Estimation and usefulness of measurement uncertainty from sampling at different spatial scales: microns to kilometres.

Uncertainty of measurement values (MU) is crucial to their reliable geochemical interpretation. MU can be estimated using the Duplicate Method, which requires the taking of a small proportion of duplicated samples, and can be applied at any spatial scale. The distance between the duplicated samples is selected to reflect the effect of analyte heterogeneity on the measurement result (i.e. estimated concentration) within each sampling target, at the particular scale of investigation. Three published case studies, at different spatial scales, are used to explain how the Duplicate Method can be applied to estimate MU. They also illustrate how MU can be used to improve geochemical interpretation and validate measurement procedures (that include sampling) by judging their fitness for purpose. At the kilometre scale, measurements from the GEMAS survey of agricultural soils across Europe are used to estimate their MU for the first time. The MU for 53 elements range from an uncertainty factor of 1.01 to over 10. The MU contributes more that 20% to the total variance for 8 of the 53 elements, showing that the measurement procedure was not fit for purpose in those cases. At the micron scale, measurements of oxygen isotopes in candidate quartz reference materials had MU that was dominated by its sampling component, caused by sometimes unacceptable heterogeneity. A third case study of Pb in soils at 12 UK sites showed that the Duplicate Method can also be used to quantify the heterogeneity (as factor 1.03 to 2.4), and that it can indicate different possible sources of an element.

Evaluation of uncertainties in in situ and ex situ gamma measurements on land areas with low contamination levels.

Previous work on the characterisation of land areas with moderate contamination levels showed that in situ measurements made with a gamma detector can achieve lower levels of the random component of uncertainty than laboratory measurements of extracted samples. This was found when the variance caused by small-scale lateral heterogeneity of contaminants was included in the uncertainty estimation. The present paper documents the results of applying the same techniques of uncertainty estimation to an area with contamination levels that were lower by a factor of 10. If the same counting times were used, it would be expected that both measurement types would be affected by higher levels of random uncertainty in the individual measurements because of increased uncertainty from counting statistics and other factors such as interpretation of gamma spectra. However, when uncertainty due to sampling was included, it was found that both measurements methods were subject to similar combined uncertainties at individual locations. Using an assumption of the depth distributions of radionuclides that was supported by ex situ measurements, in situ measurements were able to produce averaging estimates with an approximate reduction of 50% in the standard error on the mean at ~50% of the cost of the ex situ measurements.

Optimising in situ gamma measurements to identify the presence of radioactive particles in land areas.

High-coverage in situ surveys with gamma detectors are the best means of identifying small hotspots of activity, such as radioactive particles, in land areas. Scanning surveys can produce rapid results, but the probabilities of obtaining false positive or false negative errors are often unknown, and they may not satisfy other criteria such as estimation of mass activity concentrations. An alternative is to use portable gamma-detectors that are set up at a series of locations in a systematic sampling pattern, where any positive measurements are subsequently followed up in order to determine the exact location, extent and nature of the target source. The preliminary survey is typically designed using settings of detector height, measurement spacing and counting time that are based on convenience, rather than using settings that have been calculated to meet requirements. This paper introduces the basis of a repeatable method of setting these parameters at the outset of a survey, for pre-defined probabilities of false positive and false negative errors in locating spatially small radioactive particles in land areas. It is shown that an un-collimated detector is more effective than a collimated detector that might typically be used in the field.

Comparison between in situ and ex situ gamma measurements on land areas within a decommissioning nuclear site: a case study at Dounreay.

Measurements made in situ with gamma detectors and ex situ measurements of soil samples in a laboratory can have complementary roles in the assessment of radioactively contaminated land on decommissioning nuclear sites. Both in situ and ex situ methods were used to characterize (137)Cs contamination within an area at the Dounreay site in Scotland. The systematic difference (bias) between estimates of the mean activity concentration was found to be non-significant when in situ measurements were interpreted using a linear depth model, based on ex situ measurements made at two different depths. An established method of evaluating the random components of measurement uncertainty was used. The random component of analytical uncertainty in the in situ measurements, made in field conditions, was found to exceed that for the ex situ measurements, made in the controlled conditions of a laboratory. However, contamination by the target radionuclide was found to be heterogeneous over small spatial scales. This resulted in significantly higher levels of random sampling uncertainty in individual ex situ measurements. As in situ measurements are substantially less costly, a greater number of measurements can be made, which potentially reduces the uncertainty on the mean. Providing the depth profile of contaminants can be modelled with confidence, this can enable estimates of mean activity concentration over an averaging area to be made with lower overall uncertainties than are possible using ex situ methods.

