Quantitative structure activity relationship (QSAR) modelling of the degradability rate constant of volatile organic compounds (VOCs) by OH radicals in atmosphere

Photodegradation of polychlorinated biphenyls in water/nitrogen-doped silica and air/nitrogen-doped silica systems: Kinetics, mechanism and quantitative structure activity relationship (QSAR) analysis

Developing efficient and low-cost photocatalytic materials is essential for removing polychlorinated biphenyls (PCBs). In this work, the photodegradation process of fourteen representative polychlorinated biphenyls (PCBs) in both water/nitrogen-doped SiO2 (N-SiO2) and air/N-SiO2 systems was studied. The photodegradation kinetics of PCBs is consistent with the pseudo-first-order kinetic equation. The variation in the degradation effects of different PCBs in the two systems is primarily related to the position of the Cl substituent and the effective absorption wavelength range of PCBs. A total of fourteen intermediates for 4'-Dichlorobiphenyl (PCB-15), 2,2',4,4',6,6'-Hexachlorobiphenyl (PCB-155), and 2,2',3,3',4,4',5,5',6,6'-Decachlorobiphenyl (PCB-209) generated from four reaction pathways were identified based on both mass spectrometry analysis and theoretical calculations. Using the values of lnk (k denotes pseudo-first-order kinetic constants) for the 11 PCBs in the training set and the calculated molecular and structural parameters, quantitative structure-activity relationship (QSAR) models for the two systems were constructed by using multiple linear regression (MLR) method to better understand the factors affecting the photodegradation rate of PCBs. The QSAR equations were obtained with Cl atom substitution at position 3 (N3) as the main parameter, which were lnk = -1.98 - 0.19 N3 for the water/N-SiO2 system and lnk = -1.56 - 0.34 N3 for the air/N-SiO2 system, with the correlation coefficient (R2) of 0.66 and 0.73, leave-one-out cross-validation (Q2LOO) of 0.51 and 0.59, respectively, and bootstrapping validation coefficients (Q2BOOT) values of both 0.74, confirming that the models were well fitted and showed high robustness and prediction ability. This study provides valuable insights into photocatalytic degradation studies of PCBs.

Significant contribution of carbonyls to atmospheric oxidation capacity (AOC) during the winter haze pollution over North China Plain

Atmospheric carbonyl compounds play significant roles in the cycling of radicals and have exhibited surprisingly high levels in winter that were well correlated to particulate matter, for which the reason have not been clearly elucidated. Here we measured carbonyl compounds and other trace gasses together with PM2.5 over urban Jinan in North China Plain during the winter. Markedly higher carbonyl concentrations (average: 14.63 ± 4.21 ppbv) were found during wintertime haze pollution, about one to three-times relative to those on non-haze days, with slight difference in chemical composition except formaldehyde (HCHO). HCHO (3.68 ppbv), acetone (3.17 ppbv), and acetaldehyde (CH3CHO) (2.83 ppbv) were the three most abundant species, accounting for ∼75% of the total carbonylson both haze and non-haze days. Results from observational-based model (OBM) with atmospheric oxidation capacity (AOC) indicated that AOC significantly increased with the increasing carbonyls during the winter haze events. Carbonyl photolysis have supplied key oxidants such as RO2 and HO2, and thereby enhancing the formation of fine particles and secondary organic aerosols, elucidating the observed haze-carbonyls inter-correlation. Diurnal variation with carbonyls exhibiting peak values at early-noon and night highlighted the combined contribution of both secondary formation and primary diesel-fuel sources. 1-butene was further confirmed to be the major precursor for HCHO. This study confirms the great contribution of carbonyls to AOC, and also suggests that reducing the emissions of carbonyls would be an effective way to mitigate haze pollution in urban area of the NCP region.

Experimental determination and QSAR analysis of the rate constants for SO5•- reactions with aromatic micropollutants in water

S(IV)-based systems used for advanced oxidation processes (AOPs) have been constructed for the degradation of organic contaminants via oxysulfur radicals, including SO3•-, SO4•-, and SO5•-. Although SO5•- is proposed as an active species in AOPs processes, research on the reactivity of SO5•- has remained unclear. In this work, 53 target aromatic micropollutants (AMPs), including 13 phenols, 27 amines, and 13 PPCPs were selected to determine the second-order reaction rate constants for SO5•- using the competitive kinetics method, in which the [Formula: see text] values, observed at pH 4 ranged from (2.44 ± 0.00) × 105 M-1 s-1 to (4.41 ± 0.28) × 107 M-1 s-1. Quantitative structure-activity relationship (QSAR) models for the oxidation of AMPs by SO5•- were developed based on 40 [Formula: see text] values of amines and phenols, and their molecular descriptors, using the stepwise multiple linear regression method. This comprehensive model exhibited the excellent goodness-of-fit (Radj2 = 0.802), robustness (QLOO2 = 0.749), and predictability (Qext2 = 0.656), and the one-electron oxidation potential (Eox), energy of the highest occupied molecular orbital energy (EHOMO), and most positive net atomic charge on the carbon atoms (qC+) were considered the most influential descriptors for the comprehensive model, indicating that SO5•- oxidizes pollutants via single electron transfer reaction and exhibits a strong oxidation capacity, especially for pollutants containing electron-donating groups. Moreover, the [Formula: see text] values of 13 PPCPs were predicted using this comprehensive model, which suggested the practical application significance of the QSAR model. This study emphasizes the direct oxidation capacity of SO5•-, which is important to evaluate and simulate AOPs based on S(IV).

