Nitrate Contamination in Brazilian Urban Aquifers: A Tenacious Problem
Bibliographic record
Abstract
This study follows the geochemistry of nitrogen in a Cretaceous and unconfined sedimentary aquifer in the city of Urânia (Brazil) over 20 years. Although the sewer network was built in the 1970s, the nitrate contamination problem (>45 mg/L-NO3−) persists to this day. The oldest urbanization areas located in the north of the city initially used cesspits for wastewater and currently present the highest nitrate concentrations (>120 mg/L-NO3−), with the plume reaching the deeper aquifer portions (up to 100 m). The contamination is not as dramatic in the south part of the city, where urbanization including installation of the sewage network with PVC pipes that are more resistant to leak than the old ceramic networks occurred after 1985. Based on the constructive well profiles, three hydrogeochemical zones were established: shallow (<20 m deep), with average nitrate of 63 mg/L-NO3−; intermediate (20–60 m), with 30 mg/L-NO3−; and deep (>60 m), with 17 mg/L-NO3−. The current total nitrate mass in the aquifer exceeds 731 kg-NO3−. Numerical flow (Modflow) and transport (MT3D) model scenarios support the hypothesis that the nitrate contamination is caused by substantial infiltration of nitrogen through the cesspits until the 1970s, but after the 1990s, leaks from the sewer network should be responsible for the maintenance of the recently observed high concentrations of nitrate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".