Wedelolactone inhibits ferroptosis and alleviates hyperoxia-induced acute lung injury via the Nrf2/HO-1 signaling pathway.

Hyperoxia-induced acute lung injury (HALI) is a complication of oxygen therapy. Ferroptosis is a vital factor in HALI. This paper was anticipated to investigate the underlying mechanism of Wedelolactone (WED) on ferroptosis in HALI. The current study used hyperoxia to injure two models, one HALI mouse model and one MLE-12 cell injury model. We found that WED treatment attenuated HALI by decreasing the lung injury score and lung wet/dry weight ratio and alleviating pathomorphological changes. Then, the inflammatory reaction and apoptosis in HALI mice and hyperoxia-mediated MLE-12 cells were inhibited by WED treatment. Moreover, WED alleviated ferroptosis with less iron accumulation and reversed expression alterations of ferroptosis markers, including MDA, GSH, GPX4, SLC7A11, FTH1, and TFR1 in hyperoxia-induced MLE-12 cells in vitro and in vivo. Nrf2-KO mice and Nrf2 inhibitor (ML385) decreased WED's ability to protect against apoptosis, inflammatory response, and ferroptosis in hyperoxia-induced MLE-12 cells. Collectively, our data highlighted the alleviatory role of WED in HALI by activating the Nrf2/HO-1 pathway.

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks.

Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owing to the state-of-the-art modelling power of graph neural networks (GNNs) and their match with urban drainage networks in the graph structure, this work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks, which regards recent hydraulic states as initial conditions, and future runoff and control policy as boundary conditions. To incorporate hydraulic constraints and physical relationships into drainage modelling, physics-guided mechanisms are designed on top of the surrogate model to restrict the prediction variables with flow balance and flooding occurrence constraints. According to case results in a stormwater network, the GNN-based model is more cost-effective with better hydraulic prediction accuracy than the NN-based model after equal training epochs, and the designed mechanisms further limit prediction errors with interpretable domain knowledge. As the model structure adheres to the flow routing mechanisms and hydraulic constraints in urban drainage networks, it provides an interpretable and effective solution for data-driven surrogate modelling. Simultaneously, the surrogate model accelerates the predictive modelling of urban drainage networks for real-time use compared with the physics-based model.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring.

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Gpnmb silencing protects against hyperoxia-induced acute lung injury by inhibition of mitochondrial-mediated apoptosis.

Background: Hyperoxia-induced acute lung injury (HALI) is a complication to ventilation in patients with respiratory failure, which can lead to acute inflammatory lung injury and chronic lung disease. The aim of this study was to integrate bioinformatics analysis to identify key genes associated with HALI and validate their role in H2O2-induced cell injury model.Methods: Integrated bioinformatics analysis was performed to screen vital genes involved in hyperoxia-induced lung injury (HLI). CCK-8 and flow cytometry assays were performed to assess cell viability and apoptosis. Western blotting was performed to assess protein expression.Results: In this study, glycoprotein non-metastatic melanoma protein B (Gpnmb) was identified as a key gene in HLI by integrated bioinformatics analysis of 4 Gene Expression Omnibus (GEO) datasets (GSE97804, GSE51039, GSE76301 and GSE87350). Knockdown of Gpnmb increased cell viability and decreased apoptosis in H2O2-treated MLE-12 cells, suggesting that Gpnmb was a proapoptotic gene during HALI. Western blotting results showed that knockdown of Gpnmb reduced the expression of Bcl-2 associated X (BAX) and cleaved-caspase 3, and increased the expression of Bcl-2 in H2O2 treated MLE-12 cells. Furthermore, Gpnmb knockdown could significantly reduce reactive oxygen species (ROS) generation and improve the mitochondrial membrane potential.Conclusion: The present study showed that knockdown of Gpnmb may protect against HLI by repressing mitochondrial-mediated apoptosis.

Improving the interpretability of deep reinforcement learning in urban drainage system operation.