Methodology for profiling anti-androgen mixtures in river water using multiple passive samplers and bioassay-directed analyses.

The identification of endocrine disrupting chemicals in surface waters is challenging as they comprise a variety of structures which are often present at nanomolar concentrations and are temporally highly variable. Hence, a holistic passive sampling approach can be an efficient technique to overcome these limitations. In this study, a combination of 4 different passive samplers used for sampling polar (POCIS Apharm and POCIS Bpesticide) and apolar compounds (LDPE low density polyethylene membranes, and silicone strips) were used to profile anti-androgenic activity present in river water contaminated by a wastewater effluent. Extracts of passive samplers were analysed using HPLC fractionation in combination with an in vitro androgen receptor antagonist screen (YAS). Anti-androgenic activity was detected in extracts from silicone strips and POCIS A/B at (mean ± SD) 1.1 ± 0.1 and 0.55 ± 0.06 mg flutamide standard equivalents/sampler respectively, but was not detected in LDPE sampler extracts. POCIS samplers revealed higher selectivity for more polar anti-androgenic HPLC fractions compared with silicone strips. Over 31 contaminants were identified which showed inhibition of YAS activity and were potential anti-androgens, and these included fungicides, germicides, flame retardants and pharmaceuticals. This study reveals that passive sampling, using a combination of POCIS A and silicone samplers, is a promising tool for screening complex mixture of anti-androgenic contaminants present in surface waters, with the potential to identify new and emerging structures with endocrine disrupting activity.

Rapid and accurate analyses of silicon and phosphorus in plants using a portable X-ray fluorescence spectrometer.

The elemental analysis of plant material is a frequently employed tool across biological disciplines, yet accurate, convenient and economical methods for the determination of some important elements are currently lacking. For instance, digestion-based techniques are often hazardous and time-consuming and, particularly in the case of silicon (Si), can suffer from low accuracy due to incomplete solubilization and potential volatilization, whilst other methods may require large, expensive and specialised equipment. Here, we present a rapid, safe and accurate procedure for the simultaneous, nonconsumptive analysis of Si and phosphorus (P) in as little as 0.1 g dried and ground plant material using a portable X-ray fluorescence spectrometer (P-XRF). We used certified reference materials from different plant species to test the analytical performance of P-XRF and show that the analysis suffers from very little bias and that the repeatability precision of the measurements is as good as or better than that of other methods. Using this technique we were able to process and analyse 200 ground samples a day, so P-XRF could provide a particularly valuable tool for plant biologists requiring the simultaneous nonconsumptive analysis of multiple elements, including those known to be difficult to measure such as Si, in large numbers of samples.

Judging the fitness of on-site measurements by their uncertainty, including the contribution from sampling.

Chemical measurements made on-site can be very effective in underpinning environmental decisions, but they are often mistrusted in favour of measurements made on samples removed off-site to a remote, usually accredited, laboratory. The uncertainty present in all measurements, wherever they are made, includes contributions from the sampling and sample preparation processes, and also from often unsuspected systematic errors. Once this total uncertainty has been estimated in a rigorous way under statistical control for each investigation, it is demonstrated using two case studies that on-site measurements can be fit for decision-making purposes. Uncertainty from sampling and sample preparation often dominates many measurement systems, whether they are based on-site or off-site, and makes the analytical contribution less critical for judging fitness. The value of this total uncertainty can be used to make a probabilistic, rather than deterministic, classification of the contamination. Uncertainty values can also be used to calculate how the measurement method can be modified to achieve an optimal value that it fit-for-purpose (e.g. using composite samples or measurements). The rapid availability of on-site measurements, together with known uncertainty, is shown to be capable of enabling equal reliability of decisions to the off-site approach, whilst minimising the cost of the decision making process. Challenges in estimating the uncertainty of on-site measurements are identified as (i) potential ambiguity in the true value that is being estimated (i.e. the measurand), and (ii) the fact that off-site lab measurements can have values of analytical precision that are larger than those quoted by the lab (e.g. 54% rather than 30% for TPH, at 95% confidence) and which make their use in the validation of on-site measurements problematic.

