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CHAPTER ONE 

TABLE OF CONTENTS 

Section Page 

 

1.0 INTRODUCTION 1 

1.1 PLANNING 2 

1.2 IMPLEMENTATION 9 

1.3 ASSESSMENT 11 

1.4 REFERENCES AND SOURCES FOR ADDITONAL INFORMATION ON 

PROJECT QUALITY ASSURANCE AND CONTROL 

15 

1.5 GLOSSARY 16 

Figure 

1-1 PROJECT LEVEL EPA QA SYSTEM 14 

Appendix A SUMMARY OF UPDATES/CHANGES IN CHAPTER 1 25 
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CHAPTER ONE 

Project Quality Assurance and Quality Control

For a summary of changes in this version from the previously published Chapter One, please 

see Appendix A at the end of this document. 

1.0 INTRODUCTION 

 The goal of this chapter is to provide an understanding of environmental data and the need 

for quality. EPA has developed numerous guidance documents on quality assurance. This 

chapter is not intended to summarize the previously developed EPA guidance. Instead, this 

chapter will provide familiarity with regulations and guidance relating to QA and where to find them. 

 Regulations promulgated under the Resource Conservation and Recovery Act (RCRA) of 

1976, as amended; require the collection and use of environmental data by regulated entities. In 

addition, organizations often collect and use environmental data for decision making. Given the 

significant decisions to be made based on environmental data, it is critical that the data are of 

sufficient quantity and quality for their intended use and can support decision-making based on 

sound science. 

 In response to the need for quality data, it is recommended that all parties follow a structured 

system of data quality assurance and quality control (QA/QC). In addition, some of the RCRA 

regulations include specific requirements for ensuring data quality. 

 This chapter provides general guidance intended to ensure data are of sufficient quality for 

their intended use. Its intended audience is any entity, government or private party that may be 

collecting environmental data. It is designed to support the efforts of those responsible for 

preparing and reviewing project planning documents such as Quality Assurance Project Plans 

(QAPPs), those involved in implementing and assessing data collection and generation activities in 

the field or laboratory, and those who use the data for decision-making. 

 Due to the diversity of data collection efforts, it is not possible to provide all details necessary 

to meet the needs of all members of the intended audience. However, EPA has developed a 

variety of detailed QA guidance documents that are incorporated into this Chapter by reference. 

This series of quality systems documents can be accessed on the EPA's Quality web site at: 

http://www.epa.gov/quality. These documents describe in detail EPA policies and procedures for 

planning, implementing and assessing the effectiveness of quality systems. 

 EPA's quality system comprises three structural levels: policy, organization/program, and 

project. This document addresses quality at the project level of the system, including technical 

aspects of analytical method quality assurance (QA) and quality control (QC). Entities which 

desire guidance on the other two structural levels (policy and organization/program levels) can 

access such guidance at the aforementioned EPA quality web site. 

 A project's life cycle under EPA's quality system has three phases: planning, implementation, 

and assessment. This chapter is organized into these three phases. Additionally, Figure 1 is 

provided, and illustrates this process. 

 Additionally, this chapter contains general project QC guidance to be used with the 

subsequent chapters and methods in this manual. It should be noted that several methods (e.g., 
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Method 8000) also contain general QC criteria and guidance that pertain to the individual methods 

referenced therein (e.g., Methods 8081, 8082, 8260 and 8270). Individual methods may also 

contain QC criteria specific only to that method. The QC criteria in the general methods take 

precedence over chapter QC criteria. Method-specific QC criteria take precedence over general 

method QC criteria. 

 

1.1 PLANNING 

 Planning, the first phase of a project’s life cycle, involves the development of project 

objectives and acceptance or performance criteria using a systematic process. Data quality 

objectives (DQOs) and a sampling and analysis design are established to generate data of an 

appropriate type, quality and quantity to meet project objectives. The final output of this phase is 

a planning document, such as a QAPP, and/or a sampling and analysis plan (SAP) or a waste 

analysis plan (WAP). 

 This section provides guidance on activities and concepts that EPA recommends to be used 

or considered during the planning phase, when appropriate to a specific project. 

1.1.1 Systematic Planning 

Systematic planning is a process designed to ensure that the level of detail in planning is 

commensurate with the importance and intended use of the work and the availability of resources 

to accomplish it. The ultimate goal of systematic planning is to ensure collection of the 

appropriate type, quantity, and quality of data to support decisions with acceptable confidence. 

Following is a summary of EPA’s Agency-wide guidance on systematic planning. More detail can 

be found in the EPA Quality Manual for Environmental Programs (CIO-2105-P-01-0).

The systematic planning process generally involves the following elements: 

• Identification and involvement of data generators and users. 

• Identification of project schedule, milestones, resources (including budget), and any 

applicable requirements. 

• Description of the project goals and objectives (i.e., what is trying to be accomplished 

by performing this project) 

• Identification of the type (e.g., individual data points to be used to estimate risk at a site, 

multi-point composites to be used to evaluate the average concentration in a decision 

unit), quantity and quality (e.g., screening for the presence/absence of an analyte, 

definitive data supported by all method specific QC results) of data needed. Be specific 

on what kind of analytical result will be needed to make a decision, whether the 

collected results need to be comprehensive and meet well defined DQOs or are 

merely for screening purposes to make a presence/absence decision? 

• Specification of acceptance or performance criteria for ensuring the data collected 

meets the needs of the project. 

• Description of how, when, and where the data will be obtained, and identification of any 

constraints on data collection. 
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• Specification of QA and QC activities needed to assess quality performance criteria 

(e.g., QC samples for the field and laboratory, audits, technical assessments, 

performance evaluations, etc.). 

• Description of how acquired data will be analyzed (i.e., field and/or laboratory), 

evaluated, and assessed against performance criteria. If statistical assumptions are 

made as part of the planning process, the assessment must discuss how the 

assumptions will be verified as accurate, and what actions will be taken if the statistical 

assumptions are not supported by the data. 

Planners should also recognize that existing data (i.e., secondary data) can be useful in supporting 

decision making. Secondary data can provide valuable information to help design a plan for 

collecting new data, while also lowering the cost of future data collection efforts. However, the 

limitations on using any secondary data must be clearly understood and documented. For 

example, secondary data must be examined to ensure that their quality is acceptable for a given 

application. Combining secondary data with current data can be a complex operation and should 

be undertaken with care. Sometimes, statistical expertise is necessary to evaluate both data sets 

before they can be combined. If combining data sets, make sure historical data use is appropriate 

in type and quality to the current project. 

 1.1.2 DQOs 

 The DQO process, discussed in detail in the Guidance on Systematic Planning Using the 

Data Quality Objectives Process, EPA QA/G-4, is designed to produce scientific and 

resource-effective data collection designs that will support decision making with a defined level of 

confidence. 

