Applications of polyparameter linear free energy relationships in environmental chemistry

Gas/particle partitioning of PCDD/Fs: Distributions and implications for available models

Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) are semi-volatile organic compounds (SVOCs) existing in the atmosphere in the gas and particulate phase, remain persistent for a long time and pose a high risk to the environment and human health. In this study, PCDD/F measurements were made in an urban area between June 2022 and April 2023. In order to understand the fate of PCDD/Fs, the gas/particle (G/P) partitioning was studied. Although various models have been developed to determine the G/P partitioning of SVOCs, only logKp-logPL0, Junge-Pankow and Harner-Bidleman models are generally used for PCDD/Fs. In this study, nine different models (Junge-Pankow, Harner-Bidleman, Dachs-Eisenreich, Li-Ma-Yang, pp-LFER, mp-pp-LFER, QSPR, logKp-logPL0, logKp-logKOA) were employed to determine the G/P partitioning. To the best of our knowledge, pp-LFER, mp-pp-LFER and QSPR models were evaluated for PCDD/Fs for the first time in this study. In addition, the performance of the models within the equilibrium (EQ), non-equilibrium (NE) and maximum partitioning (MP) domain was investigated for PCDD/Fs for the first time in this study. Accordingly, models based on absorption in the EQ domain, adsorption in the NE domain and adsorption and absorption mechanisms in the MP domain were found to be effective in explaining the G/P transitions. It was determined that there is no equilibrium situation in the G/P partitioning. The Junge-Pankow, pp-LFER, Li-Ma-Yang and QSPR models under-predicted the particle fraction values while the other models showed a high prediction profile. The Li-Ma-Yang model showed the closest results to the measured particle fraction values, and it determined that deposition mechanisms are of non-negligible importance in the G/P partitioning of PCDD/Fs. One of the new models, the pp-LFER model, has shown remarkable success at high logKOA values. The mp-pp-LFER model, which overestimated the contribution of the adsorption mechanism, showed a very high prediction profile compared to the measured values.

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.

A Review of Mechanistic Models for Predicting Adverse Effects in Sediment Toxicity Testing

Since recognizing the importance of bioavailability for understanding the toxicity of chemicals in sediments, mechanistic modeling has advanced over the last 40 years by building better tools for estimating exposure and making predictions of probable adverse effects. Our review provides an up-to-date survey of the status of mechanistic modeling in contaminated sediment toxicity assessments. Relative to exposure, advances have been most substantial for non-ionic organic contaminants (NOCs) and divalent cationic metals, with several equilibrium partitioning-based (Eq-P) models having been developed. This has included the use of Abraham equations to estimate partition coefficients for environmental media. As a result of the complexity of their partitioning behavior, progress has been less substantial for ionic/polar organic contaminants. When the EqP-based estimates of exposure and bioavailability are combined with water-only effects measurements, predictions of sediment toxicity can be successfully made for NOCs and selected metals. Both species sensitivity distributions and toxicokinetic and toxicodynamic models are increasingly being applied to better predict contaminated sediment toxicity. Furthermore, for some classes of contaminants, such as polycyclic aromatic hydrocarbons, adverse effects can be modeled as mixtures, making the models useful in real-world applications, where contaminants seldomly occur individually. Despite the impressive advances in the development and application of mechanistic models to predict sediment toxicity, several critical research needs remain to be addressed. These needs and others represent the next frontier in the continuing development and application of mechanistic models for informing environmental scientists, managers, and decisions makers of the risks associated with contaminated sediments. Environ Toxicol Chem 2024;43:1778-1794. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Comparison of in vitro membrane permeabilities of diverse environmental chemicals with in silico predictions

The parallel artificial membrane permeability assay (PAMPA) is widely used for estimating biomembrane permeabilities of experimental drugs in pharmaceutical research. However, there are few reports of studies using PAMPA to measure membrane permeabilities of chemicals of environmental concern (CECs) outside the pharmaceutical domain, many of which differ substantially from drugs in their physicochemical properties. We applied PAMPA methods simulating gastrointestinal (PAMPA-GIT) and blood-brain barrier (PAMPA-BBB) membranes under consistent conditions to 51 CECs, including some pharmaceuticals. A backward stepwise multivariate linear regression was implemented to explore the correlation between the differences of measured permeabilities from PAMPA-GIT and PAMPA-BBB and Abraham solute descriptors. In addition, a previously reported in silico model was evaluated by comparing predicted and measured permeability results. PAMPA-GIT and PAMPA-BBB experimental permeability results agreed relatively well. The backward stepwise multivariate linear regression identified excess molar refraction and polarizability to be significant at the 0.10 level in predicting the differences between PAMPA-GIT and PAMPA-BBB. The in silico model performed well - with predicted permeability of most compounds within two-fold of experimentally measured values. We found that CECs pose experimental challenges to the PAMPA method in terms of having lower solubility and lower stability compared to most drugs.

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.

Mechanistic and data-driven perspectives on plant uptake of organic pollutants

Establishing reliable predictive models for plant uptake of organic pollutants is crucial for environmental risk assessment and guiding phytoremediation efforts. This study compiled an expanded dataset of plant cuticle-water partition coefficients (Kcw), a useful indicator for plant uptake, for 371 data points of 148 unique compounds and various plant species. Quantum/computational chemistry software and tools were utilized to compute various molecular descriptors, aiming to comprehensively characterize the properties and structures of each compound. Three types of models were developed to predict Kcw: a mechanism-driven pp-LFER model, a data-driven machine learning model, and an integrated mechanism-data-driven model. The mechanism-data-driven GBRT-ppLFER model exhibited superior performance, achieving RMSEtrain = 0.133 and RMSEtest = 0.301 while maintaining interpretability. The Shapley Additive Explanation analysis indicated that pp-LFER parameters, ESPI, FwRadicalmax, ExtFP607, and RDF70s are the key factors influencing plant uptake in the GBRT-ppLFER model. Overall, pp-LFER parameter, ESPI, and ExtFP607 show positive effects, while the remaining factors exhibit negative effects. Partial dependency analysis further indicated that plant uptake is not solely determined by individual factors but rather by the combined interactions of multiple factors. Specifically, compounds with ppLFER parameter >4, ESPI > -25.5, 0.098 < FwRadicalmax <0.132, and 2 < RFD70s < 3, are generally more readily taken up by plants. Besides, the predicted Kcw values from the GBRT-ppLFER model were effectively employed to estimate the plant-water partition coefficients and bioconcentration factors across different plant species and growth media (water, sand, and soil), achieving an outstanding performance with an RMSE of 0.497. This study provides effective tools for assessing plant uptake of organic pollutants and deepens our understanding of plant-environment-compound interactions.

