hybrid Linear 

29.943 0.170 -197.7% Constant Linear 

38.009 0.361 -196.2% Constant Hybrid 

0.893 39.665 191.2% Constant 

ELCD 0.493 0.357 -32.1% Hybrid Linear 

B - 62 
Table 9. Comparison of 16-point and 5-point 


Single-laboratory IQEs at 10% RSD (SL-IQEs 10%) for the Episode 6000 Dataset


Analyte 

2-chlorotoluene 

2-chlorotoluene 

2-hexanone 

2-nitropropane 

4-chlorotoluene 

4-chlorotoluene 

4-isopropyltoluene 

4-methyl-2-pentanone 

Acetone 

Acrylonitrile 

Allyl Chloride 

Aluminum 

Aluminum 

Ammonia as Nitrogen 2 

Antimony 

Antimony 

Arsenic 

Arsenic 

Barium 

Barium 

Benzene 

Benzene 

Beryllium 

Beryllium 

Boron 

Bromobenzene 

Bromobenzene 

Bromobenzene 

Bromochloromethane 

Bromochloromethane 

Bromodichloromethane 

Bromodichloromethane 

Bromoform 

Bromoform 

Bromomethane 

Bromomethane 

Cadmium 

Cadmium 

Calcium 

Carbon Disulfide 

Carbon Tetrachloride 

Carbontet+1,1-dcp 

Chloroace tonitrile 

Chlorobenzene 

Chlorobenzene 

Chlorobenzene 

Method 

502.2 

524.2 

524.2 

524.2 

502.2 

524.2 

524.2 

524.2 

524.2 

524.2 

524.2 

1620 

200.8 

350.3 

1620 

200.8 

1620 

200.8 

1620 

200.8 

502.2 

524.2 

1620 

200.8 

1620 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

1620 

200.8 

1620 

524.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

(μg/L except where footnoted) 

Procedure SLIQE10% 

(16) 

SLIQE10% 

(5) 

Percent 

Difference 

SL-IQE 

Model (16) 

SL-IQE 

Model (5) 

PID 0.849 0.806 -5.2% Hybrid Constant 

0.053 0.044 -19.1% Hybrid Linear 

0.442 61.796 197.2% Hybrid Constant 

0.590 17.783 187.2% Hybrid Constant 

ELCD 0.142 1 0.485 109.4% Hybrid Linear 

23.810 0.837 -186.4% Constant Constant 

0.016 1.194 194.6% Hybrid Constant 

1.785 14.514 156.2% Hybrid Constant 

2.741 59.415 182.4% Hybrid Constant 

28.056 19.275 -37.1% Constant Constant 

29.674 0.164 -197.8% Constant Hybrid 

464.069 144.530 -105.0% Constant Constant 

ICP/MS 29.684 47.196 45.6% Hybrid Constant 

0.035 0.082 78.8% Hybrid Constant 

9.551 8.364 5 -3.6% Constant Constant 

ICP/MS 0.034 0.633 179.8% Hybrid Constant 

3.097 4.656 40.2% Hybrid Constant 

ICP/MS 0.798 0.847 6.1% Hybrid Hybrid 

4.118 3.334 -21.1% Constant Constant 

ICP/MS 0.211 0.153 -32.1% Linear Constant 

PID 0.182 0.130 -33.2% Linear Linear 

0.044 0.029 -41.0% Hybrid Linear 

0.980 0.985 0.6% Hybrid Linear 

ICP/MS 0.044 0.036 -19.9% Hybrid Constant 

51.134 46.392 -9.7% Linear Hybrid 

ELCD 3.529 29.488 157.2% Linear Linear 

PID 0.100 0.057 -55.4% Linear Hybrid 

0.140 0.187 28.7% Hybrid Hybrid 

ELCD 1.598 0.057 -186.1% Linear Hybrid 

0.368 0.592 46.5% Hybrid Hybrid 

ELCD 0.424 0.465 9.1% Linear Constant 

0.128 0.111 -13.8% Hybrid Linear 

ELCD 3.393 0.068 -192.1% Constant Linear 

0.482 0.406 -17.1% Hybrid Hybrid 

ELCD 16.351 2.195 -152.7% Constant Hybrid 

0.226 0.412 58.4% Hybrid Linear 

0.410 0.400 -2.6% Hybrid Linear 

ICP/MS 0.063 0.033 -63.4% Hybrid Constant 

99.975 109.600 9.2% Linear Constant 

0.101 0.268 90.3% Hybrid Linear 

0.140 0.520 115.1% Hybrid Linear 

ELCD 0.069 1.553 183.1% Hybrid Constant 

3.310 31.753 162.2% Hybrid Constant 

ELCD 1.766 1.558 -12.5% Linear Constant 

PID 0.119 0.034 3 -110.6% Hybrid Linear 

0.059 0.831 173.3% Hybrid Constant 

B - 63

Table 9. Comparison of 16-point and 5-point 

Single-laboratory IQEs at 10% RSD (SL-IQEs 10%) for the Episode 6000 Dataset 

(μg/L except where footnoted) 

Analyte Method Procedure SL- SL- Percent SL-IQE

IQE10% 

SL-IQE 

IQE10% Difference Model (16) Model (5) 

(5) 

Chloroethane 

Chloroethane 

Chloroform 

Chloroform 

Chloromethane 

Chloromethane 

Chromium 

Chromium 

Cis-1,2-dce+2,2-dcp 

Cis-1,2-dichloroethene 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cobalt 

Cobalt 

Copper 

Copper 

Dibromochloromethane 

Dibromochloromethane 

Dibromomethane 

Dibromomethane 

Dichlorodifluoromethane 

Dichlorodifluoromethane 

Diethyl ether 

Ethyl me thacryla te 

Ethylbenzene 

Ethylbenzene 

Hardness 2 

Hexachlorobutadiene 

Hexachlorobutadiene 

Hexachloroethane 

Hexchlobutadiene+naphthalene 

Iron 

Isopropylbenzene 

Isopropylbenzene 

Lead 

Lead 

M+p xylene 

M+p xylene 

Magnesium 

Manganese 

Manganese 

Mercury 

Methacrylon itrile 

Methyl Iodide 

Methyl tert-butyl ether 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

1620 

200.8 

502.2 

524.2 

502.2 

502.2 

524.2 

1620 

200.8 

1620 

200.8 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

524.2 

502.2 

524.2 

130.2 

502.2 

524.2 

524.2 

502.2 

1620 

502.2 

524.2 

1620 

200.8 

502.2 

524.2 

1620 

1620 

200.8 

200.8 

524.2 

524.2 

524.2 

(16) 

ELCD 5.826 0.644 -160.2% Constant Linear 

0.255 0.207 -20.8% Hybrid Hybrid 

ELCD 0.025 0.033 26.1% Linear Linear 

0.121 0.092 -27.7% Hybrid Linear 

ELCD 1.734 1.049 -49.2% Linear Constant 

0.141 0.191 30.4% Hybrid Linear 

1.259 1.558 21.2% Linear Constant 

ICP/MS 1.028 1.022 -0.6% Linear Constant 

ELCD 0.039 1.055 185.7% Hybrid Constant 

0.144 0.151 4.9% Hybrid Hybrid 

ELCD 0.415 0.447 6 7.4% Linear Constant 

PID 0.017 1 0.226 172.0% Hybrid Linear 

0.141 0.085 -49.3% Hybrid Linear 

40.837 25.933 -44.6% Linear Linear 

ICP/MS N/A 4 0.001 0.0% Linear Hybrid 

47.509 32.643 -37.1% Constant Constant 

ICP/MS 1.825 1.885 3.2% Constant Constant 

ELCD 1.252 0.809 -43.0% Linear Constant 

0.288 0.167 -53.2% Hybrid Hybrid 

ELCD 1.395 0.587 -81.6% Linear Constant 

0.460 0.498 7.9% Hybrid Hybrid 

ELCD 1.091 5 2.470 77.4% Linear Constant 

0.480 0.442 -8.1% Hybrid Hybrid 

0.404 0.525 26.0% Hybrid Hybrid 

0.183 0.141 -26.0% Hybrid Linear 

PID 0.157 0.007 3 -182.9% Hybrid Linear 

0.077 0.064 -19.2% Hybrid Linear 

5.465 10.032 58.9% Linear Constant 

ELCD 0.243 0.582 82.2% Hybrid Linear 

0.228 0.232 1.7% Hybrid Linear 

0.167 0.386 78.9% Hybrid Linear 

PID 1.542 1.193 -25.6% Hybrid Constant 

996.565 5 2186.832 74.8% Linear Constant 

PID 0.129 0.032 -120.6% Linear Linear 

25.592 1.157 -182.7% Constant Constant 

5.698 6.059 6.1% Linear Constant 

ICP/MS 0.685 5.983 158.9% Linear Constant 

PID 0.222 0.240 7.6% Hybrid Constant 

24.651 0.034 -199.4% Constant Hybrid 

267.199 378.277 34.4% Linear Constant 

15.264 9.339 -48.2% Constant Constant 

ICP/MS 0.245 0.160 -41.8% Constant Constant 

ICP/MS 0.039 0.017 1 -79.4% Hybrid Hybrid 

19.062 1.111 -178.0% Constant Hybrid 

0.083 3.681 191.1% Hybrid Constant 

0.122 15.132 6 196.8% Hybrid Constant 

B - 64

Table 9. Comparison of 16-point and 5-point 

Single-laboratory IQEs at 10% RSD (SL-IQEs 10%) for the Episode 6000 Dataset 

Analyte 

Methyla crylate 

Methylene Chloride 

Methylene Chloride 

Methylm ethacryl ate 

Molybdenum 

Molybdenum 

N-butylbenzene 

N-butylbenzene 

N-propylbenzene 

N-propylbenzene 

Naphthalene 

Nickel 

Nickel 

O-xylene 

O-xylene+styrene 

P-isoproptol+1,4-dcb 

Pentachloroethane 

Sec-butylbenzene 

Sec-butylbenzene 

Selenium 

Selenium 

Silver 

Silver 

Sodium 

Styrene 

Tert-butylbenzene 

Tert-butylbenzene 

Tetrachloroethene 

Tetrachloroethene 

Tetrachloroethene 

Thallium 

Thallium 

Thorium 

Tin 

Titanium 

Toluene 

Toluene 

Total Phosphorus 2 

(μg/L except where footnoted) 

Method Procedure SL- SL- Percent SL-IQE

IQE10% 

SL-IQE 

IQE10% Difference Model (16) Model (5) 

(5) 

524.2 

502.2 

524.2 

524.2 

1620 

200.8 

502.2 

524.2 

502.2 

524.2 

524.2 

1620 

200.8 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

1620 

200.8 

1620 

200.8 

1620 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

1620 

200.8 

200.8 

1620 

1620 

502.2 

524.2 

365.2 

(16) 

0.727 0.853 16.0% Hybrid Linear 

ELCD 6.033 N/A 4 N/A Constant Constant 

0.433 0.293 -38.5% Hybrid Linear 

20.773 0.873 -183.9% Constant Linear 

7.597 11.866 43.9% Linear Constant 

ICP/MS 0.608 0.012 -192.4% Constant Constant 

PID 0.745 0.586 -24.0% Linear Linear 

0.067 1.287 180.1% Hybrid Constant 

PID 0.186 0.212 13.0% Hybrid Constant 

29.878 0.118 -198.4% Constant Hybrid 

0.108 0.256 81.1% Hybrid Hybrid 

67.206 86.054 24.6% Linear Constant 

ICP/MS 0.183 0.147 -21.9% Hybrid Constant 

0.040 0.016 -85.5% Hybrid Linear 

PID 0.181 0.305 51.0% Linear Constant 

PID 0.456 0.302 -40.8% Linear Constant 

0.551 1.036 61.1% Hybrid Linear 

PID 0.157 0.754 131.1% Hybrid Constant 

0.047 1.266 185.5% Hybrid Constant 

5.235 4.076 -24.9% Linear Linear 

ICP/MS 1.045 0.707 -38.6% Linear Hybrid 

25.842 22.813 -12.5% Linear Constant 

ICP/MS 0.056 N/A 4 N/A Linear Linear 

337.755 333.796 -1.2% Linear Linear 

0.041 0.067 49.3% Hybrid Linear 

PID 0.203 0.111 -58.9% Linear Hybrid 

0.073 0.074 1.1% Hybrid Linear 

ELCD 0.122 0.182 39.7% Hybrid Linear 

PID 0.750 0.385 -64.4% Linear Linear 

30.554 6 1.643 -179.6% Constant Linear 

2.799 2.745 -1.9% Linear Linear 

ICP/MS 0.002 0.001 -76.8% Linear Linear 

ICP/MS 0.004 0.001 -134.2% Linear Constant 

9.406 9.772 3.8% Linear Linear 

14.236 42.768 100.1% Linear Constant 

PID 0.194 0.131 -39.1% Linear Constant 

0.046 1.145 6 184.7% Hybrid Constant 

0.030 0.026 -15.8% Hybrid Linear 

Total Suspended Solids 2 

Trans-1,2-dichloroethene 

Trans-1,2-dichloroethene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,4-dichloro-2-butene 

Trichloroethene 

160.2 

502.2 

524.2 

502.2 

502.2 

524.2 

524.2 

502.2 

6.729 6.929 2.9% Hybrid Linear 

ELCD 0.191 0.081 5 -80.6% Hybrid Linear 

0.153 0.171 11.3% Hybrid Hybrid 

ELCD 0.729 0.485 -40.2% Linear Constant 

PID 0.175 0.238 30.7% Hybrid Linear 

0.218 0.101 -73.5% Hybrid Hybrid 

30.108 1.768 -177.8% Constant Hybrid 

ELCD 3.169 1.010 -103.3% Linear Constant 

B - 65

1

2

3

4

5

6

Table 9. Comparison of 16-point and 5-point 

Single-laboratory IQEs at 10% RSD (SL-IQEs 10%) for the Episode 6000 Dataset 

Analyte 

Trichloroethene 

Trichloroethene 

Trichlorofluoromethane 

Trichlorofluoromethane 

Uranium 

Vanadium 

vVnadium 

Vinyl Chloride 

Vinyl Chloride 

Wad Cyanide 

Xylene (total) 

Yttrium 

Zinc 

Zinc 

Method 

502.2 

524.2 

502.2 

524.2 

200.8 

1620 

200.8 

502.2 

524.2 

1677 

524.2 

1620 

1620 

200.8 

(μg/L except where footnoted) 

Procedure SLIQE10% 

(16) 

SLIQE10% 

(5) 

Percent 

Difference 

SL-IQE 

Model (16) 

SL-IQE 

Model (5) 

PID 0.401 0.079 -134.4% Linear Linear 

0.167 1.068 145.8% Hybrid Linear 

ELCD 4.662 1.355 -109.9% Constant Constant 

42.490 6 0.301 -197.2% Constant Hybrid 

ICP/MS 0.001 0.000 -69.1% Linear Linear 

24.338 17.798 -31.0% Hybrid Linear 

ICP/MS 1.933 2.225 14.1% Hybrid Linear 

ELCD 8.234 3.258 -86.6% Constant Linear 

0.219 0.652 99.2% Hybrid Linear 

WADCN 1.624 2.661 48.4% Linear Constant 

23.520 0.017 -199.7% Constant Hybrid 

8.962 28.689 104.8% Linear Constant 

10.452 14.257 30.8% Hybrid Constant 

ICP/MS 7.024 10.927 43.5% Linear Constant 

IQE 10% undefined, IQE 20% reported


Results reported as mg/L


IQE 10% negative, IQE 20% reported


IQE 10%, IQE 20% , IQE30% all negative based on chosen mod el (linear)


IQE 10% and IQE 20% both negative, IQE 30% reported


Hybrid model selected but did not converge; IQE 10% based on constant model instead


B - 66

Summary Statistics for Table 9 

SL-IQE10 

(16) vs. SLIQE10 (5) 

(all 

analytes) 

SL-IQE10 (16) vs. SLIQE10 (5) 

(same mod el used) 

SL-IQE10 (16) vs. 

