**SW-846 Update V ONE - i Revision 2**

**October 2012** 

### 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 24** **SW-846 Update V ONE - 1 Revision 2**

**October 2012 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 understan ding of environmental data and the need for**  **quality. EPA has developed numerous guidance docum ents 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 Conserva tion and Recovery Act (RCRA) of 1976,**  **as amended; require the collection and use of envir onmental 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 rec ommended 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 entity, 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 d ata for decision-making.** 

**Due to the diversity of data collection efforts, it is not possible to provide all details necessary t**  **o meet the needs of all members of the intended audie**  **nce. 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 effect iveness of quality systems.** 

**EPA's quality system comprises three structural lev els: policy, organization/program, and project.**  **This document addresses quality at the project leve l of the system, including technical aspects of**  **analytical method quality assurance (QA) and qualit y 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 h as three phases: planning, implementation, and**  **assessment. This chapter is organized into these t hree phases. Additionally, Figure 1 is**  **provided, and illustrates this process. SW-846 Update V ONE - 2 Revision 2** 

**October 2012 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., 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-specif**  **ic 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 syst ematic process. Data quality objectives**  **(DQOs) and a sampling and analysis design are estab lished to generate data of an appropriate**  **type, quality and quantity to meet project objectiv es. The final output of this phase is a planning**  **document, such as a QAPP, and/or a sampling and ana lysis plan (SAP) or a waste analysis plan**  **(WAP).**

**This section provides guidance on activities and co ncepts that EPA recommends to be used or**  **considered during the planning phase, when appropri ate 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 s ystematic planning is to ensure**  **collection of the appropriate type, quantity, and q uality of data to support decisions with**  **acceptable confidence. Following is a summary of E PA’s Agency-wide guidance on**  **systematic planning. More detail can be found in t he**  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 average concentration in a**  **decision unit), quantity and quality (e.g., screeni ng for the presence/absence of an**  **analyte, definitive data supported by all method sp ecific 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. SW-846 Update V ONE - 3 Revision 2** 

**October 2012** • **Description of how, when, and where the data will be obtained, and identification of**  **any constraints on data collection.**

• **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 pro vide valuable information to help**  **design a plan for collecting new data, while also l owering the cost of future data collection**  **efforts. However, the limitations on using any sec ondary data must be clearly**  **understood and documented. For example, secondary data must be examined to ensure**  **that their quality is acceptable for a given applic ation. Combining secondary data with**  **current data can be a complex operation and should be undertaken with care.**  **Sometimes, statistical expertise is necessary to ev aluate both data sets before they can**  **be combined. If combining data sets, make sure his torical 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, regard less 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 requir ements for each decision to be made**  **or question to be answered. It consists of seven p lanning steps that are summarized** 

**below.**

**1.1.2.1 Step 1: State the Problem**

**The purpose of Step 1 is to clearly define the prob lem 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). T he 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, r elease mechanisms, contaminant**  **fate and transport, exposure pathways, potential re ceptors, 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 iden tify the key questions that need**  **to be answered in order to resolve the problem(s) i dentified in Step 1. Step 2** **SW-846 Update V ONE - 4 Revision 2**

**October 2012 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 deci**  **sion statement defines which of the identified alternative actions will be pursued**  **depending on the outcomes of the study.** 

**1.1.2.3 Step 3: Identify the Information Inputs**

**The purpose of Step 3 of the DQO process is to iden tify 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 Stud y** 

**The purpose of Step 4 of the DQO process is to defi ne 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. , horizontal and vertical**  **boundaries) of the study. They can include geograp hic 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 w ill apply.** 

**1.1.2.5 Step 5: Develop the Analytic Approach**

**The purpose of Step 5 of the DQO process is to cons ider the outputs from Steps 1-4**  **and develop “If... ,then...else” decision rules that unambiguously state**  **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 res**  **ults 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 s amples, Step 6 should**  **examine consequences of making incorrect decisions, and place acceptable limits**  **on the likelihood of making decision errors. For r andom samples, Step 6 should**  **specify any statistical hypothesis to be considered and all applicable statistical tests** 

