Development and exploration of an organic contaminant fate model using poly-parameter linear free energy relationships

Batch equilibrium experiments and modeling reveal weak temperature dependence of cyclic volatile methylsiloxane sorption in soil/sediment organic carbon-water systems

The organic carbon normalized partition coefficient, KOC, describes the equilibrium distribution of a chemical between water and organic carbon in soil or sediment. It is a key parameter in evaluating chemical persistence, mass distribution, and transport using multimedia fate and transport models. Considerable uncertainty remains about the KOC values of cyclic volatile methylsiloxane (cVMS) compounds, and in particular the dependence of KOC on temperature. In this study, we used a batch equilibrium (BE) method to measure KOC values and their temperature dependence between ∼5 and 25 °C for octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) with soil and sediments. Approximate log KOC values at 25 °C were 4.5-5.0 for D4 and 5.5-6.1 for D5 with different sorbents, and decreased by 0.3 log units or less at 4-5 °C. Enthalpies of sorption, ΔHOC, obtained for the different sorbents ranged from +7.2 to +16 kJ mol-1, with average values of +7.9 and +13 kJ mol-1 for D4 and D5, respectively. These values differ in magnitude and direction from those reported elsewhere based on KOC values determined by a novel dynamic purge-and-trap (PnT) method, but are consistent with predictions based on their solvation properties. A new fugacity-based multimedia model incorporating sorption/desorption kinetics was developed and used to predict concentrations in the phases of BE and PnT systems during desorption of cVMS under different experimental and ideal conditions. Model simulations suggested that KOC values for cVMS compounds derived from the PnT systems could be influenced by sorption disequilibrium between water and solids controlled by desorption rates from the particle phase to water, and subsequent losses due to volatilization and degradation. This has the potential to result in overestimation of KOC values when fitting the experimental data of cVMS mass remaining in a PnT system over time, which could explain the observed differences between the methods.

MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC

MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC's capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO₂ in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor-liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at https://github.com/GOMC-WSU/MoSDeF-GOMC.

A critical assessment of the environmental fate of linear and cyclic volatile methylsiloxanes using multimedia fugacity models

We apply multimedia models to systematically evaluate the fate profile of cyclic volatile methyl siloxanes (VMS) D₄, D₅ and D₆, and the linear VMS L₄ and L₅ using recently reported measurements of their partition ratios between organic carbon and water (K_OC), their salting out constants (K^s), and their enthalpy of sorption to organic carbon (ΔH_OC). Our assessment follows a multi-stage strategy where the environmental fate of the chemicals is explored in generic regional models with increasing fidelity to the real system and in a region-specific model. Modeled emissions of VMS to air remained in air and were degraded or advected out of the system with overall residence times ranging from 2.4 to 2.5 days, while emissions to water resulted in accumulation in sediment and longer residence times ranging from 29.5 to 1120 days. When emitted to water the modeled residence times of VMS in the sediment exceeded the REACH criterion for persistence in freshwater sediments. Reported K_OC measurements for D₅ differ by 1 log unit, which results in a 500-day difference in the overall residence times calculated in the generic regional modeling. In the specific-region modeling assessment for Adventfjorden, Svalbard in Norway, the different K_OC measurements of D₅ resulted in a 200-day difference in overall residence times. Model scenarios that examined combinations of previously published ΔH_OC or enthalpy of phase change between octanol and water (ΔH_OW) for D₅ in combination with the range of the K_OC measurements resulted in 1100-days difference in overall residence times. Our results demonstrate that residence times of VMS in aquatic systems are highly sensitive to their degree of sorption to organic carbon, and that residence times of VMS likely exceed several persistence criteria and therefore they cannot be considered as non-persistent.

Can poly-parameter linear-free energy relationships (pp-LFERs) improve modelling bioaccumulation in fish?

