Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: a QSAR study

Experimental insights and modeling innovations: Deciphering Fe(VI) oxidation in imidazole ionic liquids through QSAR and RFR

In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants (kobs). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy (Egap) and kobs. MLR analysis further underscored Egap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees, feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed Egap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors, establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry.

Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.

Fe2+-NTA synergized UV254 photolytic defluorination of perfluorooctane sulfonate (PFOS): Enhancing through intramolecular electron density perturbation via electron acquisition

Perfluorooctane sulfonate (PFOS) is a persistent organic pollutant posing a risk in environmental persistence, bioaccumulation and biotoxicity. This study was to reach a comprehensive and deeper understanding of PFOS elimination in a UV254 photolytic treatment with the co-presence of Fe2+ and nitrilotriacetic acid trisodium salt (NTA). PFOS defluorination was noticeably enhanced in the UV/Fe2+-NTA treatment compared with UV/NTA, UV/Fe2+ and our previously studied UV/Fe3+ treatments. UV-vis, FTIR, and UPLC/MS-MS results indicated the formation of PFOS-Fe2+-NTA complex in PFOS, Fe2+ and NTA mixture. The transition energy gap of PFOS-Fe2+-NTA decreased below the excitation energy supplied by UV254 irradiation, corresponding with red shift appearing in UV-vis scanning spectrum. This favored intramolecular electron transfer from Fe2+-NTA to PFOS under UV254 irradiation to form electron-accepting PFOS. Molecular electrostatic potential and atom charge distribution analyses suggested electron density rearrangement and perturbation in the perfluorinated carbon chain of electron-accepting PFOS, leading to the decrease in bond dissociation energies. Intermediate products detection suggested the parallel defluorination pathways of PFOS desulfonation, middle carbon chain scission and direct C-F cleavage. NTA exhibited crucial functions in the UV/Fe2+-NTA treatment by holding Fe2+/Fe3+ in soluble form as a chelant and favoring water activation to generate hydrated electrons (eaq-) under UV irradiation as a photosensitizer. Fe2+ acting as the conduit for electron transfer and the bridge of PFOS anion and NTA was thought functioning best at 200 µM in this study. The degree of UV/Fe2+-NTA -synergized PFOS defluorination also depended on eaq- yield and UV254 photon flux. The structure dependence on the electron transfer process of PFOS and PFOA was explored incorporating molecular structure descriptors. Because of possessing greater potential to acquire electrons or less likeliness to donate its electrons than PFOA, PFOS exhibited faster defluorination kinetics in the published "reduction treatments" than "oxidation" ones. Whereas, PFOA defluorination kinetics were at similar level in both "reduction" and "oxidation" treatments.

AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism

The development of accurate and interpretable models for predicting reaction constants of organic compounds with hydroxyl radicals is vital for advancing quantitative structure-activity relationships (QSAR) in pollutant degradation. Methods like molecular descriptors, molecular fingerprinting, and group contribution methods have limitations, as traditional machine learning struggles to capture all intramolecular information simultaneously. To address this, we established an integrated graph neural network (GNN) with approximately 12 million learnable parameters. GNN represents atoms as nodes and chemical bonds as edges, thus transforming molecules into a graph structures, effectively capturing microscopic properties while depicting atom connectivity in non-Euclidean space. Our datasets comprise 1401 pollutants to develop an integrated GNN model with Bayesian optimization, the model achieves root mean square errors of 0.165, 0.172, and 0.189 on the training, validation, and test datasets, respectively. Furthermore, we assess molecular structure similarity using molecular fingerprint to enhance the model's applicability. Afterwards, we propose a gradient weight mapping method for model explainability, uncovering the key functional groups in chemical reactions in artificial intelligence perspective, which would boost chemistry through artificial intelligence extreme arithmetic power.

Quantitative structure-activity relationships for the reaction kinetics of trace organic contaminants with one-electron oxidants

Understanding the reactivity between trace organic contaminants (TrOCs) and radicals involved in advanced oxidation processes (AOPs) is necessary for a good process design, but the experimentally determined rate constants (k values) are not sufficient for numerous artificial TrOCs. Thus, the development of quantitative structure-activity relationships (QSARs) for predicting k values may be an effective way to address this limitation. In this work, we developed QSARs for the reactions of TrOCs with AOP-related one-electron oxidants. Specifically, 15 QSARs using Hammett constants and 8 cross-correlations were developed based on the k values of over 400 reactions between TrOCs (most contain electron-rich moieties, such as phenol, aniline, and alkoxy benzene) and 5 one-electron oxidants (SO4˙-, Br˙, Br2˙-, Cl2˙-, and CO3˙-). Overall, the developed QSARs show a good predictive performance with 94% (237/251, for Hammett constant-based QSARs) and 80% (218/274, for cross-correlations) of the k values predicted within a factor of 3. All the Hammett constant-based QSARs show negative slope values and all cross-correlations show positive relationships, suggesting all 5 one-electron oxidants mainly share similar electrophilic mechanisms with the TrOCs highlighted in this work. Previous QSAR studies on the k values of one-electron oxidants were compared and integrated into their model analysis. Furthermore, k values predicted herein from the QSARs were used to evaluate the degradation of TrOCs during UV/persulfate and UV/chlorine treatment in multiple wastewater matrices, which were demonstrated to be useful. Finally, remarks on the use of the developed QSARs were presented.

