D16 WP3-2 sampl,meas,qa-qc Final2...pling, Measuring and Quality Assurance. Draft report . 16-3 Annexes Annex 1. Definitions and terminology Annex 2. Assessing uncertainties associated

Deliverable 16: Summary Guidance and Recommendations on Sam-

pling, Measuring and Quality Assurance. Draft report.

16-1

Contract n° 006538 (SSPI)

BBRRIIDDGGEE

BBaacckkggrroouunndd ccRRiitteerriiaa ffoorr tthhee IIDDeennttiiffiiccaattiioonn ooff GGrroouunnddwwaatteerr tthhrrEEsshhoollddss

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: [email protected]

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)



16-2

Contents

1. Introduction ................................................................................................................4

2. General rules of collecting representative groundwater samples.............................5

2.1 Spatial representativity......................................................................................5

2.2 Temporal representativity .................................................................................6 3. Overview of existing approaches for assessment of uncertainty associated with sampling process ..............................................................................................................8

3.1 Fundamental concepts......................................................................................8

3.2 Sampling as a source of uncertainty of measurement.....................................9

3.3 Approaches to uncertainty estimation ............................................................10

3.3.1 Empirical approach.....................................................................................10

3.3.2 Modelling approach.....................................................................................12 4. Quality requirements for monitoring ........................................................................13

4.1 Quality of design .............................................................................................13

4.2 Quality of sampling .........................................................................................15

4.3 Quality of analytical data.................................................................................16

4.4 QA/QC programme.........................................................................................18

5. Recommended approach for assessing uncertainties associated with sampling of groundwater ....................................................................................................................20

5.1 Duplicate sampling..........................................................................................23 5.2 Calculation of uncertainty and its components...............................................25

5.2.1 Verification of data ......................................................................................25

5.2.2 Analysis of variance....................................................................................26

6. Assessing uncertainties associated with the assessment of trends.......................28

7. Conclusions .............................................................................................................28

8. References...............................................................................................................29

STATUS, CONFIDENTIALITY AND ACCESSIBILITY

Status Confidentiality Accessibility

S0 Approved/Released PU public Work-space

S1 Reviewed PP Restricted to other programme participants (including the Commission Services)

Internet

S2 Pending for review RE Restricted to a group specified by the consor-tium (including the Commission Services) Paper

S3 Draft for comments CO Confidential, only for members of the consor-tium (including the Commission Services)

S4 Under preparation



16-3

Annexes

Annex 1. Definitions and terminology Annex 2. Assessing uncertainties associated with sampling of groundwater:

GWB monitoring network (ca. 1000 km2) Annex 3. Assessing uncertainties associated with sampling of groundwater:

regional monitoring network (ca. 50000 km2). Annex 4. Assessing uncertainties associated with sampling of groundwater:

country wide monitoring network (ca. 300000 km2) Annex 5. Assessing of practical limit of detection (PLOD) for regional monitoring

network Annex 6. Assessing uncertainties associated with the assessment of trends



16-4

1. Introduction 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



16-5

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.



16-6

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

• It describes how hydrogeologistsassume 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 series 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.



16-7

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.



16-8

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 publications.

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):



16-9

− 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



16-10

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.



16-11

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 sampling 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 ε sampling, 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 2

meas = σ2sampling + σ2

analytical where σ2

sampling is 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: s2

meas = s2sampling + s2

analytical The standard uncertainty of measurement (u) can be then identified with the square root of s2

meas, given by:

u ≡ 2analytical

2samplingmeas sss +=



16-12

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 σ2

tota l is then given by: σ2

total = σ2between-targets + σ2

sampling + σ2analytical

and its best estimate becomes: s2

total = s2between-targets + s2

sampling + 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 individual 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.



16-13

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 problems 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).



16-14

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).



16-15

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



16-16

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 analyzed 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 analyzed 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



16-17

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.



16-18

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



16-19

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.



16-20

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). x stands 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



16-21

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 rejection 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): s2

total = s2geochemical + s2


Best estimate of this variability is expressed by corresponding statistical estimates of variances: s2



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 = nst ⋅



16-22

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

stk' 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.



16-23

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.



16-24

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.



16-25

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).



16-26

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. s2



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 = 1002

xs lgeochemica⋅

(%)

U’sampling = 1002

xssampling⋅

(%)

U’analytical = 1002

xsanalytical⋅

(%)



16-27

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 chemical 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



16-28

It should be noted that there are other measures of quality of sampling and analytical process with respect to chemical analyses of water. They state that maximum allowable uncertainty of analysis (Um) should lie between 10 % and 25% of the limit value if the results of monitoring provide the basis for administrative decisions (Table D16.6 – see DWD, 1998). Therefore is it suggested that these quality indicators are applied independently of the limit values proposed by Ramsey et al. (1992).

Maximum measurement variance, 20% of total variance

Maximum analytical variance, 4% of total variance

Geochemical variance Figure D16.13. Maximum allowable relative contributions of sampling and analytical variances to the total variance associated with the assessment of chemical status of groundwater body (Ramsey et al., 1992).

6. Assessing uncertainties associated with the assessment of trends Apart from assessment of chemical status of the given groundwater body, monitoring will have another important function during implementation of GWD. Namely, it will be employed to detect trends and trend reversals of groundwater quality. Specific methodology has been proposed for this purpose (Grath et al., 2001). However, as in the case of status assessment, detection of trends and trend reversals is inevitably associated with uncertainties. The first question which can be asked in this regard may concern the ability of the given monitoring site to detect the preset level of deterioration of groundwater quality (e.g. 20%) with the assumed confidence level (e.g. 95%). This can be reached by assessment of measurement uncertainty in the framework of empirical approach discussed above. (cf. Figure D16.11- right-hand panel). Example of such assessment can be found in Grath et al. (2006) and NORDTEST (2005) or at http://www.samplersguide.com.

7. Conclusions The identification and determination of uncertainties associated with sampling, preservation and transport of samples is an important part of the overall monitoring effort. The implications of recognizing the sampling process as an integral part of the monitoring activities are far reaching. They include also management issues. The strict procedures that are being applied for assessing and improving quality of the laboratory work should be applied equally to the sampling



16-29

procedures. The responsibility for the quality of the overall monitoring effort should ideally rest with one organization, and responsibilities for different parts of the process must additionally be defined. Similarly, one organization should take responsibility for estimating the uncertainty associated with the monitoring process, based on information from all participants of this endeavor. Adequate understanding of uncertainty arising from sampling process must be embedded in the broader perspective of fitness for purpose. Generally applicable approach to judging the fitness for purpose of measurements is to consider the effect of the measurement on its ultimate purpose. All analytical measurements are undertaken to support a decision. A decision can be either correct or incorrect. An incorrect decision involves extra costs, and an incorrect decision is more likely if the uncertainty is higher. However, reducing the uncertainty of a measurement result requires rapidly escala ting costs. The true cost of a decision is the sum of the measurement costs and the excess costs of incorrect decisions. This sum has a minimum value at some particular level of uncertainty, and this uncertainty can be identified with the fitness for purpose. The concept of fitness for purpose applies entirely for monitoring of groundwater quality.

8. References AMC (2001). What should be done with results below the detection limit? Mentioning the unmentionable. Royal

Society of Chemistry. Analytical Methods Committee Technical Brief No 5. AMC (2004). What is uncertainty from sampling, and why is it important? Royal Society of Chemistry. Analytical

Methods Committee Background Paper No 1. AMC (2005). Analytical and sampling strategy, fitness for purpose, and computer games. Royal Society of

Chemistry. Analytical Methods Committee Technical Brief No 20. Barcelona, M.J., J.P. Gibb, J.A. Helfrich and E.E. Garske (1985). Practical guide for ground-water sampling. SWS

Contract Report 374. Illinois State Water Survey, Department of Energy and Natural Resources, Champaign, Illinois: 94.

Broers, H. P. (2004). The spatial distribution of groundwater age for different geohydrological situations in the Netherlands: implications for groundwater quality monitoring at the regional scale. Journal of Hydrology 299: 84-106.

Broers, H.P. and B. Van der Grift (2004). Regional monitoring of temporal changes in groundwater quality. Journal of Hydrology 296: 192-220.

Broers, H.P. and F.C. Van Geer (2005). Monitoring strategies at phreatic wellfields: a 3D travel time approach. Ground Water 43: 850-862.

Codex (2004): General Guidelines on Sampling. CAC/GL-2004: 69. http://www.codexalimentarius.net/download/standards/10141/CXG_050e.pdf

Codex (2006). Uncertainty of Sampling. CX/MAS 06/27/10: 13. Currie, L.A. (1995). Nomenclature in Evaluation of Analytical Methods Including Detection and Quantification

Capabilities (IUPAC Recommendations 1995). Pure and Applied Chemistry 67/10: 1699-1723. de Zorzi, P., M. Belli, S. Barbizzi, S. Menegon and A. Deluisa (2002). A practical approach to assessment of

sampling uncertainty. Accreditation and Quality Assurance 7: 182-188. de Zorzi, P., S. Barbizzi, M. Belli, G. Ciceri, A. Fajgelj, D. Moore and U. Sansone (2003). Terminology in Soil

Sampling (IUPAC Recommendations 2003). Draft 7. International Union of Pure and Applied Chemistry, Analytical Chemistry Division: 15.

DVWK (1992). Entnahme und Untersuchungs umfang von Grundwasser proben. DVWK-Schriften 128: 36. DWD (1998). COUNCIL DIRECTIVE 98/83/EC of 3 November 1998 on the quality of water intended for human

consumption. Official Journal of the European Communities L 330, 5.12.98. Edmunds, W.M. and P. Shand ed. (2003). Natural Baseline Quality in European Aquifers: A Basis for Aquifer

Management. Final Contract Report FP5 BASELINE EVK1-CT1999-0006: 130. Ellison, S.L.R. and V.J. Barwick (1998). Estimating measurement uncertainty: reconciliation using a cause and effect

approach. Accreditation and Quality Assurance 3: 101-105. Ellison, S.L.R., M. Rosslein and A. Williams, eds. (2000). Quantifying Uncertainty in Analytical Measurement. 2nd

edition. EURACHEM/CITAC Guide CG4: 120. EPA (2002). Guidance on Choosing a Sampling Design for Environmental Data Collection for Use in Developing a

Quality Assurance Project Plan. EPA/240/R-02/005. U.S. Environmental Protection Agency: 168.



16-30

Eurachem (2006). Estimation of measurement uncertainty arising from sampling, 6th Committee Draft of the Eurachem/EUROLAB/CITAC/Nordtest Guide, April 2006

Fleming, J., H. Albus, B. Neidhart and W. Wegscheider (1997). Glossary of analytical terms (VII). Accreditation and Quality Assurance 2: 51-52.

Foster, S.S.D., P.J. Chilton and R. Helmer (2004). Groundwater Quality Monitoring. Taylor & Francis Books Ltd: 304.

