Characterization of the sorption of gaseous and organic solutes onto polydimethyl siloxane solid-phase microextraction surfaces using the Abraham model

Improved prediction of PFAS partitioning with PPLFERs and QSPRs

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

Assessing Aquatic Baseline Toxicity of Plastic-Associated Chemicals: Development and Validation of the Target Plastic Model

We developed a Target Plastic Model (TPM) to estimate the critical plastic burden of organic toxicants in five types of plastics, namely, polydimethylsiloxane (PDMS), polyoxymethylene (POM), polyacrylate (PA), low-density polyethylene (LDPE), and polyurethane ester (PU), following the Target Lipid Model (TLM) framework. By substituting the lipid-water partition coefficient in the TLM with plastic-water partition coefficients to create TPM, we demonstrated that the biomimetic nature of these plastic phases allows for the calculation of critical plastic burdens of toxicants, similar to the notion of critical lipid burdens in TLM. Following this approach, the critical plastic burdens of baseline (n = 115), less-inert (n = 73), and reactive (n = 75) toxicants ranged from 0.17 to 51.33, 0.04 to 26.62, and 1.00 × 10-6 to 6.78 × 10-4 mmol/kg of plastic, respectively. Our study showed that PDMS, PA, POM, PE, and PU are similar to biomembranes in mimicking the passive exchange of chemicals with the water phase. Using the TPM, median lethal concentration (LC50) values for fish exposed to baseline toxicants were predicted, and the results agreed with experimental values, with RMSE ranging from 0.311 to 0.538 log unit. Similarly, for the same data set of baseline toxicants, other widely used models, including the TLM (RMSE: 0.32-0.34), ECOSAR (RMSE: 0.35), and the Abraham Solvation Model (ASM; RMSE: 0.31), demonstrated comparable agreement between experimental and predicted values. For less inert chemicals, predictions were within a factor of 5 of experimental values. Comparatively, ASM and ECOSAR showed predictions within a factor of 2 and 3, respectively. The TLM based on phospholipid had predictions within a factor of 3 and octanol within a factor of 4, indicating that the TPM's performance for less inert chemicals is comparable to these established models. Unlike these methods, the TPM requires only the knowledge of plastic bound concentration for a given plastic phase to calculate baseline toxic units, bypassing the need for extensive LC50 and plastic-water partition coefficient data, which are often limited for emerging chemicals. Taken together, the TPM can provide valuable insights into the toxicities of chemicals associated with environmental plastic phases, assisting in selecting the best polymeric phase for passive sampling and designing better passive dosing techniques for toxicity experiments.

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.

Characterization of fire investigators' polyaromatic hydrocarbon exposures using silicone wristbands

BACKGROUND

Exposures to polyaromatic hydrocarbons (PAHs) contribute to cancer in the fire service. Fire investigators are involved in evaluations of post-fire scenes. In the US, it is estimated that there are up to 9000 fire investigators, compared to approximately 1.1 million total firefighting personnel. This exploratory study contributes initial evidence of PAH exposures sustained by this understudied group using worn silicone passive samplers.

OBJECTIVES

Evaluate PAH exposures sustained by fire investigators at post-fire scenes using worn silicone passive samplers. Assess explanatory factors and health risks of PAH exposure at post-fire scenes.

METHODS

As part of a cross-sectional study design, silicone wristbands were distributed to 16 North Carolina fire investigators, including eight public, seven private, and one public and private. Wristbands were worn during 46 post-fire scene investigations. Fire investigators completed pre- and post-surveys providing sociodemographic, occupational, and post-fire scene characteristics. Solvent extracts from wristbands were analyzed via gas chromatography-mass spectrometry (GC-MS). Results were used to estimate vapor-phase PAH concentration in the air at post-fire scenes.

RESULTS

Fire investigations lasted an average of 148 minutes, standard deviation ± 93 minutes. A significant positive correlation (r=0.455, p<.001) was found between investigation duration and PAH concentrations on wristbands. Significantly greater time-normalized PAH exposures (p=0.039) were observed for investigations of newer post-fire scenes compared to older post-fire scenes. Regulatory airborne PAH exposure limits were exceeded in six investigations, based on exposure to estimated vapor-phase PAH concentrations in the air at post-fire scenes.

DISCUSSION

Higher levels of off-gassing and suspended particulates at younger post-fire scenes may explain greater PAH exposure. Weaker correlations are found between wristband PAH concentration and investigation duration at older post-fire scenes, suggesting reduction of off-gassing PAHs over time. Exceedances of regulatory PAH limits indicate a need for protection against vapor-phase contaminants, especially at more recent post-fire scenes.

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.

