Biodegradation of microplastics derived from controlled release fertilizer coating: Selective microbial colonization and metabolism in plastisphere.

Coated controlled-release fertilizers (CRFs) are widely used in agriculture, and the persistent presence of residual polymer coating has raised environmental concerns. This study investigates the underlying degradation dynamics of microplastics (MPs) derived from three typical materials used in CRFs, including polyethylene (PE), epoxy (EP), and polyurethane (PU), through a soil degradation test. The formation of surface biofilm, the succession process, and metabolic characteristics of microbial community are revealed by laser scanning confocal microscope, 16S rRNA sequencing, and non-targeted metabolomics analysis. The weight loss rates of PE, EP, and PU after 807 days of degradation were 16.70 %, 2.79 %, and 4.86 %, respectively. Significant secondary MPs were produced with tears and holes appeared in the coating cross sections and pyrolysis products were produced such as ethers, acids, and esters for PE; alkanes, olefins and their branched-chain derivatives for EP; and short-chain fatty acids and benzene molecules for PU. The coating surface selectively recruited the bacteria of Chujaibacter and Ralstonia and fungus of Fusarium and Penicillium, forming biofilm composed of lipids, proteins, and living cells. The metabolism of amino acids and polymers was enhanced to protect against MP-induced stress. The metabolites or intermediates of organic acids and derivatives, oxygen-contained organic compounds, and benzenoids on CRF surface increased significantly compared with soil, but there were no significant differences among different coating types. This study provides insights to the underlying mechanisms of biodegradation and microenvironmental changes of MPs in soil.

Quantifying microplastic dispersion due to density effects.

An experimental study was conducted on how polymer density affects the transport and fate of microplastics in aquatic flows. For the first time, polypropylene (PP), polyethylene (PE), polymethyl methacrylate (PMMA), polyetheretherketone (PEEK), and polyvinyl chloride (PVC) were chemically stained and tested using solute transport techniques and velocities found among rivers in the natural environment (0.016 - 0.361 m/s). The movement of 3D-polymers with densities ranging from 0.9 - 1.4 g/cm³ was quantified in a laboratory flume scaled to simulate open-channel flows in fluvial systems. Except for PP, in most conditions microplastics exhibited similar transport characteristics to solutes regardless of density and established solute transport models were successfully implemented to predict their transport and fate. Mass recoveries and ADE routing model demonstrated microplastic deposition and resuspension was associated with polymer density below critical velocity thresholds ≤ 0.1 m/s. When density becomes the dominant force at these slower velocities, concentrations of denser than water microplastics will be momentarily or permanently deposited in channel beds and microplastics follow the classical Shields sediment transport methodology. This data is the first to provide microplastic suspension and deposition thresholds based on river velocity and polymer density, making a key contribution to research predicting microplastic fate and organismal exposure.

Physics-informed neural networks as surrogate models of hydrodynamic simulators.

In response to growing concerns surrounding the relationship between climate change and escalating flood risk, there is an increasing urgency to develop precise and rapid flood prediction models. Although high-resolution flood simulations have made notable advancements, they remain computationally expensive, underscoring the need for efficient machine learning surrogate models. As a result of sparse empirical observation and expensive data collection, there is a growing need for the models to perform effectively in 'small-data' contexts, a characteristic typical of many scientific problems. This research combines the latest developments in surrogate modelling and physics-informed machine learning to propose a novel Physics-Informed Neural Network-based surrogate model for hydrodynamic simulators governed by Shallow Water Equations. The proposed method incorporates physics-based prior information into the neural network structure by encoding the conservation of mass into the model without relying on calculating continuous derivatives in the loss function. The method is demonstrated for a high-resolution inland flood simulation model and a large-scale regional tidal model. The proposed method outperforms the existing state-of-the-art data-driven approaches by up to 25 %. This research demonstrates the benefits and robustness of physics-informed approaches in surrogate modelling for flood and hydroclimatic modelling problems.

Decline in Iran's groundwater recharge.

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.

Microplastic transport dynamics in surcharging and overflowing manholes.

