Laboratory Quality Assurance and Quality Control Data Quality Assessment and Data Usability Evaluation Guidance Document

New Jersey Department of Environmental Protection

Site Remediation Program

DATA QUALITY ASSESSMENT AND

DATA USABILITY EVALUATION

TECHNICAL GUIDANCE

Version 1.0

April 2014

Preamble

The results of analyses performed on environmental matrices are used to determine if

remediation is needed. Because of the nature of environmental matrices, limitations of analytical

methods, characteristics of analytes, and inherent error associated with any sampling and

analysis procedure, the results of environmental analysis may contain an element of uncertainty

and in some cases may be significantly biased, and therefore may not be representative of the

actual concentrations of the analytes in the environmental matrices. Thus, an evaluation of the

quality of the analytical data in relation to the intended use is important in order for the

investigator to make decisions which are supported by data of known and sufficient quality.

There are many ways to evaluate the quality of analytical data in terms of precision, accuracy,

representativeness, comparability, completeness and sensitivity in relation to the intended use

of the data. Precision, accuracy, representativeness, comparability, completeness and

sensitivity are collectively referred to as the PARCCS parameters. This guidance document

describes a NJDEP-accepted, two-step process for data evaluation. The first step in the process

consists of an assessment of data quality. The second step is an evaluation to determine

whether the data can be used to support the decisions that will be made using that data. Use of

this guidance provides consistency in evaluation and presentation of data quality information

that will facilitate review. If an alternative process is used, such a process should be

documented in order to explain the thought process and may involve a commitment of

significant resources to demonstrate that the data is of known and sufficient quality and is

usable relative to its intended purpose.

To assist the investigator in obtaining analytical data of known quality, the Work Group

developed the Data of Known Quality Protocols (DKQPs). The DKQPs include specific

laboratory Quality Assurance and Quality Control (QA/QC) criteria that produce analytical data

of known and documented quality for analytical methods. When Data of Known Quality are

achieved for a particular data set, the investigator will have confidence that the laboratory has

followed the DKQPs, has described nonconformances, if any, and the investigator has adequate

information to make judgments regarding data quality.

The Data of Known Quality performance standards are given in Appendix B of the NJDEP Site

Remediation Program, Data of Known Quality Protocols Technical Guidance, April 2014. These

protocols will enhance the ability of the investigator to readily obtain from the laboratory the

necessary information to identify and document the precision, accuracy and sensitivity of data.

Preamble .......................................................................................................................... i

1. Intended Use of Guidance Document ........................................................................ 1

2. Purpose ...................................................................................................................... 2

3. Document Overview ................................................................................................... 5

4. Procedures ................................................................................................................. 6

Figure 1: DQA and DUE Flow Chart............................................................................... 7

4.1 Data Quality Objectives ...................................................................................... 8

4.2 Uncertainty in Analytical Data ............................................................................ 9

4.3 Types of Analytical Data .................................................................................... 9

4.4 PARCCs Parameters ....................................................................................... 10

4.4.1 Precision .................................................................................................... 10

4.4.2 Accuracy .................................................................................................... 11

4.4.3 Representativeness ................................................................................... 12

4.4.4 Comparability ............................................................................................. 12

4.4.5 Completeness ............................................................................................ 13

4.4.6 Sensitivity .................................................................................................. 13

4.5 Data Quality Assessment ................................................................................. 14

4.5.1 Batch Quality Control versus Site Specific Quality Control ........................ 15

4.5.2 Evaluating Significant Quality Control Variances ....................................... 16

4.5.3 Poorly Performing Compounds .................................................................. 16

iii

4.5.4 Common Laboratory Contaminants ........................................................... 17

4.5.5 Bias ........................................................................................................... 17

4.6 Data Usability Evaluation ................................................................................. 17

4.6.1 Evaluation of Bias ...................................................................................... 20

4.6.2 General Quality Control Information .......................................................... 23

4.6.2.1 Chain of Custody Forms .......................................................................... 23

4.6.2.2 Sample Preservation Holding Times and Handling Time ........................ 23

4.6.2.3 Equipment, Trip and Field Blanks ............................................................ 26

4.6.2.4 Field Duplicates ....................................................................................... 28

4.6.3 Laboratory Quality Control Information ...................................................... 30

4.6.3.1 Data of Known Quality Conformance/Nonconformance

Summary Questionnaire .......................................................................... 31

4.6.3.2 Reporting Limits....................................................................................... 31

4.6.3.3 Method Blanks ......................................................................................... 33

4.6.3.4 Laboratory Duplicates .............................................................................. 34

4.6.3.5 Surrogates ............................................................................................... 34

4.6.3.6 Laboratory Control Samples (LCS) .......................................................... 37

4.6.3.7 Matrix Spike/Matrix Spike Duplicates and Matrix Spike/Matrix Duplicate 38

4.6.3.8 Internal Standards ................................................................................... 41

4.6.3.9 Serial Dilutions (ICP and ICP/MS) ........................................................... 42

4.6.3.10 Interference Check Solution .................................................................. 42

4.6.3.11 Matrix Spikes and Duplicates ................................................................ 44

4.6.3.12 Internal Standards for ICP/MS (for Metals) ............................................ 45

4.6.4 Using Multiple Lines of Evidence to Evaluate Laboratory QC Information . 45

4.6.5 Data Usability Evaluations for Non-DKQ Analytical Data........................... 47

4.6.6 Data Usability Evaluations Using Multiple Lines of Evidence

from DQOs and the CSM .......................................................................... 49

4.6.7 Factors to be Considered During Data Usability Evaluations .................... 50

4.6.8 Documentation of Data Quality Assessments and Data Usability

Evaluations ............................................................................................... 52

REFERENCES .............................................................................................................. 54

Appendix A Supplemental Information on Data Quality Objectives and Quality

Assurance Project Plans .......................................................................... 56

Appendix B QC Information Summary and Measurement Performance Criteria .......... 58

Appendix C QC Information to be Reviewed During Data Quality Assessments ........ 66

Appendix D Data Quality Assessment Worksheets and Summary of DKQ

Acceptance Criteria .................................................................................. 71

Appendix E Evaluating Significant QA/QC Variances .................................................. 85

Appendix F Poorly Performing Compounds ................................................................. 92

Appendix G Range of Data Usability Evaluation Outcomes ......................................... 95

Appendix G Range of Data Usability Evaluation Outcomes ......................................... 96

Appendix H Data Usability Evaluation Worksheet ...................................................... 105

Appendix I Surrogates and Internal Standards ........................................................... 109

Appendix J Supplemental Examples Using Multiple Lines of Evidence ..................... 113

Appendix K: Glossary .................................................................................................. 119

Appendix L: List of Acronyms ..................................................................................... 130

1. Intended Use of Guidance Document

This guidance is designed to help the person responsible for conducting remediation to comply

with the Department's requirements established by the Technical Requirements for Site

Remediation (Technical Rules), N.J.A.C. 7:26E. Because this guidance will be used by many

different people that are involved in the remediation of a contaminated site such as Licensed

Site Remediation Professionals (LSRPs), Non-LSRP environmental consultants and other

environmental professionals, the generic term “investigator” will be used to refer to any person

that uses this guidance to remediate a contaminated site on behalf of a remediating party,

including the remediating party itself.

The procedures for a person to vary from the technical requirements in regulation are outlined in

the Technical Rules at N.J.A.C. 7:26E-1.7. Variances from a technical requirement or guidance

must be documented and be adequately supported with data or other information. In applying

technical guidance, the Department recognizes that professional judgment may result in a range

of interpretations on the application of the guidance to site conditions.

This guidance supersedes previous Department guidance issued on this topic. Technical

guidance may be used immediately upon issuance. However, the NJDEP recognizes the

challenge of using newly issued technical guidance when a remediation affected by the

guidance may have already been conducted or is currently in progress. To provide for the

reasonable implementation of new technical guidance, the NJDEP will allow a 6-month “phase-

in” period between the date the technical guidance is issued final (or the revision date) and the

time it should be used.

This guidance was prepared with stakeholder input. The following people were on the

committee who prepared this document:

• Greg Toffoli, Chair (Department), Office of Data Quality

• Nancy Rothman, Ph.D., New Environmental Horizons, Inc.

• Rodger Ferguson, CHMM LSRP, Pennjersey Environmental Consulting

• Stuart Nagourney (Department), Office of Quality Assurance

• David Robinson, LSRP, Synergy Environmental, Inc.

• Joseph Sanguiliano (Department), Office of Data Quality

• Phillip Worby, Accutest Laboratories, Inc.

2. Purpose

The purpose of this document is to provide guidance on how to review and subsequently use

analytical data generated pursuant to the remediation of a discharge of a contaminant(s).

Laboratory Quality Assurance and Quality Control (QA/QC) is a comprehensive program used

to enhance and document the quality of analytical data. QA involves planning, implementation,

assessment, reporting, and quality improvement to establish the reliability of laboratory data.

QC procedures are the specific tools that are used to achieve this reliability.

Evaluating the quality of analytical data to determine whether the data are of sufficient quality for

the intended purpose is a two-step process. The first step of the process is a data quality

assessment (DQA) to identify and summarize any quality control problems that occurred during

laboratory analysis (QC nonconformances). The results of the DQA are used to perform the

second step, which is a data usability evaluation (DUE) to determine whether or not the quality

of the analytical data is sufficient for the intended purpose.

To assist the investigator in obtaining usable, “good’ analytical data, the NJDEP Analytical

Technical Guidance Work Group developed the Data of Known Quality Protocols (DKQPs). The

DKQPs are a collection of analytical methods that contain specific performance criteria and are

based on the conventional analytical methods published by the U.S. Environmental Protection

Agency (EPA). DKQPs have been developed for the most commonly used analytical methods.

DKQPs may be developed for other methods in the future. Analytical data generated from the

DKQPs are termed Data of Known Quality (DKQ).

When the DKQPs are followed the investigator can have confidence that the data are of known

and documented quality. This will enable the investigator to evaluate whether the quality of the

data is usable. (When the performance criteria in the DKQPs are met, it is likely that the data

will be usable for project decisions.) Information regarding the DKQPs and laboratory QA/QC is

presented in the NJDEP guidance document titled NJDEP Site Remediation Program, Data of

Known Quality Protocols Technical Guidance, April 2014 (DKQ Guidance). The DKQ Guidance

and DKQPs are published on the NJDEP web site at:

http://www.nj.gov/dep/srp/guidance/index.html#analytic_methods

The DKQP Guidance includes the “Data of Known Quality Conformance/Nonconformance

Summary Questionnaire” that the investigator may request the laboratory to use to indicate

whether the data meet the guidelines for DKQ. The guidance also describes the narrative (that

must be included as a laboratory deliverable pursuant to N.J.A.C. 7:26E Appendix A) that

describes QA/QC nonconformances. When DKQ criteria are achieved for a particular data set,

the investigator will have confidence that the laboratory has followed the DKQPs, has described

nonconformances, if any, and has adequate information to make judgments regarding data

quality.

A basic premise of the DKQPs is that good communication and the exchange of information

between the investigator and the laboratory will increase the likelihood that the quality of the

analytical data will meet project-specific Data Quality Objectives (DQOs), and therefore, will be

suitable for the intended purpose. To this end, the “Example: Project Communication Form”

has been included with the DKQP Guidance (Appendix A) to provide an outline of the

information that a laboratory should have prior to analyzing the associated samples.

The process of obtaining analytical data that are of sufficient quality for the intended purpose

and evaluating the quality of analytical data in relation to project-specific DQOs occurs

throughout the course of a project. It is the investigator’s responsibility to perform the DQA/DUE

process; therefore, the investigator’s contact with the laboratory should be limited to explaining

any issues that were not adequately addressed in the narrative (nonconformance summary)

and, if provided, a Data of Known Quality Conformance/Nonconformance Summary

Questionnaire (DKQP Guidance). It should be noted that the investigator, not the laboratory, is

responsible for the usability of data.

It is not unusual for laboratory reports to contain QC nonconformances, especially for those

analyses that have extensive analyte lists such as Method 8260B (Volatile Organics) and 8270C

(Semivolatile Organics). The chances of every analyte passing all the QC criteria are remote

and not expected. In many cases, the DQA and DUE will reveal QC nonconformances that do

not affect the usability of the analytical data for the intended purpose. In these cases, the

investigator and others who will be relying on the data may have confidence that the quality of

the data is appropriate for the intended purpose.

In other cases, the DQA and DUE will reveal QC nonconformances that will affect the usability

of the analytical data for the intended purpose. In these cases, the investigator has developed

an understanding of the limitations of the analytical data (e.g., through a conceptual site model

(CSM)) and can avoid making decisions that are not technically supported and may not be fully

protective of human health and the environment.

It is important to note that uncertainty introduced through the collection of non-representative

samples or an inadequate number of samples will, in many cases, exceed the uncertainty

caused by laboratory analysis of the samples. It is imperative that the investigator follow the

appropriate regulations and guidance documents to ensure that the number and location of

samples collected and analyzed are sufficient to provide adequate characterization of site

conditions.

This guidance does not suggest formal data validation (such as that outlined in the NJDEP Site

Remediation Program Standard Operating Procedure (SOP) for Analytical Data Validation of

Target Analyte List (TAL) – Inorganics, Revision No. 5, SOP No. 5.A.2) is to be performed in all

instances. Specifically, such documents describe formal, systematic processes for reviewing

analytical data. These processes involve, for example, verifying derived results, inspection of

raw data, review of chromatograms, mass spectra, inter-element correction factors to ascertain

that the data set meets the data validation criteria, and the DQOs specified in the quality

assurance project plan (QAPP). In most cases, use of the DKQPs will allow the investigator to

perform a DQA without conducting formal data validation. In cases where formal data validation

will be necessary, the investigator will have to evaluate the data in accordance with applicable

NJDEP and/or EPA Guidance/SOPs. Please note that if data validation is necessary, then a full

data deliverable package is required. (An example where full validation may be required could

be where site conditions have made it difficult for the laboratory to meet the quality control

requirements of a DKQP and the issuance of a RAO is in the balance.)

3. Document Overview

The DQA and DUE constitutes a two-step process that is designed to evaluate the quality of

analytical data to determine if the data are of sufficient quality for the intended purpose. The

DQA is an assessment of the laboratory quality control data, the laboratory report, and

laboratory narrative by the investigator to identify and summarize QC nonconformances. The

DUE is an evaluation by the investigator to determine if the analytical data (that may include

nonconformances) are of sufficient quality for the intended purpose. The DUE uses the results

of the DQA and evaluates the quality of the analytical data in relation to the project-specific

DQOs and the intended use of the data. The DQA should be performed in real-time when the

data are received throughout the course of a project. If issues with the data are found, an

adjustment to the project may be made in real-time, so that enough data with sufficient quality

may be gathered prior to beginning the DUE. The DUE is performed whenever the data are

used to make decisions.

4. Procedures

The process of obtaining analytical data of sufficient quality for the intended purpose and

evaluating the quality of analytical data in relation to project-specific DQOs and the CSM occurs

throughout the course of a project. This process includes the following:

• Development of project-specific DQOs in accordance with professional judgment taking

cognizance of published applicable rules and guidance documents.

• Communication with the laboratory regarding project-specific DQOs and the selection of

appropriate analytical methods with the appropriate analytical sensitivity;

• Performance of QA and QC activities during the analysis of the samples and reporting of QC

results by the laboratory;

• Performance of a DQA by the investigator when analytical results are received from the

laboratory to identify QC nonconformances; and,

• Performance of a DUE by the investigator to determine if the analytical data are of sufficient

quality for the intended purpose. The DUE uses the results of the DQA and evaluates the

quality of the analytical data in relation to the project-specific DQOs and the CSM.

This process is described in Figure 1: DQA and Due Flow Chart.

Figure 1: DQA and DUE Flow Chart**

CSM

Figure 1: DQA and DUE Flow Chart

Sampling Plan, Field

QA/QC, and Method

Selection

Analytical Data, Field

Observations,

Hydrogeological and

Physical Data

Collect Additional Lab

or Field Data

Modify/Expand

Investigation/Remediate

Collect Additional Lab

or Field Data

Modify/Expand

Investigation

Representativeness

Evaluation

Does the Information/Data

Represent the Site and

Support the CSM?

DUE - Are the

Analytical Data

Adequate for the

Intended Purpose

Based on a Review

of QC

Nonconformances

and Information?

Data is Representative and of Adequate Quality to Support Environmental Professional’s Opinion

YES

DQA – Identify Non-Conformances

Start

** State Of Connecticut, Department of Environmental Protection, Laboratory Quality Assurance and Quality

Control, Data Quality Assessment And Data Usability Evaluation Guidance Document, May 2009, Revised

December 2010.

4.1 Data Quality Objectives

DQOs are developed by the investigator to ensure that a sufficient quantity and quality of

analytical data are generated to meet the goals of the project and support defensible

conclusions that protect human health and the environment. DQOs should be developed at

the beginning of a project and revisited and modified as needed as the project progresses.

Similarly, the quality of analytical data is evaluated in relation to the DQOs throughout the

course of a project.

It is important to document the DQOs for a project in the context of the CSM so there is a

roadmap to follow during the project and so there is documentation that the DQOs were met

after the project is finished. The DQOs for a project can be documented in a project work

plan, a QAPP, environmental investigation report, or other document. DQOs are a required

QAPP element per N.J.A.C. 7:26E 2.2. Sources of detailed information regarding the

development of DQOs and QAPPs are listed in Appendix A of this document.

Typical analytical DQOs include, but are not limited to the following:

• The QA/QC criteria specified in the DKQPs or in other analytical methods with an

equivalent degree of QA/QC as in the DKQPs;

• The applicable regulatory criteria, for example, the Appendix Table 1 - Specific

Ground Water Quality Criteria noted in the Ground Water Quality Standards, N.J.A.C.

7:9C; and

• The target reporting limit (RL) for a specific substance when determining the extent

and degree of contamination.

The DQOs, which are based on the intended use of the analytical data, define how reliable

the analytical data must be to make sound, rational decisions regarding data usability. For

example, analytical data can be used by an investigator to determine if a discharge took

place, evaluate the nature and extent of a discharge, confirm that remediation is complete, or

determine compliance with an applicable standard/screening level as described in the

“Definition of Terms” above.

4.2 Uncertainty in Analytical Data

Uncertainty exists in every aspect of sample collection and analysis. For example:

• Sample collection and homogeneity;

• Sample aliquoting:

• Sample preservation;

• Sample preparation; and

• Sample analysis

The overall measurement error is a combination of the sum of all the errors associated with

all aspects of sample collection and analysis. The investigator needs to understand the

impact of these uncertainties in order to establish data of known quality.

It is important to understand this uncertainty because analytical data with an unknown

amount of uncertainty may be difficult to use. However, it may still be possible to use the

analytical data if the investigator understands the degree of uncertainty, which is assessed

using the DQA/DUE process. The intended use of the analytical data determines how much

uncertainty is acceptable and how dependable the analytical data must be.

For example, when analytical data will be used for determining if a site meets the Residential

Direct Contact Soil Remediation Standards with a goal of obtaining an unrestricted Remedial

Action Outcome (RAO), the investigator must have a greater degree of confidence in that

data and must understand whether or not the degree of uncertainty will affect the usability of

the data for its intended purpose. Conversely, in cases where contaminants are known to be

present at concentrations significantly greater than Non-Residential Direct Contact Soil

Remediation Standards and further investigation and remediation will be conducted, the

amount of uncertainty associated with that analytical data can be greater.

4.3 Types of Analytical Data

There are two types of data: data that are generated from DKQPs and data that are not. For

the data generated from DKQPs, a lesser degree of scrutiny needs to be applied since the

uncertainty of these data is better understood. For data not generated from DKQPs, a higher

degree of scrutiny may be required since these data may have greater uncertainty. The type

of data will usually determine the level of effort that is required for the DQA and DUEs. For

data generated from DKQPs, an example of the information that should be submitted in a

conformance/nonconformance summary is included in the DKQPs Guidance (“Data of

Known Quality Conformance/Nonconformance Summary Questionnaire”). Because

many environmental investigation and remediation projects have been on-going for a period

of time before the DKQPs were developed and because DKQPs are not published for all

methods of analysis, it is likely that many investigators will need to integrate the data

generated by methods other than the DKQPs with data generated in accordance with the

DKQPs. This evaluation should be performed on a site-specific basis relative to the CSM and

DQOs, but the basic principles should be similar for each situation. Section 4 of the DKQP

Guidance presents information on the types of laboratory QC information that are needed to

demonstrate equivalency with the DKQs.

4.4 PARCCs Parameters

The PARCCs parameters are used to describe the quality of analytical data in quantitative

and qualitative terms using the information provided by the laboratory quality control

information. The PARCCS parameters – precision, accuracy, representativeness,

comparability, completeness, and sensitivity – are described below. The types of QC

information that can be used to evaluate the quality of analytical data using the PARCCS

parameters are provided in Appendix B of this document. Also found in Appendix B is a table

that summarizes DKQ performance parameters and the recommended frequency for the

various types of QC elements. Acceptance criteria associated with PARCCs Parameters are

included in any site-specific QAPP and are also discussed in the “SRP Technical Guidance

for Quality Assurance Project Plans” at

http://www.nj.gov/dep/srp/guidance/index.html#analytic_methods

4.4.1 Precision

Precision expresses the closeness of agreement, or degree of dispersion, between a

series of measurements. Precision is a measure of the reproducibility of sample results.

The goal is to maintain a level of analytical precision consistent with the DQOs. As a

conservative approach, it would be appropriate to compare the greatest numeric results

from a series of measurements to the applicable regulatory criteria.

Precision is measured through the calculation of the relative percent difference (RPD) of

two data sets generated from a similar source or percent relative standard deviation

(%RSD) from multiple sets of data. The formula for RPD is presented in the definition for

precision in the Definition of Terms section of this document. For example, the analytical

results for two field duplicates are 50 milligrams per kilogram (mg/kg) and 350 mg/kg for a

specific analyte. The RPD for the analytical results for these samples was calculated to be

150%, which, although it doesn’t actually represent a numerical measure of heterogeneity,

suggests a high degree of heterogeneity in the sample matrix and a low degree of

precision in the analytical results. Duplicate results varying by this amount may require

additional scrutiny, including qualification and/or resampling. When using duplicate results

that have met DKQP acceptance criteria, the QAPP should discuss whether the average

or the higher of the two values would be used for making data usability decisions.

4.4.2 Accuracy

Accuracy is used to describe the agreement between an observed value and an accepted

reference or true value. The goal is to maintain a level of accuracy consistent with the

DQOs. Accuracy is usually reported through the calculation of percent recovery using the

formula in the definition for accuracy included in the Definition of Terms section of this

document. For example, the analytical result for a Laboratory Control Sample (LCS) is 5

mg/kg. The LCS was known to contain 50 mg/kg of the analyte. The percent recovery for

the analytical results for this analyte was calculated to be 10%, which indicates a low

degree of accuracy of the analytical results for the analyte and would indicate a low bias

of that analyte to any associated field sample in that analytical batch. Therefore, the actual

concentration of the analyte in samples is likely to be higher than reported. All of the

possible field sample collection and analytical issues which may affect accuracy should be

evaluated to determine overall accuracy of a specific reported result. These data may

require additional scrutiny with the possibility of qualification or rejection based upon the

DQO. A list of common qualifiers has been included in Appendix D of this Guidance

document.

4.4.3 Representativeness

Representativeness is a qualitative measurement that describes how well the analytical

data characterizes an area of concern. Many factors can influence how representative the

analytical results are for an area sampled. These factors include the selection of

appropriate analytical procedures, the sampling plan, matrix heterogeneity and the

procedures and protocols used to collect, preserve, and transport samples. Information to

be considered when evaluating how well the analytical data characterizes an area of

concern is presented in various SRP technical guidance documents and manuals.

For example, as part of a sampling plan, an investigator collected soil samples at

locations of stained soil near the base of several above-ground petroleum storage tanks

known to be more than seventy years old and observed to be in deteriorated condition.

The samples were analyzed for extractable petroleum hydrocarbons (EPH). The

concentrations of all EPH results were below the method RL or not detected (ND). The

investigator evaluated these results in relation to visual field observations that indicated

that petroleum-stained soil was present. The investigator questioned how well the

analytical results characterized the locations where stained soil was observed and

collected several additional samples for EPH analysis to confirm the results. The results of

the second set of samples collected from locations of stained soil indicated the presence

of EPH at concentrations of approximately 5,000 mg/kg. Therefore, the investigator

concluded that the original samples for which the analytical results were reported as ND

for EPH were not representative of the stained soil and that the second set of samples

were representative of the stained soil.