Selectivity evaluation of extraction systems

Extraction is the most common sample preparation technique prior to chromatographic analysis for samples which are too complex, too dilute, or contain matrix components incompatible with the further use of the separation system or interfere in the detection step. The most important extraction techniques are biphasic systems involving the transfer of target compounds from the sample to a different phase ideally accompanied by no more than a tolerable burden of co-extracted matrix compounds. The solvation parameter model affords a general framework to characterize biphasic extraction systems in terms of their relative capability for solute-phase intermolecular interactions (dispersion, dipole-type, hydrogen bonding) and within phase solvent-solvent interactions for cavity formation (cohesion). The approach is general and allows the comparison of liquid and solid extraction phases using the same terms and is used to explain the features important for the selective enrichment of target compounds by a specific extraction phase using solvent extraction, liquid-liquid extraction, and solid-phase extraction for samples in a gas, liquid, or solid phase. Hierarchical cluster analysis with the system constants of the solvation parameter model as variables facilitates the selection of solvents for extraction, the identification of liquid-liquid distribution systems with non-redundant selectivity, and evaluation of different approaches using liquids and solids for the isolation of target compounds from different matrices.

Wearable Passive Samplers for Assessing Environmental Exposure to Organic Chemicals: Current Approaches and Future Directions

PURPOSE OF REVIEW

We are continuously exposed to dynamic mixtures of airborne contaminants that vary by location. Understanding the compositional diversity of these complex mixtures and the levels to which we are each exposed requires comprehensive exposure assessment. This comprehensive analysis is often lacking in population-based studies due to logistic and analytical challenges associated with traditional measurement approaches involving active air sampling and chemical-by-chemical analysis. The objective of this review is to provide an overview of wearable passive samplers as alternative tools to active samplers in environmental health research. The review highlights the advances and challenges in using wearable passive samplers for assessing personal exposure to organic chemicals and further presents a framework to enable quantitative measurements of exposure and expanded use of this monitoring approach to the population scale.

RECENT FINDINGS

Overall, wearable passive samplers are promising tools for assessing personal exposure to environmental contaminants, evident by the increased adoption and use of silicone-based devices in recent years. When combined with high throughput chemical analysis, these exposure assessment tools present opportunities for advancing our ability to assess personal exposures to complex mixtures. Most designs of wearable passive samplers used for assessing exposure to semi-volatile organic chemicals are currently uncalibrated, thus, are mostly used for qualitative research. The challenge with using wearable samplers for quantitative exposure assessment mostly lies with the inherent complexity in calibrating these wearable devices. Questions remain regarding how they perform under various conditions and the uncertainty of exposure estimates. As popularity of these samplers grows, it is critical to understand the uptake kinetics of chemicals and the different environmental and meteorological conditions that can introduce variability. Wearable passive samplers enable evaluation of exposure to hundreds of chemicals. The review presents the state-of-the-art of technology for assessing personal exposure to environmental chemicals. As more studies calibrate wearable samplers, these tools present promise for quantitatively assessing exposure at both the individual and population levels.

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

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

Development of a New Polar Organic Chemical Integrative Sampler for 1,4-dioxane Using Silicone Membrane as a Diffusion Barrier

Efficient monitoring methods must be developed for 1,4-dioxane, which is suspected to be carcinogenic to humans and is highly mobile in aquatic environments. In this regard, polar organic chemical integrative samplers (POCIS) have been utilized extensively as passive samplers for determining time-weighted average concentrations of hydrophilic organic compounds. However, POCIS are difficult to apply to extremely hydrophilic known organic compounds with negative log octanol-water partition coefficient (K_ow ) values due to their limited kinetic sampling time. Using an activated carbon-based sorbent with a high adsorption capacity and a bilayer of silicone and polyethersulfone membranes that inhibit mass transfer to the sorbent, we developed a POCIS device to measure 1,4-dioxane (log K_ow -0.27) in the present study. Permeation and field calibration tests demonstrated that the use of silicone membranes effectively reduces the water-to-sorbent mass transfer rate. The sampling rate and kinetic sampling period determined by field calibration tests were 1.4 ml day^-1 and >14 days, respectively. Finally, the developed POCIS device was applied to a landfill treatment plant to determine the 1,4-dioxane concentrations. Environ Toxicol Chem 2023;42:296-302. © 2022 SETAC.

