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Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.
- Source :
-
Journal of Hazardous Materials . Sep2021, Vol. 418, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions. [Display omitted] • Smartphone-based PM monitoring device was developed. • PM concentration was predicted from holographic speckles of PMs by deep learning. • Volumetric monitoring of PMs was demonstrated without procedure of air intake. • Quasi-real-time PM monitoring was demonstrated. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03043894
- Volume :
- 418
- Database :
- Academic Search Index
- Journal :
- Journal of Hazardous Materials
- Publication Type :
- Academic Journal
- Accession number :
- 152002732
- Full Text :
- https://doi.org/10.1016/j.jhazmat.2021.126351