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Record W2606722963 · doi:10.5194/acp-17-9485-2017

A review of current knowledge concerning PM <sub>2. 5</sub> chemical composition, aerosol optical properties and their relationships across China

2017· review· en· W2606722963 on OpenAlexaff
Jun Tao, Leiming Zhang, Junji Cao, Renjian Zhang

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Natural Science Foundation of China
KeywordsAerosolEnvironmental scienceCoal combustion productsChinaBiomass burningMegacityAir quality indexNitrateAtmospheric sciencesSulfateChemical compositionApportionmentEnvironmental chemistrySea saltGeographyMeteorologyCoalChemistryEcologyPhysics

Abstract

fetched live from OpenAlex

Abstract. To obtain a thorough knowledge of PM2. 5 chemical composition and its impact on aerosol optical properties across China, existing field studies conducted after the year 2000 are reviewed and summarized in terms of geographical, interannual and seasonal distributions. Annual PM2. 5 was up to 6 times the National Ambient Air Quality Standards (NAAQS) in some megacities in northern China. Annual PM2. 5 was higher in northern than southern cities, and higher in inland than coastal cities. In a few cities with data longer than a decade, PM2. 5 showed a slight decrease only in the second half of the past decade, while carbonaceous aerosols decreased, sulfate (SO42−) and ammonium (NH4+) remained at high levels, and nitrate (NO3−) increased. The highest seasonal averages of PM2. 5 and its major chemical components were typically observed in the cold seasons. Annual average contributions of secondary inorganic aerosols to PM2. 5 ranged from 25 to 48 %, and those of carbonaceous aerosols ranged from 23 to 47 %, both with higher contributions in southern regions due to the frequent dust events in northern China. Source apportionment analysis identified secondary inorganic aerosols, coal combustion and traffic emission as the top three source factors contributing to PM2. 5 mass in most Chinese cities, and the sum of these three source factors explained 44 to 82 % of PM2. 5 mass on annual average across China. Biomass emission in most cities, industrial emission in industrial cities, dust emission in northern cities and ship emission in coastal cities are other major source factors, each of which contributed 7–27 % to PM2. 5 mass in applicable cities. The geographical pattern of scattering coefficient (bsp) was similar to that of PM2. 5, and that of aerosol absorption coefficient (bap) was determined by elemental carbon (EC) mass concentration and its coating. bsp in ambient condition of relative humidity (RH) = 80 % can be amplified by about 1.8 times that under dry conditions. Secondary inorganic aerosols accounted for about 60 % of aerosol extinction coefficient (bext) at RH greater than 70 %. The mass scattering efficiency (MSE) of PM2. 5 ranged from 3.0 to 5.0 m2 g−1 for aerosols produced from anthropogenic emissions and from 0.7 to 1.0 m2 g−1 for natural dust aerosols. The mass absorption efficiency (MAE) of EC ranged from 6.5 to 12.4 m2 g−1 in urban environments, but the MAE of water-soluble organic carbon was only 0.05 to 0.11 m2 g−1. Historical emission control policies in China and their effectiveness were discussed based on available chemically resolved PM2. 5 data, which provides the much needed knowledge for guiding future studies and emissions policies.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.009
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.082
GPT teacher head0.297
Teacher spread0.214 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations387
Published2017
Admission routes1
Has abstractyes

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