SL-IQE10 (5) 

(differe nt mo dels 

used) 

Number of Analytes 195 50 145 

Minimum: -19,971.5% -19,237.7% -19,971.5% 

25th percentile: -6,115.2% -7,243.8% -4,927.0% 

Median: -194.6% -2,442.7% 613.9% 

75th percentile: 4,562.6% 576.4% 6109.3% 

Maximum: 19,715.8% 15724.6% 19,715.8% 

Number of 

analytes 

Median % 

Difference 

Sign Test pvalue 

Wilcoxon 

p-value 

SL-IQE10 (16) vs. SLIQE10 (5) 

(all analytes) 

SL-IQE10 (16) vs. SLIQE10 (5) 

(same model used) 

SL-IQE10 (16) vs. SLIQE10 (5) 

(different models used) 

195 -194.600 0.567 0.345 

50 -2,442.7% 0.015 0.001 

145 613.9% 0.507 0.606 

B - 67

Table 10. Comparison of ACIL, USGS and EPA Limits Calculating using USGS Blank and Spiked data 

Analyte 

# blanks # spikes ACIL CRV USGS LT-MDL USGS LT-MDL (adding 

(adding median) mean) 

EPA MDL 

(Randomly selected from 

simulated 

7-replicate MDLs) 

Ammonia (FCA) 52 24 

Ammonia (FCC) 52 24 

Ammonia Low Level (FCC) 52 15 

Arsenic, Dissolved 26 24 

Arsenic, Total 26 24 

Cadmium, Dissolved by GFAA 26 24 

Cadmium, Total by GFAA 26 24 

Chromium, Dissolved by GFAA 26 24 

Chromium, Total by GFAA 26 24 

Cobalt, Dissolved by GFAA 26 24 

Cobalt, Total by GFAA 26 24 

Copper, Dissolved by GFAA 26 24 

Copper, Total by GFAA 26 24 

Lead, Dissolved by GFAA 26 24 

Lead, Total by GFAA 26 24 

Molybdenum (Wastewater) by GFAA 25 24 

Molybdenum, Dissolved by GFAA 26 24 

Nickel, Dissolved by GFAA 26 24 

Nickel, Total by GFAA 26 24 

0.0% 

1.9% 

1.9% 

3.8% 

3.8% 

0.0% 

0.0% 

3.8% 

3.8% 

0.0% 

3.8% 

3.8% 

0.0% 

3.8% 

3.8% 

0.0% 

3.8% 

0.0% 

0.0% 

0.0% 

11.5% 

21.2% 

3.8% 

7.7% 

0.0% 

0.0% 

0.0% 

3.8% 

0.0% 

3.8% 

3.8% 

0.0% 

0.0% 

3.8% 

0.0% 

0.0% 

0.0% 

19.2% 

0.0% 

11.5% 

7.7% 

3.8% 

7.7% 

0.0% 

0.0% 

0.0% 

3.8% 

0.0% 

3.8% 

3.8% 

0.0% 

0.0% 

3.8% 

0.0% 

3.8% 

0.0% 

11.5% 

0.0% 

9.6% 

1.9% 

3.8% 

3.8% 

0.0% 

0.0% 

0.0% 

0.0% 

0.0% 

3.8% 

0.0% 

0.0% 

0.0% 

3.8% 

4.0% 

0.0% 

0.0% 

0.0% 

Limit % exceeding Limit % exceeding Limit % exceeding Limit % exceeding 

0.022 0.021 0.021 0.062 

0.023 0.011 0.011 0.012 

0.006 0.003 0.004 0.006 

1.068 1.005 1.071 0.895 

1.493 0.829 0.825 1.298 

0.082 0.099 0.095 0.121 

0.075 0.084 0.089 0.130 

0.441 0.466 0.475 0.473 

0.316 0.341 0.340 3.540 

1.287 1.911 1.847 1.451 

1.639 1.053 1.093 1.076 

0.536 0.408 0.421 1.102 

0.684 0.766 0.764 26.384 

0.609 0.861 0.857 0.860 

0.774 0.780 0.736 0.678 

0.906 0.779 0.778 0.639 

0.862 1.098 1.082 1.190 

0.991 1.014 0.909 2.568 

1.765 0.936 1.167 2.076 

Nitrate/Nitrite (FCA) 52 24 0.018 0.0% 0.009 21.2% 0.010 17.3% 0.009 21.2% 

B - 68

Table 10. Comparison of ACIL, USGS and EPA Limits Calculating using USGS Blank and Spiked data 

Analyte 

# blanks # spikes ACIL CRV USGS LT-MDL USGS LT-MDL (adding 

(adding median) mean) 

EPA MDL 

(Randomly selected from 

simulated 

7-replicate MDLs) 

Limit % exceeding Limit % exceeding Limit % exceeding Limit % exceeding 

Nitrate/Nitrite (FCC) 52 15 

Nitrate/Nitrite Low Level (FCC) 52 24 

Nitrite (FCC) 52 15 

Nitrite Low Level (FCC) 52 24 

Orthophosphate (FCC) 52 24 

Orthophosphate Low Level (FCC) 52 24 

Phosphorus, Low Level Filtered 52 24 

Phosphorus, Low Level Filtered 52 24 

Phospho rus, Low Le vel in 

Wastewater 

52 24 

Selenium, Dissolved 26 24 

Selenium, Total 26 24 

Silver, Dissolved by GFAA 26 24 

SIlver, Total by GFAA 26 24 

TKN/ Ammonia (FCA) 52 24 

TKN/ Ammonia (FCC) 52 24 

TKN/ Ammonia (WCA) 52 24 

Total Phosphorus (FCA) 52 24 

Total Phosphorus (FCC) 52 24 

3.8% 

0.0% 

0.0% 

0.0% 

3.8% 

0.0% 

1.9% 

0.0% 

3.8% 

0.0% 

3.8% 

3.8% 

3.8% 

0.0% 

1.9% 

1.9% 

3.8% 

0.0% 

1.9% 

0.0% 

1.9% 

0.0% 

19.2% 

26.9% 

0.0% 

0.0% 

1.9% 

0.0% 

7.7% 

0.0% 

3.8% 

0.0% 

3.8% 

1.9% 

0.0% 

0.0% 

1.9% 

0.0% 

1.9% 

0.0% 

15.4% 

26.9% 

0.0% 

0.0% 

1.9% 

0.0% 

7.7% 

0.0% 

3.8% 

0.0% 

3.8% 

1.9% 

0.0% 

0.0% 

5.8% 

11.5% 

0.0% 

0.0% 

15.4% 

0.0% 

0.0% 

0.0% 

0.0% 

0.0% 

11.5% 

0.0% 

0.0% 

0.0% 

7.7% 

1.9% 

1.9% 

0.0% 

0.023 0.025 0.026 0.019 

0.007 0.008 0.008 0.006 

0.003 0.002 0.002 0.003 

0.001 0.002 0.002 0.002 

0.022 0.008 0.010 0.010 

0.002 0.000 0.000 0.001 

0.003 0.003 0.003 0.003 

0.003 0.003 0.003 0.004 

0.003 0.004 0.004 0.009 

1.174 1.434 1.410 1.334 

2.123 1.211 1.324 1.130 

0.088 0.159 0.158 0.122 

0.140 0.125 0.131 0.196 

0.070 0.092 0.091 0.071 

0.083 0.056 0.059 0.049 

0.483 0.081 0.104 0.071 

0.021 0.026 0.026 0.022 

0.026 0.025 0.025 0.023 

Total Phosphorus (WCA) 52 24 0.027 1.9% 0.023 1.9% 0.023 1.9% 0.021 3.8% 

B - 69

Summary Statistics for Table 10. 

% of Blanks Exceeding Limit for Dataset 

Limit Type Mean Standard Error 

ACIL CRV 1.9% 0.3% 

USGS LTMDL (adding 

median) 

4.4% 1.2% 

USGS LTMDL (adding 

mean) 

3.7% 0.9% 

EPA MDL 2.9% 0.8% 

B - 70

Table 11. Comparison of SL-IDEs and MDLs calculated With and Without Outlier Removal, 

Analyte 

1,1,1,2-tetrachloroethane 

1,1,1,2-tetrachloroethane 

1,1,1-trichloroethane 

1,1,1-trichloroethane 

1,1,2,2-tce+1,2,3-tcp 

1,1,2,2-tetrachloroethane 

1,1,2-trichloroethane 

1,1-dichloroethane 

1,1-dichloroethane 

1,1-dichloroethene 

1,1-dichloropropene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichloropropane 

1,2,4-trichlorobenzene 

1,2,4-trichlorobenzene 

1,2,4-trimethylbenzene 

1,2,4-trimethylbenzene 

1,2-dibromo-3-chloropropane 

1,2-dibromoethane 

1,2-dibromoethane 

1,2-dichlorobenzene 

1,2-dichlorobenzene 

1,2-dichloroethane 

1,2-dichloroethane 

1,2-dichloropropane 

1,2-dichloropropane 

1,3,5-trimethylbenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichloropropane 

1,3-dichloropropane 

1,4-dichlorobenzene 

1,4-dichlorobenzene 

2,2-dichloropropane 

2-butanone 

2-chlorotoluene 

2-chlorotoluene 

2-hexanone 

4-chlorotoluene 

4-chlorotoluene 

Allyl Chloride 

Aluminum 

Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE MDL 

Method Procedure Outliers Outliers Model Used Outliers Outliers 

(Kept/Dropped) Kept dropped 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

524.2 

502.2 

502.2 

502.2 

524.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

502.2 

502.2 

502.2 

524.2 

502.2 

524.2 

524.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

524.2 

1620 

200.8 

350.3 

Kept Dropped 

ELCD 0.034 0.024 E/E 0.041 

0.244 0.211 E/E 0.052 

ELCD 0.041 0.038 E/E 0.012 

0.308 0.311 E/E 0.055 

ELCD 0.179 0.123 E/E 0.064 

0.436 0.296 E/E 0.132 

ELCD 0.032 0.026 E/E 0.024 

ELCD 0.083 0.060 E/E 0.010 

0.229 0.187 E/E 0.033 

ELCD 0.234 0.165 E/E 0.038 

0.287 0.294 E/E 0.045 

ELCD 0.134 0.066 E/E 0.048 

PID 0.115 0.095 E/E 0.057 

0.275 0.256 E/E 0.070 

1.263 1.046 E/E 7.328 

ELCD 0.088 0.076 E/E 0.022 

PID 0.124 0.117 E/E 0.070 

PID 0.125 0.107 E/E 0.095 

0.144 0.134 E/E 0.012 

1.749 1.368 E/E 1.457 

ELCD 0.164 0.146 E/E 0.096 

0.326 0.290 E/E 0.127 

ELCD 0.065 0.061 E/E 0.035 

0.130 0.133 E/E 0.030 

ELCD 0.042 0.029 E/E 0.017 

0.258 0.237 E/E 0.039 

ELCD 0.043 0.031 E/E 0.023 

0.247 0.175 E/E 0.056 

0.135 0.127 E/E 0.011 

ELCD 0.118 0.073 E/E 0.035 

PID 0.126 0.106 E/E 0.093 

ELCD 0.047 0.037 E/E 0.016 

0.202 0.182 E/E 0.038 

ELCD 0.061 0.053 E/E 0.026 

0.140 0.130 E/E 0.023 

0.691 0.630 E/E 2.376 

0.833 0.696 E/E 0.417 

ELCD 0.175 0.161 E/E 0.108 

PID 0.230 0.143 E/E 0.238 

0.902 0.753 E/E 1.316 

ELCD 0.149 0.134 E/E 0.110 

0.123 0.114 E/E 0.010 

0.229 0.213 E/E 0.032 

206.975 47.299 C/E 29.555 

ICP/MS 12.747 9.371 E/E 19.145 

0.014 0.013 E/E 0.010 

0.006 

0.052 

0.012 

0.055 

0.064 

0.132 

0.018 

0.014 

0.033 

0.028 

0.045 

0.021 

0.057 

0.070 

4.014 

0.022 

0.070 

0.095 

0.026 

1.457 

0.095 

0.127 

0.035 

0.025 

0.017 

0.059 

0.029 

0.026 

0.011 

0.014 

0.067 

0.014 

0.038 

0.026 

0.023 

2.376 

0.874 

0.108 

0.086 

0.426 

0.083 

0.010 

0.029 

19.524 

0.839 

0.010 

B - 71 

Aluminum 

Ammonia as Nitrogen 2 
Table 11. Comparison of SL-IDEs and MDLs calculated With and Without Outlier Removal, 

