Environmental Sampling Design - Fundamentals of Environmental Sampling and Analysis - page 46

such a plan, a team with a broad range of expertise (or a well-informed Principal
Investigator for a small research project) is needed (Fig. 3.1). This multidisciplinary
undertaking is common for most environmental projects, such as remediation
investigation/feasibility study, site assessment, waste management, remediation
action, and risk assessment.

Project
manager

Sampling
- Data user

-C ient
-Decision maker

Field personnel

Lab analysis
-

Data analysis
- Statistician

- Field engineer

Chemist

- Geologist

- Soil specialist

QA/QC
specialist

Figure 3.1 Personnel involved in project planning: From sampling to data user

As shown in Figure 3.1, experienced field personnel, who are experts in
collecting samples and solving field problems, are called to interact with an
analytical chemist who knows how to preserve, store, and analyze samples. The
analytical chemist must also understand sampling theory and practice in addition to
measurement methods (Kratochvil et al., 1984). An engineer helps in a complex
manufacturing process to optimize sampling location and safety. A statistician will
then verify that the resulting data is suitable for any required statistical calculation or
decision. AQA/QC representative will review the applicability of standard operating
procedure (SOP), determine the numbers of QA/QC samples (blanks, spikes, and so
forth), and document the accuracy and precision of the resulting database. A data
end-user ensures that data objectives are understood and incorporated into the
sampling and analytical plan. Depending on the project, other individuals might
include a geologist, facility manager, local citizen, and an EPA representative. For
beginners in this field, exposure to a team of various expertises could be an
intimidating but a rewarding experience.

3.1.1 Data Quality Objectives

The planning process is critical in the development of DQOs. The DQOs are defined
as ''qualitative and quantitative statements that define the appropriate type of data, and
specify the tolerable levels of potential decision errors that will be used as basis for
establishing the quality and quantity of data needed to support decision'' (EPA, 2000).
The DQOs were first developed by the U.S. EPA specifically for projects under EPA's
oversight (EPA, 1993). However, its planning principles can serve as a checklist and
apply to any projects that require environmental data collection. The main idea of the
DQO process is to have the least expensive data collection scheme but not at the price
of providing answers that have too much uncertainty. The DQOs are a written