Predicting sorption of pesticides and other multifunctional organic chemicals to soil organic carbon

Evaluation and modelling of polycyclic aromatic hydrocarbon (PAH) bioavailability in soils affected by coal tar asphalt

There are large masses of coal tar asphalt present in old roads, containing high concentrations of polycyclic aromatic hydrocarbons (PAHs). Uncertainty surrounding the risk they pose causes problems during road reconstruction and for the reuse of the asphalt present. To help elucidate potential risks, a parsimonious linear equilibrium partitioning model for the bioavailability of PAHs in soils contaminated by tar asphalt particles was developed. Furthermore, a set of partitioning coefficients for PAHs between sampled coal tar binders and water were determined experimentally, as well as measurements of freely dissolved concentrations using polyoxymethylene samplers in batch tests and column recirculation experiments with various mixtures of different soils (peat and sandy loam) and tar asphalts. The model predictions of freely dissolved concentrations were conservative and within an order of magnitude of measurements in both batch and column tests. The model presented here only relies on soil organic carbon content and the fraction coal tar binder in the soil to model PAH partitioning. This model could be used for more realistic. Low tier risk assessments towards rational prioritization of sensitive areas for risk reduction efforts.

Two-phase system model to predict hydrophobic organic compound partition to heterogeneous soil dissolved organic matter across China

Soil dissolved organic matter (SDOM) is an important part of the DOM pool in terrestrial systems, influencing the transport and fate of many pollutants. In this study, SDOMs from different regions across China were compared by a series of spectroscopic methods, including UV-vis spectroscopy, fluorescence spectroscopy, and Fourier transform infrared (FTIR) spectroscopy, and the hydrophobicity was quantified by partition coefficients of SDOM in the aqueous two-phase system (KATPS). The molecular weight, aromaticity, and hydrophobicity of SDOM from different regions exhibited strong heterogeneity, KATPS combined with UV-vis and fluorescence indices can be readily used for differentiating heterogeneous SDOM, and SDOMs were compositionally and structurally different from DOMs in aquatic systems based on spectral characterization. Importantly, the two-phase system (TPS) model has only been validated by DOMs in freshwater systems, and good organic carbon‒water partition coefficient (KOC) predictive power (RMSE = 0.11) could be provided by the TPS model when applied to heterogeneous SDOM without calibration, showing its broad applicability. Our results demonstrate the applicability of the TPS model for predicting the sorption behavior of terrestrial DOM, broadening the application scope of the TPS model and indicating its potential as a routine model for the risk assessment of hydrophobic organic compounds (HOCs) in organic contaminated sites.

A Review of Mechanistic Models for Predicting Adverse Effects in Sediment Toxicity Testing

Since recognizing the importance of bioavailability for understanding the toxicity of chemicals in sediments, mechanistic modeling has advanced over the last 40 years by building better tools for estimating exposure and making predictions of probable adverse effects. Our review provides an up-to-date survey of the status of mechanistic modeling in contaminated sediment toxicity assessments. Relative to exposure, advances have been most substantial for non-ionic organic contaminants (NOCs) and divalent cationic metals, with several equilibrium partitioning-based (Eq-P) models having been developed. This has included the use of Abraham equations to estimate partition coefficients for environmental media. As a result of the complexity of their partitioning behavior, progress has been less substantial for ionic/polar organic contaminants. When the EqP-based estimates of exposure and bioavailability are combined with water-only effects measurements, predictions of sediment toxicity can be successfully made for NOCs and selected metals. Both species sensitivity distributions and toxicokinetic and toxicodynamic models are increasingly being applied to better predict contaminated sediment toxicity. Furthermore, for some classes of contaminants, such as polycyclic aromatic hydrocarbons, adverse effects can be modeled as mixtures, making the models useful in real-world applications, where contaminants seldomly occur individually. Despite the impressive advances in the development and application of mechanistic models to predict sediment toxicity, several critical research needs remain to be addressed. These needs and others represent the next frontier in the continuing development and application of mechanistic models for informing environmental scientists, managers, and decisions makers of the risks associated with contaminated sediments. Environ Toxicol Chem 2024;43:1778-1794. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Chemical Space Covered by Applicability Domains of Quantitative Structure-Property Relationships and Semiempirical Relationships in Chemical Assessments

A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure-property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol-air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.

Modeling sorption of environmental organic chemicals from water to soils

Reliable estimation of chemical sorption from water to solid phases is an essential prerequisite for reasonable assessments of chemical hazards and risks. However, current fate and exposure models mostly rely on algorithms that lack the capability to quantify chemical sorption resulting from interactions with multiple soil constituents, including amorphous organic matter, carbonaceous organic matter, and mineral matter. Here, we introduce a novel, generic approach that explicitly combines the gravimetric composition of various solid constituents and poly-parameter linear free energy relationships to calculate the solid-water sorption coefficient (Kd) for non-ionizable or predominantly neutral organic chemicals with diverse properties in a neutral environment. Our approach demonstrates an overall statistical uncertainty of approximately 0.9 log units associated with predictions for different types of soil. By applying this approach to estimate the sorption of 70 diverse chemicals from water to two types of soils, we uncover that different chemicals predominantly exhibit sorption onto different soil constituents. Moreover, we provide mechanistic insights into the limitation of relying solely on organic carbon normalized sorption coefficient (KOC) in chemical hazard assessment, as the measured KOC can vary significantly across different soil types, and therefore, a universal cut-off threshold may not be appropriate. This research highlights the importance of considering chemical properties and multiple solid constituents in sorption modeling and offers a valuable theoretical approach for improved chemical hazard and exposure assessments.

Chemodiversity of soil organic matters determines biodegradation of polychlorinated biphenyls by a graphene oxide-assisted bacterial agent

A promising strategy for degrading persistent organic pollutants (POPs) in soil is amendment with nanomaterial-assisted functional bacteria. However, the influence of soil organic matter chemodiversity on the performance of nanomaterial-assisted bacterial agents remains unclear. Herein, different types of soil (Mollisol soil, MS; Ultisol soil, US; and Inceptisol soil, IS) were inoculated with a graphene oxide (GO)-assisted bacterial agent (Bradyrhizobium diazoefficiens USDA 110, B. diazoefficiens USDA 110) to investigate the association between soil organic matter chemodiversity and stimulation of polychlorinated biphenyl (PCB) degradation. Results indicated that the high-aromatic solid organic matter (SOM) inhibited PCB bioavailability, and lignin-dominant dissolved organic matter (DOM) with high biotransformation potential was a favored substrate for all PCB degraders, which led to no stimulation of PCB degradation in MS. Differently, high-aliphatic SOM in US and IS promoted PCB bioavailability. The high/low biotransformation potential of multiple DOM components (e.g., lignin, condensed hydrocarbon, unsaturated hydrocarbon, etc.) in US/IS further resulted to the enhanced PCB degradation by B. diazoefficiens USDA 110 (up to 30.34%) /all PCB degraders (up to 17.65%), respectively. Overall, the category and biotransformation potential of DOM components and the aromaticity of SOM collaboratively determine the stimulation of GO-assisted bacterial agent on PCB degradation.

