Parameter Linear Free Energy Relationships Research Articles

Experimental determination of the partitioning of representative organic pollutants to the air-water interface.

Using glancing-angle laser-induced fluorescence (GALIF) spectroscopy as a probe, the partitioning of naphthalene, fluoranthene, pyrene, umbelliferone, phenol red, and bisphenol A from bulk solution to the air-water interface was examined in both pure water and aqueous solutions of 6 mM octanol. Previous studies provided similar Langmuir adsorption isotherms for anthracene and imidazole 2-carboxaldehyde. The surface partitioning behaviour of each compound in both environments was well described using a Langmuir adsorption model; partitioning coefficients were derived from the fits to such isotherms. Only the PAH molecules, naphthalene, fluoranthene and pyrene, saw an enhancement in the surface partitioning in octanol solution compared to pure water. The surface partitioning to pure water surfaces could be fairly well described using a one parameter linear free energy relationship based on either solubility or KOW.

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

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

Performance evaluation and prediction of activated carbon for VOCs via experiments and LFER methods

Due to the diversity and fluidity of volatile organic compounds (VOCs), it is difficult to conduct experimental studies on activated carbon (AC) used to capture VOCs in industry. Therefore, predicting the adsorption capacity of AC for VOCs is of great significance for evaluating the adsorption performance of AC and developing its promising applications. Herein, based on the experimental data of dynamic adsorption, the poly parameter linear free energy relationship (pp-LFER) model was developed to quantify the interactions between VOCs and AC. Through the dynamic adsorption curves of 25 VOCs on a fixed bed packed with the AC, the partition coefficient (PC) and adsorption capacity (q) of AC for VOCs were calculated, and the pp-LFER equations used to predict them were established. For the PC values, the pp-LFER model consists of the excess molar refractive index (E), molar polarizability (S), hydrogen-bond acidity (A), and logarithmic hexadecane-air partition coefficient (L), and the R2 of it was 0.86. For the ln q values, the developed model can predict it in R2 of 0.83, and the increased adsorption capacity was predominantly attributed to the dispersive interaction (lL). Finally, the internal and external validation results confirmed that the developed models have reliability, robustness, and predictability.

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

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Predicting the Gas/Particle Distribution of SVOCs in the Indoor Environment Using Poly Parameter Linear Free Energy Relationships.

Understanding the partitioning of semi volatile organic compounds (SVOCs) between gas phase and particle phase is essential for exposure analysis and risk assessment in the indoor environment. Numerous attempts have been made to calculate gas/particle partitioning coefficients Kip. Single-parameter adsorption and absorption models, which relate Kip to the vapor pressure Ps or the octanol/air distribution coefficient KOA are usually applied. In this work we use poly parameter Linear Free Energy Relationships (pp-LFER) to describe the partitioning behavior of 14 SVOCs with high relevance for the indoor environment. The pp-LFER concept is based on Abraham descriptors and considers interactions between molecule and particle by separate parameters. van der Waals interactions can be approximated by the logarithm of the hexadecane/air partitioning coefficient (log KHdA = L), which is a key parameter for the 14 polar but nonionizable organic esters being studied here. For many of the examined compounds experimentally determined L-values were not available and had to be measured using gas chromatography. It is shown that the pp-LFER method is a strong alternative to single-parameter approaches and gives reliable coefficients for gas/particle distribution in the indoor environment.

Skin-mimetic chromatography for prediction of human percutaneous absorption of biologically active compounds occurring in medicinal plant extracts.

