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Record W7162039302 · doi:10.82308/5102

Ozonation for the improvement of wastewater quality in lagoons

2019· dissertation· en· W7162039302 on OpenAlexaboutno aff
Beauregard Schlageter

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterEffluentChemical oxygen demandTotal suspended solidsBiochemical oxygen demandSewage treatment

Abstract

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Concerns over environmental sustainability in Canada have increased in recent years leading to changes to the discharge limits of certain contaminants like biological oxygen demand (25 mg/L), ammonia (1.25 mg NH3-N/L), and total suspended solids (25 mg/L) in effluent from wastewater treatment facilities collecting an average daily influent volume of 100 m3 or more. Contaminants of emerging concern (CECs) like pharmaceuticals, hormones, pesticides, herbicides, and other natural and synthetic compounds found in effluent from wastewater treatment facilities remain unregulated in Canada but may persist in the environment and lead to negative environmental outcomes. Given the number of Canadians who rely upon lagoons for their wastewater treatment in small, rural, and remote communities, there is a need to investigate and develop new, cost-effective strategies to improve the quality of wastewater treated in lagoons. Ozonation has been shown to improve the removal of a variety of wastewater contaminants (including CECs) from real wastewater through direct means (oxidation of compounds) and indirect means (increased dissolved oxygen, enhanced biodegradability) and was investigated in the present thesis as a potential strategy to improve wastewater treatment in lagoons. Two pilot tests were conducted to investigate the effects of ozonation on contaminant removal in lagoons. Samples of wastewater were collected from several locations in each lagoon prior to ozonation, during ozonation, and after ozonation and analyzed to determine the effect of ozonation on the removal of conventional contaminants and CECs. The samples were analyzed for biological oxygen demand (BOD), chemical oxygen demand (COD), total ammonia, unionized ammonia, total dissolved solids (TDS), total suspended solids (TSS), nitrite, nitrate, toxicity to Vibrio fischeri, and for the presence of fifteen CECs. For the first pilot test at New Credit First Nation, there were reductions in the BOD and total ammonia during the ozonation period. There was a decrease in toxicity to Vibrio fischeri and a further decrease in total ammonia during the post-ozonation period. The concentrations of three CECs (carbamazepine, gemfibrozil, and ibuprofen) decreased during the ozonation period, and the gemfibrozil concentration continued to decrease during the post-ozonation period. For the second pilot test at Rainy River First Nation, there were reductions in BOD, total ammonia, and unionized ammonia in the post-ozonation period. There was also a reduction in time from ice-off at the lagoon until compliance with the Wastewater System Effluent Regulations (WSERs) in 2018 compared to 2017 and 2016, but this time was still longer than in 2015 or 2014. Laboratory experiments with synthetic wastewater showed no improvements in removal of contaminants due to ozonation in a subsequent biodegradation process by Bacillus, Pseudomonas, and Rhodococcus species, and an increase in the formation nitrate at one of the dose tested. Direct removal of two indicator compounds (caffeine and sulfamethoxazole) by ozone was observed. The optimal dosing strategy for the removal of caffeine was different than for the removal of sulfamethoxazole. Overall, it is unclear if the pilot tests led to improvements in each lagoon and laboratory experiments with synthetic wastewater did provide evidence of an optimal ozone dosing strategy to improve the removal of contaminants. Monitoring at New Credit First Nation and Rainy River First Nation may provide further insight on the impact of ozonation on contaminant removal processes in lagoons. Further laboratory experiments using a more complex synthetic wastewater or real lagoon wastewater may also help determine the impact of ozonation on contaminant removal and the optimal dosing strategy to minimize costs associated with ozone production.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.306
Teacher spread0.289 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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