PlumeCityNet: Multi-Resolution Air Quality Forecasting

TL;DR

PlumeCityNet is a multi-resolution deep learning engine that forecasts key air pollutants using diverse data sources, providing accurate, fast, and scalable predictions suitable for various urban environments.

Contribution

The paper introduces the Scale-Unit block and a multi-resolution U-Net architecture for integrated, efficient air quality forecasting across different spatial scales.

Findings

Outperforms existing prediction methods in major cities.

Maintains high accuracy even in unseen cities.

Operates efficiently on standard CPUs, enabling frequent updates.

Abstract

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.