Reconstructing PM2.5 Levels in the United States Region using Machine Learning
The process involves downloading historical weather-related data and PM2.5 ground observation data. Then, two machine learning models are trained - one for historical data and another for forecasting - to estimate ground-level PM2.5 concentration.
Two machine learning models are employed to reconstruct the continuous spatial ground-level concentration of PM2.5 across the United States region.