PlumeCityNet: Multi-Resolution Air Quality Forecasting
This provides a scalable and computationally efficient solution for air quality forecasting in cities, benefiting public health and urban planning, though it is incremental in combining existing methods.
The paper tackles air quality forecasting for multiple pollutants by developing an engine that integrates various data sources to produce forecasts at multiple spatial resolutions up to 24 hours, achieving high out-of-sample accuracy and efficient computation on standard CPUs.
This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health: nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose diameters are below 2.5um and 10um respectively). The engine is fed with air quality monitoring stations' measurements, weather forecasts, physical models' outputs and traffic estimates to produce forecasts up to 24 hours. The forecasts are produced with several spatial resolutions, from a few dozens of meters to dozens of kilometers, fitting several use-cases needing air quality data. We introduce the Scale-Unit block, which enables to integrate seamlessly all available inputs at a given resolution to return forecasts at the same resolution. Then, the engine is based on a U-Net architecture built with several of those blocks, giving it the ability to process inputs and to output predictions at different resolutions. We have implemented and evaluated the engine on the largest cities in Europe and the United States, and it clearly outperforms other prediction methods. In particular, the out-of-sample accuracy remains high, meaning that the engine can be used in cities which are not included in the training dataset. A valuable advantage of the engine is that it does not need much computing power: the forecasts can be built in a few minutes on a standard CPU. Thus, they can be updated very frequently, as soon as new air quality monitoring stations' measurements are available (generally every hour), which is not the case of physical models traditionally used for air quality forecasting.