Enhancing insight into DOM fate in aquatic systems: Introducing the FLOTATION model.

Water quality modeling can help to understand the source, transport, transformation and fate of dissolved organic matter (DOM) in aquatic systems. However, water quality models typically use biological oxygen demand as the state variable for DOM, which poorly represents the bio-refractory fraction of the DOM pool. Furthermore, photodegradation, which has a significant impact on the fate of DOM, is often neglected in water quality models. To fill these gaps, we developed the FLOTATION (FLuorescent dOm Transport And TransformatION) model, which includes three processes: biodegradation, photodegradation, and primary production formation. We applied the model to the Nanfei River to understand the source, spatial distribution, and fate of DOM under low flow conditions. The model was set up and calibrated with the longitudinal measurements of four humic-like components (C1-C4) and one protein-like component (C5) identified by excitation-emission matrix parallel factor analysis (EEM-PARAFAC). The results showed that the simulation reproduced the longitudinal variations of all components well. The photodegradation process removed 18 %, 15 % and 21 % of the total input loadings of the humic-like components C1, C2 and C4, respectively. Algal primary production contributed 18 % of the downstream transport loading, constituting an important autochthonous source. For the protein-like C5, photodegradation and biodegradation together removed 7 % of the input loading. Our newly developed FLOATATION model can facilitate a comprehensive understanding of the fate and transport of DOM in aquatic environments.

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant.

Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making support to facilitate their operations and management. In this study, we developed a novel hybrid deep learning model by combining the temporal convolutional network (TCN) model with the long short-term memory (LSTM) network model to improve the simulation of hourly total nitrogen (TN) concentration in WWTP effluent. The developed model was tested in a WWTP in Jiangsu Province, China, where the prediction results of the hybrid TCN-LSTM model were compared with those of single deep learning models (TCN and LSTM) and traditional machine learning model (feedforward neural network, FFNN). The hybrid TCN-LSTM model could achieve 33.1 % higher accuracy as compared to the single TCN or LSTM model, and its performance could improve by 63.6 % comparing to the traditional FFNN model. The developed hybrid model also exhibited a higher power prediction of WWTP effluent TN for the next multiple time steps within eight hours, as compared to the standalone TCN, LSTM, and FFNN models. Finally, employing model interpretation approach of Shapley additive explanation to identify the key parameters influencing the behavior of WWTP effluent water quality, it was found that removing variables that did not contribute to the model output could further improve modeling efficiency while optimizing monitoring and management strategies.

Identifying the fate of dissolved organic matter in wastewater treatment plant effluent-dominated urban river based on fluorescence fingerprinting and flux budget approach.

Effluent organic matter from wastewater treatment plants (WWTPs) is an important source of dissolved organic matter (DOM) in urban rivers worldwide and is an important water quality factor. Identifying the fate of DOM in urban river is crucial for water quality management. To address this concern, a fluorescent flux budget approach was conducted to probe the fate of DOM in WWTP effluent-dominated urban river, in combination with field measurement and fluorescence fingerprinting. An urban river receiving two WWTP effluents in Hefei City, China was chosen as the study site, where longitudinal measurements of river hydrology and water quality were performed. The fluorescence fingerprinting revealed the presence of two humic-like components (C1, C4), one fulvic-like component (C2) and one protein-like component (C3) in this investigated river, among which C2 and C4 were indicative of anthropogenic influences, closely associated with treated effluents. For each fluorescent component, the WWTP effluent contributed over 80 % of the total fluorescent dissolved organic matter (FDOM) input in this river. Using the developed FDOM flux budget model, it was found that the C1 and C3 were almost conserved within the waterbody, while the C2 and C4 experienced losses due to biogeochemical reactions. The decay rates of C2 and C4 were estimated to be 0.109-0.174 d-1 and 0.096-0.320 d-1, respectively. Spatial heterogeneity of decay rates for C2 and C4 were associated with the varied chemistries of the lateral input sources including two treated effluents and one tributary flow. Our study highlights that after treated effluent is released into the receiving waterbody, the FDOM would undergo loss from the waters particularly for anthropogenic fulvic-like substance C2 and humic-like substance C4. Additionally, the quantified FDOM decay rate in actual urban water environment provides insights for river water quality management, especially when using DOM as the surrogate indicator of organic pollutants.

