Raman Spectroscopic Techniques for Detecting Microplastic in Alpine Environments

As plastic production continues to rise, its environmental consequences have become a growing concern. Secondary microplastics (MPs), derived from the breakdown of larger plastics, constitute the majority of environmental plastic pollution. This research uses Raman spectroscopy to identify types of plastic in freshwater, snow, and cave environmental samples based on their different molecular structures. Raman spectroscopy is an effective method to identify MPs due to its ability to detect low concentrations of molecules and distinguish them by their unique spectral fingerprints. We assess and improve existing methods for microplastic detection by using a near-infrared, co-aligned Raman system and explore confocal Raman microscopy to minimize the signal from background substrates. Raman spectroscopy is paired with existing machine learning algorithms to identify the types of plastic present in the sample. These methodological advances are applied to samples from various alpine locations. The resulting data will provide insight into MP pollution in alpine environments and support broader conservation efforts to preserve these ecosystems from the growing threat of plastic pollution.