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Record W2765522807 · doi:10.47339/ephj.2017.81

Follow-up study of private well users affected by groundwater arsenic in the Surrey-Langley area

2017· article· en· W2765522807 on OpenAlexvenueaboutno aff
Douglas H. Gordon, Environmental Health BCIT School of Health Sciences, Marc Zubel, Blair Choquette, Helen Heacock

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsArsenicArsenic contamination of groundwaterGroundwaterToxicantEnvironmental healthContaminated groundwaterEnvironmental scienceWater wellArsenic toxicityToxicologyWater qualityArsenic poisoningContaminationMedicineEcologyEngineeringChemistryToxicityBiologyEnvironmental remediationInternal medicine

Abstract

fetched live from OpenAlex


 Background: Arsenic is a potent toxicant and Group 1 human carcinogen which occurs naturally in certain sediments and can contaminate groundwater. In the Surrey-Langley area of British Columbia, a 2007 study of private wells found that 43% of wells tested contained arsenic concentrations above the maximum acceptable concentration (MAC) prescribed in Health Canada’s Guidelines for Canadian Drinking Water Quality. The well owners who participated in the 2007 study were informed of the results and of effective treatment methods that would remove the arsenic contamination. This is a follow-up study that surveyed affected well users approximately 10 years later in order to identify whether the well users had subsequently made any water treatment or behavioral changes to improve the quality of their drinking water, and also to determine whether knowledge translation of the arsenic risk had been effective. Methodology: This study contacted and enrolled private well users who were living at properties which had previously been included in the 2007 study and, in 2007, were found to have arsenic levels above the MAC in the groundwater. Respondents who agreed to participate completed a questionnaire designed to identify what treatment methods or behavioral methods they use to mitigate the risk posed by arsenic contamination. Pre-treatment and post-treatment samples of their drinking water were collected and the arsenic concentrations were analyzed. The effectiveness of treatment devices for arsenic removal was evaluated. The groundwater arsenic concentrations from approximately 10 years apart were compared to identify if arsenic levels had changed. Results: Of the 42 properties that participated in the 2007 study and had groundwater arsenic levels above the MAC, 17 participated in this follow-up study. 14 of the participants also took part in the 2007 study 10 years ago. 79% of participants had not known prior to taking part in the 2007 study that their drinking water contained arsenic levels above the MAC. All 79% then either installed reverse osmosis treatment devices to remove arsenic from their drinking water, or switched to using bottled water for drinking. This indicates that knowledge translation of the health risk was effective. Of the 8 properties using treatment devices rather than bottled water, to mitigate the arsenic risk, 2/8 (25%) were ineffective at reducing arsenic. In addition, arsenic groundwater concentrations were not found to have changed significantly in 10 years (p = 0.11). Conclusion: Participation in the 2007 study was viewed as useful and informative by participants. Knowledge translation of the health risk and the need for risk mitigation was effective, but 25% of treatment devices were found to be ineffective at removing arsenic from drinking water. These results suggests that further knowledge translation of the need for routine testing for arsenic in post-treated drinking water may be beneficial to affected private well users.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.265
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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