Experimental determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for pesticides and other contaminants: new measurements and recommendations

Improved prediction of PFAS partitioning with PPLFERs and QSPRs

Per- and polyfluoroalkyl substances (PFAS) are chemicals of high concern and are undergoing hazard and risk assessment worldwide. Reliable physicochemical property (PCP) data are fundamental to assessments. However, experimental PCP data for PFAS are limited and property prediction tools such as quantitative structure-property relationships (QSPRs) therefore have poor predictive power for PFAS. New experimental data from Endo 2023 are used to improve QSPRs for predicting poly-parameter linear free energy relationship (PPLFER) descriptors for calculating water solubility (SW), vapor pressure (VP) and the octanol-water (KOW), octanol-air (KOA) and air-water (KAW) partition ratios. The new experimental data are only for neutral PFAS, and the QSPRs are only applicable to neutral chemicals. A key PPLFER descriptor for PFAS is the molar volume and this work compares different versions and makes recommendations for obtaining the best PCP predictions. The new models are included in the freely available IFSQSAR package (version 1.1.1), and property predictions are compared to those from the previous IFSQSAR (version 1.1.0) and from QSPRs in the US EPA's EPI Suite (version 4.11) and OPERA (version 2.9) models. The results from the new IFSQSAR models show improvements for predicting PFAS PCPs. The root mean squared error (RMSE) for predicting log KOWversus expected values from quantum chemical calculations was reduced by approximately 1 log unit whereas the RMSE for predicting log KAW and log KOA was reduced by 0.2 log units. IFSQSAR v.1.1.1 has an RMSE one or more log units lower than predictions from OPERA and EPI Suite when compared to expected values of log KOW, log KAW and log KOA for PFAS, except for EPI Suite predictions for log KOW which have a comparable RMSE. Recommendations for future experimental work for PPLFER descriptors for PFAS and future research to improve PCP predictions for PFAS are presented.

Guidelines for descriptor assignments for the solvation parameter model by separation techniques

The solvation parameter model uses six compound descriptors to model equilibrium properties in biphasic systems formally defined as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L. The V descriptor can be assigned from structure and the E descriptor for compounds liquid at 20 °C can be calculated from its refractive index and characteristic volume. The E descriptor for compounds solid at 20 °C and the S, A, B, and L descriptors are assigned from experimental properties traditionally obtained by chromatographic, liquid-liquid partition, and solubility measurements. Here I report an efficient experimental design using the Solver method for the accurate assignment of descriptors for neutral compounds that simultaneously minimizes laboratory resources. This multi-technique approach requires 3 retention factor measurements in a 60 °C temperature range per compound on four columns by gas chromatography, 3 retention factor measurements in a 30 % (v/v) acetonitrile composition range per compound on two columns by reversed-phase liquid chromatography, and eight partition constant measurements by liquid-liquid partition in totally organic and aqueous biphasic systems for a total of 26 experimental measurements. The accuracy of the descriptor assignments was validated by comparison with the values in the Wayne State University (WSU) descriptor database taken as the best estimate of the true descriptor values. The E, S, A, B and L descriptors were assigned simultaneously by the Solver method using the above approach without significant bias and with an average absolute deviation (AAD) of 0.054, 0.018, 0.015, 0.013, and 0.040, respectively, compared with the WSU database values, corresponding to a relative absolute average deviation in percent (RAAD) of 7.2, 1.9, 3.6, 5.1, and 0.84 %, respectively, for 32 varied compounds. This streamlined approach represents a significant improvement on earlier single-technique approaches used as the starting point for the development of the multi-technique approach. For compounds of variable hydrogen-bond basicity modifications to the multi-technique approach were implemented while maintaining the same number of experimental measurements. Acceptable descriptor assignments for B/B° were obtained for compounds liquid at 20 °C for which the E descriptor was available by calculation. For solid compounds at 20 °C the E and B/B° descriptors are restricted to qualitative application where approximate values may be acceptable.

Evaluation of the Goss-modified solvation parameter model for the characterization of biphasic systems and descriptor assignments

The solvation parameter model uses six descriptors identified as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L to model the distribution of neutral compounds in biphasic systems. Abraham's version of this model uses all six descriptors with two separate linear free energy relationship models for the transfer of compounds from a gas phase to a condensed phase and between condensed phases. Goss proposed a modification to this model that uses a single calibration model regardless of the physical state for each phase and five of the descriptors employed in Abraham's model (E descriptor is eliminated). The capability of Abraham's model and the Goss-modified model to characterize the contribution of intermolecular interaction to retention for gas and reversed-phase liquid chromatographic systems and distribution in liquid-liquid partition systems is evaluated using the WSU compound descriptor database. These more accurate values for the Abraham descriptors have not been utilized previously for the evaluation of the Goss-modified model and should be more capable of discerning subtle differences in model performance. It is shown that model quality defined by statistical parameters favors Abraham's model over the Goss-modified model with differences in model quality greater for systems in which Abraham's model indicates a significant contribution from electron lone pair interactions and for systems in which one phase is a solvent containing perfluoroalkyl substituents. There is a small systematic difference for the terms describing the combined contributions of cavity formation and dispersion interactions and for interactions of a dipole-type. The contribution of hydrogen-bonding interactions is virtually identical for the two models. The model intercepts are generally different and potentially assigned to a larger contribution from lack-of-fit for the Goss-modified model. Although the Abraham model descriptors have been routinely employed for applications using the Goss-modified model the possibility that Goss-model specific descriptors should be employed was evaluated. Using the Solver method and Goss-model specific calibration models for chromatographic and liquid-liquid partition systems a new set of Goss-specific descriptors was calculated for 28 varied compounds. These descriptors show good statistical agreement with the Abraham descriptor values with an average deviation of 0.009, -0.003, -0.004, and -0.023, respectively, for the S, A, B, and L descriptors, corresponding to a relative absolute deviation in percent of 2.2 %, 3.9 %, 4.3 %, and 1.2 %, respectively.

Two-phase system model to predict hydrophobic organic compound partition to heterogeneous soil dissolved organic matter across China

Soil dissolved organic matter (SDOM) is an important part of the DOM pool in terrestrial systems, influencing the transport and fate of many pollutants. In this study, SDOMs from different regions across China were compared by a series of spectroscopic methods, including UV-vis spectroscopy, fluorescence spectroscopy, and Fourier transform infrared (FTIR) spectroscopy, and the hydrophobicity was quantified by partition coefficients of SDOM in the aqueous two-phase system (KATPS). The molecular weight, aromaticity, and hydrophobicity of SDOM from different regions exhibited strong heterogeneity, KATPS combined with UV-vis and fluorescence indices can be readily used for differentiating heterogeneous SDOM, and SDOMs were compositionally and structurally different from DOMs in aquatic systems based on spectral characterization. Importantly, the two-phase system (TPS) model has only been validated by DOMs in freshwater systems, and good organic carbon‒water partition coefficient (KOC) predictive power (RMSE = 0.11) could be provided by the TPS model when applied to heterogeneous SDOM without calibration, showing its broad applicability. Our results demonstrate the applicability of the TPS model for predicting the sorption behavior of terrestrial DOM, broadening the application scope of the TPS model and indicating its potential as a routine model for the risk assessment of hydrophobic organic compounds (HOCs) in organic contaminated sites.

