Deliverable 16: Summary Guidance and Recommendations on Sampling, Measuring and Quality Assurance. BBaacckkggrroouunndd ccRRiitteerriiaa ffoorr tthhee IIDDeennttiiffiiccaattiioonn ooff GGrroouunnddwwaatteerr tthhrrEEsshhoollddss BBRRIIDDGGEE

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Contract n° 006538 (SSPI)

BR B R ID I D GE G E

B B ac a ck kg gr r ou o u nd n d c c Ri R it te er r ia i a f fo o r r t th he e I ID De en n ti t if fi ic ca a ti t io on n o of f G Gr r ou o un n dw d wa at te er r th t hr rE E sh s ho o ld l d s s

Research for Policy Support

Deliverable 16: Summary Guidance and Recommendations on Sampling, Measuring and Quality Assurance.

Due date of deliverable: 31 September 2006 Actual submission date: 25 November 2006

The deliverable authors are responsible for the content. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

Start date of the project : 1 January 2005 Duration: 24 months

AUTHOR: Stanislaw WITCZAK

AFFILIATION: AGH–University of Science and Technology ADDRESS: 30-059 Krakow, al. Mickiewicza 30, POLAND

TEL.: +48-12-6172437 EMAIL: witczak@uci.agh.edu.pl

FURTHER AUTHORS: Jan BRONDERS, Jaroslaw KANIA, Ewa KMIECIK, Kazimierz ROZANSKI, Jadwiga SZCZEPANSKA

Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006) Dissemination Level

PU Public x

PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

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The reliability of measurements and analytical data is a prerequisite for proper assessment of the chemical status of groundwater bodies. The whole process of the data collection, starting from the sampling itself and including sample storage and treatment, analytical procedures up to the final analysis of results, should be therefore considered within an integrated strategy (ISO 5667- 14, 1998). A schematic diagram of sampling and measurement process in relation to monitoring of groundwater quality is shown in Figure D16.1. The identification and determination of uncertainties associated with sampling, preservation and transport of samples is an important part of the overall monitoring effort.

Figure D16.1 Schematic diagram of sampling and measurement process in relation to monitoring of groundwater quality (after Grath et al., 2006).

The strategy for assessment of chemical status of GWB being developed in the framework of the BRIDGE project consists of several steps (tiers) – cf. D15 (Hart, Mueller et al., 2006). The first steps consist of the determination of natural background levels (NBLs) for suite of chemical constituents of GWB and assigning adequate threshold values (TVs) for the given system. The decision whether the given GWB is in good or poor chemical status is taken in the process of comparing TV values with the results originating from representative monitoring points. The monitoring results are the end-product of the entire analytical process and as such are unavoidably subject to uncertainty. Although efforts should be undertaken to minimize this uncertainty, it is obvious that it cannot be completely eliminated and should not be neglected in the decision process.

This document reviews the current approaches towards assessing uncertainties associated with sampling, transport and storage of samples in determination of the chemical quality of

groundwater. Several examples are presented which illustrate the use of these approaches in quantifying uncertainties for monitor ing networks of various sizes. Although this document was prepared in the framework of the BRIDGE project, the proposed approaches towards

uncertainty assessment have not been implemented in the project itself. The document should be therefore viewed as informative material for Working Group C designing the implementation strategy of GWD.

conceptual model

monitoring design

sampling &

measurement data

management Reporting

modelling &

assessment

laboratory analyses WFD and Management

Objectives

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2. General rules of collecting representative groundwater samples

Monitoring and sampling of groundwater is a complex process. This complexity stems mainly from substantial spatial variability of groundwater composition, limited access to the system and lack of simple hierarchy of flow such as drainage pattern of surface water systems (cf. Figure D16.6). In some instances also temporal variability of groundwater quality has to be taken into account. It has to be emphasized that even in favourable situations the sampling process comprises only a small part of monitored GWB, whereas conclusions drawn from this sampling necessarily relate to the entire system. Therefore, representativity of the collected samples is of utmost importance. Here, only the most important issues associated with this problem are outlined. More comprehensive discussion can be found in D7 (Witczak et al, 2006) and in D17 (Scheidleder et al., 2006).

The installation of a monitoring well or a series of wells should always be preceded by careful assessment of the purposes and objectives of the monitoring system. The objectives will in many cases dictate the design parameters for the well, including well diameter, well casing and screen materials, well screen length and placement, and well screen slot size and open area. For instance, when the objective is to monitor the extent of three-dimensional contaminant plume, the well screen length should be short enough to conduct sampling of discrete intervals (typically between 0.5 to 2 meters). Moreover, the diameter of the well may need to be large enough to accommodate a pump for sampling, of sufficient capacity. The types of monitoring well completions range from single screened interval or open-borehole bedrock wells to more complex multiple -casing or multiple -screen wells Each type of well completion has its applications, advantages and disadvantages. General recommendations for the application of each well completion type are given by Nielsen ed. (2005).

2.1 Spatial representativity

Spatial representativity is straightforward only in simple situations when individual samples taken from well-defined location in an aquifer with determined interval of depth and in determined moment of time, is considered. Defining a representative monitoring network at a regional scale (GWB, aquifer) is the task which requires adequate hydrogeological knowledge of the system (Foster et al., 2004). Essential step here is establishing a conceptual model of the monitored GWB (see Figure D16.1 and D16.2). An example of such an approach is given in the guidelines of WFD implementation (WFD CIS Guidance Document No. 7, 2003)

After establishing a conceptual model of the monitored system, the next step is to define zones most suitable for monitoring. Selection of such zones will be guided by several criteria such as representativity:

(i) for specific part of the studied system (e.g. recharge/discharge zones), (ii) with respect to certain receptors (e.g. human health, surface water ecosystems, etc.),

(iii) with respect to expected anthropogenic load.

Different approaches towards establishing representative monitoring zones within the GWB have been proposed but up to now no generally accepted methodology exists (Nielsen ed., 2005;

Jousma and Roelofsen, 2004; Grath et al., 2001). For instance, a representativity index (RU) was developed as a tool for assessing the homogeneity of a network (Grath et al., 2001). A certain degree of homogeneity of the network is a statistical prerequisite for applying the arithmetic mean as preferred aggregation method, as proposed in WFD.

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• A conceptual model is a simplified representation, or working description, of how the real hydrogeological system is supposed to behave.

• It describes how hydrogeologists assume a groundwater system behaves.

Figure D16.2. Conceptual model of the monitoring system (after WFD CIS Guidance Document No. 7, 2003).

