Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Neuroprotective efficacy of the glucocorticoid receptor modulator PT150 in the rotenone mouse model of Parkinson's disease.

Parkinson's disease (PD) is the most common neurodegenerative movement disorder worldwide. Current treatments for PD largely center around dopamine replacement therapies and fail to prevent the progression of pathology, underscoring the need for neuroprotective interventions. Approaches that target neuroinflammation, which occurs prior to dopaminergic neuron (DAn) loss in the substantia nigra (SN), represent a promising therapeutic strategy. The glucocorticoid receptor (GR) has been implicated in the neuropathology of PD and modulates numerous neuroinflammatory signaling pathways in the brain. Therefore, we investigated the neuroprotective effects of the novel GR modulator, PT150, in the rotenone mouse model of PD, postulating that inhibition of glial inflammation would protect DAn and reduce accumulation of neurotoxic misfolded ⍺-synuclein protein. C57Bl/6 mice were exposed to 2.5 mg/kg/day rotenone by intraperitoneal injection for 14 days. Upon completion of rotenone dosing, mice were orally treated at day 15 with 30 mg/kg/day or 100 mg/kg/day PT150 in the 14-day post-lesioning incubation period, during which the majority of DAn loss and α-synuclein (α-syn) accumulation occurs. Our results indicate that treatment with PT150 reduced both loss of DAn and microgliosis in the nigrostriatal pathway. Although morphologic features of astrogliosis were not attenuated, PT150 treatment promoted potentially neuroprotective activity in these cells, including increased phagocytosis of hyperphosphorylated α-syn. Ultimately, PT150 treatment reduced the loss of DAn cell bodies in the SN, but not the striatum, and prohibited intra-neuronal accumulation of α-syn. Together, these data indicate that PT150 effectively reduced SN pathology in the rotenone mouse model of PD.

The developmental mechanics of divergent buckling patterns in the chick gut.

Tissue buckling is an increasingly appreciated mode of morphogenesis in the embryo, but it is often unclear how geometric and material parameters are molecularly determined in native developmental contexts to generate diverse functional patterns. Here, we study the link between differential mechanical properties and the morphogenesis of distinct anteroposterior compartments in the intestinal tract-the esophagus, small intestine, and large intestine. These regions originate from a simple, common tube but adopt unique forms. Using measured data from the developing chick gut coupled with a minimal theory and simulations of differential growth, we investigate divergent lumen morphologies along the entire early gut and demonstrate that spatiotemporal geometries, moduli, and growth rates control the segment-specific patterns of mucosal buckling. Primary buckling into wrinkles, folds, and creases along the gut, as well as secondary buckling phenomena, including period-doubling in the foregut and multiscale creasing-wrinkling in the hindgut, are captured and well explained by mechanical models. This study advances our existing knowledge of how identity leads to form in these regions, laying the foundation for future work uncovering the relationship between molecules and mechanics in gut morphological regionalization.

Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis.

Objective.Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons.Approach.We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network.Results.The proposed model achieves a mean absolute error (MAE) of18.76±5.167in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of0.95±0.09and a Frechet inception distance (FID) of145.60±8.38. The model yields a MAE of26.83±8.27to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of0.73±0.06and a FID distance equal to122.58±7.55. The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of27.89±2.22and26.08±2.95to synthesize MRI from CT input.Significance.The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.

Shared and distinctive brain networks underlying trait and state rumination.

