C.10 Study Question 10: Is the spatial coverage of the monitoring network appropriate (spatial optimization)?

Optimization and design of the monitoring program must assure sample independence while providing adequate spatial coverage of the site. This question addresses how to use statistical methods to optimize the spatial coverage of the site. Optimization can lead to decreasing or increasing the number of wells. The concepts of sufficiency and redundancy are related but different tools are available to determine if existing wells are redundant (that is, wells can be removed from monitoring) if there are sufficient wells (you may either add or remove wells). If the monitoring program is in the early stages, statistical design considerations and site investigation data can be used to establish a well network (Section 3.6). Statistical spatial optimization methods are most applicable for a site with existing large data sets. For an overview of temporal optimization methods, see Study Question 9. For effective optimization, you must establish the goal of the long-term monitoring program and identify an acceptable set of wells to determine a change.

This question can be relevant in all stages of the project life cycle: release detection, site characterization, remediation, monitoring, and closure; it is more likely that enough information exists to conduct optimization at later stages in the project life cycle.

Selecting and Characterizing the Data Set

Verify that the data set can support optimization techniques. Refer to Section 3.4: Common Statistical Assumptions for further discussion of how the following requirements may impact statistical analysis results.

Statistical Methods and Tools

Using the results of the above plots and tests as a guide, you can use more sophisticated statistical methods to evaluate the redundancy or sufficiency of sample results among wells. The two approaches highlighted for this assessment are the redundancy or spatial uncertainty analyses. Spatial optimization is a challenging objective and an active area of research. Generally these methods require a lot of data and broad spatial coverage of the plume. Optimization results should be checked versus what is known or hypothesized about contamination using the conceptual site model (CSM)A living collection of information about a site which considers factors such as environmental and land use plans, site-specific chemical and geologic conditions, and the regulatory environment (ITRC 2007b).. Be aware that in some cases uncertainty can be high and additional sampling may be required. See Appendix D for software packages.

Redundancy Analysis

Spatial Uncertainty Analysis

Interpretation of Results and Associated Uncertainty

Optimizing groundwater monitoring well networks often works best when the network is evaluated as a unit. Therefore, greater potential for project benefits exists when both spatial and temporal information are considered. However, in some cases a project could benefit by eliminating redundant wells or by adding wells to reduce uncertainties. It is important that optimization be conducted such with regard to and consistent with what is known or hypothesized using the CSMconceptual site model.

Related Study Questions

Study Question 5: Is there a trend in contaminant concentrations?

Study Question 6: Is there seasonality in the concentrations?

Study Question 9: Is the sampling frequency appropriate (temporal optimization)?

Key Words: Optimization, Efficiency, Spatial Coverage, Release Detection, Site Characterization, Remediation, Monitoring, Closure

Publication Date: December 2013

Permission is granted to refer to or quote from this publication with the customary acknowledgment of the source (see suggested citation and disclaimer).


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