Predicting Hydrocarbon Primary Biodegradation in Soil and Sediment Systems Using System Parameterization and Machine Learning

Technical complexity associated with biodegradation testing, particularly for substances of unknown or variable composition, complex reaction products, or biological materials (UVCB), necessitates the advancement of non-testing methods such as quantitative structure-property relationships (QSPRs). Models for describing the biodegradation of petroleum hydrocarbons (HCs) have been previously developed. A critical limitation of available models is their inability to capture the variability in biodegradation rates associated with variable test systems and environmental conditions. Recently, the Hydrocarbon Biodegradation System Integrated Model (HC-BioSIM) was developed to characterize the biodegradation of HCs in aquatic systems with the inclusion of key test system variables. The present study further expands the HC-BioSIM methodology to soil and sediment systems using a database of 2195 half-life (i.e., degradation time [DT]50) entries for HCs in soil and sediment. Relevance and reliability criteria were defined based on similarity to standard testing guidelines for biodegradation testing and applied to all entries in the database. The HC-BioSIM soil and sediment models significantly outperformed the existing biodegradation HC half-life (BioHCWin) and virtual evaluation of chemical properties and toxicities (VEGA) quantitative Mario Negri Institute for Pharmacological Research (IRFMN) models in soil and sediment. Average errors in predicted DT50s were reduced by up to 6.3- and 8.7-fold for soil and sediment, respectively. No significant bias as a function of HC class, carbon number, or test system parameters was observed. Model diagnostics demonstrated low variability in performance and high consistency of parameter usage/importance and rule structure, supporting the generalizability and stability of the models for application to external data sets. The HC-BioSIM provides improved accuracy of Persistence categorization, with correct classification rates of 83.9%, and 90.6% for soil and sediment, respectively, demonstrating a significant improvement over the existing BioHCWin (70.7% and 58.6%) and VEGA (59.5% and 18.5%) models. Environ Toxicol Chem 2024;43:1352-1363. © 2024 Concawe. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Identifying uncertainty in physical-chemical property estimation with IFSQSAR

This study describes the development and evaluation of six new models for predicting physical-chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol-water (KOW), octanol-air (KOA), and air-water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure-Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for "novel chemicals" in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7-1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.

Thermodynamic Analysis of Eplerenone in 13 Pure Solvents at Temperatures from 283.15 to 323.15 K

The solubility of eplerenone (EP) in 13 pure solvents (acetonitrile, N,N-dimethylformamide (DMF), acetone, 2-butanone, 4-methyl-2-pentanone, ethyl formate, methyl acetate, ethyl acetate, propyl acetate, butyl acetate, methyl propionate, ethyl propionate, ethanol, and 1-propanol) was determined by the gravimetric method at atmospheric pressure and various temperatures (from 283.15 to 323.15 K). The results showed that the solubility of EP in the selected solvents was positively correlated with the thermodynamic temperature, and the order of solubility of EP at 298.15 K was acetonitrile > DMF > 2-butanone > methyl acetate > 4-methyl-2-pentanone > methyl propionate > ethyl acetate > propyl acetate > ethyl formate > acetone > butyl acetate > ethanol >1-propanol. The modified Apelblat model, van't Hoff model, λh model, and polynomial empirical model were used for fitting the solubility data, and then the λh model was found to have the highest fitting accuracy with a minimum ARD of 7.0 × 10-3 and a minimum RMSD of 6.1 × 10-6. The solvent effect between the solute and the solvent was analyzed using linear solvation energy relationship (LSER), and the enthalpy of solvation (ΔsolH°), entropy of solvation (ΔsolS°), and Gibbs free energy of solvation (ΔsolG°) of the dissolution process of EP were calculated by the van't Hoff model, which indicated that the dissolution process of EP in the selected solvents was endothermic, nonspontaneous, and entropy-increasing. In this work, the solubility, dissolution characteristics, and thermodynamic parameters of EP were studied, which will provide data support for the production, crystallization, and purification of EP and will provide important guidance for the crystallization optimization of EP in industry.

Predicting the intrinsic membrane permeability of Caco-2/MDCK cells by the solubility-diffusion model

Membrane permeability is one of the main determinants for the absorption, distribution, metabolism and excretion of compounds and is therefore of crucial importance for successful drug development. Experiments with artificial phospholipid membranes have shown that the intrinsic membrane permeability (P0) of compounds is well-predicted by the solubility-diffusion model (SDM). However, using the solubility-diffusion model to predict the P0 of biological Caco-2 and MDCK cell membranes has proven unreliable so far. Recent publications revealed that many published P0 extracted from Caco-2 and MDCK experiments are incorrect. In this work, we therefore used a small self-generated set as well as a large revised set of experimental Caco-2 and MDCK data from literature to compare experimental and predicted P0. The P0 extracted from Caco-2 and MDCK experiments were systematically lower than the P0 predicted by the solubility-diffusion model. However, using the following correlation: log P0,Caco-2/MDCK = 0.84 log P0,SDM - 1.85, P0 of biological Caco-2 and MDCK cell membranes was well-predicted by the solubility-diffusion model.

Mechanistic modelling of amphibian body burdens after dermal uptake of pesticides from soil

Amphibians are currently considered to be covered by pesticide Environmental Risk Assessment schemes by surrogacy assumptions of exposure and susceptibility based on typical laboratory test species such as fish, mammals, and birds. While multiple reviews have shown for this approach to be adequate in the case of aquatic stages, the same cannot be definitively stated for terrestrial stages. Concerns have risen that exposure of amphibians is likely to be highly influenced by dermal absorption, primarily due to the high permeability of their skin and the lack of a protective layer, such as fur or feathers. It is thus hypothesized that dermal uptake could be a significant route of exposure. Consequently, it is necessary to determine the relative importance of different exposure routes that might affect the integrated toxicity outcome for terrestrial amphibian life-stages. Here, a one-compartment Toxicokinetic model was derived and tested using a publicly available dataset containing relevant exposure and uptake information for juvenile anurans exposed to 13 different pesticides. Modelled body burdens were then compared to measured burdens for a total of 815 individuals. Overall, a good concordance between modelled and measured values was observed, with the predicted and measured body burdens differing by a factor of 2 on average (overall R2 of 0.80 and correlation coefficient of 0.89), suggesting good predictivity of the model. Accordingly, the model predicts realistic body burdens for a variety of frog and toad species, and overall, for anurans. As the model includes rehydration (implicit in the evaluated studies) but currently does not account for metabolism, it can be seen as a worst-case assessment. We suggest toxicokinetic models, such as the one here presented, could be used to characterize dermal exposure in amphibians, screen for pesticides of concern, and prioritize risk assessment efforts, whilst reducing the need for de novo vertebrate testing.

