SW-846 Update V ONE - i Revision 2 July 2014 

### 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 INFORMATIO N 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 25SW-846 Update V ONE - 1 Revision 2 July 2014 CHAPTER ONE

Project Quality Assurance and Quality Control

For a summary of changes in this version from the p reviously 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 understa nding of environmental data and the need for quality. EPA has developed numerous guidance d ocuments on quality assurance. This chapter is not intended to summarize the previously developed EPA guidance. Instead, this chapter will provide familiarity with regulations a nd guidance relating to QA and where to find them.

Regulations promulgated under the Resource Conserv ation 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 envir onmental data for decision making. Given the significant decisions to be made based on environme ntal 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 re commended that all parties follow a structured system of data quality assurance and quality contro l (QA/QC). In addition, some of the RCRA regulations include specific requirements for ensur ing data quality.

This chapter provides general guidance intended to ensure data are of sufficient quality for their intended use. Its intended audience is any e ntity, government or private party that may be collecting environmental data. It is designed to s upport the efforts of those responsible for preparing and reviewing project planning documents such as Quality Assurance Project Plans (QAPPs), those involved in implementing and assessi ng 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, i t is not possible to provide all details necessary

to meet the needs of all members of the intended au dience. 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 acc essed 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 effectiven ess of quality systems.

EPA's quality system comprises three structural le vels: policy, organization/program, and project. This document addresses quality at the pr oject 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 qual ity web site.

A project's life cycle under EPA's quality system has three phases: planning, implementation, and assessment. This chapter is organized into the se three phases. Additionally, Figure 1 is provided, and illustrates this process.

Additionally, this chapter contains general projec t QC guidance to be used with the subsequent chapters and methods in this manual. It should be noted that several methods (e.g.,SW-846 Update V ONE - 2 Revision 2 July 2014 Method 8000) also contain general QC criteria and g uidance 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-specif ic QC criteria take precedence over general
method QC criteria.

1.1 PLANNING

Planning, the first phase of a project’s life cycl e, involves the development of project objectives and acceptance or performance criteria u sing a systematic process. Data quality objectives (DQOs) and a sampling and analysis desig n are established to generate data of an appropriate type, quality and quantity to meet proj ect objectives. The final output of this phase is a planning document, such as a QAPP, and/or a sampl ing and analysis plan (SAP) or a waste analysis plan (WAP).

This section provides guidance on activities and c oncepts that EPA recommends to be used or considered during the planning phase, when appro priate 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 o f 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 guidanc e 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, re sources (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 a verage concentration in a decision unit), quantity and quality (e.g., screening for th e 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/ab sence decision?

• Specification of acceptance or performance criteri a 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.SW-846 Update V ONE - 3 Revision 2 July 2014 • Specification of QA and QC activities needed to as sess quality performance criteria (e.g., QC samples for the field and laboratory, aud its, technical assessments, performance evaluations, etc.).

• Description of how acquired data will be analyzed (i.e., field and/or laboratory), evaluated, and assessed against performance criteri a. If statistical assumptions are made as part of the planning process, the assessmen t 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 valuab le information to help design a plan for collecting new data, while also lowering the cost o f future data collection efforts. However, the limitations on using any secondary data must be cle arly 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 ex pertise is necessary to evaluate both data sets before they can be combined. If combining data set s, 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 wil l support decision making with a defined level of

confidence.

The DQO process can be applied to any study, regar dless 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 deta il of DQO development will depend on the study objectives. The DQO process is particularly applic able 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 q uestion 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 pro blem that has created the need for the study. In describing the problem, especially f or 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, c ontaminant 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 ide ntify the key questions that need to be answered in order to resolve the problem(s) i dentified in Step 1. Step 2 should also identify any actions that may be taken based on stu dy results. The goals of the study and

the alternative actions are then combined to form d ecision 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.SW-846 Update V ONE - 4 Revision 2 July 2014 1.1.2.3 Step 3: Identify the Information Inputs

The purpose of Step 3 of the DQO process is to ide ntify the information needed to resolve the decision statement. This may include, but is not limited to:

• Primary data, including sampling and analysis meth ods

• Secondary data, including the necessary informatio n to ensure the data is of known and documented quality (e.g., sampling and an alysis methods used as well as sufficient information to evaluate the qual ity of the data)

• Action limits to be considered

• Background information about the site or process, including known or anticipated variability of the study parameters tha t may help the planning team identify critical sampling locations

