Temporal Trend of PM10 and the Associated Risk to Human Health in the Lima Metropolitan Area
Bibliographic record
Abstract
Based on the monthly average of PM10 and the 90th percentile of PM10 concentration, respectively, the study's goal was to assess the risk to human health posed by PM10 exposure for residents of the Metropolitan Area of Lima (MAL), Peru, in both the best-case and worstcase scenarios.The National Meteorology and Hydrology Service (SENAMHI) published hourly PM10 concentrations for five monitoring stations from 2010 to 2023.The air quality index (AQI) was used to evaluate the quality of the air.Since there is no toxicity value (TVs) for PM10, the yearly limit value set by the World Health Organization (WHO, 15 µg/m 3 ) and the European Union (EU, 40 µg/m 3 ) was used to generate the hazard quotient (HQ) to assess the danger to human health.The average annual PM10 concentration was higher than the annual limit set by the EU and WHO, ranging from 45.1 µg/m 3 to 96.1 µg/m 3 .According to the AQI, Lima's air quality is categorized as moderate to unhealthy, with most days having dangerous levels.While WHO AQG indicated a potential chronic non-carcinogenic health risk in most months of the year, the worst-case scenario indicated a non-carcinogenic risk for the majority of the period.In the best-case scenario and worst-case scenario based on the EU, both showed higher potential chronic non-cancer risk in the summer and spring months.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".