Microplastics are defined as plastics smaller than 5 mm down to 0.05 mm. These plastics enter the environment and undergo certain physical changes, most notably density changes and a relative increase of surface size. Microplastics can then release or absorb toxicants from the surrounding environment. These plastics may then enter the food chain from producers to top predators. In this study, microplastics were investigated in four study sites in the upper Vaal River, South Africa. The goal of the study was to determine the levels of plastics in water, sediment and a top predator, the benthic fish Clarias gariepinus. In this study, a 10% KOH digestion of water and fish, and density separation of sediment with NaCl (1.2 g cm³) was used to extract microplastics for identification. Microplastics were detected in water (3, 300 particles m⁻³), fish (7.47 particles per fish) and sediment (46.7 particles kg⁻¹). Microplastic intake was not attributed to the microplastic shape or size of the fish that ingested it. This highlights the need to understand how niche-specific microplastic concentrations are, which will not only aid in quantifying microplastics accurately in the environment but to better understand how they may influence various ecosystems.

The use of Unmanned Arial Vehicle (UAV) photogrammetry is a valuable tool to enhance our understanding of wetlands. Accurate planning derived from this technological advancement allows for more effective management and conservation of wetland areas. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool to enhance the assessment of wetland ecosystems. The UAV images were collected during a single flight within 2½ hours over a 100 ha area at the Kameelzynkraal farm, Gauteng Province, South Africa. An AKS Y-6 MKII multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. Twenty ground control points (GCPs) were surveyed using a Trimble GPS to achieve geometrical precision and georeferencing accuracy. Structure-from-Motion (SfM) computer vision techniques were used to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The geometric accuracy of the data based on the 20 GCP’s were 0.018 m for the overall, 0.0025 m for the vertical root mean squared error (RMSE) and an over all root mean square reprojection error of 0.18 pixel. The UAV products were then edited and subsequently analysed, interpreted and key attributes extracted using a selection of tools/ software applications to enhance the wetland assessment. The results exceeded our expectations and provided a valuable and accurate enhancement to the wetland delineation, classification and health assessment which even with detailed field studies would have been difficult to achieve.