Chapter 2

Quality assurance in environmentalanalysis

2.1 INTRODUCTION

The reliability of analytical measurements for environmental control isa key issue inasmuch as it affects public health, influences environ-mental quality and facilitates technological progress. Environmentalmanagement relies on information for a wide range of compounds.Therefore, unreliable or inaccurate information can lead to economiclosses through redundant measurements (e.g. in health-relatedanalyses) or unnecessary risks for the environment or human healthunless appropriate steps are taken to avoid them. Environmentalinformation should therefore be correct [1].

In the past, accuracy was the primary target in developing newanalytical methods. Today, accuracy continues to be a capital objectivebut not the sole goal for correct analyses, owing to the increasing varietyof samples to be processed and their special requirements. A resultconsisting of a single figure is more valuable, if it is delivered before thewater or food that produces it is eaten or drunk, than another withseveral accurate figures if it arrives too late [2]. Hence expeditiousness isalso significant, and so are costs, equipment availability and laboratoryefficiency.

The underlying principles of environmental analysis rely on an op-erational model that integrates the elements required to obtain repro-ducible, reliable measurements and unifies measurements for organicand inorganic compounds, however different their requirements may be.However, such principles are no all-purpose "recipe" or "formula" thatcan be applied to any determination.

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Environmental sample analyses can be addressed in many ways. Theanalytical protocol of choice in each case is dictated by the repro-ducibility demanded by the objectives, implementation time and equip-ment availability.

The analytical objectives for environmental samples differ from thosefor other samples in that reproducible measurements must be obtainedfrom very low analyte concentrations - some species occur at the part-per-million or even part-per-trillion level in highly complex samples.Advances in analytical methodology continue to lower the concentrationlevels that can be reproducibly measured; however, the demand foranalyses of increasingly lower analyte concentrations challenge furtherprogress. Many factors that have little or no effect on other analyticalmeasurements have a critical influence on the results and reproduci-bility of environmental analyses.

Environmental control and protection usually rest on measurementsthat encompass long periods of time and wide geographical areas. Onlythrough correct sampling and accurate analytical results can validconclusions on a situation and its changes be drawn. The use of certifiedreference materials (CRMs) facilitates checking for measurementaccuracy [3].

The underlying principles of environmental analysis are as follows[4]:

(a) planning;(b) quality assurance and quality control;(c) validation;(d) precision and accuracy;(e) representativeness;(/) sampling;(g) measurement; and(h) documentation and reporting.This chapter deals briefly with all these issues by way of an overview

of environmental analysis. Sampling, and data and reference materialprocessing, are discussed in separate chapters owing to their high sig-nificance to quality control and measurements.

2.2 ANALYSIS PLANNING

One essential principle of environmental analysis is correct planning,which is the sole effective pathway to valid results. Planning is intended

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to initially define the problem correctly and subsequently refine theanalytical protocol chosen according to evidence gathered in the process.This ensures efficiency and reliability in the results.

If successful results are to be delivered, those who initially request theanalyses should never be allowed to define the objectives by themselves.In fact, for the results to match the objectives, the latter should beestablished jointly by those who are to produce the results and those whoare to use them. The analytical protocol should be constructed in thelight of the real problem, i.e. in terms of the available technique andmethod, and the anticipated or required sensitivity, accuracy, reliability,precision, interferences, matrix effects, constraints, costs and analysistime.

A detailed protocol for the analytical process to be implementedshould include the objectives, a description of the required qualityassurance and quality control, the sampling and analytical method to beused, the type of measurement to be employed and the kind of docu-mentation and report to be delivered (Fig. 2.1).

The sampling model and analytical method to be used are the twomost important choices as regards reliability of the results, which,however, is also influenced by other factors [4]. Thus, the concentrationlevels of the analytes to be measured are also of consequence inasmuchas they often dictate the amount of sample, pretreatment and analyticalmethod to be used. As a rule, the lower the concentration level to bemeasured, the higher the analytical costs.

Fig. 2.1. Elements of environmental analysis planning.

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HIGH

QUALITYASSURANCE

EFFORT

LOW I

CE

LOW HiGH

ANALYTE CONCENTRATION

Fig. 2.2. Qualitative relationship between degree of confidence, concentration and qualityassurance effort.

One other factor to be considered is the confidence level required inthe analyte identification. One should check whether the initially chosen

confidence level is too high or too low for correct identification. A higher

confidence level can be achieved by performing a confirmation analysisusing a different measurement technique. A lower level can readily be

obtained by comparing spectral, chromatographic or physico-chemicalproperties for the measured analytes with reported values. The degree of

confidence needed to quantify the analyte influences the selection of the

analytical method and number of samples to be used, as well as thedesign of the quality assurance scheme. The higher the confidence level,

the greater the quality assurance effort (as can be seen in Fig. 2.2), the

latter increasing with decreasing analyte concentration. As a rule, the

higher the accuracy and precision demanded, the stricter the quality

assurance scheme and analytical costs.Validation is also essential in order to ascertain whether the required

specificity, precision and accuracy can be accomplished. Otherwise, themethod chosen should be improved or replaced. The first step in the

validation process usually involves intra-laboratory contrast unless morethan one laboratory takes part in the measurement programme (e.g.

multi-laboratory controls, surveillance or monitoring operations), inwhich case inter-laboratory comparisons are needed.

