Distribution, seasonality, and fluxes of dissolved organic matter in the Pearl River (Zhujiang) estuary, China
Bibliographic record
Abstract
Abstract. Dissolved organic carbon (DOC) concentration in the Pearl River estuary (PRE) of China was measured in May, August, and October 2015 and January 2016. Chromophoric and fluorescent dissolved organic matter (CDOM and FDOM) in the latter three seasons were characterized by absorption and fluorescence spectroscopy. CDOM and FDOM exhibited negligible seasonal variations, while DOC displayed a significant seasonality, with the average concentration being highest in May (156 µmol L−1), lowest in November (87 µmol L−1), and comparable between January (118 µmol L−1) and August (112 µmol L−1). Although DOC, CDOM, and FDOM in surface water were generally higher than in bottom water, the difference between the two layers was statistically insignificant. DOC showed little cross-estuary variations in all seasons, while CDOM and FDOM in January were higher on the west side of the estuary than on the east side. All three variables showed rapid drawdowns in the head region of the estuary (salinity <5); their dynamics in the main estuary were primarily controlled by conservative mixing, leading to linearly declining or relatively constant (for DOC in May and November only) contents with increasing salinity. The decrease in FDOM with salinity was 5 %–35 % faster than that of CDOM, which in turn was 2–3 times quicker than that of DOC. Salinity and CDOM absorption coefficients could serve as indicators of DOC in August and January. Freshwater endmembers in all seasons mainly contained fresh, protein-rich DOM of microbial origin, a large part of it likely being pollution-derived. Protein-like materials were preferentially consumed in the head region but the dominance of the protein signature was maintained throughout the estuary. Exports of DOC and CDOM (in terms of the absorption coefficient at 330 nm) into the South China Sea were estimated as 195×109 g and 266×109 m2 for the PRE and 362×109 g and 493×109 m2 for the entire Pearl River Delta. The PRE presents the lowest concentrations and export fluxes of DOC and CDOM among the world's major estuaries. DOM delivered from the PRE is, however, protein-rich and thus may enhance heterotrophs in the adjacent coastal waters. Overall, the PRE manifests lower abundance and smaller spatiotemporal variability of DOM than expected for a sizable estuary with a marked seasonality of river runoff due supposedly to the poorly forested watershed of the Pearl River, the rapid degradation of the pollution-derived DOM in the upper reach, and the short residence time of freshwater.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".