4.4.4 Comparability

Comparability refers to the equivalency of sets of data. This goal is achieved through the

use of standard or similar techniques to collect and analyze representative samples.

Comparable data sets must contain the same variables of interest and must possess

values that can be converted to a common unit of measurement. Comparability is

primarily a qualitative parameter that is dependent upon the other data quality elements.

For example, if the RLs for a target analyte were significantly different for two different

methods, the two methods may not be comparable and more importantly, it may be

difficult to use those data to draw inferences and/or make comparisons. Use caution in

combining data sets especially if the quality of the data is uncertain.

4.4.5 Completeness

Completeness is a quantitative measure that is used to evaluate how many valid

analytical data were obtained in comparison to the amount that was planned.

Completeness is usually expressed as a percentage of usable analytical data.

Completeness goals are specified for the various types of samples that will be collected

during the course of an investigation. Completeness goals are used to estimate the

minimum amount of analytical data required to support the conclusions of the investigator.

If the completeness goal is 100% for samples that will be used to determine compliance

with the applicable regulations, all of the samples must be collected, analyzed and yield

analytical data that are usable for the intended purpose. Critical samples include those

samples that are relied upon to determine the presence, nature, and extent of a release or

determine compliance with applicable regulations. The completeness goal for critical

samples is generally 100%. Overall project completeness goals are generally below 100%

(e.g., QAPP DQO for overall project completeness may be 90%) to account for losses due

to unintended issues with sample collection (e.g., well will not purge properly or possible

breakage of sample in-transit to the laboratory) or to account for quality issues which

affect usability of sample data.

4.4.6 Sensitivity

Sensitivity is related to the RL. In this context, sensitivity refers to the capability of a

method or instrument to detect a given analyte at a given concentration and reliably

quantitate the analyte at that concentration. The investigator should be concerned that the

instrument or method can detect and provide an accurate analyte concentration that is not

greater than an applicable standard and/or screening level. In general, RLs should be less

than the applicable standard and/or screening level. Analytical results for samples that are

non-detect for a particular analyte that have RLs greater than the applicable standards

and/or screening levels cannot be used to demonstrate compliance with the applicable

standards and/or screening levels.

The issue of analytical sensitivity may be one of the most difficult to address as it pertains

to data usability evaluations. Samples that are contaminated with sufficient quantity of

material, such that dilutions are performed, are a leading cause of RLs exceeding

applicable criteria. However, there may be instances where such exceedances are

insignificant relative to the site specific DQOs. As an example, the project may be on-

going and/or other compounds are “driving” the cleanup such that not meeting applicable

criteria for all compounds at that particular juncture is not an issue.

4.5 Data Quality Assessment

A DQA is the process of identifying and summarizing QC nonconformances. The DQA

process should occur throughout the course of a project. The DKQP Guidance “Data of

Known Quality Conformance/Nonconformance Summary Questionnaire”, laboratory

narrative, and analytical data package should be reviewed by the investigator soon after it is

received, so the laboratory can be contacted regarding any questions, and issues may be

resolved in a timely manner. The DQA is to be performed prior to the DUE. The level of effort

necessary to complete this task depends on the type of analytical data described above in

Section 2.3 of this guidance document. The types of QC information that are to be reviewed

as part of the DQA are described in Appendix C of this document. Results from the DQA are

used during the DUE to evaluate whether the analytical data for the samples associated with

the specific QA/QC information are usable for the intended purpose.

Appendix B of this Guidance document includes a table that summarizes the DKQ

parameters and the recommended frequencies for various types of QC information. The

actual QC checks, target acceptance criteria and information required to be reported under

the DKQPs are provided in Appendix B of the DKQ Guidance.

The DQA is usually most efficiently completed by summarizing QC nonconformances on a

DQA worksheet or another manner that documents the thought process and findings of the

DQA (e.g., NJDEP Full Laboratory Data Deliverable Form available at:

http://www.nj.gov/dep/srp/srra/forms/

Sample DQA worksheets are included in Appendix D of this document. These worksheets

may be modified by the user. Appendix D also presents a summary of selected DKQ

acceptance criteria which may be useful during the completion of DQA worksheets.

4.5.1 Batch Quality Control versus Site Specific Quality Control

Laboratory QC is performed on a group or “batch” of samples. Laboratory QC procedures

require a certain number of samples be spiked and/or analyzed in duplicate. Since a

laboratory batch may include samples from several different sites, the accuracy and

precision assessment for organic samples will not be germane to any site in the batch

except for the site from which the QC samples originated. QC samples from a specific site

are referred to as site specific QC. Since batch QC for organic samples may include

samples from different sites, it may be of limited value when evaluating precision and

accuracy for a site. For inorganic samples, the sample chosen for the QC sample pertains

to all inorganic samples in the batch because the inorganic methods themselves include

little sample-specific quality control. Typically, organic analyses require an MS/MSD pair

for every twenty samples of similar matrix (e.g., soil, water, etc.). Inorganic analyses

usually have a matrix spike and a matrix duplicate (MD) for every twenty samples;

however, an MS/MSD pair for inorganic analyses is acceptable. Information regarding

MS/MSDs is presented in Section 5.6.3.7 of this document. The results of the MS spike

can be used to evaluate accuracy, while the results of the MS and MSD analysis (or

sample and MD) can be used to assess precision. Similarly, LCSs and LCS/LCSDs are

used by laboratories as a substitute to or in addition to MS/MSD where the LCS is used to

evaluate method accuracy, while a LCS/LCSD pair can be used to evaluate both precision

and accuracy. Information regarding LCS/LCSD is presented in Section 4.6.3.6 of this

document.

There may be instances where the investigator incorporates site or project specific QC

samples as part of the DQO. Examples of where this may be appropriate are:

• Complex or unique matrix;

• Contract specific requirements;

• High profile cases;

• Sites containing contaminants such as dioxins or hexavalent chromium

If project specific QC samples are required to meet the DQO, then the investigator should

supply sufficient sample volume for the analyses.

4.5.2 Evaluating Significant Quality Control Variances

Some QC nonconformances are so significant that they must be thoroughly evaluated.

Some examples are the absence of QC analyses, gross exceedance of holding time, and

exceeding low recoveries of spikes and/or surrogates. Appendix E of this document

presents a summary of significant QC variances or gross QC failures.

If the DQA is performed when the laboratory deliverable is received it may be possible for

the investigator to request that the laboratory perform reanalysis of the sample or sample

extract within the holding time. During the DUE, data with gross QC failures in most cases

will be deemed unusable, unless the investigator provides adequate justification for its

use. However, samples with significant QC variances could be used if the results are

significantly above remedial standards/screening levels.

4.5.3 Poorly Performing Compounds

Not all compounds of interest perform equally well for a given analytical method or

instrument. Typically, this is due to the chemical properties of these compounds and/or

the limitations of the methods and instrumentation, as opposed to laboratory error. These

compounds are commonly referred to as "poor performers," and the majority of QC

nonconformances are usually attributed to these compounds. Appendix F of this

document presents a summary of compounds that are typically poorly performing

compounds. Each method specific DKQ acceptance criteria table (QAPP Worksheet)

notes the method-specific poor performers. A laboratory’s list of poorly performing

compounds should not be substantially different from this list. The investigator should,

through the QAPP, have the laboratory confirm which compounds are poor performers for

the methods used prior to the analysis of samples. This information should be used during

the DUE. The investigator may decide not to use the entire data set should “too many”

compounds fail to meet acceptance criteria as this may be an indication of general and

significant instrumental difficulties. For example, the investigator may decide that if QC

results for more than 10% of the compounds fail to meet acceptance criteria for DKQ

Method 8260 or more than 20% fail to meet criteria for DKQ Method 8270, the data may

not be usable to demonstrate that concentrations are less than applicable standards

without additional lines of evidence to support such a decision.

4.5.4 Common Laboratory Contaminants

During the course of the analysis of samples, substances at the laboratory may

contaminate the samples. The contamination in the sample may come from contaminated

reagents, gases, and/or glassware; ambient contamination; poor laboratory technique; et

cetera. A list of common laboratory contaminants can be found in Appendix G of this

document. However, not all sample contamination can be attributed to the compounds on

the laboratory contaminant list. During the DUE, the investigator must take the CSM and

site-specific information into account to support a hypothesis that the detection of common

laboratory contaminants in environmental samples is actually due to laboratory

contamination and not due to releases at the site or due to sampling efforts.

4.5.5 Bias

When QC data for analytical results indicates that low or high bias is present, this means

that the true values of the target analytes are lower or higher than the reported

concentration, respectively. Bias can also be indeterminate, which means that the

analytical results have poor analytical precision or have conflicting bias in the data.

Additionally, as bias ultimately can affect the actual concentration reported, all bias has

the potential to affect accuracy. Bias is evaluated by the investigator as part of the DUE.

Bias can be caused by many factors, including improper sample collection and

preservation, exceedances of the holding times, the nature of sample matrix, and method

performance. The sample matrix can cause matrix interferences. Typically, matrices such

as peat, coal, coal ash, clay, and silt can exhibit significant matrix interferences by binding

contaminants or reacting with analytes of concern. The investigator should contact the

laboratory to determine the appropriate laboratory methods to address these difficult

matrices. The evaluation of bias is further discussed in Section 5.6.1 of this document.

4.6 Data Usability Evaluation

The DUE is an evaluation by the investigator to determine if the analytical data are of

sufficient quality for the intended purpose and can be relied upon by the investigator with the

appropriate degree of confidence to support the conclusions that will be made using the data.

The investigator uses the results of the DQA to evaluate the usability of the analytical data

during the DUE in the context of project-specific DQOs and the CSM.

One of the primary purposes of the DUE is to determine if any bias that might be present in

the analytical results, as identified during the DQA, affects the usability of the data for the

intended purpose. The DUE can use multiple lines of evidence from different types of

laboratory QC information or from site-specific conditions described in the CSM to evaluate

the usability of the analytical data.

The initial DUE should evaluate precision, accuracy, and sensitivity of the analytical data

compared to DQOs. Representativeness, completeness, and comparability should be

evaluated as part of a DUE and should be considered when incorporating analytical data into

the CSM.

More scrutiny regarding the quality of analytical data may be necessary when the

investigator intends to use the data to demonstrate compliance with an applicable

standard/screening level than when the data are used to design additional data collection

activities or when remediation will be conducted. Data that may not be deemed to be of

sufficient quality to demonstrate compliance with applicable standard/screening level may be

useful for determining that a discharge has occurred in cases when remediation will be

conducted or to guide further data collection activities.

Typically, the most challenging DUE decisions are for situations when the analytical results

are close to, or at, the applicable standard/screening level and there are QC

nonconformances that might affect the usability of the data. In situations such as this, the

NJDEP expects that the investigator will use an approach that is protective of human health

and the environment. Coordination with the laboratory to understand QC information,

additional investigation, and re-analysis of samples may be necessary in some cases. If the

DQA is performed when the laboratory deliverable is received and issues are raised, it may

be possible to perform re-analysis of the sample extract within the holding time and still use

the sample data.

To help expedite the DUE, it may be useful to determine if the QC nonconformances

identified in the DQA are significant for a particular project. The types of questions listed

below are not inclusive. They are intended to give examples to the investigator to help

evaluate QC nonconformance for a particular project. See the DUE Worksheet provided in

Appendix I of this document for additional examples.

• Will remediation be conducted at the area of concern? If remediation will be conducted,

the investigator should use the QC information supplied by the laboratory (or request

additional assistance from the laboratory when necessary) to minimize QC issues for

the samples to be collected to evaluate the effectiveness of remediation. Alternately,

if remediation will not be conducted, the analytical data should be of sufficient quality

to demonstrate compliance with an appropriate and applicable standard/screening

level.

• Were significant QC variances reported? Analytical data with gross QC failures are

usually deemed unusable (rejected) unless the investigator provides adequate

justification for its use. Significant QC variances are discussed in Appendix E of this

document.

• Were QC nonconformances noted for substances that are not constituents of concern

at the site as supported by the CSM? QC nonconformance assessments for

contaminants that are not of concern may not be critical to meeting project DQOs.

However, limiting the list of contaminants of concern without appropriate investigation

and analytical testing (i.e., incomplete CSM) can inadvertently overlook substances

that should be identified as contaminants of concern.

• Were QC nonconformances reported for compounds that are poorly performing

compounds? If the nonconformances are noted for poorly performing compounds that

are not contaminants of concern for the site, then they have little or no impact on data

usability. However, if the nonconformances are noted for poorly performing

compounds that are compounds of concern for the site, then the investigator may

have to address these issues, including but not limited to re-sampling and/or re-

analysis. Poorly performing compounds are discussed in Section 3.3 and Appendix F

of this document.

The DUE process is discussed in detail using examples in the sections that follow. The

examples presented below are for illustrative purposes only and are not meant to be a strict

or comprehensive evaluation of all types of laboratory QC information or all the possible

outcomes of data quality evaluations. The discussion begins with examples of less complex

QC information and concludes with the use of multiple lines of evidence to evaluate more

complicated DUE issues using more than one type of laboratory QC information and

information from the CSM for a hypothetical site. The standards/screening levels identified in

the examples are for illustrative purposes and may not be consistent with actual levels.

Appendix H of this document illustrates many common QC issues and a range of potential

DUE outcomes for each issue. The DUE is usually most efficiently completed by using a

worksheet or another manner that documents the thought process and findings of the DUE.

Appendix I of this document presents a DUE Worksheet that can be used and modified as

needed to summarize the types of issues that should be discussed in the investigator written

opinion regarding data usability.

4.6.1 Evaluation of Bias

The types of bias are discussed in Section 4.5.5 of this document. Bias can be low, high

or indeterminate.

High or low bias can be caused by many factors. Investigators should be cautioned that it

is never acceptable to “adjust laboratory reported” compound concentrations or RLs

based on percent recovery.

Indeterminate or non-directional bias means that the analytical results exhibit a poor

degree of precision (e.g., as demonstrated by high RPD in sample/MD measurements) or

there are cumulative conflicting biases in the data set (e.g., surrogate recoveries for a

sample are low but LCS recoveries are high). Duplicate sample results are used to

evaluate the degree of precision between the measurements. Indeterminate bias may

occur when heterogeneous matrices, such as contaminated soil or soil containing wastes

such as slag, are sampled. The heterogeneity of the matrix causes the analytical results to

vary and may cause a large RPD between the sample results. The degree to which the

analytical results represent the environmental conditions is related to the number of

samples taken to characterize the heterogeneous matrix and how those samples are

selected and collected. For example, as a greater number of samples are analyzed, the

analytical results will better represent the concentrations of the analytes present in the

environment.

Bias for a particular result should not be evaluated until all sources of possible bias in a

sample analysis have been evaluated. Evaluating the impact of bias on one’s data set is

not always straightforward. For example, judging bias only on surrogate recovery and

ignoring LCS recovery results may lead to erroneous conclusions. Therefore, overall bias

for a result must be judged by the cumulative effects of the QC results.

Examples of the actions suggested based on the type of bias observed (L= low;

I=indeterminate; H=high; None = within limits) on non-detect data (ND) are shown below.

For the purposes of the table, bias refers to agreement with method defined QA/QC limits.

Table1: Summary Actions Due to Bias.

ACTIONS

Bias

w/in limits

Conc.

ND<Reg Lev

Further

None

ND=Reg Lev

Further

None

ND>Reg Lev

Not usable to

determine

clean areas

Not usable to

determine

clean areas

Not usable to

determine

clean areas

Not usable to

determine

clean areas

Further = Look at Site; evaluate complete data set; Reanalyze; speak to

lab; resample if necessary.

Ultimately it is the investigators’ responsibility to use professional judgment when

determining the use of any data.

• If the detected concentrations of analytes are below the applicable

standard/screening level, the bias may have limited impact on the usability of the

data. If the concentration is just below the regulatory limit, evaluation of bias can be

critical, especially when data are being used to demonstrate compliance (i.e.,

issuance of a RAO).

• If the detected concentrations of analytes are above the applicable standard/screening

level, the bias may have limited impact on the usability of the data unless these data

are being used to demonstrate compliance (i.e., issuance of a RAO).

4.6.2 General Quality Control Information

The following subsections discuss issues associated with QC information related to

sample management, preservation, holding times, and field QC samples.

4.6.2.1 Chain of Custody Forms

Chain of Custody (COC) forms are used to document the history of sample possession

from the time the sample containers leave their point of origin (usually the laboratory

performing the analyses) to the time the samples are received by the laboratory. COCs

are considered legal documents. Sometimes incorrect information is on the COC form,

such as incorrect dates, sample identification numbers, and analysis requested.

Usually these errors are found through the course of the project. However, simply

correcting this information without documentation of the problem and the resolution

may amount to falsification of the chain of custody or cause confusion. The error may

be corrected by the investigator with a single-line cross-out of the error,

initialing/signing, dating of the correction, and an explanation for the correction. If the

laboratory notices an error on the COC, this should be noted in their sample receiving

documentation and in the laboratory narrative and the laboratory should contact the

investigator. Any changes to the COC should be approved by the investigator and

documented by the laboratory.

4.6.2.2 Sample Preservation Holding Times and Handling Time

Once a sample is collected, changes in the concentrations of analytes in the sample

can occur. To minimize these changes, the sample must be collected, stored, and

preserved as specified in the analytical method and for non-aqueous volatile organic

compounds as specified in the NJDEP's N.J.A.C. 7:26E-2.1(a)8. The sample must also

be analyzed within the specified holding and handling times. The holding time for a

sample has two components. The first component is the time from when a sample is

collected to when it is prepared for analysis or, if no preparation step is required, the

time from when the sample is collected to when it is analyzed. (For environmental

samples, handling time is included in this first component.) If a test requires a

preparation step, such as solvent extraction for determination of polychlorinated

biphenyls (PCBs) or acid digestion for determination of metals, there is a second

holding-time component referred to as the extract holding time. This is the time

between when the sample is prepared and when the resultant extract or digestate is

analyzed. Failure to analyze a sample within the prescribed holding time could render

the data unusable. The laboratory should be made aware (usually in the QAPP) that if

holdings times are not going to be met, then the laboratory should contact the

investigator and check to see if the samples should still be analyzed. The use of

laboratory data from a sample with a failed holding time must be evaluated for usability.

The determination made by the investigator to use data with failed holding times is

based on the critical nature of the sample, the type of sample and the analytical results.

The conventional conclusion with organic samples that exceeded holding times is that

there is a loss of compound and the concentration may be biased low.

It should be noted that certain constituents are not necessarily adversely affected by

holding time exceedances providing the samples are preserved and stored properly. If

the contaminants of concern were PCBs, PCDDs/PCDFs and metals, holding time

exceedance may not adversely affect usability. In these situations, the data should be

qualified and discussed by the investigator. However, attempts should be made to

meet the method required holding times.

Example 1: Meeting standards –exceeded holding times

Benzene and 1,2-dichloroethane were found in a water sample at concentrations of 0.9

ug/L and 1 ug/L, respectively. This sample was to be the last round of sampling prior to

the intention of issuing a RAO. Applicable ground water quality criteria for benzene and

1,2-dichloroethane are 1 and 2 ug/L respectively. However, the data were obtained

from samples that exceeded holding time to analysis by 4 days. Because the overriding

consensus with a holding time exceedance is that data are biased low and, because of

the proximity of the concentration to the applicable criteria, the investigator should

probably not use these data.

Example 2 – Holding time exceedance – ground water monitoring

Trichloroethene, tetrachloroethene and 1,1,1-trichloroethane are present in a water

sample at concentrations of 80 ug/L, 140 ug/L and 125 ug/L, respectively. Compound-

specific ground water criteria apply in this situation for the trichloroethene,

tetrachloroethene and 1,1,1-trichloroethane at concentrations of 1 ug/L, 1 ug/L and 30

ug/L, respectively. The sample is part of a routine, quarterly monitoring program of a

contaminated ground water aquifer and the sample results are similar to those

determined from previous rounds of sampling and analyses. It is expected that

quarterly monitoring will continue for a minimum of three additional years. However, the

data were from a sample that exceeded the holding time to analysis by 3 days. Based

on this information, the data would most likely be used because there will be additional

rounds of sampling prior to terminating the remedial activities, data are consistent with

previous results and the concentrations reported were significantly above the

applicable standards such that the effect of a holding time exceedance on the accuracy

of the numbers reported would probably be negligible.

Sample preservation can be either physical or chemical. Physical preservation might

be cooling, freezing, or storage in a hermetically sealed container. Chemical

preservation refers to addition of a chemical, usually a solvent, acid, or base to prevent

loss of any analyte in the sample. An example of physical storage is the freezing of soil

samples for determination of volatile organic compounds (VOCs). This procedure and

other procedures for preserving soil samples for the determination of VOCs can be

found in the NJDEP Field Sampling Procedures Manual.

NJDEP expects that all non-aqueous samples collected for the purpose of laboratory

analysis for VOCs be collected and preserved in accordance with the procedures

described in N.J.A.C. 7:26E-2.1(a)8 and all appropriate analytical methods and

technical guidance. If proper preservation of soils sampled for volatiles is not

performed, VOCs may be biased low and may be unusable. Based on this evaluation,

additional investigation and/or remediation may be warranted. Improperly preserved

samples should not be used to determine compliance with regulatory standards and/or

criteria.

4.6.2.3 Equipment, Trip and Field Blanks

Equipment-rinsate, trip, and field blank samples can be used to evaluate contamination

in a sample as a result of improperly decontaminated field equipment or contamination

introduced during transportation or collection of the sample. Trip and field blanks

(including laboratory analyte-free water which may be used to produce an equipment-

rinsate blank) must be transported to the site with sample containers and must be

received at the site within one day of preparation in the laboratory. Blanks may be held

on-site for no more than two calendar days and must arrive back at the lab within one

day of shipment from the field. If the handling time is not met, then it is possible that the

field blanks will not represent the site conditions. Handling times are established more

from logistical reasons than from scientific reasons. It is possible that sample

containers kept on-site or in construction trailers on site have a greater chance of

picking up contamination the longer they are stored. This may present a challenge to

the investigator for scheduling sample collection activities especially following

weekends and holidays.

The investigator should be cognizant that laboratories have a limited amount of time to

prepare/extract and analyze samples, some of which may require additional effort such

as reanalysis and as such, the quicker samples get to the laboratory, the better it is for

all parties concerned.

Low concentrations of contaminants may be detected in samples as a result of non-

site-related contamination. Organic compounds typically found include, but are not

limited to, methyl ethyl ketone (MEK), acetone, and methylene chloride which are

commonly used as laboratory solvents. Bis(2-ethylhexyl) phthalate is also a common

laboratory contaminant; however, it is also observed from field sample collection

activities such as use of plastics. Additional scrutiny should be taken if these are

contaminants of concern at the site.

The presence of any analytes in any blanks is noted in the DQA review of the data.

The concentrations of the analytes in the blanks are compared to any detected analyte

concentrations in the associated samples, taking into account any dilution factors.

Analytes that are detected in the blanks, but ND in the sample, can be ignored.

Analytes detected in the laboratory method blank (not the field and/or trip blank) and

detected in any associated sample should be flagged by the laboratory with a "B" suffix

to draw attention to the data user.

SRP has been using a 3 times to 10 times policy to evaluate the potential presence of

compounds in an environmental sample when the same compounds are also found in

a blank sample. The specific policy is as follows.

If the concentration of a given compound in a sample is less than or equal to three (3)

times the concentration of that compound in the associated equipment, trip or field

blank, then it is unlikely that the compound is present in the sample. If the

concentration is between 3 and 10, although it is present in a corresponding blank, the

presence of the compound in the site sample is considered real; however, if the

concentration is greater than 10 times the concentration in the corresponding blank,

the impact of the blank on the sample results is considered negligible.

All compounds that are present in a sample at a concentration of less than or equal to

10 times the concentration in the corresponding blank should be qualified

(conventionally, a “B” qualifier is added next to the concentration of the affected

compound) to indicate possible blank contamination.

Example 3: Application of 3x Rule:

Benzene was found in a ground water sample collected at the site at concentration of 2

μg/L. Benzene is also present at a concentration of 1.0 μg/l in the associated

equipment blank. The concentration in the sample is less than 3 times but less the

concentration of the blank. Therefore, the result may not be real; however, the result

should be qualified B and discussed by the investigator.

Example 4: Application of 10x Rule

Strict validation protocols may have more robust procedures for blank qualification (e.g., in

addition to the “B” qualifier, concentrations between 3 and 10 times the associated blank should

also be reported with the "J" qualifier.) However, for the purpose of the DUE, addition of the “B”

qualifier (or other user-defined qualifier) will suffice to denote corresponding blank contamination.

