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Record W4413786034 · doi:10.1126/science.adq2840

Secondary organic aerosol in urban China: A distinct chemical regime for air pollution studies

2025· review· en· W4413786034 on OpenAlexaff
Ru‐Jin Huang, Yongjie Li, Qi Chen, Yanli Zhang, Chunshui Lin, Chak K. Chan, Jian Zhen Yu, J. A. de Gouw, Shengrui Tong, Jingkun Jiang, Weigang Wang, Xiang Ding, Xinming Wang, Maofa Ge, Weijian Zhou, Douglas R. Worsnop, Michael Boy, Merete Bilde, Ulrike Dusek, Annmarie G. Carlton, Thorsten Hoffmann, V. Faye McNeill, Marianne Glasius

Bibliographic record

VenueScience · 2025
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsIONICS Mass Spectrometry (Canada)
Fundersnot available
KeywordsAerosolHazeChinaDominance (genetics)PollutionEnvironmental scienceParticulatesAir pollutionEarth scienceEnvironmental chemistryEnvironmental protectionChemistryMeteorologyGeographyGeologyEcologyBiology

Abstract

fetched live from OpenAlex

In the past decades, China has witnessed high air pollution associated with rapid economic development, although regulatory efforts have alleviated the situation since 2013. Haze events characterized by high particulate matter (PM) levels in China are not only of enormous magnitude but also represent a distinct chemical regime. Once driven by direct emissions, these high-PM episodes are now more affected by secondary aerosol, especially secondary organic aerosol (SOA). This Review synthesizes the state of the science of SOA formation in urban China, specifically (i) how the dominance of anthropogenic precursors affects SOA formation, (ii) what are the prevailing SOA formation mechanisms, and (iii) how important are the multipollutant and multiphase processes in SOA formation and evolution. We also highlight essential directions for future studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.287
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2025
Admission routes1
Has abstractyes

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