Improved evaluation of measurement uncertainty from sampling by inclusion of between-sampler bias using sampling proficiency testing.

A realistic estimate of the uncertainty of a measurement result is essential for its reliable interpretation. Recent methods for such estimation include the contribution to uncertainty from the sampling process, but they only include the random and not the systematic effects. Sampling Proficiency Tests (SPTs) have been used previously to assess the performance of samplers, but the results can also be used to evaluate measurement uncertainty, including the systematic effects. A new SPT conducted on the determination of moisture in fresh butter is used to exemplify how SPT results can be used not only to score samplers but also to estimate uncertainty. The comparison between uncertainty evaluated within- and between-samplers is used to demonstrate that sampling bias is causing the estimates of expanded relative uncertainty to rise by over a factor of two (from 0.39% to 0.87%) in this case. General criteria are given for the experimental design and the sampling target that are required to apply this approach to measurements on any material.

Uncertainty of measurement or of mean value for the reliable classification of contaminated land.

Classification of contaminated land is important for risk assessment and so it is vital to understand and quantify all of the uncertainties that are involved in the assessment of contaminated land. This paper uses a case study to compare two methods for assessing the uncertainty in site investigations (uncertainty of individual measurements, including that from sampling, and uncertainty of the mean value of all measurements within an area) and how the different methods affect the decisions made about a site. Using the 'uncertainty of the mean value' there is shown to be no significant possibility of 'significant harm' under UK guidance at one particular test site, but if you consider the 'uncertainty of the measurements' a significant proportion (50%) of the site is shown to be possibly contaminated. This raises doubts as to whether the current method using 'uncertainty of the mean' is sufficiently robust, and suggests that 'uncertainty of measurement' information may be preferable, or at least beneficial when used in conjunction.

Chemical speciation and bioaccessibility of lead in surface soil and house dust, Lavrion urban area, Attiki, Hellas.

In the Lavrion urban area study, Hellas, a five-step sequential extraction method was applied on samples of 'soil' (n = 224), affected by long-term mining and metallurgical activities, and house dust (n = 127), for the purpose of studying the potential bioaccessibility of lead and other metals to humans. In this paper, the Pb concentrations in soil and house dust samples are discussed, together with those in rocks and children's blood. Lead is mainly associated with the carbonate, Fe-Mn oxides and residual fractions in soil and house dust. Considering the very low pH of gastric fluids (1-3), a high amount of metals, present in soil (810-152,000 mg/kg Pb) and house dust (418-18,600 mg/kg Pb), could be potentially bioaccessible. Consequently, children in the neighbourhoods with a large amount of metallurgical processing wastes have high blood-Pb concentrations (5.98-60.49 µg/100 ml; median 17.83 µg/100 ml; n = 235). It is concluded that the Lavrion urban and sub-urban environment is extremely hazardous to human health, and the Hellenic State authorities should urgently tackle this health-related hazard in order to improve the living conditions of local residents.

Uncertainty in the assessment of hazard, exposure and risk.

The terminology, concepts and current approaches to uncertainty in the assessment of hazard, exposure and risk are reviewed. Five generic questions are discussed on uncertainty, including sources, levels, when and how it should be dealt with or reduced, what are our gaps in understanding and how they can be addressed. A case study of lead exposure of children in Lavrion, Greece, is used to exemplify these questions and possible answers. Estimation of uncertainty may be improved by the use of interorganizational studies to capture sources of uncertainty that are often overlooked. Gaps identified in our understanding of uncertainty include: a limitation in the availability of basic measurements, a lack of knowledge of the environmental processes, an inability to predict the effects of mixtures, the aetiology of disease and devising procedures for optimal resource allocation in impact assessment.

The duplicate method of uncertainty estimation: are eight targets enough?

This paper presents methods for calculating confidence intervals for estimates of sampling uncertainty (s(samp)) and analytical uncertainty (s(anal)) using the chi-squared distribution. These uncertainty estimates are derived from application of the duplicate method, which recommends a minimum of eight duplicate samples. The methods are applied to two case studies--moisture in butter and nitrate in lettuce. Use of the recommended minimum of eight duplicate samples is justified for both case studies as the confidence intervals calculated using greater than eight duplicates did not show any appreciable reduction in width. It is considered that eight duplicates provide estimates of uncertainty that are both acceptably accurate and cost effective.

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling.

