Quantitative structure-biodegradability relationships for biokinetic parameter of polycyclic aromatic hydrocarbons

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.

Overestimate of remediation efficiency due to residual sodium persulfate in PAHs contaminated soil and a solution

Oxidation remediation is a commonly used technology for PAHs contaminated soil presently, but the overestimate of efficiency due to ongoing remediation by residual oxidants during extraction and testing has not been paid enough attention. In this study, persulfate was activated by Fe(II) to investigate the effects of residual oxidants on PAHs removal during detection process and the elimination effects of adding Na₂SO₃ and extending sampling time on residual oxidants. Results verified that the residual oxidants removed PAHs in extraction process, making the results lower than the actual values: the detection recovery rate η of ∑PAHs and 3-6 ring PAHs ranged from 24.3% (25% Na₂S₂O₈ treatment) to 87.4% (5% Na₂S₂O₈+4/4Fe²⁺ treatment), 20.1%-99.0%, 28.9%-87.9%, 20.8%-89.4%, and 18.6%-76.9%, respectively. After adding Na₂SO₃, the accuracy of detection results increased significantly: the η of ∑PAHs and 3-6 ring PAHs increased to 64.1%-96.5%, 58.8%-95.5%, 73.8%-114.4%, 60.6%-95.6%, and 45.4%-77.1%, respectively. After 49 days of adding oxidants, residual oxidants had no considerable effect on the detection of PAHs, indicating it was appropriate to start soil remediation verification sampling49 days after the remediation was completed. The observed results will help scientific evaluation of the remediation effects of chemical oxidation on organic contaminated soil.

The experimental determination of reliable biodegradation rates for mono-aromatics towards evaluating QSBR models

Quantitative Structure Biodegradation Relationships (QSBRs) are a tool to predict the biodegradability of chemicals. The objective of this work was to generate reliable biodegradation data for mono-aromatic chemicals in order to evaluate and verify previously developed QSBRs models. A robust biodegradation test method was developed to estimate specific substrate utilization rates, which were used as a proxy for biodegradation rates of chemicals in pure culture. Five representative mono-aromatic chemicals were selected that spanned a wide range of biodegradability. Aerobic biodegradation experiments were performed for each chemical in batch reactors seeded with known degraders. Chemical removal, degrader growth and CO₂ production were monitored over time. Experimental data were interpreted using a full carbon mass balance model, and Monod kinetic parameters (Y, K_s, q_max and μ_max) for each chemical were determined. In addition, stoichiometric equations for aerobic mineralization of the test chemicals were developed. The theoretically estimated biomass and CO₂ yields were similar to those experimentally observed; 35% (s.d ± 8%) of the recovered substrate carbon was converted to biomass, and 65% (s.d ± 8%) was mineralised to CO₂. Significant correlations were observed between the experimentally determined specific substrate utilization rates, as represented by q_max and q_max/K_s, at high and low substrate concentrations, respectively, and the first order biodegradation rate constants predicted by a previous QSBR study. Similarly, the correlation between q_max and selected molecular descriptors characterizing the chemicals structure in a previous QSBR study was also significant. These results suggest that QSBR models can be reliable and robust in prioritising chemical half-lives for regulatory screening purposes.

Discrimination of typical cyclic compounds and selection of toxicity evaluation bioassays for coal gasification wastewater (CGW) based on toxicity mechanism of actions (MOAs)

This paper originally investigated toxicity discrimination of typical cyclic compounds and bioassays selection on toxicity evaluation for coal gasification wastewater (CGW) effluent with mechanism-oriented investigation. Initially, representative cyclic toxicants were selected and classified with quantitative structure-toxicity relationship (QSTR). Nitrogen heterocyclic compounds (NHCs) and polycyclic aromatic hydrocarbons (PAHs) were basically discriminated as nonpolar narcotics with significant correlation to hydrophobicity (p < 0.05, R² = 0.8668-0.9635), while phenols were regarded as polar narcotics and reactive compounds due to slight correlation to hydrophobicity (p > 0.05, R² < 0.5). Furthermore, specific mechanism of actions (MOAs) to various organisms revealed that phenols were discriminated as critical source of acute toxicity in CGW, with short-term visible and irreversible damage. However, NHCs and PAHs, which exerted accumulation toxicity rather than acute toxicity, might result in potential mutagenicity and unpredictable risk along the food chain. Afterwards, based on species sensitivity to typical toxicants and application in real CGW effluent, non-applicability of Chlorella vulgaris (C. vulgaris) was validated in toxicity evaluation. While Daphnia magna (D. magna) was suggested as a toxicity bioassay in entire effluent due to the highest sensitivity and applicability. Tetrahymena thermophile (T. pyriformis) might be applicable in effluent with low biodegradability due to similar evaluation results (TU = 8.90) to D. magna (TU = 6.67) in aerobic effluent. Finally, the relationship between toxicity and bioavailability based on typical pollutants and model species illustrated necessity for dualism toxicity-biodegradability investigation on CGW.

