0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Remediation Sign in to save

Advances in the analysis of relevant microplastic types in agricultural soils

Zenodo (CERN European Organization for Nuclear Research) 2024
Chiara Consolaro, Rachel Hurley, Rachel Hurley, Nina Buenaventura, Sverre Hjelset, Luca Nizzetto, Luca Nizzetto

Summary

Researchers developed an optimized soil purification protocol for extracting and identifying microplastic particles from agricultural soils, systematically testing and combining multiple extraction and purification steps to improve the accuracy of polymer identification across different soil types.

Soils are one of the most challenging matrices to deal with for the extraction of microplastic particles. The challenge is to purify samples sufficiently enough to isolate the plastic particles from the rest of the matrix to allow a clear identification of the different polymer types. The soil purification method presented here is based on Möller et al., 2022, and it was designed following systematic testing on different soil types to create an efficient protocol for microplastic analysis. The protocol consists of a combination of different steps which were each tested before being added to the final protocol to identify their relevance to the effective and efficient removal of the soil matrix and isolation of plastic particles. The final protocol was subject to a rigorous validation procedure to verify the efficacy of the method. This sample processing protocol is further supplemented by advances made in the detection of mulching film fragments in soil. Specifically, a method for replacing visual analysis of the fragments larger than the size threshold for µFourier Transform Infrared (FTIR) analysis was developed using multi-spectral imaging. This method utilises a Videometer imaging device with a machine learning algorithm in conjunction with repeated spot testing using attenuated total reflectance-FTIR to train the instrument to detect the microplastics. This method is effective in separating between two types of mulching films, conventional and biodegradable black films, with a high degree of accuracy (RSDs of Also see: https://micro2024.sciencesconf.org/558587/document

Share this paper