A Data-Driven Supervised Machine Learning Approach to Estimating Global
  Ambient Air Pollution Concentrations With Associated Prediction Intervals
Liam J Berrisford Hugo Barbosa Ronaldo Menezes
Global ambient air pollution, a transboundary challenge, is typically
addressed through interventions relying on data from spatially sparse and
heterogeneously placed monitoring stations. These stations often encounter
temporal data gaps due to issues such as power outages. In response, we have
developed a scalable, data-driven, supervised machine learning framework. This
model is designed to impute missing temporal and spatial measurements, thereby
generating a comprehensive dataset for pollutants including NO$_2$, O$_3$,
PM$_{10}$, PM$_{2.5}$, and SO$_2$. The dataset, with a fine granularity of
0.25$^{\circ}$ at hourly intervals and accompanied by prediction intervals for
each estimate, caters to a wide range of stakeholders relying on outdoor air
pollution data for downstream assessments. This enables more detailed studies.
Additionally, the model's performance across various geographical locations is
examined, providing insights and recommendations for strategic placement of
future monitoring stations to further enhance the model's accuracy.