Removal of Natural Organic Matter Fractions by Two Potable Water Treatment Systems: Dual Membrane Filtration and Conventional Lime Soda Softening
Bibliographic record
Abstract
The objective of this study was to investigate the removal of dissolved organic carbon (DOC) fractions by two water treatment plants, the Portage La Prairie Water Treatment Plant (PPWTP), which uses lime/soda softening with granular activated carbon (GAC) filtration, and the Morris Water Treatment Plant (MWTP), a dual micro/nano membrane facility, located in Manitoba, Canada. The study aimed to determine the cause of reportedly high trihalomethane (THM) concentrations in plant effluent. Both the PPWTP and MWTP use surface water sources, the Assiniboine and Red River, respectively, which are reportedly high in DOC, fluctuating from 7mg/L to 18mg/L throughout the year. As a result of the high DOC in the source water both plants have reported high THM concentrations that have, in the past, exceeded the 100ppb maximum limit set by the Province of Manitoba. Solid phase extraction (SPE) was used to fractionate DOC in water samples collected during this study. The SPE method fractionated DOC into six fractions: hydrophobic acid (HPOA), hydrophobic base (HPOB), hydrophobic neutral (HPON), hydrophilic acid (HPIA), hydrophilic base (HPIB), and hydrophilic neutral (HPIN). Samples were collected from the PPWTP and Assiniboine River on November 8, 2010, January 20, 2011 and April 2, 2011 to evaluate the DOC and DOC fraction removal throughout the plant. Results found that the GAC filter was ineffective at removing DOC, often with DOC concentrations increasing after the GAC filter. The HPOA fraction, largely believed to contain the greatest THM precursors, was unaffected by GAC filter showing the potential cause for reported high THM levels at the plant. All hydrophilic fractions increased after the GAC filter and only the HPOB fraction was reduced by GAC filtration at the PPWTP. The recommendation to the PPWTP from this group was to improve the coagulation process to reduce organic loads on the GAC filter. Samples were collected from the Red River on September 25, 2010, and fractionated, to establish the relative composition of the river. The results found that the late summer composition of the Red River was 45% hydrophobic and 55% hydrophilic, with 40% of the total organic component being HPIN. On November 25, 2010 samples from the Red River and MWTP were fractionated to establish membrane removal efficiency. The results were unexpected finding that DOC increased from 8.7mg/L to 10.2mg/L. The HPIA and HPIN fractions increased after the nano filter from 0.35-1.41mg/L and 2.00-4.00mg/L, respectively. The HPOA fraction was found to be unaffected by the nano filter while the HPON, HPOB, and HPIB faction had small decreases in concentrations. However, for samples collected in February, 2011 DOC concentrations were reduced to <0.5mg/L by the nano filter. The reason for the high DOC found after the nano for the November sampling period is unclear however it is believed that (1) the samples were taken just prior to a cleaning event where filter was not removing DOC effectively or (2) that the use of citric acid to clean the nano membrane could have added a carbon source to the nano effluent. It is recommended that a pre-treatment process be implemented prior to the micro/nano membranes to reduce the DOC load on the membranes preventing both high THM concentrations and membrane fouling.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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 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".