**that will be used to assess the data. SW-846 Update V ONE - 5 Revision 2** 

**October 2012 1.1.2.7 Step 7: Develop the Plan for Obtaining Da**  **ta**

**The purpose of Step 7 of the DQO process is to deve lop 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 devel oped a guidance document** 

**called** Guidance for Choosing a Sampling Design for Environ mental 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-effec tive data collection design that**  **meets the previously identified performance or acce ptance criteria. The plan for**  **obtaining data is documented in detail by developin g a project QAPP as per**  EPA Requirements for Quality Assurance Project Plans (Q A/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 ar e 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 th is section. Section 1.1.3.1**  **discusses QAPPs, which are a key output of the syst ematic 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 comp liance with the RCRA regulations).**  **EPA has issued several documents to aid in preparin g QAPPs, including the** 

**documents noted in Section 1.1.2.7. These document s provide suggestions for**  **both EPA and non-EPA organizations on preparing, re viewing, and implementing** 
**QAPPs.**

**The primary purpose of the QAPP is to present the d ata 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 d ata 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. 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 reviewers.** 

**When preparing a QAPP, a graded approach should be used to determine the level** **SW-846 Update V ONE - 6 Revision 2**

**October 2012 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 d etailed enough to provide a clear**  **description of every aspect of the project, from si te history through assessment of**  **the planned data collection. At a minimum, the QAP P should provide sufficient** 

**detail to demonstrate 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.** 

**As described in QA/R-5 and QA/G-5, the QAPP should be composed of**  **standardized, recognizable elements covering the en tire 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 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, including the numbers, types, and locations**  **of all samples to be collected; the rationale for w hy the proposed data collection**  **effort will be sufficient to address the study obje ctives; all sampling, subsampling**  **and analytical procedures to be followed (i.e., bot h sample preparation as well as**  **determinative procedures); QC requirements for all applicable field and laboratory**  **procedures including the data quality indicators (D QIs) discussed below in**  **Section 1.1.4; instrument calibration and maintenan ce for both field and**  **laboratory equipment; use of secondary data; and da ta management.**  **Implementation of these elements ensures that appro priate methods for sampling,**  **analysis, data handling, and QC activities are empl oyed and properly** 

**documented.**

• **Assessment and Oversight - The elements in this group address the activitie**  **s for assessing the effectiveness of project implementati**  **on 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 proje ct 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 SW-846 Update V ONE - 7 Revision 2** 

**October 2012 addresses the general, common activities of a progr**  **am 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 d**  **ocument, the information that is not site or time-specific but applies throughout th**  **e program. Application-specific information is then added to the approved QAPP, eit**  **her 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 RC RA 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 prepa red various guidance documents**  **to assist in preparing WAPs and SAPs that meet vari ous regulatory requirements.**  **Examples of these guidance documents include the fo llowing:** 

• **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 required by regulation, QAPPs**  **can be prepared such that they present sufficient d etail to cover both the QAPP and**  **WAP or SAP in a single document. If a WAP or SAP i s 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 perform ance criteria for DQIs must be** 

**established and documented for each data collection effort** 

. **DQIs apply to both laboratory and field activities. At a minimum, DQI**  **s 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 rep licate measurements.**  **Field precision is assessed through the collection and analysis of field duplicates.**  **Analytical precision is estimated by duplicate/repl icate analyses, usually on**  **laboratory control samples, spiked samples and/or f ield samples. The most** **SW-846 Update V ONE - 8 Revision 2**

**October 2012 commonly used estimates of precision are the relati**  **ve standard deviation (RSD) and, when only two samples are available, the relat**  **ive percent difference (RPD).**

**1.1.4.2 Accuracy**

**Accuracy is the closeness of a measured result to a n accepted reference value.**  **Accuracy is usually measured as a percent recovery. QC analyses used to**  **measure accuracy include standard recoveries, labor atory control samples, spiked**  **samples, and surrogates.**

**1.1.4.3 Representativeness**

**Sample representativeness expresses the degree to w hich data accurately and**  **precisely represents a characteristic of a populati on, parameter variations at a**  **sampling point, a process condition, or an environm ental condition. It is**  **dependent on the proper design of the sampling prog ram and will be satisfied by**  **ensuring the approved plans were followed during sa mpling and analysis.** 

