A synthesis review on atmospheric wet deposition of particulate elements: scavenging ratios, solubility, and flux measurements
Bibliographic record
Abstract
Atmospheric dry and wet deposition of particulate matter controls its lifetime in air and contributes to the environmental burden of toxic pollutants and thus has important implications on human and ecosystem health. This synthesis review focuses on atmospheric wet deposition of particulate elements and analyzed their scavenging ratios (i.e., ratio of concentration in precipitation to that in ambient air), solubility, and wet deposition flux measurements based on published studies in the literature, aiming to gather updated knowledge that can be used for modeling their wet deposition. Our analysis finds that scavenging ratios of a specific element have a narrow range. Overall, elemental scavenging ratios for snow are approximately three times higher than those for rain. Elements that are bound to coarse (PM2.5–10) particles have larger scavenging ratios than those bound to fine (PM2.5) particles, except for Fe and Si. Solubility of elements in rainwater ranges from 8% (Fe) to 94% (Ca). Solubility is moderately correlated with scavenging ratio, possibly explaining the lower scavenging ratios of Fe and Si compared with other elements with similar fine fractions. Data collected from North America, Europe, the Middle East, and Asia show that the wet fluxes of Al and Fe are orders of magnitude greater than those of routinely monitored anthropogenic elements (Zn, Pb, Cu, Ni, Cd, Cr). Wet deposition fluxes of particulate elements in the Middle East exceed those in other regions, likely due to regional transport of dust and soil resuspension. Fluxes from all regions are a factor of two to three times greater in industrialized and urban locations than in rural and remote locations because of industrial, vehicular, and soil and mineral dust emissions. Dry deposition fluxes are usually greater than wet deposition fluxes, although to varying degrees according to co-located measurements. Based on the relationships between scavenging ratio and the elemental PM2.5 fraction under rain and snow conditions, we derived regression equations for estimating scavenging ratios of particulate elements for which measurements are limited. Such knowledge and data improve the quantification of atmospheric deposition fluxes for an expanded list of metals and metalloids and the understanding of pathways contributing to ecological risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".