Origins of Selective Oxidation in Carbon-Based Nonradical Oxidation Processes toward Organic Pollutants: Quantitative Structure-Activity Relationships (QSARs)

Metal-free carbon material-mediated nonradical oxidation processes (C-NOPs) have emerged as a research hotspot due to their excellent performance in selectively eliminating organic pollutants in aqueous environments. However, the selective oxidation mechanisms of C-NOPs remain obscure due to the diversity of organic pollutants and nonradical active species. Herein, quantitative structure-activity relationship (QSAR) models were employed to unveil the origins of C-NOP selectivity toward organic pollutants in different oxidant systems. QSAR analysis based on adsorption and oxidation descriptors revealed that C-NOP selectivity depends on the oxidation potentials of organic pollutants rather than on adsorption interactions. However, the dominance of electronic effects in selective oxidation decreases with increasing structural complexity of organic pollutants. Moreover, the oxidation threshold solely depends on the inherent electronic nature of organic pollutants and not on the reactivity of nonradical active species. Notably, the accuracy of substituent descriptors (Hammett constants) and theoretical descriptors (e.g., highest occupied molecular orbital energy, ionization potential, and single-electron oxidation potential) is significantly influenced by the complexity and molecular state of organic pollutants. Overall, the study findings reveal the origins of organic pollutant-oriented selective oxidation and provide insight into the application of descriptors in QSAR analysis.

In Silico Acute Aquatic Hazard Assessment and Prioritization Using a Grouped Target Site Model: A Case Study of Organic Substances Reported in Permian Basin Hydraulic Fracturing Operations

Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;00:1-12. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Determination of solvation parameter model compound descriptors by gas chromatography

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 the gas-liquid partition constant at 25 °C on n-hexadecane, L, to model transfer properties in gas-condensed phase biphasic systems. The E descriptor for compounds liquid at 20 °C is available by calculation using a refractive index value while E for solid compounds at 20 °C and the S, A, B, and L descriptors are determined by experiment. As a single-technique approach, it is shown that with up to 20 retention factor measurements on four columns comprising a poly(siloxane) containing methyloctyl or dimethyldiphenylsiloxane monomers (SPB-Octyl or HP-5), a poly(siloxane) containing methyltrifluoropropylsiloxane monomers (Rtx-OPP or DB-210), a poly(siloxane) containing bis(cyanopropylsiloxane) monomers (HP-88 or SGE BPX-90), and a poly(ethylene glycol) stationary phase (DB-WAXetr or HP-INNOWAX) are suitable for assigning the S, A, and L descriptors. Using the descriptors in the updated WSU compound descriptor database as target values the average absolute error in the descriptor assignments for 52 varied compounds in the temperature range 60-140 °C was 0.072 for E, 0.016 for S, 0.008 for A, and 0.022 for L corresponding to 30 %, 3.5 %, and 0.6 % as a relative average absolute error for E, S, and L, respectively. For the higher temperature range of 160-240 °C and 34 varied compounds that are liquid at 20 °C the average absolute error for the S, A and L descriptors was 0.026, 0.020, and 0.031, respectively, with the largest relative average absolute error for S of 3.2 % (< 1 % for the L descriptor). For 35 varied compounds that are solid at 20 °C the relative absolute error for the E, S, A, and L descriptors in the higher temperature range was 0.068, 0.035, 0.020, and 0.020, respectively, with a relative average absolute error for E (6.5 %), S (3.5 %) and L (0.88 %). The S, A, and L descriptor can be accurately assigned on the four-column system over a wide temperature range. The E descriptor for solid compounds at 20 °C exhibits greater variability than desirable. The B descriptor cannot be assigned by the four-column system, which lack hydrogen-bond acid functional groups, and is only poorly assigned on the weak hydrogen-bond acid ionic liquid column SLB-IL100.

Chemical Space Covered by Applicability Domains of Quantitative Structure-Property Relationships and Semiempirical Relationships in Chemical Assessments

A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure-property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol-air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.

Determination of descriptors for the principal flavor compounds of the cinnamons of commerce by gas chromatography and liquid-liquid partition

Descriptors for fourteen semivolatile organic compounds associated with the authenticity, botanical origin, and flavor potential of the cinnamons of commerce were determined using the Solver method and experimental retention factors determined by gas chromatography at several temperatures on a minimum of seven selectivity-selected, open-tubular columns and liquid-liquid partition constants in up to twenty totally organic biphasic systems. The six descriptors that encode the solvation properties of the compounds were used to predict water-gas, octanol-gas, and octanol-water partition constants commonly employed to assess environmental distribution properties. For octanol-water partition constants, log KOW, the predicted partition constants exhibited an average absolute deviation of 0.12 for log KOW experimental - log KOW predicted (n = 14). Soil-water, soil-air, urban aerosol-air, skin-water permeation, and non-specific toxicity to the fathead minnow were predicted for the same compounds to assess their potential environmental impact. The product terms of the solvation parameter model provide a useful insight into the contribution of individual intermolecular interactions to the distribution properties of the cinnamon compounds and their environmental impact.

Acetal Formation of Flavoring Agents with Propylene Glycol in E-Cigarettes: Impacts on Indoor Partitioning and Thirdhand Exposure

The widespread use of flavored e-cigarettes has led to a significant rise in teenage nicotine use. In e-liquids, the flavor carbonyls can form acetals with unknown chemical and toxicological properties. These acetals can cause adverse health effects on both smokers and nonsmokers through thirdhand exposure. This study aims to explore the impacts of these acetals formed in e-cigarettes on indoor partitioning and thirdhand exposure. Specifically, the acetalization reactions of commonly used flavor carbonyls in laboratory-made e-liquids were monitored using proton nuclear magnetic resonance (1H NMR) spectroscopy. EAS-E Suite and polyparameter linear free energy relationships (PP-LFERs) were employed to estimate the partitioning coefficients for species. Further, a chemical two-dimensional partitioning model was applied to visualize the indoor equilibrium partitioning and estimate the distribution of flavor carbonyls and their acetals in the gas phase, aerosol phase, and surface reservoirs. Our results demonstrate that a substantial fraction of carbonyls were converted into acetals in e-liquids and their chemical partitioning was significantly influenced. This study shows that acetalization is a determinant factor in the exposure and toxicology of harmful carbonyl flavorings, with its impact extending to both direct exposure to smokers and involuntary exposure to nonsmokers.