 The DQO process can be applied to any study, regardless of its size. While there is no 

regulatory obligation to use the DQO process, it is the recommended planning approach for most 

EPA data collection activities. The depth and detail of DQO development will depend on the study 

objectives. The DQO process is particularly applicable to a study in which multiple decisions must 

be reached. By using the DQO process, the planning team can clearly separate and delineate 

data requirements for each decision to be made or question to be answered. It consists of seven 

planning steps that are summarized below. 

 1.1.2.1 Step 1: State the Problem 

 The purpose of Step 1 is to clearly define the problem that has created the need for 

the study. In describing the problem, especially for more complex sites, it is often useful to 

include a conceptual site model (CSM). The CSM is a three-dimensional "picture" of site 

conditions at a discrete point in time that conveys what is known or suspected about the 

facility, including releases, release mechanisms, contaminant fate and transport, exposure 

pathways, potential receptors, and risks. 

 1.1.2.2 Step 2: Identify the Goals of the Study 

 The purpose of Step 2 of the DQO process is to identify the key questions that need 

to be answered in order to resolve the problem(s) identified in Step 1. Step 2 should also 

identify any actions that may be taken based on study results. The goals of the study and 

the alternative actions are then combined to form decision statement(s) that will resolve the 

problem. A decision statement defines which of the identified alternative actions will be 

pursued depending on the outcomes of the study. 
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 1.1.2.3 Step 3: Identify the Information Inputs 

 The purpose of Step 3 of the DQO process is to identify the information needed to 

resolve the decision statement. This may include, but is not limited to: 

• Primary data, including sampling and analysis methods 

• Secondary data, including the necessary information to ensure the data is of 

known and documented quality (e.g., sampling and analysis methods used as 

well as sufficient information to evaluate the quality of the data) 

• Action limits to be considered 

• Background information about the site or process, including known or 

anticipated variability of the study parameters that may help the planning team 

identify critical sampling locations 

 1.1.2.4 Step 4: Define the Boundaries of the Study 

 

 The purpose of Step 4 of the DQO process is to define the spatial and temporal 

boundaries for the data collection design, including where samples will be collected. Spatial 

boundaries describe the physical area (i.e., horizontal and vertical boundaries) of the study. 

They can include geographic area or volume of material. Temporal boundaries include both 

the period of time the data collection effort will represent and the timeframe to which the 

decision will apply. 

 1.1.2.5 Step 5: Develop the Analytic Approach 

 The purpose of Step 5 of the DQO process is to consider the outputs from Steps 1-4 

and develop “IfJ, thenJelse” decision rules that unambiguously state which of the 

alternative actions identified in Step 2 will be pursued. These if/then/else decisions should 

be formulated to be dependent on how the results of the study compare to an established 

action level. 

 1.1.2.6 Step 6: Specify Performance or Acceptance Criteria 

 

 The purpose of Step 6 of the DQO process is to set limits on decision errors, and to 

document those limits. For judgmental and random samples, Step 6 should examine 

consequences of making incorrect decisions, and place acceptable limits on the likelihood of 

making decision errors. For random samples, Step 6 should specify any statistical 

hypothesis to be considered and all applicable statistical tests that will be used to assess the 

data. 

 1.1.2.7 Step 7: Develop the Plan for Obtaining Data 

 The purpose of Step 7 of the DQO process is to develop the data collection plan that 

will satisfy the objectives presented in Steps 1 through 6. RCRA Waste Sampling Draft 

Technical Guidance, dated August 2002, provides guidance that may be used during 

sampling design development. EPA also developed a guidance document called Guidance 

for Choosing a Sampling Design for Environmental Data Collection (QA/G-5S), to specifically 

provide the information needed to carry out step 7 and develop a sampling design. 

 The proposed plan should be the most resource-effective data collection design that 

meets the previously identified performance or acceptance criteria. The plan for obtaining 

data is documented in detail by developing a project QAPP as per EPA Requirements for 
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Quality Assurance Project Plans (QA/R-5), which presents the requirements for QAPPs, and 

its companion document, EPA Guidance for Quality Assurance Project Plans (QA/G-5). 

1.1.3 Development of QAPPs, Waste Analysis Plans (WAPs) and Sampling and Analysis 

Plans (SAPs)

 Documentation of planning processes and outcomes are critical to the: 

• effective communication of planned activities to all participants in the process, 

• reconstruction of completed events, and 

• reconciliation of the analytical data with the project plans. 

 Two types of planning documents are discussed in this section. Section 1.1.3.1 discusses 

QAPPs, which are a key output of the systematic planning process. Section 1.1.3.2 discusses 

WAPs and SAPs. 

1.1.3.1 QAPPs 

 All environmental data collection efforts performed by or funded by EPA (e.g., 

through an EPA contractor) must be supported by an approved QAPP. EPA also 

recommends the use of QAPPs for environmental data collection efforts by other entities 

(e.g., by other regulated entities in compliance with the RCRA regulations). EPA has issued 

several documents to aid in preparing QAPPs, including the documents noted in Section 

1.1.2.7. These documents provide suggestions for both EPA and non-EPA organizations 

on preparing, reviewing, and implementing QAPPs. 

 The primary purpose of the QAPP is to present the data collection activities to be 

implemented, including all necessary QA and QC, to ensure that all data produced are of 

known and documented quality, and that the data will satisfy the stated performance criteria. 

 QAPPs, and any accompanying WAPs or SAPs, should be accessible to all 

participants throughout the life of the project. They should provide understandable 

instructions to those who must implement the QAPP, such as the field sampling team, the 

analytical laboratory, modelers, and the data reviewers. 

 When preparing a QAPP, a graded approach should be used to determine the level of 

detail needed. This will ensure that the level of information presented is consistent with the 

intended use of the results and the degree of confidence needed in the quality of the results. 

The QAPP should be detailed enough to provide a clear description of every aspect of the 

project, from site history through assessment of the planned data collection. At a minimum, 

the QAPP should provide sufficient detail to demonstrate that: 

• the project technical and quality objectives are identified and agreed upon; 

 

• the intended measurements and data generation or data acquisition methods 

are appropriate for achieving project objectives; 

 

• assessment procedures are sufficient for confirming that data of the type and 

quality needed and expected are obtained; and 

 

• any limitations on the use of the data can be identified and documented. 

 
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As described in QA/R-5 and QA/G-5, the QAPP should be composed of standardized, 

recognizable elements covering the entire project from planning through assessment. 

These elements may be divided into the following four general groups: 

• Project Management - The elements in this group address project organization 

and management; site background and history; and project objectives. These 

elements ensure that project goals are clearly defined, that the participants 

understand the goals and the approach to be used, and that the planning 

process is documented. 

