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Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;43:1161-1172. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC., (© 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.)

The target lipid model (TLM) has been previously applied to predict the aquatic toxicity of hydrocarbons and other nonionic organic chemicals and for deriving the concentrations above which 95% of species should be protected (HC5 values). Several concerns have been identified with the TLM-derived HC5 when it is applied in a substance risk assessment context. These shortcomings were addressed by expanding the acute and chronic toxicity databases to include more diverse taxonomic groups and increase the number of species. The TLM was recalibrated with these expanded databases, resulting in critical target lipid body burdens and acute-to-chronic ratios that met the required guidelines for using species sensitivity distributions in substance risk assessment. The HC5 equation was further revised to consider covarying model parameters. The calculated HC5 values derived from the revised TLM framework were validated using an independent data set for hydrocarbons comprising 106 chronic values across plants, invertebrates, and fish. Assuming a sum binomial distribution, the 95% confidence limit for a 5% failure is between 0.8 and 9.2%. Eight chronic values fell below the HC5, corresponding to an excursion of 7.5%, which falls within the expected uncertainty bounds. Thus, calculated HC5s derived from the revised TLM framework were found to be consistent with the intended protection goals. Environ Toxicol Chem 2018;37:1579-1593. © 2018 SETAC., (© 2018 SETAC.)

Polyparameter Linear Free Energy Relationships (pp-LFERs) using Abraham system parameters have many useful applications. However, developing the Abraham system parameters depends on the availability and quality of the Abraham solute parameters. Using Quantum Chemically estimated Abraham solute Parameters (QCAP) is shown to produce pp-LFERs that have lower root mean square errors (RMSEs) of predictions for solvent-water partition coefficients than parameters that are estimated using other presently available methods. pp-LFERs system parameters are estimated for solvent-water, plant cuticle-water systems, and for novel compounds using QCAP solute parameters and experimental partition coefficients. Refitting the system parameter improves the calculation accuracy and eliminates the bias. Refitted models for solvent-water partition coefficients using QCAP solute parameters give better results (RMSE = 0.278 to 0.506 log units for 24 systems) than those based on ABSOLV (0.326 to 0.618) and QSPR (0.294 to 0.700) solute parameters. For munition constituents and munition-like compounds not included in the calibration of the refitted model, QCAP solute parameters produce pp-LFER models with much lower RMSEs for solvent-water partition coefficients (RMSE = 0.734 and 0.664 for original and refitted model, respectively) than ABSOLV (4.46 and 5.98) and QSPR (2.838 and 2.723). Refitting plant cuticle-water pp-LFER including munition constituents using QCAP solute parameters also results in lower RMSE (RMSE = 0.386) than that using ABSOLV (0.778) and QSPR (0.512) solute parameters. Therefore, for fitting a model in situations for which experimental data exist and system parameters can be re-estimated, or for which system parameters do not exist and need to be developed, QCAP is the quantum chemical method of choice.

