Natural and anthropogenic factors controlling organic carbon storage in riverine wetlands along South Korea's four rivers.

This study explores carbon sequestration in South Korea's riverine wetlands, focusing on the four major rivers: Han, Yeongsan, Geum, and Nakdong. Field data from the Yeongsan River wetland, including 3D topography surveys, grainsize analyses, and loss-on-ignition measurements, were used to assess carbon stocks and their environmental drivers. The Yeongsan River was selected as a representative site due to its geomorphological, hydrological, and climatic similarities with the other three major rivers, which influence sediment transport and carbon dynamics.

Comprehensive evaluation of sodium dichloroisocyanurate (NaDCC) tablets as a novel solid-state alternative to conventional membrane cleaning agents in gravity-driven filtration systems.

Gravity-driven membrane (GDM) systems are increasingly recognized as sustainable and energy-efficient solutions for decentralized water treatment. However, membrane fouling, particularly by organic matter, remains a significant operational challenge, necessitating regular chemical cleaning to maintain performance. The present study was undertaken to investigate the cleaning efficiency of sodium dichloroisocyanurate (NaDCC) tablets, a novel solid-state alternative to conventional liquid cleaning agents such as sodium hypochlorite (NaOCl), sodium lauryl sulfate (SLS), acetic acid, and citric acid.

System Dynamics Modeling of Scale Formation in Membrane Distillation Systems for Seawater and RO Brine Treatment.

To overcome the limitations of traditional Reverse Osmosis (RO) desalination, Membrane Distillation (MD) has gained attention as an effective solution for improving the treatment of seawater and RO brine. Despite its potential, the formation of inorganic scales, particularly calcium sulfate (CaSO), continues to pose a major challenge. This research aims to explore the scaling mechanisms in MD systems through a combination of experimental analysis and dynamic modeling.

Evaluating Nanofiltration and Reverse Osmosis Membranes for Pharmaceutically Active Compounds Removal: A Solution Diffusion Model Approach.

Trace organic contaminants (TrOCs), including pharmaceutically active compounds (PhACs), present significant challenges for conventional water treatment processes and pose potential risks to environmental and human health. To address these issues, nanofiltration (NF) and reverse osmosis (RO) membrane technologies have gained attention. This study aims to evaluate the performance of NF and RO membranes in removing TrOCs from wastewater and develop a predictive model using the Solution Diffusion Model.

Evaluation of Concrete Compressive Strength Prediction Using the Maturity Method Incorporating Various Curing Temperatures and Binder Compositions.

This study aims to systematically analyze the effects of different curing temperatures, unit binder content, and the mixture ratios of ground granulated blast-furnace slag and fly ash based on ordinary Portland cement in binders on the development of concrete compressive strength. Particularly, the study evaluates strength characteristics by calculating the maturity equivalent to 28 days of curing at 20 °C. A model based on the relationship between maturity and strength was applied to predict the compressive strength, and the experimental data were analyzed to derive strength coefficients for each variable.

Comparison of adsorption-extraction workflows for improved measurements of viruses and bacteria in untreated wastewater.

The lack of standardized methods and large differences in virus concentration and extraction workflows have hampered severe acute respiratory syndrome (SARS-CoV-2) wastewater surveillance and data reporting practices. Numerous studies have shown that adsorption-extraction (AE) method holds promise, yet several uncertainties remain regarding the optimal AE workflow. Several procedural components that may influence the recovered concentrations of target DNA/RNA, including membrane types, homogenization instruments, speed and duration, and lysis buffer.

Machine learning-based prediction of adsorption capacity of metal-doped and undoped activated carbon: Assessing the role of metal doping.

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.

Mini-cores of activated carbon block simulate full-sized performance for removing organics and arsenate from drinking water.

Activated carbon block (ACB) filters are widely used in point-of-use (POU) drinking technology to remove tastes, odors, and organic compounds from drinking water, and when modified can even remove inorganic pollutants (e.g., arsenate, lead, copper).

Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete.

Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R).

Enhancing the potential application of food-waste biochar as a sustainable bio-solid fuel: Analysis of post-treatment and combustion behavior.

