  Monitoring air pollution is of vital importance to the overall health of the
population. Unfortunately, devices that can measure air quality can be
expensive, and many cities in low and middle-income countries have to rely on a
sparse allocation of them. In this paper, we investigate the use of Gaussian
Processes for both nowcasting the current air-pollution in places where there
are no sensors and forecasting the air-pollution in the future at the sensor
locations. In particular, we focus on the city of Kampala in Uganda, using data
from AirQo's network of sensors. We demonstrate the advantage of removing
outliers, compare different kernel functions and additional inputs. We also
compare two sparse approximations to allow for the large amounts of temporal
data in the dataset.