 

• Data Generation and Acquisition - The elements in this group address all 

aspects of project design and implementation, including the numbers, types, 

and locations of all samples to be collected; the rationale for why the proposed 

data collection effort will be sufficient to address the study objectives; all 

sampling, subsampling and analytical procedures to be followed (i.e., both 

sample preparation as well as determinative procedures); QC requirements for 

all applicable field and laboratory procedures including the data quality 

indicators (DQIs) discussed below in Section 1.1.4; instrument calibration and 

maintenance for both field and laboratory equipment; use of secondary data; 

and data management. Implementation of these elements ensures that 

appropriate methods for sampling, analysis, data handling, and QC activities 

are employed and properly documented. 

 

• Assessment and Oversight - The elements in this group address the activities 

for assessing the effectiveness of project implementation and associated QA 

and QC activities. The purpose of assessment is to ensure that the QA Project 

Plan is properly implemented as prescribed. 

 

• Data Validation and Usability - The elements in this group address the QA 

activities that occur after data collection or generation is completed. These 

elements address how data will be reviewed, verified and validated as well as 

how data will be assessed and reconciled with the project objectives. 

While most QAPPs will describe project- or task- specific activities, there may be occasions 

when a generic QAPP may be more appropriate. A generic QAPP addresses the general, 

common activities of a program that are to be conducted at multiple locations or over a long 

period of time. For example, a generic QAPP may be useful for a large monitoring program 

that uses the same methodology at different locations. A generic QAPP describes, in a 

single document, the information that is not site or time-specific but applies throughout the 

program. Application-specific information is then added to the approved QAPP, either in the 

form of a site-specific QAPP, QAPP Addendum, or SAP. 

 1.1.3.2 WAPs and SAPs 

 In certain cases, WAPs or SAPs are required by a RCRA regulation. For example, 

WAPs are required as part of a permit application. Where WAPs or SAPs are required by 

regulation, the applicable regulations should be reviewed to ensure that the content and 

format requirements for the WAP or SAP are understood. Additionally, it should be noted 

that EPA has prepared various guidance documents to assist in preparing WAPs and SAPs 

that meet various regulatory requirements. Examples of these guidance documents include 

the following: 
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• EPA guidance on the preparation of WAPs can be found in the document 

entitled Waste Analysis at Facilities that Generate, Treat, Store and Dispose of 

Hazardous Wastes (ECDIC 2002-011), dated April 1994. 

• EPA guidance on SAPs for delisting petitions can be found in the document 

entitled EPA RCRA Delisting Program Guidance Manual for the Petitioner, 

dated March 2000. 

• General SAP guidance can be found in EPA’s RCRA Waste Sampling Draft 

Technical Guidance, dated August 2002. 

• Chapter 9 of this document (Test Methods for Evaluating Solid Waste, 

Physical/Chemical Methods - SW-846) presents additional discussion on 

sampling plans. 

A QAPP may also be prepared, and required, as a supplement to any WAP or SAP. It 

should also be noted that if a WAP or SAP is not required by regulation, QAPPs can be 

prepared such that they present sufficient detail to cover both the QAPP and WAP or SAP in 

a single document. If a WAP or SAP is prepared along with a QAPP, it is common for these 

documents to reference one another for necessary information. To enhance the usability of 

QAPPs and WAPs/SAPs, references between the documents should be specific, providing 

the full document name, section number, subsection, and page number. 

 1.1.4 Data Quality Indicators 

 As part of systematic planning, measurement performance criteria for DQIs must be 

established and documented for each data collection effort. DQIs apply to both laboratory and 

field activities. At a minimum, DQIs should include precision, accuracy, representativeness, 

comparability, and completeness (PARCC). The following presents a discussion of PARCC and 

other DQIs. 

1.1.4.1 Precision 

 Precision measures the agreement among a set of replicate measurements. Field 

precision is assessed through the collection and analysis of field duplicates. Analytical 

precision is estimated by duplicate/replicate analyses, usually on laboratory control samples, 

spiked samples and/or field samples. The most commonly used estimates of precision are 

the relative standard deviation (RSD) and, when only two samples are available, the relative 

percent difference (RPD). 

1.1.4.2 Accuracy 

 Accuracy is the closeness of a measured result to an accepted reference value. 

Accuracy is usually measured as a percent recovery. QC analyses used to measure 

accuracy include standard recoveries, laboratory control samples, spiked samples, and 

surrogates. 

1.1.4.3 Representativeness 

 Sample representativeness expresses the degree to which data accurately and 

precisely represents a characteristic of a population, parameter variations at a sampling 

point, a process condition, or an environmental condition. It is dependent on the proper 

design of the sampling program and will be satisfied by ensuring the approved plans were 

followed during sampling and analysis. 
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1.1.4.4 Comparability 

 Comparability expresses the degree of confidence with which one data set can be 

compared to another. It is dependent upon the proper design of the sampling program and 

will be satisfied by ensuring that the approved plans are followed and that proper sampling 

and analysis techniques are applied. Further, when assessing comparability, data sets 

should be of known and documented quality. 

1.1.4.5 Completeness 

 Completeness is a measure of the amount of valid data collected compared to the 

amount planned. Measurements are considered to be valid if they are unqualified or 

qualified as estimated data during validation. Field completeness is a measure of the 

number of samples collected versus the number of samples planned. Laboratory 

completeness is a measure of the number of valid measurements compared to the total 

number of measurements planned. 

1.1.4.6 Bias 

 Bias is the systematic or persistent distortion of a measurement process that causes 

error in one direction (e.g., the sample measurement is consistently lower than the sample’s 

true value). Bias can be introduced during sampling, analysis, and data evaluation. 

Sampling bias is best addressed through the proper selection and use of sampling tools, 

uses of correct sampling and subsampling procedures to limit preferential selection or loss of 

sample media, use of random sampling designs, and use of sample handling procedures that 

limit the loss or gain of constituents to the sample media. Analytical bias refers to deviation 

in one direction (i.e., high, low or unknown) of the measured value from a known spiked 

amount. Analytical bias can be assessed by comparing a measured value in a sample of 

known concentration to an accepted reference value or by determining the recovery of a 

known amount of contaminant spiked into a sample (matrix spike). The planning team 

should specify qualitative criteria for sampling bias and quantitative criteria for analytical bias, 

typically expressed as "percent recovery." 

1.1.4.7 Reproducibility 

 Analytical reproducibility is a quantitative indicator that is used when referring to the 

uncertainty associated with the use of multiple laboratories for a specific study. The ability 

of multiple laboratories to generate the same result for splits of the same sample can be 

expressed as a measure of interlaboratory precision and bias. Specific indicators of 

precision and bias (such as range or variance) are generated using data from replicate 

samples sent to multiple laboratories. 