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

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

pH-Dependent Partitioning of Ionizable Organic Chemicals between the Silicone Polymer Polydimethylsiloxane (PDMS) and Water

The silicone polymer polydimethysiloxane (PDMS) is a popular passive sampler for in situ and ex situ sampling of hydrophobic organic chemicals. Despite its limited sorptive capacity for polar and ionizable organic chemicals (IOC), IOCs have been found in PDMS when extracting sediment and suspended particulate matter. The pH-dependent partitioning of 190 organics and IOCs covering a range of octanol-water partition constants log K _ow from -0.3 to 7.7 was evaluated with a 10-day shaking method using mixtures composed of all chemicals at varying ratios of mass of PDMS to volume of water. This method reproduced the PDMS-water partition constant K _PDMS/w of neutral chemicals from the literature and extended the dataset by 93 neutral chemicals. The existing quantitative structure-activity relationship between the log K _ow and K _PDMS/w could be extended with the measured K _PDMS/w linearly to a log K _ow of -0.3. Fully charged organics were not taken up into PDMS. Thirty-eight monoprotic organic acids and 42 bases showed negligible uptake of the charged species, and the pH dependence of the apparent D _PDMS/w(pH) could be explained by the fraction of neutral species multiplied by the K _PDMS/w of the neutral species of these IOCs. Seventeen multiprotic chemicals with up to three acidity constants pK _a also showed a pH dependence of D _PDMS/w(pH) with the tendency that the neutral and zwitterionic forms showed the highest D _PDMS/w(pH). D _PDMS/w(pH) of charged species of more hydrophobic multiprotic chemicals such as tetrabromobisphenol A and telmisartan was smaller but not negligible. Since these chemicals show high bioactivity, their contribution to mixture effects has to be considered when testing passive sampling extracts with in vitro bioassays. This work has further implications for understanding the role of microplastic as a vector for organic micropollutants.

Determining chemical air equivalency using silicone personal monitors

BACKGROUND

Silicone personal samplers are increasingly being used to measure chemical exposures, but many of these studies do not attempt to calculate environmental concentrations.

OBJECTIVE

Using measurements of silicone wristband uptake of organic chemicals from atmospheric exposure, create log K_sa and k_e predictive models based on empirical data to help develop air equivalency calculations for both volatile and semi-volatile organic compounds.

METHODS

An atmospheric vapor generator and a custom exposure chamber were used to measure the uptake of organic chemicals into silicone wristbands under simulated indoor conditions. Log K_sa models were evaluated using repeated k-fold cross-validation. Air equivalency was compared between best-performing models.

RESULTS

Log K_sa and log k_e estimates calculated from uptake data were used to build predictive models from boiling point (BP) and other parameters (all models: R² = 0.70-0.94). The log K_sa models were combined with published data and refined to create comprehensive and effective predictive models (R²: 0.95-0.97). Final estimates of air equivalency using novel BP models correlated well over an example dataset (Spearman r = 0.984) across 5-orders of magnitude (<0.05 to >5000 ng/L).

SIGNIFICANCE

Data from silicone samplers can be translated into air equivalent concentrations that better characterize environmental concentrations associated with personal exposures and allow direct comparisons to regulatory levels.

Linear Solvation Energy Relationships (LSERs) for Robust Prediction of Partition Coefficients between Low Density Polyethylene and Water Part I: Experimental Partition Coefficients and Model Calibration

When equilibrium of leaching is reached within a product's duty cycle, partition coefficients polymer/solution dictate the maximum accumulation of a leachable and thus, patient exposure by leachables. Yet, in the pharmaceutical and food industry, exposure estimates based on predictive modeling typically rely on coarse estimations of the partition coefficient, with accurate and robust models lacking. This first part of the study aimed to explore linear solvation energy relationships (LSERs) as high performing models for the prediction of partition coefficients polymer/water. For this, partition coefficients between low density polyethylene (LDPE) and aqueous buffers for 159 compounds spanning a wide range of chemical diversity, molecular weight, vapor pressure, aqueous solubility and polarity (hydrophobicity) were determined and complimentary data collected from the literature (n=159, MW: 32 to 722, logK_i,O/W: -0.72 to 8.61 and logK_i,LDPE/W: -3.35 up to 8.36). The chemical space represented by this compounds set is considered indicative for the universe of compounds potentially leaching from plastics. Based on the dataset for the LDPE material purified by solvent extraction, a LSER model for partitioning between LDPE and water was calibrated to give:logK_i,LDPE/W=-0.529+1.098E_i-1.557S_i-2.991A_i-4.617B_i+3.886V_i. The model was proven accurate and precise (n = 156, R² = 0.991, RMSE = 0.264). Further, it was demonstrated superior over a log-linear model fitted to the same data. Nonetheless, it could be shown that log-linear correlations against logK_i,O/W can be of value for the estimation of partition coefficients for nonpolar compounds exhibiting low hydrogen-bonding donor and/or acceptor propensity. For these nonpolar compounds, the log - linear model was found to be: logK_i,LDPE/W=1.18logK_i,O/W-1.33 (n = 115, R²=0.985, RMSE = 0.313). In contrast, with mono-/bipolar compounds included into the regression data set, an only weak correlation was observed (n = 156, R² = 0.930, RMSE = 0.742) rendering the log-linear model of more limited value for polar compounds. Notably, sorption of polar compounds into native (non-purified) LDPE was found to be up to 0.3 log units lower than into purified LDPE. To identify maximum (i. e. worst-case) levels of leaching in support of chemical safety risk assessments on systems attaining equilibrium before end of shelf-life, it appears adequate to utilize LSER - calculated partition coefficients (in combination with solubility data) by ignoring any kinetical information.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor

Polydimethylsiloxane-air partition coefficient (K_PDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict K_PDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the R_adj², Q_BOOT² and Q_ext² are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental K_PDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

Reliable Prediction of the Octanol-Air Partition Ratio

The octanol-air equilibrium partition ratio (K_OA ) is frequently used to describe the volatility of organic chemicals, whereby n-octanol serves as a substitute for a variety of organic phases ranging from organic matter in atmospheric particles and soils, to biological tissues such as plant foliage, fat, blood, and milk, and to polymeric sorbents. Because measured K_OA values exist for just over 500 compounds, most of which are nonpolar halogenated aromatics, there is a need for tools that can reliably predict this parameter for a wide range of organic molecules, ideally at different temperatures. The ability of five techniques, specifically polyparameter linear free energy relationships (ppLFERs) with either experimental or predicted solute descriptors, EPISuite's KOAWIN, COSMOtherm, and OPERA, to predict the K_OA of organic substances, either at 25 °C or at any temperature, was assessed by comparison with all K_OA values measured to date. In addition, three different ppLFER equations for K_OA were evaluated, and a new modified equation is proposed. A technique's performance was quantified with the mean absolute error (MAE), the root mean square error (RMSE), and the estimated uncertainty of future predicted values, that is, the prediction interval. We also considered each model's applicability domain and accessibility. With an RMSE of 0.37 and a MAE of 0.23 for predictions of log K_OA at 25 °C and RMSE of 0.32 and MAE of 0.21 for predictions made at any temperature, the ppLFER equation using experimental solute descriptors predicted the K_OA the best. Even if solute descriptors must be predicted in the absence of experimental values, ppLFERs are the preferred method, also because they are easy to use and freely available. Environ Toxicol Chem 2021;40:3166-3180. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (K_PE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (K_PE-W), LDPE-air (K_PE-A) and LDPE-seawater (K_PE-SW) partition coefficients. These developed models have satisfying predictability (R²_adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSE_tra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q²_ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSE_ext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown K_PE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol–water and air–water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R² = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA’s model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model—a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)—performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R² = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.

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

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

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

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

Passive air sampling for semi-volatile organic chemicals

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

Comparing passive dosing and solvent spiking methods to determine the acute toxic effect of pentachlorophenol on Daphnia magna

Pentachlorophenol (PCP) is a widespread and persistent hydrophobic organic pollutant in the environment despite its restricted public use. Risk assessment of such hydrophobic organic compounds (HOCs) is challenging because sorption and volatilization issues during toxicity test often lead to inconsistent exposure concentration. Considering the hydrophobicity of the PCP, in this study, a passive dosing format was applied by adopting a silicone O-ring as a reservoir and evaluated its applicability on the determination of PCP on Daphnia magna. Results obtained with passive dosing method were compared with that of solvent spiking method. We hypothesized that the passive dosing method may provide more reliable and accurate toxicity results than conventional solvent spiking approach. As a result, the partition coefficient of PCP between methanol and a test medium (log K_MeOH:ISO) was 2.1, which enabled the maintenance of reliable exposure concentration throughout the experiment. In the acute toxicity tests, passive dosing and solvent spiking showed similar EC50 values of 576 and 485 µg/L for 24 h, and 362 and 374 µg/L for 48 h, respectively, which overlap with EC50 values of previous studies. Altogether, both methods were suitable for the acute toxicity assessment of hydrophobic PCP.

Analysis of volatile metabolites from in vitro biofilms of Pseudomonas aeruginosa with thin-film microextraction by thermal desorption gas chromatography-mass spectrometry