Although trait and state rumination play a central role in the exacerbation of negative affect, evidence suggests that they are weakly correlated and exert distinct influences on emotional reactivity to stressors. Whether trait and state rumination share a common or exhibit distinct neural substrate remains unclear. In this study, we utilized functional near-infrared spectroscopy (fNIRS) combined with connectome-based predictive modeling (CPM) to identify neural fingerprints associated with trait and state rumination. CPM identified distinctive functional connectivity (FC) profiles that contribute to the prediction of trait rumination, primarily involving FC within the default mode network (DMN) and the dorsal attention network (DAN) as well as FC between the DMN, control network (CN), DAN, and salience network (SN). Conversely, state rumination was predominantly associated with FC between the DMN and CN. Furthermore, the predictive features of trait rumination can be robustly generalized to predict state rumination, and vice versa. In conclusion, this study illuminates the importance of both DMN and non-DMN systems in the emergence and persistence of rumination. While trait rumination was associated with stronger and broader FC than state rumination, the generalizability of the predictive features underscores the presence of shared neural mechanisms between the two forms of rumination. These identified connectivity fingerprints may hold promise as targets for innovative therapeutic interventions aimed at mitigating rumination-related negative affect.

Carbon savings from sugarcane straw-derived bioenergy: Insights from a life cycle perspective including soil carbon changes.

Sugarcane straw removal for bioenergy production will increase substantially in the next years, but this may deplete soil organic carbon (SOC) and exacerbate greenhouse gas (GHG) emissions. These aspects are not consistently approached in bioenergy life cycle assessment (LCA). Using SOC modeling and LCA approach, this study addressed the life cycle GHG balance from sugarcane agroindustry in different scenarios of straw removal, considering the potential SOC changes associated with straw management in sugarcane-cultivated soils in Brazil. Long-term simulations showed SOC losses of up to -0.5 Mg ha-1 yr-1 upon complete straw removal, whereas the moderate removal had little effects on SOC and the maintenance of all straw in the field increased SOC accumulation by up to 0.4 Mg ha-1 yr-1. Our analysis suggests that accounting for SOC changes in LCA calculations could lower the net GHG benefits of straw-derived bioenergy, whose emissions intensity varied according to soil type. Overall, SOC depletion induced by complete straw removal increased the life cycle GHG emissions of straw-derived bioenergy by 26 % (3.9 g CO2eq MJ-1) compared to a scenario without taking SOC changes into account. Straw removal for cellulosic ethanol could be effective for mitigating GHG emissions relative to gasoline, but it was not advantageous for bioelectricity generation depending on the energy sources that are displaced. Therefore, straw-induced change of SOC stocks is a critical factor to model life cycle GHG emissions of straw-derived bioenergy.

Nitrate and ammonium removal in constructed wetlands: Experimental insights and zero-dimensional numerical modeling.

Constructed wetlands (CWs) have emerged as effective wastewater treatment systems, mimicked natural wetland processes but engineered for enhanced pollutant removal efficiency. Ammonium (NH4+) and nitrate (NO3-) are among common pollutants in wastewater, posing significant environmental and health risks. The primary objective of this study is to compares the performance of CWs using gravel and three sizes of natural pumice, along with phragmites australis, in horizontal and horizontal-vertical CWs for nitrate and ammonium removal in the complementary treatment of domestic wastewater. Additionally, the study aims to develop and validate a numerical model using MATLAB software to predict the removal efficiency of these pollutants, thereby contributing to the optimization of CW design and operation. The model operates as a zero-dimensional model based on the law of mass conservation, treating the wetland as a completely mixed reactor, thus avoiding complexities associated with solute movement in porous media. It accurately could predict removal efficiency of chemical, biochemical, and biological indicators while considering active and passive absorption mechanisms by plant uptake. Notably, the determination of coefficients in the model equation does not rely on potentially error-prone laboratory measurements due to sampling issues. Instead, optimization techniques alongside field data robustly estimate these coefficients, ensuring reliability and practicality. Results indicate that higher pollutant concentrations increase reaction rates, particularly enhancing CW efficiency in ammonium removal. Pumice, especially in larger sizes, exhibits superior absorption due to increased porosity and surface area. Overall, the model accurately predicts nitrates concentrations, demonstrating its potential for CW performance optimization and confirming the significance of effective pollutant removal strategies in wastewater treatment.

