Evaluating Indoor Air Quality Using LIBS and SPAMS

There is no question that most of the modern world is spending an increasingly amount of their time (and life) indoors. Previous studies have estimated that people on average spend 90% (or 22 out of 24 hours) of their life indoors (1).

The development of modern appliances, such as air conditioning units and heaters, have allowed people to retreat into their homes and feel comfortable when doing so. In the 21st century, the development of many technology-inspired hobbies such as watching television and playing video games has further decreased the need for people to stay outdoors. The Covid-19 pandemic also precipitated a huge shift in the workplace, allowing more employees to stay in their homes for work as well, without the need to commute.

Health and wellness are becoming integral to house designs, including the use of non-toxic materials, improved indoor air quality systems, and layouts that encourage physical activity. Generated by AI. | Image Credit: © BMMP Studio - stock.adobe.com

As a result, it is important that there are effective ways to monitor air quality inside the home. A recent study led by Yuzhu Liu from Nanjing University of Information Science & Technology and Jinan University explored this topic. Published in Optics and Lasers in Engineering, Liu and his team explores how laser-induced breakdown spectroscopy (LIBS) and single-particle aerosol mass spectrometry (SPAMS) can monitor indoor air pollution—specifically in environments where electronic welding is performed (2).

Indoor air quality is defined as the quality of the air present in an enclosed structure, such as a home or school building (1). Because indoor environments are confined spaces, levels of pollutants could be significantly greater than outside concentrations, even sometime up to five times the amount (1). Part of what contributes to this effect starts with the work that is done to maintain the home. For examples, activities such as electronic welding produce significant amounts of smoke laden with hazardous metal particles, which can be a health concern (2). Detecting and analyzing these pollutants in real time has traditionally been a challenge, prompting researchers to develop more effective solutions (2).

This study used LIBS and SPAMS to conduct an in situ analysis of pollutants generated during electronic welding (2). Both of these techniques come with their own distinct advantages. For LIBS, it can use lasers to analyze smoke, which helps analysts learn about the presence of metals like lead (Pb) and tin (Sn) (2). Meanwhile, SPAMS complements LIBS by capturing isotopic and abundance data for these metals, offering a deeper understanding of their distribution and concentration (2).

The researchers discovered a few important findings. First, they realized the benefits of using SPAMS technology, which provided them with isotopic profiles of lead and tin that were accurate (2). Second, they discovered that carbon concentrations increase proportionally with operation time, highlighting another significant pollutant (2). And finally, advanced data analysis techniques, such as principal component analysis (PCA) and error back propagation artificial neural networks (BP-ANN), helped classify and identify pollutants with varying concentrations effectively (2).

The use of LIBS for elemental analysis, combined with SPAMS for isotopic data, creates a comprehensive toolkit for identifying and quantifying pollutants in real time (2). The incorporation of machine learning further enhances the precision and efficiency of this method, opening avenues for broader applications in air quality management (2).

Indoor air pollution from electronic welding operations has been a persistent issue, often overlooked despite its severe implications for health and safety. By pioneering a dual-technology system, Liu and colleagues have laid the groundwork for addressing this gap, offering a practical solution that could be implemented across various industrial settings (2).

Although the study focused on electronic welding smoke, the researchers suggest that the LIBS-SPAMS framework could be adapted for other indoor environments with different sources of air pollution (2). Further refinement of the machine learning algorithms and sensor technologies could make this system even more versatile and accessible.

References

1. American Lung Association, Lung Association Launches Healthy and Efficient Homes Project to Promote Healthy Indoor Air Quality. Lung.org. Available at: https://www.lung.org/media/press-releases/on-average,-americans-spend-90-of-their-time-indo (accessed 2024-11-26).
2. Wan, E.; Zhang, Q.; Li, L.; et al. The Online in situ Detection of Indoor Air Pollution via Laser Induced Breakdown Spectroscopy and Single Particle Aerosol Mass Spectrometer Technology. Opt. Laser Eng. 2024, 174, 107974. DOI: 10.1016/j.optlaseng.2023.107974