TRAINING.

Overview

By the end of this course, you will be able to:

• Design fit‑for‑purpose sampling programs to support contaminated site assessment and decision‑making
• Select and apply appropriate statistical methods to analyze and interpret environmental data
• Develop defensible sampling strategies aligned with regulatory objectives and site conditions
• Evaluate data quality, uncertainty, and variability to support compliance and risk‑based decisions
• Interpret sampling results in relation to criteria, standards, and site‑specific background conditions
• Apply spatial interpolation methods to analyze contaminant distribution data

Description

Assessing contaminated sites requires you to make technically defensible decisions based on data that is often sparse, variable, and uncertain. Sampling programs that are poorly designed or statistical analyses that are misapplied can lead to incorrect conclusions about site conditions, regulatory noncompliance, unnecessary remediation costs, or unresolved risk. Designing a sampling strategy that appropriately balances uncertainty, regulatory expectations, site complexity, and practical constraints is therefore a critical professional responsibility.

This course focuses on planning, implementing, and evaluating sampling programs for contaminated site assessment in a structured and defensible manner. You will examine how sampling objectives, contaminant behaviour, geologic and hydrogeologic conditions, and spatial variability influence sampling design and data interpretation. The course emphasizes the selection and application of appropriate statistical methods, the treatment of outliers and uncertainty, and the role of data quality and QA/QC in supporting reliable conclusions.

Through applied examples and practical frameworks, this course equips you with the skills to develop fit‑for‑purpose sampling plans, analyze environmental data with confidence, and interpret results in a way that supports clear, defensible decision‑making. The tools and approaches presented are intended to be directly transferable to real site investigation and environmental site assessment projects across a range of contaminated site scenarios.

Who Should Attend

This course is designed for:

• Environmental engineers and engineering technologists
• Environmental consultants involved in site investigation and remediation projects
• Technical professionals responsible for soil sampling, data interpretation, and reporting
• Practitioners working in contaminated site assessment, monitoring, or regulatory compliance
• Early‑career to senior professionals seeking to strengthen applied sampling and data analysis skills

More Information

Time: 10:00 AM - 6:00 PM Eastern Time

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Please note: You can check other time zones here.

Syllabus

Introduction and overview

• Background and Purpose
• Review of Regulatory Context and Legislation

Statistical Data Analyses

• Statistical Distributions
• Methods of Determining Statistical Distributions
• Identification and Treatment of Outliers
• Statistical Analysis Methods

Soil Sampling Methods

• CCME Suggested Operating Procedures
• Test Pits / Auger / Boreholes / Split Spoon
• Soil Descriptions
• Quality Assurance Considerations

Conceptual Site Model

• Contaminants Characterization 
• Site Characterization
• Contaminated Area & Background

Sampling Strategies and Data Analyses

• Preparation of a Sampling Plan / Sampling Design Strategies
• Sampling Grids for Determining “HOT SPOTS”
• Quality Assurance / Quality Control (QA/QC)
• Sample Handling and Storage Requirements
• Analysis Methods
• Validation and Interpretation of Data
• Comparison of Criteria and Standards
• Comparison of Site Data to Site Specific Background Concentrations

Spatial Interpolation of Contaminant Data

• Deterministic Methods (Inverse Distance Weighting, Spline, Nearest Neighbor)
• Geostatistical Methods (Kriging)
• Machine Learning

Questions and Answers

Concluding Remarks and Final Adjournment

Instructor

Preston Sorenson, Ph.D., P.Ag.
Preston is currently a Research Associate at the University of Saskatchewan, where he is conducting research related to predictive soil mapping in the Canadian Prairies since January 2020. Preston has a B.Sc. in Land Use and Environmental Studies from the University of Saskatchewan, a M.Sc. in Soil Science from the University of Alberta, and a Ph.D. in Soil Science from the University of Alberta. His research has focused on improving maps of soil properties, using the resulting data to improve understanding of soil dynamics from landscape to provincial scales, and linking mapping results with biogeochemical models to forecast soil carbon change over time. He is also actively involved in the development of data analysis tools for the analysis of soil contaminant data including the mapping and visualizing of contaminants. Preston specializes in the use of machine learning and modelling to solve complex soil and environmental data analysis challenges. He is a registered member of the Saskatchewan Institute of Agrologists.