Predicting COVID-19 vaccine uptake: Comparing the health belief model and theory of planned behavior.

The COVID-19 outbreak has had a significant impact on the global health landscape, underscoring the crucial role that vaccinations play in achieving herd immunity and reducing the effects of pandemics. Given the importance of this issue, it is imperative to gain a deeper understanding of the various factors that influence individuals' decisions to seek vaccination. This study aimed to compare the prediction level of the Health Belief Model (HBM), the Theory of Planned Behavior (TPB), and a combined model in explaining the intention of adults to receive COVID-19 immunization. A cross-sectional online survey was conducted among adults (n = 505) in Saudi Arabia. The survey contained variables related to the HBM and TPB. The prediction level of the two models as well as a combined model were evaluated utilizing Structural Equation Modeling (SEM). Among the recruited 505 participants, 88% fell within the 18 to 30 age range, and 54.5% were male. The proposed HBM model accounted for 68% of the variation in intention, whereas the TPB model explained 78.2% of the variation in COVID-19 vaccination intention. The combined model showed greater explanatory power (82%). The variables of susceptibility (ß = 0.20, p < .001), severity (ß = 0.49, p < .001), advantages (ß = 0.63, p < .001), and obstacles (ß = - 0.24, p < .001), perceptions of behavioral control (ß = 1.58, p < .001) and attitudes (ß = 0.44, p < .001) were found to significantly predict increased vaccination intentions in the combined model. However, the subjective norm construct did not significantly predict vaccination intentions (ß = 0.06, p = .34). The TPB has greater explanatory power than the HBM in predicting the intention to obtain COVID-19 vaccination. However, the combined model showed a greater prediction level. Understanding and identifying people's perceived health beliefs and practices is critical for developing successful COVID-19 intervention methods.

Evaluating spatiotemporal groundwater quality changes in the Rhodope coastal aquifer system (NE Greece) employing a GIS-assisted hybrid approach of multivariate statistics and inverse geochemical modeling.

This research employs a GIS-assisted approach of multivariate statistics and inverse geochemical modeling to unravel the processes driving groundwater salinization in a complex aquifer system. Multivariate statistical methods define the end-member water groups, identifying dominant processes explaining hydrogeochemical variance in wet and dry season water chemistry datasets. Mineral saturation indices (SIs) and inverse geochemical modeling (IGM) investigate potential geochemical reactions and mixing processes responsible for the observed groundwater compositions and their spatiotemporal evolution along reversed flow paths caused by overexploitation in the Rhodope aquifer system. Results reveal that a concise set of reactant and product phases, including CO2(g), H2O, calcite, gypsum, halite, celestite, plagioclase, K-feldspar, illite, and Ca-montmorillonite, along with ion exchange processes (CaX2, MgX2, and NaX), explains the hydrogeochemical evolution of groundwater along reversed flow paths between genetically and compositionally different surface and groundwater bodies. Systematic changes in water chemistry along the flow paths are attributed to mixing of surface waters and/or different groundwater end-members, dilution by a freshwater component, water-rock interaction (WRI) processes, and ion exchange involving Ca/Mg- and/or Na-clays. The chemical evolution represented by IGMs initiates with the mixing of Aegean seawater and Aspropotamos River, incorporating WRI and ion exchange processes (Mg- and Na-clays) to produce the water chemistry of Vistonida Lake, the only surface water body with hydraulic interaction with the groundwater system in the study area. Statistically-defined end-member water groups effectively explain the groundwater flow system and evolutionary processes between hydraulically connected surface and groundwater bodies. Overall, the fusion of multivariate statistical analysis (MVSA), inverse geochemical modeling (IGM), and GIS techniques proves potent and comprehensive, enhancing understanding of groundwater dynamics, improving prediction accuracy, aiding proficient management, and facilitating data-driven decision-making within the realm of groundwater assessment and management.

Building up a model family for inflammations.

