State of the Art Report on Multivariate Chemometric Methods in Environmental Forensics☆

Abstract

Environmental forensic analysis has evolved signi®cantly from the early days of qualitative chemical ®ngerprint evaluations. The need for quantitative rigor has made the use of numerical methods critical in identifying and mapping contaminant sources in complex environmental systems. Given multiple contaminant sources, the environmental scientist is faced with the challenge of unraveling the contributions of multiple plumes with overlapping spatial and temporal distributions. The problem may be addressed through a multivariate statistical approach, but there is a mindboggling array of the available``chemometric'' methods. This paper provides an overview of these methods, along with a review of their advantages, disadvantages, and pitfalls. Methods discussed include principal component analysis and several receptor-modeling techniques.

Hierarchical cluster analysis of polychlorinated dioxins and furans in Michigan, USA, soils: Evaluation of industrial and background congener profiles

“…A constant-row-sum transformation was used, in which the sum of each row was converted to unity and the natural-logarithm-transformed concentration value of each congener in each sample was converted to a fraction of unity. Finally, a range transformation, as described by Johnson et al [24], was applied to each congener. The natural-logarithm and constant-row-sum transformations reduce the influence of samples with high concentrations, while the range transformation reduces the influence of congeners with high variability.…”

“…64-72, 2010 # 2009 SETAC Printed in the USA DOI: 10.1002/etc. 24 All Supplemental Data may be found in the online version of this article. * To whom correspondence may be addressed (ttowey@limno.com).…”

Comparison of a new variant of PMF with other receptor modeling methods using artificial and real sediment PCB data sets

“…This weighting allows smaller values to be given high uncertainties so they influence the objective function (Q) a small amount, and large data points to be given a high uncertainty so outliers do not overly influence the factor formation (Paatero and Tapper, 1994). Recent reviews of receptor modeling techniques were provided by Henry (2002), Hopke (2003), and Johnson and Ehrlich (2002).…”

A User's Guide for Determining the Sources of Contaminants in Sediments

“…Excellent summaries of statistical and numerical methods commonly used can be found in Gauthier, 2002) and Johnson and Ehrlich, 2002), respectively. One significant advantage of chemometric analyses is that they provide a strictly mathematical means of analyzing data, thus removing any biases of the interpreter.…”