Toward Property-Based Regulation

An expanding web of adverse impacts on people and the environment has been steadily linked to anthropogenic chemicals and their proliferation. Central to this web are the regulatory structures intended to protect human and environmental health through the control of new molecules. Through chronically insufficient and inefficient action, the current chemical-by-chemical regulatory approach, which considers regulation at the level of chemical identity, has enabled many adverse impacts to develop and persist. Recognizing the link between fundamental physicochemical properties and hazards, we describe a new paradigm─property-based regulation. By regulating physicochemical properties, we show how governments can delineate and enforce safe chemical spaces, increasing the scalability of chemical assessments, reducing the time and resources to regulate a substance, and providing transparency for chemical designers. We highlight sparse existing property-based approaches and demonstrate their applicability using bioaccumulation as an example. Finally, we present a path to implementation in the United States, prescribing roles and steps for government, nongovernmental organizations, and industry to accelerate this transition, to the benefit of all.

Quantitative structure-activity relationship(QSAR) models for color and COD removal for some dyes subjected to electrochemical oxidation

Electrochemical oxidation is an efficient method for the destruction of dyes in wastewater streams. The experimental conditions during electrochemical oxidation (EO) and molecular structure of a dye greatly influence the extent of degradation. The extent of degradation for a variety of dyes by EO can be predicted conveniently by the use of Quantitative structure-activity Relationship (QSAR) models. An abundant amount of published data on dye degradation by EO using highly variable experimental conditions lies unutilized to prepare QSAR models. In this study, an effort is made to use published experimental data on EO of aqueous dyes after applying an easy method of normalization, to prepare QSAR models for percent color and COD removal. Normalized color and COD removal were obtained by multiplying the reported removal by volume of reactor and concentration of dye; and divided by total current passed and the time of electrolysis. More than 15 molecular descriptors were computed using Schrodinger-suit 2018-3. The multiple linear regression (MLR) approach was used to develop normalized color and COD removal models. The quantum chemical descriptors: highest occupied molecular orbital energy (HOMO) and lowest unoccupied molecular orbital energy (LUMO), polar surface area (PSA), hydrogen bond donor count (HBD), and number of atoms were found significant. The statistical indices: goodness-of-fit, R² > 0.75, and internal and external validations, Q²_LOOCV and Q²_ext, > 0.5, satisfied the criteria for predictive models and indicated that the method of normalization used in this study is adequate. Developed QSAR models are quite simple, interpretable, and transparent.

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

Background

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a progressive neurodegenerative disorder that gradually affects cognitive function and eventually causes death. Most approved drugs can only treat the disease alleviating the disease symptoms; therefore, there is a need to develop drugs that can treat this illness holistically. The medical community is searching for new drugs and new drug targets to cure this disease. In this study, QSAR, molecular docking evaluation, and ADMET/pharmacokinetics assessment were used as modeling methods to identify the compounds with outstanding physicochemical properties.

Results

The 37 MAO-B compounds were screened using the aforementioned methods and yielded a model with the following molecular properties: AATS1v, AATS3v, GATS4m, and GATS6e. Good statistical values were R2train = 0.69, R2adj = 0.63, R2pred = 0.57, LOF = 0.23, and RMSE = 0.38. The model was validated using an evaluation set that confirmed its robustness. The molecular docking was also utilized using crystal structure of human monoamine oxidase B in complex with chlorophenylchromone-carboxamide with ID code of 6FW0, and three compounds were identified with outstanding high binding affinity (13 = − 30.51 kcal mol−1, 31 = − 31.85 kcal mol−1, and 33 = − 33.70 kcal mol−1), and better than the Eldepryl (referenced) drug (− 11.40 kcal mol−1).

Conclusions

These three compounds (13, 31, and 33) were analyzed for ADMET/pharmacokinetics evaluation and found worthy of further analysis as promising drug candidates to cure AD and could also serve as a template to design several monoamine oxidase B inhibitors in the future to cure AD.

