Systematic review of environmental and human health risk assessments in municipal solid waste management
Bibliographic record
Abstract
Abstract Effective risk assessment is critical for ensuring safe and sustainable municipal solid waste (MSW) management, supporting data-driven decision-making and regulatory compliance by identifying hazards, evaluating their impacts, and guiding targeted mitigation strategies. This study uses the PRISMA method to systematically review 72 studies published in the past decade on risk assessments for various MSW facilities, providing a comprehensive overview of current practices while identifying key trends, gaps, and opportunities for improvement. Results indicate that approximately 60% of environmental assessments identified risks, with over half focusing on human health. While diverse MSW facilities, including dumpsites, composting, incineration, energy-from-waste (EfW), and recycling, were investigated, landfills accounted for 49% of the reviewed studies, underscoring their global prevalence. The findings emphasize the need for continuous pollution monitoring, even in facilities initially deemed low-risk, and highlight the importance of a standardized methodology that integrates analytical tools with statistical software to address inconsistencies in risk assessment indices. Such standardization would enhance mitigation effectiveness, support evidence-based policymaking, and optimize resource allocation, ultimately fostering safer and more sustainable MSW management systems. Graphical abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.035 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".