Air quality is the product of complex mechanisms which involve long-distance transport phenomena, physico-chemical transformations, and variations on a local scale. Pollution data produced at ground level by fixed pollution measurement stations or measured by satellites provides only a snapshot of air quality for that exact time, and it is therefore difficult to know from these observations alone what the air pollution will be even a few hours in the future.
So how do scientists predict pollution one, two or seven days, in advance all over the world?
To understand this, we have to start with the fact that the evolution of air quality is intrinsically linked to the weather. This means that pollution is dispersed or transported from one place to another depending on the wind. To add to that, sunshine induces a chemical transformation of certain pollutants, heavy rains cause the particulate matter to fall to the ground, and a difference of temperature between air layers can trap pollution at ground level! In other words, it’s complicated.
When it comes to predicting air quality, we create models that account for all these parameters. Scientists create these models using powerful calculation tools based on atmospheric sciences (similar to those used to predict weather). They combine them with millions of pollution data points measured in real time by satellite, or at ground level. This allows the models to produce an estimate of pollution levels on a large scale and over several days.
In the same way that weather predictions help you decide if you should bring an umbrella before leaving home, pollution predictions are useful to better plan activities (sports, for example) or take adequate measures to reduce exposure.