Monthly Year-Round Characteristics and Ocean Export of Riverine Organic Matter: Relationship with Microplastics
Bibliographic record
Abstract
Rivers play a crucial role in transporting carbon from terrestrial watersheds to oceans. Understanding the quantity and characteristics of riverine organic matter discharged into the ocean is essential for predicting changes in marine and global organic carbon cycles. Riverine organic matter, comprising both allochthonous and autochthonous fractions, is influenced by shifts in watershed sources driven by climate change, as well as socio-economic transformations that affect its production and characteristics. Plastics, a significant source of allochthonous organic carbon, could contribute substantially to rivers, and microplastics (MPs) generated from plastic degradation may alter carbon cycling within river systems through interactions with other organic materials. Despite their importance, MP exports from rivers to oceans remain poorly quantified and rarely measured in terms of carbon mass, with even less understanding of their interactions with other forms of riverine organic matter. To address this gap, we have investigated the five major rivers in South Korea, accounting for 90% of the freshwater discharge. Here, we present the preliminary results for three major rivers, representing Korean fluvial system connected to the Yellow Sea. Both particulate and dissolved organic matters were characterized in quantitative and qualitative terms by monthly sampling at each river-mouth station, including particulate organic carbon (POC), chlorophyll-a, transparent exopolymer particles (TEP), Coomassie stainable particles (CSP), and MPs for particulate forms and dissolved organic carbon (DOC) and dissolved organic matter (CDOM and FDOM) of dissolved forms. Considering the spatiotemporal variability of organic matters and MPs, river samples were collected three times a day at 2-3 hour intervals and in each sampling by compositing the samples taken from horizontally three cross-sectioned sites and vertically 3–5 water column layers per site. This study aims to quantify the monthly loads of total organic carbon (POC and DOC) entering the ocean from these rivers, assess the relationships between various forms of organic matter, and determine the relative contribution of MP-derived organic carbon to total organic carbon. Our results are expected to provide valuable insights into the ocean load and their inter-relationships of various organic matter forms originated from fluvial system, and their potential impact on the marine carbon cycle.Acknowledgement: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2024-00356940).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".