Spatial mapping and analysis of aerosols during a forest fire using computational mobile microscopy

TL;DR

This study introduces c-Air, a portable mobile microscopy platform that enables real-time spatial mapping and analysis of aerosols during forest fires, revealing increased PM concentrations and soot-like particles near fire sites.

Contribution

The paper presents a novel portable device combining microscopy and machine learning for field analysis of aerosols during forest fires, addressing limitations of laboratory-based methods.

Findings

PM concentration increases near fire sites, especially for particles smaller than 2 microns.

Aerosols are predominantly soot-like, strongly absorbing, and asymmetric.

c-Air shows potential for real-time forest fire aerosol monitoring.

Abstract

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.