Seasonal aerosol acidity and liquid water content: impact on aerosol concentration and nitrogen deposition fluxes in a urban Canadian environment.
Bibliographic record
Abstract
Aerosol acidity and liquid water content (LWC) affect aerosol concentration and composition as well as the fate of the precursor compounds ammonia (NH3) and nitric acid (HNO3) [1,2]. Together with temperature, aerosol acidity and LWC determine the gas-particle partitioning of such precursors. In warm seasons, high aerosol acidity and low LWC promote the partitioning of NH3 to particulate phase as ammonium, while at the same time drive aerosol nitrate to the gas phase as HNO3. In cold seasons, the opposite effect can be observed. Given that the dry deposition rate of gaseous NH3 and HNO3 is up to 10 times faster than the particle phase, the conditions that favour the partitioning of these species to the gas phase also determine the dry deposition rates of reduced and oxidized nitrogen. This process has consequences for the accumulation of aerosols in the boundary layer, as well as the transport and deposition flux of nitrogen species[2]. In the present work, we explore the seasonal variation of aerosol acidity and liquid water content and their estimated effect on nitrogen dry deposition velocity using data collected over three years in Toronto, Canada, from January 2016 to December 2018. Aerosol acidity, in terms of H+ concentration, has large inter- and intra-seasonal variability, ranging between 5 and almost 3 orders of magnitude, respectively. By applying the framework developed in Nenes et al. 2020 [1], aerosol formation during winter is sensitive to HNO3 levels (pH range ~3 and ~6, LWC range ~0.4 and ~ 35.0 μg m-3), whereas in summer it tends to be insensitive to both NH3 and HNO3 (pH range ~1.4 and ~ 4, LWC range ~0.04 and ~10.0 μg m-3) This insensitive regime indicates that emissions of other precursors such as SOx and organic aerosol are major sources of aerosol variability in summer. In terms of nitrogen dry deposition, the seasonal variation experiences two regimes: in winter, the deposition is fast for NH3 and slow for HNO3, whereas in summer, both deposition of NH3 and HNO3 are fast. In conclusion, the analysis of ambient aerosol data using aerosol pH and liquid water content suggest that in Toronto, emission controls of NOx in winter and of SOx in summer would be most beneficial for air quality. [1] Nenes A., Pandis S., Weber R.J., Russell A., ACP, 20, 3249–3258, 2020 [2] Nenes A., Pandis S., Kanakidou M., Russell A., Song S., Vasilakos P., Weber R.J., ACPD, 20, 266, 2020
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".