Analyte 

Antimony 

Arsenic 

Barium 

Barium 

Benzene 

Beryllium 

Beryllium 

Bromobenzene 

Bromobenzene 

Bromobenzene 

Bromochloromethane 

Bromodichloromethane 

Bromodichloromethane 

Bromoform 

Bromoform 

Bromomethane 

Cadmium 

Cadmium 

Calcium 

Carbon Tetrachloride 

Carbontet+1,1-dcp 

Chlorobenzene 

Chlorobenzene 

Chloroethane 

Chloroethane 

Chloroform 

Chloromethane 

Chloromethane 

Chromium 

Chromium 

Cis-1,2-dce+2,2-dcp 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cobalt 

Cobalt 

Copper 

Copper 

Dibromochloromethane 

Dibromochloromethane 

Dibromomethane 

Dibromomethane 

Dichlorodifluoromethane 

Diethyl Ether 

Ethyl Me thacryla te 

Ethylbenzene 

Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE MDL 

Method Procedure Outliers Outliers Model Used Outliers Outliers 

Kept Dropped (Kept/Dropped) Kept 

0.008 

0.226 

1.702 

0.018 

0.030 

0.528 

0.007 

0.131 

0.012 

0.044 

0.013 

0.004 

0.043 

0.006 

0.123 

0.477 

0.127 

0.004 

36.726 

0.038 

0.029 

0.011 

0.026 

0.011 

0.048 

0.043 

0.070 

0.045 

0.310 

0.073 

0.013 

0.007 

0.057 

0.036 

9.820 

0.001 

6.046 

0.037 

0.006 

0.051 

0.007 

0.102 

0.071 

0.120 

0.035 

0.021 

dropped 

200.8 ICP/MS E/E 0.178 

ICP/MS 0.366 0.347 E/E 0.226 

1.837 1.441 C/C 1.702 

ICP/MS 0.084 0.068 E/E 0.033 

PID 0.079 0.074 E/E 0.030 

0.448 0.430 E/E 0.528 

ICP/MS 0.024 0.021 E/E 0.007 

ELCD 0.765 0.242 L/E 0.131 

PID 0.050 0.046 E/E 0.012 

0.211 0.195 E/E 0.044 

ELCD 0.482 0.390 L/L 0.013 

ELCD 0.075 0.065 E/E 0.004 

0.205 0.190 E/E 0.043 

ELCD 1.513 1.504 C/C 0.006 

0.400 0.363 E/E 0.123 

ELCD 7.293 7.427 C/C 0.267 

0.191 0.159 E/E 0.127 

ICP/MS 0.022 0.022 E/E 0.004 

41.358 36.054 L/L 36.726 

0.314 0.288 E/E 0.038 

ELCD 0.072 0.068 E/E 0.029 

ELCD 0.460 0.378 L/L 0.011 

PID 0.064 0.055 E/E 0.030 

ELCD 2.598 2.357 C/C 0.108 

0.395 0.362 E/E 0.066 

ELCD 0.032 0.026 E/E 0.043 

ELCD 0.250 0.150 E/E 0.070 

0.253 0.302 E/E 0.045 

0.496 0.464 E/E 0.310 

ICP/MS 0.408 0.207 L/E 0.073 

ELCD 0.055 0.052 E/E 0.013 

ELCD 0.074 0.062 E/E 0.007 

PID 0.082 0.138 E/E 0.057 

0.173 0.145 E/E 0.038 

16.463 15.625 E/E 9.820 

ICP/MS 0.074 0.074 C/C 0.001 

21.189 14.718 C/C 6.046 

ICP/MS 0.798 0.160 C/E 0.037 

ELCD 0.436 0.413 L/L 0.009 

0.287 0.210 E/E 0.051 

ELCD 0.460 0.344 L/L 0.007 

0.388 0.319 E/E 0.102 

ELCD 0.240 0.069 E/E 0.009 

0.376 0.301 E/E 0.120 

0.273 0.246 E/E 0.045 

PID 0.078 0.073 E/E 0.021 

0.019 0.014 

200.8 

1620 

200.8 

502.2 

1620 

200.8 

502.2 

502.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

1620 

200.8 

1620 

524.2 

502.2 

502.2 

502.2 

502.2 

524.2 

502.2 

502.2 

524.2 

1620 

200.8 

502.2 

502.2 

502.2 

524.2 

1620 

200.8 

1620 

200.8 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

502.2 

B - 72 
Table 11. Comparison of SL-IDEs and MDLs calculated With and Without Outlier Removal, 

Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE MDL 

Analyte Method Procedure Outliers Outliers Model Used Outliers Outliers 

Kept Dropped 

0.198 0.184 E/E 0.033 

ELCD 0.094 0.081 E/E 0.043 

PID 0.597 0.490 E/E 0.649 

373.590 42.840 L/E 90.409 

PID 0.060 0.047 E/E 0.020 

0.120 0.107 E/E 0.011 

2.423 1.855 E/E 1.647 

ICP/MS 0.204 0.133 E/E 0.655 

PID 0.121 0.114 E/E 0.090 

105.998 100.489 E/E 103.033 

6.808 2.183 C/E 6.856 

ICP/MS 0.109 0.018 C/E 0.031 

ICP/MS 0.027 0.024 E/E 0.004 

0.718 0.492 E/E 0.356 

0.601 0.477 E/E 0.220 

0.314 0.279 E/E 0.082 

0.535 0.480 E/E 0.225 

3.034 2.683 E/E 2.455 

ICP/MS 0.271 0.027 1 C/C 0.004 

PID 0.141 0.105 E/E 0.030 

PID 0.092 0.071 E/E 0.040 

0.186 0.219 E/E 0.048 

25.560 23.853 E/E 20.219 

ICP/MS 0.083 0.057 E/E 0.146 

PID 0.116 0.087 E/E 0.059 

PID 0.159 0.131 E/E 0.073 

0.408 0.351 E/E 0.553 

PID 0.081 0.068 E/E 0.055 

ICP/MS 0.416 0.324 E/E 0.192 

10.668 10.718 E/L 4.907 

ICP/MS 0.012 0.010 E/E 0.004 

PID 0.074 0.082 E/E 0.029 

ELCD 0.061 0.054 E/E 0.018 

PID 0.156 0.131 E/E 0.062 

0.469 0.393 E/E 0.085 

ICP/MS 0.001 0.001 E/E 0.000 

ICP/MS 0.001 0.001 E/E 0.001 

3.932 3.700 E/E 3.670 

5.376 4.732 E/E 4.777 

PID 0.064 0.056 E/E 0.070 

0.146 0.136 E/E 0.020 

3.005 3.060 E/E 1.170 

ELCD 0.081 0.073 E/E 0.041 

ELCD 0.098 0.083 E/E 0.012 

PID 0.092 0.088 E/E 0.058 

0.223 0.188 E/E 0.051 

(Kept/Dropped) Kept dropped 

Ethylbenzene 

Hexachlorobutadiene 

Hexchlobutadiene+naphthalene 

524.2 

502.2 

502.2 

Iron 1620 

502.2 

524.2 

1620 

200.8 

502.2 

1620 103.033 

1620 

200.8 

200.8 

524.2 

524.2 

524.2 

524.2 

1620 

200.8 

502.2 

502.2 

524.2 

1620 

200.8 

502.2 

502.2 

524.2 

502.2 

200.8 

1620 

200.8 

502.2 

502.2 

502.2 

524.2 

200.8 

200.8 

1620 

1620 

502.2 

524.2 

160.2 

502.2 

502.2 

502.2 

524.2 

0.023 

0.043 

0.649 

19.188 

0.020 

0.010 

1.288 

0.131 

0.090 

Isopropylbenzene 

Isopropylbenzene 

Lead 

Lead 

M+p xylene 

Magnesium 

Manganese 

Manganese 

Mercury 

Methacrylon itrile 

Methyla crylate 

Methylene Chloride 

Methylm ethacryl ate 

Molybdenum 

Molybdenum 

N-butylbenzene 

N-propylbenzene 

Naphthalene 

Nickel 

Nickel 

O-xylene+styrene 

P-isoproptol+1,4-dcb 

Pentachloroethane 

Sec-butylbenzene 

Selenium 

Silver 

Silver 

Tert-butylbenzene 

Tetrachloroethene 

Tetrachloroethene 

Tetrachloroethene 

Thallium 

Thorium 

Tin 

Titanium 

Toluene 

Toluene 

Total Suspended Solids 2 

Trans-1,2-dichloroethene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

1.176 

0.012 

0.004 

0.336 

0.220 

0.082 

0.225 

2.455 

0.002 

0.083 

0.040 

0.048 

20.219 

0.075 

0.043 

0.054 

0.207 

0.036 

0.192 

4.250 

0.004 

0.029 

0.018 

0.062 

0.027 

0.000 

0.001 

3.670 

4.663 

0.071 

0.018 

0.980 

0.041 

0.012 

0.058 

0.051 

B - 73 
Table 11. Comparison of SL-IDEs and MDLs calculated With and Without Outlier Removal, 

Analyte 

Trichloroethene 

Trichloroethene 

Trichloroethene 

Trichlorofluoromethane 

Trichlorofluoromethane 

Uranium 

Vinyl Chloride 

Wad Cyanide 

Yttrium 

Zinc 

Zinc 

Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE MDL 

Method Procedure Outliers Outliers Model Used Outliers Outliers 

Kept Dropped (Kept/Dropped) Kept dropped 

502.2 E/E 0.012 

PID 0.097 0.078 E/E 0.027 

0.332 0.333 E/E 0.061 

ELCD 2.079 1.762 C/C 0.108 

0.384 0.528 E/E 0.087 

ICP/MS 0.000 0.000 E/E 0.000 

ELCD 3.672 3.577 C/C 0.270 

WADCN 0.701 0.665 L/L 0.572 

3.247 3.078 E/E 1.923 

4.500 4.135 E/E 2.597 

ICP/MS 1.598 1.016 E/E 0.900 

ELCD 0.059 0.049 0.012 

502.2 

524.2 

502.2 

524.2 

200.8 

502.2 

1677 

1620 

1620 

200.8 

0.027 

0.061 

0.012 

0.087 

0.000 

0.270 

0.550 

1.923 

2.597 

0.585 1

 Constant m odel used b ecause IDE did not conve rge for chose n model (E xponential) 2

 Results reported as mg/L 

Summary Statistics for Table 11. 

Percent Difference 

(Positive if limit with 

outliers kept>limit with 

outliers removed) 

# Analytes Minimum 25th 

Perce ntile 

Median 75th 

Perce ntile 

Maximum 

SL-IDE (all) 149 -51.6% 7.1% 14.3% 24.4% 164.2% 

SL-IDE (same model used) 141 -51.6% 6.9% 13.7% 22.2% 164.2% 

SL-IDE (different model 

used) 

8 -0.5% 93.4% 114.7% 135.9% 158.9% 

MDL 60 -115.4% 4.4% 30.2% 75.6% 183.7% 

B - 74

Table 12. Comparison of SL-IQEs and MLs calculated With and Without Outlier Removal, Episode 6000 

Analyte 

1,1,1,2-tetrachloroethane 

1,1,1,2-tetrachloroethane 

1,1,1-trichloroethane 

1,1,1-trichloroethane 

1,1,2,2-tce+1,2,3-tcp 

1,1,2,2-tetrachloroethane 

1,1,2-trichloroethane 

1,1-dichloroethane 

1,1-dichloroethane 

1,1-dichloroethene 

1,1-dichloropropene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichloropropane 

1,2,4-trichlorobenzene 

1,2,4-trichlorobenzene 

1,2,4-trimethylbenzene 

1,2,4-trimethylbenzene 

Method 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

524.2 

502.2 

502.2 

502.2 

524.2 

Data (�g/L except where footnoted) 

Procedure 

SL-IQE (10%) ML 

Outliers 

Kept 

Outliers 

Dropped 

Model Used 

(Kept/Dropped) 

Outliers 

Kept 

Outliers 

Dropped 

ELCD 0.030 0.023 H/H 0.2 0.02 

0.181 0.142 H/H 0.2 0.2 

ELCD 0.830 2.207 L/C 0.05 0.05 

0.240 0.157 H/H 0.2 0.2 

ELCD 5.514 5.290 5 C/C 0.2 0.2 

0.569 0.318 H/H 0.5 0.5 

ELCD 0.060 0.030 L/H 0.1 0.05 

ELCD 0.527 0.311 L/L 0.05 0.05 

0.115 25.620 5 H/C 0.1 0.1 

ELCD 3.796 3.827 L/L 0.1 0.1 

0.180 0.090 H/H 0.2 0.2 

ELCD 0.851 0.117 L/L 0.2 0.1 

PID 0.248 0.190 H/H 0.2 0.2 

0.216 0.217 H/H 0.2 0.2 

11.316 5.134 L/L 20 10 

ELCD 0.401 0.226 L/L 0.1 0.1 

PID 0.439 0.429 L/L 0.2 0.2 

PID 0.653 0.621 L/L 0.5 0.5 

20.896 21.013 C/C 0.05 0.1 

1,2-dibromo-3-chloropropane 

1,2-dibromoethane 

1,2-dibromoethane 

1,2-dichlorobenzene 

1,2-dichlorobenzene 

1,2-dichloroethane 

1,2-dichloroethane 

1,2-dichloropropane 

1,2-dichloropropane 

1,3,5-trimethylbenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichloropropane 

1,3-dichloropropane 

1,4-dichlorobenzene 

1,4-dichlorobenzene 

2,2-dichloropropane 

2-butanone 

2-chlorotoluene 

2-chlorotoluene 

2-hexanone 

4-chlorotoluene 

4-chlorotoluene 

Allyl Chloride 

Aluminum 

Aluminum 

Ammonia as Nitrogen 2 

Antimony 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

502.2 

502.2 

502.2 

524.2 

502.2 

524.2 

524.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

524.2 

1620 

200.8 

350.3 

200.8 

71.182 5 72.198 5 C/C 5 5 

ELCD 0.592 0.560 L/L 0.5 0.2 

0.417 0.418 H/H 0.5 0.5 

ELCD 0.183 0.114 L/H 0.1 0.1 

0.085 0.067 H/H 0.1 0.1 

ELCD 0.065 0.031 H/H 0.05 0.05 

0.222 0.168 H/H 0.1 0.2 

ELCD 0.102 0.038 L/H 0.1 0.1 

0.196 0.085 H/H 0.2 0.1 

23.744 23.877 C/C 0.05 0.05 

ELCD 0.936 0.463 L/L 0.1 0.05 

PID 0.465 0.401 L/L 0.2 0.2 

ELCD 0.054 0.059 L/H 0.05 0.05 

0.139 0.151 H/H 0.1 0.1 

ELCD 0.101 0.079 H/H 0.1 0.1 

0.078 0.077 H/H 0.1 0.1 

38.009 38.299 C/C 10 10 

0.893 0.534 H/H 2 2 

ELCD 0.493 0.439 H/H 0.5 0.5 

PID 0.849 0.770 H/L 1 0.2 

0.442 0.518 H/H 5 2 

ELCD 0.142 1 0.517 H/H 0.5 0.2 

23.810 23.941 C/C 0.05 0.05 

29.674 29.866 C/C 0.1 0.1 

464.069 156.043 C/L 100 50 

ICP/MS 29.684 31.466 H/L 50 2 

0.035 0.032 H/H 0.05 0.05 

ICP/MS 0.034 0.020 H/H 0.5 0.02 

B - 75 
Table 12. Comparison of SL-IQEs and MLs calculated With and Without Outlier Removal, Episode 6000 

Arsenic 

Barium 

Barium 

Benzene 

Beryllium 

Beryllium 

Bromobenzene 

Bromobenzene 

Bromobenzene 

Bromochloromethane 

Bromodichloromethane 

Bromodichloromethane 

Bromoform 

Bromoform 

Bromomethane 

Cadmium 

Cadmium 

Calcium 

Carbon Tetrachloride 

Carbontet+1,1-dcp 

Chlorobenzene 

Chlorobenzene 

Chloroethane 

Chloroethane 

Chloroform 

Chloromethane 

Chloromethane 

Chromium 

Chromium 

Cis-1,2-dce+2,2-dcp 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cobalt 

Cobalt 

Copper 

Copper 

Dibromochloromethane 

Dibromochloromethane 

Dibromomethane 

Dibromomethane 

Dichlorodifluoromethane 

Diethyl Ether 

Ethyl Me thacryla te 

Ethylbenzene 

Ethylbenzene 

Hexachlorobutadiene 

Data (�g/L except where footnoted) 

Procedure 

SL-IQE (10%) ML 

Outliers 

Kept 

Outliers 

Dropped 

Model Used 

(Kept/Dropped) 

