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.

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.

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".

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

Octanol/water partition coefficient (log P), octanol/air partition coefficient (log K_OA) and bioconcentration factor (log BCF) are important physiochemical properties of organic substances. Quantitative structure-property relationship (QSPR) models are a promising alternative method of reducing and replacing experimental steps in determination of log P, log K_OA and log BCF. In the current study, we propose a new QSPR model based on a deep belief network (DBN) to predict the physicochemical properties of polychlorinated biphenyls (PCBs). The prediction accuracy of the proposed model was compared to the results of previous reported models. The predictive ability of the DBN model, validated with a test set, is clearly superior to the other models. All results showed that the proposed model is robust and satisfactory, and can effectively predict the physiochemical properties of PCBs without highly reliable experimental values.