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Record W2169580185

Optimizing Solvent Extraction of PCBs from Soil

2009· dissertation· en· W2169580185 on OpenAlexaboutno aff
Maureen O'Connell

Bibliographic record

VenueUWSpace (University of Waterloo) · 2009
Typedissertation
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsSolvent extractionExtraction (chemistry)Environmental scienceSolventEnvironmental chemistryChemistryChromatographyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Polychlorinated biphenyls (PCBs) are carcinogenic persistent contaminants. Although their manufacturing in North America ceased in the late 1970s, their high heat resistance made their use widespread over their production lifetime. As a result, PCB contamination has occurred globally and in particular has plague brownfield redevelopment in urban environments. The remediation of PCB contaminated soil or sediments has historically been dealt with through the expensive and unsustainable practice of excavation followed by off-site disposal or incineration. One potential technology that has shown some success with on-site remediation of PCB contamination is solvent extraction. Solvent extraction is technically simple; it involves excavating the contaminated soil, placing it in a vessel and adding solvent. The PCBs are extracted by the solvent and the treated soil is returned for use on site. Although successful at removing a large quantity of PCBs from some soils, this technology can be improved upon by extracting additional PCB mass and making the extraction more efficient and suitable for colder climates.
\nThis thesis aimed to identify the factors controlling PCB extraction with solvents in order to optimize PCB extraction as it is applied on different soil types and in various climates. The research investigated the impact of elevated moisture contents (≤ 20% by weight) on solvent extraction efficiency, the effects of low temperatures (<5ºC) on solvent extraction, and developed a kinetic model to represent PCB solvent extraction. As past research has shown, weathered PCB in soil is more difficult to remove. Contaminated field samples from Southern Ontario, Canada were used for this work, rather than synthetically prepared samples.
\nThe impact of elevated moisture contents and low temperature on extraction efficiency was determined through a series of screening experiments using polar and non-polar solvents at both 20ºC and 4ºC. It was hypothesized that improved extractions may be possible with combinations of polar and non-polar solvents. Based on the results of these screening experiments, a factorial experiment was designed using solvent combinations to further assess the role of moisture contents and low temperatures. The role of PCB mass distribution among grain sizes was also evaluated to see if optimization based on grain size separation is possible. Finally, experiments were performed to generate data suitable for the development of a kinetic model that incorporates key factors affecting solvent extraction.
\nFour suitable solvents for solvent extraction in Ontario were identified through a literature review and these were used for this work: isopropyl alcohol (polar), ethanol (polar), triethylamine (non-polar) and isooctane (non-polar). Triethylamine outperformed isooctane and performed best on its own rather than in combination with polar solvents. An interaction between soil moisture content and choice of polar solvent (isopropyl alcohol versus ethanol) was established: a given polar solvent achieves optimal PCB extraction at a specific moisture content range. Temperature was also identified as significantly influencing PCB extraction. Although it was determined that PCBs were distributed unevenly amongst grain sizes, a simple relationship between grain size and fractional organic carbon or organic content was not found.
\nA simple two-compartment kinetic model was developed which is suitable for predicting the PCB concentrations extracted up to 24 hours. The model incorporates both temperature and soil to solvent ratio in order to estimate PCB concentration extracted.

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 categoriesMeta-epidemiology (narrow)
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 score0.048
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)0.0000.000

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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