Predicting air pollution changes due to temperature increases in two Brazilian capitals using machine learning – a necessary perspective for a climate resilient health future.
Given that climate change can exacerbate the health impacts of air pollutants, we evaluated the impact of temperature increase scenarios on air pollutant levels (O3, PM2.5, and PM10) in Porto Alegre and Recife, Brazil. Air pollutants and meteorological data were collected, and simulations were perfo...
| Publicado en: | International Journal of Environmental Health Research Vol. 35; no. 11; pp. 3392 - 3407 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189081366&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189081366 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09603123 57L jtl: International Journal of Environmental Health Research issn: 09603123 maglogo: Y pubinfo: dt: Nov2025 vid: 35 iid: 11 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 189081366 184222996 189081366 189081366 10.1080/09603123.2025.2486598 189081366 ppf: 3392 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting air pollution changes due to temperature increases in two Brazilian capitals using machine learning – a necessary perspective for a climate resilient health future. aug: au: Adler Tavella, Ronan Scursone, Gabriel Fuscald dos Santos da Silva, Leopoldo Nadaleti, Willian Cézar Adamatti, Diana Francisca El Khouri Miraglia, Simone Georges da Silva Júnior, Flavio Manoel Rodrigues affil: Instituto de Ciências Ambientais, Químicas e Farmacêuticas, Universidade Federal de São Paulo, Diadema, Brazil sug: subj: Air Pollutants, Environmental Analysis Air Pollution Analysis Temperature Climate Change Urban Areas Brazil Machine Learning Prediction Models Environmental Monitoring Environmental Health Human Funding Source Brazil Computer Simulation Support Vector Machine Pearson's Correlation Coefficient Factor Analysis Air Pollution, Indoor Public Health Seasons One-Way Analysis of Variance Comparative Studies Data Analysis Software Descriptive Statistics Humidity Particulate Matter Analysis Association (Research) Ultraviolet Rays Forecasting Atmospheric Pressure Heat Cold Ozone Analysis ab: Given that climate change can exacerbate the health impacts of air pollutants, we evaluated the impact of temperature increase scenarios on air pollutant levels (O3, PM2.5, and PM10) in Porto Alegre and Recife, Brazil. Air pollutants and meteorological data were collected, and simulations were performed using a Support Vector Machine model with radial basis function kernel, applying temperature increases of 0.5°C, 1.0°C, 1.5°C, and 2.0°C to predict future pollutant concentrations. The data were analyzed seasonally and annually. Pearson correlation and principal component analyses (PCA) explored the relation with meteorological conditions. Simulations revealed that rising temperatures do not uniformly lead to increased pollutant concentrations; instead, the effects are highly dependent on local meteorological and climatic conditions. In Porto Alegre, O3 levels increased throughout the year, with a peak of 14.14% during the summer in the + 2.0°C scenario, while PM2.5 and PM10 also showed marked seasonal increases. Conversely, in Recife, O3 levels decreased in some seasons but increased during autumn, with particulate matter levels also rising during the summer. The findings underscore the need for health systems to consider these dynamics in their management strategies through location-specific investigations and emphasize the importance of policy-driven adaptive measures to build climate-resilient health systems. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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