Urban organic aerosol composition in eastern China differs from north to south: molecular insight from a liquid chromatography–mass spectrometry (Orbitrap) study
Bibliographic record
Abstract
Air pollution by particulate matter in China affects human health, the ecosystem and the climate. However, the chemical composition of particulate aerosol, especially of the organic fraction, is still not well understood. In this study, particulate aerosol samples with a diameter of ≤2.5 µm (PM 2.5 ) were collected in January 2014 in three cities located in northeast, east and southeast China, namely Changchun, Shanghai and Guangzhou. Organic aerosol (OA) in the PM 2.5 samples was analyzed by an ultrahigh-performance liquid chromatograph (UHPLC) coupled to a high-resolution Orbitrap mass spectrometer in both negative mode (ESI-) and positive mode electrospray ionization (ESI+). After non-target screening including the assignment of molecular formulas, the compounds were classified into five groups based on their elemental composition, i.e., CHO, CHON, CHN, CHOS and CHONS. The CHO, CHON and CHN groups present the dominant signal abundances of 81 %–99.7 % in the mass spectra and the majority of these compounds were assigned to mono- and polyaromatics, suggesting that anthropogenic emissions are a major source of urban OA in all three cities. However, the chemical characteristics of these compounds varied between the different cities. The degree of aromaticity and the number of polyaromatic compounds were substantially higher in samples from Changchun, which could be attributed to the large emissions from residential heating (i.e., coal combustion) during wintertime in northeast China. Moreover, the ESI- analysis showed higher H/C and O/C ratios for organic compounds in Shanghai and Guangzhou compared to samples from Changchun, indicating that OA undergoes more intense photochemical oxidation processes in lower-latitude regions of China and/or is affected to a larger degree by biogenic sources. The majority of sulfur-containing compounds (CHOS and CHONS) in all cities were assigned to aliphatic compounds with low degrees of unsaturation and aromaticity. Here again, samples from Shanghai and Guangzhou show a greater chemical similarity but differ largely from those from Changchun. It should be noted that the conclusions drawn in this study are mainly based on comparison of molecular formulas weighted by peak abundance and thus are associated with inherent uncertainties due to different ionization efficiencies for different organic species.
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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 | 0.000 | 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) | 0.001 | 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".