Occurrence of emerging contaminants in pet hair and indoor air: integrative health risk assessment using multiple ToxCast endpoints

Indoor exposome is a growing concern, including a mixture of legacy and emerging contaminants. Recent studies suggest that indoor pollutants may accumulate in pet hair, a part of indoor exposome, increasing health risks to pet owners; however, the source and hazards of pollutants associated with pet hair are largely unknown. Here, we found that hydrophobic pollutants often had higher indoor concentrations than hydrophilic ones, and polycyclic aromatic hydrocarbons (PAHs) were the most dominant fractions (61.1%) in indoor air exposome while polycyclic musks (PCMs) had the highest concentrations among all contaminant classes in indoor dust (1559 ± 1598 ng g-1 dw) and pet hair (2831 ± 2458 ng g-1 dw). The levels of hygiene-related contaminants (PCMs, current-use pesticides (CUPs), and antibiotics) were higher in pet hair than dust due to direct contact during applications. Health risk assessment using toxicity thresholds from high-throughput screening data showed that human health risks from the five classes of indoor contaminants (PAHs, PCMs, organophosphate esters, CUPs, and antibiotics) via inhalation, ingestion, and dermal contact were within acceptable limits, but the children may be exposed to a higher risk than the adults. The thresholds estimated from the ToxCast data using endpoint sensitivity distribution make the exposome risk assessment feasible in the absence of benchmarks, which is beneficial for including a mixture of emerging pollutants in risk assessment.

Intermolecular Interactions, Solute Descriptors, and Partition Properties of Neutral Per- and Polyfluoroalkyl Substances (PFAS)

The environmental partition properties of perfluoroalkyl and polyfluoroalkyl substances (PFAS) must be understood for their transport and fate analysis. In this study, isothermal gas chromatographic (GC) retention times of 60 neutral PFAS were measured using four columns with different stationary phase polarities, which indicated varying polar interactions exerted by these substances. The GC data were combined with new octanol/water partition coefficient data from this study and existing partition coefficient data from the literature and used to determine the polyparameter linear free energy relationship (PP-LFER) solute descriptors. A complete set of the solute descriptors was obtained for 47 PFAS, demonstrating the characteristic intermolecular interaction properties, such as hydrogen bonding capabilities influenced by the electron-withdrawing perfluoroalkyl group. The partition coefficients between octanol and water, air and water, and octanol and air predicted by the PP-LFER models agreed with those predicted by the quantum chemically based model COSMOtherm, suggesting that both models are highly accurate for neutral PFAS and can fill the current large data gaps in partition property data. A chemical partitioning space plot was generated by using the PP-LFER-predicted partition coefficients, showing the primary importance of the air phase for the environmental distribution of nonpolar and weakly polar PFAS and the increasing significance of organic phases with increasing PFAS polarity.

Revised descriptors for polycyclic aromatic and related hydrocarbons for the prediction of environmental properties using the solvation parameter model

Revised descriptors for twenty-five polycyclic aromatic and related hydrocarbons (PAHs) forming a component of the Wayne State University (WSU) descriptor database are provided for use with the solvation parameter model. The descriptors are determined by the Solver method using experimental data for calibrated gas-liquid and reversed-phase liquid chromatographic retention factors and liquid-liquid partition constants in totally organic biphasic systems. The characteristic solvation properties of the PAHs are accounted for mainly by the additional dispersion interactions (E descriptor) and dipole-type interactions (S descriptor) resulting from the availability of easily polarizable electrons that complement typical dispersion interactions for saturated hydrocarbons. The descriptors afford acceptable prediction of the water-air partition constant (average absolute deviation AAD = 0.17, n = 22), octanol-air partition constant (AAD = 0.12, n = 20), and water-octanol partition constant (AAD = 0.10, n = 23). A two-parameter model containing only the V and B descriptors provides an unbiased prediction of aqueous solubility for the PAHs with an AAD = 0.26 (n = 22). The descriptors estimated by convenient chromatographic and partition constant measurements are demonstrated to be a viable alternative to the experimental determination of environmental properties otherwise only available by tedious, expensive, and low data throughput experimental techniques.

Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning

With an increase in production and application of various engineering nanomaterials (ENMs), they will inevitably be released into the environment. Adsorption of various organic chemicals onto ENMs will impact on their environmental behavior and toxicology. It is unrealistic to experimentally determine adsorption equilibrium constants (K) for the vast number of organics and ENMs due to high cost in expenditure and time. Herein, appropriate molecular dynamics (MD) methods were evaluated and selected by comparing experimental K values of seven organics adsorbed onto graphene with the MD-calculated ones. Machine learning (ML) models on K of organics adsorption onto graphene and black phosphorus nanomaterials were constructed based on a benchmark data set from the MD simulations. Lasso models based on Mordred descriptors outperformed ML models built by support vector machine, random forest, k-nearest neighbor, and gradient boosting decision tree, in terms of cross-validation coefficients (Q2 > 0.90). The Lasso models also outperformed conventional poly-parameter linear free energy relationship models for predicting logK. Compared with previous models, the Lasso models considered more compounds with different functional groups and thus have broader applicability domains. This study provides a promising way to fill the data gap in logK for chemicals adsorbed onto the ENMs.

Predicting aqueous sorption of organic pollutants on microplastics with machine learning

Microplastics (MPs) are ubiquitously distributed in freshwater systems and they can determine the environmental fate of organic pollutants (OPs) via sorption interaction. However, the diverse physicochemical properties of MPs and the wide range of OP species make a deeper understanding of sorption mechanisms challenging. Traditional isotherm-based sorption models are limited in their universality since they normally only consider the nature and characteristics of either sorbents or sorbates individually. Therefore, only specific equilibrium concentrations or specific sorption isotherms can be used to predict sorption. To systematically evaluate and predict OP sorption under the influence of both MPs and OPs properties, we collected 475 sorption data from peer-reviewed publications and developed a poly-parameter-linear-free-energy-relationship-embedded machine learning method to analyze the collected sorption datasets. Models of different algorithms were compared, and the genetic algorithm and support vector machine hybrid model displayed the best prediction performance (R2 of 0.93 and root-mean-square-error of 0.07). Finally, comparison results of three feature importance analysis tools (forward step wise method, Shapley method, and global sensitivity analysis) showed that chemical properties of MPs, excess molar refraction, and hydrogen-bonding interaction of OPs contribute the most to sorption, reflecting the dominant sorption mechanisms of hydrophobic partitioning, hydrogen bond formation, and π-π interaction, respectively. This study presents a novel sorbate-sorbent-based ML model with a wide applicability to expand our capacity in understanding the complicated process and mechanism of OP sorption on MPs.

