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Record W4320066200 · doi:10.1289/isee.2022.o-pk-33

The impact of weather changes on air quality and health in Brazil

2022· article· en· W4320066200 on OpenAlexaff
Weeberb J. Réquia, Francisco Jablinski Castelhano, Ana Clara Neme Pedroso, Igor Cobelo Ferreira, Rafael Borge, Henrique Llacer Roig, Matthew D. Adams, Heresh Amini, Petros Koutrakis

Bibliographic record

VenueISEE Conference Abstracts · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental scienceAir quality indexAir pollutionRelative humidityWind speedAir pollutantsGeneralized additive modelClimatologyPollutantPollutionHumidityAtmospheric sciencesMeteorologyGeographyMathematicsStatisticsBiologyEcology

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.130
GPT teacher head0.411
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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