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Environmental fate & pathways

Bioaccumulation: aquatic / sediment

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Description of key information

Log BCF from regression-based method = 0.634 (BCF = 4.309 L/kg wet-wt)
Log BCF Arnot-Gobas method (upper trophic) = -0.046 (BCF = 0.8994)

Key value for chemical safety assessment

BCF (aquatic species):
4.309 L/kg ww

Additional information

Based on REACH regulation ANNEX IX column 2, the study need not be conducted if direct and indirect exposure of the aquatic compartment is unlikely. The substance is not water soluble and RMM applied to production and use sites described within the attached CSR prevent water release.

An estimated value with BCFBAF v. 3.01 is reported.

Estimation Methodology

The original estimation methodology used by the original BCFWIN program is described in a document prepared for the U.S. Environmental Protection Agency (Meylan et al., 1997). The estimation methodology was then published in journal article (Meylan et al, 1999).

The BCFBAF Program updates the BCF estimation methodology of the BCFWIN program by using an updated and better evaluated BCF database for selecting training and validation datasets. The exact same regression methodology used to derive the original BCFWIN method was used to derive the BCFBAF method for estimating BCF.

Experimental BCF Data

The measured BCF values used in the revised regressions were selected from a quality reviewed BCF database (Arnot and Gobas, 2006); details of the data quality review methods are described in Arnot and Gobas (2006). Single BCF values were selected for each compound (median values were generally selected for compounds with multiple values).

The BCF values selected for the BCFBAF training and validation datasets are available in via Internet download at:http://esc.syrres.com/interkow/EpiSuiteData.htm ... A substructure searchable version of the data can be downloaded at: http://esc.syrres.com/interkow/EpiSuiteData_ISIS_SDF.htm

Estimation Methodology

The following is a brief summary of the estimation methodology:

The BCFBAF method classifies a compound as either ionic or non-ionic. Ionic compounds include carboxylic acids, sulfonic acids and salts of sulfonic acids, and charged nitrogen compounds (nitrogen with a +5 valence such as quaternary ammonium compounds). All other compounds are classified as non-ionic.

Training Dataset Included:

466 Non-Ionic Compounds

61 Ionic Compounds (carboxylic acids, sulfonic acids, quats)

Methodology for Non-Ionic was to separate compounds into three divisions by Log Kow value as follows:

Log Kow < 1.0

Log Kow 1.0 to 7.0

Log Kow > 7.0

For Log Kow > 7.0 the derived QSAR estimation equation is:

Log BCF = -0.49 Log Kow + 7.554 + Σ correction factors

(n = 35, r2 = 0.634, Q2 = 0.57, std dev = 0.538, avg dev = 0.396)

The previous BCFWIN equation:

Log BCF = -1.37 Log Kow + 14.4 + Σ correction factors

Certain super-hydrophobic chemicals (Log Kow >7.0) selected from the empirical database had reported BCF values with measured water concentrations that exceed water solubility limits. These BCF values were corrected based on estimates of water solubility limits (Arnot and Gobas, 2006).

log BCF = 0.50 (log Kow < 5.0)

log BCF = 1.00 (log Kow 5.0 to 6.0)

log BCF = 1.75 (log Kow 6.0 to 8.0)

log BCF = 1.00 (log Kow 8.0 to 9.0)

log BCF = 0.50 (log Kow > 9.0)

Estimation Domain

Appendix E gives for each correction factor the maximum number of instances of that factor in any of the 527 training set compounds (the minimum number of instances is of course zero, since not all compounds had every correction factor). The minimum and maximum values for molecular weight and logKow are listed below. Currently there is no universally accepted definition of model domain. However, users may wish to consider the possibility that bioconcentration factor estimates are less accurate for compounds outside the MW and logKow ranges of the training set compounds, and/or that have more instances of a given correction factor than the maximum for all training set compounds. It is also possible that a compound may have a functional group(s) or other structural features not represented in the training set, and for which no fragment coefficient was developed; and that a compound has none of the fragments in the model’s fragment library. In the latter case, predictions are based on molecular weight alone. These points should be taken into consideration when interpreting model results.

Training Set (527 Compounds):

Molecular Weight:

Minimum MW: 68.08(Furan)

Maximum MW: 991.80 Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6-bis[[4-[[2-(sulfooxy)ethyl]sulfonyl]phenyl]azo]-, tetrasodium salt)

Maximum MW: 959.17 Non-Ionic: (Benzene, 1,1 -oxybis[2,3,4,5,6-pentabromo-)

Average MW: 244.00

Log Kow:

Minimum LogKow: -6.50 Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6-bis[[4-[[2-(sulfooxy)ethyl]sulfonyl]phenyl]azo]-, tetrasodium salt)

Minimum LogKow: -1.37 Non-Ionic: (1,3,5-Triazine-2,4,6-triamine)

Maximum LogKow: 11.26 (Benzenamine, ar-octyl-N-(octylphenyl)-)