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Steven Wilcox. DQO an Integrated approach to the characterisation of contaminated sites Radioactive and non-radioactive contamination. Overview Step by step process. Promotes better communication between individuals involved in any environmental program.
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Steven Wilcox DQO an Integrated approach to the characterisation of contaminated sitesRadioactive and non-radioactive contamination
Overview Step by step process. Promotes better communicationbetween individuals involved in any environmental program. A team can develop acceptance criteria for the quality of the data collected and for the quality of the decision. The planning process is the first element, which gives an open methodology to the decision process Enables decision-makers to show what has been undertaken Systematic Planning (Overview) • Planning Phase: • The survey design is developed and documented • Implementation Phase: • The survey design is carried out • Assessment Phase: • Then the data are validated to ensure that the results of data collection activities support the objectives of the survey • Decision: • A decision is made, based on the conclusions drawn from the assessment process
The industry has started to adopt the assistance of the EPA’s Data Quality Objectives (DQO)-based methodology and support tools to facilitate better, faster, and cost effective method to meet regulatory requirements. The adopted DQO process consists of seven-main steps that lead to the detailed Sampling and Analysis Plans (SAP). This process gives transparency to any assumptions made. This approach relies on the use of statistical techniques to support decisions. Systematic planning: how do we do it? Step 1: State the Problem Step 2: Identify the goals of the study (Decision) Step 3: Identify the information inputs Step 4: Define the boundaries of the study Step 5: Develop the Analytical approach (Decision rules) Step 6: Specify performance or acceptance criteria (tolerance for decision errors) Step 7: Develop the plan for obtaining data (sampling design)
Designed to answer: Why are data needed? What type of data is needed? Where should the data be collected? When should data be collected? How will the data be used to make decisions? How often are you willing to make decision errors based on the data? How much data is needed? Why do it? Avoids unnecessary waste of time and money Enable the collection of the right amount of data Enables the sampling to be representative Enables the balancing of resources against cost Minimises bad data Reduces the possibility of third-party challenges Reduces unnecessary re-work Reduce unnecessary clean up Data Quality Objectives (DQO) process
Systematic planning: Why do it? • Using the DQO Process will help to ensure that when a data collection endeavor has been completed it will have accomplished two goals: • Provided sufficient data to make required decisions within a reasonable uncertainty. • Collect only the minimum amount of necessary data. • The DQO Process achieves this by determining the quality and quantity of data needed while minimizing costs • Don't collect (and pay for) data you • don't need • won't use • can't use
Systematic planning: Why do it? • Tried and Tested in the US and Various Companies in the UK • Promotes better communication between individuals involved in any environmental program. • Best Practice in achieving defensible sampling Plans • Auditable process • The Benefits: • Allows better planning, control and understanding of the issues • One controllable system. • Provides a more defensible approach and gives a clear and precise methodology • Multiple cost savings (time & resources) • Software is freely available
Project Example The DQO methodology has been used on a number of projects to date. • One example is a large area on a nuclear site. (Radiological & non Radiological) • This project comprised demolishing many buildings and clearing a large area of surrounding land and was completed in 3-4 months. • Previous unknown contamination was discovered and dealt with appropriately before the site was declared clean for redevelopment. • The previous methodology would have resulted in many more samples being taken with no improved confidence. • This resulted in a well documented, defensible approach • Multiple Projected savings