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Record W1584899462 · doi:10.5194/acp-15-13633-2015

Absorption coefficient of urban aerosol in Nanjing, west Yangtze River Delta, China

2015· article· en· W1584899462 on OpenAlexaff
Bingliang Zhuang, T. J. Wang, Jane Liu, Yan Ma, Chengjie Yin, S. Li, Min Xie, Yong Han, Jialei Zhu, Xiu‐Qun Yang, Congbin Fu

Bibliographic record

VenueAtmospheric chemistry and physics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersYoung Scientists FundNational Oceanic and Atmospheric AdministrationNatural Science Foundation for Young Scientists of Shanxi ProvinceFundamental Research Funds for the Central UniversitiesPriority Academic Program Development of Jiangsu Higher Education InstitutionsNanjing UniversityNational Natural Science Foundation of China
KeywordsAethalometerEnvironmental scienceAerosolDeltaAtmospheric sciencesRadiative forcingAngstrom exponentClimatologySeasonalityYangtze riverDiurnal temperature variationChinaMeteorologyGeographyPhysicsChemistryGeology

Abstract

fetched live from OpenAlex

Abstract. Absorbing aerosols can significantly modulate short-wave solar radiation in the atmosphere, affecting regional and global climate. The aerosol absorption coefficient (AAC) is an indicator that assesses the impact of absorbing aerosols on radiative forcing. In this study, the near-surface AAC and absorption Ångström exponent (AAE) in the urban area of Nanjing, China, are characterized on the basis of measurements in 2012 and 2013 using the seven-channel Aethalometer (model AE-31, Magee Scientific, USA). The AAC is estimated with direct and indirect corrections, which result in consistent temporal variations and magnitudes of AAC at 532 nm. The mean AAC at 532 nm is about 43.23 ± 28.13 M m−1 in the urban area of Nanjing, which is much lower than that in Pearl River Delta and the same as in rural areas (Lin'an) in Yangtze River Delta. The AAC in the urban area of Nanjing shows strong seasonality (diurnal variations); it is high in cold seasons (at rush hour) and low in summer (in the afternoon). It also shows synoptic and quasi-2-week cycles in response to weather systems. Its frequency distribution follows a typical log-normal pattern. The 532 nm AAC ranging from 15 to 65 M m−1 dominates, accounting for more than 72 % of the total data samples in the entire study period. Frequent high pollution episodes, such as those observed in June 2012 and in winter 2013, greatly enhanced AAC and altered its temporal variations and frequency distributions. These episodes are mostly due to local emissions and regional pollution. Air masses flowing from northern China to Nanjing can sometimes be highly polluted and lead to high AAC at the site. AAE at 660/470 nm from the Schmid correction (Schmid et al., 2006) is about 1.56, which might be more reasonable than from the Weingartner correction (Weingartner et al., 2003). Low AAEs mainly occur in summer, likely due to high relative humidity (RH) in the season. AAC increases with increasing AAE at a fixed aerosol loading. The RH–AAC relationship is more complex. Overall, AAC peaks at RH values of around 40 % (1.3 < AAE < 1.6), 65 % (AAE < 1.3 and AAE > 1.6), and 80 % (1.3 < AAE < 1.6).

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 score0.057
Threshold uncertainty score0.112

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)0.0010.000

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 head0.010
GPT teacher head0.200
Teacher spread0.191 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

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