Depth or depth interval(s) of the monitoring wells should take into account spatial structure of groundwater flow and objectives of the monitoring network. In unconfined systems the screen length, and especially the depths of the observation wells should be carefully chosen, depending on the transit time of water from the surface to the monitoring well and the degradation and retardation rates of contaminants in question.

2.2 Temporal representativity

Temporal representativity is related to minimum frequency of sampling which is required to detect trends or trend reversals of groundwater quality changes in the investigated GWB (Grath et al., 2001).

Detection and understanding of groundwater quality changes with time requires combining time serie s information, concentration–depth profiles, and age dating. In most cases, simple statistical evaluation of the available groundwater quality data restricted to a single well is not sufficient for effective detection of trends. Also information about spatial structure of groundwater flow and spatial distribution of hydrochemical zones in the system is required. Other complicating factors for trend analysis are long travel times to observation wells, spatial and temporal variations of anthropogenic load, groundwater age (especially deeper groundwater), reactive properties in the subsurface and finally temporal variations caused by meteorological effects (e.g. infiltration changes).

Transit time–based approach for monitoring design in case of unconfined systems is proposed by Broers (2004), Broers and Van der Grift (2004) and Broers and Van Geer (2005). In this approach, information about the transit time is based on flow patterns and simple formula (see Figure D16.3). This information can also be derived from tracer data.

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It should be emphasized that temporal changes of groundwater composition observed at the given monitoring site may not only reflect varying anthropogenic load of contaminants but may also be a consequence of response of the given GWB to pumping (upconing by pumping, sea water intrusion) or due to physical handlings on groundwater such as flow cycles due to irrigation in phreatic aquifers (Walraevens et al., 2003).

Figure D16.3. Groundwater flow and isochrones patterns in a homogeneous unconfined aquifer with constant groundwater recharge, N. (a) elementary concept with formula to calculate transit time of water, tz , through the aquifer with the porosity ε (b) concept used for the set-up of the monitoring networks, (c) hypothetical case with drainage system. Local flow systems in (c) result in distortion of the vertical pattern of isochrones and larger variations in groundwater age in the drained areas (after Broers and Van der Grift, 2004).

Frequency of monitoring should be tuned to phys ical and chemical characteristics of the system, such as groundwater flow conditions, recharge rates, groundwater flow veloc ities and reactive processes (Zhou, 1996). Frequency of sampling during initial stages of monitoring should be higher than that adopted for routine operation of the monitoring network in order to characterize short-term (seasonal) changes of the monitored parameters which can be superimposed on general trends. Frequency of sampling should be higher also in the case of low precision of analyses associated with specific contaminants. In general, sampling frequency should be tailored to the properties of the system being monitored. Too rigid rules are not recommended.

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3. Overview of existing approaches for assessment of uncertainty associated with sampling process

The main purpose of a physical act of measurement is to enable decisions to be made. In case of a groundwater body, the measurements of physico-chemical parameters of groundwater are an indispensable first step in assessing the chemical status of GWB. The reliability of the decision whether the given GWB is in good or poor status heavily depends on knowing the uncertainty of the measurement results. If the uncertainty of measurements is underestimated, for example because the sampling process is not taken into account, then erroneous decisions can be made that can have substantial financial consequences. Therefore, it is essential that effective procedures are available for estimating the uncertainties arising from all parts of the measurement process, including sampling. Judgment on whether the contribution to the measurement uncertainty arising from the analytical procedure in the laboratory is acceptable, can only be made with knowledge of the uncertainty originating in the remaining steps of the entire measurement procedure (Eurachem, 2006).

Abundant literature exists on the theory of sampling and measurement process as well as on its practical implications for the assessment of uncertainty associated with the physical act of measurement. In addition to publications in scientific journals (e.g. AMC, 2004; de Zorzi et al., 2002; Kurfurst et al., 2004; Love, 2002; Ramsey, 1998, 2002, 2004; Ramsey et al., 1992, 1995, Thompson, 1998, 1999; Thompson and Maguire, 1993; Thompson et al., 2002; van der Veen and Alink, 1998), also several guides and international ISO regulations on this subject have been published (e.g. Codex, 2004, 2006; de Zorzi et al., 2003; Ellison et al., 2000; Eurachem, 2006; EPA, 2002; Gron et al., 2005; NORDTEST, 2006; IAEA, 2004; ISO, 1993; ISO 5667-14, 1998; ISO/IEC 17025, 2005; ISO 5725-1-1994/Cor 1, 1998; ISO 5725-2-1994/Cor 1, 2002; ISO 5725-3-1994/Cor 1, 2001; ISO 5725-4, 1994; ISO 5725-5-1998/Cor 1, 2005; ISO/TS 21748, 2004; Konieczka et al., 2004; Magnusson et al., 2004; Prichard, 2004; Quevauviller ed., 1995;

Taylor and Kuyatt, 1994). The remaining part of this chapter follows the concepts and approaches recommended in those public ations.

3.1 Fundamental concepts

Uncertainty of measurement is defined in metrological terminology as “A parameter,

associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand.”(ISO, 1993, Eurachem, 2006). The

‘parameter’ may be, for example, a range, a standard deviation, an interval (like a confidence interval) or other measures of dispersion such as relative standard deviation. When

measurement uncertainty is expressed as a standard deviation, the parameter is known as

‘standard uncertainty’, usually given the symbol u. Uncertainty is associated with each measurement result. A complete measurement result typically includes an indication of the uncertainty in the form x ± ?u, where x is the measurement result and u an indication of the uncertainty. This form of expressing a result is an indication to the end-user of the result that, with a reasonable confidence, the result implies that the value of the measurand is within this interval. The ‘measurand’ stands for quantity, such as a length, mass, or concentration of a material, which is being measured (Eurachem, 2006).

Although uncertainty is related to other concepts such as accuracy, error, trueness, bias and precision, there are important differences between them (Eurachem, 2006):

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− Uncertainty is a range of values attributable on the basis of the measurement result and other known effects, whereas error is a single difference between a result and a ‘true (or reference) value’;

− Whereas uncertainty includes allowances for all effects which may influence the result (i.e.

both random and systematic errors), precision only includes the effects which vary during the observations (i.e. only some random errors);

− Uncertainty is valid for correct application of measurement and sampling procedures but it is not intended to make allowance for gross errors caused by failure of measuring

equipment and/or mistakes of the operator.

Figure D16.4 illustrates the influence of systematic and random effects on the measurement uncertainty.