A machine learning based approach for estimating site-specific partition coefficient Kd of organic compounds: Application to nonionic pesticides

The partitioning coefficient K_d for a specific compound and location is not only a key input parameter of fate and transport models, but also critical in estimating the safety environmental concentration threshold. In order to reduce the uncertainty caused by non-linear interactions among environmental factors, machine learning based models for predicting K_d were developed in this work based on literature datasets of nonionic pesticides including molecular descriptors, soil properties, and experimental settings. The equilibrium concentration (C_e) values were specifically included for the reason that a varied range of K_d corresponding to a given C_e occurred in a real environment. By transforming 466 isotherms reported in the literature, 2618 paired equilibrium concentrations of liquid-solid (C_e-Q_e) data points were obtained. Results of SHapley Additive exPlanations revealed that soil organic carbon, C_e, and cavity formation were the most important. The distance-based applicability domain analysis was conducted for the 27 most frequently used pesticides with 15952 pieces of soil information from the HWSD-China dataset by setting three C_e scenarios (i.e., 10, 100, and 1000 μg L^-1). It was revealed the groups of compounds showing log K_d < 0.06 and log K_d > 1.19 were composed mostly of those with log K_ow of -0.800 and 5.50, respectively. When log K_d varied between 0.100 and 1.00, it was impacted by interactions among soil types, molecular descriptors, and C_e comprehensively, which accounted for 55% of the total 2618 calculations. It could be concluded that site-specific models developed in this work are necessary and practicable for the environmental risk assessment and management of nonionic organic compounds.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

Assessing the Persistence and Mobility of Organic Substances to Protect Freshwater Resources

Persistent and mobile organic substances are those with the highest propensity to be widely distributed in groundwater and thereby, when emitted at low-levels, to contaminate drinking water extraction points and freshwater environments. To prevent such contamination, the European Commission is in the process of introducing new hazard classes for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances within its key chemical regulations CLP and REACH. The assessment of persistence in these regulations will likely be based on simulated half-life, t _1/2, thresholds; the assessment of mobility will likely be based on organic carbon-water distribution coefficient, K _OC, thresholds. This study reviews the use of t _1/2 and K _OC to describe persistence and mobility, considering the theory, history, suitability, data limitations, estimation methods, and alternative parameters. For this purpose, t _1/2, K _OC, and alternative parameters were compiled for substances registered under REACH, known transformation products, and substances detected in wastewater treatment plant effluent, surface water, bank filtrate, groundwater, raw water, and drinking water. Experimental t _1/2 values were rare and only available for 2.2% of the 14 203 unique chemicals identified. K _OC data were only available for a fifth of the substances. Therefore, the usage of alternative screening parameters was investigated to predict t _1/2 and K _OC values, to assist weight-of-evidence based PMT/vPvM hazard assessments. Even when considering screening parameters, for 41% of substances, PMT/vPvM assessments could not be made due to data gaps; for 23% of substances, PMT/vPvM assessments were ambiguous. Further effort is needed to close these substantial data gaps. However, when data is available, the use of t _1/2 and K _OC is considered fit-for-purpose for defining PMT/vPvM thresholds. Using currently discussed threshold values, between 1.9 and 2.6% of REACH registered substances were identified as PMT/vPvM. Among the REACH registered substances detected in drinking water sources, 24-30% were PMT/vPvM substances.

Retrieval, Selection, and Evaluation of Chemical Property Data for Assessments of Chemical Emissions, Fate, Hazard, Exposure, and Risks

Reliable chemical property data are the key to defensible and unbiased assessments of chemical emissions, fate, hazard, exposure, and risks. However, the retrieval, evaluation, and use of reliable chemical property data can often be a formidable challenge for chemical assessors and model users. This comprehensive review provides practical guidance for use of chemical property data in chemical assessments. We assemble available sources for obtaining experimentally derived and in silico predicted property data; we also elaborate strategies for evaluating and curating the obtained property data. We demonstrate that both experimentally derived and in silico predicted property data can be subject to considerable uncertainty and variability. Chemical assessors are encouraged to use property data derived through the harmonization of multiple carefully selected experimental data if a sufficient number of reliable laboratory measurements is available or through the consensus consolidation of predictions from multiple in silico tools if the data pool from laboratory measurements is not adequate.

Applicability Domain of Polyparameter Linear Free Energy Relationship Models Evaluated by Leverage and Prediction Interval Calculation

Polyparameter linear free energy relationships (PP-LFERs) are accurate and robust models employed to predict equilibrium partition coefficients (K) of organic chemicals. The accuracy of predictions by a PP-LFER depends on the composition of the respective calibration data set. Generally, extrapolation outside the domain defined by the calibration data is likely to be less accurate than interpolation. In this study, the applicability domain (AD) of PP-LFERs was systematically evaluated by calculating the leverage (h) and prediction interval (PI). Repeated simulations with experimental data showed that the root mean squared error of predictions increased with h. However, the analysis also showed that PP-LFERs calibrated with a large number (e.g., 100) of training data were highly robust against extrapolation error. For such PP-LFERs, the common definition of extrapolation (h > 3 h_mean, where h_mean is the mean h of all training compounds) may be excessively strict. Alternatively, the PI is proposed as a metric to define the AD of PP-LFERs, as it provides a concrete estimate of the error range that agrees well with the observed errors, even for extreme extrapolations. Additionally, published PP-LFERs were evaluated in terms of their AD using the new concept of AD probes, which indicated the varying predictive performance of PP-LFERs in the existing literature for environmentally relevant compounds.

Sorption and Mobility of Charged Organic Compounds: How to Confront and Overcome Limitations in Their Assessment

Permanently charged and ionizable organic compounds (IOC) are a large and diverse group of compounds belonging to many contaminant classes, including pharmaceuticals, pesticides, industrial chemicals, and natural toxins. Sorption and mobility of IOCs are distinctively different from those of neutral compounds. Due to electrostatic interactions with natural sorbents, existing concepts for describing neutral organic contaminant sorption, and by extension mobility, are inadequate for IOC. Predictive models developed for neutral compounds are based on octanol-water partitioning of compounds (Kow) and organic-carbon content of soil/sediment, which is used to normalize sorption measurements (KOC). We revisit those concepts and their translation to IOC (Dow and DOC) and discuss compound and soil properties determining sorption of IOC under water saturated conditions. Highlighting possible complementary and/or alternative approaches to better assess IOC mobility, we discuss implications on their regulation and risk assessment. The development of better models for IOC mobility needs consistent and reliable sorption measurements at well-defined chemical conditions in natural porewater, better IOC-, as well as sorbent characterization. Such models should be complemented by monitoring data from the natural environment. The state of knowledge presented here may guide urgently needed future investigations in this field for researchers, engineers, and regulators.

