Chemical composition, sources, and deposition fluxes of water-soluble inorganic ions obtained from precipitation chemistry measurements collected at an urban site in northwest China
Bibliographic record
Abstract
Precipitation samples were collected at an urban site in Xi'an, northwest China during March to November in 2009 and were then analyzed to determine the pH and concentrations of water-soluble inorganic ions (Na(+), NH(4)(+), K(+), Mg(2+), Ca(2+), SO(4)(2-), NO(3)(-), Cl(-), and F(-)) in precipitation. The pH of precipitation ranged from 4.1 to 7.6 for all of the samples with an annual volume-weighted mean of 6.4. While a large portion of the precipitation events were weakly acidic or alkaline, around 30% of the precipitation events in the autumn were strongly acidic. Precipitation events with air masses from the northeast and the southeast were weakly acidic while those with air masses from the northwest and the southwest were alkaline. SO(4)(2-), Ca(2+), NH(4)(+), and NO(3)(-) were dominant ions in the precipitation, accounting for 37%, 25%, 18%, and 9%, respectively, of the total analyzed ions. Ca(2+) and NH(4)(+) were found to be the major neutralizers of precipitation acidity; however, the contribution of Mg(2+), although much lower than those of Ca(2+) and NH(4)(+), was important, in many cases, in changing the precipitation from weakly acidic to weakly alkaline. Enrichment factor analysis confirmed that SO(4)(2-) and NO(3)(-) were produced from anthropogenic sources, Ca(2+), K(+), and 80% Mg(2+) were from crustal sources, and Na(+), Cl(-), and ∼20% of Mg(2+) were from marine sources. The annual wet depositions were estimated to be 3.5 t km(-2) per year for sulfur; 2.3 t km(-2) per year for nitrogen, of which 0.8 t km(-2) per year was oxidized nitrogen and 1.5 t km(-2) per year was reduced nitrogen; and 3.0 t km(-2) per year for Ca(2+).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".