Abstract Understanding the effects of climate change on watershed health is essential for effective ecosystem management. The main purpose of the current research is to evaluate the health of the Nekarood Watershed in northern Iran using a conceptual model based on reliability (Rel), resilience (Res), and vulnerability (Vul) under various climate change scenarios. Climate data for the periods 2021–2050 and 2051–2080 were simulated using six models under the Shared Socioeconomic Pathways (SSP) scenarios and downscaled with the LARS-WG model. The physically based Soil and Water Assessment Tool (SWAT) model was employed to predict discharge, sediment, nitrate (NO3 −), and phosphate (PO4 3−) under these scenarios. Under baseline conditions, the watershed’s Rel, Res, and Vul indices were 0.46, 0.53, and 0.85, respectively, while the watershed health index (WHI) was 0.59, indicating a moderate status based on high flow discharge, low flow discharge, sediment, NO3 −, and PO4 3− at the watershed outlet. Under the SSP126 scenario, the WHI is projected to change by − 2.7% for 2021–2050 and − 6.6% for 2051–2080. For SSP245, the changes are − 6.1% and + 0.7%; for SSP370, − 5.7% and − 5.0%; and for SSP585, − 3.2% and − 18.7% for the respective future periods. This spatial modeling approach enhances decision-makers’ understanding of temporal changes in the Nekarood Watershed and supports the simulation of climate impacts on land characteristics, including pollutant loads and overall watershed health.
Around three quarters of Bitcoin transactions take place off-chain. Despite their significance, the vast majority of the empirical literature on cryptocurrencies focuses on on-chain transactions. This paper presents one of the first analysis of both on- and off-chain demand- and supply-side factors. Two hypotheses relating on-chain and off-chain demand and supply drivers to the Bitcoin price are tested in an ARDL model with daily data from 2019 to 2024. Our estimates document the differential contributions of on-chain and off-chain drivers on the Bitcoin price. Off-chain demand pressures have a significant impact on the Bitcoin price in the long-run. In the short-run, both demand and supply drivers significantly affect the Bitcoin price. Regarding transactions on the blockchain, only on-chain demand pressures are statistically significant - both in the long- and short-run. These findings confirm the dual nature of the Bitcoin price dynamics, where also market fundamentals affect the Bitcoin price in addition to speculative drivers. Bitcoin whale trading has less significant impact on price in the long-run, while is more pronounced contemporaneously and one-period lag.
Abstract In the next decades, some countries will suffer from water shortage because of the increased population and the increased percentage of economic development. Accordingly, it is necessary to find the alternative way to solve this problem and investigate the development. Nanoscale graphene pieces known as graphene quantum dots (GQDs) exhibit distinctive characteristics that render them intriguing contenders for an array of novel uses. Cellulose acetate (CA) membranes were prepared from CA powder, formamide, and acetone with different concentrations of graphene quantum dots using the technique of phase inversion. The produced membranes were subjected to Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and contact angle studies to characterise their structural, morphological, hydrophilic, and biofouling behaviour. The graphene quantum dots additives proved to enhance the performance as well as antibiofouling characteristics of the CA-RO membranes. The CA membrane showed a flow of water 42.8 L/m2 h and a 99.4% salt rejection rate using a 10,000 ppm NaCl solution after annealing.
Hajar Barkhor, Mohammad Ali Nasseri, Alireza Amarzadeh
et al.
