A mass balance approach to investigating geochemical controls on secondary water quality impacts at a crude oil spill site near Bemidji, MN
Bibliographic record
Abstract
Secondary water quality impacts can result from a broad range of coupled reactions triggered by primary groundwater contaminants. Data from a crude-oil spill research site near Bemidji, MN provide an ideal test case for investigating the complex interactions controlling secondary impacts, including depleted dissolved oxygen and elevated organic carbon, inorganic carbon, CH4, Mn, Fe, and other dissolved ions. To better understand these secondary impacts, this study began with an extensive data compilation of various data types, comprising aqueous, sediment, gas, and oil phases, covering a 260m cross-sectional domain over 30years. Mass balance calculations are used to quantify pathways that control secondary components, by using the data to constrain the sources and sinks for the important redox processes. The results show that oil constituents other than BTEX (benzene, toluene, ethylbenzene, o-, m- and p-xylenes), including n-alkanes and other aromatic compounds, play significant roles in plume evolution and secondary water quality impacts. The analysis underscores previous results on the importance of non-aqueous phases. Over 99.9% of the Fe(2+) plume is attenuated by immobilization on sediments as Fe(II) and 85-95% of the carbon biodegradation products are outgassed. Gaps identified in carbon and Fe mass balances and in pH buffering mechanisms are used to formulate a new conceptual model. This new model includes direct out-gassing of CH4 and CO2 from organic carbon biodegradation, dissolution of directly produced CO2, and sorption with H(+) exchange to improve pH buffering. The identification of these mechanisms extends understanding of natural attenuation of potential secondary impacts at enhanced reductive dechlorination sites, particularly for reduced Fe plumes, produced CH4, and pH perturbations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".