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Record W2902606379 · doi:10.1002/rem.21580

Stabilized hydrogen peroxide for the remediation of hydrocarbons and MTBE in high temperature and saline groundwater

2018· article· en· W2902606379 on OpenAlexaff
Mansor Kashir, Rick McGregor

Bibliographic record

VenueRemediation Journal · 2018
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSequoia Environmental Remediation (Canada)
Fundersnot available
KeywordsBTEXHydrogen peroxideEthylbenzeneChemistryEnvironmental chemistryHydrocarbonCitric acidEnvironmental remediationBenzeneGroundwaterPlumeContaminationOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Abstract A field pilot‐scale test was conducted to determine if the use of citric acid‐stabilized hydrogen peroxide (H 2 O 2 ) is effective in reducing dissolved concentrations of petroleum hydrocarbon compounds (PHCs) and the additive methyl tert‐butyl ether (MTBE) in impacted high salinity groundwater. The test was carried out adjacent to an operational hydrocarbon fuel facility in Western Saudi Arabia. The pilot test was based on the results of laboratory tests, which suggested that hydrogen peroxide stabilized with citric acid (C 6 H 8 O 7 ) would enhance the degradation of the dissolved PHCs and associated MTBE within a saline groundwater. A 7.5 weight percent hydrogen peroxide solution was injected into a series of injection wells positioned to target a PHC‐impacted plume within an unconfined aquifer. The plume contains total benzene, toluene, ethylbenzene, and xylenes (BTEX) at concentrations up to 6,890 micrograms per liter (μg/L). The MTBE concentration within the groundwater was detected at concentrations of up to 55,182 μg/L, whereas the groundwater salinity was approximately 7,000 milligrams per liter (mg/L). A total of 9,012 liters of citric acid‐stabilized hydrogen peroxide solution was injected over three events spaced over a 1‐month period. The results of the pilot test indicated that injection of stabilized hydrogen peroxide was effective in reducing the concentration of dissolved PHCs within the plume, including BTEX and other aromatic hydrocarbons. The average concentration decrease for total BTEX was 72% with up to 97.4% reduction being measured in a sample collected from one key monitoring well during the treatment period. MTBE was also effectively treated during the pilot test with an average MTBE degradation of 50% being realized during the test and up to 86% concentration decrease being measured in some groundwater samples. While reductions in MTBE concentrations were noted, no increase in tertiary butyl alcohol was measured, which is a promising finding. The general water quality did not fluctuate significantly between pre‐ and postinjection with the pH, oxidation–reduction potential, and dissolved oxygen remaining relatively constant. The dissolved iron, nitrate, and sulfate concentrations vary during the injection period with nitrate decreasing to below detection limits following the final injection, whereas sulfate decreased in two of the three monitoring wells following the injection. Dissolved iron concentrations remained relatively constant in two of the three monitoring wells (NJBP45 and NJBP46) during the injection events, whereas dissolved iron in monitoring well NJBP44 decreased from a baseline concentration of 1.03 μg/L to 0.09 μg/L, suggesting that the ferrous iron in solution was being oxidized to ferric iron. Measurements of the preinjection microbiological community indicated that the community is diverse within the injection area based on deoxyribonucleic acid extraction by polymerase chain reaction as well as sequencing and clustering. Subsequent analyses of the microbiological community postinjection indicated that the community diversity and biomass was reduced with a shift to a more aerobic population, at least over the short term. Results of compound specific isotope analyses are consistent with the concentration data and show petroleum hydrocarbon and MTBE was degraded by the citric acid‐stabilized hydrogen peroxide treatment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.060
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes1
Has abstractyes

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