Abstract This research was designed to evaluate the performance of the CuFe12O19/CuS/Xenon system in the degradation of tetracycline in aqueous solutions. In this study, after green synthesis of nanocomposite using the extract of the Artemisia plant, its properties were determined by XRD, FTIR, FESEM, TEM, BET, XPS, DRS, DLS, EDS, VSM, and PL. In addition, parameters affecting the photocatalytic degradation of tetracycline, including time, pH, TC initial concentration, and nanocomposite dose, were assessed. The findings showed that the degradation efficiency increases with increasing pH and catalyst dosage. Under optimum circumstances (pH = 9, nanocomposite dose of 0.5 g/L, and time 200 min), the process efficiency with concentration of 20 mg/L was 100%. The kinetics of the degradation rate of tetracycline obeyed the pseudo-first-order equation. In addition, the results show that after six consecutive cycles, the synthesized catalyst’s ability did not significantly reduce. The results of the mineralization tests revealed that the COD and TOC degradation of the synthetic solution of tetracycline with a concentration of 20 mg/L reached 87.25% and 73.06%, respectively, in the optimal reaction conditions. The scavenger experiments confirmed that OH plays the most crucial role in the decomposition process of tetracycline. Generally, the CuFe12O19/CuS/Xenon photocatalytic system can effectively degradation tetracycline from aqueous environments.
Abstract The anoxic-oxic (A/O) process is the most common biological method for removing nitrogen (N) from wastewater, but the antibiotic resistance profile of N-metabolizing microbes in A/O processes remains largely underexplored. Here we demonstrated a significant positive correlation between various types of N-metabolizing genes and antibiotic resistance genes (ARGs) in swine wastewater A/O processes across China. We assembled 180 high-quality genomes of dominant N-metabolizing microbes (12.6% of the total metagenome-assembled genomes), all harboring transcriptionally active ARGs. And Pseudomonas was identified as the primary N-metabolizing genus and major ARG host. Among 1110 culturable N-metabolizing isolates, 22.34% were Pseudomonas strains showing high N removal capacity and multi-antibiotic resistance. Moreover, plasmid-mediated ARG transfer further heightened resistance risks. Overall, these findings highlight a significant ARG risk among predominant N-metabolizing microbes in A/O treatment processes, underscoring the urgency of balancing N removal performance with resistance control in wastewater treatment processes.
Hossein Dehghanisanij, Mohammad Mehdi NakhjavaniMoghadam, Elahe Kanani
et al.
Abstract This study aimed to optimize water productivity and wheat yield in the rainfed wheat systems of the Honam plain, a critical region in the upper Karkheh River basin of Iran. In the first two years of research (2013–2014 and 2014–2015), the prevailing status of the region was investigated with regards to wheat yield and rainfall productivity under rainfed conditions. Thereafter, different management scenarios were defined and investigated to improve wheat yield, rainfall productivity, and water productivity. In the second year of research (2014–2015), the best management scenarios selected from the first two years were tested in some selected rainfed wheat farms in the Honam plain. The results showed that wheat biomass and grain yields from these best scenarios under rainfed and single irrigation (SI) conditions could be accurately predicted using the AquaCrop model. At the model validation stage, the RMSE was 0.16 for grain yield and 0.32 ton ha−1 for biomass and the NRMSE was 5 and 4%, respectively. Whether for grain yield or crop biomass, the coefficient of determination was about 0.86. The proposed scenarios for AquaCrop modelling were then trialed for rainfed wheat and showed better agronomic advantages than the traditional crop management practices. By applying a single irrigation in spring, the mean total water productivity (rainfall + irrigation) for wheat increased to 0.70 kg m−3, being 74% higher than that under rainfed conditions. The best management plan in the Honam plain was the combination of superior crop management with single irrigation in spring (60 mm) during the mid-flowering period, which increased the grain yield by 176% and rainfall productivity by 134%. The results from this management scenario were satisfactorily simulated by the AquaCrop model.
Peoples Water Data proves that a people-powered, open-data model can deliver reliable household water evidence at scale, driving equitable decisions, accountability, and safer water where it is most needed.
