Static headspace-gas chromatography with mass spectrometry for the assessment of the bioaccumulation of volatile organic compounds associated with microplastics in animal tissues

Abstract

Background

The excessive increase in the production of plastics worldwide and their mismanagement have turned these materials into a threat to the environment and living organisms. Plastics undergo erosion and degradation processes releasing additives, monomers of their formulation, and even generating one of the most worrying emerging pollutants: microplastics. These contaminants have been found in both aquatic and terrestrial ecosystems, so it is clear the need to develop a methodology to determine the accumulation of these pollutants and their associated compounds in living organisms.

Results

This work presents a new combination of static headspace with gas chromatography coupled to mass spectrometry (SHS-GC-MS) for the determination of volatile organic compounds (VOCs) associated with microplastic materials in animal tissue samples. A total of 48 samples were analysed, corresponding to different organs (brain, heart, fat, liver, muscle, lung, kidney) of lamb, pig, rabbit, turkey, chicken, and beef, and 60 VOCs related to the presence of plastics were tentatively identified. Moreover, the identity of 25 VOCs was confirmed and they were also quantified, with contents in the 1.12–920 ng g⁻¹ range. The application of chemometric tools allowed to evaluate the relationship between the type of animal organ and the presence of contaminants associated with microplastics, showing a tendency towards a greater accumulation in kidney and liver. Verification of the origin of the VOCs found was carried out by means of a thermogravimetric analysis - mass spectrometry study.

Significance

The developed analytical approach generates useful and valuable information to better understand the exposure pathways of microplastics and their associated compounds, as well as the related migration and accumulation processes may happen in animal organs. This methodology has demonstrated high sensitivity, precision, robustness, and automation, as well as minimal sample handling.