Enhancing machine learning models for total organic carbon prediction by integrating geospatial parameters in river watersheds.

This study utilizes machine learning (ML) algorithms to develop a robust total organic carbon (TOC) prediction model for river waters in the Geumho River sub-basins, South Korea, considering both non-rain and rain events. The model incorporates geospatial parameters such as land use, slope, flow rate, and basic water quality metrics including biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and suspended solids (SS). A key aspect of this research is examining how land use information enhances the model's predictive accuracy. We compared two ML algorithms-extreme gradient boosting (XGBoost) and deep neural networks (DNN)-with a traditional multiple linear regression (MLR) approach. XGBoost outperformed the others, achieving an R2 value between 0.61 and 0.68 in the test dataset and demonstrating significant improvement during rain events with an R2 of 0.77 when including land use data. In contrast, this enhancement was not observed with the MLR model. Feature importance analysis using Shapley values highlighted COD as the primary predictor for non-rain events, while during rain events, COD, TP, TN, SS and agricultural land collectively influenced TOC levels. This study significantly advances understanding of TOC variability across different land use scenarios in river systems and underscores the importance of integrating geospatial and water quality parameters to enhance TOC prediction, particularly during rain events. This methodology provides a valuable framework for developing river management strategies and monitoring long-term TOC trends, especially in scenarios with gaps in essential monitoring data.

Photo-induced leaching behaviors and biodegradability of dissolved organic matter from microplastics and terrestrial-sourced particles.

Recent studies have increasingly focused on the occurrence of plastic leachate and its impacts on aquatic ecosystems. Nonetheless, the environmental fate of this leachate in the presence of abundant natural organic matter (NOM)-a typical scenario in environments contaminated with plastics-remains underexplored. This study investigates the photo-induced leaching behaviors of dissolved organic matter (DOM) from terrestrial-sourced particles (forest soil and leaf litter) and microplastics (MPs), specifically polystyrene (PS) and polyvinyl chloride (PVC), over a two-week period. We also examined the biodegradability and spectroscopic characteristics of the leached DOM from both sources. Our results reveal that DOM from microplastics (MP-DOM) demonstrates more persistent leaching behavior compared to terrestrial-derived DOM, even with lesser quantities per unit of organic carbon. UV irradiation was found to enhance DOM leaching across all particle types. However, the photo-induced leaching behaviors of fluorescent components varied with the particle type. The MP group exhibited a broader range and higher biodegradability (ranging from 19.7% to 61.6%) compared to the terrestrial-sourced particles (ranging from 3.7% to 16.5%). DOM leached under UV irradiation consistently showed higher biodegradability than that under dark conditions. Furthermore, several fluorescence characteristics of DOM, such as the protein/phenol-like component (%C2), terrestrial humic-like component (%C3), and humification index (HIX)-traditionally used to indicate the biodegradability of natural organic matter-were also effective in assessing MP-DOM (with correlation coefficients R2 = 0.6055 (p = 0.003), R2 = 0.5389 (p = 0.007), and R2 = 0.4640 (p = 0.015), respectively). This study provides new insights into the potential differences in environmental fate between MP-DOM and NOM in aquatic environments heavily contaminated with MPs.

Changes in the Dissolved Organic Matter Characteristics Released from Sediment According to Precipitation in the Namhan River with Weirs: A Laboratory Experiment.

In this study, changes in the properties of dissolved organic matter (DOM) released from sediments into water layers were investigated. To analyze the spatial and temporal variation in dissolved organic carbon (DOC), sediment and bottom water samples were collected upstream of the Gangcheon, Yeoju, and Ipo weirs of the Namhan River during the rainy and non-rainy seasons. The initial DOC was correlated with precipitation (R2 = 0.295, p = 0.034) and residence time (R2 = 0.275, p = 0.040). The change in the bottom water DOC concentration resulted from the DOC released from the sediments, which may cause water quality issues in the bottom water. The fluorescence analysis revealed that the DOM contained higher levels of hydrophilic and low-molecular-weight (LMW) organic matter in the non-rainy season and higher levels of hydrophobic and high-molecular-weight (HMW) organic matter in the rainy season. Since the Namhan River is the main resource of drinking water for the Seoul metropolitan area, our results can help to optimize the drinking water treatment process by reflecting the DOM characteristics that vary with the seasons. Furthermore, the statistical analysis confirmed that the nutrient content of pore-water and sediment can be used to estimate the DOM release rate from the sediment to the water layer. The results of this study provide a better understanding of DOM movement in aquatic ecosystems and the influences of rainfall on the water quality of the surface waterbody.