OCLI Optimised Contaminated Land Investigation. Dr Paul Taylor, Centre for Environmental Research, University of Sussex.

Experienced practitioners of site investigations on contaminated land take account of a range of factors in designing a site investigation. Nevertheless, 
the expenditure on an investigation is often influenced, indirectly or directly, by the amount that the Client is willing to pay. This, in turn, may depend on 
the site value, with more being spent where a site is to be redeveloped at considerable expense or is liable to be subject to public scrutiny. This tailoring or 
adaptation of a site investigation to meet financial constraints is currently largely intuitive. What this research aims to do is to provide a rigorous methodology 
that quantifies the extent of such an adaptation of a site investigation method. This will allow the transparent justification of higher expenditure on the 
investigation at some sites, and lower expenditure for other sites. It will also allow less experienced practitioners to arrive at optimal expenditure for 
a given site investigation. This approach is totally general and applies whether the investigation is being made in the context of general threshold values 
for contaminant concentrations (e.g. ICRCL, 1987) or for site-specific risk assessment (e.g. CLEA). In both cases the expenditure that can be justified for 
the investigation can usefully be set by comparison with the potential financial consequences of mis-classifying the land, or the potential risk that it posses 
to a particular receptor. This is the concept of an optimised contaminated land investigation (OCLI). It provides estimates of the uncertainty arising from 
sampling and the chemical analysis, in order to optimise the investigation methods.      
      
The idea of applying cost-benefit analysis, while already in use for the definition of 'reasonable' remediation (e.g. approach developed by WS Atkins), 
has not been applied to site investigation previously, partially due to the lack of a suitable criterion for the optimisation. This proposal investigates 
the feasibility and practicality of using the uncertainty of the measurements of contaminant concentration as this criterion. Uncertainty makes a good criterion, 
as it makes explicit that the investigator can never know the extent of the contamination perfectly, but increased expenditure allows absolute certainty 
to be approached, to the extent required for the particular purpose specified. In this circumstance, the site investigation can be said to be 'fit-for-purpose'. 
Uncertainty has been identified as one of the key parameters limiting the development of brownfield sites. New methods have recently been described for the 
estimation of uncertainty in contaminated land investigations. A general methodology has also been described that assesses the fitness-for-purpose of 
any analytical method using uncertainty of the measurements made. A combination of these two new methodologies has enabled this new approach to optimizing 
contaminated land investigations. In a preliminary application of this OCLI approach to a site contaminated with lead, a clear cost minimum was evident in 
the function relating uncertainty to cost, expressed as 'expectation of loss'.      
      
If the uncertainty is reduced below this minimum point, then extra cost is incurred for higher quality sampling and analysis. If the uncertainty is allowed 
to increase beyond this point, then extra cost will be incurred due to misclassification of the land. This latter cost could result from contaminated areas 
being missed (a false negative), and the subsequent litigation or loss of corporate reputation that this would cause, if discovered. Alternatively, uncontaminated 
areas could be classified erroneously as contaminated (a false positive), and money wasted on unnecessary remediation. In either case extra resources allocated 
to the site investigation would be justified if these financial consequences are large. On the contrary, if the cost of a potential error is small, or the probability 
of its occurrence is very low, then a less expensive site investigation is justified. In a second part of the OCLI method, it is possible to make an optimal split 
of this overall optimal uncertainty, between its two components from sampling and analysis. Thus if uncertainty needs to be reduced, and sampling is the limiting 
factor, then increased expenditure on sampling is justified (e.g. taking larger samples). This requires the OCLI methodology to match the expenditure on 
an investigation to the financial sensitivity of the site. If this sensitivity is high, then misclassification of areas within the site will cause large expense, 
for any of the reasons outlined above. Ultimately both parts of the OCLI method can be made into a robust 'decision support tool' to help routine practitioners.