Cited by Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India

Machine learning-driven prediction of nitrate-N adsorption efficiency by Fe-modified biochar: Refined model tuning and identification of crucial features DOI

Chen Li,

Xie Guixian,

Jing Li

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 107026 - 107026

Published: Jan. 22, 2025

Language: Английский

Citations

Application of machine learning models for PM2.5 prediction in bengaluru using precursor air pollutants and meteorological data DOI

Gourav Suthar,

Saurabh Singh

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(3)

Published: March 1, 2025

Language: Английский

Citations

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

Ronan Adler Tavella, Gabriel Fuscald Scursone, Leopoldo dos Santos da Silva

et al.

International Journal of Environmental Health Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: April 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.

Language: Английский

Citations

Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India DOI

Saurabh Singh,

Gourav Suthar

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 22, 2024

Language: Английский

Citations