Abstract A comprehensive modeling framework utilizing hydrodynamic and sediment transport models (MIKE Hydro River, MIKE 21 FM, MIKE 21C) was applied to the 33.57 km stretch of the Banshadhara River from Banshadhara bridge, Gunupur to Kashinagar town. Daily discharge, water levels, and sediment data from 2013 to 2022 of Gunupur and Kashinagar gauge stations was used as model input, and calibration–validation of models. Additionally, satellite remote sensing data used for determined Manning’s roughness (n) values as well compared the model outputs. Calibration procedures ensured high accuracy, with MIKE Hydro River achieving 98.3% agreement between observed and simulated water levels. 1D sediment transport model showing significant variability in total bed load values ranging from 0.3 to 3.83 m^3/s. The Wilcock & Crowe gravel bed load formula accurately predicted bed load transport within the expected magnitude, albeit tending to overestimate transport, indicative of supply-limited conditions. 2D MIKE 21 FM simulations highlighted flow dynamics, with rapid flood arrival at Kashinagar and varying current speeds along the river, especially at bends. MIKE 21C, focused on the central portion, indicated erosion processes at river bends with high discharge. Sediment transport model accuracy was approximately 86.7%, capturing general trends despite occasional discrepancies. Over a 10-year simulation, erosion trends and potential river course shifting were observed, particularly near bends. The analysis provides valuable insights for river management, flood risk mitigation, water resource management, infrastructure planning, and environmental preservation in the Banshadhara River basin and similar environments.
Water supply for domestic and industrial purposes, Environmental sciences
Fateme Kargar, Akram Bemani, Mohammad Hoseein Sayadi
et al.
AbstractIntroduction: In recent years, the consumption of antibiotics has increased significantly all over the world due to health reasons related to human and animal health. The presence of these compounds even in very small amounts causes adverse effects on the environment and living organisms. The purpose of this study is to review different technologies for removing antibiotics from aquatic environments with an emphasis on new technologies.Methods: In this study, a review of library studies and articles in the Google Scholar, Science Direct, Springer, Scopus, Elsevier, and PubMed databases are used to obtain up-to-date and valid content.Findings: The results showed that membrane bioreactor systems among biological methods, despite the high cost due to the use of filtration, produce higher quality wastewater and are more effective than other biological treatment systems. Also, the investigation of biological methods showed that the efficiency of removing antibiotics varied from 40 to 95 percent. In recent years, advanced oxidation methods are a suitable option for removing antibiotics, which can completely remove antibiotics, and are also a cheap and environmentally friendly technology. In this study, several antibiotic pollutant purification technologies and their advantages and disadvantages were examined and it was shown that biological and physicochemical technologies, especially advanced oxidation processes, are capable of removing pollutants. They are medicinal. Although photo catalysts have been successfully developed for wastewater treatment on a laboratory scale, more extensive research is needed before the commercialization of these processes.
Abstract Membrane distillation (MD) is a versatile low-temperature separation process used for desalinating saline solutions with high salt rejection rates. Its current drawbacks include low flux and high energy demand. This study presents localized electrically induced heating using ceramic-coated metallic spacers to improve MD performance. We coated Ni-Cr spacers with MgO via electrolytic deposition and calcination, optimizing for a crack-free protective surface. Smaller wire diameter Ni-Cr exhibited superior heating. When a periodic current of 0.2 A cm−2 was applied, permeate flux increased by 15% although energy consumption only increased by 4%. Continuous supply of high-grade electrical energy added no further performance improvement as compared to periodic application. Our work highlights a spacer-based approach for localized Joule heating in MD systems without compromising membrane structure, while exploring coating systems to protect conductive spacers and optimizing schemes for electrically controlled performance.
Hafiz Usama Imad, Raza Ali Sanjrani, Sajid Hassan
et al.