Evaluation of the permeability and potential toxicity of polycyclic aromatic hydrocarbons to pulmonary surfactant membrane by the parallel artificial membrane permeability assay model

Polycyclic aromatic hydrocarbons (PAHs) can penetrate and accumulate in the pulmonary surfactant (PS) membranes, leading to abnormalities of biological macromolecules and the destruction of membrane structure and properties. In the present study, the bioavailability, apparent permeability, effective permeability and residual coefficient of 10 PAHs on PS membrane was assessed by the parallel artificial membrane permeability assay (PAMPA). The influence of various forces on permeability is obtained by analyzing the correlation between parameters and physicochemical properties. Research shows that octanol-water partition coefficient (K_ow) cannot directly predict permeability, and permeability has no significant relationship with polarity. Dispersion, induction, coupling/polarization promote permeation, while hydrogen bonded acid and n-n electron pair inhibit permeation. Further surface pressure-area (π-A) isotherms test and Brewster angle microscope observation manifested that there are huge differences in the transmembrane ability and effects on the membrane of PAHs with different structures. This work has considerable significance that will help to evaluate the bioavailability and human health risk of PAHs.

Molecular Docking as a Tool to Examine Organic Cation Sorption to Organic Matter

Molecular docking simulations were performed to examine the structural effects of organic cations on their sorption to organic matter. A set of benzylamine compounds was used to assess the sorption trends arising from the systematic structural differences between ring or nitrogen substituents. Binding simulations were performed using AutoDock 4.2 with Schulten's proposed soil organic matter as a representative organic matter structure. The calculated binding energies for the sorbate compounds correlated strongly with the measured sorption energies for Pahokee peat, indicating that the simulated binding energies and their associated sorbate orientations were representative of the experimental conditions. Graphical docking orientations showed primary, secondary, and tertiary aminium compounds to form hydrogen-bond interactions with deprotonated carboxylic acid groups in a pocket of the organic matter structure. Quaternary ammonium compounds formed pi-pi or cation-pi interactions with the aromatic groups elsewhere in the same organic matter pocket. Ring substituents showed no clear trends in sorption energies with the substituent group type for primary aminium compounds. Rather, substituent groups altered the simulated van der Waals, electrostatic, hydrogen-bond, and desolvation energy contributions to the overall sorption energies, in part because of the variations in docking orientations between compounds. Increasing methyl substitution of the aminium nitrogen group was associated with an increase in van der Waals energy contributions and a decrease in electrostatic energy contributions to the overall compound sorption energies because of aminium charge delocalization into methyl substituents and steric hindrance from methyl substituents to form specific interactions. The findings illustrate how molecular docking can be used to explore the effects of organic cation structure on sorption interactions with organic matter.

Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon

We recently discovered that transformation of the neonicotinoid insecticidal pharmacophore alters sorption propensity to activated carbon, with products adsorbing less than parent compounds. To assess the environmental fate of novel transformation products that lack commercially available standards, researchers must rely on predictive approaches. In this study, we combined computationally derived quantitative structure-activity relationship (QSAR) parameters for neonicotinoids and neonicotinoid transformation products with experimentally determined Freundlich partition constants (log K F for sorption to carbon nanotubes [CNTs] and granular activated carbon [GAC]) to model neonicotinoid and transformation product sorption. QSAR models based on neonicotinoid sorption to functionalized/nonfunctionalized CNTs (used to generalize/simplify neonicotinoid-GAC interactions) were iteratively generated to obtain a multiple linear regression that could accurately predict neonicotinoid sorption to CNTs using internal and external validation (within 0.5 log units of the experimentally determined value). The log K F,CNT values were subsequently related to log K F,GAC where neonicotinoid sorption to GAC was predicted within 0.3 log-units of experimentally determined values. We applied our neonicotinoid-specific model to predict log K F,GAC for a suite of novel neonicotinoid transformation products (i.e., formed via hydrolysis, biotransformation, and chlorination) that do not have commercially available standards. We present this modeling approach as an innovative yet relatively simple technique to predict fate of highly specialized/unique polar emerging contaminants and/or transformation products that cannot be accurately predicted via traditional methods (e.g., pp-LFER), and highlights molecular properties that drive interactions of emerging contaminants.

Prediction of hydrophobic organic compound partition to algal organic matter through the growth cycle of Microcystis aeruginosa

Algal organic matter (AOM) is an important source for the dissolved organic matter (DOM) pool in aquatic systems, particularly in eutrophic waters. In this study, we reported the dynamic pattern of AOM hydrophobicity during the growth cycle of Microcystis aeruginosa using the partition coefficients of AOM in the aqueous two-phase system (K_ATPS) as a simple quantitative measure. AOM hydrophobicity had significant and non-monotonic changes during the growth cycle. It increased in the lag and early exponential phases, then decreased in the late exponential and stationary phases, and rebounded in the decline phase. AOM hydrophobicity determined using the resin fractionation, SUVA₂₅₄, and nuclear magnetic resonance methods shared similar non-monotonic pattern. Nevertheless, the correlations among these indicators were poor. The partition behavior of polycyclic aromatic hydrocarbons and chlorobenzenes to AOM was assessed based the K_ATPS dataset and the two-phase system (TPS) model. The TPS model showed good prediction power for the partition behavior of AOM with an RMSE of 0.23, suggesting that it was applicable to AOM from Microcystis aeruginosa. Our results indicate that algae activity will influence the overall hydrophobicity of the DOM pool depending on the growth phase, resulting in changes in the bioavailability of hydrophobic organic compounds in aquatic systems.

