or a machine-learning model. These two models generate results in totally different ways.
Chemical transport models use lots of known chemical and physical formulas to calculate the presence and production of air pollutants. They use data from emissions inventories reported by local agencies that list pollutants from known sources, such as wildfires, traffic, and data from meteorology that provides atmospheric information, such as wind, precipitation, temperature and solar radiation.
These models simulate the flow and chemical reactions of the air pollutants. However, their simulations involve multiple variables with huge uncertainties. Cloudiness, for example, changes the incoming solar radiation and thus the photochemistry. This can make the results less accurate. The EPA’s AirNow air pollution forecasts use machine learning. During wildfire events, a smoke-transport and dispersion model helps to simulate the spread of smoke plumes. This map is the forecast for Aug. 9, 2023. Yellow indicates moderate risk; orange indicates unhealthy air for sensitive groups.
The downside of machine-learning models is that they do not consider any chemical and physical mechanisms, as chemical transport models do. Also, the accuracy of machine-learning projections under extreme conditions, such as heat waves or wildfire events, can be off if the models weren’t trained on such data.