Garrett, R.G. and Goss, T.I. (1980) UANOVA: Fortran IV program for unbalanced nested analysis of variance. Comput. Geosci., 6: 35-60

Grath, J., A. Scheidleder, S. Uhlig, K. Weber, M. Kralik, T. Keimel and D. Gruber (2001). The EU Water Framework Directive: Statistical aspects of the identification of groundwater pollution trends, and aggregation of monitoring results. Final Report. Austrian Federal Ministry of Agriculture and Forestry, Environment and Water Management (Ref.: 41.046/01-IV1/00 and GZ 16 2500/2-I/6/00), European Commission (Grant Agreement Ref.: Subv 99/130794), in kind contributions by project partners. Vienna. www.wfdgw.net.

Grath, J. et al. (2006). Monitoring Guidance for Groundwater. Final draft (v. 10.0). Drafting group GW1 Groundwater Monitoring: 47.

Gron, C., J.A. Falkenberg, J.S. Andersen, M. Borresen, A. Pettersen, S. Nilsson, K. Hakansson and J. Laiho (2005). Quality Control Manual for Field Measurements. NORDTEST report TR 581, project 04210: 86. http://www.nordicinnovation.net/nordtestfiler/tec581.pdf

GWD (2005). Directive of the European Parliament and of the Council on the protection of groundwater against pollution. Draft.

Hart, A., D. Müller et al. (2006). D15: Preliminary Methodology to derive Environmental Threshold Values. BRIDGE Project: 48. http://www.wfd-bridge.net (10.02.2006)

IAEA (2004). Soil sampling for environmental contaminants. IAEA-TECDOC-1415: 75. ISO (1993). Guide to the Expression of Uncertainty in Measurement. Geneva, ISBN 92-67-10188-9. ISO 5667-11 (1993). Water quality -- Sampling -- Part 11: Guidance on sampling of groundwaters. ISO 5667-14 (1998). Water quality -- Sampling -- Part 14: Guidance on quality assurance of environmental water

sampling and handling. ISO 5667-18 (2001). Water quality -- Sampling -- Part 18: Guidance on sampling of groundwater at contaminated

sites. ISO 5667-3 (2003). Water quality -- Sampling -- Part 3: Guidance on the preservation and handling of water samples. ISO/IEC 17025 (2005). General requirements for the competence of testing and calibration laboratories. ISO 5725-1:1994/Cor 1 (1998) -- Accuracy (trueness and precision) of measurement methods and results -- Part 1:

General principles and definitions. ISO 5725-2:1994/Cor 1 (2002). Accuracy (trueness and precision) of measurement methods and results -- Part 2:

Basic method for the determination of repeatability and reproducibility of a standard measurement method. ISO 5725-3:1994/Cor 1 (2001). Accuracy (trueness and precision) of measurement methods and results -- Part 3:

Intermediate measures of the precision of a standard measurement method. ISO 5725-4 (1994). Accuracy (trueness and precision) of measurement methods and results -- Part 4: Basic methods

for the determination of the trueness of a standard measurement method. ISO 5725-5:1998/Cor 1 (2005). Accuracy (trueness and precision) of measurement methods and results -- Part 6: Use

in practice of accuracy values. ISO/TS 21748 (2004). Guidance for the use of repeatability, reproducibility and trueness estimates in measurement

uncertainty estimation. Jousma, G. and F.J. Roelofsen (2004). World-wide inventory on groundwater monitoring. IGRAC, Utrecht. Kalevi, K. and J. Gustafsson (2006). D7, Chpt.9: Analytical aspects concerning to set threshold values for substances

in groundwater. [In:] Griffioen J. et al. (2006). D7: State-of-the-art knowledge on behaviour and effects of natural and anthropogenic groundwater pollutants relevant for the determination of groundwater threshold values. BRIDGE Project. Final reference report : 9.1-9.15. www.wfd-bridge.net (03.07.2006)

Kolpin, D.W. and M.R. Burkart (1991). Work plan for regional reconnaissance for selected herbicides and nitrate in ground water of the Mid-continental United States. U.S. Geological Survey Open-File Report 91-59: 18.

Konieczka, P., J. Namiesnik, B. Zygmunt, E. Bulska, A. Switaj-Zawadka, A. Naganowska, E. Kremer, M. Rompa (2004). Quality assessment and quality control of analytical results – QA/QC. Gdansk, Centre of Excellence in Environmental Analysis and Monitoring.

Kurfurst, U., A. Desaules, A. Rehnert and H. Muntau (2004). Estimation of measurement uncertainty by the budget approach for heavy metal content in soils under different land use. Accreditation and Quality Assurance 9: 64-75.

Leidel, N.A., K.A. Busch and J.R. Lynch (1977). Occupational exposure sampling strategy manual. USDHEW, NIOSH Publication No. 77-173.

Love, J.L. (2002). Chemical metrology, chemistry and the uncertainty of chemical measurements. Accreditation and Quality Assurance 7: 95-100.



16-31

Magnusson, B., T. Naykki, H. Hovind and M. Krysell (2004). Handbook for Calculation of Measurement Uncertainty in Environmental Laboratories. 2nd edition. NORDTEST report TR 537, project 1589-02: 41. http://www.nordicinnovation.net/nordtestfiler/tec537.pdf

McNaught, A.D. and A. Wilkinson ed. (1997). IUPAC Compendium of Chemical Terminology “The Gold Book”. 2nd edition. IUPAC website: http://www.chem.qmul.ac.uk/iupac/bibliog/gold.html

Nielsen, D.M. ed. (1991). Practical handbook of ground-water monitoring. Lewis Publishers: 717. Nielsen, D.M. ed. (2005). Practical handbook of environmental site characterization and ground-water monitoring.

2nd ed. CRC Press: 1328. NORDTEST (2003). Certification of Samplers: A Nordtest Certification Scheme Proposal. Approved 2003-05

NORDTEST DOC GEN 049: 6. NORDTEST (2005). NORDTEST Sampler Certification. Scheme handbook v. 1-0. NT ENVIR 008: 60. NORDTEST (2006). Internal Quality Control – Handbook for Chemical Laboratories. 2nd edition. NORDTEST

report TR 569: 46. http://www.nordicinnovation.net/nordtestfiler/tec569_ed_2.pdf

Quevauviller, P. ed. (1995). Quality Assurance in Environmental Monitoring—Sampling and Sample Pre-treatment. VCH Publishers, Weinheim: 335.

Quevauviller, P. (2005). Groundwater monitoring in the context of EU legislation: reality and integration needs. Journal of Environmental Monitoring 7: 89-102.

Parker, L.V. (1994). The Effects of Ground Water Sampling Devices on Water Quality: A Literature Review. Ground Water Monitoring and Remediation: 130-141.

Prichard, E. (2004). QUACHA Training Course Book QUality Assurance for CHemical Analysis. SWIFT-WFD. Screening methods for Water data InFormaTion in support of the implementation of the Water Framework Directive. SWIFT-WFD: [email protected]; http://www.ema.fr/infos_recherche/i_recherche_LGEI-Swift.html

Ramsey, M.H. (1998). Sampling as a source of measurement uncertainty: techniques for quantification and comparison with analytical sources. Journal of Analytical Atomic Spectrometry 13: 97-104.

Ramsey, M.H. (2002). Appropriate rather than representative sampling, based on acceptable levels of uncertainty. Accreditation and Quality Assurance 7: 274-280.

Ramsey, M.H. (2004). Improving the Reliability of Contaminated Land Assessment using Statistical Methods: Part 1 - Basic Principles and Concepts. CL:AIRE technical bulletin TB7: 4.

Ramsey, M.H., M. Thompson, M. Hale (1992). Objective evaluation of the precision requirements for geochemical analysis using robust analysis of variance. Journal of Geochemical Exploration 44: 23-36.

Ramsey, M.H., A. Argyraki and M. Thompson (1995). On the Collaborative Trial in Sampling. Analyst 120: 2309-2312.

Scheidleder, A. et al. (2006 D17, Report on the integrated data aggregation methodology . BRIDGE Project. Final reference report.

Szczepanska, J. and E. Kmiecik (2005). Estimation of groundwater chemical status on the base of monitoring investigation (in polish). Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, Krakow: 278.

Taylor, B.N. and Ch.E. Kuyatt (1994). Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. NIST Technical Note 1297: 20.

Thompson, M. (1998). Uncertainty of sampling in chemical analysis. Accreditation and Quality Assurance 3: 117-121.

Thompson, M. (1999). Sampling: the uncertainty that dares not speak its name. Journal of Environmental Monitoring 1: 19N-21N.

Thompson, M. and M. Maguire (1993). Estimating and Using Sampling Precision in Surveys of Trace Constituents of Soils. Analyst 118: 1107-1110.

Thompson, M., B.J. Coles and J.K. Douglas (2002). Quality control of sampling: Proof of concept. Analyst 127: 174-177.

van der Veen, A.M.H. and A. Alink (1998). A practitioner’s approach to the assessment of the quality of sampling, sample preparation, and subsampling. Accreditation and Quality Assurance 3: 20-26.

Walraevens, K. et al. (2003). Baseline Trends. [In:] Edmunds, W.M. and P. Shand, ed. (2003). Natural Baseline Quality in European Aquifers: A Basis for Aquifer Management. Final Contract Report FP5 BASELINE EVK1-CT1999-0006: 130.

WFD CIS Guidance Document No. 7 (2003). Monitoring under the Water Framework Directive. Produced by Working Group 2.7 – Monitoring. ISBN 92-894-5127-0. European Communities.

WFD CIS WG_C (2005). Groundwater summary report. Technical report on groundwater body characterization, monitoring and risk assessment issues as discussed at the WG C workshop in 2003-2004.

Witczak, S. and A. Adamczyk (1994). Guidebook on pollutants and indicator of pollution, Volume I (in polish). State Env. Agency, Warsaw: 111.

Witczak, S., J. Kania and K. Rozanski (2006). D7, Chpt.10: Sampling and monitoring practices. [In:] Griffioen J. et al. (2006). D7: State-of-the-art knowledge on behaviour and effects of natural and anthropogenic groundwater pollutants relevant for the determination of groundwater threshold values. BRIDGE Project.



16-32

Final reference report: 10.1-10.23. www.wfd-bridge.net (03.07.2006)

Zhou, Y. (1996). Sampling frequency for monitoring the actual state of groundwater systems. Journal of Hydrology 180: 301-318.



