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Record W2123595598 · doi:10.5194/acpd-15-27253-2015

Baseline carbon monoxide and ozone in the northeast US over 2001–2010

2015· article· en· W2123595598 on OpenAlexaffabout
Y. Zhou, Huiting Mao, Kenneth L. Demerjian, Christian Hogrefe, Jane Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersU.S. Environmental Protection Agency
KeywordsBaseline (sea)OzoneEnvironmental scienceNOxAtmospheric sciencesClimatologyCarbon monoxideGeographyMeteorologyChemistryGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Baseline carbon monoxide (CO) and ozone (O3) were studied at seven rural sites in the northeast US during varying periods over 2001–2010. Interannual and seasonal variations of baseline CO and O3 were examined for the effects of changes in anthropogenic emissions, stratospheric intrusion, transport pathways and O3 photochemistry. Baseline CO generally exhibited decreasing trends at most sites, except at Castle Spring (CS), an elevated (~ 400 m a.s.l.) site in rural central New Hampshire. Over April 2001–December 2010, baseline CO at Thompson Farm (TF), Pinnacle State Park (PSP), and Whiteface Mountain (WFM) decreased at rates ranging from −4.3 to −2.5 ppbv yr−1. Baseline CO decreased significantly at a rate of −2.3 ppbv yr−1 at Mt. Washington (MWO) over April 2001–March 2009, and −3.5 ppbv yr−1 at Pack Monadnock (PM) over July 2004–October 2010. Unlike baseline CO, baseline O3 did not display a significant long term trend at any of the sites, resulting probably from opposite trends in NOx emissions worldwide and possibly from the overall relatively constant mixing ratios of CH4 in the 2000s. In looking into long term trends by season, wintertime baseline CO at MWO and WFM, the highest sites, did not exhibit a significant trend, probably due to the competing effects of decreasing CO emissions in the US and increasing emissions in Asia. Springtime and wintertime baseline O3 at TF increased significantly at a rate of 2.4 and 2.7 ppbv yr−1, respectively, which was likely linked to nitrogen oxides (NOx) emissions reductions over urban areas and possible resultant increases in O3 due to less titration by NO in urban plumes. The effects of meteorology on baseline O3 and CO were investigated. A negative correlation was found between springtime baseline O3 and the North Atlantic oscillation (NAO) index. It was found that during positive NAO years, lower baseline O3 in the northeast US was linked to less solar radiation flux, weakened stratospheric intrusion, and intensified continental export. The lowest baseline CO at Appledore Island (AI), PM, TF, PSP, WFM and the lowest baseline O3 at AI, PM, and PSP in summer 2009 were linked to the negative phase of the Arctic oscillation (AO), when more frequent cyclone activities brought more clean Arctic air to midlatitudes. It was also found that forest fires played a major role in determining baseline CO in the northeast US In summer, ~ 38% of baseline CO variability at AI, CS, MWO, TF, PSP, and WFM could be explained by CO emissions from forest fires in Russia and ~ 22 % by emissions from forest fires in Canada. Long-range transport of O3 and its precursors from biomass burning contributed to the highest baseline O3 in summer 2003 at AI, CS, MWO, TF, and WFM. The findings of this study suggested impacts of increasing Asian emissions, NOx emissions from the Northeast Urban corridor, global biomass burning emissions, and meteorological conditions (e.g. cyclone activity, AO, and NAO) should all be considered when designing strategies for meeting and maintaining National Ambient Air Quality Standards (NAAQS) and evaluating the air quality in the northeast US.

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 score0.128
Threshold uncertainty score0.255

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.211
Teacher spread0.190 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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