Technique Analysis of Filter-Clogging Particulate Matter in Eddy Covariance Systems in a Volcanic Environment

The eddy covariance (EC) technique is a key tool in environmental monitoring, enabling continuous and non-invasive measurement of carbon dioxide (CO2) fluxes at the ecosystem–atmosphere interface. In environments characterized by high levels of airborne particulates, such as volcanic regions, the reliability of enclosed-path EC measurements can be compromised by frequent filter clogging, potentially affecting data continuity, and increasing maintenance requirements. This study investigates whether the chemical and mineralogical signatures of particulate matter accumulated on clogged Swagelok pre-Licor filters can be used to identify dominant particle sources and provide insights into filter clogging processes. A multi-analytical workflow combining scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS), portable Raman spectroscopy, and hyperspectral imaging (HSI) was applied to recovered filter residues. The combined approach provided complementary chemical, mineralogical, and morphological information, allowing discrimination among volcanogenic material (e.g., glass shards, crystals, and lithic fragments), aeolian lithogenic dust, including Saharan inputs, and biogenic particles such as plant fibers. The results revealed two dominant particulate groups, volcanogenic mineral phases and biogenic material, with a minor contribution from wind-transported lithogenic dust. Volcanogenic phases, enriched in Si, Al, and Fe, dominated the inorganic fraction, whereas O-, C-, and N-rich particles were mainly associated with local biogenic sources. No clear evidence of significant anthropogenic contributions was identified. These findings demonstrate that multi-analytical characterization of particles accumulated on EC pre-filters can provide qualitative source attribution and valuable information on the processes responsible for filter loading and clogging. By linking particle characteristics with meteorological and environmental conditions, this approach has the potential to support site-specific, predictive, and event-driven maintenance strategies, contributing to improved EC data quality and more efficient long-term monitoring in high-aerosol environments.