MétaCan
Menu
← Back to cohort
Record W4253593651 · doi:10.5194/acp-2018-968

Increased inorganic aerosol fraction contributes to air pollution and haze in China

2018· preprint· en· W4253593651 on OpenAlexfundno aff
Yonghong Wang, Yuesi Wang, Lili Wang, Tuukka Petäjä, Qiaozhi Zha, Chongshui Gong, Sixuan Li, Bo Hu, Markku Kulmala

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationDalhousie University
KeywordsAerosolHazeRelative humidityEnvironmental chemistryPollutionSulfateAir quality indexNitrateAir pollutionEnvironmental scienceChemistryAtmospheric sciencesMeteorologyGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract. The detailed formation mechanism of increased number of haze events in China is still not very clear. Here, we found that reduced surface visibility and an increase in satellite derived columnar concentration of inorganic precursor concentrations are connected with each other. Typically higher inorganic mass fractions lead to increased aerosol water uptake and light scattering ability in elevated relative humidity. Satellite observation of aerosol precursors of NO2 and SO2 showed increased concentrations during study period. Our in-situ measurement of aerosol chemical composition in Beijing also confirmed increased contribution of inorganic aerosol fraction as a function of increased particle pollution level. Our investigations demonstrate that the increased inorganic fraction in the aerosol particles is a key component in the frequently occurring haze days during studying period, and particularly the reduction of nitrate, sulfate and their precursor gases would contribute towards better air quality in China.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.210
Teacher spread0.202 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicAtmospheric chemistry and aerosols→French-language works237,207→