The surface aerosol optical properties in the urban area of Nanjing, west Yangtze River Delta, China
Bibliographic record
Abstract
Abstract. Observational studies of aerosol optical properties are useful for reducing uncertainties in estimations of aerosol radiative forcing and forecasting visibility. In this study, the observed near-surface aerosol optical properties in urban Nanjing are analysed from March 2014 to February 2016. Results show that near-surface urban aerosols in Nanjing are mainly from local emissions and the surrounding regions. They have lower loadings but are more scattering than aerosols in most cities in China. The annual mean aerosol extinction coefficient (EC), single-scattering albedo (SSA) and asymmetry parameter (ASP) at 550 nm are 381.96 Mm−1, 0.9 and 0.57, respectively. The aerosol absorption coefficient (AAC) is about 1 order of magnitude smaller than its scattering coefficient (SC). However, the absorbing aerosol has a larger Ångström exponent (AAE) value, 1.58 at 470∕660 nm, about 0.2 larger than the scattering aerosols (SAE). All the aerosol optical properties follow a near-unimodal pattern, and their values are mostly concentrated around their averages, accounting for more than 60 % of the total samplings. Additionally, they have substantial seasonality and diurnal variations. High levels of SC and AAC all appear in winter due to higher aerosol and trace gas emissions. AAE (ASP) is the smallest (largest) in summer, possibly because of high relative humidity (RH) which also causes considerably larger SC and smaller SAE, although intensive gas-to-particle transformation could produce a large number of finer scattering aerosols in this season. Seasonality of EC is different from the columnar aerosol optical depth. Larger AACs appear during the rush hours of the day while SC and back-scattering coefficient (Bsp) only peak in the early morning. Aerosols are fresher in the daytime than at night-time, leading to their larger Ångström exponent and smaller ASP. Different temporal variations between AAC and SC cause the aerosols to be more absorbing (smaller SSA) in autumn, winter and around rush hours. ASP has a good quasi-log-normal growth trend with increasing SC when RH is below 60 %. The correlation between AAC and SC at the site is close but a little smaller than that in suburban Nanjing in spring. Atmospheric visibility decreases exponentially with increasing EC or SC, more sharply in spring and summer, and it could be further deteriorated with increasing SSA and ASP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".