The effect of the assigned descriptors for phthalate esters on the characterization of their separation properties using the solvation parameter model

Revised descriptors are determined for fifteen phthalate esters for use in the solvation parameter model and form part of the Wayne State University (WSU) compound descriptor database. For thirteen phthalate esters a comparison is made with the same compounds in the Abraham descriptor database. Gas chromatographic retention factors on poly(methyloctylsiloxane), SPB-Octyl, and poly(cyanopropylphenyldimethylsiloxane), DB-225, stationary phases are used to facilitate an assessment of the contribution of cavity formation and dispersion interactions, L descriptor, and dipole-type interactions, S descriptor, to the experimental retention factors (log k) for the phthalate esters with minimum interference from competing intermolecular interactions. The results indicate a systematic overprediction of the cavity and dispersion interaction term and underprediction of dipole-type interactions for the Abraham descriptors compared with the WSU descriptors for the phthalate esters. The average absolute deviation (AAD) for 13 phthalate esters on SPB-Octyl is 0.039 (WSU descriptors) compared with 0.252 (Abraham descriptors) and for 9 phthalate esters on DB-225 0.030 (WSU descriptors) compared with 0.167 (Abraham descriptors). The results for dipole-type interactions are confirmed and extended to include the hydrogen-bond basicity of the phthalate esters, B descriptor, by evaluation of partition constants in aqueous biphasic systems and the n-heptane-2,2,2-trifluoroethanol biphasic system. Differences in the contribution of the hydrogen-bond basicity of the phthalate esters to the experimental partition constants are largely random with respect to database selection but important for the accurate prediction of the partition constants. The AAD for the partition constant for 15 phthalate esters is 0.063 (WSU descriptors) compared with 0.320 (Abraham descriptors) for the heptane-2,2,2-trifluoroethanol biphasic system and 0.13 (WSU descriptors) compared with 0.25 (Abraham descriptors) for 9 phthalate esters in the octanol-water biphasic system. The WSU descriptors for the phthalate esters exhibit a better fit with the experimental data for separation systems and are free of the extreme values predicted for the Abraham descriptors for several phthalate esters.

The effect of descriptor database selection on the physicochemical characterization and prediction of water-air, octanol-air and octanol-water partition constants using the solvation parameter model

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors (McGowan's characteristic volume, excess molar refraction, dipolarity/polarizability, hydrogen-bond acidity and basicity, and the gas-liquid partition constant on n-hexadecane at 298.15 K) are curated in two publicly accessible databases for hundreds (WSU compound descriptor database) or thousands (Abraham compound descriptor database). These databases were developed independently using different approaches resulting in descriptor values that vary for many compounds. Previously, it was shown that the two descriptor databases are not interchangeable, and the WSU descriptor database consistently demonstrated improved model performance for chromatographic systems where the uncertainty in the dependent variable was minimized by suitable quality control and calibration procedures. In this report we wish to evaluate whether the same conclusions are true for models with a dependent variable containing significant measurement uncertainty. To evaluate this hypothesis, we assembled databases for water-air, octanol-air, and octanol-water partition constants reported by multiple laboratories using various measurement methods. It was found that database selection has little effect on model quality or model predictive capability but significantly affects the assignment of the contribution of individual intermolecular interactions to the dependent variable. The latter information is database specific, and a quantitative comparison of system constants should be restricted to models using the same compound descriptor database.

Experimental Determination of Air/Water Partition Coefficients for 21 Per- and Polyfluoroalkyl Substances Reveals Variable Performance of Property Prediction Models

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals of high environmental concern. However, reliable data for the air/water partition coefficients (Kaw), which are required for fate, exposure, and risk analysis, are available for only a few PFAS. In this study, Kaw values at 25 °C were determined for 21 neutral PFAS by using the hexadecane/air/water thermodynamic cycle. Hexadecane/water partition coefficients (KHxd/w) were measured with batch partition, shared-headspace, and/or modified variable phase ratio headspace methods and were divided by hexadecane/air partition coefficients (KHxd/air) to obtain Kaw values over 7 orders of magnitude (10-4.9 to 102.3). Comparison to predicted Kaw values by four models showed that the quantum chemically based COSMOtherm model stood out for accuracy with a root-mean-squared error (RMSE) of 0.42 log units, as compared to HenryWin, OPERA, and the linear solvation energy relationship with predicted descriptors (RMSE, 1.28-2.23). The results indicate the advantage of a theoretical model over empirical models for a data-poor class like PFAS and the importance of experimentally filling data gaps in the chemical domain of environmental interest. Kaw values for 222 neutral (or neutral species of) PFAS were predicted using COSMOtherm as current best estimates for practical and regulatory use.

Investigation on the adsorption affinity of organic micropollutants on seaweed and its QSAR study

Seaweed, one of the most abundant biomaterials, can be used as a biosorbent to remove organic micropollutants. In order to effectively use seaweed to remove a variety of micropollutants, it is vital to rapidly estimate the adsorption affinity according to the types of micropollutants. Thus, the isothermal adsorption affinities of 31 organic micropollutants in neutral or ionic form on seaweed were measured, and a predictive model using quantitative structure-adsorption relationship (QSAR) modeling was developed. As a result, it was found that the types of micropollutants had a significant effect on the adsorption of seaweed, as expected, and QSAR modeling with a predictability (R²) of 0.854 and a standard error (SE) of 0.27 log units using a training set could be developed. The model's predictability was internally and externally validated using leave-one-out cross validation and a test set. Its predictability for the external validation set was R² = 0.864, SE = 0.171 log units. Using the developed model, we identified the most important driving forces of the adsorption at the molecular level: Coulomb interaction of the anion, molecular volume, and H-bond acceptor and donor, which significantly affect the basic momentum of molecules on the surface of seaweed. Moreover, in silico calculated descriptors were applied to the prediction, and the results revealed reasonable predictability (R² of 0.944 and SE of 0.17 log units). Our approach provides an understanding of the adsorption process of seaweed for organic micropollutants and an efficient prediction method to estimate the adsorption affinities of seaweed and micropollutants in neutral and ionic forms.

