Machine Learning for Hourly Air Pollution Prediction – Global (ML-HAPPG)

This dataset contains estimates of air pollution levels across the globe for every hour of the year 2022. It covers five major air pollutants that can affect human health and the environment. The data cover major air pollutants, including Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each air pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios. The spatial coverage of the dataset includes the entire globe, structured as an evenly spaced grid, with each grid square covering an area of 0.25 degrees (0.25 degrees x 0.25 degrees). Data points correspond to the centre of these grid squares. There is also training data used for the model from real-world monitoring stations.