Fecal Contamination in the Surface Waters of a Rural- and an Urban-Source Watershed
Bibliographic record
Abstract
Surface waters are commonly used as source water for drinking water and irrigation. Knowledge of sources of fecal pollution in source watersheds benefits the design of effective source water protection plans. This study analyzed the relationships between enteric pathogens (Escherichia coli O157:H7, Salmonella spp., and Campylobacter spp. [C. jejuni, C. lari and C. coli]), water quality (turbidity, temperature, and E. coli), and human and ruminant–cow Bacteroidales and mitochondrial DNA (mtDNA)-based fecal source tracking (FST) markers in two source watersheds. Water samples (n = 329) were collected at 10 sites (five in each watershed) over 18 mo. The human Bacteroidales marker (HF183) occurred in 9 to 10% of the water samples at nine sampling sites; while a forested site in the urban watershed tested negative. Ruminant–cow Bacteroidales markers (BacR and CowM2) only appeared in the rural watershed (6%). The mtDNA markers (HcytB and AcytB) showed the same pattern but were less sensitive due to lower fecal concentrations. Higher prevalences (P < 0.05) of Campylobacter spp. (41 vs. 16% for the rural and urban watershed, respectively) and E. coli O157:H7 (12 vs. 3%) were observed in the rural watershed, while Salmonella spp. levels were comparable (23–28%). Densities of E. coli ≥100 colony-forming units (CFU) 100 mL−1 increased the odds (P < 0.05) of detecting the enteric bacterial pathogens. The water turbidity levels (nephelometric turbidity units [NTU] ≥ 1.0) similarly predicted (P < 0.05) pathogen presence. Storm events increased (P < 0.01) pathogen and fecal marker concentrations in the waterways. The employment of multiple FST methods suggested failing onsite wastewater systems contribute to human fecal pollution in both watersheds. Core Ideas Human marker (HF183) detection revealed human fecal contamination in both watersheds. Ruminant markers (BacR and CowM2) only occurred in the rural watershed (6%). The mtDNA markers were less sensitive due to lower initial fecal copy numbers. E. coli ≥100 CFU 100 mL−1 or turbidity ≥1 NTU increased the odds of pathogen presence. Storm events increased detection of pathogens and MST markers in the rural watershed.
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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 | 0.004 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".