Outliers 

Kept 

Outliers 

Dropped 

ICP/MS 0.798 0.747 H/H 1 1 

4.118 3.231 C/C 5 5 

ICP/MS 0.211 0.191 L/L 0.1 0.05 

PID 0.182 0.149 L/H 0.1 0.1 

0.980 0.975 H/H 2 2 

ICP/MS 0.044 0.038 H/H 0.02 0.02 

ELCD 3.529 0.594 L/H 0.5 0.5 

PID 0.100 0.022 L/L 0.05 0.05 

0.140 0.143 H/H 0.2 0.2 

ELCD 1.598 1.344 L/L 0.05 0.05 

ELCD 0.424 0.323 L/L 0.02 0.02 

0.128 0.131 H/H 0.2 0.2 

ELCD 3.393 3.350 C/C 0.02 0.02 

0.482 0.484 H/H 0.5 0.5 

ELCD 16.351 16.541 C/C 1 2 

0.410 0.422 H/L 0.5 0.5 

ICP/MS 0.063 0.068 H/H 0.02 0.02 

99.975 88.075 L/L 100 100 

0.140 0.061 H/H 0.1 0.1 

ELCD 0.069 4.481 H/C 0.1 0.1 

ELCD 1.766 1.514 L/L 0.05 0.05 

PID 0.119 0.100 H/H 0.1 0.1 

ELCD 5.826 5.285 C/C 0.5 0.05 

0.255 0.202 H/H 0.2 0.2 

ELCD 0.025 0.006 L/H 0.2 0.2 

ELCD 1.734 0.766 L/L 0.2 0.2 

0.141 0.187 H/H 0.2 0.2 

1.259 1.072 L/L 1 1 

ICP/MS 1.028 0.636 L/L 0.2 0.2 

ELCD 0.039 0.038 H/H 0.05 0.05 

ELCD 0.415 0.131 L/H 0.02 0.02 

PID 0.017 1 0.262 H/H 0.2 0.2 

0.141 0.070 H/H 0.1 0.1 

40.837 39.614 L/L 50 50 

ICP/MS N/A 3 N/A 3 N/A 0.005 0.005 

47.509 33.000 C/C 20 20 

ICP/MS 1.825 1.706 C/C 0.1 0.1 

ELCD 1.252 1.189 L/L 0.02 0.02 

0.288 0.177 H/H 0.2 0.2 

ELCD 1.395 1.099 L/L 0.02 0.02 

0.460 0.473 H/H 0.5 0.5 

ELCD 1.091 4 5.023 L/C 0.02 0.2 

0.404 0.400 H/H 0.5 0.5 

0.183 0.109 H/H 0.2 0.1 

PID 0.157 0.149 H/H 0.1 0.1 

0.077 0.047 H/H 0.1 0.1 

ELCD 0.243 0.194 H/H 0.2 0.2 

Analyte Method 

200.8 

1620 

200.8 

502.2 

1620 

200.8 

502.2 

502.2 

524.2 

502.2 

502.2 

524.2 

502.2 

524.2 

502.2 

1620 

200.8 

1620 

524.2 

502.2 

502.2 

502.2 

502.2 

524.2 

502.2 

502.2 

524.2 

1620 

200.8 

502.2 

502.2 

502.2 

524.2 

1620 

200.8 

1620 

200.8 

502.2 

524.2 

502.2 

524.2 

502.2 

524.2 

524.2 

502.2 

524.2 

502.2 

B - 76 
Table 12. Comparison of SL-IQEs and MLs calculated With and Without Outlier Removal, Episode 6000 

Data (�g/L except where footnoted) 

Procedure 

SL-IQE (10%) ML 

Outliers 

Kept 

Outliers 

Dropped 

Model Used 

(Kept/Dropped) 

Outliers 

Kept 

Outliers 

Dropped 

Hexchlobutadiene+naphthalene PID 1.542 1.216 H/H 2 2

996.565 4 151.265 L/H 200 50 

PID 0.129 1.928 L/C 0.1 0.1 

25.592 25.726 C/C 0.05 0.05 

5.698 4.449 L/L 5 5 

ICP/MS 0.685 0.281 L/H 2 0.5 

PID 0.222 0.217 H/H 0.2 0.2 

267.199 259.424 L/L 500 500 

15.264 5.629 C/L 20 5 

ICP/MS 0.245 0.071 C/L 0.1 0.05 

ICP/MS 0.039 0.033 H/H 0.02 0.02 

19.062 19.451 C/C 1 1 

0.727 0.586 H/H 1 1 

0.433 0.390 H/H 0.2 0.2 

20.773 20.951 C/C 1 1 

7.597 6.737 L/L 10 10 

ICP/MS 0.608 0.011 C/H 0.01 0.005 

PID 0.745 0.397 L/L 0.1 0.2 

PID 0.186 0.128 H/H 0.2 0.2 

0.108 0.166 H/H 0.2 0.2 

67.206 58.049 L/L 100 100 

ICP/MS 0.183 0.116 H/H 0.5 0.2 

PID 0.181 0.140 L/H 0.2 0.2 

PID 0.456 0.330 L/L 0.2 0.2 

0.551 0.406 H/H 2 1 

PID 0.157 0.101 H/H 0.2 0.1 

ICP/MS 1.045 0.607 L/H 0.5 0.5 

25.842 25.005 L/L 20 20 

ICP/MS 0.056 0.027 L/L 0.02 0.02 

PID 0.203 0.121 L/L 0.1 0.1 

ELCD 0.122 0.092 H/H 0.05 0.05 

PID 0.750 0.664 L/L 0.2 0.2 

30.554 5 0.275 C/H 0.2 0.1 

ICP/MS 0.002 0.002 L/L 0.002 0.002 

ICP/MS 0.004 0.001 L/H 0.002 0.002 

9.406 8.651 L/L 10 10 

14.236 13.166 L/L 20 20 

PID 0.194 0.084 L/L 0.2 0.2 

0.046 0.039 H/H 0.05 0.05 

Analyte Method 

502.2 

Iron 

Isopropylbenzene 

Isopropylbenzene 

Lead 

Lead 

M+p xylene 

Magnesium 

Manganese 

Manganese 

Mercury 

Methacrylon itrile 

Methyla crylate 

Methylene Chloride 

Methylm ethacryl ate 

Molybdenum 

Molybdenum 

N-butylbenzene 

N-propylbenzene 

Naphthalene 

Nickel 

Nickel 

O-xylene+styrene 

P-isoproptol+1,4-dcb 

Pentachloroethane 

Sec-butylbenzene 

Selenium 

Silver 

Silver 

Tert-butylbenzene 

Tetrachloroethene 

Tetrachloroethene 

Tetrachloroethene 

Thallium 

Thorium 

Tin 

Titanium 

Toluene 

Toluene 

1620 

502.2 

524.2 

1620 

200.8 

502.2 

1620 

1620 

200.8 

200.8 

524.2 

524.2 

524.2 

524.2 

1620 

200.8 

502.2 

502.2 

524.2 

1620 

200.8 

502.2 

502.2 

524.2 

502.2 

200.8 

1620 

200.8 

502.2 

502.2 

502.2 

524.2 

200.8 

200.8 

1620 

1620 

502.2 

524.2 

Total Suspended Solids 2 

Trans-1,2-dichloroethene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trans-1,3-dichloropropene 

Trichloroethene 

Trichloroethene 

Trichloroethene 

160.2 

502.2 

502.2 

502.2 

524.2 

502.2 

502.2 

524.2 

6.729 7.441 H/L 5 

ELCD 0.191 0.159 H/H 0.2 

ELCD 0.729 0.610 L/L 0.05 

PID 0.175 0.173 H/H 0.2 

0.218 0.124 H/H 0.2 

ELCD 3.169 0.041 1 L/L 0.05 

PID 0.401 0.332 L/L 0.1 

0.167 0.237 H/H 0.2 

B - 77 

5 

0.2 

0.05 

0.2 

0.2 

0.05 

0.1 

0.2 
Table 12. Comparison of SL-IQEs and MLs calculated With and Without Outlier Removal, Episode 6000 

Trichlorofluoromethane 

Trichlorofluoromethane 

Uranium 

Vinyl Chloride 

Wad Cyanide 

Yttrium 

Zinc 

Zinc 

Data (�g/L except where footnoted) 

Procedure 

SL-IQE (10%) ML 

Outliers 

Kept 

Outliers 

Dropped 

Model Used 

(Kept/Dropped) 

Outliers 

Kept 

Outliers 

Dropped 

ELCD 4.662 3.950 C/C 0.5 0.05 

42.490 5 0.228 C/H 0.2 0.2 

ICP/MS 0.001 0.001 L/H 0.001 0.001 

ELCD 8.234 8.020 C/C 1 1 

WADCN 1.624 1.543 L/L 2 2 

8.962 8.501 L/L 5 5 

10.452 11.630 H/L 10 10 

ICP/MS 7.024 2.291 L/H 2 2 

Analyte Method 

502.2 

524.2 

200.8 

502.2 

1677 

1620 

1620 

200.8 

1 IQE 10% undefined, IQE 20% reported
 2 Results reported as mg/L
 3

 IQE 10%, IQE 20% and IQE 30% all neg ative based on chosen model (linear)
 4

 IQE 10% and IQE 20% both negative, IQE 30% reported
 5

 Hybrid model selected but did not converge; IQE 10% based on constant model instead


Summary Statistics for Table 12 

Perce nt Differ ence (P ositive if 

limit with outliers kept> limit 

with outliers removed) 

# Analytes Minimum 25th Perce ntile Median 75th Perce ntile Maximum 

SL-IQE (all) 148 -198.2% 1.0% 16.3% 50.2% 197.9% 

SL-IQE (same model used) 117 -176.3% 0.0% 2.8% 23.7% 194.9% 

SL-IQE (different model used) 31 -198.2% -7.7% 53.1% 107.1% 197.9% 

ML 31 -163.6% 66.7% 66.7% 120.0% 184.6% 

B - 78

Table 13. Comparison of SL-IDEs calculated using different Model Types, Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE, Based on Given Model 

Analyte 

1,1,1,2-tetrachloroethane 

1,1,1,2-tetrachloroethane 

1,1,1-trichloroethane 

1,1,1-trichloroethane 

1,1,2,2-tce+1,2,3-tcp 

1,1,2,2-tetrachloroethane 

1,1,2-trichloroethane 

1,1,2-trichloroethane 

1,1-dichloroethane 

1,1-dichloroethane 

1,1-dichloroethene 

1,1-dichloroethene 

1,1-dichloropropanone 

1,1-dichloropropene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichlorobenzene 

1,2,3-trichloropropane 

1,2,4-trichlorobenzene 

1,2,4-trichlorobenzene 

1,2,4-trichlorobenzene 

1,2,4-trimethylbenzene 

1,2,4-trimethylbenzene 

Method Procedure 

502.2 ELCD


524.2


502.2 ELCD


524.2


502.2 ELCD


524.2


502.2 ELCD


524.2


502.2 ELCD


524.2


502.2 ELCD


524.2


524.2


524.2


502.2 ELCD


502.2


524.2


524.2


502.2 ELCD


502.2


524.2


502.2


524.2


PID 

PID 

PID 

1,2-dibromo-3-chloropropane 

1,2-dibromoethane 

1,2-dibromoethane 

1,2-dichlorobenzene 

1,2-dichlorobenzene 

1,2-dichlorobenzene 

1,2-dichloroethane 

1,2-dichloroethane 

1,2-dichloropropane 

1,2-dichloropropane 

1,3,5-tmb+4-chlorotoluene 

1,3,5-trimethylbenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichlorobenzene 

1,3-dichloropropane 

1,3-dichloropropane 

1,4-dichlorobenzene 

1,4-dichlorobenzene 

1-chlorobutane 

2,2-dichloropropane 

2-butanone 

2-chlorotoluene 

2-chlorotoluene 

2-chlorotoluene 

524.2


502.2 ELCD


524.2


502.2 ELCD


502.2


524.2


502.2 ELCD


524.2


502.2 ELCD


524.2


502.2


524.2


502.2 ELCD


502.2


524.2


502.2 ELCD


524.2


502.2 ELCD


524.2


524.2


524.2


524.2


502.2 ELCD


502.2


524.2


PID 

PID 

PID 

PID 

Constant Linear Exponential Hybrid RSD

0.687 0.000 0.034 0.010 184% 

11.051 -1.234 0.244 0.078 166% 

0.985 0.016 0.041 0.010 183% 

14.141 -0.836 0.308 0.098 166% 

2.597 -0.222 0.179 N/A 1 123% 

12.456 -1.517 0.436 0.248 160% 

0.476 0.016 0.032 0.016 169% 

7.245 -0.407 0.319 0.127 158% 

0.801 0.083 0.083 0.067 140% 

11.355 -0.642 0.229 0.049 167% 

1.167 0.305 0.234 0.213 96% 

18.473 -2.042 0.335 0.050 168% 

15.292 4.713 6.372 6.513 58% 

13.573 -0.554 0.287 0.073 167% 

0.942 0.117 0.134 0.117 125% 

0.640 0.134 0.115 0.083 109% 

18.047 -1.759 0.275 0.090 168% 

12.464 3.599 1.263 0.041 129% 

0.739 0.082 0.088 0.069 135% 

0.688 0.113 0.124 0.100 112% 

14.387 -1.058 0.224 0.059 168% 

0.889 0.125 0.125 0.108 123% 

9.319 -0.074 0.144 0.020 169% 

34.167 -7.305 1.749 N/A 1 128% 

0.543 0.184 0.164 0.160 71% 

8.173 -0.811 0.326 0.184 158% 

0.653 0.037 0.065 0.045 151% 

0.895 0.136 0.148 0.121 117% 

12.369 -1.392 0.130 0.036 170% 

0.951 -0.041 0.042 0.022 157% 

7.061 -0.485 0.258 0.097 161% 

0.733 0.015 0.043 0.024 173% 

9.388 -0.729 0.247 0.085 164% 

1.526 0.084 0.114 0.073 160% 

10.590 -0.059 0.135 0.016 170% 

0.775 0.230 0.118 0.103 103% 

0.773 0.102 0.126 0.099 121% 

12.273 -1.099 0.143 0.033 170% 

0.578 0.015 0.047 0.028 164% 

6.432 -0.320 0.202 0.061 163% 

0.654 0.050 0.061 0.033 152% 

11.443 -1.116 0.140 0.034 169% 

13.444 -0.406 0.220 0.024 169% 

17.294 -0.134 0.691 0.152 161% 

14.170 -1.296 0.833 0.384 153% 

1.533 0.051 0.175 0.166 146% 

0.977 0.272 0.230 0.187 90% 

11.146 -0.639 0.136 0.023 170% 

B - 79

Table 13. Comparison of SL-IDEs calculated using different Model Types, Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE, Based on Given Model 

