Reliable Prediction of the Octanol-Air Partition Ratio

QSPR modeling for the prediction of partitioning of VOCs and SVOCs to indoor fabrics: Integrating environmental factors

Porous fabrics have a significant impact on indoor air quality by adsorbing and emitting chemical substances, such as volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs). Understanding the partition behavior between organic compound molecules and indoor fabrics is crucial for assessing their environmental fate and associated human exposure. The physicochemical properties of fabrics and compounds are fundamental in determining the free energy of partitioning. Moreover, environmental factors like temperature and humidity critically affect the partition process by modifying the thermal and moisture conditions of the fabric. However, existing methods for determining the fabric-air partition coefficient are limited to specific fabric-chemical combinations and lack a comprehensive consideration of indoor environmental factors. In this study, large amounts of experimental data on fabric-air partition coefficients (Kfa) of (S)VOCs were collected for silk, polyester, and cotton fabrics. Key molecular descriptors were identified, integrating the influences of physicochemical properties, temperature, and humidity. Subsequently, two typical quantitative structure-property relationship (QSPR) models were developed to correlate the Kfa values with the molecular descriptors. The fitting performance, robustness, and predictive ability of the two QSPR models were evaluated through statistical analysis and internal/external validation. This research provides insights for the high-throughput prediction of the environmental behaviors of indoor organic compounds.

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.

Metrics for estimating vapour pressure deviation from ideality in binary mixtures

A novel method is introduced for estimating the degree of interactions occurring between two different compounds in a binary mixture resulting in deviations from ideality as predicted by Raoult's law. Metrics of chemical similarity between binary mixture components were used as descriptors and correlated with the Root-Mean Square Error (RMSE) associated with Raoult's law calculations of total vapour pressure prediction, including Abraham descriptors, sigma moments, and several chemical properties. The best correlation was for a quantitative structure-activity relationship (QSAR) equation using differences in Abraham parameters as descriptors (r2 = 0.7585), followed by a QSAR using differences in COSMO-RS sigma moment descriptors (r2 = 0.7461), and third by a QSAR using differences in the chemical properties of log KAW, melting point, and molecular weight as descriptors (r2 = 0.6878). Of these chemical properties, Δlog KAW had the strongest correlation with deviation from Raoult's law (RMSE) and this property alone resulted in an r2 of 0.6630. These correlations are useful for assessing the expected deviation in Raoult's law estimations of vapour pressures, a key property for estimating inhalation exposure.

Prioritizing molecular formulae identified by non-target analysis through high-throughput modelling: application to identify compounds with high human accumulation potential from house dust

Because it is typically not possible to pursue compound identification efforts for all chemical features detected during non-target analysis (NTA), the need for prioritization arises. Here we propose a strategy that ranks chemical features detected in environmental samples based on a model-derived metric that quantifies a feature's attribute that makes it desirable to elucidate its structure, e.g., a high potential for bioaccumulation in humans or wildlife. The procedure involves the identification of isomers that could plausibly represent the molecular formulae assigned to NTA-detected chemical features. For each isomer, the prioritization metric is calculated using properties predicted with high-throughput methods. After the molecular formulae are ranked based on the average values of the prioritization metric calculated for all isomers assigned to a formula, the highest ranked molecular formulae are prioritized for structure elucidation. We applied this workflow to features identified in house dust, using the ratio of chemical intake through dust ingestion to chemical concentration in blood (dose-to-concentration ratio, DCR) as the prioritization metric. Collections of isomers for the molecular formulae were assembled from the PubChem database and DCR was estimated using partitioning and biotransformation properties predicted for each isomer using quantitative structure property relationships. The ten top-ranked molecular formulae with notably lower average DCR-values represented mostly compounds already known to be indoor pollutants of concern, such as two polybrominated diphenyl ethers, bis(2-ethylhexyl) tetrabromophthalate, tetrabromobisphenol A, tris(1,3-dichloroisopropyl)phosphate and the azo dye disperse blue 373.

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.

Octanol/Air Partition Coefficient─A General-Purpose Fragment Model to Predict Log Koa from Molecular Structure

The octanol/air partition coefficient K_oa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, K_oa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient K_ow and the air/water partition coefficient K_aw for multimedia fate modeling. Experimental log K_oa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting log K_oa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms: 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne: -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe: 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (80:20%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-K_oa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding K_oa indirectly determined through K_ow and K_aw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) K_oa.

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.

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).