1.1.4.8 Repeatability 

 Repeatability is a quantitative indicator that in used within a single laboratory (i.e., 

intralaboratory precision). It is determined when the laboratory, analyst, test method and 

equipment remain constant and random aliquots of the same sample are analyzed within a 

short period of time. 

1.1.4.9 Sensitivity 

 Sensitivity is an instrument’s or method’s minimum concentration that can be reliably 

measured or reported (i.e., or lower limit of quantitation [LLOQ]). 
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 Please note the glossary section to this chapter includes further discussion of the 

DQIs. 

1.2 IMPLEMENTATION 

Implementation is the second phase of the project life cycle. The implementation phase includes 

the following steps: 

1.2.1 Standard Operating Procedures (SOPs) 

 SOPs are written documents that describe, in detail, the routine procedures to be followed for 

a specific operation, analysis, or action. Please note, in most cases, referencing a given method 

or method number is not sufficient, unless the method is performed exactly as written (e.g., a 

method-defined parameter). Laboratories must have an SOP to demonstrate that their procedure 

meets the conditions of the referenced method. Information on how to prepare SOPs can be 

found in EPA’s Guidance for the Preparation of Standard Operating Procedures (QA/G-6), dated 

April 2007. 

 SOPs enable tasks to be accomplished reproducibly, even if there are changes in the 

personnel performing them. Consistent use of an approved SOP ensures conformance with 

organizational practices, reduction in the frequency of errors, and improved data comparability and 

defensibility. SOPs also serve as resources for training and for ready reference and 

documentation of proper procedures. 

 The SOPs, and/or procedures described in the QAPP, must be followed for all project 

activities from sample collection to validation. If project-specific modifications to SOPs are 

needed, they must also be presented in the QAPP. Field and laboratory SOPs should be 

provided for the areas discussed below, as applicable: 

• Sample Custody SOPs that describe sample receipt and handling; sample storage; 

sample security and tracking; and holding times 

• Sample collection 

• Analytical Method SOPs, including subsampling, sample preparation/clean-up, 

calibration, QC, and analysis 

• Reagent/Standard Preparation and Traceability SOPs

• Equipment Calibration and Maintenance SOPs 

• Corrective Action SOPs 

• Data Reduction SOPs 

• Data Reporting SOPs 

• Records Management SOPs 

• Waste Disposal SOPs 

As indicated above, the QC associated with each method may be provided in appropriate SOPs. 

The types of QC parameters to include in the SOPs and QAPP are defined in the glossary of this 

chapter. Since the QC acceptance limits are frequently adjusted more often than SOPs are 

revised, it may be impractical to update SOPs as these limits change. Therefore, field and 

laboratory QC limits may need to be presented in QAPP tables rather than SOPs. 
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 1.2.2 Conducting the Study/Experiment 

 The data collection effort is performed in accordance with approved plans (e.g., QAPP). 

The QAPP will present all proposed methods (i.e., including sampling, subsampling, preservation, 

preparation, clean-up and determinative methods), as well as a rationale for why the proposed 

methods are sufficient to meet the project DQOs. 

 The QAPP should also indicate whether the proposed analyses include method-defined 

parameters and/or whether the flexible approach to methods will be utilized. For the flexible 

approach, the QAPP must also demonstrate that the proposed methods are adequate to reach the 

study goals outlined in the DQOs. 

1.2.2.1 Method-defined parameters 

 Method-defined parameters (MDP), such as the Toxicity Characteristic Leaching 

Procedure (TCLP), have been written into regulations, the methods should be followed 

without deviation, and should not to be modified on a project basis. For a list of 

method-defined parameters, see Vol 40 CFR 260.11 references or the Methods Innovation 

Rule (MIR), in Volume 70, Number 113 of the Federal Register (70 FR 34537 Table 3). Some 

method-defined parameters are discussed in Chapter 8 of this manual. 

1.2.2.2 Flexible Approach to Environmental Measurements 

 The Flexible Approach to Environmental Measurement was finalized in 2008 as 

EPA’s preferred approach for environmental monitoring except in cases where methods are 

required by regulation. 

The goals of the flexible approach are to: 

• Provide method users flexibility when choosing sampling and analytical 

approaches to meet regulatory requirements for measurements 

• Develop processes for validation to confirm measurements meeting quality 

requirements 

• Increase collaboration with stakeholders to develop validation processes for 

new measurement technology 

• Ensure timely assessment of new or modified technologies, methods, and 

procedures. 

Chapter Two of this manual entitled, "Choosing the Correct Procedure” provides additional 

guidance regarding the selection of appropriate methods and method flexibility. 

1.2.3 Technical Assessments 

 Technical Assessments (otherwise known as audits) are systematic and objective 

examinations of a program or project to determine whether environmental data generation 

activities and related results comply with planning documents. Technical assessments can be 

performed to evaluate all phases of a project including field sampling, laboratory analysis, 

validation and data handling/management. 

 The QAPP should identify all personnel responsible for performing technical assessments, 

the authority of the auditor (e.g., do they have the authority to stop work if significant deviations 

from planning documents are found), and the planned audit frequency. Additionally, if technical 

assessments are planned or required as part of a project, the QAPP should provide the applicable 
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technical assessment checklists. These checklists establish and document the scope of all 

proposed technical assessments. The development of these technical assessment checklists 

should overseen by an independent party (i.e., someone not involved in the day to day operations 

of a project), such as the organization’s QA personnel. Further, the QAPP should establish a 

process for correcting technical assessment findings and confirming implementation of any 

corrective actions taken as a result of the findings. 

 While most technical assessments will be scheduled in advance with an organization, there 

may be occasions when unannounced technical assessments are needed. However, an 

unannounced audit may not reveal a representative picture of project activities if it occurs when the 

project is not active, or activities that are not within the scope of the technical assessment are 

occurring. 

 Technical assessments may be planned as part of the pre-award activities, or throughout the 

life of a project. For example, pre-award audits, including performance evaluation (PE) samples, 

are useful tools in determining the ability of a laboratory to perform the proposed analytical work. 

Additionally, technical assessments may also be used as an investigative tool where problems 

may be suspected. The EPA document Guidance on Technical Audits and Related Assessments 

for Environmental Data Operations (QA/G-7), dated January 2000, provides detailed information 

on various types of technical audits and includes an example checklist for a technical systems 

audit of a laboratory measurement system. 

1.3 ASSESSMENT 

 Following planning (Sec. 1.1) and implementation (Sec. 1.2), assessment is the third and 

final phase of the project data generation life cycle. The purpose of this phase is to evaluate 

whether the data are of the type, quantity and quality needed to meet project DQOs. Assessment 

can involve many different complex activities, including the use of statistical tools. This section 

provides an introduction and overview of these data assessment activities. 

