Global health costs of ambient PM2·5 from combustion sources: a modelling study supporting air pollution control strategies
Bibliographic record
Abstract
Background Climate actions targeting combustion sources can generate large ancillary health benefits via associated air-quality improvements. Therefore, understanding the health costs associated with ambient fine particulate matter (PM 2·5 ) from combustion sources can guide policy design for both air pollution and climate mitigation efforts. Methods In this modelling study, we estimated the health costs attributable to ambient PM 2·5 from six major combustion sources across 204 countries using updated concentration–response models and an age-adjusted valuation method. We defined major combustion sources as the sum of total coal, liquid fuel and natural gas, solid biofuel, agricultural waste burning, other fires, and 50% of the anthropogenic fugitive, combustion, and industrial dust source. Findings Global long-term exposure to ambient PM 2·5 from combustion sources imposed US$1·1 (95% uncertainty interval 0·8–1·5) trillion in health costs in 2019, accounting for 56% of the total health costs from all PM 2·5 sources. Comparing source contributions to PM 2·5 concentrations and health costs, we observed a higher share of health costs from combustion sources compared to their contribution to population-weighted PM 2·5 concentration across 134 countries, accounting for more than 87% of the global population. This disparity was primarily attributed to the non-linear relationship between PM 2·5 concentration and its associated health costs. Globally, phasing out fossil fuels can generate 23% higher relative health benefits compared to their share of PM 2·5 reductions. Specifically, the share of health costs for total coal was 36% higher than the source's contributions to corresponding PM 2·5 concentrations and the share of health costs for liquid fuel and natural gas was 12% higher. Other than fossil fuels, South Asia was expected to show 16% greater relative health benefits than the percentage reduction in PM 2·5 from the abatement of solid biofuel emissions. Interpretation In most countries, targeting combustion sources might offer greater health benefits than non-combustion sources. This finding provides additional rationale for climate actions aimed at phasing out combustion sources, especially those related to fossil fuels and solid biofuel. Mitigation efforts designed according to source-specific health costs can more effectively avoid health costs than strategies that depend solely on the source contributions to overall PM 2·5 concentration. Funding The Health Effects Institute, the National Natural Science Foundation of China, and NASA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".