Food-waste biochar holds significant potential as a bio-solid fuel for achieving carbon neutrality; however, its high content of sodium (Na), potassium (K), calcium (Ca), chlorine (Cl), and nitrogen, inhibits its potential use. This study explored the effects of post-treatment with ascorbic, acetic, citric, and iminodiacetic acids on the properties of food-waste biochar and volatile ionic substances to establish a foundation for assessing both the environmental impact and practical use of food waste. Post-treatment with organic acids achieved 92% Cl-removal efficiency and induced deformation of the functional groups of food-waste biochar surfaces, leading to the re-adsorption of alkali and alkaline earth metals.

Rapid degradation of perfluorooctane sulfonic acid (PFOS) and perfluorononanoic acid (PFNA) through bimetallic catalyst of FeO/MnO and unravelling the effect of support SiO.

Perfluoroalkyl substances (PFAS) are emerging contaminants present in various water sources. Their bioaccumulation and potential toxicity necessitate proper treatment to ensure safe water quality. Although iron-based monometallic photocatalysts have been reported to exhibit rapid and efficient PFAS degradation, the impact of bimetallic photocatalysts is unknown.

Application of tryptophan-like fluorescence index to quantify the trace organic compounds removal in wastewater ozonation.

The effectiveness of ozonation, one of the techniques known for destroying organic contaminants from wastewater, depends on the composition of the wastewater matrix. The required ozone (O) dose is determined based on the target compounds during ozonation. Hydroxyl radicals are quantified using a probe compound.

Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving.

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted.

Development and Experimental Verification of Inorganic Electromagnetic Pulse Shielding Paint for Building Interiors Using Carbon-Based Materials.

The term electromagnetic pulse (EMP) generally refers to high-power electromagnetic waves and can be classified into EMPs caused by nuclear weapons, non-nuclear EMPs, and EMPs caused by natural phenomena. EMPs can cause catastrophic damage to any electronic device consisting of electromagnetic components, including communications devices and transportation. In this study, the shielding effectiveness of paint was evaluated depending on the type and content of carbon material and binder.

Removal of trivalent chromium ions in model contaminated groundwater using hexagonal boron nitride as an adsorbent.

The feasibility of using hexagonal boron nitride (h-BN) to treat heavy metal Cr(III) from model contaminated groundwater was evaluated in this study by adsorption experiments and characterizations. To the best of our knowledge, this study is the first attempt to conduct the adsorption of Cr(III) by h-BN under various experimental conditions such as exposure time, ratio of adsorbates and adsorbents, solution pH, background ions with different ionic strength, and the presence of humic acids (HA) in model contaminated groundwater. The optimized h-BN showed excellent maximum adsorption capacity (i.

Shear Wave Velocity Determination of a Complex Field Site Using Improved Nondestructive SASW Testing.

The nondestructive spectral analysis of surface waves (SASW) technique determines the shear wave velocities along the wide wavelength range using Rayleigh-type surface waves that propagate along pairs of receivers on the surface. The typical configuration of source-receivers consists of a vertical source and three vertical receivers arranged in a linear array. While this approach allows for effective site characterization, laterally variable sites are often challenging to characterize.

Laboratory Evaluation of Storage Stability for Asphalt Binder Modified with Crumb Rubber and Styrene-Isoprene-Styrene Depending on Evaluation Factors and Blending Condition.

Modified asphalt binders are still considered important in asphalt pavement. However, the comprehensive use of various modifiers is limited due to storage stability issues. Moreover, there is a scarcity of detailed analyses regarding the degree of separation for asphalt binders among each method despite the utilization of various methods to assess the storage stability of binders.

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration.

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles.

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs).

Quantifying regional low flows under data scarce conditions.

Estimating low flow quantiles is essential for regulating minimum flow requirements and ensuring water availability, ecological health, and overall system sustainability. This study aims to quantify regional low flows by analyzing low-flow time series in data-limited environments. We utilized annual minimum 7-day instantaneous streamflow data collected from gaging stations.