1.3.1 Data Verification and Validation 

 Data verification and/or validation is the first step in the assessment process. Validation and 

verification are defined in the EPA Guidance on Environmental Data Verification and Data 

Validation (QA/G-8), dated November 2002. According to QA/G-8, verification is the process of 

evaluating the completeness, correctness, and conformance/compliance of a data set against a 

specified set of requirements. QA/G-8 defines validation as an analyte and sample specific 

process that extends the evaluation of data beyond data verification to determine its analytical 

quality. For the purposes of this chapter, it should be noted that the meaning of verification and 

validation is often program, organization and/or system specific. Further, these terms are used 

interchangeably at times, and in other cases have very different meanings. Therefore, it is critical 

that the procedures for verification and/or validation be clearly documented in the approved QAPP. 

 In general, data verification/validation involves the examination and review of project data 

(e.g., field and laboratory data for all applicable QC measurements) to confirm that the sampling 

and analysis protocols specified in the QAPP (and any other planning or contractual documents) 

were followed. Data verification/validation also involves examining whether the data met the 

method performance and acceptance criteria established in the project QAPP. When these 

criteria are not met, data verification/validation procedures should include qualification of the data

(e.g., the J qualifier is commonly used to indicate estimated data and the R qualifier is commonly 

used to indicate rejected data). The reasons for any failure to meet performance criteria also 
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need to be documented during this phase of assessment (e.g., in a data verification/validation 

report). 

 In addition to QA/G-8, EPA has published several references which provide more detailed 

information on what should be included in data verification and validation procedures. These 

include EPA regional and programmatic validation guidance documents that may apply to 

particular projects. It is also common for EPA guidance from one program or region to be 

modified for use in another area. While this may be acceptable, any modifications or changes to 

the established validation procedures should be included in the approved QAPP. 

 Data verification/validation may be performed at various phases throughout a project. As 

such, the procedures for verifying and/or validating data may need to be described in laboratory 

SOPs and/or QA Plans, in addition to the project documents like the QAPP. Corrective action 

based on verification/validation findings may also need to be described in these documents. 

 Data verification/validation activities may be performed internally (e.g., when the laboratory 

or data collector review their own data), or externally by an independent entity (i.e., a party that is 

not associated with the data collection effort). Internal and external data verification/validation 

procedures should be defined in the QAPP or other project documents. 

 A data verification/validation report should be generated to document the procedures 

followed as well as the findings and qualifications applied during data verification/validation. The 

report should include a list of the samples collected, field information about how the samples were 

collected, the analyses performed on the samples, the results of the analysis and the quality of the 

reported data, at a minimum. The usability of this report can be enhanced by listing the samples 

and analytes affected by each QC criteria exceedance, as well as the extent of the exceedance, 

and any qualifiers applied. Any other observations noted by the data reviewer that may impact 

the usability of the data should also be documented. 

1.3.2 Data Quality Assessment 

 Data quality assessment (DQA) is the second step in the assessment process. DQA is 

often needed because data verification/validation alone is generally not sufficient to determine 

whether a data set can be used for its intended purpose. Typically, the DQA follows the data 

verification and/or validation step. 

 In general, the DQA should include an evaluation of overall trends or biases in the data and 

associated QC results, as well as how the data may be affected. For random data sets, the DQA 

should evaluate the validity of any statistical assumptions made during the planning phase. If the 

statistical assumptions made during the planning phase are not supported by the data, 

recommendations for corrective action should be presented. All DQA findings should be 

summarized in a report. 

 Guidance documents available from EPA that discuss the DQA process include Data Quality 

Assessment: A Reviewer’s Guide (QA/G-9R), dated February 2006, and Data Quality 

Assessment: Statistical Tools for Practitioners (QA/G-9S), dated February 2006. 

 The DQA process described in this guidance is a five-step process. 

 Step 1: Review the Data Quality Objectives and Sampling Design 

 Step 2: Conduct a Preliminary Data Review 

 Step 3: Select the Statistical Method 

 Step 4: Verify the Assumptions of the Statistical Method 
SW-846 Update V ONE - 13 Revision 2 

 July 2014 

 Step 5: Draw Conclusions from the Data 

 It is recommended that the data user refer to these guidance documents, because they 

provide extensive information about DQA and the statistical tools that can be employed. 

 Although the DQA process described in QA/G-9R and QA/G-9S includes a significant 

amount of statistical procedures, it should be noted that the DQA process is not only applicable to 

random data sets, where statistics can be used to assess the data. A DQA should also be 

performed on judgmental, or biased, data sets. The process for how any data set will be 

assessed should be determined during the planning phase, and documented in the QAPP or other 

site documents. 

 
SW-846 Update V ONE - 14 Revision 2 

 July 2014 

FIGURE 1-1 

PROJECT LEVEL EPA QA SYSTEM

Technical 

Assessments 

Standard 

Operating 

Procedures

Conduct Study/ 

Experiment 

QA 

Project Plan 

Systematic Planning

(e.g., DQO Process) 

Data Quality

Assessment 

Data Verification 

& Validation

Planning Implementation

Assessment
SW-846 Update V ONE - 15 Revision 2 

 July 2014 

1.4. REFERENCES AND SOURCES FOR ADDITIONAL QA/QC INFORMATION 

1. USEPA. Guidance on Systematic Planning Using the Data Quality Objectives Process, EPA 

QA/G-4. Quality Staff, Office of Environmental Information, United States Environmental 

Protection Agency, Washington, D.C. February 2006. 

2. USEPA. EPA Quality Manual for Environmental Programs CIO 2105-P-01-0. Office of 

Environmental Information Quality Staff, United States Environmental Protection Agency, 

Washington, D.C. May 5, 2000. 

 

3. USEPA. Guidance on Choosing a Sampling Design for Environmental Data Collection, EPA 

QA/G-5S. Quality Staff, Office of Environmental Information, United States Environmental 

Protection Agency, Washington, D.C. December 2002. 

4. USEPA. Guidance on Environmental Data Verification and Data Validation, EPA QA/G-8. 

Quality Staff, Office of Environmental Information, United States Environmental Protection 

Agency, Washington, D.C. November 2002. 

5. USEPA. Data Quality Assessment: A Reviewer’s Guide, QA/G-9R. Quality Staff, Office 

of Environmental Information, United States Environmental Protection Agency, Washington, 

D.C. February 2006. 

6. USEPA. Data Quality Assessment: Statistical Tools for Practitioners, QA/G-9S. Quality 

Staff, Office of Environmental Information, United States Environmental Protection Agency, 

Washington, D.C. February 2006. 

7. USEPA. Guidance for Preparing Standard Operating Procedures, EPA QA/G-6. Quality 

Staff, Office of Environmental Information, United States Environmental Protection Agency, 

Washington, D.C. April 2007. 

8. USEPA. RCRA Waste Sampling Draft Technical Guidance. Office of Solid Waste, United 

States Environmental Protection Agency, Washington, D.C. August 2002. 

