PM2.5 Chemical Composition Analysis in Different Functional Subdivisions in Tangshan, China
Bibliographic record
Abstract
In this study, the 24-h PM2.5 (i.e., the fine particles with aerodynamic diameter of ≤ 2.5 µm) samples were collected at two different functional subdivisions in the city of Tangshan during the period of July 2012 to April 2013. The months of July, October, January, and April were chosen to represent four different typical seasons. The total PM2.5 mass was measured. PM2.5 samples were used for the analysis of inorganic elements, ions, Organic Carbon (OC) and Elemental Carbon (EC). PM2.5 concentration in the industrial subdivision were generally higher than those in the residential subdivision. The annual mean PM2.5 concentrations were 196 µg m–3 for the industrial subdivision and 116 µg m–3 for the residential subdivision. The coefficients of divergence (CD) calculated for spring, summer, autumn and winter were 0.67, 0.35, 0.65, and 0.33, respectively. The Enrichment Factor Method (EFM) was used to help determine the original sources of these inorganic elements. Secondary water-soluble ions (SO42–, NO3–, and NH4+) were the major water soluble ions in the PM2.5 of Tangshan, and they are present in the atmosphere as (NH4)2SO4 and NH4NO3. The Sulfur Oxidation Ratio (SOR) and Nitrogen Oxidation Ratio (NOR) show that the precursor conversion ratio was highest in summer. At both subdivisions, winter had the highest monthly average OM concentration while summer had the lowest.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".