π-π stack driven competitive /complementary adsorption of aromatic compounds on MIL-53(Al)

In this study, the selective adsorption of aromatic compounds on mesoporous MIL-53(Al) was investigated, and followed the order: Biphenyl (Biph) > Triclosan (TCS) > Bisphenol A (BPA) > Pyrogallol (Pyro) > Catechol (Cate) > Phenol (Phen), and exhibited high selectivity toward TCS in binary compounds. In addition to hydrophobicity and hydrogen bonding, π-π interaction/stacking predominated, and more evidently with double benzene rings. TCS-containing halogens could increase π interaction on the benzene rings via forming Cl-π stacking with MIL-53(Al). Moreover, site energy distribution confirmed that complementary adsorption mainly occurred in the Phen/TCS system, as evidenced by ΔQ^pri (the decreased solid-phase TCS concentration of the primary adsorbate) < Q^sec (the solid-phase concentrations of the competitor (Phen)). In contrast, competitive sorption occurred in the BPA/TCS and Biph/TCS systems within 30 min due to ΔQ^pri = Q^sec, followed by substitution adsorption in the BPA/TCS system, but not for the Biph/TCS system, likely attributed to the magnitude of energy gaps (E_g) and bond energy of TCS (1.80 eV, 362 kJ/mol) fallen between BPA (1.74 eV, 332 kJ/mol) and Biph (1.99 eV, 518 kJ/mol) according to the density-functional theory of Gaussian models. Biph with a more stable electronic homeostasis than TCS lead to the occurrence of substitution adsorption in the TCS/BPA system, but not in the TCS/Biph system. This study provides insight into the mechanisms of different aromatic compounds on MIL-53(Al).

Effective PFAS degradation by electrochemical oxidation methods-recent progress and requirement

The presence of per- and poly-fluoroalkyl substances (PFASs) in water is of global concern due to their high stability and toxicity even at very low concentrations. There are several technologies for the remediation of PFASs, but most of them are inadequate either due to limited effectiveness, high cost, or production of a large amount of sludge. Electrochemical oxidation (EO) technology shows great potential for large-scale application in the degradation of PFASs due to its simple procedure, low loading of chemicals, and least amount of waste. Here, we have reviewed the recent progress in EO methods for PFAS degradation, focusing on the last 10 years, to explore an efficient, cost-effective, and environmentally benign remediation technology. The effects of important parameters (e.g., anode material, current density, solution pH, electrolyte, plate distance, and electrical connector type) are summarized and evaluated. Also, the energy consumption, the consequence of different PFASs functional groups, and water matrices are discussed to provide an insight that is pivotal for developing new EO materials and technologies. The proposed degradation pathways of shorter-chain PFAS by-products during EO of PFAS are also discussed.

Structural Features Promoting Photocatalytic Degradation of Contaminants of Emerging Concern: Insights into Degradation Mechanism Employing QSA/PR Modeling

Although heterogeneous photocatalysis has shown promising results in degradation of contaminants of emerging concern (CECs), the mechanistic implications related to structural diversity of chemicals, affecting oxidative (by HO•) or reductive (by O2•−) degradation pathways are still scarce. In this study, the degradation extents and rates of selected organics in the absence and presence of common scavengers for reactive oxygen species (ROS) generated during photocatalytic treatment were determined. The obtained values were then brought into correlation as K coefficients (MHO•/MO2•−), denoting the ratio of organics degraded by two occurring mechanisms: oxidation and reduction via HO• and O2•−. The compounds possessing K >> 1 favor oxidative degradation over HO•, and vice versa for reductive degradation (i.e., if K << 1 compounds undergo reductive reactions driven by O2•−). Such empirical values were brought into correlation with structural features of CECs, represented by molecular descriptors, employing a quantitative structure activity/property relationship (QSA/PR) modeling. The functional stability and predictive power of the resulting QSA/PR model was confirmed by internal and external cross-validation. The most influential descriptors were found to be the size of the molecule and presence/absence of particular molecular fragments such as C − O and C − Cl bonds; the latter favors HO•-driven reaction, while the former the reductive pathway. The developed QSA/PR models can be considered robust predictive tools for evaluating distribution between degradation mechanisms occurring in photocatalytic treatment.

Quantitative structure-activity relationship (QSAR) guides the development of dye removal by coagulation

QSAR modeling could be a promising tool for guiding the development of novel and cost-effective environmental technologies. As an example, it could be widely used to analyze the degradation rules of organic pollutants in various decomposition methods. However, a lack of systematic research on a particular removal method is significant in revealing the decomposition rule of pollutants more accurately and guiding industrial applications. In this study, six coagulation systems (MnO₂/Fe(OH)₃/AlCl₃/FeCl₃/CaCl₂/MgCl₂) were used as examples to remove 38 dyes under three pH conditions, and the characteristics and differences of these systems were explored by QSAR modeling. The results showed that the removal effect by MnO₂ under acidic and neutral conditions and Fe(OH)₃ under acidic conditions were quantitatively described mainly by bond order (BO) and Fukui index (f (+) and f (0)), which reflected that oxidative degradation dominated. In contrast, most of the critical parameters of other systems were molecular descriptors represented by ∑q(O) (the total charge of all the oxygen atoms in the molecule) and SAA (surface area of a molecule), which reflected that electrostatic adsorption and hydrogen-bond adsorption processes dominated.

