Lechthaler, S.E., Hildebrandt, L., Stauch, G., & Schüttrumpf, H. (2020): Canola Oil Extraction in Conjunction with a Plastic Free Separation Unit Optimises Microplastics Monitoring in Water and Sediment. Anal. Methods, doi:10.1039/D0AY01574A
Microplastics are widely distributed in the environment and to define contamination hot spots, environmental samples have to be analysed by means of cost-as well as time-efficient and reliable standardised protocols. Due to the lipophilic characteristics of plastics, oil extraction as a fast and density-independent separation process is beneficial for the crucial extraction step. It was extensively validated (480 experiments) in two test setups by using canola oil and a cost-effective, plastic-free separation unit with spiked microplastics (19 different polymer types) in the density range from ρ = 11–1760 kg m−3 and in the size range from 0.02–4.4 mm. Thus, an innovative, new method combination was developed and profoundly validated for water and sediment samples using only a short settling time of 15 minutes. Some experiments were also carried out with zinc chloride to obtain additional reference data (particles ≤ 359 μm). The total mean recovery rate was 89.3%, 91.7% within the larger microplastic fraction and 85.7% for the small fraction. Compared to zinc chloride (87.6%), recovery rates differed not significantly with oil (87.1%). Furthermore, size limits were set, since the method works best with particles 0.02 mm ≥ d ≤ 3 mm. The proposed method exhibits higher efficiency (84.8% for 20–63 μm) for the potentially most harmful microplastic size fraction than the classic setup using brine solution. As a result, oil is a comparably effective separation medium and offers further advantages for separating water and sediment samples due to its density independence, simple and fast application and environmental friendliness. Based on this, a new extraction protocol is presented here that confirms oil separation as a sound and effective separation process in microplastic analysis and identifies previously missing information.