Predicting air pollution changes due to temperature increases in two Brazilian capitals using machine learning – a necessary perspective for a climate resilient health future

International Journal of Environmental Health Research, Год журнала: 2025, Номер unknown, С. 1 - 15

Опубликована: Апрель 2, 2025

Given that climate change can exacerbate the health impacts of air pollutants, we evaluated impact temperature increase scenarios on pollutant levels (O3, PM2.5, and PM10) in Porto Alegre Recife, Brazil. Air pollutants meteorological data were collected, simulations performed using a Support Vector Machine model with radial basis function kernel, applying increases 0.5°C, 1.0°C, 1.5°C, 2.0°C to predict future concentrations. The analyzed seasonally annually. Pearson correlation principal component analyses (PCA) explored relation conditions. Simulations revealed rising temperatures do not uniformly lead increased concentrations; instead, effects are highly dependent local climatic In Alegre, O3 throughout year, peak 14.14% during summer + scenario, while PM2.5 PM10 also showed marked seasonal increases. Conversely, decreased some seasons but autumn, particulate matter summer. findings underscore need for systems consider these dynamics their management strategies through location-specific investigations emphasize importance policy-driven adaptive measures build climate-resilient systems.

Язык: Английский

A review of the application of response surface methodology in nanofiltration: Insights into process modeling, parametric analysis, and optimization

Separation Science and Technology, Год журнала: 2025, Номер unknown, С. 1 - 15

Опубликована: Май 27, 2025

Язык: Английский