A Portable Infrared System for Identification of Particulate Matter.
Javier NúñezArjen BoersmaRobin KoldeweijJoseph TrimboliPublished in: Sensors (Basel, Switzerland) (2024)
Occupational exposure to airborne dust is responsible for numerous respiratory and cardiovascular diseases. Because of these hazards, air samples are regularly collected on filters and sent for laboratory analysis to ensure compliance with regulations. Unfortunately, this approach often takes weeks to provide a result, which makes it impossible to identify dust sources or protect workers in real time. To address these challenges, we developed a system that characterizes airborne dust by its spectro-chemical profile. In this device, a micro-cyclone concentrates particles from the air and introduces them into a hollow waveguide where an infrared signature is obtained. An algorithm is then used to quantitate the composition of respirable particles by incorporating the infrared features of the most relevant chemical groups and compensating for Mie scattering. With this approach, the system can successfully differentiate mixtures of inorganic materials associated with construction sites in near-real time. The use of a free-space optic assembly improves the light throughput significantly, which enables detection limits of approximately 10 µg/m 3 with a 10 minute sampling time. While respirable crystalline silica was the focus of this work, it is hoped that the flexibility of the platform will enable different aerosols to be detected in other occupational settings.
Keyphrases
- particulate matter
- air pollution
- health risk assessment
- health risk
- human health
- polycyclic aromatic hydrocarbons
- drinking water
- cardiovascular disease
- heavy metals
- machine learning
- water soluble
- high throughput
- optical coherence tomography
- room temperature
- type diabetes
- mass spectrometry
- gestational age
- cardiovascular risk factors
- climate change
- quantum dots
- optic nerve
- high resolution
- low cost
- highly efficient
- preterm birth
- sensitive detection
- metal organic framework