Statistical methods, environmental

Gilbert, R.O. (1987) Statistical Methods for Environmental Pollution Monitoring, Wiley, Chichester. 

This paper discusses the role that statistics can play In environmental sampling. The primary difference between an Investigation based on statistical considerations and one that Is not Is the degree of objectivity that can be Incorporated Into the evaluation of the quality and uncertainty of the study results. Statistical methods In the planning stage can also aid In optimizing allocation of resources. 

Barnard TE (1995) Environmental Sampling, in The handbook of environmental chemistry (Ed. O. Hutzinger), Vol 2, Part G Chemometrics in environmental chemistry - statistical methods (Vol. Ed. J. Einax), Springer, Berlin Heidelberg New York, 1-47... 

Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York... 

PCA [12, 16] is a multivariate statistics method frequently applied for the analysis of data tables obtained from environmental monitoring studies. It starts from the hypothesis that in the group of original data, there is a set of reduced factors or dominant components (sources of variation) which influence the observed data variance in an important way, and that these factors or components cannot be directly measured (they are hidden factors), since no specific sensors exist for them or, in other words, they cannot be experimentally observed. 

Measurements of the environmental parameters in the monitoring regime provide sets of series of quantitative characteristics for the system of data processing, which cannot be analyzed because of their stationarity. There are many ways to overcome time dependence and thereby remove the contradiction between the applicability of statistical methods and the level of observational data stationarity. One such way consists in partitioning a series of noise-loaded measurements into quasi-stationary parts (Borodin et al., 1996 Krapivin el al., 2004). 

Barnett V. (2003). Environmental Statistics Methods and Applications. Wiley, London, 320 pp. 

Borodin L.F. and Gordina L.I. (1983). An algorithm of randomized line-broken approximation. In N.A. Armand (ed.), Statistical Methods for the Data Processing of Environmental Remote Sensing. IREE Publ., Moscow, pp. 100-104 [in Russian]. 

Because data analysis is of central interest, particularly in the application of chemometric methods in the field of environmental research, a rough list of important multivariate statistical methods is given below (Tab. 1-1). 

To conclude, the stochastic and error-influenced character of environmental data requires the use of mathematical and statistical methods for further analysis. 

A third and often neglected reason for the need for care fill application of chemometric methods is the problem of the type of distribution of environmental data. Most basic and advanced statistical methods are based on the assumption of normally distributed data. But in the case of environmental data, this assumption is often not valid. Figs. 1-7 and 1-8 demonstrate two different types of experimentally found empirical data distribution. Particularly for trace amounts in the environment, a log-normal distribution, as demonstrated for the frequency distribution of N02 in ambient air (Fig. 1-7), is typical. 

Green, R.G. Sampling Design and Statistical Methods for Environmental Biologists, Wiley, New York, Chichester, Brisbane, Toronto, 1979... 

Breen, J.J., Robinson, P.E. (Eds.) Environmental Applications of Chemometrics, ACS Symposium Series, Vol. 292, American Chemical Society, Washington, D.C., 1985 Devillers, I, Karcher, W. (Eds.) Applied Multivariate Analysis in SAR and Environmental Studies, Kluwer Academic Publishers, Dordrecht, Boston, London, 1991 Einax, J. (Ed.) Chemometrics in Environmental Chemistry. Statistical Methods, in the series Hutzin-ger, O. (Ed.) Handbook of Environmental Chemistry, Vol. 2, Part G, Springer, Berlin, 1995 Einax, J. (Ed.) Chemometrics in Environmental Chemistry. Applications, in the series Hutzinger, O. 

In environmental chemistry in particular it cannot be the last step of the investigation to produce data tables on pollutant concentrations. Multivariate statistical methods offer a tool for the investigation of the multifactorial and complex events in the environment. 

Commonly the compromising conditions of routine environmental monitoring lead to restrictions on the accuracy and the precision of sampling and analysis. The purpose of this section is to show that under these conditions multivariate statistical methods are a useful tool for qualitative extraction of new information about the degree of stress of the investigated areas, and for identification of emission sources and their seasonal variations. The results represented from investigation of the impact of particulate emissions can, in principle, be transferred to other environmental analytical problems, as described in the following case studies. 

By application of multivariate statistical methods for consideration of the overall environmental situation of a river it is possible to optimize the sampling strategy. 

Quantitative studies by means of parametric statistical methods are, however, often very unreliable because of high environment-related variations very often amounting to several orders of magnitude [FORSTNER and WITTMANN, 1983 EINAX, 1990], In other words environmental data sets often contain values which are extremely high or low, i.e. they are outliers in the statistical sense. Also, because environmental data are often not normally distributed, the application of parametric statistical methods results in distorted reflections of reality. 

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