Influence of small‐ and large‐scale variables on the chemical and isotopic compositions of urban rainwater, as illustrated by a case study in Ashdod, Israel
Bibliographic record
Abstract
The source of air pollution must be identified to select the appropriate monitoring activities and remedial measures. The present case study illustrates how analyses of (1) regional‐scale synoptics and air mass trajectories, (2) local‐scale atmospheric measurements (wind direction and speed, air temperature, dust load, sulfur and nitrogen oxide concentrations in the air, rain amount, and intensity) and (3) chemical and isotopic compositions of rainwater from 46 rain events, collected in the coastal city of Ashdod, Israel, were used to identify the various sources contributing to rainwater salts and contaminants and their timing. Rainwater affected by the Mediterranean Sea was characterized by a large marine fraction of salts, high chloride concentrations, and low values of δ18O and δD. These rain events were associated with the Cyprus Low system, which typically prevails in midwinter. Rainwater affected by continental sources had a small contribution of marine salts, high bicarbonate and calcium concentrations, and high values of δ18O and δD. These rains were related to continental trajectories from the Red Sea and north African coast prevailing during the fall and spring. Rainwater affected by anthropogenic sources was characterized by low marine fraction, high sulfate concentrations, and low values of δ34S. Whereas remote anthropogenic source was associated with the Red Sea trough system and were characterized by high nitrate concentrations (representing desert dust), the local anthropogenic source was characterized by high air concentrations of sulfur oxides at the local monitoring stations and high potassium and low nitrate concentrations. The identification of local anthropogenic source suggests that measures taken to reduce emissions from local oil refineries and power stations are likely to reduce the overall air pollution in the study area.
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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 | 0.000 | 0.001 |
| 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".