Simultaneous analysis of several plasticizer classes in different matrices by on-line turbulent flow chromatography-LC-MS/MS

The development of methodologies for the determination of plasticizers is essential for assessing the environmental and human impact resulting from the use of plastics. A fast analytical method with on-line purification based on turbulent flow chromatography (TFC) coupled to tandem mass spectrometry (MS-MS) has been developed for the analysis of ten phthalates, four alternative plasticizers (including adipates and citrates), and 20 organophosphate esters (OPEs). The method has been validated for the determination of plasticizers across different matrices. Analytical parameters showed acceptable recoveries ranging between 50 and 125%, RSDs lower than 20%, and mLODs of 0.001-2.08 ng g-1 wet weight (ww), 0.002-0.30 ng g-1, and 0.001-0.93 ng m-3 for foodstuffs, face masks, and ambient air, respectively. These methodologies were applied to foodstuff samples purchased in grocery stores, reusable and self-filtering masks, and indoor air measured in different locations. Plasticizers were detected in all the analyzed samples, with values up to 22.0 μg g-1 ww, 6.78 μg g-1, and 572 ng m-3 for foodstuffs, face masks, and indoor air, respectively. The contribution of each family to the total plasticizer content varied between 1.3 and 87%, 0.5 and 98%, and 0.5 and 65% for phthalates, alternative plasticizers, and OPEs, respectively. These findings highlighted the need for analytical methodologies capable of simultaneously assessing a wide number of plasticizers with minimal extraction steps. This capability is crucial in order to obtain more conclusive insights into the impact of these pollutants on both the environment and human health, arising from different sources of exposure such as foodstuffs, plastic materials, and atmospheric air.

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study was supported by the Spanish Ministry of Science and Innovation (Project EXPOPLAS (PID2019-110576RB-I00), Grants CEX2018-000794-S, PRE2020-093018, and PRE2020-091979 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”), and by the Generalitat de Catalunya (Consolidated Research Group 2021 SGR01150).

Peer reviewed