Abstract Deltas are landforms that are formed at the regions where a river meets an ocean. When the freshwater of a river joins the saline water of an ocean, a transitional state of water is obtained. This zone of transition varies on the quantity of freshwater entering the ocean and the quality of both waters. Indus River is facing severe challenges due to climate change which has resulted in reduced water flow in the river. Consequently, seawater intrusion takes place which causes coastal erosion, stunted mangrove growth, and the narrowing of the river towards the seaside. The groundwater in the deltaic regions is unfit for drinking which forces an increased reliance on surface water. Sindh Province of Pakistan, particularly the coastal regions, suffers from severe water quality problems, affecting approximately 41% of the population. The population of this region is deprived of good-quality water for their daily use. This research aims to evaluate the physiochemical parameters of water in the Indus River to identify the zone of salinity inundation and determine safe zones for accessing surface water in the Indus Delta in the Sujawal district. Water samples were collected from Khobar Creek, ending in the Arabian Sea in the areas of Shah Bandar and Kharo Chan at an approximate interval of 5 km. These samples were analyzed for various physical and chemical properties. Properties were compared spatially to develop a relationship between distance and quality of water. Results indicate that the values of dissolved solids in collected samples are between 21,450 and 900 mg/L, pH was between 6.66 and 7.63, conductivity of the samples varied from 33,000 to 1385 µS/cm, acidity values were between 921.14 and 399.84 mg/L, hardness was in the range of 3654 to 192 mg/L, and the turbidity was in between 21 and57 NTU. It was revealed from the results that seawater is having a severe influence upto 15 km from the mouth of the creek in which not a single parameter except the pH was complying with WHO recommendations. In the interval of 15 to 30 km, some properties of water were complying with WHO recommendations, while some of them were deviating from the limits. It has been concluded that turbidity was improving while going toward the ocean, while Total Dissolved Solids, hardness, and chloride concentrations were increasing as water was getting closer to the sea while pH was found almost unaffected. The findings emphasize the need for addressing environmental degradation, social hardships, and economic challenges caused by these water quality issues. Ultimately, the quality of water is crucial for various activities specifically for drinking purposes, and its suitability for specific purposes is of utmost importance.
Water supply for domestic and industrial purposes, Environmental sciences
Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy
et al.
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.
In this paper, we introduce the R package mcmsupply which implements Bayesian hierarchical models for estimating and projecting modern contraceptive market supply shares over time. The package implements four model types. These models vary by the administration level of their outcome estimates (national or subnational estimates) and dataset type utilised in the estimation (multi-country or single-country contraceptive market supply datasets). mcmsupply contains a compilation of national and subnational level contraceptive source datasets, generated by IPUMS and Demographic and Health Survey microdata. We describe the functions that implement the models through practical examples. The annual estimates and projections with uncertainty of the contraceptive market supply, produced by mcmcsupply at a national and subnational level, are the first of their kind. These estimates and projections have diverse applications, including acting as an indicator of family planning market stability over time and being utilised in the calculation of estimates of modern contraceptive use.
Ankush Meshram, Markus Karch, Christian Haas
et al.
Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity mechanisms. The unavailability of network infrastructure information makes designing the security policies or configuring the cybersecurity countermeasures such as Network Intrusion Detection Systems (NIDS) challenging. An empirical solution is to self-learn the network infrastructure information of an industrial system from its monitored network traffic to make the network transparent for downstream analyses tasks such as anomaly detection. In this work, a Python-based industrial communication paradigm-aware framework, named PROFINET Operations Enumeration and Tracking (POET), that enumerates different industrial operations executed in a deterministic order of a PROFINET-based industrial system is reported. The operation-driving industrial network protocol frames are dissected for enumeration of the operations. For the requirements of capturing the transitions between industrial operations triggered by the communication events, the Finite State Machines (FSM) are modelled to enumerate the PROFINET operations of the device, connection and system. POET extracts the network information from network traffic to instantiate appropriate FSM models (Device, Connection or System) and track the industrial operations. It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device.
Ananta Mukherjee, Peeyush Kumar, Boling Yang
et al.
This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain confidential. Organizations within the supply chain are modeled as agents, each seeking to optimize their own objectives while interacting with others. As each organization's strategy is contingent on neighboring strategies, maintaining privacy of state and action-related information is crucial. To tackle this challenge, we propose a game-theoretic, privacy-preserving mechanism, utilizing a secure multi-party computation (MPC) framework in MARL settings. Our major contribution is the successful implementation of a secure MPC framework, SecFloat on EzPC, to solve this problem. However, simply implementing policy gradient methods such as MADDPG operations using SecFloat, while conceptually feasible, would be programmatically intractable. To overcome this hurdle, we devise a novel approach that breaks down the forward and backward pass of the neural network into elementary operations compatible with SecFloat , creating efficient and secure versions of the MADDPG algorithm. Furthermore, we present a learning mechanism that carries out floating point operations in a privacy-preserving manner, an important feature for successful learning in MARL framework. Experiments reveal that there is on average 68.19% less supply chain wastage in 2 PC compared to no data share, while also giving on average 42.27% better average cumulative revenue for each player. This work paves the way for practical, privacy-preserving MARL, promising significant improvements in secure computation within supply chain contexts and broadly.