Figure D16.4. Random and systematic effects on analytical results and measurement uncertainty (after NORDTEST, 2006).These effects are illustrated by the performance of someone practicing at a target – the reference value or true value. Each point represents a reported analytical result. The two circles are illustrating different requirements on analytical quality. In the lower left target requirement 1 is fulfilled and requirement 2 is fulfilled in all cases except the upper right. The upper left target represents a typical situation for most laboratories.

3.2 Sampling as a source of uncertainty of measurement

The act of taking a sample introduces uncertainty into the reported measurement result wherever the objective of the measurement is defined in term of the analyte concentration in the sampling target and not simply in the laboratory sample. This is the case of groundwater quality

monitoring.

Sampling protocols are never perfect in that they can never describe the action required for every possible eventuality that may arise in the real world in which sampling occurs (Eurachem, 2006). Awareness of these sources of uncertainty is important in the design and implementation of methods for the estimation of uncertainty. Similar arguments can be made for the uncertainty that arises in the process of physical treatment of a sample (e.g. preservation, transportation, storage) preceding treatment undertaken in the laboratory. The methods employed for sampling

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should aim to reduce these errors to a minimum. Moreover, adequate procedures are required to estimate the uncertainty of the final measurement result arising from all of these steps.

Heterogeneity of the sampling target, in this case a groundwater body, will always lead to uncertainty of the analyzed quantity (Codex, 2006; Eurachem, 2006; Ramsey, 1998; Ramsey et al., 1995). This heterogeneity can be quantified in a separate experiment (see below), but if the aim is to estimate the average concentration of the given quantity characterizing the entire GWB, this heterogeneity is just one cause of the measurement uncertainty.

3.3 Approaches to uncertainty estimation

There are two broad approaches to the estimation of uncertainty. One, described as ‘empirical’

or ‘top down’, uses replication of the whole measurement procedure as a way of direct estimation of the uncertainty of the final result of the measurement. The second approach, described as ‘modelling’, ‘theoretical’, or ‘bottom up’, aims to quantify all the sources of uncertainty individually, and then uses a model to combine them into overall uncertainty characterizing the final result. Both approaches can be used together to study the same measurement system (Eurachem, 2006; Ramsey, 1998, 2002).

The overall objective of any approach is to obtain a sufficiently reliable estimate of the overall uncertainty of measurement (Eurachem, 2006). This means that not necessarily all individual sources of uncertainty are to be quantified; only that the combined effect be assessed. If, however, the overall level of uncertainty is found to be unacceptably high, i.e. the

measurements are not fit for purpose, specific action must be taken to reduce the uncertainty.

3.3.1 Empirical approach

The empirical approach is intended to obtain a reliable estimate of the uncertainty, without necessarily knowing the sources of uncertainty individually (Eurachem, 2006; Ramsey, 1998, 2002; Thompson, 1998). It is possible to describe the general type of uncertainty sources, such as random or systematic effects, and to subdivide these as those arising from the sampling process or the analytical process. Estimates of the magnitude of each of these effects can be made separately from the properties of the measurement methods, such as sampling precision (for random effects arising from sampling) or analytical bias (for systematic effects arising from chemical analysis). These estimates can be combined to produce an estimate of the uncertainty in the measurement result.

The overall uncertainty of measurements arises from four broad classes of errors (Eurachem, 2006; Ramsey, 2002). These four classes are the random errors arising from the methods of both the sampling and analysis, and also the systematic errors arising from these methods. These errors have traditionally been quantified as the sampling precision, analytical precision,

sampling bias and the analytical bias, respectively (cf. Table D16.1). If errors belonging to these four classes are quantified, separately or in combinations, it is possible to estimate the

uncertainty of the measurements that these methods produce.

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Table D16.1. Estimation of uncertainty contributions in the empirical approach (after Eurachem, 2006).

Effect class Process

Random (precision) Systematic (bias) Analysis e.g. duplicate analyses e.g. certified reference materials Sampling duplicate samples Reference Sampling Target

Inter-Organisational Sampling Trial Sampling and analytical precision can be estimated by duplication of a proportion (e.g. 10%) of the samples and analyses respectively. Analytical bias can be estimated by measuring the bias against certified reference materials, or by taking it directly from the validation of the analytical method. Procedures for estimating sampling bias include the use of a Reference Sampling Target or they utilize measurements from Inter-Organisational Sampling Trials in which the sampling bias potentially introduced by each participant is included in the estimate of

uncertainty based on the overall variability (Eurachem, 2006; Ramsey, 2002). Although some of the components of uncertainty associated with systematic effects may be difficult to estimate, it may be unnecessary to do so if there is evidence that systematic effects are small and under good control.

A statistical model describing the relationship between the measured and true values of analyte concentration is needed for estimation of uncertainty using the empirical approach. This random effects model considers a single measurement of analyte concentration (x), on one sample (composite or single), from one particular samplin g target (Eurachem, 2006):

x = Xtrue + εsampling + εanalysis

where Xtrue is the true value of the analyte concentration in the sampling target, i.e. equivalent to the value of the measurand. For instance, this could be the concentration of a given element or constituent in sampled groundwater. The total error due to sampling is εsampling, and the total analytical error is εanalysis.

In an investigation of a single sampling target, if the sources of variation are independent, the measurement variance is given by:

σ ²meas = σ²sampling + σ²analytical

where σ²samplingis the between-sample variance on one target and σ²analytical is the between- analysis variance on one sample.

If statistical estimates of variance (s²) are used to approximate these parameters, then we get:

s²meas = s²sampling + s²analytical

The standard uncertainty of measurement (u) can be then identified with the square root of s²meas, given by:

u ≡ s_meas= s_sampling² +s_analytical²

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In a survey across several sampling targets (several monitoring points in case of a GWB), the model needs to be extended to include the variance of the concentration between the targets. If the sources of variation are independent, the total variance σ²tota l is then given by:

σ²total = σ²between-targets + σ²sampling + σ²analytical

and its best estimate becomes:

s²total = s²between-targets + s²sampling + s²analytical

The empirical approach adopted for estimating uncertainty associated with monitoring of groundwater quality is discussed in detail in chapter 5 below.

3.3.2 Modelling approach

The modelling approach, also known as ‘bottom-up’ approach, has been described for

measurement methods in general and applied to analytical measurements (Ellison et al., 2000).

