Impact of particle number and mass size distributions of major chemical components on particle mass scattering efficiency in urban Guangzhou in southern China
Bibliographic record
Abstract
To grasp the key factors affecting particle mass scattering efficiency (MSE), particle mass and number size distribution, PM 2.5 and PM 10 and their major chemical compositions, and the particle scattering coefficient ( b sp ) under dry conditions were measured at an urban site in Guangzhou, southern China, during 2015–2016. On an annual average, 10±2 %, 48±7 % and 42±8 % of PM 10 mass were in the condensation, droplet and coarse modes, respectively, with mass mean aerodynamic diameters (MMADs) of 0.78±0.07 in the droplet mode and 4.57±0.42 µm in the coarse mode. The identified chemical species mass concentrations can explain 79±3 %, 82±6 % and 57±6 % of the total particle mass in the condensation, droplet and coarse mode, respectively. Organic matter (OM) and elemental carbon (EC) in the condensation mode, OM, (NH 4 ) 2 SO 4 , NH 4 NO 3 , and crustal element oxides in the droplet mode, and crustal element oxides, OM, and CaSO 4 in the coarse mode, were the dominant chemical species in their respective modes. The measured b sp can be reconstructed to the level of 91±10 % using Mie theory with input of the estimated chemically resolved number concentrations of NaCl, NaNO 3 , Na 2 SO 4 , NH 4 NO 3 , (NH 4 ) 2 SO 4 , K 2 SO 4 , CaSO 4 , Ca(NO 3 ) 2 , OM, EC, crustal element oxides and unidentified fraction. MSEs of particle and individual chemical species were underestimated by less than 13 % in any season based on the estimated b sp and chemical species mass concentrations. Seasonal average MSEs varied in the range of 3.5±0.1 to 3.9±0.2 m 2 g −1 for fine particles (aerodynamic diameter smaller than 2.1 µm), which was mainly caused by seasonal variations in the mass fractions and MSEs of the dominant chemical species (OM, NH 4 NO 3 , (NH 4 ) 2 SO 4 ) in the droplet mode. MSEs of the dominant chemical species were determined by their lognormal size-distribution parameters, including MMADs and standard deviation ( σ ) in the droplet mode.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".