2.4 Scale-Specific Sampling Considerations
Soil-contaminant interactions contribute to concentration heterogeneity and data variability, which operate at progressively larger spatial scales.
- ITRC
- ITRC ISM Public Pages
- Incremental Sampling Methodolgy Homepage
- 1.0 Introduction
- 2.0 Nature of Soil Sampling and Incremental Sampling Principles
- 2.1 Introduction
- 2.2 Soil Heterogeneity and Variation in Contaminant Concentrations
- 2.3 Foundational Concepts of Sampling
- 2.4 Scale-Specific Sampling Considerations
- 2.5 Gy Theory and the Source of Sampling Error
- 2.6 Three Sampling Approaches
- 2.6.1 Discrete Sampling
- 2.6.1.1 Heterogeneity and discrete samples
- 2.6.1.2 Discrete sampling plans
- 2.6.1.3 Interpreting results of discrete sampling
- 2.6.1.4 When discrete sampling may be successful
- 2.6.2 Composite Sampling
- 2.6.2.1 Beneficial uses of compositing
- 2.6.2.2 Poorly designed composite sampling
- 2.6.3 Incremental Sampling Methodology
- 3.0 Systematic Planning and Decision Unit Designation
- 3.1 Overview of Systematic Planning
- 3.2 Nature and Intent of Decision Units and Sampling Units
- 3.3 Decision Units
- 3.3.1 Defining Decision Units
- 3.3.2 Types of Decision Units
- 3.3.3 Vertical Definition of DUs
- 3.3.4 Exposure Area Decision Units
- 3.3.4.1 Residential exposure area decision units
- 3.3.4.2 Commercial/industrial exposure area decision units
- 3.3.4.3 Ecological exposure area decision units
- 3.3.4.4 Exposure area decision units based on preferential exposure
- 3.3.4.5 Example exposure area decision units
- 3.3.5 Source Area Decision Units
- 3.3.5.1 Smaller source area decision units within exposure area DUs
- 3.3.5.2 Example source area decision units
- 3.3.6 Decision Units Surrounding Source Areas
- 3.3.7 Subsurface Decision Units
- 3.3.8 Stockpile Decision Units
- 3.3.9 Excavation Decision Units
- 3.3.10 Decision Units for Very Large Areas
- 3.3.11 Other Types of Decision Units
- 3.4 Establishing New Decision Units Based on Previous Results
- 3.5 Hot Spots
- 4.0 Statistical Sampling Designs for ISM
- 4.1 Factors that Complicate Estimating the Mean Concentration
- 4.2 Uncertainty in Estimates of the Decision Unit Mean
- 4.3 Evaluating the Performance of Sampling Approaches
- 4.3.1 Definitions of Performance Metrics
- 4.3.1.1 Coverage and magnitude of UCL errors
- 4.3.1.2 Bias in estimated mean
- 4.3.1.3 Relative standard deviation of replicate samples
- 4.3.2 Simulation Study Approach
- 4.3.3 Objectives of the Simulation Studies
- 4.3.4 Simulation Study Findings on ISM Performance
- 4.3.4.1 Sample size (number of increments and replicates)
- 4.3.4.2 Effects of sampling pattern
- 4.3.4.3 Partitioning the DU
- 4.3.4.4 Relative standard deviation
- 4.4 Areas for Further Study
- 5.0 Field Implementation, Sample Collection and Processing
- 6.0 Laboratory Sample Processing and Analysis
- 6.1 Introduction
- 6.2 Laboratory Processing
- 6.2.1 Volatile Organic Compounds
- 6.2.2 Semivolatile Organic Compounds and Inorganic Contaminants of Concern
- 6.2.2.1 Identification of sample
- 6.2.2.2 Bulk sample mass reduction via sample splitting
- 6.2.2.3 Sample conditioning
- 6.2.2.4 Sample mixing
- 6.2.2.5 Particle size reduction
- 6.2.2.6 Particle size selection
- 6.2.2.7 Analytical splitting and subsampling techniques
- 6.3 Laboratory Analysis
- 6.4 Quality Assurance/Quality Control
- 7.0 Making Decisions Using ISM Data
- 7.1 Introduction
- 7.2 Decision Mechanisms
- 7.2.1 Decision Mechanism 1: Comparison of One ISM Sample from the DU to the Action Level
