Effect of Methanol on the Microbial Community Structure of Biofilters Treating Dimethyl Sulphide
Bibliographic record
Abstract
Odour emissions resulting from reduced sulphur compounds in the kraft pulping industry are frequently found in dilute, high flowrate air streams that are costly to treat using incineration and thermal oxidation. Biofiltration, an air treatment method involving passing air through a packed bed of microorganisms, has emerged as a promising treatment strategy for these dilute waste gas streams. However, biodegradation of dimethyl sulphide (DMS) in biofilters is rather poor and is limiting the application of biofiltration to odour streams rich in DMS. Recently, our group has shown that co-treatment of DMS with methanol can increase DMS removal significantly. In this thesis, the effect of methanol on the microbiology of two biofilters treating DMS was explored. Microbial community analysis revealed that the addition of methanol led to a significant increase of up to an order of magnitude in the abundance of Hyphomicrobium spp. in a biofilter co-treating DMS and methanol compared to a biofilter treating DMS alone with no significant difference in the abundance of Thiobacillus spp. between the two biofilters. Further to the biofiltration experiments, the growth kinetics of Hyphomicrobium spp. and Thiobacillus spp. on DMS and methanol in an enrichment culture created from a biofilter co-treating DMS and methanol were studied. A specific growth rate of 0.099 h-1 and 0.11 h-1 was determined for Hyphomicrobium spp. and Thiobacillus spp., respectively, growing on DMS at pH 7, double the highest maximum specific growth rate for bacterial growth on DMS reported to date in the literature. As the pH decreased from pH 7 to pH 5, the specific growth rate of Hyphomicrobium spp. decreased significantly by 85% in the mixed culture while the specific growth rate of Thiobacillus spp. remained similar through the same pH shift. When methanol was used as a substrate, the specific growth rate of Hyphomicrobium spp. declined much less over the same pH range (up to 30%). These results suggest that addition of methanol to biofilters co-treating DMS and methanol can increase DMS removal rates by increasing the abundance of DMS-degrading Hyphomicrobium spp. at pH levels not conducive to high growth rates on DMS alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".