The main aim of this study was to predict quantitatively human percutaneous absorption of chosen compounds commonly occurring in plants which can be used as medicinal extracts in the drug and beauty industries. The most important human percutaneous descriptors, i.e. logKp (logarithm of the water/skin partition coefficient) and logJmax (logarithm of the maximum flux of solutes penetrating the skin), of fatty acids and polyphenols were determined using both in vitro and in silico methods. For in vitro determination of human percutaneous absorption, micellar liquid chromatography based on hexadecyltrimethylammonium bromide, sodium dodecyl sulfate and polyoxyethylene (23) lauryl ether (Brij35) was used. Human percutaneous absorption was characterized by entirely new QSAR/QRAR models based on retention, lipophilic, steric and electronic data as well as on the linear free energy relationship parameters. Many different correlations between human skin absorption and different physicochemical parameters were performed, e.g. the in silico estimated logKp value was correlated with the retention parameter logkw (logarithm of the retention factor extrapolated to pure water) from the systems imitating a cutaneous environment (R2 = 0.92). Moreover, the influence of lipophilicity on percutaneous absorption was examined. The obtained correlation was excellent (R2 = 0.95).

Approaches for estimating PUF-air partitions coefficient for semi-volatile organic compounds: A critical comparison

Partition coefficients between polyurethane foam (PUF) and air (KPUF-Air) are important when using PUF as a passive air sampler for semi-volatile organic compounds (SVOCs) and when considering the fate of SVOCs indoors where PUF is a common material. Here, KPUF-Air for selected SVOCs was estimated using published methods, since measured data are unavailable for most of these compounds. Estimates of KPUF-Air were within one order of magnitude for SVOCs having values of log octanol-air partition coefficient (KOA) of 5, but differed by nearly three orders of magnitude for SVOCs with log KOA of 12. Of all the methods, the correlation developed using experimental measurements gave the lowest estimates for the high KOA compounds, likely because the compounds did not reach equilibrium throughout the PUF sample. The surface area/volume ratio of the PUF sample was shown to influence the observed correlation, a reflection of the equilibration status of the PUF. For quantitative comparison, the poly parameter linear free energy relationship (pp-LFER) model of Kamprad and Goss (2007) was used here as a “surrogate” standard. The correlations developed with vapor pressure and KOA produced estimates that were closest to those obtained using the pp-LFER model. COSMO-RS theory, in which intimate and unimpeded contact is assumed between the compound in air and PUF molecules, gave lower estimates for low KOA compounds, but good average agreement for high KOA compounds. When used in modeling applications, the selection of the method for estimating KPUF-Air should reflect the configuration of the products containing PUF and the model assumptions regarding compound homogeneity within the PUF.

New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds.

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set.

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

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

Modeling Nonlinear Adsorption with a Single Chemical Parameter: Predicting Chemical Median Langmuir Binding Constants.

Procedures for accurately predicting linear partition coefficients onto various sorbents (e.g., organic carbon, soils, clay) are reliable and well established. However, similar procedures for the prediction of sorption parameters of nonlinear isotherm models are not. The purpose of this paper is to present a procedure for predicting nonlinear isotherm parameters, specifically the median Langmuir binding constants, K̃L, obtained utilizing the single-chemical parameter log-normal Langmuir isotherm developed in the accompanying work. A reduced poly parameter linear free energy relationship (pp-LFER) is able to predict median Langmuir binding constants for graphite, charcoal, and Darco granular activated carbon (GAC) adsorption data. For the larger F400 GAC data set, a single pp-LFER model was insufficient, as a plateau is observed for the median Langmuir binding constants of larger molecular volume sorbates. This volumetric cutoff occurs in proximity to the median pore diameter for F400 GAC. A log-linear relationship exists between the aqueous solubility of these large compounds and their median Langmuir binding constants. Using this relationship for the chemicals above the volumetric cutoff and the pp-LFER below the cutoff, the median Langmuir binding constants can be predicted with a root-mean square error for graphite (n = 13), charcoal (n = 11), Darco GAC (n = 14), and F400 GAC (n = 44) of 0.129, 0.307, 0.407, and 0.424, respectively.