16-33

Annexes

Annex 1. Definitions and terminology Annex 2. Assessing uncertainties associated with sampling of groundwater:

GWB monitoring network (ca. 1000 km2) Annex 3. Assessing uncertainties associated with sampling of groundwater:

regional monitoring network (ca. 50000 km2). Annex 4. Assessing uncertainties associated with sampling of groundwater:

country wide monitoring network (ca. 300000 km2) Annex 5. Assessing of practical limit of detection (PLOD) for regional monitoring

network Annex 6. Assessing uncertainties associated with the assessment of trends



16-34

Annex 1

Definitions and terminology



16-35

Definitions and terminology

(source: de Zorzi et al., 2003; Ellison et al., 2000; Eurachem, 2006; ISO 5725; ISO/IEC 17025;

McNaught and Wilkinson, 1997) Accuracy The closeness of agreement between a test result and the accepted reference value. Analyte 1) The property of the sample that is measured. 2) The component of a system to be analysed. Bias The difference between the expectation of the test result and an accepted reference value. Blank sample A sample known to be without content of the analyte, that is used for quality control of measurements. Certified reference material Material where the content of the analyte has been certified by analyses by a number of analytical laboratories, i.e.: where the true value is known. Combined standard uncertainty The result of the combination of standard uncertainty components. Control sample A sample with known content of the analyte, that is used for quality control of measurements. Coverage factor A number that, when multiplied by the combined standard uncertainty, produces an interval (the expanded uncertainty) about the measurement result that may be expected to encompass a large, specified fraction (e.g. 95%) of the distribution of values that could be reasonably attributed to the measurand. Duplicate (Replicate) sample One of the two (or more) samples obtained separately at the same time by the same sampling procedure. Error of measurement The incorrectness of a single measurement, i.e.: the difference between a single measurement and the true value. Includes both systematic and random effects. Expanded uncertainty Obtained by multiplying the combined standard uncertainty by a coverage factor. Fitness for Purpose The degree to which data produced by a measurement process enables a user to make technically and administratively correct decisions for a stated purpose.



16-36

Geochemical uncertainty Part of the total uncertainty, that characterises the dispersion of the values caused by heterogeneity of the sampled groundwater system. Heterogeneity The degree to which a property or constituent is not uniformly distributed throughout an investigated system. Interlaboratory comparison Analysis of samples from the same primary sample at different laboratories in order to compare the correctness of analytical results from different laboratories. Level of confidence The probability that the value of the measurand lies within the quoted range of uncertainty. Limit of Detection (LOD) 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. Limit of Quantification (LOQ) Derived from the analogous expression as LOD, with the k factor typically chosen between 6 and 10. Measurand Particular quantity subject to measurement. Measurement uncertainty Parameter, associated with the result of a measurement, that characterises the dispersion of the values that could reasonably be attributed to the measurand. It consists of sampling uncertainty and analytical uncertainty. Practical Limit of Detection (PLOD) 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. 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. Precision The closeness of agreement between independent test results obtained under defined conditions. Random error of result A component of the error which, in the course of a number of test results for the same characteristic, remains constant or varies in an unpredictable way. Representative sample Sample resulting from a sampling plan that can be expected to reflect adequately the properties of interest in the studied system.



16-37

Sampler Person (or group of persons) carrying out the sampling procedures at the sampling point. Sampling bias The part of the total measurement bias attributable to the sampling. Sampling location The place where sampling occurs within the sampling target. Sampling precision The part of the total measurement precision attributable to the sampling. Sampling procedure Operational requirements and/or instructions relating to the use of a particular sampling protocol; i.e. the planned method of selection, withdrawal and preparation of sample(s). Sampling uncertainty The part of the total measurement uncertainty attributable to sampling. Spiked sample A sample with added a known amount of the analyte. Standard deviation The positive square root of the variance. Standard uncertainty The estimated standard deviation. Systematic error of result A component of the error which, in the course of a number of test results for the same characteristic, remains constant or varies in a predictable way. True value The “true” content of the analyte in a material. The true value is in theory never known exactly but for practical purposes, the certified values of reference materials and concentrations spiked to are often considered true values. Trueness The closeness of agreement between the average value obtained from a large series of test results and an accepted reference value. Variance A measure of the dispersion of a set of measurements; the sum of the squared deviations of the observations from their average, divided by one less than the number of observations.



16-38

Annex 2

Examples of assessing uncertainties associated with sampling of groundwater:

Groundwater body monitoring network (ca. 1000 km2)



16-39


Background information

Title/Name of case study: Estimation of uncertainty associated with monitoring of GWB.

Type of case study: GWB monitoring network.

Objective of case study: Demonstration of the balanced design of the duplicate method for estimation of uncertainty associated with monitoring of GWB.

GWD focus : Groundwater monitoring.

Specific contributions: Estimation of the total uncertainty components attributed to geochemical variance, sampling variance and analytical variance.

Characterisation:

The investigated GWB is an unconsolidated, porous, multi-layered system which includes Quaternary and Sarmatian sandy aquifers. Monitoring network consists in total of 49 wells. Thirty seven belong to Sarmatian and deeper Quaternary buried valley which is hydraulically connected to Sarmatian. In shallow Quaternary aquifer (0 to 30 m deep) 12 sites are located. Quaternary aquifer is under anthropogenic influence. The natural background levels of selected elements have been determined for the Sarmatian aquifer. The monitoring network was used for the assessment of chemical status of the investigated GWB. Important part of chemical status assessment was estimation of expanded uncertainty and its components, associated with the monitoring. The assessment of expanded overall uncertainty was based on a pilot study comprising collection of duplicate water samples from 14 wells randomly selected.

Scenario:

The empirical approach was selected assess the overall uncertainty associated with the operation of the GWB monitoring network, attributed to geochemical, sampling and analytical variances. The balanced design of the duplicate method was used (chapter 5.1). Fourteen wells were selected for the study. One sampling campaign was carried out. The duplicate samples were obtained using single sampling protocol and single sampler. Four analyses per site and element were performed (cf. Figure D16.11). Forty three elements were analyzed.

Sampling protocol:

Sampling was done using sampling protocol for groundwater monitoring developed by the operator of the network. Samples were collected from the well heads using submersible pumps. Four sub-samples were collected: (i) 500 ml of unfiltered water for major ion analysis, (ii) 100 ml of filtered and acidified (pH< 2, with HNO3) water for ICP-MS and ICP-AES analyses, (iii) 100 ml of filtered water for DOC analysis. Samples were transported to the laboratory at 4oC. Analyses were performed at laboratory which implements extensive quality assurance and analytical quality control programmes.

Verification of analytical data:

Ionic balance of major ions, rejection of analyses with balance error >10%. Identification and rejection of outliers.

Calculation of uncertainty and its components

The analysis of variance was carried out for individual parameters using ROBAN code



16-40

(chapter 5.2.2). The following information was derived: • The total variance separated into geochemical, sampling and analytical variance • Calculation of standard and relative uncertainties (u) associated with these

components. • Calculation of expanded uncertainty(U) for 95% confidence level • Calculation of relative uncertainties (U’) related to mean values of the analyzed

element for normal and duplicate samples.

Calculation of uncertainty and its components has been shown in detail for three elements (As, Mn, Fe). The summary table (A2.13) comprises the results of variance and uncertainty assessment for selected elements for which the analyzed concentrations > LOD.

The estimated components of the total variance can be used during assessment of GWB chemical status (see chapter 5). The example is given for three elements (As, Mn, Fe).

Conclusions:

The geochemical variance is dominating in the monitored GWB for all elements except arsenic and beryllium. In these two cases the contribution of the measurement variance to the total variance is higher than the limiting value (Figure D16.13). Sampling variance is very high especially for arsenic, and efforts should be made to reduce it (for instance, by improving the sampling protocol).



16-41


The empirical approach was selected assess the overall uncertainty associated with the operation of the GWB monitoring network, attributed to geochemical, sampling and analytical variances.

The balanced design of the duplicate method was used. Fourteen wells were selected for the study. One sampling campaign was carried out. The duplicate samples were obtained using single sampling protocol and single sampler. Four analyses per site and element are performed. Forty three elements were analysed (pH, SEC, HCO3, Li, Al, V, Co, Ni, Cu, As, Se, Mo, Ag, Cd, Sb, Pb, Hg, B, Ba, Ca, Cr, Fe, K, Mg, Mn, Na, SiO2, Sr, Tl, Zn, Cl, Br, HPO4, SO4, DOC, F, NH4, NO2, NO3, phenols, cyanides, trichloroethylene, tetrachloroethylene). Calculation of uncertainty and its components is shown in detail for three elements (AS, Mn, Fe). The summary table (Table A2.13) comprises the results for selected analyzed elements with the concentrations > LOD. 1. Verification of analytical data and calculation of uncertainty 1.1 Ionic balance The limiting relative error of the ionic balance of 10% has been adopted. In this case no values can be rejected. Histogram of errors associated with analysis of ionic balance is shown in Figure A2.1.

Figure A2.1. Histogram of errors associated with analysis of ionic balance.



16-42

1.2 Example calculations for selected elements: As Table A2.1. Concentrations of As [µg/L] in the pairs of analyses after verification based on ionic balance.

No of monitoring site Analysis n.1.1*

Analysis n.1.2*

Analysis n.2.1*

Analysis n.2.2*

KK332/41 2.308 2.22 1.765 1.906 KK332/43 0.979 1.019 0.874 0.906 KK332/45 1.211 1.143 1.635 17.959 KK332/47 0.894 0.924 0.659 0.716 KK332/49 1.898 1.79 0.407 0.346 KK332/51 7.702 7.189 3.736 3.973 KK332/53 3.76 3.967 2.555 2.528 KK332/55 2.13 2.101 0.393 0.393 KK332/57 5.371 5.945 0.163 1.656 KK332/50 3.744 3.897 1.115 1.137 KK332/6 0.468 0.443 0.326 0.291 KK332/19 0.339 0.324 0.247 0.201 KK332/20 0.344 0.31 0.285 0.258 KK332/24 0.38 0.354 0.281 0.253

*) - see Fig. D16.11 1.2.1 Identification of outliers A B C

Figure A2.2. Graphical tests for outlier identification in the framework of empirical approach of uncertainty assessment (measured element: As). 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 (according to NORDTEST, 2006). One outlier was identified (site KK332/45). This pair of duplicates has been rejected.



16-43

1.2.2 Output of ROBAN code Results for 13 pairs of samples (after outlier rejection): CLASSICAL ANOVA RESULTS Mean = 1.6955769 Standard Deviation (Total) = 1.8820817 Total variance 3.49835 Geochemical Sampling Analysis ----------- -------- -------- Sums of Squares 122.613 49.863 1.5017 Standard Deviation 1.263 1.374 0.2403 Variance 1.595 1.888 0.0138 Percentage Variance 45.043 53.325 1.6305 Results for 14 pair of samples (without outlier rejection) for Robust ANOVA calculations: ROBUST ANOVA RESULTS: Mean = 1.7555505 Standard Deviation (Total) = 1.8669986 Total variance 3.4856 Geochemical Sampling Analysis Measurement ----------- -------- -------- ----------- Standard Deviation 1.321 1.317 0.07257 1.3192 Variance 1.745 1.735 0.00526 1.73 Percentage Variance 50.072 49.776 0.151 49.927 Relative Uncertainty - 150.063 8.2677 150.29 (% at 95% confidence)

Table A2.2. Parameter estimates derived from ROBAN.

