Wastewater Flow and Pathogen Transport from At‐Grade Line Sources to Shallow Groundwater
Bibliographic record
Abstract
On‐site wastewater treatment systems are commonly used in sparsely populated areas where capital‐intensive centralized wastewater treatment facilities are not feasible. The primary objective of this work was to investigate vadose zone and groundwater transport of a bromide (Br) tracer and naturally occurring Escherichia coli applied to the soil surface in secondarily treated wastewater at a public rest stop in central Alberta, Canada, with seasonally fluctuating water table (between 0.2 and 1.5 m) over a 1‐yr period. A transect within the wastewater application field was instrumented with 10 nests of three monitoring wells (N = 30). We found that travel times for Br and E. coli were most likely related to vadose zone thickness under wastewater application lines, with Br and E. coli initially detected in monitoring wells within 4 d at locations where the vadose zone was 0.2 to 0.4 m thick. When the vadose zone thickness increased to ≥0.9 m, however, E. coli levels in the monitoring wells decreased dramatically despite continued high surface application of E. coli. The observed travel times were consistent with those calculated assuming piston flow. Therefore, the risk of groundwater contamination from wastewater at this site is greatest during times when high wastewater applications (high facility use) and shallow water table conditions coincide. We recommend that detailed knowledge of vadose zone and groundwater hydrology be used to guide the design of on‐site wastewater treatment systems and also to assess the probability of human exposure to E. coli and other pathogens that are transported to groundwater. Core Ideas Transport under temporally variable boundary (surface flux and water table elevation) was studied. Optimum vadose zone thickness for effective mitigation of E. coli transport was identified. Requirements for assessing risks of on‐site wastewater treatment systems were investigated.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".