Analyte 

2-hexanone 

2-nitropropane 

4-chlorotoluene 

4-chlorotoluene 

4-isopropyltoluene 

4-methyl-2-pentanone 

Acetone 

Acrylonitrile 

Allyl Chloride 

Aluminum 

Aluminum 

Ammonia as Nitrogen 2 

Antimony 

Antimony 

Arsenic 

Arsenic 

Barium 

Barium 

Benzene 

Benzene 

Beryllium 

Beryllium 

Boron 

Bromobenzene 

Bromobenzene 

Bromobenzene 

Bromochloromethane 

Bromochloromethane 

Bromodichloromethane 

Bromodichloromethane 

Bromoform 

Bromoform 

Bromomethane 

Bromomethane 

Cadmium 

Cadmium 

Calcium 

Carbon Disulfide 

Carbon Tetrachloride 

Carbontet+1,1-dcp 

Chloroace tonitrile 

Chlorobenzene 

Chlorobenzene 

Chlorobenzene 

Chloroethane 

Chloroethane 

Chloroform 

Chloroform 

Method Procedure 

524.2 

524.2 

502.2 ELCD 

524.2 

524.2 

524.2 

524.2 

524.2 

524.2 

1620 

200.8 ICP/MS 

350.3 

1620 

200.8 ICP/MS 

1620 

200.8 ICP/MS 

1620 

200.8 ICP/MS 

502.2 

524.2 

1620 

200.8 ICP/MS 

1620 

502.2 ELCD 

502.2 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

1620 

200.8 ICP/MS 

1620 

524.2 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

502.2 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

PID 

PID 

PID 

Constant Linear Exponential Hybrid RSD

22.744 -5.136 0.902 0.188 161% 

18.337 -3.854 1.082 0.254 156% 

1.792 -0.022 0.149 0.112 140% 

10.619 -0.329 0.123 0.013 170% 

9.108 0.162 0.117 0.007 192% 

20.121 -5.006 1.195 0.773 150% 

22.659 -1.723 2.120 1.092 141% 

13.467 -1.190 1.333 0.715 139% 

13.324 -0.815 0.229 0.051 168% 

206.975 88.830 51.697 N/A 1 70% 

41.919 12.689 12.747 12.961 73% 

0.078 0.009 0.014 0.013 114% 

4.260 3.728 3.562 3.596 9% 

0.229 0.027 0.019 0.015 144% 

2.131 1.510 1.410 1.390 22% 

2.023 0.257 0.366 0.345 114% 

1.837 1.522 1.300 1.306 17% 

0.257 0.085 0.084 0.079 69% 

0.802 0.036 0.079 0.060 152% 

8.619 -0.122 0.125 0.019 169% 

1.587 0.365 0.448 0.431 83% 

0.170 0.013 0.024 0.018 134% 

38.617 20.625 21.161 20.805 35% 

1.685 0.765 0.499 0.515 65% 

0.569 0.028 0.050 0.032 157% 

12.851 -1.691 0.211 0.060 168% 

0.939 0.482 0.162 0.157 85% 

8.929 -0.807 0.345 0.161 159% 

0.617 0.111 0.075 0.060 125% 

8.020 -0.455 0.205 0.056 165% 

1.513 1.161 0.381 0.381 66% 

10.207 -1.309 0.400 0.211 159% 

7.293 5.796 4.313 N/A 1 26% 

12.379 -1.072 0.280 0.096 166% 

0.364 0.208 0.191 0.180 37% 

0.040 0.022 0.022 0.026 31% 

54.321 41.358 37.020 37.410 19% 

14.835 -1.181 0.239 0.040 168% 

15.266 -1.197 0.314 0.056 167% 

1.998 -0.007 0.072 0.020 162% 

11.548 -0.814 1.569 1.453 119% 

0.982 0.460 0.189 0.183 83% 

0.749 0.020 0.064 0.048 160% 

10.276 -0.665 0.133 0.026 169% 

2.598 2.161 1.091 1.053 45% 

14.465 -0.836 0.395 0.104 165% 

0.732 0.006 0.032 0.004 185% 

9.385 -0.399 0.225 0.051 166% 

B - 80

Table 13. Comparison of SL-IDEs calculated using different Model Types, Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE, Based on Given Model 

Analyte 

Chloromethane 

Chloromethane 

Chromium 

Chromium 

Cis-1,2-dce+2,2-dcp 

Cis-1,2-dichloroethene 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cis-1,3-dichloropropene 

Cobalt 

Cobalt 

Copper 

Copper 

Dibromochloromethane 

Dibromochloromethane 

Dibromomethane 

Dibromomethane 

Dichlorodifluoromethane 

Dichlorodifluoromethane 

Diethyl Ether 

Ethyl Me thacryla te 

Ethylbenzene 

Ethylbenzene 

Hardness 2 

Hexachlorobutadiene 

Hexachlorobutadiene 

Hexachloroethane 

Method Procedure 

502.2 ELCD 

524.2 

1620 

200.8 ICP/MS 

502.2 ELCD 

524.2 

502.2 ELCD 

502.2 

524.2 

1620 

200.8 ICP/MS 

1620 

200.8 ICP/MS 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

502.2 ELCD 

524.2 

524.2 

524.2 

502.2 

524.2 

130.2 

502.2 ELCD 

524.2 

524.2 

PID 

PID 

Hexchlobutadiene+naphthalene 502.2 

1620 

502.2 

524.2 

1620 

200.8 ICP/MS 

502.2 

524.2 

1620 

1620 

200.8 ICP/MS 

200.8 ICP/MS 

524.2 

524.2 

524.2 

524.2 

502.2 ELCD 

524.2 

524.2 

1620 

200.8 ICP/MS 

PID 

PID 

Iron 

Isopropylbenzene 

Isopropylbenzene 

Lead 

Lead 

M+p xylene 

M+p xylene 

Magnesium 

Manganese 

Manganese 

Mercury 

Methacrylon itrile 

Methyl Iodide 

Methyl tert-butyl ether 

Methyla crylate 

Methylene Chloride 

Methylene Chloride 

Methylm ethacryl ate 

Molybdenum 

Molybdenum 

PID 

Constant Linear Exponential Hybrid RSD

1.130 0.453 0.250 0.233 82% 

19.617 -2.484 0.253 0.056 169% 

1.090 0.528 0.496 0.471 46% 

0.672 0.408 0.284 0.290 44% 

1.893 -0.048 0.055 0.012 164% 

11.249 -0.960 0.234 0.062 167% 

0.716 0.083 0.074 0.061 138% 

0.933 0.039 0.082 0.013 167% 

7.072 -0.454 0.173 0.062 165% 

30.100 16.339 16.463 16.102 35% 

0.074 -0.012 -0.004 -0.001 192% 

21.189 16.989 14.754 14.861 18% 

0.798 0.404 0.205 0.207 69% 

0.784 0.436 0.144 0.141 81% 

8.159 -0.667 0.287 0.126 161% 

0.836 0.460 0.192 0.184 73% 

7.135 -0.585 0.388 0.203 153% 

2.194 0.348 0.240 0.153 133% 

24.275 -4.798 0.560 0.183 166% 

12.008 -1.243 0.376 0.175 162% 

10.053 -0.957 0.273 0.079 164% 

0.888 0.020 0.078 0.060 160% 

11.939 -0.776 0.198 0.032 168% 

3.658 2.362 2.258 2.385 25% 

0.997 0.105 0.094 0.065 144% 

17.734 -2.203 0.308 0.092 167% 

18.095 -2.155 0.288 0.069 168% 

1.442 0.793 0.597 0.523 50% 

486.971 373.590 125.364 124.648 66% 

0.856 0.025 0.060 0.033 168% 

11.414 -0.141 0.120 0.012 170% 

3.976 2.396 2.423 2.437 28% 

1.007 0.265 0.204 0.200 94% 

1.701 0.005 0.121 0.088 170% 

10.994 -0.206 0.142 0.016 170% 

145.717 112.074 105.998 106.575 16% 

6.808 4.201 2.993 3.033 42% 

0.109 0.065 0.034 0.034 59% 

0.827 0.006 0.027 0.016 185% 

8.883 -0.181 0.718 0.356 145% 

12.103 -0.866 0.193 0.035 168% 

10.845 -1.117 0.225 0.053 167% 

13.820 -1.522 0.601 0.315 157% 

2.841 1.822 -3.178 N/A 1 651% 

8.787 -0.455 0.314 0.188 159% 

9.597 -0.342 0.535 0.244 154% 

4.908 3.163 3.034 3.042 26% 

0.271 0.096 0.180 -0.007 88% 

B - 81

Table 13. Comparison of SL-IDEs calculated using different Model Types, Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE, Based on Given Model 

Analyte 

Naphthalene 

N-butylbenzene 

N-butylbenzene 

Nickel 

Nickel 

N-propylbenzene 

N-propylbenzene 

o-xylene 

o-xylene+styrene 

Pentachloroethane 

P-isoproptol+1,4-dcb 

Sec-butylbenzene 

Sec-butylbenzene 

Selenium 

Selenium 

Silver 

Silver 

Sodium 

Styrene 

Tert-butylbenzene 

Tert-butylbenzene 

Tetrachloroethene 

Tetrachloroethene 

Tetrachloroethene 

Thallium 

Thallium 

Thorium 

Tin 

Titanium 

Toluene 

Toluene 

Total Phosphorus 2 

Total Suspended Solids 2 

Method Procedure 

524.2 

502.2 

524.2 

1620 

PID 

200.8 ICP/MS 

502.2 PID 

524.2 

524.2 

502.2 

524.2 

502.2 

502.2 

524.2 

1620 

PID 

PID 

PID 

200.8 ICP/MS 

1620 

200.8 ICP/MS 

1620 

524.2 

502.2 

524.2 

PID 

502.2 ELCD 

502.2 

524.2 

1620 

PID 

200.8 ICP/MS 

200.8 ICP/MS 

1620 

1620 

502.2 

524.2 

PID 

365.2 

Constant Linear Exponential Hybrid RSD

14.829 -0.891 0.186 0.044 169% 

0.714 0.215 0.141 0.135 92% 

10.237 -0.145 0.152 0.028 169% 

50.587 26.333 25.560 24.898 39% 

1.023 0.176 0.083 0.072 136% 

0.785 0.075 0.092 0.066 139% 

13.415 -0.751 0.284 0.061 167% 

11.622 -0.802 0.198 0.017 168% 

1.372 0.043 0.116 0.082 160% 

11.186 -0.793 0.408 0.237 159% 

1.583 0.091 0.159 0.118 150% 

0.942 0.053 0.081 0.052 156% 

11.240 0.080 0.140 0.020 194% 

4.161 2.054 1.975 1.971 43% 

2.090 0.406 0.416 0.364 104% 

13.219 11.098 10.668 10.801 10% 

0.048 0.020 0.012 0.010 77% 

169.136 141.290 138.768 140.811 10% 

10.516 -0.600 0.141 0.017 169% 

0.854 0.038 0.074 0.050 158% 

11.706 -0.323 0.186 0.030 169% 

0.927 0.029 0.061 0.031 169% 

1.027 0.114 0.156 0.127 126% 

13.627 -0.451 0.469 N/A 1 132% 

1.726 1.185 1.153 1.161 21% 

0.003 0.001 0.001 0.001 73% 

0.032 0.002 0.001 0.000 176% 

5.755 3.991 3.932 3.986 20% 

8.500 6.012 5.376 5.419 23% 

0.731 0.044 0.064 0.051 152% 

9.778 -0.303 0.146 0.019 169% 

0.018 0.014 0.013 0.013 16% 

4.317 3.195 3.005 2.977 19% 

0.922 0.067 0.081 0.060 151% 

13.734 -0.953 0.300 0.062 167% 

0.666 0.201 0.098 0.087 104% 

0.650 0.052 0.092 0.068 135% 

6.714 -0.432 0.223 0.096 161% 

14.301 -1.059 1.250 0.782 141% 

1.006 0.035 0.059 0.038 169% 

0.914 0.066 0.097 0.069 146% 

12.510 -0.619 0.332 0.065 165% 

2.079 1.656 1.107 1.076 32% 

19.248 -2.147 0.384 N/A 1 136% 

0.002 0.000 0.000 0.000 116% 

22.721 9.967 10.630 10.693 46% 

2.762 0.730 0.864 0.840 75% 

trans-1,2-dichloroethene 

trans-1,2-dichloroethene 

trans-1,3-dichloropropene 

trans-1,3-dichloropropene 

trans-1,3-dichloropropene 

trans-1,4-dichloro-2-butene 

Trichloroethene 

Trichloroethene 

Trichloroethene 

Trichlorofluoromethane 

Trichlorofluoromethane 

Uranium 

Vanadium 

Vanadium 

Vinyl Chloride 

160.2


502.2 ELCD


524.2


502.2 ELCD


502.2


524.2


524.2


502.2 ELCD


502.2


524.2


502.2 ELCD


524.2


200.8 ICP/MS


1620


200.8 ICP/MS


502.2 ELCD


PID 

PID 

3.672 3.036 1.756 1.690 39% 

B - 82

Table 13. Comparison of SL-IDEs calculated using different Model Types, Episode 6000 Data 

(μg/L except where footnoted) 

SL-IDE, Based on Given Model 

Analyte 

Vinyl Chloride 

Wad Cyanide 

Xylene (total) 

Yttrium 

Zinc 

Zinc 

Method Procedure 

524.2 

1677 WADCN 

524.2 

1620 

1620 

200.8 ICP/MS 1

 Hybrid model failed to converge 2 Results reported as mg/L 

Constant Linear Exponential Hybrid RSD

22.292 -3.345 0.365 0.083 168% 

1.023 0.701 0.620 0.638 25% 

10.490 -0.264 0.128 0.008 170% 

4.569 3.520 3.247 3.279 17% 

14.628 3.804 4.500 4.425 76% 

7.561 2.537 1.598 1.610 86% 

Summary Statistics for Table 13 

Method # Analytes Minimum 25th Percentile Median 75th Perce ntile Maximum 

All 198 8.5% 81.8% 151.1% 166.7% 650.6% 

502.2 65 25.7% 103.5% 140.1% 159.9% 650.6% 

524.2 81 58.2% 159.2% 166.0% 168.5% 194.5% 

1620 26 8.5% 18.1% 26.8% 42.4% 83.0% 

200.8 21 31.0% 72.5% 88.0% 134.5% 191.6% 

B - 83

Table 14. Comparison of SL-IQEs calculated using different Model Types, Episode 6000 Data 

(μg pt w ootnoted) 