Benzene was found in a ground water sample collected at the site at concentration of 4

μg/L. Benzene is also present at a concentration of 0.5 μg/l in the associated

equipment blank. The concentration in the sample is greater than 3 times but less than

10 times the concentration of the blank. Therefore, the result is real and should be

qualified B which may indicate quantitative uncertainty. Additional site investigation

may be warranted including an evaluation of the sampling protocol.

Example 5: Application of 10x Rule

Benzene was found in a ground water sample collected at the site at concentration of

20 μg/L. Benzene is also present at a concentration of 0.5 μg/l in the associated

equipment blank. The concentration in the sample is greater than 10 times the

concentration of the blank. Therefore, the sample result is considered real and may be

used.

The investigator should review all blank related results in relation to the CSM for the

site, including results for other samples in the vicinity, in order to determine if this

evaluation is reasonable before concluding that a compound is or is not site related.

This policy cannot be used to eliminate detections of analytes that can be attributed to

a release or a potential release. Special attention should be paid to concentrations that

may be blank related at or near regulatory/screening levels.

4.6.2.4 Field Duplicates

Field duplicates are replicate or split samples collected in the field and submitted to the

laboratory as two different samples. Field duplicates measure both field and laboratory

precision. Blind duplicates are field duplicate samples submitted to the laboratory

without being identified as duplicates. Duplicate samples are used to evaluate the

sampling technique and homogeneity/heterogeneity of the sample matrix. The results

of field duplicates are reported as the RPD between the sample and duplicate results.

As a conservative approach, the higher of the two results for field duplicate samples

would be compared to the applicable regulatory criteria.

In general, solid matrices have a greater amount of heterogeneity than liquid matrices.

When the RPD for detected constituents (concentrations greater than the RL) is

greater than or equal to 50 percent for nonaqueous matrices or greater than or equal to

30 percent for aqueous matrices, the investigator is advised to consider the

representativeness of the sample results in relation to the CSM. If the field duplicates

are not collected and analyzed from your site, then field duplicate precision

cannot be part of your DUA.

Field duplicate results should be evaluated along with any laboratory duplicate results

that are available in an attempt to identify whether the issue is related to the sample

matrix, collection techniques, or the laboratory analysis of the sample. (Laboratory

duplicates are obtained from one environmental sample in one sample container that is

extracted and analyzed twice. Refer to Section 4.3.4 of this guidance document for

additional information.) If the laboratory duplicates are acceptable, but the field

duplicates are not, the likely source of this lack of reproducibility is heterogeneity of the

matrix or the sampling or compositing technique. If the laboratory duplicates are not

acceptable, laboratory method performance may be the source for the lack of

reproducibility. The RL for the analyte in question must be considered in this evaluation

because, typically, analytical precision decreases as the results get closer to the RL.

One could also evaluate precision by comparing a sample result to a sample duplicate

result (no spiking is performed), although representativeness of the samples could be a

factor when evaluating the results of duplicate analyses. Furthermore, if the results for

a specific analyte are ND in both samples, the evaluation of precision, through

calculation of RPD, cannot be performed

Example 6: Duplicate Sample Results – Heterogeneity

Duplicate soil sample analytical results for lead for two soil samples were 500 mg/kg

and 1,050 mg/kg. The RL was 1 mg/kg. The RPD for these samples is approximately

71 percent, which is greater than the guideline of 50 percent. The lack of precision for

these sample results indicate that the samples are heterogeneous and may not be

representative of the site location for lead. The investigator is advised to consider the

representativeness of the sample results in relation to the CSM. Additional

investigation and analysis are needed to evaluate the actual concentrations and

distribution of lead at the site.

4.6.3 Laboratory Quality Control Information

The DKQPs and commonly used analytical methods for environmental samples have

been verified to produce reliable data for most matrices encountered. The reliability of

the results to represent environmental conditions is predicated on many factors

including:

• The sample must be representative of field conditions;

• The sample must be properly preserved and analyzed within handling and holding

times;

• The preparation steps used to isolate the analytes from the sample matrix must be

such that no significant amounts of the analytes are lost;

• The analytical system should not have contamination above the RL;

• The analytical system must be calibrated and the calibration verified prior to sample

analysis; and

• No significant sample matrix interferences are present which would affect the

analysis.

With the exception of the first bullet, the laboratory can provide the data user with

laboratory QC information that provides insight into these key indicators. The

determination that a sample is representative of the field conditions is based on

reviewing the CSM, the sampling plan, the field team’s SOPs and field logs, and the

results for other samples including field and laboratory duplicates.

The primary laboratory QC data quality information that the investigator considers

during the DQA are the DKQP “Data of Known Quality

Conformance/Nonconformance Summary Questionnaire”, the chain of custody

form, sample preservation, handling and holding times, RLs, laboratory and field

duplicates, surrogates, MSs and MSDs (when requested by the investigator), method

blanks, and laboratory control samples. However, there are other non-standard types of

QC information (e.g., regulator pressure from a canister) that are required to be

reported by the DKQPs that are described in Appendix B of the DKQP Guidance.

4.6.3.1 Data of Known Quality Conformance/Nonconformance Summary Questionnaire

The DKQP “Data of Known Quality Conformance/Nonconformance Summary

Questionnaire” is used by the laboratory to certify whether the data meet the

requirements for “Data of Known Quality.” The DKQP “Data of Known Quality

Conformance/Nonconformance Summary Questionnaire” is presented in

Appendix A of the DKQ Guidance and can be found at the NJDEP website at

http://www.nj.gov/dep/srp/guidance/index.html#analytic_methods

. All of the

questions on the “Data of Known Quality Conformance/Nonconformance Summary

Questionnaire” should be answered, the questionnaire should be signed, and a

narrative of nonconformances included with the analytical data package. If all of the

questions are not answered, or the questionnaire is not signed, or if a narrative of

nonconformances is not included with the data package, then the investigator should

contact the laboratory to obtain a properly completed questionnaire and/or the

missing narrative. If the laboratory cannot supply the requested information, the

investigator should demonstrate equivalency with the DKQPs for the data set by

following the guidance presented in Sections 5 and 6 of the DKQP Guidance.

4.6.3.2 Reporting Limits

The RL is the lowest concentration that a method can achieve for a target analyte

with the necessary degree of accuracy and precision. As defined in N.J.A.C. 7:26E

2.1(a)3, the RL for an organic compound is derived from the lowest concentration

standard for that compound used in the calibration of the method as adjusted by

sample-specific preparation and analysis factors (for example, sample dilutions and

percent solids). The RL for an inorganic compound is derived from the concentration

of that analyte in the lowest level check standard (which could be the lowest

calibration standard in a multi-point calibration curve). RLs are method and

laboratory-specific. Laboratories are required to report the RLs for all compounds for

all samples per Appendix A of N.J.A.C. 7:26E.

RLs and their association with meeting standards and/or screening levels present

one of the most significant challenges to laboratories and investigators. A commonly

occurring scenario that arises is with volatile analyses and default impact to ground

water standards where multiple aliphatic compounds are present, the sample is

diluted because of the presence of one analyte with a very high standard, resulting in

an inability to “see down to the standard” for another compound. This frequently

occurs where samples from petroleum discharges areas are required to be diluted

due to the presence of compounds such as xylenes and/or ethyl benzene and the

0.005 mg/kg default impact to ground water soil screening level for benzene cannot

be attained. Dilutions occur not only to obtain an accurate concentration but also to

prevent temporary damage to the instrumentation. Where laboratories are having

difficulties reporting down to a low value, laboratories should perform and report

sample results that are derived from the lowest level of dilution

Multiple dilutions and alternative methods of analysis (e.g., gas chromatography with

a photoionization detector, Method 8010/8020) should be considered to obtain the

desired levels of quantitation.

Example 7: Reporting Limits and Dilution Factor

Results for soil samples tested for PCE (a primary driver at the site) are ND, with a

RL of 1,000 μg/kg) with a dilution factor of 20. Dilutions of the samples were

performed when the laboratory determined by pre-screening the samples that

undiluted analyses may cause contamination of the instrument that is difficult and

time consuming to remove and because the analyte concentrations would be above

the calibration curve. However, based on other analyses, it was determined that

there are other drivers that would result in the site undergoing remediation. In this

instance, as the remediation will also remove the PCE (even if it is above a

regulatory criteria), it would be acceptable for the laboratory to report PCE as ND

with a RL greater than the regulatory level.

Example 8: Reporting Limits and Dilutions

Results for a soil sample tested for BTEX are ND for benzene at a RL of 10 m/Kg

and xylenes was detected at 800 mg/Kg. The sample required a dilution of 100 due

to the concentration of xylene. (The Residential Direct Contact Soil Remediation

Standard for benzene is 2 mg/Kg and 12000 mg/Kg for xylenes.) However, while the

concentration for xylene is below its regulatory level, the ND for benzene at the RL is

above the regulatory level. If further delineation is to occur at the site, then the

exceedance of benzene should be noted but should not prevent the investigator from

proceeding with the remediation. If however, this analysis was to be used for

purposes of issuing a RAO, reanalysis and or resampling may be required and/or

further remediation may be required prior to resampling and reanalysis. If it is

absolutely necessary for benzene to be evaluated at or below the regulatory level

with high levels of xylene in the sample, the laboratory should be contacted to

discuss analytical options which may include alternative methodologies, sample

preparation and/or methods of detection.

Example 9: Reporting Limits

The ND result for PCE for a groundwater sample has a RL of 12 μg/L. The GWQS

for PCE is 1 μg/L. Additionally, the data cannot be used to show that PCE is not

present at a concentration less than the RL of 12 μg/L. However, this sample was to

be used to demonstrate compliance with the GWQS for PCE. Therefore, the data are

not usable for this project decision.

4.6.3.3 Method Blanks

Most analytical methods require method blanks. The purpose of the method blank is

to determine the presence and concentration of any contamination associated with

the processing or analysis of the samples at the laboratory. Laboratories are required

to summarize method blank results for all samples per Appendix A of N.J.A.C. 7:26E.

Ideally, method blanks should not contain any detected analytes above the RL, but

for certain tests, low levels of common contaminants are not unusual because of the

nature of the typical commercial analytical laboratory. Common laboratory

contaminants or artifacts include methylene chloride, acetone, MEK, for VOCs and/or

any phthalate for SVOCs. A summary of common laboratory contaminants is

presented in Appendix G of this document.

The presence of any analytes in any method blanks that are detected should be

noted during the review of the data. The concentrations of contaminants in method

blanks are compared to any detected analyte concentrations in the associated

samples, including field and trip blanks taking into account any dilution factors.

Analytes present in the blanks, but ND in the sample can be ignored. Analytes

detected in the laboratory method blank and detected in any associated sample

should be flagged by the laboratory with a "B" suffix to draw attention to the data

user.

Refer to Section 5.6.2.3 of this document for further information on blank action.

4.6.3.4 Laboratory Duplicates

Laboratory duplicates measure laboratory precision. The analytical results for

laboratory duplicates are reported as the RPD between the sample and duplicate

results. Laboratory duplicates are replicate samples and are prepared by taking two

aliquots from one sample container. Duplicate results are only used to determine

precision and not compliance with a standard and/or criteria.

Laboratory duplicate results should be evaluated along with any field duplicate

results to identify whether any precision issues are related to the sample matrix and

collection techniques or to the laboratory analysis of the sample. Information

regarding the interpretation of duplicate sample results can be found in Section 4.2.4

of this document.

4.6.3.5 Surrogates

A surrogate is an organic compound that is similar to the target analyte(s) in

chemical composition and behavior in the analytical process but is not normally

found in environmental samples. Laboratories are required to summarize surrogate

recoveries for all samples per Appendix A of N.J.A.C. 7:26E. Spiking the samples

(including any batch QC such as method blanks and LCSs) with surrogate

compounds prior to extraction and/or analysis and determining the percent recovery

of the spiked surrogate compound evaluates sample matrix effects, accuracy, and

laboratory performance on individual samples. The surrogate concentration is

measured using the same procedures used to measure other analytes in the sample.

Certain analyses that have extensive target compound lists require several

surrogates.

If the reported recovery for a surrogate is outside acceptance criteria for VOCs, then

all VOC results should be considered to be biased high or low depending on whether

the surrogate was higher or lower than the acceptance criteria. For SVOCs, if two or

more surrogates in the same fraction (acid SVOC surrogates or base neutral SVOC

surrogates) are outside acceptance criteria, all results in that fraction should be

considered to be biased high or low depending on whether the surrogate was higher

or lower than the acceptance criteria. For SVOCs, by understanding which

surrogates are related to which target compounds, the percent recovery of a

surrogate can be related to constituents of concern, which may be useful in

evaluating whether or not the data are useable. If a surrogate is not within the DKQ

criteria, the associated quantitative data may be suspect and may require further

scrutiny. Information regarding the surrogates for volatiles, SVOCs, chlorinated

pesticides and aroclors are presented in the tables in Appendix J of this document.

The evaluation of interfering matrix effects or high concentrations of target

compounds that may mask the detection of surrogate recoveries is a complex issue

and not straightforward in some cases. Common problems include the presence of

non-target compounds. The review and evaluation of surrogate compound results

involves the evaluation of multiple lines of evidence and is described in Section 4.4

of this document. Data from surrogate results should be used in conjunction with

other QC data, such as LCS and MS. The performance standards for surrogates are

presented in the DKQ protocols (Appendix B of the DKQ Guidance) and in Appendix

D of this document.

Surrogate recoveries may be affected when the sample or sample extract undergoes

dilution. Under severe instances, the surrogates may be “diluted out” and no

surrogate recovery is reported. When surrogate recoveries are affected due to

dilutions, the investigator may have to increase his/her reliance on other QC

information such as internal standard response, LCS and MS.

Example 10: Surrogates – High Recovery

A soil sample analyzed by Method 8270 was collected to determine if further

remediation was needed.

• The percent recovery for the surrogate pyrene-d10 was reported to be 159% and

for the surrogate benzo(a)pyrene-d12 was reported to be 145%. The method

specifies that the recovery limits for SVOC surrogates must be within 30 to 130

percent.

• Benzo(a)pyrene was reported at a concentration of 10 mg/kg, which is greater

than the Residential Direct Contact Soil Remediation Standard of 0.2 mg/Kg

applicable in this example.

Since the reported concentration of benzo(a)pyrene is well above the regulatory level

for benzo(a)pyrene, the reported QC information has no bearing on the usability of

the results and therefore further remediation is needed.

Example 11: Surrogates – Low Recovery

A soil sample was analyzed by Method 8260. The intended use of the analytical data

was to determine if contaminants were present at concentrations that exceed the

applicable regulatory level (Impact to Ground Water Screening Level in this

example).

• The percent recovery for the surrogate Toluene-d8 was reported to be 20

percent. The DKQ protocol specifies that the recovery limits for surrogates

should be within 70 to 130 percent for this method. Because the reported

recovery for this surrogate is outside acceptance criteria for VOCs, then all VOC

results may be biased low.

• 1,1,1-Trichloroethane was reported at a concentration of 0.1 mg/Kg, which is

just below the regulatory level (of 0.2 mg/Kg).

The reported percent recovery for the surrogate toluene-d8 indicates a potential low

bias for 1,1,1-trichloroethane. Because the reported concentration of 1,1,1-

trichloroethane is just below the regulatory level, the reported potential low bias

means the results should not be used to determine that 1,1,1-trichloroethane is

present at a concentration less than the regulatory level. Before drawing any

conclusions regarding the effect of the low bias reported by the surrogate, the

investigator should consider using multiple lines of evidence, as described in Section

4.4 of this document. This example is evaluated further in Appendix J of this

document, with Example J-1 using multiple lines of evidence.

4.6.3.6 Laboratory Control Samples (LCS)

Laboratory control samples (sometimes referred as blank spikes) are used to monitor

the accuracy of the analyst(s) performing the laboratory method. The LCS should

contain all target analytes. By evaluating the accuracy of the LCS analysis (percent

recovery of the target analytes), one can evaluate the laboratory performance of the

entire analytical process. The evaluation of results of LCS involves the evaluation of

multiple lines of evidence, as described in Section 4.6.4 of this document. Data from

LCS should be used in conjunction with other QC data. The performance standards

for LCS are presented in the DKQ protocols (Appendix B of the DKQ Guidance) and

in Appendix D of this document. When required by the method, laboratories are

required to summarize LCS recoveries associated with the samples from your site

per Appendix A of N.J.A.C. 7:26E.

Example 12: Laboratory Control Samples – Low Recovery

Groundwater samples were analyzed by DKQ Method 8260. The purpose of

sampling was to determine compliance with Regulatory criteria. The GWQS for

benzene is 1 μg/L.

• The results for the LCS indicate a 54 percent recovery for benzene. The DKQ

protocol specifies that the recovery limits for the LCS should be within 70 to 130

percent.

• The analytical results were ND for benzene at a RL of 0.5 μg/l.

The results of the laboratory control sample indicate a possible low bias in the

accuracy of the method. The results reported could have been affected by the low

bias of the method, and therefore it is possible that benzene may not have been ND

below the GWQS. Before drawing any conclusions regarding the effect of the low

bias reported associated with the LCS, the investigator should consider using

multiple lines of evidence, as described in Section 4.6.4 of this document.

Resampling and reanalysis may be appropriate. This example is further evaluated in

Appendix J of this document, with Example J-2 using multiple lines of evidence.

Example 13: Laboratory Control Samples – High Recovery

Groundwater samples were analyzed using DKQ Method 8260. The purpose of

sampling was to evaluate groundwater contamination prior to the start of

remediation. The GWQS for trichloroethene (TCE) is 1 μg/.

• The LCS indicates a 190 percent recovery for TCE, which was detected in the

sample at a concentration of 10 μg/L. DKQ Method 8260 specifies that the

recovery limits for the LCS should be within 70 to 130 percent.

The results for the LCS sample indicate a potential high bias. However, the reported

concentration of TCE is over the GWQS. Therefore, this high bias does not affect the

usability of the data for the intended purpose.

4.6.3.7 Matrix Spike/Matrix Spike Duplicates and Matrix Spike/Matrix Duplicate

The purpose of a MS sample is to determine whether the sample matrix contributes

bias to the analytical results. A MS is an environmental sample to which known

quantities of target analytes are added or spiked by the laboratory prior to sample

analysis. A matrix spike/matrix spike duplicate (MS/MSD) pair is prepared by spiking

two aliquots of an environmental sample with all target analytes. (Please keep in

mind that at such time in an investigation where site-specific concerns have reduced

the number of target analytes/compounds from a “full” list to a subset thereof, then

the MS/MSD fortifications may contain only the site-specific compounds of concern.)

Certain protocols do not require spiking with all analytes. However, DKQPs, with the

exception of air methods, do require the spiking of all target analytes. The two

aliquots are analyzed separately, and the results are compared. A MS can be used

to evaluate method accuracy, while a MS/MSD pair can be used to evaluate both

precision and accuracy. MS should not be performed on trip, equipment, or field

blanks. For analysis of samples for organic analytes, a MS/MSD pair is typically

performed. For inorganic analysis, a matrix spike/matrix duplicate (MS/MD) is

typically performed, although a MS/MSD pair is acceptable. Samples chosen for

MS/MSD and MS/MD should be chosen from samples that are similar in

geological/chemical characteristics to those actual site samples. It should be noted

that samples chosen are frequently from “other sites”. Although this practice is not

prohibited (and the use of site-specific QA/QC is generally not required), MS/MSD

and MS/MD results need to be used with discretion. It may or may not add value to

the data assessment process. When required by the method, laboratories are

required to summarize MS/MSD results per Appendix A of N.J.A.C. 7:26E.

To evaluate accuracy one must compare the results of the unspiked sample against

the spiked sample. To evaluate precision, the results of the matrix spike are

compared to those for the matrix spike duplicate. To evaluate accuracy, the percent

recoveries of the matrix spike compounds in both the sample and the duplicate are

compared (taking into consideration any concentration of the compounds in the

unspiked sample). Poor recoveries may be the result of matrix interference and

indicate that the sample results have a significant bias. The RPD between a set of

duplicate results (either a sample and duplicate pair or a MS/MSD pair) is used to

evaluate precision. High RPDs may indicate a lack of sample homogeneity. Poor

recoveries or high RPDs can also be caused by laboratory error, which would affect

the interpretation of results.

The sample submitted for MS/MSD evaluation should be representative of the

potentially contaminated matrix. Ideally, the sample selected for MS/MSD should be

spiked at a concentration which will allow for measurement of the spiked sample

matrix. (If the concentrations of compounds of concern in the unspiked sample are

very high, then it may be necessary to spike the sample at a high level

concentration.) The laboratory will need additional sample quantity when MS/MSDs

are requested and the need for these QC samples must be addressed prior to

sample collection.

The evaluation of precision and accuracy using MS/MSDs or sample/duplicate

results is a complex issue and not straightforward in some cases. For organics, the

results of the MS/MSD only impact the sample used for the spike while for metals,

the MS/MSD or MS/MD affect the entire associated batch. Common problems

include interfering matrix effects or high concentrations of target compounds or non-

target compounds that mask the detection or quantitation of spiked compounds. This

review and evaluation involves the evaluation of multiple lines of evidence, as

described in Section 4.6.4 of this document. Data from MS results should be used in

conjunction with other QC data, such as LCS, duplicate samples, and surrogates.

The performance standards for MS/MSDs are presented in the DKQ protocols

(Appendix B of the DKQ Guidance) and in Appendix D of this document.

Example 14: Matrix Spike/Matrix Spike Duplicates – Low Recovery

A water sample was evaluated for metals by DKQ Method 6010. The intended

purpose of the analysis was to confirm that remediation was needed.

• Lead was detected at 4 ug/L. The effective GWQS is 5 ug/L.

• The MS/MSD percent recoveries for lead were 28 percent and 32 percent. The

DKQ protocol specifies that MS/MSD spike recovery limits should be from 75

percent to 125 percent.

• The RPD for the MS/MSD pair is 13.3 percent. The DKQ protocol specifies that

RPD should be less than 30 percent for the MS/MSD pair.

• All other QC criteria were within the DKQ protocol acceptance criteria.

The RPD for the MS/MSD was well within the acceptance criteria specified in DKQ

protocol, indicating acceptable laboratory precision for the site matrix for the method

of analysis. The MS/MSD percent recoveries indicated a potential low bias for lead.

Therefore, these results should not be used to indicate lead was below the GWQS

for lead.

Care must be taken in evaluating the MS/MSD recoveries if the unspiked sample

contains high concentrations of compounds used in the spike.

Example 15 Matrix Spike/Matrix Spike Duplicates – High Recovery

A residential soil sample was analyzed by DKQ Method 8260 for VOCs. The

intended use of the data is to determine compliance with the residential direct

contact soil remediation standard.

• TCE was reported at a concentration of 8 mg/kg, which is just above the

residential direct contact soil standard of 7 mg/kg.

• The percent recoveries for TCE generated by a MS/MSD pair are 180 and 185

percent respectively. According to the DKQ protocol, the recovery limits for the

MS/MSD should be within 70 to 130 percent.

• The RPD for the MS/MSD pair is 2.7 percent. The RPD should be less than 30

percent for the MS/MSD pair.

The spike recoveries indicate a potential high bias for trichloroethene. Because of

the reported high bias and the sample result just above the soil standard, the actual

concentration of TCE in the sample may be lower and may be less than the soil

standard. However, the investigator cannot adjust the concentrations of the reported

values lower. The RPD for the MS/MSD pair was within the acceptance criteria

specified in DKQ protocol; therefore, MS/MSD results show an acceptable degree of

the precision. Further evaluation of these results in conjunction with multiple lines of

evidence, as described in Section 4.6.4 of this document, is needed to assess this

potential high bias. This example is evaluated further in Appendix J of this document,

with Example J-3 using multiple lines of evidence.

4.6.3.8 Internal Standards

The purpose of an internal standard is to determine the existence and magnitude of

instrument drift and physical interferences. Internal standard performance criteria

ensure that the instrument’s sensitivity and response are stable (i.e., the analytical

behavior of compounds is uniform in each analytical run) during each analysis.

Laboratories are required to submit internal standard summaries for all samples per

Appendix A of N.J.A.C. 7:26E.

Per the analytical methods, target compounds are associated to and quantitated with

specific internal standards. Refer to Appendix J in this guidance document for

specific compound-to-internal standard associations. When results deviate from

acceptance criteria, the analytical results are considered unreliable and thus qualified

as estimated values. When internal standard acceptance criteria are not met, all

quantitative data associated with the non-compliant internal standard may be

suspect. When the internal standard result is below the lower limit of the acceptance

range, RLs may be suspect. Information regarding and internal standards for

Volatiles and SVOCs and their corresponding target compounds and surrogates are

presented in Appendix J of this document.