Cystic fibrosis (CF) is an autosomal recessive inherited disease which leads to a production of thickened mucus in the airways. These conditions are conducive to poly-microbial infections, like chronic lung infection, in which Pseudomonas aeruginosa (P. aeruginosa) is the major pathogenic bacterium colonizing CF lungs at the end of the lifetime of CF patients. This in vitro study uses a P. aeruginosa biofilm model under partly cystic fibrosis conditions, with a sampling of volatile extracellular metabolites. The gas sampling was done with thin-film microextraction (TFME) and commercial polydimethylsiloxane (PDMS) films, whereas the analysis of loaded films was done by gas chromatography coupled to quadrupole mass spectrometry and thermodesorption (TD-GC-qMS). For this purpose, two commercially available films were characterized by means of thermogravimetry coupled to a qMS with atmospheric pressure photo ionization (TG-APPI-qMS), regarding homogeneity and temperature stability. The selected film was cleaned using a method developed in this study. The TD-GC-qMS method was successfully used for standards of volatile metabolites which were known to be produced by P. aeruginosa. Limits of detection and quantification of the method for middle and less polar compounds in low nanomolar range (0.5 nM and 1.5 nM) were achieved. The developed method was finally applied to investigate the extracellular volatile metabolites produced by biofilms of the strain P. aeruginosa DSM 50071 under aerobic and anaerobic conditions. In sum, eleven metabolites could be found under both conditions. Furthermore, it was shown in this study that different oxygen conditions (aerobic and anaerobic) resulted in emitting different extracellular volatile metabolites. Specific metabolites, like 1-undecene (aerobic) and 2-undecanone (anaerobic), could be identified. The results are promising, in that the biofilm model may be applicable for the identification of P. aeruginosa under clinical conditions. Furthermore, the model could be the basis for studying extracellular volatile metabolites from different mono- or co-cultures of various bacteria, as well as the implementation of pulmonary conditions, like these in CF lungs. This possibility allows the development of a non-invasive “at-bedside” breath analysis method for CF patients in focus of various bacterial infections.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R²) of 0.949 and cross-validation coefficient (Q_Loo²) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

Polyethersulfone as suitable passive sampler for waterborne hydrophobic organic compounds - Laboratory calibration and field test in the Sosiani river, Kenya

We report the application of polyethersulfone (PES) membrane as a cost-saving and less labour-intensive single-phase passive sampler for waterborne hydrophobic organic compounds (HOCs) like organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polyaromatic hydrocarbons (PAHs). The uptake kinetics of 31 HOCs from water to porous polyethersulfone (PES) membranes and their partitioning behaviour were investigated in laboratory studies. Sampling rates (R_s) of HOCs with PES were determined in a range from 1.15 to 12.9 L/d. The uptake of test chemicals and the elimination of analogous (pre-loaded) performance reference compounds (PRCs) showed anisotropy, both under laboratory and field conditions, implying that PRCs are not suitable for determining in situ sampling rates with PES. The PES-water partition coefficients (K_pw) are, on average, ten times higher than the related K_ow. A Linear Solvation Energy Relationship for modelling the measured log K_pw with PES under inclusion of all available published data yields a poor fit in comparison to what is usually obtained with homogeneous polymers like polydimethylsiloxane or low-density polyethylene. At least a strong linear relationship was found between log R_s and log K_pw for the narrow log K_ow range of HOCs investigated in this work which can be used for interpolation to other HOCs in this range. The PES membranes were also tested in a field trial in a tropical river against the well-established silicone rubber (SR) sheets. With laboratory-based R_s for PES generated under field-relevant temperature and water flow velocity it was possible to obtain time-weighted average concentrations in the lower ng/L range which are comparable (within a factor of two) with those derived from accumulated amounts in SR sheets (using in situ sampling rates).

Predicting Sorbent-Air Partition Coefficients for Terpenoids at Multiple Temperatures

Partition coefficients describe the relative concentration of a chemical equilibrated between two phases. In the design of air samplers, the sorbent-air partition coefficient is a critical parameter, as is the ability to extrapolate or predict partitioning at a variety of temperatures. Our specific interest is the partitioning of plant-derived terpenes (hydrocarbons formed from isoprene building blocks) and terpenoids (with oxygen-containing functional groups) in polydimethylsiloxane (PDMS) sorbents. To predict K P D M S / A I R as a function of temperature for compounds containing carbon, hydrogen, and oxygen, we developed a group contribution model that explicitly incorporates the van't Hoff equation. For the 360 training compounds, predicted K P D M S / A I R values strongly correlate (R² > 0.987) with K P D M S / A I R values measured at temperatures from 60 °C to 200 °C. To validate the model with available literature data, we compared predictions for 50 additional C₁₀ compounds, including 6 terpenes and 22 terpenoids, with K P D M S / A I R values measured at 100 °C and determined an average relative error of 3.1 %. We also compared predictions with K P D M S / A I R values measured at 25 °C. The modeling approach developed here is advantageous for properties with limited experimental values at a single temperature.