Evidence of stockpile contamination for legacy polychlorinated biphenyls and organochlorine pesticides in the urban environment of Cyprus (Eastern Mediterranean): Influence of meteorology on air level variability and gas/particle partitioning based on equilibrium and steady-state models

The present study investigated comprehensively the atmospheric occurrence and fate of an extensive range of polychlorinated biphenyls (PCBs; forty-two congeners), organochlorine pesticides (OCPs; twenty-seven emerging and legacy agrochemicals) and polycyclic aromatic hydrocarbons (PAHs; fifty parent and alkylated members, including the non USEPA-16 listed toxic ones), in both gas and particulate phase of the scarcely monitored atmosphere over Cyprus for the first time. Parent-metabolite concentration ratios suggested fresh application for dichlorodiphenyl-trichloroethanes (DDTs), dicofol, hexachlorocyclohexanes, endosulfan and chlorothalonil, particularly during spring (April-May). Regressions of logarithms of partial pressure against ambient temperature revealed that secondary recycling from contaminated terrestrial surfaces regulates the atmospheric level variability of PCBs, DDTs, aldrin, chlordane, dicofol, heptachlor and endosulfan. Enthalpies of surface-air exchange (∆H_SA) calculated from Clausius-Clapeyron equations were significantly correlated to vaporization enthalpies (∆H_V) determined by chromatographic techniques, corroborating presence of potential stockpile-contaminated sites around the study area. The Harner-Bidleman equilibrium model simulating urban areas, and the Li-Jia empirical model, predicted better the partitioning behavior of PAHs (<four-ring parent and alkylated members), PCBs (<hexa-chlorobiphenyls), and OCPs, respectively. For heavier PAHs and PCBs, partitioning coefficients (K_P) were inadequately predicted by the Li-Ma-Yang steady-state model, probably due to local human activities and regional transport in the study area.

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.

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).

A holistic modeling framework for estimating the influence of climate change on indoor air quality

The IPCC 2021 report predicts rising global temperatures and more frequent extreme weather events in the future, which will have different effects on the regional climate and concentrations of ambient air pollutants. Consequently, changes in heat and mass transfer between the inside and outside of buildings will also have an increasing impact on indoor air quality. It is therefore surprising that indoor spaces and occupant well-being still play a subordinate role in the studies of climate change. To increase awareness for this topic, the Indoor Air Quality Climate Change (IAQCC) model system was developed, which allows short and long-term predictions of the indoor climate with respect to outdoor conditions. The IAQCC is a holistic model that combines different scenarios in the form of submodels: building physics, indoor emissions, chemical-physical reaction and transformation, mold growth, and indoor exposure. IAQCC allows simulation of indoor gas and particle concentrations with outdoor influences, indoor materials and activity emissions, particle deposition and coagulation, gas reactions, and SVOC partitioning. These key processes are fundamentally linked to temperature and relative humidity. With the aid of the building physics model, the indoor temperature and humidity, and pollutant transport in building zones can be simulated. The exposure model refers to the calculated concentrations and provides evaluations of indoor thermal comfort and exposure to gaseous, particulate, and microbial pollutants.

QSPRs for Predicting Equilibrium Partitioning in Solvent–Air Systems from the Chemical Structures of Solutes and Solvents

Poly-parameter Linear Free Energy Relationships (PPLFERs) based on the Abraham solvation model are a useful tool for predicting and interpreting equilibrium partitioning of solutes in solvent systems. The focus of this work is neutral organic solutes partitioning in neutral organic liquid solvent-air systems. This is a follow-up to previous work (Brown, 2021) which developed predictive empirical correlations between solute descriptors and system parameters, allowing system parameters to be predicted from the solute descriptors of the solvent. A database of solute descriptors, and a database of system parameters supplemented by empirical predictions, form the basis for the development of new Quantitative Structure Property Relationships (QSPRs). A total of 11 QSPRs have been developed for the E, S, A, B and L solute descriptors, and the s, a, b, v, l, and c system parameters. The QSPRs were developed using a group-contribution method referred to as Iterative Fragment Selection. The method includes robust internal and external model validation and a well-defined Applicability Domain, including estimates of prediction uncertainty. System parameters can also be predicted by combining the solute descriptor QSPRs and the empirical correlations. The predictive power of PPLFERs applied using different combinations of experimental data, empirical correlations, and QSPRs are externally validated by predicting partition ratios between solvents and air. The uncertainty for predicting the log10KSA of diverse solutes in diverse solvents using only the new QSPRs and empirical correlations is estimated to be one log10 unit or less.

Thirdhand smoke from tobacco, e-cigarettes, cannabis, methamphetamine and cocaine: Partitioning, reactive fate, and human exposure in indoor environments

A source of chemical exposure to humans, thirdhand smoke (THS) refers to the contamination that persists indoors following the cessation of a smoking event. The composition of thirdhand smoke depends on the type of substance from which it originates. Although past studies have investigated the effects of tobacco THS on indoor air quality and human health, few have focused on the chemical composition and health impacts of other sources and components of THS. Here we review the state of knowledge of the composition and partitioning behavior of various types of indoor THS, with a focus on THS from tobacco, e-cigarettes, cannabis, and illicit substances (methamphetamine and cocaine). The discussion is supplemented by estimates of human exposure to THS components made with a chemical fate and exposure model. The modeling results show that while very volatile THS compounds (i.e., aromatics) are likely to be taken up by inhalation, highly water-soluble compounds tended to be dermally absorbed. Conversely, minimally volatile THS compounds with low solubility are predicted to be ingested through hand-to-mouth and object-to-mouth contact.

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.