Transfer Learning Approach to Multitarget Temperature-Dependent Reaction Rate Prediction

Accurate prediction of temperature-dependent reaction rate constants of organic compounds is of great importance to both atmospheric chemistry and combustion science. Extensive work has been done on developing automated mechanism generation systems but the lack of quality reaction rate data remains a huge bottleneck in the application of highly detailed mechanisms. Machine learning prediction models have been recently adopted to alleviate the data gap in thermochemistry and have great potential to do the same for kinetic data with the recent release of quality reaction rate data compilations. The ultimate goal is to formulate easily accessible, general-purpose, temperature-dependent, and multitarget models for the prediction of reaction rates. To that end, we propose a model that depends on the well-known Morgan fingerprints as well as learned representations transferred from the QM9 data set. We propose the use of an Arrhenius-based loss where predictions of the three modified-Arrhenius parameters (A, n, and B = E_a/R) are given instead of the direct prediction of reaction rate constants. Our model is >35% more accurate compared to a baseline model of feed forward network (FFN) on Morgan fingerprints.

Prediction of Second-Order Rate Constants of Sulfate Radical with Aromatic Contaminants Using Quantitative Structure-Activity Relationship Model

Predicting the second-order rate constants between aromatic contaminants and a sulfate radical (kSO4•−) is vital for the screening of pollutants resistant to sulfate radical-based advanced oxidation processes. In this study, a quantitative structure-activity relationship (QSAR) model was developed to predict the values for aromatic contaminants. The relationship between logkSO4•− and three molecular descriptors (electron density, steric energy, and ratio between oxygen atoms and carbon atoms) was built through multiple linear regression. The goodness-of-fit, robustness, and predictive ability of the model were characterized statistically with indicators showing that the model was reliable and applicable. Electron density was found to be the most influential descriptor that contributed the most to logkSO4•−. All data points fell within the applicability domain, and no outliers existed in the training set. The comparison with other models indicates that the QSAR model performs well in elucidating the mechanism of the reaction between aromatic compounds and sulfate radicals.

Estimating PM2.5 concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China

With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters ≤ 2.5 µm (PM_2.5) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies on estimating PM_2.5 concentrations have been performed using statistical regression models and satellite remote sensing. However, the accuracy of PM_2.5 concentration estimates is limited by traditional regression models; machine learning methods have high predictive power, but fewer studies have been performed on the complementary advantages of different approaches. This study estimates PM_2.5 concentrations from satellite remote sensing-derived aerosol optical depth (AOD) products, meteorological data, terrain data and other predictors in 2015 in Shaanxi, China, using a combined genetic algorithm-support vector machine (GA-SVM) method, after which the spatial clustering pattern was explored at the season and year levels. The results indicated that temperature (r = -0.684), precipitation (r = -0.602) and normalized difference vegetation index (NDVI) (r = -0.523) were significantly negatively correlated with the PM_2.5 concentration, while AOD (r = 0.337) was significantly positively correlated with the PM_2.5 concentration. Compared to conventional land use regression (LUR) and SVM models and previous related studies, the GA-SVM method demonstrated a significantly better prediction accuracy of PM_2.5 concentration, with a higher 10-fold cross-validation coefficient of determination (R²) of 0.84 and lower root mean square error (RMSE) and mean absolute error (MAE) of 12.1 μg/m³ and 10.07 μg/m³, respectively. Y-scrambling test shows that the models have no chance correlation. The central and southern parts of Shaanxi have high PM_2.5 concentrations, which are mainly due to the pollutant emissions and meteorological and topographical conditions in those areas. There was a positive spatial agglomeration characteristic of regional PM_2.5 pollution, and the spatial spillover effect of PM_2.5 pollution for seasonal and annual variations does exist. In general, the GA-SVM method is robust and accurately estimates PM_2.5 concentrations via a novel modeling framework application and high-quality spatiotemporal information. It also has great significance for the exploration of PM_2.5 pollution estimation and high-precision mapping methods, especially early warning in high-risk areas. Finally, the prevention and control of atmospheric pollution should take pollution control measures from major cities and surrounding cities, and focus on the joint pollution control measures for plain cities.

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.

Predicting the rate constants of volatile organic compounds (VOCs) with ozone reaction at different temperatures

Based on the bond order, fukui indices and other related descriptors, as well as temperature, a new QSAR model was established to predict the rate constant (kO3) of VOCs degradation by O3. 302 logkO3 values (178-409 K) of 149 VOCs were divided into training set (242 logkO3) and test set (60 logkO3), respectively, which were used to construct and verify the QSAR model. The optimal model (R2 = 0.83, q2 = 0.82, Qext2 = 0.72) shows that EHOMO, BOx and q(C-)n have a greater influence on the value of logkO3. In addition, fukui indices and logkO3 are well correlated. The applicability domains of the current models can be used to predict kO3 of a wide range of VOCs at different temperatures.