Kerstin Nylund

Analysis of some polyhalogenated organic pollutants in sediment and sewage sludge

Abstract A laminated sediment core collected in the southern part of the Baltic Proper was analysed for DDT compounds, polychlorinated biphenyls (PCB) including the coplanar congeners, polybrominated diphenyl ethers (PBDE) and polychlorinated naphthalenes (PCN). Two sewage sludge samples were also analysed for the same compounds. The results of the sediment analyses indicate the presence of PCB, DDT and PBDE compounds in sediment layers dating from the 1950s and later. Neither coplanar PCB nor PCN were detected at any level of the sediment core. The PCB concentrations indicate a slight increase in levels during the last decades, while there were no changes in the levels of DDT compounds. In contrast, TeBDE levels increase 4- to 8-fold and one PeBDE congeners levels increase 10- to 20-fold. The results of sewage sludge analyses showed the concentration of the individual PBDE to be at about the same level as for the individual PCB congeners. The congener pattern in sludge samples indicates low-chlorinated products to be the main PCN source.

Multiresidue method for the gas-chromatographic analysis of some polychlorinated and polybrominated pollutants in biological samples

SummaryA multiresidue method for the analysis of a number of chlorinated and brominated organic compounds in biological samples has been developed. The method includes gas-chromatographic analysis of hexachlorinated and hexabrominated benzenes (HCB and HBB), polychlorinated and polybrominated biphenyls (PCB and PBB), polychlorinated phenols (PCP), guaiacols (PCG), paraffins (CP) and naphthalenes (PCN), polybrominated diphenyl ethers (PBDE) and the pesticides DDT, polychlorinated terpenes (PCC or toxaphene), chlordanes, hexachlorocyclohexanes (HCH), aldrin and dieldrin. The use of charcoal column in the clean-up procedure makes it also possible to analyze the planar PCB congeners (pPCB). Cod muscle samples were spiked with 10 and 50 times the detection levels of the compounds to be analyzed. The mean recoveries, relative to internal standards, for 40 compounds or compound groups were between 51 and 120%.

Modeling relationships between Baltic Sea herring (Clupea harengus) biology and contaminant concentrations using multivariate data analysis.

Baltic Sea herring (Clupea harengus) is a pelagic, zoo-planktivorous fish and young (2-5 years old) individuals of this species are sampled annually in the Swedish marine monitoring program. This study determined concentrations of organochlorines (OCs) and brominated flame retardants (BFRs) in dorsal muscle from herring (n = 60) of varying age (2-13 years), weight (25-200 g), and body length (16-29 cm) caught at three locations in the Swedish part of the Baltic Proper. In order to ensure that the fish biology was as varied as possible, though still similar from all sampling sites, the fish to be chemically analyzed were selected from a large number of fish with determined biology using Multivariate Design. In statistical evaluation of the data, univariate and multivariate data analysis techniques, e.g. principal components analysis (PCA), partial least-squares regression (PLS), and orthogonal PLS (OPLS), were used. The results showed that the fish are exposed to a cocktail of contaminants and levels are presented. Significant OPLS models were found for all biological variables versus concentrations of OCs and BFRs, showing that fish biology covaries with fish contaminant concentrations. Correlation coefficients were as high as 0.98 for e.g. βHCH concentration (wet weight) versus the lipid content. Lastly, the OC concentrations in herring muscle were modeled against the BFR concentrations to determine whether concentrations of either could be used to predict the other. It was found that OPLS models allowed BFR concentrations to be predicted from OC concentrations with high, but varying, accuracy (R(2)Ys between 0.93 to 0.75). Thus, fish biology and contaminant concentrations are interwoven, and fish biological parameters can be used to calculate (predict) contaminant concentrations. It is also possible to predict the BFR concentrations in an individual fish from its concentrations of OCs with very high accuracy.