EU-SICHERHEITSDATENBLATT Dieselkraftstoff ... - Schmierstoffe

EU-SICHERHEITSDATENBLATT Dieselkraftstoff ... - Schmierstoffe EU-SICHERHEITSDATENBLATT Dieselkraftstoff ... - Schmierstoffe

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4.0 Bioaccumulation Assessment of Petroleum hydrocarbon Blocks Guidance provided by the ECHA (ECHA 2008b) on how to interpret the Annex XIII criteria and the recent discussion on the proposed revision of those criteria, indicate that the assessment of B/vB properties (B: BCF of >2000,

(see Appendix 2). Since BCF predictions derived from this model are based on a default lipid content of 10.7%, predictions were adjusted to 5% lipid content as recommended in REACH Guidance Chapter R.7c (ECHA, 2008a). Default values for particulate and dissolved organic carbon concentrations of 0.5 mg/l that are assumed in the BCFBAF model were also used as conservative defaults in these calculations. The marked influence that fish biotransformation exerts on the predicted BCFs derived from this model is evident (see second figure in each of the sections below). BAF predictions included in the BCFBAF model were judged to be inappropriate for hydrocarbons since metabolism in the gut (which effectively reduces the default dietary assimilation efficiency assumed in food chain model calculations) is ignored. The decision to exclude BAF model predictions in this analysis is supported by experimental dietary BMF data (see below) demonstrating the critical role of gut metabolism in limiting biomagnification of hydrocarbons via the diet. Experimental data Aqueous and dietary bioaccumulation data are reported in tables in each hydrocarbon class section. Dietary bioaccumulation tests (Anon, 2004) offer a number of practical advantages over traditional aqueous BCF tests, particularly for more hydrophobic test substances. These data may be used to calculate a biomagnification factor (BMF) which is defined as the concentration ratio of test substance in fish tissue at steady-state to that in the administered diet. If expressed on a lipid normalized basis, substances that exhibit a BMF significantly above one may undergo biomagnification while substances with a BMF well below one may exhibit trophic dilution in the food chain. Experimental elimination rate data derived from these studies can also be combined with allometric equations for estimating the uptake clearance (k 1 ) to estimate a BCF for the test substance (Parkerton et al. 2008): The BCF, normalized to 5% lipid content is calculated with the following equation: BCF C C fish water ku t 0.693 1 2 5 x L fish where k u is the uptake rate constant (520·W -0.32 ) (Sijm et al. 1995), t 1/2 is the growthcorrected half-life, derived from the slope of the depuration plot and the fish growth rate during the dietary test, and L fish a correction for bioavailability which limits uptake for more hydrophobic substances. For such substances, complexation to low concentrations of organic carbon in dilution water is unavoidable. As a result, the ratio of chemical to oxygen gill transfer efficiencies declines as predicted by the following relationship (Gobas and Arnot 2003): 1 , X poc = 0, X doc = 2×10 -6 (1 (0.35 X K ) (0.08 X K ) poc OW doc OW 29

(see Appendix 2). Since BCF predictions derived from this model are based on a default<br />

lipid content of 10.7%, predictions were adjusted to 5% lipid content as recommended in<br />

REACH Guidance Chapter R.7c (ECHA, 2008a). Default values for particulate and<br />

dissolved organic carbon concentrations of 0.5 mg/l that are assumed in the BCFBAF<br />

model were also used as conservative defaults in these calculations. The marked<br />

influence that fish biotransformation exerts on the predicted BCFs derived from this<br />

model is evident (see second figure in each of the sections below). BAF predictions<br />

included in the BCFBAF model were judged to be inappropriate for hydrocarbons since<br />

metabolism in the gut (which effectively reduces the default dietary assimilation<br />

efficiency assumed in food chain model calculations) is ignored. The decision to exclude<br />

BAF model predictions in this analysis is supported by experimental dietary BMF data<br />

(see below) demonstrating the critical role of gut metabolism in limiting biomagnification<br />

of hydrocarbons via the diet.<br />

Experimental data<br />

Aqueous and dietary bioaccumulation data are reported in tables in each hydrocarbon<br />

class section. Dietary bioaccumulation tests (Anon, 2004) offer a number of practical<br />

advantages over traditional aqueous BCF tests, particularly for more hydrophobic test<br />

substances. These data may be used to calculate a biomagnification factor (BMF) which<br />

is defined as the concentration ratio of test substance in fish tissue at steady-state to that<br />

in the administered diet. If expressed on a lipid normalized basis, substances that exhibit<br />

a BMF significantly above one may undergo biomagnification while substances with a<br />

BMF well below one may exhibit trophic dilution in the food chain. Experimental<br />

elimination rate data derived from these studies can also be combined with allometric<br />

equations for estimating the uptake clearance (k 1 ) to estimate a BCF for the test substance<br />

(Parkerton et al. 2008):<br />

The BCF, normalized to 5% lipid content is calculated with the following equation:<br />

BCF<br />

C<br />

<br />

C<br />

fish<br />

water<br />

ku<br />

t<br />

<br />

0.693<br />

1<br />

2<br />

5<br />

x<br />

L<br />

fish<br />

where k u is the uptake rate constant (520·W -0.32 ) (Sijm et al. 1995), t 1/2 is the growthcorrected<br />

half-life, derived from the slope of the depuration plot and the fish growth rate<br />

during the dietary test, and L fish <br />

a correction for bioavailability which limits uptake for more hydrophobic substances. For<br />

such substances, complexation to low concentrations of organic carbon in dilution water<br />

is unavoidable. As a result, the ratio of chemical to oxygen gill transfer efficiencies<br />

declines as predicted by the following relationship (Gobas and Arnot 2003):<br />

1<br />

<br />

, X poc = 0, X doc = 2×10 -6<br />

(1 (0.35<br />

X K ) (0.08<br />

X K )<br />

poc<br />

OW<br />

doc<br />

OW<br />

29

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