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

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.

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.

Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS

With advances in machine learning (ML) techniques, the quantitative structure–activity relationship (QSAR) approach is becoming popular for evaluating chemicals. However, the QSAR approach requires that the chemical structure of the target compound is known and that it should be convertible to molecular descriptors. These requirements lead to limitations in predicting the properties and toxicities of chemicals distributed in the environment as in the PubChem database; the structural information on only 14% of compounds is available. This study proposes a new ML-based QSAR approach that can predict the properties and toxicities of compounds using analytical descriptors of mass spectrum and retention index obtained via gas chromatography–mass spectrometry without requiring exact structural information. The model was developed based on the XGBoost ML method. The root-mean-square errors (RMSEs) for log K_o-w, log (molecular weight), melting point, boiling point, log (vapor pressure), log (water solubility), log (LD₅₀) (rat, oral), and log (LD₅₀) (mouse, oral) are 0.97, 0.052, 51, 23, 0.74, 1.1, 0.74, and 0.6, respectively. The model performed well on a chemical standard mixture measurement, with similar results to those of model validation. It also performed well on a measurement of contaminated oil with spectral deconvolution. These results indicate that the model is suitable for investigating unknown-structured chemicals detected in measurements. Any online user can execute the model through a web application named Detective-QSAR (http://www.mixture-platform.net/Detective_QSAR_Med_Open/). The analytical descriptor-based approach is expected to create new opportunities for the evaluation of unknown chemicals around us.

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.

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.

Development of retention time indices for comprehensive multidimensional gas chromatography and application to ignitable liquid residue mapping in wildfire investigations

In this study, we introduce a simple three-step workflow for a universally applicable RI system, to be used in GC×GC analysis of ignitable liquid residue (ILR) for arson investigations. The proposed RI system applies a combination of two well-established GC RI systems: non-isothermal Kovats (K) index in the first dimension and Lee (L) index in the second dimension. The proposed KLI RI system showed very good correlations when compared with predicted values and existing RI systems (r² = 0.97 in first dimension, r² = 0.99 in second dimension) and was valid for a wide range of analyte concentrations and operational settings (coefficient of variance (CV) < 1% in first dimension, < 10% in second dimension). Using the KLI RI, an ILR classification contour map was created to assist with the identification of ILR types within ASTM E1618. The contour map was successfully applied to neat fuels and a fire scene sample, highlighting the application to wildfire investigation. Standardizing the RI process and establishing acceptable error margins allows the exploration and comparison of comprehensive data generated from GC×GC analysis of ILRs regardless of location, time, or system, further enhancing comprehensive and tenable chemometric analyses of samples. Overall, the KLI workflow was inexpensive, quick to apply, and user-friendly with its simple 3-step design.

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.