A Review of the Environmental Impacts of Post-Consumer Electronic Cigarette Waste and Disposal
Bibliographic record
Abstract
INTRODUCTION: The dramatic rise of e-cigarette use has raised significant environmental concerns, largely due to unclear disposal guidance from manufacturers and regulatory agencies. This review aimed to synthesize existing research on the environmental impacts of post-consumer e-cigarette waste and disposal. METHODS: We conducted a comprehensive search of the databases Medline, Embase, Web of Science, and Google Scholar on the environmental impacts of e-cigarette waste from market inception in 2004 to June 2024. Following abstract and title screening, full-text review, and extraction by two authors, 18 papers were identified for inclusion in the study. RESULTS: Thematic analysis revealed the following results: (1) The chemical, metallic, and electrical composition of e-cigarettes qualifies these devices as hazardous and electronic waste products. (2) E-liquid, along with its chemical and flavoring constituents, negatively impacts aquatic and terrestrial organisms. (3) Although less harmful to the environment than tobacco cigarettes, e-cigarettes still pose environmental risks, particularly those that are disposable or contain disposable components (ie, pods/cartridges). (4) Consumer disposal practices are often unsafe and ineffective. CONCLUSIONS: In jurisdictions where e-cigarettes are legally sold, we recommend that regulatory agencies establish clear, enforceable guidelines for e-cigarette disposal and recycling. This should include well-defined manufacturer responsibility frameworks, including displaying recycling information on their products and assuming responsibility for recycling costs. Public education campaigns are also needed to raise awareness of e-cigarette waste issues. Further research into the environmental impact of e-cigarette waste and innovative recycling systems is crucial to better understand and address this emerging issue. IMPLICATIONS: Previous reviews on e-cigarette waste and disposal have primarily called attention to gaps in research, offering limited insight into the full scope of its environmental harm. This review offers the most thorough synthesis to date, detailing how e-cigarettes pose risks to ecosystems due to their chemical, metallic, and electrical components. Findings from this review underscore the need for urgent regulatory guidelines on disposal and recycling, enhanced public awareness, and manufacturer accountability to mitigate the environmental impact of post-consumer e-cigarette 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".