An algorithm prediction of the pollutant concentrations can be translated into air quality index, same as actual measurements.
It also helps assess risks to the environment and the climate caused by poor air quality standards.
Accurate forecasting can also lead to ease in planning day-to-day activities, avoiding locations with high alert areas, and implementing effective pollution control measures.
[1] Advanced approaches in air quality forecasting combine historical data with data generated via on-ground sensors and satellite observations to provide insights, analysis, and forecasts from global to street-level air pollution.
It also takes into consideration local factors like traffic, regional weather patterns, or emissions in the atmosphere.