It initially identifies all of the sources of uncertainty, quantifies the contributions from each source, and then combines all of the contributions, as an uncertainty budget, to give an estimate of the combined standard uncertainty. In the process, the measurement method is separated into all of its individ ual steps. This can usefully take the form of a cause-and-effect, or ‘fish-bone’, diagram (de Zorzi et al., 2002; Ellison and Barwick, 1998; Ellison et al., 2000; Eurachem, 2006;

Kurfurst et al., 2004). The cause-and-effect diagram for groundwater sampling is shown in Figure D16.5.The uncertainty of measurement generated by each of these steps is estimated independently, either empirically or by other methods. The combined uncertainty is then calculated by summing the uncertainty from all of the steps using a model that adds the

variances representing individual sources of uncertainties. This approach is well established for analytical methods (Ellison et al., 2000) but has only recently been applied to the process of sampling (de Zorzi et al., 2002; Kurfurst et al., 2004).

Figure D16.5. Schematic cause-effect diagram for the assessment of uncertainties associated with monitoring of groundwater quality.

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4. Quality requirements for monitoring

Monitoring of groundwater bodies for the purpose of establishing thresholds of selected chemical constituents is a complex process influenced by numerous of factors (Grath et al.

2006; Quevauviler, 2005; WFD CIS Guidance Document No. 7, 2003; WFD CIS WG_C, 2005). Its overall quality depends on both “hardware” which can be identified as adequate design and construction of monitoring wells as well as their proper distribution on the

monitored area, and “software” which can be related to the sampling process and evaluation of the levels of the monitored parameters in the laboratory and in the field.

Sampling of groundwater for monitoring purposes is always associated with errors resulting from incomplete knowledge of the system (inadequate conceptual model, poorly defined recharge and discharge zones, etc.), deficiencies in design and operation of the monitoring network (improper construction materials, improper sampling technologies, inadequate frequency of sampling), and other factors. Recommendations how to minimize typical errors associated with monitoring can be found in numerous guidelines and ISO regulations (e.g. ISO 5667-11, 1993; ISO 5667-14, 1998; ISO 5667-18, 2001; ISO 5667-3, 2003; Gron et al., 2005;

Nielsen ed., 2005; NORDTEST, 2003, 2005).

Errors and faults leading to non-representative sampling may originate already at the initial stages of the monitoring process, such as site selection and design of monitoring wells. They may also occur at subsequent stages of the monitoring process aimed at determination of groundwater quality.

4.1 Quality of design

The design of a monitoring network should fulfil specific compliance criteria which can be formulated in terms of required maximum allowable confidence interval for the monitored parameter(s) in time or space within a groundwater body or a group of groundwater bodies. The design should be documented and subject to peer review (Grath et al., 2006).

A selected monitoring site should be representative for the monitored groundwater system, taking into account the three-dimensional character of groundwater flow. Even in simple situation like that presented in Figure D16.6, sufficient hydrogeological information is required for proper design of the monitoring network. Preferential flowpaths, particularly in fissured and karstic -fissured aquifers, pose serious proble ms for localization of the monitoring sites.

Geophysical methods may be useful in solving this problem.

A minimum number of 3 sampling points per GW-body is required (Grath et al., 2001). The replacement of sampling points should be kept as low as possible. In case of changes of monitoring stations it should be assured that these changes do not affect the outcome of the assessment.

Homogeneity of the monitoring network is an important element of the network design and can be checked by statistical means. The Representativiy Index (RU) is recommended as adequate statistical tool for assessing homogeneity of a monitoring network (Grath et al., 2001; D17 – Scheidleder et al., 2006). A certain degree of homogeneity of the network is a statistical prerequisite for the admissibility of applying the arithmetic mean as the proposed data aggregation method. If the groundwater body is hydrogeologically heterogeneous and if a spatially homogeneous monitoring network is not feasible or sensible , the monitoring has to be developed in a hydrogeologically representative way (Grath at al., 2001, 2006).

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Errors and faults leading to non-representative sampling may originate already at the initial stages of the monitoring process, such as site selection and design of monitoring wells.

Differences in hydraulic head even in homogenous aquifer are common due to boundary

conditions (downward flow in the recharge areas and upward seepage in discharge areas such as rivers, springs, etc. Vertical flow can take place not only through the screen but also through gravel pack along the screen. In each case, a good conceptual model of the flow system plays a crucial role.

Figure D16.6. Three-dimensional characteristics of a groundwater system (after Nielsen ed., 1991; modified). The results obtained from the monitoring well which is situated in the shallow part of the monitored groundwater system (B) may lead to wrong conclusion that adjacent source of pollution does not effect the near-by river since there is no contaminated water in the profile. A cluster of monitoring wells with short screens (2-4 m – cf. D) will allow correct assessment of the situation. Long screen (well C) may cause vertical flow through the well from points with slightly higher hydraulic head to the lower one.

The completion of a monitoring well may disturb natural groundwater flow and its chemical composition. Such processes like reaction of water with the materials of screen, casing, pumps, etc. (material factor), as well as hydrogeological processes like changing of hydraulic head or contact with atmosphere via a monitoring well should be taken into account (Jousma and Roelofsen, 2004; Nielsen ed., 2005). The design of monitoring wells should also include the materials used for construction. In the case of monitoring wells selected among existing exploitation and observation wells, thorough checking of the construction is necessary. The elements which may lead to non-representative sampling should be eliminated or, as a minimum requirement, described in the sampling protocols. Contamination may be the result of improper material used in well construction and equipment or may be due to materials and processes applied during well drilling and completion. Construction of a monitoring well should prevent inflow of water from other aquifer and/or from land surface. Good practice during design of a monitoring well is mainly attributed to: (i) proper localization of screen within the profile, (ii) suppressing of vertical flow through proper screening of the well, (iii) usage of non-reactive materials in well completion (D7 – Witczak et al., 2006).

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4.2 Quality of sampling

Quality requirements for sampling must be formulated in terms of the maximum acceptable uncertainty associated with the sampling process (cf. chapter 5). Steps involved in the process of groundwater sampling and typical sources of errors which contribute to the overall

uncertainty of sampling are summarized in Figure D16.7.

The water samples collected and analysed as a part of the monitoring effort should be

representative for the groundwater composition. The following general rules apply for selection of containers: (i) contamination by substances that could be leached from the material of the container should be absent; (ii) interaction between water sample and container (sorption, desorption, etc.) should be minimized. Before adopting new types of containers for sampling it is advisable to perform blank test.