SL-IQE 10%, Based on Given Model RSD 1 

/L exce here f

Analyte Method Procedure

1,1,1,2-tetrachloroethane 502.2 ELCD 

1,1,1,2-tetrachloroethane 524.2 

1,1,1-trichloroethane 502.2 ELCD 

1,1,1-trichloroethane 524.2 

1,1,2,2-tce+1,2,3-tcp 502.2 ELCD 

1,1,2,2-tetrachloroethane 524.2 

1,1,2-trichloroethane 502.2 ELCD 

1,1,2-trichloroethane 524.2 

1,1-dichloroethane 502.2 ELCD 

1,1-dichloroethane 524.2 

1,1-dichloroethene 502.2 ELCD 

1,1-dichloroethene 524.2 

1,1-dichloropropanone 524.2 

1,1-dichloropropene 524.2 

1,2,3-trichlorobenzene 502.2 ELCD 

1,2,3-trichlorobenzene 502.2 PID 

1,2,3-trichlorobenzene 524.2 

1,2,3-trichloropropane 524.2 

1,2,4-trichlorobenzene 502.2 ELCD 

1,2,4-trichlorobenzene 502.2 PID 

1,2,4-trichlorobenzene 524.2 

1,2,4-trimethylbenzene 502.2 PID 

1,2,4-trimethylbenzene 524.2 

1,2-dibromo-3-chloropropane 524.2 

1,2-dibromoethane 502.2 ELCD 

1,2-dibromoethane 524.2 

1,2-dichlorobenzene 502.2 ELCD 

1,2-dichlorobenzene 502.2 PID 

1,2-dichlorobenzene 524.2 

1,2-dichloroethane 502.2 ELCD 

1,2-dichloroethane 524.2 

1,2-dichloropropane 502.2 ELCD 

1,2-dichloropropane 524.2 

1,3,5-tmb+4-chlorotoluene 502.2 PID 

1,3,5-trimethylbenzene 524.2 

1,3-dichlorobenzene 502.2 ELCD 

1,3-dichlorobenzene 502.2 PID 

1,3-dichlorobenzene 524.2 

1,3-dichloropropane 502.2 ELCD 

1,3-dichloropropane 524.2 

1,4-dichlorobenzene 502.2 ELCD 

1,4-dichlorobenzene 524.2 

1-chlorobutane 524.2 

2,2-dichloropropane 524.2 

2-butanone 524.2 

2-chlorotoluene 502.2 ELCD 3.438 1.364 0.452 0.493 97.4% 

2-chlorotoluene 502.2 PID 2.176 1.249 0.597 0.849 56.9% 

2-chlorotoluene 524.2 24.990 -2.436 0.308 0.053 169.5% 

Constant Linear Exponential Hybrid 

1.541 0.000 0.078 0.030 182.6% 

24.612 -4.974 0.556 0.181 165.7% 

2.208 0.830 0.096 0.058 126.0% 

31.494 -4.112 0.704 0.240 165.7% 

5.514 -1.416 0.430 N/A 2 120.9% 

27.377 -5.971 1.001 0.569 159.1% 

1.067 0.060 0.075 0.040 162.6% 

15.923 -1.175 0.726 0.290 157.7% 

1.795 0.527 0.200 0.178 113.2% 

25.290 -2.390 0.521 0.115 166.8% 

2.617 3.796 0.627 0.886 75.6% 

41.142 -28.559 0.767 0.129 167.7% 

30.102 12.705 15.558 15.041 43.2% 

30.229 -2.582 0.655 0.180 166.2% 

2.113 0.851 0.334 0.341 92.1% 

1.435 0.482 0.279 0.248 91.5% 

40.193 -12.045 0.628 0.216 167.9% 

27.394 11.316 2.981 0.166 117.0% 

1.658 0.401 0.212 0.186 114.4% 

1.544 0.439 0.303 0.276 94.7% 

32.041 -5.251 0.510 0.141 168.0% 

1.993 0.653 0.309 0.291 99.2% 

20.896 -0.243 0.326 0.048 168.6% 

71.182 -145.715 4.217 N/A 2 125.6% 

1.218 0.592 0.401 0.381 60.5% 

17.963 -2.444 0.743 0.417 157.5% 

1.465 0.183 0.154 0.121 136.6% 

1.992 0.638 0.367 0.346 93.6% 

27.734 -6.758 0.294 0.085 169.7% 

2.132 0.266 0.100 0.065 155.8% 

15.586 -1.407 0.585 0.222 160.5% 

1.643 0.102 0.101 0.065 162.6% 

20.909 -2.433 0.562 0.196 164.1% 

3.422 0.396 0.268 0.189 147.0% 

23.744 -0.208 0.305 0.037 169.5% 

1.738 0.936 0.289 0.267 85.9% 

1.732 0.465 0.309 0.288 99.3% 

27.518 -4.866 0.324 0.076 169.5% 

1.287 0.054 0.110 0.067 159.5% 

14.324 -0.934 0.458 0.139 162.8% 

1.467 0.218 0.144 0.101 136.4% 

25.657 -5.226 0.316 0.078 169.3% 

29.943 -1.682 0.499 0.060 168.5% 

38.009 -15.752 1.607 0.464 159.8% 

30.407 -4.569 1.934 0.893 151.2% 

B - 84

Table 14. Comparison of SL-IQEs calculated using different Model Types, Episode 6000 Data 

(μg pt w ootnoted) 

SL-IQE 10%, Based on Given Model RSD 1 

/L exce here f

Analyte Method Procedure Constant Linear Exponential Hybrid 

2-hexanone 524.2 47.881 -30.174 2.102 0.442 160.2% 

2-nitropropane 524.2 38.203 -16.221 2.531 0.590 153.7% 

4-chlorotoluene 502.2 ELCD 4.017 0.161 0.383 N/A 3 142.4% 

4-chlorotoluene 524.2 23.810 -1.231 0.278 0.032 169.9% 

4-isopropyltoluene 524.2 20.421 0.528 0.265 0.016 189.9% 

4-methyl-2-pentanone 524.2 41.919 -23.810 2.804 1.785 147.6% 

Acetone 524.2 47.703 -8.481 5.137 2.741 136.5% 

Acrylonitrile 524.2 28.056 -3.845 3.129 1.651 135.6% 

Allyl Chloride 524.2 29.674 -3.694 0.521 0.121 167.7% 

Aluminum 1620 464.069 255.899 130.746 N/A 2 59.4% 

Aluminum 200.8 ICP/MS 93.989 37.673 30.404 29.684 64.5% 

Ammonia as Nitrogen 4 350.3 0.175 0.052 0.035 0.035 90.3% 

Antimony 1620 9.551 8.719 8.275 8.104 7.5% 

Antimony 200.8 ICP/MS 0.525 0.073 0.044 0.034 140.8% 

Arsenic 1620 4.705 3.542 3.240 3.097 20.0% 

Arsenic 200.8 ICP/MS 4.629 0.692 0.859 0.798 110.3% 

Barium 1620 4.118 3.475 2.973 2.934 16.4% 

Barium 200.8 ICP/MS 0.589 0.211 0.197 0.183 66.6% 

Benzene 502.2 PID 1.798 0.182 0.189 0.155 139.7% 

Benzene 524.2 19.325 -0.385 0.284 0.044 168.9% 

Beryllium 1620 3.559 0.964 1.044 0.980 78.3% 

Beryllium 200.8 ICP/MS 0.382 0.041 0.057 0.044 127.8% 

Boron 1620 86.584 51.134 49.514 47.266 31.9% 

Bromobenzene 502.2 ELCD 3.704 3.529 1.408 1.417 50.7% 

Bromobenzene 502.2 PID 1.277 0.100 0.118 0.079 149.8% 

Bromobenzene 524.2 28.621 -7.963 0.479 0.140 167.7% 

Bromochloromethane 502.2 ELCD 2.106 1.598 0.399 0.379 77.6% 

Bromochloromethane 524.2 19.625 -2.531 0.787 0.368 158.8% 

Bromodichloromethane 502.2 ELCD 1.384 0.424 0.178 0.148 108.8% 

Bromodichloromethane 524.2 17.863 -1.404 0.465 0.128 164.9% 

Bromoform 502.2 ELCD 3.393 2.540 0.922 0.877 64.3% 

Bromoform 524.2 22.334 -4.327 0.914 0.482 157.9% 

Bromomethane 502.2 ELCD 16.351 5.779 N/A 3 N/A 2 67.6% 

Bromomethane 524.2 27.570 -5.134 0.637 0.226 165.3% 

Cadmium 1620 0.816 0.505 0.445 0.410 34.1% 

Cadmium 200.8 ICP/MS 0.090 0.065 0.054 0.063 23.1% 

Calcium 1620 121.796 99.975 86.815 84.600 17.4% 

Carbon Disulfide 524.2 33.263 -7.679 0.545 0.101 168.3% 

Carbon Tetrachloride 524.2 34.000 -7.521 0.718 0.140 166.8% 

Carbontet+1,1-dcp 502.2 ELCD 4.480 0.105 0.167 0.069 181.2% 

Chloroace tonitrile 524.2 24.059 -2.331 3.679 3.310 114.7% 

Chlorobenzene 502.2 ELCD 2.202 1.766 0.477 0.458 72.9% 

Chlorobenzene 502.2 PID 1.679 0.092 0.151 0.119 152.8% 

Chlorobenzene 524.2 23.041 -2.418 0.300 0.059 169.2% 

Chloroethane 502.2 ELCD 5.826 4.368 2.730 2.613 39.2% 

Chloroethane 524.2 31.932 -4.186 0.907 0.255 164.1% 

Chloroform 502.2 ELCD 1.640 0.025 0.075 0.011 183.1% 

Chloroform 524.2 20.902 -1.329 0.511 0.121 165.6% 

B - 85

Table 14. Comparison of SL-IQEs calculated using different Model Types, Episode 6000 Data 

(μg/L except where footnoted) 

Analyte Method Procedure 

SL-IQE 10%, Based on Given Model RSD 1 

Constant Linear Exponential Hybrid 

Chloromethane 502.2 ELCD 2.533 1.734 0.650 0.678 65.0% 

Chloromethane 524.2 43.690 -89.292 0.577 0.141 169.0% 

Chromium 1620 2.444 1.259 1.141 1.062 44.0% 

Chromium 200.8 ICP/MS 1.538 1.028 0.681 0.669 41.7% 

Cis-1,2-dce+2,2-dcp 502.2 ELCD 4.244 0.218 0.127 0.039 178.0% 

Cis-1,2-dichloroethene 524.2 25.054 -3.865 0.532 0.144 166.4% 

Cis-1,3-dichloropropene 502.2 ELCD 1.604 0.415 0.177 0.151 117.3% 

Cis-1,3-dichloropropene 502.2 PID 2.077 0.222 0.196 N/A 3 129.7% 

Cis-1,3-dichloropropene 524.2 15.751 -1.358 0.391 0.141 164.7% 

Cobalt 1620 67.490 40.837 38.691 36.682 31.5% 

Cobalt 200.8 ICP/MS 0.166 -0.022 -0.009 0.002 138.6% 

Copper 1620 47.509 39.683 34.348 33.546 16.6% 

Copper 200.8 ICP/MS 1.825 0.984 0.487 0.477 67.2% 

Dibromochloromethane 502.2 ELCD 1.757 1.252 0.349 0.330 76.3% 

Dibromochloromethane 524.2 18.012 -2.066 0.653 0.288 160.3% 

Dibromomethane 502.2 ELCD 1.874 1.395 0.475 0.447 67.3% 

Dibromomethane 524.2 15.614 -1.663 0.885 0.460 152.6% 

Dichlorodifluoromethane 502.2 ELCD 4.918 -0.244 0.732 0.654 116.1% 

Dichlorodifluoromethane 524.2 53.352 30.938 1.297 0.480 118.6% 

Diethyl Ether 524.2 26.391 -4.619 0.860 0.404 161.4% 

Ethyl Me thacryla te 524.2 22.094 -3.192 0.621 0.183 164.1% 

Ethylbenzene 502.2 PID 1.991 0.128 0.188 0.157 148.8% 

Ethylbenzene 524.2 26.591 -3.326 0.450 0.077 168.2% 

Hardness 4 130.2 8.005 5.465 5.109 5.258 23.0% 

Hexachlorobutadiene 502.2 ELCD 2.236 0.753 0.228 0.243 109.3% 

Hexachlorobutadiene 524.2 39.496 -21.961 0.703 0.228 167.2% 

Hexachloroethane 524.2 40.301 -19.924 0.657 0.167 168.0% 

Hexchlobutadiene+naphthalene 502.2 PID 3.234 2.358 1.524 1.542 37.5% 

Iron 1620 1091.863 -281.500 N/A 3 N/A 3 N/A 

Isopropylbenzene 502.2 PID 1.919 0.129 0.141 0.088 158.1% 

Isopropylbenzene 524.2 25.592 -0.498 0.270 0.029 170.2% 

Lead 1620 8.914 5.698 5.587 5.489 25.9% 

Lead 200.8 ICP/MS 2.305 0.685 0.478 0.462 90.4% 

M+p xylene 502.2 PID 3.813 0.031 0.285 0.222 167.3% 

M+p xylene 524.2 24.651 -0.743 0.321 0.037 169.5% 

Magnesium 1620 326.719 267.199 247.396 240.982 14.4% 

Manganese 1620 15.264 10.195 7.113 6.899 39.5% 

Manganese 200.8 ICP/MS 0.245 0.156 0.079 0.076 57.3% 

Mercury 200.8 ICP/MS 1.854 0.019 0.063 0.039 183.8% 

Methacrylon itrile 524.2 19.062 -0.518 1.655 0.815 143.5% 

Methyl Iodide 524.2 26.956 -3.833 0.439 0.083 168.3% 

Methyl tert-butyl ether 524.2 23.940 -4.171 0.511 0.122 166.5% 

Methyla crylate 524.2 29.913 -5.560 1.386 0.727 156.1% 

Methylene Chloride 502.2 ELCD 6.033 5.201 -4.095 N/A 2 10.5% 

Methylene Chloride 524.2 19.701 -1.528 0.717 0.433 158.9% 

Methylm ethacryl ate 524.2 20.773 -1.043 1.228 0.561 152.7% 

Molybdenum 1620 11.003 7.597 7.049 6.869 23.9% 

Molybdenum 200.8 ICP/MS 0.608 0.260 N/A 3 0.026 98.3% 

B - 86

Table 14. Comparison of SL-IQEs calculated using different Model Types, Episode 6000 Data 

(μg pt w ootnoted) 

SL-IQE 10%, Based on Given Model RSD 1 

/L exce here f

Analyte Method Procedure

N-butylbenzene 502.2 PID 

N-butylbenzene 524.2 

N-propylbenzene 502.2 PID 

N-propylbenzene 524.2 

Napthalene 524.2 

Nickel 1620 

Nickel 200.8 ICP/MS 

O-xylene 524.2 

O-xylene+styrene 502.2 PID 

P-isoproptol+1,4-dcb 502.2 PID 

Pentachloroethane 524.2 

Sec-butylbenzene 502.2 PID 

Sec-butylbenzene 524.2 

Selenium 1620 

Selenium 200.8 ICP/MS 

Silver 1620 

Silver 200.8 ICP/MS 

Sodium 1620 

Styrene 524.2 

Tert-butylbenzene 502.2 PID 

Tert-butylbenzene 524.2 

Tetrachloroethene 502.2 ELCD 

Tetrachloroethene 502.2 PID 

Tetrachloroethene 524.2 

Thallium 1620 

Thallium 200.8 ICP/MS 

Thorium 200.8 ICP/MS 

Tin 1620 

Titanium 1620 

Toluene 502.2 PID 

Toluene 524.2 

Total Phosphorus 4 365.2 

Total Suspended Solids 4 160.2 

Trans-1,2-dichloroethene 502.2 ELCD 

Trans-1,2-dichloroethene 524.2 

Trans-1,3-dichloropropene 502.2 ELCD 

Trans-1,3-dichloropropene 502.2 PID 

Trans-1,3-dichloropropene 524.2 

Trans-1,4-dichloro-2-butene 524.2 

Trichloroethene 502.2 ELCD 

Trichloroethene 502.2 PID 

Trichloroethene 524.2 

Trichlorofluoromethane 502.2 ELCD 

Trichlorofluoromethane 524.2 

Uranium 200.8 ICP/MS 

Vanadium 1620 

Vanadium 200.8 ICP/MS 

Vinyl Chloride 502.2 ELCD 

Constant Linear Exponential Hybrid 

1.601 0.745 0.343 0.325 79.3% 

22.952 -0.521 0.345 0.067 168.6% 

1.759 0.351 0.221 0.186 120.2% 

29.878 -3.650 0.647 0.148 166.5% 

33.249 -4.704 0.422 0.108 169.1% 

113.424 67.206 60.455 57.072 35.2% 

2.341 0.800 0.202 0.183 115.1% 

25.884 -3.313 0.450 0.040 168.4% 

3.077 0.181 0.272 0.202 153.2% 

3.550 0.456 0.380 0.312 134.9% 

24.914 -3.372 0.934 0.551 158.6% 

2.112 0.346 0.196 0.157 134.2% 

25.203 0.279 0.316 0.047 193.4% 

9.268 5.235 4.657 4.474 38.3% 

4.686 1.045 0.957 0.829 99.7% 

29.640 25.842 24.547 24.294 9.5% 

0.107 0.056 0.030 0.034 62.6% 

379.229 337.755 323.935 317.747 8.1% 

23.420 -2.180 0.318 0.041 169.3% 

1.916 0.203 0.177 0.135 143.6% 

26.246 -1.197 0.423 0.073 168.4% 

2.078 0.415 0.145 0.122 135.5% 

2.303 0.750 0.392 0.400 94.7% 

30.554 -2.553 1.080 N/A 2 131.8% 

3.870 2.799 2.661 2.614 19.9% 

0.007 0.002 0.002 0.002 70.9% 

0.074 0.004 0.003 0.001 174.7% 

12.904 9.406 9.064 8.971 18.7% 

19.058 14.236 12.443 12.213 21.9% 

1.640 0.194 0.153 0.124 140.6% 

21.925 -1.050 0.330 0.046 168.8% 

0.040 0.032 0.030 0.030 14.1% 

9.679 7.570 6.985 6.729 17.3% 

2.068 0.795 0.197 0.191 108.7% 

30.588 -4.773 0.684 0.153 166.3% 

1.492 0.729 0.237 0.212 89.8% 

1.457 0.206 0.221 0.175 122.1% 

14.821 -1.254 0.506 0.218 161.1% 

30.108 -3.685 2.938 1.819 137.8% 

2.256 3.169 0.141 0.120 108.1% 

2.049 0.401 0.235 0.209 122.7% 

27.861 -2.666 0.759 0.167 164.9% 

4.662 5.166 3.222 3.308 23.8% 

42.490 -50.543 0.881 N/A 2 135.7% 

0.005 0.001 0.001 0.001 112.1% 

50.943 26.049 25.112 24.338 40.8% 

6.320 1.828 2.022 1.933 72.6% 

8.234 4.775 3.544 3.828 42.3% 

B - 87

Table 14. Comparison of SL-IQEs calculated using different Model Types, Episode 6000 Data 