4.6.3.9 Serial Dilutions (ICP and ICP/MS)

The purpose of a serial dilution is to determine whether or not physical or chemical

interferences exist (on an analyte-specific basis) with the analysis of samples for

metals due to the sample matrix. If an analyte concentration is sufficiently high (i.e.,

minimally, a factor of 10 above a RL) an analysis of a 1:5 dilution should agree within

+/- 10% of the original sample result. Serial dilutions are required for analyses by

ICP and less frequently by ICP/MS. Analytes whose concentrations are outside the

10% difference in sample concentration (i.e., 90 – 110%) are quantitatively qualified.

Laboratories are required to submit serial dilution summaries for all samples per

Appendix A of N.J.A.C. 7:26E.

4.6.3.10 Interference Check Solution

The commonly occurring analytes aluminum, iron, magnesium and calcium may

cause interferences with the detection and/or quantitation of other analytes. The

instrument can correct for these interferences. The purpose of the Interference

Check Solution (ICS) is to demonstrate the instrument’s ability to overcome

interferences and report data for analytes of concern within an acceptable accuracy

of 80 – 120% of the actual spiked amount. The effects of the ICS results are applied

to all samples within the associated analytical batch. Laboratories are required to

submit ICS summaries for all samples per Appendix A of N.J.A.C. 7:26E.

In general the ICP sample data can be accepted if the concentrations of the

aluminum, iron, magnesium and calcium in the field sample are found to be less than

or equal to their respective concentrations in the ICS. If analytes aluminum, iron,

magnesium and calcium are present in a field sample at levels greater than the ICS,

then the following should occur:

Example 16: ICS Low Recovery

Groundwater samples were analyzed by DKQ Method 6010. The purpose of

sampling was to determine compliance with Regulatory criteria. The GWQS for

Arsenic and Cadmium are 3 ug/L and 4 ug/L, respectively.

• The results for the ICS indicate a 54 percent recovery for arsenic and 60 percent

recovery for cadmium. The DKQ protocol specifies that the recovery limits for the

ICS should be within 80 to 120 percent.

• The analytical results were both at the GWQS of 3 ug/L and 4 ug/L for Arsenic

and Cadmium, respectively.

The results of the ICS indicate a possible low bias in the accuracy of the method.

The results reported could have been affected by the low bias of the method, and

therefore it is possible that arsenic and cadmium may be above the GWQS. Before

drawing any conclusions regarding the effect of the low bias reported associated with

the ICS, the investigator should consider using multiple lines of evidence, as

described in Section 4.6.4 of this document. Resampling and reanalysis may be

appropriate. This example is further evaluated in Appendix J of this document, with

Example J-4 using multiple lines of evidence.

Example 17: ICS – High Recovery

Soil samples were analyzed using DKQ Method 6010. The purpose of sampling was

to evaluate if the soil samples exceeded the residential direct contact soil

remediation standard for lead. The residential direct contact soil remediation

standard for lead is 400 mg/kg.

• The ICS indicates a 150 percent recovery for lead, which was detected in the

sample at a concentration of 1000 mg/kg. The DKQ protocol for method 6010

specifies that the recovery limits for the ICS should be within 80 - 120 percent.

The results for the ICS sample indicate a potential high bias. However, the reported

concentration of lead is much greater than the applicable standard. Therefore, this

high bias does not affect the usability of the data for the intended purpose. Further

remediation would be required.

4.6.3.11 Matrix Spikes and Duplicates

The purpose of a matrix spike and duplicate is to determine whether the sample

matrix contributes bias to the analytical results. The sample that is spiked should be

representative of the soil type from the site under investigation/remediation.

Documenting the effect of the matrix for a given preparation batch consisting of

similar sample characteristics should include the analysis of at least one matrix spike

and one duplicate unspiked sample or one matrix spike/matrix spike duplicate pair.

The decision of whether to prepare and analyze duplicate samples or MS/MSD

should be based on knowledge of the samples in the sample batch or as noted in the

QAPP. If samples are expected to contain target analytes, then the laboratory may

use one MS and a duplicate analysis of an unspiked field sample. If samples are not

expected to contain target analytes, then the laboratory should use a MS/MSD.

Unknown source investigations should employ the use of a MS/MSD.

Sample requirements are specified in the DKQ methods attached to the NJDEP Site

Remediation Program, Data of Known Quality Protocols Technical Guidance April

2014. Actions to be taken on affected samples are the same as those noted in

Section 5.6.3.7 above. However qualifications of data affected by MS, MS/MSD/ and

duplicate outliers affect all samples associated with the corresponding digestion

batch.

4.6.3.12 Internal Standards for ICP/MS (for Metals)

The purpose of internal standards is to determine the existence and magnitude of

instrument drift and physical interferences. Internal standards are added to every

sample, calibration standard and QC sample. Laboratories are required to submit

internal standard summaries for all samples per Appendix A of N.J.A.C. 7:26E. If the

QC criteria are not met, then the sample must be diluted five-fold and reanalyzed

with the appropriate amounts of internal standard. If the first dilution does not correct

the deficiency, then the procedure should be repeated until the internal standard

intensities fall within the method-defined acceptance criteria.

4.6.4 Using Multiple Lines of Evidence to Evaluate Laboratory QC Information

The use of several different types of laboratory QC information as multiple lines of

evidence to understand complex QC issues is an important component of DUEs. A

conclusion about possible bias in data should not be drawn until the results of all QC

samples are assessed since cumulative quality control effects may confound results.

The following examples illustrate the evaluation of commonly reported QC information

using a “multiple lines of evidence” approach. The investigator should seek

experienced assistance, as needed, when evaluating QC data involving multiple lines

of evidence. These examples are intended to build on the information presented earlier

in this document. Additional examples using multiple lines of evidence are also

presented in Appendix J of this document.

Example 18: Multiple Lines of Evidence – Low Recovery for LCS and MS/MSD

A soil sample was analyzed by DKQ Method 8260 for VOCs. The intended purpose of

the analysis was to evaluate the concentrations of VOCs that were present at a release

area.

• The reported concentrations of the constituents of concern are just below (e.g.,

the concentrations are 9 ug/Kg and the regulatory levels are 10 ug/Kg) the

applicable regulatory criteria.

• The percent recoveries for TCE generated by a MS/MSD pair are low and are

less than 45 percent. According to the DKQ protocol, the recovery limits for the

MS/MSD should be within 70 to 130 percent.

• LCS percent recoveries are low and are less than 35 percent. The DKQ protocol

specifies that the recovery limits for the LCS should be within 70 to 130 percent.

About 25% of the DKQ Method 8260 target compounds, including TCE, are

outside of the acceptance criteria specified in the DKQ protocol.

In this example, the most important QC component is the LCS data as it is indicative of

the overall performance of the laboratory.MS/MSDs evaluate method precision and

accuracy in relation to the sample matrix. LCSs evaluate the laboratory's performance.

The QC sample results indicate consistent low bias associated with both the sample

analysis and the laboratory's performance for the analysis of TCE; however, the LCS

results indicate laboratory performance issues. The LCS is a measure of how well the

laboratory can perform a given method in a clean sample matrix. Failure to get

adequate LCS recoveries can indicate a problem with the compound-specific results

for the samples associated with the LCS. Therefore, the actual concentrations of the

constituents of concern may be higher than reported and actually above the regulatory

level.

The investigator may need to contact the laboratory for guidance on how to best

resolve issues associated with the failure of an LCS to meet acceptance criteria.

Reanalysis of the samples (if within holding time), use of alternative analytical

methods, or collection of additional samples may be necessary to obtain data that

could be used to demonstrate that the reported concentrations are less than the

applicable regulatory criteria.

Example 19: Multiple Lines of Evidence – Low MS/MSD Recovery

A soil sample was analyzed by DKQ Method 8260 for VOCs. The intended purpose of

the analysis was to evaluate the concentrations of VOCs that were present due to a

discharge.

• The reported concentrations of the constituents of concern are ND, and the RLs

are well below (e.g., a factor of 100 times lower) the applicable regulatory criteria.

• The MS/MSDs are from the site being investigated.

• The MS/MSD recoveries were outside acceptance limits. Recoveries were in the

40-50% range. According to the DKQ protocol, the recovery limits for the

MS/MSD should be within 70 to 130 percent.

• The results for the surrogates and the LCS were within acceptance limits.

The results for the surrogates and the laboratory control sample indicate laboratory and

method performance are acceptable indicating that the data are not biased due based

on these QC indicators. The results for the MS/MSD indicate a potential low bias, but

as no compounds were detected and the RLs were far below the regulatory criteria,

there is no significant impact on the usability of the data.

4.6.5 Data Usability Evaluations for Non-DKQ Analytical Data

In order to evaluate if Non-DKQ data can be used to support environmental decision-

making, the investigator should go through a multi-step evaluation process. One

objective of that evaluation would be to make a decision as to whether additional data

collection is necessary to corroborate the Non-DKQ data or whether the quality of the

Non-DKQ data is such that it could be used for its intended purpose without the

collection of additional data. Such an evaluation process includes the following steps:

• The QAPP should identify acceptance criteria for the non-DKQP methods and

the associated DQOs.

• Perform a DQA and DUE to evaluate precision, accuracy and sensitivity. The

investigator must evaluate the RLs, method detection limits (if available),

handling and holding times, sample preservation, and results of QC measures

(surrogates, LCS, MS/MSD or MS/MD, method blank results). Review any data

narratives which may explain issues with sample receipt and analysis.

• Consider such factors as the age of previously generated data, limitations and

benefits of analytical method(s), laboratory QA/QC results, and how any of those

factors might affect the quality of the data or the usability of the data with respect

to its intended purpose.

• Determine whether any newer data corroborate the older results and whether all

sets of data are consistent with the CSM.

• Review available field collection information, preservation techniques, filtering, et

cetera for the older samples to evaluate how those techniques compare to

current knowledge and how any differences from more recent scientific

perspectives might affect the quality of the data.

• Consider decisions that have already been made based on the old data.

• Consider future decisions that will be made based on the old data.

• Consider any other site-specific factors.

NJDEP expects that more scrutiny regarding the quality of previously generated data

will be necessary when the investigator intends to use that data to demonstrate

compliance with applicable regulations than when that data are used to design

additional data collection activities.

If the investigator does not fully understand all of the issues associated with the data

quality assessment of non-DKQP, then it is highly recommended that they consult with

experts more knowledgeable in this field. The investigator may seek additional

guidance from the Department’s or USEPA Region 2 SOPs. Region 2 data validation

guidance documents and SOPs may be found at

http://www.epa.gov/region2/qa/documents.htm

4.6.6 Data Usability Evaluations Using Multiple Lines of Evidence from DQOs and

the CSM

Using multiple lines of evidence during a DUE is not limited to the use of analytical QC

data. Multiple lines of evidence using DQOs and CSM can also be used to determine if

the quality of the analytical data is adequate for the intended purpose. The DQOs are

used to determine if a sufficient quantity and quality of analytical data was generated to

meet the goals of the project and support defensible conclusions that are protective of

human health and the environment. Information regarding the DQOs is presented in

Section 2.1 of this guidance document. The investigator will also evaluate the analytical

data in relation to the CSM to determine if any significant data gaps result from the

quality of the data. For these evaluations, the SRP expects that the investigator will

use an approach that is fully protective of human health and the environment. This

evaluation includes, but is not limited to, the following actions:

• Evaluate the analytical data to determine if the DQOs for precision, accuracy,

representativeness, comparability, completeness and sensitivity are met.

• Evaluate the entire body of information (type, amount, and quality data) available

for the specific area/discharge for which the data are presumed to be

representative.

• Determine whether the data are consistent with the CSM and if any significant

data gaps are present.

• Consider the effects of having insufficient and/or inaccurate information relative

to the risk to potential receptors and the risk to human health and the

environment.

• Consider the source of data (e.g., whether the data were generated by the

investigator’s own firm or some other firm, the investigator’s own involvement

with the project, the method of collection for the samples, and the reporting

methods by other firms/laboratories generating the data). Perform a critical

review of these data to evaluate its reliability.

• Consider any other site-specific factors.

In addition to the items listed above the reader should also refer to the Data Usability

Evaluation Worksheet presented in Appendix H-2 for further information to consider

during this evaluation.

4.6.7 Factors to be Considered During Data Usability Evaluations

Factors that must be considered during DUEs are presented below:

• Adjusting analytical results reported by the laboratory based on laboratory QC

information is not appropriate. For example, if the results for a matrix spike

indicate a percent recovery of 150%, it is not scientifically valid to adjust the

results downward by 50 %. If a contaminant is reported in a blank, it is never

appropriate to subtract the concentration of the concentration found in the blank

from the sample results.

• False positives can occur due to contamination from commonly used laboratory

contaminants, interferences in laboratory methods themselves or sample

preservation procedures. For example, methyl ethyl ketone can be formed when

sodium bisulfate is used to preserve a soil sample for volatile organic compound

analysis. The investigator should contact the laboratory for assistance when the

results do not make sense in relation to the CSM.

• In addition to evaluating high or low bias, it is also necessary to consider

indeterminate or non-directional bias caused by high RPDs or conflicting biases

in the data. High RPDs may indicate a lack of sample homogeneity and raise

questions regarding the representativeness of the sample.

• The investigator is responsible for evaluating overall data quality and usability

and should not ask the laboratory to perform the DQA nor the DUE of their data

(e.g., it is not appropriate to have the laboratory complete the NJDEP Full

Laboratory Data Deliverable Form). If the laboratory is required by the

investigator to complete the NJDEP Full Data Deliverable Form and/or the

NJDEP Reduced Deliverable Form, the investigator is forewarned that they and

not the laboratory are responsible for the content of that information.

• It is important that the meaning of laboratory acceptance criteria be understood

when evaluating QC results. The purpose of acceptance criteria is to define a

range where data are acceptable as reported. Any data within an acceptable

recovery window is appropriate for use. When QC results and information are

within acceptance criteria, the reported value is “accepted” as the concentration

that should be used for decision-making purposes.

• Results from surrogate analytes do not automatically indicate that a QC issue

exists for a specific compound. Matrix spikes are used to evaluate the

performance of a specific compound on the spiked sample.

• Soil and sediment results should be reported on a dry-weight basis. Tissues are

reported on a wet-weight basis. If sample results are reported incorrectly the

laboratory should be contacted for assistance.

• Sample heterogeneity issues or RL issues are to be considered when evaluating

total results and results following SPLP or Toxicity Characteristic Leaching

Procedure (TCLP) extraction. For example, the total sample results of analysis

for total VOCs are “ND,” while the results for the SPLP or TCLP leachate indicate

the presence of VOCs at substantial concentrations.

• It is inappropriate to conclude in all instances that because the matrix spike and

matrix spike duplicate results are biased low, the contaminants are bound up in a

sample matrix that has not undergone some form of treatment, and therefore the

low bias is irrelevant. (There may be instances where the compounds of concern

do exhibit low MS/MSD recoveries due to a treatment of the matrix designed for

exactly that purpose.) The investigator should contact the laboratory to

determine, if possible, how to overcome such matrix interference issues. An

evaluation to determine if a compound is bound up in the sample matrix is

outside of the scope of this document and may involve a significant study.

• It is important to work with the laboratory to minimize analytical difficulties or bias.

There are several options for sample clean-up and analysis. Typically, sediment

samples for pesticides or PCBs need extensive sample clean-up because

naturally occurring interferences can cause analytical problems. Should the

resultant effect of cleanup be an increase in the RL, the laboratory should contact

the investigator and inquire as to how the laboratory is to proceed.

4.6.8 Documentation of Data Quality Assessments and Data Usability Evaluations

Documentation of the thought process used, as well as the outcomes of the DQA and

DUE is an essential task that is necessary to support the investigator’s decisions

regarding the usability of the analytical data for the intended purpose. This

documentation is a thoughtful and succinct evaluation and presentation of the findings

and conclusions of the DQA and DUE process. NJDEP expects that this

documentation will be presented in the documents submitted to the Department where

the analytical data are used to support the investigator’s opinion that the quality of

analytical data is appropriate, or not appropriate, for the intended purpose(s).

As stated previously, there are various ways to document this information, including the

DQA Worksheets in Appendix D, NJDEP Full Laboratory Data Deliverable Form in

http://www.nj.gov/dep/srp/srra/forms/

, DUE Worksheet in Appendix I of this document

and the text of the document that uses the analytical data. The DQA and DUE

worksheets may be modified by the user as deemed appropriate, provided the end

result meets the objectives expressed in this guidance document.

Typical documentation of a DQA and DUE includes a written summary regarding data

usability and DQA and DUE Worksheets. The report that presents the analytical data

should also include:

• The laboratory reports, laboratory narratives, and “Data of Known Quality

Conformance/Nonconformance Summary Questionnaire” and chain of custody

form;

• Project communication forms (if used); and

• Any other pertinent information.

The investigator should work with the laboratory to receive the analytical data in a

convenient format, particularly if the laboratory report is provided in electronic format.

The use of electronic deliverables from the laboratory can make the transfer of data

into computer spreadsheets and databases more efficient, which in turn will improve

efficiency when performing the DQA and DUE.

REFERENCES

Connecticut Department of Environmental Protection, Guidance for Collecting and Preserving

Soil and Sediment Samples for Laboratory Determination of Volatile Organic Compounds,

Version 2.0, effective March 1, 2006.

Connecticut Department of Environmental Protection, Site Characterization Guidance

Document, effective September 2007.

Connecticut Department of Environmental Protection, Laboratory Quality Assurance and Quality

Control Guidance, Reasonable Confidence Protocols, Guidance Document, effective November

19, 2007.

Connecticut Department of Environmental Protection, Reasonable Confidence Protocols, for

various analytical methods.

Florida Department of Environmental Protection, Process for Assessing Data Usability, DEP-EA

001/07, MWB Draft 9/6/07 (v2).

Massachusetts Department of Environmental Protection, Bureau of Waste Site Cleanup, The

Compendium of Quality Assurance and Quality Control Requirements and Performance

Standards for Selected Analytical Methods Used in Support of Response Actions for the

Massachusetts Contingency Plan (MCP).

Massachusetts Department of Environmental Protection, Bureau of Waste Site Cleanup, MCP

Representativeness Evaluations and Data Usability Assessments, Policy #WSC-07-350,

September 19, 2007.

U.S. Army Corps of Engineers, Environmental Quality Assurance for HTRW Projects, Engineer

Manual. October 10, 1997, EM 200 1-6.

United States Environmental Protection Agency (EPA) Quality Assurance guidance document:

Guidance on Systematic Planning Using the Data Quality Objective Process (QA/G-4), February

2006, EPA/240/B-06/001.

U.S. Environmental Protection Agency, Office of Superfund Remediation and Technology

Innovation (5201G), Introduction to the Contract Laboratory Program, April 2004, EPA 540-R-

03-005, OWSER 9240.1-41.

U.S. Environmental Protection Agency, Guidance on Environmental Data Verification and Data

Validation, EPA QA/G-8, November 2002, EPA/240/R-02/002.

U.S. Environmental Protection Agency, EPA New England, Region 1, Quality Assurance Unit

Staff, Office of Environmental Measurement and Evaluation Data Validation Functional

Guidelines For Evaluating Environmental Analyses”, July 1996, Revised December 1996.

U.S. Environmental Protection Agency, EPA New England Region I, Compendium of Quality

Assurance Project Plan, Requirements and Guidance, Final October 1999, Attachment A.

U.S. Environmental Protection Agency, EPA-NE, Region I, Quality Assurance Project Plan

Manual, Draft, September 1998, Table 4, pg. 83-87.

U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response,

Guidance for Data Usability in Risk Assessment (Part A), Final, April 1992, EPA 9285.7, PB92-

963356.

U.S. Environmental Protection Agency, Guidance for Quality Assurance Project Plans, February

1998, EPA QA/G-5 EPA 600-R-98-018.

U.S. Environmental Protection Agency, Office of Emergency Response, Quality Assurance

Guidance for Conducting Brownfields Site Assessments, September 1998, EPA 540-R-98-038,

OSWER 9230.0-83P, PB98-963307.

Appendix A

Supplemental Information on Data Quality Objectives

and Quality Assurance Project Plans

APPENDIX A

UPPLEMENTAL INFORMATION ON DATA QUALITY OBJECTIVES

AND QUALITY ASSURANCE PROJECT PLANS

Data Quality Objectives (DQOs) are project-specific goals for an environmental investigation that address the

generation, assessment, and intended use of the data associated with that investigation. DQOs express the

qualitative and quantitative measures that will be used to determine whether the amount and quality of data

associated with the investigation are sufficient and sufficiently accurate to draw the conclusions that will be

necessary. Information on developing Data Quality Objectives can be found in the United States Environmental

Protection Agency (EPA) Quality Assurance guidance document: Guidance on Systematic Planning Using the

Data Quality Objective Process (QA/G-4), February 2006, EPA/240/B-06/001.

A Quality Assurance Project Plan (QAPP) documents the planning, implementation, and assessment

procedures for a particular project, as well as any specific quality assurance and quality control activities. It

integrates all the technical and quality aspects of the project in order to provide a "blueprint" for obtaining the

type and quality of environmental data and information needed for a specific decision or use. All work

performed or funded by EPA that involves the acquisition of environmental data must have an approved

QAPP. In these instances, the State of New Jersey Department of Environmental Protection and EPA must

review all QAPPs prior to the commencement of any monitoring component of the project. All QAPPs shall be

written in conformance with N.J.A.C. 7:26E 2.2 and the Site Remediation Program’s “Technical Guidance for

Quality Assurance Project Plans”. These and other quality assurance documents can be accessed at the

following websites:

www.epa.gov/region1/lab/qa/qualsys.html and,

http://www.nj.gov/dep/srp/guidance/index.html

Appendix B

QC Information Summary and

Measurement Performance Criteria

APPENDIX B-1

UMMARY OF QUALITY CONTROL CHECKS AND SAMPLES

QC Sample or Activity used to

Assess Measurement Performance

Frequency*

Measurement

Performance

Criteria

Field Duplicate One in 20 samples per matrix for each parameter

See Appendix D-4

Site Specific Matrix Spike, Matrix Spike Duplicate

(MS/MSD) Pair

One in 20 samples, one MS/MSD per matrix for

each parameter

Laboratory Control Sample, Laboratory Control Sample

Duplicate (LCS/LCSD) Pair

One per batch of up to 20 samples per matrix

Field Blank Project specific

Equipment Blank

One in 20 samples with non-dedicated

equipment

Trip Blank

One per cooler (VOCs only) per event for VOCs

and volatile organic compounds

Performance Evaluation Sample Project specific

Inter-Lab Split Samples Project specific

Methanol Trip Blank Project specific

*Frequency determined by method and/or project-specific requirements

APPENDIX B-2

YPES OF INFORMATION USED TO EVALUATE

PRECISION, ACCURACY, REPRESENTATIVENESS, COMPARABILITY,

OMPLETENESS AND SENSITIVITY

QC Element Laboratory Measures Field Measures

Precision

Laboratory Control Sample/

Laboratory Control Sample Duplicate

Pair

Field Duplicates

Matrix Spike/Matrix Spike Duplicates pairs

(collect samples for)

Matrix Duplicate (collect samples for)

Matrix Spike Duplicates

Historical Data Trends Appropriate Sampling Procedure

Accuracy

Laboratory Control Samples

Matrix Spikes/Matrix Spike Duplicates

(collect samples for)

Matrix Spikes and Matrix Spike

Duplicates

Inclusion of “Blind” Samples

Internal Standards Appropriate Sampling Procedures

Surrogate Recovery Appropriate Sample Containers

Initial Calibration Appropriate Sample Preservation

Continuing Calibration Handling & Holding Times

Standard Reference Material Equipment Blank/Field Blank

Representativeness

Laboratory Homogenization

Appropriate Sampling Procedures

Appropriate Sample Containers

Appropriate Sub-sampling Appropriate Sample Preservation

Appropriate Dilutions Incorporation of Field Screening Data

“As Received” Sample Preservation

Meeting Hold Times

Appropriate Number of Samples

Comparability

Gas Chromatography/Mass

Spectrometry Tuning

Comparison to Previous Data Points

Calibration Comparison to Similar Data Points

Analytical Method Followed Similar Methods of Analysis used

Completeness

Percent Sample Per Batch Analyzed

and Reported

Percent Planned Samples Collected

All Critical Samples Reported and

Unqualified

All Critical Samples Collected

Sensitivity

Method Blanks Equipment Blank/Field Blanks

Instrument Blanks Appropriate Sample Volume or Weight

Reporting Limit

(Lowest Calibration Standard)

Appropriate Analytical Method

Adapted from Massachusetts Department of Environmental Protection, Bureau of Waste Site Cleanup, MCP

Representativeness Evaluations and Data Usability Assessments, Policy #WSC-07-350, September 19, 2007.

