MétaCan
Menu
Back to cohort
Record W2714878629 · doi:10.5194/acp-2017-215

The optical, physical properties and direct radiative forcing of urban columnar aerosols in Yangtze River Delta, China

2017· article· en· W2714878629 on OpenAlexaff
Bingliang Zhuang, Tijian Wang, Jane Liu, Huizheng Che, Yong Han, Yu Fu, Shu Li, Min Xie, Mengmeng Li, Pulong Chen, Huimin Chen, Xiu‐Qun Yang, Jianning Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaEuropean Commission
KeywordsAerosolSingle-scattering albedoSun photometerAngstrom exponentEnvironmental scienceRadiative forcingRadiative transferAtmospheric sciencesScatteringAlbedo (alchemy)MeteorologyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract. The aerosol optical and physical properties as well as its direct radiative forcing (DRF) in urban area of Nanjing (urNJ) are investigated, based on the measurements of Cimel sun-photometer combined with a radiation transfer model. We find that the annual mean 550 nm aerosol optical depth (AOD) of the total aerosols is about 0.65, dominated by scattering aerosols (about 94 %), resulting in a mean single scattering albedo (SSA) of 0.93 at 550 nm and refractive index of 1.44 + 0.0084i at 440 nm during the sampling period. The scattering aerosol has larger size than the absorbing aerosol, with Ångström exponents (AE) of 1.19 at 440/870 nm, 0.13 smaller than the latter one. The coarse mode fraction for the scattering aerosol (18.03 %) is much smaller than the absorbing aerosol's (43.91 %). Thus, the fine mode aerosols presents more scattering (SSA = 0.95) while the coarse aerosol is more absorption (SSA = 0.82). Analysis implies that there are about 15 % and 27.5 % occurrences of dust and black carbon dominated mixing aerosols, respectively, during the sampling period. All the optical properties follow a simple unimodal pattern. Aerosols in urNJ have a two-mode lognormal pattern in volume size distribution, peaking at the radius of 0.148 and 2.94 µm, and the AOD positively depends on them. Although the fine mode aerosol has a much smaller sizes than the coarse one, they have the same level of the volume concentrations (about 0.12 µm3/cm3) due to much higher fraction of the fine aerosol. Estimations present that the mean aerosol DRFs at the top of atmosphere (TOA) are −10.69, −16.45, +5.76 W/m2, respectively, for the total, scattering and absorbing aerosols in clear sky. At the surface, the DRFs are 1.1–2.5 times stronger than those at TOA, and the fine aerosol DRFs in these three type of aerosols account for 83.7 %, 91.7 % and 67.2 %, respectively, to their totals. Normally, aerosol DRFs is not very sensitive (no more than 5 %) to its profiles in clear sky condition (extreme cases excepted), although both aerosol scattering and absorption could become weaker to some extent if more aerosols were in lower layers. Both the aerosol properties and DRFs have substantial seasonality.

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 score0.065
Threshold uncertainty score0.130

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicAtmospheric aerosols and cloudsFrench-language works237,207