Particulates and Gaseous Emission from the Indian Cropland Agricultural Sector and Health Burden Attributable to Emitted Primary PM<sub>2.5</sub>
Bibliographic record
Abstract
The agricultural sector significantly contributes to atmospheric pollution, impacting air quality through activities such as tillage, planting, fertilizer application, harvesting, crop residue burning, and grain handling. The outcome of this work is the documentation of the emission inventory of particulates (PM 10 and PM 2.5 ) and gaseous emissions (SO 2, CO, NO x, NH 3, and volatile organic compounds (VOCs)) from the agricultural industry, particularly crop production. Further, the emissions of elemental carbon (EC), organic carbon (OC), and polycyclic aromatic hydrocarbons (PAHs), which are part of particulate matter (PM), were calculated along with greenhouse gases (CO 2, CH 4, and N 2 O) coming from the agricultural sector for 2021, with projections for 2051. Total greenhouse gas emissions in 2021 were 408 Tg, while PM 10 and PM 2.5 emissions were approximately 2.5 and 1.1 Tg, respectively. The health impact of primary agricultural PM 2.5 was quantified, revealing an estimated approximately 4 million disability-adjusted life years (DALYs) and 0.13 million deaths attributable to these emissions in 2021. The findings highlight the urgent need to reduce emissions at their source and ensure sustainable agricultural practices. This study provides critical data for policymakers to address air quality and health challenges. Furthermore, the developed emission inventory will serve as a valuable resource for researchers conducting air quality modeling and environmental impact assessments.
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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 | 0.002 |
| 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) | 0.002 | 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 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".