Partitioning into phosphatidylcholine-cholesterol membranes: liposome measurements, coarse-grained simulations, and implications for bioaccumulation

Membrane-water partitioning is an important physical property for the assessment of bioaccumulation and environmental impact. Here, we advance simulation methodology for predicting the partitioning of small molecules into lipid membranes and compare the computational predictions to experimental measurements in liposomes. As a step towards high-throughput screening, we present an automated mapping and parametrization procedure to produce coarse-grained models compatible with the Martini 3 force field. The methodology is general and can also be used for other applications where coarse-grained simulations are appropriate. This article addresses the effect on membrane-water partitioning of adding cholesterol to POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) membranes. Nine contrasting neutral, zwitterionic and charged solutes are tested. Agreement between experiment and simulation is generally good, with the most challenging cases being permanently charged solutes. For all solutes, partitioning is found to be insensitive to membrane cholesterol concentration up to 25% mole fraction. Hence, for assessment of bioaccumulation into a range of membranes (such as those found in fish), partitioning data measured in pure lipid membranes are still informative.

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.

Molecular Dynamics Simulation Prediction of the Partitioning Constants (KH, Kiw, Kia) of 82 Legacy and Emerging Organic Contaminants at the Water-Air Interface

The tendency of organic contaminants (OCs) to partition between different phases is a key set of properties that underlie their human and ecological health impacts and the success of remediation efforts. A significant challenge associated with these efforts is the need for accurate partitioning data for an ever-expanding list of OCs and breakdown products. All-atom molecular dynamics (MD) simulations have the potential to help generate these data, but existing studies have applied these techniques only to a limited variety of OCs. Here, we use established MD simulation approaches to examine the partitioning of 82 OCs, including many compounds of critical concern, at the water-air interface. Our predictions of the Henry's law constant (K_H) and interfacial adsorption coefficients (K_iw, K_ia) correlate strongly with experimental results, indicating that MD simulations can be used to predict K_H, K_iw, and K_ia values with mean absolute deviations of 1.1, 0.3, and 0.3 logarithmic units after correcting for systematic bias, respectively. A library of MD simulation input files for the examined OCs is provided to facilitate future investigations of the partitioning of these compounds in the presence of other phases.

A machine learning based approach for estimating site-specific partition coefficient Kd of organic compounds: Application to nonionic pesticides

The partitioning coefficient K_d for a specific compound and location is not only a key input parameter of fate and transport models, but also critical in estimating the safety environmental concentration threshold. In order to reduce the uncertainty caused by non-linear interactions among environmental factors, machine learning based models for predicting K_d were developed in this work based on literature datasets of nonionic pesticides including molecular descriptors, soil properties, and experimental settings. The equilibrium concentration (C_e) values were specifically included for the reason that a varied range of K_d corresponding to a given C_e occurred in a real environment. By transforming 466 isotherms reported in the literature, 2618 paired equilibrium concentrations of liquid-solid (C_e-Q_e) data points were obtained. Results of SHapley Additive exPlanations revealed that soil organic carbon, C_e, and cavity formation were the most important. The distance-based applicability domain analysis was conducted for the 27 most frequently used pesticides with 15952 pieces of soil information from the HWSD-China dataset by setting three C_e scenarios (i.e., 10, 100, and 1000 μg L^-1). It was revealed the groups of compounds showing log K_d < 0.06 and log K_d > 1.19 were composed mostly of those with log K_ow of -0.800 and 5.50, respectively. When log K_d varied between 0.100 and 1.00, it was impacted by interactions among soil types, molecular descriptors, and C_e comprehensively, which accounted for 55% of the total 2618 calculations. It could be concluded that site-specific models developed in this work are necessary and practicable for the environmental risk assessment and management of nonionic organic compounds.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

The influence of descriptor database selection on the solvation parameter model for separation processes

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors characterize the 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. McGowan's characteristic volume and the excess molar refraction for liquids are available by calculation (E requires and experimental refractive index). The other descriptors and excess molar refraction for solids are experimental quantities and subject to greater variation or are estimated using computational or empirical models. Solute descriptors for several thousand compounds are available in the Abraham descriptor database and for several hundred compounds in the WSU descriptor database. These publicly accessible databases were developed independently using different approaches and for many compounds provide different descriptor values. In this report we evaluate the effect of mixing descriptors from the two databases on modeling chromatographic retention factors and liquid-liquid partition constants. It is shown that the two descriptor databases are not interchangeable. The WSU descriptor database consistently demonstrates improved model quality as determined by statistical parameters. Model system constants exhibit a general dependence on database selection with an approximately linear trend as a function of the fraction of compounds assigned descriptors from either database. There is no general model performance advantage to using mixed descriptor datasets and no real cause for concern for relatively large datasets containing < 15 % of compounds with descriptors assigned from the other database. For small datasets, descriptor quality is an important variable for adequate model performance.

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.

Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS

As an essential environmental property, the aqueous solubility quantifies the hydrophobicity of a compound. It could be further utilized to evaluate the ecological risk and toxicity of organic pollutants. Concerned about the proliferation of organic contaminants in water and the associated technical burden, researchers have developed QSPR models to predict aqueous solubility. However, there are no standard procedures or best practices on how to comprehensively evaluate models. Hence, the CRITIC-TOPSIS comprehensive assessment method was first-ever proposed according to a variety of statistical parameters in the environmental model research field. 39 models based on 13 ML algorithms (belonged to 4 tribes) and 3 descriptor screening methods, were developed to calculate aqueous solubility values (log K_ws) for organic chemicals reliably and verify the effectiveness of the comprehensive assessment method. The evaluations were carried out for exhibiting better predictive accuracy and external competitiveness of the MLR-1, XGB-1, DNN-1, and kNN-1 models in contrast to other prediction models in each tribe. Further, XGB model based on SRM (XGB-1, C = 0.599) was selected as an optimal pathway for prediction of aqueous solubility. We hope that the proposed comprehensive evaluation approach could act as a promising tool for selecting the optimum environmental property prediction methods.

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.

Aromatic amines leachate from cigarette butts into aquatic environments: Is there risk for water organisms?

There are many toxics, such as aromatic amines (AAs), in cigarette butts (CBs). As CBs are the most abundant litter worldwide, these chemicals may leach into water bodies. In the present work, for the first time, the levels of AAs leachates from CBs in distilled water (DW) and river water (RW) samples were evaluated at different exposure times ranging from 15 min to 30 days. The mean leachate levels of AAs in DW and RW samples were in the range of 0.2-566 and 0.2-596 ng L^-1, respectively, with overall mean values of 569 and 556 ng L^-1. There was no significant difference (p > 0.05) between the total AAs levels as well as the level of each examined AA in DW and RW samples. Aniline (ANL) had the highest leaching rate from CBs into water. The mean leachates of AAs from CBs into water were ranked as: ANL> 1-naphthylamine (1-NA)> 2-naphthylamine (2-NA) > 2,6-dimethylaniline (2, 6-DMA)> ∑toluidine (∑TOL)> o-anisidine (o-ASD)> ∑aminobiphenyl (∑ABP). Ecological risk assessment showed that ∑7AAs, ANL, p-TOL, o-TOL, 2-NA, and ∑ABP had medium risks to sensitive crustaceans and fish. As AAs are not the only hazardous chemicals which may leach from CBs into aquatic environments, restrictions on littering CBs into the environment are required due to the release of different toxics ultimately causing adverse effects on aquatic organisms.