Polyparameter Linear Free Energy Relationships for Partitioning of Neutral Organic Compounds to Storage Lipids

Storage lipids are an important compartment in the bioaccumulation of neutral organic compounds. Reliable models for predicting storage lipid-water and storage lipid-air partition coefficients (Kislip/w and Kislip/a), as well as their temperature dependence, are considered useful. Polyparameter linear free energy relationships (PP-LFERs) are accurate, general, and mechanistically clear models for predicting partitioning-related physicochemical quantities. About a decade ago, PP-LFERs were calibrated for Kislip/w at the physiological temperature of 37 °C. However, to date, a comprehensive collection and sufficiently reliable PP-LFERs for Kislip/w and Kislip/a at the most common standard temperature of 25 °C are still lacking. In this study, experimentally based Kislip/w and/or Kislip/a values at 25 °C for 278 compounds were extensively collected or converted from the literature. Subsequently, PP-LFERs were calibrated for Kislip/w and Kislip/a at 25 °C, performing well over 10 orders of magnitude with root-mean-square errors of 0.17-0.21 log units for compounds with reliable descriptors. Furthermore, standard internal energy changes of transfer from water or air to storage lipids for 158 compounds were derived and used to calibrate PP-LFERs for estimating the temperature dependence of Kislip/w or Kislip/a. Additionally, using PP-LFERs, low-density polyethylene was confirmed to be a better storage lipid analogue than silicone and polyoxymethylene in the equilibrium passive sampling of nonpolar and H-bond acceptor polar compounds.

Assessment of liquid-liquid partition for the assignment of descriptors for the solvation parameter model

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and McGowan's characteristic volume, V, to model transfer properties between condensed phases. The V descriptor is assigned from structure. For compounds liquid at 20 °C the E descriptor can be assigned from the characteristic volume and its refractive index. The E descriptor for compounds solid at 20 °C and the S, A, and B descriptors are experimental properties traditionally assigned from chromatographic, liquid-liquid partition, and solubility measurements. In this report liquid-liquid partition constants in totally organic and aqueous biphasic systems are evaluated as a standalone technique for descriptor assignments. Using six totally organic biphasic systems the S, A, and B descriptors were assigned with an average absolute deviation (AAD) of about 0.04, 0.03, and 0.04, respectively, compared with the best estimate of the true descriptor values for 65 compounds. The E descriptor for compounds solid at 20 °C can only be estimated with an AAD of approximately 0.1. For six aqueous biphasic systems the B descriptor is assigned with a lower AAD of 0.028 and higher AAD of 0.08 and 0.05 for the S and A descriptors, respectively, than for the totally organic biphasic systems for compounds with a reliable value for the E descriptor. The preferred system for descriptor assignments utilizes both totally organic biphasic systems (heptane-1,1,1-trifluoroethanol, isopentyl ether-propylene carbonate, isopentyl ether-ethanolamine, heptane-ethylene glycol, heptane-formamide, and 1,2-dichloroethane-ethylene glycol) and aqueous biphasic systems (octanol-water, cyclohexane-water) with the possible substitution of some systems with alternative systems of similar selectivity. For 55 varied compounds this combination of eight organic and aqueous biphasic systems resulted in an AAD of approximately 0.03, 0.02, and 0.02 for the S, A, and B descriptors compared to the best estimate of the true descriptor value. For 30 compounds solid at 20 °C the AAD for the E descriptor of 0.11 is poorly assigned. The relative average absolute deviation in percent (RAAD) corresponds to 9.7 %, 3.1 %. 4.0 % and 8.3 % for E, S, A, and B, respectively, for the eight biphasic systems. Liquid-liquid partition is compared to reversed-phase liquid and gas chromatography as a standalone technique for descriptor assignments.

Photo-assisted transformation of furosemide: Exploring transformation pathways, structure database and suspect and non-target workflows for comprehensive screening of unknown transformation products in wastewaters and landfill leachates

In recent years, transformation products-(TPs) of pharmaceuticals in the environment have received considerable attention. In this context, here, a customized overview of transformation of Furosemide-(FRS) in aqueous matrices treated by photo-oxidation is provided as a proof of concept. Hence, the primary goal of the study was to display an integrated strategy by combining the target (parent-molecule) and suspect screening-(SS) approaches (TPs) in order to build an in-house High-Resolution mass spectrometry (HRMS) database able to provide reference information (chromatographic/spectral) for environmental investigations in complex matrices (wastewaters/landfill leachates). Data analysis was performed by optimizing a SS workflow. Additional confirmation for the proposed structural elucidation was provided by correlating retention time to the proposed structure employing three prediction models. This approach was applied for the tentative identification of 35 TPs of FRS, 28 of which are reported herein for the first time. Finally, SS and non-target analysis (NTA) have been successfully applied for retrospective screening of FRS and its TPs in real samples. The findings demonstrated that SS allows the proper identification of TPs of FRS in complex matrices proving its outstanding importance compared to NTA. In total, six TPs were identified by SS with potential ecotoxicological implications for two of them according to in silico risk assessment.

Chlorpyrifos fate in the Arctic: Importance of analyte structure in interactions with Arctic dissolved organic matter

The insecticide and current use pesticide chlorpyrifos (CLP) is transported via global distillation to the Arctic where it may pose a threat to this ecosystem. CLP is readily detected in Arctic environmental compartments, but current research has not studied its partitioning between water and dissolved organic matter (DOM) nor the role of photochemistry in CLP's fate in aquatic systems. Here, the partition coefficients of CLP were quantified with various types of DOM isolated from the Arctic and an International Humic Substances Society (IHSS) reference material Suwannee River natural organic matter (SRNOM). While CLP readily partitions to DOM, CLP exhibits a significantly higher binding constant with Arctic lacustrine DOM relative to fluvial DOM or SRNOM. The experimental partitioning coefficients (KDOC) were compared to a calculated value estimated using poly parameter linear free energy relationship (pp-LFER) and was found to be in good agreement with SRNOM, but none of the Arctic DOMs. We found that Arctic KDOC values decrease with increasing SUVA254, but no correlations were observed for the other DOM compositional parameters. DOM also mediates the photodegradation of CLP, with stark differences in photo-kinetics using Arctic DOM isolated over time and space. This work highlights the chemo-diversity of Arctic DOM relative to IHSS reference materials and highlights the need for in-depth characterization of DOM that transcends the current paradigm based upon terrestrial and microbial precursors.

Recalibrating polyparameter linear free energy relationships and reanalyzing mechanisms for partition of nonionic organic compounds to low-density polyethylene passive sampler

Equilibrium passive sampling techniques based on the low-density polyethylene (LDPE) film are increasingly used for determining the concentration of contaminants in water and air. Reliable models capable of predicting LDPE-water and LDPE-air partition coefficients (K_iLDPEw and K_iLDPEa) would be very useful. In previous studies, polyparameter linear free energy relationships (PP-LFERs) based on Abraham's solute descriptors were calibrated for LDPE-water and LDPE-air systems. Unfortunately, a portion of unreliable partition coefficients and solute descriptors were included in the calibration sets of these previous studies, leading to unexpected system parameters and predictive performance in the regression results. In this study, more reliable PP-LFERs were recalibrated for LDPE-water and LDPE-air systems (20‒25 °C) using carefully collected reliable partition coefficients and solute descriptors of various polar and nonpolar compounds (over one hundred and with low redundancy) from the literature, as well as the robust regression method. The PP-LFERs performed well with root-mean-square errors of 0.15-0.25 log units and successfully predicted K_iLDPEw and K_iLDPEa values spanning over 10 orders of magnitude for compounds with reliable descriptors. The partitioning mechanisms of compounds to LDPE were also reanalyzed and compared in detail with n-alkanes (C₆-C₁₆). Generally, LDPE is more prone to form dispersion interactions with solutes than n-alkanes, while it is more difficult to form cavities in LDPE. In addition, the crystallinity of LDPE is not the sole reason for the distinct constant terms presenting in PP-LFERs for LDPE-water and n-hexadecane-water systems.