Micropollutant degradation by UV/H2O2 in drinking water: Facilitated prediction through combination of model simulation and portable measurement

Ultraviolet-based advanced oxidation processes (UV-AOPs) are highly effective for micropollutant degradation. However, it is considerably time and labor consuming to evaluate the practical performance of UV-AOPs. This study developed a novel method, through combination of model simulation with portable measurement (MS-PM), to facilitate prediction of the photon fluence-based rate constant of micropollutant degradation (k'_p,MP) by UV/H₂O₂, a commercially available UV-AOP. Model simulation was performed with photochemical, hydroxyl radical (HO^•) concentration steady-state approximation, and quantitative structure-activity relationship models; and portable measurement was conducted on a mini-fluidic photoreaction system to quantify the HO^• scavenging capacity (HRSC) of a water matrix. The method was established and further verified experimentally in seven test waters by taking sulfamethazine (SMN) as a model micropollutant. A lower k'_p,SMN was predicted in a water matrix with a higher HRSC, for example, 57.5 and 347.8 m² einstein^-1 in the raw water (HRSC = 5.91 × 10⁵ s^-1) and sand-filtered effluent (HRSC = 5.25 × 10⁴ s^-1) of a drinking water treatment plant at an H₂O₂ dose of 25 mg L^-1, respectively. The predicted values agreed generally well with the experimental ones. The MS-PM method has advantages of high efficiency and convenience, low cost, and acceptable accuracy, which will significantly facilitate the design and field assessment of UV-AOPs for micropollutant removal from drinking water.

The structure-activity relationship of aromatic compounds in advanced oxidation processes：a review

Advanced oxidation processes (AOPs) are widely used as efficient technologies to treat highly toxic and harmful substances in wastewater. Taking the most representative aromatic compounds (monosubstituted benzenes, substituted phenols and heterocyclic compounds) as examples, this paper firstly introduces their structures and the structural descriptors studied in AOPs before, and the influence of structural differences in AOPs with different reactive oxygen species (ROS) on the degradation rate was discussed in detail. The structure-activity relationship of pollutants has been previously analyzed through quantitative structure-activity relationship (QSAR) model, in which ROS is a very important influencing factor. When electrophilic oxidative species attacks pollutants, aromatic compounds with electron donating groups are more favorable for degradation than aromatic compounds with electron donating groups. While nucleophilic oxidative species comes to the opposite conclusion. The choice of advanced oxidation processes, the synergistic effect of various active oxygen species and the used catalysts will also change the degradation mechanism. This makes the structure-dependent activity relationship uncertain, and different conclusions are obtained under the influence of various experimental factors.

Quantum parameter analysis of the adsorption mechanism by freshly formed ferric hydroxide for synthetic dye and antibiotic wastewaters

In this study, the adsorption effect by freshly formed ferric hydroxide (FFFH) for the removal of 47 synthetic dye and antibiotic wastewaters under different pH conditions (i.e., pH = 4, 7, and 10) was investigated. The average total organic carbon (TOC) removal rates (R_exp) of pollutants under acidic, neutral, and alkaline conditions were 27.10 ± 3.21%, 15.16 ± 2.48%, and 9.72 ± 2.81%, respectively. By analyzing the characteristics of FFFH measured by SEM, XRD, FT-IR, TGA and BET, the properties of pollutants, and the values of R_exp, one can conclude that the large specific surface area and rich hydroxyl groups on the surface of FFFH were the reasons for its adsorption capacity for organic pollutants, and the electrostatic adsorption was the main reason for higher removal rate in acidic condition. Subsequently, to better elucidate the intrinsic factors influencing the removal rates at the molecular structure level, three optimal quantitative structure-activity relationship (QSAR) models were established by using multiple linear regression (MLR) analysis. Results of model validations (e.g., regression coefficient, internal and external verifications, and Y-randomization) showed that the established models exhibited excellent stability, reliability, and robustness with the values of R² = 0.7544, 0.7764, 0.7528, Q²_INT = 0.6451, 0.6836, 0.6228, and Q²_EXT = 0.5890, 0.6029, 0.7298 under acidic, neutral, and alkaline conditions, respectively. The results of quantum parameter analysis revealed that the adsorption mechanism of FFFH for dyes and antibiotics mainly includes the activity of adsorption site, the behavior of electron transfer and the strength of molecular polarity.

Prediction of Reaction Yield for Buchwald-Hartwig Cross-coupling Reactions Using Deep Learning

Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction yield. However, none of them were at a practical level in terms of predictive ability. In this study, we propose a message passing neural network (MPNN) model for chemical yield prediction, focusing on the Buchwald-Hartwig cross-coupling HTE data set. As an initial atom embedding in MPNN model, we propose to use the Mol2Vec feature vectors pre-trained using a large compound database. Predictive ability of the proposed model was higher than that of previously reported five models for the three out of five data sets. Moreover, visualization of important atoms based on self-attention mechanism was in favor of Mol2Vec as an atom embedding rather than other embeddings including previously employed simple representations.

