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

Screening for compounds with bioaccumulation potential in breast milk using their retention behavior in two-dimensional gas chromatography

Discovery of emerging pollutants in breast milk will be helpful for understanding the hazards to human health. However, it is difficult to identify key compounds among thousands present in complex samples. In this study, a method for screening compounds with bioaccumulation potential was developed. The method can decrease the number of compounds needing structural identification because the partitioning properties of bioaccumulative compounds can be mapped onto GC×GC chromatograms through their retention behaviors. Twenty pooled samples from seven provinces in China were analyzed. 1,286 compounds with bioaccumulation potential were selected from over 3,000 compounds. Sixty-two compounds, including aromatic compounds, phthalates, and phenolics etc., were identified with a high level of confidence and then quantified. Among them, twenty-seven compounds were found for the first time in breast milk. Three phthalate plasticizers and two phenolic antioxidants were found in significantly higher concentrations than other compounds. A toxicological priority index approach was applied to prioritize the compounds considering their concentrations, detection frequencies and eight toxic effects. The prioritization indicated that 13 compounds, including bis(2-ethylhexyl) phthalate, dibutyl phthalate, 1,3-di-tert-butylbenzene, phenanthrene, 2,6-di-tert-butyl-1,4-benzoquinone, 2,4-di-tert-butylphenol, and others, showed higher health risks. Meanwhile, some compounds with high risk for a particular toxic effect, such as benzothiazole and geranylacetone, were still noteworthy. This study is important for assessing the risks of human exposure to organic compounds.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

Addressing new chemicals of emerging concern (CECs) in an indoor office

Indoor pollutants change over time and place. Exposure to hazardous organics is associated with adverse health effects. This work sampled gaseous organics by Tenax TA tubes in two indoor rooms, i.e., an office set as samples, and the room of chassis dynamometer (RCD) set as backgrounds. Compounds are analyzed by a thermal desorption comprehensive two-dimensional gas chromatography-quadrupole mass spectrometer (TD-GC × GC-qMS). Four new chemicals of emerging concern (CECs) are screened in 469 organics quantified. We proposed a three-step pipeline for CECs screening utilizing GC × GC including 1) non-target scanning of organics with convincing molecular structures and quantification results, 2) statistical analysis between samples and backgrounds to extract useful information, and 3) pixel-based property estimation to evaluate the contamination potential of addressed chemicals. New CECs spotted in this work are all intermediate volatility organic compounds (IVOCs), containing mintketone, isolongifolene, β-funebrene, and (5α)-androstane. Mintketone and sesquiterpenes may be derived from the use of volatile chemical products (VCPs), while (5α)-androstane is probably human-emitted. The occurrence and contamination potential of the addressed new CECs are reported for the first time. Non-target scanning and the measurement of IVOCs are of vital importance to get a full glimpse of indoor organics.

Predicting aqueous sorption of organic pollutants on microplastics with machine learning

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

Reliable prediction of sensory irritation threshold values of organic compounds using new models based on linear free energy relationships and GC×GC retention parameters

The human sensory irritation threshold (SIT) is an important biochemical parameter for the exposure assessment of organic air pollutants. First, we recalibrated the Abraham solvation models (ASMs) for 9 SIT endpoints by curating 720 individual experimental SIT values to find an accurate and parsimonious ASM variant, which exhibited root mean square error (RMSE) = 0.174-0.473 log unit. Second, we report linear free energy relationships - henceforth called partition models (PMs) - which exploit the correlations of 9 SIT endpoints with the linear combinations of partition coefficients for octanol-water and air-water systems showing RMSE = 0.221-0.591 log unit. These PMs can easily be integrated into widely used EPI-Suite™ screening tool. The explanatory and predictive performance of PMs were like parameter-intensive ASMs. Third, we present GC × GC models that are based on the retention times of the nonpolar analytes on the comprehensive two-dimensional gas chromatography (GC × GC), which successfully described the SIT variance (R²=0.959-0.996) and depicted a strong predictive power (RMSE = 0.359-0.660 log unit) for an independent set of nonpolar analytes. Taken together, PMs allow easy SIT screening of organic chemicals compared to ASMs. Unlike ASMs, our GC × GC models can be applied to estimate SIT of complex nonpolar mixtures.