(μg pt w ootnoted) 

SL-IQE 10%, Based on Given Model RSD 1 

/L exce here f

Analyte Method Procedure Constant Linear Exponential Hybrid 

Vinyl Chloride 524.2 49.647 49.158 0.837 0.219 113.0% 

Wad Cyanide 1677 WADCN 2.277 1.624 1.414 1.424 24.2% 

Xylene (total) 524.2 23.520 -0.952 0.290 0.019 169.8% 

Yttrium 1620 10.244 8.962 7.839 7.516 14.3% 

Zinc 1620 32.799 12.850 10.999 10.452 64.0% 

Zinc 200.8 ICP/MS 17.301 7.024 3.817 3.741 80.4% 1

 Calculation in cludes pos itive IQEs only
 2

 Given model did not converge
 3

 IQE 10% could not be calculated based on given model
 4

 Results reported as mg/L


Summary Statistics for Table 14 

Method # analytes Minimum 25th Percentile Median 

All 197 7.5% 72.6% 135.6% 

502.2 65 10.5% 79.3% 114.4% 

524.2 81 43.2% 157.9% 165.7% 

1620 25 7.5% 16.6% 23.9% 

75th Perce ntile Maximum 

165.3% 193.4% 

142.4% 183.1% 

168.4% 193.4% 

38.3% 78.3% 

200.8 21 23.1% 66.6% 90.4% 115.1% 183.8% 

B - 88

Table 15. Comparison of SL-IDEs and SL-IQEs Calculated Using Different Software 

Analyte Model Type Limit QCalc Excel

1,1-dichloroethene (502.2) 

1,2,4-trichlorobenzene (502.2, ELCD) 

1,3,5-trimethylbenzene (524.2) 

Antimony (1620) 4 

Arsenic (200.8) 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

SAS 1 

0.2135 

0.886 

0.2337 

0.627 

0.3051 

3.796 

1.167 

2.617 

0.0694 

0.186 

0.0880 

0.212 

0.0817 

0.401 

0.740 

1.658 

0.0157 

0.037 

0.1349 

0.305 

-0.0586 

-0.208 

10.590 

23.744 

3.5960 

8.104 

3.5616 

8.275 

3.7283 

8.719 

4.260 

9.551 

0.3449 

0.798 

0.3661 

0.859 

0.2570 

0.692 

2.023 

4.629 

-0.0338 0.3180 2 

-0.87 2.006 

0.2307 0.2367 

0.622 

0.3059 

3.7 3.693 

1.169 

2.604 

0.0688 0.1072 

0.19 0.297 

0.0874 0.0888 

0.212 

0.0821 

0.40 0.399 

0.741 

1.651 

0.0157 -4.10E-07 

0.04 -6.00E-06 

0.1345 0.1367 

not calc 3 

-0.0595 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 not ca lc 3 not ca lc 3 

Constant IDE 

IQE 10 

Linear 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

10.448 

23.269 

3.5724 3.8364 

8.10 8.578 

3.5380 3.5853 

8.270 

3.7511 

8.72 8.713 

4.266 

9.502 

0.3433 0.3675 

0.80 0.837 

0.3643 0.3734 

0.858 

0.2623 

0.69 0.691 

2.056 

4.611 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

B - 89

Table 15. Comparison of SL-IDEs and SL-IQEs Calculated Using Different Software 

Analyte Model Type Limit Excel

Hybrid IDE


IQE 10


Bromoform (524.2) Exponential IDE


IQE 10


Linear IDE


IQE 10


Constant IDE


IQE 10


QCalc SAS 1 

0.2165 -0.0094 0.2113 

0.48 -0.132 0.482 

0.4097 0.4157 0.3998 

not calc 3 0.914 

-1.3717 -1.3091 

not calc 3 not ca lc 3 -4.327 

10.355 10.207 

22.220 22.334 

0.1048 -0.0035 0.1036 

0.25 -0.057 0.255 

0.3999 0.4028 0.3953 

not calc 3 0.907 

-0.8594 -0.8365 

not calc 3 not ca lc 3 -4.186 

14.518 14.465 

31.769 31.932 

0.0600 0.1254 0.0606 

0.15 0.351 0.151 

0.0734 0.0750 0.0740 

0.176 0.177 

0.0833 0.0830 

0.41 0.412 0.415 

0.718 0.716 

1.598 1.604 

0.1397 0.4531 0.1406 

0.33 1.081 0.330 

0.1430 0.1502 0.1441 

0.348 0.349 

0.4389 0.4359 

1.25 1.252 1.252 

0.786 0.784 

1.750 1.757 

0.2001 0.3318 0.2005 

0.46 0.752 0.462 

0.2033 0.2086 0.2038 

0.477 0.478 

0.2705 0.2650 

0.68 0.684 0.685 

1.024 1.007 

2.296 2.305 

Hybrid IDE


IQE 10


Chloroethane (524.2) Exponential IDE


IQE 10


Linear IDE


IQE 10


Constant IDE


IQE 10


Hybrid IDE


IQE 10


Cis-1,3-dichloropropene (502.2 ELCD) Exponential IDE


IQE 10


Linear IDE


IQE 10


Constant IDE


IQE 10


Hybrid IDE


IQE 10


Dibromochloromethane (502.2) Exponential IDE


IQE 10


Linear IDE


IQE 10


Constant IDE


IQE 10


Hybrid IDE


IQE 10


Lead (200.8) Exponential IDE


IQE 10


IDE


IQE 10


Linear 

Constant IDE


IQE 10


B - 90

Table 15. Comparison of SL-IDEs and SL-IQEs Calculated Using Different Software 

Analyte Model Type Limit QCalc Excel

M+p Xylene (502.2) 

Methylmethacrylate (524.2) 

Sec-butylbenzene (524.2) 

Selenium (200.8) 

Selenium (1620) 

Sodium (1620) 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

SAS 1 

0.0883 

0.222 

0.1205 

0.285 

0.0052 

0.031 

1.701 

3.813 

0.2441 

0.561 

0.5350 

1.228 

-0.3415 

-1.043 

9.597 

20.773 

0.0195 

0.047 

0.1397 

0.316 

0.0798 

0.279 

11.240 

25.203 

0.3637 

0.829 

0.4159 

0.957 

0.4059 

1.045 

2.090 

4.686 

1.9709 

4.474 

1.9754 

4.657 

2.0539 

5.235 

4.161 

9.268 

140.8112 

317.747 

138.7678 

323.935 

0.0876 0.0872 

0.22 0.255 

0.1197 0.1208 

0.285 

0.0053 

0.03 0.030 

1.704 

3.795 

0.2522 -0.0267 

0.56 -0.364 

0.5528 0.5615 

not calc 3 

-0.3617 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 not ca lc 3 not ca lc 3 

Constant IDE 

IQE 10 

Linear 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

9.734 

20.667 

0.0194 0.0205 

0.05 0.050 

0.1388 0.1403 

0.316 

0.0803 

0.28 0.279 

11.258 

25.074 

0.3565 0.4600 

0.83 1.045 

0.4076 0.4159 

0.957 

0.4057 

1.04 1.044 

2.082 

4.668 

-0.3256 2.2850 

-4.47 5.107 

1.9742 2.0045 

4.653 

2.0809 

5.23 5.231 

4.195 

9.221 

139.8852 145.2512 

317.64 326.198 

137.8479 139.6656 

323.711 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

B - 91

Table 15. Comparison of SL-IDEs and SL-IQEs Calculated Using Different Software 

Model Type Limit QCalc Excel SAS 1 

142.1564 141.2901 

337.63 337.515 337.755 

169.406 169.136 

377.295 379.229 

0.0175 -5.70E-08 0.0174 

0.04 -8.40E-07 0.041 

0.1407 0.1423 0.1405 

not calc 3 0.318 

-0.6099 -0.6000 

not calc 3 not ca lc 3 -2.180 

10.555 10.516 

23.301 23.420 

10.6227 11.4032 10.6931 

24.33 25.889 24.338 

10.5597 10.7036 10.6304 

25.094 25.112 

10.0290 9.9671 

26.04 26.029 26.049 

22.757 22.721 

50.684 50.943 

0.0840 -2.30E-07 0.0834 

0.22 -9.78E-07 0.219 

0.3671 0.3701 0.3649 

not calc 3 0.837 

-3.4286 -3.3451 

49.30 not ca lc 3 49.158 

22.474 22.292 

49.394 49.647 

3.2571 3.6382 3.2787 

7.51 8.305 7.516 

3.2251 3.2726 3.2468 

7.833 7.839 

3.5420 3.5202 

8.96 8.955 8.962 

4.576 4.569 

10.192 10.244 

Analyte 

Styrene (524.2) 

Vanadium (1620) 

Vinyl Chloride (524.2) 

Yttrium (1620) 

1

 Calculated using SAS programs written by EPA to run IDE and IQE calculations. Results are the same as those


presented in Tables 2 and 4.
 2

 Limits in bold indicate the calculated IDE or IQE based on the model suggested as most appropriate based on the


given software.


3

 No value could be calculated d ue to model not con verging.
 4

 Based on statistical tests, QCalc determined that the constant model should be used to calculate the IDE and IQE.


However, determination of the IDE and IQE using the constant model is not run by this program.


Linear IDE 

IQE 10 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

Hybrid IDE 

IQE 10 

Exponential IDE 

IQE 10 

IDE 

IQE 10 

Linear 

Constant IDE 

IQE 10 

B - 92

Table 16. Summary Statistics of Ratios Comparing IDEs/IQEs using different Software Packages 

Comparison 

Ratio 

QCalc/ SAS 

Excel/ SAS 

QCalc/ Excel 

Model Type 

Hybrid 

Exponential 

Linear 

Hybrid 

Exponential 

Linear 

Constant 

Hybrid 

Exponential 

Linear 

Maximum 25th 75th Limit Minimum Median Perce ntile Perce ntile 

IDE -0.17 0.99 0.99 1.00 1.03 

IQE 10 -1.00 0.99 1.00 1.00 1.07 

IDE 0.98 0.99 0.99 1.00 1.03 

IQE 10 0.97 0.99 1.00 1.00 1.00 

IDE -0.11 -0.000003 1.10 1.32 3.22 

IQE 10 -0.65 -0.000009 1.06 1.35 3.27 

IDE 1.00 1.01 1.01 1.02 1.05 

IQE 10 0.99 1.00 1.00 1.00 1.00 

IDE 1.00 1.01 1.01 1.02 1.06 

IQE 10 0.97 1.00 1.00 1.00 1.00 

IDE 0.99 1.00 1.00 1.01 1.02 

IQE 10 0.98 0.99 0.99 1.00 1.00 

IDE -365,000 -12.85 0.54 0.93 1.01 

IQE 10 -225,000 -2.07 0.52 0.91 1.01 

IDE 0.96 0.98 0.99 0.99 0.99 

IQE 10 0.99 1.00 1.00 1.00 1.00 

B - 93

Table 17. Comparison of Simulated 7-replicate ACIL CRVs to Overall CRV, ACIL Blanks 

Analyte # Blanks * Overa ll 

CRV 

# 

7-replicate 

CRVs 

Mean of 

Simulated 7-

replicate 

CRVs 

Range of 

Simulated 

7-replicate 

CRVs 

Range of 

Days between 

1st and Last 

of 7 

consecutive 

replicates 

% short-term 

CRVs 

exceeding 

Overall CRV 

Barium 26 0.0039 20 0.0039 0.0011 to 

0.0083 

7 to 26 30 

Cadmium 33 0.0012 27 0.0014 0.0004 4 to 

0.0019 

11 to 24 67 

Chromium 55 0.0048 49 0.0051 0.0014 to 

0.0117 

7 to 20 29 

Copper 52 0.0035 46 0.0039 0.0010 to 

0.0059 

7 to 20 78 

Silver 45 0.0105 39 0.0100 0.0019 to 

0.0326 

7 to 20 28 

simulated 

*

 Analyzed over a period of 3 months 

Table 18. Comparison of Simulated 7-replicate ACIL CRVs to Overall CRV, ACIL Blanks 

After Outlier Removal 

Analyte # Blanks * Overa ll 

CRV 

# simulated 

7-replicate 

CRVs 

Mean of 

Simulated 7-

replicate CRVs 

Range of 

Simulated 

7-replicate 

CRVs 

Range of 

Days 

between 1st 

and Last of 7 

consecutive 

replicates 

% short-term 

CRVs 

exceeding 

Overall CRV 

Barium 25 0.0020 19 0.0021 0.0011 to 

0.0029 

11 to 26 74 

Chromium 54 0.0040 48 0.0044 0.0014 to 

0.0080 

7 to 20 56 

Silver 42 0.0031 36 0.0038 0.0019 to 

0.0058 

8 to 21 72 

*

 Analyzed over a period of 3 months 

B - 94

Appendix C 

Example Calculations 

This Appendix is included to support Appendices B of this Assessment Document, by providing 

example calculations of the single-laboratory variants of the Interlaboratory Detection Estimate (SL-IDE) and 

Interlaboratory Quantitation Estimate (SL-IQE) as described in ASTM D6091 and ASTM D6512, respectively. 

Example calculations of the method detection limit (MDL) and minimum level of quantitation (ML) also are 

included. The example calculations provided in this Appendix were used in the data analyses presented in 

Appendix B. 

All abbreviations and symbols used in the SL-IDE and SL-IQE calculations match those given in the 

ASTM procedures. The linear and exponential standard deviation models and all recovery models were fit 

using the PROC REG procedure in SAS Version 8.1. The hybrid standard deviation model was fit using 

Newton’s Non-Linear Least Squares procedure as described in ASTM D6512, programmed using SAS Version 

8.1. The dataset used in these examples is that included for 1,1,1,2- tetrachloroethane in EPA’s Episode 6000 

(see Chapter 1 and Appendix B of this document for descriptions of datasets). 

