An Exploration of the Interplay between the Measurement Uncertainty and the Number of Samples in Contaminated Land Investigations |
| |
| Authors: | Katy A Boon Peter Rostron Michael H Ramsey |
| |
| Institution: | Department of Biology and Environmental Science, School of Life Sciences, University of Sussex, Falmer, Brighton, BN1 9QG, UK |
| |
| Abstract: | 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. |
| |
| Keywords: | measurement uncertainty number of samples optimisation fitness‐for‐purpose sampling incertitude de mesure nombre d’ é chantillons optimisation aptitude à l’ emploi é chantillonnage |
|
|