**1.1.4.4 Comparability**

**Comparability expresses the degree of confidence wi th 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 plans are followed and**  **that proper sampling and analysis techniques are ap plied.** 

**1.1.4.5 Completeness**

**Completeness is a measure of the amount of valid da ta 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 numbe r of samples planned.**  **Laboratory completeness is a measure of the number of valid measurements**  **compared to the total number of measurements planne d.** 

**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 mea surement is consistently lower**  **than the sample’s true value). Bias can be introdu ced during sampling, analysis,**  **and data evaluation. Sampling bias is best address ed through the proper**  **selection and use of sampling tools, uses of correc t sampling and subsampling**  **procedures to limit preferential selection or loss of sample media, use of random**  **sampling designs, and use of sample handling proced ures that limit the loss or**  **gain of constituents to the sample media. Analytic al bias refers to deviation in**  **one direction (i.e., high, low or unknown) of the m easured 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 r eference value or by**  **determining the recovery of a known amount of conta minant spiked into a sample**  **(matrix spike). The planning team should specify q ualitative criteria for sampling**  **bias and quantitative criteria for analytical bias, typically expressed as "percent**  **recovery." SW-846 Update V ONE - 9 Revision 2** 

**October 2012 1.1.4.7 Reproducibility** 

**Analytical reproducibility is a quantitative indica tor 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 th e same result for splits of the**  **same sample can be expressed as a measure of interl aboratory precision and**  **bias. Specific indicators of precision and bias (s uch as range or variance) are**  **generated using data from replicate samples sent to multiple laboratories.** 

**1.1.4.8 Sensitivity**

**Sensitivity is an instrument’s or method’s minimum concentration that can be**  **reliably measured (i.e., instrument detection limit [IDL] or limit of quantitation**  **[LOQ]).**

**Please note the glossary section to this chapter in cludes 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 detail , the routine procedures to be followed**  **for a specific operation, analysis, or action. Ple ase note, in most cases, referencing a**  **given method or method number is not sufficient, un less the method is performed exactly**  **as written (e.g., a method-defined parameter). Lab oratories must have an SOP to**  **demonstrate that their procedure meets the conditio ns 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 ap proved SOP ensures conformance**  **with organizational practices, reduction in the fre quency of errors, and improved data**  **comparability and defensibility. SOPs also serve a s 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. I f 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, a s applicable:** **SW-846 Update V ONE - 10 Revision 2**

**October 2012** • **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 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.**

**1.2.2 Conducting the Study/Experiment**

**The data collection effort is performed in accordan ce with approved plans (e.g., QAPP).**  **The QAPP will present all proposed methods (i.e., i ncluding sampling, subsampling,**  **preservation, preparation, clean-up and determinati ve 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 t hat the proposed methods are**  **adequate to reach the study goals outlined in the D QOs.** 

**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 mo dified 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 paramete rs are discussed in**  **Chapter 8 of this manual.**

**1.2.2.2 Flexible Approach to Environmental Measure ments** 

**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** **SW-846 Update V ONE - 11 Revision 2**

**October 2012** • **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) a re 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 projec t 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 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 project, the**  **QAPP should provide the applicable technical assess ment checklists. These checklists**  **establish and document the scope of all proposed te chnical assessments. The**  **development of these technical assessment checklist s should overseen by an**  **independent party (i.e., someone not involved in th e day to day operations of a project),**  **such as the organization’s QA personnel. Further, t he QAPP should establish a process**  **for correcting technical assessment findings and co nfirming 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 a ssessments are needed.**  **However, an unannounced audit may not reveal a repr esentative picture of project**  **activities if it occurs when the project is not act ive, 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 audi ts, including performance evaluation**  **(PE) samples, are useful tools in determining the a bility 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 Enviro nmental Data Operations (QA/G-7)

**, dated January 2000, provides detailed information o**  **n various types of technical audits and includes an example checklist for a technical syste**  **ms audit of a laboratory measurement system.** 

**1.3 ASSESSMENT**

**Following planning (Sec. 1.1) and implementation (S ec. 1.2), assessment is the third and final**  **phase of the project data generation life cycle. T he purpose of this phase is to evaluate whether**  **the data are of the type, quantity and quality need ed to meet project DQOs. Assessment can** **SW-846 Update V ONE - 12 Revision 2**

