E. coli Levels Associated with Source Waters and Household Handling Practices of Potable Water in Peri-urban Phnom Penh, Cambodia
Bibliographic record
Abstract
Cambodia has made progress towards addressing Sustainable Development Goal 6.1. “By 2030, achieve universal and equitable access to safe and affordable drinking water for all”, but challenges remain in fully realizing this target. We begin this paper by reviewing current country-wide access to safe and affordable water and subsequently report on the results of water sampling done for E. coli in a peri-urban area north of Phnom Penh that was conducted between 2018 and 2020. The sampling examined E. coli levels in source waters, including rivers, ponds, a large lake/wetland, wells, rainwater harvesting systems, piped water, and bottled water. Sampling from household storage containers and in-home drinking cups also was done to assess the effects that handling practices might have on exposure to E. coli. We show that country-wide, as well as in the peri-urban study area, there has been increased access to piped water. Piped water and commercially-available bottled water (0.5–1.5 L PET bottles) had the lowest E. coli levels in our study area, although such bottled water is not an affordable alternative for many peri-urban families. Surface pond water and the Tonle Sap River contained the highest E. coli levels and would pose the greatest risk associated with direct consumption. Handling practices may impact drinking water quality, as a significant difference (p=0.2) was found in E. coli levels between samples taken from commercially-available 0.5–1.5 L PET bottles and from household cups into which the bottled water was poured. There also was a significant difference (p<0.05) in E. coli levels between piped water sampled directly from the tap and piped water stored in bulk household containers. The geometric mean concentration of E. coli in large, covered, traditional outdoor storage jars used for rainwater harvesting was nearly 10 times lower than the same type of jars that were not covered, although due to the small sample size and variability in the data, the difference was not significant (p=0.5). Despite the increasing availability of piped water service in the study area, we found a diversity of water source practices, including use of rainwater harvesting, surface water, 20 L bottled water, and wells. These source waters can be safe, but must be routinely monitored. The study illustrates the advantages of field-based testing for effective screening of E. coli in peri-urban areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".