Exploring controls on perfluorocarboxylic acid (PFCA) gas-particle partitioning using a model with observational constraints

The atmospheric fate of perfluorocarboxylic acids (PFCAs) has attracted much attention in recent decades due to the role of the atmosphere in global transport of these persistent chemicals. There is a gap in our understanding of gas-particle partitioning, limited by availability of reliable atmospheric measurements, partitioning properties, and models of gas-particle interactions. The gas-particle equilibrium phase partitioning of C2-C16 PFCAs in the atmosphere were modeled here by taking account of both deprotonation and phase partitioning equilibria among air, aerosol liquid water, and particulate water-insoluble organic matter using a range of available PFCA partitioning properties. We systematically varied water and organic matter content to simulate the full range of atmospheric conditions. Except in severe organic matter pollution episodes, shorter-chain PFCAs are predicted to mainly partition between air and aqueous phase, while for PFCAs with carbon chains longer than 12, organic matter is more likely to be the dominant particle phase reservoir. The model framework underestimated the particle fraction of C2-C8 PFCAs compared with several ambient observations, with larger discrepancies observed for longer-chain PFCAs. The discrepancy could result from externally mixed dust components, non-ideality of aerosol liquid water, surfactant descriptions at phase boundaries, and missed interactions between organic matter and charged PFCA molecules. Reliable measurements of ambient PFCAs with high time resolution and the measurement of uptake parameters by particle-relevant components will be beneficial to more reliable environmental fate modeling of ambient PFCAs.

A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms

The liposome/water partition coefficient (K_lip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the K_lip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict K_lip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the K_lip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds

Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

In silico package models for deriving values of solute parameters in linear solvation energy relationships

Environmental partitioning influences fate, exposure and ecological risks of chemicals. Linear solvation energy relationship (LSER) models may serve as efficient tools for estimating environmental partitioning parameter values that are commonly deficient for many chemicals. Nonetheless, scarcities of empirical solute parameter values of LSER models restricted the application. This study developed and evaluated in silico methods and models to derive the values, in which excess molar refraction, molar volume and logarithm of hexadecane/air partition coefficient were computed from density functional theory; dipolarity/polarizability parameter, solute H-bond acidity and basicity parameters were predicted by quantitative structure-activity relationship models developed with theoretical molecular descriptors. New LSER models on four physicochemical properties relevant with environmental partitioning (n-octanol/water partition coefficients, n-octanol/air partition coefficients, water solubilities, sub-cooled liquid vapour pressures) were constructed using the in silico solute parameter values, which exhibited comparable performance with conventional LSER models using the empirical solute parameter values. The package models for deriving the LSER solute parameter values, with advantages that they are free of instrumental determinations, may lay the foundation for high-throughput estimating environmental partition parameter values of diverse organic chemicals.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Exploring the role of octanol-water partition coefficient and Henry's law constant in predicting the lipid-water partition coefficients of organic chemicals

Partition coefficients for storage lipid-water (logKlw) and phospholipid-water (logKpw) phases are key parameters to understand the bioaccumulation and toxicity of organic contaminants. However, the published experimental databases of these properties are dwarfs and current estimation approaches are cumbersome. Here, we present partition models that exploit the correlations of logKlw, and of logKpw with the linear combinations of the octanol-water partition coefficient (logKow) and the dimensionless Henry's law constant (air-water partition coefficient, logKaw). The calibrated partition models successfully describe the variations in logKlw data (n = 305, R2 = 0.971, root-mean-square-error (rmse) = 0.375), and in logKpw data (n = 131, R2 = 0.953, rmse = 0.413). With the inputs of logKow and logKaw estimated from the U.S. EPA's EPI Suite, our models of logKlw and logKpw have exhibited rmse = 0.52 with respect to experimental values indicating suitability of these models for inclusion in the EPI Suite. Our models perform similar to or better than the previously reported models such as one parameter partition models, Abraham solvation models, and models based on quantum-chemical calculations. Taken together, our models are robust, easy-to-use, and provide insight into variations of logKlw and logKpw in terms of hydrophobicity and volatility trait of chemicals.

Identifying organic chemicals not subject to bioaccumulation in air-breathing organisms using predicted partitioning and biotransformation properties

Because the respiration processes contributing to the elimination of organic chemicals deviate between air- and water-breathing organisms, existing and widely used procedures for identifying chemicals not subject to bioaccumulation in aquatic organisms based on the octanol-water partition ratio K_OW need to be complemented with similar procedures for organisms respiring air. Here, we propose such a procedure that relies on the comparison of a compound's predicted K_OW , octanol-air partition ratio K_OA , and biotransformation half-life HL_B with three threshold values, below which elimination is judged to be sufficiently rapid to prevent bioaccumulation. The method allows for the consideration of the effect of dissociation on the efficiency of urinary and respiratory elimination. Explicit application of different types of the prediction error, such as the 95% prediction interval or the standard error, allows for variable tolerance for false-negative decisions, that is, the potential to judge a chemical as not bioaccumulative even though it is. A test with a set of more than 1000 diverse organic chemicals confirms the applicability of the prediction methods for a wide range of compounds and the procedure's ability to categorize approximately four-fifth of compounds as being of no bioaccumulation concern, suggesting its usefulness to screen large numbers of commercial chemicals to identify those worthy of further scrutiny. The test also demonstrates that a screening based solely on K_OW and K_OA would be far less effective because the fraction of chemicals that can be judged as sufficiently volatile and/or sufficiently water soluble for rapid respiratory and urinary elimination based on the partitioning properties predicted for their neutral form is relatively small. Future improvements of the proposed procedure depend largely on the development of prediction methods for the biotransformation kinetics in air-breathing organisms and for the potential for renal reabsorption. Integr Environ Assess Manag 2022;18:1297-1312. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Quantum Chemical Calculation and Evaluation of Partition Coefficients for Classical and Emerging Environmentally Relevant Organic Compounds

Octanol/water (K_OW), octanol/air (K_OA), and hexadecane/air (K_HdA) partition coefficients are calculated for 67 organic compounds of environmental concern using computational chemistry. The extended CRENSO workflow applied here includes the calculation of extensive conformer ensembles with semiempirical methods and refinement through density functional theory, taking into account solvation models, especially COSMO-RS, and thermostatistical contributions. This approach is particularly advantageous for describing large and nonrigid molecules. With regard to K_OW and K_HdA, one can refer to many experimental data from direct and indirect measurement methods, and very good matches with results from our quantum chemical workflow are evident. In the case of the K_OA values, however, good matches are only obtained for the experimentally determined values. Larger systematic deviations between data computed here and available, nonexperimental quantitative structure-activity relationship literature data occur in particular for phthalic acid esters and organophosphate esters. From a critical analysis of the coefficients calculated in this work and comparison with available literature data, we conclude that the presented quantum chemical composite approach is the most powerful so far for calculating reliable partition coefficients because all physical contributions to the conformational free energy are considered and the structure ensembles for the two phases are generated independently and consistently.

Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds

Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (K_PE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the K_PE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (K_mic-w) and the LDPE-micelle partition coefficient (K_PE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the K_PE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental K_PE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.

Spatiotemporal Distribution of Hydrophobic Organic Contaminants in Spiked-Sediment Toxicity Tests: Measuring Total and Freely Dissolved Concentrations in Porewater and Overlying Water

The sediment-water interface of spiked-sediment toxicity tests is a complex exposure system, where multiple uptake pathways exist for benthic organisms. The freely dissolved concentration (C_free ) in sediment porewater has been proposed as a relevant exposure metric to hydrophobic organic contaminants (HOCs) in this system. However, C_free has rarely been measured in spiked-sediment toxicity tests. We first developed a direct immersion solid-phase microextraction method for measuring C_free in overlying water and porewater in a sediment test using polydimethylsiloxane-coated glass fibers, resulting in sensitive and repeatable in situ measurements of HOCs. Then, we measured C_free and total dissolved concentrations (C_diss ) in the sediment test systems with the freshwater amphipod Hyalella azteca and thoroughly evaluated the temporal and spatial profiles of four HOCs (phenanthrene, pyrene, benzo[a]pyrene, and chlorpyrifos). Furthermore, we examined the relationship between the measured concentrations and the lethality of H. azteca. We found that the test system was far from an equilibrium state for all four chemicals tested, where C_diss in overlying water changed over the test duration and a vertical C_free gradient existed at the sediment-water interface. In porewater C_diss was larger than C_free by a factor of 170 to 220 for benzo[a]pyrene because of the strong binding to dissolved organic carbon. Comparison of the median lethal concentrations of chlorpyrifos in the sediment test and those in water-only tests indicates that C_free in porewater was the most representative indicator for toxicity of this chemical. The method and findings presented in the present study warrant further research on the chemical transport mechanisms and the actual exposure in sediment tests using different chemicals, sediments, and test species. Environ Toxicol Chem 2021;40:3148-3158. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Automated Coarse-Grained Mapping Algorithm for the Martini Force Field and Benchmarks for Membrane-Water Partitioning

With a view to high-throughput simulations, we present an automated system for mapping and parameterizing organic molecules for use with the coarse-grained Martini force field. The method scales to larger molecules and a broader chemical space than existing schemes. The core of the mapping process is a graph-based analysis of the molecule's bonding network, which has the advantages of being fast, general, and preserving symmetry. The parameterization process pays special attention to coarse-grained beads in aromatic rings. It also includes a method for building efficient and stable frameworks of constraints for molecules with structural rigidity. The performance of the method is tested on a diverse set of 87 neutral organic molecules and the ability of the resulting models to capture octanol-water and membrane-water partition coefficients. In the latter case, we introduce an adaptive method for extracting partition coefficients from free-energy profiles to take into account the interfacial region of the membrane. We also use the models to probe the response of membrane-water partitioning to the cholesterol content of the membrane.

Partitioning between polyurethane foam and the gas phase: data compilation, uncertainty estimation and implications for air sampling

Polyurethane foam (PUF) is frequently applied for sampling semi-volatile organic compounds (SVOCs) in the gas phase. Equilibrium partition coefficients (KPUF/G) often are used to estimate the potential for breakthrough during active air sampling (AAS) and to correct for non-linear uptake during passive air sampling (PAS). KPUF/G is either determined experimentally or estimated, in both cases incurring uncertainties that can be carried over to other parameters. Here, a dataset of 547 measured KPUF/G values and chemical information for 281 distinct chemicals was compiled from the peer reviewed literature. Measured log KPUF/G were compared with predicted values to identify potential bias in data generated with a particular experimental approach. An analysis of the measured data suggests that the uncertainty of unbiased log KPUF/G values is at best 0.2 log units at 15 °C (e.g. for hexachlorobenzene and fluoranthene), but most likely much higher. This implies that inherent passive air sampling rates obtained from the loss of a depuration compound (SR) and breakthrough volumes during AAS can presently not be known with an uncertainty of less than ca. 50%. During short PAS deployment periods, the uncertainty in the effective sampling volume (Vair) derives mainly from the uncertainty in the SR, whereas the uncertainty in KPUF/G of the target compound will become important and even the main source of uncertainty for Vair if deployments are long or target chemicals are relatively volatile. This in turn implies that the uncertainty of Vair cannot be smaller than the uncertainty of SR and KPUF/G and therefore again is at least ca. 50%. We strongly recommend that the uncertainty of air concentrations obtained by non-linear PAS is quantified and reported and we outline a procedure on how to do that. Because the uncertainty in KPUF/G of target and depuration chemicals generally exceeds 30%, it may often be necessary to conduct Monte Carlo simulation.

Relook on the Linear Free Energy Relationships Describing the Partitioning Behavior of Diverse Chemicals for Polyethylene Water Passive Samplers

Over the past 3 decades, low-density polyethylene (PE) passive sampling devices have been widely used to scout organic chemicals in air, water, sediments, and biotic phases. Experimental partition coefficient data, required to calculate the concentrations in environmental compartments, are not widely available. In this study, we developed and rigorously evaluated linear free energy relationships (LFERs) to predict the partition coefficient between the PE and the water phase (log K _pe-w). Poly-parameter (pp) LFERs based on Abraham solute parameters performed better (root-mean-square error, rmse = 0.333-0.350 log unit) in predicting log K _pe-w compared to the two one-parameter (op) LFERs built on n-hexadecane-water and octanol-water partition coefficients (rmse = 0.41-0.42 log unit), indicating that one parameter is not able to account for all types of interactions experienced by a chemical during PE-water exchange. Dimensionality analyses show that the calibration dataset used to train pp-LFERs fulfills all the requirements to obtain a robust model for log K _pe-w. Van der Waals interactions of the molecule tend to favor the PE phase, and polar interactions of the molecule favor the water phase. The PE phase is the most sensitive to polarizable chemicals compared to other commonly used passive sampling polymeric phases such as polydimethylsiloxane, polyoxymethylene, and polyacrylate. For op-LFERs, the PE phase is better represented by the hexadecane phase than by the octanol phase. A computational method based on the conductor-like screening model for real solvents theory did good job in estimating log K _pe-w for chemicals that were neither very hydrophobic nor very hydrophilic in nature. Our models can be used to reliably predict the log K _pe-w values of simple neutral organic chemicals. This study provides insights into the partitioning behavior of PE samplers compared to other commonly used passive samplers.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Two-Phase System Model to Assess Hydrophobic Organic Compound Sorption to Dissolved Organic Matter

The equilibrium partition of organic compounds to dissolved organic matter (DOM) is an essential process that affects their environmental risks. Traditional models cannot accurately assess this process as the variability of DOM is not properly accounted for. The two-phase system (TPS) model was developed with the consideration of the variability that stems from both organic compounds and DOM. In this study, we examined the applicability of the TPS model for the prediction of the organic carbon-water partition coefficient (K_OC) of hydrophobic organic compound (HOC) sorption to aquatic and sediment DOM using a diverse set of 17 organic compounds and 53 DOM samples. The TPS model showed good predictive power (RMSE < 0.20) without calibration, outperforming currently used linear free energy relationship models (RMSE > 0.28). The significance of DOM properties in the sorption behavior was quantitatively analyzed based on the TPS model. The spatial pattern of K_OC for HOC sorption to aquatic DOM in Lake Taihu, the third largest freshwater lake in China, was assessed using the TPS model. Our results suggest that the TPS model has great potential to facilitate the routine assessment of the partition behavior of HOCs in aquatic systems for environmental risk assessment.

