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OCR for page 89
Chemical and Biological Methods
INTRODUCTION
The purpose of this section is to set forth general pr inciples in study
design and in chemical and biological methodology. The specific
examples cited should aid the reader in critically evaluating oil
pollution research literature and present a general guide to the
present state of the art in this area of research.
We strongly advocate that scientists be properly educated and
trained in the fundamentals of the various disciplines of biology,
chemistry, ~
.
or marine sciences before engaging in the difficult research
problems associated with petroleum pollution in the mar ine environment.
Thus, it is not the intent of this section to provide a detailed
account or "cookbook " approach to par ticular measurements .
General Strateg ies of Study Design
·
In the east 10 vear s a number or new scat 1S1: coal
· _ _
A discussion of statistical techniques used in studies of the fate and
effects of oil in the environment would need to cover most of the areas
in modern statistics. The more important general problems that have
arisen in oil spill research will be br lefty discussed and areas of
study identified where recent developments in statistics may contr ibute
to their solution. For the most part, discussions of the role of
sampling and statistical design in oil spill studies have been confined
to the use of classical statistical methods
Cox et al., 1977) . _ , _ , _ _ _
methods have been developed for a wide range of environmental
problems . These methods, used in water qual ity research, air
pollution, and environmental sampl ing, have obvious appl ication to the
study of oil in the mar ine environment .
_
~,
d iscussed and
see, for instance, G .V .
In comparison with the esoteric techniques of organic chemistry,
f luid dynamics , and faunal analysis employed in studies of the effect
of oil in the environment, the basic concepts of statistical design and
analysis are relatively straightforward. Yet, statistical problems
have often been cited as the cause of many of the def iciencies in oil
pollution studies. The main issue is to design an experiment, set of
exper iments, or sampl ing program to answer a specif ic set of questions
89
OCR for page 90
go
within a specified set of cost, manpower, and time constraints. It is
assumed that invest igator s can agree on a general model for a g iven
system. When the questions, constraints, and model have been specif led,
the next step is to allocate samples and experimental treatments
optimally so that the scientific and regulatory questions being asked
can be answered clear ly. I f a problem is less sharply defined, the
Dual ity and efficiency of the statistical design decrease. Studies of
oil pollution in the mar ine environment are particularly sub ject to
this effect. In the past, but less frequently now, fortunately, several
large environmental research programs were constructed by committees in
which scientific questions, cost constraints, model processes, and
statistical des ~ gn were decided in a more or less haphazard and illog i-
cal order . Thus, rather than being the cause of the problem, improper
statistical design and data analysis were more often symptoms of
fundamental cliff iculties within the research plan.
The nature of pollution studies is such that some of the work must
be done in areas where pollution has occurred, in what might be called
an unplanned experiment. The objective, in most cases, is to assign a
causal relationship between a particular event and some effect in the
area. There is also an interest in drawing conclusions that could be
appl fed in other r elated cases. In a classical randomized experiment,
a set of areas is selected and then oil treatments are randomly assigned
to half of the areas. In most field studies of oil in the environment,
the treatment, for example, of the concentration of oil in the sediment,
has not been assigned by the investigator as part of the experiment
design, but rather is a consequence of human error and natural forces
that dispersed the oil unevenly in different areas. This does not
preclude making reasonable inferences about the causal nature of the
event observed; however, one must make stronger assumptions about the
underlying nature of the data and use rather different analyses. There
is a large literature in statistics on the problems associated with
observational studies. Recently McKinlay (1975) has reviewed the
literature on observation studies, particularly those influencing human
populations. Cochran (1951, 1968) was an early contributor in this
area and still offers the definitive work. R.H. Green (1979) gives a
relatively clear analysis of this problem in an environmental setting.
A s imple example serves to illustrate the problem of observational
data. The following Is a f ictitious data set used to illustrate
Simpson's paradox" (Lindley and Norick, 1981) in an environmental
setting. Table 3-1 describes the results of a large, well-designed,
random survey of an oil spill area. The 200 samples were classified as
to whether they fell above or below the median oil concentration and
whether the biomass of the benthic community fell above or below the
median. Table 3-1 shows clearly that there is a positive correlation
between high oil concentration and high biomass concentration. Suppose,
however, we again divide the samples into two subareas, those below 5 m
depth and those above 5 m depth, thus spl itting the data into two
separate tables. We see that the correlation in Table 3-1 is reversed
when we look at the two subpopulations separately. The explanation for
this is obvious: although the sampling program was random, the
d istr ibution of oil in the sediment (the treatment) was not; heavy
OCR for page 91
91
TABLE 3-1 Matrix of Oil Concentrations in Sediments and Correlations
With Benthic Organism Biomass
Oil Concentration Groups (~)
Below Median
Concentration
Biomass Concentration Groups
Above Median
Concentration
c-~ i men t a t A 1 1 Ma to r n-lath A; ; n .R t~dv A r ea
Below median concentration
Above median concentration
Sediment Above and Below 5 m Water Depth
Above 5-m water depth
Below median concentration
Above median concentration
Below 5-m water depth
Below median concentration
Above median concentration
70
30
70
10
o
20
30
70
30
o
10
70
NOTE: Hypothetical case (see text).
in entire data set.
