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Record W4388906073 · doi:10.1007/978-3-031-34447-3_15

In Situ Chemical Oxidation of Petroleum Hydrocarbons

2023· book-chapter· en· W4388906073 on OpenAlexaff
Neil R. Thomson

Bibliographic record

VenueEnvironmental contamination remediation and management · 2023
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPotassium permanganatePotassium persulfatePersulfateHydrogen peroxideOzoneChemistryEnvironmental remediationSodium persulfateEnvironmental chemistryInorganic chemistryOrganic chemistryContaminationPolymerCatalysis

Abstract

fetched live from OpenAlex

Abstract In situ chemical oxidation (ISCO) is a mature treatment technology that involves the delivery of a chemical oxidant into a target treatment zone (TTZ) to destroy petroleum hydrocarbon (PHC) compounds, and thereby reduce risk to human health and the environment. Commonly used chemical oxidants include hydrogen peroxide, sodium percarbonate, ozone, sodium or potassium permanganate, and sodium or potassium persulfate. All these oxidants can degrade environmentally relevant PHCs except that permanganate is non-reactive toward benzene. Ozone is delivered into the TTZ as a gas while the other oxidants are typically delivered as a concentrated liquid. ISCO should be considered part of integrated remediation strategy and not used in isolation. This chapter provides a reader with an introduction to key aspects of ISCO that are relevant to applications at sites contaminated with PHCs. Following a discussion of the TTZ, it then examines the fundamentals of the common oxidants used. Next the interaction of chemical oxidants with aquifer materials is explored, and a description of relevant transport considerations is provided. This is followed with an overview of methods that can be used to deliver an oxidant to treat PHCs in a TTZ. This chapter closes with a summary of the important takeaway messages.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
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)0.0010.001

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 head0.187
Teacher spread0.180 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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