Certain groundwater constituents and parameters are unstable and need special treatment during sampling and adequate transport conditions. The following processes can be held responsible for lack of stability: (i) precipitation of certain constituents (CaCO3, metal hydroxides,

phosphates, etc.) and/or volatilization of others (CO2, O2, H2S, CN, Hg, VOCs); (ii) oxidation of certain constituents by atmospheric oxygen (Fe²⁺, sulphides, organics); (iii) adsorption of heavy metals and some organic compounds on the walls of the container; (iv) microbiological activity in the sample (ISO 5667-3, 2003).

Figure D16.7. Steps involved in the process of groundwater sampling and sources of errors (after Barcelona et al., 1985).

Cross-contamination is a difficult problem associated with subsequent sampling of a series of monitoring wells, which comprise both contaminated and pristine parts of the monitored aquifer system (Parker, 1994). Good but expensive solution is the use of disposable equipment

(connecting tubes, filters, bailers etc.) for each well. Problem with cross-contamination is less severe when sampling begins at non-contaminated monitoring sites. Ideally, the water sample

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should have a minimum contact with the equipment and should be collected without contact with the atmosphere (D7 – Witczak et al., 2006).

Sampling in the field is often carried out under difficult conditions (dust, solar radiation, vapours of fuel, car exhausts, freezing, etc.). Thus, efforts should be made to minimize the influence of those factors in order to maintain required quality of sampling. Training of personnel involved in groundwater sampling process plays an important role (NORDTEST, 2003, 2005). Last not least, common sense in solving problems associated with sampling and monitoring is necessary.

4.3 Quality of analytical data

Chemical analyses of samples carried out in the framework of groundwater quality monitoring should be performed by an accredited laboratory which employs validated analytical methods.

In such case, the analytical uncertainties related to the analyzed constituents of groundwater will be known and reported.

It is a good practice to check the consistency of chemical data using well-established methods such as ionic balance of major ions and consistency of the data with respect to redox conditions in the analy zed water samples. Ionic balance is based on the assumption of electrical neutrality of the solution; the total charge of kations in the solution should be equal to the total charge of anions, within the assumed uncertainty level. The following ions should be determined as a minimum, to make such control reliable: Cl^-, SO4

2-, HCO3

-, K⁺, Na⁺, Mg²⁺ and Ca²⁺. The limits set for the uncertainty of the ionic balance will depend on the objectives of groundwater

monitoring. Typically, the limit between 5 and 10% is used.

Independently of the ionic balance method one may also scrutinize the available analytical data with respect to redox conditions occurring in the analy zed waters (DVWK, 1992). Table D16.2 summarizes examples of analyses which were classified as suspicious with respect to redox conditions.

Table D16.2. Examples of suspicious chemical analyses with regard to redox conditions after DVWK, 1992).

Redox indicator value Suspicious analyses of other elements Fe²⁺ > 0.05 mg/L

Mn²⁺ > 0.05 mg/L NO2

- > 0.05 mg/L NH4

+ > 0.1 mg/L O2 > 5 mg/L

H2S > 0.01 mg/L NO3

- > 2.0 mg/L Fe²⁺ > 0.2 mg/L

H2S > 0.1 mg/L NO3

- > 2.0 mg/L Mn²⁺ > 0.05 mg/L

H2S > 0.1 mg/L H2S > 0.1 mg/L

for 8.0 < pH > 5.5

NO3

- > 1.0 mg/L for (Ca²⁺ + Mg²⁺) > 1 mmol/L

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In addition to the uncertainty of the analytical results reported by the laboratory, they are other indicators of the quality of analytical work. They refer to the ability of the given

method/laboratory to measure or detect the given element (analyte) in the sampling target.

Limit of Detection (LOD)

According to IUPAC Compendium of Chemical Terminology (McNaught and Wilkinson ed., 1997) limit of detection (in analysis), is expressed as the concentration, cL, or the quantity, qL, derived from the smallest measure, xL, that can be detected with reasonable certainty for a given analytical procedure. The value of xL is given by the equation:

LOD = xL = xbi + ksbi

where xbi is the mean of the blank measurements, sbi is the standard deviation of the blank measurements, k is a numerical factor chosen according to the confidence level desired.

Typically, the factor k is chosen as 3 (Currie , 1995; Fleming et al., 1997).

Limit of Quantification (LOQ)

Limit of quantification is derived from the analogous expression as LOD, with the k factor typically chosen between 6 and 10 (Currie , 1995; Fleming et al., 1997; Konieczka et al. 2004).

Practical Limit of Detection (PLOD)

The LOD and LOQ values refer to laboratory conditions. To take into account possible impact of the sampling process on those values, the so-called practical limit of detection (PLOD) is often introduced. The PLOD is defined as the lowest concentration level that can be reliably achieved on field blank samples within specified limits of precision and accuracy, during routine laboratory operating conditions (Szczepanska and Kmiecik, 2005). The field blank samples are taken with the same equipment as the normal samples but the medium is high- purity deionized water. They are processed, transported and stored in the same way as normal samples.

Example of determination of LOD and PLOD values in a field study is shown in Table D16.3.

The PLOD values substantially higher than LOD values indicate that the sampling process is a source of significant uncertainty. Through quantification of the PLOD values the manager of the monitoring network can assess the quality of the sampling process and take appropriate

measures to improve its quality.

Specific problem related to the LOD, LOQ and PLOD values discussed above, is reporting of the analytical results which fall below those limit values. Various opinions exist in the literature on how to report such values. The following options exist (AMC, 2001): (i) not determined; (ii) less than LOD; (iii) a value of zero; (iv) an arbitrary fraction of LOD, e.g. LOD/2; (v) the result found, with a statement of its uncertainty. According to AMC (2001) the last option is the recommended one (reporting the value found, accompanied by its uncertainty) because it retains most information. Other authors (e.g. Edmunds and Shand ed., 2003; Grath et al., 2001; Nielsen ed., 2005) recommend option (iv) i.e. values below LOD are reported as LOD/2.

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Table D16.3. Example of quantification of LOD and PLOD values in a field study (after Szczepanska and Kmiecik, 2005).

