A spectral-decomposition aging index for microplastics in aquatic environments

Microplastics (MPs) undergo aging that alters their surface properties and ecological risks, with photodegradation and microbial colonization as dominant pathways in natural waters. However, traditional infrared indices (e.g., carbonyl index) are inadequate for quantifying aging in oxygen‑containing MPs (e.g., polylactic acid [PLA]) due to spectral interference from inherent functional groups and are further confounded by biofilm signals. To address this, a spectral-decomposition aging index (SDAI) was developed based on multivariate curve resolution-alternating least squares decomposition of infrared spectra. SDAI replaces single-peak ratios with full-spectrum decomposition, enabling cross-polymer comparison under complex spectral conditions. Core consistency diagnostics determined the optimal component number (two for photoaging, three for microbial aging), yielding a robust model with > 99.7% explained variance and reproducible SDAI values across replicate measurements (standard deviation ≤ 0.19). SDAI was validated on six aquatic-relevant polymers, including both laboratory-generated spectra (polyvinyl chloride [PVC], polyethylene terephthalate [PET], polycaprolactone [PCL]) and publicly available spectral datasets (polyethylene [PE], polypropylene [PP], PLA). For conventional MPs (PE, PP, PVC), SDAI trends were consistent with traditional indices; for oxygen-containing polymers (PET, PCL, PLA), SDAI captured monotonic aging trajectories where conventional indices limited; in microbially aged samples, SDAI simultaneously resolved aging-related and biofilm-associated signals. Therefore, SDAI provides a robust and transferable framework for comparative MP aging assessment, particularly under complex spectral conditions, while its application to field-weathered samples will require further validation.