This book contains the proceedings of a workshop, 'Statistical Methods for the Assess ment of Point Source Pollution', held September 12-14, 1988, at the Canada Centre for Inland Waters in Burlington, Ontario, Canada. The objectives of the workshop were to: a) advance the art, science, and application of statistical methods to current water quality issues by stimulating discussions and disseminating ideas and information. The emphasis was on statistical problems associated with monitor ing and controlling discharges from industries and municipalities and assessing the impact of these discharges on receiving water quality, b) provide a forum for managers, engineers, scientists, and statisticians to present and discuss techniques for evaluating water quality data and planning monitoring activities, c) provide a published state-of-the art summary of the application of statistical methods for the assessment of point source discharges and their impact on water qUality. The papers contained in this volume cover a number of topics that are of concern not only for monitoring and assessing point source pollution but also for other environmental problems.

This book discusses a broad range of statistical design and analysis methods that are particularly well suited to pollution data. It explains key statistical techniques in easy-to-comprehend terms and uses practical examples, exercises, and case studies to illustrate procedures. Dr. Gilbert begins by discussing a space-time framework for sampling pollutants. He then shows how to use statistical sample survey methods to estimate average and total amounts of pollutants in the environment, and how to determine the number of field samples and measurements to collect for this purpose. Then a broad range of statistical analysis methods are described and illustrated. These include: * determining the number of samples needed to find hot spots * analyzing pollution data that are lognormally distributed * testing for trends over time or space * estimating the magnitude of trends * comparing pollution data from two or more populations New areas discussed in this sourcebook include statistical techniques for data that are correlated, reported as less than the measurement detection limit, or obtained from field-composited samples. Nonparametric statistical analysis methods are emphasized since parametric procedures are often not appropriate for pollution data. This book also provides an illustrated comprehensive computer code for nonparametric trend detection and estimation analyses as well as nineteen statistical tables to permit easy application of the discussed statistical techniques. In addition, many publications are cited that deal with the design of pollution studies and the statistical analysis of pollution data. This sourcebook will be a useful tool for applied statisticians, ecologists, radioecologists, hydrologists, biologists, environmental engineers, and other professionals who deal with the collection, analysis, and interpretation of pollution in air, water, and soil.

As an area of statistical application, environmental epidemiology and more speci cally, the estimation of health risk associated with the exposure to - vironmental agents, has led to the development of several statistical methods and software that can then be applied to other scienti c areas. The stat- tical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noise ratio in the data is low and the targets of inference are inherently small risks. These constraints typically lead to the development and use of more sophisticated (and pot- tially less transparent) statistical models and the integration of large hi- dimensional databases. New technologies and the widespread availability of powerful computing are also adding to the complexities of scienti c inves- gation by allowing researchers to t large numbers of models and search over many sets of variables. As the number of variables measured increases, so do the degrees of freedom for in uencing the association between a risk factor and an outcome of interest. We have written this book, in part, to describe our experiences developing and applying statistical methods for the estimation for air pollution health e ects. Our experience has convinced us that the application of modern s- tistical methodology in a reproducible manner can bring to bear subst- tial bene ts to policy-makers and scientists in this area. We believe that the methods described in this book are applicable to other areas of environmental epidemiology, particularly those areas involving spatial{temporal exposures.

Introducing a novel approach to setting environmental pollution standards that allow for proper treatment of uncertainty and variation, this book surveys the forms of standards and proposes a new kind of "statistically verifiable ideal standard." Setting Environmental Standards includes: a current analysis regarding the treatment of uncertainty and variation in environmental standard setting a review of basic principles in standard setting, including costs, actions and effects, and benefits examples where uncertainty and variation have been well-treated in current practice as well as examples where clear deficiencies are apparent specific proposals for the future approach to setting environmental pollution standards - encompassing the anticipated elements of uncertainty and variability The issues discussed serve statisticians as well as those persons involved with environmental standards. Scientists in agencies responsible for setting standards, in organizations advising such agencies or working in industries subject to these standards, will find Setting Environmental Standards an invaluable reference.

This easy-to-understand introduction emphasizes the areas of probability theory and statistics that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, but without omitting importa

Kontaminationen so früh wie möglich erkennen und damit so früh wie möglich beseitigen: Statistische Methoden, die diesem Ziel dienen, werden hier vorgestellt. Die besprochenen Strategien zum Nachweis und zur Quantifizierung von Schadstoffen lassen sich auf alle Umweltkompartimente anwenden. Praktische Details werden anhand von Fallstudien anschaulich erläutert.