APPENDIX B-3

NFORMATION DERIVED FROM QUALITY CONTROL CHECKS AND SAMPLES

Data Quality

Indicator

(Type of

Information

Provided)

Checks

and

Samples

Sources of Measurement Error

Sample Collection

Sample

Transport

Laboratory

Sampling

Equipment

Sample

Container

Preserva-

tion

Technique

Sample

Matrix

Shipment

Process

Sample

Storage at

Laboratory

Sample

Prepara-

tion

Reagents

Sample

Prepara-

tion

Equipment

Analytical

Method

Reagents

Analytical

Equipment

Purpose

Accuracy/Bias

(Contamination)

Equipment

Blank

(Rinsate

Blank)

X X X X X X X X X

To evaluate carryover

contamination resulting

from successive use of

sampling equipment.

Bottle Blank

(per Lot #)

X X X X X

To evaluate

contamination

introduced from the

sample container.

VOA Trip

Blank

X X X X X X X X

To evaluate

contamination

introduced during

shipment.

Storage

Blank

X X X X X

To evaluate cross

contamination

introduced during

sample storage.

Method

Blank

X X X X

To evaluate

contamination

introduced during

sample preparation

and/or analysis by

laboratory, including

reagents, equipment,

sample handling and

ambient laboratory

conditions.

Reagent

Blank

(per Lot #)

X X X X

To evaluate

contamination

introduced by specific

method reagents.

Instrument

(System)

Blank

X X

To evaluate

contamination

originating from the

analytical reagents

instrumentation.

Data Quality

Indicator

(Type of

Information

Provided)

Checks

and

Samples

Sources of Measurement Error

Sample Collection

Sample

Transport

Laboratory

Sampling

Equipment

Sample

Container

Preserva-

tion

Technique

Sample

Matrix

Shipment

Process

Sample

Storage at

Laboratory

Sample

Prepara-

tion

Reagents

Sample

Prepara-

tion

Equipment

Analytical

Method

Reagents

Analytical

Equipment

Purpose

Accuracy/Bias

Matrix

Spike

X X X X X

To determine laboratory

preparatory and

analytical bias for

specific compounds in

specific sample

matrices.

Surrogate

Spike

X X X X X

To evaluate laboratory

preparatory and

analytical bias for

specific sample

matrices.

Accuracy/Bias

Laboratory

Control

Sample

(LCS)

X X X X

To evaluate the

laboratory’s ability to

accurately identify and

quantitate target

compounds in a

reference matrix at a

known concentration,

usually mid-range of the

calibration curve.

Perfor-

mance

Evaluation

Samples-

Ampulated

Single Blind

X X X X

To evaluate sample

handling procedures

from field to laboratory.

To evaluate the

laboratory’s ability to

accurately identify and

quantitate target

compounds in a

reference matrix.

Frequently used for data

quality assessments

and for laboratory self-

assessments and

external assessments.

Perfor-

mance

Evaluation

Sample-Full

Volume

Single Blind

X X X X X X X X

Data Quality

Indicator

(Type of

Information

Provided)

Checks

and

Samples

Sources of Measurement Error

Sample Collection

Sample

Transport

Laboratory

Sampling

Equipment

Sample

Container

Preserva-

tion

Technique

Sample

Matrix

Shipment

Process

Sample

Storage at

Laboratory

Sample

Prepara-

tion

Reagents

Sample

Prepara-

tion

Equipment

Analytical

Method

Reagents

Analytical

Equipment

Purpose

Accuracy/Bias

Perfor-

mance

Evaluation

Sample

Double

Blind

X X X X X X X X

To evaluate sample

handling procedures

from field to laboratory.

To evaluate the

laboratory’s ability to

accurately identify and

quantitate target

compounds in a

reference matrix.

Laboratory

Fortified

Blank (LFB)

Laboratory

Control

Sample

(LCS)

X X X X

A type of LCS used to

evaluate laboratory

(preparatory and

analytical) sensitivity

and bias for specific

compounds in a

reference matrix at the

quantitation limit

concentrations.

Accuracy/Bias

Initial

Calibration

X X

To ensure that the

instrument is capable of

producing acceptable

qualitative and

quantitative data.

Continuing

Calibration/

Continuing

Calibration

Verification

X X

To ensure the accuracy

and stability of the

instrument response.

Instrument

Perfor-

mance

Check

Sample

X X

To verify that an

instrument can

accurately identify and

quantitate target

analytes at specific

concentration levels.

Data Quality

Indicator

(Type of

Information

Provided)

Checks

and

Samples

Sources of Measurement Error

Sample Collection

Sample

Transport

Laboratory

Sampling

Equipment

Sample

Container

Preserva-

tion

Technique

Sample

Matrix

Shipment

Process

Sample

Storage at

Laboratory

Sample

Prepara-

tion

Reagents

Sample

Prepara-

tion

Equipment

Analytical

Method

Reagents

Analytical

Equipment

Purpose

Accuracy/Bias

(Preservation)

Cooler

Temp.

Blank

(VOC only)

To evaluate whether or

not samples were

adequately cooled

during shipment.

Sensitivity

Low-level

calibration

standard

X X X X

A standard used to

evaluate accuracy and

sensitivity at a specific

concentration. Used to

evaluate laboratory

sensitivity and bias for

specific compounds in a

reference matrix at the

quantitation limit

concentrations.

Method

Detection

Limit

Studies

X (if

performed

using

same

reference

matrix)

X X X X

A statistical

determination that

defines the minimum

concentration of a

substance that can be

measured and reported

with 99% confidence

that the analyte

concentration is greater

than zero.

Sensitivity

Low Point

of Initial

Calibration

Curve

(Reporting

Limit)

X X

To ensure that the

instrument is capable of

producing acceptable

qualitative and

quantitative data at the

lowest concentration

that sample results will

be reported; the

Reporting Limit.

Data Quality

Indicator

(Type of

Information

Provided)

Checks

and

Samples

Sources of Measurement Error

Sample Collection

Sample

Transport

Laboratory

Sampling

Equipment

Sample

Container

Preserva-

tion

Technique

Sample

Matrix

Shipment

Process

Sample

Storage at

Laboratory

Sample

Prepara-

tion

Reagents

Sample

Prepara-

tion

Equipment

Analytical

Method

Reagents

Analytical

Equipment

Purpose

Precision

Field

Duplicates

X X X X X X X X X X

To measure overall

precision by evaluating

cumulative effects of

both field and laboratory

precision.

Laboratory

Duplicates

X X X X X

To evaluate laboratory

preparatory and

analytical precision.

Matrix

Spike

Duplicates

X X X X

To determine laboratory

preparatory and

analytical bias and

precision for specific

compounds in specific

sample matrices.

Analytical

Replicates

(e.g.,

duplicate

injections)

To evaluate analytical

precision for

determinative

instrumentation.

Internal

Standards

To evaluate instrument

precision and stability.

Inter-laboratory

Comparability

Field Splits X X X X X X

To evaluate sample

handling procedures

from field to laboratory

and to evaluate inter-

laboratory comparability

and precision.

Notes:

Not all of the types of QC checks and samples listed in this table are standard deliverables that are reported or required by the RCPs.

Table adapted from Region I, EPA New England Compendium of Quality Assurance Project Plan Requirements and Guidance, Final October 1999, Attachment A: Region I, EPA-NE Quality Assurance Project

Plan Manual, Draft, September 1998, Table 4, pages 83-87.

Appendix C

QC Information to be Reviewed During

Data Quality Assessments

APPENDIX C

UALITY CONTROL INFORMATION TO BE EVALUATED

URING DQA AND DUES

NJDEP expects that the investigator will evaluate all laboratory reported QC information and

nonconformances in accordance with this guidance. Nonconformances that are found may be

noted on the DQQ Worksheets found in Appendix D of this document, the SRP Full Laboratory

Data Deliverables form and the SRP Reduced Laboratory Data Deliverables section appearing

in key documents.

The information below summarizes standard, required deliverables to obtain Data of Known

Quality. The QC information that must be reviewed during the DQA by the investigator includes,

but is not limited to the following:

STANDARD DKQ DELIVERABLES

Laboratory Report Inspection

Goal: Determine if all laboratory deliverables are provided and complete:

Tasks:

• Review the laboratory report to determine that the following items are present for all

sample batches:

o DKQ Conformance/Nonconformance Summary

Questionnaire(C/NCSQ)

o Narrative identifying QC nonconformances;

o Analytical results;

o Chain of Custody Form; and,

o Quality control results, including but not limited to:

 Method Blanks;

 Laboratory Control Samples (LCS);

 MS/MSD (when requested);

 Surrogates (as appropriate for method); and,

 Other QC results and information provided in the laboratory report.

• Review information on the C/NCSQ to determine that:

o All the questions in the C/NCSQ are answered;

o The C/NCSQ is dated and signed; and,

o The narrative includes an explanation for the questions which were answered “NO.”

• Review the laboratory narrative to identify QC nonconformances:

o Review the narrative for significant findings (i.e., QC nonconformances that could affect

usability of the reported results) and request additional information from the

laboratory, if applicable.

• Review the Chain of Custody Form for completeness and correctness:

o Review Chain of Custody Form to ensure form is complete and correct;

o Verify sample identification numbers and collection information;

o Verify that there is an acceptance signature for each relinquished signature documenting

the delivery of the samples to the laboratory facility. Check for errors in noted dates and

times;

o Correct any errors with a single line cross-out, initial/date and note reason for correction;

and,

o Contact the laboratory for help or clarification if needed.

Data of Known Quality Evaluation

Goal: Determine if Data of Known Quality was achieved.

Tasks: Review the C/NCSQ to determine if data are of known quality was achieved.

Chain of Custody (COC) Evaluation

Goal: Evaluate the information presented on the Chain of Custody Form to determine if any

QC issues or nonconformances are present.

Tasks:

o Determine whether Handling Time was met;

o Determine if samples appropriately preserved/refrigerated/iced; and,

o Determine if samples were received by the laboratory an appropriate temperature.

Sample Result Evaluation

Goal: Determine if sample results have been properly reported.

Tasks: Evaluate the sample results:

• Determine that reporting limits (RLs) were noted;

• Verify that concentrations greater than the RL were reported;

• Verify that concentration reported below the RLs are qualified “J”

• Verify that the results for soils and sediments were reported in mg/kg on a dry weight basis;

• Verify that results for aqueous samples are reported in ug/L;

• Verify that air vapor samples are reported in ug/m3;

• Check dilution factor to see if a dilution was performed and if so, the RL adjusted

accordingly;;

• Determine that RLs are less than, or equal to the regulatory criteria; and,

• Determine if sample results are provided for the each requested analysis

Sample Preservation and Holding Times Evaluation

Goal: Determine if samples were preserved properly and analyzed within holding times.

Tasks:

• Review the chain of custody and or narrative to determine if the samples were preserved in

accordance with the requirement of the DKQ Method reported.

• Review the narrative to determine if the holding time specified in the DKQ Method was met.

• Review the chain of custody for other sample/method-specific QA (e.g. vacuum

readings on vapor canisters).

Method, Field or Trip Blank Evaluation

Goal: Determine the existence and magnitude of contamination resulting from laboratory or

field activities.

Task: Review all blank data and narratives for possible contamination.

Field Duplicates and Laboratory Duplicates

Goal: Evaluate Precision

Task: Review all duplicate sample information.

Laboratory Control Samples Evaluation

Goal: Evaluate accuracy of laboratory method.

Task: Review the narrative to determine if nonconformances were noted in the laboratory

narrative.

Surrogate Results Evaluation

Goal: Evaluate accuracy in the sample matrix.

Task: Review the narrative to determine if nonconformances were noted in the laboratory

narrative.

Matrix Spike/Matrix Spike Duplicate Results Evaluation

Goal: Evaluate accuracy (Matrix Spike) and precision (Matrix Spike Duplicate) in the sample

matrix.

Task: Review the narrative to determine if nonconformances were noted in the laboratory

narrative.

Other Information and QC Information:

Other Laboratory Information:

Evaluate precision, accuracy, representativeness, comparability, completeness, and sensitivity

as appropriate

Review information provided.

Appendix D

Data Quality Assessment Worksheets and

Summary of DKQ Acceptance Criteria

APPENDIX D-1

NSTRUCTIONS FOR THE USE OF THE

ATA QUALITY ASSESSMENT WORKSHEETS

The worksheets presented in Appendices D-2 and D-3 are two examples of Data Quality

Assessment Worksheets (DQA Worksheets) that may be used to summarize the QC

nonconformances that are reported for a laboratory deliverable for each sample in one place.

The “NJDEP Site Remediation Program Full Laboratory Data Deliverable Form” must be

submitted to the Department pursuant to N.J.A.C. 7:26E-2.1(a)15 when submitting analytical

results for samples of Immediate Environmental Concern (IEC), potable well samples, and

vapor intrusion cases pursuant to N.J.A.C 7:26E-1.14, 1.17, and 1.18 and for polychlorinated

dibenzo-p-dioxins/polychlorinated dibenzofurans and all hexavalent chromium soil samples

pursuant to N.J.A.C. 7:26E-2.1. This form and instructions are available on the NJDEP website

at http://www.nj.gov/dep/srp/srra/forms/

. These worksheets are intended to be a starting point

and can be modified by the user. A summary of the QC information to be reviewed as part of a

Data Quality Assessment is presented in Appendix C of this document. It is the investigator’s

responsibility to complete these worksheets (i.e., they should not be completed by the

laboratory).

If needed, the NJDEP DKQ acceptance criteria for each of the common analytical methods can

also be found in Appendix D-4 of this document and Appendix B of the DKQ Guidance.

Appendix D-2, DQA Worksheet 1

QC for DKQ deliverables and other information is shown on the left hand side of the form. QC

nonconformances, if any, are circled and described on the right hand side of the form. A space

for notes is also provided on the right hand side of this form.

Appendix D-3, DQA Worksheet 2

This one page worksheet can be used to list all of the nonconformances for a sample in one

place. To help streamline data entry this form can be filled out electronically by using a

spreadsheet program. For smaller projects, it may be useful to add a columns to list applicable

regulatory criteria and preliminary DUE findings.

APPENDIX D-2

ATA QUALITY ASSESSMENT WORKSHEET 1

PAGE __ OF __

PROJECT:

FILE NUMBER:

LABORATORY WORK ORDER

REVIEWER: DATE:

BLANKS

Compound

Notes

Method Blank, VOCs

>RL?

Method Blank, SVOCs

>RL?

Method Blank, VPH

>RL?

Method Blank, EPH

>RL?

Method Blank, PCBS

>RL?

Method Blank, Pest

>RL?

Method Blank, Metals

>RL?

Method Blank, Total Cyanide

>RL?

Method Blank, ETPH

>RL?

Method Blank Hex Chrome

>RL?

Field Blank

>RL?

Trip Blank

>RL?

VPH Blank (methanol)

>RL?

Blank Soil VOCs (methanol)

>RL?

Blank Soil VOCs

(water/bisulfate) circle

>RL?

LCS

Low Bias

High Bias

Compound

Notes

VOCs

<10%

> 10% & < LCL

>UCL

SVOCs

<10%

> 10% & < LCL

>UCL

VPH

<10%

> 10% & < LCL

>UCL

EPH

<10%

> 10% & < LCL

>UCL

PCB

<10%

> 10% & < LCL

>UCL

PEST

<10%

> 10% & < LCL

>UCL

Hex Chrome

<70%

>70 % & <LCL

> UCL

Metals

<10%

> 10% & < LCL

>UCL

Total Cyanide

<10%

> 10% & < LCL

>UCL

ETPH

<10%

> 10% & < LCL

>UCL

SURROGATES

Low Bias

High Bias

Compound

Notes

VOCs

<10%

> 10% & < LCL

>UCL

SVOCs

<10%

> 10% & < LCL

>UCL

VPH

<10%

> 10% & < LCL

>UCL

EPH

<10%

> 10% & < LCL

>UCL

PCB

<10%

> 10% & < LCL

>UCL

PEST

<10%

> 10% & < LCL

>UCL

MS/MSDS

Low Bias

High Bias

QC Source

RPDS

Notes

VOCs

<10%

> 10% & < LCL

>UCL

Batch? Site?

SVOCs

<10%

> 10% & < LCL

>UCL

Batch? Site?

EPH

<10%

> 10% & < LCL

>UCL

Batch? Site?

PCB

<10%

> 10% & < LCL

>UCL

Batch? Site?

PEST

<10%

> 10% & < LCL

>UCL

Batch? Site?

Hex Chrome

<50%

> 505 & < LCL

>UCL

Batch?

Metals

<10%

> 10% & < LCL

>UCL

Batch? Site?

Total Cyanide

<10%

> 10% & < LCL

>UCL

Batch? Site?

ETPH

<10%

> 10% & < LCL

>UCL

Batch? Site?

FIELD DUPLICATES RPDS

Soil

Water

Compound

Notes

VOCs

RPD > 50%

RPD > 30%

SVOCs

RPD > 50%

RPD > 30%

VPH

RPD > 50%

RPD > 30%

EPH

RPD > 50%

RPD > 30%

PCB

RPD > 50%

RPD > 30%

PEST

RPD > 50%

RPD > 30%

Metals

RPD > 50%

RPD > 30%

Total Cyanide

RPD > 50%

RPD > 30%

EPH

RPD > 50%

RPD > 30%

LAB DUPLICATES RPDS

Soil

Water

Compound

Notes

RPD > 50%

RPD > 30%

Batch? Site?

Hex Chrome

RPD >20%

Batch

Reasonable Confidence Achieved? Y/N Significant QC Variances Noted? Y/N Requested Reporting Limits Achieved? Y/N

Preservation Requirements Met? Y/N Holding Time Requirements Met? Y/N

Abbreviations: RL = Reporting Limit; LCS = Laboratory Control Sample; SV = Significant QC Variance; LCL= RCP Lower Control Limit; UCL= RCP Upper Control Limit; RPD =

Relative Percent Difference; VOCs = Volatile Organic Compounds; SVOCs = Semivolatile Organic Compounds; VPH = Volatile Petroleum Hydrocarbons; EPH = Extractable

Petroleum Hydrocarbons; PCBs = Polychlorinated Biphenyls; Pest = Pesticides; ETPH Extractable Total Petroleum Hydrocarbons

APPENDIX D-3

ATA QUALITY ASSESSMENT WORKSHEET 2

Project:

File Number:

Reviewer:

Date:

Notes:

Sample

Number(s)

Compound(s)

Quality Control

Nonconformance

Percent

Recovery

Relative

Percent

Difference

High/Low or

Indeterminate

Bias

Comments

Note other QC nonconformances below (data package inspection, reasonable confidence. chain of custody, sample result, sample

preservation and holding time evaluations.

Notes:

Bias High: Reported result may be lower. Reporting Limit (RL) is acceptable as reported.

Bias Low: Reported results may be higher. Reporting Limit (RL) may be higher than reported.

Bias Indeterminate: Reported result may be biased; however, it’s unclear whether the results may be biased low or high.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

QC Parameter Holding Time (1)

Method

Blank

Site Specific Matrix Spike/Matrix Spike

Duplicate

Laboratory Control Sample

Method 6010

Trace Metals

Inductively Coupled

Plasma-Atomic

Emission

Spectrometry

Aqueous soil,

sediment, and high

concentration

waste samples,

180 days. Mercury

28 days.

Target

analytes

must be <

RL.

Percent recovery limits must be between 75-

125%.

If MS/MSD run,

for aqueous samples, if concentration > 5x the

RL, RPD < 20%. If concentration < 5x RL,

difference ± RL;

for solids, if concentration > 5x RL, RPD <

35%. If concentration < 5x RL, difference ± 2x

RL.

LCS recoveries ± 20% for

aqueous samples and within

vendor control (95%

confidence limits) for solids.

Method 6020

Trace Metals

Inductively Coupled

Plasma-Mass

Spectrometry

Aqueous, soil,

sediment, and high

concentration

waste samples,

180 days. Mercury

28 days.

Target

analytes

must be <

RL.

Percent recovery limits must be between 75-

125%.

If MS/MSD run,

for aqueous samples, if concentration > 5x the

RL, RPD <20%. If concentration < 5x RL,

difference ± RL;

for solids, if concentration > 5x RL, RPD <

35%. If concentration < 5x RL, difference ± 2x

RL.

LCS recoveries ± 20% for

aqueous samples and within

vendor control (95%

confidence limits) for solids.

Method 7000 Series

Metals

(Flame and Graphite

Furnace Atomic

Absorption

Spectroscopy)

Aqueous, soil,

sediment, and high

concentration

waste samples,

180 days.

Target

analytes

must be <

RL.

Percent recovery limits must be between 75-

125%.

If MS/MSD run,

for aqueous samples, if concentration > 5x the

RL,

RPD ± 20%, if concentration < 5x RL,

difference ± RL;

for solids, if concentration > 5x RL, RPD ±35%.

If concentration < 5x RL, difference ± 2x RL.

LCS recoveries ± 20% for

aqueous samples and within

vendor control (95%

confidence limits) for solids.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

Parameter

Holding Time (1)

Method

Blank

Site-Specific

Matrix

Spike/Matrix

Spike

Duplicate

Site-Specific

Matrix Spike/

Matrix Spike

Duplicate

(Aqueous

Only)

Site-Specific

Sample Matrix

Duplicate

Site-Specific

Soluble and

Insoluble Cr6+

Matrix Spike

(Solid Only)

Laboratory

Control

Sample

Method 7196

Hexavalent

Chromium

Aqueous 24 hours;

Soil/sediment samples,

digest within 30 days.

Analyze digestate within

7 days of preparation.

High concentration waste

samples Digest within 30

days. Analyze digestate

within 7 days of

preparation.

Soil/sediment pH and

ORP

24 hours of sample

preparation.

Soil/sediment, ferrous

iron and sulfide 7 days

Cr6+

must be

< RL

(Not Applicable

(Matrix spike

only for

Hexavalent

Chromium, not

MS/MSD pair)

Percent

recovery limits

must be

between 75-

125%.

Must be

performed on a

Site field

sample.

Aqueous/

Soil/Sediment:

RPD ≤ 20%; a

control limit of +

RL if original or

duplicate is < 4

times the RL.

Percent

recovery limits

must be

between 75-

125%.

LCS recoveries

±20% for

aqueous

samples and

within vendor

control (95%

confidence

limits) for solids

or the NIST

2701 control

limits.

Method

7470/7471

Mercury Cold

Vapor Atomic

Absorption

Spectroscopy

Aqueous, soil, sediment,

and high concentration

waste samples, 28 days.

Mercury

must be

<RL

Percent

recovery limits

must be

between 75-

125%.

Not applicable

For aqueous

samples RPD ±

20% if conc.

>5x the RL. If

conc. < 5x RL,

the limit is ±

RL.

For solids RPD

±35% if conc.

>5x the RL. If

conc. < 5x the

RL, limit is ± the

RL.

Not applicable

LCS recoveries

±20% for

aqueous

samples and

within vendor

control (95%

confidence

limits) for

solids.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

Parameter

Holding Time (1) Method Blank Surrogates

Site-Specific

Matrix

Spike/Matrix Spike

Duplicate

Laboratory

Control Sample

Endrin and

DDT

Breakdown

Standard

Method

8021

Volatile

Organic

Com-

pounds

Aqueous 14 days (2)

Soil/sediment, 14 days if

preserved. 48 hours if

unpreserved (Note 3).

High concentration waste

samples, 14 days.

Target analytes

must be < RL

except for

common lab

contaminants

which must be <

3x the RL

(contaminants

are acetone,

methylene

chloride, and 2-

butanone).

Laboratory

determined percent

recoveries must be

between 70-130%

for individual

surrogate

compounds.

Laboratory

determined recovery

limits may be

outside 70-130 %

limits for difficult

matrices (e.g.

waste, sludges,

etc.).

Laboratory

determined percent

recoveries should

be between 70-130

% for target

compounds.

RPD’s should be ≤

30%.

Laboratory

determined

percent recoveries

must be between

70-130% for target

compounds.

Not

applicable

Method

8081

Pesticides

Aqueous, 7 days to

extraction. 40 days from

extraction to analysis.

Soil/sediment samples, 14

days to extraction. 40

days from extraction to

analysis. Up to one year

for samples frozen within

48 hours of collection

(Note 1).

High concentration waste

samples 14 days to

extraction. 40 days from

extraction to analysis.

Target analytes

must be < RL.

Recovery limits lab

generated and

within maximum

range of 30-150%

for both compounds

on both columns.

Labs must develop

own in-house limits,

which fall within 30-

150% limits.

