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Record W3004222395 · doi:10.5194/acp-21-8111-2021

The role of coarse aerosol particles as a sink of HNO <sub>3</sub> in wintertime pollution events in the Salt Lake Valley

2021· article· en· W3004222395 on OpenAlexafffund
Amy Hrdina, J. G. Murphy, A. Gannet Hallar, John C. Lin, Alexander Moravek, Ryan Bares, Ross Petersen, Alessandro Franchin, A. M. Middlebrook, Lexie Goldberger, Ben H. Lee, Munkh Baasandorj, Steven S. Brown

Bibliographic record

VenueAtmospheric chemistry and physics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsYork UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationUniversity of Utah
KeywordsParticulatesEnvironmental chemistryAmmoniumPollutionNitrateSnowpackAerosolTrace gasSnowInorganic ionsSink (geography)Environmental scienceSea saltChemistryHazeAtmospheric sciencesIonMeteorologyGeology

Abstract

fetched live from OpenAlex

Wintertime ammonium nitrate (NH 4 NO 3 ) pollution events burden urban mountain basins around the globe. In the Salt Lake Valley of Utah in the United States, such pollution events are often driven by the formation of persistent cold-air pools (PCAPs) that trap emissions near the surface for several consecutive days. As a result, secondary pollutants including fine particulate matter less than 2.5 µm in diameter (PM 2.5 ), largely in the form of NH 4 NO 3 , build up during these events and lead to severe haze. As part of an extensive measurement campaign to understand the chemical processes underlying PM 2.5 formation, the 2017 Utah Winter Fine Particulate Study, water-soluble trace gases and PM 2.5 constituents were continuously monitored using the ambient ion monitoring ion chromatograph (AIM-IC) system at the University of Utah campus. Gas-phase NH 3 , HNO 3 , HCl, and SO 2 along with particulate NH4+, Na + , K + , Mg 2+ , Ca 2+ , NO3-, Cl − , and SO42- were measured from 21 January to 21 February 2017. During the two PCAP events captured, the fine particulate matter was dominated by secondary NH 4 NO 3 . The comparison of total nitrate (HNO 3 + PM 2.5 NO3-) and total NH x (NH 3 + PM 2.5 NH4+) showed NH x was in excess during both pollution events. However, chemical composition analysis of the snowpack during the first PCAP event revealed that the total concentration of deposited NO3- was nearly 3 times greater than that of deposited NH4+. Daily snow composition measurements showed a strong correlation between NO3- and Ca 2+ in the snowpack. The presence of non-volatile salts (Na + , Ca 2+ , and Mg 2+ ), which are frequently associated with coarse-mode dust, was also detected in PM 2.5 by the AIM-IC during the two PCAP events, accounting for roughly 5 % of total mass loading. The presence of a significant particle mass and surface area in the coarse mode during the first PCAP event was indicated by size-resolved particle measurements from an aerodynamic particle sizer. Taken together, these observations imply that atmospheric measurements of the gas-phase and fine-mode particle nitrate may not represent the total burden of nitrate in the atmosphere, implying a potentially significant role for uptake by coarse-mode dust. Using the NO3- : NH4+ ratio observed in the snowpack to estimate the proportion of atmospheric nitrate present in the coarse mode, we estimate that the amount of secondary NH 4 NO 3 could double in the absence of the coarse-mode sink. The underestimation of total nitrate indicates an incomplete account of the total oxidant production during PCAP events. The ability of coarse particles to permanently remove HNO 3 and influence PM 2.5 formation is discussed using information about particle composition and size 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 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 score0.083
Threshold uncertainty score0.164

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.006
GPT teacher head0.192
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

Citations18
Published2021
Admission routes2
Has abstractyes

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