A new approach for assessing the state of environment using isometric log-ratio transformation and outlier detection for computation of mean PCDD/F patterns in biota.

Abstract

To assess the state of the environment, various compartments are examined as part of monitoring programs. Within monitoring, a special focus is on chemical pollution. One of the most toxic substances ever synthesized is the well-known dioxin 2,3,7,8-TCDD (2,3,7,8-tetra-chlor-dibenzo-dioxin). Other PCDD/F (polychlorinated-dibenzo-dioxin and furan) can act toxic too. They are ubiquitary and persistent in various environmental compartments. Assessing the state of environment requires knowledge of typical local patterns of PCDD/F for as many compartments as possible. For various species of wild animals and plants (so called biota), I present the mean local congenere profiles of ubiquitary PCDD/F contamination reflecting typical patterns and levels of environmental burden for various years. Trends in time series of means can indicate success or failure of a measure of PCDD/F reduction. For short time series of mean patterns, it can be hard to detect trends. A new approach regarding proportions of outliers in the corresponding annual cross-sectional data sets in parallel can help detect decreasing or increasing environmental burden and support analysis of time series. Further, in this article, the true structure of PCDD/F data in biota is revealed, that is, the compositional data structure. It prevents direct application of statistical standard procedures to the data rendering results of statistical analysis meaningless. Results indicate that the compositional data structure of PCDD/F in biota is of great interest and should be taken into account in future studies. Isometric log-ratio (ilr) transformation is used, providing data statistical standard procedures that can be applied too. Focusing on the identification of typical PCDD/F patterns in biota, outliers are removed from annual data since they represent an extraordinary situation in the environment. Identification of outliers yields two advantages. First, typical (mean) profiles and levels of PCDD/F contamination can be identified. Second, decreasing (increasing) proportions of outliers could indicate decreasing (increasing) numbers of extraordinary environmental burden rendering the success of PCDD/F reduction strategies for biota. Therefore, probabilities and proportions of outlier contamination are estimated too. To reveal the enormous influence of the method of outlier detection, the applied two well-known procedures are compared, that is, robust Mahalanobis distance and a projection pursuit-based approach.