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Correct planning of environmental analyses also demands an exactknowledge of the degree of quality assurance required. The mere use ofvalidation methods, reference laboratories and personal experience doesnot warrant reliable results. All analytical work must be controlled by aquality assurance system in order to ascertain that the results are highlylikely to be correct. Using a rudimentary method based on sound per-sonal experience can justify accepting a lower degree of control than thatrequired by new, complex procedures which the operator may scarcely ornever have implemented. In any case, one should check that the methodchosen measures the target analyte. This is currently verified by using ahyphenated technique (e.g. GC-MS, where mass spectrometry is used toconfirm the results of gas chromatographic measurements).

2.3 QUALITY ASSURANCE

The term "quality" encompasses two closely related factors that areusually employed synonymously and hence are prone to confusion.External quality is concerned with the features of the results (accuracyand representativeness) and the analytical process (precision, sensi-tivity, sampling, throughput, costs and personnel factors).

The quality policy of social and corporate bodies has given rise to suchapproaches as quality management and quality system. This hierarchicalsequence ends in the so-called quality assurance (QA). Quality assur-ance is a substantial component of environmental analysis planning(Fig. 2.1). It is interesting to note its two-way relationship to theobjectives, to be included in the quality policy; this in turn shouldprecisely define external (environmental) quality and internal quality(analytical control), and establish pertinent relations between them.

Quality assurance as applied to analytical laboratories can be definedas the set of planned activities intended to ensure that the analyticalinformation produced meets the quality requisites of the requestingbody, client or user, essentially in terms of accuracy and represent-ativeness, which are the two analytical properties that can be directlyascribed to the results [5-7]. This entails improving basic analyticalproperties (precision, sensitivity, selectivity and correct sampling) byraising the quality of analytical work inside and outside the laboratory,and that of the materials, equipment and software used in environ-mental analyses. However, QA should be approached with qualitycompromises in mind, compromises which arise from the inverse

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relationships between the previous properties and those that defineproductivity in environmental analysis (viz. expeditiousness, economyand personnel safety and comfort) [8].

Notwithstanding some controversies in the abundant literature onquality, QA is widely acknowledged to consist of two main components,namely: quality control and quality assessment. The borderline be-tween the two is rather diffuse and often leads to contradictoryassignation of activities to each. The choice adopted here (Fig. 2.1)establishes a clear-cut distinction that can be adapted to other, alsocorrect approaches.

2.3.1 Quality control

Quality control can be defined as the specific set of activities intended toexamine both the analytical process and its results in terms of quality.Controlling quality in this respect entails continuously checking labora-tory organization, equipment, reagents and the sample custody chain,among others, which lead to statistical control and to achieving theaccuracy demanded from the measurement process. Equipment calibra-tion, blank analyses, the use of suitable reference materials and skilledpersonnel, and close surveillance of every operation by the person incharge of the work team are typical components of quality controlschemes.

Statistical control (viz. checking for measurement reproducibility) isthe primary requisite, above accuracy - in fact, irreproducible measure-ments can hardly be accurate. In practice, such control is done withShewhart charts and blind samples. Control charts allow potentialdiscrepancies in the results provided by a proven method to be identi-fied. They are plots of the results obtained from measurements of areference sample or working standard [3] which can contain a materialsimilar to that in the unknown samples (with certified high uniformityand stability, however). Working standards are also called referencematerials (RMs) and are comparable to the certified reference materials(CRMs) used for additional confirmation of the accuracy of a method.

Control charts are typically constructed from a series of 15-20measurements of the reference material performed over a long period inorder to obtain an estimate of the mean and standard deviation asmeasures of the precision of the method [9]. In the Shewhart chart ofFig. 2.3, the vertical axis represents concentrations and the horizontalaxis the sequential number of each measurement. Horizontal lines

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z20

!iIt

Iz

Ouz0O

1 8 16 24

SAMPLE NUMBER

Fig. 2.3. Typical X control chart (in arbitrary units).

corresponding to the mean, "warning" and "alarm" concentration (thelatter two are equal to the mean 2 and 3 standard deviations, andcorrespond to a risk of 5% and 1%, respectively, that the results will beoutliers) are drawn (X control chart). When the reference material isunaffordable, the vertical axis is used to represent differences betweenduplicate determinations (R control chart).