Passive air sampling for semi-volatile organic chemicals

During passive air sampling, the amount of a chemical taken up in a sorbent from the air without the help of a pump is quantified and converted into an air concentration. In an equilibrium sampler, this conversion requires a thermodynamic parameter, the equilibrium sorption coefficient between gas-phase and sorbent. In a kinetic sampler, a time-averaged air concentration is obtained using a sampling rate, which is a kinetic parameter. Design requirements for kinetic and equilibrium sampling conflict with each other. The volatility of semi-volatile organic compounds (SVOCs) varies over five orders of magnitude, which implies that passive air samplers are inevitably kinetic samplers for less volatile SVOCs and equilibrium samplers for more volatile SVOCs. Therefore, most currently used passive sampler designs for SVOCs are a compromise that requires the consideration of both a thermodynamic and a kinetic parameter. Their quantitative interpretation depends on assumptions that are rarely fulfilled, and on input parameters, that are often only known with high uncertainty. Kinetic passive air sampling for SVOCs is also challenging because their typically very low atmospheric concentrations necessitate relatively high sampling rates that can only be achieved without the use of diffusive barriers. This in turn renders sampling rates dependent on wind conditions and therefore highly variable. Despite the overall high uncertainty arising from these challenges, passive air samplers for SVOCs have valuable roles to play in recording (i) spatial concentration variability at scales ranging from a few centimeters to tens of thousands of kilometers, (ii) long-term trends, (iii) air contamination in remote and inaccessible locations and (iv) indoor inhalation exposure. Going forward, thermal desorption of sorbents may lower the detection limits for some SVOCs to an extent that the use of diffusive barriers in the kinetic sampling of SVOCs becomes feasible, which is a prerequisite to decreasing the uncertainty of sampling rates. If the thermally stable sorbent additionally has a high sorptive capacity, it may be possible to design true kinetic samplers for most SVOCs. In the meantime, the passive air sampling community would benefit from being more transparent by rigorously quantifying and explicitly reporting uncertainty.

Inclusion of molecular descriptors in predictive models improves pesticide soil-air partitioning estimates

The soil-air exchange of pesticides is one potential fate and exposure pathways, and this process is generally thought to be governed by soil properties and environmental conditions. The experimental determination of soil-air partitioning coefficient (Ksa) is laborious and costly and typically, Ksa's are predicted from a semiempirical or a simple linear regression approach with soil and environmental variables. Here we developed a model that combined linear regression of soil, environmental and molecular parameters with the quantitative structural-property relationship (QSPR) to predict Ksa for pesticides. The values of theoretical descriptors of pesticides were calculated and the best descriptors selected using the Boruta Algorithm. Seventy-six experimental logKsa values for 17 pesticides were used in model development. Multiple linear regression (MLR) with a soil (organic carbon fraction), physicochemical (octanol-air partitioning coefficient), environmental (temperature and humidity) and molecular descriptor (Gmin, a 2D E-state molecular parameter), called as MLR-QSPR combined model exhibited better predictability (adj. r² = 0.95) of logKsa compared to MLR (adj. r² = 0.87) or QSPR (adj. r² = 0.82) itself. MLR-QSPR also showed the best performance in five-fold cross-validation (adj. r² = 0.94) and test set verification (adj. r² = 0.96). The developed model was validated and characterized by the applicability domain. Results showed that the proposed MLR-QSPR approach is highly predictive and statistically robust with >95% of predictions within ±0.5 log unit of the measured Ksa. Therefore, this approach can be used in estimating the soil-air partitioning of pesticides to better predict it's fate and transport in environments.

Machine learning-based prediction of toxicity of organic compounds towards fathead minnow

Predicting the acute toxicity of a large dataset of diverse chemicals against fathead minnows (Pimephales promelas) is challenging. In this paper, 963 organic compounds with acute toxicity towards fathead minnows were split into a training set (482 compounds) and a test set (481 compounds) with an approximate ratio of 1 : 1. Only six molecular descriptors were used to establish the quantitative structure–activity/toxicity relationship (QSAR/QSTR) model for 96 hour pLC₅₀ through a support vector machine (SVM) along with genetic algorithm. The optimal SVM model (R² = 0.756) was verified using both internal (leave-one-out cross-validation) and external validations. The validation results (q_int² = 0.699 and q_ext² = 0.744) were satisfactory in predicting acute toxicity in fathead minnows compared with other models reported in the literature, although our SVM model has only six molecular descriptors and a large data set for the test set consisting of 481 compounds.

A quantitative structure–toxicity relationship of 963 chemicals against fathead minnow was developed by using support vector machine and genetic algorithm.

Development of predictive models for silicone rubber-water partition coefficients of hydrophobic organic contaminants

The silicone rubber passive sampling technique is extensively applied to monitor the aqueous freely dissolved concentration of hydrophobic organic compounds (HOCs). The silicone rubber-water partition coefficient (Ksrw) is an important parameter to accurately measure the concentrations of chemicals using passive sampling devices. In this study, two theoretical linear solvation energy relationship (TLSER) models and a quantitative structure-property relationship (QSPR) model were developed for predicting the Ksrw of HOCs. The 119 model compounds studied here included 31 personal care products, such as musks, UV-filters, and organophosphate flame retardants, as well as "conventional" pollutants, such as polycyclic aromatic hydrocarbons and polychlorinated biphenyls. The statistical parameters indicated that the final QSPR model with seven descriptors for all 119 chemicals had a satisfactory goodness-of-fit (Radj2 = 0.898), robustness (QLOO2 = 0.881) and predictive ability (Qext-F1,2,32 = 0.897-0.926). In comparison, the results of one TLSER model with four descriptors for 113 chemicals (Radj2 = 0.826, QLOO2 = 0.790, Qext-F1,2,32 = 0.805-0.837) and another TLSER model with one descriptor for 5 chemicals (Radj2 = 0.747, QLOO2 = 0.647) were also acceptable. The applicability domains of the obtained models covered chemicals containing hydroxyl, imino groups, carbonyl groups, ether bonds, halogen atoms, sulfur atoms, phosphorus atoms, nitro groups, and cyano groups. In addition, the structural features governing the partition behavior of chemicals between silicone rubber and water were explored through interpretation of appropriate mechanisms.

Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models

Obtaining accurate measurements of the partition coefficients between sorbent materials and water is of major importance for the analysis of the adsorption behavior and dissolved concentrations of organic compounds in the environment. In the passive-sampling approach, polydimethylsiloxane (PDMS) has a wide range of applications. Therefore, we established a poly-parameter linear-free energy relationship (pp-LFER) and a quantitative structure-activity relationship (QSAR) model to predict the log K_PDMS-w values for a large dataset of 290 organic chemicals from 11 diverse classes. For the pp-LFER model, E (excess molar refractivity), A (molecular H-bond donor ability), V (McGowan volume), and B (the H-bond acceptor properties) were introduced as the main correlated variables. However, the obtained model is much limited in terms of acquiring available descriptors. For this reason, we developed a QSAR model, and CrippenLogP (Crippen octanol-water partition coefficient), RNCG (Relative negative charge-most negative charge/total negative charge), ATSC4e (Centered Broto-Moreau autocorrelation-lag4/weighted by Sanderson electronegativities) and GATS6p (Geary autocorrelation-lag6/weighted by polarizabilities) were selected as the significant parameters. The predictive power and functional reliability of the presented models were confirmed with validation methods as described in previous studies. The adjusted determination coefficients (R²_adj) of 0.851 and 0.922 and leave-one-out cross-validated (Q²_LOO) of 0.841 and 0.907 revealed that the models have good predictive power and generalizability. Thus, the proposed models are simple yet accurate tools for predicting the log K_PDMS-w values and providing new insights to further understand the adsorption mechanism of organic compounds.

