Many organisations are currently dealing with long standing legacy issues in clean up, decommissioning and demolition projects. Industry is required to ensure that all bulk articles, substances and waste arisings are adequately characterised and assigned to the correct disposal routes in compliance with UK legislation and best practice. It is essential that data used to support waste sentencing is of the correct type, quality and quantity, and that it is appropriately assessed in order to support defensible, confident decisions that account for inherent uncertainties. AMEC has adopted the Data Quality Objectives (DQO) based methodology and the software package Visual Sample Plan (VSP) to provide a better, faster, and more cost effective approach to meeting regulatory and client requirements, whilst minimising the time spent gathering data and assessing the information. The DQO methodology is based on a scientific approach that requires clear objectives to be established from the outset of a project and that there is a demonstration of acceptability of the results. Through systematic planning, the team develops acceptance or performance criteria for the quality of the data collected and for the confidence in the final decision. The systematic planning process promotes communication between all departments and individuals involved in the decision-making process thus the planning phase gives an open and unambiguous method to support the decisions and enables the decision-makers (technical authorities on the materials of concern) to document all assumptions. The DQO process allows better planning, control and understanding of all the issues. All types of waste can be sentenced under one controllable system providing a more defensible position. This paper will explain that the DQO process consists of seven main steps that lead to a detailed Sampling and Analysis Plan (SAP). The process gives transparency to any assumptions made about the site or material being characterised and identifies individuals involved. The associated calculation effort is reduced using the statistically based sampling models produced with VSP. The first part of this paper explains the DQO based methodology and Visual Sample Plan and the second part shows how the DQO process has been applied in practice.

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