Sources and Fate of the Antiandrogenic Fluorescent Dye 4-Methyl-7-Diethylaminocoumarin in Small River Systems

Recently, the potent antiandrogen 4-methyl-7-diethylaminocoumarin (C47) and its potential transformation products 4-methyl-7-ethylaminocoumarin (C47T1) and 4-methyl-7-aminocoumarin (C47T2) were identified as novel environmental contaminants. We assessed for the first time the sources, distribution, and fate of these compounds in aquatic systems using the Holtemme River (Saxony-Anhalt, Germany), which is a hotspot for these contaminants. To this end, wastewater-treatment plant (WWTP) influent and effluent samples, surface water samples over 3 years, and the longitudinal profiles in water, sediment, and gammarids were analyzed. From the longitudinal profile of the river stretch, the WWTP of Silstedt was identified as the sole point source for these compounds in the River Holtemme, and exposure concentrations in the low micrograms per liter range could be recorded continuously over 3 years. Analysis of WWTP influent and effluent showed a transformation of approximately half of the C47 into C47T1 and C47T2 but no complete removal. A further attenuation of the three coumarins after discharge into the river could be largely attributed to dilution, while transformation was only approximately 20%, thus suggesting a significant persistence in aquatic systems. Experimentally derived partitioning coefficients between water and sediment organic carbon exceeded those predicted using the OPERA quantitative structure-activity relationship tools and polyparameter linear free-energy relationships by up to 93-fold, suggesting cation binding as a significant factor for their sorption behavior. Near-equilibrium conditions between water and sediment were not observed close to the emitting WWTP but farther downstream in the river. Experimental and predicted bioaccumulation factors for gammarids were closely matching, and the concentrations in field-sampled gammarids were close to steady state with exposure concentrations in the water phase of the river. Environ Toxicol Chem 2021;40:3078-3091. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Prediction of organic pollutant removal using Corynebacterium glutamicum fermentation waste

The disposal of bio-waste (e.g., Corynebacterium glutamicum) produced by the fermentation industry is a serious problem and has a negative impact on economic returns. Some fermentation waste can be recycled as livestock feed, but much cannot be used. Therefore, other recycling methods must be developed to increase its applications, for example, as an environmentally friendly adsorbent for the removal or recovery of chemicals. To broaden its application as an adsorbent, we carried out comprehensive experimental and theoretical analysis. From the experiments, adsorption affinity values between C. glutamicum and micropollutants were measured, and, based on the experimental values, we developed a predictive model. The experimental results reveal that the degree of adsorption is dependent on the structural properties of the micropollutants. In particular, the adsorbent has remarkable adsorption ability toward cations, whereas anionic and neutral compounds interact weakly with the adsorbent. In addition, we found that adsorption is affected by the sodium chloride concentration. Briefly, an increase in salt concentration increases the adsorption of anions, whereas the opposite behavior is observed for cations. In contrast, the adsorption of neutral compounds was not affected by the presence of salt. The modeling studies revealed that a linear free energy relationship model can be used to predict the adsorption affinity. Based on the developed model, we found that hydrogen-bond basicity, anionic coulombic interactions, and molecular volume are the main contributing factors to the adsorption model. However, to achieve the best predictability (a coefficient of determination (R²) of 0.902), additional parameters, such as the dipolarity/polarizability and dispersive interaction, should be included. This indicates that adsorption is a product of complex interactions.

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.

Two-Phase System Model to Assess Hydrophobic Organic Compound Sorption to Dissolved Organic Matter

The equilibrium partition of organic compounds to dissolved organic matter (DOM) is an essential process that affects their environmental risks. Traditional models cannot accurately assess this process as the variability of DOM is not properly accounted for. The two-phase system (TPS) model was developed with the consideration of the variability that stems from both organic compounds and DOM. In this study, we examined the applicability of the TPS model for the prediction of the organic carbon-water partition coefficient (K_OC) of hydrophobic organic compound (HOC) sorption to aquatic and sediment DOM using a diverse set of 17 organic compounds and 53 DOM samples. The TPS model showed good predictive power (RMSE < 0.20) without calibration, outperforming currently used linear free energy relationship models (RMSE > 0.28). The significance of DOM properties in the sorption behavior was quantitatively analyzed based on the TPS model. The spatial pattern of K_OC for HOC sorption to aquatic DOM in Lake Taihu, the third largest freshwater lake in China, was assessed using the TPS model. Our results suggest that the TPS model has great potential to facilitate the routine assessment of the partition behavior of HOCs in aquatic systems for environmental risk assessment.

Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Predicting plant cuticle-water partition coefficients (K_cw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental K_cw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict K_cw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logK_cw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R²_adj,tra) of 0.93, bootstrapping coefficient (Q²_BOOT) of 0.92, external validation coefficient (Q²_ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13-31%), double bond (9-17%) or nitrogen-containing heterocycles (9-17%), π/n-electron pairs interactions exhibit obvious positive contributions to logK_cw. In conclusion, the proposed pp-LFER model is beneficial for predicting logK_cw of potential organic pollutants directly from their molecular structures.

Evaluation and development of models for estimating the sorption behaviour of pharmaceuticals in soils

Sorption is one of the key process that affects the fate and mobility of pharmaceuticals in the soil environment. Several models have been developed for estimating the sorption of organic chemicals, including ionisable compounds, in soil. However, the applicability of these models to pharmaceuticals has not been extensively tested. In this study, we generated a high-quality dataset on the sorption of twenty-one pharmaceuticals in different soil types and used these data to evaluate existing models and to develop new improved models. Sorption coefficients (Kd) of the pharmaceuticals ranged from 0.2 to 1249.2 L/kg. Existing models were unable to adequately estimate the measured sorption data. Using the data, new models were developed, incorporating molecular and soil descriptors, that outperformed the published models when evaluated against external data sets. While there is a need for further evaluation of these new models against broader sorption datasets obtained at environmentally relevant concentrations, in the future they could be highly useful in supporting environmental risk assessment and prioritization efforts for pharmaceutical ingredients.

Adsorption site-dependent transport of diclofenac in water saturated minerals and reference soils

Use of reclaimed water for irrigation is a main way for pharmaceutical compounds such as diclofenac getting into the soil environment. However, the role of minerals, especially iron oxides, in the diclofenac adsorption to soils with low soil organic matter (SOM) is still in the lack of evaluation. In this study, adsorption of diclofenac onto six minerals (five nature minerals-hematite, goethite, magnetite, kaolinite and aluminium oxide and one engineered mineral-activated aluminia) and five reference soils was investigated by column chromatography. Adsorption of diclofenac onto minerals and soils was totally reversible and interactions such as H-bonding were the primary mechanisms. Adsorption affinity of iron oxides was much higher than that of nature silicon and aluminum oxides. Diclofenac tended to be adsorbed by mineral surface -OH groups with high thermodynamic stability, which were dehydroxylated at high temperature. Compared with the SOM-dominated sorption of naphthalene, adsorption of diclofenac onto soils was controlled by bonding with surface -OH groups of iron oxides. Adsorption coefficients of diclofenac onto soils can be well predicted by contents of extracted Fe by diethylenetriamine pentaacetic acid (DTPA) instead of total iron oxides contents, suggesting that the bonding was adsorption site-dependent. These findings highlighted the importance of iron oxides in the adsorption of diclofenac (an anionic pharmaceutical compound) in soils with relatively low SOM (e.g., 1.03-3.45%). It also indicated that contents of effective surface -OH groups and DTPA-Fe were the promising parameters to develop the predictive models for diclofenac adsorption onto minerals and soils, respectively.

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.

