A comprehensive overview of the ionic composition of rainwater across Latin America
Bibliographic record
Abstract
Wet atmospheric deposition, primarily through rainfall, plays a crucial role in removing atmospheric pollutants, particularly water-soluble particles and dissolved gases, thereby contributing to atmospheric cleansing. This review article examines the ionic composition of rainwater in Latin America, based on scientific studies retrieved from the Web of Science and Scopus databases. The findings reveal that anthropogenic sources of ionic compounds in rainwater include fossil fuel combustion, fertilizer application, vehicular emissions, and industrial activities, which primarily contribute to acidifying compounds. In contrast, neutralizing compounds (e.g., Ca2+ and Mg2+) originate from both human activities and natural sources, such as marine aerosols and dust resuspension. Elevated concentrations of acidifying compounds have been documented in urban and industrial areas, whereas rural regions predominantly exhibit neutralizing ions. In certain countries, such as Brazil and Costa Rica, reductions in the sulfur content of fuels have led to trends of rainwater neutralization. The ratios of ionic compounds (e.g., SO42−/NO3− and NH4+/NO3−) and rainwater pH were analyzed to identify emission sources and regional patterns, revealing geographical, seasonal, and local influences on emissions. However, documentation on the impacts of rainwater acidification in Latin America remains limited, with most studies focusing on Brazil. Mexico, Argentina, and Brazil have been identified as the most productive countries in this research area. Nevertheless, studies on the ionic composition of rainwater in Latin America remain scarce and have declined over the past 5 years. This decline is concerning given the region’s rich biodiversity, which may be adversely affected by rainwater acidification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".