The transport of microplastics within urban water systems remains poorly understood, with little prior research on their behaviour within manhole configurations. This study represents the first to measure and model the transport dynamics of microplastics within circular and square manholes under different hydraulic scenarios. The transport and fate of polyethylene (PE) was quantified and compared to solutes (Rhodamine WT dye) using energy losses, residence time distributions (RTDs), and mixing models within surcharging and overflowing manholes. The bulk mass of solute and PE concentrations followed similar flow paths across all conditions except for 17.3 ± 7.9 % of PE mass that was immobilized in a dead zone above the inlet pipe for manholes with a surcharge to pipe diameter ratio ≥2. Consequently, these microplastics only exit after a significant change in hydraulic regime occurs, causing microplastics to be at risk of being contaminated over a prolonged duration. No significant mixing differences for PE and solutes were found between manhole geometries. The deconvolution method outperformed the ADZ model with goodness of fit (Rt2) values of 0.99 (0.60) and 1.00 (0.89) for PE and solute mixing, respectively. This establishes the deconvolution method as the most accurate and appropriate model to accurately predict microplastic mixing in manholes and urban drainage systems.

Modelling impacts of climate change and anthropogenic activities on inflows and sediment loads of wetlands: case study of the Anzali wetland.

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.

Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.

The computational limitations of complex numerical models have led to adoption of statistical emulators across a variety of problems in science and engineering disciplines to circumvent the high computational costs associated with numerical simulations. In flood modelling, many hydraulic and hydrodynamic numerical models, especially when operating at high spatiotemporal resolutions, have prohibitively high computational costs for tasks requiring the instantaneous generation of very large numbers of simulation results. This study examines the appropriateness and robustness of Gaussian Process (GP) models to emulate the results from a hydraulic inundation model. The developed GPs produce real-time predictions based on the simulation output from LISFLOOD-FP numerical model. An efficient dimensionality reduction scheme is developed to tackle the high dimensionality of the output space and is combined with the GPs to investigate the predictive performance of the proposed emulator for estimation of the inundation depth. The developed GP-based framework is capable of robust and straightforward quantification of the uncertainty associated with the predictions, without requiring additional model evaluations and simulations. Further, this study explores the computational advantages of using a GP-based emulator over alternative methodologies such as neural networks, by undertaking a comparative analysis. For the case study data presented in this paper, the GP model was found to accurately reproduce water depths and inundation extent by classification and produce computational speedups of approximately 10,000 times compared with the original simulator, and 80 times for a neural network-based emulator.

Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams.

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 (Dx), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of Dx 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 Dx and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx 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 Dx 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 Dx data (i.e., 100%) is the best model to compute Dx in streams. Considering the significant inherent uncertainty reported in the previous Dx models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (Dx) in turbulent environmental flow systems.

Sliding Mode Observer Design for decentralized multi-phase flow estimation.

Robust flow measurement in multi-phase systems has extensive applications in understanding, design, and operation of complex environmental, energy and industrial processes. The nonlinearity and spatiotemporal variability of the interactions between different flow phases makes the multi-phase flow measurement a challenging task. Two Sliding Mode Observer (SMO) schemes are proposed in this study for the state estimation of a decentralized multi-phase flow measurement system. The developed observers are shown to be theoretically valid and numerically applicable for a real-life case study data. The multi-phase flow system considered in this paper can be described as two interconnected sub-systems including fluid and gas sub-systems, and two scenarios are considered in the design of the observers. The first scenario considers the interconnections as bounded disturbance (SMOD), while the second scenario considers the interconnections as an uncertainty (SMOU). Hence, the Sliding Mode Observers are adopted to mitigate the effects of disturbance in the system and uncertainties in the parameters. Numerical simulations are conducted using MATLAB and dynamic HYSYS simultaneously, using the data obtained from field-based multi-phase flow measurements. The results demonstrate the appropriateness and robustness of the proposed Sliding Mode Observer (SMO) for estimation of the multi-phase fluid specifications including the density, velocity, and the volume phases fraction in each subsystem. The analysis of the results highlights that the proposed model is computationally efficient with fast transient response, accurate tracking capability of the real process data, and very low steady-state error. This study shows that choosing an appropriate Lyapunov-Krasovsky function results in the asymptotic stability of the decentralized system and improves the performance of the proposed observers. Uncertainty analysis is conducted on the velocity estimation results obtained from the Sliding Observers. Overall, SMOU method shown better performance with RMSE of 0.24%, while RMSE of 0.46% was achieved for the SMOD. Comparison of the numerical results with the field-based flow measurement, as a benchmark, shows that although uncertainty in SMOU is approximately half of the uncertainty in SMOD, state estimation for both schemes was achieved in a finite time with high order of precision. It was shown that both observers developed in this study are well capable of estimating the multi-phase flow variables and states.

