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Environmental statistics and data analysis
Many random processes occur in nature. To make accurate predictions
about the manner in which man’s activities may alter these processes and affect
their outcomes, it is necessary to construct models that faithfully represent real-
ity, including its random components. In the environmental field, the purpose
of most models is to make accurate predictions about the effect of environmen-
tal pollution control activities on environmental quality or on human exposure
to pollutants. Although modeling the fate and transport of pollutants through
the environment is well advanced, few models have been developed adequately
to include the random, or stochastic, nature of environmental phenomena. Sto-
chastic models treat the phenomenon being modeled probabilistically, thus in-
cluding the random components in a statistical framework. Because random
phenomena abound in the environment, stochastic modeling often is more im-
portant than other kinds of modeling. Despite the importance of stochastic
models in environmental analyses and decision-making, few reference works
are available covering these techniques and showing how to apply them to en-
vironmental problems. Of particular importance for concentrations measured
in the environment are the right-skewed distributions, such as the lognormal,
and techniques to take into account source controls, such as rollback models
(see Chapter 9).
To help fill the need for a reference work on environmental statistics, this
book seeks to develop a comprehensive and. understandable framework for ap-
plying probabilistic techniques to environmental problems of all kinds. It in-
cludes statistical models for environmental decision-making, data analysis, and
field survey design, along with the theoretical basis for each model wherever
possible. A model that has a sound theoretical basis is more likely to make ac-
curate predictions than one that does not. This book also includes a consider-
able body of original material, not previously published, on new theories and
insights to help explain observed environmental phenomena and the control of
these phenomena. The new theories are included to help guide data analysts
and decision makers in applying statistical models to practical problems in the
environment.
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