Large sub-regional differences of ammonia seasonal patterns over India reveal inventory discrepancies
Bibliographic record
Abstract
Abstract Ammonia (NH 3 ) is a key precursor of haze particles and fine particulate matter (PM 2.5 ) and its spatiotemporal variabilities are poorly constrained. In this study, we present measurements of NH 3 over the Indian subcontinent region from the Infrared Atmospheric Sounder Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) satellite instruments. This region exhibits a complex emission profile due to the number of varied sources, including crop burning, fossil fuel combustion, fertilizer application, livestock and industrial sources. Observations from the CrIS and IASI instruments are oversampled to a resolution of 0.02° × 0.02°. Five regions with distinct spatiotemporal NH 3 profiles are determined using k-means clustering. Maximum NH 3 columns are seen in July over the western India with column densities of 6.2 × 10 17 mol cm −2 and 7.2 × 10 17 mol cm −2 respectively for IASI and CrIS. The seasonality of measured NH 3 columns show annual maxima occurring in spring in Eastern India and Bangladesh and in mid-summer for the western Indo-Gangetic plain. Our observational constraints suggest that the impact of local farming practices on NH 3 emissions is not well captured in emission inventories such as Coupled Model Intercomparison Project Phase 6 (CMIP6), which exhibits peaks in the late spring and autumn. The spatial variability in the seasonal patterns of NH 3 is also not captured by the single emissions profile used in CMIP6 for India. The high-resolution maps obtained from these measurements can be used to improve NH 3 emission inventories in order to understand its sources for more accurate predictions of air quality in the Indian subcontinent. Our study points to the need for regionally specific emissions inventories for short-lived species such as NH3 that have heterogeneous emissions profiles due to specific agricultural practices and other emission source characteristics.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".