1.1.2.4 Step 4: Define the Boundaries of the Stu dy

The purpose of Step 4 of the DQO process is to def ine the spatial and temporal boundaries for the data collection design, includin g where samples will be collected. Spatial boundaries describe the physical area (i.e., horizo ntal and vertical boundaries) of the study. They can include geographic area or volume of mater ial. 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 con sider the outputs from Steps 1-4 and develop “IfJ , thenJelse” decision rules that unambiguously stat e which of the alternative actions identified in Step 2 will be pu rsued. 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 Acceptanc e 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 s amples, Step 6 should examine consequences of making incorrect decisions, and pla ce 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 stat istical tests that will be used to assess the

data.

1.1.2.7 Step 7: Develop the Plan for Obtaining Da ta

The purpose of Step 7 of the DQO process is to dev elop the data collection plan that will satisfy the objectives presented in Steps 1 th rough 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 Da ta 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-effe ctive data collection design that meets the previously identified performance or acce ptance criteria. The plan for obtaining data is documented in detail by developing a projec t QAPP as per EPA Requirements forSW-846 Update V ONE - 5 Revision 2 July 2014 Quality Assurance Project Plans (QA/R-5), which presents the requirements for QAPPs, and its companion document, EPA Guidance for Quality Assurance Project Plans (Q A/G-5)

.

1.1.3 Development of QAPPs, Waste Analysis Plans (W APs) and Sampling and Analysis Plans (SAPs)

Documentation of planning processes and outcomes a re critical to the:

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

• reconstruction of completed events, and

• reconciliation of the analytical data with the pro ject plans.

Two types of planning documents are discussed in t his section. Section 1.1.3.1 discusses QAPPs, which are a key output of the systematic pla nning process. Section 1.1.3.2 discusses

WAPs and SAPs.

1.1.3.1 QAPPs

All environmental data collection efforts performe d 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 wi th the RCRA regulations). EPA has issued several documents to aid in preparing QAPPs, includ ing the documents noted in Section 1.1.2.7. These documents provide suggestions for b oth 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 wil l satisfy the stated performance criteria.

QAPPs, and any accompanying WAPs or SAPs, should b e accessible to all participants throughout the life of the project. T hey should provide understandable instructions to those who must implement the QAPP, such as the field sampling team, the analytical laboratory, modelers, and the data revie wers.

When preparing a QAPP, a graded approach should b e 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 confi dence needed in the quality of the results. The QAPP should be detailed enough to provide a cle ar description of every aspect of the project, from site history through assessment of th e planned data collection. At a minimum, the QAPP should provide sufficient detail to demons trate that:

• the project technical and quality objectives are i dentified and agreed upon;

• the intended measurements and data generation or d ata acquisition methods are appropriate for achieving project objectives;

• assessment procedures are sufficient for confirmin g that data of the type and quality needed and expected are obtained; and

• any limitations on the use of the data can be iden tified and documented.SW-846 Update V ONE - 6 Revision 2 July 2014 As described in QA/R-5 and QA/G-5, the QAPP should be composed of standardized, recognizable elements covering the entire project f rom planning through assessment. These elements may be divided into the following fo ur general groups:

• Project Management - The elements in this group address project organ ization and management; site background and history; and pr oject objectives. These elements ensure that project goals are clearly defi ned, that the participants understand the goals and the approach to be used, a nd that the planning process is documented.

• Data Generation and Acquisition - The elements in this group address all aspects of project design and implementation, inclu ding the numbers, types, and locations of all samples to be collected; the r ationale for why the proposed

data collection effort will be sufficient to addres s the study objectives; all sampling, subsampling and analytical procedures to be followed (i.e., both sample preparation as well as determinative procedu res); QC requirements for all applicable field and laboratory procedures incl uding 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 eleme nts ensures that appropriate methods for sampling, analysis, data ha ndling, and QC activities are employed and properly documented.

• Assessment and Oversight - The elements in this group address the activitie s for assessing the effectiveness of project implemen tation 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 gene ration is completed. These elements address how data will be reviewed, verifie d and validated as well as how data will be assessed and reconciled with the p roject objectives.

