• 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

Example 1

Example 1: Sampling Redundancy Analysis in Visual Sample Plan (VSP)
Application: Monitoring Program Optimization
Application Summary: The sampling redundancy module in VSP was used to identify redundant wells for a semiannual water level gauging program, and to identify a statistically defensible sampling frequency for metals analysis.
Methods: Well redundancy analysis using global kriging weights and root mean square error (RMSE) analysis, temporal variogram analysis for temporal optimization.
Data Requirements: VSP guidance recommends at least 30-50 data points for well redundancy analysis, and 20-30 observations for temporal variogram analysis.
Reference: Matzke et al. 2014
Approach: The well redundancy analysis requires fitting a variogram using a normal score transform of the data to accommodate skewed data (see Figure 50). After the user kriges the data, VSP determines a global kriging weight for each well by adding the kriging weights for that data point for all locations in the kriging grid. The wells with higher global kriging weights have greater impacts on the interpolation. VSP then ranks the wells by their global kriging weight, the lowest ranked data location is removed from the data set, and the remaining data are kriged. This process is repeated until the maximum number of wells is removed from the data set. At each step, VSP calculates the root mean square error (RMSE) between the base interpolation with all wells, and the interpolation generated at that step. A graph of RMSE versus wells removed can then be used to determine a reasonable number to remove without significantly reducing the accuracy of the interpolation (see Figure 51). To support this decision, the user can visually compare maps of the base case (all wells) to those generated after eliminating any number of wells; see Figure 52 (Matzke et al. 2014). The sampling frequency analysis involves completing single well variography to evaluate the range of temporal correlation at each well. The fitted range parameter conceptually represents the minimum time interval between independent sampling events (see Figure 53). Note that VSP also has an iterative thinning module for temporal optimization that is described in the ITRC GSMC-1 guidance document.


Results: The well redundancy analysis indicates that approximately 30 wells may be removed from the gauging program without significantly affecting the accuracy of the interpolation. The range of temporal correlation of the data is greater than 365 days, supporting a reduction in sampling frequency from semiannual to annual.

gro_fig_9_1

Figure 50. Example fitted model for the semivariogram of normal scores of the water level data.

gro_fig_9_2

Figure 51. RMSE error plot versus wells removed. A companion table links well identifiers to RMSE results.

gro_fig_9_3

Figure 52. Comparison of interpolation maps using all locations and the trimmed data set.

gro_fig_9_4

Figure 53. Variogram analysis for arsenic (As) at well MW-3, with estimated range of 800 days.

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.