Deep reinforcement learning (DRL) has been increasingly used as an adaptive and efficient solution for real-time control (RTC) of the urban drainage system (UDS). Despite the promising potential of DRL, it is a black-box model whose control logic and control consequences are difficult to be understood and evaluated. This leads to issues of interpretability and poses risks in practical applications. This study develops an evaluation framework to analyze and improve the interpretability of DRL-based UDS operation. The framework includes three analysis methods: Sobol sensitivity analysis, tree-based surrogate modelling, and conditional probability analysis. It is validated using two different DRL approaches, i.e., deep Q-learning network (DQN) and proximal policy optimization (PPO), which are trained to reduce combined sewer overflow (CSO) discharges and flooding in a real-world UDS. According to the results, the two DRLs have been shown to perform better than a rule-based control system that is currently being used. Sobol sensitivity analysis indicates that DQN is particularly sensitive to the flow of links and rainfall, while PPO is sensitive to all the states. Tree-based surrogate models effectively reveal the control logic behind the DRLs and indicate that PPO is more comprehensible but DQN is more forward-looking. Conditional probability analysis demonstrates the potential control consequences of the DRLs and identifies three situations where the DRLs are ineffective: a) the storage of UDS is fully utilized; b) peak flows have already passed through actuators; c) a substantial amount of water enters one location simultaneously. The proposed evaluation framework enhances the interpretability of DRL in UDS operations, fostering trust and confidence from operators, stakeholders, and regulators.

Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich.

Three-dimensional lake hydrodynamic model is a powerful tool widely used to assess hydrological condition changes of lake. However, its computational cost becomes problematic when forecasting the state of large lakes or using high-resolution simulation in small-to-medium size lakes. One possible solution is to employ a data-driven emulator, such as a deep learning (DL) based emulator, to replace the original model for fast computing. However, existing DL-based emulators are often black-box and data-dependent models, causing poor interpretability and generalizability in practical applications. In this study, a data-driven emulator is established using deep neural network (DNN) to replace the original model for fast computing of three-dimensional lake hydrodynamics. Then, the Koopman operator and transfer learning (TL) are employed to enhance the interpretability and generalizability of the emulator. Finally, the generalizability of DL-based emulators is comprehensively analyzed through linear regression and correlation analysis. These methods are tested against an existing hydrodynamic model of Lake Zurich (Switzerland) whose data was provided by an open-source web-based platform called Meteolakes/Alplakes. According to the results, (1) The DLEDMD offers better interpretability than DNN because its Koopman operator reveals the linear structure behind the hydrodynamics; (2) The generalization of the DL-based emulators in three-dimensional lake hydrodynamics are influenced by the similarity between the training and testing data; (3) TL effectively improves the generalizability of the DL-based emulators.

A dataset of low-carbon energy transition index for Chinese cities 2003-2019.

Cities are at the heart of climate change mitigation as they account for over 70% of global carbon emissions. However, cities vary in their energy systems and socioeconomic capacities to transition to renewable energy. To address this heterogeneity, this study proposes an Energy Transition Index (ETI) specifically designed for cities, and applies it to track the progress of energy transition in Chinese cities. The city-level ETI framework is based on the national ETI developed by the World Economic Forum (WEF) and comprises two sub-indexes: the Energy System Performance sub-index, which evaluates the current status of cities' energy systems in terms of energy transition, and the Transition Readiness sub-index, which assesses their socioeconomic capacity for future energy transition. The initial version of the dataset includes ETI and its sub-indexes for 282 Chinese cities from 2003 to 2019, with annual updates planned. The spatiotemporal data provided by the dataset facilitates research into the energy transition roadmap for different cities, which can help China achieve its energy transition goals.

Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning.

The real-time control (RTC) of urban drainage systems can make full use of the capabilities of existing infrastructures to mitigate combined sewer overflow (CSO) and urban flooding. Despite the benefits of RTC, it may encounter potential risks and failures, which need further consideration to enhance its robustness. Besides failures of hardware components such as sensors and actuators, the RTC performance is also sensitive to communication failures between the devices that are spatially distributed in a catchment-scale system. This paper proposes a decentralized control strategy based on multi-agent reinforcement learning to enhance communication robustness and coordinate the decentralized control agents through centralized training. To investigate different control structures, a centralized and a fully decentralized strategy are also developed based on reinforcement learning (RL) for comparison. A benchmark drainage model and a real-world drainage model are formulated as two cases, and the control agents are trained to control the orifices or pumps for CSO or flooding mitigation in each case. The three RL strategies reduce the CSO volume by 5.62-9.30% compared with a static baseline in historical rainfalls of the benchmark case and reduce the CSO and flooding volume by 14.39-21.36% compared with currently-used rule-based control in synthetic rainfalls of the real-world case. Benefitting from centralized training, the decentralized agents can achieve similar performance to the centralized agent. The decentralized control also enhances the communication robustness with smaller performance loss than the centralized control when observation communication fails, and provides a robust backup at the local level to limit the uncertainties when action commands from the centralized agent are lost. The results and findings indicate that multi-agent RL contributes to a coordinated and robust solution for RTC of urban drainage systems.