In silico study for inhibiting thyroid hormone sulfotransferase activity by halogenated phenolic chemicals

Thyroid hormones (THs) are essential to proper growth and development of human bodies. Inhibiting the sulfation metabolism of THs has been demonstrated to be an important way for some environmental pollutants, such as halogenated phenolic compounds, to interfere THs homeostasis, thereby causing health problems. However, the important property characteristics that govern the sulfation inhibition of these chemicals are not well understood, and the experimental data on inhibition potential is limited. In this work, an in silico approach was developed to investigate the structure-activity relationship for their sulfotransferases (SULTs) inhibition. A series of quantum chemical descriptors that quantify the electronic and energy properties of 22 halogenated phenolic compounds have been calculated to establish a predictive model and analyzed their corresponding contributions to SULTs inhibition. Density functional theory (DFT) B3LYP/6-31G** has been employed to optimize molecular geometries to obtain a total of 15 descriptors for every compound. The implementation of linear regression shows three descriptors that represent molecular mass, positive charges on hydrogen atoms, and energy of frontier orbitals strongly correlate with SULTs inhibition potential. This indicates molecular size, hydrogen-bond strength, and nucleophilic-electrophilic reactivity may play important roles in SULTs inhibition. The derived regression model has good statistical performance (r² = 0.84, rms = 0.35), and different validation strategies indicate it can serve as an efficient predictive tool for other chemicals in application domain but with no experimental data, consequently assisting in their THs sulfation inhibition and health risk assessment.

Sorption properties and behaviour at laboratory scale of selected pharmaceuticals using batch experiments

Despite the increasing public concern about the frequent occurrence of pharmaceuticals in the water bodies, their transport and fate are not yet well known; in particular in groundwater. In this laboratory study, batch experiments were conducted to investigate the sorption behaviour of selected pharmaceuticals. The choice of compounds was motivated by their chemical properties as well as by their occurrence at the relevant field sites. It included: antipyrine, atenolol, caffeine, carbamazepine, ciprofloxacin, diclofenac, ketoprofen, ofloxacin, and sulfamethoxazole. Sorption behaviour has already been investigated for some of these compounds (e.g. carbamazepine), but for the others (e.g. antipyrine, ketoprofen), extensive studies are missing so far. For the experiments, artificial and actual aquifer materials from complementary field sites were selected: technical coarse quartz sand and sediments from alluvial Vistrenque Aquifer, France (sandy loam), and fluvio-deltaic Baix Fluvià Aquifer, Spain (sandy clay loam, clay, and medium sand). In these field sites occurrence of pharmaceuticals in groundwater was previously stated, and the presented laboratory experiments were complementary to the field investigations. Five concentration steps for determining the sorption isotherms were investigated. Correlation analysis showed dependencies of K-coefficients of individual compounds and sediment properties; however, no clear, universal patterns for all compounds were found. Batch experiments suggest that sorption behaviour was governed by compound-specific properties rather than by sediment properties. These results contribute to the understanding sorption behaviour of pharmaceuticals in heterogeneous sediments, although some inconsistencies were revealed between laboratory scale results and field scale observations.

Prediction of Apolar Compound Sorption to Aquatic Natural Organic Matter Accounting for Natural Organic Matter Hydrophobicity Using Aqueous Two-Phase Systems

The equilibrium partitioning of organic compounds to natural organic matter (NOM) plays a key role in their environmental fate as well as bioavailability. In this study, a prediction model for organic compound sorption to NOM was theoretically derived based on two-phase systems. In this model, the hydrophobicity of NOM was scaled by their partition coefficients in an aqueous two-phase system (K_ATPS) and that of organics was scaled by their octanol-water partition coefficients (K_OW). The model uses only K_ATPS and K_OW as variables. Coefficients in the model were determined using a data set including the organic carbon-water partition coefficient (K_OC) of four polycyclic aromatic hydrocarbons (PAHs) sorption to 10 NOM samples collected from surface waters. The resulting model was validated using additional NOM samples and reference NOM, which suggested good prediction power for PAH sorption to aquatic NOM. The model performance was compared with commonly used linear free energy relationship models, and its applicability was discussed. Sorption behavior unexpected by this model is attributed to additional sorption mechanisms other than partitioning. Overall, this approach allows prediction of K_OC for apolar organic compound sorption to aquatic NOM simply using their respective partition coefficients in two-phase systems based on a specific model.

Mechanisms and quantification of adsorption of three anti-inflammatory pharmaceuticals onto goethite with/without surface-bound organic acids

Nowadays non-steroidal anti-inflammatory drugs (NSAIDs) are often detected in surface water and groundwater. In this study, effects of environmental factors, i.e., solution pH, ionic strength, temperature and surface-bound organic acids, on bonding of three typical NSAIDs (ketoprofen, naproxen and diclofenac) onto goethite were systematically investigated. Column chromatography, batch experiments, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy and surface complexation modeling were used to probe the adsorption mechanisms. Bonding of three NSAIDs onto goethite was totally reversible, ionic strength-dependent and endothermic (adsorption enthalpy 2.86-9.75 kJ/mol). These evidences supported H-bonding mechanism, which was further explained by ATR-FTIR observation and a triple planes model. Surface-bound organic acids (phthalic acid, trimellitic acid and pyromellitic acid) by inner-sphere complexation with goethite were hard to be desorbed. Surface-bound phthalic acid increased the uptake of NSAIDs but surface-bound trimellitic acid and pyromellitic acid reduced their adsorption. The reason is that the adsorbed phthalic acid can result in a more hydrophobic surface while adsorbed trimellitic acid and pyromellitic acid increased the surface negative charge and polarity. Finally, adsorption of NSAIDs onto goethite with/without surface-bound organic acids was well described by a free energy model, in which contributions of interactions (e.g., H-bonding and van der Waals) were evaluated.

QSAR modelling for predicting adsorption of neutral, cationic, and anionic pharmaceuticals and other neutral compounds to microalgae Chlorella vulgaris in aquatic environment

Environmental fate or transport of pharmaceutical waste depends on the adsorptive interactions of pharmaceuticals with various environmental phases e.g. soil, sediment, microalgae, and bacteria etc. Therefore, it is important to understand these adsorptive interactions. As part of the study, we studied the adsorptive interaction of 30 chemicals with microalgae, i.e. Chlorella vulgaris, because it is ubiquitous and its surface area occupies a high proportion in aquatic environments. For this study, isotherms between C. vulgaris and 30 micropollutants in neutral and ionic forms (i.e. 15 cations, 5 anions, and 10 neutrals) were experimentally measured, and their adsorptive affinities were then theoretically predicted based on the concept of the linear free energy relationship. For modeling, the dataset was divided into a training set and a test set, where the training set was used for model development and the test set was performed for model validation. This process was repeated ten times. Finally, we suggested one model which has high predictability in R² of 0.96 and standard error (SE) of 0.17 log unit for the training set, R² of 0.818 and SE = 0.217 log unit for the test set, and R² of 0.926 and SE of 0.169 log unit for the total dataset. Moreover, it was found that dispersive force, H-bond basicity, molecular volume, and electrostatic interaction of anion significantly contribute to the model developed based on the entire dataset. Here, dispersive and hydrophobic interactions (proportional to the magnitude of molecular size) are main attractive forces, while the rest cases are repulsive. In addition, it was found that the adsorption property of the surface of C. vulgaris differs from those of Gram negative bacteria Escherichia coli and dissolved organic matters in an aquatic environment.