Parameter Method- ANOVA robust* )

Geochemical variance 1.74

Geochemical variance [% of total variance] 50.08

Sampling variance 1.74

Sampling variance [% of total variance] 49.77

Analytical variance 0.0053

Analytical variance [% of total variance] 0.15

Measurement variance 1.73

Measurement variance [% of total variance] 49.93 *) – ROBAN code allows presence only 10% of outliers for ANOVA robust calculations (with-out outliers rejection).



16-44

ANOVA robust

50.08%49.77%

0.15%

Geochemical variance

Sampling variance

Analytical variance

Figure A2.3. Relative contributions of sampling and analytical variances to the total variance for As. Conclusion: Geochemical and sampling variances are dominating.

For further steps (estimation of expanded uncertainty and relative uncertainty) the results of ANOVA robust calculations are used.

Table A2.3. Calculated contributions to the overall uncertainty .

Parameter Value

ugeochem [µg/L] 1.3

Ugeochem [µg/L] 2.6

U’geochem [%] 150.5

usampling [µg/L] 1.3

Usampling [µg/L] 2.6

U’sampling [%] 150.1

uanalytical [µg/L] 0.073

Uanalytical [µg/L] 0.15

Uanalytical [%] 8.34

Umeasurement [µg/L] 2.6

U’measurement.[%] 150.3

utotal [µg/L] 1.9

Utotal [µg/L] 3.7

U’total[%] 212.8



16-45

Calculation of maximum allowable uncertainty of analysis for As (Um - see chapter 5.2.2): According to Table D16.6, for As we have Um = 10% ⋅TV = 0.1⋅ 10 = 1 [µg/L]. It means that estimated Umeas > Um. Therefore, action should be taken to reduce the measurement uncertainty via improvement of sampling protocol. 1.3 Calculation of the standard uncertainty of the mean, associated with the

assessment of chemical status of GWB The values of variances and uncertainties calculated above can be used in the course of assess-ment of chemical status of GWB (see chapter 5). Deterministic approach is recommended (Fig-ure D16.9) The expanded uncertainty of the mean value (U) is being compared with correspond-ing maximum acceptable uncertainty (Umax ). For illustration, three different values of k’ factor (usually determined by expert judgment) have been used in Table A2.4.

Table A2.4. Expanded uncertainty of the mean value (U), calculated during assessment of chemical status of the studied GWB with Umax. Indica-tor

n Mean value

U (mean value)

NBL (range) sNBL k’ t Umax

As 49 1.8 1.8/√49=0.26 0.0018-13.7 3.4245 1 2 0.98

As 49 1.8 1.8/√49=0.26 0.0018-13.7 3.4245 1.25 2 1.22 As 49 1.8 1.8/√49=0.26 0.0018-13.7 3.4245 1.5 2 1.47 For arsenic U < Umax. Consequently , the calculated mean value can be compared with the TV value and the decision can be taken with respect to the chemical status of monitored GWB. The calculated uncertainty of the mean has in this situation only informative meaning.



16-46

1.4 Example calculations for selected elements: Mn Table A2.5. Concentrations of Mn [mg/L] in the pairs of analyses after verification based on ionic balance.


Analysis n.1.2*

Analysis n.2.1*

Analysis n.2.2*



Figure A2.4. Graphical tests for outlier identification in the framework of empirical approach of uncertainty assessment (measured element: As). 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 (according to NORDTEST, 2006). No outliers were identified



16-47

1.4.2 Output of ROBAN code Results for 14 pairs of samples (without outlier rejection): CLASSICAL ANOVA RESULTS Mean = 0.45572 Standard Deviation (Total) = 0.37198 Total variance 0.13831 Geochemical Sampling Analysis ----------- -------- -------- Sums of Squares 6.883 0.2757 0.05979 Standard Deviation 0.357 0.0937 0.04621 Variance 0.1274 0.008779 0.002135 Percentage Variance 92.1128 6.3437 1.5434 ROBUST ANOVA RESULTS: Mean = 0.41176704 Standard Deviation (Total) = 0.29984686 Total variance 0.089852 Geochemical Sampling Analysis Measurement ----------- -------- -------- ----------- Standard Deviation 0.2805 0.1051 0.01233 0.1058 Variance 0.0787 0.011 0.000152 0.01119 Percentage Variance 87.538 12.292 0.1691 12.4618 Relative Uncertainty - 51.062 5.9892 51.4125 (% at 95% confidence)


Parameter Method- ANOVA robust* )








Measurement variance [% of total variance] 12.46

*) – ROBAN code allows presence only 10% of outliers for ANOVA robust calculations (with-out outliers rejection).



16-48

ANOVA robust

87.54%

12.29% 0.17%


Sampling variance

Analytical variance

Figure A2.5. Relative contributions of sampling and analytical variances to the total variance for Mn. Conclusion: Geochemical variance is dominating.



Parameter Value




usampling [µg/L] 0.11

Usampling [µg/L] 0.22

U’sampling [%] 53.44

uanalytical [µg/L] 0.012

Uanalytical [µg/L] 0.024

Uanalytical [%] 5.83


U’measurement.[%] 53.44

utotal [µg/L] 0.30

Utotal [µg/L] 0.60

U’total[%] 145.64



16-49

Calculation of maximum allowable uncertainty of analysis for Mn (Um - see chapter 5.2.2): According to Table D16.6, for Mn we have Um = 10% ⋅TV = 0.1⋅ 50 = 5 [ug/L]. It means that estimated Umeas < Um, and the results of analyses are acceptable. 1.4.3 Calculation of the standard uncertainty of the mean, associated with the assess-

ment of chemical status of GWB The values of variances and uncertainties calculated above can be used in the course of assess-ment of chemical status of GWB (see chapter 5). Deterministic approach is recommended (Fig-ure D16.9) The expanded uncertainty of the mean value (U) is being compared with correspond-ing maximum acceptable uncertainty (Umax ). For illustration, three different values of k’ factor (usually determined by expert judgment) have been used in Table A2.8.


n Mean value

U (mean value)

NBL (range)

sNBL k’ t Umax

Mn 49 0.27 0.27/√49=0.039 0.001-0.686 0.1713 1 2 0.049 Mn 49 0.27 0.27/√49=0.039 0.001-0.686 0.1713 1.25 2 0.061 Mn 49 0.27 0.27/√49=0.039 0.001-0.686 0.1713 1.5 2 0.073 For manganese U < Umax. Consequently, the calculated mean value can be compared with the TV value and the decision can be taken with respect to the chemical status of monitored GWB. The calculated uncertainty of the mean has in this situation only informative meaning.



16-50

1.5 Example calculations for selected elements: Fe Table A2.9. Concentrations of Fe [mg/L] in the pairs of analyses after verification based on ionic balance.


Analysis n.1.2*

Analysis n.2.1*

Analysis n.2.2*



Figure A2.6. Graphical tests for outlier identification in the framework of empirical approach of uncertainty assessment (measured element: Fe). 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 (according to NORDTEST, 2006). One outlier was identified (site KK332/53). This pair of duplicates has been rejected.



16-51

1.5.2 Output of ROBAN code Results for 13 pairs of samples (after outlier rejection): CLASSICAL ANOVA RESULTS: Mean = 2.39225 Standard Deviation (Total) = 3.07634 Total variance 9.46388 Geochemical Sampling Analysis ----------- -------- -------- Sums of Squares 453.302 0.3737 0.670331 Standard Deviation 3.0719 0.0385 0.160567 Variance 9.4365 0.00148 0.02576 Percentage Variance 99.7119 0.01566 0.272424 Results for 14 pair of samples (without outlier rejection) for Robust ANOVA calculations: ROBUST ANOVA RESULTS: Mean = 2.03998 Standard Deviation (Total) = 2.06791 Total variance 4.27625 Geochemical Sampling Analysis Measurement ----------- -------- -------- ----------- Standard Deviation 2.0662 0.07218 0.03989 0.0824 Variance 4.269 0.0052 0.0016 0.0068 Percent. Variance 99.8409 0.12185 0.0372 0.1590 Relative Uncertainty - 7.077 3.91 8.0857 (% at 95% confidence)


Parameter Method -- ANOVA robust*)









*) – ROBAN code allows presence only 10% of outliers for ANOVA robust calculations (with-out outliers rejection).



16-52

ANOVA robust

99.84%

0.12% 0.04%


Sampling variance

Analytical variance

Figure A2.7. Relative contributions of sampling and analytical variances to the total variance for Fe. Conclusion: Geochemical variance is dominating.


Table A2.11. Calculated contributions to the overall uncertainty.

Parameter Value

ugeochem [mg/L] 2.1

Ugeochem [mg/L] 4.2


usampling [mg/L] 0.072

Usampling [mg/L] 0.14

U’sampling [% ] 7.06

uanalytical [mg/L] 0.040

Uanalytical [mg/L] 0.080

U’analytical [%] 3.92

Umeas [mg/L] 0.17

U’meas.[%] 8.08

utotal [mg/L] 2.1

Utotal [mg/L] 4.2

U’total[%] 202.76



16-53

Calculation of maximum allowable uncertainty of analysis for Fe (Um - see chapter 5.2.2): According to Table D16.6, for iron we have Um = 10% ⋅TV = 0.1⋅ 0.2 = 0.02 [mg/L]. It means that estimated Umeas > Um. Because the analysis of variance for this element shows that geo-chemical variance is dominating, possible reduction of Umeas can be achieved by increase of the number of monitoring points. 1.5.3 Calculation of the standard uncertainty of the mean, associated with the assess-

ment of chemical status of GWB The values of variances and uncertainties calculated above can be used in the course of assess-ment of chemical status of GWB (see chapter 5). Deterministic approach is recommended (Fig-ure D16.9) The expanded uncertainty of the mean value (U) is being compared with correspond-ing maximum acceptable uncertainty (Umax ). For illustration, three different values of k’ factor (usually determined by expert judgment) have been used in Table A2.12.


N Mean value

U (mean value)

NBL (range) sNBL k’ t Umax

Fe 49 1.93 1.93/√49=0.28 0.002-7.188 1.7965 1 2 0.51

Fe 49 1.93 1.93/√49=0.28 0.002-7.188 1.7965 1.25 2 0.64 Fe 49 1.93 1.93/√49=0.28 0.002-7.188 1.7965 1.5 2 0.77 For iron U < Umax. Consequently , the calculated mean value can be compared with the TV value and the decision can be taken with respect to the chemical status of monitored GWB. The calcu-lated uncertainty of the mean has in this situation only informative meaning. 2. Results of parameter estimates derived from ROBAN code for selected elements The results of calculation of uncertainty and its components for a suite of other elements ana-lyzed in the framework of this study are listed in the summary table (Table A2.13). This table contains only elements with the concentrations > LOD.



16-54

Table A2.13a. Parameter estimates derived from ROBAN code for selected indicators.

