Enhancing oil-spill bioremediation in sub-Arctic soils through the rational use of nutrients and silica nanocarriers for biostimulation
Notice bibliographique
Résumé
There are several thousand petroleum-contaminated sites in remote sub-Arctic Canada and effective site remediation strategies are required to ensure site cleanup with minimum environmental risks. Bioremediation, the bacterial degradation of petroleum hydrocarbons to less hazardous end products is a cost-effective technology that can be easily applied in situ or ex situ at remote sites. This thesis examines how parameters such as dose and mode of biostimulation (addition of inorganic N,P nutrients), microbiology and hydrocarbon degrading gene abundances, and soil aggregate structure influence biodegradation rates and extents in different sub-Arctic soils under representative field conditions in summer months. The first objective examines the rational basis for nutrient biostimulation. Results of a meta-analysis of 58 peer-reviewed studies and microcosm experiments with a sub-Arctic soil over a range of N, P doses showed similar findings: absence of universally favorable N, P doses and N doses > 1,000 mg/kg were unfavorable for enhancing biodegradation rates. Studying interrelationships between biostimulation impact, microbial community, and degradative gene abundance is critical to identify monitoring parameters to assess bioremediation potential. Thus, the second objective was to examine effects of Arctic diesel addition (~3500 mg/kg) and biostimulation (70 mg-N/kg, 78 mg-P/kg) on 7 unsaturated sub-Arctic soils at site summer temperatures. Surprisingly, the significant differences between nutrient amended and unamended soil microbiology did not reflect the relatively small difference in hydrocarbon reductions observed between both systems. One possibility is that nutrient–driven soil organic matter degradation or degradation of metabolites produced from parent hydrocarbon degradation influenced microbial community composition and activity. To develop strategies to provide nutrients at oil-water interface, an important location for degradation of oils containing poorly soluble (high molecular weight hydrocarbons), the third objective examined phosphate delivery specifically to the oil-water interface, an active habitat for hydrocarbon degraders. Core-shell nanoparticles were synthesized containing a hydroxyapatite (nHAP) core with a mesoporous silica shell, functionalized with oleic acid to enable interfacial attachment. nHAP dissolution in aqueous media from the composite nanoparticles was demonstrated. Batch reactors containing 1% hexadecane (model oil) showed 5–fold enhanced interfacial growth of Dietzia maris (a well-known hydrocarbon degrader) when dosed with the nanoparticles, compared to systems not dosed with the nanoparticles. Significantly larger cellular aggregate sizes at the oil-water interface were also observed. Finally, the fourth objective assessed the implications of porosities of soil aggregate interiors on biostimulation efficiency, considering the diffusion of nutrients, oxygen into soil aggregate pores necessary to achieve extensive biodegradation of hydrocarbons. X-Ray Micro Computed Tomography was used to image soil aggregates in a non-destructive manner at voxel sizes of 2.39–3.09 µm, and bioaccessible intra–aggregate porosities were quantified via image analysis. Soils with lower porosities yielded lower biodegradation rates and extents, implying lower porosities have reduced nutrient and oxygen availability in intra–aggregate regions, adversely impacting bioremediation behavior. The sustainability implications of this thesis, relevant to SDGs 8,11,13,14, involve a basis for reducing soil N, P addition, and improve remediation measures for diesel contaminated remote Northern regions, also home to Canada’s Traditional communities. Overall, this thesis examines biostimulation levels, soil microbiology, aggregate microstructure, and core-shell nutrient nanocarriers as parameters towards environment friendly, effective, and targeted bioremediation strategies for petroleum contaminated sub-Arctic soils
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».