  Calculating an Air Quality Index (AQI) typically uses data streams from air
quality sensors deployed at fixed locations and the calculation is a real time
process. If one or a number of sensors are broken or offline, then the real
time AQI value cannot be computed. Estimating AQI values for some point in the
future is a predictive process and uses historical AQI values to train and
build models. In this work we focus on gap filling in air quality data where
the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario
is where one or a number of air, weather and traffic sensors are offline and
explores prediction accuracy under such situations. The work is part of the
MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the
DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI,
weather and CCTV traffic images for air pollution prediction.
