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Record W4256153249 · doi:10.5194/acp-2021-471

Aerosol Responses to Precipitation Along North American Air Trajectories Arriving at Bermuda

2021· preprint· en· W4256153249 on OpenAlexfundno aff
Hossein Dadashazar, Majid Alipanah, Miguel Ricardo A. Hilario, Ewan Crosbie, Simon Kirschler, Hongyu Liu, Richard H. Moore, Andrew J. Peters, Amy Jo Scarino, Michael A. Shook, K. L. Thornhill, Christiane Voigt, Hailong Wang, Edward L. Winstead, Bo Zhang, Luke D. Ziemba, Armin Sorooshian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersPacific Northwest National LaboratoryDivision of Ocean SciencesNational Oceanic and Atmospheric AdministrationNuclear Safety and Security CommissionBattelleNational Aeronautics and Space AdministrationU.S. Department of EnergyCompute CanadaHarvard UniversityNational Science Foundation
KeywordsAerosolEnvironmental scienceParticulatesAtmospheric sciencesPrecipitationSulfateVolume (thermodynamics)Mass concentration (chemistry)Sea saltPollutionClimatologyEnvironmental chemistryChemistryMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. North American pollution outflow is ubiquitous over the western North Atlantic Ocean, especially in winter, making this location an ideal natural laboratory for investigating the impact of precipitation on aerosol particles along air mass trajectories. We take advantage of observational data collected at Bermuda to seasonally assess the sensitivity of aerosol mass concentrations and volume size distributions to accumulated precipitation along trajectories (APT). The mass concentration of particulate matter with aerodynamic diameter less than 2.5 µm normalized by the enhancement of carbon monoxide above background (PM2.5/∆CO) at Bermuda was used to estimate the degree of aerosol loss during transport to Bermuda. Results for December–February (DJF) show most trajectories come from North America and have the highest APTs, resulting in significant reduction (by 53 %) in PM2.5/∆CO under high APT conditions (> 13.5 mm) relative to low APT conditions (< 0.9 mm). Moreover, PM2.5/∆CO was most sensitive to increases in APT up to 5 mm (−0.044 µg m−3 ppbv−1 mm−1) and less sensitive to increases in APT over 5 mm. While anthropogenic PM2.5 constituents (e.g., black carbon, sulfate, organic carbon) decrease with high APT, sea salt in contrast was comparable between high and low APT conditions owing to enhanced local wind and salt emissions in high APT conditions. The greater sensitivity of the fine mode volume concentrations (versus coarse mode) to wet scavenging is evident from AERONET volume size distribution data. A combination of GEOS-Chem model simulations of 210Pb submicron aerosol tracer and its gaseous precursor 222Rn reveal that (i) surface aerosol particles at Bermuda are most impacted by wet scavenging in winter/spring (due to large-scale precipitation) with a maximum in March, whereas convective scavenging plays a substantial role in summer; and (ii) North American 222Rn tracer emissions contribute most to surface 210Pb concentrations at Bermuda in winter (~75–80 %), indicating that air masses arriving at Bermuda experience large-scale precipitation scavenging while traveling from North America. A case study flight from the ACTIVATE field campaign on 22 February 2020 reveals a significant reduction in aerosol number and volume concentrations during air mass transport off the U.S. East Coast associated with increased cloud fraction and precipitation. These results highlight the sensitivity of remote marine boundary layer aerosol characteristics to precipitation along trajectories, especially when the air mass source is continental outflow from polluted regions like the U.S. East Coast.

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 score0.159
Threshold uncertainty score0.315

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)0.0010.000

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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