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QSBMR - Quantitative Structure Biomagnification Relationships: Physicochemical and Structural Descriptors Important for the Biomagnification of Organochlorines and Brominated Flame Retardants
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Physiology and Developmental Biology, Environmental Toxicology. (Computional Ecotoxicology)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Physiology and Developmental Biology, Environmental Toxicology.
2006 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 20, no 8-10, 392-401 p.Article in journal (Refereed) Published
Abstract [en]

The aim of this project is to establish models to predict the biomagnification of contaminants present in Baltic Sea biota. In this paper a quantitative model that we term QSBMR-Quantitative Structure Biomagnification Relationships is presented. This model describes the relationship between the biomagnification factors (BMFs) for several organochlorines (OCs) and brominated flame retardants (BFRs), for example, polychlorinated biphenyls (PCBs), polybrominated diphenylethers (PBDEs) and hexabromocyclododecane (HBCD), and their descriptors, for example, physico-chemical properties and structural descriptors. The concentrations of contaminants in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg from the Baltic Sea were used. The BMFs were calculated with the randomly sampled ratios (RSR) method that denotes the BMFs with a measure of the variation. In order to describe the physico-chemical properties and chemical structures, approximately 100 descriptors for the contaminants were generated: (a), by using the software (TSAR); (b) finding log Kow values from the literature, and (c) creating binary fingerprint variables that described the position of the chlorine and bromine for the respective PCB and PBDE molecules. Partial least squares (PLS) regression was used to model the relationship between the contaminants' BMF and the descriptors and the resulting QSBMR revealed that more than 20 descriptors in combination were important for the biomagnification of OCs and BFRs between herring and guillemot. The model including all contaminants (R2X=0.73, R2Y=0.87 and Q2=0.63, three components) explained approximately as much of the variation as the model with the PCBs alone (R2X=0.83, R2Y=0.87 and Q2=0.58, two components). The model with the BFRs alone (R2X=0.68, R2Y=0.88 and Q2 = 0.41, two components) had a slightly lower Q2 than the model including all contaminants. For validation, a training set of seven contaminants was selected by multivariate design (MVD) and a model was established. This model was then used to predict the BMFs of the test set (seven contaminants not included in the model). The resulting R2 for the regression Observed BMF versus Predicted BMF was high (0.65). The good models showed that descriptors important for the biomagnification of OCs and BFRs had been used. These types of models will be useful for in silico predictions of the biomagnification of new, not yet investigated, compounds as an aid in risk assessments.

Place, publisher, year, edition, pages
2006. Vol. 20, no 8-10, 392-401 p.
Keyword [en]
Biomagnification, preditictive models, Risk assessment, persistent organic pollutants
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-93812DOI: 10.1002/cem.1014ISI: 000247019400009OAI: oai:DiVA.org:uu-93812DiVA: diva2:167407
Available from: 2005-11-24 Created: 2005-11-24 Last updated: 2017-12-14Bibliographically approved
In thesis
1. QSBMR Quantitative Structure Biomagnification Relationships: Studies Regarding Persistent Environmental Pollutants in the Baltic Sea Biota
Open this publication in new window or tab >>QSBMR Quantitative Structure Biomagnification Relationships: Studies Regarding Persistent Environmental Pollutants in the Baltic Sea Biota
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

I have studied persistent environmental pollutants in herring (Clupea harengus), in adult guillemot (Uria aalge) and in guillemot eggs from the Baltic Sea. The studied contaminants were organochlorines (OCs); dichlorodiphenyltrichloroethanes (DDTs), polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), hexachlorocyclohexanes (HCHs), and brominated flame retardants (BFRs); polybrominated diphenylethers (PBDEs) and hexabromocyclododecane (HBCD). The highest concentration in both species was shown by p,p′DDE with a concentration in guillemot egg (geometric mean (GM) with 95% confidence interval) of 18200 (17000 – 19600) ng/g lipid weight. The BFR with the highest concentration in guillemot egg was HBCD with a GM concentration of 140 (120 – 160) ng/g lw.

To extract additional and essential information from the data, not possible to obtain using only univariate or bivariate statistics, I used multivariate data analysis techniques; principal components analysis (PCA), partial least squares regression (PLS), soft independent modelling of class analogy (SIMCA), and PLS discriminant analysis (PLS-DA). I found e.g.; that there are significant negative correlations between egg weight and the concentrations of HCB and p,p'DDE; that concentrations of OCs and BFRs in the organisms co-varied so that concentrations of OCs can be used to calculate concentrations of BFRs; and, that several contaminants (e.g. HBCD) had higher concentration in guillemot egg than in guillemot muscle, that several (e.g. BDE47) showed no concentration difference between muscle and egg and that one contaminant (BDE154) showed higher concentration in the guillemot muscles than in egg.

In this thesis I developed a new method, “randomly sampled ratios” (RSR), to calculate biomagnification factors (BMFs) i.e. the ratio between the concentration of a contaminant in an organism and the concentration of the same contaminant in its food. With this new method BMFs are denoted with an estimate of variation. Contaminants that biomagnify are e.g., p,p′DDE, CB118, HCB, βHCH and all of the BFRs. Those that do not biomagnify are e.g., p,p′DDT, αHCH and CB101.

Lastly, to investigate which of the contaminants descriptors (physical-chemical/other properties and characteristics) that correlates to the biomagnification of the contaminants, I modeled the contaminants’ respective BMFRSR versus ~100 descriptors and showed that ~20 descriptors in combination were important, either favoring or counteracting biomagnification between herring and guillemot.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2005. 60 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 125
Keyword
Biology, Biomagnification, Multivariate modelling of environmental data, Transport of contaminants, Environmental toxicology, Biologi
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-6173 (URN)91-554-6412-2 (ISBN)
Public defence
2005-12-15, Lindahlssalen, EBC, Norbyvägen 18 A, Evolutionsbiologiskt centrum, Norbyvägen 18 A, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2005-11-24 Created: 2005-11-24 Last updated: 2009-04-03Bibliographically approved

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Lundstedt-Enkel, KatrinLundstedt, TorbjörnÖrberg, Jan

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