A linear solvation energy relationship model of organic chemical partitioning to particulate organic carbon in soils and sediments

The sorption behaviors of 4 cyclic and linear volatile methyl siloxane (VMS) compounds between water and organic matter in 3 United Kingdom soils were studied by a batch equilibrium method using13C-enriched sorbates. Sorption and desorption kinetics and isotherms were determined for octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5), octamethyltrisiloxane (L3), and decamethyltetrasiloxane (L4). Concentrations of [13C]-VMS in the soil and aqueous phases were measured directly by extraction and gas chromatography–mass spectrometry techniques. All VMS compounds were sorbed rapidly, reaching constant distributions in all soils by 24 h. Desorption kinetics were very rapid, with reattainment of equilibrium within 1 h. In the main, linear isotherms were observed for aqueous concentrations at or below 4% of the solubility limits. The average sorption organic carbon partition coefficient (log KOC) values across soils were 4.23 for D4, 5.17 for D5, 4.32 for L3, and 5.13 for L4, with standard deviations of 0.09 to 0.34. Desorption KOC values were systematically greater by 0.1 log units to 0.3 log units. The linear isotherms and low variation in KOC values across soils suggested partitioning-dominated sorption of the VMS. Compared with traditional hydrophobic organic compounds, KOC values for the VMS compounds were significantly lower than expected on the basis of their octanol–water partition coefficients. A linear free energy relationship analysis showed that these differences could be rationalized quantitatively in terms of the inherent characteristics of the VMS compounds, combined with the differences in solvation properties of organic matter and octanol. Environ Toxicol Chem 2014; 33:1937–1945.

log K = c + v V + e E + s S + a A + b B

Reviews of the solute descriptors (upper case letters in Equation ) and their determination are available elsewhere .…”

Section: Resultsmentioning

confidence: 99%

“…[32]. Several LFER equations based on the form of the solvation parameter model often used for partitioning between condensed phases, Equation 5, have been reported for correlation of K OC values of nonionic organic compounds with their Abraham solute descriptors [14,15,30,33,34].…”

Section: Analysis Of K Oc and Comparison With K Owmentioning

confidence: 99%

Determination of soil–water sorption coefficients of volatile methylsiloxanes

Kozerski

Miller

et al. 2014

“…The LSER for humic acid is reasonably robust with an RMSE ¼ 0.43 that is comparable to the best log K POC models [39]. However, it should be noted that approximately one-half of the data is for Aldrich humic acid.…”

Section: Discussionmentioning

confidence: 88%

A linear solvation energy relationship model of organic chemical partitioning to dissolved organic carbon

Kipka

Toro

2011

Self Cite

Predicting the association of contaminants with both particulate and dissolved organic matter is critical in determining the fate and bioavailability of chemicals in environmental risk assessment. To date, the association of a contaminant to particulate organic matter is considered in many multimedia transport models, but the effect of dissolved organic matter is typically ignored due to a lack of either reliable models or experimental data. The partition coefficient to dissolved organic carbon (K(DOC)) may be used to estimate the fraction of a contaminant that is associated with dissolved organic matter. Models relating K(DOC) to the octanol-water partition coefficient (K(OW)) have not been successful for many types of dissolved organic carbon in the environment. Instead, linear solvation energy relationships are proposed to model the association of chemicals with dissolved organic matter. However, more chemically diverse K(DOC) data are needed to produce a more robust model. For humic acid dissolved organic carbon, the linear solvation energy relationship predicts log K(DOC) with a root mean square error of 0.43.

“…This is also observed across all of the different exposure systems where the different symbols overlap and show the same trends and magnitudes (Supplemental Data, Figure S3). For comparison, typical environmental fate and effects models used to estimate partitioning (e.g., log K OW , log organic carbon partition coefficient [ K OC ] , or toxicity ) have RMSEs of log residuals typically in the range of 0.3 to 0.5.…”

Section: Resultsmentioning

confidence: 99%

Investigating the role of dissolved and droplet oil in aquatic toxicity using dispersed and passive dosing systems

Redman

Butler

Letinski

et al. 2016

Characterization of the aquatic toxicity of oil is needed to support hazard assessment and inform spill response. Natural processes and mitigation strategies involving dispersant use can result in exposures to both dissolved and droplet oil that are not typically differentiated when oil exposures are characterized in toxicity tests. Thus, the impact of droplets on aquatic toxicity is largely uncharacterized. To improve the understanding of the role of droplets, acute toxicity tests with Daphnia magna and Americamysis bahia were performed with Endicott crude oil in low-energy mixing systems with and without Corexit 9500 dispersant. Exposures were also prepared by placing crude oil in silicone tubing and passively dosing test media to provide dissolved oil exposures without droplets. A framework is described for characterizing dissolved phase exposures using both mechanistic modeling and passive sampling measurements. The approach is then illustrated by application to data from the present study. Expression of toxicity in terms of toxic units calculated from modeled dissolved oil concentrations or passive sampling measurements showed similar dose responses between exposure systems and organisms, despite the gradient in droplet oil. These results indicate that droplets do not appreciably contribute to toxicity for the 2 species investigated and further support hazard evaluation of dispersed oil on the basis of dissolved exposure metrics. Environ Toxicol Chem 2017;36:1020-1028. © 2016 SETAC.