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Record W3016965376 · doi:10.1002/clen.202000118

In Situ Bioremediation of Toluene‐Polluted Vadose Zone: Integrated Column and Wetland Study

2020· article· en· W3016965376 on OpenAlexaff
Shreejita Basu, Brijesh Kumar Yadav, Shashi Mathur, Pankaj Kumar Gupta

Bibliographic record

VenueCLEAN - Soil Air Water · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBiodegradationBioremediationTolueneVadose zoneEnvironmental scienceEnvironmental chemistryContaminationSoil waterWetlandSoil scienceChemistryEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Abstract A vertical soil column setup integrated with wetlands is developed to study the biodegradation and transport of toluene, a light non‐aqueous phase liquid (LNAPL), in the subsurface environment. LNAPL‐contaminated water is applied to infiltrate from the top of the soil column. The observed and simulated breakthrough curves show high equilibrium concentration at top ports rather than at lower ports, indicating effective toluene biodegradation with soil depth. The observed equilibrium concentration of toluene is higher in the case of unplanted wetland, asserting an accelerated biodegradation rate in the planted case. A difference in the relative concentration of toluene between input and output fluxes at 100 h is found as 13.34% and 30.86% for planted and unplanted wetland setups, respectively. Estimated biodegradation rates show that toluene degradation is 2.5 times faster in the planted wetland setup. In addition, the difference in the observed bacterial count and dissolved oxygen prove that toluene degraded aerobically at a faster rate in the planted setup. Simulations show that as time reached 80–100 h, there is no significant change in concentration profile, thereby confirming the equilibrium condition. The results of this study will be useful to frame plant‐assisted bioremediation techniques for LNAPL‐contaminated soil–water resources in the field.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.514

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 head0.009
GPT teacher head0.203
Teacher spread0.195 · 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 designObservational
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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCLEAN - Soil Air WaterSame topicConstructed Wetlands for Wastewater TreatmentFrench-language works237,207