Optimal Data Gathering
Development of optimal sampling schemes and design tools throughout the environmental remediation cycle can result in significant cost avoidance, streamlined regulatory acceptance, and defensible remediation planning. PNNL offers support for the Data Quality Objectives (DQO) process, from training to implementation, and on through data collection, analysis, and project completion.
The DQO process is a planning tool for data collection activities. It provides a basis for balancing decision uncertainty with available resources. The DQO process involves developing appropriate sampling design and spatial analysis plans.
Further, the Visual Sample Plan (VSP), developed at PNNL, provides statistical solutions to sampling design, world-class mathematical and statistical algorithms, and a user-friendly visual interface. VSP provides the answers to two important questions in sample planning: How many samples are needed? And Where should the samples be taken?
PNNL also has extensive experience in designing efficient experimental test plans to yield confident decisions, manage uncertainties, and provide an adequate number of tests, samples, and analyses. Designs are developed to enable identification, isolation, control, and quantification of uncertainty sources. Statistical models are employed to quantify uncertainties and ensure confident decisions. Statistical designs ensure the suitability, efficiency, traceability, and defensibility of data and the resulting conclusions. PNNL statisticians have broad experience in these areas with especially strong capabilities in design and analysis of mixture experiments and experiments on irregularly shaped regions.
Contact: Brent Pulsipher