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

when a generic QAPP may be more appropriate. A generic QAPP addre sses the general, common activities of a program that are to be condu cted 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 locatio ns. A generic QAPP describes, in a single document, the information that is not site o r 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 R CRA 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 re viewed to ensure that the content and format requirements for the WAP or SAP are understo od. Additionally, it should be noted that EPA has prepared various guidance documents to assist in preparing WAPs and SAPs that meet various regulatory requirements. Example s of these guidance documents include the following:SW-846 Update V ONE - 7 Revision 2 July 2014 • EPA guidance on the preparation of WAPs can be fou nd 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 b e 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 sup plement to any WAP or SAP. It

should also be noted that if a WAP or SAP is not re quired by regulation, QAPPs can be prepared such that they present sufficient detail t o cover both the QAPP and WAP or SAP in a single document. If a WAP or SAP is prepared alo ng with a QAPP, it is common for these documents to reference one another for necessary in formation. To enhance the usability of QAPPs and WAPs/SAPs, references between the documen ts 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 perfor mance 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 includ e precision, accuracy, representativeness, comparability, and completeness (PARCC). The follo wing presents a discussion of PARCC and
other DQIs.

1.1.4.1 Precision

Precision measures the agreement among a set of re plicate measurements. Field precision is assessed through the collection and an alysis of field duplicates. Analytical precision is estimated by duplicate/replicate analy ses, usually on laboratory control samples, spiked samples and/or field samples. The most comm only used estimates of precision are the relative standard deviation (RSD) and, when onl y 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 co ntrol 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 populati on, parameter variations at a sampling point, a process condition, or an environmental con dition. It is dependent on the proper design of the sampling program and will be satisfie d by ensuring the approved plans were followed during sampling and analysis.SW-846 Update V ONE - 8 Revision 2 July 2014 1.1.4.4 Comparability

Comparability expresses the degree of confidence w ith which one data set can be compared to another. It is dependent upon the prop er design of the sampling program and will be satisfied by ensuring that the approved pla ns 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 d ata collected compared to the amount planned. Measurements are considered to be valid if they are unqualified or qualified as estimated data during validation. Fie ld completeness is a measure of the number of samples collected versus the number of sa mples planned. Laboratory completeness is a measure of the number of valid me asurements 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 measuremen t is consistently lower than the sample’s true value). Bias can be introduced during samplin g, 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 u se of sample handling procedures that limit the loss or gain of constituents to the sampl e media. Analytical bias refers to deviation in one direction (i.e., high, low or unknown) of th e measured value from a known spiked amount. Analytical bias can be assessed by compari ng 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 (m atrix spike). The planning team should specify qualitative criteria for sampling bi as and quantitative criteria for analytical bias, typically expressed as "percent recovery."

1.1.4.7 Reproducibility

Analytical reproducibility is a quantitative indic ator that is used when referring to the uncertainty associated with the use of multiple lab oratories for a specific study. The ability of multiple laboratories to generate the same resul t 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 th e 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 quant itation [LLOQ]).SW-846 Update V ONE - 9 Revision 2 July 2014 Please note the glossary section to this chapter i ncludes further discussion of the
DQIs.

1.2 IMPLEMENTATION

Implementation is the second phase of the project l ife cycle. The implementation phase includes the following steps:

1.2.1 Standard Operating Procedures (SOPs)

SOPs are written documents that describe, in detai l, 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 meth od 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. Inf ormation 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 ap proved SOP ensures conformance with organizational practices, reduction in the frequenc y of errors, and improved data comparability and defensibility. SOPs also serve as resources for tr aining 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. I f project-specific modifications to SOPs are needed, they must also be presented in the QAPP. F ield and laboratory SOPs should be provided for the areas discussed below, as applicab le:

• Sample Custody SOPs that describe sample receipt a nd handling; sample storage; sample security and tracking; and holding times

• Sample collection

• Analytical Method SOPs, including subsampling, sam ple 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 met hod may be provided in appropriate SOPs. The types of QC parameters to include in the SOPs a nd QAPP are defined in the glossary of this chapter. Since the QC acceptance limits are freque ntly adjusted more often than SOPs are revised, it may be impractical to update SOPs as th ese limits change. Therefore, field and laboratory QC limits may need to be presented in QA PP tables rather than SOPs.SW-846 Update V ONE - 10 Revision 2 July 2014 1.2.2 Conducting the Study/Experiment

The data collection effort is performed in accorda nce with approved plans (e.g., QAPP). The QAPP will present all proposed methods (i.e., i ncluding sampling, subsampling, preservation, preparation, clean-up and determinative methods), a s 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 p roposed 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 Toxic ity Characteristic Leaching Procedure (TCLP), have been written into regulation s, 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 re ferences 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 Measurem ents

The Flexible Approach to Environmental Measurement was finalized in 2008 as EPA’s preferred approach for environmental monitori ng except in cases where methods are required by regulation.

