Quantitative structure-property relationship (QSPR) models for predicting the physicochemical properties of polychlorinated biphenyls (PCBs) using deep belief network

Assessment of legacy and alternative halogenated organic pollutants in outdoor dust and soil from e-waste sites in Nigeria: Concentrations, patterns, and implications for human exposure

E-waste is often processed informally, particularly in developing countries, resulting in the release of harmful chemicals into the environment. This study investigated the co-occurrence of selected persistent organic pollutants (POPs), including legacy and alternative halogenated flame retardants (10 polybrominated diphenyl ethers (PBDEs), decabromodiphenyl ethane (DBDPE), syn and anti-dechlorane plus (DP)), 32 polychlorinated biphenyls (PCBs) and 12 organochlorine pesticides (OCPs), in 20 outdoor dust and 49 soil samples from 7 e-waste sites in Nigeria. This study provides the first report on alternative flame retardants (DBDPE and DP) in Nigeria. The total concentration range of the selected classes of compounds was in the order: ∑10PBDEs (44-12300 ng/g) > DBDPE (4.9-3032 ng/g) > ∑2DP (0.7-278 ng/g) > ∑32PCBs (4.9-148 ng/g) > ∑12OCPs (1.9-25 ng/g) for dust, and DBDPE (4.9-9647 ng/g) > ∑10PBDEs (90.3-7548 ng/g) > ∑32PCBs (6.1-5025 ng/g) > ∑12OCPs (1.9-250 ng/g) > ∑2DP (2.1-142 ng/g) for soil. PBDEs were the major contributors to POP pollution at e-waste dismantling sites, while PCBs were the most significant contributors at e-waste dumpsites. DBDPE was found to be significantly associated with pollution at both e-waste dismantling and dumpsites. Estimated daily intake (EDI) via dust and soil ingestion and dermal adsorption routes ranged from 1.3 to 2.8 ng/kg bw/day and 0.2-2.9 ng/kg bw/day, respectively. In the worst-case scenario, EDI ranged from 2.9 to 10 ng/kg bw/day and 0.8-5.8 ng/kg bw/day for dust and soil, respectively. The obtained intake levels posed no non-carcinogenic risk, but could increase the incidence of cancer at some of the studied e-waste sites, with values exceeding the USEPA cancer risk lower limit (1.0 × 10-6). Overall, our results suggest that e-waste sites act as emission point sources of POPs.

Machine learning-assisted crystal engineering of a zeolite

It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced Na₂O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).

Application of machine learning and deep learning methods for hydrated electron rate constant prediction

Accurately determining the second-order rate constant with e_aq^- (k_eaq-) for organic compounds (OCs) is crucial in the e_aq^- induced advanced reduction processes (ARPs). In this study, we collected 867 k_eaq- values at different pHs from peer-reviewed publications and applied machine learning (ML) algorithm-XGBoost and deep learning (DL) algorithm-convolutional neural network (CNN) to predict k_eaq-. Our results demonstrated that the CNN model with transfer learning and data augmentation (CNN-TL&DA) greatly improved the prediction results and overcame over-fitting. Furthermore, we compared the ML/DL modeling methods and found that the CNN-TL&DA, which combined molecular images (MI), achieved the best overall performance (R²_test = 0.896, RMSE_test = 0.362, MAE_test = 0.261) when compared to the XGBoost algorithm combined with Mordred descriptors (MD) (0.692, RMSE_test = 0.622, MAE_test = 0.399) and Morgan fingerprint (MF) (R²_test = 0.512, RMSE_test = 0.783, MAE_test = 0.520). Moreover, the interpretation of the MD-XGBoost and MF-XGBoost models using the SHAP method revealed the significance of MDs (e.g., molecular size, branching, electron distribution, polarizability, and bond types), MFs (e.g, aromatic carbon, carbonyl oxygen, nitrogen, and halogen) and environmental conditions (e.g., pH) that effectively influence the k_eaq- prediction. The interpretation of the 2D molecular image-CNN (MI-CNN) models using the Grad-CAM method showed that they correctly identified key functional groups such as -CN, -NO₂, and -X functional groups that can increase the k_eaq- values. Additionally, almost all electron-withdrawing groups and a small part of electron-donating groups for the MI-CNN model can be highlighted for estimating k_eaq-. Overall, our results suggest that the CNN approach has smaller errors when compared to ML algorithms, making it a promising candidate for predicting other rate constants.