2.3.2 Quality assessment

The other cornerstone of quality assurance is quality assessment, whichinvolves a set of operations that examine quality control directly (seeFig. 2.1) in order to ensure that all actions are performed correctly andunder supervision. Quality assessment can also include direct analysis ofthe results, which is the most frequent source of confusion in thisrespect. Quality assessment activities can be carried out by the follow-ing: (a) the staff directly involved in ordinary analytical tasks (e.g.constructing control charts, analysing blind samples, introducing newpersonnel or methodologies); (b) staff from the same body as thelaboratory but belonging to a different section or department; and (c)outside personnel. Audits, performed by workers outside the environ-mental analytical laboratory (b and c above), are commonplace in this

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context. Audits can be qualitative (for systems) and involve the visualand documental inspection of the quality control systems and analyticalprocesses, or quantitative (for performance), which entail direct analysisof the results produced. Mixed (qualitative-quantitative) audits areusually more comprehensive and rigorous than the previous two andnormally entrusted to outside specialists.

One other major consideration is the accreditation of environmentallaboratories. Accreditations are issued by a national institution at therequest of the laboratory and granted for a specific activity and a givenlength of time; they are therefore voluntarily solicited, and of partial andtemporary nature, even though they can be renewed. An accreditation isa formal written acknowledgement that the laboratory concerned iscompetent to perform a given type of analysis (specific analytes inspecific samples). There is a current trend for external laboratory auditsto be comprehensive and include quantitative assessment (direct anal-ysis of results), which can be accomplished in two ways, namely: (a) byusing certified reference materials supplied by the accrediting institu-tion, materials which are usually expensive and scanty - particularlythose for environmental analyses ; and (b) by taking part in proficiencytesting schemes, which are inter-laboratory exercises designed for thestatistical comparison of the results produced by many laboratoriesanalysing the same sample for the same analytes. This choice is onlyacceptable provided the exercise is organized by an independent,renowned institution [10].

Good laboratory practices (GLPs) are one other approach to environ-mental analytical control that share some of the above considerationsbut have some special features of their own. GLPs can be defined as aseries of rules, operating procedures and compulsory practices (enforcedby national governments, for example) that are issued by such institu-tions as EPA, OECD, EU, FDA, etc., to regulate the quality of theinformation produced by environmental analytical laboratories. Amongother measures, they entail establishing a quality assurance unit (QAU)via an internal audit inasmuch as the associated personnel are notdirectly answerable to the laboratory officials, but to the quality depart-ment or managers of the institution. In connection with GLPs are stan-dard operational procedures (SOPs), which are detailed descriptions ofthe wide variety of specific operations a laboratory can carry out [12,13].

Implementing a quality assurance system in an environmental lab-oratory requires the concourse of human (personnel willingness andavailability) and technical resources (appropriate instrumental equip-

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ment and computer and chemometric systems) for the so-called "quali-metrics", without which QA is utterly impossible.

2.3.3 Validation

Validation is an increasingly used approach to quality despite the factthat the underlying idea is inherent in quality assurance activities. Inthis context, to validate is to formally demonstrate that a given system(the object) has done what it was supposed to do (i.e. possesses somefeatures) and continues to do it (to possess such features). Validationcontrols are applicable to various objects such as sampling models,analytical methods, data, results, instruments, apparatuses, etc. [13].

Corrective actions are a substantial element of quality in environ-mental analysis even though - perhaps for obvious reasons - they arenot systematically included in QA systems. There are two main types ofcorrective actions. Internal actions are an integral part of QA and aconsequence of quality assessment activities; they are intended tocorrect deficiencies in quality systems when they fail to meet theirobjectives. External corrective actions are of a generic type and affect theorganization, work and other aspects of the analytical laboratory; theyalso derive from QA activities and are intended to ensure that the resultsmeet the planned objectives for an environmental analysis. In theabsence of corrective actions, quality assurance systems make no sense.

The following sections discuss the five data quality indicators (DQIs)established by EPA in their Quality Assurance Project: precision, bias,representativeness, completeness and comparability.

2.4 PRECISION AND ACCURACY

Precision is defined as the degree of consistency between data producedby repeated measurements and is the origin for the statistical concept ofdispersion. On the other hand, accuracy is the degree of consistencybetween measured data (or their mean) and the true value. These twoconcepts are illustrated graphically in Fig. 2.4. If the true value lies atthe centre of the circle, then method A will be accurate and precise,method B will be precise but not accurate, method C will be neitheraccurate nor precise, and method D will never be accurate because it isnot precise. Dispersion or variability in the results arises from fortuitoussources called random or indefinite errors, whereas inaccuracy

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A B

ACCURATE PRECISE BUTAND PRECISE INACCURATE

D

IMPRECISE IMPRECISE ANDAND INACCURATE THEREFORE

INACCURATE

Fig. 2.4. Accuracy and precision of a method.

originates from controllable sources - it can thus be avoided - whichgive rise to systematic or definite errors also known collectively as"bias". In some countries, bias is used to refer to the overall error, i.e. thesummation of errors from both types of sources (random and systematicerrors). In fact, the overall error is the best choice for defining theaccuracy of a method.