Efficacy and mechanism of action of Yanxiao Di'naer formula for non-alcoholic steatohepatitis treatment based on metabolomics and RNA sequencing.

ETHNOPHARMACOLOGICAL RELEVANCE: Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is a crucial component of this disease spectrum. The Yanxiao Di'naer formula (YXDNE) is an Uyghur medical extract that has been used in folk medicine to treat hepatitis for a long time. However, the role and mechanism of action of YXDNE in NASH treatment remains unclear. OBJECTIVE: The objective of this study was to assess the effectiveness of YXDNE in treating NASH induced by injections of carbon tetrachloride combined with a high-fat high-cholesterol diet (HFHCD), and to clarify the underlying mechanisms. METHODS: The compounds in the YXDNE extract were analysed for classification and proportions using ultra-performance liquid chromatography-mass spectrometry. The efficacy of YXDNE in treating abnormal lipid metabolism was evaluated in L02 cells in vitro. In addition, a C57BL/6 mouse model of NASH was established to evaluate the therapeutic efficacy of YXDNE in vivo. Metabolomics and RNA sequencing were used to analyse the therapeutic effects of YXDNE on the liver. The corresponding signalling pathways were found to target AMPKα1, PPARα, and NF-κB. The efficacy of YXDNE was validated using inhibitors or silencing RNA (siRNA) against AMPKα1 and PPARα. RESULTS: This study confirmed that YXDNE treatment ameliorated NASH in a murine model of this disease. Metabolomics analysis suggested that YXDNE efficacy was associated with fatty acid catabolism and AMPK signalling pathways. RNA sequencing results showed that YXDNE efficacy in treating NASH was highly correlated with the AMPK, PPARα and NF-κB pathways. Both in vitro and in vivo experimental data demonstrated that YXDNE affected the expression of p-AMPKα1, PPARα, p-NF-κB, IκB, and p-IκB. The efficacy of YXDNE in treating NASH in vitro was cancelled when AMPK was inhibited with Compound C or PPARα was modulated via siRNA. CONCLUSIONS: YXDNE may have a therapeutic effect on abnormal lipid metabolism in L02 cells and in a murine model of NASH by affecting the AMPKα1/PPARα/NF-κB signalling pathway. Therefore, YXDNE has the potential for clinical application in the prevention and treatment of NASH.

A smart sewer detection approach based on wavelet denoising of in-sewer temperature sensing measurement.

Urban sewer detection is important for the proper conveyance of sanitary water to wastewater treatment plant prior to environmental discharge. An effective approach to address this important process still needs to be developed. This study introduced a novel data-driven approach to sewer detection utilizing in-sewer distributed temperature sensing (DTS) measurement combined with wavelet-based denoising of DTS data. It underlines that the effective denoising of DTS data, and consequently the accurate determination of DTS noise threshold, is pivotal to reliable sewer detection. DTS background noise is chiefly influenced by the threshold rescaling. A reliable DTS background noise threshold was found to be ±0.25 °C in a field study, established with the threshold rescaling of a level-dependent estimation of level noise, and the associated threshold selection rule of heuristics threshold or minimum maximum variance. Deviation from this threshold could hamper the identification of true inflow or infiltration points. Applying the established threshold to the study site, our study identified two sewer problematic points including a groundwater infiltration point, and a clean water inflow point based on generated three-value image. Further interpretation of the three-value image revealed that both groundwater infiltration and clean water inflow into the sewer exhibited intermittent instead of constant behavior, which was due to time-variable water head difference associated with sewage discharge variation over the daily period and rainfall events. Thus, the methodology offers considerable potential for urban sewer detection, especially for its performance to capture intermittent sewer infiltrations and inflows without draining sewers.