Online Air Pollution Inference using Concept Recurrence and Transfer Learning

Pollution from wood burners has profound health implications for the general population. Typically, monitoring the level of airborne particulate matter, PM
2.5
, in these areas often requires making inferences about missing or corrupted readings. Air Quality inference in these cases often poses critical challenges. The factors can evolve over time, changing the distribution of data. Such changes in the distribution of data are known as concept drift. Moreover, air pollution inference for a location typically would require historical data to be collected for the location. We investigate five air quality studies in New Zealand rural towns. We explore two different research problems: (1) an adaptive recurrent drift algorithm to model recurrence patterns in PM
2.5
levels for a town with the ability to recover after accuracy deterioration after a concept drift using an adaptive recurrent drift algorithm, and (2) transfer learning for the data stream whereby we reuse a pre-trained air pollution inference model from a town as the starting point for an air pollution inference model on another town. We further investigate the relationship between the changes we detected and changes within the prediction horizon. We showed that the average accuracy of the air quality inference for the five towns is between 70% and 94% using the recurrent drift algorithm. We also show that transfer learning was advantageous between two of the five towns.