Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 72615
A Fast and Cost-Effective Method to Monitor Microplastics in Compost and Soil

Authors: Petar Mandaliev, Raffael Schreiber, Robin S. Gilli

Abstract:

In Switzerland, approx. 16`000 t of plastic are applied to agricultural land annually, and approx. 160 t of plastic remains in the soil per year. Here, composts contaminated with plastic are the main source of mi-croplastics. These inputs can lead to plastic concentrations in topsoil (0-25 cm) of up to 200 mg kg-1. The presence of macro- (>5 mm), micro- (1 μm-5mm), and nano-plastics (<1 μm) in soils is an issue of increasing concern as they can pose a risk to soil ecosystems and freshwater and potentially to human health. There are no standardised methods for rapid in situ identification of plastics in compost. Current methods for calculating the plastic load of compost and identifying plastics consist of on-site sampling campaigns combined with physical laboratory extraction and subsequent particle characterisation. Such characterisation methods are far too time-consuming to be used as standard in practice. In this study, we successfully applied a fast and cost-effective method for the accurate detection of microplastics in compost and soil based on high-resolution hyperspectral imaging com-bined with machine learning and artificial neural networks for pattern recognition. The results show that the proposed approach is a promi-sing tool for determining and quantifying microplastics with a particle size of 0.5 to 5 mm in compost or directly on the soil surface.

Keywords: compost, hyperspectral imaging, machine learning, microplastic, soil

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