The first approach outline the basic physics & dynamic process, chemical reaction of true atmosphere and the various kind of emission. The second approach use statistical tools to capture the pattern of historical meteorology, air quality dataset and train a predictive model. With evaluating the model for unseen data instances predicting, it can be used for air quality forecasting.

Many researchers choose numerical models to achieve their target as computation results based on the emulation of real world, could reproduce spatial-temporal variation of pollutants to some extent.

Machine learning is becoming the cutting-edge of data science even in all scientific field and showing its great ability in non-linear problems(secondary pollutants(O3, SOA, etc) are formed through non-linear chemistry reactions).

What are the possibilities to use a machine learning approach to improve air quality forecasting?