The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City DOI Creative Commons
Ronan Adler Tavella,

Daniele Feijó das Neves,

Gustavo de Oliveira Silveira

и другие.

Atmosphere, Год журнала: 2025, Номер 16(4), С. 363 - 363

Опубликована: Март 24, 2025

This study investigated the relationship between surface meteorological variables and levels of air pollutants (O3, PM10, PM2.5) in scenarios simulated temperature increases Rio Grande, a medium-sized Brazilian city with strong industrial influence. utilized five years daily data (from 1 January 2019 to 31 December 2023) model atmospheric conditions two pollutant 21 2021 20 simulate how would respond annual °C 2 °C, employing Support Vector Machine, supervised machine learning algorithm. Predictive models were developed for both averages seasonal variations. The predictive analysis results indicated that, when considering averages, concentrations showed decreasing trend as temperatures increased. same pattern was observed scenarios, except during summer, O3 increased rise. greatest reduction occurred winter (decreasing by 10.33% 12.32% under warming respectively), while PM10 PM2.5, most significant reductions spring. lack correlation levels, along their other variables, explains Grande. research provides important contributions understanding interactions climate change, pollution, factors similar contexts.

Язык: Английский

The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City DOI Creative Commons
Ronan Adler Tavella,

Daniele Feijó das Neves,

Gustavo de Oliveira Silveira

и другие.

Atmosphere, Год журнала: 2025, Номер 16(4), С. 363 - 363

Опубликована: Март 24, 2025

This study investigated the relationship between surface meteorological variables and levels of air pollutants (O3, PM10, PM2.5) in scenarios simulated temperature increases Rio Grande, a medium-sized Brazilian city with strong industrial influence. utilized five years daily data (from 1 January 2019 to 31 December 2023) model atmospheric conditions two pollutant 21 2021 20 simulate how would respond annual °C 2 °C, employing Support Vector Machine, supervised machine learning algorithm. Predictive models were developed for both averages seasonal variations. The predictive analysis results indicated that, when considering averages, concentrations showed decreasing trend as temperatures increased. same pattern was observed scenarios, except during summer, O3 increased rise. greatest reduction occurred winter (decreasing by 10.33% 12.32% under warming respectively), while PM10 PM2.5, most significant reductions spring. lack correlation levels, along their other variables, explains Grande. research provides important contributions understanding interactions climate change, pollution, factors similar contexts.

Язык: Английский

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