Particulates and Gaseous Emission from Indian Agricultural Sector and Health Burden Attributable to Agricultural PM2.5
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 (CRB), and grain handling. The outcome of this work is the docu- mentation of emission inventory of particulates (PM10 and PM2.5) and gaseous emission (SO2, CO, NOx, NH3, and volatile organic compounds (VOCs)) from the agricultural industry. Further the emission of EC, OC, and (Polycyclic Aromatic Hydrocarbons) PAHs, which are part of particulate matter (PM), were calculated along with green- house gases (CO2, CH4 and N2O) coming from the agricultural sector for 2021, with projections for 2051. Total greenhouse gas emission in 2021 were 377 Tg, while PM10 and PM2.5 emissions were approximately 2.5 Tg and 1.1 Tg, respectively. The health impact of agricultural PM2.5 was quantified, revealing an estimated approximately 4 million Disability-Adjusted Life Years (DALYs) and 0.14 million deaths attributable to these emissions in 2021. The findings highlight the urgent need for technological advancements to reduce emissions at their source, ensuring sustainable agricultural practices. This study provides critical data for policymakers to address air quality and health challenges. Furthermore, the emission inventory developed will serve as a valuable resource for researchers conducting air quality modeling and environmental impact assessments. Synopsis: Emission inventory for various agricultural activities is not yet available for India. This study of aggregation of emission inventory for India supports efforts to improve public health, reduce pollution, and promote long-term sustainability in agriculture.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".