『**Abstract**

A model is presented for estimating the value of information
of sampling programs for contaminated soil. The purpose is to
calculate the optimal number of samples when the objective is
to estimate the mean concentration. A Bayesian risk-cost-benefit
decision analysis framework is applied and the approach is design-based.
The model explicitly includes sample uncertainty at a complexity
level that can be applied to practical contaminated land problems
with limited amount of data. Prior information about the contamination
level is modelled by probability density functions. The value
of information is expressed in monetary terms. The most cost-effective
sampling program is the one with the highest expected net value.
The model was applied to a contaminated scrap yard in Goteborg（最初のoの頭に¨）, Sweden, contaminated by metals. The
optimal number of samples was determined to be in the range of
16-18 for a remediation unit of 100 m^{2}. Sensitivity
analysis indicates that the perspective of the decision-maker
is important, and that the cost of failure and the future land
use are the most important factors to consider. The model can
also be applied for other sampling problems, for example, sampling
and testing of wastes to meet landfill waste acceptance procedures.

Keywords: Contamination; Value of information; Data worth; Soil;
Cost-effectiveness; Bayesian analysis』

**Introduction
Methodology
** Decision analysis framework

Procedure of VOIA

Step 1: sampling program

Step 2: prior information

Step 3: probability estimation

Prior probabilities

Including sample uncertainty

Weighing sample uncertainty with prior information

Preposterior probabilities

Step 4: cost estimation

Step 5: estimation of the value of information

Application

Sampling approach

Prior information

Probability estimation

Cost estimation

Results

Sensitivity analysis by Monte Carlo simulation

Conclusions and discussion

Acknowledgments

References

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