Feasibility Analysis of Municipal Solid Waste Incineration for Harmless Treatment of Potentially Virulent Waste
Bibliographic record
Abstract
The outbreak of major health emergencies, such as the COVID-19 pandemic, has posed numerous challenges to waste management. Environmentally sound treatment of such epidemic-related municipal solid waste (MSW) plays a vital role in interrupting virus transmission. In this study, the furnace type, incineration process and control parameters of an MSW incinerator were comparatively analyzed with those of a medical waste incinerator and hazardous waste incinerator according to China’s MSW incineration pollution control standards. In addition, changes in flue gas emissions data before, during and after the outbreak of the pandemic were empirically analyzed. The study revealed the following: (1) the feasibility of MSW incinerators to meet the harmless disposal of potentially viral municipal solid waste (PVMSW); (2) the priority order of incinerator types for MSW incinerators in the disposal of potentially virulent waste was grate furnace incinerator > fluidized bed incinerator > cement kiln; and (3) when MSW incinerators treated PVMSW, the emissions of dioxin compounds in the flue gas fluctuated between 0.00052 and 0.031 ng TEQ/m3, HCl emissions fluctuated between 1.6 and 23.742 mg/m3, CO emissions fluctuated between 0.18 and 59.15 mg/m3, heavy metal emissions fluctuated between 0.000008 and 0.855 mg/m3, and particulate matter emissions fluctuated between 0.64 and 12.13 mg/m3. All emissions met the flue gas emission standards. This study verified the feasibility of using MSW incinerators to treat PVMSW during a sudden major pandemic and provided a theoretical basis for the environmentally sound collaborative treatment of PVMSW and a reference for the emergency management and sustainable development of MSW.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".