Snow scavenging and phase partitioning of nitrated and oxygenated aromatic hydrocarbons in polluted and remote environments in central Europe and the European Arctic
Bibliographic record
Abstract
Nitrated and oxygenated polycyclic aromatic hydrocarbons (N/OPAHs) are emitted in combustion processes and formed in polluted air. Their environmental cycling through wet deposition has hardly been studied. Fresh snow samples at urban and rural sites in central Europe, as well as surface snow from a remote site in Svalbard, were analysed for 17 NPAHs, 8 OPAHs, and 11 nitrated mono-aromatic hydrocarbons (NMAHs), of which most N/OPAHs as well as nitrocatechols, nitrosalicylic acids, and 4-nitroguaiacol are studied for the first time in precipitation. In order to better understand the scavenging mechanisms, the particulate mass fractions ( θ ) at 273 K were predicted using a multi-phase gas-particle partitioning model based on polyparameter linear free energy relationships. ∑NPAH concentrations were 1.2–17.6 and 8.8–19.1 ng L −1 at urban and rural sites, whereas ∑OPAHs were 79.8–955.2 and 343.3–1757.4 ng L −1 at these sites, respectively. 9,10-anthraquinone was predominant in snow aqueous and particulate phases. NPAHs were only found in the particulate phase with 9-nitroanthracene being predominant followed by 2-nitrofluoranthene. Among NMAHs, 4-nitrophenol showed the highest abundance in both phases. The levels found for nitrophenols were in the same range or lower than those reported in the 1980s and 1990s. The lowest levels of ∑N/OPAHs and ∑NMAHs were found at the remote site (3.5 and 390.5 ng L −1 , respectively). N/OPAHs preferentially partitioned in snow particulate phase in accordance with predicted θ , whereas NMAHs were predominant in the aqueous phase, regardless of θ . It is concluded that the phase distribution of non-polar N/OPAHs in snow is determined by their gas-particle partitioning prior to snow scavenging, whereas that for polar particulate phase substances, i.e. NMAHs, is determined by an interplay between gas-particle partitioning in the aerosol and dissolution during in- or below-cloud scavenging.
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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 | 0.001 | 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".