  Air pollutants, such as particulate matter, strongly impact human health.
Most existing pollution monitoring techniques use stationary sensors, which are
typically sparsely deployed. However, real-world pollution distributions vary
rapidly in space and the visual effects of air pollutant can be used to
estimate concentration, potentially at high spatial resolution. Accurate
pollution monitoring requires either densely deployed conventional point
sensors, at-a-distance vision-based pollution monitoring, or a combination of
both.
  This paper makes the following contributions: (1) we present a high temporal
and spatial resolution air quality dataset consisting of PM2.5, PM10,
temperature, and humidity data; (2) we simultaneously take images covering the
locations of the particle counters; and (3) we evaluate several vision-based
state-of-art PM concentration prediction algorithms on our dataset and
demonstrate that prediction accuracy increases with sensor density and image.
It is our intent and belief that this dataset can enable advances by other
research teams working on air quality estimation.
