A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis.

The identification of continuous pollution sources for rivers is of great concern for emergency response. Most studies focused on instantaneous river pollution sources and associated incidents. There is a dire need to address continuous pollution sources, as pollutant discharge may impose a major impact on the water ecosystem. Therefore, in this study, a novel inverse model is proposed to identify the continuous point sources in river pollution incidents that would estimate the source strength, location, release time, and spill time. The proposed inverse model combines the advanced DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and the forward transport advection-dispersion equation to infer the posterior probability distribution of source parameters for quantifying uncertainties. In addition, the performance of the DREAM-based model is compared with those of the Metropolis-Hastings (MH)-based and genetic algorithm (GA)-based models. The results show that the DREAM-based model performs accurately for both the hypothetical and the field tracer cases. The comparative analysis shows that the DREAM-based model performs better in saving computation time, improving the accuracy of results, and reconstructing pollutant concentrations. Observation errors significantly influence the accuracy of the identification results from the DREAM-based model. In addition, a comprehensive sensitivity analysis of the DREAM-based model is conducted. The identification results from the DREAM-based model are sensitive to the dispersion coefficient and river velocity. The accuracy of the inverse model could be improved by increasing the monitoring number and by monitoring locations closer to the spill site. The findings of this study can improve decision-making during emergency responses to sudden river pollution incidents.

Oral Troxerutin Alleviates Depression Symptoms in Mice by Modulating Gut Microbiota and Microbial Metabolism.

SCOPE: A growing body of evidence suggests that the harmful gut microbiota in depression patients can play a role in the progression of depression. There is limited research on troxerutin's impact on the central nervous system (CNS), especially in depression. The study finds that troxerutin effectively alleviates depression and anxiety-like behavior in mice by increasing the abundance of beneficial bacteria like Lactobacillus and Firmicutes while decreasing the abundance of harmful bacteria like Proteobacteria, Bacteroides, and Actinobacteria in the gut. Furthermore, the research reveals that troxerutin regulates various metabolic pathways in mice, including nucleotide metabolism, caffeine metabolism, purine metabolism, arginine biosynthesis, histidine metabolism, 2-oxocarboxylic acid metabolism, biosynthesis of amino acids, glycine, serine and threonine metabolism, and Arginine and proline metabolism. CONCLUSIONS: In conclusion, the study provides compelling evidence for the antidepressant efficacy of troxerutin. Through the investigation of the role of intestinal microorganisms and metabolites, the study identifies these factors as key players in troxerutin's ability to prevent depression. Troxerutin achieves its neuroprotective effects and effectively prevents depression and anxiety by modulating the abundance of gut microbiota, including Proteobacteria, Bacteroides, and Actinobacteria, as well as regulating metabolites such as creatine.

Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis.

Source identification plays a vital role in implementing control measures for sudden river pollution incidents. In contrast to single-point source identification problems, there have been no investigations into inverse identification of multi-point emissions. In this study, an inverse model is developed based on the observed time series of pollutant concentrations and the DiffeRential Evolution Adaptive Metropolis (DREAM) method to identify multi-point sources with uncertainty quantification. We aim to simultaneously determine source mass, release location and time of multi-point sources. The newly developed DREAM-based model has been tested and verified through both numerical and field data case studies in terms of accuracy, reliability, and computational time. Adapted cases with single-point, two-point and three-point sources in the Songhua River are conducted to test the applicability of the modeling approach, respectively. The developed model can correctly quantify source parameters with a relative error that does not exceed ±0.63%, although it shows that an increase of emission sources may slightly increase the identification error. Among the three source parameters, the identification error of the release time tends to rise more obviously in response to the increase in the number of pollution sources. It is also found that the identification accuracy is primarily sensitive to the river velocity, followed by the dispersion coefficient and the river cross-sectional area. Furthermore, good monitoring strategies, including reducing observation errors, shortening monitoring interval time and selecting the proper monitoring distance between the monitoring and the source sites, help to achieve a better application of the developed model in river pollution incidents.

Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis.

Identification of pollution point source in rivers is strenuous due to accidental chemical spills or unmanaged wastewater discharges. It is crucial to take physical characteristics into account in the estimation of pollution sources. In this study, an integrated inverse modeling framework is developed to identify a point source of accidental water pollution based on the contaminant concentrations observed at monitoring sites in time series. The modeling approach includes a Markov chain Monte Carlo method based on Bayesian inference (Bayesian-MCMC) inverse model and a genetic algorithm (GA) inverse model. Both inverse models can estimate the pollution sources, including the emission mass quantity, release time, and release position in an accidental river pollution event. The developed model is first tested for a hypothetical case with field river conditions. The results show that the source parameters identified by the Bayesian-MCMC inverse model are very close to the true values with relative errors of 0.02% or less; the GA inverse model also works with relative errors in the range of 2%-7%. Additionally, the uncertainties associated with model parameters are analyzed based on global sensitive analysis (GSA) in this study. It is also found that the emission mass of pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects release position and release time. A real case study in the Fen River is further conducted to test the applicability of the developed inverse modeling approach. Results confirm that the Bayesian-MCMC model performs better than the GA model in terms of accuracy and stability for the field application. The findings of this study would support decision-making during emergency responses to river pollution incidents.

Deposition Kinetics of Colloidal Manganese Dioxide onto Representative Surfaces in Aquatic Environments: The Role of Humic Acid and Biomacromolecules.

The initial deposition kinetics of colloidal MnO2 on three representative surfaces in aquatic systems (i.e., silica, magnetite, and alumina) in NaNO3 solution were investigated in the presence of model constituents, including humic acid (HA), a polysaccharide (alginate), and a protein (bovine serum albumin (BSA), using laboratory quartz crystal microbalance with dissipation monitoring equipment (QCM-D). The results indicated that the deposition behaviors of MnO2 colloids on three surfaces were in good agreement with classical Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. Critical deposition concentrations (CDC) were determined to be 15.5 mM NaNO3 and 9.0 mM NaNO3 when colloidal MnO2 was deposited onto silica and magnetite, respectively. Both HA and alginate could largely retard the deposition of MnO2 colloids onto three selected surfaces due to steric repulsion, and HA was more effective in decreasing the deposition rate relative to alginate. However, the presence of BSA can provide more attractive deposition site and thus lead to greater deposition behavior of MnO2 colloids onto surfaces. The dissipative properties of the deposited layer were also influenced by surface type, electrolyte concentration, and organic matter characteristics. Overall, these results provide insights into the deposition behavior of MnO2 colloids on environmental surfaces and have significant implications for predicting the transport potential of common MnO2 colloids in natural environments and engineered systems.

Effects of C/N ratio on nitrous oxide production from nitrification in a laboratory-scale biological aerated filter reactor.

Emission of nitrous oxide (N2O) during biological wastewater treatment is of growing concern. This paper reports findings of the effects of carbon/nitrogen (C/N) ratio on N2O production rates in a laboratory-scale biological aerated filter (BAF) reactor, focusing on the biofilm during nitrification. Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and microelectrode technology were utilized to evaluate the mechanisms associated with N2O production during wastewater treatment using BAF. Results indicated that the ability of N2O emission in biofilm at C/N ratio of 2 was much stronger than at C/N ratios of 5 and 8. PCR-DGGE analysis showed that the microbial community structures differed completely after the acclimatization at tested C/N ratios (i.e., 2, 5, and 8). Measurements of critical parameters including dissolved oxygen, oxidation reduction potential, NH4+-N, NO3--N, and NO2--N also demonstrated that the internal micro-environment of the biofilm benefit N2O production. DNA analysis showed that Proteobacteria comprised the majority of the bacteria, which might mainly result in N2O emission. Based on these results, C/N ratio is one of the parameters that play an important role in the N2O emission from the BAF reactors during nitrification.