2.4.1 Precision assessment

Repeatability, reproducibility and inter- and intra-laboratory variabilityare all statistical terms used to assess the quality of measurements.Repeatability expresses the variation of a datum produced from a singlemethod by a single analyst and/or instrument over a short period(within-day analyses), whereas reproducibility denotes the variationover a long period of time or between several analysts and laboratories(between-day analyses). Intra-laboratory variability represents differ-ences in the results produced by the same laboratory measuring thesame sample repeatedly. On the other hand, inter-laboratory variabilitydenotes differences in the results obtained by different laboratoriesmeasuring portions of the same sample. Because of the ambiguousnature of these terms, laboratories should clarify the assumed meanings

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of the terms employed in describing the experimental conditions used. Aproperly designed and executed quality assurance scheme helps dimin-ish inter- and intra-laboratory variability.

The standard deviation is a commonplace statistical parameter forassessing precision; in fact, it is a measure of result repeatability. Itsvariant, the relative standard deviation, is more practical inasmuch as itrelates variability with the measured quantity (e.g. a concentration)[14].

Even if a laboratory makes systematic use of appropriate analyticaland validation methods, and quality assurance procedures, analyticalmeasurements frequently produce anomalous data (outliers) that differtoo greatly from the mean to be considered highly unlikely. Outliers canbe detected by using special statistical techniques. Zero and negativereadings are often considered outliers; however, when the workingconcentrations are close to the limit of detection, some analyses canreasonably give a zero value by accident [15]. In any case, outliers shouldbe not only excluded from the list of results but also identified and thestatistical reasons for their exclusion clearly explained.

2.4.2 Accuracy assessment

Unless the true value of the datum to be measured is known, accuracycannot be evaluated. The true value can be determined from the resultsfor samples of known composition; this makes reference materialshighly useful for assessing method accuracy, which, however, can also beevaluated with other means.

Taylor [16] reported several procedures for assessing accuracy in-cluding (a) the use of spiked or surrogate samples; (b) comparing themethod applied with an independent method or one of known accuracy;(c) using certified reference materials (CRMs); and (d) comparing theresults with those obtained by other laboratories. Each of these proce-dures has its own advantages and disadvantages. In any case, the use ofspikes and surrogates for this purpose is widely discouraged.

Each measurement has its own sources of error, which add up to thoseinherent in the analyst's actions. In some methods, errors can arise fromoperations (e.g. matrix digestion in ICP-AES) that are unnecessary inothers (e.g. neutron activation spectroscopy). If an independent methodis available it will usually be the best choice for validating the results. Ifboth methods provide consistent results, then the tested method can beassumed to be free of systematic errors. The more different the two

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methods are, the more correct such a conclusion will be. For example, ifboth methods include a common pretreatment step that introducessome error, the error may go unnoticed in both cases. Failing theinstruments required for comparison with another method, accuracycan be assessed in terms of analyte recovery from spiked samples.However, in interpreting recovery results, one should bear in mind thatspiked samples do not completely simulate natural samples in somecases.

Participating laboratories in intercomparison exercises frequentlyarrive at inconsistent results. Hence, their participation and a criticaldiscussion of the results make a good method for achieving a highaccuracy. However, such exercises cannot be scheduled for every singleanalytical field.

Occasionally, discrepancies between the results of participating labo-ratories originate during calibration. In fact, calibration has beenidentified as a major source of systematic errors in environmental anal-yses. Calibration-related errors arise from the presence of impurities,incorrect stoichiometries or compound identities, erroneous dilution,ignoring density and temperature in preparing standard solutions froma base volume, etc. Other major sources of error include sample altera-tions between sampling and analysis, corrections with spurious blanksand the difficulty of determining every single form of an analyte. Thelast is particularly significant when the analyte concentration in thesample is extremely low.

Determining the accuracy of an analytical procedure entails investi-gating every single step of the analytical process from sampling to resultdelivery. If every step is controlled and all uncertainties are determined,then the procedure can be considered traceable and its results correct.The traceability of a method should thus also be considered in evalu-ating its accuracy.

Reference materials, which are indispensable for assessing calibra-tion accuracy, and traceability, are both dealt with in Chapter 5.

2.5 REPRESENTATIVENESS

This is a property of analytical results that expresses their degree ofconsistency with the environmental problem addressed. A result that isaccurate and has a low uncertainty but is not representative is a poorresult inasmuch as it does not help solve the underlying problem.

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Representativeness thus denotes the degree of consistency betweenexternal (environmental) quality and internal (analytical) quality [17].

Representativeness is seldom formally considered an analytical prop-erty even though its significance is often implicitly or explicitly acknow-ledged. While representativeness arises largely from quality sampling, acomprehensive definition of this concept includes several other notions.

