Trends of inorganic sulfur and nitrogen species at an urban site in western Canada (2004–2018)
Bibliographic record
Abstract
A 15-year (2004–2018) record of measurement data of gaseous and particulate sulfur and nitrogen pollutants in air collected at an urban site in Burnaby in western Canada was analyzed for generating their decadal trends using three different methods, including linear regression, Mann-Kendall test and Theil-Sen trend estimator (MK-TS), and ensemble empirical mode decomposition (EEMD). Annual mean concentration of SO2 and SO42− decreased by about 59% and 42%, respectively, during the 15-year period. The slower decreases of SO42− than SO2 were mainly caused by the increased O3 concentration and temperature in spring and summer, which promoted conversion of SO2 to SO42− through gas-phase reaction, and by the increased aerosol pH value and availability of H2O2 in winter, which enhanced aqueous-phase SO42− formation. Accordingly, the sulfur oxidation ratio (SOR) increased by 23% or more in spring, summer, and winter during the 15-year period. Annual mean concentrations of NO2 and NO3− declined by 36% and 38%, respectively, during this period. On seasonal basis, NO3− decreased faster than NO2 in autumn and slower in winter. The non-linear responses of NO3− to NO2 concentration decreases were more evident in winter than the other seasons, partly due to the increased particulate NO3− fraction caused by decreased temperature, increased aerosol pH value, and enhanced NO3− formation caused by increased O3 concentrations. Annual mean concentration of NH3 showed small increases due to stable NH3 emission and reduced conversion of NH3 to NH4+. NH4+ concentration decreased by 51% during the 15-year period. These results suggest that reduced oxidants levels are likely responsible for weakened formation of secondary inorganic aerosols, besides emission reductions for SO2 and NO2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".