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Soil Background and Risk Assessment

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About ITRC
1 Introduction
1 Introduction
1.1 Audience
1.2 Purpose
1.3 Use of Background in the Risk Assessment Process
1.4 Limitations
2 Soil Background Definition 
2 Soil Background Definition
2.1 Natural Soil Background
2.2 Anthropogenic Ambient Soil Background
2.3 Additional Background Definitions
3 Establishing Soil Background
3 Establishing Soil Background
3.1 Introduction
3.2 Conducting a Soil Background Study
3.3 Choosing an Area for a Soil Background Study
3.4 Sampling
3.5 Laboratory Analysis
3.6 Using an Existing Soil Background Study
3.7 Background Dataset Analysis
3.8 Establishing Default or Site-Specific Soil Background
3.9 Extracting Site-Specific Background Dataset from an On-site Dataset
4 Using Soil Background in Risk Assessment
4 Using Soil Background in Risk Assessment
4.1 Representative Site Concentration to Compare to a BTV
4.2 Using Default Background
4.3 Using Site-Specific Background
4.4 Use of Background for Remedial Goals
4.5 Additional Considerations
5 Geochemical Evaluations
5 Geochemical Evaluations
5.1 Geochemistry Is Not Statistics
5.2 Uses of Geochemical Evaluations
5.3 General Methodology
5.4 Nondetects
5.5 Key Geochemical Processes
5.6 Extracting Background Data from Existing Data
6 Using Geochemical Evaluations in Risk Assessment
6 Using Geochemical Evaluations in Risk Assessment
6.1 Using Geochemical Evaluations During COPC Selection
6.2 Using Geochemical Evaluations During Risk Characterization
6.3 Considerations
7 Environmental Forensics Related to soil Background
7 Environmental Forensics Related to soil Background
7.1 Introduction
7.2 Polycyclic Aromatic Hydrocarbons
7.3 Total Petroleum Hydrocarbons (TPH)
7.4 Polychlorinated Biphenyls (PCBs)
7.5 Polychlorinated Dibenzo-p-Dioxins and Dibenzofurans (PCDD/F)
7.6 Perfluoroalkyl Substances (PFAS)
7.7 Remote Sensing
8 Conceptual Site Model and Data Quality Objectives
8 Conceptual Site Model and Data Quality Objectives
8.1 Conceptual Site Model
8.2 Data Quality Objectives
9 Sampling
9 Sampling
9.1 Background Reference Areas
9.2 Sample Depth
9.3 Sample Size
9.4 Sample Methods
9.5 Sampling Design
9.6 Sample Collection Methods
9.7 Sample Handling
10 Analytical Methods
10 Analytical Methods
10.1 Introduction
10.2 Obtaining Reliable Analytical Data
10.3 Analytical Limits
10.4 Sample Preparation
10.5 Analytical Test Methods
11 Statistics
11 Statistics
11.1 Data Requirements
11.2 Data Distribution
11.3 Treatment of Nondetects
11.4 Graphical displays
11.5 Outliers
11.6 Confidence Interval Limit, Coefficient, and Limit
11.7 Statistical Values Used to Represent Background
11.8 Statistical Tests to Compare Site and Background Datasets
11.9 Statistical Software
12 Regulatory Framework from State Survey
12 Regulatory Framework from State Survey
12.1 Description of State Survey
12.2 Overview of State Survey Results
12.3 State Survey Results
13 Existing Guidance and Studies
14 Case Studies
14 Case Studies
14.1 Minnesota Pollution Control Agency (MPCA) Soil Background Case Study
14.2 Former Firearms Training Range Soil Background Case Study
14.3 Region 4 RARE Urban Background Study
14.4 Geochemical Evaluation Case Study—Statistical Outlier is an Uncontaminated Soil Sample
14.5 Geochemical Evaluation Case Study—Statistical Outlier Is a Contaminated Soil Sample
14.6 Geochemical Evaluation Case Study–Contaminated Soil Sample Is Not a Statistical Outlier
14.7 Environmental Forensics Case Study—PAHs from Leaked Petroleum Versus Contaminated Fill
Frameworks
Frameworks
Framework 1
Framework 2
Framework 3
Appendices
Appendix A. Upper Limits Used to Estimate Background Threshold Values
Appendix B. Index Plots
Appendix C. Additional Sources of Information for PAHs in Soil
Additional Information
Team Contacts
Glossary
Acronyms
Acknowledgments
References
ITRC & EJ/DEI
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Soil Background and Risk Assessment
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Appendix A. Upper Limits Used to Estimate Background Threshold Values