The importance of analytical results can be approached in four dif-ferent, hierarchical ways as regards both scope and significance. Thus,the results can be consistent with (a) the samples (aliquots) that are ineffect subjected to the analytical procedure; (b) the object (bulk sample),which is the origin of sampling; (c) the analytical problem, whichdemands an accurate definition of the expected analytical result andcareful planning of sampling, the analytical method, data processing,report production, etc.; and (d) the environmental problem, which in factraises the need for quality results (with allowance for some compromisessuch as accuracy or precision versus expeditiousness) and conditions theanalytical problem. Obviously, representativeness decreases from (a) to(d). If a result is only representative for the environmental samples thatreach the laboratory, it can be a poor result inasmuch as it may not beconsistent with the analytical problem addressed. Conversely, if theresult is consistent with the environmental problem faced, it will berepresentative of the other problems addressed to obtain it.

2.6 COMPLETENESS

Completeness is a measure of the amount of correct data obtained from ameasure system in relation to the amount one should have obtainedunder normal correct conditions. The basis for the projected objective(e.g. 90% completeness) and the anticipated consequences of failing toaccomplish it must be discussed in advance. The step(s) where data arespecially prone to be lost should be identified and alternative proceduressuggested to enforce the predetermined completeness.

Potential sources of data losses include, but are not limited to, thefollowing: special sampling places that cannot be accessed at the timedata are acquired, sample breakage or spillage during handling or trans-port, instrument malfunctioning (e.g. in continuous analysers) andexceedingly long periods preceding sample analysis.

With in situ measurement schemes such as environmental aircontrols where a lost sample (e.g. an average of one every hour) cannotbe replaced, a simple calculation is performed to obtain a numerical

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value in order to ensure completeness. In other types of samplingschemes (e.g. collection of discrete samples that are subsequently trans-ferred to the laboratory), lost samples can be replaced with otherscollected later or a larger than required number of samples can initiallybe collected in order to offset potential losses and ensure that theprojected completeness level is attained.

Whatever the situation, lost data should be accounted for: how andwhere in the measurement chain they were lost and why (field samplingcomplications, transport, breakage, storage, instrument failure, etc.).This information is pivotal in designing refined sampling schemes.

2.7 COMPARABILITY

Comparability expresses the confidence with which a data series can becompared with another. For such a comparison to be feasible, thefollowing requirements should be met:

(a) The objectives should be identical in both cases (for example,sampling stations in each network should be located in such a way as tohave the same likelihood of collecting representative samples).

(b) Measurements and data should also be identical (i.e. the analytesof interest should be the same, their conditions expressed in the sameunits, and all obtained under the same working concentrations). Inaddition, the sampling procedure and the analytical method used foreach analyte should be the same for all the stations in a network.

(c) Quality assurance and quality control schemes should be con-sistent with those for existing data series.

2.8 SAMPLING

This section advances some general concepts of sampling in order toplace it in the context of environmental analysis. For a more detaileddiscussion, read Chapter 3, which is entirely devoted to this topic.

The quality and usefulness of analytical data rely heavily on the use ofvalid samples, which in turn rests on an appropriate sampling program.Sample collection and its design are often quite complex. The purpose ofsampling is to obtain substances that are representative of the situationstudied. Some sampling plans and schemes occasionally entail collectingsamples at specific places and times.

All these aspects of the sampling scheme should be planned andjustified in detail, which entails defining a relationship between the

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sampling protocol to be used and the analytical results to be obtained. Asampling scheme should include a reasoned justification of the samplingpoints chosen, the number of samples to be collected, the sampleacquisition time and the magnitude of the oscillations expected from theheterogeneity of the sample addressed. It should also describe theprocedures used for collecting and labelling samples, conditioningcontainers, and storing and pretreating samples.

An acceptable sampling scheme should include at least the following:(a) a sampling program considering all critical aspects of the study andthe uncertainty introduced by the number of samples collected and thevariability in the sample set; (b) instructions for sample collection,labelling, preservation and transport intended to avoid any alterationsbefore the analytical procedure is applied; and (c) skilled personnel forimplementing sampling techniques and the specific procedure chosen.

The nature of the inner walls of containers and the sample-containercontact time should also be considered, as should the special conditionsoccasionally required during sample transport (e.g. refrigeration, dark-ness) in order to ensure that the samples reach the laboratory unaltered.

Samples must be protected from losses, alteration and tampering. Inaddition to these general precautions, if samples are involved in a legalsuit, then they should be protected with wax, a chain or a similar seal.

Because environmental samples are typically heterogeneous innature, a large number of them must usually be analysed in order toobtain reliable compositional data. One measurement of an individualsample can never be representative of an environmental problem, butonly of the sample concerned. If an average concentration must bedetermined, then a large number of samples should be collected atrandom and then combined and mixed in order to obtain a sample ofreasonably homogeneous composition from which representative sub-samples can be taken for analysis. Conversely, constructing compositionand variability profiles entails collecting many samples and analysingthem on an individual basis.

As a rule, the number of samples to be collected and the quality of thesampling procedure applied must be anticipated in order to facilitatecharacterization of the problem addressed and increase the usefulness ofthe final result. If the sampling plan is not imposed (e.g. by legislation),the analyst will have to decide what error and confidence level areacceptable. Once these are determined, the minimum number ofsamples needed to meet the selected confidence limits for the protocolshould be estimated.

