Source Apportionment of Aerosol Ammonium in an Ammonia‐Rich Atmosphere: An Isotopic Study of Summer Clean and Hazy Days in Urban Beijing
Bibliographic record
Abstract
Abstract Aerosol ammonium (NH 4 + ) can be a major component of fine particles, especially during severe haze episodes. The abatement of ammonia (NH 3 ) emissions is important for reducing fine particles, but NH 3 sources remain poorly constrained and are largely unregulated in China and most other regions. This study uses stable isotopes to interpret the role NH 3 sources play in generating different sized NH 4 + aerosols in Beijing between 21 June and 4 July 2013 with fine particle concentrations of 20–242 μg/m 3 . The concentrations and nitrogen stable isotope composition of aerosol NH 4 + (δ 15 N‐NH 4 + ) were both elevated during the five haze episodes that were sampled. These increases were driven by enhancements in the fine mode as opposed to substantial increases in the coarse mode aerosol. After accounting for the isotope fractionation that occurs during gas‐to‐particle partitioning (17.7‰ to 28.2‰), the “initial” (prepartitioning) δ 15 N‐NH 3 values were estimated to be −35‰ for a clean period (i.e., a nonhazy day) and ranged from −14.3‰ to −22.8‰ for hazy days. Source apportionment using the “IsoSources” isotopic mixing model indicated that the dominant contribution to NH 3 shifted from agricultural sources during the clean period (86%) to fossil fuel emissions (54%–81%) during hazy days and when back trajectories rotate from the northwest to the west and/or south. These results together suggest that even in summer, fossil fuel‐related sources from Beijing and the surrounding areas are the major source of NH 3 during haze events and that controlling these sources may be important for alleviating particulate matter pollution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".