Detailed Speciation of Semi-Volatile and Intermediate-Volatility Organic Compounds (S/IVOCs) in Marine Fuel Oils Using GC × GC-MS

Ship emissions contribute substantial air pollutants when at berth. However, the complexity and diversity of the marine fuels utilized hinder our understanding and mapping of the characteristics of ship emissions. Herein, we applied GC × GC-MS to analyze the components of marine fuel oils. Owing to the high separation capacity of GC × GC-MS, 11 classes of organic compounds, including b-alkanes, alkenes, and cyclo-alkanes, which can hardly be resolved by traditional one-dimensional GC-MS, were detected. Significant differences are observed between light (-10# and 0#) and heavy (120# and 180#) fuels. Notably, -10# and 0# diesel fuels are more abundant in b-alkanes (44~49%), while in 120# and 180#, heavy fuels b-alkanes only account for 8%. Significant enhancement of naphthalene proportions is observed in heavy fuels (20%) compared to diesel fuels (2~3%). Hopanes are detected in all marine fuels and are especially abundant in heavy marine fuels. The volatility bins, one-dimensional volatility-based set (VBS), and two-dimensional VBS (volatility-polarity distributions) of marine fuel oils are investigated. Although IVOCs still take dominance (62-66%), the proportion of SVOCs in heavy marine fuels is largely enhanced, accounting for ~30% compared to 6~12% in diesel fuels. Furthermore, the SVOC/IVOC ratio could be applied to distinguish light and heavy marine fuel oils. The SVOC/IVOC ratios for -10# diesel fuel, 0# diesel fuel, 120# heavy marine fuel, and 180# heavy marine fuel are 0.085 ± 0.046, 0.168 ± 0.159, 0.504, and 0.439 ± 0.021, respectively. Our work provides detailed information on marine fuel compositions and could be further implemented in estimating organic emissions and secondary organic aerosol (SOA) formation from marine fuel storage and evaporation processes.

Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances

Comprehensive two-dimensional gas chromatography (GCxGC) offers unrivaled separation of petroleum substances, which can contain thousands of constituents or more. However, interpreting substance compositions from GCxGC data is costly and requires expertise. To facilitate environmental risk assessments, industries provide aggregated compositional information known as "hydrocarbon blocks" (HCBs), but these proprietary methods do not transparently associate the HCBs with GCxGC chromatogram data. These obstacles frustrate efforts to study the environmental risks of petroleum substances and associated environmental samples. To address this problem, we developed a GCxGC elution model for user-defined petroleum substance compositions. We calibrated the elution model to experimental GCxGC retention times of 56 known hydrocarbons by fitting three tunable model parameters to two candidate instrument methods. With the calibrated model, we simulated retention times for a library of 15,447-15,455 hydrocarbon structures (plus 40-48 predicted as chromatographically unretained) spanning 11 classes of petroleum substance constituents in the C₁₀-C₃₀ range. The resulting simulation data reveal that GCxGC retention times are quantitatively associated with hydrocarbon class and carbon number information throughout the GCxGC chromatogram. These innovations enable the development of transparent and efficient technical methods to investigate the chemical compositions and environmental properties of petroleum substances, including in environmental and lab-weathered samples.

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

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

Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches

Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.

Passive sampling of chlorinated paraffins by silicone: Focus on diffusion and silicone-water partition coefficients

Short-chain chlorinated paraffins (SCCPs) are under regulation through the European Water Framework Directive and were recently classified as POPs. Consecutively, the increasing use of middle-chain chlorinated paraffins (MCCPs) becomes of growing concern. Knowledge on the occurrence of chlorinated paraffins (CPs) is still scarce particularly in water phase. To achieve sufficient method sensitivity, the passive sampling approach, acting as a relevant alternative to usual grab sampling, has been considered only very recently for the monitoring of CPs in water. The present work aimed at determining the diffusion coefficients in silicone (D_s) and the silicone-water partition coefficients (K_sw) of various CP groups, having different chlorine contents and carbon chain lengths, in four commercial CP mixtures. Log D_s (-10.78 to -10.21) was found to vary little and to be high for the groups of CPs studied. Thus, their uptake in silicone is controlled by the water boundary layer, which allows to consider the release of performance and reference compounds for in-field estimation of the sampling rate. Moreover, CPs partitioned strongly towards silicone rubbers. Both the chlorination degree and the carbon chain length of CPs cause large uncertainties in the partitioning between silicone and water (log K_sw between 4.85 and 6.30), indicating that instead of an average value, differentiated K_sw should be used to estimate aqueous CPs more accurately. Even so, the probable influence of chlorine atoms position on polarity and partitioning may be an argument for favoring sampling in the kinetic stage.

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.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (K_PUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the K_PUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.

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.

