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Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion ( D x ), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of D x in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of D x and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of D x estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the D x values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth- factor  = 0.56) that brackets the highest percentage of true D x data (i.e., 100%) is the best model to compute D x in streams. Considering the significant inherent uncertainty reported in the previous D x models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing ( D x ) in turbulent environmental flow systems.

Background Curcumin is herbal compound that has been shown to have anti-cancer effects in pre-clinical and clinical studies. The anti-cancer effects of curcumin include inhibiting the carcinogenesis, inhibiting angiogenesis, and inhibiting tumour growth. This study aims to determine the Clinical effects of curcumin in different types of cancers using systematic review approach. Methods A systematic review methodology is adopted for undertaking detailed analysis of the effects of curcumin in cancer therapy. The results presented in this paper is an outcome of extracting the findings of the studies selected from the articles published in international databases including SID, MagIran, IranMedex, IranDoc, Google Scholar, ScienceDirect, Scopus, PubMed and Web of Science (ISI). These databases were thoroughly searched, and the relevant publications were selected based on the plausible keywords, in accordance with the study aims, as follows: prevalence, curcumin, clinical features, cancer. Results The results are derived based on several clinical studies on curcumin consumption with chemotherapy drugs, highlighting that curcumin increases the effectiveness of chemotherapy and radiotherapy which results in improving patient’s survival time, and increasing the expression of anti-metastatic proteins along with reducing their side effects. Conclusion The comprehensive systematic review presented in this paper confirms that curcumin reduces the side effects of chemotherapy or radiotherapy, resulting in improving patients’ quality of life. A number of studies reported that, curcumin has increased patient survival time and decreased tumor markers’ level.

Flow through a granular, natural porous media can erode it chemically by dissolving and thus shrinking the solid particles, or mechanically, by removing them. The two‐way interplay between the transport of fluids and dissolved solutes and alteration of the porous structure and hence the medium's transport properties is of interest to processes ranging from subsurface energy storage to contamination in groundwater. Here, we conduct a quantitative pore‐scale analysis through a combination of numerical simulations and microfluidic experiments. We find that erosion enhances solute dispersion at low Pe (diffusion‐dominated) and diminishes it at high Pe. Residence time distribution reveals that mechanical erosion tends to induce non‐Fickian transport more than chemical erosion, which we attribute to the differences in their effects on pore size distribution. Plain Language Summary Quantifying the dispersion coefficient is one of the most fundamental aspects of transport in porous materials with direct application in energy storage/recovery, or pollutant/nutrient transport in subsurface media. Dispersion arises from the availability of variable flow paths, resulting in a spectrum of transient times that solute particles can experience for transport through porous media. Porous media are often heterogeneous structures with irregular flow paths, making the prediction of solute spreading challenging. In nature, a common phenomenon that impacts the geometry of media is erosion. We analyze solute spreading under single‐phase conditions in media with different degree/type of erosion. We found a non‐monotonic erosion‐dispersion relation. Depending on the transport regime, our investigation shows that erosion can either enhance solute spreading (for diffusion‐dominated transport) or limit it (for advection‐dominated transport). While mechanical erosion through particle migration creates media with a multi‐modal variation in pore size distribution, chemical erosion, which involves particle shrinkage, primarily widens pore spaces without significantly altering the initial pore size distribution. These findings mark a crucial advancement in predicting the fate of injected or released solute species into natural porous media, particularly in the context of ongoing changes in pore space properties driven by erosion. Key Points Erosion promotes solute spreading at low Pe (diffusion‐dominated) regime, while suppressing it at high Pe (advection‐dominated) Mechanical erosion, unlike chemical erosion, alters pore space morphology toward a multi‐modal variation in pore sizes Compared to chemical erosion, mechanical erosion causes a shift in transport toward a more non‐Fickian spreading

Understanding the effects of climate change and anthropogenic activities on the hydrogeomorpholgical parameters in wetlands ecosystems is vital for designing effective environmental protection and control protocols for these natural capitals. This study develops methodological approach to model the streamflow and sediment inputs to wetlands under the combined effects of climate and land use / land cover (LULC) changes using the Soil and Water Assessment Tool (SWAT). The precipitation and temperature data from General Circulation Models (GCMs) for different Shared Socio-economic Pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downscaled and bias-corrected with Euclidean distance method and quantile delta mapping (QDM) for the case of the Anzali wetland watershed (AWW) in Iran. The Land Change Modeler (LCM) is adopted to project the future LULC at the AWW. The results indicate that the precipitation and air temperature across the AWW will decrease and increase, respectively, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Streamflow and sediment loads will reduce under the sole influence of SSP2-4.5 and SSP5-8.5 climate scenarios. An increase in sediment load and inflow was observed under the combined effects of climate and LULC changes, this is mainly due to the projected increased deforestation and urbanization across the AWW. The findings suggest that the densely vegetated regions, mainly located in the zones with steep slope, significantly prevents large sediment load and high streamflow input to the AWW. Under the combined effects of the climate and LULC changes, by 2100, the projected total sediment input to the wetland will reach 22.66, 20.83, and 19.93 million tons under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The results highlight that without any robust environmental interventions, the large sediment inputs will significantly degrade the Anzali wetland ecosystem and partly-fill the wetland basin, resulting in resigning the wetland from the Montreux record list and the Ramsar Convention on Wetlands of International Importance.

Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran—a global hotspot for groundwater depletion. Using an extended database comprising abstractions from over one million groundwater wells, springs, and qanats, from 2002 to 2017, here we show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge. This decline is primarily attributed to unsustainable water and environmental resources management, exacerbated by decadal changes in climatic conditions. However, it is important to note that the former’s contribution outweighs the latter. Our results show the average annual amount of nationwide groundwater recharge (i.e., ~40 mm/yr) is more than the reported average annual runoff in Iran (i.e., ~32 mm/yr), suggesting the surface water is the main contributor to groundwater recharge. Such a decline in groundwater recharge could further exacerbate the already dire aquifer depletion situation in Iran, with devastating consequences for the country’s natural environment and socio-economic development. Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran. The authors here show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge.

Accurately predicting suspended sediment concentration (SSC) in fluvial systems is essential for environmental monitoring, flood management, and riverine engineering applications. This study introduces a novel hybrid approach for forecasting SSC by leveraging advanced deep learning algorithms. Daily datasets from the U.S. Geological Survey, including discharge (Q) and SSC measurements, were analyzed from 2007 to 2017 at two key locations on the Mississippi River: Chester (CH) and Thebes (TH). The proposed framework integrates feedforward neural networks (FFNN), long short-term memory (LSTM) networks, stochastic gradient descent (SGD), and radial basis function (RBF) models, augmented with a first-order differencing technique. Additionally, hybrid models, including Supervised FFNN-LSTM and Supervised FFNN-SGD, were developed to enhance predictive performance. The dataset was partitioned into training (70%, 2,747 d) and testing (30%, 1,178 d) subsets, with daily temporal resolution. Six input scenarios incorporating lagged parameters were evaluated using performance metrics, including the correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scatter index (SI), and Willmott’s index (WI). Sensitivity analysis identified SSCt-1 (i.e., one day before) as the most influential predictor for short-term forecasting. Among the models, the SFFNN-LSTM-6 achieved the highest performance, with CC values of 0.976 for CH and 0.960 for TH, demonstrating the ability to predict SSC effectively even in the absence of current-day discharge data. The proposed hybrid models exhibited exceptional robustness across diverse flow regimes, including extreme environmental conditions, establishing a reliable tool for SSC forecasting in complex fluvial systems. [Display omitted] •Machine learning techniques are used for sediment concentration predictions in fluvial systems.•Hybrid machine learning approaches can robustly predict suspended sediment concentration.•Sensitivity analysis shows (SSCt-1) is most influential in predicting sediment concentration in rivers.•SFFNN-LSTM-6 model can accurately predict SSC in data-scarce conditions.•Our proposed model improved SSC predictions across varying flow regimes.

This paper outlines a hybrid modeling approach to facilitate weather-based operation and energy optimization for the largest Italian wastewater treatment plant (WWTP). Two clustering methods, K-means algorithm and Gaussian mixture model (GMM) based on the expectation-maximization (EM) algorithm, were applied to an extensive dataset of historical and meteorological records. This study addresses the problem of determining the intrinsic structure of clustered data when no information other than the observed values is available. Two quantitative indexes, namely the Bayesian information criterion (BIC) and the Silhouette coefficient using Euclidean distance, as well as two general criteria, were implemented to assess the clustering quality. Furthermore, seven weather-based influent scenarios were introduced to the process simulation model, and sets of aeration strategies are proposed. The results indicate that incorporating weather-based aeration strategies in the operation of the WWTP improves plant energy efficiency.

Turbidity currents are frequently observed in natural and man-made environments, with the potential of adversely impacting the performance and functionality of hydraulic structures through sedimentation and reduction in storage capacity and an increased erosion. Construction of obstacles upstream of hydraulic structures is a common method of tackling adverse effects of turbidity currents. This paper numerically investigates the impacts of obstacle’s height and geometrical shape on the settling of sediments and hydrodynamics of turbidity currents in a narrow channel. A robust numerical model based on LES method was developed and successfully validated against physical modelling measurements. This study modelled the effects of discretization of particles size distribution on sediment deposition and propagation in the channel. Two obstacles geometry including rectangle and triangle were studied with varying heights of 0.06, 0.10 and 0.15 m. The results show that increasing the obstacle height will reduce the magnitude of dense current velocity and sediment transport in narrow channels. It was also observed that the rectangular obstacles have more pronounced effects on obstructing the flow of turbidity current, leading to an increase in the sediment deposition and mitigating the impacts of turbidity currents.

Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic and climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic ecosystems can provide a better understanding of their current ecosystem health and functions. Different levels of environmental risk are defined by considering the categories of risk and the probability and severity of each in the environment. Determining environmental risk levels provides a general overview of ecosystem function. This mechanism increases the visibility of risk levels and their values in three distinct states (i.e., low, moderate, and high) associated with ecosystem function. The Bayesian belief network (BBN) is a novel tool for determining environmental risk levels and monitoring the effectiveness of environmental planning and management measures in reducing the levels of risk. This study develops a robust methodological framework for determining the overall level of risks based on a combination of varied environmental risk factors using the BBN model. The proposed model is adopted for a case study of Shadegan International Wetlands (SIWs), which consist of a series of Ramsar wetlands in the southwest of Iran with international ecological significance. A comprehensive list of parameters and variables contributing to the environmental risk for the wetlands and their relationships were identified through a review of literature and expert judgment to develop an influence diagram. The BBN model is adopted for the case study location by determining the states of variables in the network and filling the probability distribution tables. The environmental risk levels for the SIWs are determined based on the results obtained at the output node of the BBN. A sensitivity analysis is performed for the BBN model. We proposed model-informed management strategies for wetland risk control. According to the BBN model results, the SIWs ecosystems are under threat from a high level of environmental risk. Prolonged drought has been identified as the primary contributor to the SIWs’ environmental risk levels.

Assessment of climate change impacts on wind characteristics is crucial for the design, operation, and maintenance of coastal and offshore infrastructures. In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU-ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%.