Characterization of the Microbial Community in A Continuous Garbage Treatment Process
Notice bibliographique
Résumé
Previously, we developed a “static type” garbage treatment system whose performance exceeds that of conventional garbage treatment systems. However, we could not explain the system’s excellent performance especially from a microbial ecosystem’s stand point (Matsuda et al, 2017). Microbial activity causes the decomposition of organic materials in a garbage treatment process. However, we do not know which microbes are effective in garbage decomposition and what relationship exists between the succession of a microbial community and the decomposition of organic materials in a garbage composting process.  This study analyzed the relationship between a continuous operation garbage treatment system’s microbial community and the decomposition of organic matter and evaluated for effective microbes in the same garbage treatment system. Although many research articles have been published on the composting processes throughout the world, most of them employ batch processes and not continuous processes. Conversely, most real-world practical composting plants adopt a continuous operation system in which fresh feed is usually input once a day with continuous aeration and intermittent mixing.  To comprehensively analyze the microbial community, three different approaches were adopted in this study; 1) colony observation, 2) DNA analysis, and 3) the enzymatic activities of each colony. In our experiment, the working volume was 10 L of leaf mold and 40 g/(day L) of garbage (the organic load) was input to the reactor every day at a fixed time. Dog food with 70 % moisture content was used as a model garbage substrate. The reactor’s internal temperature, the total reactor’s weight and the sampled residue’s weight, moisture content and pH were measured before inputting garbage. Additionally, the reactor’s internal temperature was measured six hours later. The internal temperature was about 55 ℃ at the highest without heating. The organic matter decomposition rate was about 50 % and the weight reduction rate was over 90 %, implying successful garbage decomposition was achieved. The microbial composition and the number of the colonies seen on the medium plate changed every day and did not realize a “steady state.” Thus an extremely efficient microbe does not exist. Only eleven microbes were isolated; yet many more microbes must exist in the system but were not counted due to a high dilution rate. From our DNA analysis, the PCR-DGGE profile of the microbial community in the garbage residue showed that bands of isolated colonies were detected in the same positions as the bands of garbage residue, which contained all kinds of microbes. Nine microbes were identified using 16S rRNA genome from eleven isolated ones. The identified microbes were of different bacterial species and their characteristics were examined from the stand point of nutritional property and enzymatic activity.  The garbage decomposition process consists of two steps, solubilization and metabolization. Extracellular enzymes act during solubilization of solid garbage residue, and intracellular enzymes work when water-soluble substances are taken up into bacterial cells and metabolized. Protease and amylase activity were measured to assess extracellular enzymatic activity and dehydrogenase activity was measured to evaluate intracellular enzymatic activity. The enzyme activities of bacterial strains significantly differed by strain. Results from this study suggest complementary microbial activity and that Bordetella trematum, Bacillus cereus, Bacillus subtills subtills and Streptomyces thermocarboxydus effectively decompose garbage.
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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 ».