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.

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It is argued that the current division between field sampling and chemical analysis is counterproductive in terms of ensuring that geochemical measurement results are fit for their intended purpose. An integrated approach to the whole measurement process has many advantages including no dependence on the two assumptions that either the samples are necessarily representative if taken with a correct protocol, or that the measurement results can be assumed to be true values of chemical concentration. The measurement results then require values of measurement uncertainty, including that from sampling as well as from chemical analysis. This enables the user of the measurement results, rather than the producer, to judge their fitness for a specific purpose. Case studies are used to illustrate the practicality and benefits of this new approach, including the use of measurement results with optimal, but relatively high, levels of uncertainty to make reliable decisions. This contrasts with the traditional assumption that pursuit of the lowest possible measurement uncertainty is the best approach.

Measurements taken to characterise environmental contamination contain uncertainty, which is generated by both field sampling and chemical analyses. Recently devised techniques have been applied for the first time to estimate this uncertainty in the commercial monitoring and assessment of contaminated land. The uncertainty reduces the reliability of the classification of the land that is made following a site investigation. The possible misclassification of areas of land, as a result of measurement uncertainty, can lead to substantial financial penalties, resulting from litigation or unnecessary remediation. Previous studies have developed methods for the estimation and financial optimisation of measurement uncertainty. These methods have now been applied to a series of six contrasting site investigations, which were conducted by various commercial organisations. The previous uses of these sites included a gas works, a tin mine and railway sidings. The measurement uncertainty was successfully estimated for each of the six investigations, showing its applicability to a wide range of different sampling methods, such as trial pits, window sampling and augering. The measurement uncertainty ranged widely between sites from 25% to 158%, indicating that investigations can differ widely in their reliability. The field sampling tended to generate the largest component of the measurement uncertainty when compared to the contribution from the chemical analysis. The Optimised Contaminated Land Investigation (OCLI) method was applied to each site, with the initial aim of estimating the financial losses that could be incurred as a result of misclassifying the land, due to the uncertainty. It showed that the expectation of loss value per sampling location ranged from only £58 at one site to over £ 11 000 at another. The optimal level of uncertainty that produced the minimal financial loss was then calculated for each site. It provided a reduction in the expectation of loss for the whole site of over £ 10 000 at two of the sites and over £90 000 at two others. These findings demonstrate that implementing concepts of uncertainty can have practical benefits in environmental monitoring, and can enable improvements to be made in the quality of sampling and hence of measurements in general.

In the assessment of potentially contaminated land, the number of samples and the uncertainty of the measurements (including that from sampling) are both important factors in the planning and implementation of an investigation. Both parameters also effect the interpretation of the measurements produced, and the process of making decisions based upon those measurements. However, despite their importance, previously there has been no method for assessing if an investigation is fit‐for‐purpose with respect to both of these parameters. The Whole Site Optimised Contaminated Land Investigation (WSOCLI) method has been developed to address this issue, and to allow the optimisation of an investigation with respect to both the number of samples and the measurement uncertainty, using an economic loss function. This function was developed to calculate an ‘expectation of (financial) loss’, incorporating costs of the investigation itself, subsequent land remediation, and potential consequential costs. To allow the evaluation of the WSOCLI method a computer program ‘OCLISIM’ has been developed to produce sample data from simulated contaminated land investigations. One advantage of such an approach is that as the ‘true’ contaminant concentrations are created by the program, these values are known, which is not the case in a real contaminated land investigation. This enables direct comparisons between functions of the ‘true’ concentrations and functions of the simulated measurements. A second advantage of simulation for this purpose is that the WSOCLI method can be tested on many different patterns and intensities of contamination. The WSOCLI method performed particularly well at high sampling densities producing expectations of financial loss that approximated to the true costs, which were also calculated by the program. WSOCLI was shown to produce notable trends in the relationship between the overall cost (i.e., expectation of loss) and both the number of samples and the measurement uncertainty, which are: (a) low measurement uncertainty was optimal when the decision threshold was between the mean background and the mean hot spot concentrations. (b) When the hot spot mean concentration is equal to or near the decision threshold, then mid‐range measurement uncertainties were optimal. (c) When the decision threshold exceeds the mean of the hot spot, mid‐range measurement uncertainties were optimal. The trends indicate that the uncertainty may continue to rise if the difference between hot spot mean and the decision threshold increases further. (d) In any of the above scenarios, the optimal measurement uncertainty was lower if there is a large geochemical variance (i.e., heterogeneity) within the hot spot. (e) The optimal number of samples for each scenario was indicated by the WSOCLI method, and was between 50 and 100 for the scenarios considered generally; although there was significant noise in the predictions, which needs to be addressed in future work to allow such conclusions to be clearer.