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Record W4252925609 · doi:10.1029/2012gl053926

Satellite constraints of Nitrogen Oxide (NOX) emissions from India based on OMI observations and WRF-Chem simulations

2012· article· en· W4252925609 on OpenAlexfundno aff
Sachin D. Ghude, Gabriele Pfister, Chinmay Jena, Ronald van der A, L. K. Emmons, Rajesh Kumar

Bibliographic record

VenueGeophysical Research Letters · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersDalhousie UniversityDepartment of Science and Technology, Ministry of Science and Technology, IndiaIndian Institute of Technology MadrasUniversity Corporation for Atmospheric ResearchNational Science Foundation
KeywordsOzone Monitoring InstrumentEmission inventoryNOxTroposphereWeather Research and Forecasting ModelEnvironmental scienceSatelliteNitrogen oxideAtmospheric sciencesMeteorologyAtmospheric researchTropospheric ozoneClimatologyAir quality indexGeographyGeologyChemistryPhysics

Abstract

fetched live from OpenAlex

[1] In this work, we map and develop for the first time an independent satellite constrained NOx emission inventory for India for 2005 using an inverse technique and iterative procedure. We used OMI tropospheric NO2 column retrievals over the Indian region, with tropospheric NO2 columns simulated by the WRF-Chem model using the INTEX-B emission inventory. We determined the local relationship between modeled emissions and tropospheric columns and iteratively apply this relationship to OMI observations to derive an optimized NOx emission inventory on a 0.5° × 0.5° grid. The optimized total NOx emissions for India amount to 1.9 TgN/y and agree within 25% with EDGARv4.1 and the INTEX-B estimate. Our top-down inventory captures many of the missing hotspots in the original inventory and suggests that the INTEX-B inventory overestimates emissions over the Western and Eastern Indo-Gangetic region and underestimates point sources. We further evaluate the effect of the top-down inventory on surface ozone, which clearly indicates significant changes in spatial distribution.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.056
GPT teacher head0.295
Teacher spread0.239 · 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 teacher head, not a consensus.

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

Citations47
Published2012
Admission routes1
Has abstractyes

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