Multimodal optical characterization of ambient particulate matter-loaded filters using speckle pattern analysis and hyperspectral imaging

Abstract Particulate matter (PM) is a critical environmental pollutant requiring accurate and real-time monitoring. In this study, we investigate $$\hbox {PM}_{2.5}$$ deposition on glass fiber filters collected across all four seasons using a dual-channel optical system that combines dynamic speckle pattern analysis with hyperspectral imaging. The speckle-based channel captures temporal fluctuations in scattered light intensity associated with particulate deposition, while the hyperspectral channel resolves the spectral signatures and optical absorption of the deposited aerosols. The system operates in a non-contact, in-situ configuration at the filter site without disrupting standard sampling workflows. One month of filter data per season is analyzed over a full year. Spring and summer exhibited the lowest dynamic activity, autumn showed an intermediate behavior, and winter exhibited the highest dynamic activity and optical response. This behavior is characterized by the inertia moment (IM), a dimensionless speckle-based descriptor of temporal intensity fluctuations associated with particulate deposition. The mean IM increased from $$9.901 \pm 0.081$$ in summer to $$18.125 \pm 0.116$$ in winter, compared with a baseline IM of $$0.342 \pm 0.033$$ obtained from unexposed blank filters. In addition, comparison with the independent $$\hbox {PM}_{2.5}$$ measurements from the BAM-1020 monitor yielded a similarity index of approximately 60-70% for the corresponding temporal behavior, providing an independent reference for evaluating the optical response. These results demonstrate that the proposed speckle-based descriptors provide a measurable optical response associated with particulate loading and its temporal and seasonal variability. The proposed platform is non-destructive, remote, and directly deployable at existing filter collection sites without modifying established sampling protocols, offering a scalable approach for long-term PM monitoring and air-quality assessment.