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Record W2155191484 · doi:10.1002/2013jd021357

Effects of crop residue burning on aerosol properties, plume characteristics, and long‐range transport over northern India

2014· article· en· W2155191484 on OpenAlexaff
Dimitris G. Kaskaoutis, Sarvan Kumar, Deepti Sharma, Ramesh P. Singh, Shailesh Kumar Kharol, Manish Sharma, Abhay Kumar Singh, Sachchidanand Singh, Atinderpal Singh, Dinesh Pratap Singh

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsDalhousie University
FundersNational Oceanic and Atmospheric AdministrationIndian Institute of Technology KanpurNational Aeronautics and Space Administration
KeywordsAerosolEnvironmental scienceModerate-resolution imaging spectroradiometerAngstrom exponentAtmospheric sciencesPlumeCrop residueHYSPLITParticulatesTrace gasSea saltClimatologyMeteorologyGeographySatelliteAgricultureGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract Aerosol emissions from biomass burning are of specific interest over the globe due to their strong radiative impacts and climate implications. The present study examines the impact of paddy crop residue burning over northern India during the postmonsoon (October–November) season of 2012 on modification of aerosol properties, as well as the long‐range transport of smoke plumes, altitude characteristics, and affected areas via the synergy of ground‐based measurements and satellite observations. During this period, Moderate Resolution Imaging Spectroradiometer (MODIS) images show a thick smoke/hazy aerosol layer below 2–2.5 km in the atmosphere covering nearly the whole Indo‐Gangetic Plains (IGP). The air mass trajectories originating from the biomass‐burning source region over Punjab at 500 m reveal a potential aerosol transport pathway along the Ganges valley from west to east, resulting in a strong aerosol optical depth (AOD) gradient. Sometimes, depending upon the wind direction and meteorological conditions, the plumes also influence central India, the Arabian Sea, and the Bay of Bengal, thus contributing to Asian pollution outflow. The increased number of fire counts (Terra and Aqua MODIS data) is associated with severe aerosol‐laden atmospheres (AOD 500 nm > 1.0) over six IGP locations, high values of Ångström exponent (>1.2), high particulate mass 2.5 (PM 2.5 ) concentrations (>100–150 µgm −3 ), and enhanced Ozone Monitoring Instrument Aerosol Index gradient (~2.5) and NO 2 concentrations (~6 × 10 15 mol/cm 2 ), indicating the dominance of smoke aerosols from agricultural crop residue burning. The aerosol size distribution is shifted toward the fine‐mode fraction, also exhibiting an increase in the radius of fine aerosols due to coagulation processes in a highly turbid environment. The spectral variation of the single‐scattering albedo reveals enhanced dominance of moderately absorbing aerosols, while the aerosol properties, modification, and mixing atmospheric processes differentiate along the IGP sites depending on the distance from the aerosol source, urban influence, and local characteristics.

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 score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations333
Published2014
Admission routes1
Has abstractyes

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