Sorption of α-, β-, γ- and δ-hexachlorocyclohexane isomers to three widely different biochars: Sorption mechanisms and application

Within this study different biochars (BC) with widely varying characteristics have been tested as materials for the adsorption of hexachlorocyclohexane's (HCH) isomers (α, β, γ and δ) from water. Three BCs produced from digestate (700 °C), greenhouse tomato waste (550 °C) and durian shell (400 °C) were tested. The BCs demonstrated variable physico-chemical characteristics, especially with respect to surface area, with CO₂-surface area ranging from 5.4 to 328.6 m² g^-1 and iron content ranging from 0.0733 to 11.17 g kg^-1. Isotherm tests were carried out to understand which mechanisms drive HCH uptake to BC, to assess whether stereochemistry affects adsorption and to assess whether competitive sorption occurs. Log K_F values ranged from 3.7 to 5.8 (μg kg^-1) (μg L^-1)^-n for the various isomers on the three biochars. No competition (t-test, P < 0.0001) was observed between α-, β-, γ- and δ-HCH. Freundlich adsorption constants of α-, γ- and δ-HCH followed the order: BC digestate > BC greenhouse tomato waste > BC durian shell, in contrast to β-HCH which followed the order: BC durian shell > BC greenhouse tomato waste > BC digestate. In addition to stereochemistry, sorption coefficients were affected most strongly by BC surface area and iron content, in addition to specific HCH/BC matrix interactions. In this study the iron content of a carbonaceous material has been investigated, for the first time, as a factor that can affect the sorption of HCHs.

Bonding of monocarboxylic acids, monophenols and nonpolar compounds onto goethite

Adsorption of a diverse set of chemicals onto goethite was evaluated by column chromatography. The pH of the effluents was 4.7-5.2. Van der Waals forces dominate the exothermic adsorption of 8 nonpolar compounds (e.g., PAHs and chlorobenzenes). H-bonding is responsible for the adsorption of 32 monocarboxylic acids (i.e., benzoic acids, naphthoic acids and acidic pharmaceuticals) and their adsorption tends to be endothermic. Steric effects significantly decreased the bonding of monocarboxylic acids with ortho-substitutions. Exothermic adsorption of 10 monophenols is controlled by weak H-bonding. Bonding of these 50 solutes onto goethite is totally reversible. In contrast, inner-sphere complexation of phthalic acid and chlortetracycline with goethite occurred according to their low desorption ratio (1.1%-54.4%). Polyparameter linear free energy relationship (PP-LFER) models were established to provide acceptable fitting results of the goethite-solute distribution coefficients (RMSE = 0.32 and 0.30 at 25 °C and 5 °C, respectively). It is worthy to note that steric effects must be considered to get a better prediction for compounds with ortho-substitutions.

Sorption of cationic organic substances onto synthetic oxides: Evaluation of sorbent parameters as possible predictors

Knowledge on the sorption behavior of cationic organic substances in aquatic systems is vital for their risk assessment due to the increasing detection of such chemicals in the hydrosphere. Their sorption behavior is strongly influenced by sorption processes onto mineral surfaces (e.g., oxides, clays). To contribute to the development of prediction tools, the impact of sorbent characteristics on the sorption strength was studied in a highly-idealized model system. In addition to the properties of the solid phase, the concentration of other ions in direct competition for sorption sites and the molecular structure of the sorbate were changed to separate ion exchange and non-ion exchange processes. The study includes in total 120 systematic column experiments using five extensively characterized synthetic oxides (three silica gels, two aluminum oxides), three probe molecules (two structurally related cationic substances, one neutral compound), and four distinctively different NaCl concentrations. The results show that the concentration of OH groups on the sorbent surface is a meaningful descriptor for the observed variations in sorption capacity onto different oxides. Compound-specific linear correlations were obtained, enabling the prediction of sorption coefficients. In addition, a more complex sorption behavior of organic cations compared to uncharged molecules were observed as demonstrated by the sorption results at different electrolyte concentrations. Thus, the study provides an important step towards a better principal mechanistic understanding of organic cation sorption. However, further work using other sorbents including natural ones and other probe molecules is needed to verify the identified relationships within the scope of developing reliable prediction models for cation sorption.

Assessing residual status and spatial variation of current-use pesticides under the influence of environmental factors in major cash crop growing areas of Pakistan

The status of seven currently used pesticides were assessed under the influence of soil parameters in surface soils of cash crop growing areas of Pakistan. Chlorpyrifos occurred in highest mean concentration (1.18 mg kg^-1). Selected pesticides exhibited higher affinity towards both organic carbon and black carbon fractions. The δ¹³C stable carbon isotopic fraction of inorganic carbon was also used as a tracer and disclosed high retention of total organic carbon in Swat and Swabi sites. Statistical analysis revealed that carbon storage was primarily influenced by altitude and temperature. Soil clay mineral oxides of aluminum and iron positively correlated with organic carbon and selected pesticides (chlorpyrifos and cyprodinil). Soil to plant bio-concentration ratios predicted heightened uptake of azinfos and diazinon in major cash crop bio mass. Occupational risk via soil ingestion expressed no significant threat to the farmer community.

Ranking REACH registered neutral, ionizable and ionic organic chemicals based on their aquatic persistency and mobility

The contaminants that have the greatest chances of appearing in drinking water are those that are mobile enough in the aquatic environment to enter drinking water sources and persistent enough to survive treatment processes. Herein a screening procedure to rank neutral, ionizable and ionic organic compounds for being persistent and mobile organic compounds (PMOCs) is presented and applied to the list of industrial substances registered under the EU REACH legislation as of December 2014. This comprised 5155 identifiable, unique organic structures. The minimum cut-off criteria considered for PMOC classification herein are a freshwater half-life >40 days, which is consistent with the REACH definition of freshwater persistency, and a log D_oc < 4.5 between pH 4-10 (where D_oc is the organic carbon-water distribution coefficient). Experimental data were given the highest priority, followed by data from an array of available quantitative structure-activity relationships (QSARs), and as a third resort, an original Iterative Fragment Selection (IFS) QSAR. In total, 52% of the unique REACH structures made the minimum criteria to be considered a PMOC, and 21% achieved the highest PMOC ranking (half-life > 40 days, log D_oc < 1.0 between pH 4-10). Only 9% of neutral substances received the highest PMOC ranking, compared to 30% of ionizable compounds and 44% of ionic compounds. Predicted hydrolysis products for all REACH parents (contributing 5043 additional structures) were found to have higher PMOC rankings than their parents, due to increased mobility but not persistence. The fewest experimental data available were for ionic compounds; therefore, their ranking is more uncertain than neutral and ionizable compounds. The most sensitive parameter for the PMOC ranking was freshwater persistency, which was also the parameter that QSARs performed the most poorly at predicting. Several prioritized drinking water contaminants in the EU and USA, and other contaminants of concern, were identified as PMOCs. This identification and ranking procedure for PMOCs can be part of a strategy to better identify contaminants that pose a threat to drinking water sources.

