Impact of particle number and mass size distributions of major chemical components on particle mass scattering efficiency in urban Guangzhou of South China
Bibliographic record
Abstract
Abstract. To grasp the key factors affecting particle mass scattering efficiency (MSE), particle mass and number size distribution, bulk PM2.5 and PM10 and their major chemical compositions, and particle scattering coefficient (bsp) under dry condition were measured at an urban site in Guangzhou, south China during 2015–2016. On annual average, 10 ± 2 %, 48 ± 7 % and 42 ± 8 % of PM10 mass were in the condensation, droplet and coarse modes, with mass median aerodynamic diameters (MMADs) of 0.21 ± 0.00, 0.78 ± 0.07 and 4.57 ± 0.42 μm, respectively. 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, (NH4)2SO4, NH4NO3 and crustal element oxides in the droplet mode, and crustal element oxides, OM and CaSO4 in the coarse mode were the dominant chemical species in their respective modes. The measured bsp can be reconstructed to the level of 91 ± 10 % using Mie theory with input of the estimated chemically-resolved number concentrations of NaCl, NaNO3, Na2SO4, NH4NO3, (NH4)2SO4, K2SO4, CaSO4, Ca(NO3)2, OM, EC, crustal element oxides and unidentified fraction. MSEs of bulk particle and individual chemical species were underestimated by less than 13 % in any season based on the estimated bsp and chemical species mass concentrations. Seasonal average MSEs varied in a small range of 3.5 ± 0.1 to 3.9 ± 0.2 m2 g−1 for fine particles, which was mainly caused by seasonal variations of the mass fractions and MSEs of OM 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".