Evaluation of the pollutant interactions between different overlying water and sediment in simulated urban sewer system by excitation-emission matrix fluorescence spectroscopy.

The water quality in the sewer systems can be significantly influenced by the interaction between sediment and overlying water, which are still many doubts about the impact of pollutants transformation, degradation sequence, and reaction time. In this study, the exchanging processes between sewer sediment and four different overlying waters were evaluated in simulated urban sewer systems (dark and anaerobic environments). Dissolved organic matter (DOM) was used as an indicator to reflect the mitigation and exchange processes of pollutants. Excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) as an effective method for deciphering DOM properties was applied. There are three findings: (1) Three main processes (biological degradation, desorption, and adsorption) happened in the simulated sewer systems, in which the predominant pathway in the interaction process is biological degradation though consuming amino acid components. (2) The characteristics of overlying water could induce significant changes in sediment signatures; the amino acid-like components are more susceptible to degradation, and the humic-like compositions are more readily absorbed by sediments. (3) The reaction time is another significant factor (14 days was the turning point of the processes). This study unravels the transformation processes in sediment and different overlying waters, which provides the theoretical foundation for urban sewer efficient management and operation.

Quantitative source apportionment of dissolved organic matters in wet weather overflows of storm drainage systems based on degradation potential index and end member mixing model.

Dissolved organic matter (DOM) in wet weather overflows (WWFs) of storm drainage systems mainly originates from anthropogenic sources, such as paved runoff, illegally discharged domestic sewage and the retained sediment. This study provides a promising method to quantitatively apportion the WWF DOM of storm drainage systems using degradation potential index (DPI) and end member mixing (EMM) model. DPI is derived from excitation-emission matrix parallel factor analysis (EEM-PARAFAC), which can endow the end members and itself of WWF DOM with numerical features, and thus help quantify the source contributions of WWF DOM in EMM model. Findings show that (1) DPI was a reliable tool in the quantitative source apportionment of WWF DOM, owing to its features of small variance within source and large variances between sources; (2) DPI combined with EMM model could help identify the factors that induce significant impacts on the source contributions of WWF DOM, such as the storm pumping discharge and antecedent dry days in our case study; (3) the identified factors could guide the development of effective strategies for WWF DOM control, e.g. sediment management in our case.

Revealing the characteristics of dissolved organic matter in urban runoff at three typical regions via optical indices and molecular composition.

Dissolved organic matter (DOM) plays a major role in ecological systems and influences the fate and transportation of many pollutants. Despite the significance of DOM, understanding of how environmental and anthropogenic factors influence its composition and characteristics is limited, especially in urban stormwater runoff. In this article, the chemical properties (pollutant loads, molecular weight, aromaticity, sources, and molecular composition) of DOM in stormwater extracted from three typical end-members (traffic, residential, and campus regions) were characterized by UV-visible (UV-vis) spectroscopy, excitation-emission matrix spectroscopy combined with parallel factor analysis (EEM-PARAFAC), and ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). There are three findings: (1) The basic properties of DOM in stormwater runoff varied obviously from three urban fields, and the effect of initial flush was also apparent. (2) The DOM in residential areas mainly came from autochthonous sources, while allochthonous sources primarily contributed to the DOM in traffic and campus areas. However, it was mainly composed of terrestrial humic-like components with CHO and CHON element composition and HULO and aliphatic formulas. (3) The parameters characterizing DOM were primarily related to terrestrial source and aromaticity, but their correlations varied. Through the combination of optical methods and UPLC-Q-TOF spectrometry, the optical and molecular characteristics of rainwater are effectively revealed, which may provide a solid foundation for the classification management of stormwater runoff in different urban regions.

Statistical comparison between SARIMA and ANN's performance for surface water quality time series prediction.