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The number of samples to be used can be calculated by using variousprocedures (see Chapter 3). Thus, a statistical procedure can be usedwhen the distribution or standard deviation of the data set is known orcan be assumed. As a rule, analytical data are assumed to obey thenormal or Gaussian distribution law, but this is not always the case -especially with the very low concentrations (a few ppm or ppt) en-countered in environmental analyses. In such cases, a normal logarith-mic distribution (one where the logarithm of the concentration fits anormal or Gaussian distribution) is more appropriate. Therefore, thefirst thing the analyst should check is that the statistical distributionused is the most appropriate.

2.8.1 Use of blanks and control samples

The typically high complexity of environmental samples makes the useof various types of blanks essential.

Field blanks are obtained from a source similar to the sample butcontain none of the analytes of interest. If unavailable, they must beprepared from samples that simulate the sample matrix very closelyexcept for the analytes. In many cases, synthetic or simulated blanks arethe sole available choice.

When localized pollution (e.g. a hazardous dumping or sewage unloadpoint) is to be studied, concentration measurements must also be madeat pollution-free places (controls). Such places and the number of samp-les collected at each should be carefully chosen in order to be able toestablish the significance of any apparent differences worth noting. Asthe concentrations in the test and control samples approach, the anal-ysis of the latter gains significance and the use of statistical proceduresis increasingly essential.

Field blanks spiked with the analytes at known concentrations areused as field control samples to recover analytes as a way of assessing agiven analytical method. Unpolluted samples from control places to whichthe analyte of interest has been added provide the most comprehensiveinformation inasmuch as they simulate any existing matrix effects. Asdefined here, the term "spiked blank" is incorrect since it is intended todesignate a reference material added to a liquid that is thus made a"standard solution." If no blanks can be obtained from control places,then spiked simulated or synthetic field blanks can be used instead.

The purpose of spiking a field blank or sample (control field samples)while samples are being collected instead of spiking laboratory blank orsamples (control laboratory samples) prior to analysis is to identify

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potential matrix effects arising from the time and/or conditions wherethe sample is collected, transported and stored before it is analysed.Even though spiking control field samples is the most useful choice, thetechnical difficulties involved in most cases entail spiking them ontransfer to the laboratory, which may take place hours, days or evenweeks after collection.

Recoveries from control field and laboratory samples may differ as theresult of a differential behaviour of naturally occurring and externallyadded substances. For example, if an analyte is strongly bound to anyother matrix component in the sample, it may not be so readily re-covered as from the blank matrix, which is spiked and then immediatelyextracted.

2.9 ANALYTICAL DETERMINATION

The analytical determination proper is preceded by a number of stepsthat make up the analytical method and warrant some comment.

Thus, after the sample is collected, the analytical process often includesone or more sample conditioning steps based on (a) physical operations(sieving, mixing and grinding, drying) and (b) chemical treatments (dis-solution, extraction, digestion, fractionation, derivatization, pH adjust-ment, and addition of preservatives, standards or other materials). Thesephysical and chemical operations not only complicate analyses, but alsoare the potential sources of deviations, variations, contamination andmechanical losses. Therefore, sample preparation must be carefully plan-ned so it can faithfully reflect the sample history. In addition, test samplesshould be subjected to the same history and treatment as the analyte.

Analytical measurements should only be made after a validated suit-able technique and method have been chosen; control and calibrationruns should be performed periodically in order to minimize random andsystematic errors in the process. Current analytical techniques rely onpowerful instruments, most of which are automated and afford the deter-mination of concentrations as low as 10-10_10-10- 2 g/g at a high throughput.However, such a high sensitivity does not necessarily imply a high accu-racy. The procedure of choice should ensure the required precision by useof minimally contaminating devices and with the highest possible re-coveries. By keeping the number of procedural steps and operations to aminimum, the risk of contamination from various sources (reagents,solvents, vessels, containers, the atmosphere, internal standards, etc.)will be minimized.

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Analytical methods rely on measurements of an electrical signal thatmust be correlated with the concentration of the substance of interest inthe unknown sample. Such correlation is established via a calibrationcurve.

Environmental analytical measurement plans should be designed inaccordance with the established quality assurance scheme by using thefollowing elements: calibration standards, field samples, field blanks,spiked field or laboratory blanks and reference samples. Field blanks areneeded to identify the presence of spurious analytes, interferences andbase concentrations of the analyte of interest. Spiked field and lab-oratory samples are required to determine the recovery. Referencesamples are used to validate the quality assurance scheme. Finally,calibration standards are required to establish the base for quantitationof the analyte of interest. The frequency with and order in whichmeasurements of samples and blanks are to be made should be definedin the protocol developed at the planning stage.

