Decadal changes in anthropogenic source contribution of PM <sub>2.5</sub> pollution and related health impacts in China, 1990–2015
Bibliographic record
Abstract
Abstract. Air quality in China has changed dramatically in response to rapid development of economy and policies. In this work, we investigate the changes of anthropogenic source contribution to ambient fine particulate matter (PM2.5) 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 PM2.5 exposure and related premature mortality over China during 1990–2015 with 5-yr intervals, by using an integrated model framework of bottom-up emission inventory, chemical transport model, and the Global Exposure Mortality Model (GEMM). The national anthropogenic PM2.5-related premature mortality estimated with GEMM for the nonaccidental deaths due to noncommunicable diseases and lower respiratory infections rose from 1.26 million (95 % 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 PM2.5-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 PM2.5-related premature mortality [734,000 (95 % CI: 615,500, 844,900), 35 % of total], followed by residential (25 %), agriculture (23 %), transportation (10 %) and power (6 %). The decadal changes in source contribution to PM2.5-related premature mortality in China represents a combined impact of socioeconomic development and clean air policy. For example, active control measures have successfully reduced pollution from power sector, while contribution from industrial and transportation sector continuously increased due to more prominent growth of activity rates. Transition in fuel consumption dominated the decrease of contribution from residential sector. In the meanwhile, contribution from agriculture sector continuously increased due to persistent NH3 emissions and enhanced formation of secondary inorganic aerosols under a NH3 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".