Decadal changes in anthropogenic source contribution of PM <sub>2.5</sub> pollution and related health impacts in China, 1990–2015
Bibliographic record
Abstract
Air quality in China has changed dramatically in response to rapid development of the economy and to policies. In this work, we investigate the changes in anthropogenic source contribution to ambient fine particulate matter (PM 2.5 ) air pollution and related health impacts in China during 1990–2015 and elucidate the drivers behind the decadal transition. We estimate the contribution of five anthropogenic emitting sectors to ambient PM 2.5 exposure and related premature mortality over China during 1990–2015 with 5-year intervals, by using an integrated model framework of a bottom-up emission inventory, a chemical transport model, and the Global Exposure Mortality Model (GEMM). The national anthropogenic PM 2.5 -related premature mortality estimated with the GEMM for nonaccidental deaths due to noncommunicable diseases and lower respiratory infections rose from 1.26 million (95 % confidence interval (CI) [1.05, 1.46]) in 1990 to 2.18 million (95 % CI [1.84, 2.50]) in 2005; then, it decreased to 2.10 million (95 % CI [1.76, 2.42]) in 2015. In 1990, the residential sector was the leading source of the PM 2.5 -related premature mortality (559 000, 95 % CI [467 000, 645 900], 44 % of total) in China, followed by industry (29 %), power (13 %), agriculture (9 %), and transportation (5 %). In 2015, the industrial sector became the largest contributor of PM 2.5 -related premature mortality (734 000, 95 % CI [615 500, 844 900], 35 % of total), followed by the residential sector (25 %), agriculture (23 %), transportation (10 %), and power (6 %). The decadal changes in source contribution to PM 2.5 -related premature mortality in China represent a combined impact of socioeconomic development and clean-air policy. For example, active control measures have successfully reduced pollution from the power sector, while contributions from the industrial and transportation sectors have continuously increased due to more prominent growth in activity rates. A transition in fuel consumption has dominated the decrease in the contribution from residential sector. Meanwhile, the contribution from the agriculture sector has continuously increased due to persistent NH 3 emissions and enhanced formation of secondary inorganic aerosols under an NH 3 -rich environment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".