Global probabilistic forecasting of air pollution

Key points

• Fine particulate matter (PM2.5) refers to particulate matter with an aerodynamic diameter of 2.5 micrometre or less.1
• It comes from human activities, such as traffic and industry, and from wildfires, desert dust and gases emitted by vegetation.1
• Operational forecasts run on grids tens of kilometres wide, which hide the pollution breathed by nearly two hundred million people a day.1
• Machine learning has transformed weather forecasting, but data-driven forecasts of air pollution still blur extremes and lose skill as the air travels.1

Sources (1)

• [1]Global probabilistic forecasting of air pollution
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:36 AM

  Here we introduce Foehn, a generative model that learns from global kilometre-scale estimates of PM2.5 and computes where the air comes from.

  Fine particulate matter (PM2.5) refers to particulate matter with an aerodynamic diameter of 2.5 micrometre or less.

Extractive summary: sentences quoted from the sources.