Chlordane components (CHLs) and their metabolites (heptachlor, cis-heptachlor epoxide, U82, MC4, transchlordane, MC5, cis-chlordane, MC7, oxychlordane, MC6- and trans- and cis-nonachlor) and aldrin, dieldrin, endrin, isodrin,endosulfan 1,endosulfan 2,and mirex were quantified in the soft tissues of blue mussel, a whole crab, and whole fishes collected from the spatially different sites in the Gulf of Gdansk. Six to twelve chlordane compounds and metabolites and dieldrin were detected in all organisms examined while aldrin, endrin, isodrin, endosulfans 1 and 2, and mirex were not found above the detection limit of the method. The lipid weight based concentrations in Baltic biota were relatively small and ranged from 12 to 150 and 7.6-77 ng/g, while between 0.16 and 6.8 and 0.10-6.6 ng/g in fresh tissue, respectively. The profile (%) of chlordane compounds was very similar between various fish species with trans-nonachlor (28 +/- 17), cis-chlordane (23 +/- 18), oxychlordane (13 +/- 7), and heptachlor epoxide (11 +/- 5) as major constituents and was totally different in crab with oxychlordne as the most dominating (> 65%) compound. Blue mussel, lamprey, and three-spined stickleback exhibited a smallest ability to metabolize CHLs, and such fishes as cod, lesser sand-eel, sand-eel, pikeperch, perch, round goby, flounder, and herring showed a slightly better ability, while crab was able to effectively metabolize most of CHL compounds except trans-nonachlor. A value of the quotient of the trans-nonachlor to cis-chlordane concentrations (N/C quotient) was 1.0 in blue mussel, 3.1 in crab, and between 0.9 and 1.8 in fish. Both the small concentrations of CHLs in all organisms and the values of N/C quotients close to I imply on a long-range aerial transport through movement of the air masses from the remote regions of the northern hemisphere as a main source of this pesticide in the Gulf of Gdansk. The interdependences between the CHL profiles for various fish species and between different sampling sites were examined using the principal component analysis (PCA) method. Applying the PCA model the first four significant components explained 90% (43% + 23% + 15% + 8%) of the total variance in the data matrix.
