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Supplementary material to "Long-term Atmospheric Deposition of Nitrogen and Sulfur and Assessment of Critical Loads Exceedances at Canadian Rural Locations"

2022· preprint· en· W4283654183 on OpenAlexaffabout
Irene Cheng, Leiming Zhang, Zhuanshi He, Hazel Cathcart, Daniel Houle, Amanda Cole, Jian Feng, Jason M. O’Brien, A. M. Macdonald, Julian Aherne, Jeffrey R. Brook

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsTrent UniversityPublic Health OntarioUniversity of TorontoEnvironment and Climate Change Canada
Fundersnot available
KeywordsSulfurTerm (time)Deposition (geology)Environmental scienceNitrogenAtmospheric sciencesEnvironmental chemistryMaterials scienceChemistryGeologyPhysicsMetallurgyGeomorphology

Abstract

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S1 Daily average dry deposition velocities (Vd) of N and S species S1.1 Spatial patternsDaily average Vd for gas-phase compounds (SO2, HNO3) and particulate-phase compounds (pSO4 2-, pNH4 + and pNO3 -) for the 2000-2018 period are summarized in Table S2.For gaseous compounds, the mean daily Vd (cm/s) among 15 CAPMoN sites were 1.2 for HNO3 and 0.46 for SO2.For particulate sulfate (pSO4 2-), ammonium (pNH4 + ) and nitrate (pNO3 -), the mean daily Vd were 0.16, 0.15 and 0.21 cm/s, respectively.Vd of N and S compounds exhibited strong variability between sites.The regions with higher Vd for N and S compounds include the west coast, southeast and Atlantic (Table S2).According to the land use data surrounding a CAPMoN site (Table S1), the west coast and Atlantic sites have higher Vd likely because of nearby land use coverage that is associated with higher Vd.For example, water surfaces and forests.In particular, evergreen needleleaf or broadleaf trees are typically associated with larger leaf area index (LAI) and hence larger Vd.Meteorological conditions also differ substantially across Canadian sites, which can drive the spatial variability in Vd. S1.2 Cold vs. warm seasonal patternsVd of gaseous N and S compounds were slightly greater in the warm season than cold season at most of the sites based on the monthly variations in Fig. S1.This was also the case for particulate nitrate due to the higher fraction of nitrate in coarse PM during the warm season, which is based on size-fractionated measurements previously conducted at CAPMoN sites (Zhang et al., 2008).Given that Vd of coarse PM (PM2.5-10) is larger than that of fine PM (PM2.5), a higher fraction in coarse PM results in higher Vd.During the cold season, nitrate is predominantly associated with fine PM at CAPMoN sites (Zhang et al., 2008).Vd of sulfate and ammonium were slightly higher in the cold season than warm season.This pattern is likely attributed to meteorology perhaps higher wind speeds in the cold season. S1.3 Long-term annual trendsLong-term annual trends in Vd were estimated using Theil-Sen slopes of the seasonal average Vd, which have been seasonally adjusted using LOESS (locally estimated scatterplot smoothing).Statistically significant trends in Vd (p<0.05) are shown in Fig. S2.Among the sites, annual trends in Vd (cm/s of change per year) ranged from 2.4-8.8x 10 -3 for HNO3, 9.0 x 10 -4 to 5.1 x 10 -3 for SO2, 3.0 x 10 -4 to 1.0 x 10 - 3 for sulfate, 3.0-9.0x 10 -4 for ammonium and 2.7 x 10 -4 to 1.5 x 10 -3 for nitrate.Overall, the increasing annual trends in Vd were very small indicating that Vd of a chemical specie stays constant over time.Small fluctuations in Vd over time reflect meteorological variability.Thus, the main factor driving the long-term annual dry deposition trends are the ambient air concentrations.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.505
Threshold uncertainty score0.705

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5050.070

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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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