This document discusses optimization techniques for sampling strategies in long-term environmental monitoring of gamma dose rates. It presents regression kriging and simulated annealing as methods for optimizing sampling locations to minimize prediction errors while accounting for spatial autocorrelation. An example application to a gamma radiation monitoring network in Europe demonstrates that minor changes to the existing network could improve mapped predictions along borders. The document notes several shortcomings and areas for further improvement, such as dealing with extreme values and incorporating dynamic and multi-criteria optimization that considers monitoring purposes and constraints.

1. Sampling Optimization Trade-offs for Long-term Monitoring of Gamma Dose Rates S.J. Melles, G.B.M. Heuvelink, C.J.W. Twenh ö fel, and U. St ö hlker Presented at ICCSA, GEOG-AN-MOD July 2, 2008, Perugia, Italy

5. Part 2: Methods to predict or describe spatial variability in environmental variables

10. Part 2: Example: γ - dose rate radiation

12. Spatially correlated residuals notice directionality in residuals 0 frequency 60 0 40 -40

13. Modelled with aniso variogram spherical and linear component in all directions, but spherical is dominant in swesterly and linear in ortho

14. Regression kriging prediction 30 80 nSv/hr

15. Kriging prediction error variance 65 85 nSv/hr

16. Part 3: Optimization

19. Minimizing the objective function (RKSE) Current design Optimized design

20. Examining the trade-offs? Costs

21. Part 4: What have we learned? Where to from here?

23. Doesn’t that look like even spatial coverage sampling? Current thinned design ( 50% stations) Optimized design (50% stations) Npp

26. THANK YOU!

27. Appendix

28. P of accepting a worsening design Delta f is the change in MUKV

31. Dynamic case