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 mobilemicroscopy- 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 μm. 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.
Bibliographical noteKAUST Repository Item: Exported on 2022-06-28
Acknowledgements: The authors acknowledge the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the National Science Foundation (NSF) Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, the NSF INSPIRE Award, the NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, the Office of Naval Research (ONR), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), the Vodafone Americas Foundation, the Mary Kay Foundation, the Steven & Alexandra Cohen Foundation, and KAUST. This work is based on research performed in a laboratory renovated by the NSF under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA). The authors also acknowledge Dr. Zoltán Göröcs from UCLA for providing information on the La Tuna Forest Fire.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.