Tijin H. G. Saji, José Manuel Vicent-Luna, Thijs J. H. Vlugt
et al.
Hydrogen peroxide plays a key role in many environmental and industrial chemical processes. We performed classical Molecular Dynamics and Continuous Fractional Component Monte Carlo simulations to calculate thermodynamic properties of H2O2 in aqueous solutions. The quality of the available force fields for H2O2 developed by Orabi & English, and by Cordeiro was systematically evaluated. To assess which water force field is suitable for predicting properties of H2O2 in aqueous solutions, four water force fields were used, namely the TIP3P, TIP4P/2005, TIP5P-E, and a modified TIP3P force field. While the computed densities of pure H2O2 in the temperature range of 253-353 K using the force field by Orabi & English are in excellent agreement with experimental results, the densities using the force field by Cordeiro are underestimated by 3%. The TIP4P/2005 force field in combination with the H2O2 force field developed by Orabi & English can predict the densities of H2O2 aqueous solution for the whole range of H2O2 mole fractions in very good agreement with experimental results. The TIP4P/2005 force field in combination with either of the H2O2 force fields can predict the viscosities of H2O2 aqueous solutions for the whole range of H2O2 mole fractions in good agreement with experimental results. The diffusion coefficients for H2O2 and water molecules using the TIP4P/2005 force field with either of the H2O2 force fields are almost constant for the whole range of H2O2 mole fractions. The Cordeiro force field for H2O2 in combination with either of the water force fields can predict the Henry coefficients of H2O2 in water in better agreement with experimental values than the force field by Orabi & English.
Abdul Basit Haneef, Faisalul Ameen K, Muhammad Uvais P K
The term microbe is an umbrella term used to denote organisms of micro size that mainly includes bacteria, fungus and protists. This paper is focused on collecting the informations and giving an overview on the microbes and its applications in treatment of contaminated water, and it ’s industrial application for the production and enhancement in industrial products. The methodology- searching the databases like “PubMed”, “Research gate” and “google scholar” using the keywords like “microbes of dairy industry”, “bacteria for leather industry” and “microbial application in industries”. The microbes were selected for listing by coinciding different articles retrieved from the databases by making the names of the microbes as the keywords. findings are- the microbes like and some others contribute to dairy industry, , and , and some lipase and protease yielding bacteria are used in food, medical leather industries respectively. Moreover, the application of and in treatment of contaminated water are also included in this paper. This review article is highly recommendable and helpful for various industries to find out the microbes they need to culture and treat contaminated water.
Abstract Rainwater harvesting (RWH) systems use runoff to meet water supply demand and may also benefit runoff reduction. We develop the Rainwater Analysis and Simulation Program (RASP) model to simulate an RWH system using storage volume, roof area, irrigated area, and water demand as inputs. A cost-benefit model of RWH was developed with subject to reliability target of water supply and runoff capture in each simulation. Near-optimal solutions were identified for each case using a constrained nonlinear programming algorithm. We apply the model to evaluate the RWH system in Bandung Area, West Java. Various types of buildings, ranging from residential houses, schools, dormitories, offices, apartments, condominiums, mosques, and hospitals, are studied to provide several optimal parameters for tank volume and number of users for various land-use scenarios. The resulting parameters are obtained through optimization to get minimal RWH’s capital costs and water purchase costs by applying several criteria related to the financial feasibility of the RWH system and by keeping the runoff coefficient not exceeding the pre-development value. The simulation results can be used as a guide for planners to consider the application of RWH in several types of buildings.