Sediment multivariate data analysis

Sakurai, T., Suzuki, N., Morita, M., 2002. Examination of dioxin fluxes recorded in dated aquatic-sediment cores in the Kanto region of Japan using multivariate data analysis. 

Ahlf, W. and Wild-Metzko, S. (1992) Bioassay responses to sediment elutriates and multivariate data analysis for hazard assessment of sediment-bound chemicals, Hydrobiologia, 235, pp. 415-418. 

The sediment from Amerikahaven (site 10) was found to contain unexpectedly low contaminant levels during sampling in 1996 (see also De Boer et al., 2001). This was attributed to repeated dredging activity. The sediment was therefore sampled a second time in September 1997 at a non-dredged site. Analysis of this sediment showed considerably higher contaminant levels. These results are considered more representative of this location and were therefore used instead of the 1996 data in the multivariate statistical analysis of biomarker data. Sediment bioassays were however conducted with the material collected in 1996 and these data for location no. 10 were used for multivariate analysis when sediment chemistry was included. 

Corresponding to the dimension d = 2, the poset shown in Fig. 19 can alternatively be visualized by a two-dimensional grid as is shown in Fig. 22. Both visualizations have their advantages. Structures within a Hasse diagram, e.g., successor sets, or sets of objects separated from others by incomparabilities, can be more easily disclosed by a representation like that of Fig. 19. In multivariate statistics reduction of data is typically performed by principal components analysis or by multidimensional scaling. These methods minimize the variance or preserve the distance between objects optimally. When order relations are the essential aspect to be preserved in the data analysis, the optimal result is a visualization of the sediment sites within a two-dimensional grid. 

Principal Component Analysis (PCA) is being used increasingly to interpret multivariate data, such as concentrations of metals in sediments. PCA could clearly separate metal patterns in clean, less-clean, and highly contaminated sediments and showed correlations between Co and Mn, Zn and Pb, and a relatively weak relationship between Cu and Cd [71]. 

This work required a large amount of subsidiary R D in (1) hydrodynamic sediment-plant mesocosm design, replication, and monitoring, (2) synthetic and analytical chemistry, including the synthesis of commercially unavailable standards and development analytical approaches to detect minor differences in organic chemicals between time points and treatments and (3) sensor design, time series data acquisition and wavelet analysis of non-stationary series [6], and covariance structure modeling of mesocosm and ecosystem data [1]. Basic questions (e.g., what constitutes a true spatiotemporal replicate in a multivariate, multiply colinear system What is the minimum number of indicator variables needed to characterize the states of such a system and how often do they need to be sampled in space and time ) arose and had to... 

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