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Preface
Summary
Table of Contents
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Statistical Procedures of Environmental Monitoring Data and Risk Assessment
Author: McBean, Edward A. / Rovers, Frank
Cover: Hard cover
List Price: $76.00
Published by Prentice Hall
Date Published: 06/1998
ISBN: 0136750184
Preface
The intent in this book is to carefully explain features of various
statistical analysis procedures. Unlike much of the professional
literature, however, this text makes special effort to describe statistical
techniques in terms comprehensible to the nonstatistician. This is
accomplished by downplaying mathematical notation, comprehensively
explaining the development of equations, and emphasizing example
applications. Thus, as successive problems of environmental-monitoring
interpretation are developed, the text describes through use of simple
examples how each procedure is utilized. References are provided, with
particular emphasis on works describing applications reported in the
technical literature. Problems included at the end of the chapters stress
fundamentals and increase the usefulness of this book as a classroom text.
The collection and laboratory analyses of samples needed to
characterize environmental quality are already expensive. Further, as
society expresses increasing concern for environmental protection and as
instrumentation technology can detect contaminants at ever lower
concentrations, expenditures for monitoring environmental quality will rise
with time.
As a direct consequence of the rising costs of environmental
monitoring, it is essential to use available environmental quality data
effectively. Effective utilization involves answering questions such as,
"Is the environmental quality acceptable?" and, "Is the environmental
quality improving or deteriorating?" Responding to these types of questions
requires interpretation of data, and this stage of assessment is beset with
difficulties. Some difficulties with interpreting environmental-monitoring
results:
(i) Since the data are frequently expensive to accumulate, the data
sets being interpreted are usually very modest in size.
(ii) The data may involve a vector of chemical and biological
constituent measurements because consideration must typically be given to a
range of constituents. Correlation between the constituents may help the
infilling of missing data or the identification of outlier data.
(iii) Early detection of any deterioration in environmental quality is
highly desirable because early detection may provide the opportunity for
controlling the problem at a lower cost before the problem magnifies. Any
procedure for identifying early warning signals must not, however, falsely
identify a problem of apparent environmental deterioration when one does
not actually exist; nor should it fail to identify a problem when one does
exist.
(iv) The vagaries of nature introduce significant noise and sources of
variability such as seasonality effects. This can make the identification
of trends more difficult.
(v) The derivation of quantitative risk assessments is in many ways
data dependent. But will the information returned by these risk estimates
be worth additional data collection efforts?
The net result of difficulties such as the five mentioned is that making
sense of environmental quality data necessarily involves statistical
interpretation. Statistical interpretation procedures must be sensitive to
small changes in environmental quality and yet recognize the potentially
substantial costs of any additional data collection requirement.
The need for the statistical interpretation of environmental
quality data is widespread. The range of concerns for each environmental
media-air quality, surface water quality, groundwater quality, and soil
contamination-are similar in many respects. Yet there is no single
statistical analysis procedure universally applicable to the variety of
problems associated with environmental quality data. Instead, the
practitioner needs to have an array of statistical procedures available. A
multitude of statistical analysis tests are available, but each of the
tests possess assumptions that may or may not be appropriate for specific
circumstances. Computer programs now becoming widely available facilitate
use of various procedures. The difficulty remains for the student and the
practitioner to learn which conditions dictate a particular procedure and
which conditions render it highly inappropriate.
Following the introduction (Chapter 1), the book is organized into
four parts as follows:
- Part I Chapters 2 through 5 develop the fundamental measures used to
describe data and the distributions employed to describe the data.
- Part II Chapters 6 and 7 describe procedures commonly utilized to detect
changes occurring over time, the detection of outliers and the mathematical
procedures for quantifying coincidental behavior in data sets.
- Part III Chapters 8 through 11 describe the bases used in hypothesis
testing to determine when there are differences in environmental quality at
various locations. Problems of censored data are considered as they
influence the utilization of alternative tests.
- Part IV Chapter 12 focuses on the interrelationships between risk
assessment and the data upon which the risk characterization procedures
rely. Simulation procedures for risk characterization using sampling
methodologies from probability distributions of data are described.
Authors of the Book
Edward A. McBean (B.A.Sc. from the University of British Columbia and S.M.,
C.E., and Ph.D. from the Massachusetts Institute of Technology) is an
associate of Conestoga-Rovers and Associates and president of CRA
Engineering Inc. Dr. McBean's experience includes more than 20 years as a
faculty member at the University of Waterloo and the University of
California. Much of the focus of Dr. McBean's research and professional
work has been on the specific problem of interpretating environmental
quality data. He is the author of more than 300 technical articles and has
authored or edited eight books.
Frank A. Rovers (B.A.Sc. and M.A.Sc. from the University of Waterloo) is
president of Conestoga-Rovers and Associates, an environmental engineering
company with more than 850 employees located in 29 offices. Mr. Rovers has
been involved for more than 25 years in a very large number of
environmental engineering problems dealing with the complete spectrum of
environmental quality issues. Frank is the author of numerous technical
journal articles dealing with the interpretation of environmental quality
data.
Both authors have been heavily involved in the teaching of
professional development courses, including those at the University of
Wisconsin-Madison, University of Toronto, Nova Scotia Technical College,
and UCLA.
Acknowledgements
We are under no delusion that the work reported in this book is just our
work. Clearly the material is the product of many people's efforts. Our
intent was to assemble and organize the considerable range of experience
and understanding culled from literature about statistical evaluation of
environmental quality data.
In addition to the literature, we have drawn upon the experience
and efforts of many individuals, and for this assistance we are grateful.
During the years preceding publication, the authors worked closely with
many colleagues and students, among them:
- The employees of CRA who so generously provided examples. The
advice and assistance of many is acknowledged, with special mention of John
Donald, Klaus Schmidtke, Darrell O'Donnell, Mark Schwark, and Wes Dyck.
- All the people who examined drafts of the book and whose comments
for improvements were valuable. In particular, useful comments by Bill
Lennox, Aditya Tyagi, and many students are gratefully acknowledged.
- The secretarial staff at CRA who so obligingly "revised the last
revision." In this respect, special acknowledgement must be given to Maria
Manoli, who continued to remain cheerful in the face of numerous rewrites.
- Melissa McBean, whose preparation of figures for this book is also
gratefully acknowledged.
To all of the above, we owe our sincere thanks for their assistance.
In an undertaking of the magnitude of this text, it is not possible
to avoid errors, and for this we apologize in advance. Any corrections,
criticisms, or suggestions for improvements will be greatly appreciated by
the authors. We would also welcome any additional information and data that
would make future editions of the book more complete.
Edward A. McBean
Frank A. Rovers
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