Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air

Earth and Space Science, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Abstract An automated air quality forecasting system (AI‐Air) was developed to optimize and improve for different typical cities, combined with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Model (CUACE), used in a inland city of Zhengzhou coastal Haikou China. The performance evaluation results show that PM 2.5 forecasts, correlation coefficient (R) is increased by 0.07–0.13, mean error (ME) root square (RMSE) decreased 3.2–3.5 3.8–4.7 μg/m³. Similarly, O 3 R value improved 0.09–0.44, ME RMSE values are reduced 7.1–22.8 9.0–25.9 μg/m³, respectively. Case analyses operational also indicate AI‐Air can significantly pollutant concentrations effectively correct underestimation, or overestimation phenomena compared CUACE model. Additionally, explanatory were performed assess key meteorological factors affecting cities topographic climatic conditions. highlights potential AI techniques forecast accuracy efficiency, promising applications field forecasting.

Language: Английский

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Artificial intelligence and public health

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 127 - 157

Published: Jan. 1, 2025

Language: Английский

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Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning

Toxics, Journal Year: 2025, Volume and Issue: 13(3), P. 217 - 217

Published: March 16, 2025

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims assess the of aviation-related on quality at Alexandroupolis Regional Airport, Greece, evaluate role meteorological factors in pollution dispersion. Using machine learning models, we analyzed data, including CO2, NOx, CO, HC, SOx, PM2.5, fuel consumption, parameters from 2019–2020. Results indicate that NOx CO2 showed highest correlation with traffic volume consumption (R = 0.63 0.67, respectively). Bayesian Linear Regression emerged as most accurate achieving an R2 value 0.96 0.97, respectively, for predicting PM2.5 concentrations. Meteorological had a moderate influence, precipitation negatively correlated (−0.03), while temperature wind speed limited effects emissions. A significant decline aviation was observed 2020, decreasing by 28.1%, 26.5%, 35.4% compared 2019, reflecting COVID-19 travel restrictions. Carbon dioxide extensive percentage distribution, accounting 75.5% total emissions, followed fuels, which accounted 24%, remaining pollutants, such more minor impacts. These findings highlight need optimized management regional airports, integrating predictive monitoring supporting policy interventions mitigate pollution.

Language: Английский

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Next-Generation Air Quality Management: Unveiling Advanced Techniques for Monitoring and Controlling Pollution

Aerosol Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Language: Английский

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Forecasting the concentration of the components of the particulate matter in Poland using neural networks

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Language: Английский

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