The paper presents an approach for overcoming modeling problems of typical life science applications with partly unknown mechanisms and lacking quantitative data: A model family of reaction-diffusion equations is built up on a mesoscopic scale and uses classes of feasible functions for reaction and taxis terms. The classes are found by translating biological knowledge into mathematical conditions and the analysis of the models further constrains the classes. Numerical simulations allow comparing single models out of the model family with available qualitative information on the solutions from observations. The method provides insight into a hierarchical order of the mechanisms. The method is applied to the clinics for liver inflammation such as metabolic dysfunction-associated steatohepatitis or viral hepatitis where reasons for the chronification of disease are still unclear and time- and space-dependent data is unavailable.

Construction and application of product optimisation design model driven by user requirements.

User requirements serve as the primary reference content in product design. The effective capture of crucial user requirements, followed by the development of a product technical solution aligned with these requirements, stands as a pivotal approach to enhancing design efficiency. In order to explore the problem of generating and decision-making of product technical solutions in the case of complex user demands, this study constructs a user requirements driven product optimization design model, which is used to complete the generation and decision-making of product design solutions in a more reasonable way. The model unfolds across three key stages: Firstly, a user requirements importance ranking system is crafted leveraging Kano Model and Pairwise Analysis. Next, employing the Functional Analysis System Techniques (FAST) theory and the Quality Function Deployment (QFD) theory, user requirements undergo transformation into technical solutions. Finally, these technical solutions are amalgamated into diverse technical combinations, with decisions facilitated by a game theory model to yield the optimal overall design solution. The new optimal design model reduces the influence of subjectivity and ambiguity in the process of user requirements analysis, increases the reliability of the transformation of user requirements into technical solutions, and improves the efficiency of the generation and decision-making of product design solutions under multiobjective situations. The model proposed in this study is exemplified through the development of a medicated bath water heater. Results indicate that the technical solution derived from the model surpasses similar products in terms of user satisfaction, thereby validating its feasibility.

Simulation of hexavalent chromium removal by electrocoagulation using iron anode in flow-through reactor.

An electrocoagulation (EC) model is developed for hexavalent chromium reduction and precipitation, using iron electrodes. Parallel removal mechanisms such as adsorption of chromium on ferrihydrite and direct reduction at the cathode is assumed negligible due to low concentration of Cr(VI). The reaction model presented for batch system represents species complexation, precipitation/dissolution, acid/base, and oxidation-reduction reactions. Batch reactor simulation is verified using experimental data obtained by Sarahney et al. (2012), where the effect of initial chromium concentration, pH, volumetric current density, and ionic strength is considered (Sarahney et al., 2012). The model couples multicomponent ionic transport in MATLAB with chemical reaction model in PHREEQC, as a widely used computational programming tool and a geochemical reaction simulator with comprehensive geochemistry databases. The suggested current density is 0.05-0.3mA/cm2 and the surface to volume ratio in batch reactor is considered 0.017 1/cm. Design parameters are presented for operation of a flow-through hexavalent chromium removal using electrocoagulation by iron electrode to treat Cr(VI) in range of 10-50 mg/L. The operational parameters for a flow-through EC reactor for Cr(VI) removal is suggested to follow [Formula: see text] .

Unveiling temporal dynamics of PTSD and its functional impairments: A longitudinal study in UK healthcare workers.