Prediction of Biodegradability for Polycyclic Aromatic Hydrocarbons Using Various In Silico Modeling Methods

Polycyclic aromatic hydrocarbons (PAHs) have attracted great concern as global environmental pollutants. In this work, the quantitative structure-biodegradability relationship (QSBR) study has been done to predict the biodegradability of PAHs and develop the correlation between the biodegradability and the molecular structures. The structural chemistry and quantum chemistry descriptors were used to represent molecular structures. Three in silico modeling methods, i.e., multiple linear regression (MLR), radial basis function neural network, and back-propagation artificial neural network (BPANN), are utilized to construct the linear and nonlinear prediction models and provide some insights into the structural characteristics affecting the biodegradability of PAHs. The stability of these QSBR models was tested by leave-one-out cross-validation, and the cross-validated correlation coefficients (q²) were 0.6109, 0.6887, and 0.6586, respectively. The correlation coefficients (R²) of the three models for the training set were 0.7811, 0.8883, and 0.9667, respectively. The comparison of the three models showed that the BPANN model produced a statistically more significant model than the other two models. On the basis of molecular structure, the dominant molecular structure descriptor affecting biodegradability of PAHs were analyzed and discussed.

Overview of the state of the art of processes and technical bottlenecks for coal gasification wastewater treatment

CGWW is major waste stream resulting from a number of activities of the low/medium temperature gasification unit that occurs during the production of natural gas. The resulting effluent contains a broad spectrum of organic and inorganic contaminants and exerts a negative influence on the environment, mainly due to the presence of toxic and refractory compounds. So far, various technologies have been applied for treatment of CGWW, while few reviews are available in the literature. Thus, this review attempts to offer a comprehensive picture about CGWW. An overview about pretreatment, biological and advanced processes for treatment of CGWW is presented, and the degradation mechanism of toxic and refractory pollutants is also elaborated. Technical bottlenecks existing in the operation of coal chemical industries, including foam proliferation, odors and biotoxicity risk, are detailed analyzed. Finally, the prospects of treatment for CGWW are discussed based on the concept of "wastewater is money". The review can be provided as an effective technical support for the construction and operation of coal gasification industries.

Classification of biodegradable materials using QSAR modelling with uncertainty estimation

The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure-activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model's predictions. Almost never is there a means of assessing the uncertainty in an individual prediction. Without an uncertainty estimate, it is impossible to assess the trustworthiness of any particular prediction, which leaves the model with a low utility for regulatory purposes. In the present work, a QSAR model with uncertainty estimates is used to predict biodegradability for a set of substances from a publicly available data set. Separation was performed using a partial least squares discriminant analysis model, and the uncertainty was estimated using bootstrapping. The uncertainty prediction allows for confidence intervals to be assigned to any of the model's predictions, allowing for a more complete assessment of the model than would be possible through a traditional statistical analysis. The results presented here are broadly applicable to other areas of modelling as well, because the calculation of the uncertainty will clearly demonstrate where additional tests are needed.

Hasse diagram as a green analytical metrics tool: ranking of methods for benzo[a]pyrene determination in sediments

This study presents an application of the Hasse diagram technique (HDT) as the assessment tool to select the most appropriate analytical procedures according to their greenness or the best analytical performance. The dataset consists of analytical procedures for benzo[a]pyrene determination in sediment samples, which were described by 11 variables concerning their greenness and analytical performance. Two analyses with the HDT were performed—the first one with metrological variables and the second one with “green” variables as input data. Both HDT analyses ranked different analytical procedures as the most valuable, suggesting that green analytical chemistry is not in accordance with metrology when benzo[a]pyrene in sediment samples is determined. The HDT can be used as a good decision support tool to choose the proper analytical procedure concerning green analytical chemistry principles and analytical performance merits.