DeepPlume: Very High Resolution Real-Time Air Quality Mapping

TL;DR

This paper introduces DeepPlume, a real-time, high-resolution air quality prediction engine that integrates multiple data sources to provide detailed street-level pollution maps for health and navigation applications.

Contribution

The paper presents a novel high-resolution prediction engine that combines official measurements, atmospheric models, land cover, and traffic data, differing from previous Plume Labs systems.

Findings

Achieves predictions at a resolution of a few dozen meters.

Capable of real-time street-level air quality mapping.

Applicable to innovative routing and health-related applications.

Abstract

This paper presents an engine able to predict jointly the real-time concentration of the main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose size are below 2.5 um and 10 um). The engine covers a large part of the world and is fed with real-time official stations measures, atmospheric models' forecasts, land cover data, road networks and traffic estimates to produce predictions with a very high resolution in the range of a few dozens of meters. This resolution makes the engine adapted to very innovative applications like street-level air quality mapping or air quality adjusted routing. Plume Labs has deployed a similar prediction engine to build several products aiming at providing air quality data to individuals and businesses. For the sake of clarity and reproducibility, the engine presented here has been built specifically for this paper and differs quite significantly from the one used in Plume Labs' products. A major difference is in the data sources feeding the engine: in particular, this prediction engine does not include mobile sensors measurements.