Interactions between lake dissolved organic matter and heavy metals: implications for ecological risk assessment
Bibliographic record
Abstract
Dissolved organic matter (DOM) plays a key role in influencing the environmental behavior of heavy metals in lake ecosystems. Through mechanisms such as complexation, ion exchange, and physical adsorption, DOM regulates the speciation, transport, and bioavailability of heavy metals, thereby shaping their ecological risks and fate. Here, we provide a comprehensive review of the sources and molecular composition of lake DOM, with particular attention to humic substances, proteins, and polysaccharides, and highlight the importance of functional groups such as carboxyl group and phenolic hydroxyl group in metal binding. The mechanisms of DOM-heavy metal interactions are discussed in detail. These include σ-ligand bonding, which relies on the donation of lone pair electrons from O/N-containing functional groups to metal orbitals. Also covered are π–d electron interactions, often initiated by photoexcitation of aromatic moieties in DOM to facilitate electron transfer, and multi-site adsorption, a process governed by the combined effects of electrostatic attraction, hydrogen bonding, and the porous structure of DOM. Additionally, the effects of environmental factors (temperature, pH, and light) and biological factors (microbial activity and aquatic plant decomposition) on DOM-heavy metal dynamics are examined. Although substantial progress has been made, key challenges remain in understanding the microscale mechanisms, capturing real-time changes in natural waters, and assessing long-term ecological impacts. Future research should prioritize multi-scale approaches. This entails employing advanced techniques like Fourier-transform ion cyclotron resonance mass spectrometry to elucidate molecular mechanisms, while also advancing in situ monitoring technologies and establishing long-term observation networks to resolve real-time dynamics and assess cumulative ecological impacts. This review provides a theoretical basis for understanding DOM-heavy metal interactions and supports future efforts in ecological risk assessment and the sustainable management of lake environments.
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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) | 0.004 | 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".