An Interpretable Ensemble Learning Framework for the Prediction of Hourly Ground-Level Ozone in Industrial Cities
Published: 22 July 2025| Version 2 | DOI: 10.17632/2czdvf96nn.2
Contributor:
Shenao FanDescription
Meteorological variables, including temperature, atmospheric pressure, relative humidity, wind speed, and wind direction, were obtained from the Liaoning Meteorological Monitoring Network. Boundary-layer height data were sourced from the ERA5 reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).Hourly concentrations of air pollutants(PM2.5, PM10, SO2, CO and O3).The complete dataset spans the period from January 2020 to April 2025 and underwent rigorous preprocessing, including the removal of missing values, correction of anomalies, and standardization of data formats.
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Institutions
Shenyang Aerospace University
Categories
Air Pollution, Machine Learning, Ozone