Source influence on emission pathways and ambient PM <sub>2.5</sub> pollution over India (2015–2050)
Bibliographic record
Abstract
Abstract. India currently experiences degraded air quality, with future economic development leading to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015–2050, under specific pathways of diffusion of cleaner and more energy efficiency technologies. The impacts of individual source-sectors on PM2.5 concentrations were assessed through GEOS-Chem model simulations of spatially and temporally resolved particulate matter concentrations, followed by population-weighted aggregation to national and state levels. PM2.5 pollution is a pan-India problem, with a regional character, not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 standard (40 µg/m3). Future evolution of emissions under current regulation or under promulgated or proposed regulation, yield deterioration in future air-quality in 2030 and 2050. Only under a scenario where more ambitious measures are introduced, promoting a total shift away from traditional biomass technologies and a very large shift (80–85 %) to non-fossil electricity generation was an overall reduction in PM2.5 concentrations below 2015 levels achieved. In this scenario, concentrations in 20 states and six union territories would fall below the national standard. However, even under this ambitious scenario, 10 states (including Delhi) would fail to comply with the national standard through to 2050. Under present day (2015) emissions, residential biomass fuel use for cooking and heating is the largest single sector influencing outdoor air pollution across most of India. Agricultural residue burning is the next most important source, especially in north-west and north India, while in eastern and peninsular India, coal burning in thermal power plants and industry are important contributors. The relative influence of anthropogenic dust and total dust is projected to increase in all future scenarios, largely from decreases in the influence of other PM2.5 sources. Overall, the findings suggest a large regional background of PM2.5 pollution (from residential biomass, agricultural residue burning and power plant and industrial coal), underlying that from local sources (transportation, brick kiln, distributed diesel) in highly polluted areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".