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Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.

The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.

Diss Factsheets

Administrative data

Endpoint:
adsorption / desorption
Remarks:
adsorption/desorption
Type of information:
(Q)SAR
Adequacy of study:
supporting study
Reliability:
2 (reliable with restrictions)
Justification for type of information:
QSAR prediction: migrated from IUCLID 5.6

Data source

Reference
Reference Type:
publication
Title:
EPI-Suite - KOCWIN v2.00
Author:
U.S. Environmental Protection Agency
Year:
2008
Bibliographic source:
EPI Suite was developed by the EPA's Office of Pollution Prevention Toxics and Syracuse Research Corporation (SRC), Copyright 2000-2008 U.S. Environmental Protection Agency; http://www.epa.gov/oppt/exposure/pubs/episuitedl.htm

Materials and methods

Test guideline
Qualifier:
according to guideline
Guideline:
other: REACH guidance on QSARs R.6, May 2008
Deviations:
not applicable
Principles of method if other than guideline:
This property indicates the binding capacity (or stickiness) of a substance to solid surfaces, and so is essential for understanding environmental partitioning behaviour. Substances with a Koc below 500–1,000 L/kg are generally unlikely to adsorb to sediment. To avoid extensive testing of chemicals, a log Koc (or log Pow) ≥3 can be used as a trigger value for sediment effects assessment. Strong binding behaviour to soil particles (e.g. log Pow >5, log Koc >4) might justify immediate longterm soil organism toxicity testing if particular sensitivity and/or persistence is anticipated.
The program EpiSuite 4.0 (PCKOCWIN) predicts logKow values using two methods. First the MCI methodology and second a method based on the logPow. Overall, the MCI methodology is somewhat more accurate than the log Kow methodology, although both methods yield good results. If the Training datasets are combined in to one dataset of 516 compounds (69 having no corrections plus 447 with corrections), the MCI methodology has an r2, standard deviation and average deviation of 0.916, 0.330 and 0.263, respectively, versus 0.86, 0.429 and 0.321 for the logKow methodology.
GLP compliance:
no
Type of method:
other: calculation
Media:
soil

Test material

Constituent 1
Chemical structure
Reference substance name:
Dimethyl sulphate
EC Number:
201-058-1
EC Name:
Dimethyl sulphate
Cas Number:
77-78-1
Molecular formula:
C2H6O4S
IUPAC Name:
dimethyl sulfate
Details on test material:
Smiles code: O=S(=O)(OC)OC (generated with ACD/ChemSketch version 12.01, Februar 2009 (Freeware), Copyright 1994-2009)

Study design

HPLC method

Details on study design: HPLC method:
no details available - QSAR calculation

Batch equilibrium or other method

Analytical monitoring:
no
Computational methods:
The following table gives statistical information for the MCI (Molecular Connectivity Index, first-order) training and validation Datasets. The statistics pertain to the experimental logKoc and the MCI estimated logKoc:

Training Training Validation
No with Data set
Corrections Corrections
number 69 447 158
r2 corr coef 0.967 0.900 0.850
std deviation 0.247 0.340 0.583
avg deviation 0.199 0.273 0.459

The following table gives statistical information for the log Kow-based regression: training and
validation sets. The statistics pertain to the experimental logKoc and the logKow estimated logKoc:

Training Training Validation
No with Data set
Corrections Corrections
number 68 447 150
r2 corr coef 0.877 0.855 0.778
std deviation 0.478 0.396 0.679
avg deviation 0.371 0.307 0.494

Overall, the MCI methodology is somewhat more accurate than the log Kow methodology, although both methods yield good results. If the Training datasets are combined in to one dataset of 516 compounds (69 having no corrections plus 447 with corrections), the MCI methodology has an r2, standard deviation and average deviation of 0.916, 0.330 and 0.263, respectively, versus 0.86, 0.429 and 0.321 for the logKow methodology.

Both results were given under results and discussions.

Results and discussion

Adsorption coefficientopen allclose all
Type:
log Koc
Value:
0.9
Remarks on result:
other: Koc estimate from MCI
Type:
log Koc
Value:
1.2
Remarks on result:
other: Koc estimate from log Kow

Applicant's summary and conclusion

Validity criteria fulfilled:
yes
Conclusions:
According to the reference, the adsorption/desorption coefficient (log Koc) of DMS is calculated to be 0.9 (estimated from MCI) and 1.2 (estimated from log Kow), respectively.