Metabolic profiling of Daphnia magna exposure to a mixture of hydrophobic organic contaminants in the presence of dissolved organic matter

The hydrophobic organic contaminants triclosan, triphenyl phosphate (TPhP) and diazinon sorb to dissolved organic matter (DOM) and this may alter their bioavailability and toxicity. ¹H nuclear magnetic resonance (NMR)-based metabolomics was used to investigate how DOM at 1 and 5 mg organic carbon/L may alter the metabolome of Daphnia magna from exposure to equitoxic mixtures of triclosan, TPhP and diazinon. These contaminants have different modes of action toward D. magna. The contaminant concentrations in each mixture were an equal percentage of their lethal concentration to 50% of the population (LC₅₀) values, which equates to 1250 μg/L TPhP, 330 μg/L triclosan and 0.9 μg/L diazinon. The ternary mixture exposure at 1% LC₅₀ values did not alter the D. magna metabolome. Contaminant mixture exposures at 5%, 10%, and 15% LC₅₀ values decreased glucose, serine and glycine concentrations and increased asparagine and threonine concentrations, suggesting disruptions in energy metabolism. The contaminant mixture had a unique mode of action in D. magna and DOM at 1 and 5 mg organic carbon/L did not change this mode of action. The estimated sorption of triclosan, TPhP or diazinon to DOM at 1 or 5 mg organic carbon/L in this experimental design was calculated to be <50% for each contaminant. This suggests that the mode of action of the contaminant mixture was not altered by DOM because the two environmentally relevant concentrations of DOM may have not substantially altered contaminant bioavailability. Our results indicate that DOM may not inevitably mitigate or alter the sub-lethal toxicity of a mixture of hydrophobic organic contaminants. This indicates the complexity of predicting the molecular-level toxicity of environmental mixtures. For adequate risk assessment of freshwater ecosystems, it is vital to account for the combined sub-lethal toxicity of an environmental mixture of contaminants.

Prediction of Apolar Compound Sorption to Aquatic Natural Organic Matter Accounting for Natural Organic Matter Hydrophobicity Using Aqueous Two-Phase Systems

The equilibrium partitioning of organic compounds to natural organic matter (NOM) plays a key role in their environmental fate as well as bioavailability. In this study, a prediction model for organic compound sorption to NOM was theoretically derived based on two-phase systems. In this model, the hydrophobicity of NOM was scaled by their partition coefficients in an aqueous two-phase system (K_ATPS) and that of organics was scaled by their octanol-water partition coefficients (K_OW). The model uses only K_ATPS and K_OW as variables. Coefficients in the model were determined using a data set including the organic carbon-water partition coefficient (K_OC) of four polycyclic aromatic hydrocarbons (PAHs) sorption to 10 NOM samples collected from surface waters. The resulting model was validated using additional NOM samples and reference NOM, which suggested good prediction power for PAH sorption to aquatic NOM. The model performance was compared with commonly used linear free energy relationship models, and its applicability was discussed. Sorption behavior unexpected by this model is attributed to additional sorption mechanisms other than partitioning. Overall, this approach allows prediction of K_OC for apolar organic compound sorption to aquatic NOM simply using their respective partition coefficients in two-phase systems based on a specific model.

Distribution characteristics of poly-brominated diphenyl ethers between water and dissolved organic carbon from anaerobic digestate: Effects of digestion conditions

It is becoming increasingly urgent to investigate the partition coefficients (expressed as log K_DOC values) of polybrominated diphenyl ethers (PBDEs) in dissolved organic carbon (DOC) present in wastewater. In the current study, after 72 h of equilibration, the concentrations of four common PBDEs were measured in the presence of four DOC solutions from two laboratories and two full-scale anaerobic digestion plants. Sixteen log K_DOCs were determined by calculation and unit conversion. The results for the laboratory samples, such as log K_DOCs for 2,2',4,4',5,5'-hexabromodiphenyl ether being 6.38 and 5.46 at different reaction temperatures during the cultivate procedure, suggest that a thermophilic environment promotes the solubility of PBDEs to a greater extent than mesophilic conditions. DOC composition directly influences the solubility of PBDEs, even at the same cultivating temperature: the highest log K_DOCs for 2,2',4,4',5,6'-hexabromodiphenyl ether were 6.71 and 6.33 in different full-scale plant digestates. A linear regression with an R² of 0.9863 was used to construct a model describing the potential relationship between log K_DOC and the composition of DOC, which includes proteins, polysaccharides and lipids, and which takes into account the positions of bromine atoms, for use in predicting the log K_DOC values of PBDEs in different water systems.

SSP silicone-, lipid- and SPMD-water partition coefficients of seventy hydrophobic organic contaminants and evaluation of the water concentration calculator for SPMD

Passive sampling is increasingly applied for monitoring neutral hydrophobic compounds (HOC) in various environmental media like water, sediment, air and also soft biota tissue. Passive samplers for HOC are often constructed from permeable polymers like silicone and polyethylene (PE), while also SPMD are often applied. Their HOC uptake can be converted to freely dissolved or equivalent lipid-based concentrations using appropriate partition coefficients with or without the use of kinetic uptake models to adjust for non-equilibrium. To facilitate such conversions for seventy HOC partition coefficients are derived by combining polymer-water for Altesil™ silicone and PE, with new and earlier published polymer-polymer, polymer-lipid partition coefficients. Derived SSP silicone-water, lipid-water (K_lip/w), and SPMD-water (K_spmd/w) partition coefficients demonstrate good agreement with literature data, except for K_spmd/w. For SPMD, this work demonstrates a linear K_spmd/w - K_ow relationship (R² = 0.99) in contrast to the parabolic K_spmd/w - K_ow relationship utilized in the USGS "SPMD Water Concentrations Calculator". Following a thorough evaluation of this Calculator it is recommended that in combination with revised K_spmd/w, a radical different model approach should be used for obtaining accurate water concentrations from passive sampling with SPMD.

Uptake, distribution and elimination of chemicals in fish - Which physiological parameters are the most relevant for toxicokinetics?

Bioconcentration and toxicity studies are regularly conducted for the risk assessment of chemicals. If such tests yield different results for different fish species, this can either be due to differences in toxicokinetics or to differences in toxicodynamics. Here we investigate which physiological parameters could cause major differences in the toxicokinetics in fish. To this end it is important to distinguish physiological parameters that affect the sorption capacity of the fish from those that affect kinetic processes. Variability in the lipid content of a fish is the most influential parameter for the sorption capacity of fish and therefore most relevant for the total concentration in fish under steady-state conditions when metabolism is not relevant. In terms of kinetics, ventilation rate, uptake efficiency from food and metabolism are the most influential factors. While ventilation rate can roughly be estimated from allometric scaling equations, little general information is available on the uptake efficiency from food. The metabolism rate constant appears to be the single most influential toxicokinetic factor. This information cannot be estimated but must be determined experimentally, preferably from in vitro experiments.

