Modeling Photo-oxidative Degradation of Aromatics in Water. Optimization Study Using Response Surface and Structural Relationship Approaches

Heterogeneous Photodegradation Behavior of Liquid Crystal Monomers in Dust: Quantitative Structure-Activity Relationship and Product Identification

The heterogeneous photodegradation behavior of liquid crystal monomers (LCMs) in standard dust (standard reference material, SRM 2583) and environmental dust was investigated. The measured photodegradation ratios for 23 LCMs in SRM and environmental dust in 12 h were 11.1 ± 1.8 to 23.2 ± 1.1% and 8.7 ± 0.5 to 24.0 ± 2.8%, respectively. The degradation behavior of different LCM compounds varied depending on their structural properties. A quantitative structure-activity relationship model for predicting the degradation ratio of LCMs in SRM dust was established, which revealed that the molecular descriptors related to molecular polarizability, electronegativity, and molecular mass were closely associated with LCMs' photodegradation. The photodegradation products of the LCM compound 4'-propoxy-4-biphenylcarbonitrile (PBIPHCN) in dust, including •OH oxidation, C-O bond cleavage, and ring-opening products, were identified by nontarget analysis, and the corresponding degradation pathways were suggested. Some of the identified products, such as 4'-hydroxyethoxy-4-biphenylcarbonitrile, showed predicted toxicity (with an oral rat lethal dose of 50%) comparable to that of PBIPHCN. The half-lives of the studied LCMs in SRM dust were estimated at 32.2-82.5 h by fitting an exponential decay curve to the observed photodegradation data. The photodegradation mechanisms of LCMs in dust were revealed for the first time, enhancing the understanding of LCMs' environmental behavior and risks.

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.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

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.

Prediction of biodegradability of aromatics in water using QSAR modeling

The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD₅) and chemical (COD) oxygen demand values determined for parent compounds ((BOD₅/COD)₀), as well as for their reaction mixtures in half-life achieved by UV-C/H₂O₂ process ((BOD₅/COD)_t1/2). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD₅/COD)₀ and (BOD₅/COD)_t1/2, respectively, according to the values of statistical parameters R² and Q². Hence, 3-variable model predicting (BOD₅/COD)₀ possessed R²=0.863 and Q²=0.799 for training set, and R²=0.710 for test set, while 5-variable model predicting (BOD₅/COD)_1/2 possessed R²=0.886 and Q²=0.788 for training set, and R²=0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H₂O₂.