• Skip to main content
itrc_logo

Geospatial Analysis for Optimization at Environmental Sites

Navigating this Website
Overview
Fact Sheets
Fact Sheets Overview
Fact Sheet 1: Do You Need Geospatial Analysis?
Fact Sheet 2: Are Conditions Suitable for Geospatial Analysis?
Fact Sheet 3: How is Geospatial Analysis Applied?
Fact Sheet 4: What Software is Available to Help?
PM's Tool Box
PM's Tool Box Overview
Review Checklist
Choosing Methods
Common Misapplications
Optimization Questions
Geospatial Analysis Support for Optimization Questions in the Project Life Cycle
Data Requirements
General Considerations
Methods for Optimization
Geospatial Methods for Optimization Questions in the Project Life Cycle Stages
Release Detection
Site Characterization
Remediation
Monitoring
Closure
Documenting Results
Fundamental Concepts
Fundamental Concepts for Geospatial Analysis
Basic Data Concepts for Geospatial Analysis
Interpolation Methods and Model Prediction
Uncertainty in Geospatial Analyses
Characteristics of Interpolation Methods
Work Flow
Work Flow for Conducting Geospatial Analysis
Geospatial Analysis Work Flow Overview
Perform Exploratory Data Analysis
Select Geospatial Method
Build Geospatial Model
Evaluate Geospatial Method Accuracy
Generate Geospatial Analysis Results
Using Results
Using Analysis Results for Optimization
Plume Intensity and Extent
Trend Maps
Estimating Quantities
Hot Spot Detection
Sample Spacing
Estimating Concentrations Based on Proxy Data
Background Estimation
Quantifying Uncertainty
Remedial Action Optimization
Monitoring Program Optimization
Examples
Examples Overview
Example 1
Example 2
Example 3
Example 4
Methods
Methods Overview
Simple Geospatial Methods
More Complex Geospatial Methods
Advanced Methods
Index of Methods
Software
Software Overview
Software Comparison Tables
Software Descriptions
Workshops and Short Courses
Case Studies
Case Studies Overview
Superfund Site Monitoring Optimization (MAROS)
PAH Contamination in Sediments—Uncertainty Analysis (Isatis)
Optimization of Long-Term Monitoring at Former Nebraska Ordnance Plant (GTS; Summit Envirosolutions)
Optimization of Lead-Contaminated Soil Remediation at a Former Lead Smelter (EVS/MVS)
Extent of Radiological Contamination in Soil at Four Sites near the Fukushima Daiichi Power Plant, Japan (ArcGIS)
Optimization of Groundwater Monitoring at a Research Facility in New Jersey (GWSDAT)
Optimization of Sediment Sampling at a Tidally Influenced Site (ArcGIS)
Stringfellow Superfund Site Monitoring Optimization (MAROS)
Lead Contamination in Soil (ArcGIS)
Stakeholder Perspectives
Additional Information
Project Life Cycle Stages
History of Remedial Process Optimization
Additional Resources
Acronyms
Glossary
Index of Methods
Acknowledgments
Team Contacts
Document Feedback

 

Geospatial Analysis for Optimization at Environmental Sites
HOME

Quantifying Uncertainty

One of the objectives in optimization is to understand and manage the uncertainties throughout the remedial efforts in order to achieve the remedial goals with sufficient confidence. The general topic of Attainment of Closure Goals is related to Quantifying Uncertainty (see General Topics, Table 2). More complex and advanced geospatial analyses can help quantify uncertainty. Analyzing the estimated uncertainties may help inform decisions regarding future sampling locations, areas requiring remediation, or attainment of closure goals. The analyses provide maps of uncertainty in the estimated/interpolated values.

Understanding the Results: ▼Read more

Kriging, with cross-validation: After transforming or detrending the data, or both if necessary, fit the empirical variogram using several models. Select several neighborhoods. Perform cross-validation in order to evaluate the accuracy of the alternative models and neighborhoods. Then use the cross-validation to compare the accuracy of each model (and neighborhood) in order to choose the most suitable one. Through the analysis of the error terms, the most suitable model (the most accurate) and neighborhood are chosen for further estimates. The more accurate the model, the more likely it is that the kriging results are representative of the variable of interest.

Contour kriging variance: The precision of the predictions generated from kriging can be measured using the prediction standard error or variance. By creating a map of the standard error or variance, the areas that may require additional sampling can be identified. To use this approach to evaluate potential additional sampling locations, first place a hypothetical sample in a zone of concern (high variance), assign it a random value of the variable of interest (for example, concentration), and then recalculate the map of variance from this new sample point to help determine whether the decrease in variance is significant.

Conditional simulation: Perform a Gaussian (normal distribution) transformation of the raw distribution. Fit a theoretical variogram, select a grid of prediction locations, and perform several simulations of concentrations (>100 for instance). Prepare maps of the probability of exceeding the remediation cleanup target concentrations. Estimate volumes of contamination for each simulation. The probability maps correspond to the risk of occurrence of contamination. Focus on blocks showing 30% to 60% probability of exceeding the threshold. In these blocks, the occurrence of contamination is not accurately estimated and represents the areas of uncertainty. The volume associated with these blocks can be estimated. Further investigations may be needed in these areas in order to improve the estimate of the risk of contamination and to minimize the uncertainty of the volume estimate.

image_pdfPrint this page/section



GRO

web document
glossaryGRO Glossary
referencesGRO References
acronymsGRO Acronyms
ITRC
Contact Us
About ITRC
Visit ITRC
social media iconsClick here to visit ITRC on FacebookClick here to visit ITRC on TwitterClick here to visit ITRC on LinkedInITRC on Social Media
about_itrc
Permission is granted to refer to or quote from this publication with the customary acknowledgment of the source (see suggested citation and disclaimer). This web site is owned by ITRC • 1250 H Street, NW • Suite 850 • Washington, DC 20005 • (202) 266-4933 • Email: [email protected] • Terms of Service, Privacy Policy, and Usage Policy ITRC is sponsored by the Environmental Council of the States.