Parameter Units LOD PLOD PLOD/LOD

Aluminium mg/L 0.015 0.08 5.3

Boron mg/L 0.005 0.23 46

Cadmium mg/L 0.001 0.001 1

Calcium mg/L 0.1 4.8 48

Chloride mg/L 5 5 1

Chromium mg/L 0.003 0.01 3.3

Copper mg/L 0.002 0.01 5

Fluoride mg/L 0.1 0.1 1

Iron mg/L 0.03 2.2 73.3

Lead mg/L 0.0017 0.0075 4.4

Magnesium mg/L 0.1 0.7 7

Manganese mg/L 0.01 0.03 3

Mercury mg/L 0.0002 0.0037 18.5

Nickel mg/L 0.001 0.02 20

Ammonia (NNH4) mg/L 0.04 0.1 2.5

Nitrate (NNO3) mg/L 0.1 0.2 2

Nitrite (NNO2) mg/L 0.001 0.003 3

Potassium mg/L 0.01 1.5 150

Sodium mg/L 0.1 0.1 1

Sulphate mg/L 10 10 1

Zinc mg/L 0.01 0.075 7.5

DOC mg/L 0.2 2.33 11.7

Trichloroethene mg/L 0.00003 0.019 633.3

Tetrachloroethene mg/L 0.000005 0.0056 1120

Anionic detergents mg/L 0.0001 0.0001 1

4.4 QA/QC programme

To maintain the required level of quality of the monitoring activities, appropriate measures should be undertaken. Usually they will have a form of a dedicated QA/QC programme, developed and executed along with the operation of the monitoring network. Such programme usually consists of two independent parts; one focused on field activities and another one on laboratory issues. An adequate QA/QC programme accompanying monitoring of groundwater quality should be obligatory for monitoring networks comprising more than 50 sites, and for smaller networks when results of monitoring provide the basis for making administrative decisions with financial consequences (Nielsen ed., 1991).

The dedicated QA/QC programme focused on field activities of the network, in addition to other measures, usually comprises collection of additional control samples during regular sampling campaigns. These control samples should constitute 10 to 30% of the overall number of samples collected within the network (Nielsen ed., 1991). The following types of control samples are usually collected (Kolpin, Burkart, 1991; Leidel et al., 1977; Witczak and Adamczyk, 1994;

Szczepanska, Kmiecik, 2005):

(i) field blank samples collected with the same equipment as was used for collecting the normal samples, but with high-purity deionized water as a sample medium. They should

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comprise at least 3% of the total number of samples collected. They were processed, transported and stored in the same way as normal samples and are used for determination of the practical limit of detection (PLOD);

(ii) samples collected from randomly selected sites as duplicates of the normal samples (at least 6% of the total number of samples collected). They are used for quantification of the uncertainty associated with sampling (cf. chapter 5);

(iii) spiked samples (at least 1% of the total number of samples collected) of known composition or a reference material addition, enabling the assessment of accuracy and thus the detection of systematic errors and biases during sampling;

Three separate quality classes for field measurements have been defined (Table D16.4). The definitions of classes combine the type of measurement obtained with the intended use of the measurements in order to enable establishment of viable and sufficient but not excessive quality requirements.

Groundwater monitoring for assessment of quality status falls into category of quantitative methods (compliance testing of concentration against maximum value).

Table D16.4 Definition of quality classes for field measurements (after Gron et al., 2005).

Figure D16.8. Costs as a function of uncertainty (after AMC, 2005; Eurachem, 2006). Line A represents the costs of measurement, Line B the costs of incorrect decisions. The sum of these costs shows a minimum at uncertainty C.

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According to AMC (2005), specific quality requirements for sampling and analysis should be formulated in terms of the maximum acceptable uncertainties associated with these steps. In practice, this formulation will be guided by two factors: the existing regulations and the costs.

Smaller uncertainty means that the chance of making incorrect decision is smaller (Figure D16.8). However, at the same time more money should be put into the monitoring effort (more sampling points, sophisticated analytical tools).

5. Recommended approach for assessing uncertainties associated with sampling of groundwater

Reliable assessment of groundwater quality is a principal goal of monitoring activities. This assessment can be done in two ways (Figure D16.9): deterministic and probabilistic. In the deterministic approach the results of monitoring are taken as granted and compared against the adopted threshold value(s) to make decision with respect to good or poor chemical status of investigated groundwater. The probabilistic approach takes the uncertainties associated with sampling and measurement into account in the decision process.

Figure D16.9. Comparison between (a) deterministic and (b) probabilistic classification of the chemical status of groundwater body (after Ellison et al., 2000; Ramsey, 2002, 2004; IAEA, 2004; modified). xstands for mean concentration of the given element derived for the

monitored groundwater body. Expanded uncertainty attached to the mean values is labeled by U.

In the process of assessment of chemical status of GWB, the results of analyses from individual monitoring points are aggregated into single numbers representing the elements being

monitored, by averaging the individual results. The minimum number of points allowing meaningful averaging is three, according to Grath et al. (2001). In the framework of

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deterministic approach, the average value of the given element is then compared with appropriate threshold value (TV) leading to the statement about good or poor status of GWB (GWD, 2005).

In the probabilistic approach the uncertainty becomes a part of the decision process. Although various specific rules can be defined in this respect, some general recommendations exist (Eurachem, 2006; Ellison et al., 2000). One should decide whether the decision requires proof of compliance, proof of non-compliance, or a ‘shared risk’ approach and set an appropriate level of confidence. For proof of compliance, the result and its uncertainty interval must be entirely within the permitted range. This might be the case of testing chemical status of groundwater against threshold values. For proof of non-compliance, the result and its uncertainty interval must be entirely outside the permitted range. For shared risk approaches, one should set a range for acceptable measurement results based on the permitted interval, adjusted to provide a specified probability of false acceptance and false reje ction rates.

It is evident that both individual results of monitoring and the average values used in the decision process are associated with uncertainty which is created in the course of determination of groundwater quality. The need for recognizing uncertainty of sampling and measurement as an important element of assessment of groundwater quality has been expressed by Working Group C GW1. In their Monitoring Guidance for Groundwater (Grath et al., 2006) they recommend: “Estimates of the confidence in the monitoring results should be determined and reported in accordance with WFD requirements. The reported confidence must as a minimum describe the uncertainty arising from the monitoring processes and the variability (in time or space) of the parameters monitored. Moreover, the Working Group recommends further in this document that “If the initially required confidence has not been obtained, the consequences for the monitoring objectives must be evaluated and the need for adjustment of the monitoring programme specified”. This leads to specific quality requirements with respect to design and functioning of the monitoring system (cf. chapter 4).