A wide range of studies have characterized different types of biosorbent, with regard to their interactions with chemicals. This has resulted in the development of poly-parameter linear free energy relationships (pp-LFERs) for the estimation of partitioning of neutral organic compounds to biological phases (e.g., storage lipids, phospholipids and serum albumins). The aims of this study were to explore and evaluate the influence of implementing pp-LFERs both into a one-compartment fish model and a multi-compartment physiologically based toxicokinetic (PBTK) fish model and the associated implications for chemical risk assessment. For this purpose, fish was used as reference biota, due to their important role in aquatic food chains and dietary exposure to humans. The bioconcentration factor (BCF) was utilized as the evaluation metric. Overall, our results indicated that models incorporating pp-LFERs (R² = 0.75) slightly outperformed the single parameter (sp) LFERs approach in the one-compartmental fish model (R² = 0.72). A pronounced enhancement was achieved for compounds with log K_OW between 4 and 5 with increased R² from 0.52 to 0.71. The minimal improvement was caused by the overestimation of lipid contribution and underestimation of protein contribution by the sp-approach, which cancelled each other out. Meanwhile, a greater improvement was observed for multi-compartmental PBTK models with consideration of metabolism, making all predictions fall within a factor of 10 compared with measured data. For screening purposes, the K_OW-based (sp-LFERs) approach should be sufficient to quantify the main partitioning characteristics. Further developments are required for the consideration of ionization and more accurate quantification of biotransformation in biota.

Screening-level models to estimate partition ratios of organic chemicals between polymeric materials, air and water

Polymeric materials flowing through the technosphere are repositories of organic chemicals throughout their life cycle. Equilibrium partition ratios of organic chemicals between these materials and air (KMA) or water (KMW) are required for models of fate and transport, high-throughput exposure assessment and passive sampling. KMA and KMW have been measured for a growing number of chemical/material combinations, but significant data gaps still exist. We assembled a database of 363 KMA and 910 KMW measurements for 446 individual compounds and nearly 40 individual polymers and biopolymers, collected from 29 studies. We used the EPI Suite and ABSOLV software packages to estimate physicochemical properties of the compounds and we employed an empirical correlation based on Trouton's rule to adjust the measured KMA and KMW values to a standard reference temperature of 298 K. Then, we used a thermodynamic triangle with Henry's law constant to calculate a complete set of 1273 KMA and KMW values. Using simple linear regression, we developed a suite of single parameter linear free energy relationship (spLFER) models to estimate KMA from the EPI Suite-estimated octanol-air partition ratio (KOA) and KMW from the EPI Suite-estimated octanol-water (KOW) partition ratio. Similarly, using multiple linear regression, we developed a set of polyparameter linear free energy relationship (ppLFER) models to estimate KMA and KMW from ABSOLV-estimated Abraham solvation parameters. We explored the two LFER approaches to investigate (1) their performance in estimating partition ratios, and (2) uncertainties associated with treating all different polymers as a single "bulk" polymeric material compartment. The models we have developed are suitable for screening assessments of the tendency for organic chemicals to be emitted from materials, and for use in multimedia models of the fate of organic chemicals in the indoor environment. In screening applications we recommend that KMA and KMW be modeled as 0.06 ×KOA and 0.06 ×KOW respectively, with an uncertainty range of a factor of 15.

Mapping environmental partitioning properties of nonpolar complex mixtures by use of GC × GC

Comprehensive two-dimensional gas chromatography (GC × GC) is effective for separating and quantifying nonpolar organic chemicals in complex mixtures. Here we present a model to estimate 11 environmental partitioning properties for nonpolar analytes based on GC × GC chromatogram retention time information. The considered partitioning properties span several phases including pure liquid, air, water, octanol, hexadecane, particle natural organic matter, dissolved organic matter, and organism lipids. The model training set and test sets are based on a literature compilation of 648 individual experimental partitioning property data. For a test set of 50 nonpolar environmental contaminants, predicted partition coefficients exhibit root-mean-squared errors ranging from 0.19 to 0.48 log unit, outperforming Abraham-type solvation models for the same chemical set. The approach is applicable to nonpolar organic chemicals containing C, H, F, Cl, Br, and I, having boiling points ≤402 °C. The presented model is calibrated, easy to apply, and requires the user only to identify a small set of known analytes that adapt the model to the GC × GC instrument program. The analyst can thus map partitioning property estimates onto GC × GC chromatograms of complex mixtures. For example, analyzed nonpolar chemicals can be screened for long-range transport potential, aquatic bioaccumulation potential, arctic contamination potential, and other characteristic partitioning behaviors.