Laboratory

determined percent

recovery limits must

be between 30-

150%

RPD’s ≤ 20% for

water and ≤ 30%

for solids

Laboratory

determined

percent recovery

limits must be

between 40-140%

except for difficult

analytes, which

must be between

30-140%

recovery.

Breakdown

must be ≤

15% for each

compound.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

Parameter

Holding Time (1) Method Blank Surrogates

Site Specific Matrix

Spike/Matrix Spike

Duplicate

Laboratory Control

Sample

Method 8151

Chlorinated

Herbicides

Aqueous 7 days to

extraction, 40 days from

extraction to analysis

Soil/Sediment, 14 days to

extraction. 40 days from

extraction to analysis. Up to

one year for samples frozen

within 48 hours of collection.

(Note 4)

High concentration waste

samples, 14 days to

extraction. 40 days from

extraction to analysis

Target analytes must

be <RL.

Recovery limits lab

generated and within

30-150% for both

compounds on both

columns.

Labs must develop own

in-house limits that fall

within 30-150% limits.

If surrogate exceeds

limits on one column

and herbicide

concentrations reported

at > RL but dual column

precision not acceptable

(RPD > 40%), re-extract

and reanalyze samples.

Laboratory determined

percent recovery limits

must be between 30-

150%, RPDs ≤ 20%

waters and ≤ 30%

solids.

Laboratory determined

percent recovery limits

must be between 40-

140% except in-house

limits for Dinoseb.

Labs expected to

develop own in-house

control limits that meet

or exceed limits listed

above.

Method 8082

Polychlori-

nated

Biphenyls

Aqueous 7 days to

extraction, 40 days from

extraction to analysis.

Soil/Sediment 14 days to

extraction. 40 days from

extraction to analysis. Up to

one year for samples frozen

within 48 hours of collection.

(Note 4)

High concentration waste

samples, excluding

transformer oils, 14 days to

extraction. 40 days from

extraction to analysis.

Transformer/Waste Oils, 1

Target analytes must

be <RL.

Recovery limits lab

generated and within

maximum range of 30-

150% for both

compounds on both

columns.

Labs must develop own

in-house limits that fall

within 30-150% limits.

Laboratory determined

percent recovery limits

for AR-1016/1260 must

be between 40-140%.

Recoveries for all

Aroclors or Congeners

40-140%

Congeners must

contain all target

congeners.

RPD’s ≤ 20% for waters

and ≤ 30% for solids.

Laboratory determined

percent recovery limits

must be between 40-

140%.

Labs are required to

develop own in-house

limits that meet or

exceed limits listed

above.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

Parameter

Holding Time (1) Method Blank Surrogates

Site Specific Matrix

Spike/Matrix Spike

Duplicate

Laboratory Control

Sample

Method

8260

Volatile

Organic

Com-

pounds

Aqueous, 14 days, 7 days

if unpreserved (2)

Soil/Sediment, 14 days if

preserved. 48 hours if

unpreserved.

(Note 3).

High concentration waste

samples, 14 days.

Target analytes must

be <RL except for

common lab

contaminants which

must be <3x the RL

(Contaminants are

acetone, methylene

chloride, and 2-

butanone).

Laboratory determined

percent recoveries must

be between 70-130%

for individual surrogate

compounds.

Laboratory determined

recovery limits may be

outside 70-130% limits

for difficult matrices

(e.g. waste, sludges,

etc.).

Laboratory determined

percent recoveries

should be between 70-

130% for target

compounds.

RPDs should be ≤ 30%

Laboratory determined

percent recoveries must

be between 70-130%

for target compounds.

Can also be used as

CCAL.

Lab may have difficult

compounds out of

criteria as long as within

40-160% recovery.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

QC Parameter Holding Time (1) Method Blank Surrogates

Site Specific Matrix

Spike/Matrix Spike

Duplicate

Laboratory Control

Sample

Method 8270

Semivolatile

Organic

Compounds

Aqueous, 7 days to

extraction. 40 days from

extraction to analysis

Soil/sediment, 14 days to

extraction. 40 days from

extraction to analysis.

Up to one year for

samples frozen within 48

hours of collection.

(Note 4)

High concentration

waste samples 14 days

to extraction. 40 days

from extraction to

analysis.

Target analytes

must be < RL

except for

common lab

contaminants

which must be

< 5x the RL

(Contaminants

are phthalates).

Soil recovery limits

lab generated and

within 30-130%.

Water recovery limits

lab generated and

within 30-130% for

base-neutrals, 15-

110% for acid

compounds.

Laboratory

determined percent

recovery limits must

be between 70-130%

except 20-160% for

difficult compounds.

RPD’s ≤ 20% for

waters and ≤ 30% for

soils.

Laboratory determined

percent recovery limits must

be between 70-130% except

20-160% for difficult

compounds.

Method

9010/9012/9014

Total Cyanide

Aqueous, soil, sediment

and high concentration

waste samples: Cyanide

14 days from collection

to analysis, (from date

when thawed if solid

samples frozen). Can

maintain samples up to 1

year if frozen

Cyanide must

be < RL.

Not applicable

Percent recovery

limits must be

between 75-125%.

For aqueous samples

RPD ≤ 20%

For solids RPD ≤

35%

LCS recoveries ±20% for

aqueous samples and within

vendor control (95%

confidence limits) for solids.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

QC Parameter Holding Time (1) Method Blank Surrogates

Site Specific Matrix

Spike/Matrix Spike

Duplicate

Laboratory Control

Sample

Fractionation

Check

Standard

NJDEP

Extractable

Petroleum

Hydrocarbons

(EPH)

Aqueous, soil,

and sediments,

samples must be

extracted within

14 days of

collection.

Extracts must be

analyzed within

40 days of

extraction.

All components

should be < 5

times their

respective

MDLs.

Labs develops

own in-house

limits which must

be within 40-140%

for each surrogate.

Sample recoveries

and must be within

40-140%.

Conc. of

fractionating

surrogates

naphthalene and

methylnaphthalene

in aliphatic fraction

< 5% total conc. of

those 2

compounds (in the

batch-related LCS

or LCSD)

Lab develops own in-

house recovery range

but percent

recoveries should be:

Fractionated =

between 40 and

140% for each

carbon range.

Non-Fractionated =

between 40 and

140% for each

compound

RPDs should be ≤

50% for waters and

soils/sediments if

MSD is performed.

Percent recoveries

between 40 and

140% for all

compounds in the

LCS; n-nonane

must be between

25-140%.

If #2 fuel used as

the LCS, percent

recoveries must be

between 40 and

140% for the #2-fuel

Retention times of

surrogates in LCS

must be within

retention time

windows

Every lot of

silica gel/SPE

cartridges

checked.

Percent

recoveries

between 40 and

140% for each

compound,

except for n-

nonane which

must be

between 25-

140%.

APPENDIX D-4

UMMARY OF DKQ ACCEPTANCE CRITERIA

Notes:

Not all method QA/QC deliverables are listed here. .

(1) See the Method for specific preservation requirement for each method.

(2) If aqueous samples effervesce upon addition of hydrochloric acid, samples must be collected unpreserved and stored at 4 ±

2º Celsius. Holding time is 7-days from collection.

(3) Samples should be collected and stored according to N.J.A.C. 7:26E-2.1(a)8.

(4) If the freezing option is selected, the sample must be frozen within 48 hours of collection. The holding time recommences

when thawing begins. The total holding time is calculated from the time of collection to freezing plus the time allowed for

thawing. The total elapsed time must be less than 14 days. Although the USEPA removed the holding time requirements for

PCBs, NJDEP still requires the method – specified holding times to be followed.

Abbreviations:

CCAL Continuing Calibration

Cr Chromium

EPA United States Environmental Protection Agency

EPH Extractable Petroleum Hydrocarbons

LCS Laboratory Control Sample

LCSD Laboratory Control Sample Duplicate

ORP Oxidation Reduction Potential

RPD Relative Percent Difference

RL Reporting Limit

YR Year

Appendix D-5

Common Laboratory Data Qualifiers

Organics:

U This flag indicates the compound was analyzed for but not detected at a listed and

appropriately adjusted reporting level.

J This flag indicates an estimated value. This flag may be used when:

(1) estimating a concentration for TICs where a 1:1 response is assumed;

(2) the mass spectral and Retention Time (RT) data indicate the presence of a

compound that meets the volatile and semivolatile GC/MS identification criteria,

and the result is less than the adjusted Reporting Limit; and

(3) the RT data indicate the presence of a compound that meets the pesticide

and/or Aroclor identification criteria, and the result is less than the adjusted

Reporting Limit but greater than zero. For example, if the sample's adjusted

Reporting Limit is 5.0 μg/L, but a concentration of 3.0 μg/L is calculated, report it

as 3.0J.

N This flag indicates presumptive evidence of a compound. This flag is only used for

TICs, where the identification is based on a mass spectral library search and must

be used in combination with the J flag. It is applied to all TIC results. For generic

characterization of a TIC, such as chlorinated hydrocarbon, or for an "unknown" (no

matches ≥ 85%), the "N" flag is not used.

P This flag is used for pesticide and Aroclor target compounds when there is greater

than 40% relative percent difference (RPD) for detected concentrations between the

two GC columns (see Form X). The "P" flag is not used unless a compound is

identified on both columns.

C This flag applies to pesticide and Aroclor results when the identification has been

confirmed by GC/MS. If GC/MS confirmation was attempted but was unsuccessful,

do not apply this flag; use a laboratory-defined flag instead (such as the X-qualifier).

B This flag is used when the analyte is found in the associated method blank as well

as in the sample. It indicates probable blank contamination and warns the data user

to take appropriate action. This flag shall be used for a TIC as well as for a positively

identified target compound. Blank contaminants are flagged "B" only when they are

detected in the sample.

E This flag identifies compounds whose response exceeds the response of the

highest standard in the initial calibration range of the instrument for that specific

analysis. (If one or more compounds of concern have a response greater than the

response of the highest standard in the initial calibration, the sample or extract

should be diluted and reanalyzed according to the specifications of the method and

a new result reported.)

D If a sample or extract is reanalyzed at a dilution factor greater than 1(e.g., when the

response of an analyte exceeds the response of the highest standard in the initial

calibration), the D qualifier is attached to the sample result.

Organics (continued):

A This flag indicates that a TIC is a suspected Aldol-condensation product.

S This flag is used to indicate an estimated value for Aroclor target compounds where

a valid 5-point initial calibration was not performed prior to the analytes detection in

a sample. If an "S" flag is used for a specific Aroclor, then a reanalysis of the sample

is required after a valid 5-point calibration is performed for the detected Aroclor.

(Obtained from the USEPA Contract Laboratory Program Statement of Work for Organics

Analysis Multi-Media, Multi-Concentration SOM1.1May 2005 (revised in SOMO1.2).

Inorganics:

X The reported value is estimated due to interferences.

* QC analyses are outside control limits.

D The reported value is from a dilution.

J The reported value was less than the CRQL, but greater than or equal to the MDL.

U The result was less than the MDL. For Hardness, if the results for both Ca and Mg

were less than their respective MDLs.

N Spiked sample recovery not within control limits.

E The reported value is estimated due to the presence of interference. An explanatory

note should be included in a comments section.

Obtained From USEPA Contract Laboratory Program Statement of Work for Inorganic

Superfund Methods (Multi-Media, Multi-Concentration) ISM01.2 January 2010.

Appendix E

Evaluating Significant QA/QC Variances

APPENDIX E

VALUATING SIGNIFICANT QA/QC VARIANCES

On occasion, the investigator may encounter Quality Control (QC) nonconformances that are so

excessive that they must be considered as significant or gross violations of QC criteria. Causes

may range from problems associated with the sampled medium, such as severe matrix

interference, or may be the result of improper sample handling and management. Whatever the

cause, the investigator must determine whether or not the data associated with such significant

QC violations can be used in making the environmental decisions for which the associated

samples were collected.

In general, data associated with significant QC violations will be of limited use in decision-

making, and it is the responsibility of the investigator to demonstrate that such data are, in fact,

usable for a particular purpose. It should be understood that the same data set with the same

QC issues may be usable for one purpose but not for another. It is certainly possible that data

associated with significant violations of QC might be used for qualitative or screening purposes,

but it is highly unlikely that such data would be suitable for demonstrating compliance with

applicable regulations. However, samples with significant QC variances can be used to

determine that remediation is needed. The extent to which such data may be relied upon clearly

depends on the intended use of that data.

It is possible to review a data set with significant QC violations and, depending on the intended

purpose, the investigator may choose to use or qualify the data in one case and reject it in

another. For example, if significant QC failures occur, but an analyte is detected and the

purpose of the sample analysis is to characterize environmental matrices to determine if a

release has occurred, the investigator can reasonably justify using that data to determine that

there was, in fact, a release of the specific compounds that were detected. The data may not be

usable to determine all of the contaminants that may have been released (i.e., determine the full

nature of the release), and it should be clearly understood that additional measures should be

taken to ensure that QC results for sampling during follow-up portions of the investigation are

within acceptable limits.

If significant QC failures occur and the purpose of the sampling was to conclusively demonstrate

compliance with regulations, then it is unlikely that the data will be usable for that purpose.

If there are years of previous data or many other samples from a particular release area that are

consistent with the results of the data associated with significant QC failures and site conditions

have not changed as demonstrated through subsequent data, then it is possible that the data

with poor QC could be used with qualification. If the data with poor QC appear anomalous

relative to previous results, then it is unlikely that they can be relied on to draw final conclusions.

QC results for laboratory data associated with investigation and remediation projects should

always be evaluated with respect to the intended use of that data and the project-specific or

task-specific data quality objectives that were established for types of decisions that will be

made using that data. NJDEP expects that data with significant QC failures will be deemed

unusable, unless the investigator provides adequate justification for the use of such data and

qualifies the data accordingly, such as indicating that such data is used as qualitative, rather

than quantitative, information. Once the investigator comes to the conclusion that data are

unusable, NJDEP expects that any data deemed unusable will not be used to demonstrate

compliance with regulatory criteria.

The following paragraphs identify typical types and causes of significant QC violations and

provide a discussion of the factors that an investigator should consider when evaluating whether

or not the associated data is usable.

General QC Infractions

Sample Receipt Issues

• Field and trip blanks were not received at the site within 1 day of their preparation at the

laboratory;

• Blanks and associated samples were held longer than 2 days on-site and/or did not arrive

back at the laboratory within 1 day of shipment;

• Samples to be analyzed are received outside a temperature of 4 +/-2º Celsius (C);

• Samples received above a maximum temperature of 12ºC more than 24 hours from

collection; and

• Lack of evidence of cooling with ice or use of artificial ice substitutes, such as “blue ice,”

which are not acceptable as evidence of cooling if the sample temperature is outside the

acceptance limits specified in the DKQ protocols and the three prior bullets above.

Sample Containers

Any improper sample container, as described in the applicable analytical method, or a sample

container that is not properly sealed or has been otherwise compromised, should be considered

to be a significant QC infraction.

Sample Preservation

Analytical results from samples that are not preserved in accordance with the requirements of

the analytical method should be considered to be a significant QC infraction.

Analysis Holding-time Excursions (total holding time from collection)

Analytical results that are greater than the applicable regulatory criteria can be considered

usable, regardless of the holding time, as long as the intended use of the data is to identify

locations where concentrations of contaminants exceed those criteria. However, analytical

results less than regulatory criteria that were analyzed and/or extracted after more than two

times the holding time has passed should not be considered usable unless the investigator can

provide the rationale for the use of the data. Similarly, if samples for which analytical results are

greater than regulatory criteria were subject to holding-time issues and such results are

intended for use in demonstrating compliance in any way, such as using an alternative criterion,

those results must be considered in a manner similar to results that are less than regulatory

criteria.

Calibration Issues

If calibration issues are reported the investigator should contact the laboratory, as needed, for

guidance. Although reporting of calibration QC is not required under the DKQs on a routine

basis, the DKQ protocols require that the laboratory narrate nonconformance of calibration

issues, as described in the DKQ protocols for various analytical methods. The following

calibration issues are among those that would be considered significant QC infractions:

• Instrument not calibrated by an initial calibration (ICAL);

• No continuing calibration standard analyzed within 24 hrs of ICAL;

• Gas Chromatography/Mass Spectrometry tune criteria significantly out of criteria (greater

than 20 percent for any one atomic mass unit); and

• Relative Response Factor (RRF) less than 0.05 (with no technical justification for low

RRF), for DKQ Methods 8260B and 8270C should result in rejection of all results

reported as below the reporting limit for associated samples.

Reporting Issues

Issues of suspected data fraud should be forwarded to the appropriate authorities, e.g. the

NJDEP Office of Quality Assurance.

Professional Judgment

In some cases, it is appropriate to reject data based on professional judgment. These cases

include, but are not limited to the following:

• Severely poor overall instrument performance;

• Low percent solids (less than 10 percent); and

• Multiple QC nonconformances and gross failures.

Significant QC Violations for Specific Analytes

The following situations are considered to be significant QC violations. If any of the following

issues are reported, the investigator is encouraged to contact the laboratory for guidance.

Inorganic Compounds

LCS recovery is less than 50 percent of the control limit - An LCS less than 50 percent of

control limit may be off-set by matrix spike data within acceptance criteria to reasonably

determine that the problem is only associated with the LCS.

MS recovery is less than 30 percent for all affected analytes in a batch, with the exception of

hexavalent chromium if supported by Oxidation Reduction Potential (ORP) and pH data which

indicates reducing conditions Hexavalent chromium readily reduces to trivalent chromium in a

reducing environment.

Organic Compounds

LCS recovery is less than 10 percent - Usability of results reported as below the reporting limit

for analytes with LCS recovery less than 10 percent is severely limited and would require

substantial justification by the investigator.

Surrogate recoveries for organics less than 10 percent - Usability of results reported as below

the reporting limit for analytes associated with surrogates with LCS recovery less than 10

percent is severely limited and would require substantial justification by the investigator.

MS/MSD recoveries for organics less than 10 percent - Usability of results reported as below

the reporting limit for affected compound in the unspiked sample (i.e., field sample used for

MS/MSD only) is severely limited and would require substantial justification by the investigator.

The investigator should also evaluate how these results may affect the usability of other sample

results in the batch.

Internal standard area counts in a sample are less than 20 percent of associated calibration

check standard area counts – generally associated non-detects for analytes which are

quantitated using the internal standard are rejected and would not be usable for project

decisions.

Fractionation Check Standard (FCS) recovery for EPH for any analyte included in the FCS that

is not between 40% and 140% (with lower recoveries permissible for n-Nonane but recovery

must be >25%) - results are generally rejected and are not usable for project decisions.

Endrin/DDT Breakdown Check Standard, breakdown should be less than 15 percent - Non-

detected results for endrin or DDT, whichever compound is affected, should be rejected and

detected results for the breakdown projects should be considered biased high. This indicates

the equipment was in need of maintenance at the time of analysis.

Dual column precision percent difference is greater than 100 percent for single response

pesticides and herbicides - Reject all results for affected pesticides and herbicides. Dual

columns are used to confirm the presence of analytes.

Dual column precision percent difference is greater than 500 percent for multi-response

pesticides and polychlorinated biphenyls - Reject all results for affected data.

Appendix F

Poorly Performing Compounds

PPENDIX F

OORLY PERFORMING COMPOUNDS

Method 8260

The following compounds are poorly performing compounds: acetone, bromoform,

bromomethane, 1,2-dibromo-3-chloropropane, dichlorodifluoromethane, cis-1,3-

dichloropropene, 1,4-dioxane, 2-hexanone, 2-butanone (MEK), 4-methyl-2-petanone (MIBK),

naphthalene, styrene, and 1,1,2,2-tetrachloroethane. (See EPA Methods 8000 and 8260 for

more detail.) Acetone, 2-hexanone, MEK and MIBK are water soluble and are therefore poor

purgers; they are not easily purged from the water sample onto the trap. 1,4-Dioxane has poor

purging efficiency and is subject to poor recovery if chlorinated solvents are present in the

sample. 1,4-dioxane should not be analyzed by Method 8260; a modified version of Method

8270 is to be used. Naphthalene is a relatively high boiling compound for volatiles, and is also

poorly purged from the sample. The remaining compounds, bromoform, bromomethane, 1,2-

dibromo-3-chloropropane, dichlorodifluoromethane, cis-1,3-dichloropropene, styrene, and

1,1,2,2-tetrachloroethane, are easily degraded by heat as found in the injection port of the gas

chromatograph or can react in certain sample matrices resulting in poor recovery. Additionally

bromomethane and dichlorodifluoromethane are gases and are sometimes lost from the trap

during analysis.

Poorly Performing Compounds are those compounds whose characteristics are such that routine analytical method

criteria are difficult to achieve. In the data assessment and data usability evaluation, added scrutiny should be given

to the “analytical behavior” of these compounds.

Method 8270

The following compounds are poorly performing compounds: 4-chloraniline, 4-chloro-3-

methylphenol, 4,6-dinitro-2-methylphenol, 2,4-dinitrophenol, 1,4-dioxane,

hexachlorocyclopentadiene, 2-nitroaniline, 3-nitroaniline, 4-nitroaniline, 4-nitrophenol,

pentachlorophenol, phenol, pyridene, 2,4,5-trichlorophenol and 2,4,6-trichlorophenol. (See EPA

Methods 8000 and 8270 for more detail.) Most of these compounds are thermally reactive and

are potentially lost in the injection port of the gas chromatograph. All of the phenolics are

reactive with base and relatively water soluble. They are sometimes poorly extracted from

aqueous samples and if a soil sample has a basic pH, may not be extracted at all. 1,4-Dioxane

has poor extraction efficiency; however, Method 8270 has been modified by the department to

include an option. The isotopically labeled compound 1,4-dioxane-d8 is added to the sample

prior to extraction and is used both as an internal standard (to quantitate 1,4-dioxane) and a

surrogate (1,4-Dioxane-d4 is used to quantitate 1,4-dioxane-d8 as a surrogate). This option is

available for certification by the NJDEP Office of Quality Assurance.

Appendix G

Range of Data Usability Evaluation Outcomes

Appendix G

Range of Data Usability Evaluation Outcomes

The table which follows provides the data reviewer and the investigator with options addressing

how to use data. It discusses what to look for and how to use data that may be qualified due to

a variety of issues. The user is cautioned that each element of quality needs to be addressed

before deciding that data are not usable. Any of the elements should undergo review by the

investigator to determine if there is anything that is correctable prior to a usability evaluation.

Data with certain quality assurance deficiencies may be usable in certain circumstances. In the

worst case scenario, depending on the severity of the deficiency, the data may be unusable.

APPENDIX G

ATA USABILITY OUTCOMES

Quality Control

Element (Sample

Type, Analysis,

Condition or

Characteristic)

Type of

Nonconformance

Possible Causes

Major PARCCS

Parameters

Affected (Note

Possible Effects on Data Usability (Note 3)

Chain of Custody

Chain broken,

incomplete, or not

kept

Missing signatures, missing

seals, missing dates or times,

type of analysis requested not

listed

Completeness

If confirmed that sample set is complete and

samples not compromised, data are usable.

Sample labeling

Sample labels

unreadable, missing,

or not attached to

containers

Failure to protect label from

moisture, failure to use

appropriate marker or labels,

improper standard operating

procedure (SOP)

Representative-

ness

Completeness

If the sample can be unambiguously identified,

then samples are usable.

Sample labeling

Samples mislabeled

or labeled

incompletely

Sampler error

Improper SOP

Representative-

ness

If the sample can be unambiguously identified,

then samples are usable.

Sample containers

Plastic containers for

organic analytes

Samplers unaware of

container requirements,

improper SOP, failure to read

SOP, SOP incorrect,

insufficient quantity of correct

containers samplers used

containers on-hand

Representative-

ness

Accuracy

Completeness

Possible phthalate interference and/or volatile loss

may be present.

Sample containers

Glass containers for

metals

Samplers unaware of

container requirements,

improper SOP, failure to read

SOP, SOP incorrect,

insufficient containers

Representative-

ness

Accuracy

Completeness

Possible inorganic contamination may be present.

Headspace

Bubbles in water

inside volatile organic

chemical (VOC) vial

Poor sampling technique, caps

not sealed tightly, septum caps

not used, water vials not

completely filled, improper

Representative-

ness

Accuracy

Completeness

Loss of volatiles may occur.

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

Quality Control

Element (Sample

Type, Analysis,

Condition or

Characteristic)

Type of

Nonconformance

Possible Causes

Major PARCCS

Parameters

Affected (Note

Possible Effects on Data Usability (Note 3)

SOP

Preservation – soil and

sediment samples

VOC soil or sediment

samples not properly

preserved

Varies

Accuracy

Representative-

ness

Completeness

Comparability

Loss of volatiles may occur.

