Do soils and geology always protect groundwater from bacterial contamination?
Bibliographic record
Abstract
Photo courtesy of Emmanuelle Arnaud Countless ways exist for water-borne bacteria to die or get stuck in soil and geologic sediments. This is why scientists tend to assume that a thick layer of these materials will keep pathogens in surface-applied manures from seeping down into groundwater. University of Guelph geologist Emmanuelle Arnaud and her colleagues thought so, too—until they conducted the work that appears in the September–October issue of the Journal of Environmental Quality. To their surprise, they detected E. coli bacteria in groundwater one week after an application of liquid swine manure on a farm field, even though 12 m of soil and glacial sediments lay in between. So surprised were they, in fact—especially by the apparent speed of the microbes’ movement—that “we spent a lot of time thinking: Is this really plausible?” says Arnaud, who led the research with her student, Anna Best. “It requires further testing, but I think it's a fair hypothesis for future work—that this is indeed happening. Now we have to figure out exactly how it can happen and how prevalent it may be elsewhere.” Arnaud points out that soil scientists often study the fate of bacterial contaminants in the uppermost soil layers. People also routinely find bacteria in drinking water wells. But rarely has research examined what's occurring in the middle region—or the vadose zone—which extends from the land surface to the top of the groundwater table. “So, ours was a very basic hypothesis,” she says. “Will we find bacteria making their way down and what are some of the factors that affect their transport?” Those questions are becoming more critical as animal agriculture intensifies around the globe. But they gained especial importance in Ontario after an outbreak of E. coli O157:h7 and Campylobacter was linked to groundwater contamination by manure-borne pathogens. As part of a larger study of non-point source pollution of groundwater by both nitrate and E. coli, Arnaud and an interdisciplinary team carried out their work with funding from the Ontario Ministry of Agriculture and Rural Affairs. Although the scientists did detect low levels of E. coli in groundwater before and after their experiment, what they observed one week after the manure application was a spike in E. coli concentrations that lasted for about five weeks. Because the scientists didn't fingerprint the E. coli, they can't conclusively say that the bacteria cultured from the groundwater came from the manure. But the timing is persuasive. “To us, it's a really telling signal: We applied the manure, and a week later the bacteria showed up,” Arnaud says. “That's super-fast travel.” This paper is part of a JEQ special collection on Microbial Transport and Fate in the Subsurface The question then became: Why was it so fast? The geologic materials underlying the research site are fairly coarse-grained and permeable. So, “if bacteria were going to get through, they would get through in a place like this,” Arnaud says. At the same time, their calculations indicate that bacteria in bulk matrix water would take four to seven years to travel through the site's 12 m of sediments, even assuming the materials all fell into the upper range of estimated permeability. The team therefore suspects that preferential flow pathways are behind the quicker transport. The site isn't tile-drained, but previous research there uncovered movement of fecal bacteria through soil macropores. Below the soil, the researchers also found areas of coarse-grained materials that are likely connected to fractures in the bedrock. And there may be fractures in finer-grained deposits that would otherwise be expected to slow water flow and protect the underlying aquifer. If this type of geological information were collected more often, Arnaud thinks it could improve estimates of microbial transport rates through the subsurface and help refine the vulnerability maps that scientists use to determine the susceptibility of aquifers to contamination. At the very least, the matter deserves a closer look. “We can't take these results too far. The next step would be to do actual tracer studies and then check if we can replicate those fast [transport] times,” Arnaud says. “But it's nice to start to put numbers on this and to ask: Should we rethink how we perceive these thicker vadose zones?” Adapted from Arnaud, E., A. Best, B. Parker, R. Aravena, and K. Dunfield. 2015. Transport of Escherichia coli through a thick vadose zone. J. Environ. Qual. 44(5). Access the full article online at https://doi.org/10.2134/jeq2015.02.0067
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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) | 0.001 | 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 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".