  Forest fires are a major source of particulate matter (PM) air pollution on a
global scale. The composition and impact of PM are typically studied using only
laboratory instruments and extrapolated to real fire events owing to a lack of
analytical techniques suitable for field-settings. To address this and similar
field test challenges, we developed a mobile-microscopy and
machine-learning-based air quality monitoring platform called c-Air, which can
perform air sampling and microscopic analysis of aerosols in an integrated
portable device. We tested its performance for PM sizing and morphological
analysis during a recent forest fire event in La Tuna Canyon Park by spatially
mapping the PM. The result shows that with decreasing distance to the fire
site, the PM concentration increases dramatically, especially for particles
smaller than 2 microns. Image analysis from the c-Air portable device also
shows that the increased PM is comparatively strongly absorbing and asymmetric,
with an aspect ratio of 0.5-0.7. These PM features indicate that a major
portion of the PM may be open-flame-combustion-generated element carbon
soot-type particles. This initial small-scale experiment shows that c-Air has
some potential for forest fire monitoring.