"pySiRC": Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants

We developed a web application structured in a machine learning and molecular fingerprint algorithm for the automatic calculation of the reaction rate constant of the oxidative processes of organic pollutants by ^•OH and SO₄^•- radicals in the aqueous phase-the pySiRC platform. The model development followed the OECD principles: internal and external validation, applicability domain, and mechanistic interpretation. Three machine learning algorithms combined with molecular fingerprints were evaluated, and all the models resulted in high goodness-of-fit for the training set with R² > 0.931 for the ^•OH radical and R² > 0.916 for the SO₄^•- radical and good predictive capacity for the test set with R_ext² = Q_ext² values in the range of 0.639-0.823 and 0.767-0.824 for the ^•OH and SO₄^•- radicals. The model was interpreted using the SHAP (SHapley Additive exPlanations) method: the results showed that the model developed made the prediction based on a reasonable understanding of how electron-withdrawing and -donating groups interfere with the reactivity of the ^•OH and SO₄^•- radicals. We hope that our models and web interface can stimulate and expand the application and interpretation of kinetic research on contaminants in water treatment units based on advanced oxidative technologies.

A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants

The Monte Carlo algorithm was applied to formulate a robust quantitative structure-property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES and hydrogen-suppressed molecular graph for both classes of compounds. Approximately 75% of the total experimental data set was randomly divided into training and invisible training sets, while approximately 25% was divided into calibration and validation sets. The authenticity and robustness of the developed QSPR models were also judged by the Index of Ideality of Correlation. In QSPR modelling of aliphatic compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.852-0.905, 0.815-0.894, 0.839-0.897 and 0.737-0.867, respectively. Whereas, in the QSPR modelling of phenyl-based compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.867-0.896, 0.852-0.865, 0.816-0.850 and 0.760-0.762, respectively. The structural attributes, which are promoters of l o g K e a q - increase/decrease are also extracted from the SMILES notation for mechanistic interpretation. These QSPR models can also be applied to compute the reaction rate constants of organic contaminants.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (K_POM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log K_POM-w with R²_adj of 0.825. The values of statistical parameters including R²_adj, Q²_ext, RMSE_tra and RMSE_ext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.

Two-dimensional and Three-dimensional quantitative structure-activity relationship models for the degradation of organophosphate flame retardants during supercritical Water oxidation

Organophosphate flame retardants (OPFRs) have been increasingly utilized as flame retardants in various fields due to the phasing out of polybrominated diphenyl ethers. To achieve a better understanding of the degradation of OPFRs undergoing supercritical water oxidation (SCWO) process, two-dimensional and three-dimensional quantitative structure-activity relationship (2D-QSAR and 3D-QSAR) models were established to investigate the factors influencing the total carbon degradation rates (k_TOC). Results of the QSAR models demonstrated reliable results to estimate the k_TOC values, but varied in the influencing factors. Two distinct degradation mechanisms were subsequently proposed based on the distribution of LUMO in molecules for the 2D-QSAR model. CoMFA and CoMSIA methods were applied to develop the 3D-QSAR models. Steric fields were observed to influence k_TOC values more than electrostatic fields in the CoMFA model with the contribution rates of 87.2% and 12.8%, respectively. In the CoMSIA model, influence on k_TOC values varies between different types of fields with the hydrophobic field being the most influential at 62.1%, followed by the steric field at 25.7% and then the electrostatic field at 10.8%. Results from this study generated critical knowledge of influencing factors on OPFRs degradation and yielded theoretical basis for estimating removal behaviors of OPFRs undergoing SCWO process.

Integration of target analyses, non-target screening and effect-based monitoring to assess OMP related water quality changes in drinking water treatment

The ever-increasing production and use of chemicals lead to the occurrence of organic micro-pollutants (OMPs) in drinking water sources, and consequently the need for their removal during drinking water treatment. Due to the sheer number of OMPs, monitoring using targeted chemical analyses alone is not sufficient to assess drinking water quality as well as changes thereof during treatment. High-resolution mass spectrometry (HRMS) based non-target screening (NTS) as well as effect-based monitoring using bioassays are promising monitoring tools for a more complete assessment of water quality and treatment performance. Here, we developed a strategy that integrates data from chemical target analyses, NTS and bioassays. We applied it to the assessment of OMP related water quality changes at three drinking water treatment pilot installations. These installations included advanced oxidation processes, ultrafiltration in combination with reverse osmosis, and granular activated carbon filtration. OMPs relevant for the drinking water sector were spiked into the water treated in these installations. Target analyses, NTS and bioassays were performed on samples from all three installations. The NTS data was screened for predicted and known transformation products of the spike-in compounds. In parallel, trend profiles of NTS features were evaluated using multivariate analysis methods. Through integration of the chemical data with the biological effect-based results potential toxicity was accounted for during prioritization. Together, the synergy of the three analytical methods allowed the monitoring of OMPs and transformation products, as well as the integrative biological effects of the mixture of chemicals. Through efficient analysis, visualization and interpretation of complex data, the developed strategy enabled to assess water quality and the impact of water treatment from multiple perspectives. Such information could not be obtained by any of the three methods alone. The developed strategy thereby provides drinking water companies with an integrative tool for comprehensive water quality assessment.