Measurement uncertainty is a vital issue within analytical science. There are strong arguments that primary sampling should be considered the first and perhaps the most influential step in the measurement process. Increasingly, analytical laboratories are required to report measurement results to clients together with estimates of the uncertainty. Furthermore, these estimates can be used when pursuing regulation enforcement to decide whether a measured analyte concentration is above a threshold value. With its recognised importance in analytical measurement, the question arises of 'what is the most appropriate method to estimate the measurement uncertainty?'. Two broad methods for uncertainty estimation are identified, the modelling method and the empirical method. In modelling, the estimation of uncertainty involves the identification, quantification and summation (as variances) of each potential source of uncertainty. This approach has been applied to purely analytical systems, but becomes increasingly problematic in identifying all of such sources when it is applied to primary sampling. Applications of this methodology to sampling often utilise long-established theoretical models of sampling and adopt the assumption that a 'correct' sampling protocol will ensure a representative sample. The empirical approach to uncertainty estimation involves replicated measurements from either inter-organisational trials and/or internal method validation and quality control. A more simple method involves duplicating sampling and analysis, by one organisation, for a small proportion of the total number of samples. This has proven to be a suitable alternative to these often expensive and time-consuming trials, in routine surveillance and one-off surveys, especially where heterogeneity is the main source of uncertainty. A case study of aflatoxins in pistachio nuts is used to broadly demonstrate the strengths and weakness of the two methods of uncertainty estimation. The estimate of sampling uncertainty made using the modelling approach (136%, at 68% confidence) is six times larger than that found using the empirical approach (22.5%). The difficulty in establishing reliable estimates for the input variable for the modelling approach is thought to be the main cause of the discrepancy. The empirical approach to uncertainty estimation, with the automatic inclusion of sampling within the uncertainty statement, is recognised as generally the most practical procedure, providing the more reliable estimates. The modelling approach is also shown to have a useful role, especially in choosing strategies to change the sampling uncertainty, when required.

Effect of scale of Cd heterogeneity and timing of exposure on the Cd uptake and shoot biomass, of plants with a contrasting root morphology.

A pot experiment was conducted to investigate the influence of spatial heterogeneity of Cd distribution in soil on shoot biomass, shoot metal concentration and total shoot Cd uptake by lettuce (Lactuca sativa, variety Tom Thumb) and Indian mustard (Brassica juncea). Five different soil treatments had similar overall concentration of Cd per pot, but different scales of heterogeneity and also timing of plant exposure during the growth cycle. The presence and scale of heterogeneity and timing of exposure were found to have significant effects on shoot biomass for both plants (with one exception). The mean values of Cd mass taken up were significantly affected by the presence of heterogeneity and timing only for lettuce. Only the scale of heterogeneity affected the uptake of Cd by Indian mustard, presumably because of its larger root system (approximately 18 cm, compared with approximately 5 cm for lettuce). These findings have important implications for phytoremediation, and for human health risk assessment where leafy vegetables are grown in situations with highly elevated Cd concentrations.

Spatial contaminant heterogeneity: quantification with scale of measurement at contrasting sites.

Material within the terrestrial environment is rarely homogeneously distributed, either spatially or temporally. One consequence of heterogeneity is that uncertainty is usually generated in measurements that are taken with the aim of characterising the environment. For example, a measurement of analyte concentration within soil taken from one sampling location on contaminated land can vary substantially when compared against another sample taken at effectively the same nominal location. The measurement uncertainty arising from the heterogeneity can substantially limit the reliability of the interpretations made upon environmental investigations. The sampling uncertainty usually outweighs the analytical uncertainty from the laboratory, often by a factor of 20 or more. One approach to reducing the uncertainty is to design a more suitable sampling strategy. This might be achieved by predicting the degree of heterogeneity prior to the investigation, but this is often difficult to achieve accurately. Another approach, which was investigated here, is to actually characterise the heterogeneity prior to the main investigation using rapid and inexpensive technology, such as in situ measurement techniques. In situ portable X-ray fluorescence (PXRF) and X-ray microprobe (XMP) techniques were employed to test the feasibility of this approach. Two contrasting contaminated land sites were chosen to characterise the two-dimensional spatial heterogeneity of heavy metal contamination in topsoil at a range of scales (50 m to 0.001 m). The spatial heterogeneity of contaminants, expressed as relative standard deviations, was found to differ between the two sites by a factor of two, largely due to the mode of deposition of pollution. The study also indicated that the heterogeneity did not change systematically with the scale of measurement between sampling locations at either site.

