The impact of weather changes on air quality and health in Brazil
Bibliographic record
Abstract
Background and Aim: Weather changes affect air quality that is also a leading contributor to disease burden. The current evidence on the relationship between weather changes and air pollution is mainly based on the environmental processes occurring in North America and Europe. As a response to this gap in knowledge, in this study, we quantified the past weather-related changes in ambient air pollution in Brazil over 16 years (2003-2018). Then we estimated the excess mortalities associated with this impact of long-term weather changes on air quality. Methods: We applied generalized additive models (GAMs) to fit adjusted (with meteorological variables) and unadjusted models. The difference of slopes estimated by the models without and with adjusting for these meteorological variables represents the impact of weather changes on pollutant trends, defined in our study as "weather penalty”. Results: Overall, ambient air pollution levels in Brazil during the period 2003-2018 have decreased in most of the Brazilian regions. We estimated significant trends in meteorological variables, indicating an increase in temperature, relative humidity, and wind speed in all Brazilian regions over the 16-study period. Our findings suggest that PM2.5 was the pollutant most impacted by weather changes. For the 16-year period of analysis, we estimated a weather penalty ranging from 1.58 μg/m−3 (CI 95%:1.25;1.91) to 0.41 μg/m−3 (CI 95%:0.28;0.53) among the different Brazilian regions. If weather parameters had remained constant, PM2.5 would have decreased by 1.10 µg/m3 (95%CI: 0.74; 1.46) in the South and by 2.25 µg/m3 (95%CI: 2.72; 1.79) in the Midwest. Over the 16-year study period, the weather impact on PM2.5 in Brazil was associated with over 6,500 excess deaths. Conclusions: The evidence of historical weather penalty should be of interest to policy makers to devise future strategies related to environmental health and climate change. Keywords: Air pollution, Climate Change, Long-term impact
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".