Concentration levels, spatial variations and exchanges of polychlorinated biphenyls (PCBs) in ambient air, surface water and sediment in Bursa, Türkiye

In this study, ambient air, surface water and sediment samples were simultaneously collected and analyzed for PCBs to investigate their levels, spatial variations and exchanges between these three compartments at different sampling sites for 12 months in Bursa, Türkiye. During the sampling period, a total of 41 PCB concentrations were determined in the ambient air, surface water (dissolved and particle phase) and sediment. Thus, 945.9 ± 491.6 pg/m³ (average ± STD), 53.8 ± 54.7 ng/L, 92.8 ± 59.3 ng/L and 71.4 ± 38.7 ng/g, respectively. The highest concentrations of PCBs in the ambient air and in water particulate phase were measured at the industrial/agricultural sampling site (1308.6 ± 252.1 pg/m³ and 168.7 ± 21.2 ng/L, respectively), ∼ 4-10 times higher than background sites; while the highest concentrations in the sediment and dissolved phase were measured at the urban/agricultural sampling sites (163.8 ± 27.0 ng/L and 145.7 ± 15.3 ng/g, respectively), ∼ 5-20 times higher than background sites. PCB transitions between ambient air-surface water (f_A/f_W) and surface water-sediment (f_W/f_S) were investigated by fugacity ratio calculations. According to the fugacity ratios obtained, volatilization from the surface water to the ambient air was observed at all sampling sites (98.7 % of f_A/f_W ratios are <1.0). Additionally, it has been determined that there is a transport from the surface water to the sediment (100.0 % of f_W/f_S ratios are higher than 1.0). The flux values in ambient air-surface water and surface water-sediment environments ranged from -1.2 to 1770.6 pg/m²-day and from -225.9 to 0.001 pg/m²-day, respectively. The highest flux values were measured for PCBs with low chlorine content (Mono-, Di-Cl PCBs), while the lowest flux values were measured for the high chlorine content PCBs (Octa-, Nona- and Deca-Cl PCBs). As it was determined in this study that surface waters contaminated by PCBs have the potential to pollute both air and sediments, it will be important to take measures to protect surface waters.

Toxicity of polychlorinated biphenyls in aquatic environments - A review

The assessment of polychlorinated biphenyls (PCBs) and their congeners resulting from the pollution of all environmental media is inherently related to its persistence and ubiquitous nature. In principle, determination of this class of contaminants are limited to the determination of their concentrations in the various environmental matrices. For solving many problems in this context, knowledge of the emission sources of PCBs, transport pathways, and sites of contamination and biomagnification is of great benefit to scientists and researchers, as well as many regulatory organizations. By far the largest amounts of PCBs, regardless of their discharged points, end up in the soil, sediment and finally in different aquatic environments. By reviewing relevant published materials, the source of origin of PCBs in the environment particularly from different pollution point sources, it is possible to obtain useful information on the nature of different materials that are sources of PCBs, or their concentrations and their toxicity or health effects and how they can be removed from contaminated media. This review focuses on the sources of PCBs in aquatic environments and critically reviews the toxicity of PCBs in aquatic animals and plants. The review also assesses the toxicity equivalency factors (TEFs) of PCBs providing valuable knowledge to other scientists and researchers that enables regulatory laws to be formulated based on selective determination of concentrations regarding their maximum permissible limits (MPLs) allowed. This review also supplies a pool of valuable information useful for designing decontamination technologies for PCBs in media like soil, sediment, and wastewaters.

Dynamic calibration of process-wide partial-nitritation modeling with airlift granular for nitrogen removal in a full-scale wastewater treatment plant

A main challenge in rapid nitrogen removal from rejected water in wastewater treatment plants (WWTPs) is growth of biomass by nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB). In this study, partial nitritation (PN) coupled with air-lift granular unit (AGU) technology was applied to enhance nitrogen-removal efficiency in WWTPs. For successful PN process at high-nitrogen-influent conditions, a pH of 7.5-8 for high free-ammonia concentrations and AOB for growth of total bacterial populations are required. The PN process in a sequential batch reactor (SBR) with AGU was modeled as an activated sludge model (ASM), and dynamic calibration using full-scale plant data was performed to enhance aeration in the reactor and improve the nitrite-to-ammonia ratio in the PN effluent. In steady-state and dynamic calibrations, the measured and modeled values of the output were in close agreement. Sensitivity analysis revealed that the kinetic and stoichiometric parameters are associated with growth and decay of heterotrophs, AOB, and NOB microorganisms. Overall, 80% of the calibrated data fit the measured data. Stage 1 of the dynamic calibration showed NO₂ and NO₃ values close to 240 mg/L and 100 mg/L, respectively. Stage 2 showed NH₄ values of 200 mg/L at day 30 with the calibrated effluent NO₂ and NO₃ value of 250 mg/L. In stage 3, effluent NH₄ concentration was 200 mg/L at day 60.