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

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

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

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

Calibration of polydimethylsiloxane and polyurethane foam passive air samplers for measuring semi volatile organic compounds using a novel exposure chamber design

Passive air sampling is increasingly used for air quality monitoring and for personal sampling. In a novel experimental exposure chamber study, 3 types of polydimethylsiloxane (PDMS, including sheet and wristband) and 1 type of polyurethane foam (PUF) passive air samplers were tested for gas-phase uptake of 200 semi volatile organic compounds (SVOCs) during six months. For 155 SVOCs including PAH, PCB, phthalates, organophosphate esters, musk compounds, organochlorine- and other pesticides, a normalized generic uptake rate (Rs) of 7.6 ± 1.3 m³ d^-1 dm^-2 and a generic mass transfer coefficient (MTC) of 0.87 ± 0.15 cm s^-1 at a wind speed of 1.3 m s^-1 were determined. Variability of sampling rates within and between passive sampling media and analyte groups was not statistically significant, supporting the hypothesis of air-side controlled uptake regardless of sampling material. A statistical relationship was developed between the sampling rate and windspeed which can be used to obtain a sampling rate applicable to specific deployment conditions. For 98 SVOCs, partition coefficients (Ksampler-air) for PUF and PDMS were obtained, which determine the duration of linear uptake and capacity of the sampler for gas-phase uptake. Ksampler-air for PDMS were approximately 10 times higher than for PUF, suggesting that PDMS can be deployed for longer time per volume of sampler, while uptake remains in the linear phase. Statistical relationships were developed to estimate Kpuf-air and Kpdms-air from Koa. These results improve the understanding of the performance of PDMS and PUF passive samplers and contribute to the development of PDMS for the use as a promising personal sampler.

Characterization of sorption properties of high-density polyethylene using the poly-parameter linearfree-energy relationships

High-density polyethylene (HDPE) is a known sorbent for non-ionic organic compounds in technical applications. Nevertheless, there is little information available describing sorption to industrial HDPE for a broad range of compounds. With a better understanding of the sorption properties of synthetic polymers, environmental risk assessment would achieve a higher degree of accuracy, especially for microplastic interactions with organic substances. Therefore, a robust methodology for the determination of sorbent properties for non-ionic organic compounds by HDPE is relevant for the understanding of molecular interactions for both technical use and environmental risk assessment. In this work, sorption properties of HDPE material used for water pipes were characterized using a poly-parameter linear free-energy relationship (ppLFER) approach. Sorption batch experiments with selected probe sorbates were carried out in a three-phase system (air/HDPE/water) covering an aqueous concentration range of at least three orders of magnitude. Sorption in the concentration range below 10^-2 of the aqueous solubility was found to be non-linear and the Freundlich model was used to account for this non-linearity. Multiple regression analysis (MRA) using the determined distribution coefficients and literature-tabulated sorbate descriptors was performed to obtain the ppLFER phase descriptors for HDPE. Sorption properties of HDPE were then derived from the ppLFER model and statistical analysis of its robustness was conducted. The derived ppLFER model described sorption more accurately than commonly used single-parameter predictions, based i.e., on log K_o/w. The ppLFER predicted distribution data with an error 0.5 log units smaller than the spLFERs. The ppLFER was used for a priori prediction of sorption by the characterized sorbent material. The prediction was then compared to experimental data from literature and this work and demonstrated the strength of the ppLFER, based on the training set over several orders of magnitude.

Technical basis for using passive sampling as a biomimetic extraction procedure to assess bioavailability and predict toxicity of petroleum substances

Solid-phase microextraction fibers coated with polydimethylsiloxane (PDMS) provide a convenient passive sampling format to characterize bioavailability of petroleum substances. Hydrocarbons absorb onto PDMS in proportion to both freely dissolved concentrations and partitioning properties of the individual constituents, which parallels the mechanistic basis used to predict aquatic toxicity in the PETROTOX model. When deployed in a non-depletive manner, combining SPME with thermal desorption and quantification using gas chromatography-flame ionization creates a biomimetic extraction (BE) procedure that has the potential to simplify aquatic hazard assessments of petroleum substances since the total moles of all hydrocarbons sorbed to the fiber can be related to toxic thresholds in target lipid of aquatic organisms. The objective of this work is to describe the technical basis for applying BE measurements to predict toxicity of petroleum substances. Critical BE-based PDMS concentrations corresponding to adverse effects were empirically derived from toxicity tests on different petroleum substances with multiple test species. The resulting species sensitivity distribution (SSD) of PDMS effect concentrations was then compared and found consistent with the previously reported target lipid-based SSD. Further, BE data collected on samples of aqueous media dosed with a wide range of petroleum substances were highly correlated to predicted toxic units derived using the PETROTOX model. These findings provide justification for applying BE in environmental hazard and risk evaluations of petroleum substances and related mixtures.

