Selection of the best sorbent material to capture ammonia emissions using passive flux samplers
Bibliographic record
Abstract
The agricultural sector is the most significant contributor to global ammonia (NH 3 ) emissions, affecting ecosystems and human health. In this context, passive flux samplers (PFS) have emerged as a cost-effective and practical alternative for monitoring air emissions. The PFS comprises a tubular body with inlet and outlet openings and a sorbent medium that passively collects the target pollutant. This study aims to identify the most efficient acid-coated sorbent among glass microfiber filters, solid glass beads, zeolite, and biochar for the determination of NH 3 emissions employing PFS. A comprehensive comparative analysis between the four acid-coated sorbents was conducted, involving aerodynamic analysis, sorption kinetics, variability, and precision of the data. Additionally, a weighted performance index was developed to objectively rank each material to be used in estimating NH 3 emissions. The results of this study highlight microfiber glass filters as the most effective acid-coated matrix, combining favorable aerodynamic behaviors with low variability (28%). In contrast, porous materials such as zeolite and biochar showed high promising sorption capacities (12 573 μg and 3 928 μg, respectively) but exhibited high variability (58% and 37%), limiting their reliability under field conditions. The methodology developed, the weighted index to select the best sorbent, lays the groundwork for a standardized, reproducible, and transferable approach to sorption evaluation in PFS devices, contributing to more reliable, scalable, and cost-efficient air quality monitoring strategies. • Acid-coated sorbents enhance PFS performance for NH 3 emission assessment • Sorbent type influences the orifice constant and the k factor of PFS • High sorption variability was observed for both biochar and zeolite • Weighted performance index identified glass microfiber filters as the best sorbent
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".