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Record W2916891707 · doi:10.5194/acp-19-8471-2019

Impact of particle number and mass size distributions of major chemical components on particle mass scattering efficiency in urban Guangzhou in southern China

2019· article· en· W2916891707 on OpenAlexaff
Jun Tao, Zhisheng Zhang, Yunfei Wu, Leiming Zhang, Zhijun Wu, Peng Cheng, Mei Li, Laiguo Chen, Renjian Zhang, Junji Cao

Bibliographic record

VenueAtmospheric chemistry and physics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Natural Science Foundation of China
KeywordsParticle (ecology)Particle sizeCondensationChemical compositionChemistryMass concentration (chemistry)Particle-size distributionAnalytical Chemistry (journal)Total organic carbonRange (aeronautics)Mass fractionScatteringMineralogyEnvironmental chemistryMaterials scienceMeteorologyPhysicsGeologyOpticsPhysical chemistry

Abstract

fetched live from OpenAlex

To grasp the key factors affecting particle mass scattering efficiency (MSE), particle mass and number size distribution, PM 2.5 and PM 10 and their major chemical compositions, and the particle scattering coefficient ( b sp ) under dry conditions were measured at an urban site in Guangzhou, southern China, during 2015–2016. On an annual average, 10±2 %, 48±7 % and 42±8 % of PM 10 mass were in the condensation, droplet and coarse modes, respectively, with mass mean aerodynamic diameters (MMADs) of 0.78±0.07 in the droplet mode and 4.57±0.42 µm in the coarse mode. The identified chemical species mass concentrations can explain 79±3 %, 82±6 % and 57±6 % of the total particle mass in the condensation, droplet and coarse mode, respectively. Organic matter (OM) and elemental carbon (EC) in the condensation mode, OM, (NH 4 ) 2 SO 4 , NH 4 NO 3 , and crustal element oxides in the droplet mode, and crustal element oxides, OM, and CaSO 4 in the coarse mode, were the dominant chemical species in their respective modes. The measured b sp can be reconstructed to the level of 91±10 % using Mie theory with input of the estimated chemically resolved number concentrations of NaCl, NaNO 3 , Na 2 SO 4 , NH 4 NO 3 , (NH 4 ) 2 SO 4 , K 2 SO 4 , CaSO 4 , Ca(NO 3 ) 2 , OM, EC, crustal element oxides and unidentified fraction. MSEs of particle and individual chemical species were underestimated by less than 13 % in any season based on the estimated b sp and chemical species mass concentrations. Seasonal average MSEs varied in the range of 3.5±0.1 to 3.9±0.2 m 2 g −1 for fine particles (aerodynamic diameter smaller than 2.1 µm), which was mainly caused by seasonal variations in the mass fractions and MSEs of the dominant chemical species (OM, NH 4 NO 3 , (NH 4 ) 2 SO 4 ) in the droplet mode. MSEs of the dominant chemical species were determined by their lognormal size-distribution parameters, including MMADs and standard deviation ( σ ) in the droplet mode.

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 score0.100
Threshold uncertainty score0.199

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 communication0.0010.000
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 head0.011
GPT teacher head0.259
Teacher spread0.249 · 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

Citations46
Published2019
Admission routes1
Has abstractyes

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