Chemical source profiles of fine particles for five different sources in Delhi
Bibliographic record
Abstract
Increasing emissions from sources such as construction and burning of biomass from crop residues, roadside and municipal solid waste have led to a rapid increase in the atmospheric concentrations of fine particulate matter (≤2.5 μm; PM2.5) over many Indian cities. Analyses of their chemical profiles are important for receptor models to accurately estimate the contributions from different sources. We have developed chemical source profiles for five important pollutant sources - construction (CON), paved road dust (PRD), roadside biomass burning (RBB), solid waste burning (SWB), and crop residue burning (CPB) - during three intensive campaigns (winter, summer and post-monsoon) in and around Delhi. We obtained chemical characterisations of source profiles incorporating carbonaceous material such as organic carbon (OC) and elemental carbon (EC), water-soluble ions (F−, Cl−, NO2−, NO3−, SO42−, PO43−, Na+ and NH4+), and elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Br, Rb, Sr, Ba, and Pb). CON was dominated by the most abundant elements, K, Si, Fe, Al, and Ca. PRD was also dominated by crustal elements, accounting for 91% of the total analysed elements. RBB, SWB and CPB profiles were dominated by organic matter, which accounted for 94%, 86.2% and 86% of the total PM2.5, respectively. The database of PM emission profiles developed from the sources investigated can be used to assist source apportionment studies for accurate quantification of the causes of air pollution and hence assist governmental bodies in formulating relevant countermeasures.
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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 | 0.000 | 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) | 0.000 | 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 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".