- 7.2.2 Decision Mechanism 2: Comparison of the Mean of Replicate Data from the DU to the Action Level
- 7.2.3 Decision Mechanism 3: Comparison of the 95% UCL on the Mean of Replicate Data from the DU to the Action Level
- 7.2.4 Decision Mechanism 4: Comparison to Background
- 7.2.5 Decision Mechanism 5: Combining DUs
- 7.2.6 Decision Mechanism 6: Extrapolating from Sampled to Unsampled Areas
- 7.2.7 Decision Mechanism 7: Evaluating Oversized DUs
- 7.3 Assessment of Error
- 8.0 Regulatory Challenges and Opportunities for ISM
- 8.1 Introduction
- 8.2 Perception Issues
- 8.3 Regulatory Challenges for ISM
- 8.4 State of Knowledge, Experience, and Training
- 8.5 Implementation Issues
- 8.5.1 Systematic Planning
- 8.5.1.1 CSM
- 8.5.1.2 Sampling objectives and developing the decision unit
- 8.5.2 Lab Availability
- 8.5.3 Costs
- 8.5.4 Challenges in Developing and Using ISM Data
- 8.5.4.1 Validation of statistical analysis within ISM
- 8.5.4.2 Meeting regulatory requirements for average and maximum concentration estimates
- 8.5.4.3 Decision unit versus exposure unit
- 8.5.4.4 Comparison of discrete samples and ISM samples
- 8.5.4.5 Comparison of ISM means and "not to exceed" basis regulation
- 8.5.4.6 Decisions based on a single ISM
- 8.5.4.7 Background/geochemical limitations
- 8.5.4.8 Extrapolation between and within DUs
- 8.5.5 Matrix and Parameter Issues
- 8.5.5.1 Laboratory experience
- 8.5.5.2 Sample processing
- 8.5.1 Systematic Planning
- 8.6 Summary
- 9.0 Case Study Summaries
- 10. Stakeholder and Tribal Input
- 11.0 References
- Appendix A - Statistical Simulations
- Table of Contents
- List of Tables
- List of Figures
- A.1 Statistical Simulation Studies Introduction
- A.2 Probability Distributions (PD-1)
- A.3 Spatial Autocorrelation Maps (M-1)
- A.4 Maps of RDX and HMX (M-2A and M2-B)
- A.5 Bulk Material Sampling Maps (M3-A to M3-C)
- A.6 Glossary of Terms and Concepts
- A.6.1 Gy Sampling Principles Applied to Bulk Material
- A.6.2 Bulk Material Sampling
- A.6.3 Sample Representativeness and Bias
- A.6.4 Heterogeneities
- A.6.4.1 Compositional heterogeneity (CH)
- A.6.4.2 Distributional heterogeneity (DH)
- A.6.4.3 Small-scale (short-range) DH
- A.6.4.4 Sample support
- A.6.4.5 Fundamental error (FE)
- A.6.4.6 Large-scale (long-range) DH
- A.6.5 Homogeneity
- A.6.5.1 Homogeneous bulk material
- A.6.5.2 Homogeneous contaminant concentration
- A.6.6 Sampling Patterns
- A.6.7 MIS Software to Generate Homogeneous and Heterogeneous DUs
- A.7.1 Estimation of FE and Var (FE)
- A.7.2 Computation of Coefficient of Variation (CV) of ISM Replicates
- A.7.3 Performance Metric Equations
- A.8 References
- Appendix B - August 2009 Survey Results
- Appendix C - Case Studies
- C.1 CASE STUDY 1: KURE ATOLL, HAWAII
- C.1.1 Background and Previous Investigations
- C.1.2 DU-IS Investigation (2008)
- C.1.2.1 DU-IS Investigation Approach
- C.1.2.2 DU Designation and Investigation Objectives
- C.1.2.3 Landfill DU Characterization
- C.1.2.4 Landfill DU ISM Results
- C.1.2.5 Use of Discrete Data to Identify Localized Spill Area
- C.1.2.6 Targeted Spill Area DU ISM Results
- C.1.3 Comparison of ISM and Discrete Soil Data
- C.1.4 References
- C.2 Case Study 2: Petroleum Contaminated Soil Stockpile
- C.3 Case Study 3: Former Golf Course Field Demonstration of ISM
- C.4 Case Study 4: Hawaiian Homelands Development, Kapolei, Oahu, Hawaii
- C.1 CASE STUDY 1: KURE ATOLL, HAWAII
- Appendix D - ISM Team Contacts
- Appendix E - Glossary
- Appendix F - Acronyms
- Site Map
