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Record W7133034590

Investigation on the effects of design and operational variables on the efficacy of biosand filters

2014· article· en· W7133034590 on OpenAlexaboutno aff

Bibliographic record

VenueLehigh Preserve · 2014
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Quality (philosophy)Water treatmentDeveloping countryWater quality
DOInot available

Abstract

fetched live from OpenAlex

The following paper reports on the efforts made to assist in the overall implementation of one specific household water treatment (HWT) for improving water quality for people in developing countries, biosand filters (BSFs). It is recognized that BSFs are not applicable for every situation or community. When BSFs were first developed for household applications, the minimum sand bed depth was determined to be 50 cm, based on existing Canadian regulations for water treatment through large-scale, high-capacity sand filters. We questioned this basic assumption, and investigated whether smaller, lighter, and cheaper BSFs (with a shorter sand bed depth) are as effective as the traditional large, concrete filter. The overall project objective was to assess the efficacy, effectiveness, and acceptability of a smaller biosand filter, both in the laboratory and in the field, with the overall goal of demonstrating successful performance and acceptability of the smaller BSFs to reduce implementation costs, allowing more households to be reached. Hopefully, the results presented herein will provide additional insight and quantified data on the operational considerations and removal capabilities of various types of full-scale BSFs to aid in the justification and support for future implementation efforts. In section one, the background and scope of the problem of water access and quality in developing countries is reviewed, including a brief overview of several household water treatment technologies that are currently used. The introduction, section two, provides a detailed description of the biosand filter and the experimental setup that was the focus of the laboratory research. Sections three through six contain the manuscript style descriptions of the four studies conducted, including the results and conclusions. The last and final section, section seven, is a summary of conclusions including findings and lessons learned gained in from the execution and evaluation of this research. The research conducted and reported herein tested the general hypothesis that biosand filtration can be effective on a smaller, cheaper scale than currently practiced with the concrete BSF. In particular, we investigated how the efficacy of the CAWST BSF compared to smaller bucket-sized BSFs with respect to removal of turbidity, total coliforms, E. coli, MS2 coliphage, and Cryptosporidium parvum oocysts from raw drinking water supplies. Specifically, the research attempted to answer the following questions regarding BSF performance: (1) Are the removal efficiencies of smaller BSFs significantly different from the concrete BSF? (2) Is removal efficiency impacted by the turbidity of the source water? (3) To what extent do slight disturbances affect the performance of the bucket BSFs? (4) Can the BSF be modified (i.e., by the addition of rusty nails in the diffuser basin) to significantly improve the removal of viruses in the BSF? (5) How is the removal efficiency impacted by the length of the pause period? (6) If smaller sized BSFs can offer an acceptable level of removal (based on the laboratory results), how will a smaller BSF perform in the field and will it be acceptable to end-users?Four separate studies (Sections 3.0 - 7.0 and summarized below), were conducted to answer the questions outlined above. Effect of sand bed depth and media age on bacteria and turbidity removal The main objective of the first study was to build several full-scale BSFs, simulate real-world usage conditions, and assess the long-term efficacy (9-month study period) for particulate and bacteria removal. Four replicates of three different filter designs were built: the traditional concrete BSF, and two scaled-down versions that use a 5-gal and 2-gal bucket, respectively, as the casing material. The major difference among the three BSF designs was the depth of the sand layer: approximately 54, 15, and 10 cm for the concrete, 5-gal bucket, and 2-gal bucket BSFs, respectively. This study investigated (1) how the efficacy of the CAWST (Centre for Affordable Water and Sanitation Technology version 10) BSF performed with respect to removal of turbidity and E. coli from raw drinking water supplies, (2) whether biosand filtration could be effective with scaled-down 5-gal and 2-gal bucket BSFs, (3) the effects of low and high turbidity feed water on filter performance and maintenance, and (4) the effects of filter maintenance (i.e., cleaning) on filter performance. All bucket-sized filters, and two of the concrete filters, had hydraulic loading rates (HLRs) in the range of 0.2-0.3 m3/(m2*hr) for the majority of the testing period. The smaller sand bed depths in the bucket-sized filters did not impact filter performance with respect to turbidity and E. coli removal or the effluent levels of turbidity and E. coli. All filters produced effluents with a mean turbidity of \u0026lt;0.6 NTU. In addition, 78%, 74%, and 72% percent of effluent samples for the concrete, 5-gal, and 2-gal filters, respectively, had E. coli concentrations \u0026lt;1 CFU/100 mL. Based on the data collected in this study, the CAWST v10 concrete filter was able to achieve 98.1 - 98.4% turbidity removal and 3.8 - 4.0 log E. coli removal. The scaled-down BSFs, constructed in 5-gal (15cm bed depth) and 2-gal (10cm bed depth) buckets, were shown to be as effective (p-values \u0026gt;0.05) as the CAWST v10 concrete (54cm bed depth) configuration for both turbidity and E. coli removal. Alternating the influent turbidity between periods of high and low turbidity (~50 and ~5 NTU, respectively) did not influence either turbidity removal or E. coli removal. Periodic filter maintenance (i.e., cleaning the top of the sand bed) exhibited no correlation to either removal values or effluent levels of either E. coli or turbidity (p\u0026lt;0.05 and

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.245
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes1
Has abstractyes

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