Understanding and Modeling Removal of Anionic Organic Contaminants (AOCs) by Anion Exchange Resins

Ionic organic contaminants (OCs) are a growing concern for water treatment and the environment and are removed inefficiently by many existing technologies. This study examined removal of anionic OCs by anion exchange resins (AXRs) as a promising alternative. Results indicate that two polystyrene AXRs (IRA910 and IRA96) have higher sorption capacities and selectivity than a polyacrylate resin (A860). For the polystyrene resins, selectivity follows: phenolates ≥ aromatic dicarboxylates > aromatic monocarboxylates > benzenesulfonate > aliphatic carboxylates. This trend can be explained based on hydration energy, the number of exchange groups, and aromaticity and hydrophobicity of the nonpolar moiety (NPM) of the anions. For A860, selectivity only varies within a narrow range (0.13-1.64). Despite the importance of the NPM of the anions, neutral solutes were sorbed much less, indicating synergistic combinations of electrostatic and nonelectrostatic interactions in the overall sorption. By conducting multiple linear regression between Abraham's descriptors and nature log of selectivity, induced dipole-related interactions and electrostatic interactions were found to be the most important interaction forces for sorption of the anions, while solute H-bond basicity has a negative effect. A predictive model was then developed for carboxylates and phenolates based on the poly parameter linear free energy relationships established for a diverse range of 16 anions and 5 neutral solutes, and was validated by accurate prediction of sorption of five test solutes within a wide range of equilibrium concentrations and that of benzoate at different pH.

Interaction Mechanisms and Predictive Model for the Sorption of Aromatic Compounds onto Nonionic Resins

Understanding interaction mechanisms between porous sorbents and organic compounds is important in selecting or custom-synthesizing an appropriate sorbent. In this study, sorption isotherms of a set of 14 (XAD-4&7) or 11 (MN200) aromatic compounds were measured for three nonionic resins, and a phase conversion approach (from aqueous phase to n-hexadecane or gas phase) was applied to separate sorbate-sorbent interactions from the overall involved interactions. Subsequently, contributions of individual interactions to the overall ΔG were quantified by poly parameter linear free energy relationships (pp-LFERs). Cavity energy (V), energy costs for creating cavities in bulk water, is the dominant driving force for the sorption from aqueous phase. Meanwhile, sorption was substantially abated by H-bonding accepting capacities of the solutes (B) due to the high electron accepting capacity of water molecules. Solute’s H-bonding donating capacity (A) and polarity/polarizability (S) are predominantly responsible for the n-hexadecane or gas-phase converted sorptions; V is also important in the gas-phase converted sorption. XAD-7 has larger A and S coefficients than XAD-4 and MN200 for both the original and converted analyses, while the opposite is true for V coefficients. More promisingly, a predictive model, developed based on the sorption of 7 simple aromatic compounds by the resins, can accurately estimate the sorption behaviors of 7 other relatively complex aromatic compounds within a wide range of concentrations.

Experimentally Determined Soil Organic Matter–Water Sorption Coefficients for Different Classes of Natural Toxins and Comparison with Estimated Numbers

Although natural toxins, such as mycotoxins or phytoestrogens are widely studied and were recently identified as micropollutants in the environment, many of their environmentally relevant physicochemical properties have not yet been determined. Here, the sorption affinity to Pahokee peat, a model sorbent for soil organic matter, was investigated for 29 mycotoxins and two phytoestrogens. Sorption coefficients (K(oc)) were determined with a dynamic HPLC-based column method using a fully aqueous mobile phase with 5 mM CaCl(2) at pH 4.5. Sorption coefficients varied from less than 10(0.7) L/kg(oc) (e.g., all type B trichothecenes) to 10(4.0) L/kg(oc) (positively charged ergot alkaloids). For the neutral compounds the experimental sorption data set was compared with predicted sorption coefficients using various models, based on molecular fragment approaches (EPISuite's KOCWIN or SPARC), poly parameter linear free energy relationship (pp-LFER) in combination with predicted descriptors, and quantum-chemical based software (COSMOtherm)). None of the available models was able to adequately predict absolute K(oc) numbers and relative differences in sorption affinity for the whole set of neutral toxins, largely because mycotoxins exhibit highly complex structures. Hence, at present, for such compounds fast and consistent experimental techniques for determining sorption coefficients, as the one used in this study, are required.