Implications of polar organic chemical integrative sampler for high membrane sorption and suitability of polyethersulfone as a single-phase sampler

Polar organic chemical integrative sampler (POCIS) contains sorbent, which is typically enclosed between two polyethersulfones (PES) membranes. A significant PES uptake is reported for many contaminants, yet, aqueous concentration is mainly correlated with the sorbent uptake using first-order kinetics. Under high PES sorption, the first-order kinetics often provide erroneous sampling rate for the sorbent phase due to increased membrane resistance. This work evaluated the uptake of four high PES sorbing chemicals, i.e., three Cl- and CH₃-substituted nitrobenzenes and one chlorinated aniline using POCIS and the potential of a single-phase PES sampler using laboratory experiments. POCIS calibration results demonstrated that both sorbent and membrane had similar affinity for the target compounds. A rapid PES sorption occurred in the earlier days (<7 days) followed by a gradual increase in the PES phase concentration (equilibrium not achieved after 60 days). Especially, the membrane was the primary sink for 3,4-dichloroaniline and 3,4-dichloronitrobenzene for up to 14 and 31 days, respectively. On the other hand, the single-phase PES sampler showed similar mass uptake as POCIS and reached equilibrium within 19 days under static condition, indicating its potential suitability in the equilibrium regime. PES-water partition coefficient of the target compounds was between 1.2 and 6.5 L/g. Finally, we present a poly-parameter linear-free energy relationship (pp-LFER) using published data to predict the PES-water partition coefficients. The pp-LFER models showed moderate predictability as indicated by R²_adj values between 0.7 and 0.9 for both internal and external data set consisting of a wide range of hydrophobic and hydrophilic compounds (-0.1 ≤ logK_OW ≤ 7.4). The proposed pp-LFER model can be used to screen high PES-sorbing chemicals to increase the reliability and accuracy of aqueous concentration prediction from POCIS sampling and to select the most appropriate sampling approach for new compounds.

In silico model-based exploration of the applicability of parallel artificial membrane permeability assay (PAMPA) to screen chemicals of environmental concern

Parallel Artificial Membrane Permeability Assay (PAMPA) is an in vitro laboratory method for screening the transmembrane permeability of chemicals. Stemming from medicinal chemistry, PAMPA has the potential for use in the cost-effective high-throughput evaluation of chemicals of environmental concern. However, many chemicals of environmental concern differ substantially from pharmaceuticals in hydrophobicity and volatility. Here, we develop an in silico mass balance model to explore the impacts of chemical properties on chemical mass transfer in PAMPA and PAMPA's applicability to hydrophobic or volatile chemicals of environmental concern. The model's performance is evaluated by agreement between predicted and measured permeabilities of 1383 chemicals. The model predicts that the PAMPA measured permeability can be highly uncertain for hydrophobic chemicals because of considerable retention by the artificial membrane and for volatile chemicals because of substantial volatilization to the headspace. Notably, the permeabilities of hydrophobic chemicals are remarkably sensitive to specific experimental conditions, for example, the frequency of stirring and incubation time, challenging the comparison between measurements under different conditions. For hydrophobic chemicals, the PAMPA measured permeability may largely indicate the permeability of the unstirred water layer over the membrane, instead of the "intrinsic" permeability of the membrane, and therefore, may not be of interest for environmental exposure and risk assessments. The model also predicts that the time for mass transfer of highly hydrophobic chemicals to reach the steady state likely exceeds the incubation time, which violates the steady-state assumption used in calculating permeability from measured concentrations. Overall, our theoretical analysis underscores the importance to consider chemical properties when applying the current design of PAMPA to chemicals of environmental concern.

Assessing the Persistence and Mobility of Organic Substances to Protect Freshwater Resources

Persistent and mobile organic substances are those with the highest propensity to be widely distributed in groundwater and thereby, when emitted at low-levels, to contaminate drinking water extraction points and freshwater environments. To prevent such contamination, the European Commission is in the process of introducing new hazard classes for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances within its key chemical regulations CLP and REACH. The assessment of persistence in these regulations will likely be based on simulated half-life, t _1/2, thresholds; the assessment of mobility will likely be based on organic carbon-water distribution coefficient, K _OC, thresholds. This study reviews the use of t _1/2 and K _OC to describe persistence and mobility, considering the theory, history, suitability, data limitations, estimation methods, and alternative parameters. For this purpose, t _1/2, K _OC, and alternative parameters were compiled for substances registered under REACH, known transformation products, and substances detected in wastewater treatment plant effluent, surface water, bank filtrate, groundwater, raw water, and drinking water. Experimental t _1/2 values were rare and only available for 2.2% of the 14 203 unique chemicals identified. K _OC data were only available for a fifth of the substances. Therefore, the usage of alternative screening parameters was investigated to predict t _1/2 and K _OC values, to assist weight-of-evidence based PMT/vPvM hazard assessments. Even when considering screening parameters, for 41% of substances, PMT/vPvM assessments could not be made due to data gaps; for 23% of substances, PMT/vPvM assessments were ambiguous. Further effort is needed to close these substantial data gaps. However, when data is available, the use of t _1/2 and K _OC is considered fit-for-purpose for defining PMT/vPvM thresholds. Using currently discussed threshold values, between 1.9 and 2.6% of REACH registered substances were identified as PMT/vPvM. Among the REACH registered substances detected in drinking water sources, 24-30% were PMT/vPvM substances.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

The octanol–air partition ratio (KOA) describes the partitioning of a chemical between air and octanol and is often used to approximate other partitioning phenomena in environmental chemistry (e.g., blood–air, atmospheric particulate matter–air, polyurethane foam-air). Such partitioning processes often occur at environmental temperatures other than 25 °C. Enthalpies $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ or internal energies $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of phase transfer are used to express the temperature dependence of the KOA. Existing poly-parameter linear free energy relationships (ppLFERs) for predicting $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ were developed using a relatively small dataset. In this work we utilize a recently developed comprehensive KOA database to create and curate a $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ dataset containing 195 chemicals and use this dataset in the development of new predictive equations. Using the QSAR development platform QSARINS we evaluate the use of Abraham descriptors, other molecular descriptors, and the log10KOA at 25 °C as variables in different multilinear regression equations for $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ . The $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of neutral organic chemicals can be reliably predicted using only the log10KOA (RMSEEXT = 6.86 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.94), only the solute’s hydrogen acidity A and the logarithm of the hexadecane–air partition ratio L (RMSEEXT = 7.23 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.93), or A and log10KOA (RMSEEXT = 6.76 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.95).

