Retrospective Isotopic Analysis of Summertime Urban Atmospheric Sulfate in South Asia Using Improved Source Constraints
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The emissions of sulfur dioxide (SO 2 ), a harmful atmospheric pollutant, are on the rise in South Asia. Sulfate, formed from the oxidation of SO 2, often comprises 10%–67% of aerosol mass and has a profound impact on climate, air quality/human health, and the environment. The potential drivers of sulfate-linked urban air pollution in South Asian megacities─facing a choking air pollution crisis─remain poorly understood due to a lack of systematic observations. Here, we conducted stable S-isotope (δ 34 S) fingerprinting of sulfate aerosols in summertime megacity Delhi in South Asia to evaluate the potential drivers. With newly developed region-specific isotopic endmembers in this study, a statistical source apportionment of urban atmospheric sulfate was feasible. Results show that coal combustion (80 ± 12%) and oil combustion (14 ± 11%), followed by road dust (4 ± 3%) and biomass burning (2 ± 2%), were major contributors to atmospheric sulfate in summertime Delhi. Retrospective analysis showed a marked isotopic shift in stable sulfate isotopic composition in summertime megacity Delhi wherein the average δ 34 S value was 4 ± 1‰ in 2015 and 2 ± 1‰ in 2021, respectively. This was evidently linked to changes in the dominant fuel type as sulfate sourced from coal combustion (oil combustion) significantly increased ∼ 20% (decreased ∼ 20%) during this period. With no clear increase in the number of thermal power plants in and around Delhi, we speculate that the substantial increase in coal-derived sulfate could plausibly be linked to a rise in the informal industries such as brick production and food and agricultural product processing operations, whose SO 2 emissions remain challenging to estimate. While further observations from the region are warranted, the findings here suggest that the continued dependence on coal in developing nations of South Asia could be one of the reasons for rising SO 2 levels.
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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 | 0.001 |
| 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) | 0.001 | 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 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".