Detection of suspended macroplastics using acoustic doppler current profiler (ADCP) echo

Plastic pollution has become an enormous environmental problem, endangering ecosystems, livelihoods, safety and human health. Large quantities of plastics are trapped in or transported by rivers. Monitoring methods mostly focus on plastics floating at the surface or deposited on riverbanks, while a substantial part of plastics may be transported below the water surface. Available underwater monitoring methods rely on nets and large equipment, making them labour-intensive, expensive and invasive. The measurements are, therefore, limited to occasional point measurements. In this paper, we explore the potential of echo sounding for the monitoring of suspended macroplastic (plastic items bigger than 5 mm). We performed tests in a controlled (basin), a semi-controlled (harbour) and an uncontrolled (river) environment using the high-end Acoustic Doppler Current Profiler (ADCP). This device is already in use for the estimation of flow velocity and suspended sediment concentrations using the wide network of ADCPs in the Netherlands and other countries. In the undisturbed controlled environment, 25 items varying in size, material, and orientation could be detected up to at least 4.6 m from the ADCP. The semi-controlled experiments showed that most of these items can also be detected among other naturally occurring scatterers, such as aquatic life, organic material and air bubbles. The field tests under natural conditions, combining ADCP and net measurements, showed that ADCP data can be calibrated towards a correct order of magnitude estimate of plastic transport. The coupling of the ADCP data to item characteristics such as size, material and orientation is still challenging, but more research into, for example, the signature of items may enable distinguishing item characteristics. This fundamental knowledge, combined with repetitions of validated field measurements under different flow conditions, is needed for the development of a robust monitoring method. Such a method m