The goals of the flexible approach are to:

• Provide method users flexibility when choosing sam pling and analytical approaches to meet regulatory requirements for meas urements

• Develop processes for validation to confirm measur ements meeting quality requirements

• Increase collaboration with stakeholders to develo p validation processes for new measurement technology

• Ensure timely assessment of new or modified techno logies, methods, and procedures.

Chapter Two of this manual entitled, "Choosing the Correct Procedure” provides additional guidance regarding the selection of appropriate met hods 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 w hether environmental data generation activities and related results comply with planning documents. Technical assessments can be performed to evaluate all phases of a project inclu ding 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 th e 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 pr oject, the QAPP should provide the applicableSW-846 Update V ONE - 11 Revision 2 July 2014 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., some one not involved in the day to day operations of a project), such as the organization’s QA person nel. Further, the QAPP should establish a process for correcting technical assessment finding s and confirming implementation of any corrective actions taken as a result of the finding s.

While most technical assessments will be scheduled in advance with an organization, there may be occasions when unannounced technical assessm ents are needed. However, an unannounced audit may not reveal a representative p icture of project activities if it occurs when the project is not active, or activities that are not w ithin the scope of the technical assessment are occurring.

Technical assessments may be planned as part of th e 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 la boratory to perform the proposed analytical work. Additionally, technical assessments may also be use d as an investigative tool where problems may be suspected. The EPA document Guidance on Tec hnical Audits and Related Assessments for Environmental Data Operations (QA/G-7), dated J anuary 2000, provides detailed information on various types of technical audits and includes a n 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 cyc le. The purpose of this phase is to evaluate whether the data are of the type, quantity and qual ity needed to meet project DQOs. Assessment can involve many different complex activities, incl uding 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 s tep in the assessment process. Validation and

verification are defined in the EPA Guidance on Env ironmental Data Verification and Data Validation (QA/G-8), dated November 2002. Accordin g to QA/G-8, verification is the process of evaluating the completeness, correctness, and confo rmance/compliance of a data set against a specified set of requirements. QA/G-8 defines vali dation 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 shou ld be noted that the meaning of verification and validation is often program, organization and/or sy stem specific. Further, these terms are used interchangeably at times, and in other cases have v ery different meanings. Therefore, it is critical that the procedures for verification and/or validat ion 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 a ny other planning or contractual documents)

were followed. Data verification/validation also i nvolves examining whether the data met the method performance and acceptance criteria establis hed 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 perfor mance criteria alsoSW-846 Update V ONE - 12 Revision 2 July 2014 need to be documented during this phase of assessme nt (e.g., in a data verification/validation report).

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

information on what should be included in data veri fication and validation procedures. These include EPA regional and programmatic validation gu idance documents that may apply to particular projects. It is also common for EPA gui dance from one program or region to be modified for use in another area. While this may b e acceptable, any modifications or changes to the established validation procedures should be inc luded in the approved QAPP.

Data verification/validation may be performed at v arious phases throughout a project. As such, the procedures for verifying and/or validatin g data may need to be described in laboratory SOPs and/or QA Plans, in addition to the project do cuments 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 per formed internally (e.g., when the laboratory or data collector review their own data), or extern ally by an independent entity (i.e., a party that i s not associated with the data collection effort). I nternal and external data verification/validation procedures should be defined in the QAPP or other p roject documents.

A data verification/validation report should be ge nerated 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 collect ed, field information about how the samples were collected, the analyses performed on the samples, t he 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 exceedanc e, 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 i n the assessment process. DQA is

often needed because data verification/validation a lone is generally not sufficient to determine whether a data set can be used for its intended pur pose. Typically, the DQA follows the data verification and/or validation step.

In general, the DQA should include an evaluation o f 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 ass umptions made during the planning phase. If the statistical assumptions made during the planning ph ase are not supported by the data, recommendations for corrective action should be pre sented. 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 fi ve-step process.