Ionic Liquids: Predictions of Physicochemical Properties with Experimental and/or DFT-Calculated LFER Parameters To Understand Molecular Interactions in Solution

In this article, we present evolutionary models to predict the octanol-water partition coefficients (log P), water solubilities, and critical micelle concentrations (CMCs) of ionic liquids (ILs), as well as the anionic activity coefficients and hydrophobicities in pure water and octanol-water. They are based on a polyparameter linear free energy relationship (LFER) using measured and/or DFT-calculated LFER parameters: hydrogen-bonding acidity (A), hydrogen-bonding basicity (B), polarizability/dipolarity (S), excess molar refraction (E), and McGowan volume (V) of IL ions. With both calculated or experimental LFER descriptors of IL ions, the physicochemical parameters were predicted with an errors of 0.182-0.217 for the octanol-water partition coefficient and 0.131-0.166 logarithmic units for the water solubility. Because experimentally determined solute parameters of anions are not currently available, the CMC, anionic activity coefficient, and hydrophobicity were predicted with quantum-chemical methods with R(2) values of at least 0.99, as well as errors below 0.168 logarithmic units. These new approaches will facilitate the assessment of the technical applicability and environmental fate of ionic compounds even before their synthesis.

Predicting Sorption of Pesticides and Other Multifunctional Organic Chemicals to Soil Organic Carbon

Chemicals of current environmental concern are often multifunctional and more polar and more complex than classical pollutants such as polychlorinated biphenyls (PCB) or polycyclic aromatic hydrocarbons (PAH). Traditional models for predicting the partitioning in the environment such as group contribution methods or correlations with octanol-water partitioning cannot be expected to work well for such complex chemicals. In contrast, poly parameter Linear Free Energy Relationships (pp-LFERs) have been proven to describe partitioning of polar and nonpolar chemicals in all kinds of sorbing systems. Here, a pp-LFER model for soil-water partitioning was calibrated with data for 79 polar and nonpolar compounds that cover a very wide range of the relevant intermolecular interactions. The data set used for the model calibration in this work is more diverse and covers a wider range of the chemical space than other pp-LFERs published so far. Subsequently, the experimental data for about 50 pesticides and pharmaceuticals -not involved in the model calibration- were used as independent validation of this new calibrated model. The model performs well with a standard error of 0.25 log units for fitting the calibration data and with a root-mean-square error of 0.4 log units for the pesticides and pharmaceuticals. The validation with the independent data set for pesticides and pharmaceuticals also shows that the pp-LFER model reported here performs better compared to earlier published pp-LFER models and to the traditional log Kow correlation.

Classification of drugs according to their milk/plasma concentration ratio

The classification of drugs was done according to their milk/plasma concentration ratio (M/P) by using counter propagation artificial neural network (CP-ANN). The features of each drug were encoded by linear free energy relationship (LFER) parameters. These descriptors were used as inputs for developing linear discriminant analysis, quadratic discriminant analysis, least square support vector machine and CP-ANN models to distinguish the potential risk of 154 drugs as high risk (with M/P > 1) and low risk (with M/P < 1) for lactating women. The accuracy of classification for training, internal and external test sets was 100.00%, 100.00% and 90.00%, respectively for CP-ANN model, as the best model. The obtained results revealed the applicability of CP-ANN in classification of drugs based on their M/P values, using LFER parameters.

Theoretical and Experimental Simulation of the Fate of Semifluorinated n-Alkanes during Snowmelt

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

Prediction of biomagnification factors for some organochlorine compounds using linear free energy relationship parameters and artificial neural networks

