Assessing Point of Use Water Treatment Technologies under Real-Use Conditions: The Field Challenge Test Technique
Bibliographic record
Abstract
Point of use water treatment (POUWT) technologies can be the final and sometimes only barrier against waterborne illness in contexts where there is insufficient access to a safely managed on-premises water supply. Microbiological effectiveness of POUWT devices is currently evaluated under controlled laboratory conditions using water spiked with virus, bacteria, and/or protozoa or their surrogates to measure log10 reduction values or LRVs, in a process called challenge testing. However, laboratory-based POUWT challenge tests do not adequately assess microbe reduction under real-use conditions, thus omitting variations relative to factors such as user behaviours and water quality. The overall aim of this work was to develop a method with which POUWT technologies can be evaluated under real-use conditions, which we refer to as the field challenge test technique. To this end, we validated the use of probiotic Escherichia coli (E. coli Nissle, EcN) and S. cerevisiae (baker’s yeast) as field-appropriate, food-safe surrogates for pathogenic bacteria and protozoans, respectively. We implemented the innovative field challenge test technique using validated EcN and S. cerevisiae surrogates. In summer 2021, 144 one-on-one surveys were conducted of backcountry campers in the Juan de Fuca provincial park in British Columbia, Canada. The field challenge test consisted of spiking a 1 L sample of water with EcN and S. cerevisiae and requesting participants to treat the spiked water as they normally would, using their own POUWT device. Post-treatment water samples were enumerated in comparison to the original spike to calculate LRVs. Using field challenge testing, we were able to ascertain the performance of POUWT methods under real-use conditions. Our field-based LRVs were generally lower than claimed by POUWT device manufacturers for the bacterial microbe class, but for the protozoan microbe class, LRVs were similar to those claimed by manufacturers. Using the framework of quantitative microbial risk assessment (QMRA), we quantified and compared health risk estimates when using laboratory-gathered vs field-gathered LRVs of POUWT devices. Health risks attributable to the bacterial pathogen class were higher based on field-gathered LRVs (i.e., obtained by field challenge testing) in comparison to corresponding manufacturer-claimed LRVs.For the protozoan pathogen class, calculated health risks were similar due to homogeneity between field-obtained and manufacturer-claimed LRVs. The field challenge technique and corresponding QMRA analysis have numerous implications, including validation of POUWT sanitary inspection criteria, quantifying health impacts of contextual factors, or to inform technology selection.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".