Concentration-dependent polyparameter linear free energy relationships to predict organic compound sorption on carbon nanotubes

Adsorption of organic compounds on carbon nanotubes (CNTs), governed by interactions between molecules and CNTs surfaces, is critical for their fate, transport, bioavailability and toxicity in the environment. Here, we report a promising concentration-dependent polyparameter linear free energy relationships (pp-LFERs) model to describe the compound-CNTs interactions and to predict sorption behavior of chemicals on CNTs in a wide range of concentrations (over five orders of magnitude). The developed pp-LFERs are able to capture the dependence of the k_i on equilibrium concentration. The pp-LFERs indexes [r, p, a, b, v] representing different interactions are found to have a good relationship with the aqueous equilibrium concentrations of compounds. This modified model can successfully interpret the relative contribution of each interaction at a given concentration and reliably predict sorption of various chemicals on CNTs. This approach is expected to help develop a better environmental fate and risk assessment model.

Model-based exploration of the drivers of mountain cold-trapping in soil

A pollutant is said to undergo mountain cold-trapping if it is found at higher concentrations in a surface medium (soil, snow, foliage) high on a mountain, where it is colder, than in the same medium lower on the mountain. The processes that lead to mountain cold-trapping in soil were explored for a set of hypothetical Perfectly Persistent Pollutants (PPPs) by varying several environmental parameters in a fugacity based fate and transport box model. These parameters were: the spatial scale of the mountain; the rate and location of rain; the amount of particles in the atmosphere; the presence and magnitude of the upslope temperature gradient. The relative potential of each hypothetical PPP to exhibit mountain cold-trapping was expressed in terms of its Mountaintop Contamination Potential (MCP). The PPPs with the highest MCPs were those that neither were deposited from the atmosphere to the surface in the lower zones in the model nor left the model domain without being deposited at all. The simulations revealed that under most conditions wet-gaseous deposition is the biggest driver of mountain cold-trapping in soils, and its effects are greatly enhanced by large negative temperature gradients and increased precipitation upslope. Dry-gaseous and wet-and-dry-particle deposition processes cause similar PPPs to exhibit mountain cold-trapping, and the contributions to MCP by the dry processes are of the same magnitude as wet-particle deposition. Dry gaseous deposition alone is insufficient to cause mountain cold-trapping in soils under any conditions modelled here. Those measuring organic contaminants in mountains should expect to find that mountains with different climates cold-trap different pollutants, and that some mountains may not exhibit upslope enrichment of any species.

Predicting hexadecane-air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data

A group contribution-based quantitative structure-property relationship (QSPR) for the hexadecane-air equilibrium partition coefficients (L) of organic chemicals is developed using the iterative fragment selection (IFS) approach. This new QSPR includes in its training and external validation data sets L values for a large number of structurally complex chemicals measured by the same group using consistent methods. The resulting QSPR has better predictive power than other prediction methods trained primarily using data for chemicals of simpler structures, and measurements of L values from diverse sources. For a subset of chemicals in which the L values have non-additive effects caused by intramolecular hydrogen bonds, the new QSPR gives much better performance in comparison to the most commonly used prediction method.

Prediction and experimental evaluation of soil sorption by natural hormones and hormone mimics

Surface runoff from manure-fertilized fields is a significant source of endocrine-disrupting compounds (EDCs) in the environment. Sorption by soils may play a major role in the environmental fate of manure-borne EDCs, including 17α- and 17β-estradiol (17α-E2 and 17β-E2), estrone (E1), melengestrol acetate (MGA), 17α- and 17β-trenbolone (17α-TB and 17β-TB), trendione (TND), and zeranol (α-ZAL). As a measure of sorption behavior, the organic carbon-normalized partition coefficients (K(OC)) of 17β-E2, E1, MGA, and α-ZAL were experimentally determined for three agricultural soils with initial EDC concentrations spanning from ∼0.01 to >1 μM. Sorption isotherms were linear for most solute-soil combinations. Measured K(OC) values were compared to those predicted using a suite of single-parameter and polyparameter linear free energy relationships (sp- and pp-LFERs). Sp-LFER models were based on experimentally determined octanol-water partition coefficients (K(OW)), whereas pp-LFER solute descriptors were calculated indirectly from experimentally determined solvent-water partition coefficients or the program ABSOLV. Log K(OC) predictions by sp-LFERs were closest to the experimentally determined values, whereas pp-LFER predictions varied considerably due to uncertainties in both solute and sorbent descriptors determined by ABSOLV or estimates using the partition coefficient approach.

