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Record W2804606984

Detection and quantification of reactive atmospheric nitrogen species in remote ecosystems

2017· dissertation· en· W2804606984 on OpenAlexaboutno aff
Bryan K. Place

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2017
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsReactive nitrogenEnvironmental scienceEcosystemTerrestrial ecosystemTransectAquatic ecosystemNitrogen cycleEutrophicationEnvironmental chemistryNitrogenAtmospheric sciencesEcologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Anthropogenic inputs of nitrogen to the environment have increased by over 150 % \nin the last 150 years causing concern for vital biophysical processes on Earth. Thus being \nable to measure these increased inputs in terrestrial, aquatic and atmospheric \nenvironments is essential to understanding how the global nitrogen cycle has been \nimpacted since the industrial revolution. With respect to the atmosphere, emissions of \nreduced and oxidized forms of nitrogen have increased largely due to the anthropogenic \nactivities of agriculture and combustion, respectively. Emissions of these nitrogenous \nspecies not only impact regions adjacent to their point sources, but also have the ability to \ninfluence ecosystems hundreds of kilometers away due to the long-range transport of \nsome of these compounds. This can impact sensitive remote ecosystems positively or \nnegatively by either stimulating growth or causing acidification, eutrophication and \nbiodiversity shifts. Therefore developing analytical techniques that are capable of \nmeasuring oxidized and reduced atmospheric inputs to remote ecosystems is of great \nimportance. \nIn part I of this work a method employing custom-built physisorption-based passive \nsamplers coupled with ion chromatography analysis was developed to sample \natmospheric nitric acid (HNO₃(g)) in remote ecosystems. The developed HNO₃(g) sampling \nmethod was able to detect HNO₃(g) mixing ratios as low as 2 parts per trillion by volume \n(pptv) over a monthly sampling period, following a rigorous quality assurance and quality \ncontrol procedure. The passive samplers were installed across the Newfoundland and \nLabrador – Boreal Ecosystem Latitudinal Transect (NL-BELT) in the summer of 2015, and average mixing ratios of HNO₃(g) at the NL-BELT field sites from 2015-16 were \ndetermined to be in the tens of parts per trillion by volume (pptv) range. The dry \ndeposition flux of HNO₃(g) as nitrogen (N) to the field sites ranged from 3 – 16 mg N yr-1. \nThrough an air mass back trajectory analysis, coupled with a steady-state chemical box \nmodel approximation, it was determined that the HNO₃(g) quantities observed at a single \nNL-BELT site likely originated from local production and regional transport from central \nand eastern Newfoundland, with an additional contribution from the down welling of \nperoxyacetyl nitrates from the upper troposphere, possibly occurring during the spring \nand early summer. \nIn part II of this work, an ion chromatography method was developed to speciate \nand quantify alkylamines (NR₃(g)). NR₃(g) have been shown to influence Earth’s climate \nand may be an important source of new nitrogen to remote ecosystems. The developed \nmethod was shown to be sensitive, accurate, and robust in separating and quantifying 11 \natmospheric alkylamines, including 3 sets of alkylamine isomers, from 5 common \natmospheric inorganic cations. The method was able to detect NR₃(g) at a picogram per \ninjection level, and the method performed robustly in the presence of a complex biomassburning \nmatrix containing amounts of inorganic cations up to 3 orders of magnitude \nlarger than the NR₃(g) quantified in the samples. Thus the ion chromatography method can \nbe applied to the remote atmosphere where alkylamine concentrations are often detected \nin quantities 1000 times less than other atmospheric cations. In the biomass-burning \nparticle samples tested using the ion chromatography method unprecedented quantities of \ndimethylamine and diethylamine were observed, with the summed molar quantity \nexceeding that of ammonium in the 100 – 560 nm particle diameter fraction. \nThe applicability of these atmospheric measurement techniques to measure and \nquantify HNO₃(g) and NR₃(g) has been demonstrated for remote ecosystems and will \nhopefully allow for a greater understanding of these two species roles’ in remote \nenvironments.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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