Recalcitrant compounds removal from wastewater: new biotechnological approaches using fungi and bacteria
Notice bibliographique
Résumé
Recalcitrant compounds limit the efficiency of conventional biological processes for wastewater treatment, representing one of the major issues in the field. Among anthropogenic effluents, Landfill Leachate (LFL) is a strongly polluted wastewater whose treatment requires more sustainable technologies to remove its recalcitrant fraction. Although different strategies have been developed for LFL treatment, several drawbacks, such as high costs and complexity, are still unresolved. Fungi, and especially White-Rot Fungi (WRF) with their extracellular enzymes, are gaining considerable research interests among bio-based industries as they have resulted effective toward several types of pollutants such as textile and pharmaceuticals ones. Besides the promising results achieved with WRF on difficult wastewaters, at the moment only a limited number of studies has been reported about the use of WRF on LFL treatment. In this study, a WRF fungus, Bjerkandera adusta MUT 2295, was selected as a candidate for the old LFL treatment. Strain selection was performed through a biodegradation experiment in which decolorisation potential was used as main criteria, in which B. adusta was able to remove the color up to 40% and produce the extracellular enzyme manganese-dependent peroxidase (MnP). The efficiency of B. adusta toward old LFL (Canada) was tested through batch and continuous tests, using biomass attached onto polyurethane foam cubes (PUFs). In particular, several batch tests were performed to evaluate MnP, Chemical Oxygen Demand (COD) and soluble COD (sCOD) removal, as a function of a) target effluent, b) co-substrate type and concentration and c) LFL initial COD and sCOD. Diverse target effluents have been tested including landfill leachate (Canada) and the two solutions containing synthetic recalcitrant compounds, which were prepared with tannic and humic acid. COD removal of 48%, 61%, and 48% was obtained in raw leachate and in the synthetic solutions containing tannic and humic acids, respectively. The high COD removal achieved using tannic acid (61%), encouraged the exploitation of the selected fungi to treat such effluent, while further optimization were required for LFL and humic acid treatment. To optimize process performance, diverse types of co-substrates were used, including milled cellulose from beverage cup waste material. Batch tests have proved such co-substrate suitable for the bioremediation of LFL using B. adusta. Continuous experiments in non-sterile conditions were performed using B. adusta attached onto PUF cubes in bench-scale packed-bed reactors, including two bioreactors fed with LFL and one with a synthetic solution prepared with tannic acid. Bioreactors, operated with LFL, were dosed with an initial start-up of glucose (Rg) or cellulose (Rc). An additional glucose dosage was provided in both reactors, leading to remarkable performance increases. The highest COD and sCOD removals were 63% and 53% in Rg, and 54% and 51% in Rc. The reactor fed with tannic acid (R1) was active for 210 days without co-substrate addition, achieving an average sCOD removal of 53% and a maximum removal of 81%, proving that B. adusta was suitable for long-term treatment of tannic acid containing effluents in non-sterile conditions. As a final step, in this study the bioremediation potential of two fungal strains toward old LFL (Canada) was also evaluated, including an autochthonous strain isolated from LFL and the allochthonous white-rot fungus Phanerochaete chrysosporium. The experiment was performed through batch tests and the two fungal strains were cultured in both suspended and attached forms, using PUFs. For both conditions, with suspended and attached biomass, the two strains were tested alone and combined. The highest sCOD removal was achieved using immobilized biomass, reaching maximum sCOD abatements of 41% and 33% with Phanerochaete chrysosporium and the autochthonous strain, respectively. These results were associated with the detection of laccase (LaC) activity, suggesting the presence of a degradative process of LFL, activated by the two fungi. Hence, these two fungal strains could be exploited to remove LFL recalcitrant fraction in longer experiments and on wider scale, through bioreactors.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 tête enseignante, 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 ».