Preservation –

aqueous samples

No preservative or

wrong pH

No preservative added or

improper amount of

preservative added

Representative-

ness

Accuracy

Completeness

This is an analyte- and method-dependent issue.

Loss of analytes may occur.

Preservation –

aqueous samples

Wrong preservative

Improper SOP, failure to read

SOP, SOP incorrect, correct

preservative unavailable

Representative-

ness

Accuracy

Completeness

This is an analyte- and method-dependent issue.

Loss of analytes may occur

Preservation

Improper

temperature

(temperature outside

4 ± 2° C Note (4)

Insufficient ice, samples too

cold, shipping container

inadequately insulated,

samples adequately cooled at

time of sampling and during

shipping, transit time too long

or too short for samples to

reach temperature

Representative-

ness

Accuracy

Completeness

Loss of analytes may occur if temperature is too

high. If temperature is too low, check container for

integrity.

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

Quality Control

Element (Sample

Type, Analysis,

Condition or

Characteristic)

Type of

Nonconformance

Possible Causes

Major PARCCS

Parameters

Affected (Note

Possible Effects on Data Usability (Note 3)

NJDEP certification

status

Laboratory not

certified or approved

for specific analytes

by NJDEP.

Varies

All may be

affected

Except in limited circumstances, data should not

be used.

Handling or Holding

times

Handling and/or

Holding times

exceeded

Excessive analysis time; tardy

ship date; inappropriate

shipping method; slow

laboratory turn-around time.

Representative-

ness

Accuracy

Completeness

Loss of analytes may occur.

(Note 5)

Analysis method

Wrong method used

to analyze samples

Incorrect laboratory method

specified on chain of custody

form; laboratory/analyst

unaware of requirement;

failure to read SOP; SOP

incorrect.

Representative-

ness

Comparability

Completeness

Accuracy

Sensitivity

Except in limited circumstances, data should not

be used.

Reporting Limit (RL) RL too high

Insufficient measures to

combat interferences (i.e.,

cleanup, background

correction); insufficient

sample; high dilution factor;

wrong or inappropriate

method.

Comparability

Completeness

Sensitivity

If the RL for site-specific compounds of concern >

the standards/screening levels, then NDs cannot

be used to determine compliance.

If a compound is detected and the RL is elevated,

the data are usable.

Method blank (MB)

Method blank absent

(Note 6)

Improper SOP

Representative-

ness

Accuracy

Completeness

Data may contain false positives and in some

circumstances, data should not be used.

Method blank (MB) Contamination

Contaminated reagents,

gases, glassware; ambient

contamination; poor laboratory

technique.

Representative-

ness

Accuracy

Completeness

Data may contain false positives and/or high bias

Equipment blank (EB)

or Rinsate blank

Contamination

Improper decontamination of

field sampling equipment;

contaminated rinsate water,

Representative-

ness

Accuracy

Data may contain false positives and/or high bias

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

Quality Control

Element (Sample

Type, Analysis,

Condition or

Characteristic)

Type of

Nonconformance

Possible Causes

Major PARCCS

Parameters

Affected (Note

Possible Effects on Data Usability (Note 3)

containers, or preservatives.

Completeness

Trip blank (TB) for

analysis of VOCs

Trip blank absent

TB not included; Improper

SOP; TB broken during

shipment; TB lost during

shipment.

Representative-

ness

Accuracy

Completeness

Data may contain false positives and/or high bias.

Trip blank for analysis

of VOCs

Contamination

Cross-contamination during

shipment or storage;

contaminated reagent water,

glassware, or preservatives

Representative-

ness

Accuracy

Completeness

Data may contain false positives and/or high bias.

Laboratory Control

Sample (LCS)

LCS absent (Note 7) Improper laboratory SOP

Accuracy

Completeness

Comparability

Complete evaluation of the data may not be

possible.

LCS, Laboratory

Control Sample

Duplicate (LCSD),

blank spike (BS), blank

spike duplicate (BSD)

Low recoveries

Method failure; improper

spiking; degraded spiking

solution; failed spiking device.

Accuracy

Completeness

Comparability

Data may contain false negatives and/or low bias

LCS, LCSD, BS, BSD High recoveries

Method failure; improper

spiking; degraded spiking

solution; failed spiking device;

contaminated reagents, gases,

glassware, etc.

Accuracy

Completeness

Comparability

Data may contain false positives and/or high bias

LCS, LCSDs High RPDs

Method failure; improper

spiking; failed spiking device;

contaminated reagents, gases,

glassware, etc.

Representative-

ness

Precision

Completeness.

Comparability

Poor precision exists in the analytical procedure.

Surrogates in MB,

LCS, LCSD, BS, BSD

Low recoveries

Method failure; improper

spiking; degraded spiking

solution; failed spiking device.

Accuracy

Completeness

Laboratory performance should be questioned.

100

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

Quality Control

Element (Sample

Type, Analysis,

Condition or

Characteristic)

Type of

Nonconformance

Possible Causes

Major PARCCS

Parameters

Affected (Note

Possible Effects on Data Usability (Note 3)

Surrogates in MB,

LCS, LCSD, BS, BSD

High recoveries

Method failure; improper

spiking; degraded spiking

solution; failed spiking device;

contaminated reagents, gases,

glassware. etc.

Accuracy

Completeness

Laboratory performance should be questioned

Surrogates in samples Low recoveries

Matrix effects; inappropriate

method; method failure;

improper spiking; degraded

spiking solution; failed spiking

device.

Accuracy

Completeness

Data may contain false negatives and/or low bias.

Surrogates in samples High recoveries

Matrix effects; inappropriate

method; method failure;

improper spiking; degraded

spiking solution; failed spiking

device; contaminated

reagents, gases, glassware,

etc.

Accuracy

Completeness

Data may contain false positives and/or high bias.

MS, MSD (Note 8)

Low recoveries (Note

Matrix effects; inappropriate

method; method failure;

inadequate cleanup;

inadequate background

correction; failure to use

method of standard additions;

improper spiking; degraded

spiking solution; failed spiking

device.

Accuracy Data may contain false negatives and/or low bias.

MS, MSD (Note 8)

High recoveries (Note

Matrix effects; inappropriate

method; method failure;

inadequate cleanup;

inadequate background

correction; failure to use

method of standard additions;

improper spiking; degraded

Accuracy

Data may contain false positives and/or high bias.

Qualify sample results greater than the RL (i.e.,

possible matrix effects).

101

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

Quality Control

Element (Sample

Type, Analysis,

Condition or

Characteristic)

Type of

Nonconformance

Possible Causes

Major PARCCS

Parameters

Affected (Note

Possible Effects on Data Usability (Note 3)

spiking solution; failed spiking

device; contaminated

reagents, gases, glassware,

etc.

MS, MSD (Note 8)

High Relative Percent

Difference

Sample heterogeneity;

inadequate sample mixing for

non-voc samples in the

laboratory or the field; samples

misidentified; method failure;

improper spiking; failed spiking

device, duplicate spiking of a

sample, contaminated

reagents, gases, glassware,

etc.

Representative-

ness

Precision

The sample itself may be heterogeneous leading

to poor precision (high variability).

Dilution factors

Extremely high

dilution factors

High concentrations of

interferences or analytes;

inappropriate analytical

method used or selected

Accuracy

Comparability

Completeness

Samples with high RLs may not meet DQO and

RLs may become greater than regulatory criteria.

Field Duplicates

Field duplicates are

not comparable

within DQOs

Sample inhomogeneity;

insufficient mixing in field;

samples not split but

collocated (Note 10);

insufficient mixing in

laboratory.

Representative-

ness

Precision

The sample itself may be heterogeneous leading

to poor precision (high variability). The sample

may not be representative of site conditions.

102

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

This table was adapted from US Army Corps of Engineers, Environmental Quality Assurance for HTRW Projects, Engineer Manual. October 10, 1997, EM 200 1-

6, table 3-1.

103

APPENDIX G (CONTINUED)

ANGE OF DATA USABILITY OUTCOMES

Notes:

(1) Entries in the Possible Causes, PARCCS Parameters Affected, Effect on Data, and Possible Data Evaluation columns assume only one type of failure

occurring at any one time. The cumulative or synergistic effects of more than one failure type occurring simultaneously make data usability evaluation more

complex. Data usability evaluations involving multiple failure types are beyond the scope of this table. Not all possible QC failures and outcomes are illustrated on

this table.

(2) The PARCCS parameters most affected are listed. All of the PARCCS parameters may affected in some cases. Any failure that results in invalid data affects

Completeness.

(3) All data usability evaluations are subject to discretion of the investigator taking into account project DQOs, and the intended use of the analytical data. The

DQA and DUE thought process must be documented in the report using the data.

(4) Refrigeration not required for trace metals (excluding mercury).

(5) Exceeding holding times on some analyses can produce false positives (i.e., carbonates, dissolved oxygen, etc.) and high bias (i.e., pH, carbonates, dissolved

oxygen, etc.). High bias and false positives can also occur when degradation products of contaminants are also themselves analytes, i.e., when 4,4'-DDT is

present and holding times are exceeded, high bias and false positives for the degradation products 4,4 DDD, 4,4 DDE, 4,4 DDT, 2,4 DDD, 2,4 DDE, 4,4’-DDT can

occur.

(6) Method blanks are not appropriate for all analyses, i.e. pH, conductivity, % solids, etc.

(7) Laboratory control samples are not appropriate for all analyses, i.e. pH, conductivity, % solids, etc.

(8) Matrix spike and matrix spike duplicates are performed at the request of the investigator and may not be present.

(9) Note that when the native sample concentrations are significantly greater than the effective spike concentration that the conclusion of the matrix effect is only

tentative. As a general rule of thumb, the native sample concentration should be no more than four times higher than the effective matrix spike concentration of for

the matrix effect to be considered probably present.

(10) Conventional sampling protocols for some analyte classes (i.e., VOCs) prohibit sample mixing and splitting because it results in the loss of analytes. Field and

QC samples for these analytes are more appropriately collected as sample pairs.

104

Appendix H

Data Usability Evaluation Worksheet

105

APPENDIX H-1

NSTRUCTIONS FOR USE OF THE

ATA USABILITY EVALUATION WORKSHEET

The Data Usability Evaluation Worksheet (DUE Worksheet) can be used to document the

investigator’s thought process during a DUE of the QC nonconformances that were cataloged

as part of the DQA. A description of the “Nonconformance DQA Review Elements” listed in the

left hand column can be found in Appendix C of this document. The DUE worksheet is available

below in Appendix H-2 and can be modified by the user.

106

APPENDIX H-2

ATA USABILITY EVALUATION WORKSHEET

Project Name: ___________________________________________________

Laboratory: _____________________________________________________

Sample Delivery Group: ___________________________________________

Sample Delivery Group Number: ___________________________________

Date Samples Collected: ___________________________________________

Reviewer: _______________________________________________________

Describe the intended use of the data:

Nonconformance

DQA Review

Elements

Briefly Summarize DQA Nonconformances

Laboratory Report

Inspection

Reasonable

Confidence

Evaluation

Chain of Custody

Evaluation

Sample Result

Evaluation

Sample

Preservation and

Holding Time

Evaluation

Blank Evaluation

Laboratory Control

Samples

Surrogates

Site Specific Matrix

Spikes and Matrix

Spike Duplicates

Tentatively

Identified

Compounds

Other QC data

107

APPENDIX H-2 (CONTINUED)

ATA USABILITY EVALUATION WORKSHEET

Provide a summary statement describing how the analytical data set relied upon is of adequate quality

and of sufficient accuracy, precision, and sensitivity for the intended purpose. Questions for the

investigator to consider during the DUE include, but are not limited to, the following, please see the text of

this guidance for additional information:

How will the analytical data be used:

• Is this the initial site investigation to determine if and what contamination exists?

• Will the analytical results be used to determine compliance with Regulatory criteria (e.g. post

excavation samples)?

• Will remedial action be conducted?

• Has remedial action been conducted?

• Are the results going to be used to guide further remedial investigation?

• Are the results going to be used to guide further remedial action (including monitored natural

attention of groundwater)?

• Evaluate seasonal variability, or homogeneity in an environmental sample?

Laboratory QC Information

• If the results are close to a regulatory limit, does any QC bias affect the interpretation of the data?

• Are significant QC variances reported?

• Are the biases high or low?

• Are the identified QC nonconformances related to results for substances that are reported as

“ND,” and the reporting limits are less than regulatory criteria?

• Are the nonconformances related to poorly performing compounds that are not constituents of

concern?

• Are the nonconformances related substances that are not constituents of concern?

• How do the nonconformances affect “NDs” and reported concentrations?

DQOs

• Were the DQOs precision, accuracy, representativeness, comparability, completeness and

sensitivity met?

• Are all critical samples usable for the intended purpose(s)?

• Does sample homogeneity or heterogeneity affect the representativeness of the samples?

CSM

• Do any analytical QC nonconformances create significant data gaps in the conceptual site

model?

• Evaluate the entire body of information (type, amount, and quality data) available for the specific

area/release for which the data are presumed to be representative. Determine whether any

newer data corroborate the older results and whether both sets of data are consistent with the

CSM.

• Consider the risk of being wrong based on risk to potential receptors and the risk to human health

and the environment.

• Consider the source of data (e.g., whether the data were generated by the investigator’s own firm

or some other firm, the investigator’s own involvement with the project, method of collection for

the samples, and reporting methods by other firms/laboratories generating the data). Perform a

critical review of these data to evaluate its reliability.

• Consider any other site-specific factors.

108

Appendix I

Surrogates and Internal Standards

109

APPENDIX I-1

EMI-VOLATILE INTERNAL STANDARDS AND THEIR CORRESPONDING

ARGET COMPOUNDS AND SURROGATES

This table lists the commonly used (e.g. DKQ Methods 8260 and 8270) internal standards and their associated target compounds and surrogates

for semi-volatiles. If the laboratory data indicates a problem with the internal standard(s) and/or surrogate(s), this table can be used to evaluate

which target compounds are effected. For instance, if the surrogate 1,2-Dichloroethane-d4 had a low recovery, the compounds listed in the same

column would potentially be effected as well, and low bias should be suspected unless otherwise indicated by additional QC data.

1,4-Dichlorobenzene-d4

Naphthalene-d8

Acenaphthene-d10

Phenanthrene-d10

Chrysene-d12

Perylene-d12

Aniline

Nitrobenzene

Hexachlorocyclopentadiene

4,6-Dinitro-2-methylphenol

Pyrene

Di-n-octyl phthalate

Phenol

Isophorone

2,4,6-Trichlorophenol

4-Bromophenyl-

phenylether

Butylbenzylphthalate

Benzo(b) fluoranthene

bis-(2-Chloroisopropyl ether)

2-Nitrophenol

2,4,5-Trichlorophenol

N-Nitroso-diphenylamine

3,3'-Dichlorobenzidine

Benzo(k) fluoranthene

2-Chlorophenol

2,4-Dimethylphenol

2-Chloronaphthalene

Hexachlorobenzene

Benzo(a)anthracene

Benzo(a)pyrene

2-Methylphenol

bis-(2-Chloro

ethoxy)methane

2-Nitroaniline

Pentachlorophenol

Chrysene

Indeno(1,2,3-cd)-pyrene

Pyridine

2,4-Dichlorophenol

Dimethylphthalate

Phenanthrene

bis-(2-Ethylhexyl)phthalate

Dibenzo(a,h)-anthracene

2,2'-oxybis-(1-

Chloropropane)

Naphthalene

2,6-Dinitrotoluene

Anthracene

Terphenyl-d14 (surr)

Benzo(g,h,i)perylene

4-Methylphenol

4-Chloroaniline

Acenaphthylene

Carbazole

N-Nitroso-di-n- propylamine

Hexachlorobutadiene

3-Nitroaniline

Di-n-butylphthalate

Hexachloroethane

4-Chloro-3- methylphenol

Acenaphthene

Fluoranthene

2-Fluorophenol (surr)

2-Methylnaphthalene

2,4-Dinitrophenol

Pentachloronitro-benzene

Phenol-d5 (surr)

1,2,4-Trichlorobenzene

4-Nitrophenol

2,4,6-Tribromophenol (surr)

2-Chlorophenol-d4 (surr)

Nitrobenzene-d5 (surr)

Dibenzofuran

1,2-Dichlorobenzene-d4 (surr)

2,4-Dinitrotoluene

Diethylphthalate

Fluorene

4-Chlorophenylphenylether

4-Nitroaniline

1,2,4,5-Tetrachlorobenzene

2-Fluorobiphenyl (surr)

110

APPENDIX I-2

OLATILE INTERNAL STANDARDS AND THEIR CORRESPONDING

ARGET COMPOUNDS AND SURROGATES

1,4-Difluorobenzene (I.S.)

Chlorobenzene-d5 (IS)

1,4-Dichlorobenzene-d4 (IS)

Dichlorodifluoromethane

1,1,1-Trichloroethane

Bromoform

Chloromethane

Cyclohexane

1,3-Dichlorobenzene

Vinyl chloride

Carbon tetrachloride

1,4-Dichlorobenzene

Bromomethane

Benzene

1,2-Dichlorobenzene

Chloroethane

Trichloroethene

1,2-Dibromo-3-chloropropane

Trichlorofluoromethane

Methylcyclohexane

1,2,4-Trichlorobenzene

1,1-Dichloroethene

1,2-Dichloropropane

1,2,3-Trichlorobenzene

1,1,2-Trichloro-1,2,2-trifluoroethane

Bromodichloromethane

1,2-Dichlorobenzene-d4 (DMC)

Acetone

cis-1,3-Dichloropropene

Carbon disulfide

4-Methyl-2-pentanone

Methyl acetate

Toluene

Bromochloromethane

trans-1,3-Dichloropropene

Methylene chloride

1,1,2-Trichloroethane

trans-1,2-Dichloroethene

Tetrachloroethene

Methyl tert-butyl ether

2-Hexanone

1,1-Dichloroethane

Dibromochloromethane

cis-1,2-Dichloroethene

1,2-Dibromoethane

2-Butanone

Chlorobenzene

Chloroform

Ethylbenzene

1,2-Dichloroethane

m,p-Xylene

1,4-Dioxane

o-Xylene

Vinyl chloride-d3 (DMC)

Styrene

Chloroethane-d5 (DMC)

Isopropylbenzene

1,1-Dichloroethene-d2 (DMC)

1,1,2,2-Tetrachloroethane

2-Butanone-d5 (DMC)

Benzene-d6 (DMC)

Chloroform-d (DMC)

1,2-Dichloropropane-d6 (DMC)

1,2-Dichloroethane-d4 (DMC)

trans-1,3-Dichloropropene-d4 (DMC)

1,4-Dioxane-d8 (DMC)

Toluene-d8 (DMC)

2-Hexanone-d5 (DMC)

1,1,2,2-Tetrachloroethane-d2 (DMC)

111

APPENDIX I-3

Surrogates for Chlorinated Pesticides and Aroclors

Decachlorobiphenyl

Tetrachloro-m-xylene

112

Appendix J

Supplemental Examples Using Multiple Lines of Evidence

113

APPENDIX J

UPPLEMENTAL EXAMPLES USING MULTIPLE LINES OF EVIDENCE

These examples illustrate how multiple lines of evidence may be used to address QC

nonconformances.

Example J-1: Surrogates – Low Recovery, Expanded Version of Example 11

A soil sample was analyzed by DKQ Method 8260. The intended use of the analytical data was

to determine if contaminants were present at concentrations that exceed the Impact to Ground

Water Screening Level (IGWSL).

The percent recovery for the surrogate Toluene-d8 was reported to be 20 percent. The method

specifies that the recovery limits for surrogates must be within 70 to 130 percent. Because the

reported recovery for this surrogate is outside acceptance criteria for Volatile Organic

Compounds (VOCs), then all VOC results are biased low.

• 1,1,1-Trichloroethane was reported at a concentration of 0.1 mg/kg, which is just below

the applicable criteria of 0.2 mg/kg.

• MS/MSD percent recoveries from a soil sample collected at the site, from substantially the

same type of unconsolidated material as the sample, were within the RCP acceptance

criteria for all compounds reported by RCP Method 8260. 1,1,1-Trichloroethane was not

detected (ND) as a target compound in the MS/MSD sample.

• The RPD for the MS/MSD for pair for 1,1,1-trichloroethane is 13.3 percent. The method

specifies that relative percent difference must be less than 30 percent for the MS/MSD

pair.

• All other quality control criteria were within the DKQ acceptance criteria.

The reported percent recovery for the surrogate toluene-d8 indicates a potential low bias for all

volatile organic compounds. Because the reported concentration of 1,1,1-trichloroethane is just

below the IGWSL, the reported potential low bias associated with the surrogate recovery means

the results should not be used to solely determine that 1,1,1-trichloroethane is present at a

concentration less than the regulatory criteria. Multiple lines of evidence such as matrix spikes

114

and matrix spike duplicates were used to evaluate this data set further. However, the MS/MSD

percent recoveries for soil samples collected at the site, from substantially the same type of

unconsolidated material as the sample were reported within DKQ acceptance criteria.

Conclusion: The evaluation of these results using multiple lines of evidence would not prevent

the investigator from concluding that 1,1,1-trichloroethane is not present at a concentration

greater than the regulatory criteria.

Example J-2: Laboratory Control Samples – Low Recovery, Expanded Version of Example 12

Ground water samples were analyzed by DKQ Method 8260. The purpose of sampling was to

determine compliance with regulatory criteria. The GWQS for benzene is 1 μg/l.

• The results for the LCS indicate a 54 percent recovery for benzene. The method specifies

that the recovery limits for the LCS must be within 70 to 130 percent.

• The analytical results were ND for benzene at a reporting limit of 0.5 μg/l.

• The surrogate recoveries are within the DKQ method acceptance criteria.

• The MS/MSD percent recoveries from a water sample collected at the site, from

substantially the same aquifer as the sample, were within the DKQ method acceptance

criteria for all compounds reported by DKQ Method 8260. Benzene was ND as a target

compound in the MS/MSD sample.

• The RPD for the MS/MSD for pair for Benzene is 23.3 percent. The method specifies that

the RPD must be less than 30 percent for the MS/MSD pair.

• All other QC criteria are within the RCP acceptance criteria.

The results of the laboratory control sample indicate a potential low bias in the accuracy of the

method. Therefore, the results reported could have been affected by the low bias of the

associated with the method, and the results should not solely be used to determine if benzene is

present at a concentration greater than the GWQS. Multiple lines of evidence such as

surrogates, and matrix spikes and matrix spike duplicates were used to evaluate this data set

further. However, the surrogate recoveries were within DKQ method acceptance indicating an

acceptable degree of accuracy with the analytical method. In addition, the MS/MSD percent

115

recoveries from a water sample collected at the site, from substantially the same aquifer were

reported within DKQ acceptance criteria.

Conclusion: The evaluation of these results using multiple lines of evidence would indicate to

the investigator that benzene is below the applicable GWQS.

116

Example J-3: MS/MSD High Recoveries, Expanded Version of Example 15

A residential soil sample was analyzed by DKQ Method 8260 for VOCs. The intended use of

the data is to determine compliance with the residential direct contact soil remediation standard.

• Trichloroethene (TCE) was reported at a concentration of 8 mg/kg, which is just above

residential direct contact soil standard of 7 mg/kg.

• The percent recoveries for TCE generated by a MS/MSD pair are 180 and 185 percent

respectively. According to the DKQ method, the recovery limits for the MS/MSD should

be within 70 to 130 percent.

• The RPD for the MS/MSD pair is 2.7 percent. The relative percent difference should be

less than 30 percent for the MS/MSD pair.

• The surrogates are within DKQ acceptance criteria.

• In a duplicate sample, TCE was reported at a concentration of 10 mg/kg, which is just

above the residential direct contact soil standard of 7 mg/kg. The relative percent

difference between the original and duplicate sample is 18.2 percent, which indicates an

acceptable degree of precision between the two samples.

• All other QC criteria were within the DKQ acceptance criteria.

The spike recoveries indicate a potential high bias for TCE. Because of the reported high bias

and the sample result just above the residential direct contact soil standard of 7 mg/kg., the

actual concentration of TCE in the sample may be lower and may be less than the residential

direct contact soil standard of 7 mg/kg. However, the investigator cannot adjust the

concentrations of the reported values lower. The RPD for the MS/MSD pair was within the

acceptance criteria specified in DKQ method, and therefore, MS/MSD results show an

acceptable degree of the precision. Because of the reported high bias associated with the

MS/MSD pair, the MS/MSD results should not be used solely to determine if TCE is present at a

concentration greater than the residential direct contact soil remediation standard.

