Chemical and isotopic interpretation of major ion compositions from precipitation: a one-year temporal monitoring study in Wrocław, SW Poland
Bibliographic record
Abstract
The chemical compositions (Na+, NH4 +, K+, Mg2+, Ca2+, Cl−, NO2 −, NO3 −, SO4 2−, HCO3 −) of wet precipitation and nitrogen isotope compositions δ15N(NH4 +) were studied from January to December 2010 in Wrocław (SW Poland). Results of a principle component analysis show that 82 % of the data variability can be explained by three main factors: 1) F1 (40 %) observed during vegetative season (electrical conductivity, HCO3 −, NO3 −, NO2 −, NH4 + and SO4 2−), mainly controlling rainwater mineralization; 2) F2 (26 %) observed during vegetative and heating seasons (K+, Ca2+ and Mg2+), probably representing a combination of two processes: anthropogenic dusts and fertilizers application in agricultural fields, and 3) F3 (16 %) reported mainly during heating season (Na+ and Cl−) probably indicating the influence of marine aerosols. Variations of δ15N(NH4 +) from −11.5 to 18.5 ‰ identify three main pathways for the formation of NH4 +: 1) equilibrium fractionation between NH3 and NH4 +; 2) kinetic exchange between NH3 and NH4 +; 3) NH4 + exchange between atmospheric salts particles and precipitation. The coupled chemical/statistical analysis and δ15N(NH4 +) approach shows that while fossil fuels burning is the main source of NH4 + in precipitation during the heating season, during the vegetative season NH4 + originates from local sewage irrigation fields in Osobowice or agricultural fertilizers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".