their transport, or mean velocity of movement through the atmosphere, their diffusion, chemical transformation, and ground deposition. The forecast temporally resolution is usually daily or hourly and the spatial resolution can change from block resolution to dozens of km resolution. Most forecasts of air quality cover two to five days. 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. Challenges Meteorological conditions such as thermal inversions can prevent surface air from rising, trapping pollutants near the surface, which makes accurate forecasts of such events crucial for air quality modeling. Urban air quality models require a very fine computational mesh, requiring the use of high-resolution mesoscale weather models; in spite of this, the quality of numerical weather guidance is the main uncertainty in air quality forecasts. Uses By knowing the air quality forecast one can decide how to act, e.g. due to air pollution health effects, one can prepare ahead of time and choose the best time to do an outdoor activity. Deciding whether to put on a skin care ointment. Find the cleanest route for driving, walking or cycling. Deciding whether to leave the windows open or closed. Governments can utilize air quality forecasts to implement effective pollution control measures. See also Numerical weather prediction References Air pollution