Multiple linear regression and artificial neural networks (ANNs) as feature mapping techniques were used for the prediction of the biomagnification factor (BMF) of some organochlorine pollutants. As independent variables, or compound descriptors, the Abraham descriptors often employed in linear free energy relationships were used. Much better results were obtained from the nonlinear ANN model than from multiple linear regression. The average absolute error, average relative error and root mean square error in the calculation of log (BMF) by the ANN model were 0.055, 0.051 and 0.097 for the training set and 0.11, 0.086 and 0.175 for the internal validation set, respectively. The degree of importance of each descriptor was evaluated by carrying out a sensitivity analysis approach for the nonlinear model. The results obtained reveal that the order of importance is the pollutant volume, the pollutant dipolarity/polarizability and the pollutant excess molar refraction. In order to examine the credibility of the obtained ANN model the leave-many-out cross-validation test was applied which gave Q 2= 0.827 and SPRESS = 0.15. Also the Y-scrambling procedure was applied to the ANN model in order to examine the effect of chance correlations. The results obtained reveal that it is possible to predict the BMFs of organochlorine pollutants using a nonlinear ANN model with Abraham descriptors as inputs.

Evaluating coal tar–water partitioning coefficient estimation methods and solute–solvent molecular interactions in tar phase

Equilibrium partitioning coefficients between an industrial coal tar sample and water ( K CT/w) were determined for 41 polar and nonpolar solutes in batch systems. Together with literature values, 69 K CT/w data were analyzed using the following model approaches: Raoult’s law, the single parameter linear free energy relationship (SPLFER) with octanol–water partitioning coefficients ( K ow), the linear solvation energy relationships (LSERs), SPARC and COSMOtherm. Estimations by Raoult’s law and the SPLFER agreed well with the experimental log K CT/w values for the investigated coal tar, with root mean square errors (RMSE) of 0.31 and 0.33, respectively. LSER resulted in as good estimations (RMSE = 0.29) as the previous two. The LSER analysis revealed significant hydrogen (H)-bond acceptor properties of the studied coal tar phase. Using naphthalene as a surrogate solvent for the coal tar phase, SPARC and COSMOtherm provided fairly good predictions (RMSE of 0.63 and 0.65, respectively) of log K CT/w, without any additional empirical parameter. Further calculations using SPARC and COSMOtherm for partitioning between water and other tar-components (e.g., benzofuran, phenol and quinoline) suggested that minor components in coal tar do not significantly influence K CT/w of nonpolar solutes, and that Raoult’s law and the SPLFER thus may be generally applied to these types of solutes, e.g., polycyclic aromatic hydrocarbons and alkylbenzenes, regardless of coal tar compositions. In contrast, partitioning of H-bonding solutes (e.g., phenols) can significantly vary depending on the amount of polar tar-components such as N-heterocyclic aromatic compounds. Therefore, the presented successful applications of Raoult’s law and SPLFER to the studied coal tar could be a special case, and these simple approaches may not provide reasonable estimations for partitioning of H-bonding solutes from compositionally different coal tars.

Ambient Gas/Particle Partitioning. 2: The Influence of Particle Source and Temperature on Sorption to Dry Terrestrial Aerosols

In a companion paper we reported that, for apolar and most polar compounds, the dominating sorption mechanism governing ambient gas/particle partitioning under dry conditions is absorption into a water-insoluble organic matter (WIOM) phase, whereas under moist conditions, polar and ionized compounds can partition additionally into a mixed-aqueous phase. In order to understand how sorption into the WIOM varies for particles from diverse terrestrial locations, we looked at over 500 equilibrium gas/particle partitioning constants, Kip, measured at a specific temperature and relative humidity (15 degrees C, 50% RH), covering aerosol samples from all seasons and various locations. The data indicate that for every sample the WIOM exhibits similar intermolecular interactions with gas-phase organic compounds. For a given compound, the Kip values usually vary within a factor 3 for different aerosol samples, though they could vary by up to an order of magnitude. This is most likely due to variations in the WIOM weight fraction. Fitted poly parameter linear free energy relationships (PP-LFERs) were validated by giving good predictions of Kip values for many SVOCs in the literature, including n-alkanes, organochlorines, PCBs, though not PAHs as much of the particle-bound PAHs are likely nonexchangeable with the air phase. This study also investigated the influence of temperature on partitioning to WIOM, and found thatthe temperature dependence of Kip values can be reasonably predicted using the pure compound's enthalpy of vaporization.