Indicator Unit Geochemical variance

Geochemical vari-ance [% of total]

Sampling variance

Sampling variance [% of total]

Analytical variance

Analytical variance [% of total]

Measurement variance

Measurement variance [% of total]

Al ug/L 6.651241 81.55 0.140625 1.72 1.364224 16.73 1.504849 18.45 As ug/L 1.745041 50.09 1.734489 49.76 0.005329 0.15 1.739818 49.91 B mg/L 0.002704 95.12 0.000121 4.31 0.000016 0.57 0.000137 4.88 Ba mg/L 0.002304 96.92 0.000025 1.04 0.000049 2.04 0.000074 3.08 Be ug/L 0.0036 51.17 0.001764 25.61 0.0016 23.23 0.003364 48.84 Ca mg/L 2848.037 99.61 8.191044 0.29 2.917264 0.1 11.10831 0.39 Cd ug/L 0.002916 86.30 0.000361 10.73 0.0001 2.97 0.000461 13.7

Cu ug/L 0.731025 92.26 0.0324 4.09 0.0289 3.65 0.0613 7.74 Fe mg/L 4.269 99.84 0.0052 0.12 0.0016 0.04 0.0068 0.16 K mg/L 2.518569 91.85 0.027225 0.99 0.196249 7.16 0.223474 8.15 Li mg/L 0.000784 96.91 0.000025 2.97 0.000001 0.12 0.000026 3.09 Mg mg/L 79.8521 99.59 0.225625 0.28 0.1024 0.13 0.328025 0.41 Mn mg/L 0.078961 87.59 0.011025 12.25 0.000144 0.16 0.011169 12.41 Mo ug/L 0.002025 97.94 0.002025 1.55 0.000676 0.52 0.002701 2.07 Na mg/L 669.1534 99.87 0.3481 0.05 0.5184 0.08 0.8665 0.13

P mg/L 0.0225 95.17 0.001024 4.32 0.000121 0.51 0.001145 4.83 Pb ug/L 0.316969 69.37 0.125316 27.42 0.014641 3.2 0.139957 30.62 Sb ug/L 0.007225 82.23 0.001521 17.21 0.000049 0.55 0.00157 17.76 Si mg/L 23.07842 99.59 0.054756 0.24 0.039601 0.17 0.094357 0.41 SO4 mg/L 12637.13 99.92 6.630625 0.05 3.7636 0.05 10.394225 0.1 Sr mg/L 0.4225 99.86 0.0004 0.09 0.000196 0.05 0.000596 0.14 U ug/L 0.080089 98.07 0.001089 1.89 0.000025 0.04 0.001114 1.93

Zn mg/L 0.000841 98.45 0.000004 0.48 0.000009 1.07 0.000013 1.55

A2.13b. Calculated contributions to the overall uncertainty for selected indicators.



16-55

Indicator Unit ugeochem Ugeochem Ugeochem

[%] usampling Usampling

Usampling [%]

uanalytical Uanalytical Uanalytical

[%] Umeas

Um *)

Umeas.[%] utotal Utotal Utotal

[%]

Al ug/L 2.6 5.2 138.81 0.38 0.76 20.18 1.2 2.4 62.86 2.4 20 66.02 2.9 5.8 153.71

As ug/L 1.3 2.6 150.49 1.32 2.64 150.04 0.073 0.146 8.32 2.6 1 150.27 1.9 3.8 212.72

B mg/L 0.052 0.104 130 0.011 0.022 27.5 0.0040 0.008 10 0.023 0.1 29.26 0.053 0.106 132.5

Ba mg/L 0.048 0.096 12.78 0.0050 0.010 13.31 0.0070 0.014 18.63 0.017 - 22.89 0.049 0.098 130.47

Be ug/L 0.06 0.12 89.89 0.042 0.084 62.92 0.040 0.080 59.93 0.12 - 86.89 0.083 0.166 124.34

Ca mg/L 53.4 106.8 119.12 2.9 5.8 6.39 1.7 3.4 3.81 6.7 - 7.44 43.5 87 97.03

Cd ug/L 0.054 0.108 137.14 0.019 0.038 48.25 0.010 0.020 25.39 0.043 - 54.52 0.058 0.116 147.3

Cu ug/L 0.85 1.7 162.08 0.18 0.36 34.12 0.17 0.34 32.22 0.49 200 46.93 0.89 1.78 168.72

Fe mg/L 2.1 4.2 202.56 0.072 0.144 7.06 0.040 0.080 3.92 0.17 0.02 8.08 2.1 4.2 202.76

K mg/L 1.6 3.2 73.47 0.17 0.34 7.64 0.44 0.88 20.51 0.95 - 20.62 1.7 3.4 76.66

Li mg/L 0.028 0.056 163.74 0.0050 0.010 29.24 0.0010 0.0020 5.84 0.010 - 29.82 0.029 0.058 169.59

Mg mg/L 8.9 17.8 111.52 0.48 0.96 5.93 0.32 0.64 3.99 1.1 - 7.15 8.9 17.8 111.74

Mn mg/L 0.28 0.56 136.51 0.11 0.22 51 0.012 0.024 5.83 0.21 5 51.33 0.30 0.60 145.74

Mo ug/L 0.36 0.72 179.22 0.045 0.090 22.53 0.026 0.052 13.02 0.10 - 26.02 0.36 0.72 181.23

Na mg/L 25.9 51.8 219.68 0.59 1.18 5.01 0.72 1.44 6.11 1.9 20 7.9 25.9 51.8 219.81

Pb ug/L 0.56 1.12 135.82 0.35 0.70 85.4 0.12 0.24 29.19 0.75 1 90.25 0.68 1.36 163.09

Sb ug/L 0.085 0.17 215.87 0.039 0.078 99.05 0.0070 0.014 17.77 0.079 1.25 100.63 0.094 0.188 238.73

SO4 mg/L 112.42 224.83 177.56 2.58 5.15 4.07 1.94 3.88 3.06 6.45 25 4.53 112.46 224.92 177.64

Sr mg/L 0.65 1.3 173.96 0.020 0.040 5.35 0.014 0.028 3.75 0.049 - 6.53 0.65 1.3 174.23

U ug/L 0.24 0.48 280.16 0.033 0.066 38.85 0.0050 0.010 5.89 0.067 - 39.29 0.24 0.48 282.52

Zn mg/L 0.029 0.058 130.93 0.0020 0.004 9.03 0.0030 0.006 13.54 0.0070 - 16.27 0.029 0.058 130.93

*) according to Table D16.6



16-56

It is apparent from Table A2.13 that geochemical variance is dominating for all elements except arse-nic and beryllium. In these two cases the contribution of measurement variance to the total variance is higher than the limit ing value (Figure D16.13). Sampling variance is very high, especially for arsenic, for which the extended uncertainty Umeas>Um. Efforts should be made to reduce it (for instance, by improving the sampling protocol).



16-57

Annex 3

Assessing uncertainties associated with sampling of groundwater:

Regional monitoring network (ca. 50000 km2)



16-58


Background information Title/Name of case study: Estimation of uncertainty associated with groundwater monitoring Type of case study: Regional monitoring network Objective of case study: Demonstration of the use of simplified version of the duplicate method for the estimation of uncertainty associated with sampling and analysis GWD focus : groundwater monitoring Specific contributions: Estimation of the total uncertainty components attributed to geochemical variance and measuring variance (sampling variance + analytical variance). Characterisation: The regional monitoring of groundwater quality is associated with river basin. It covers the area of about 50,000 km2. Monitoring network consists of 172 monitoring sites (wells and springs). The manager of the monitoring network decided to carry out pilot study aimed at assessment of uncer-tainty associated sampling and analyses of water samples collected by the network. The pilot study was implemented in the form of two sampling campaigns. Scenario: The empirical approach was selected to assess the overall uncertainty associated with the operation of regional groundwater monitoring network. The simplified duplicate method was used (see Chapter 5.1). Thirty seven monitoring sites were randomly selected. Two analyses per site and element are performed. Forty three elements were analysed (pH, SEC, Eh, K, Na, Mg, Ca, N-NH4, N-NO3, N-NO2, Al, Cl, Fe, SO4, Mn, HPO4, SiO2, F, B, Cr, Zn, Cd, Cu, Ni, Pb, Hg, aggressive CO2, A254, DOC, ChZTMn, organic nitrogen, phenols, hydrocarbons, detergents, chloroform, tetrachloroethylen, trichloroethylen, DDT, DDE, DDD, Lindan, methoxychlor, benzo-a-pyrene). Sampling protocol: Sampling was done using the sampling protocol for groundwater monitoring developed by the op-erator of the network. Samples were collected from the well heads using submersible pumps and from springs, using peristaltic pump. Four sub-samples were collected: (i) 500 ml of unfiltered water for major ion analysis, (ii) 100 ml of filtered and acidified (pH< 2, with HNO3) water for ICP-AES analyses, (iii) 100 ml of filtered water for DOC analysis, (iv) 250 ml of filtered water for Hg analysis. Samples were transported to the laboratory at 4oC. Analyses were performed at an accredited laboratory using accredited methods subject to the required quality assurance and ana-lytical quality control (ISO 17025, 2005). Verification of analytical data: Ionic balance of major ions, rejection of analyses with balance error >10% Identification and rejection of outliers Calculation of uncertainty and its components The analysis of variance was carried out for individual parameters using ROB2 code (chapter 5.2.2). The following information was derived:

• The total variance separated into geochemical and measurement (sampling and analytical) variance

• Calculation of standard and relative uncertainties (u) associated with these components. • Calculation of expanded uncertainty (U) for 95% confidence level • Calculation of rela tive uncertainties (U’) related to the mean values of the analyzed ele-

ment for normal and duplicate samples. Calculation of uncertainty and its components has been shown in detail for one element (Hg). The summary table (A3.5) comprises the results for selected elements with the concentrations > LOD. Conclusions: The geochemical variance is dominating in the regional monitoring network for all elements. For three elements (Hg, Pb, Fe) the expanded measurement uncertainty turned out to be higher that the limiting value. Efforts should be made to reduce it.



16-59


The empirical approach was selected assess the overall uncertainty associated with the operation of the GWB groundwater monitoring network, attributed to geochemical, sampling and analytical variances.

The unbalanced design of the duplicate method was used. Thirty seven wells were selected for the study. One sampling campaign was carried out. The duplicate samples were obtained using single sampling protocol and single sampler. Two analyses per site and element are performed. Forty three elements were analysed (pH, SEC, Eh, K, Na, Mg, Ca, N-NH4, N-NO3, N-NO2, Al, Cl, Fe, SO4, Mn, HPO4, SiO2, F, B, Cr, Zn, Cd, Cu, Ni, Pb, Hg, aggressive CO2, A254, DOC, ChZTMn, organic nitro-gen, phenols, hydrocarbons, detergents – DETAN, chloroform, tetrachloroethylen, trichloroethylen, DDT, DDE, DDD, Lindan, methoxychlor, benzo-a-pyrene). Calculation of uncertainty and its components has been shown in detail for one element (Hg). The summary table (Table A3.5) comprises the results for selected analyzed elements with the concentra-tions > LOD. 1. Verification of analytical data and calculation of uncertainty 1.1 Ionic balance The limiting relative error of the ionic balance of 10% has been adopted. In this case 13 pairs of analy-sis has been rejected. Histogram of errors associated with analysis of ionic balance, after rejection of faulty analyses, is shown in Figure A3.1.

