Cigarette butts in the environment: a growing global threat?
Bibliographic record
Abstract
Cigarette butts (CBs) are composed of cellulose acetate and are a significant source of anthropogenic waste. More than 4 trillion CBs are improperly discarded in natural and urban environments, resulting in the contamination of a variety of ecosystems. The goal of the present study was to obtain information regarding environmental contamination of CBs through a comprehensive systematic review. A literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. “Cigarette butt” and “cigarette filter” were used as keywords in searches in Google Scholar, Scopus, PubMed, ScienceDirect, and SpringerLink databases, where the abstracts were separated, organized, and analysed using IRaMuTeQ software. The review identified 116 articles published in 23 countries, with publication growth observed over the years. Through descending hierarchical classification, two groups and four classes were recognized, whereby different terminologies were specified by factorial correspondence and similarity analyses. The four classes were categorized as follows: (1) ecotoxicological studies, with information about the lethal and sublethal effects of CBs on different organisms; (2) public policies, with discussion pertaining to the problem and possible measures and actions aimed at reducing CB contamination; (3) contamination of public areas, with studies addressing the potential dispersion of this material in the environment; and (4) physicochemical aspects, with evidence of the potential for contamination caused by the components contained in the cigarette filters. However, despite an increasing number of publications over the years and a variety of studies regarding the environmental effects of CBs, there is still an absence of information within each class, requiring further research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.017 |
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; both teacher heads agree on what is shown here.
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".