The performance comparison studies of the autoregressive integrated moving average model (ARIMA) and the artificial neural network (ANN) were mostly carried out between the selected model structures through trial-and-error, strongly influenced by model structure uncertainty. This research aims to make up for this inadequacy. First, a surface water quality prediction case study including eight monitoring sites in China was introduced. Second, the ARIMA and ANN's performance was compared statistically between 6912 Seasonal ARIMA (SARIMA) and 110,592 feedforward ANN with different model structures, based on the mean square error (MSE) distributions depicted by boxplots. In a statistical view, the ANN models obtained a significantly lower median value and a more concentrated distribution of validation MSEs, which indicated lighter overfitting and better generalization ability. Furthermore, the optimal SARIMA models' performance is inferior to even the median of the ANN models in the case study. In contrast with the previous comparisons among selected models, the statistical comparison in this study shows lower uncertainty.

Complexing characteristics between Cu(â ¡) ions and dissolved organic matter in combined sewer overflows: Implications for the removal of heavy metals by enhanced coagulation.

Enhanced coagulation has been widely used in storm tanks to remove heavy metal ions (HMs) from combined sewer overflows (CSOs), but faces challenges on removing the HMs bound to dissolved organic matter (DOM) with small molecular weight (MW). DOM ubiquitously existing in CSOs generally contains a large distribution range of MW, which can significantly impact the MW distribution of HMs by complexing reaction, thereby adding uncertainties for the removal efficiency of coagulation. Therefore, realizing the potential MW distribution of the HMs bound to CSO-DOM is greatly important for cost-effectively removing HMs from CSOs in the coagulation process. This paper presents a comprehensive approach of ultrafiltration, fluorescence quenching titration, excitation-emission matrix parallel factor analysis, complexation model, and two-dimensional correlation fluorescence spectroscopy for exploring the MW-based complexing characteristics between Cu(II) ions and CSO-DOM components. Results show that: (1) Cu(II) ions that bound to the CSO-DOM were mainly distributed in the MW range of <5 kDa, which makes them very difficult to be removed from CSOs by coagulation technique. (2) Concentration effect and molecular composition exerted great impacts on the MW distribution of the Cu(II) ions bound to CSO-DOM. (3) The humic-like component of terrestrial origin with the MW range of 100 kDa∼0.45 µm possessed high binding stability, capacity, and priority with Cu(II) ions, and they could be used at a high concentration to promote the removal efficiency of coagulation for Cu(Ⅱ) ions of CSOs by competitive complexation and inter-molecular bridging.

Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data.

Due to the influence of buildings on the distribution of flood and their economic and social attributes, 3D spatial information such as the size of buildings and the flooded ratio of buildings relative to their height has an increasing impact on urban flood risk. However, existing flood risk assessment methods mainly use data in 2D and analysis methods are mostly 2D. In this study, flood variation processes were analyzed in the form of 3D dynamic visualization by coupling an urban drainage model and a flood simulation model with 3D visualization methods. By further combining with 3D building models, the 3D spatial information of buildings related to flood was obtained. In order to study the influence of 3D information on flood risk and combine with other multi-source heterogeneous data for integrated analysis, a 3D visualization assessment and analysis method for flood risk, coupled with the projection pursuit-particle swarm optimization algorithm (PP-PSO) was established (3DVAAM-PP-PSO). A case study from Chaohu City, China, was used to demonstrate the method. The results showed that the PP-PSO algorithm can process high-dimensional information and obtain the objective weight of each index. The 3D information from the influenced buildings had an impact on the evaluation results, which needed to be considered. Through the 3D visualization analysis, the overall distribution of flood risk and that around the buildings were obtained in multi-perspectives. The flood risk during different rainfall return periods were analyzed intuitively and comparatively. This study furnishes a novel method for flood risk assessment and analysis by making the most of 3D spatial information.

Transfer learning for neural network model in chlorophyll-a dynamics prediction.

Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models need to be improved. In this study, Transfer learning (TL) is employed to optimize neural network models (including feedforward neural networks (FNN), recurrent neural networks (RNN) and long short-term memory (LTSM)) and overcome these problems. Models using TL are able to reduce the influence of mutable data distribution and enhance generalization ability. Thus, it can improve the accuracy of prediction and maintain high performance in long-term applications. Also, TL is compared with parameter norm penalties (PNP) and dropout-two other methods used to improve model generalization ability. In general, TL has a better prediction effect than PNP and dropout. All the models, including FNN with different architectures, RNN and LSTM, as well as models optimized by PNP, dropout, and TL, are applied to an estuary reservoir in eastern China to predict chlorophyll-a dynamics at 5-min intervals. According to the results of this study, (i) models with TL produce the best prediction results; (ii) the original models and the models with PNP and dropout lose their ability to predict within 3 months, while TL models retain a high prediction accuracy.

