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Record W1582101783 · doi:10.1029/2012jd018463

Shipboard measurements of gaseous elemental mercury along the coast of Central and Southern California

2012· article· en· W1582101783 on OpenAlexaff
P. S. Weiss‐Penzias, E. J. Williams, B. M. Lerner, T. S. Bates, Cassandra J. Gaston, Kimberly A. Prather, A. Vlasenko, Shao‐Meng Li

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBayOutflowUpwellingOceanographyEnvironmental scienceCruiseDiel vertical migrationMercury (programming language)LatitudeMorningGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Gaseous elemental mercury (GEM) in the atmosphere was measured during an oceanographic cruise in coastal waters between San Diego and San Francisco, California during the CalNex 2010 campaign. The goal of the measurements was to quantify GEM in the various environments that the ship encountered, from urban outflow, the Port of Long Beach and associated shipping lanes, coastal waters affected by upwelling, the San Francisco Bay, and the Sacramento ship channel. Mean GEM for the whole cruise was 1.41 ± 0.20 ng m–3, indicating that background concentrations were predominantly observed. The ship's position was most often in waters off the coast of Los Angeles (74% of time with latitude < 34.3°N) and mean GEM for this section was not significantly (P > 0.05) higher than the whole cruise mean. South of 34.3°N, GEM was observed to vary diurnally and as a function of wind direction, displaying significantly higher concentrations at night and in the morning associated with general transport from the land to the sea. GEM and CO concentrations were positively correlated with a slope of 0.0011 ng m–3 ppbv–1 (1.23 × 10–7 mol mol–1) during periods identified as “Los Angeles urban outflow”, which given the inventoried CO emissions for the region, suggests a larger source of GEM than is accounted for by the inventory. The timing of the diel maximum in GEM (9:00 local time) was intermediate between the maxima of CO and NO2 (6:00) and that of NO and SO2 (10:00–12:00), suggesting that a mixture of urban and industrial sources were contributing to GEM. There was no observable postsunrise dip in GEM concentrations due to reaction with atomic chlorine in the polluted coastal atmosphere. On three occasions, significantly higher GEM concentrations were observed while in the Port of Long Beach (~ 7 ng m–3), and analyses of wind directions, ratios of GEM with other copollutants, and the composition of single particles, suggest that these plumes originated from the local waste incinerator in the Port area. A plume encounter from a large cargo ship allowed for the estimation of a mass‐based emission factor for GEM (0.05 ± 0.01 mg kg–1 fuel burned). GEM enhancements observed in the Carquinez Straits, were lower than expected based on the observed NOx/SO2 ratios in the plumes and emissions inventories of the nearest oil refineries. In a region north of Monterey Bay known for upwelling, GEM in the air was positively correlated with dimethyl sulfide (DMS) in seawater and in the air. Using the observed GEM/DMS(g) relationship and the calculated mean DMS ocean‐atmosphere flux for the cruise, an ocean‐atmosphere flux of GEM of 0.017 ± 0.009 µmol m–2 d–1 was estimated. This flux was on the upper end of previously reported GEM ocean‐atmosphere fluxes and should be verified with further measurements of Hg species in seawater and air.

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 score0.318
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.059
GPT teacher head0.325
Teacher spread0.266 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

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