BIODIESEL AT THE CROSSROADS OF CLEAN ENERGY AND CANCER PREVENTION: A DOUBLE-EDGED SWORD IN SUSTAINABLE TRANSPORT
Bibliographic record
Abstract
Air pollution is a major environmental risk factor for cancer, driven in part by emissions from the transportation sector. Biodiesel, a renewable alternative to petrodiesel, has emerged as a promising strategy to reduce toxic emissions from diesel engines. This review critically examines the relationship between biodiesel use and cancer risk, evaluating its impact across key pollutants including particulate matter (PM), unburned hydrocarbons (UHCs), carbon monoxide (CO), and nitrogen oxides (NOₓ). Biodiesel combustion generally results in lower emissions of PM, UHCs, and CO, pollutants known to induce DNA damage, oxidative stress, and chronic inflammation, all of which contribute to carcinogenesis. These benefits are largely attributed to biodiesel’s intrinsic oxygen content, absence of aromatic compounds, and higher cetane number, which collectively promote more complete combustion. However, biodiesel is consistently associated with increased NOₓ emissions, which can lead to secondary carcinogenic pollutants like ozone and PAHs. Additionally, potential cancer risks may arise during biodiesel production and processing. The paper concludes that while biodiesel significantly improves air quality and reduces several cancer-related exposures, optimizing its formulation and combustion conditions is essential to minimize unintended health trade-offs. Biodiesel thus represents both an opportunity and a challenge in the broader effort to achieve cleaner energy and cancer prevention.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".