Formation process and mechanism of humic acid-kaolin complex determined by carbamazepine sorption experiments and various characterization methods

To explore the formation process and mechanism of organic matter and organic-mineral complex under humification and mineralization conditions, a series of samples including humic acid, kaolin, and humic acid-kaolin complex were prepared using a subcritical water treatment method (SWT) under specific temperature, pressure and reaction time conditions. HA was used as a surrogate for natural organic matter because it has a similar abundant pore structure, variety of carbon types, and chemical components. These samples were used in carbamazepine (CBZ) sorption experiments and characterized by a variety of techniques. The polymerization of humic acid under the conditions of increased temperature and pressure resulted in an increase in specific surface area and molecular quantity. In addition, the degree of aromaticity rose from 59.52% to 70.90%. These changes were consistent with the transformation from 'soft carbon' to 'hard carbon' that occurs in nature. The results of sorption experiments confirmed the interaction between humic acid and kaolin from the difference between the predicted and actual Q_e values. The conceptual model of humic acid-kaolin complex could be deduced and described as follows. Firstly, the aromatic components of humic acid preferentially combine with kaolin through the intercalation effect, which protects them from the treatment effects. Next, the free carboxyl groups and small aliphatic components of humic acid interact on the surface of kaolin, and these soft species transform into dense carbon through cyclization and polymerization. As a result, humic acid-kaolin complex with a mineral core and dense outer carbonaceous patches were formed.

Repeatability of n-octanol/water partition coefficient values between liquid chromatography measurement methods

The n-octanol/water partition coefficient (K_OW) is a physical/chemical property that is extensively used for regulatory and environmental risk and exposure assessments. The K_OW value can estimate various chemical properties such as water solubility, bioavailability, and toxicity using quantitative structure-activity relationships which demands an accurate knowledge of this property. The present investigation aims to compare outcomes of three commonly cited methods of K_OW measurement in the literature for six hydrophobic chemicals with insecticidal functions as well as highly volatile petroleum constituents. This measurement has been difficult to obtain for the selected pyrethroid insecticides, cypermethrin, and bifenthrin and is a novel measurement for the latter: polycyclic aromatic sulfur heterocycles, dibenzothiophene (DBT), and three of its alkyl derivatives except for DBT. The K_OW values were obtained using two liquid chromatographic methods with isocratic and gradient programming, and the slow-stirring method following OECD 117 and 123 guidelines, respectively. The mean log K_OW values of bifenthrin, cypermethrin, DBT, methyl-DBT, dimethyl-DBT, and diethyl-DBT were 8.4 ± 0.1, 6.0 ± 0.3, 4.8 ± 0.0, 5.4 ± 0.1, 6.0 ± 0.1, and 6.8 ± 0.0 using the HPLC method with gradient programing. The K_OW values were significantly reproducible within a method, however, not between the methods. Results suggest assessing a chemical's property and environmental risk and exposure solely based on the K_OW value should be practiced with caution.

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.

The presence and partitioning behavior of flame retardants in waste, leachate, and air particles from Norwegian waste-handling facilities

Flame retardants in commercial products eventually make their way into the waste stream. Herein the presence of flame retardants in Norwegian landfills, incineration facilities and recycling sorting/defragmenting facilities is investigated. These facilities handled waste electrical and electronic equipment (WEEE), vehicles, digestate, glass, combustibles, bottom ash and fly ash. The flame retardants considered included polybrominated diphenyl ethers (∑BDE-10) as well as dechlorane plus, polybrominated biphenyls, hexabromobenzene, pentabromotoluene and pentabromoethylbenzene (collectively referred to as ∑FR-7). Plastic, WEEE and vehicles contained the largest amount of flame retardants (∑BDE-10: 45,000-210,000μg/kg; ∑FR-7: 300-13,000μg/kg). It was hypothesized leachate and air concentrations from facilities that sort/defragment WEEE and vehicles would be the highest. This was supported for total air phase concentrations (∑BDE-10: 9000-195,000pg/m³ WEEE/vehicle facilities, 80-900pg/m³ in incineration/sorting and landfill sites), but not for water leachate concentrations (e.g., ∑BDE-10: 15-3500ng/L in WEEE/Vehicle facilities and 1-250ng/L in landfill sites). Landfill leachate exhibited similar concentrations as WEEE/vehicle sorting and defragmenting facility leachate. To better account for concentrations in leachates at the different facilities, waste-water partitioning coefficients, K_waste were measured (for the first time to our knowledge for flame retardants). WEEE and plastic waste had elevated K_waste compared to other wastes, likely because flame retardants are directly added to these materials. The results of this study have implications for the development of strategies to reduce exposure and environmental emissions of flame retardants in waste and recycled products through improved waste management practices.

History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments

Chemical property estimation is a key component in many industrial, academic, and regulatory activities, including in the risk assessment associated with the approximately 1000 new chemical pre-manufacture notices the United States Environmental Protection Agency (US EPA) receives annually. The US EPA evaluates fate, exposure and toxicity under the 1976 Toxic Substances Control Act (amended by the 2016 Frank R. Lautenberg Chemical Safety for the 21^st Century Act), which does not require test data with new chemical applications. Though the submission of data is not required, the US EPA has, over the past 40 years, occasionally received chemical-specific data with pre-manufacture notices. The US EPA has been actively using this and publicly available data to develop and refine predictive computerized models, most of which are housed in EPI Suite™, to estimate chemical properties used in the risk assessment of new chemicals. The US EPA develops and uses models based on (quantitative) structure-activity relationships ([Q]SARs) to estimate critical parameters. As in any evolving field, (Q)SARs have experienced successes, suffered failures, and responded to emerging trends. Correlations of a chemical structure with its properties or biological activity were first demonstrated in the late 19^th century and today have been encapsulated in a myriad of quantitative and qualitative SARs. The development and proliferation of the personal computer in the late 20^th century gave rise to a quickly increasing number of property estimation models, and continually improved computing power and connectivity among researchers via the internet are enabling the development of increasingly complex models.

Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals

The octanol-air partition coefficient (K_OA) is a key parameter describing the partition behavior of organic chemicals between air and environmental organic phases. As the experimental determination of K_OA is costly, time-consuming and sometimes limited by the availability of authentic chemical standards for the compounds to be determined, it becomes necessary to develop credible predictive models for K_OA. In this study, a polyparameter linear free energy relationship (pp-LFER) model for predicting K_OA at 298.15 K and a novel model incorporating pp-LFERs with temperature (pp-LFER-T model) were developed from 795 log K_OA values for 367 chemicals at different temperatures (263.15-323.15 K), and were evaluated with the OECD guidelines on QSAR model validation and applicability domain description. Statistical results show that both models are well-fitted, robust and have good predictive capabilities. Particularly, the pp-LFER model shows a strong predictive ability for polyfluoroalkyl substances and organosilicon compounds, and the pp-LFER-T model maintains a high predictive accuracy within a wide temperature range (263.15-323.15 K).

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors

The soil sorption partition coefficient logK_oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK_oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK_oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.