2.9.1 Calibration and standardization

Calibration is the process by which the values assigned to the physicalstandards used or the scales of the measuring instruments employed arechecked for correctness. A typical calibration uses mass, volume andlength standards, but often includes checking devices that measuretemperature, pH or chemical composition. An analytical measurementcan only be valid if all the instruments used during the analytical process(balances, volumetric glassware, thermometers, analysers, etc.) are pro-perly calibrated. The word "standardization" is used to refer to the valueof the response function of an analytical instrument to a substance ofknown concentration (a standard) with a view to constructing a cali-bration curve.

The accuracy of calibration depends strongly on the reproducibility ofthe standards used in the intercomparisons involved. Whenever pos-sible, it should be performed by subjecting the net signal for the analyteconcentration to regression analysis.

At least three - but, preferably, more - different concentrations of acalibration standard should be measured (in triplicate). The standardconcentrations should be comparable with that of the analyte in thesample. No datum outside the calibration range for the methodologyemployed should be included.

One other major requisite is that calibration should be performed

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under the same instrumental and chemical conditions to be used duringthe measurement process. The frequency of calibration will be dictatedby the accuracy required and the stability of the instrument employed.

Internal calibration entails the addition of a reference material to thesample. On the other hand, external calibration involves using a sep-arate reference material. The internal standard chosen should simulatethe analyte of interest, while external standards are usually the sameanalytes to be analysed. The relation between the response of an in-ternal standard and the analyte is known as the response constant and isused to calculate the analyte concentration.

2.9.2 Recovery

Analyte recoveries are markedly influenced by various factors such asanalyte concentrations, sample matrix and storage time. Because therecovery usually changes with the concentration, the analyte and spikedconcentrations should be as similar as possible. If the spiked and/oranalyte concentration are very close to the base concentration, re-coveries can vary over a wide range.

Matrix effects can significantly alter recoveries, especially for organiccompounds. Therefore, for a spiked standard to be valid, it should bedetermined in the same matrix as the sample. Significant differences inrecoveries for organic spiking standards added to industrial wastewaters are frequently encountered as a result of samples being collectedover a period of several days (or a few hours in some cases) because thecomposition of such samples often changes markedly with time. Storageover long periods can also alter recoveries.

Variability in the recovery is occasionally addressed by using isotopicdilution: if an isotope of the analyte is added, then the labelled substancewill behave identically with the unlabelled analyte.

Whenever possible, homogeneous working standards should be usedas they contain naturally acquired analyte concentrations. Unfortu-nately, environmental reference materials are frequently scanty (partic-ularly organic materials, as shown in Chapter 5).

2.9.3 Selectivity

Interferences with environmental analyses are quite commonplaceowing to the typical complexity of environmental samples and the lowselectivity of most available methodologies. Appropriate controls must

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therefore be used to ascertain the absence of interferences or, in theirpresence, that they are removed or their reactivity suppressed. Inter-ferents are usually removed by using a separation technique, whichmost often also allows the preconcentration of analytes present in traceamounts (see Chapter 4). Interferences can be either present in thesample or introduced during the analytical process. The analyticalprotocol used should include provisions for identifying potential sourcesof sample contamination and distinguishing between intrinsic (con-stitutional) interferences and those added during sample manipulation,transfer or analysis.

2.9.4 Limit of detection and limit of quantitation

Choosing whether to detect a given analyte is one of the most crucialdecisions in microtrace analysis. The question to answer is whether ameasured value is significantly different from that for the sample blank.

The limit of detection (LOD) is defined as the lowest determinableconcentration that is statistically different from that for a blank. Theconcept and the statistical basis for its evaluation have been revised byLong and Winefordner [18].

If S t is the total measured value for a sample, Sb that for the blank ando the standard deviation of measurements, then the analytical signalwill be given by the difference St - Sb. For a normal distribution, such adifference can be shown to be greater than zero with a confidence limit of99% when the difference exceeds 3 times the standard deviation, i.e.

S t - Sb > 3a

Therefore, the recommended value for the LOD is 3o, i.e.

LOD = Sb + 3a

The method detection limit (MDL) denotes the lowest analyte concen-tration that a given method can reproducibly determine indifferently ina blank or sample; therefore, LOD approaches MDL as Sb approacheszero. One related concept, the instrument detection limit (IDL) definesthe smallest signal above background noise that an instrument candetect reproducibly.

Occasionally, IDL and LOD are operationally identical. In practice,the extent to which the analytical signal exceeds peak-to-peak noise is an

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indication of whether the instrument can detect the analyte in question.IDL and, especially, MDL, are important parameters for comparing andselecting instruments and methods. The experimental determination ofMDL has been discussed in detail by Glaser et al. [19].

The limit of quantitation (LOQ) is defined as the concentration levelabove which quantitative results can be obtained with a given degree ofconfidence. Confidence in the apparent analyte concentration increasesas the analytical signal rises above LOD. The recommended LOQ valueis 10 times the standard deviation, which corresponds to an uncertaintyof + 30% in the measured value (10(o ± 3y) at a confidence level of 99%,i.e.