Figure A3.1. Histogram of errors associa ted with analysis of ionic balance.



16-60

1.2 Example calculations for selected element: Hg Table A3.1. Concentrations of Hg [µg/L] in the pairs of analyses after verification based on ionic balance.

No of monitoring site Analysis n.1*

Analysis n.2*

RMWP86 0.4 0.3

RMWP82 0.4 0.7

RMWP523 0.3 0.3 RMWP865 0.2 0.2

RMWP58 1.8 1.8

RMWP91 0.6 0.4

RMWP28 5.8 4.2

RMWP35 0.6 0.5

RMWP19 2.6 2.5

RMWP20 0.8 0.9

RMWP74 2.4 2.3

RMWP79 2.5 2.3

RMWP46 2.2 2.3

RMWP2 1.4 2.5

RMWP58 1.5 1.1

RMWP86 1 2.5

RMWP51 0.5 2.5

RMWP6 2.5 2.5

RMWP103 1.3 2.4

RMWP9 3.8 3.6

RMWP54 4.3 3.2

RMWP17 0.6 0.5

RMWP60 1.2 1.8 *) - see Fig. D16.11 1.2.1 Identification of outliers A B C

Figure A3.2. Graphical tests for outlier identification in the framework of empirical approach of un-certainty assessment (measured element: Hg [ug/L]). 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 (according to NORDTEST, 2006). No outliers were identified.



16-61

1.2.2 Output of ROB2 code Results for 23 pairs of samples (without outlier rejection): CLASSICAL ANOVA RESULTS: Mean = 1.739 Geochemical Measurement (temporal) (sampling + analysis) ------------- ----------- Sums of Squares - 68.269 6.62 Standard Deviation - 1.186 0.536 Variance 1.4066 0.2873 Total standard deviation - 1.302 ROBUST ANOVA RESULTS: mean = 1.632 Geochemical Measurement (temporal) (sampling + analysis) ------------- ----------- Standard Deviation - 1.138 0.301 Variance 1.295 0.0906 Total standard deviation - 1.177 Further calculations (estimation of uncertainty) are made for robust estimates (k = 2):

Table A3.3. Parameter estimates derived from ROB2

Parameter Method -- ANOVA Robust







16-62

ANOVA robust

93.46%

6.54%



Figure A3.3. Relative contributions of sampling and analytical variances to the total variance for Hg. Conclusion: geochemical variance is dominating.

Table A3.4. Calculated contributions to the overall uncertainty

Parametr Value



U’geochem[%] 134.8

umeasurement [µg/L] 0.30


U’measurement [%] 36.88

utotal [µg/L] 1.2

Utotal [µg/L] 2.4

U’total[%] 147.05

Calculation of maximum allowable uncertainty of analysis for Hg : According to Table D16.6, for Hg we have Um = 10% TV = 0.1×1 = 0.1 [µg/L]. It means that esti-mated Umeas > Um. therefore, action should be taken to reduce the measurement uncertainty via im-provement of sampling protocol and/or analytical variance.



16-63

2. Results of parameter estimates derived from ROBAN code for selected elements The results of calculation of uncertainty and its components for a suite of other elements analyzed in the framework of this study are listed in the summary table (Table A3.5). This table contains only elements with the concentrations > LOD.


Indicator Unit Geochemical variance

Geochemical variance [%]


Measurement variance [%]

Ca mg/L 2182.57 99.19 17.76 0.81

Cl mg/L 70.325 99.99 0.004 0.01

DOC mg/L 0.998 96.26 0.0388 3.74

F mg/L 0.766 99.5 0.0038 0.5

Fe mg/L 0.6416 99.45 0.0035 0.54

HCO3 mg/L 15146.96 99.99 2.26 0.01

Hg ug/L 1.295 93.46 0.09 6.54

K mg/L 1.927 96.36 0.07 3.54

Mg mg/L 60.95 98.88 0.689 1.12

Mn mg/L 0.0234 99.66 0.000081 0.34

Na mg/L 31.85 99.75 0.0795 0.25

N-NO3 mg/L 13.148 99.98 0.0021 0.02

Pb ug/L 4.09 86.92 0.615 13.08

SiO2 mg/L 57.27 99.96 0.02 0.04

SO4 mg/L 2817.27 99.82 5.12 0.18

Tetrachloroethylen ug/L 0.126 93.95 0.0081 6.05

Zn ug/L 0.048 99.53 0.00023 0.47



16-64

Table A3.5b. Calculated contributions to the overall uncertainty for selected indicators. Idicator Unit ugeochem Ugeochem U’geochem

[%] umeas Umeas Um

*) U'meas [%]

utotal Utotal U’total [%]

Ca mg/L 46.7 93.4 108.7 4.2 8.4 - 9.8 46.9 93.8 109.1

Cl mg/L 8.4 16.8 97.94 0.064 0.128 25 0.75 8.4 16.8 97.94

DOC mg/L 0.99 1.99 122.91 0.20 0.40 - 24.24 1.0 2.0 125.37

F mg/L 0.88 1.76 291.67 0.062 0.124 0.15 20.67 0.88 1.76 292.33

Fe mg/L 0.80 1.60 293.03 0.059 0.118 0.02 21.58 0.80 1.60 293.76

HCO3 mg/L 123 246 93.94 1.5 3.0 - 1.15 123 246 93.95

Hg ug/L 1.1 2.2 134.8 0.3 0.6 0.1 36.88 1.2 2.4 147.05

K mg/L 1.4 2.8 135.41 0.27 0.54 - 25.95 1.4 2.8 137.95

Mg mg/L 7.8 15.6 122.57 0.83 1.66 - 13.03 7.8 15.6 123.26

Mn mg/L 0.15 0.30 241.82 0.009 0.018 0.005 14.22 0.15 0.30 243.4

Na mg/L 5.6 11.2 117.83 0.28 0.56 20 5.89 5.6 11.2 117.97

N-NO3 mg/L 3.6 7.2 186.93 0.046 0.092 1.1 2.37 3.6 7.2 186.93

Pb ug/L 2.0 4.0 114.65 0.78 1.56 1 44.45 2.2 4.4 122.93

SiO2 mg/L 7.6 15.2 102.89 0.14 0.28 - 1.94 7.6 15.2 102.92

SO4 mg/L 53 106 158.23 2.3 4.6 25 6.75 53 106 158.43

Tetrachloroethylen ug/L 0.36 0.72 183.31 0.09 0.18 2.5 46.47 0.37 0.74 188.99

Zn ug/L 0.22 0.44 230.32 0.015 0.03 - 15.85 0.22 0.44 230.32

*) according to Table D16.6

It is apparent from Table A3.5 that geochemical variance is dominating for all measured ele-ments. As in case of Hg, for Fe and Pb the extended uncertainty Umeas>Um. Efforts should be made to reduce it (for instance, by improving the sampling protocol).



16-65

Annex 4

Examples of assessing uncertainties associated with sampling of groundwater:

Country-wide monitoring network (ca. 300000 km2)



16-66

Country-wide monitoring network (ca. 300000 km2)

Background information Title/Name of case study: Estimation of uncertainty associated with groundwater monitoring Type of case study: State monitoring network Objective of case study: Demonstration of the use of simplified version of the duplicate method for the estimation of uncertainty associated with sampling and analysis GWD focus : groundwater monitoring Specific contributions: Estimation of measurement uncertainty related to geochemical vari-ance and measurement variance (sum of sampling variance and analytical variance) for large monitoring network. Characterisation:

Country-wide groundwater quality monitoring network covers the area of about 300,000 km2. Monitoring network consists of 600 monitoring sites (wells and springs). A pilot study was designed to estimate contribution of measurement variance (sampling + analytical) to the total uncertainty associated with operation of the network. Duplicate water samples were collected from 11 randomly selected monitoring sites.

Scenario: The empirical approach was selected to assess measurement variance (sampling uncertainty + analytical uncertainty). The simplified version of duplicate method was used (see Chapter 5.1). Eleven wells were selected for the pilot study. One sampling campaign was carried out. Dupli-cate samples were obtained using a single sampling protocol and by a single person (sampler). Two analyses per site and element are performed (cf. Figure D16.10 ). Forty elements were analysed (pH, SEC, NH4, HCO3, TOC, CN, Li, Al, V, Co, Ni, Cu, As, Se, Mo, Ag, Cd, Sb, Pb, Hg, B, Ba, Ca, Cr, Fe, K, Mg, Mn, Na, SiO 2, Sr, Ti, Zn, F, Cl, NO2, Br, NO3, HPO4, SO4). Sampling protocol: Sampling was done using the sampling protocol for groundwater monitoring developed by the operator of the network. Samples were collected from the well heads using submersible pumps. Three sub-samples were collected: (i) 500 ml of unfiltered water for major ion analy-sis, (ii) 100 ml of filtered and acidified (pH< 2, with HNO3) water for ICP-MS and ICP-AES, (iii) 100 ml of filtered water for DOC analysis. Samples were transported to the laboratory at 4oC. Analyses were performed at an accredited laboratory using accredited methods subject to the required quality assurance and analytical quality control (ISO 17025, 2005). Estimates of the analytical uncertainty and analytical detection limits were obtained from the QA/QC pro-gramme implemented by the laboratory. Verification of analytical data: Ionic balance of major ions, rejection of analyses with balance error >10% Identification and rejection of outliers Calculation of uncertainty and its components The analysis of variance was carried out for individual parameters using ROB2 code (chapter 5.2.2). The following information was derived:

• The total variance separated into geochemical and measurement (technical) variance • Calculation of standard and relative uncertainties (u) associated with these compo-

nents. • Calculation of expanded uncertainty(U) for 95% confidence level • Calculation of relative uncertainties(U’) relative to mean values of the mean values of

the analyzed element for normal and duplicate samples.



16-67

Calculation of uncertainty and its components has been shown in detail for one element (Cu). Table A4.5 comprises the results for all elements with the concentrations > LOD. Conclusions: The geochemical variance is dominating in the country-wide monitoring network for all ana-lysed elements except of Cu. The expanded measurement uncertainty turned out to be higher that the limiting value only for F. Efforts should be made to reduce it.



16-68

Country wide monitoring network (ca. 300000 km2) The empirical approach was selected assess the overall uncertainty associated with the operation of the country wide monitoring network, attributed to geochemical and measurement variances.

