Introduction to Environmental Data Acquisition - Fundamentals of Environmental Sampling and Analysis - page 2

2. To measure ambient background concentration and assess the degree of
pollution and to identify the short- and long-term trends.
3. To detect accidental releases and evaluate the risk and toxicity to human and
biota.
4. To study the fate and transport of contaminants and evaluate the efficiency
of remediation systems.
This introductory chapter briefly discusses the basic process of environmental data
acquisition and errors associated with field sampling and laboratory analysis. A
unique feature of this text is to treat sampling and analysis as an entity. This is to say
that sampling and analysis are closely related and dependent on each other. The data
quality depends on the good work of both sampler and analyst.
The importance of sampling is obvious. If a sample is not collected properly, if it
does not represent the system we are trying to analyze, then all our careful lab work
is useless! A bad sampler will by no means generate good reliable data. In some
cases, even if sampling protocols are properly followed, the design of sampling is
critical, particularly when the analytical work is so costly.
Then what will be the data quality after a right sample is submitted for a lab
analysis? The results now depend on the chemist who further performs the lab
analysis. The importance of sample analysis is also evident. If the analyst is unable
to define an inherent level of analytical error (precision, accuracy, recovery, and so
forth), such data are also useless. The analyst must also know the complex nature of
a sample matrix for better results. The analyst needs to communicate well with the
field sampler for proper sample preservation and storage protocols.
1.1.1 Importance of Scientifically Reliable
and Legally Defensible Data
All environmental data should be scientifically reliable. Scientific reliability means
that proper procedures for sampling and analysis are followed so that the results
accurately reflect the content of the sample. If the result does not reflect the sample,
there is no claim of validity. Scientifically defective data may be a result of
unintentional or deliberate efforts. The examples include the following:
An incorrect sampling protocol (bad sampler)
An incorrect analytical protocol (bad analyst)
The lack of a good laboratory practice (GLP)
The falsification of test results.
Good laboratory practice (GLP) is a quality system concerned with the
organizational process and the conditions under which studies are planned,
performed, monitored, recorded, archived, and reported. The term ''defensible''
means ''the ability to withstand any reasonable challenge related to the veracity,
integrity, or quality of the logical, technical, or scientific approach taken in a