9. USPEA. Guidance for Quality Assurance Project Plans, EPA QA/G-5. Quality Staff, Office 

of Environmental Information, United States Environmental Protection Agency, Washington, 

D.C. December 2002. 

10. USEPA. EPA Requirements for Quality Assurance Project Plans, EPA QA/R-5. Quality 

Staff, Office of Environmental Information. United States Environmental Protection Agency, 

Washington, D.C. March 2001. 

11. USEPA. RCRA Delisting Program Guidance Manual for the Petitioner. United States 

Environmental Protection Agency, March 23, 2000. 

12. USEPA. Waste Analysis at Facilities That Generate, Treat, Store and Dispose of 

Hazardous Wastes, ECDIC 2002-011. Office of Waste Programs Enforcement, United 

States Environmental Protection Agency. April 1994. 

13. USEPA. Guidance on Technical Audits and Related Assessments for Environmental Data 

Operations, QA/G-7. Quality Staff, Office of Environmental Information, United States 

Environmental Protection Agency, Washington, D.C. January 2000. 
SW-846 Update V ONE - 16 Revision 2 

 July 2014 

1.5. GLOSSARY 

Also see the following for a glossary of quality-related terms developed by EPA: 

http://www.epa.gov/fem/pdfs/Env_Measurement_Glossary_Final_Jan_2010.pdf

ACCURACY The degree of agreement between an observed value and an accepted 

reference value. When applied to a set of observed values, accuracy 

includes a combination of a random error (precision) and systematic 

error (bias) components. 

ANALYTICAL BATCH A group of samples, including quality control samples, which are 

processed together using the same method, the same lots of reagents, 

and at the same time or in continuous, sequential time periods. 

Samples in each batch should be of similar composition and share 

common internal quality control standards. For QC purposes, if the 

number of samples in a batch is limited to 20; laboratory QC samples 

are not included in the batch count. 

Each batch should be uniquely identified within the laboratory for 

tracking purposes. Samples collected from the same site would 

normally be grouped together for batching purposes within the 

constraints imposed by the method holding times and batch size. 

Samples prepared in the same batch would normally be analyzed 

together on a single instrument. However, laboratories may find it 

necessary to group multiple clients’ samples into a single batch. Under 

these circumstances, additional matrix QC samples (i.e., duplicates, 

matrix spikes) may be needed to evaluate the effect of the matrix from 

each site on method performance. 

BIAS The constant or systematic distortion of a measurement process, 

different from random error, which manifests itself as a persistent 

positive or negative deviation from the known or true value. This can 

result from improper data collection, poorly calibrated analytical or 

sampling equipment, or limitations or errors in analytical methods and 

techniques. 

Bias can be assessed by comparing a measured value to an accepted 

reference value in a sample of known concentration or by determining 

the recovery of a known amount of contaminant spiked into a sample 

(matrix spike). Thus, the bias (B) due to matrix effects based on a 

matrix spike is calculated as: 

 B = (xs - xu ) - K 

where: 

 xs = measured value for spiked sample, 

 xu = measured value for unspiked sample, and 

 K = known value of the spike in the sample. 

Using the following equation yields the percent recovery (%R). 

 %R = 100 (xs - xu)/ K 
SW-846 Update V ONE - 17 Revision 2 

 July 2014 

 

BLANK Blanks are generally considered to be acceptable if target analyte 

concentrations are less than 1⁄2 the LLOQ or are less than 

project-specific requirements. Blanks may contain analyte 

concentrations greater than acceptance limits if the associated 

samples in the batch are unaffected (i.e. targets are not present in 

samples or sample concentrations are ≥10X the blank). Other criteria 

may be used depending on the needs of the project. For method 

specific details see methods 6010, 6020 for inorganics and Method 

8000 for organics). 

See also Calibration Blank, Equipment Blank, Method Blank, Reagent 

Blank and Trip Blank. 

CALIBRATION BLANK A calibration blank is a sample of analyte-free media that can be used 

along with prepared standards to calibrate the instrument. A 

calibration blank may also be used to verify absence of instrument 

contamination (e.g., initial calibration blank and continuing calibration 

blank). 

CALIBRATION CHECKS Calibration check analyses are used to assess calibration drift and 

memory effects over time for each analytical system. These analyses 

may include zero, span (low and high) to cover the full calibration 

range, and mid-range checks, depending on the method. 

CALIBRATION CURVE A plot of instrument response to an analyte versus known 

concentrations or amounts of analyte standards. Calibration 

standards are prepared by successively diluting a standard solution to 

produce working standards which cover the working range of the 

instrument. Standards should be prepared at the frequency specified 

in the appropriate method. The calibration standards should be 

prepared using the same type of acid or solvent and at the same 

concentration as the samples following sample preparation. This is 

applicable to organic and inorganic chemical analyses. 

CO-LOCATED 

SAMPLES 

A type of field duplicate where independent samples are collected as 

close as possible to the same point in space and time. They are two 

separate samples taken from the same source, stored in separate 

containers, and analyzed independently by the same method and 

laboratory. These duplicates are useful in documenting the precision 

of the sampling process. 

 

COMPARABILITY The degree to which different methods or data agree or can be 

represented as similar. Comparability describes the confidence that 

two data sets can contribute to a common analysis and interpolation. 

 

COMPLETENESS A measure of the amount of valid data obtained from a measurement 

system compared with the amount that was expected to be obtained 

under correct, normal conditions. Percent completeness is calculated 

as: 

% Completeness = x x 100

y 

 where: 
SW-846 Update V ONE - 18 Revision 2 

 July 2014 

x = amount of valid data obtained 

y = amount of data expected to be obtained 

DATA QUALITY 

INDICATORS (DQIs) 

The quantitative statistics and qualitative descriptors that are used to 

interpret the degree of acceptability or utility of data to the user. The 

principal indicators of data quality are precision, bias, accuracy, 

representativeness, comparability, completeness, and sensitivity. 

DATA QUALITY 

OBJECTIVES (DQOs) 

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. 

DATA VALIDATION The process of evaluating the available data against the project DQOs 

to make sure that the objectives are met. Data validation may be very 

rigorous, or cursory, depending on project DQOs. The available data 

reviewed will include analytical results, field QC data, laboratory QC 

data, and may also include field records. 

DUPLICATE See Co-located Samples, Laboratory Duplicate, Field Duplicate, Field 

Split Samples, and Matrix Spike Duplicate. 

EQUIPMENT BLANK A sample of analyte-free media which has been used to rinse the 

sampling equipment. It is collected after completion of 

decontamination and prior to sampling at a location. This blank is 

useful in documenting adequate decontamination of sampling 

equipment. 

EQUIPMENT RINSATE See Equipment Blank. 

FIELD BLANK Field blanks include any sample submitted from the field that is 

identified as a blank. These include trip blanks, rinsates, equipment 

blanks, etc.