Single-Laboratory IDE (SL-IDE) 

The procedure for calculating the IDE that is described in ASTM D6091 stipulates use of data from 

multiple laboratories. However, because analytes in the Episode 6000 dataset were only measured by a single 

laboratory, EPA calculated a variant of the IDE which was called the single-laboratory IDE (SL-IDE). The SLIDE and the analyses performed using the SL-IDE are described in greater detail in Appendix B of this 

Assessment document. 

In order to calculate the SL-IDE, means and standard deviations are needed for each spike level. The 

means and standard deviations for 1,1,1,2-tetrachloroethane are listed in Table 1. 

Table 1. Mean and Standard Deviation Calculated at each Spike Level 

Spike (ug/L) N Mean (ug/L) 

0.01 7 0.0016 

0.015 7 0.001 

0.02 7 0.0007 

0.035 7 0.0057 

0.05 7 0.0081 

0.075 7 0.0263 

0.1 6 0.0295 

0.15 7 0.0536 

0.0017 

0.0010 

0.0036 

0.0024 

0.0202 

0.0039 

0.0046 

0.20 7 0.0991 0.0158 

SD (ug/L) 

0.0018 

C - 14

Spike (ug/L) N Mean (ug/L) SD (ug/L) 

0.35 7 0.235 0.0078 

0.50 7 0.3744 0.0257 

0.75 6 0.6193 0.0262 

1.0 8 0.8368 0.0814 

2.0 7 1.9560 0.0980 

5.0 8 5.0994 0.2382 

10.0 7 10.4453 0.5469 

In order to choose the appropriate model to calculate the IDE, significance tests were used. 

The fitted unweighted linear model was: 

S = 0.000039515 + 0.05326 * T, where T corresponds to spike concentration 

The slope of this model was significantly greater than 0, and therefore the constant model was rejected. 

The fitted unweighted exponential model (fit by natural log-transforming standard deviations) was: 

Log(S)= -5.02407 + 0.54851 * T 

The slope of this model was significantly greater than 0, thus, the linear model was rejected. 

Based on this assessment, the exponential model was used in Appendix B to calculate the IDE for this analyte. 

While the exponential model was chosen as the most appropriate model for this analyte, the calculation of the 

SL-IDE using all four model types is presented in this Appendix. This was done to provide a step-by-step 

example for the calculation of the SL-IDE using all of the different model types. 

Constant model: The pooled within-spike variance was first calculated using the equation below: 

where: si

 is the standard deviation of the results for spike level i, and 

ni

 is the number of replicates for spike level i. 

The calculated pooled within-spike variance (g2) is 0.024, and the square root of this value, g, equals 0.155. 

C - 15

A linear regression model was then fit for the mean results for the 16 spike levels. The estimates of slope and 

intercept for this model are: a = -0.089 and b=1.0478, respectively. 

Based on these results: 

YC = (k1 * g) + a = (0.155 * k1) - 0.089 = (0.155 * 2.6) - 0.089 = 0.3137 

where: YC = the recovery critical value as defined in ASTM D6091, and 

k1 = 2.6 (a conservative number based on the total n of 112) 

LC = (YC - a)/b = (0.3137 + 0.089) / 1.0478 = 0.3848 

where: LC = the true concentration critical value as defined in ASTM D6091. 

IDE = LC + (k2 * g)/b = 0.3848 + (1.86 * 0.155)/1.0478 = 0.660 

where: k2 = 1.86 (a conservative number based on the total n of 112). 

Linear Model: 

An unweighted linear regression model was fit, predicting standard deviation based on concentration, using 

PROC REG in SAS Version 8.1. The estimated parameters are: g = 0.0000392 and h = 0.05326. Based on these 

parameters, weights for the recovery model were calculated for each spike value. For each concentration, the 

weight was calculated as: 

The calculated weights are given in Table 2. 

, for each true concentration Ti

. 

Table 2. Calculated Weights based on Linear Model 

Spike (ug/L) Est. SD (ug/L) Weight 

0.01 0.00057 3,058,709 

0.015 0.00084 1,423,673 

0.02 0.00110 819,854 

0.035 0.00190 276,031 

0.05 0.00270 136,940 

0.075 0.00403 61,454 

0.1 0.00537 34,736 

0.15 0.00803 15,514 

C - 16

Spike (ug/L) Est. SD (ug/L) Weight 

0.20 0.01069 8,748 

0.35 0.01868 2,865 

0.50 0.02667 1,406 

0.75 0.03999 625.4 

1.0 0.05330 352 .0 

2.0 0.10657 88.1 

5.0 0.26635 14.1 

10.0 0.53267 3.52 

Using these weights, the fitted recovery model estimates were a = -0.00898 and b = 0.6860. Based on these 

results: 

YC = (k1 * g) + a = (0.0000392 * 2.6) - 0.00898 = -0.00888, and 

LC = (YC - a)/b = (-0.00888 + 0.00898) / 0.6860 = 0.00015 

For the linear model, the SL-IDE must be calculated recursively. The initial estimate of the SL-IDE, LD0, was: 

LD0 = LC + (k2*s(0)) / b = 0.00025. 

Each following estimate was calculated using the recursive formula: 

Results of the recursive LD calculations are given in Table 3. 

Table 3. Recursive SL-IDE Calculations, Linear Model 

LD estimate LD estimate 

run 

0 0.000255 

1 0.000291 

2 0.000297 

3 0.000297 

The recursive estimates of LD converge to 6 decimal places by the third iteration. Therefore, the linear model 

estimate of the IDE = 0.000297 ug/L. 

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Exponential Model: 

An unweighted linear regression model was fit, predicting natural log-transformed standard deviation based on 

concentration. The estimated parameters are: g = 0.00658 and h = 0.54851. Based on these parameters, weights 

for the recovery model were calculated for each spike value. For each concentration, the weight was calculated 

as: 

, for each true concentration Ti. 

The calculated weights are given in Table 4. 

Table 4. Calculated Weights based on Exponential Model 

Spike (ug/L) Est. SD (ug/L) Weight 

0.01 0.00661 22,861 

0.015 0.00663 22,736 

0.02 0.00665 22,611 

0.035 0.00671 22,242 

0.05 0.00676 21,879 

0.075 0.00685 21,287 

0.1 0.00695 20,711 

0.15 0.00714 19,606 

0.20 0.00734 18,560 

0.35 0.00797 15,744 

0.50 0.00865 13,355 

0.75 0.00993 10,152 

1.0 0.01138 7,717 

2.0 0.01970 2,576 

5.0 0.10213 96 

10.0 1.58566 0.40 

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Using these weights, the fitted recovery model estimates were a = -0.04585, and b = 0.91696. Based on these 

results: 

YC = (k1 * g) + a = (0.00658 * 2.6) - 0.04585 = -0.0287, and 

LC = (YC - a)/b = (-0.0287 + 0.04585) / 0.91696 = 0.0187 

For the Exponential model, the SL-IDE must be calculated recursively. The initial estimate of the SL-IDE, LD0, 

was: 

LD 0 = LC + (k2*s(0)) / b = 0.03199. 

Each following estimate was calculate using the recursive formula: 

Results of the recursive LD calculation are given in Table 5, below. 

Table 5. Recursive SL-IDE Calculations, Exponential Model 

LD estimate run LD estimate 

0 0.031993 

1 0.032229 

2 0.032231 

The recursive estimates of LD converge to 6 decimal places by the second iteration. Therefore, the exponential 

model estimate of the IDE = 0.032231 ug/L. 

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Hybrid Model: 

The Hybrid model was fit using Newton’s Method for Non-linear Least Squares. Summary statistics from this fit of the hybrid model are presented 

in Table 6, using the same notation as shown in ASTM D6512-00. 

Table 6. Summary Statistics from Newton’s Non-Linear Least Squares 

Run g h u v c d p q �g �h dg% dh% 

0 0.00095 0.05465 1,254330 4,285 19,889 2 x 10 -10 555.95 -0.592 0.00048 -0.00237 50.5 43.4 

1 0.00143 0.05228 981,892 4,275 15,368 3 x 10 -10 41.83 -1.132 0.00005 -0.00044 3.45 8.5 

2 0.00148 0.05184 958,193 4,309 15,092 3 x 10 -10 4.47 -0.123 5 x 10 -6 -0.00005 0.37 0.9 

Because dg% (the percent difference between the last 2 estimates of g) and dh% (the percent difference between the last 2 estimates of h) were both 

less than 1% in run 2, the model converged, and the estimated parameters of the hybrid model were: 

g = g run 2 +� g run 2 = 0.00148 + 0.000005 = 0.00149 

h = h run 2+ � h run 2 = 0.05184 - 0.00005 = 0.05179 

Using these fitted parameters, the weights for the recovery model were calculated as shown in Table 7. 

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Table 7. Calculated Weights, Hybrid Model 

Spike (ug/L) Est. SD (ug/L) Weight 

0.01 0.00158 403,037 

0.015 0.00168 355,066 

0.02 0.00181 304,351 

0.035 0.00234 181,881 

0.05 0.00299 112,141 

0.075 0.00416 57,811 

0.1 0.00539 34,447 

0.15 0.00791 15,987 

0.20 0.01046 9,134 

0.35 0.01819 3,024 

0.50 0.02594 1,487 

0.75 0.03887 662 

1.0 0.05181 373 

2.0 0.10358 93.2 

5.0 0.25893 14.9 

10.0 0.51786 3.73 

Using these weights, the fitted recovery model estimates were a = -0.01471, and b = 0.74338. Based on 

these results: 

YC = (k1 * g) + a = (0.00149 * 2.6) - 0.01471 = -0.01085, and 

LC = (YC - a)/b = (-0.01085 + 0.01471) / 0.74338 = 0.00520 

LD had to be calculated recursively. The initial estimate of LD was: 

LD 0 = LC + (k2*s(0)) / b = 0.00893. 

Each following estimate was calculated using the recursive formula: 

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Results of the recursive LD calculation are given in Table 8. 

Table 8. Recursive SL-IDE Calculations, Hybrid model 

LD estimate run 

0 

1 

2 

0.008925 

0.009101 

0.009108 

3 0.009108 

LD estimate 

The recursive estimates of LD converge to 6 decimal places by the third iteration. Therefore, the hybrid model 

estimate of the IDE = 0.009108 ug/L. 

Single-Laboratory IQE (SL-IQE) 

The procedure for the IQE described in ASTM D6512 stipulates use of data from multiple laboratories. 

However, because analytes in the Episode 6000 dataset were only measured by a single laboratory, EPA 

calculated a variant of the IQE which was called the single-laboratory IDE (SL-IQE). The SL-IQE and the 

analyses performed using the SL-IQE are described in greater detail in Appendix B of this Assessment 

document. 

Fitting and selection of models in the IQE calculation process are identical to the IDE calculation process 

except: 

•	 The Hybrid model was considered in model selection instead of the Exponential model, based on 

significance tests for curvature as described in 6.3.3.2 (g) - (i) of ASTM D6512. 

•	 A bias-correction adjustment factor is applied to calculated standard deviations prior to modeling as 

described in 6.3.3.2 (b) of ASTM D6512. 

Therefore, the example calculation begins with the fitted model parameters for each model type, and 

demonstrates the calculation of each IQE value. 

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Constant model: 

Using the same steps for fitting the constant model as described in the SL-IDE example, the fitted precision and 

recovery model parameters are determined to be: 

g = 0.1615


a = -0.0894, and b = 1.0478.


The IQE (10%) was calculated as: IQE (10%) = (g/b)*(100/10) = 1.541 

The IQE (20%) was calculated as: IQE (20%) = (g/b)*(100/20) = 0.770 

The IQE (30%) was calculated as: IQE (30%) = (g/b)*(100/30) = 0.514 

Linear model: 

Using the same steps for fitting the linear model as described in the SL-IDE example, the fitted precision and 

recovery model parameters are determined to be: 

g = 4.2 x 10 -7, h = 0.0555 

a = -0.0087, b = 0.6810 

The IQE (10%) was calculated as: IQE (10%) = g/(b*(10/100)-h) = 3.3 x 10 -5 

The IQE (20%) was calculated as: IQE (20%) = g/(b*(20/100)-h) = 5.2 x 10 -6 

The IQE (30%) was calculated as: IQE (30%) = g/(b*(30/100)-h) = 2.8 x 10 -6 

Hybrid model: 

Using the same steps for fitting the hybrid model as described in the SL-IDE example, the fitted precision and 

recovery model parameters are determined to be: 

g = 0.00155, h = 0.0540 

a = -0.0147, b = 0.7434 

The IQE (10%) was calculated as: 

= 0.0304 

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The IQE (20%) was calculated as: 

= 0.0112 

The IQE (30%) was calculated as: 

= 0.0072 

Exponential model: 

Using the same steps for fitting the constant model as described in the SL-IDE example, the fitted precision and 

recovery model parameters are determined to be: 

g = 0.0069, h = 0.5482 

a = -0.0459, b = 0.9170 

For the Exponential model, the IQE must be solved recursively. The initial estimate of the IQE was set to the 

IDE (re-calculated using bias-corrected standard deviations, and therefore not matching the IDE presented in 

the example above). The IQE was then re-calculated using the estimate from the prior round, based on the 

equation below: 

, 

where: Z i = 10, 20 or 30, depending on the IQE being calculated. 

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Results of the recursive calculations for the IQEs are given in Table 9. 

Table 9. Recursive SL-IDE Calculations, Exponential model 

Run IQE (10%) IQE (20%) IQE (30%) 

0 0.0355 0.0355 0.0355 

1 0.0763 0.0381 0.0254 

2 0.0780 0.0382 0.0253 

3 0.0781 0.0382 0.0253 

4 0.0781 0.0382 0.0253 

MDL/ML 

This section gives an example calculation of the MDL and ML determined using the Episode 6000 data, and 

presented in Appendix B. Due to the nature of the study design, MDLs could not be determined following the 

MDL procedure directly. Therefore, the MDL was calculated based on the results of the two lowest spike 

levels with all positive results for which the standard deviations were not significantly different. 

The lowest two spike levels with all positive, non-zero results are 0.050 �g/L and 0.075 �g/L. From Table 1, 

the standard deviations at these concentrations are 0.0024 �g/L and 0.0202 �g/L, respectively. The F test was 

then run on the variances at these two spike levels: 

The critical value for the F test at �=0.10, where both variances are based on 7 results, is 3.05. Because 70.385 

> 3.05, the variance at the higher concentration is significantly greater than the variance at the lower 

concentration, and these two concentrations cannot be used to calculate the MDL. 

The next lowest spike level (0.10 �g/L) has only 6 results, but all results are greater than 0. Therefore, an F test 

was run comparing variances at 0.075 �g/L and 0.10 �g/L. From Table 1, the standard deviation at 0.10 �g/L is 

0.0039 �g/L. The results of the F test are: 

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The critical value for this F test is 3.11, slightly higher than for the prior comparison due to the fewer number of 

results at the higher spike level. Because 0.037 < 3.11, the variance at the higher spike level is not significantly 

greater than the variance at the lower spike level. Therefore, the MDL is calculated based on these two spike 

levels: 

The ML is determined by first multiplying the pooled standard deviation (0.015 �g/L from the calculation 

above) by 10. This yields a result of 0.15 �g/L. Based on the ML rounding scheme, this becomes 0.2 �g/L. 

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