Field evidence of brown carbon absorption enhancement linked to organic nitrogen formation in Indo-Gangetic Plain
Bibliographic record
Abstract
Atmospheric brown carbon (BrC), a short-lived climate forcer, absorbs solar radiation and is a substantial contributor to warming of Earth’s atmosphere. BrC composition, absorption properties, and their evolution are poorly represented in climate models, especially for aqueous media (e.g., fog, clouds). Aqueous phenomena such as clouds and fog are quite prevalent during wintertime in Indo-Gangetic Plain (IGP). In this study, we examined the evolution of water-soluble BrC (WS-BrC) mass absorption efficiency at 365 nm (MAEWS-BrC-365) and chemical characteristics, viz., low-volatile fraction of organic carbon and water-soluble organic nitrogen (WSON) to water-soluble organic carbon (WSOC) ratio (org-N/C) on diurnal scale and under varying aerosol liquid water content (ALWC) and relative humidity (RH) conditions during wintertime in central IGP. Diurnally, highest MAEWS-BrC-365 was observed during late night and morning time and was coincided with enhanced WSON formation and relative abundance of low-volatile organics. The lower MAEWS-BrC-365 during the afternoon time was due to photochemical aging; whereas fresh WS-BrC emissions due to increased biomass burning and vehicular emissions activities caused lower MAEWS-BrC-365 values during evening and early night time. Further, we observed that MAEWS-BrC-365 and org-N/C ratio increased with decrease in ALWC and RH, signifying that drying of bulk aerosol particle and aqueous droplets would be potentially accelerating the formation of nitrogen containing organic chromophores resulting in enhancement in WS-BrC absorptivity. We also obtained that direct radiative forcing of WS-BrC relative to that of EC was ~19% during wintertime at Kanpur and ~40% of this contribution was in UV-region. These findings highlight the importance of incorporating this atmospheric phenomenon in BrC framework for fog and clouds in global climate models.
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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 | 0.001 |
| 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 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".