**October 2012 involve many different complex activities, includin**  **g 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 st ep in the assessment process. Validation and** 

**verification are defined in the EPA** Guidance on Environmental Data Verification and Dat a Validation **(QA/G-8), dated November 2002. According 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/o r validation be clearly documented in the**  **approved QAPP.**

**In general, data verification/validation involves t he examination and review of project data (e.g.,**  **field and laboratory data for all applicable QC mea surements) to confirm that the sampling and**  **analysis protocols specified in the QAPP (and any o ther planning or contractual documents) were** 

**followed. Data verification/validation also involv es 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 sh ould 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 also need to be**  **documented during this phase of assessment (e.g., i n a data verification/validation report).** 

**In addition to QA/G-8, EPA has published several re ferences 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 va rious 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 documen ts like the QAPP. Corrective action based**  **on verification/validation findings may also need t o be described in these documents.** 

**Data verification/validation activities may be perf ormed internally (e.g., when the laboratory or**  **data collector review their own data), or externall y by an independent entity (i.e., a party that is n**  **ot associated with the data collection effort). Inter**  **nal and external data verification/validation procedures should be defined in the QAPP or other p**  **roject documents.**

**A data verification/validation report should be gen erated to document the procedures followed as**  **well as the findings and qualifications applied dur ing data verification/validation. The report**  **should include a list of the samples collected, fie ld 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 e xceedance, 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.** **SW-846 Update V ONE - 13 Revision 2**

**October 2012 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 i s generally not sufficient to determine whether**  **a data set can be used for its intended purpose. T ypically, 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 ass umptions made during the planning phase. If**  **the statistical assumptions made during the plannin g phase 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 fiv e-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 Method**  **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 Q A/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 t he 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 documente d in the QAPP or other site documents.** **SW-846 Update V ONE - 14 Revision 2**

**October 2012** 

## 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**

**October 2012 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**  **Environmental 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**

**October 2012 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 observe d 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 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 )/ K** **SW-846 Update V ONE - 17 Revision 2**

**October 2012 BLANK** 

**CALIBRATION BLANK**

**CALIBRATION CHECKS See Calibration Blank, Equipment Blank, Method Blan**  **k, and Trip**

**Blank.**

**A calibration blank is a sample of analyte-free med ia 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 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 c over 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. 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 documenting the prec ision**  **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:**

**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.** **SW-846 Update V ONE - 18 Revision 2**

**October 2012 DATA QUALITY**  **OBJECTIVES (DQOs)**

**DATA VALIDATION Qualitative and quantitative statements derived fro**  **m the DQO Planning Process that clarify the purpose of the st**  **udy, define 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.**

**The process of evaluating the available data agains t the project DQOs**  **to make sure that the objectives are met. Data val idation may be**  **very rigorous, or cursory, depending on project DQO s. The available**  **data reviewed will include analytical results, fiel d 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**

**FIELD BLANK See Equipment 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 to measure amb ient**  **contamination in the field. If this type of field blank is requested, a**  **container of reagent water or a solid bland materia l 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., VOCs**  **in soil), and in these cases co-located samples may be more**  **appropriate. SW-846 Update V ONE - 19 Revision 2** 

**October 2012 INITIAL** 

**DEMONSTRATION of PROFICIENCY (IDP)** 

**INSTRUMENT DETECTION LIMIT (IDL)** 

**INTERNAL STANDARD 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 pro**  **cedure).**

**Please see individual methods for additional guidan ce on IDP.** 

**IDLs are typically established for metal and inorga nic analytes. IDLs** 

**are useful means to evaluate the instrument noise l evel and response**  **changes over time for each analyte from a series of reagent blank**  **analyses to obtain a calculated concentration. The y are not to be**  **confused with the LLOQ, nor should they be used in establishing this**  **limit. It may be helpful to compare the calculated IDLs to the**  **established LLOQ, however, it should be understood that the LLOQ**  **needs to be verified according to the guidance in t he determinative** 