Step 1: Review the Data Quality Objectives and Sa mpling Design Step 2: Conduct a Preliminary Data Review Step 3: Select the Statistical Method Step 4: Verify the Assumptions of the Statistical MethodSW-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 thes e guidance documents, because they provide extensive information about DQA and the sta tistical 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 note d that the DQA process is not only applicable to random data sets, where statistics can be used to a ssess 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 p hase, 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

AssessmentSW-846 Update V ONE - 15 Revision 2 July 2014 1.4. REFERENCES AND SOURCES FOR ADDITIONAL QA/QC IN FORMATION

1. USEPA. Guidance on Systematic Planning Using the Data Qual ity Objectives Process, EPA
QA/G-4 . Quality Staff, Office of Environmental Informati on, United States Environmental Protection Agency, Washington, D.C. February 2006.

2. USEPA. EPA Quality Manual for Environmental Programs CIO 2 105-P-01-0 . Office of Environmental Information Quality Staff, United Sta tes Environmental Protection Agency, Washington, D.C. May 5, 2000.

3. USEPA. Guidance on Choosing a Sampling Design for Environm ental Data Collection, EPA QA/G-5S. Quality Staff, Office of Environmental In formation, United States Environmental Protection Agency, Washington, D.C. December 2002.

4. USEPA. Guidance on Environmental Data Verification and Dat a 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 Environ mental Protection Agency, Washington, D.C. February 2006.

6. USEPA. Data Quality Assessment: Statistical Tools for Pra ctitioners , 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 Procedure s

, 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 Environ mental Protection Agency, Washington,

D.C. December 2002.

10. USEPA. EPA Requirements for Quality Assurance Project Plan s , 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 Peti tioner . 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 Enforce ment, United States Environmental Protection Agency. April 1994

.

13. USEPA. Guidance on Technical Audits and Related Assessment s for Environmental Data Operations , QA/G-7. Quality Staff, Office of Environmental I nformation, 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-re lated terms developed by EPA:

http://www.epa.gov/fem/pdfs/Env_Measurement_Glossar y_Final_Jan_2010.pdf

ACCURACY The degree of agreement between an observed value and an accep ted 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 qual ity control samples, which are processed together using the same method, the same lots of reagents, and at the same time or in continuous, sequential t ime periods. Samples in each batch should be of similar composit ion and share common internal quality control standards. For QC purposes, if the number of samples in a batch is limited to 20; labo ratory 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 b e analyzed together on a single instrument. However, laborato ries 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 mea surement process, different from random error, which manifests itself as a persistent positive or negative deviation from the known or tr ue value. This can result from improper data collection, poorly calibr ated analytical or sampling equipment, or limitations or errors in ana lytical 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 spike d into a sample (matrix spike). Thus, the bias (B) due to matrix e ffects based on a matrix spike is calculated as:

B = (x

s - x u ) - K

where:

x s = measured value for spiked sample,

x u = measured value for unspiked sample, and K = known value of the spike in the sample.

Using the following equation yields the percent rec overy (%R).

%R = 100 (x

s - x u )/ KSW-846 Update V ONE - 17 Revision 2 July 2014 BLANK Blanks are generally considered to be accepta ble 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 th e associated samples in the batch are unaffected (i.e. targets a re 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 inorgan ics 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 o f analyte-free media that can be used along with prepared standards to calibrate the inst rument. A calibration blank may also be used to verify absenc e of instrument contamination (e.g., initial calibration blank and continuing calibration blank).

CALIBRATION CHECKS Calibration check analyses are used to assess calib ration 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 metho d.

CALIBRATION CURVE A plot of instrument response to an analyte versus known concentrations or amounts of analyte standards. Ca libration standards are prepared by successively diluting a s tandard solution to produce working standards which cover the working r ange of the

inst rument. Standards should be prepared at the freque ncy specified in the appropriate method. The calibration standar ds should be prepared using the same type of acid or solvent and at the same concentration as the samples following sample prepa ration. This is applicable to organic and inorganic chemical analys es.

CO-LOCATED

SAMPLES A type of field duplicate where independent samples are collected as close as possible to the same point in space and ti me. 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 documen ting the precision of the sampling process.

COMPARABILITY The degree to which different methods or data agree or can be represented as similar. Comparability describes th e confidence that two data sets can contribute to a common analysis a nd interpolation.

COMPLETENESS A measure of the amount of valid data obtained from a measurement system compared with the amount that was expected t o be obtained under correct, normal conditions. Percent complete ness 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 descrip tors 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, an d sensitivity.