The USEPA has issued several comprehensive documents dealing with calculating upper limits to compute BTV estimates ((USEPA 2002), (USEPA 2002), (USEPA 2006), (USEPA 2009), (USEPA 2015)) based upon a background dataset. However, there is no general consensus among practitioners about the statistic (upper limits) that should be used to estimate a BTV. For the sake of a typical reader and completeness, a brief description of upper limits (with interpretation), including upper percentiles, UPLs, UTLs, and USLs, used to estimate BTVs is provided in this appendix. Mathematical details and formulae to compute these statistics for datasets with and without NDs are given in ProUCL 5.1 Technical Guide. In the following the background population is also referred as the target population.  

Upper percentile: A value, based on the data, below which a selected percentage (for example, 95%) of the data points will fall. In comparison with other BTV estimates (for example, UTL 95-95), the use of the 95th percentile yields a higher number of false positives, resulting in potentially unnecessary cleanup decisions, especially when many observations coming from a population (comparable to the background population) are compared with the sample percentile, p0.95.  

Upper prediction limits (UPL): Let UPL95 represent a 95% UPL for a future/next observation. One is 95% sure that a single future value from the target (background) population will be less than or equal to the UPL95 with a confidence coefficient of 0.95 (95%). If an on-site value, x_onsite, is less than the UPL95, it may be concluded that x_onsite comes from the background population with a CC of 0.95. A UPL95 is not meant to be used to perform more than one future comparison.  

However, in practice, users tend to use a UPL95 for many future comparisons, which results in a higher number of false positives (locations declared contaminated when in fact they are clean). When k (>1) future comparisons are made with a UPL95, some of those values will exceed UPL95 just by chance, each with a probability of 0.05. For proper comparison, UPLs need to be computed according to the number, k, of comparisons that will be performed. For details refer to ProUCL 5.1 Technical Guide.  

Upper tolerance limit (UTL): A UTL (1-α)-p (for example, UTL 95-95) based upon an established background dataset represents that limit such that p% (for example, = 95%) of the sampled data will be less than or equal to that limit with a CC equal to (1-α) * 100% (for example, =95%). A UTL (1-α)-p represents a (1–α) * 100% upper confidence limit for the pth percentile of the underlying background population. It is expected that at least p% of the observations coming from the background population will be less than or equal to the UTL (1-α)-p with a CC equal to (1-α) * 100%. Specifically, a UTL 95-90 represents an upper tolerance limit providing coverage to at least 90% of the observations of the target population with CC=95%.  

A UTL 95-90 assumes that as much as 10% of the observations can exceed the background UTL 95-90 when site concentrations are not different from the background population, and a UTL 95-90 can declare 10% of the observations coming from the background population as not coming from the background population just by chance, with a probability of 0.95. A UTL is used when many comparisons are planned. For a UTL 95-90, 10 exceedances per 100 comparisons (of background values) can result just by chance for an overall CC of 0.95. The number (and not percentage) of false positives can become large when many values from the target population are compared with a UTL.  

Upper simultaneous limit (USL): A (1 – α) * 100% USL based upon an established background dataset is meant to provide simultaneous coverage for all sample observations in the background dataset (Singh and Nocerino, 1995) with probability (1-α). It is expected that all observations (present and future) belonging to the target (background, comparable to background) population will be less than or equal to a 95% USL (USL95) with a CC=0.95.  

Like a UTL, a USL is used when any number (small or large) of on-site observations are compared with a BTV estimate. Unlike a UTL, a USL does not assume a priori that a certain percentage of background observations may not belong to the background population. Depending upon the variability of the background data, some of these statistics (for example, USL95, UTL 95-95) may exceed the largest value in the background dataset. To account for data variability of all sampled and unsampled locations, critical values associated with a USL95 increase as the sample size increases.  

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