The unbalanced design of the duplicate method was used. Eleven wells were selected for the study. One sampling campaign was carried out. The duplicate samples were obtained using single sampling protocol and single sampler. Two analyses per site and element are performed. Forty elements were analysed (pH, SEC, NH4, HCO3, TOC, CN, Li, Al, V, Co, Ni, Cu, As, Se, Mo, Ag, Cd, Sb, Pb, Hg, B, Ba, Ca, Cr, Fe, K, Mg, Mn, Na, SiO 2, Sr, Ti, Zn, F, Cl, NO2, Br, NO3, HPO4, SO4). Calculation of uncertainty and its components has been shown in detail for one element (Cu). The summary table (Table A4.7) comprises the results for selected analyzed elements with the concentrations > LOD. 1. Verification of analytical data and calculation of uncertainty 1.1 Ionic balance The limiting relative error of the ionic balance of 10% has been adopted. In this case one value can be rejected. Histogram of errors associated with analysis of ionic balance is shown in Figure A4.2.

Figure A4.1. Histogram of errors associated with analysis of ionic balance.



16-69

1.2 Example calculations for selected elements: F Table A4.1. Concentrations of F [mg/L] in the pairs of analyses after verification based on ionic balance.


Analysis n.2*

MK12 0.72 0.76 MK414 0.62 0.41 MK415 0.35 0.38 MK446 0.71 0.64 MK946 0.2 0.2 MK974 0.5 0.59 MK1151 0.66 0.05 MK1662 0.82 0.78 MK1699 0.57 0.3 MK1745 0.78 0.67


Figure A4.2. Graphical tests for outlier identification in the framework of empirical approach of uncertainty assessment (measured element: F). 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 (according to NORDTEST, 2006). There are no outliers.



16-70

1.2.2 Output of ROB2 code Results for 10 pairs of samples. CLASSICAL ANOVA RESULTS: Mean = 0.536 Geochemical Measurement (temporal) (sampling + analysis) ------------- ----------- Sums of Squares - 0.704 0.259 Standard Deviation - 0.162 0.161 Total standard deviation - 0.228 ROBUST ANOVA RESULTS: mean = 0.538 Geochemical Measurement (temporal) (sampling + analysis) Standard Deviation - 0.205 0.106 Total standard deviation - 0.231 Both robust and classical estimates are given for comparison. Results are in the same units as input data.


Parameter Method -- ANOVA Robust





ANOVA robust

78.90%

21.10%



Figure A4.3. Relative contributions of sampling and analytical variances to the total variance for F. Conclusion: the measurement variance exceeds the limiting value (20%).



16-71

Further calculations (estimation of uncertainty) are made for robust estimates (k = 2):


Parametr Value

ugeochem [mg/L] 0.21

Ugeochem [mg/L] 0.41


umeasurement [mg/L] 0.11

Umeasurement [mg/L] 0.21


utotal [mg/L] 0.23

Utotal [mg/L] 0.46

U’total[%] 85.87

Calculation of maximum allowable uncertainty of analysis for F (Um - see chapter 5.2.2): According to Table D16.6, for F we have Um = 10% TV = 0.1×1.5 = 0.15 [mg/L]. It means that estimated Umeas > Um. therefore, action should be taken to reduce the measurement uncertainty via improvement of sampling protocol and/or analytical variance.



16-72

1.3 Example calculations for selected elements: Cu Table A4.4. Concentrations of Cu [µg/L] in the pairs of analyses after verification based on ionic balance.


Analysis n.2*

MK12 0.75 0.19 MK414 0.28 0.28 MK415 0.33 0.28 MK446 0.76 0.8 MK946 0.78 0.51 MK974 2.3 1.05 MK1151 0.28 0.77 MK1662 0.41 0.33 MK1699 0.26 0.63 MK1745 0.18 0.33


Figure A4.4. Graphical tests for outlier identification in the framework of empirical approach of uncertainty assessment (measured element: Cu). 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 (according to NORDTEST, 2006). There are no outliers.



16-73

1.3.2 Output of ROB2 code Results for 10 pairs of samples. CLASSICAL ANOVA RESULTS: Mean = 0.575 Geochemical Measurement (temporal) (sampling + analysis) Sums of Squares - 3.183 1.179 Standard Deviation - 0.343 0.343 Total standard deviation - 0.486 ROBUST ANOVA RESULTS: mean = 0.489 Geochemical Measurement (temporal) (sampling + analysis) Standard Deviation - 0.127 0.268 Total standard deviation - 0.297 Further calculations (estimation of uncertainty) are made for robust estimates (k = 2):


Method Parameter

ANOVA Robust





ANOVA robust

18.58%

81.42%



Figure A4.5. Relative contributions of sampling and analytical variances to the total variance for Cu.



16-74

Conclusion: the measurement variance cannot be accepted as it exceeds the limiting value (20%).


Parametr Value







utotal [µg/L] 0.297

Utotal [µg/L] 0.594

U’total[%] 121.47

Calculation of the maximum allowable uncertainty of analysis of Cu (Um - see chapter 5.2.2): According to Table D16.6, for Cu we have Um = 10% TV = 0.1×2 = 0.2 [mg/L] = 200 [µg/L]. It means that estimated Umeas < Um. Consequently, despite of relative ly high measurement variance associated with the determination of Cu (ca. 81%), it can be accepted in the discussed case.



16-75

2. Results of parameter estimates derived from ROBAN code for selected elements The results of calculation of uncertainty and its components for a suite of other elements ana-lyzed in the framework of this study are listed in the summary table (Table A4.7). This table contains only elements with the concentrations > LOD.


Indicator Unit Geochem ical

variance

Geochem ical variance [%

of total]

Measure-ment vari-

ance

Measure-ment vari-ance [% of

total]

pH 0.064 80.98 0.015 19.02

SEC mS/cm 0.032 99.92 0.000025 0.08

Al ug/L 9.88 91.57 0.91 8.43

Ba ug/L 427.24 99.89 0.45 0.11

Ca mg/L 678.39 99.97 0.19 0.03

Cl mg/L 91.13 99.93 0.061 0.07

Cu ug/L 0.016 18.58 0.071 81.42

F mg/L 0.042025 78.94 0.011 21.06

HCO3 mg/L 10205.85 99.35 66.52 0.65

K mg/L 4.17 99.74 0.011 0.26

Li ug/L 63.79 99.63 0.24 0.37

Mg mg/L 48.99 99.93 0.036 0.07

Mn ug/L 3796.04 99.96 1.39 0.04

Mo ug/L 0.43 98.59 0.0060 1.41

Na mg/L 18.15 99.31 0.13 0.69

NO2 mg/L 0.00019 84.00 0.000036 16.00

NO2 mg/L 85.86 99.99 0.0048 0.01

SiO2 mg/L 61.73 99.95 0.033 0.05

SO4 mg/L 556.91 99.78 1.25 0.22

Sr ug/L 2574.55 98.78 31.89 1.22



16-76

A4.7b. Calculated contributions to the overall uncertainty for selected indicators.

Indicator Unit ugeochem Ugeochem U’geochem [%] umeas Umeas

Um *)

U’meas.[%] utotal Utotal U’total [%]

SEC mS/cm 0.18 0.36 63.48 0.0050 0.010 250 1.77 0.18 0.37 63.48

Al ug/L 3.1 6.3 96.00 0.95 1.9 20 29.13 3.3 6.6 100.34

Ba ug/L 20 41 172.61 0.67 1.3 - 5.61 21 41 172.7

Ca mg/L 26 52 63.36 0.43 0.86 - 5.61 26 52 63.38

Cl mg/L 9.6 19 158.51 0.25 0.49 25 4.1 9.6 19 158.57

Cu ug/L 0.13 0.25 51.4 0.27 0.53 200 109.61 0.29 0.59 121.47

F mg/L 0.21 0.41 76.21 0.11 0.21 0.15 39.41 0.23 0.46 85.87

HCO3 mg/L 100 200 72.33 8.2 16 - 6.04 100 200 72.44

K mg/L 2.0 4.1 155.09 0.10 0.21 - 7.9 2.1 4.1 155.27

Li ug/L 8.0 16 178.10 0.49 0.97 - 10.86 8.0 16 178.44

Mg mg/L 7.0 14 93.49 0.19 0.38 - 2.55 7.0 14 93.53

Mn ug/L 61 120 208.57 1.2 2.4 5 4 62 123 208.86

Mo ug/L 0.65 1.3 140.06 0.078 0.16 - 16.76 0.66 1.3 141.11

Na mg/L 4.3 8.5 133.65 0.35 0.71 20 11.11 4.3 8.6 134.12

NO2 mg/L 0.014 0.028 103.70 0.0060 0.012 0.050 44.44 0.015 0.030 111.11

NO3 mg/L 9.3 18 284.58 0.069 0.14 5.0 2.12 9.3 18 284.58

SiO2 mg/L 0.56 1.1 135.82 0.35 0.75 - 90.25 0.68 1.4 163.09

SO4 mg/L 2.1 4.2 170.28 0.30 0.61 25 25.13 2.1 4.2 172.16

Sr ug/L 0.085 0.17 215.87 0.039 0.079 - 100.63 0.090 0.19 238.73 *) calculated according to Table D16.6

It is apparent from Table A4.7 that the measurement variance exceeds the preset value of 20 % for Cu and F. The extended measurement uncertainty (Umeas) is larger than Um only for F. Efforts should be made to reduce it (for instance, by improving the sampling protocol).



16-77

Annex 5

Assessing of practical limit of detection (PLOD)

for regional monitoring network



16-78

Assessing of practical limit of detection (PLOD)

for regional monitoring network

Background information Title/Name of case study: Estimation of practical limit of detection (PLOD) during ground-water quality monitoring Type of case study: regional monitoring network Objective of case study: Assessment of practical limit of detection (PLOD) during monitoring of groundwater quality GWD focus : groundwater monitoring Characterisation:

The regional monitoring of groundwater quality is associated with river basin. It covers the area of about 50,000 km2. Monitoring network consists of 172 monitoring sites (wells and springs). The manager of the monitoring network decided to carry out pilot study aimed at assessment of practical limit of detection (PLOD) during monitoring of groundwater quality.

Scenario: Field blank samples were collected with the same equipment as was used for collecting the normal samples, but with high-purity de-ionized water as a sample medium. Fifteen blank samples were collected. They were processed, transported and stored in the same way as nor-mal samples. The blank samples were treated in the laboratory as normal samples. The calculation of PLOD was carried out in the laboratory using analogous methodology to that adopted for derivation of laboratory limit of detection (LOD).

Sampling protocol: Sampling was done using the sampling protocol for groundwater monitoring developed by the operator of the network. Samples were collected from the well heads using immersible pumps. Four sub-samples of de-ionized water were collected from one large container brought from the laboratory: (i) 500 ml of unfiltered water for major ion analysis, (ii) 100 ml of filtered and acidified (pH< 2, with HNO3) water for ICP-MS and ICP-AES analyses, (iii) 100 ml of filtered water for DOC analysis, (iv) 250 ml of filtered water for Hg analysis. Samples were trans-ported to the laboratory at 4oC. Analyses were performed at an accredited laboratory using accredited methods subject to the required quality assurance and analytical quality control (ISO 17025, 2005). Calculation of PLOD:

The value of PLOD was derived from the equation analogous to that used by the laboratory to derive LOD: PLOD = xbi + 3.28sbi where xbi is the mean of the blank measurements, sbi is the standard deviation of the blank measurements. Example calculations are presented for one element (Pb) and the summary table A5.2 is given for analysed elements Conclusions: The practical detection limit obtained in the pilot study turned out to be only 2 times lower than the maximum permissible concentration of Pb in drinking water. Therefore, specific measures should be undertaken to improve the situation and lower the PLOD. Since the PLOD value turned out to be almost five times higher than laboratory LOD, sampling protocol has to be improved to avoid contamination during sampling.



