Sunnyside Drinking Water Project: Examining Chlorinated Disinfectant By-products, Resident Perceptions and Practices, and Municipal Responses in Securing Safe Drinking Water in the Town of Sunnyside NL \n
Bibliographic record
Abstract
The Sunnyside drinking water project is a university-community partnership between researchers at the Environmental Policy Institute- Grenfell Campus and municipal leaders of the Town of Sunnyside. This project explores the persistent drinking water-related challenges facing Sunnyside, eastern Newfoundland and Labrador (NL), primarily through the viewpoint of local residents. These challenges include: threats to source water; effective water treatment, disinfection and distribution, including dealing with high levels of disinfectant by-products \n(DBPs) as a result of their current disinfection system; and developing strategies for addressing these issues. Many rural municipalities in Newfoundland and Labrador face similar issues, which present a challenge to municipalities in ensuring residents access to clean, safe drinking water. \nThis report is structured through five main parts: \n1. Introduction: Community profile, project rationale and methodology; \n2. Sunnyside drinking water survey report: Analysis and discussion of the drinking water survey conducted in Sunnyside during the fall of 2014; \n3. Chlorinated disinfectant by-products and drinking water in Sunnyside: Literature review and discussion of the potential health impacts posed by DBPs in publically supplied water systems, policy responses and alternative technologies; \n4. Community Water Forum: Discussion of the results of the Community Water Forum presentations, which took place in Sunnyside in May 2015, and; \n5. Policy recommendations: Concluding thoughts and policy recommendations. \nOne issue that frequently arose over the course of the project is the lack of regulatory emphasis placed on federal water quality guidelines related to DBPs in NL. Arriving at an enforceable level, however, would require consideration of feasible technological solutions and/or significant increases in provincial investment into the water treatment systems of hundreds of small rural \ncommunities with high levels of DBPs. Until such time, increased public health education and support is needed for municipal leaders and the public at large. Thus, our main policy recommendation for the Government of NL is to develop and support a multi-faceted education campaign around the potential health impacts of DBPs, the role of chlorination in publicly supplied drinking water, and solutions and alternatives available in preventing and/or mitigating DBP impacts. Education (and research) is also needed on the environmental and social impacts of bottled water in the province, currently the most common drinking water source for Sunnyside residents. DBPs are a collective responsibility shared by residents, municipal and senior levels of government. Municipal governments share in the responsibility of educating their residents about the impacts of both DBPs and bottled water. Home-treatment technologies also offer an alternative for residents in reducing DBP exposure, particularly in the short-term. We are cautious in recommending this as a long-term solution due to issues of affordability and social equity, the need for proper maintenance, and difficulties finding appropriate systems. In general, \nwe argue that it is the responsibility of public institutions (i.e. the government) to ensure that the \nappropriate water systems are in place to deliver safe drinking water to residents.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".