A deep neural network combined with molecular fingerprints (DNN-MF) to develop predictive models for hydroxyl radical rate constants of water contaminants

This work combined a Deep Neural Network (DNN) with molecular fingerprints (MF) to develop models to predict the OH radical rate constants of 593 organic contaminants. Molecular descriptors, most often used in establishing quantitative structural-activity relationships (QSARs), were not used here because of their complicated generation processes that rely on advanced physicochemical and computational knowledge. Instead, we only fed the most basic information of the contaminant structures, i.e., MF encoding the types of atoms and how they are connected, to DNN and DNN then developed predictive models automatically. Here, a dataset containing 457 contaminants and their OH rate constants was first used to develop predictive models by DNN-MF. The hence developed models showed comparable accuracy to the traditional QSARs. The root mean square error (RMSE) values of the test sets were 0.358-0.384. The length of 2048 bits for the MF and 3 hidden layers (each with 1024 neurons) were found to be the optimal parameters for DNN. The model containing additional 89 micorpollutants in the training set was then successfully applied to predict the OH rate constants of 17 organophosphorus flame retardants and 29 additional micropollutants, with comparable accuracy to the reported molecular descriptors-based QSARs.

Norm index for predicting the rate constants of organic contaminants oxygenated with sulfate radical

The degradation of organic contaminants in aquatic systems has raised immense attention worldwide, and the second-order rate constant ([Formula: see text]) of water pollutants oxidized by sulfate radical anion is an important index for assessing the degradation efficiency of organics. Herein, a new norm mathematical formula is defined. Based on this, four new descriptors are proposed and a QSPR model is developed for predicting [Formula: see text] using 30 families of emerging organic pollutants in water. The statistical results fully prove that this model has good fitting effect and stability with R² of 0.8862, Q²_LOO of 0.8466, and Q²_5-fold of 0.8329, respectively. The validation results including cross validation, applicability domain analysis, and model comparison show that this model has good robustness, predictive performance, and reliability. These decent results indicate that the new norm mathematical formula is effective in calculating descriptors and the norm indexes have a great application for evaluating the transformation fate of organic pollutants by sulfate radical in aquatic systems.

Rate constants of hydroxyl radicals reaction with different dissociation species of fluoroquinolones and sulfonamides: Combined experimental and QSAR studies

Hydroxyl radicals (·OH) initiated degradation is an important process governing fate of aquatic organic micropollutants (OMPs). However, rate constants for aqueous reaction of OMPs with ·OH (k_OH) are available only for a limited number of OMPs, which complicates fate assessment of OMPs. Furthermore, molecular structures of many OMPs contain ionizable groups, and the OMPs may dissociate into different anionic/cationic species with different reactivity towards ·OH. Therefore, it is of importance to determine k_OH of ionizable OMPs, and to develop quantitative structure-activity relationship (QSAR) models for predicting k_OH of OMPs at different ionization forms. Herein k_OH values of 9 fluoroquinolones (FQs) and 11 sulfonamides (SAs) at 3 dissociation forms (FQ^±/FQ⁺/FQ^-, SA⁰/SA⁺/SA^-) were determined by competition kinetics experiments. A QSAR model using theoretical molecular structural descriptors was subsequently developed. The QSAR model successfully corroborated previous experimental results, exhibited good statistical performance, and is capable to predict k_OH for FQs and SAs with different dissociation forms at environmentally relevant pH conditions. As organic ions have rarely been included in previous QSAR studies, the newly developed model that covers both neutral molecules and ions is of significance for future QSAR development as well as fate assessment of ionizable OMPs.

UV direct photolysis of halogenated disinfection byproducts: Experimental study and QSAR modeling

UV direct photolysis has been used as a promising process to remove halogenated disinfection byproducts (DBPs) generated in water. In this study, experimental studies and modeling approaches were applied to investigate the UV direct photolysis rate constants for 40 kinds of halogenated DBPs. The fluence-based pseudo-first-order rate constants for the removal of halogenated DBPs under UV photolysis spanned more than 2 orders of magnitude, with a range of (0.23-29.84) × 10^-4 cm² mJ^-1. DBPs with higher number of halogenated substituents featured higher photolysis rate constants. The degradation efficiencies of DBPs were also affected by the species of halogen substituents, which followed the trend of iodo- > bromo- > chloro- DBPs. A quantitative structure-activity relationship (QSAR) model was established on the basis of the observed degradation rate constant values, which contained a quantum-chemical descriptor (E_LUMO-E_HOMO) and a molecular descriptor (Eta_C). The calculated parameters of the developed model indicated its good robustness and high reliability. The developed QSAR model can predict the degradation rate constants for DBPs within factors of 1/3 to 3. The model was validated using application domain and visualized in a Williams plot. The selected descriptors for QSAR model can explain the reaction mechanism for UV direct photolysis.

Quantitative structure-activity relationship models for predicting reaction rate constants of organic contaminants with hydrated electrons and their mechanistic pathways

The hydrated electron (e_aq^-)-based reduction processes are promising for removing organic pollutants in water engineering systems. The reductive kinetics, especially the second order rate constants ( [Formula: see text] ) of e_aq^- with organic compounds, is important for evaluating and modeling the advanced reduction processes. In this study, the [Formula: see text] values for aliphatic compounds and phenyl-based compounds are, for the first time, modeled by the quantitative structure-activity relationship (QSAR) method. The structural features governing the reactivity of two classes of organic compounds toward e_aq^- were revealed, and the energy of the lowest unoccupied molecular orbital (E_LUMO), one-electron reduction potential (E_RED) and polarizability (α) were found to be the important molecular parameters in both two models. The built QSAR models provide robust predictive tools for estimating the removal of emerging pollutants using e_aq^- during wastewater treatment processes. Additionally, quantum chemical calculations were employed to probe into the mechanism and feasibility of the single electron transfer (SET) pathway in the e_aq^--based reduction process. The thermodynamic investigation suggests that the compounds with electron-withdrawing groups tend to possess higher [Formula: see text] and lower Gibbs free energy (ΔG_SET) and Gibbs free energies of activation (∆^‡G_SET^∘) than the ones with electron-donating groups, indicating the SET process occurs more readily. It is also found that the refractory halogenated compounds can achieve dehalogenation via the SET pathway.

Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study

Extensive commercial use of aromatic hydrocarbons results with significant amounts of these chemicals and related by-products in waters, causing a severe ecological and health threat, thus requiring an increased attention. This study was aimed at developing models for prediction of the initial toxicity of the aromatic water-pollutants (expressed as EC₅₀ and TU₀) as well as the toxicity of their intermediates at half-life of the parent pollutant (TU_1/2). For that purpose, toxicity toward Vibrio fischery was determined for 36 single-benzene ring compounds (S-BRCs), diversified by the type, number and position of substituents. Quantitative structure-activity relationship (QSAR) methodology paired with genetic algorithm optimization tool and multiple linear regression was applied to obtain the models predicting the targeted toxicity, which are based on pure structural characteristics of the tested pollutants, avoiding thus additional experimentation. Upon derivation of the models and extensive analysis on training and test sets, 4-, 4- and 5-variable models (for EC₅₀ and TU₀, TU_1/2, respectively) were selected as the most predictive possessing 0.839<R²< 0.901 and 0.789<Q²< 0.859. The analysis of the selected descriptors indicated three major structural characteristics influencing the toxicity: electronegativity, geometry and electrotopological states of the molecule. Degradation kinetics determining as well the pathways of intermediates formation, reflected over ionization potential, was found to be an important parameter determining the toxicity in half-life.

Key structural features promoting radical driven degradation of emerging contaminants in water

Diverse contaminants of emerging concern (CECs) can be found in nowadays aquatic environment, possessing high potential to cause adverse ecological and human health effects. Due to their recalcitrance, conventional water treatment methods are shown to be inadequately effective. Thus, their upgrade by advanced oxidation processes, involving the generation of highly reactive species (HO and SO₄^-), is highly demanded. In order to assess the susceptibility of CECs by HO and SO₄^-, as well as to determine the corresponding reaction rate constants k_HO and k_SO4^-, the complex experimental studies has to be maintained. The alternative is the application of modeling approaches which correlate structural characteristics with activities/properties of interest, i.e. quantitative structure activity/property relationship (QSAR/QSPR). In this study k_HO and k_SO4^- of fifteen selected CECs were determined by competitive kinetics, and afterward used to elucidate key structural features promoting their degradation. In that purpose, QSPR models were constructed using multiple linear regression (MLR) combined with genetic algorithm (GA) approach. The models were submitted to the internal and external validation (using additional set of 17 CECs). Selected 3-variable models predicting k_HO and k_SO4^- were characterized with high accuracy and predictivity (R² = 0.876 and Q² = 0.847 and R² = 0.832 and Q² = 0.778, respectively). Although selected models at the first sight include descriptors derived through complicated calculation procedures, their weighting schemes indicate on their relevance and transparency toward established reaction theories and differences regarding radical type.

2D-QSAR and 3D-QSAR simulations for the reaction rate constants of organic compounds in ozone-hydrogen peroxide oxidation

Synergistic oxidation of ozone (O₃) and hydrogen peroxide (H₂O₂) is an effective water treatment for the elimination of organic pollutants. In this study, 23 organic compounds were conducted to study the reaction rate constants during O₃-H₂O₂ oxidation. Then, two- and three-dimensional quantitative structure-activity relationship (QSAR) models were established to investigate the factors influencing the reaction rate constants by using multiple linear regression method and comparative molecular similarity index analysis (CoMSIA) method, respectively. Both of the two models showed good performance on predicting the reaction rate constants, the associated statistical indices of 2D-QSAR and 3D-QSAR models were R² = 0.898 and 0.952, q² = 0.841 and 0.951, Q_ext² = 0.968 and 0.970, respectively. But varied in the influence factors, as for the 2D-QSAR model, three quantum chemical parameters, included dipole moment, the largest change of charge in each atom during the nucleophilic attack, the maximum positive partial charge on a hydrogen atom linked with a carbon atom affected the reaction rate. While in the 3D-QSAR model, the electrostatic field played the most important role in evaluating the reaction rate with the contribution of 35.8%, followed by hydrogen bond acceptor and hydrophobic fields with the contribution of 24.9% and 23.2%, respectively. These two models provided predictive tools to study the influencing factors for the degradation of organics and might potentially be applied for estimating the removal properties of unknown organics in O₃-H₂O₂ oxidation process.

The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds

Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirical constants. We selected polychlorinated biphenyls (PCBs) as an object to investigate the intrinsic correlation between 16 quantum chemical descriptors and Hammett constants. The results exhibited extremely high linearity for ∑ with Qxx/yy/zz, α and EHOMO based on the meta-position grouping. Polychlorinated dibenzodioxins (PCDDs) and polychlorinated naphthalenes (PCNs) congeners, as two independent compounds, validated the reliability of the relationship. The meta-substituent grouping method between ∑ and α was successfully used to predict the rate constant (k) for •OH oxidation of PCBs, as well as the octanol/water partition coefficient (logKOW) and aqueous solubility (-logSW) of PCDDs, and exhibited excellent agreement with experimental measurements. Revealing the intrinsic correlation underlying the empirical constant and quantum chemical descriptors can develop simpler and higher efficient model application in predicting the environmental behavior and chemical properties of compounds.

