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A Spectral-Decomposition Aging Index for Microplastics in Aquatic Environments
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Scientists developed a better tool for measuring how microplastics break down and "age" in water over time—something that matters because aged plastic particles can release chemicals or absorb pollutants differently than fresh ones. Older testing methods struggled with certain plastic types (like biodegradable PLA) and got confused by bacterial buildup on the particles, but this new approach works accurately across many common plastic types. This gives researchers a more reliable way to track how microplastics change in the environment, which is an important step toward understanding their real-world risks to ecosystems and, potentially, human health.
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.
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