Particulate Monitoring

Jan. 1, 2019
Yichen Wu, Maxim Batalin and Aydogan Ozcan explain how c-Air shows elevated particulates in air during test overhaul operations.

Download the full PDF SMARTER: Advancing Health-Related Technology here.

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

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 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.

More from SMARTER: Advancing Health-Related Technology

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.

About the Author

Maxim Batalin

Maxim Batalin, PhD, is an associate director at the UCLA Engineering Institute for Technology Advancement working with the UCLA Ozcan Research Group on new program development and project management. Batalin is focusing on the area of intelligent system innovations with applications to biomedical, environmental and sensor/actuator networks fields.

About the Author

Aydogan Ozcan

Aydogan Ozcan, PhD, is the chancellor’s professor at UCLA and an HHMI Professor with the Howard Hughes Medical Institute, leading the Bio- and Nano-Photonics Laboratory at UCLA Electrical Engineering and Bioengineering Departments, and is also the associate director of the California NanoSystems Institute (CNSI) at UCLA. 

About the Author

Yichen Wu

Yichen Wu is a PhD candidate at the Bio-Photonics Lab led by Professor Aydogan Ozcan in the ECE Department at UCLA. His research focuses on combining computational algorithms with optics to build novel imaging and sensing tools, and their applications in biomedical diagnosis and air quality monitoring.

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