  Air pollutants, such as particulate matter, negatively impact human health.
Most existing pollution monitoring techniques use stationary sensors, which are
typically sparsely deployed. However, real-world pollution distributions vary
rapidly with position and the visual effects of air pollution 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.
  The main contribution of this paper is that to the best of our knowledge, it
is the first publicly available, high temporal and spatial resolution air
quality dataset containing simultaneous point sensor measurements and
corresponding images. The dataset enables, for the first time, high spatial
resolution evaluation of image-based air pollution estimation algorithms. It
contains PM2.5, PM10, temperature, and humidity data. We evaluate several
state-of-art vision-based PM concentration estimation algorithms on our dataset
and quantify the increase in accuracy resulting from higher point sensor
density and the use of images. It is our intent and belief that this dataset
can enable advances by other research teams working on air quality estimation.
Our dataset is available at
https://github.com/implicitDeclaration/HVAQ-dataset/tree/master.