Critical assessment of pendimethalin in terms of persistence, bioaccumulation, toxicity, and potential for long-range transport

Pendimethalin (PND, CAS registry number 40487-42-1) is a dinitroaniline herbicide that selectively controls broad-leaf and grassy weeds in a variety of crops and in noncrop areas. It has been on the market for about 30 yr and is currently under review for properties related to persistence (P), bioaccumulation (B), and toxicity (T) in the European Union (EU). A critical review of these properties as well as potential for long-range transport (LRT) was conducted. Pendimethalin has a geometric mean (GM) half-life of 76-98 d in agriculturally relevant soils under aerobic conditions in the lab. The anaerobic half-life was 12 d. The GM for field half-lives was 72 d. The GM half-life for sediment-water tests in the lab was 20 d and that in field aquatic cosms ranged from 45 to 90 d. From these data PND is not persistent as defined in the Annex II of EC regulation 1107/2009. The GM bioconcentration factor for PND was 1878, less than the criterion value. This was consistent with lack of biomagnification or accumulation in aquatic and terrestrial food chains. The GM no-observed-effect concentration (NOEC) value for fish was 43 µg/L, and 11 µg/L for algae. These do not trigger the criterion value for toxicity. In air, the DT50 of PND was estimated to be 0.35 d, which is well below the criterion of 2 d for LRT under the United Nations Economic Commission for Europe (UNECE) Aarhus protocol. Modeling confirmed lack of LRT. Because of its volatility, PND may be transported over short distances in air and was found in samples in local and semiremote regions; however, these concentrations are not of toxicological concern. Unlike other current-use pesticides, PND has not been found in samples from remote regions since 2000 and there is no apparent evidence that this herbicide accumulates in food chains in the Arctic.

Joint interpretation of enantiomer and stable isotope fractionation for chiral pesticides degradation

Chiral pesticides are important contaminants affecting the health and functioning of aquatic systems. The combination of stable isotope and enantiomer analysis techniques has been recently proposed to better characterize the fate of these contaminants in natural and engineered settings. We introduce a modeling approach with the aim of unifying and integrating the interpretation of isotopic and enantiomeric fractionation. The model is based on the definition of enantiomer-specific isotopologues and jointly predicts the evolution of concentration, enantiomer fractionation, as well as changes in stable isotope ratios of different elements. The method allows evaluating different transformation pathways and was applied to investigate enzymatic degradation of dichlorprop (DCPP), enzymatic degradation of mecoprop methyl ester (MCPPM), and microbial degradation of α-hexachlorocyclohexane (α-HCH) by different bacterial strains and under different redox conditions. The model accurately reproduces the isotopic and enantiomeric data observed in previous experimental studies and precisely captures the dual-dimensional trends characterizing different reaction pathways. Furthermore, the model allows testing possible combinations of enantiomer analysis (EA), compound specific isotope analysis (CSIA), and enantiomer specific isotope analysis (ESIA) to identify and assess isotope and enantiomer selective reaction mechanisms.

One-year study of polycyclic aromatic compounds at an urban site in Grenoble (France): Seasonal variations, gas/particle partitioning and cancer risk estimation

21 PAHs, 27 oxy-PAHs and 32 nitro-PAHs were measured every third day over a year in both gaseous (G) and particulate PM10 (P) phases in ambient air of Grenoble (France). Mean total concentrations (G+P) of PAHs and oxy-PAHs were in the same range and about 10ngm(-3). Nitro-PAHs were 50 to 100 times less concentrated averaging 100pgm(-3). Polycyclic aromatic compound (PAC) concentrations were 5 to 7 times higher in "cold" period (October to March) than in "warm" period (April to September). Seasonal variations may be explained by higher primary emissions from residential heating, especially biomass burning in "cold" season. Meteorological conditions and influence of the geomorphology around Grenoble, with the formation of thermal inversion layers leading to the stagnation of pollutants, were additional key parameters. Maximum individual PAC concentrations were observed during two PM10 pollution events in December and February-March. Chemical processes and secondary formation of oxy- and nitro-PAH were probably enhanced by the accumulation of the pollutants during these events. PAC gas/particle partitioning depended on compound molecular weight and vapour pressure. Gas/particle partitioning of oxy- and nitro-PAHs were evaluated using a multi-phase poly-parameter linear free energy relationship model. The PAC cancer risk was assessed using toxic equivalency factors available in the literature (19 PAHs, 10 nitro-PAHs and 1 oxy-PAH). Overall, particle-bound PACs contributed about 76% of the cancer risk. While PAHs accounted for most of the total PAC cancer risk, oxy- and nitro-PAHs could account for up to 24%. The risk quantification across substance classes is limited by toxicological data availability.

From Clothing to Laundry Water: Investigating the Fate of Phthalates, Brominated Flame Retardants, and Organophosphate Esters

The accumulation of phthalate esters, brominated flame retardants (BFRs) and organophosphate esters (OPEs) by clothing from indoor air and transfer via laundering to outdoors were investigated. Over 30 days cotton and polyester fabrics accumulated 3475 and 1950 ng/dm(2) ∑5phthalates, 65 and 78 ng/dm(2) ∑10BFRs, and 1200 and 310 ng/dm(2) ∑8OPEs, respectively. Planar surface area concentrations of OPEs and low molecular weight phthalates were significantly greater in cotton than polyester and similar for BFRs and high molecular weight phthalates. This difference was significantly and inversely correlated with KOW, suggesting greater sorption of polar compounds to polar cotton. Chemical release from cotton and polyester to laundry water was >80% of aliphatic OPEs (log KOW < 4), < 50% of OPEs with an aromatic structure, 50-100% of low molecular weight phthalates (log KOW 4-6), and < detection-35% of higher molecular weight phthalates (log KOW > 8) and BFRs (log KOW > 6). These results support the hypothesis that clothing acts an efficient conveyer of soluble semivolatile organic compounds (SVOCs) from indoors to outdoors through accumulation from air and then release during laundering. Clothes drying could as well contribute to the release of chemicals emitted by electric dryers. The results also have implications for dermal exposure.

Field estimates of polyurethane foam - air partition coefficients for hexachlorobenzene, alpha-hexachlorocyclohexane and bromoanisoles

Partition coefficients of gaseous semivolatile organic compounds (SVOCs) between polyurethane foam (PUF) and air (KPA) are needed in the estimation of sampling rates for PUF disk passive air samplers. We determined KPA in field experiments by conducting long-term (24-48 h) air sampling to saturate PUF traps and shorter runs (2-4 h) to measure air concentrations. Sampling events were done at daily mean temperatures ranging from 1.9 to 17.5 °C. Target compounds were hexachlorobenzene (HCB), alpha-hexachlorocyclohexane (α-HCH), 2,4-dibromoanisole (2,4-DiBA) and 2,4,6-tribromoanisole (2,4,6-TriBA). KPA (mL g(-1)) was calculated from quantities on the PUF traps at saturation (ng g(-1)) divided by air concentrations (ng mL(-1)). Enthalpies of PUF-to-air transfer (ΔHPA, kJ mol(-1)) were determined from the slopes of log KPA/mL g(-1) versus 1/T(K) for HCB and the bromoanisoles, KPA of α-HCH was measured only at 14.3 to 17.5 °C and ΔHPA was not determined. Experimental log KPA/mL g(-1) at 15 °C were HCB = 7.37; α-HCH = 8.08; 2,4-DiBA = 7.26 and 2,4,6-TriBA = 7.26. Experimental log KPA/mL g(-1) were compared with predictions based on an octanol-air partition coefficient (log KOA) model (Shoeib and Harner, 2002a) and a polyparameter linear free relationship (pp-LFER) model (Kamprad and Goss, 2007) using different sets of solute parameters. Predicted KP values varied by factors of 3 to over 30, depending on the compound and the model. Such discrepancies provide incentive for experimental measurements of KPA for other SVOCs.