Optimising uncertainty in physical sample preparation.

Uncertainty associated with the result of a measurement can be dominated by the physical sample preparation stage of the measurement process. In view of this, the Optimised Uncertainty (OU) methodology has been further developed to allow the optimisation of the uncertainty from this source, in addition to that from the primary sampling and the subsequent chemical analysis. This new methodology for the optimisation of physical sample preparation uncertainty (u(prep), estimated as s(prep)) is applied for the first time, to a case study of myclobutanil in retail strawberries. An increase in expenditure (+7865%) on the preparatory process was advised in order to reduce the s(prep) by the 69% recommended. This reduction is desirable given the predicted overall saving, under optimised conditions, of 33,000 pounds Sterling per batch. This new methodology has been shown to provide guidance on the appropriate distribution of resources between the three principle stages of a measurement process, including physical sample preparation.

Effect of alkaline pH and associated Zn on the concentration and total uptake of Cd by lettuce: comparison with predictions from the CLEA model.

An eight-fold underestimate of the potential Cd exposure to humans via ingestion of lettuce grown in moderately alkaline soil has been measured experimentally. Current models of Cd uptake by leafy vegetables, which are used in risk assessment (e.g. CLEA in UK) predict higher concentration factors in acid than in alkaline soils. Experimental evidence shows that Cd uptake, although it decreases with increasing pH from acid to neutral soils, increases again in alkaline soils, confirming recent finding from other workers. The concentration of Zn in the soil also significantly affects the uptake of Cd, although this is not included in the current prediction models either. The effect of Zn on the uptake of Cd by plants is greater in slightly alkaline soils (pH 7.7) than in slightly acidic or neutral soils. High concentrations of Zn in soil (1000 mg/kg), which are often associated with elevated Cd levels, further increase the Cd concentration factor to values 12 times higher than that predicted by the CLEA model. This is due in part to the effect of the high soil Zn on reducing the above-ground biomass of the plants.

Two-stage application of the optimised uncertainty method: a practical assessment.

Uncertainty estimates from routine sampling and analytical procedures can be assessed as being fit for purpose using the optimised uncertainty (OU) method. The OU method recommends an optimal level of uncertainty that should be reached in order to minimise the expected financial loss, given a misclassification of a batch as a result of the uncertainty. Sampling theory can used as a predictive tool when a change in sampling uncertainty is recommended by the OU method. The OU methodology has been applied iteratively for the first time using a case study of wholesale butter and the determination of five quality indicators (moisture, fat, solids-not-fat (SNF), peroxide value (PV) and free fatty acid (FFA)). The sampling uncertainty (s(samp)) was found to be sub-optimal for moisture and PV determination, for 3-fold composite samples. A revised sampling protocol was devised using Gy's sampling theory. It was predicted that an increase in sample mass would reduce the sampling uncertainty to the optimal level, resulting in a saving in expectation of loss of over pounds 2000 per 20 tonne batch, when compared to current methods. Application of the optimal protocol did not however, achieve the desired reduction in s(samp) due to limitations in sampling theory. The OU methodology proved to be a useful tool in identifying broad weaknesses within a routine protocol and assessing fitness for purpose. However, the successful routine application of sampling theory, as part of the optimisation process, requires substantial prior knowledge of the sampling target.

Heterogeneity of cadmium concentration in soil as a source of uncertainty in plant uptake and its implications for human health risk assessment.

The major route of exposure of humans to the toxic element cadmium (Cd) is via the consumption of vegetables homegrown on Cd contaminated soil. It is well known that soil pH is one of the main soil properties controlling bioavailability of Cd in plants. This is acknowledged in human health risk assessment models that incorporate pH dependant concentration factors (CF=plant Cd/soil Cd). However, variation in spatial heterogeneity of nutrients and heavy metals in soil can have a profound effect on plant performance and uptake of nutrients and heavy metals. Here we show for lettuce (variety Crispino) that variation in plant-scale heterogeneity of Cd in soil affects bioavailability and hence CF by a factor of 2. Plant yield is also significantly affected. This has important implications for both human health risk assessment, as variation in CF affects predicted exposure, and for phytoremediation where an optimal combination of plant yield and contaminant accumulation is required.