Impacts of COVID-19 Outbreak on the Municipal Solid Waste Management: Now and beyond the Pandemic
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The COVID-19 pandemic disrupted the municipal essential services, including municipal solid waste (MSW) management. This study has reviewed the literature on MSW and solid medical waste (SMW) management systems, waste management initiatives specific to this pandemic, as well as their impacts now and beyond. Waste segregation and separate treatment of waste streams play important roles in reducing the environmental, health, and social impacts of waste and waste management. The global warming potential of MSW and SMW were found to be varied from −0.64 to 520 kg CO 2 equiv/tonne and −52.1 to 3730 kg CO 2 equiv/tonne, respectively, which widely depend on the sterilization and disposal processes. Similarly, MSW and SMW disposal costs varied from 90 to $242/tonne and 12 to $1530.0/tonne, respectively. Various changes made to waste collection and management because of the COVID-19 pandemic affected waste segregation and recycling. Since the start of the pandemic, various sectors, including the food, waste management, and healthcare sectors, relied on the increased use of single-use plastics to prevent transmission of COVID-19. An environmentally friendly alternative (biodegradable/compostable) to widely used single-use plastics is desired for easing waste management problems. Although various initiatives are underway to manage growing volumes of MSW and SMW, while controlling the spreading of infectious diseases, the movable grate incineration technology coupled with an adequate disinfection process presents a potential solution in managing the COVID-19 waste challenges. The proper disinfection method and technological choices can mitigate the risk of spreading infections and can improve the waste management system’s sustainability, especially the contaminated waste.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".