16-79

Assessing of practical limit of detection (PLOD) for regional monitoring network

Field blank samples were collected with the same equipment as was used for collecting the normal samples, but with high-purity de-ionized water as a sample medium. Fifteen blank sam-ples were collected. They were processed, transported and stored in the same way as normal samples. The blank samples were treated in the laboratory as normal samples. The calculation of PLOD was carried out in the laboratory using analogous methodology to that adopted for derivation of laboratory limit of detection (LOD).

1.Calculation of PLOD

The following results of Pb content (in µg/dm3) in 15 field blank samples were obtained:

1, 6, 22, 9, 4, 4, 4, 4, 4, 3, 2, 2, 3, 3, 2.

The results are reported in chronological order. The learning process of the sampling team is apparent (cf. Figure A5.3.)

Figure A5.1 shows the histogram of the Pb results for field blank samples.

Figure A5.1. Results of Pb analyses (in µg/L) in field blank samples



16-80

1.1. Identification of outliers

The identification of outliers was carried out using control charts. They are shown in Figure A5.2.

Figure A5.2. Control charts for outlier rejection in the set of 15 determinations of Pb content in field blank samples. Upper panel: individual results of Pb determinations as a function of samples number. Lower panel: moving range chart. CL – control limit; UCL – upper control limit; LCL – lower control limit

It is apparent from the control charts presented in Figure A5.2 that sample No. 3 reveals anomalously high Pb content and should be rejected from further treatment of the data.

1.2. Calculation of practical limit of detection (PLOD)

The practical limit of detection (PLOD) for Pb was derived from the equation analogous to that used by the laboratory to derive the limit of detection (LOD):

PLOD = xbi + 3.28sbi where xbi is the mean of the blank measurements, sbi is the standard deviation of the blank measurements.

The mean Pb content in the 14 field blank samples was equal xbi = 3.6 µg/L, while the standard deviation was equal sbi = 1.2 µg/L. This gives the PLOD value for Pb equal: PLOD = 7.5 µg/L



16-81

In Table A5.1 the calculated practical limit of detection is compared with laboratory detection limit and the maximum permissible concentration of Pb in drinking water.

The practical limit of detection can be also derived using graphical method (cf. Fig. A5.3).

Table A5.1. Comparison of PLOD obtained in the present study with laboratory LOD and MPL value for drinking water.

Parameter Value [ug/L]

LOD (laboratory) 1.7

PLOD – calculated 7.5

Maximum permissible level of lead in drinking water according to (DWD, 1998)

10

Pb concentration [ug/L]

0 20 40 60 80 100

P(x

)

0.1

1

10

30

50

70

90

99

99.9 3.28

1

0

-1

-2

PLOD

Figure A5.3. Graphical estimation of PLOD. P(x) stands for probability. Numbers on the right-

hand side of the graph indicate standard deviations.

It is apparent that practical detection limit is less than 2 times lower than the maximum permissible concentration of this element in drinking water. Therefore, specific measures should be undertaken to improve the situation and lower the PLOD. It is also apparent from the table that PLOD is almost five times higher than laboratory LOD. Consequently sampling protocol has to be improved to avoid contamination during sampling.



16-82

Sampling in the field is often carried out under difficult conditions (dust, solar radiation, vapours of fuel, car exhausts, freezing, etc.). Therefore, training of personnel involved in groundwater sampling process plays an important role . This is illustrated in Figure A5.4 showing improving skills of the sampling team for determination of Zn in groundwater.

Figure A5.4. Reduction of Zn concentration in blank samples during sampling of a sequence of monitoring wells. Training of sampling team results in lowering the blank levels of certa in con-taminants (example taken from regional groundwater monitoring of Upper Vistula River Basin, southern Poland – after Witczak, 1994)



16-83

Annex 6

Assessing uncertainties associated with the assessment of trends



16-84

Assessing uncertainties associated with the assessment of trends

Background information

Title/Name of case study: Estimation of uncertainties associated with the assessment of trends Type of case study: Individual monitoring site Objective of case study: Assessment the ability of the given monitoring site to detect preset level of deterioration of groundwater quality (20%) within the assumed confidence level (95%). GWD focus : groundwater monitoring Characterisation: Artesian deep well located in confined Jurassic carbonate fissured aquifer. The well belongs to network of wells fulfilling the role of emergency supply for urban population. Scenario: The ability of the given monitoring site to detect the preset level of deterioration of groundwater quality (e.g. 20%) with the assumed confidence level (e.g. 95%) was tested.The well was sampled in specific time intervals (two weeks) and water quality was measured (cf. Figure D16.11). Sampling protocol: Sampling was done using the sampling protocol developed for groundwater monitor ing. Samples were collected from the well head. Three sub-samples were collected: (i) 500 ml of unfiltered water for major ion analysis, (ii) 100 ml of filtered and acidified (pH< 2, with HNO3) water for ICP-MS and ICP-AES analyses, (iii) 100 ml of filtered water for DOC analysis. Samples were transported to the laboratory at 4oC. Analyses were performed at laboratory which implements extensive quality assurance and analytical quality control programmes. Calculation of uncertainty and its components The analysis of variance was carried out for individual parameters using ROB2 code (chapter 5.2.2). The following information was derived:

• The total variance separated into temporal and measurement (sampling and analytical) variance

• Calculation of standard and relative uncertainties (u) associated with these compo-nents.

• Calculation of expanded uncertainty (U) for 95% confidence level • Calculation of relative uncertainties (U’) related to the mean values of the analyzed

element for normal and duplicate samples. Calculation of uncertainty and its components has been shown in detail for one element (Zn). Conclusions: In the discussed case the relative measurement uncertainty (U’measurement) for Pb and K was found to be higher than the preset value (20%). Consequently, the conditions for detection of trend with respect to these two elements are unfavourable . The relative uncertainties derived for other three elements (Na, Mg, Ca) are lower than 20 % which means that trend can be meaningfully detected.



16-85

Assessing uncertainties associated with the assessment of trends Artesian deep well (ZK) is located in confined Jurassic carbonate fissured aquifer. The well belongs to network of wells used as emergency water supply for urban population. The wells are open to public. Between December 2005 and May 2006, 13 duplicate samples have been collected. Average interval between consecutive samplings was ca. two weeks. 1. Verification of analytical data and calculation of uncertainty 1.1 Ionic balance The limiting rela tive error of the ionic balance of 10% has been adopted. Table A6.1 summa-rizes the results of ionic balance for both normal and duplicate samples. The histogram of error of ionic balance is shown in Figure A6.1. It is apparent that analyses turned out to be of good quality (mean error 0.49 %) and all results have been accepted for further treatment. Table A6.1. Error of ionic balance for the analyzed set of normal and duplicate samples

No of sample Index Error of analysis [%]

T1 N 5.82 T2 D 5.24 T3 N 3.89 T4 D 6.03 T5 N 1.36 T6 D 0.75 T7 N 0.27 T8 D 2.42 T9 N -0.72 T10 D 0.17 T11 N -0.09 T12 D 0.21 T13 N 3.00 T14 D 2.46 T15 N -2.38 T16 D -0.32 T17 N -0.05 T18 D -4.61 T19 N -4.18 T20 D -3.48 T21 N -4.13 T22 D -3.44 T23 N 2.13 T24 D 2.55 T25 N 1.21 T26 D -1.31



16-86

Figure A6.1 Histogram of error of ionic balance for the analysed set of normal and du-plicate samples 1.2 Example calculations for selected elements: Zn Table A6.2. Concentrations of Zn [µg/L] in the pairs of analyses after verification based on ionic balance.

No S1 S2

1 5 5 2 5 5 3 16 5 4 9 7 5 5 5 6 18 10 7 5 5 8 21 10 9 15 16 10 16 17 11 15 18 12 13 11 13 13 14



16-87

1.2.1 Identification of outliers A B C

Figure A2.3. 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 (according to NORDTEST, 2006). No values can be rejected. 1.2.2 Output of ROB2 code Results for 13 pairs of samples: CLASSICAL ANOVA RESULTS: Mean = 10.923 Geochemical Measurement (temporal) (sampling + analysis) ------------- ----------- Sums of Squares - 540.846 163.000 Standard Deviation - 4.033 3.541 Total standard deviation - 5.367 ROBUST ANOVA RESULTS: mean = 10.923 Geochemical Measurement (temporal) (sampling + analysis) ------------- ----------- Standard Deviation - 5.241 1.722 Total standard deviation - 5.516 Further calculations (estimation of uncertainty) are made for robust estimates (k = 2):



16-88

Table A6.3. Parameter estimates derived from ROB2 Method

Parameter ANOVA Robust





ANOVA robust

90.25%

9.75%



Figure A2.4. Relative contributions of sampling and analytical variances to the total variance for Zn. Conclusion: the calculated measurement variance is not exceeding the limiting value (20%) and therefore can be accepted.


Parametr Value


Ugeochem [µg/L] 10

U’geochem[%] 95.2




utotal [µg/L] 5.5

Utotal [µg/L] 11

U’total[%] 100.7



16-89

Apart from assessment of chemical status of the given groundwater body, monitoring will have another important function during implementation of GWD. Namely, it will be employed to detect trends and trend reversals of groundwater quality. Specific methodology has been proposed for this purpose (Grath et al., 2001). However, as in the case of status assessment, detection of trends and trend reversals is inevitably associated with uncertainties. The first question which can be asked in this regard may concern the ability of the given monitoring site to detect the preset level of deterioration of groundwater quality (e.g. 20%) with the assumed confidence level (e.g. 95%). In the discussed case the relative measurement uncertainty (U’measurement) is equal 31.1 % of the mean value (10.92 µg/L). Therefore, one can conclude that conditions for detection of trend are unfavourable in this case and action has to be taken to identify the reasons of such high measurement uncertainty.

Table A6.5. Calculated relative uncertainties for selected major ions

Indicator Ugeochem [%] U’meas.[%]

Utotal [%]

K 21.55 15.84 26.72

Na 6.25 6.44 8.98

Mg 6.31 5.18 8.16

Ca 5.37 4.34 6.90

Table A6.5 shows relative uncertainties derived for selected major ions. It is apparent that only in case of potassium the measurement uncertainty is higher that the present value (20%). For other three elements (Na, Mg, Ca) the conditions for trend detection are favourable.

Documents

D16 WP3-2 sampl,meas,qa-qc Final2...pling, Measuring and Quality Assurance. Draft report . 16-3 Annexes Annex 1. Definitions and terminology Annex 2. Assessing uncertainties associated