Different effects of surface heterogeneous atoms of porous and non-porous carbonaceous materials on adsorption of 1,1,2,2-tetrachloroethane in aqueous environment

The surface heterogeneous atoms of carbonaceous materials (CMs) play an important role in adsorption of organic pollutants. However, little is known about the surface heterogeneous atoms of CMs might generate different effect on adsorption of hydrophobic organic compounds by porous carbonaceous materials - activated carbons (ACs) and non-porous carbonaceous materials (NPCMs). In this study, we observed that the surface oxygen and nitrogen atoms could decrease the adsorption affinity of both ACs and NPCMs for 1,1,2,2-tetrachloroethane (TeCA), but the degree of decreasing effects were very different. The increasing content of surface oxygen and nitrogen ([O + N]) caused a sharper decrease in adsorption affinity of ACs (slope of lg (k_d/SA) vs [O + N]: -0.098∼-0.16) than that of NPCMs (slope of lg (k_d/SA) vs [O + N]: -0.025∼-0.059) for TeCA. It was due to the water cluster formed by the surface hydrophilic atoms that could block the micropores and generate massive invalid adsorption sites in the micropores of ACs, while the water cluster only occupied the surface adsorption sites of NPCMs. Furthermore, with the increasing concentration of dissolved TeCA, the effect of surface area on adsorption affinity of NPCMs for TeCA kept constant while the effect of [O + N] decreased due to the competitive adsorption between water molecule and TeCA on the surface of NPCMs, meanwhile, both the effects of micropore volume and [O + N] on adsorption affinity of ACs for TeCA were decreased due to the mechanism of micropore volume filling. These findings are valuable for providing a deep insight into the adsorption mechanisms of CMs for TeCA.

A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals

The octanol-air partition coefficient (K_OA) is a key parameter describing the partition behavior of organic chemicals between air and environmental organic phases. As the experimental determination of K_OA is costly, time-consuming and sometimes limited by the availability of authentic chemical standards for the compounds to be determined, it becomes necessary to develop credible predictive models for K_OA. In this study, a polyparameter linear free energy relationship (pp-LFER) model for predicting K_OA at 298.15 K and a novel model incorporating pp-LFERs with temperature (pp-LFER-T model) were developed from 795 log K_OA values for 367 chemicals at different temperatures (263.15-323.15 K), and were evaluated with the OECD guidelines on QSAR model validation and applicability domain description. Statistical results show that both models are well-fitted, robust and have good predictive capabilities. Particularly, the pp-LFER model shows a strong predictive ability for polyfluoroalkyl substances and organosilicon compounds, and the pp-LFER-T model maintains a high predictive accuracy within a wide temperature range (263.15-323.15 K).

3D-QSAR predictions for α-cyclodextrin binding constants using quantum mechanically based descriptors

Binding of organic chemicals to α-cyclodextrin (αCD) is a typical example for host-guest complexation that is influenced by the 3D-structure of both the binding site (host) and the solute (guest). Prediction of the binding constant is challenging and requires a successful representation of the binding site-solute interactions in the 3D-space. In this study, we tested if a 3D quantitative structure activity relationship (3D-QSAR) model with quantum mechanically based local sigma profiles (LSPs) derived from the COSMOsar3D method is capable of predicting αCD binding constants from the most recent literature and how the model performs in comparison to a standard comparative molecular field analysis and to a reference 2D-QSAR. The results showed that the new 3D-QSAR model was more predictive than both reference models (RMSE 0.45 vs 0.53/0.52, R² 0.70 vs 0.53/0.68). Furthermore, only the new model captured the differences in the binding constants between structural isomers of aliphatic alcohols and allowed an extrapolation of the prediction to another literature data set. The high performance of the 3D-QSAR model with LSPs tested in this study and its theoretical robustness suggest that this modeling approach should be applicable to other binding processes including protein binding.

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors

The soil sorption partition coefficient logK_oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK_oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK_oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.

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.

PCDD/F and PCB water column partitioning examination using natural organic matter and black carbon partition coefficient models

A 9-year water dataset from the Houston Ship Channel (HSC) was analyzed to understand partitioning in polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins/polychlorinated dibenzofurans (PCDD/Fs). Total PCBs had more mass as dissolved (74%) whereas total PCDD/Fs did not (11%). Generally, the limited number of PCDD/Fs (only 2378 substituted) explained these differences though differences in chemical behavior beyond log K ow also likely influence partitioning. The particular fractionation seen in the HSC also seemed related to a wide variation in particulate organic carbon (POC)/dissolved organic carbon (DOC) ratio (0.42-180%). Published and unaltered linear free energy and linear solvation energy relationships for DOC, POC, and particulate black carbon (BC) resulted in predictions that were at best 27% (PCB) and 25% root-mean-square error (RMSE) (PCDD/F) partition fraction compared to observed (using estimated BC/POC fractions of 10 and 25%, respectively). These results show, at least in light of the uncertainties in this data (e.g., precise fraction of BC), that a 25% accuracy in model prediction of operationally dissolved or suspended fraction for any one PCB or PCDD/F congener is the best prediction that may be expected. It is therefore recommended that site-specific data be used to calibrate most any water column-partitioning model if it is to be expected to describe what actually occurs in field conditions.

Root Uptake of Pharmaceuticals and Personal Care Product Ingredients

Crops irrigated with reclaimed wastewater or grown in biosolids-amended soils may take up pharmaceuticals and personal care product ingredients (PPCPs) through their roots. The uptake pathways followed by PPCPs and the propensity for these compounds to bioaccumulate in food crops are still not well understood. In this critical review, we discuss processes expected to influence root uptake of PPCPs, evaluate current literature on uptake of PPCPs, assess models for predicting plant uptake of these compounds, and provide recommendations for future research, highlighting processes warranting study that hold promise for improving mechanistic understanding of plant uptake of PPCPs. We find that many processes that are expected to influence PPCP uptake and accumulation have received little study, particularly rhizosphere interactions, in planta transformations, and physicochemical properties beyond lipophilicity (as measured by Kow). Data gaps and discrepancies in methodology and reporting have so far hindered development of models that accurately predict plant uptake of PPCPs. Topics warranting investigation in future research include the influence of rhizosphere processes on uptake, determining mechanisms of uptake and accumulation, in planta transformations, the effects of PPCPs on plants, and the development of predictive models.

