  Particulate Matter (PM) is a form of air pollution that visually degrades
urban scenery and is hazardous to human health and the environment. Current
monitoring devices are limited in measuring average PM over large areas.
Quantifying the visual effects of haze in digital images of urban scenery and
correlating these effects to PM levels is a vital step in more practically
monitoring our environment. Current image haze extraction algorithms remove
haze from the scene for the sole purpose of enhancing vision. We present two
algorithms which bridge the gap between image haze extraction and environmental
monitoring. We provide a means of measuring atmospheric scattering from images
of urban scenery by incorporating temporal knowledge. In doing so, we also
present a method of recovering an accurate depthmap of the scene and recovering
the scene without the visual effects of haze. We compare our algorithm to three
known haze removal methods. The algorithms are composed of an optimization over
a model of haze formation in images and an optimization using a constraint of
constant depth over a sequence of images taken over time. These algorithms not
only measure atmospheric scattering, but also recover a more accurate depthmap
and dehazed image. The measurements of atmospheric scattering this research
produces, can be directly correlated to PM levels and therefore pave the way to
monitoring the health of the environment by visual means. Accurate atmospheric
sensing from digital images is a challenging and under-researched problem. This
work provides an important step towards a more practical and accurate visual
means of measuring PM from digital images.