QSPR models for predicting the adsorption capacity for microplastics of polyethylene, polypropylene and polystyrene

Microplastics have become an emerging concerned global environmental pollution problem. Their strong adsorption towards the coexisting organic pollutants can cause additional environmental risks. Therefore, the adsorption capacity and mechanisms are necessary information for the comprehensive environmental assessments of both microplastics and organic pollutants. To overcome the lack of adsorption information, five quantitative structure–property relationship (QSPR) models were developed for predicting the microplastic/water partition coefficients (log K_d) of organics between polyethylene/seawater, polyethylene/freshwater, polyethylene/pure water, polypropylene/seawater, and polystyrene/seawater. All the QSPR models show good fitting ability (R² = 0.811–0.939), predictive ability (Q²_ext = 0.835–0.910, RMSE_ext = 0.369–0.752), and robustness (Q_cv² = 0.882–0.957). They can be used to predict the K_d values of organic pollutants (such as polychlorinated biphenyls, chlorobenzene, polycyclic aromatic hydrocarbons, antibiotics perfluorinated compounds, etc.) under different pH conditions. The hydrophobic interaction has been indicated as an important mechanism for the adsorption of organic pollutants to microplastics. In sea waters, the role of hydrogen bond interaction in adsorption is considerable. For polystyrene, π–π interaction contributes to the partitioning. The developed models can be used to quickly estimate the adsorption capacity of organic pollutants on microplastics in different types of water, providing necessary information for ecological risk studies of microplastics.

QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds

The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K_PA-w). The major variables influencing K_PA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R²) and cross-validation coefficient (Q²) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K_PA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.

Mixture Touch: A Web Platform for the Evaluation of Complex Chemical Mixtures

Risk assessment of chemical mixtures isRisk assessment of chemical mixtures is challenging because information about the chemical structure, concentration, properties, and toxicity, down to the individual compounds, is generally not readily accessible. To cope with this challenge, we think Mixture Touch- a web platform that offers a one-window solution, for free, for the risk assessment of complex mixtures that are analyzed with comprehensive two-dimensional gas chromatography (GC × GC). GC × GC is a powerful analytical technique for target and nontarget analysis of complex mixtures. Our web platform allows users to visualize the GC × GC data, conduct spectral identification, estimate properties, and analyze potential risks based on established methods. For illustration purpose, we show how to assess the aquatic bioaccumulation potential of short-chain chlorinated paraffin (SCCP), which is an industrially manufactured mixture. The platform readily demonstrated that most of the SCCP congeners did not have the tendency to accumulate in aquatic organisms but in humans. The platform can bridge the gap between the GC × GC experts, GC × GC users, analytical experts, and risk assessors. It could enhance the level of risk assessments of mixtures utilizing the high performance of the state-of-the-art analytical instruments.

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.

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.

Comprehensive two-dimensional gas-chromatography-based property estimation to assess the fate and behavior of complex mixtures: A case study of vehicle engine oil

A method was developed to estimate the properties and assess the potential environmental risk of analytes in a complex mixture by comprehensive two-dimensional gas chromatography (GC × GC). A GC × GC-based estimation model was calibrated for 12 physicochemical properties that were relevant to the environment or to biological organisms, including human beings. Vehicle engine oil that had been contaminated by numerous compounds during its use was investigated as a case study to which the GC × GC model could be applied. Engine-oil samples were collected from a vehicle at intervals over a distance of 11407 km. The carbon and nitrogen contents in the oil remained unchanged at 83%-84% and 2%-5%, respectively, during the run; however, in excess of 100 compounds were present in the oil upon completion of the run. Post analyses of the studied mixture samples were performed with the developed GC × GC model, which links mass spectral information for structural identification. The GC × GC model allows us to classify the detected analytes in complex mixtures in terms of their properties, such as their aquatic bioaccumulation potential. The application of the model showed that the analyzed engine oil contained in excess of 100 compounds that could accumulate in aquatic biota and reach the arctic via long-range transport, which suggests that the components in the complex mixture of engine oil could pose a risk. The newly developed model that was derived in this study shows great potential for use in the mixture assessment.

Development and evaluation of MTLSER and QSAR models for predicting polyethylene-water partition coefficients

Current study was aimed to make further improvements in measuring low density polyethylene (LDPE) -water partition coefficient (K_PE-w) for organic chemicals. Modified theoretical linear solvation energy relationship (MTLSER) model and quantitative structure activity relationship (QSAR) model were developed for predicting K_PE-w values from chemical descriptors. With the MTLSER model, α (average molecular polarizability), μ (dipole moment) and q^- (net charge of the most negative atoms) as significant variables were screened. With the QSAR model, main control factors of K_PE-w values, such as CrippenLogP (Crippen octanol-water partition coefficient), CIC0 (neighborhood symmetry of 0-order) and GATS2p (Geary autocorrelation-lag2/weighted by polarizabilities) were studied. As per our best knowledge, this is the first attempt to predict polymer-water partition coefficient using the MTLSER model. Statistical parameters, correlation coefficient (R²) and cross-validation coefficients (Q²) were ranging from 0.811 to 0.951 and 0.761 to 0.949, respectively, which indicated that the models appropriately fit the results, and also showed robustness and predictive capacity. Mechanism interpretation suggested that the main factors governing the partition process between LDPE and water were the molecular polarizability and hydrophobicity. The results of this study provide an excellent tool for predicting log K_PE-w values of most common hydrophobic organic compounds, within the applicability domains to reduce experimental cost and time for innovation.