Removals of pesticides and pesticide transformation products during drinking water treatment processes and their impact on mutagen formation potential after chlorination

Removal efficiencies of 28 pesticide transformation products (TPs) and 15 parent pesticides during steps in drinking water treatment (coagulation-sedimentation, activated carbon adsorption, and ozonation) were estimated via laboratory-scale batch experiments, and the mechanisms underlying the removal at each step were elucidated via regression analyses. The removal via powdered activated carbon (PAC) treatment was correlated positively with the log K_ow at pH 7. The adjusted coefficient of determination (r²) increased when the energy level of the highest occupied molecular orbital (HOMO) was added as an explanatory variable, the suggestion being that adsorption onto PAC particles was largely governed by hydrophobic interactions. The residual error could be partly explained by π-π electron donor-acceptor interactions between the graphene surface of the PAC particles and the adsorbates. The removal via ozonation correlated positively with the energy level of the HOMO, probably because compounds with relatively high energy level HOMOs could more easily transfer an electron to the lowest unoccupied molecular orbital of ozone. Overall, the TPs tended to be more difficult to remove via PAC adsorption and ozonation than their parent pesticides. However, the TPs that were difficult to remove via PAC adsorption did not induce strong mutagenicity after chlorination, and the TPs that were associated with strong mutagenicity after chlorination could be removed via PAC adsorption. Therefore, PAC adsorption is hypothesized to be an effective method of treating drinking water to reduce the possibility of post-chlorination mutagenicity associated with both TPs and their parent pesticides.

Quantitative structure-activity relationships for reactivities of sulfate and hydroxyl radicals with aromatic contaminants through single-electron transfer pathway

Sulfate radical anion (SO₄^•-) and hydroxyl radical (OH) based advanced oxidation technologies has been extensively used for removal of aromatic contaminants (ACs) in waters. In this study, we investigated the Gibbs free energy (ΔG_SET^∘) of the single electron transfer (SET) reactions for 76 ACs with SO₄^•- and OH, respectively. The result reveals that SO₄^•- possesses greater propensity to react with ACs through the SET channel than OH. We hypothesized that the electron distribution within the molecule plays an essential role in determining the ΔG_SET^∘ and subsequent SET reactions. To test the hypothesis, a quantitative structure-activity relationship (QSAR) model was developed for predicting ΔG_SET^∘ using the highest occupied molecular orbital energies (E_HOMO), a measure of electron distribution and donating ability. The standardized QSAR models are reported to be ΔG^°_SET=-0.97×E_HOMO - 181 and ΔG^°_SET=-0.97×E_HOMO - 164 for SO₄^•- and OH, respectively. The models were internally and externally validated to ensure robustness and predictability, and the application domain and limitations were discussed. The single-descriptor based models account for 95% of the variability for SO₄^•- and OH. These results provide the mechanistic insight into the SET reaction pathway of radical and non-radical bimolecular reactions, and have important applications for radical based oxidation technologies to remove target ACs in different waters.

The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (k _OH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: [Formula: see text], RMS _train = 0.21, while for the test set is [Formula: see text], RMS _test = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.

Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach

Designing of advanced oxidation process (AOP) requires knowledge of the aqueous phase hydroxyl radical (^●OH) reactions rate constants (k _OH), which are strictly dependent upon the pH and temperature of the medium. In this study, pH- and temperature-dependent quantitative structure-property relationship (QSPR) models based on the decision tree boost (DTB) approach were developed for the prediction of k _OH of diverse organic contaminants following the OECD guidelines. Experimental datasets (n = 958) pertaining to the k _OH values of aqueous phase reactions at different pH (n = 470; 1.4 × 10⁶ to 3.8 × 10¹⁰ M^-1 s^-1) and temperature (n = 171; 1.0 × 10⁷ to 2.6 × 10¹⁰ M^-1 s^-1) were considered and molecular descriptors of the compounds were derived. The Sanderson scale electronegativity, topological polar surface area, number of double bonds, and halogen atoms in the molecule, in addition to the pH and temperature, were found to be the relevant predictors. The models were validated and their external predictivity was evaluated in terms of most stringent criteria parameters derived on the test data. High values of the coefficient of determination (R ²) and small root mean squared error (RMSE) in respective training (> 0.972, ≤ 0.12) and test (≥ 0.936, ≤ 0.16) sets indicated high generalization and predictivity of the developed QSPR model. Other statistical parameters derived from the training and test data also supported the robustness of the models and their suitability for screening new chemicals within the defined chemical space. The developed QSPR models provide a valuable tool for predicting the ^●OH reaction rate constants of emerging new water contaminants for their susceptibility to AOPs.