Two distinct mechanisms upon absorption of volatile organic compounds into siloxane polymers

The response of polysiloxane materials to volatile organic compounds (VOCs) including benzene, toluene, ethylbenzene, and toluene (BTEX), as well as cyclohexane, acetone, methanol and isopropanol is studied using thin film large-angle refractometry. Refractive index and thickness changes are measured to quantify the diffusion rate and partition coefficients associated with the absorption and desorption of VOC vapours into polydimethylsiloxane (PDMS) and polydiphenylsiloxane (PDPS) - PDMS copolymer films. Absorption of volatile solvent vapours into siloxane polymers is found to follow two distinct mechanisms with different absorption rates. These mechanisms are also associated with different excess volumes of mixing and may be accompanied by a polymer restructuring step.

Effects of soil water saturation on sampling equilibrium and kinetics of selected polycyclic aromatic hydrocarbons

Passive sampling can be applied for measuring the freely dissolved concentration of hydrophobic organic chemicals (HOCs) in soil pore water. When using passive samplers under field conditions, however, there are factors that might affect passive sampling equilibrium and kinetics, such as soil water saturation. To determine the effects of soil water saturation on passive sampling, the equilibrium and kinetics of passive sampling were evaluated by observing changes in the distribution coefficient between sampler and soil (K_sampler/soil) and the uptake rate constant (k_u) at various soil water saturations. Polydimethylsiloxane (PDMS) passive samplers were deployed into artificial soils spiked with seven selected polycyclic aromatic hydrocarbons (PAHs). In dry soil (0% water saturation), both K_sampler/soil and k_u values were much lower than those in wet soils likely due to the contribution of adsorption of PAHs onto soil mineral surfaces and the conformational changes in soil organic matter. For high molecular weight PAHs (chrysene, benzo[a]pyrene, and dibenzo[a,h]anthracene), both K_sampler/soil and k_u values increased with increasing soil water saturation, whereas they decreased with increasing soil water saturation for low molecular weight PAHs (phenanthrene, anthracene, fluoranthene, and pyrene). Changes in the sorption capacity of soil organic matter with soil water content would be the main cause of the changes in passive sampling equilibrium. Henry's law constant could explain the different behaviors in uptake kinetics of the selected PAHs. The results of this study would be helpful when passive samplers are deployed under various soil water saturations.

Sorbent material characterization using in-tube extraction needles as inverse gas chromatography column

In-tube extraction is a full automated enrichment technique that consists of a stainless-steel needle, packed with sorbent material for the extraction of volatile and semivolatile compounds. In principle, all particulate sorbents used for enrichment in air or headspace analysis can be used. However, the selection of the sorbents is merely based on empirical considerations rather than on experimental data, which is caused by a lack of knowledge about the relevant physicochemical properties of the sorbent. Especially, the knowledge of hydrostatic, advective, diffusive, and dispersion mechanisms in addition to sorption enthalpies are important for combined transport and sorption models. To provide these missing parameters, we developed and evaluated a method in which an ordinary in-tube extraction needle was employed directly as column for sorbent characterization by inverse gas chromatography. As probe compounds, benzene, ethyl acetate, and 3-methyl-1-butanol were used to determine thermodynamic parameters such as sorption enthalpy, partitioning constant between the solid and gas phase, and kinetic parameters such as the diffusion coefficient, dispersion coefficient, and apparent permeability, exemplarily. As sorbent, three commercially available phases were characterized to demonstrate the applicability of the method.

Predicting Partitioning and Diffusion Properties of Nonpolar Chemicals in Biotic Media and Passive Sampler Phases by GC × GC

The chemical parameters needed to explain and predict bioavailability, biodynamics, and baseline toxicity are not readily available for most nonpolar chemicals detected in the environment. Here, we demonstrate that comprehensive two-dimensional gas chromatography (GC × GC) retention times can be used to predict 26 relevant properties for nonpolar chemicals, specifically: partition coefficients for diverse biotic media and passive sampler phases; aquatic baseline toxicity; and relevant diffusion coefficients. The considered biotic and passive sampler phases include membrane and storage lipids, serum and muscle proteins, carbohydrates, algae, mussels, polydimethylsiloxane, polyethylene, polyoxymethylene, polyacrylate, polyurethane, and semipermeable membrane devices. GC × GC-based chemical property predictions are validated with a compilation of 1038 experimental property data collected from the literature. As an example application, we overlay a map of baseline toxicity to fathead minnows onto the separated analyte signal of a polychlorinated alkanes (chlorinated paraffins) technical mixture that contains 7820 congeners. In a second application, GC × GC-estimated properties are used to parametrize multiphase partitioning models for mammalian tissues and organs. In a third example, we estimate chemical depuration kinetics for mussels. Finally, we illustrate an approach to screen the GC × GC chromatogram for nonpolar chemicals of potentially high concern, defined based on their GC × GC-estimated biopartitioning properties, diffusion properties, and baseline toxicity.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (K_pew). For chemicals with the octanol-water partition coefficient (log K_ow) <8, a TLSER model with V_x (McGowan volume) and qA^- (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R²) and cross-validated coefficient (Q²). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log K_OW>8, a TLSER model with V_x and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water.