LSER model for organic compounds adsorption by single-walled carbon nanotubes: Comparison with multi-walled carbon nanotubes and activated carbon

LSER models for organic compounds adsorption by single and multi-walled carbon nanotubes and activated carbon were successfully developed. The cavity formation and dispersion interactions (vV), hydrogen bond acidity interactions (bB) and π-/n-electron interactions (eE) are the most influential adsorption mechanisms. SWCNTs is more polarizable, less polar, more hydrophobic, and has weaker hydrogen bond accepting and donating abilities than MWCNTs and AC. Compared with SWCNTs and MWCNTs, AC has much less hydrophobic and less hydrophilic adsorption sites. The regression coefficients (e, s, a, b, v) vary in different ways with increasing chemical saturation. Nonspecific interactions (represented by eE and vV) have great positive contribution to organic compounds adsorption, and follow the order of SWCNTs > MWCNTs > AC, while hydrogen bond interactions (represented by aA and bB) demonstrate negative contribution. These models will be valuable for understanding adsorption mechanisms, comparing adsorbent characteristics, and selecting the proper adsorbents for certain organic compounds.

Organic Carbon/Water and Dissolved Organic Carbon/Water Partitioning of Cyclic Volatile Methylsiloxanes: Measurements and Polyparameter Linear Free Energy Relationships

The sorption of cyclic volatile methyl siloxanes (cVMS) to organic matter has a strong influence on their fate in the aquatic environment. We report new measurements of the partition ratios between freshwater sediment organic carbon and water (KOC) and between Aldrich humic acid dissolved organic carbon and water (KDOC) for three cVMS, and for three polychlorinated biphenyls (PCBs) that were used as reference chemicals. Our measurements were made using a purge-and-trap method that employs benchmark chemicals to calibrate mass transfer at the air/water interface in a fugacity-based multimedia model. The measured log KOC of octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5), and dodecamethylcyclohexasiloxane (D6) were 5.06, 6.12, and 7.07, and log KDOC were 5.05, 6.13, and 6.79. To our knowledge, our measurements for KOC of D6 and KDOC of D4 and D6 are the first reported. Polyparameter linear free energy relationships (PP-LFERs) derived from training sets of empirical data that did not include cVMS generally did not predict our measured partition ratios of cVMS accurately (root-mean-squared-error (RMSE) for logKOC 0.76 and for logKDOC 0.73). We constructed new PP-LFERs that accurately describe partition ratios for the cVMS as well as for other chemicals by including our new measurements in the existing training sets (logKOC RMSEcVMS: 0.09, logKDOC RMSEcVMS: 0.12). The PP-LFERs we have developed here should be further evaluated and perhaps recalibrated when experimental data for other siloxanes become available.

Influence of Temperature, Relative Humidity, and Soil Properties on the Soil-Air Partitioning of Semivolatile Pesticides: Laboratory Measurements and Predictive Models

Soil-air partition coefficient (Ksoil-air) values are often employed to investigate the fate of organic contaminants in soils; however, these values have not been measured for many compounds of interest, including semivolatile current-use pesticides. Moreover, predictive equations for estimating Ksoil-air values for pesticides (other than the organochlorine pesticides) have not been robustly developed, due to a lack of measured data. In this work, a solid-phase fugacity meter was used to measure the Ksoil-air values of 22 semivolatile current- and historic-use pesticides and their degradation products. Ksoil-air values were determined for two soils (semiarid and volcanic) under a range of environmentally relevant temperature (10-30 °C) and relative humidity (30-100%) conditions, such that 943 Ksoil-air measurements were made. Measured values were used to derive a predictive equation for pesticide Ksoil-air values based on temperature, relative humidity, soil organic carbon content, and pesticide-specific octanol-air partition coefficients. Pesticide volatilization losses from soil, calculated with the newly derived Ksoil-air predictive equation and a previously described pesticide volatilization model, were compared to previous results and showed that the choice of Ksoil-air predictive equation mainly affected the more-volatile pesticides and that the way in which relative humidity was accounted for was the most critical difference.

A framework for assessing the retardation of organic molecules in groundwater: Implications of the species distribution for the sorption-influenced transport

The pH-dependent molecule speciation (charge state) in solution strongly influences the transport of ionizable organic compounds in the aquatic environment. Therefore, the sorption behavior is complex and reliable predictions only based on physico-chemical sorbate, sorbent and solution properties are challenging. A short overview of underlying sorption processes causing retardation during the solute transport in aquifers is completed by a description of approaches for estimating respective sorption coefficients/retardation factors and discussed together with their limitations. Based on these initial considerations, a systematic framework is proposed, which allows the assessment of transport properties of organic molecule species by their chemical nature (neutral, acidic, basic, ampholytic). As a result, the transport properties of many (ionizable) organic molecules of interest can be assessed and even first presumptions for the sorption behavior of new and not yet investigated molecules can be derived.

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

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

Sorption mechanism and predictive models for removal of cationic organic contaminants by cation exchange resins

Understanding the sorption mechanism of organic contaminants on cation exchange resins (CXRs) will enable application of these resins for the removal of cationic organic compounds from contaminated water. In this study, sorption of a diverse set of 12 organic cations and 8 neutral aromatic solutes on two polystyrene CXRs, MN500 and Amberlite 200, was examined. MN500 showed higher sorbed concentrations due to its microporous structure. The sorbed concentrations followed the same trend of aromatic cations > aliphatic cations > neutral solutes for both resins. Generally, solute-solvent interactions, nonpolar moiety of the solutes, and resin matrix can affect selectivity of the cations. Sorbed concentrations of the neutral compounds were significantly less than those of the cations, indicating a combined effect of electrostatic and nonelectrostatic interactions. By conducting multiple linear regression between Gibbs free energy of sorption and Abraham descriptors for all 20 compounds, polarity/polarizability (S), H-bond acidity (A), induced dipole (E), and electrostatic (J(+)) interactions were found to be involved in the sorption of the cations by the resins. After converting the aqueous sorption isotherms to sorption from the ideal gas-phase by water-wet resins, a more significant effect of J(+) was observed. Predictive models were then developed based on the linear regressions and validated by accurately estimating the sorption of different test set compounds with a root-mean-square error range of 0.91-1.1 and 0.76-0.85 for MN500 and Amberlite 200, respectively. The models also accurately predicted sorption behavior of aniline and imidazole between pH 3 and 10.