LOQ = Sb + 10(

The limit of detection is more suitable for establishing the lower limitof the working range for the measurement method. Such a range encom-passes values from the lower limit to a higher level where the responseceases to be linear that is occasionally referred to as the "linear limit."

The LOD and LOQ concepts are illustrated graphically in Fig. 2.5.The scale is expressed in standard deviation units for the measurementprocess, which has been assumed identical for all the measurementsshown. LOD lies 3 above the average difference between the totalsignal (St) and the blank signal (Sb), whereas LOQ is 10o above S b.

While LOD is sometimes adopted as the sensitivity of a measurementmethod, the two concepts should never be confused. Sensitivity is taken

TOTAL SIGNAL(S)

+ 30 3

Zero LOD LOQHIGH UNCERTAINTY LOW-CERTAINTY QUANTITATION

REGION / QUANTITATION REGIONREGION

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

s, t T(S, + 3) (S + 10o)

(IN UNITS)

Fig. 2.5. Relationship of LOD and LOQ with the analytical signal.

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to be the slope of the calibration curve, which is calculated by a least-squares regression procedure that takes account of the standarddeviation and background noise for the analytical signal.

Quantitative interpretations, decision-making and corrective actionsshould all rely on LOQ or values above it.

Finally, it should be noted that LOD and LOQ are not two intrinsicconstants for each methodology; rather, they depend on the precisionachieved by a laboratory implementing a given method, which can varywidely (e.g. with the type of matrix). Consequently, reported LOD valuesshould only be used as estimates.

2.10 DOCUMENTED ANALYTICAL REPORTS

Delivery of the analytical report is the last step in the analytical process.The report must provide information justifying every step takenthroughout the process.

The laboratory must record and file the analytical reports it issues foras long as required to meet legal demands (if they ever arise) or, simply,for its own use in the future. The analytical chemist is responsible fordelivering a comprehensive description and interpretation of data, andissuing the report in a comprehensible, appropriate manner.

The report should present the results in clear terms and state whetherthey were corrected for blank or recovery measurements. It shoulddescribe the precision evaluation experiments performed and the exactnumber of measurements made if average values are reported. Also, if apublished methodology was used, it should be duly cited, as should anymodification introduced relative to its original implementation.

Finally, reports should contain enough information to allow users tounderstand the interpretations of the data issued, thereby avoiding riskfactors in making decisions in the light of the results contained in theanalytical report.

REFERENCES

1 W.P. Cofino, "Quality Assurance in Environmental Analysis", in Environ-mental Analysis, D. Barcel6 (Ed.), Elsevier Science Publishers, Amsterdam,1993.

2 L.R. Pittwell, Analytical Proc., 25 (1988) 192.

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3 E.A. Maier, Trends Anal. Chem., 10 (1991) 340.4 L.H. Keith, W. Crummett, J. Deegan, Jr, R.A. Libby, J.K. Taylor and G.

Wentler, Anal. Chem., 55 (1983) 2210.5 P.J. Howanitz and J.H. Howanitz (Eds), Laboratory Quality Assurance,

McGraw-Hill Inc., New York, 1987.6 F.M. Garfield, Quality Assurance. Principles for Analytical Laboratories,

AOAC, Arlington VA, 1991.7 J.K. Taylor, Quality Assurance of Chemical Measurements, 7th edn, Lewis

Publishers, Michigan, 1990.8 M. Valcdrcel and A. Rios, Anal. Chem., 65 (1993) 781A.9 ASTM Committee E-11, ASTM. Manual on Presentation of Data and Con-

trol Chart Analysis, STP 15D-ASTP, Philadelphia, 1976.10 C.H. Dempsey and J.D. Petty, Laboratory Accreditation and Data Certifica-

tion. A System for Success, Lewis Publishers, Michigan, 1991.11 EPA. Final Rule for Good Laboratory Practices Standards under the Toxic

Substances: Control Act, 40 CFR, Part 792, Federal Register, 48, November1983.

12 W.Y. Garner and M.S. Barge. Good Laboratory Practices: An AgrochemicalPerspective, ACS Symposium Series 369, 1988.

13 J.K. Taylor, Statistical Techniques for Data Analysis, Lewis Publishers,Michigan, 1990.

14 C. Miller and J.N. Miller, Statistics for Analytical Chemistry, Ellis Hor-woood, Chichester, 1988.

15 L.B. Rogers, et al., Chem. Eng. News, 60(23) (1982) 44.16 J.K. Taylor, Anal. Chem., 55 (1983) 600A.17 A. Rios and M. Valcarcel, Analyst, 119 (1984) 109.18 G.L. Long and J.D. Winefordner, Anal. Chem., 55 (1983) 712A.19 J.A. Glaser, D.L. Foerst, G.D. McKee, S.A. Quave and W.L. Budde, Environ.

Sci. Technol., 15 (1981) 1426.

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