Modeling the aqueous phase reactivity of hydroxyl radical towards diverse organic micropollutants: An aid to water decontamination processes

The rate constants of the hydroxyl radical reactions (k_OH) with organic micropollutants (OMPs) in aqueous medium are important in designing the advanced oxidation processes (AOPs) for their removal. In this study, a quantitative structure-property relationship (QSPR) model for the prediction of k_OH of diverse and emerging OMPs was developed in accordance with the OECD guidelines. A large experimental data set (n = 995) comprised of compounds with k_OH values ranging from 7.9 × 10⁵ to 6.8 × 10¹⁰ M^-1 s^-1 was considered and several molecular descriptors were calculated. As a result, five descriptors were found to be important in predicting the k_OH values which related to the electronegativity, topological polar surface area, double bonds, average molecular weight, and halogen atoms in the molecule. The optimal model was validated internally and externally and several statistical stringent parameters were derived. High values of the coefficient of determination (R²) and small root mean squared error (RMSE) in the training (0.954; 0.17) and validation (0.925; 0.14) sets indicated high generalization and predictivity of the developed model. Other statistical parameters derived from the training and validation data also supported the robustness of the model. The proposed model outperformed the earlier QSARs reported for k_OH prediction. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of organic micropollutants to AOPs.

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 a QSAR model for predicting aqueous reaction rate constants of organic chemicals with hydroxyl radicals

Reaction with hydroxyl radicals (˙OH) is an important removal pathway for organic pollutants in the aquatic environment. The aqueous reaction rate constant (k_OH) is therefore an important parameter for fate assessment of aquatic pollutants. Since experimental determination fails to meet the requirement of being able to efficiently handle numerous organic chemicals at limited cost and within a relatively short period of time, in silico methods such as quantitative structure-activity relationship (QSAR) models are needed to predict k_OH. In this study, a QSAR model with a larger and wider applicability domain as compared with existing models was developed. Following the guidelines for the development and validation of QSAR models proposed by the Organization for Economic Co-operation and Development (OECD), the model shows satisfactory performance. The applicability domain of the model has been extended and contained chemicals that have rarely been covered in most previous studies. The chemicals covered in the current model contain functional groups including [double bond splayed left]C[double bond, length as m-dash]C[double bond splayed right], -C[triple bond, length as m-dash]C-, -C₆H₅, -OH, -CHO, -O-, [double bond splayed left]C[double bond, length as m-dash]O, -C[double bond, length as m-dash]O(O)-, -COOH, -C[triple bond, length as m-dash]N, [double bond splayed left]N-, -NH₂, -NH-C(O)-, -NO₂, -N[double bond, length as m-dash]C-N[double bond splayed right], [double bond splayed left]N-N[double bond splayed right], -N[double bond, length as m-dash]N-, -S-, -S-S-, -SH, -SO₃, -SO₄, -PO₄, and -X (F, Cl, Br, and I).

Three-dimensional electro-Fenton oxidation of N-heterocyclic compounds with a novel catalytic particle electrode: high activity, wide pH range and catalytic mechanism

A novel three-dimensional (3D) heterogeneous electro-Fenton (EF) system with sludge deserved activated carbon from sewage and iron sludge (SAC-Fe) as catalytic particle electrodes (CPEs) was constructed in this study.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

QSPR prediction of the hydroxyl radical rate constant of water contaminants

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.

Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study

The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed.

Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach

The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure-property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed.

QSAR models for removal rates of organic pollutants adsorbed by in situ formed manganese dioxide under acid condition

Manganese dioxide formed in oxidation process by potassium permanganate exhibits promising adsorptive capacity which can be utilized to remove organic pollutants in wastewater. However, the structure variances of organic molecules lead to wide difference of adsorption efficiency. Therefore, it is of great significance to find a general relationship between removal rate of organic compounds and their quantum parameters. This study focused on building up quantitative structure activity relationship (QSAR) models based on experimental removal rate (r(exp)) of 25 organic compounds and 17 quantum parameters of each organic compounds computed by Gaussian 09 and Material Studio 6.1. The recommended model is rpre = -0.502-7.742 f(+)x + 0.107 E HOMO + 0.959 q(H(+)) + 1.388 BOx. Both internal and external validations of the recommended model are satisfied, suggesting optimum stability and predictive ability. The definition of applicability domain and the Y-randomization test indicate all the prediction is reliable and no possibility of chance correlation. The recommended model contains four variables, which are closely related to adsorption mechanism. f(+)x reveals the degree of affinity for nucleophilic attack. E HOMO represents the difficulty of electron loss. q(H(+)) reflect the distribution of partial charge between carbon and hydrogen atom. BO x shows the stability of a molecule.

Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical

The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling

Quantitative structure-property relationship (QSPR) models which predict hydroxyl radical rate constants (kOH) for a wide range of emerging micropollutants are a cost effective approach to assess the susceptibility of these contaminants to advanced oxidation processes (AOPs). A QSPR model for the prediction of kOH of emerging micropollutants from their physico-chemical properties was developed with special attention to model validation, applicability domain and mechanistic interpretation. In this study, 118 emerging micropollutants including those experimentally determined by the author and data collected from the literature, were randomly divided into the training set (n=89) and validation set (n=29). 951 DRAGON molecular descriptors were calculated for model development. The QSPR model was calibrated by applying forward multiple linear regression to the training set. As a result, 7 DRAGON descriptors were found to be important in predicting the kOH values which related to the electronegativity, polarizability, and double bonds, etc. of the compounds. With outliers identified and removed, the final model fits the training set very well and shows good robustness and internal predictivity. The model was then externally validated with the validation set showing good predictive power. The applicability domain of the model was also assessed using the Williams plot approach. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of micropollutants to AOPs.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.