Percentages are of total samples
concentrations of oil appear in the shallower areas exactly where the
highest biomass is. Inference under these conditions has been studied
by Lindley and Novick (1981) and by Blyth (1972~. The reader is
referred to these articles for various proposed solutions to this
problem. Needless to say, this situation can arise in more subtle ways
than that presented above; for instance, chronic oil pollution is
usually associated with other industrial and urban activity which could
also have an impact on the system.
Sampling Procedures and Equipment
The requirements of specialized sampling procedures and equipment are
discussed in the following sections on chemical and biological methods.
Statistical Design of Analytic Procedures in the Laboratory
Often the complexities of environmental studies lead one to overlook
the importance of careful design of laboratory analytical procedures.
Proper design can prevent confounding of laboratory effects with
natural processes that we wish to study. For example, where the same
analysis is undertaken by two laboratories, samples should be assigned
OCR for page 92
92
so that possible differences in analytic techniques are not confounded
with natural variation. For instance, assigning all samples from area
A to laboratory X and all samples from area B to laboratory Y will
certainly confound the ef feet of analytic bias between laborator ies
with true var. iations in concentration between the two areas. There is
a lengthy and very useful literature on statistical design and quality
control in laboratory situations (Tietjen and Beckman, 1974~.
Presenting Results From Complex Oil Pollution Studies
One of the more dif.icult problems in environmental oil pollution
studies is to present the data and analysis in a comprehensible way.
In most cases the data f rom a study consist of many different kinds of
measurements made on samples distributed in both space and time. The
data analysis problem is to decipher relationships among samples and
variables and then to present these relationships in a compact way.
Often, classical statistical analysis (analysis of variance, regression,
etc.) used in reports and papers describing oil spill events provides
little insight into the character of the processes and the data under
investigation. Many of the techniques of exploratory data analysis can
be used to investigate these relationships. J.W. Tukey and P. Tukey
(personal communication, 1981) have described different techniques for
displaying multivariate data. Cleveland and Kleiner (1975) and R.H.
Green (1979) have given a number of methods for graphically presenting
multivariate environmental data. This is one area where a great deal
more work needs to be done.
Designing Ecological Experiments to Study the Effects of Pollutants
The discussion of problems one can encounter in observational studies
should make clear the reasons for going to planned experiments to study
the impact of oil or other marine pollutants. W. Smith et al. (1981)
provide a general discussion of the design of ecosystem experiments.
Large experiments, such as the No. 2 fuel oil experiment conducted at
the University of Rhode Island Marine Ecosystems Research Laboratory
(see Biological Methods section), present certain problems not encoun-
tered in usual experimental design situations. First, since such a
system is partially open to the environment, all conditions within the
system cannot be precisely controlled. The second problem is that
since each experimental unit replicated is costly to maintain and
measure, the number of exper imental units repl icated is relatively
small.
These k inds of exper iments occur over t ime, and thus, par t of the
exper imental design is to determine the treatment time course. Careful
des ign of t ime ser ies exper iments can help to overcome the problems
mentioned above. There is a growing literature in this area t~qulholland
and Gowdy, 1977; Glass et al., 1975; Box and T'ao, 1975; Huitema, 1979;
Brockway et al ., 1979 ~ .
OCR for page 93
93
An Oil Spill Survey Design
Environmental sampling problems have been well studied from a statisti-
cal design perspective. Sampling spatially and time-varying processes
has been discussed by W. Smith (1979) and others. Moss and Tasker
(1979) give a review of these methods in water quality research. In
addition, composite sampling and other techniques for efficient sampling
and analysis of environmental data have been investigated (Elder et
al., 1981; R.H. Green, 1979; Smeach and Jernigan, 1977~.
W. Smith (1979) developed a specific plan for the oil spill survey
problem involving a two stage sampling scheme where, in the first stage,
a large number of samples are collected in a systematic survey (grid or
modified grid). Easy to record data, such as sediment type or if oil
is present, are recorded in the first stage.
In the second stage of the sampling program, the data from the first
stage are used to stratify the samples. The original samples can then
be subsampled for the more costly chemical and biological analysis. In
oil spill work this procedure has the great advantage that the first
stage of the program can be implemented on short notice. The cliff icult
sampl ing question can be postponed to the second stage of the program
when the legal engineer ing and scientific questions that the oil spill
raises have been better defined.
Summary
The interweaving of the various facets of observational and experimental
studies of inputs, fate, and effects of petroleum in the marine environ-
ment demands careful attention to the combined requirements of many
different types of sampling and measurement methods. It is not appro-
priate in a report of this type to review critically all aspects of
environmental and engineering measurements relevant to oil pollution
studies. For example, meteorological, physical oceanographic, and
sedimentological measurements are often of importance to these studies.
The reader is referred to texts and reviews in these areas of study for
description and evaluation of methodology. Physical parameters such as
specific gravity, viscosity, and pour point of crude oils and petroleum
products are not extensively discussed because they have been well
defined in American Society for Testing and Materials (ASTM) protocols
(R.C. Clark and Brown, 1977) and have not been the subject of
controversy in oil pollution research and monitoring activities.
Chemical methods for measur ing petroleum and petroleum compounds
and biolog ical methods for measur ing var ious levels of ef feats of
petroleum in the mar ine environment have been sub jects of cant inuing
controversy and debate since an increase in studies of petroleum
pollution in the late 1960s. For this reason chemical and biological
methodology is descr ibed and evaluated in the following sections .
Representative terms from entire chapter:
oil pollution
