Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning.

Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L25(56)) and 16 (L16(44)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ10), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ10, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ10, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ10, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R2) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.

Trends, Issues and Future Directions of Urban Health Impact Assessment Research: A Systematic Review and Bibliometric Analysis.

Health impact assessment (HIA) has been regarded as an important means and tool for urban planning to promote public health and further promote the integration of health concept. This paper aimed to help scientifically to understand the current situation of urban HIA research, analyze its discipline co-occurrence, publication characteristics, partnership, influence, keyword co-occurrence, co-citation, and structural variation. Based on the ISI Web database, this paper used a bibliometric method to analyze 2215 articles related to urban HIA published from 2012 to 2021. We found that the main research directions in the field were Environmental Sciences and Public Environmental Occupational Health; China contributed most articles, the Tehran University of Medical Sciences was the most influential institution, Science of the Total Environment was the most influential journal, Yousefi M was the most influential author. The main hotspots include health risk assessment, source appointment, contamination, exposure, particulate matter, heavy metals and urban soils in 2012-2021; road dust, source apposition, polycyclic aromatic hydrocarbons, air pollution, urban topsoil and the north China plain were always hot research topics in 2012-2021, drinking water and water quality became research topics of great concern in 2017-2021. There were 25 articles with strong transformation potential during 2020-2021, but most papers carried out research on the health risk assessment of toxic elements in soil and dust. Finally, we also discussed the limitations of this paper and the direction of bibliometric analysis of urban HIA in the future.

Tanshinone IIA Ameliorates Inflammation Response in Osteoarthritis via Inhibition of miR-155/FOXO3 Axis.

BACKGROUND: Osteoarthritis (OA) is the most common joint disorder characterized by degeneration of the articular cartilage and joint destruction with an associated risk of mobility disability in elderly people. Although a lot of achievements have been made, OA is still regarded as an incurable disease. Therefore, the pathological mechanisms and novel therapeutic strategies of OA need more investigation. METHODS: MTT assay was conducted to measure the viability of chondrocytes after LPS treatment. Cell apoptosis was analyzed by annexin V/propidium iodide labeling. ELISA was used to determine the concentrations of interleukin (IL)-1ß, IL-6, and tumor necrosis factor (TNF)-α in the culture supernatant of chondrocytes. The expression level of miR-155, IL-1ß, FOXO3, TNF-α, IL-6, caspase-3, and caspase-9 in chondrocytes was analyzed by RT-qPCR or Western blot. RESULTS: We found that LPS led to inflammatory responses, cell apoptosis, and increased miR-155 expression in human articular chondrocytes. Tanshinone IIA could inhibit LPS-induced inflammation and cell apoptosis of chondrocytes via regulating the expression of miR-155 and FOXO3. miR-155 directly targeted the 3'-UTR of FOXO3 to regulate its expression. CONCLUSIONS: Taken together, our data suggest tanshinone IIA ameliorates inflammation response in OA via inhibition of the miR-155/FOXO3 axis, and provide some evidences that tanshinone IIA could be designed and developed as a new promising clinical therapeutic drug for OA patients.

Simplified continuous simulation model for investigating effects of controlled drainage on long-term soil moisture dynamics with a shallow groundwater table.

Accurate modeling of soil water content is required for a reasonable prediction of crop yield and of agrochemical leaching in the field. However, complex mathematical models faced the difficult-to-calibrate parameters and the distinct knowledge between the developers and users. In this study, a deterministic model is presented and is used to investigate the effects of controlled drainage on soil moisture dynamics in a shallow groundwater area. This simplified one-dimensional model is formulated to simulate soil moisture in the field on a daily basis and takes into account only the vertical hydrological processes. A linear assumption is proposed and is used to calculate the capillary rise from the groundwater. The pipe drainage volume is calculated by using a steady-state approximation method and the leakage rate is calculated as a function of soil moisture. The model is successfully calibrated by using field experiment data from four different pipe drainage treatments with several field observations. The model was validated by comparing the simulations with observed soil water content during the experimental seasons. The comparison results demonstrated the robustness and effectiveness of the model in the prediction of average soil moisture values. The input data required to run the model are widely available and can be measured easily in the field. It is observed that controlled drainage results in lower groundwater contribution to the root zone and lower depth of percolation to the groundwater, thus helping in the maintenance of a low level of soil salinity in the root zone.