Polydimethylsiloxane-air partition ratios for semi-volatile organic compounds by GC-based measurement and COSMO-RS estimation: Rapid measurements and accurate modelling

Polydimethylsiloxane (PDMS) shows promise for use as a passive air sampler (PAS) for semi-volatile organic compounds (SVOCs). To use PDMS as a PAS, knowledge of its chemical-specific partitioning behaviour and time to equilibrium is needed. Here we report on the effectiveness of two approaches for estimating the partitioning properties of polydimethylsiloxane (PDMS), values of PDMS-to-air partition ratios or coefficients (KPDMS-Air), and time to equilibrium of a range of SVOCs. Measured values of KPDMS-Air, Exp' at 25 °C obtained using the gas chromatography retention method (GC-RT) were compared with estimates from a poly-parameter free energy relationship (pp-FLER) and a COSMO-RS oligomer-based model. Target SVOCs included novel flame retardants (NFRs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs), organophosphate flame retardants (OPFRs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Significant positive relationships were found between log KPDMS-Air, Exp' and estimates made using the pp-FLER model (log KPDMS-Air, pp-LFER) and the COSMOtherm program (log KPDMS-Air, COSMOtherm). The discrepancy and bias between measured and predicted values were much higher for COSMO-RS than the pp-LFER model, indicating the anticipated better performance of the pp-LFER model than COSMO-RS. Calculations made using measured KPDMS-Air, Exp' values show that a PDMS PAS of 0.1 cm thickness will reach 25% of its equilibrium capacity in ∼1 day for alpha-hexachlorocyclohexane (α-HCH) to ∼ 500 years for tris (4-tert-butylphenyl) phosphate (TTBPP), which brackets the volatility range of all compounds tested. The results presented show the utility of GC-RT method for rapid and precise measurements of KPDMS-Air.

Equations for water-triolein partition coefficients for neutral species; comparison with other water-solvent partitions, and environmental and toxicological processes

Linear free energy relationships, LFERs, have been constructed for water-triolein partition coefficients for neutral species. It is shown that separate equations are required for wet and dry triolein. From a comparison of the equation coefficients for water-wet triolein with those for 52 other water-solvent systems it is shown that there is little correspondence between triolein and any of the 52 other solvents - only the water-isopropyl myristate system is close to the water-wet triolen system. A comparison of equation coefficients for the water-wet triolein system with LFER coefficients of 16 environmentally important processes shows that wet triolein is not a suitable model for any of the processes, although a number of other water-solvent systems are possible models for some of the environmental processes. A comparison of LFER coefficients with those of 17 aqueous toxicological processes reveals that most of the water-solvent systems, including water-wet triolein, will be poor models for any of the toxicological systems, but the water-lower alcohol systems show promise as models for a number of the toxicological systems. Our method of comparison of coefficients for LFERs that have exactly the same independent variables can be extended to various other types of system.

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.

Experimental Solubility Approach to Determine PDMS-Water Partition Constants and PDMS Activity Coefficients

Freely dissolved aqueous concentration and chemical activity are important determinants of contaminant transport, fate, and toxic potential. Both parameters are commonly quantified using Solid Phase Micro-Extraction (SPME) based on a sorptive polymer such as polydimethylsiloxane (PDMS). This method requires the PDMS-water partition constants, KPDMSw, or activity coefficient to be known. For superhydrophobic contaminants (log KOW >6), application of existing methods to measure these parameters is challenging, and independent measures to validate KPDMSw values would be beneficial. We developed a simple, rapid method to directly measure PDMS solubilities of solid contaminants, SPDMS(S), which together with literature thermodynamic properties was then used to estimate KPDMSw and activity coefficients in PDMS. PDMS solubility for the test compounds (log KOW 7.2-8.3) ranged over 3 orders of magnitude (4.1-5700 μM), and was dependent on compound class. For polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins (PCDDs), solubility-derived KPDMSw increased linearly with hydrophobicity, consistent with trends previously reported for less chlorinated congeners. In contrast, subcooled liquid PDMS solubilities, SPDMS(L), were approximately constant within a compound class. SPDMS(S) and KPDMSw can therefore be predicted for a compound class with reasonable robustness based solely on the class-specific SPDMS(L) and a particular congener's entropy of fusion, melting point, and aqueous solubility.