On the replacement of empirical parameters in multimedia mass balance models with QSPR data

Official OECD recommendations give the highest priority to application of purely empirical phys/chem data (partition coefficients, environmental half-live times etc.) in multimedia mass balance modeling of environmental overall persistence and long-range transport for potentially hazardous chemicals. We have demonstrated that the replacement of the empirical data with those predicted by employing Quantitative Structure-Property Relationships (QSPR) technique did not significantly decrease the performance of The Tool 2.0--the OECD multimedia mass balance model. To prove this, we compared each other the output results (overall persistence -P(OV); characteristic traveling distance -CTD and transport efficiency TE) obtained from 6 of multimedia models. The models utilized combinations of experimentally determined and QSPR-predicted values of the partition coefficients and half-live times. For predicting phys/chem data, we utilized 2 QSPRs developed in our laboratory and the EPI Suite package (US EPA). We did not observe any statistically significant (p<0.05) differences between the models. This conclusion is important, because it leads to reducing time and costs of laboratory studies required during the risk assessment procedure. Moreover, regarding the obtained results, we proposed to replace the single-threshold approaches established by majority of international regulations to screen substances for persistence, bioaccumulation and long-range transport potential with the approaches taking into account uncertainty of the results and/or probability of passing a given threshold.

Chemical activity as an integrating concept in environmental assessment and management of contaminants

It is suggested that chemical activity in environmental media can serve as an integrating concept for holistic evaluations of contaminants, including their fate and effects. In support of this assertion, information underlying the thermodynamic principles and the relationships between monitored and modeled concentrations and activities are presented. The toxicological significance of activity is discussed, with emphasis on substances that exert baseline narcosis. Illustrations are given of the application of activity using models and monitoring data for chemical risk assessment and management. It is argued that the proximity of prevailing multimedia environmental activities to activities causing toxic effects is a particularly insightful metric of environmental contamination for both narcotics and reactive toxic substances.

Theoretical and experimental simulation of the fate of semifluorinated n-alkanes during snowmelt

Semifluorinated n-alkanes (SFAs) are highly fluorinated anthropogenic chemicals that are released into the environment through their use in ski waxes. Nothing is known about their environmental partitioning in general and their fate during snowmelt in particular. Properties were estimated for a range of SFAs with different chain lengths and degrees of fluorination using the SPARC calculator and poly parameter linear free energy relationships (ppLFERs). The calculations resulted in very low water solubility and vapor pressures and, consequently, high log KOW and log KOA values. Artificially produced snow in a cold room was spiked with a range of SFAs and subsequently melted with infrared lamps. Melt water, particles, and air samples taken during melting were analyzed. Both calculations and experiments showed that SFAs used in ski waxes will bind to particles or snow grain surfaces during snowmelt and thus are predicted to end up on the soil surface in skiing areas.

Assessment of chemical screening outcomes based on different partitioning property estimation methods

Screening is widely used to prioritize chemicals according to their potential environmental hazard, as expressed in the attributes of persistence, bioaccumulation (B), toxicity and long range transport potential (LRTP). Many screening approaches for B and LRTP rely on the categorization of chemicals based on a comparison of their equilibrium partition coefficients between octanol and water (K(OW)), air and water (K(AW)) and octanol and air (K(OA)) with a threshold value. As experimental values of the properties are mostly unavailable for the large number of chemicals being screened, the use of quantitative structure-property relationships (QSPRs) and other computational chemistry methods becomes indispensable. Predictions by different methods often deviate considerably, and flawed predictions may lead to false positive/negative categorizations. We predicted the partitioning properties of 529 chemicals, culled from previous prioritization efforts, using the four prediction methods EPI Suite, SPARC, COSMOtherm, and ABSOLV. The four sets of predictions were used to screen the chemicals against various LRTP and B criteria. Screening results based on the four methods were consistent for only approximately 70% of the chemicals. To further assess whether the means of estimating environmental phase partitioning has an impact, a subset of 110 chemicals was screened for elevated arctic contamination potential based on single-parameter and poly-parameter linear free energy relationships respectively. Different categorizations were observed for 5 out of 110 chemicals. Screening and categorization methods that rely on a decision whether a chemical's predicted property falls on either side of a threshold are likely to lead to a significant number of false positive/negative outcomes. We therefore suggest that screening should rather be based on numerical hazard or risk estimates that acknowledge and explicitly take into account the uncertainties of predicted properties.