Mapping and estimation of contaminated volumes using geostatistics

OBJECTIVES

• Understand and master the use of geostatistical solutions to characterize chemical pollution or radiological contamination.
• Generate robust contamination maps with a quantified level of accuracy.
• Objectively delineate the impacted areas and quantify the uncertainty level associated with estimated volumes.

OUTLINES

• Half of the course is devoted to methodological presentations, the second half to practical exercises on real-life cases to deepen the understanding of concepts. The focus is on illustrations and practical applications of the concepts covered.
• Computer exercises with Kartotrak.
• Course material provided.

WHO SHOULD ATTEND

Engineers, technicians, consultancies, project owners, prime contractors, public bodies, and industrial operators who wish a practical introduction to the geostatistical methods for pollution/contamination characterization.

COURSE CONTENT

Day 1: Analyze data and their variability in space

• Better understand pollution data and put them back in their environmental context: taking into account historical and contextual data (lithology, aerial views, Digital Elevation Model, etc.) and 2D/3D visualization.
• Validate data: use statistical methods for analysis and quality control (e.g., correlations, histograms).
• Understand the concept of spatial variability and the operational implications for the feasibility of some cleanup methods.
• Quantify the spatial variability of a pollutant: calculation, interpretation, and modeling of the variogram.

Day 2: Pollution/contamination mapping

• Discover usual interpolation methods and their limits of application.
• Understand the principles and properties of 2D and 3D kriging, and implement the technique to rigorously map pollution/contamination.
• Use kriging results to identify the areas that require further characterization.
• Go further with multivariate geostatistics.

Day 3: Quantify contaminated soil volumes

• Understand the difference between kriging and conditional simulations: why simulations are essential to a rigorous analysis of the uncertainties.
• Learn how to implement conditional simulations to:
  – Generate probability maps of exceeding a regulatory threshold (2D/3D).
  – Generate distribution curves of the surface/volume of soil requiring remediation according to the risk of exceeding a given threshold.
  – Calculate the pollutant mass from soil density and pollutant concentration.
• Derive the surface, volume, and mass of soil requiring remediation according to the ‘’Pareto-Sol’’ principle.
• Generate excavation maps from 3D-interpolated contamination maps.
• Compare the efficiency of the different cleanup scenarios.

PREREQUISITES

This course does not require prior knowledge in geostatistics. Some basic knowledge of statistics is an asset.