MétaCan
Menu
← Back to cohort
Record W4232058144 · doi:10.5194/acp-2021-433

Response of particle number concentrations to Clean Air Action: Lessons from the first long-term aerosol measurements in a typical urban valley, West China

2021· preprint· en· W4232058144 on OpenAlexaff
Suping Zhao, Yu Ye, Jianglin Li, Daiying Yin, Shaofeng Qi, Dahe Qin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsScience North
FundersYouth Innovation Promotion AssociationGuangzhou Science and Technology Program key projectsYouth Innovation Promotion Association of the Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsParticle numberRange (aeronautics)Particle sizeEnvironmental scienceParticle (ecology)Atmospheric sciencesCluster (spacecraft)AerosolNucleationMass concentration (chemistry)PhysicsChemistryMaterials scienceMeteorologyPlasmaBiology

Abstract

fetched live from OpenAlex

Abstract. The strictest ever Clean Air Action (CAA) has been implemented by Chinese government since 2013 to alleviate the severe haze pollution. The PM2.5 mass concentration was found to be largely reduced in response to emission mitigation policies, but response of particle number concentrations (PNCs) to CAA was less evaluated in the previous studies, which may be largely different from PM2.5 mass due to newly formed particle impacts. In this work, the first in-situ observation of particle number size distributions (PNSDs) during 2012–2019 in urban Lanzhou was used to analyze long-term PNCs variations and CAA impacts. The average number of particles in nucleation (N13–25, particle number in the size range of 13–25 nm), Aitken (N25–100, particle number in the size range of 25–100 nm) and accumulation (N100–800, particle number in the size range of 100–800 nm) modes were respectively 2514.0 cm−3, 10768.7 cm−3, and 3258.4 cm−3, and N25–100 accounted for about 65.1 % of total PNCs during the campaign. K-means clustering technique was used to classify the hourly mean PNSDs into six clusters, and each cluster corresponded to a specific source and influencing factor. The polluted clusters governed the winter PNCs before 2016, and their occurrence was less and less frequent after 2016, which was largely dominated by reduction in primary emissions. However, the contribution of new particle formation (NPF) events to summer N13–25 decreased from 50 % to about 10 % during 2013 to 2015, and then increased to reach around 60 % in 2019. The trends of size-resolved PNCs for each cluster were quantified by Theil-Sen regression. The size-segregated PNCs exhibited downward trends for all clusters during 2012–2015, especially in spring. The annual relative slopes of spring PNCs varied from −54.7 % to −17.2 %, −42.6 % to −14.1 %, and −40.7 % to −17.5 % per year for 13–25, 25–100, and 100–800 nm size ranges, and the reduction in the polluted clusters was much larger than NPF clusters. The ultrafine particle number was increased and the amplitude was much greater during 2016–2019. The annual relative slopes of N13–25 varied between 8.0 % in fall and 135.5 % in spring for NPF cluster. In response to CAA, the increased daytime net radiation, higher ambient temperature and lower relative humidity at noon for NPF events also could partly explain the higher N13–25 induced by the more frequent nucleation events after 2016, especially in spring. The air mass were mainly from the adjacent regions of urban Lanzhou and less affected by long-range transport for NPF events, and the thus particles were not easily grown by coagulation during transport processes, which was helpful for occurrence of NPF events. Therefore, some effective control measures cooperatively controlled particle number and mass should be took for the Chinese megacities.

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 score0.141
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.370
Teacher spread0.236 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAir Quality and Health Impacts→French-language works237,207→