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Diss Factsheets
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EC number: 220-985-2 | CAS number: 2958-36-3
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Adsorption / desorption
Administrative data
- Endpoint:
- adsorption / desorption
- Remarks:
- adsorption/desorption
- Type of information:
- (Q)SAR
- Remarks:
- Migrated phrase: estimated by calculation
- Adequacy of study:
- supporting study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- 1. SOFTWARE
Estimation Programs Interface Suite™ for Microsoft® Windows v 4.10. US EPA, United States Environmental Protection Agency, Washington, DC, USA.
2. MODEL (incl. version number)
KOCWIN Program (v2.00) - MCI method
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
O=C(c(c(N)ccc1CL)c1)c(c(ccc2)CL)c2
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF or providing a link]
- Defined endpoint:
log Koc – soil adsorption coefficient of organic compounds.
- Unambiguous algorithm:
log Koc = 0.5213 MCI + 0.60 + ΣPfN
MCI – molecular connectivity index, ΣPfN - summation of the products of all applicable correction factor coefficients available in the data set multiplied by the number of times (N) that factor is counted for the structure.
- Defined domain of applicability:
Currently, there is no universally accepted definition of model domain. The training set of the model contains diverse molecules, so that the fragment library is abundant. It is however possible that a compound has functional groups or other structural features that are not represented in the training set and for which no fragment coefficients were developed. Additionally, there can be more instances of a given fragment than the maximum for all training set compounds. These points should be taken into consideration while interpreting test results.
Molecular weight limits of the training set: 32-665 g/mol
Log Kow limits: -2.11-9.10
- Appropriate measures of goodness-of-fit and robustness and predictivity:
for the statistics, training data set has been split up into two subsets: the one containing non-polar substances with no fragments subjected to corrections (i.e. those with ΣPfN = 0) and the one containing the remaining ones. For the non-polar set: N = 69 compounds, correlation coefficient R2= 0.967, standard deviation sd = 0.247 and average deviation ad = 0.199. For the second set: N = 447 compounds, correlation coefficient R2= 0.9, standard deviation sd = 0.34 and average deviation ad = 0.273. For the external validation data set: N = 158 compounds, correlation coefficient R2= 0.85, standard deviation sd = 0.583 and average deviation ad = 0.459. For the 516 compounds in the training set, 93% are within 0.6 log units and 100% within 1 log unit.
- Mechanistic interpretation:
The methodology and relationship between the forst order molecular connectivity index (MCI) and adsorption coefficient is outlined in the reference paper: Meylan, W., P.H. Howard and R.S. Boethling, "Molecular Topology/Fragment Contribution Method for Predicting Soil Sorption Coefficients", Environ. Sci. Technol. 26: 1560-7 (1992). MCI was initially successfully used to predict soil sorption coefficients for non-polar organics, and the developed new estimation method based on MCIand series of statistically derived fragment contribution factors made it useful also for the polar ones.
5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
- Descriptor domain:
The substance is in the molecular weight range and in the logKow range of the compounds in the training set.
- Structural and mechanistic domains:
the correction factors for the substance do not exceed the maximum number in any individual compound of the training set.
- Similarity with analogues in the training set:
the most similar substance within the training and validation set is 2-chlorobenzamide (CAS 609-66-5) with a Dice index >50%. According to the model documentation this substance has a measured logKoc of 1.51 and an estimated logKoc of 1.45 (MCI method) indicating that the QSAR model works well for the most similar substance.
6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]
The organic carbon dependent sorption behavior is important to describe the sorption of organic non-ionic substances on sediment/soil/sludge. As the substance is organic and non-ionisable the prediction of the logKoc is not only reliable but also relevant for exposure and risk assessment purposes.
Data source
Reference
- Reference Type:
- study report
- Title:
- Unnamed
- Year:
- 2 014
- Report date:
- 2014
Materials and methods
Test guideline
- Qualifier:
- according to guideline
- Guideline:
- other: QSAR calculation
Results and discussion
Adsorption coefficient
- Type:
- log Koc
- Value:
- 3.167
- Remarks on result:
- other: calculated with EPIWIN 4.1 (Koc Estimate from MCI)
Applicant's summary and conclusion
- Conclusions:
- The estimated Log Koc is 3.1667 (calculated with EPIWIN 4.1 (Koc Estimate from MCI)).
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