Field blanks may also be obtained by the sampling organization to 

measure ambient contamination in the field. If this type of field blank is 

requested, a container of reagent water or a solid blank material is 

opened in the field for a predefined period of time. The sample is then 

sent to the laboratory for analysis. 

FIELD DUPLICATES Field duplicates are useful in documenting the precision of the 

sampling process. Field duplicates are used to assess improper 

homogenization of the samples in the field; reproducibility of sample 

preparation and analysis; and, heterogeneity of the matrix. 

See also Co-located Samples and Field Split Samples. 

FIELD SPLIT SAMPLES A type of field duplicate where the sample is homogenized and then 

divided into two or more aliquots so that variability can be evaluated, 

(i.e., often between laboratories or methods). Homogenization may 

have an impact on sample integrity for some sample types (e.g., VOCs 
SW-846 Update V ONE - 19 Revision 2 

 July 2014 

in soil), and in these cases co-located samples may be more 

appropriate. 

INITIAL 

DEMONSTRATION of 

PROFICIENCY (IDP) 

Prior to the analysis of samples an initial demonstration of method 

proficiency is accomplished through the successful calibration of 

method-specific instruments according to project requirements and 

criteria set forth in the applicable analytical methodology. This initial 

demonstration should be performed prior to independently running an 

analytical method, and should be repeated if other changes occur (e.g., 

instrument repair, significant change in procedure). 

Please see individual methods for additional guidance on IDP. 

INTERNAL STANDARD Internal standards may be spiked into prepared field samples and QC 

samples (or sample extracts). Their recovery is generally used to 

demonstrate the lack of severe matrix effect in the instrumental 

analysis by setting criteria for the internal standard response in 

comparison to a response in a sample with a known lack of matrix 

effect (i.e., a standard). Internal standards are also used to account 

for matrix effects and/or variability in instrument response by 

normalizing the response of the target analytes and surrogates, 

thereby decreasing measurement bias to the extent that their behavior 

mimics that of the target analytes. 

LABORATORY 

CONTROL 

SAMPLE 

The Laboratory Control Sample (LCS) is analyzed to assess general 

method performance based on the ability of the laboratory to 

successfully recover target analytes from a control matrix. The LCS is 

similar in composition to the method blank in that it is an aliquot of 

analyte-free water or analyte-free solid (e.g., Ottawa sand, anhydrous 

sodium sulfate, or other purified solid) to which known amounts of the 

method analytes are added. LCS analyses help determine if the 

system is running properly (i.e., within pre-defined limits). Although 

the frequency of LCS analysis should be determined by the needs of a 

project, typically one LCS is prepared and analyzed for every analytical 

batch. The LCS sample is prepared and analyzed in the same 

analytical batch and in exactly the same manner as the other routine 

samples. The recovery of the target analytes in the LCS analysis 

demonstrates whether the methodology is in control and the laboratory 

is capable of making unbiased measurements. The results of the LCS 

are evaluated in conjunction with other QC information to determine the 

acceptability of the data generated for that batch of samples. 

Acceptance criteria for LCS recovery are calculated based on 

statistical treatment of historical LCS recovery data through the use of 

control charts. 

LABORATORY 

DUPLICATE 

The analysis or measurements of the variable of interest performed 

identically on two sub-samples of the same sample, usually taken from 

the same container. The results from duplicate analyses are used to 

evaluate analytical or measurement precision and include variability 

associated with sub-sampling and the matrix, but not the precision of 

field sampling, preservation, or storage internal to the laboratory. 

Laboratory duplicate analysis entails removing two aliquots from the 
SW-846 Update V ONE - 20 Revision 2 

 July 2014 

same sample and taking them through the same preparative and 

analytical procedures to evaluate analytical precision. Laboratory 

duplicates are more commonly used to assess precision for inorganic 

and radiological constituents, while precision for organic analyses is 

usually assessed by determining the RPD between matrix spike and 

matrix spike duplicates. The frequency of laboratory duplicate 

analysis will depend on project requirements. 

LOWER LIMIT OF 

QUANTITATION (LLOQ) 

The lowest point of quantitation which, in most cases, is the lowest 

concentration in the calibration curve. The LLOQ is initially verified by 

spiking a clean control material (e.g., reagent water, method blanks, 

Ottawa sand, diatomaceous earth, etc.) at the LLOQ and processing 

through all preparation and determinative steps of the method. 

Laboratory-specific recovery limits should be established when 

sufficient data points exist. Individual methods may recommend 

procedures for verifying the LLOQ and acceptance limits for use until 

the laboratory has sufficient data to determine acceptance limits. 

LLOQs should be determined at a frequency established by the 

method, laboratory’s quality system, or project. 

Please see individual methods for additional guidance on implementing 

LLOQ (e.g., 8000, 6020).

MATRIX The material of which the sample is composed or the substrate 

containing the analyte of interest, such as waste water, storm water 

and biosolids. Also called medium or media. 

MATRIX SPIKE Matrix spikes are aliquots of environmental samples to which known 

concentrations of certain target analytes have been added before 

sample preparation, cleanup, and determinative procedures have been 

implemented. Matrix spike analysis would normally be included with 

each preparation batch of samples processed. Under ideal 

circumstances, the original, unspiked, field sample will be analyzed 

first, to determine the concentration in the unspiked sample. 

However, if this approach is not practical, the samples may be spiked 

at the midpoint of the calibration range or at the same level as the LCS. 

The matrix spike analysis is used to assess the performance of the 

method by measuring the effects of interferences caused by the 

sample matrix and reflects the bias of the method for the particular 

matrix in question. 

MATRIX SPIKE 

DUPLICATES 

Matrix spike duplicates are additional replicates of matrix spike 

samples that are subjected to the sample preparation and analytical 

scheme as the original sample. A matrix spike duplicate sample 

would normally be included with each preparation batch of samples 

processed. Analysis of spiked duplicate samples ensures a positive 

value, allowing for estimation of analytical precision. 

Matrix spike duplicates are used to document the precision as well as 

bias of a method in a given sample matrix. When critical decisions are 

based on the matrix spike and matrix spike duplicate recoveries, the 

laboratory should maintain control charts for these samples to monitor 
SW-846 Update V ONE - 21 Revision 2 

 July 2014 

precision and bias for each particular matrix. 