PTSD has been associated with negative long-term consequences, including social and occupational impairments. Yet, a nuanced understanding of the interplay between PTSD symptoms and distinct domains of impairments on a short-term basis (weeks/ months) at the within-person level remains underexplored. In a large sample (nwave 1 = 1096, nwave 7 = 304) of UK healthcare workers assessed across seven assessment waves during the COVID-19 pandemic (spaced 6 weeks apart), we employed exploratory graphical vector autoregression models (GVAR) models to discern within-person temporal (across time) and contemporaneous (within same time window) dynamics between PTSD symptoms and functional impairment domains. The contemporaneous network highlighted strong co-occurrences between different symptoms and impairments. The temporal network revealed a mutually reinforcing cycle between intrusion and avoidance symptoms. Intrusion symptoms showed the highest out-strength (i.e., most predictive symptom), predicting avoidance symptoms, elevated sense of current threat, and various functional impairments. Avoidance symptoms, elevated after increased levels of intrusions, predicted work impairments that in turn were associated with difficulties in fulfilling other obligations. Our findings underscore the dynamics between perceived threat and intrusions, and the role intrusions may play in predicting a cascade of adverse effects. Targeted interventions aimed at mitigating intrusions may disrupt this negative cycle.

Stand age diversity (and more than climate change) affects forests' resilience and stability, although unevenly.

Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Unfortunately, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a state-of-the-science biogeochemical, biophysical, validated process-based model on historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions (i.e., forests are left to develop undisturbed). Such a model, forced by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario to disentangle the effect of climate change, spanned several age classes as representative of the current European forests' context, for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with longstanding ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO2 and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age class diversity -lacking in most, if not all, the current Global Vegetation Models - for reliable and robust assessments of the impacts of climate change on future forests' stability and resilience capacity. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method.

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

Behind the heterogeneity in the long-term course of first-episode psychosis: Different psychotic symptom trajectories are associated with different patterns of cannabis and stimulant use.

BACKGROUND: Data-driven classification of long-term psychotic symptom trajectories and identification of associated risk factors could assist treatment planning and improve long-term outcomes in psychosis. However, few studies have used this approach, and knowledge about underlying mechanisms is limited. Here, we identify long-term psychotic symptom trajectories and investigate the role of illness-concurrent cannabis and stimulant use. METHODS: 192 participants with first-episode psychosis were followed up after 10 years. Psychotic symptom trajectories were estimated using growth mixture modeling and tested for associations with baseline characteristics and cannabis and stimulant use during the follow-up (FU) period. RESULTS: Four trajectories emerged: (1) Stable Psychotic Remission (54.2 %), (2) Delayed Psychotic Remission (15.6 %), (3) Psychotic Relapse (7.8 %), (4) Persistent Psychotic Symptoms (22.4 %). At baseline, all unfavorable trajectories (2-4) were characterized by more schizophrenia diagnoses, higher symptom severity, and longer duration of untreated psychosis. Compared to the Stable Psychotic Remission trajectory, unstable trajectories (2,3) showed distinct associations with cannabis/stimulant use during the FU-period, with dose-dependent effects for cannabis but not stimulants (Delayed Psychotic Remission: higher rates of frequent cannabis and stimulant use during the first 5 FU-years; Psychotic Relapse: higher rates of sporadic stimulant use throughout the entire FU-period). The Persistent Psychosis trajectory was less clearly linked to substance use during the FU-period. CONCLUSIONS: The risk for an adverse long-term course could be mitigated by treatment of substance use, where particular attention should be devoted to preventing the use of stimulants while the use reduction of cannabis may already yield positive effects.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations.

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

Free water surface constructed wetlands: review of pollutant removal performance and modeling approaches.