As pointed out above, in case of groundwater quality monitoring, the variability of the

monitored elements stems from three main sources: (i) spatial and/or temporal variability of the given element across the monitored system (between-targets variance or geochemical variance), (ii) errors during sampling, transport and storage (sampling variance), (iii) analytical errors (analytical variance):

s²total = s²geochemical + s²sampling + s²analytical

Best estimate of this variability is expressed by corresponding statistical estimates of variances:

s²total = s²geochemical + s²sampling + s²anal ytical

Classical statistics can be employed to express standard uncertainty on the mean u as s/√n, where the standard deviation (s) is divided by the square root of the number of measurements (n) used for its calculation. This shows that as n increases, the uncertainty on the mean value decreases. However, the estimate of standard deviation has also uncertainty, expressed as a standard error on the standard deviation (s/√2n). The uncertainty on the mean value is therefore multiplied by the value of the 't' statistic that reflects this extra uncertainty for low value of n.

Therefore the expanded uncertainty of mean U:

U = n

s t⋅

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Next steps accompanying the assessment of chemical status of GWB will depend on the value of the calculated standard uncertainty of the mean. They are outlined below:

(i) the expanded uncertainty of the mean value (U) derived in the above outlined way is compared to the assumed maximum acceptable uncertainty (Umax). If U < Umax, the calculated mean value is compared to the TV value and the decision is made using deterministic approach. In this case the calculated uncertainty of the mean has only informative meaning.

(ii) if U = Umax, specific action is required to improve the situation and reduce the uncertainty below the preset level. As indicated above, the uncertainty attached to the mean is

composed of three elements and efforts should be directed towards minimizing one or more components of the total variance. Because contributions of these partial uncertainties to the overall uncertainty associated with the mean values are a priori not known, reduction of this overall uncertainty below the required limit should be preceded by identification of its structure. This can be done in the framework of the empirical approach outlined above.

Uncertainty related to geochemical variabilit y can be reduced to the required level by increasing the number of monitoring points, guided by the conceptual model of the system.

Reduction of analytical errors can be reached by using appropriate analytical methods and instruments and by adequate QA/QC procedures in the laboratory (D7 – Kalevi and Gustafsson, 2006). The third component (uncertainty associated with sampling) can be reduced via appropriate sampling methodologies and protocols.

Selection of adequate maximum acceptable uncertainty (Umax) against which the calculated uncertainty of the mean value is compared, is not a straightforward process. It should be guided by the knowledge of the system and the objectives of the monitoring. In any case, it will always involve some degree of expert judgment. If the geochemical variability is relatively low and the mean value is significantly lower than the threshold value, one may adopt certain fraction of TV as Umax. One may also adopt Umax taking into account the required precision of determination of the given element as reported by DWD (1998).

In cases when geochemical variability is dominating, Umax can be derived from determination of natural background level (NBL) for the given element in the monitored GWB. One can adopt the following operational relationship to derive Umax in such cases:

Umax = k’⋅UNBL

where UNBL is the calculated standard deviation for the given element, derived using the natural distribution within the monitored GWB, divided by the square root of the number of monitoring sites (n) used to derive the mean value used for the status assessment n, and multiplied by the value of 't' statistic that reflects extra uncertainty for low value of n. Numerical factor k’ allows for broadening of the natural distribution of the given element due to anthropogenic influences in the system. The numerical value of this factor is usually determined by expert judgment. This leads to the following expression for Umax:

Umax =

n s k't⋅ ^NBL

In the following chapters (chapters 5.1 and 5.2) individual steps in the process of assessing the overall uncertainty of the mean and its components using the empirical approach are briefly discussed. They are illustrated by ‘real-world’ examples presented in the Annexes.

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5.1 Duplicate sampling

Several methods exist in the framework of the empirical approach outlined in chapter 3.3.1 to estimate the uncertainty resulting from sampling,. They are summarized in Table D16.5.

The duplicate method (No.1) is the simplest and probably most cost-effective of the four methods presented in Table D16.5 (Eurachem, 2006). The duplicate samples (test samples) are obtained using a single sampling protocol and by a single person (sampler). In this method, when applied to monitoring of chemical status of groundwater, the sampler is taking a small portion of groundwater samples in duplicates. The duplicates are taken from at least eight monitoring sites, randomly selected, representing typical composition of the monitored GWB.

Duplicated samples are taken by repeating the same sampling protocol, with permitted variations that reflect the ambiguity in the sampling protocol. All duplicate samples are transported to the laboratory together with other samples where test portions are drawn from both test samples and analysed in duplicate. This system of duplicated sampling and chemical analysis is known as a ‘balanced design’ (Figure D16.10). It should be noted that the duplicate method does not include any contribution from sampling bias, which must be estimated separately using, for example, multiple samplers, multiple protocols and/or inter-organizational sampling trials, as in the other three methods shown in Table D16.5.

Table D16.5. Empirical approaches to derive combined uncertainty including sampling (Eurachem, 2006).

Components estimated No. Method Samplers Protocols

Psamp Bsamp Panal Banal

1 Duplicates Single Single Yes No Yes No¹

2 Protocols Single Multiple Between protocols Yes No¹

3 CTS Multiple Single Between samplers Yes Yes²

4 SPT Multiple Multiple Between protocols +between samplers

Yes Yes²

1Analytical bias information may be obtained by including certified reference materials in the analytical run;

2Analytical bias is partially or completely included in collaborative exercises where multiple laboratories are involved;

(Panal – precision of analytical method, Banal – bias of analytical method, Psamp – precision of sampling method, Bsamp – bias of sampling method, CTS – Collaborative Trial in Sampling, SPT – Sampling Proficiency Test)

The test portions are then analysed anonymously by appropriate analytical method under repeatability conditions (e.g. distributed randomly within an analytical batch). If estimates of the analytical portion of the measurement uncertainty have been made independently by the

laboratory, this will be useful for comparison with estimates made by this method.

The balanced design outlined above makes allowance only for random errors associated with sampling and analysis. Assessment of eventual analytical biases and/or systematic errors induced by sampling process requires additional measures (cf. Table D16.5).

If only one monitoring site exists, then all eight duplicates can be taken from it, but the uncertainty estimate will only be applicable to that one site (Figure D16.11).

If analytical uncertainty is known or assessed independently, the sampling scheme can be simplified by analysing only two duplicated samples per site.

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Figure D16.10. (a) - Balanced design for duplicate sampling (after Eurachem, 2006; modified).

(b) – simplified version of unbalanced duplicate method with only one analysis per sample for calculation geochemical variance and measurement (sampling + analytical) variance (after Garret and Goss, 1980)

Figure D16.11. Strategy of duplicate sampling and analysis for the assessment of combined uncertainty during monitoring of chemical status of GWB (after Thompson, 1998; modified).