METHOD BLANK Method blanks are analyzed to assess background interference or 

contamination that exists in the analytical system that might lead to the 

reporting of elevated concentration levels or false positive data. The 

method blank is defined as an interference-free blank matrix, similar to 

the sample matrix, to which all reagents are added in the same 

volumes or proportions as used in sample preparation and carried 

through the complete sample preparation, cleanup, and determinative 

procedures. For aqueous analyses, analyte-free reagent water would 

typically be used. For soil analyses, a purified solid matrix (e.g., sand) 

would typically be used, except for metals analyses. Method blank 

results are evaluated in conjunction with other QC information to 

determine the acceptability of the data generated for that batch of 

samples. The method blank results should be below the LLOQ for the 

target analytes being tested; otherwise, corrective action should be 

taken. A method blank is included with the analysis of every analytical 

batch of 20 samples or less or as stated in the QAPP or method, 

whichever is more frequent. 

ORGANIC-FREE 

REAGENT WATER 

All references to water in the methods refer to water in which an 

interferant is not observed at the LLOQ for the compounds of interest. 

Preparation of organic-free reagent water may depend on the types 

of interferants of concern for the analysis and may include boiling, 

sparging with an inert gas, reverse osmosis purification, distillation, 

particle filtration, activated carbon filtration, ion exchange filtration, 

etc. 

POST-DIGESTION 

SPIKES 

Post-digestion spike samples are typically prepared for inorganic 

analyses when pre-digestion/pre-distillation matrix spike recoveries 

are outside the required control limits. They are prepared by spiking 

a known amount of standard to the sample digestate. The recovery 

data from the post digestion spike analyses are used to further assess 

if matrix effects may be a source of measurement bias in sample 

quantitation. 

PRECISION The agreement among a set of replicate measurements without 

assumption of knowledge of the true value. Precision is estimated by 

means of duplicate/replicate analyses of separate aliquots of the same 

sample (not multiple runs of a single digestion/extraction). These 

samples should contain concentrations of analyte at or above the 

LLOQ, and may involve the use of matrix spikes. The most commonly 

used estimates of precision are: 

Relative standard deviation (RSD), also known as the coefficient of 

variation (CV): 

 RSD = CV = (100)*(S/

_

x) 

where: 

_

x = the arithmetic mean of the i measurements, and 

S = the square root of the variance of i measurements; and, 
SW-846 Update V ONE - 22 Revision 2 

 July 2014 

Relative percent difference (RPD) when only two results are compared:

 =

| − 	

|




 + 	

2




∗ 	100

where: x1 and x2 are measurements of independently prepared aliquots

of the same sample or replicate samples. 

PROJECT Single or multiple data collection activities that are related through the 

same planning sequence. 

QUALITY CONTROL 

SAMPLE 

A sample made from standards or matrix and used to verify 

acceptability of the results from preparation and/or analysis of a batch 

of samples. Examples of laboratory quality control samples are 

method blanks, laboratory duplicates, and laboratory control samples; 

field quality control samples are field blanks, trip blanks, field 

duplicates, and matrix spikes. 

QUALITY ASSURANCE 

PROJECT PLAN 

(QAPP) 

A formal document describing in comprehensive detail the necessary 

quality assurance, quality control, and other technical activities that 

should be implemented to ensure that the results of the work 

performed will satisfy the stated performance criteria. 

REAGENT BLANK Reagent blanks are analyzed to assess background interference or 

contamination that exists in the analytical system that might lead to the 

reporting of elevated concentration levels or false positive data. The 

reagent blank is defined as an interference-free blank matrix, to which 

all reagents are added in the same volumes or proportions as used in 

sample preparation but are NOT carried through the complete sample 

preparation, cleanup, and determinative procedures. The purpose of 

a reagent blank is to gauge contamination from chemical inputs and 

instrumental factors only. A reagent blank is NOT run as part of a 

digestion or preparation method. 

REAGENT GRADE Analytical reagent (AR) grade, ACS reagent grade, and reagent grade 

are synonymous terms for reagents which conform to the current 

specifications of the Committee on Analytical Reagents of the 

American Chemical Society. 

REAGENT WATER Water that has been generated by any method which would achieve 

the performance specifications for ASTM Type II water. For organic 

analyses, see the definition of organic-free reagent water. 

REFERENCE 

MATERIAL 

A material containing known quantities of target analytes in solution or 

in a homogeneous matrix. It is used to document the bias of the 

analytical process. 

REPEATABILITY The degree of agreement between mutually independent test results 

produced by the same analyst using the same test method and 

equipment on random aliquots of the same sample within a short 
SW-846 Update V ONE - 23 Revision 2 

 July 2014 

REPRESENTATIVENESS 

period of time. 

A measure of the degree to which data accurately represent a 

characteristic of a population, a parameter variation at a sampling 

point, a process condition, or an environmental condition. 

REPRODUCIBILITY Reproducibility is defined as the closeness of the agreement between 

the results of measurements of the same analyte carried out under 

variable conditions of measurement. 

STANDARD ADDITION The addition of a known amount of analyte to the sample in order to 

determine the relative response of the detector to an analyte within the 

sample matrix. The relative response is then used to assess either an 

operative matrix effect or the sample analyte concentration. 

SURROGATE Surrogates are most commonly used to monitor the performance of 

organic analyses using methods such as high performance liquid 

chromatography (HPLC), gas chromatography (GC), and gas 

chromatography/mass spectrometry (GC/MS). Surrogate spikes are 

added to field samples and QC samples for organic analyses at known 

amounts, and their recoveries are used to assess matrix effects and, to 

some extent, verify proper processing and instrument performance for 

each sample. The analytes used as surrogates mimic the behavior of 

the target analyte(s) throughout sample preparation and instrument 

determination. Surrogates are organic compounds which are similar 

to the target analytes in chemical composition and behavior in the 

analytical process, but are not normally found in the environmental 

samples. Surrogates added to LCS samples and blanks are used to 

assess recovery in a matrix known to be free from interference. This 

information can be used to determine the magnitude of matrix 

interference effects on environmental sample results. 

TRIP BLANK A sample of analyte-free media taken from the laboratory to the 

sampling site and returned to the laboratory unopened. Trip blanks 

should be prepared at a frequency of one per day of sampling during 

which samples are collected for volatile organic constituents (VOCs). 

Trip blanks are prepared prior to the site visit at the time sample 

containers are shipped to the site. The trip blank should accompany 

the sampling kits throughout all the sample collection and transport 

operations. This blank will not be opened during the sampling 

activities and will be used to assess sample VOC contamination 

originating from sample transport, shipping, or site conditions. A trip 

blank is used to document contamination attributable to shipping and 

field handling procedures. 

 
SW-846 Update V ONE - 24 Revision 2 

 July 2014 

APPENDIX A 

SUMMARY OF UPDATES/CHANGES IN CHAPTER 1 

1. The entire Chapter has been rewritten and reorganized to reflect changes in the EPA data 

quality system approach. 

2. The revision number was changed to two and the date published to July 2014. 

3. This appendix was added to document changes made during the editorial process. 

4. The document was updated to match the current SW-846 style guidelines. 

5. Figure 1 was added based on information in EPA QA/G-8, November 2002, Figure 1.