Free water surface constructed wetlands (FWSCWs) for the treatment of various wastewater types have evolved significantly over the last few decades. With an increasing need and interest in FWSCWs applications worldwide due to their cost-effectiveness and other benefits, this paper reviews recent literature on FWSCWs' ability to remove different types of pollutants such as nutrients (i.e., TN, TP, NH4-N), heavy metals (i.e., Fe, Zn, and Ni), antibiotics (i.e., oxytetracycline, ciprofloxacin, doxycycline, sulfamethazine, and ofloxacin), and pesticides (i.e., Atrazine, S-Metolachlor, imidacloprid, lambda-cyhalothrin, diuron 3,4-dichloroanilin, Simazine, and Atrazine) that may co-exist in wetland inflow, and discusses approaches for simulating hydraulic and pollutant removal processes. A bibliometric analysis of recent literature reveals that China has the highest number of publications, followed by the USA. The collected data show that FWSCWs can remove an average of 61.6%, 67.8%, 54.7%, and 72.85% of inflowing nutrients, heavy metals, antibiotics, and pesticides, respectively. Optimizing each pollutant removal process requires specific design parameters. Removing heavy metal requires the lowest hydraulic retention time (HRT) (average of 4.78 days), removing pesticides requires the lowest water depth (average of 0.34 m), and nutrient removal requires the largest system size. Vegetation, especially Typha spp. and Phragmites spp., play an important role in FWSCWs' system performance, making significant contributions to the removal process. Various modeling approaches (i.e., black-box and process-based) were comprehensively reviewed, revealing the need for including the internal process mechanisms related to the biological processes along with plants spp., that supported by a further research with field study validations. This work presents a state-of-the-art, systematic, and comparative discussion on the efficiency of FWSCWs in removing different pollutants, main design factors, the vegetation, and well-described models for performance prediction.

Understanding Photovoltage Enhancement in Metal-Insulator Semiconductor Photoelectrodes with Metal Nanoparticles.

A metal-insulator-semiconductor (MIS) structure holds great potential to promote photoelectrochemical (PEC) reactions, such as water splitting and CO2 reduction, for the storage of solar energy in chemical bonds. The semiconductor absorbs photons, creating electron-hole pairs; the insulator facilitates charge separation; and the metal collects the desired charge and facilitates its use in the electrochemical reaction. Despite these attractive features, MIS photoelectrodes are significantly limited by their photovoltage, a combination of the voltage generated from photon absorption minus the potential drop across the insulator. Herein, we use multiscale continuum modeling of the carrier, electrolyte, and interfacial transport to identify strategies for mitigating the deleterious potential drop across the insulator and enabling high MIS photovoltages. To this end, we model Ni/SiO2/n-Si photoanodes that employ a planar Ni film or Ni nanoparticles (np-MIS) and validate both models using experimental polarization curves and photovoltage measurements from the literature. The simulations reveal that the insulator potential drop is lower and hence achieves higher photovoltages for np-MIS structures than MIS structures because the electrolyte screens charge trapped at defect states between the semiconductor and the insulator. This electrolyte charge screening phenomenon can be further leveraged by using low loadings or small nanoparticles, which not only minimize the interfacial potential drop but also improve the photocurrent by enabling more light absorption. These insights contribute to the optimization of the np-MIS structures for sustainable energy conversion.

Solid-liquid partitioning and speciation of Pb(II) and Cd(II) on goethite under high pH conditions, as examined by subnanomolar heavy metal analysis, X-ray absorption spectroscopy, and surface complexation modeling.

The adsorption of heavy metals on iron oxides generally increases with pH and is almost complete at neutral to slightly alkaline pH. However, almost complete adsorption on a linear scale does not imply sufficient removal of the heavy metals in terms of their toxicity. Here, we elucidated the chemical reactions that determine the solid-liquid partitioning of Pb(II) and Cd(II) on goethite at high pH. While the removal of both heavy metals was almost complete on a linear scale above pH 7 for Pb(II) and pH 9 for Cd(II), the dissolved metal concentrations decreased on a logarithmic scale with pH, reaching minima at around pH 10 for Pb(II) and pH 10-11 for Cd(II), and then they increased with pH thereafter. The XAFS spectra of Pb(II)- or Cd(II)-adsorbed goethite prepared at pH > 11 were almost the same as those at neutral pH, suggesting that removal of the heavy metals from solution was achieved by a single adsorption reaction over the entire pH range. Based on the observed macroscopic and microscopic adsorption behaviors at high pH, a robust surface complexation model was developed to predict the solid-liquid partitioning of divalent heavy metals over the entire pH range.