DATA QUALITY OBJECTIVES (DQOs) Qualitative and quantitative statements derived fro m the DQO Planning Process that clarify the purpose of the study, defi ne the most appropriate type of information to collect, determi ne the most appropriate conditions from which to collect that i nformation, and specify tolerable levels of potential decision erro rs.

DATA VALIDATION The process of evaluating the available data against th e project DQOs to make sure that the objectives are met. Data val idation may be very rigorous, or cursory, depending on project DQOs. T he 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 Duplic ate, Field Duplicate, Field Split Samples, and Matrix Spike Duplicate.

EQUIPMENT BLANK A sample of analyte-free media whic h has been used to rinse the sampling equipment. It is collected after completi on of decontamination and prior to sampling at a location . This blank is useful in documenting adequate decontamination of s ampling equipment.

EQUIPMENT RINSATE See Equipment Blank.

FIELD BLANK Field blanks include any sample submitt ed 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 o rganization to

measure ambient contamination in the field. If thi s 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 doc umenting the precision of the sampling process. Field duplicates are used to ass ess improper homogenization of the samples in the field; reprodu cibility 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 variabili ty can be evaluated, (i.e., often between laboratories or methods). Hom ogenization may have an impact on sample integrity for some sample types (e.g., VO CsSW-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 demonst ration of method proficiency is accomplished through the successful calibration of method-specific instruments according to project re quirements and criteria set forth in the applicable analytical met hodology. This initial demonstration should be performed prior to independ ently 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 guidan ce on IDP.

INTERNAL STANDARD Internal standards may be spiked into prepared field samples and QC samples (or sample extracts). Their recovery is gen erally used to

demonstrate the lack of severe matrix effect in the instrumental analysis by setting criteria for the internal stand ard response in comparison to a response in a sample with a known l ack 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 t hat 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 labo ratory 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., Ott awa sand, anhydrous sodium sulfate, or other purified solid) to which k nown amounts of the method analytes are added. LCS analyses help deter mine if the system is running properly (i.e., within pre-define d 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 th e LCS analysis demonstrates whether the methodology is in control and the laboratory is capable of making unbiased measurements. The re sults of the LCS

ar e evaluated in conjunction with other QC informatio n 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 da ta through the use of

control charts.

LABORATORY

DUPLICATE The analysis or measurements of the variable of int erest performed identically on two sub- samples of the same sample, usually taken from the same container. The results from duplicate anal yses are used to evaluate analytical or measurement precision and in clude variability associated with sub-sampling and the matrix, but no t the precision of field sampling, preservation, or storage internal t o the laboratory.

Laboratory duplicate analysis entails removing two aliquots from theSW-846 Update V ONE - 20 Revision 2 July 2014 same sample and taking them through the same prepar ative and analytical procedures to evaluate analytical precis ion. Laboratory duplicates are more commonly used to assess precisi on for inorganic and radiological constituents, while precision for organic analyses is usually assessed by determining the RPD between mat rix spike and matrix spike duplicates. The frequency of laborato ry duplicate analysis will depend on project requirements.

LOWER LIMIT OF QUANTITATION (LLOQ) The lowest point of quantitation which, in most cas es, is the lowest

concentration in the calibration curve. The LLOQ is initially verified by spiking a clean control material (e.g., reagent wat er, 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 estab lished when sufficient data points exist. Individual methods m ay recommend procedures for verifying the LLOQ and acceptance li mits for use until the laboratory has sufficient data to determine acc eptance limits. LLOQs should be determined at a frequency establish ed by the method, laboratory’s quality system, or project.

Please see individual methods f or 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 w ater, 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 proc edures 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 unspik ed sample. However, if this approach is not practical, the sam ples 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 per formance of the method by measuring the effects of interferences ca used by the sample matrix and reflects the bias of the method f or the particular matrix in question.

MATRIX SPIKE

DUPLICATES Matrix spike duplicates are additional replicates o f matrix spike samples that are subjected to the sample preparatio n and analytical scheme as the original sample. A matrix spike dupl icate sample would normally be included with each preparation ba tch of samples processed. Analysis of spiked duplicate samples en sures a positive value, allowing for estimation of analytical precis ion.

Matrix spike duplicates are used to document the pr ecision as well as bias of a method in a given sample matrix. When critical decisions are based on the matrix spike and matrix spike duplicat e recoveries, the laboratory should maintain control charts for these samples to monitorSW-846 Update V ONE - 21 Revision 2 July 2014 precision and bias for each particular matrix.