Multiple lines of evidence, including surrogate recoveries, duplicate samples and, were used to

further evaluate this data set. The surrogate recoveries are within the range specified in the

117

DKQ method. The duplicate sample results indicate that the concentration of TCE is above the

residential direct contact soil standard of 7 mg/kg.

Conclusion: The evaluation of these results using multiple lines of evidence would indicate that

TCE is above the applicable residential direct contact soil standard of 7 mg/kg.

(Note: Using the same example as above for a non-residential site, the conclusion is that the

concentration of TCE is below the non-residential direct contact soil remediation standard (20

mg/kg); however, the concentration of TCE would exceed the default impact to ground water

criteria (0.007 mg/kg) necessitating the evaluation of that pathway.)

Example J4- ICS Low Recoveries, Expanded Example 16

Ground water samples were analyzed by DKQ Method 6010. The purpose of sampling was to

determine compliance with Regulatory criteria. The GWQS for Arsenic and Cadmium are 3

ug/L and 4 ug/L, respectively.

• The results for the ICS indicate a 54 percent recovery for arsenic and 60 percent

recovery for cadmium. The DKQ protocol specifies that the recovery limits for the ICS

should be within 80 to 120 percent.

• The analytical results were both at the GWQS of 3 ug/L and 4 ug/L for Arsenic and

Cadmium, respectively.

• The Matrix spike results for arsenic and cadmium were below the QC limit of 75% at 65%

and 60 percent, respectively.

• The duplicate results were both acceptable for arsenic and cadmium.

Due to the proximity of the sample results to the GWQS, multiple lines of evidence should be

evaluated. The results of the ICS indicate a possible low bias in the accuracy of the method.

The result of the MS provides additional evidence that results are biased low. The duplicate

result demonstrates acceptable precision.

Conclusion: Based on the data reviewed one would conclude that the sample results are likely

above the applicable GWQS and additional sampling and analyses would be recommended.

118

Appendix K:

Glossary

119

Term Definition

Accuracy

Accuracy describes the closeness of agreement between an observed

value and an accepted reference value that is accepted as the true

value. Accuracy is typically evaluated using spikes (laboratory control

samples, surrogate spikes, and mat

rix spikes) and blanks (trip, field,

and method), or any other standard subjected to the entire analytical

process. Accuracy is usually reported as a percentage of the observed

value divided by the reference value (percent recovery) using the

following equation:

%R = observed value X 100

reference value

Where %R = percent recovery

Acid Semivolatile

Organic Compound

Surrogates

Acid surrogates are compounds routinely used with semi-volatile

methods

that exhibit similar chemical behavior to acidic organic

compounds such as phenols. Common acid surrogates include: 2-

Fluorophenol, phenol-d5 (a deuterated phenol), and 2,4,6-

Tribromophenol. (See also surrogate).

Analyte

Analyte means the substance being measured by an analytical

procedure.

Analytical Batch

An analytical batch is a group of samples that are processed and

analyzed as a unit. For quality control purposes, the maximum number

of samples in a batch is 20 per matrix.

120

Term Definition

Applicable

Standard/Screening

Level

Residential Direct Contact Health Based Criteria and Soil Remediation

Standards (RDC SRS),

http://www.nj.gov/dep/srp/regs/rs/rs_rule.pdf

Nonresidential Direct Contact Health Based Criteria and Soil

Remediation Standards (NRDC SRS),

http://www.nj.gov/dep/srp/regs/rs/rs_rule.pdf

Default Impact to Ground water Soil Screening Levels for

Contaminants;

http://www.nj.gov/dep/srp/guidance/rs/partition_equation.pdf

Default Leachate Criteria for Class II Ground Water (Synthetic

Precipitation Leachate Procedure);

http://www.nj.gov/dep/srp/guidance/rs/splp_guidance.pdf

Specific Ground Water Quality Criteria (Groundwater Quality

Standards);

http://www.nj.gov/dep/rules/rules/njac7_9c.pdf

Surface Water Quality Criteria for Toxic Substances (SWQC);

http://www.nj.gov/dep/rules/rules/njac7_9b.pdf

Maximum Contaminant Levels (MCL) for State Regulated VOCs;

http://www.state.nj.us/dep/rules/rules/njac7_10.pdf

NJDEP MASTER TABLE GENERIC VAPOR INTRUSION

SCREENING LEVELS including

• Vapor Intrusion Groundwater Screening Levels (GWSL);

• Vapor Intrusion Residential Indoor Air Screening Level (RIASL);

• Vapor Intrusion Nonresidential Indoor Air Screening Level

(NRIASL);

All at http://www.nj.gov/dep/srp/guidance/vaporintrusion/vig_tables.pdf

NJDEP Action Levels for Indoor Air ;

http://www.nj.gov/dep/srp/guidance/vaporintrusion/vig_tables.pdf

Vapor Intrusion Health Department Notification levels (HDNL);

http://www.nj.gov/dep/srp/guidance/vaporintrusion/vig_tables.pdf

Extractable Petroleum Hydrocarbons (EPH);

http://www.nj.gov/dep/srp/guidance/srra/eph_method.pdf

121

Term Definition

Applicable

Standard/Screening

Level (continued)

Hexavalent Chromium Cleanup Criterion;

http://www.state.nj.us/dep/srp/guidance/rs/chrome_criteria.pdf

Ecological Screening Criteria;

http://www.nj.gov/dep/srp/guidance/ecoscreening/esc_table.pdf

Site specific criteria developed for the investigation and remediation

according to the applicable NJDEP guidance.

Area of Concern

"Area of concern" means any existing or former distinct location or

environmental medium where any hazardous substance, hazardous

waste, or pollutant is known or suspected to have been discharged,

generated, man

ufactured, refined, transported, stored, handled,

treated, or disposed, or where any hazardous substance, hazardous

waste, or pollutant has or may have migrated, including, but not limited

to, each current and former objects and/or areas defined in N.J.A.C.

7:26E-1.8.

Base Neutral

Semivolatile Organic

Surrogates

Base neutral semivolatile organic surrogates exhibit similar chemical

behavior to the base-neutral semivolatile organic compounds. Common

examples include: Nitrobenzene-d5, 2-Fluorobiphenyl, and terphenyl-

d14. (See also surrogate).

Bias

Bias is the deviation of the measured value from the true value. This

can be analytical bias within the analytical procedure, or it can be due

to matrix effects. There is inherent bias within all analytical procedur

es.

Quality control measurement tools that can be used to evaluate bias

include laboratory control samples, check standards, matrix spikes, or

any other standards used for analysis.

NJDEP, Remediation Standards, N.J.A.C. 7:26D

NJDEP, Remediation Standards, N.J.A.C. 7:26D.

NJDEP, Development of Site-Specific Impact to Ground Water Soil Remediation Standards Using the

Soil-Water Partition Equation, December 2008, http://www.nj.gov/dep/srp/guidance/rs/

NJDEP, Guidance for the use of the Synthetic Precipitation Leaching Procedure to Develop Site-

Specific Impact to Ground Water Remediation Standards, June 2, 2008,

http://www.nj.gov/dep/srp/guidance/rs/

NJDEP, Groundwater Quality Standards, N.J.A.C. 7:9C

NJDEP, Surface Water Quality Standards, N.J.A.C. 7:9B

NJDEP, Safe Drinking Water Act Regulations, N.J.A.C. 7:10

NJDEP, Vapor Intrusion Technical Guidance, criteria dated March 2013.,

http://www.nj.gov/dep/srp/guidance/vaporintrusion/

Ibid.

NJDEP, Protocol for Addressing Extractable Petroleum Hydrocarbons, Version 5.0, August 9, 2010,

http://www.nj.gov/dep/srp/guidance/srra/eph_protocol.pdf

NJDEP, Chromium Soil Cleanup Criteria, April 2010,

NJDEP, Ecological Screening Criteria, March 10, 2009,

http://www.nj.gov/dep/srp/guidance/ecoscreening

122

Term Definition

Calibration Curve/Initial

Calibration

A calibration curve/initial calibration curve is generated by analyzing a

series of standards and plotting instrument response versus

concentration. A calibration curve is used to calibrate an analytical

system. Calibration criteria are specified in each analytical method.

Check Standard

A check standard is a solution of one or more analytes that is used to

document laboratory performance. This check standard can go by

many different names including laboratory control samples and

laboratory fortified blank. Consult with the laboratory to understand the

naming scheme used to identify such standards. This standard can

also be used to check the validity of a purchased stock or calibration

standard.

Comparability

Comparability refers to the equivalency of two sets of data.

Comparability may be achieved through the use of standard or similar

techniques to collect and analyze representative samples. Comparable

data sets must contain the same variables of interest and must

possess values that can be converted to a common unit of

measurement. Comparability is normally a qualitative parameter that is

dependent upon other data quality elements.

Completeness

Completeness is a measure of the amount of valid data obtained from

a measurement system compared to the amount that was expected to

be obtained under correct, normal conditions.

Conceptual Site Model Defined in NJDEP Conceptual Site Model Technical Guidance, 2012.

Contaminant or

Contamination

Contamination or contaminant means any discharged hazardous

substance as defined pursuant to N.J.S.A. 58:10-

23.11b, hazardous

waste as defined pursuant to N.J.S.A. 13:1E-38, or pollutant as defined

pursuant to N.J.S.A. 58:10A-3.

Contaminant of

Potential Ecological

Concern (COPEC)

COPEC means a substance detected at a contaminated site that has

the poten

tial to adversely affect ecological receptors because of its

concentration, distribution, and mode of toxicity; contaminants with

concentrations above their respective New Jersey Surface Water

Quality Standards or ecological screening criteria are identifi

ed as

contaminants of potential ecological concern.

Control Sample

Control sample means a quality control sample introduced into a

process to monitor the performance of a system.

Critical Sample

Critical samples are user defined where the completeness go

al is

usually 100 percent.

Data of Known Quality

When “Data of Known Quality” is achieved for a particular data set, the

investigator will have “Data of Known Quality” that the laboratory has

followed the Data of Known Quality Protocols, has described non-

conformances, if any, and has adequate information to make

judgments regarding data quality.

123

Term Definition

Data of Known Quality

Protocols (DKQPs)

DKQPs include specific laboratory quality assurance and quality control

(QA/QC) criteria that produce analytical data o

f known and

documented quality. The DKQ protocols are shown in Appendix B of

the NJDEP Site Remediation Program, DATA OF KNOWN QUALITY

PROTOCOLS TECHNICAL GUIDANCE, April 2014. (DKQ Guidance)

Data Quality

Objectives (DQOs)

DQOs, developed by the investigator, are qualitative and quantitative

statements derived from the DQO Planning Process that clarify the

purpose of the study, define the most appropriate type of information to

collect, determine the most appropriate conditions from which to collect

that

information, and specify tolerable levels of potential decision

errors.

Environmental Sample

An environmental sample is a sample of soil, groundwater, surface

water, soil vapor, sediment, air, or any other environmental matrix

collected for analysis.

Equipment-Rinsate

Blank

An equipment-rinsate blank is a sample of analyte-free water that is

used to rinse the sampling equipment. An equipment-rinsate blank is

collected after decontamination to assess potential contamination from

inadequate decontamination of field equipment. An equipment-rinsate

blank can also be used to evaluate the potential for field sampling

equipment to leach contaminants into a sample and cause cross

contamination.

Field Blank

A field blank is analyte-free matrix, usually water, prepared in the

laboratory and transported to the sampling location along with the

empty sample containers. At the sampling location the matrix is used to

fill randomly selected sample containers and then returned to the

laboratory for analysis. The field bla

nk is treated as a sample in all

respects, including exposure to sampling location conditions, storage,

preservation, and all analytical procedures. Field blanks are used to

assess any contamination contributed from sampling location

conditions and the transport, handling, and storage of the samples.

Field Duplicates

Field duplicates are replicates collected from the same location in the

field and submitted to the laboratory as two distinct samples.

Duplicates are used to evaluate precision, sample homogen

eity, and

field sample collection activities.

Field Reagent Blank See “Field Blank.”

Gas Chromatography/

Mass Spectrometry

Gas Chromatography/Mass Spectrometry is an analytical procedure in

which a gas chromatograph is connected to a mass spectrometer. The

technique allows for both accurate identification and quantitation of

analytes.

Handling Time

The maximum amount of time for a QC sample (e.g., field or trip

blanks) to be transported to a site and/or the maximum amount of time

for transport of site f

ield samples and field QC samples back to the

laboratory. Samples held beyond the allowed handling time may be

considered biased low or invalid, depending on the intended use of the

data (see NJDEP Field Sampling Procedures manual, August 2005).

124

Term Definition

Holding Time

The holding time is the maximum time that a sample may be held, after

the sample is taken prior to preparation and/or analysis and still be

considered valid or not compromised. Holding times can include time to

extraction and time allowed after extraction before analysis and time

allowed prior to digestion and after digestion prior to analysis based on

method specific requirements. Samples analyzed past the holding time

are determined to be compromised and

may be considered invalid,

depending on the intended use of the data.

Instrument Blank

An instrument blank is analyte-free matrix (e.g., distilled water)

processed through the instrumental steps of the measurement process;

used to determine instrument contamination. Typically gas

chromatography methods (excluding volatile organic compounds) use

pure solvent as an instrument blank while metals and wet chemistry

techniques use water or acidified water. Gas chromatography methods

for volatile organic compounds use either acidified water or methanol.

Internal Standards

For certain analytical methods, internal standards are compounds that

are added, immediately prior to analysis, at a known concentration to

every standard, blank, sample, and quality control sample. Internal

standards are used to calibrate t

he analytical system by plotting the

response of the internal standards versus the compound(s) of interest.

Internal standards should closely match the chemical behavior of the

compound(s) of interest and be known not to be present in the sample.

Laboratory Control

Sample (LCS)

A LCS is a sample matrix, free from the analytes of interest, spiked with

verified known amounts of analytes or a material containing known and

verified amounts of analytes from the same source as the calibration

standards. It is generally used to establish intra-laboratory or analyst

specific precision and bias or to assess the performance of all or a

portion of the measurement system. The LCS is carried through the

analysis along with the samples. LCSs are also known as laboratory

fortified blanks or blank spikes.

Laboratory Fortified

Blank

See “Laboratory Control Sample.”

License Site

Remediation

Professional

(investigator)

An individual who is licensed by the board pursuant to section 7 of P.L.

2009, c.60 (C.58:10C-7) or the department pursuant to section 12 of

P.L. 2009, c.60 (C.58:10C-12).

Matrix Duplicates

Matrix duplicates refer to the replicate analyses of samples taken from

the same sample container and prepared in the laboratory. Matrix

duplicates are used to evaluate precision and sample homogeneity.

Matrix Interference

Matrix interferences are manifestations of non-target analytes or

physical/ chemical characteristics of a sample that prevents the

quantification of the target analyte (i.e., the compound or element of

interest being effectively quantified by the test method) as it is routinely

performed, typically adversely impacting the reliability of the

determination. For example, some matrices including silt, clay, coal,

ash, and peat effectively bind analytes which may lead to low biased

results for certain extraction/analysis procedures. Co-eluting peaks in a

GC chromatogram may result in a high bias for an analyte of concern.

125

Term Definition

Matrix

The matrix is the material of which the sample is composed or the

substrate (e.g

., surface water, ground water, drinking water, soil,

sediment, air) that may or may not contain an analyte of interest.

Matrix Spike

A matrix spike is an aliquot of an environmental sample to which known

quantities of target analytes are added in the la

boratory. The matrix

spike is analyzed in an identical manner as a sample. The purpose of a

matrix spike sample is to determine the quantitative accuracy of the

overall analytical procedure for determining the analytes of concern in

the sample.

Matrix Spike Duplicate

A matrix spike duplicate is an intra-laboratory split sample, with both

aliquots spiked with identical concentrations of method analytes. The

spiking occurs prior to sample preparation and analysis. The results are

used to document the precision and accuracy of a method in a given

sample matrix. See also “Matrix Spike.”

Method Blank

A method blank is an “analyte-free” matrix that is treated exactly as a

sample including exposure to all glassware, equipment, solvents,

reagents, labeled compounds, internal standards, and surrogates that

are used with samples. The method blank is used to determine if

analytes or interferences are present in the laboratory environment, the

reagents, or the apparatus. A method blank may also be referred to as

a laboratory reagent blank.

Nonconformance

A nonconformance is an occurrence during the processing or analysis

of a sample that deviates from the quality control performance criteria

of the analytical method. Examples of nonconformances include, but

are not li

mited to, missed holding times, temperature excursions,

recoveries of surrogates or matrix spikes outside of performance

criteria, initial or continuing calibration failures.

Non-target compounds

Non-targeted compound means a compound detected in a sample

using a specific analytical method that is not a targeted analyte (see

below), a surrogate compound, a system monitoring compound, a

deuterated monitoring compound or an internal standard compound.

PARCCS Parameters

The PARCCS parameters are precision, accuracy, representativeness,

comparability, completeness, and sensitivity.

Performance

Evaluation Sample

See “Proficiency Test Sample.”

Petroleum (or

Petroleum Product)

Petroleum" or "petroleum products" means oil or petroleum of any kind

and in any form, including, but not limited to, oil, petroleum, gasoline,

kerosene, fuel oil, oil sludge, oil refuse, oil mixed with other wastes,

crude oils, and substances or additives to be utilized in the refining or

blending of crude petroleum or petroleum stock in this State. However,

any compound designated by specific chemical name on the list of

hazardous substances adopted by the department pursuant to this

section shall not be considered petroleum or a petroleum product for

the purposes of P.L.1976, c.141, unle

ss such compound is to be

utilized in the refining or blending of crude petroleum or petroleum

stock in this State.

126

Term Definition

Precision

Precision is the consistency of measurement values quantified by

measures of dispersion such as the sample standard deviation.

Precision must be defined in context –

e.g., for a certain analyte,

matrix, method, perhaps concentration, lab or group of labs. Precision

for laboratory and field measurements can be expressed as the relative

percent difference (RPD) between two duplicate

determinations or

percent relative standard deviation (%RSD) between multiple

determinations.

Proficiency Test

Sample

Proficiency test sample is a sample provided to a laboratory for the

purpose of demonstrating that the laboratory and the individual analyst

performing the test

can successfully analyze the sample within

acceptable limits. The true value of the sample is unknown by the

analyst.

Quality Assurance

Project Plan (QAPP)

A QAPP is a document which describes the procedures necessary to

produce a

n orderly assemblage of detailed procedures designed to

produce data of sufficient quantity and quality to meet the data quality

objectives for a specific data collection activity.

Quality

Assurance/Quality

Control (QA/QC)

QA is an integrated system of management activities involving

planning, implementation, assessment, reporting, and quality

improvement to establish the reliability of laboratory data to ensure that

a process, item, or service is of the type and quality needed and

expected by the client. QC procedures are the specific tools that are

used to achieve this reliability. QC is the overall system of technical

activities whose purpose is to measure and control the quality of a

product or service so that it meets the needs of users. QC procedures

measure the performance of an analytical method in relation to the QC

criteria specified in the analytical method. QC information documents

the quality of the analytical data.

Qualified Data

Qualified data are analytical results that have an affixed code placed

there by laboratories, and/or individuals conducting independent data

review, to denote that quality control requirements or other evaluation

criteria are not met. Data reviewers assess these and other criteria to

determine the usability of data.

Reagent water

Reagent water is water (generally that has been generated by any

purification method) demonstrated to be free from the analytes of

interest and potentially interfering substances at the method detection

limit for the analyte.

Reasonable

Confidence

When “Reasonable Confidence” is achieved for a particular data set,

the investigator will have “Reasonable Confidence” that the laboratory

has followed the Reasonable Confidence Protocols, has described

nonconformances, if any, and has adequate inf

ormation to make

judgments regarding data quality.

Rejected Data

Rejected data are data that have failed to meet QC requirements

and/or method specific/contractual requirements to such an extent that

the data are determined to be unusable.

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Term Definition

Relative Percent

Difference (RPD)

The RPD is defined by the following equation:

RPD = A-B x 100

((A+B)/2)

Where A = Analytical results from first measurement and

B = Analytical results from the second measurement.

Reporting Limit

As per N.J.A.C. 7:26E-1.8, “Reporting limit" means, for a compound

analyzed by a particular method, the sample equivalent concentration

(i.e., based on sample specific preparation and analysis factors), for

organics, associated with the lowest concentration standard used in the

calibration of the method and for inorganics, derived from the

concentration of that analyte in the lowest level check standard (which

could be the lowest calibration standard in a multi-

point calibration

curve).

Representativeness

Representativeness is a qualitative measurement that describes how

well the analytical data characterizes a discharge or area of concern

under investigation as part of an environmental site assessment. Many

factors can influence how representative the analytical results are for a

discharge

. These factors include, the selection of appropriate

analytical procedures, the sampling plan, and the procedures and

protocols used to collect, preserve, and transport samples.

Sensitivity

Sensitivity refers to the ability of an analytical procedure to detect and

quantify an analyte at a given concentration.

Spike

A known quantity of an analyte added to a sample for the purpose of

determining recovery or efficiency (analyst spikes), or for quality control

(blind spikes).

Split Sample

A split sample is prepared when aliquots of sample taken from the

same container and then analyzed independently. Split samples are

usually taken after mixing or compositing and are used to document

intra- or inter-laboratory precision.

Standards

Standards are solutions that contain known concentration of target

analytes. Examples include stock standards and calibration standards.

Surrogate

A surrogate is an organic non-target analyte that has similar chemical

properties to the analyte of interest. The surrogate standard is added to

the sample in a known amount and used to evaluate the response of

the analyte to preparation and analysis procedures. The surrogate

concentration is measured using the same procedures used to

measure other analytes in the sample. Surrogate recoveries are used

to evaluate the performance of the analysis.

Target Analytes

Target analytes are the compounds included on the list of analytes for

an analytical method. Site-specific target analytes are defi

ned in the

QAPP.

Tentatively Identified

Compound (TIC)

As per N.J.A.C. 7:26E-1.8, TIC means a non-targeted compound

detected in a sample using a GC/MS analytical method which has been

tentatively identified using a mass spectral library search. An estimated

concentration of the TIC is also determined.

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Term Definition

Trip Blank

Trip blanks originate within the laboratory. Trip blanks are sample

containers that have been filled with analyte-free reagent water carried

with other sample containers out to the field and back to the lab without

being exposed to sampling procedures. Trip blanks are used to

ascertain if sample containers may have been contaminated during

transportation and storage.

Turn-Around Time

The turn-around time is the amount of time it takes for the laboratory to

report the analytical results to the customer following the submittal of

the samples to the laboratory.

Uncertainty

A measure of the total variability associated with sampling and

measuring that includes the two major error components: systematic

error (bias) and random error.

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Appendix L:

List of Acronyms

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List of Acronyms

% R Percent Recovery

BEHP bis(2-ethylhexyl) phthalate

ºC Degrees Celsius

CCAL Continuing Calibration

CFR Code of Federal Regulations

Cr Chromium

CSM Conceptual Site Model

DDT Dichloro-diphenyl-trichloroethane

DKQ Data of Known Quality

DQA Data Quality Assessment

DQO Data Quality Objective

DUE Data Usability Evaluation

EPA United States Environmental Protection Agency

EPH Extractable Petroleum Hydrocarbons

Hg Mercury

ICAL Initial Calibration

LSRP Licensed Site Remediation Professional

LCL Lower Control Limit

LCS Laboratory Control Sample

LFB Laboratory Fortified Blank

MEK Methyl Ethyl Ketone

MIBK 4-Methyl-2-petanone

μg/kg Micrograms per Kilogram

μg/l Micrograms per Liter

mg/kg Milligrams per Kilogram

MS/MSD Matrix Spike/Matrix Spike Duplicate

ND Not Detected (i.e., below the Reporting Limit)

PAHs Polycyclic Aromatic Hydrocarbons, also known as Polynuclear Aromatic Hydrocarbons

PARCCS Precision, accuracy, representativeness, comparability, completeness, and

sensitivity

PCBs Polychlorinated Biphenyls

PCE Tetrachloroethene, also known as Tetrachloroethylene or Perchloroethylene

Pest Pesticides

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QA/QC Quality Assurance/Quality Control

QAPP Quality Assurance Project Plan

RL Reporting Limit

RPD Relative Percent Difference

RRF Relative Response Factor

SOP Standard Operating Procedure

SPLP Synthetic Precipitation Leaching Procedure

SVOCs Semi-volatile Organic Compounds

TCLP Toxicity Characteristic Leaching Procedure

TICs Tentatively Identified Compounds

TCE Trichloroethene

UCL Upper Control Limit

VOCs Volatile Organic Compounds

Work Group NJDEP Analytical Methods Technical Guidance Work Group

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