Revolutionizing cesium monitoring in seawater through electrochemical voltammetry and machine learning.

Monitoring radioactive cesium ions (Cs) in seawater is vital for environmental safety but remains challenging due to limitations in the accessibility, stability, and selectivity of traditional methods. This study presents an innovative approach that combines electrochemical voltammetry using nickel hexacyanoferrate (NiHCF) thin-film electrode with machine learning (ML) to enable accurate and portable detection of Cs. Optimizing the fabrication of NiHCF thin-film electrodes enabled the development of a robust sensor that generates cyclic voltammograms (CVs) sensitive to Cs⁺ concentrations as low as 1 ppb in synthetic seawater and 10 ppb in real seawater, with subtle changes in CV patterns caused by trace Cs⁺ effectively identified and analyzed using ML.

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl, pH, and HCO). We applied nine different decision tree-based models and evaluated their prediction performances.

Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory.

Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals.

Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data.

The distribution coefficient (K) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, K depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict K values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea.

Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.

Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming.

Autonomous real-time control for membrane capacitive deionization.

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy.

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters.

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O) and hydroxyl radical (OH) (i.e., ∫[O]dt and ∫[OH]dt).

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data.

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations.

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models.

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships.

Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater.

Assessing the cyanobacteria disinfection in sewage and its compliance with international-standards requires determining the concentration and viability, which can be achieve using Imaging Flow Cytometry device called FlowCAM. The objective is to thoroughly investigate the sonolytic morphological changes and disinfection-performance towards toxic cyanobacteria existing in sewage using the FlowCAM. After optimizing the process conditions, over 80% decline in cyanobacterial cell counts was observed, accompanied by an additional 10-15% of cells exhibiting injuries, as confirmed through morphological investigation.

Predicting the distribution coefficient of cesium in solid phase groups using machine learning.

The migration and retention of radioactive contaminants such as Cesium (Cs) in various environmental media pose significant long-term storage challenges for nuclear waste. The distribution coefficient (K) is a critical parameter for assessing the mobility of radioactive contaminants and is influenced by various environmental conditions. This study presents machine-learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict the K values for Cs in solid phase groups.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry.

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS).

Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data.

Biological early warning system (BEWS) has been globally used for surface water quality monitoring. Despite its extensive use, BEWS has exhibited limitations, including difficulties in biological interpretation and low alarm reproducibility. This study addressed these issues by applying machine learning (ML) models to eight years of in-situ BEWS data for Daphnia magna.

Long short-term memory models of water quality in inland water environments.

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data.

Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency.

Climate warming is linked to earlier onset and extended duration of cyanobacterial blooms in temperate rivers. This causes an unpredictable extent of harm to the functioning of the ecosystem and public health. We used Microcystis spp.

Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal.

Photocatalytic reactions with semiconductor-based photocatalysts have been investigated extensively for application to wastewater treatment, especially dye degradation, yet the interactions between different process parameters have rarely been reported due to their complicated reaction mechanisms. Hence, this study aims to discern the impact of each factor, and each interaction between multiple factors on reaction rate constant (k) using a decision tree model. The dyes selected as target pollutants were indigo and malachite green, and 5 different semiconductor-based photocatalysts with 17 different compositions were tested, which generated 34 input features and 1527 data points.

Automatic classification of microplastics and natural organic matter mixtures using a deep learning model.

Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators.

Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental driving variables.

Early warning systems for harmful cyanobacterial blooms (HCBs) that enable precautional control measures within water bodies and in water works are largely based on inferential time-series modelling. Among deep learning techniques, convolutional neural networks (CNNs) are widely applied for recognition of pictorial, acoustic and thermal images. Time-frequency images of environmental drivers generated by wavelets may provide crucial signals for modelling of HCBs to be recognized by CNNs.

Molecular-level chemical composition of aerosol and its potential source tracking at Antarctic Peninsula.

Marine organic aerosols play crucial roles in global climatic systems. However, their chemical properties and relationships with various potential organic sources still need clarification. This study employed high-resolution mass spectrometry to investigate the identity, origin, and transportation of organic aerosols in pristine Antarctic environments (King Sejong Station; 62.

Ocean-atmosphere interactions: Different organic components across Pacific and Southern Oceans.

Sea spray aerosol (SSA) particles strongly influence clouds and climate but the potential impact of ocean microbiota on SSA fluxes is still a matter of active research. Here-by means of in situ ship-borne measurements-we explore simultaneously molecular-level chemical properties of organic matter (OM) in oceans, sea ice, and the ambient PM aerosols along a transect of 15,000 km from the western Pacific Ocean (36°13'N) to the Southern Ocean (75°15'S). By means of orbitrap mass spectrometry and optical characteristics, lignin-like material (24 ± 5 %) and humic material (57 ± 8 %) were found to dominate the pelagic Pacific Ocean surface, while intermediate conditions were observed in the Pacific-Southern Ocean waters.

Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning.

Urban rainfall events can lead to the runoff of pollutants, including industrial, pesticide, and pharmaceutical chemicals. Transporting micropollutants (MPs) into water systems can harm both human health and aquatic species. Therefore, it is necessary to investigate the dynamics of MPs during rainfall events.