Left-hand panel: n monitoring sites in GWB. Right-hand panel: single monitoring well.

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5.2 Calculation of uncertainty and its components

The results of analysis of duplicate samples collected in the monitoring network provide the basis for quantitative assessment of the combined uncertainty associated with monitoring of groundwater quality. Differences between analyses of normal and duplicate samples collected from the same site allow quantitative estimation of uncertainty associated with sampling while differences between separate analyses of aliquots of the same sample lead to estimation of analytical uncertainty. When a simplified version of the balanced design for duplicate sampling is adopted, only combined uncertainty linked to sampling and analysis can be derived.

5.2.1 Verification of data

Verification of analytical data obtained in the course of implementing the empirical approach of uncertainty assessment comprises two major steps: (i) verification of consistency of chemical analyses using ionic balance and redox conditions as tools; (ii) identification and rejection of outliers. Step (i) has been described in some detail in chapter 4.3. Identification of outliers can be done using various tools. If anomalous results are caused by evident malfunction of

instrumentation or unexpected problems occurring during sampling, they can be flagged and removed by the sampler already in the field. Various methods have been developed to assess the quality of analytical data with respect to occurrence of outliers.

Application of three different methods (scatter plot, chart of differences and range chart) for testing the quality of analytical determinations of Zn content in groundwater, in a set of 10 pairs (normal and duplicate samples) is illustrated in Figure D16.12. It is apparent that two outliers have been consistently identified with all three methods. In addition to charts, also standard statistical methods for outlier rejection such as Q-Dixon, Graf or Grubbs tests, can be adopted.

A B C

Figure D16.12. Examples of graphical tests for outlier identification in the framework of empirical approach of uncertainty assessment (measured element: Zn). A – scatter plot; B – chart of differences; C – range chart (absolute differences between normal and duplicate samples). The control limits (broken line and heavy red line) are set in the following way: for n=2 (pair of samples); s = mean range/1.128; upper warning limit (broken line) is +2.83s;

upper action limit is +3.69s (NORDTEST, 2006).

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5.2.2 Analysis of variance

The analysis of variance can be carried out using ROBAN code which is based on ANOVA analysis. ROBAN code is available from http://team.sp.se/analyskvalitet/sampling/default.aspx.

Although both verified and unverified sets of data can be used as input data for ROBAN calculations , the code is able to handle only 10% of anomalous values in the analysed dataset.

Therefore, it is recommended to carry on verification of the data using the methods outlined in the previous chapter (5.2.1.). The ROBAN code is designed to handle data sets derived from the balanced design of duplicate method (four analyses per site and element). Examples illustrating the use of ROBAN code are presented in the Annexes.

In cases when simplified version of the duplicate method is being adopted (two analyses per site and element) the analysis of variance can be done using an earlier version of the ROBAN code (ROB2 – Ramsey, 1998). Examples of using the simplified version of the duplicate method are presented in Annexes.

The ROBAN code, when used together with the balanced design of the duplicate method, provides independent estimates of all three components of the total variance (geochemical, sampling, analytical) and their percentage contribution.

s²total = s²geochemical + s²sampling + s²analytical

In addition, it calculates standard (u) and relative (U’) uncertainties associated with these components.

ugeochemical = sgeochemical

usampling = ssampling

uanalytical = sanalytical

The expanded uncertainty, for example for approximately 95% confidence level, requires the value of standard uncertainty to be multiplied by a coverage factor of 2 (Ellison et al., 2000).

The expanded uncertainty (U) is then calculated as U = 2u:

Ugeochemical = 2⋅sgeochemical

Usampling = 2⋅ssampling

Uanalytical = 2⋅sanalytical

ROBAN code reports also relative uncertainties (U’) relative to the mean values of the analyzed element for normal and duplicate samples:

U’geochemical = 2 100

x s_geochemica_l

⋅ (%)

U’sampling = 2 100

x s_sampling

⋅ (%)

U’analytical = 2 100

x s_analytical

⋅ (%)

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The relative uncertainty is more widely applicable than the standard uncertainty, as it does not change appreciably as a function of concentration at values well above the analytical limit of detection (> 10 times). Other coverage factors can be selected as appropriate.

The analysis of variance allows an insight into the structure of the total uncertainty associated to the determination of the chemic al status of GWB. Figure D16.13 shows limiting values for relative contributions to the total variance originating from measurement (sampling and analysis) and from analysis alone, as suggested by Ramsey et al. (1992). If the relative

contributions to the total variance obtained with the aid of duplicate method are higher than the values shown in Figure D16.13, specific action is required to reduce them.

If the requirement shown in Figure D16.13 is fulfilled, and despite of that the overall uncertainty is still higher than the maximum allowable uncertainty (U > Umax cf. discussion above) efforts should be made to reduce the geochemical variance by increasing the number of monitoring sites.

Table D16.6. Proposed maximum acceptable uncertainty (Um) related to the maximum permissible levels (MPL) for the selected elements and their required precision (DWD, 1998).

Parameters Maximum

Permissible Level, MPL

( mg/L )

Required precision of

MPL, PR

( % )

Proposed maximum acceptable uncertainty Um

Um = MPL×PR

( mg/L )

Aluminium 0.2 10 0.020

Ammonium 0.5 10 0.050

Antimony 0.005 25 0.0013

Arsenic 0.01 10 0.0010

Benzene 0.001 25 0.00025

Boron 1 10 0.10

Cadmium 0.005 10 0.00050

Chloride 250 10 25

Chromium 0.05 10 0.0050

Conductivity 2 500 10 250

Copper 2 10 0.20

Cyanide 0.05 10 0.0050

Fluoride 1.5 10 0.15

Iron 0.2 10 0.020

Lead 0.01 10 0.0010

Manganese 0.05 10 0.0050

Mercury 0.001 10 0.00010

Nickel 0.02 10 0.0020

Nitrate 50 10 5.0

Nitrite 0.5 10 0.050

Pesticides 0.0001 25 0.000025

Selenium 0.01 10 0.0010

Sodium 200 10 20

Sulphate 250 10 25

Tetrachloroethene and Trichloroethene

0.01 25 0.0025

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Deliverable 16: Summary Guidance and Recommendations on Sampling, Measuring and Quality Assurance.

Contents

1. Introduction

2. General rules of collecting representative groundwater samples

3. Overview of existing approaches for assessment of uncertainty associated with sampling process

4. Quality requirements for monitoring

5. Recommended approach for assessing uncertainties associated with sampling of groundwater