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Record W2187780561 · doi:10.5194/acp-16-2435-2016

Evaluation and application of multi-decadal visibility data for trend analysis of atmospheric haze

2016· article· en· W2187780561 on OpenAlexafffund
Chi Li, Randall V. Martin, Brian L. Boys, Aaron van Donkelaar, Sacha Ruzzante

Bibliographic record

VenueAtmospheric chemistry and physics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsQueen's UniversityDalhousie University
FundersArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaGoddard Institute for Space StudiesNational Park ServiceUniversity of California, DavisU.S. Environmental Protection AgencyKillam TrustsNational Centers for Environmental InformationDesert Research InstituteNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsHazeEnvironmental scienceVisibilityAerosolClimatologyMeteorologyAtmospheric researchAtmospheric sciencesTrend analysisGeographyStatisticsGeologyMathematics

Abstract

fetched live from OpenAlex

There are few multi-decadal observations of atmospheric aerosols worldwide. This study applies global hourly visibility (Vis) observations at more than 3000 stations to investigate historical trends in atmospheric haze over 1945–1996 for the US, and over 1973–2013 for Europe and eastern Asia. A comprehensive data screening and processing framework is developed and applied to minimize uncertainties and construct monthly statistics of inverse visibility (1/Vis). This data processing includes removal of relatively clean cases with high uncertainty, and change point detection to identify and separate methodological discontinuities such as the introduction of instrumentation. Although the relation between 1/Vis and atmospheric extinction coefficient ( b ext ) varies across different stations, spatially coherent trends of the screened 1/Vis data exhibit consistency with the temporal evolution of collocated aerosol measurements, including the b ext trend of −2.4 % yr −1 (95 % CI: −3.7, −1.1 % yr −1 ) vs. 1/Vis trend of −1.6 % yr −1 (95 % CI: −2.4, −0.8 % yr −1 ) over the US for 1989–1996, and the fine aerosol mass (PM 2.5 ) trend of −5.8 % yr −1 (95 % CI: −7.8, −4.2 % yr −1 ) vs. 1/Vis trend of −3.4 % yr −1 (95 % CI: −4.4, −2.4 % yr −1 ) over Europe for 2006–2013. Regional 1/Vis and Emissions Database for Global Atmospheric Research (EDGAR) sulfur dioxide (SO 2 ) emissions are significantly correlated over the eastern US for 1970–1995 ( r = 0.73), over Europe for 1973–2008 ( r ∼ 0.9) and over China for 1973–2008 ( r ∼ 0.9). Consistent "reversal points" from increasing to decreasing in SO 2 emission data are also captured by the regional 1/Vis time series (e.g., late 1970s for the eastern US, early 1980s for western Europe, late 1980s for eastern Europe, and mid 2000s for China). The consistency of 1/Vis trends with other in situ measurements and emission data demonstrates promise in applying these quality assured 1/Vis data for historical air quality studies.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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

Citations41
Published2016
Admission routes2
Has abstractyes

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