Cost-effective range characterization is a challenge for range management and remediation, particularly when characterizing the spatial distribution of munitions constituents (MC) over large areas. Incremental sampling methods (ISM) are often required within the Department of Defense (DoD) for characterizing MCs on ranges. Recent projects funded by SERDP and ESTCP have improved the sample handling and laboratory process necessary for ISM. The objective of this project is to address a current need for the application of statistical tools in a systematic planning process, such as the U.S. Environmental Protection Agency’s (EPA) Data Quality Objectives (DQO) process, to optimize incremental sampling approaches over large areas. The statistically defensible sampling strategy to be demonstrated will allow DoD to control the risk of incorrect statistical sampling decisions that inherently exist and have a demonstrated strategy for use on military ranges.
This project will demonstrate the recently developed ISM-based sampling design tools in the statistical software package Visual Sample Plan (VSP). The project leverages 5 years of DOE-supported developments of prototype ISM sampling methods/tools and more than 10 years of SERDP and ESTCP and other DoD-funded research on representative ISM sampling methods. All ISM options within VSP have been vetted mathematically and will be demonstrated in practice. These options include:
These tools will improve DoD’s capability to meet DQO goals using ISM. Site characterization personnel will be able to select the appropriate statistical sampling design given specific constraints and information. With DQOs established, this demonstration will provide tools for selecting the optimal number, configuration, placement, and allocation of increments and ISM samples. Specific cost benefits include statistically optimized incremental sample collection, reduced laboratory analyses with spatial mapping objectives, and. spatial footprint reduction within wide area decision units. (Anticipated Project Completion - 2018)