Bedform characteristics and biofilm community development interact to modify hyporheic exchange.

The physical and biological attributes of riverine ecosystems interact in a complex manner which can affect the hydrodynamic behaviour of the system. This can alter the mixing characteristics of a river at the sediment-water interface. Research on hyporheic exchange has increased in recent years driven by a greater appreciation for the importance of this dynamic ecotone in connecting and regulating river systems. An understanding of process-based interactions driving hyporheic exchange is still limited, specifically the feedbacks between the physical and biological controlling factors. The interplay between bed morphology and sediment size on biofilm community development and the impact on hyporheic exchange mechanisms, was experimentally considered. Purpose built recirculating flume systems were constructed and three profiles of bedform investigated: i) flat, ii) undulating λ = 1 m, ii) undulating λ = 0.2 m, across two different sized sediments (0.5 mm and 5 mm). The influence of biofilm growth and bedform interaction on hyporheic exchange was explored, over time, using discrete repeat injections of fluorescent dye into the flumes. Hyporheic exchange rates were greatest in systems with larger sediment sizes (5 mm) and with more bedforms (undulating λ = 0.2). Sediment size was a dominant control in governing biofilm growth and hyporheic exchange in systems with limited bedform. In systems where bedform was prevalent, sediment size and biofilm appeared to no longer be a control on exchange due to the physical influence of advective pumping. Here, exchange rates within these environments were more consistent overtime, despite greater microbial growth. As such, bedform has the potential to overcome the rate limiting effects of biotic factors on hyporheic exchange and sediment size on microbial penetration. This has implications for pollutant and nutrient penetration; bedforms increase hydrological connectivity, generating the opportunity to support microbial communities at depth and as such, improve the self-purification ability of river systems.

Clinical effects of curcumin in enhancing cancer therapy: A systematic review.

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.

Large eddy simulation of turbidity currents in a narrow channel with different obstacle configurations.

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.

Application of unsupervised learning and process simulation for energy optimization of a WWTP under various weather conditions.

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.

Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return.

Ambitious energy targets in the 2020 European climate and energy package have encouraged many stakeholders to explore and implement measures improving the energy efficiency of water and wastewater treatment facilities. Model-based process optimization can improve the energy efficiency of wastewater treatment plants (WWTP) with modest investment and a short payback period. However, such methods are not widely practiced due to the labor-intensive workload required for monitoring and data collection processes. This study offers a multi-step simulation-based methodology to evaluate and optimize the energy consumption of the largest Italian WWTP using limited, preliminary energy audit data. An integrated modeling platform linking wastewater treatment processes, energy demand, and production sub-models is developed. The model is calibrated using a stepwise procedure based on available data. Further, a scenario-based optimization approach is proposed to obtain the non-dominated and optimized performance of the WWTP. The results confirmed that up to 5000 MWh annual energy saving in addition to improved effluent quality could be achieved in the studied case through operational changes only.

Longitudinal dispersion of microplastics in aquatic flows using fluorometric techniques.

Microplastics are an emerging environmental contaminant. Existing knowledge on the precise transport processes involved in the movement of microplastics in natural water bodies is limited. Microplastic fate-transport models rely on numerical simulations with limited empirical data to support and validate these models. We adopted fluorometric principles to track the movement of both fluorescent dye and florescent stained microplastics (polyethylene) in purpose-built laboratory flumes with standard fibre-optic fluorometers. Neutrally buoyant microplastics behaved in the same manner as a solute (Rhodamine) and more importantly displayed classical fundamental dispersion theory in uniform open channel flow. This suggests Rhodamine, a fluorescent tracer, can be released into the natural environment with the potential to mimic microplastic movement in the water column.