As stated by the U.S.
Environmental Protection Agency (EPA), each year the EPA and the
regulated community spend approximately $5 billion collecting
environmental data for scientific research, regulatory decision
making, and regulatory compliance. While these activities are
necessary for effective environmental protection, it is the goal of
EPA and the regulated community to minimize expenditures related to
data collection by eliminating unnecessary, duplicative, or overly
precise data. At the same time, the data collected should have
sufficient quality and quantity to support defensive decision
making. The most effective way to accomplish both of these goals is
to establish criteria for defensible decision making before the
study begins, and then to develop a data collection design based on
these criteria. To facilitate this approach, the Quality Assurance
Management Staff (QAMS) of EPA has developed the Data Quality
Objectives (DQO) Process. Thru the DQO process, the effectiveness,
efficiency, and defensibility of the data and the related decisions
can be approached in a resource-effective manner.
The DQO Process
is a strategic planning approach based on the Scientific Method that
is used to prepare for a data collection activity. It provides a
systematic procedure for defining the criteria that a data
collection design should satisfy, including when, where & how to
collect samples, the tolerable level of decision errors for the
study, and how many samples to collect. DQO combines both
qualitative and quantitative aspects of the data used in the
With MCGI on the project, decisions are
made with an understanding of scientific issues and the legal
implications of every technical decision.
The DQO Process
look forward to the privilege of serving your project needs.