Grid inhomogeneous solvation theory for cross-solvation in rigid solvents

Grid Inhomogeneous Solvation Theory (GIST) has proven useful to calculate localized thermodynamic properties of water around a solute. Numerous studies have leveraged this information to enhance structure-based binding predictions. We have recently extended GIST toward chloroform as a solvent to allow the prediction of passive membrane permeability. Here, we further generalize the GIST algorithm toward all solvents that can be modeled as rigid molecules. This restriction is inherent to the method and is already present in the inhomogeneous solvation theory. Here, we show that our approach can be applied to various solvent molecules by comparing the results of GIST simulations with thermodynamic integration (TI) calculations and experimental results. Additionally, we analyze and compare a matrix consisting of 100 entries of ten different solvent molecules solvated within each other. We find that the GIST results are highly correlated with TI calculations as well as experiments. For some solvents, we find Pearson correlations of up to 0.99 to the true entropy, while others are affected by the first-order approximation more strongly. The enthalpy-entropy splitting provided by GIST allows us to extend a recently published approach, which estimates higher order entropies by a linear scaling of the first-order entropy, to solvents other than water. Furthermore, we investigate the convergence of GIST in different solvents. We conclude that our extension to GIST reliably calculates localized thermodynamic properties for different solvents and thereby significantly extends the applicability of this widely used method.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (K_PUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the K_PUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.

Solvation Thermodynamics in Different Solvents: Water-Chloroform Partition Coefficients from Grid Inhomogeneous Solvation Theory

Reliable information on partition coefficients plays a key role in drug development, as solubility decisively affects bioavailability. In a physicochemical context, the partition coefficient of a solute between two different solvents can be described as a function of solvation free energies. Hence, substantial scientific efforts have been made toward accurate predictions of solvation free energies in various solvents. The grid inhomogeneous solvation theory (GIST) facilitates the calculation of solvation free energies. In this study, we introduce an extended version of the GIST algorithm, which enables the calculation for chloroform in addition to water. Furthermore, GIST allows localization of enthalpic and entropic contributions. We test our approach by calculating partition coefficients between water and chloroform for a set of eight small molecules. We report a Pearson correlation coefficient of 0.96 between experimentally determined and calculated partition coefficients. The capability to reliably predict partition coefficients between water and chloroform and the possibility to localize their contributions allow the optimization of a compound's partition coefficient. Therefore, we presume that this methodology will be of great benefit for the efficient development of pharmaceuticals.

Active learning-driven quantitative synthesis–structure–property relations for improving performance and revealing active sites of nitrogen-doped carbon for the hydrogen evolution reaction

A data-driven quantitative synthesis–structure–property relation methodology to elucidate correlations between catalyst synthesis conditions, structural properties and observed performance, providing a systematic way to optimize practical catalysts.

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health

Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to in vitro methods to predict the toxicological effects of chemicals on human health. In this study, we assessed a deep-learning based QSAR (DL-QSAR) model to predict the qualitative and the quantitative effects of EDCs on the human endocrine system, and especially sex-hormone binding globulin (SHBG) and estrogen receptor (ER). Statistical analyses of the qualitative responses indicated that the accuracies of all three DL-QSAR methods were above 90%, and greater than the other statistical and machine learning models, indicating excellent classification performance. The quantitative analyses, as assessed using deep-neural-network-based QSAR (DNN-QSAR), resulted in a coefficient of determination (R²) of 0.80 and predictive square correlation coefficient (Q²) of 0.86, which implied satisfactory goodness of fit and predictive ability. Thus, DNN was able to transform sparse molecular descriptors into higher dimensional spaces, and was superior for assessment qualitative responses. Moreover, DNN-QSAR demonstrated excellent performance in the discipline of computational chemistry by handling multicollinearity and overfitting problems.

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol-water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol-water (log k _OW) and hexadecane-water (log k _HW) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log k _OW and log k _HW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree-Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log k _OW) values were retrieved from the PubChem database, and KowWin log k _OW values were determined as well. For 24 of these compounds, log k _OW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log k _OW, and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log k _HW values are found to be strongly correlated to log k _OW for most compounds.

A real time QSAR-driven toxicity evaluation and monitoring of iron containing fine particulate matters in indoor subway stations

A fine particulate matter less than 2.5 µm (PM_2.5) in the underground subway system are the cause of many diseases. The iron containing PMs frequently confront in underground stations, which ultimately have an impact on the health of living beings especially in children. Hence, it is necessary to conduct toxicity assessment of chemical species and regularized the indoor air pollutants to ensure the good health of children. Therefore, in this study, a new indoor air quality (IAQ) index is proposed based on toxicity assessment by quantitative structure-activity relationship (QSAR) model. The new indices called comprehensive indoor air toxicity (CIAT) and cumulative comprehensive indoor air toxicity (CCIAT) suggests the new standards based on toxicity assessment of PM_2.5. QSAR based deep neural network (DNN) exhibited the best model in predicting the toxicity assessment of chemical species in particulate matters, which yield lowest RMSE and Q_F3² values of 0.6821 and 0.8346, respectively, in the test phase. After integration with a standard concentration of PM_2.5, two health risk indices of CIAT and CCIAT are introduced based on toxicity assessment results, which can be use as the toxicity standard of PM_2.5 for detail IAQ management in a subway station. These new health risk indices suggest more sensitive air pollutant level of iron containing fine particulate matters or molecular level contaminants in underground spaces, alerting the health risk of adults and children in "unhealthy for sensitive group".