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Record W2563177019 · doi:10.5194/acp-17-10071-2017

Global deposition of total reactive nitrogen oxides from 1996 to 2014 constrained with satellite observations of NO <sub>2</sub> columns

2017· article· en· W2563177019 on OpenAlexafffund
Jeffrey A. Geddes, Randall V. Martin

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsDeposition (geology)SatelliteChemical transport modelAtmospheric sciencesSCIAMACHYNitrogenEnvironmental scienceReactive nitrogenNitrateClimatologyAtmosphere (unit)MeteorologyAerosolChemistryGeographyTroposphereGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract. Reactive nitrogen oxides (NOy) are a major constituent of the nitrogen deposited from the atmosphere, but observational constraints on their deposition are limited by poor or nonexistent measurement coverage in many parts of the world. Here we apply NO2 observations from multiple satellite instruments (GOME, SCIAMACHY, and GOME-2) to constrain the global deposition of NOy over the last 2 decades. We accomplish this by producing top-down estimates of NOx emissions from inverse modeling of satellite NO2 columns over 1996–2014, and including these emissions in the GEOS-Chem chemical transport model to simulate chemistry, transport, and deposition of NOy. Our estimates of long-term mean wet nitrate (NO3−) deposition are highly consistent with available measurements in North America, Europe, and East Asia combined (r = 0.83, normalized mean bias = −7 %, N = 136). Likewise, our calculated trends in wet NO3− deposition are largely consistent with the measurements, with 129 of the 136 gridded model–data pairs sharing overlapping 95 % confidence intervals. We find that global mean NOy deposition over 1996–2014 is 56.0 Tg N yr−1, with a minimum in 2006 of 50.5 Tg N and a maximum in 2012 of 60.8 Tg N. Regional trends are large, with opposing signs in different parts of the world. Over 1996 to 2014, NOy deposition decreased by up to 60 % in eastern North America, doubled in regions of East Asia, and declined by 20 % in parts of western Europe. About 40 % of the global NOy deposition occurs over oceans, with deposition to the North Atlantic Ocean declining and deposition to the northwestern Pacific Ocean increasing. Using the residual between NOx emissions and NOy deposition over specific land regions, we investigate how NOx export via atmospheric transport has changed over the last 2 decades. Net export from the continental United States decreased substantially, from 2.9 Tg N yr−1 in 1996 to 1.5 Tg N yr−1 in 2014. Export from China more than tripled between 1996 and 2011 (from 1.0 to 3.5 Tg N yr−1), before a striking decline to 2.5 Tg N yr−1 by 2014. We find that declines in NOx export from some western European countries have counteracted increases in emissions from neighboring countries to the east. A sensitivity study indicates that simulated NOy deposition is robust to uncertainties in NH3 emissions with a few exceptions. Our novel long-term study provides timely context on the rapid redistribution of atmospheric nitrogen transport and subsequent deposition to ecosystems around the world.

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)0.001
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 score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations92
Published2017
Admission routes2
Has abstractyes

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