Methods for obtaining accurate predictions of solvent-water partitioning for neutral organic chemicals (e.g., K ow ) are well-established. However, methods that provide comparable accuracy are not available for predicting the solvent-water partitioning of ionic species. Previous methods for addressing charge contributions to solvent-water partitioning rely on charged solute descriptors which are obtained from regressions to neutral species descriptors as well as charged descriptors which are specific to unique charge-functionalities and structural moieties. This paper presents a method for obtaining Abraham poly-parameter linear free energy relationship (pp-LFER) descriptors using quantum chemical calculations and molecular structure, only. The method utilizes a large number of solvent-water systems to overcome large errors in individual quantum chemical computations of ionic solvent-water partition coefficients. The result is a single set of quantum-chemically estimated Abraham solute parameters (QCAP) which are solvent-independent, and can be used to predict the solvent-water partitioning of ionic species. Predictions of solvent-water partition coefficients for ionic species using quantum-chemically estimated Abraham parameters (QCAPs) are shown to provide improved accuracy compared over both existing Absolv-estimated Abraham solute parameters (AAP) as well as direct a priori quantum chemical (QC) calculations for partitioning of anionic solutes in 4 organic solvent-water systems (RMS = 0.740, 2.48 and 0.426 for the Absolv, QC and QCAP methods, respectively). For quaternary amine cations in the octanol-water system the RMS errors of the solvent-water partition coefficients were larger and similar between the two Abraham models (RMSE = 0.997 and 1.16, for the AAP and QCAP methods, respectively). Both methods showed significant improvement over direct QC calculations (RMSE = 2.82)., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The bioconcentration factor (BCF) of neutral and weakly polar organic chemicals in fish is modeled using independently calibrated models of chemical partitioning (freely dissolved fraction of chemical in the aqueous phase [φsys ] and wet-weight fish-water partition coefficient [KFW ]), respiratory exchange (respiratory update rate constant [k1 ], and respiratory elimination rate constant [k2  = k1 /KFW ]), and biotransformation (whole-body biotransformation rate constant [kM ]) as BCF = φsys KFW /(1 + kM /k2 ). Existing k1 models tend to overestimate for chemicals with log KOW  < 3.5, which constituted 30% to 50% of the examined chemicals. A revised k1 model covering a wider log KOW range (0-8.5) is presented k1  = (5.46 × 10(-6) MW + 0.261/KOW )(-1) , where MW is the molecular weight. The biotransformation rate constant kM is modeled using biota internal partitioning and Abraham parameters as reactivity descriptors. The reductionist model was tested using 3 different BCF data sets (US Environmental Protection Agency's Estimation Programs Interface [EPI], n = 548; Hertfordshire, n = 210; Arnot-Gobas, n = 1855) and compared with the following 3 state-of-the-art models: 1) the EPI Suite BCFBAF module, 2) the European Commision's Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR), and 3) the EPI/Arnot mechanistic kinetic model. The reductionist model performed comparably with the alternative models (root mean square errors [RMSEs] = 0.72-0.77), with only 5 fitting parameters and no training against experimental BCFs. Respiratory elimination and biotransformation dominate the total depuration (i.e., [k2  + kM ]/kT  ≥ 0.8) for approximately 98% of the data entries, thus validating the reductionist approximation. Mechanistic models provide greater insights into bioaccumulation and are more sensitive to biological variation. All three BCF data sets and relevant properties and checkpoint values necessary for reproducing predictions of the reductionist model have been documented. The present study shows that a streamlined mechanistic model of BCF is possible for assessment purposes., (Copyright © 2013 SETAC.)

A model for whole-body in vivo biotransformation of neutral and weakly polar organic chemicals in fish is presented. It considers internal chemical partitioning and uses Abraham solvation parameters as reactivity descriptors. It assumes that only chemicals freely dissolved in the body fluid may bind with enzymes and subsequently undergo biotransformation reactions. Consequently, the whole-body biotransformation rate of a chemical is retarded by the extent of its distribution in different biological compartments. Using a randomly generated training set (n = 64), the biotransformation model is found to be: log (HLφfish ) = 2.2 (±0.3)B - 2.1 (±0.2)V - 0.6 (±0.3) (root mean square error of prediction [RMSE] = 0.71), where HL is the whole-body biotransformation half-life in days, φfish is the freely dissolved fraction in body fluid, and B and V are the chemical's H-bond acceptance capacity and molecular volume. Abraham-type linear free energy equations were also developed for lipid-water (Klipidw ) and protein-water (Kprotw ) partition coefficients needed for the computation of φfish from independent determinations. These were found to be 1) log Klipidw  = 0.77E - 1.10S - 0.47A - 3.52B + 3.37V + 0.84 (in Lwat /kglipid ; n = 248, RMSE = 0.57) and 2) log Kprotw  = 0.74E - 0.37S - 0.13A - 1.37B + 1.06V - 0.88 (in Lwat /kgprot ; n = 69, RMSE = 0.38), where E, S, and A quantify dispersive/polarization, dipolar, and H-bond-donating interactions, respectively. The biotransformation model performs well in the validation of HL (n = 424, RMSE = 0.71). The predicted rate constants do not exceed the transport limit due to circulatory flow. Furthermore, the model adequately captures variation in biotransformation rate between chemicals with varying log octanol-water partitioning coefficient, B, and V and exhibits high degree of independence from the choice of training chemicals. The present study suggests a new framework for modeling chemical reactivity in biological systems., (Copyright © 2013 SETAC.)

High volume in situ surface water samples were collected from a tidal tributary of the Delaware Estuary using an Infiltrex sampling system equipped with a 1 μm particle filter and a XAD-2 resin column. Particulate and dissolved phase polychlorinated biphenyl (PCB) congeners were analyzed using high resolution gas chromatography/high resolution mass spectrometry to obtain detection levels in the femtograms per liter range. The data were fit to a four-phase equilibrium partitioning model including freely dissolved PCB, PCB bound to particulate organic carbon (POC), PCB bound to dissolved organic carbon (DOC), and PCB bound to black carbon (BC). Isotherms were assumed to be linear for POC and DOC and nonlinear for BC. The partition coefficient between BC and dissolved PCB was assumed to depend on the dihedral angle between the phenyl rings. Following parameter optimization, the correlation coefficient between the log of the modeled and measured apparent distribution coefficient Kp,app was 0.94, and the RMSE was 0.189 log units. Including BC in the model reduces the dissolved PCB phase concentration in the water column for all congeners, especially for the non-ortho and mono-ortho substituted congeners.

