Identification of persistent oil residues in Prince William Sound, Alaska using rapid spectroscopic techniques.

Spectroscopic techniques including X-ray fluorescence (XRF) and attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) are used to examine oil residues persisting on shorelines in Prince William Sound that originate from the 1989 Exxon Valdez oil spill and oil released as a consequence of the 1964 Great Alaska earthquake. When coupled to classification models, ATR-FTIR and XRF spectral data can be used to distinguish between the two sources of oil with 92% and 86% success rates for the two techniques respectively. Models indicate that the ATR-FTIR data used to determine oil source includes the CO stretch, the twisting-scissoring of the CH2 group, and the CC stretch. For XRF data, decision tree models primarily utilize the abundance of nickel and zinc present in the oil as a means to classify source. This approach highlights the utility of rapid, field-based spectroscopic techniques to distinguish different inputs of oil to coastal environments.

Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers.

To advance our understanding of the environmental fate and transport of macro- and micro-plastic debris, robust and reproducible methods, technologies, and analytical approaches are necessary for in situ plastic-type identification and characterization. This investigation compares four spectroscopic techniques: attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), near-infrared (NIR) reflectance spectroscopy, laser-induced breakdown spectroscopy (LIBS), and X-ray fluorescence (XRF) spectroscopy, coupled to seven classification methods, including machine learning classifiers, to determine accuracy for identifying type of both consumer plastics and marine plastic debris (MPD). With machine learning classifiers, consumer plastic types were identified with 99, 91, 97, and 70% success rates for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. The classification of MPD had similar or lower success rates, likely arising from alterations to the plastic from environmental weathering processes with success rates of 99, 81, 76, and 66% for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. Success rates indicate that ATR-FTIR, NIR reflectance spectroscopy, and LIBS coupled with machine learning classifiers can be used to identify both consumer and environmental plastic samples.