An integrated assessment of drainage system reconstruction based on a drainage network model.

In order to mitigate urban flooding and combined sewer overflows, an integrated assessment method was proposed to identify the optimum reconstruction scheme of a drainage system by considering environment, economy, and society. The integrated assessment framework consisted of the drainage system model establishment, analytic hierarchy process theory, and regret value method. Five drainage system reconstruction schemes for Chaohu city were proposed in this study, and they were evaluated according to nine assessment factors by the integrated assessment method at the initial and future stages. The integrated assessment results show that setting up interceptive equipment for a combined drainage network is the optimal reconstruction scheme at both the initial and future stages of the life cycle. This means that an interceptive combined drainage network is better than a separate drainage network or setting up storage tanks in particular situations from a comprehensive perspective.

CBR-based integration of a hydrodynamic and water quality model and GIS-a case study of Chaohu City.

Monitoring on urban water environment and analysis of engineering improvement measures are intricate and time-consuming tasks. In previous studies, the integration of hydrodynamic and water quality models and geographical information system (GIS) usually takes three approaches: loose coupling, tight coupling, and full coupling. However, this paper adopted a special loose coupling approach-case-based reasoning (CBR) to develop an integrated decision support system. This was characterized by invoking the case base stored in the GIS platform as the output of the model. The fused capability of model's water quality predication and strong spatial data processing analysis of GIS can be realized at the same time by integration. The functionality of the integrated system was illustrated through a case study of Chaohu, a medium-sized city in China, which includes case retrieval, result interpretation, and the visual display in the GIS platform. Results verified the feasibility and operability of the developed method. As a useful tool, the integrated decision support system makes it simpler and more convenient for decision makers to make decisions efficiently and quickly.

Modelling the effects of water diversion and combined sewer overflow on urban inland river quality.

In order to assist and optimize the operation of a clean water diversion project for the medium-sized inland rivers in Chaohu, China, an integrated hydrodynamic and water quality model was used in this study. Sixteen diversion scenarios and five sewage interception scenarios were defined to assess the improvement of water quality parameters including ammonia nitrogen (NH3-N), total phosphorus (TP) and chemical oxygen demand (COD) under different diverted water flows, diverting times, diverting points, diverting routines and sewage interception proportions. An index of pollutant removal rate per unit diverted water flow (PRUWF) was proposed to evaluate the effect of the clean water diversion. Results show that operating conditions played important roles in water quality improvement of medium-sized inland rivers. The optimal clean water diversion was operated under the conditions of a flow rate of 5 m3/s for 48 h with an additional constructed bridge sluice. A global sensitivity analysis using the Latin Hypercube One-Factor-at-a-Time (LH-OAT) method was conducted to distinguish the contributions of various driving forces to inland river water restoration. Results show that sewage interception was more important than diverted water flow and diverting time with respect to water quality improvement, especially for COD.

Anthropogenic Influences of Paved Runoff and Sanitary Sewage on the Dissolved Organic Matter Quality of Wet Weather Overflows: An Excitation-Emission Matrix Parallel Factor Analysis Assessment.

The quality of dissolved organic matter (DOM) in a wet weather overflow (WWF) can be broadly influenced by anthropogenic factors, such as nonpoint sources of paved runoff and point sources of sanitary sewage within the drainage networks. This study focused on the anthropogenic influences of the paved runoff and sanitary sewage on the DOM quality of WWF using excitation-emission matrix parallel factor analysis (EEM-PARAFAC). Results show that (1) EEM-PARAFAC fitted terrestrial humic-like, anthropogenic humic-like, tryptophan-like, and tyrosine-like components can be regarded as indicators to identify the types of sewage overflows and the illicit connection status of drainage systems. (2) A short emission wavelength (em: 302-313 nm) peak of the tyrosine-like component occurred in the reserved sanitary sewage, while a type of longer emission wavelength (em: 321-325 nm) peak came from the sump deposit. These tyrosine-like components were gradually evacuated in the initial phase of the overflow process with the fading of their EEM signals. Fluorescence signal transformations of all the components confirmed the potential ability of EEM-PARAFAC to monitor the dynamic changes of the primary pollutant sources. (3) The input of the newly increased sanitary sewage had a dominant influence on the quality and yield of the WWF DOM.