Particulate Monitoring

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Smoke is ubiquitous on the fireground. Recently, we have come to better understand the composition of smoke and all of its components, including particulate matter (PM). PM with a general diameter of 10μm or smaller, called PM10, can cause serious health problems as it can become lodged deep in the lungs and enter the bloodstream. The PM2.5 category represents particles with a diameter of 2.5μm or smaller and has been declared a cause of cancer by the World Health Organization.¹

Currently, state-of-the-art PM monitoring is performed by stationary instruments, such as beta-attenuation monitoring (BAM) systems, which are bulky and expensive. Various commercial portable devices are also available, although they focus primarily on counting particles in several size ranges.² PM from fires has a unique chemical composition and morphology,³ and a more detailed analytical approach may improve understanding of PM and the dangers it presents.

Introducing c-Air

Figure 1: Lens-free microscope-based air-sampler, c-Air. (a) 3D computer-aided-design (CAD) overview of the device, including (A) rechargeable battery, (B) vacuum pump (13 L/min), (C) illumination module with fiber-coupled LEDs in red (624 nm), green (527 nm), and blue (470 nm), (D) impaction-based air sampler with (E) a sticky coverslip on top of (F) the image sensor. (b) Photo of the c-Air device. A U.S. 25-cent coin (quarter) is placed next to the device for scale.

In collaboration with the SMARTER program, we have advanced the development and evaluation of a lensless microscopy-based PM monitoring device, called c-Air. Figure 1 shows c-Air utilizing a micro-pump, an impaction-based air-sampler (i.e., an impactor), and a lens-free holographic on-chip microscope. The impactor consists of an impaction nozzle and a sticky sampling coverslip. A pump drives the airstream through the nozzle at high speed. The PM inside the stream impacts with and is collected by the sticky coverslip. Particles captured by the impaction sampler are subsequently imaged by the computational microscope, and the data is reconstructed and analyzed by the custom-developed image processing algorithms⁴ to obtain characterization of individual particles. The device is powered by a rechargeable lithium polymer battery. It is controlled and automated by a microprocessor.

Recently, we have performed a successful validation experiment estimating the number of PM with c-Air running alongside a BAM system, with the particle counts closely tracking each other.⁵ Furthermore, we have also demonstrated the accuracy of c-Air estimating the size of particles with approximately 93 percent accuracy.⁵ And we have successfully demonstrated the application of c-Air to monitoring and tracking the evolution of PM concentration and morphological features during a live wildfire.⁶ These results showed that the c-Air system can be used for accurate PM measurement and characterization, while offering a portable and inexpensive alternative to existing state-of-the-art systems.

Using c-Air on structural burns

Unlike wildfires, structural fires often produce PM of non-biological origin, which may also include additional toxic particles and chemicals. Recently, in collaboration with our colleagues—Dr. Denise Smith and her team from Skidmore College, as well as Shawn Brimhall, a fire protection specialist from the Division of Homeland Security and Emergency Services, State of New York—we have performed experiments with c-Air during several structural burns with the objective to monitor PM distribution and morphology during an extended post-suppression period (representing overhaul).

The first five burns were wood-based, followed by two wood pallets (100–120 pounds of class A material). The last three burns were “sofa”-based and each included overstuffed material with plywood composite, polyurethane foam, and synthetic materials over the top.

Figure 2: Particle counts post-suppression detected by c-Air. TSP is total suspended particles, PM10 stands for particles.

Figure 2 shows plots of counts for the total suspended particles (TSP) and PM10 detected at different post-suppression time intervals after a wood-only burn (experiment 3, Fig. 2a) and after a “sofa” burn (experiment 8, Fig. 2b). Note that in both cases, c-Air detected a significant number of particles still present in the air until 9–12 minutes post-suppression. Importantly, these values remained elevated despite the fact that interior fire instructors indicated that the room was “clear” of smoke and they thought it was OK to go off air. The time required for the PM counts to subside depends on many factors, including the scale of the fire and environmental conditions. This result emphasizes the importance of firefighters continuing to wear a breathing apparatus during post-suppression activities and the need for appropriate tools to monitor air quality.

Looking ahead

We are continuing to develop the device to improve durability, increase range of PM concentrations, and extend analysis to vapor particles as well as solid phase or particles. Our goal is ultimately to provide firefighters with a low-cost portable monitor that measures PM concentration and its characteristics during emergency operations.

To learn more about c-Air and Dr. Aydogan Ozcan’s research lab, visit innovate.ee.ucla.edu/welcome.html and org.ee.ucla.edu.

To learn more about the SMARTER project and related studies, please visit skidmore.edu/responder.

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References

1. Loomis, D., Grosse, Y., Lauby-Secretan, B., et al. "The carcinogenicity of outdoor air pollution." Lancet Oncol. 2013; 14: 1262–1263.

2. Model 804 Handheld Particle Counter | Met One Instruments. Available at: metone.com/products/indoor-particle-monitors/model-804 (accessed: 08/13/2018).

3. Lighty, J.S., Veranth, J.M. and Sarofim, A.F., “Combustion Aerosols: Factors Governing Their Size and Composition and Implications to Human Health,” J. Air Waste Manag. Assoc. 2000. 50(9),1565–1618.

4. McLeod, E. and Ozcan, A., “Unconventional methods of imaging: computational microscopy and compact implementations." 2016. Rep. Prog. Phys. 79(7), 076001.

5. Wu, Y., Shiledar, A., Li, Y., et al. "Air Quality Monitoring Using Mobile Microscopy and Machine Learning.” 2017. Light: Science & Applications 6, page e17046, doi: 10.1038/lsa.2017.46.

6. Wu, Y., Shiledar, A., Wong, J., et al. “Spatial mapping and analysis of aerosols during a forest fire using computational mobile microscopy." 2018. Proc. SPIE 10485, Optics and Biophotonics in Low-Resource Settings IV, 104850T; doi: 10.1117/12.2288889.