Toxicokinetic Modeling of the Transfer of Non-Dioxin-like Polychlorinated Biphenyls (ndl-PCBs) from Feed and Soil into Laying Hens and Their Eggs

Non-dioxin-like polychlorinated biphenyls (ndl-PCBs) are a subclass of persistent bioaccumulative pollutants able to enter the food chain. Toxicokinetic models for the transfer of the six ndl-PCB congeners (PCBs 28, 52, 101, 138, 153, and 180) from contaminated feed and soil into chicken eggs and meat are presented. Three independent controlled feeding study datasets were used to estimate the model parameters and four studies for evaluating the model performance. The yolk deposition of ndl-PCBs is modeled in a novel way that mimics the physiology of yolk growth and development, resulting in improved prediction of the experimental data without introducing an ad hoc time delay between ovulation and oviposition. Using the models, the highest level of 2.4 μg/kg dry matter (DM) was calculated for the sum of ndl-PCBs in laying hen feed to ensure that the current maximum levels in meat and eggs (40 ng/g fat) will not be exceeded. It is also shown how this highest level in feed should be adapted in case soil, in addition to feed, is also a source of ndl-PCBs for free-range chickens.

Retrieval, Selection, and Evaluation of Chemical Property Data for Assessments of Chemical Emissions, Fate, Hazard, Exposure, and Risks

Reliable chemical property data are the key to defensible and unbiased assessments of chemical emissions, fate, hazard, exposure, and risks. However, the retrieval, evaluation, and use of reliable chemical property data can often be a formidable challenge for chemical assessors and model users. This comprehensive review provides practical guidance for use of chemical property data in chemical assessments. We assemble available sources for obtaining experimentally derived and in silico predicted property data; we also elaborate strategies for evaluating and curating the obtained property data. We demonstrate that both experimentally derived and in silico predicted property data can be subject to considerable uncertainty and variability. Chemical assessors are encouraged to use property data derived through the harmonization of multiple carefully selected experimental data if a sufficient number of reliable laboratory measurements is available or through the consensus consolidation of predictions from multiple in silico tools if the data pool from laboratory measurements is not adequate.

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).

Non-targeted screening of volatile organic compounds in a museum in China Using GC-Orbitrap mass spectrometry

Non-targeted analysis (NTA) was used in identifying volatile organic compounds (VOCs) in a museum in China with the gas chromatograph (GC)-Orbitrap-mass spectrometer (MS). Approximately 230 VOCs were detected, of which 117 were observed at 100% frequency across all sampling sites. Although some were common in indoor environments, most of the detected VOCs were rarely reported in previous studies on museum environments. Some of the detected VOCs were found to be associated with the materials used in furnishings and the chemicals applied in conservation treatment. Spearman's correlation analysis showed that several classes of VOCs were well correlated, suggesting their common sources. Compared with compounds in outdoor air, indoor VOCs had a lower level of unsaturation and more portions of chemically reduced compounds. Hierarchical cluster analysis (HCA) were performed. The results suggested that the sampling adsorbents chosen may have a large impact and that a single type of adsorbent may not be sufficient to cover a wide range of compounds in NTA studies. The MonoTrap adsorbent containing octadecylsilane (ODS) and activated carbon (AC) is suitable for aliphatic polar compounds that contain low levels of oxygen, whereas the MonoTrap ODS and silica gel are good at sampling aliphatic and aromatic hydrocarbons with limited polarity. Principle component analysis (PCA) showed that the indoor VOCs changed significantly at different times in the museum; this may have been caused by the removal of artifacts and refurbishment of the gallery between sampling events. A comparison with compounds identified by chamber emission tests showed that decorative materials may have been one of the main sources of indoor VOCs in the museum. The VOCs identified in the present study are likely to be present in other similar museums; therefore, further examination may be warranted of their potential impacts on cultural heritage artifacts, museum personnel, and visitors.

Leaching of microplastic-associated additives in aquatic environments: A critical review

Microplastic pollution has attracted significant attention as an emerging global environmental problem. One of the most important issues with microplastics is the leaching of harmful additives. This review summarizes the recent advances in the understanding of the leaching phenomena in the context of the phase equilibrium between microplastics and water, and the release kinetics. Organic additives, which are widely used in plastic products, have been introduced because they have diverse physicochemical properties and mass fractions in plastics. Many theoretical and empirical models have been utilized in laboratory and field studies. However, the partition or distribution constant between microplastics and water (K_p) and the diffusivity of an additive in microplastics (D) are the two key properties explaining the leaching equilibrium and kinetics of hydrophobic organic additives. Because microplastics in aquatic environments undergo dynamic weathering, leaching of organic additives with high K_p and/or low D cannot be described by a leaching model that only considers microplastic and water phases with a fixed boundary. Surface modifications of microplastics as well as biofilms colonizing microplastic surfaces can alter the leaching equilibrium and kinetics and transform additives. Further studies on the release of hydrophobic organic additives and their transformation products under various conditions are required to extend our understanding of the environmental fate and transport of these additives in aquatic environments.

Prediction of heel build-up on activated carbon using machine learning

Determining the long-term performance of adsorbents is crucial for the design of air treatment systems. Heel buildup i.e., the accumulation of non-desorbed/ non-desorbable adsorbates and their reaction byproducts, on the surface/pores of the adsorbent is a primary cause of adsorption performance deterioration. However, due to the complexity of heel buildup mechanisms, theoretical models have yet to be developed to map the extent of heel buildup to the adsorption/desorption parameters. In this work, two machine learning (ML) algorithms (XGBoost and neural network (NN)) were applied to predict volatile organic compounds (VOCs) cyclic heel buildup on activated carbons (ACs) by considering the adsorbent characteristics, adsorbate properties and regeneration conditions. The NN algorithm showed better performance in prediction of cyclic heel buildup (R² = 0.94) than XGBoost (R² = 0.81). To analyze interaction between heel buildup and adsorbent characteristics, adsorbate properties, and regeneration conditions, partial dependency plots were generated. The proposed ML-based heel prediction methods can be ultimately used to: (i) optimize adsorption/desorption operating conditions to minimize heel buildup on activated carbon in cyclic adsorption/desorption processes and (ii) quickly screen various adsorbents for efficient adsorption/desorption of a particular family of VOCs by excluding adsorbents prone to high heel formation.