**method. IDLs can be estimated as the mean of the blank resu**  **lts plus three times the standard deviation of 10 replicate**  **analyses of the reagent blank solution. (Use zero for the mean if t**  **he mean is negative). Each measurement should be performed as**  **though it were a separate analytical sample (i.e., each measu**  **rement must be followed by a rinse and/or any other procedure norm**  **ally performed between the analysis of separate samples). IDLs sh**  **ould be determined at least once using new equipment, after**  **major instrument maintenance such as changing the detector, and/or a**  **t a frequency designated by the project. An instrument log book**  **should be kept with the dates and information pertaining to each I**  **DL performed.**

**Internal standards may be spiked into prepared fiel d 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 th**  **at 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 a**  **nalyzed in the same analytical batch and in exactly the same manne**  **r as the other SW-846 Update V ONE - 20 Revision 2** 

**October 2012 routine samples. The recovery of the target analyt**  **es in the LCS analysis demonstrates whether the methodology is in**  **control and the laboratory is capable of making unbiased measuremen**  **ts. The results of the LCS are evaluated in conjunction wit**  **h other QC information to determine the acceptability of the d**  **ata generated for that batch of samples. Acceptance criteria for LCS**  **recovery are calculated based on statistical treatment of histor**  **ical LCS recovery data 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 the**  **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 i s initially verified**  **by spiking a clean control material (e.g., reagent water, method**  **blanks, Ottawa sand, diatomaceous earth, etc.) at t he LLOQ and**  **processing through all preparation and determinativ e steps of the**  **method. Laboratory-specific recovery limits should be established**  **when sufficient data points exist. Individual meth ods may**  **recommend procedures for verifying the LLOQ and acc eptance limits**  **for use until the laboratory has sufficient data to determine acceptance**  **limits statistically. LLOQs should be determined a t a frequency**  **established by the method, laboratory’s quality sys tem, or project.** 

**Please see individual methods for additional guidan ce 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. U**  **nder 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 SW-846 Update V ONE - 21 Revision 2** 

**October 2012 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 monitor**  **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 rea gent water**  **would typically be used. For soil analyses, a puri fied solid matrix**  **(e.g., sand) would typically be used, except for me tals analyses.**  **Method blank results are evaluated in conjunction w ith other QC**  **information to determine the acceptability of the d ata generated for**  **that batch of samples. The method blank results sh ould be below the**  **LLOQ for the target analytes being tested; otherwis e, corrective action** 

**should be taken. A method blank is included with t he 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**

**POST-DIGESTION**

**SPIKES 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 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 measure ment bias in**  **sample quantitation. SW-846 Update V ONE - 22 Revision 2** 

**October 2012 PRECISION The agreement among a set of replicate me**  **asurements without assumption of knowledge of the true value. Precisi**  **on is estimated by means of duplicate/replicate analyses. These sampl**  **es should contain concentrations of analyte above the LLOQ, a**  **nd may involve the use of matrix spikes. The most commonly used e**  **stimates 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,** 

**Relative percent difference (RPD) when only two res ults are**  **compared:**

**RPD = (100) (x 1**  **- x 2**  **)/[(x 1**  **+ x 2**  **)/2]**

**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 for an analytical batc**  **h. Examples of quality control samples are method blanks, laboratory dupli**  **cates, laboratory control samples, 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 See method blank.**

**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.** **SW-846 Update V ONE - 23 Revision 2**

**October 2012 REPRESENTATIVE** 

**NESS A measure of the degree to which data accurately an**  **d precisely represent a characteristic of a population, a param**  **eter variation at a sampling point, a process condition, or an environm**  **ental condition.**

**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 for organic a nalyses at known**  **concentrations, and their recovery is used to asses s matrix effects**  **and, to some extent, verify proper processing and i nstrument**  **performance for each sample. The analytes used as surrogates**  **mimic the behavior of the target analyte(s) through out preparation an**  **d analysis. Surrogates are organic compounds which a**  **re similar to the target analytes in chemical composition and behavio**  **r in the analytical process, but are not normally found in the environm**  **ental samples. Surrogates added to LCS samples and blanks are used**  **to assess recovery in a matrix known to be free from interfer**  **ence. 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 nstituents (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** 

**October 2012 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 Revision 2, u**  **pdated for release October 2012. 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.**