Good practices for getting reliable and useful information about soil contamination uncertainty from easily conducted statistical analyses

Transcription

1 Good practices for getting reliable and useful information about soil contamination uncertainty from easily conducted statistical analyses J.-B. Mathieu, M. H. Garcia, V. Garcia (KIDOVA) 13 th Intersol congress Lille, France, March 18-20,

2 Estimation of in-place mass of contaminant: A spatial statistical issue Contaminations: More or less continuous phenomenon Locally known from direct & indirect data Aim: Assess soil contamination Issue: Sparse data estimation uncertainty Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

3 Estimation of in-place mass of contaminant: A spatial statistical issue Contaminations: More or less continuous phenomenon Locally known from direct & indirect data Aim: Assess soil contamination Issue: Sparse data estimation uncertainty Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

4 Estimation of in-place mass of contaminant: A spatial statistical issue Contaminations: More or less continuous phenomenon Locally known from direct & indirect data Aim: Assess soil contamination Issue: Sparse data estimation uncertainty Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

5 Estimation of in-place mass of contaminant: A spatial statistical issue Contaminations: More or less continuous phenomenon Locally known from direct & indirect data Aim: Assess soil contamination Issue: Sparse data estimation uncertainty Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

6 Estimation of in-place mass of contaminant: A spatial statistical issue Contaminations: More or less continuous phenomenon Locally known from direct & indirect data Aim: Assess soil contamination Issue: Sparse data estimation uncertainty Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

7 Quantifying uncertainty: A decision-making tool Supplementary survey Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

8 A two step estimation approach Correcting preferential sampling & data redundancy Using data declustering methods Polyhedral declustering Cell-declustering Bivariate declustering Kriging-based declustering Supplementary survey Quantifying estimation uncertainty about in-place masse of contaminants, contaminated soil volumes Using bootstrap method Random resampling with replacement of a dataset a large number of times Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

9 Outline Introduction to basic statistics Declustering methods Bootstrap technique Application to an actual case study Conclusions Supplementary survey Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

10 Outline Introduction to basic statistics Declustering methods Bootstrap technique Application to an actual case study Conclusions Supplementary survey Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

11 Introduction to basic statistics Univariate statistics 0.88 Histogram 400 ppm Cumulative distribution function Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

12 Introduction to basic statistics Confidence intervals Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

13 Introduction to basic statistics (cont.) Relation/correlation between contaminants Scatterplots + bivariate histograms (density map) Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

14 Outline Introduction to basic statistics Declustering methods Polyhedral Cell-declustering Bootstrap technique Supplementary survey Application to an actual case study Conclusions Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

15 Polyhedral declustering Data declustering methods Weight Volume of closest neighborhood No user-defined parameters Mean = 600 ppm (instead of 2000 ppm) Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

16 Cell declustering Weight 1 / Nb data in cell Function of: cell volume grid origin Data declustering methods (cont.) O 1 Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

17 Cell declustering Weight 1 / Nb data in cell Data declustering methods (cont.) Vol = 60 m 3 Mean = 1550 ppm O 1 Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

18 Cell declustering Weight 1 / Nb data in cell Data declustering methods (cont.) Vol = 60 m 3 Mean = 1650 ppm O 1 O 2 Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

19 Cell declustering Weight 1 / Nb data in cell Data declustering methods (cont.) Vol = 400 m 3 Mean = 1000 ppm O 1 O 2 Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

20 Cell declustering Weight 1 / Nb data in cell Data declustering methods (cont.) Vol = m 3 Mean = 2000 ppm O 1 O 2 Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

21 Cell declustering Weight 1 / Nb data in cell Function of: cell volume grid origin Data declustering methods (cont.) Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

22 Outline Introduction to basic statistics Declustering methods Bootstrap technique Application to an actual case study Conclusions Supplementary survey Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

23 Principle: Bootstrap technique Random resampling with replacement of a dataset a large number of times Algorithm: N = Number of available weighted data Repeat a high number of times (N itr > ): Randomly draw with replacement N data from the dataset Compute weighted statistics from the drawn data Save the computed statistics (in-place mass of contaminant, ) Analyze the distribution of the N itr saved statistics values Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

24 Outline Introduction to basic statistics Declustering methods Bootstrap technique Application to an actual case study Conclusions Supplementary survey Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

