Revealing Drivers of Haze Pollution by Explainable Machine Learning

Many places on earth still suffer from a high level of atmospheric fine particulate matter (PM2.5) pollution. Formation of a particulate pollution event or haze episode (HE) involves many factors, including meteorology, emissions, and chemistry. Understanding the direct causes of and key drivers behind the HE is thus essential. Traditionally, this is done via chemical transport models. However, substantial uncertainties are introduced into the model estimation when there are significant changes in the emissions inventory due to interventions (e.g., the COVID-19 lockdown). Here we applied a Random Forest model coupled with a Shapley additive explanation algorithm, a post hoc explanation technique, to…

• MO
  Ministry of Science and Technology of the People's Republic of China

  Award: 2016YFC0208505
• TS
  Tianjin Science and Technology Committee

  Award: PTZWHZ00120