METHOD BLANK Method blanks are analyzed to assess b ackground 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 preparatio n and carried through the complete sample preparation, cleanup, a nd determinative procedures. For aqueous analyses, analyte- free reagent water would typically be used. For soil analyses, a purified s olid 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 f or 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 analysi s of every ana lytical batch of 20 samples or less or as stated in the QAP P or method, whichever is more frequent.

ORGANIC-FREE

REAGENT WATER All references to water in the methods refer to wat er in which an
interferant is not observed at the LLOQ for the com pounds of interest. Preparation of organic-free reagent water may depe nd on the types of interferants of concern for the analysis and may include boiling, sparging with an inert gas, reverse osmosis purific ation, distillation, particle filtration, activated carbon filtration, i on 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 use d to further assess if matrix effects may be a source of measurement bi as in sample quantitation.

PRECISION The agreement among a set of replicate me asurements without assumption of knowledge of the true value. Precision is estimate d by means of duplicate/replicate analyses of separate aliquots of the same sample (not multiple runs of a single digestion/ext raction). These samples should contain concentrations of analyte at or above the
LLOQ , and may involve the use of matrix spikes. The mo st commonly used estimates of precision are:

Relative standard deviation (RSD), also known as th e 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 measuremen ts; and,SW-846 Update V ONE - 22 Revision 2 July 2014 Relative percent difference (RPD) when only two res ults are compared:

 = |   −  |

  + 

2 ∗ 	100

where: x 1 and x 2 are measurements of independently prepared aliquots of the same sample or replicate samples.

PROJECT Single or multiple data collection activiti es 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/o r analysis of a batch of samples. Examples of laboratory quality control samples are method blanks, laboratory duplicates, and laborator y control samples ; field quality control samples are field blanks, tri p blanks, field duplicates, and matrix spikes.

QUALITY ASSURANCE

PROJECT PLAN (QAPP) A formal document describing in comprehensive detai l the necessary quality assurance, quality control, and other techn ical activities that should be implemented to ensure that the results of the work performed will satisfy the stated performance crite ria.

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 propo rtions 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 chem ical 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, a nd reagent grade are synonymous terms for reagents which conform to the current specifications of the Committee on Analytical Reage nts 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 wat er. For organic analyses, see the definition of organic-free reagen t water.

REFERENCE

MATERIAL A material containing known quantities of target an alytes in solution or in a homogeneous matrix. It is used to document th e bias of the analytical process.

REPEATABILITY The degree of agreement between mutually independen t test results produced by the same analyst using the same test me thod and equipment on random aliquots of the same sample wit hin a shortSW-846 Update V ONE - 23 Revision 2 July 2014REPRESENTATIVENESS period of time.

A measure of the degree to which data accurately re present a characteristic of a population, a parameter variati on at a sampling point, a process condition, or an environmental con dition.

REPRODUCIBILITY Reproducibility is defined as the c loseness of the agreement between the results of measurements of the same analyte car ried 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 t o assess either an operative matrix effect or the sample analyte conce ntration.

SURROGATE Surrogates are most commonly used to moni tor the performance of organic analyses using methods such as high perform ance liquid chromatography (HPLC), gas chromatography (GC), and gas chromatography/mass spectrometry (GC/MS). Surrogat e spikes are added to field samples and QC samples fo r organic analyses at known amounts, and their recoveries are used to assess matrix effects and, to some extent, verify proper processing and instrumen t 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 wh ich are similar to the target analytes in chemical composition and behavior in the analytical process, but are not normally found in t he environmental samples. Surrogates added to LCS samples and blank s are used to assess recovery in a matrix known to be free from i nterference. This information can be used to determine the magnitude of matrix interference effects on environmental sample result s.

TRIP BLANK A sample of analyte-free media taken fro m the laboratory to the sampling site and returned to the laboratory unopen ed. Trip blanks should be prepared at a frequency of one per day of sampling during which samples are collected for volatile organic co nstit uents (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 collect ion and transport operations. This blank will not be opened during t he sampling activities and will be used to assess sample VOC co ntamination originating from sample transport, shipping, or sit e conditions. A trip

blank is used to document contamination attributabl e 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 reorga nized to reflect changes in the EPA data quality system approach. 2. The revision number was changed to two and the d ate 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 Q A/G-8, November 2002, Figure 1.