25 Application on an actual case study Exploration and remediation assessment scenario: Organochlorines pollution : PCE, TCE & cis-dce 2 successive exploration surveys Preliminary 13 boreholes, 49 lab samples analyzed for organochlorines and 89 PID measurements Supplementary 33 new boreholes, 238 lab samples analyzed for organochlorines and 813 PID measurements Based on the survey and site history data Single remediation zone identified as the most contaminated In-place contaminant masses in the remediation zone required to decide about the remediation strategy and costs Two geostatistical models available as references models (GeoSiPol, 2012) Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

26 Application on an actual case study Restricted data-set Full data-set Site Site Remediation zone Remediation zone Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

27 Statistics on grade data in the remediation zone Restricted dataset Mean = 820 ppm Mean = 1175 ppm Mean = 1283 ppm Volume à traiter = m 3 Masse volumique moyenne : 1.55 t/m 3 Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

28 Contaminant masses in the remediation zone Restricted dataset (49 samples) Method Estimated (mean) 95% confidence interval Bootstrap on unweighted data 5.6 [ 1.3; 11.5 ] Bootstrap on polyhedral declustered data 8.0 [ 1.6; 16.9 ] Bootstrap on cell declustered data 8.7 [ 1.9; 17.3 ] Geostatistical simulation 25.3 [ 5.3; 63.9 ] Full dataset (287 samples) Method Estimated (mean) 95% confidence interval Bootstrap on unweighted data 27.2 [ 12.9; 45.4 ] Bootstrap on polyhedral declustered data 24.7 [ 12.6 ; 40.5 ] Bootstrap on cell declustered data 23.3 [ 11.3; 36.1 ] Geostatistical simulation 20.0 [ 11.6; 32.9 ] Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

29 Outline Introduction to basic statistics Declustering methods Bootstrap technique Application to an actual case study Conclusion Supplementary survey Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

30 Conclusions Simple statistical methods can be used in soil contamination studies different declustering methods are available the more advanced the method is, the better generally it performs. They provide a relevant quantification of the uncertainty attached to delimited remediation zones: in-place mass of contaminants other soil contamination assessments (volumes ) Uncertainty is greatly dependent on number of data spatial distribution of the data Declustering methods may be imprecise but are enough for: Identifying highly uncertain situations Additional information required Providing precise enough estimations for early study stages They are a good alternative to a full geostatistical study at early stages of environmental studies Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

31 References Chilès,J.-P. and Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty, John Wiley & Sons: New York. Deutsch, C.V. and Journel, A.G. (1997). GSLIB: Geostatistical Software Library and Users Guide, Oxford University Press, New York, second edition. Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC. ISBN Garcia M.H., Mathieu J.-B. and Garcia V. (2014). Methodological and practical aspects of geostatistical bootstrap for quantifying global and local soil contamination uncertainty. accepted at geoenv 2014, Paris, juillet GeoSiPol (2012). Etudes de démonstration de l intérêt de la géostatistique dans le domaine des sites et sols pollués, GeoSiPol Association and working group co-founded by FSS International r&d (now KIDOVA), Géovariances and the Paris School of Mines, GeoSiPol (2005). Géostatistique appliquée aux sites et sols pollués Manuel méthodologique et exemples d application, GeoSiPol Association and working group co-founded by FSS International r&d (now KIDOVA), Géovariances and the Paris School of Mines, Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, Oxford university press: New York. Isaaks, E.H. and Srivastava, R. M. (1989). Introduction to applied geostatistics, Oxford University Press. Kaskassian S., Gleize T., Chastanet J. and Côme J.-M. (2013). Projet ATTENA Phase 2, Tâche : mise en œuvre des guides méthodologiques MACAOH par BURGEAP sur le Site 1bis (solvants chlorés), Rapport Final, 3 vol., p.296, Mathieu J.-B., Kaskassian S. and Garcia M.H. (2014). Apport de la géostatistique au diagnostic des sites et sols pollués : prolongement d un cas d étude de démonstration GeoSiPol. Submitted at 3ème Rencontres Nationales de la Recherche sur les Sites et Sols Pollués, ADEME, Paris, novembre Pyrcz, M. J. and Deutsch, C. V. (2003). Declustering and debiasing. Newsletter, 19. uofaweb.ualberta.ca Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol

32 Acknowlegement Thanks for your attention We thank BURGEAP for providing us with the ATTENA research project dataset. We are also grateful to GeoSiPol for allowing us to present results from the demonstration geostatistical studies Soil contamination uncertainty assessment from easily conducted statistical analyses Intersol