From Nuisance to Resource: Understanding Microbial Sources of Contamination in Urban Stormwater-Impacted Bodies of Water Intended for Water Reuse Activities
Bibliographic record
Abstract
Harvesting stormwater provides the province of Alberta, Canada, with a strategy to address the growing demands on water resources due to climate change and projected population growth. However, stormwater reuse poses a variety of challenges due to the potential of this source to be of low water quality, and to be contaminated with human and animal feces, and thus enteric bacterial pathogens including Campylobacter spp., Salmonella spp., and pathogenic E. coli. Storm events are correlated with an increased prevalence of disease, likely due to the mobilization of pathogens in the environment, leading to increased exposure and transmission risks. The contamination of water with human and/or animal excreta possesses significant risks to human health - albeit the risks associated with pathogens found in sewage are greater than those associated with animal wastes. Several recent studies have demonstrated that human feces are commonly found in urban stormwater systems, and therefore, urban stormwater risks associated with its use must be better understood in terms of contamination sources. Consequently, an objective of this thesis was to identify sources of contamination and select enteric bacterial pathogens, due to consequences associated with illness, present in various urban stormwater-impacted bodies of water in Calgary and Airdrie, Alberta, Canada. Throughout the 2017 sampling season (i.e., May – September) 700 samples were collected from various stormwater-impacted bodies of water in Alberta, Canada. Bacteriodes-specific markers were used to identify sources of contamination (i.e., Human, Dog, Muskrat, Ruminant, Birds, and Canadian Goose) and pathogens present (i.e., Arcobacter spp., Campylobacter spp., Salmonella spp., and shiga-toxin producing E. coli [STEC]) through qPCR. Culture-based methods for Campylobacter spp. and Arcobacter spp. were used on select stormwater samples to further determine the risks. Routine testing of fecal indicator bacteria (FIB) using culture-based methods (i.e., coliforms, thermotolerant coliforms, E. coli and Enterococcus spp.) and molecular-based methods (e.g., qPCR Enterococcus spp.) was done to assess overall microbial water quality and for comparing stormwater quality against existing water quality standards. This thesis research study will help to bring about a better understanding as to the risks from pathogens in the stormwater-impacted bodies of water in Alberta and aid in the development of governmental regulations for water reuse (e.g., baselines, treatment, and long-term municipal planning). Trends from this research show that the urban stormwater ponds face poor water quality, frequently contaminated with human fecal contamination, and the presence of pathogens. The USEPA guidelines for recreational water were violated in 20% of water samples for E. coli (i.e., running geometric mean) and in 17% of samples for Enterococcus spp. (i.e., STV). Microbial source tracking results reflected that there were two dominant sources of fecal pollution in the urban stormwater ponds: human and seagull (i.e., HF183 in 28% of samples and LeeSg in 9% of samples, respectively). The most dominant pathogen present was A. butzleri in 25% of stormwater samples. In order to determine the pathogenic nature of A. butzleri, a virulence gene screen was performed on nine putative virulence genes. The results from this additional testing indicate that the A. butzleri present in the urban stormwater ponds may be pathogenic.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".