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Record W2946363599 · doi:10.5194/acp-19-15691-2019

Wintertime spatial distribution of ammonia and its emission sources in the Great Salt Lake region

2019· article· en· W2946363599 on OpenAlexaff
Alexander Moravek, J. G. Murphy, Amy Hrdina, John C. Lin, Christopher Pennell, Alessandro Franchin, A. M. Middlebrook, D. L. Fibiger, Caroline C. Womack, Erin E. McDuffie, Randal S. Martin, Kori D. Moore, Munkhbayar Baasandorj, Steven S. Brown

Bibliographic record

VenueAtmospheric chemistry and physics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsDalhousie UniversityYork UniversityUniversity of Toronto
FundersNational Oceanic and Atmospheric AdministrationUtah State University
KeywordsAerosolParticulatesEnvironmental scienceAtmospheric sciencesAmmoniumTrace gasAir quality indexNitrateEnvironmental chemistryReactive nitrogenSulfateAmmoniaChemistryNitrogenMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

Ammonium-containing aerosols are a major component of wintertime air pollution in many densely populated regions around the world. Especially in mountain basins, the formation of persistent cold-air pools (PCAPs) can enhance particulate matter with diameters less than 2.5 µm (PM 2.5 ) to levels above air quality standards. Under these conditions, PM 2.5 in the Great Salt Lake region of northern Utah has been shown to be primarily composed of ammonium nitrate; however, its formation processes and sources of its precursors are not fully understood. Hence, it is key to understanding the emission sources of its gas phase precursor, ammonia (NH 3 ). To investigate the formation of ammonium nitrate, a suite of trace gases and aerosol composition were sampled from the NOAA Twin Otter aircraft during the Utah Winter Fine Particulate Study (UWFPS) in January and February 2017. NH 3 was measured using a quantum cascade tunable infrared laser differential absorption spectrometer (QC-TILDAS), while aerosol composition, including particulate ammonium ( p NH 4 ), was measured with an aerosol mass spectrometer (AMS). The origin of the sampled air masses was investigated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model and combined with an NH 3 emission inventory to obtain model-predicted NH x (=NH3+pNH4) enhancements. Enhancements represent the increase in NH 3 mixing ratios within the last 24 h due to emissions within the model footprint. Comparison of these NH x enhancements with measured NH x from the Twin Otter shows that modelled values are a factor of 1.6 to 4.4 lower for the three major valleys in the region. Among these, the underestimation is largest for Cache Valley, an area with intensive agricultural activities. We find that one explanation for the underestimation of wintertime emissions may be the seasonality factors applied to NH 3 emissions from livestock. An investigation of inter-valley exchange revealed that transport of NH 3 between major valleys was limited and PM 2.5 in Salt Lake Valley (the most densely populated area in Utah) was not significantly impacted by NH 3 from the agricultural areas in Cache Valley. We found that in Salt Lake Valley around two thirds of NH x originated within the valley, while about 30 % originated from mobile sources and 60 % from area source emissions in the region. For Cache Valley, a large fraction of NO x potentially leading to PM 2.5 formation may not be locally emitted but mixed in from other counties.

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 score0.158
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.186
Teacher spread0.180 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

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