Predicting the association of contaminants with particulate organic matter in the environment is critical in determining the fate and bioavailability of chemicals. A ubiquitous measure of contaminant association with soil and sediment particulate organic matter is the organic carbon partition coefficient K(OC) . Chemical class-specific models relating the K(OC) to the octanol-water partition coefficient K(OW) have been used to predict the partitioning to organic carbon in the water column and sediment for nonpolar hydrophobic pollutants and some polar pollutants. A single linear solvation energy relationship (LSER) is proposed as a simpler and chemically based alternative for predicting K(OC) for a more diverse set of compounds. A chemically diverse set of K(OC) data is used to obtain a more robust and more universally representative model of organic carbon partitioning than previously available LSER models. The resulting model has a root mean square error (RMSE) of prediction for log K(OC) of RMSE = 0.48 for the fitted data set and RMSE = 0.55 for an independent data set. An analysis of LSER coefficients highlights the relative importance of hydrogen bonding interactions., (Copyright © 2011 SETAC.)

Predicting the association of contaminants with both particulate and dissolved organic matter is critical in determining the fate and bioavailability of chemicals in environmental risk assessment. To date, the association of a contaminant to particulate organic matter is considered in many multimedia transport models, but the effect of dissolved organic matter is typically ignored due to a lack of either reliable models or experimental data. The partition coefficient to dissolved organic carbon (K(DOC)) may be used to estimate the fraction of a contaminant that is associated with dissolved organic matter. Models relating K(DOC) to the octanol-water partition coefficient (K(OW)) have not been successful for many types of dissolved organic carbon in the environment. Instead, linear solvation energy relationships are proposed to model the association of chemicals with dissolved organic matter. However, more chemically diverse K(DOC) data are needed to produce a more robust model. For humic acid dissolved organic carbon, the linear solvation energy relationship predicts log K(DOC) with a root mean square error of 0.43., (Copyright © 2011 SETAC.)

The soil sorption coefficient, K(OC), is an important property affecting the environmental fate of organic molecules. Difficulties associated with measuring K(OC) have led to many attempts to predict this property, but most rely on empirical descriptors for the soil phase determined from correlations with measured K(OC) data, and are thereby limited by the data quality and diversity. A new method is presented to predict K(OC) for nonionic organic compounds that requires only molecular structures. No calibration is performed. Using model humic acid (HA) and fulvic acid (FA) molecular structures from the literature, the soil organic matter is modeled as an organic solvent composed of HA or FA molecules. K(OC) is predicted as an organic solvent-water partition coefficient using the quantum mechanics-based model COSMO-SAC. The log K(OC) values for a set of 440 diverse, environmentally relevant chemicals are predicted with a root-mean-square error of 0.84-1.08, depending on which model HA or FA is used.

A novel model has been developed to describe the kinetics of Zn adsorption and desorption to soils. The model incorporates the mechanistic-based equilibrium model WHAM (Windermere humic aqueous model) to account for the chemical variation during the reaction (e.g., pH and Zn2+ concentration), the heterogeneity of binding sites of soil organic matter (SOM), and the nonlinear binding of Zn to SOM. To test the model, kinetic experiments were conducted using a stirred-flow method. Six soils, with low clay fractions and covering a wide range in SOM concentrations, and various Zn concentrations and pHs were studied. Under these experimental conditions, SOM is found to be the major adsorbent for Zn binding. The fast and slow Zn reactions with soils were associated, respectively, with the monodentate and bidentate binding sites of humic substances in WHAM. The model has only three fitting parameters, the two desorption rate coefficients for the fast (monodentate) and slow (bidentate) reaction sites which are constant and independent of soil type, and the reactive organic matter fraction of the total SOM in each soil. All other parameters are derived from WHAM. The model is able to predict Zn release from spiked soils including the effects of Ca competition.

Ionic silver is toxic to aquatic organisms at low concentrations. However, complexation to binding sites on natural organic matter has been shown to reduce silver toxicity. Research indicates that there is a need to develop reliable methods for characterizing silver binding at very low silver ion concentrations where strong binding sites have a significant influence on silver speciation. This study provides an analytical method for measuring silver binding using a solubility-enhancement procedure. Preliminary experimental results are provided that demonstrate strong silver binding in the presence of natural organic matter at very low silver ion concentrations.