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Microplastic particles are found in environmental compartments all over the world and receive a great deal of attention, especially in the aquatic environment. Currently, a particularly high input of microplastics via Asian rivers is assumed, but so far, there are hardly any data through field measurements. Three rivers in South India were considered for this purpose to focus on their microplastic load. The emphasis was on the comparison of microplastic concentrations in urban and rural rivers. While two rivers in the megacity Chennai (Tamil Nadu) were found to have an average microplastic concentration of 0.4 microplastic particles/L, a rural river near Munnar (Kerala) had an average concentration of 0.2 microplastic particles/L. Rough estimates of annual microplastic discharge from the Adyar River (Chennai) into the Bay of Bengal are found to be as high as 11.6 trillion microplastic particles. This study should be one of the first baseline studies for microplastic loads in South Indian streams and should be complemented with further environmental sampling before, during and after the monsoon season to get more detailed information on the storage and transportation of fluvial microplastics under different weather conditions. [ABSTRACT FROM AUTHOR]

Environmental monitoring of aquatic systems is the key requirement for sustainable environmental protection and future drinking water supply. The quality of water resources depends on the effectiveness of water treatment plants to reduce chemical pollutants, such as nitrates, pharmaceuticals, or microplastics. Changes in water quality can vary rapidly and must be monitored in real-time, enabling immediate action. In this study, we test the feasibility of a deep UV Raman spectrometer for the detection of nitrate/nitrite, selected pharmaceuticals and the most widespread microplastic polymers. Software utilizing artificial intelligence, such as a convolutional neural network, is trained for recognizing typical spectral patterns of individual pollutants, once processed by mathematical filters and machine learning algorithms. The results of an initial experimental study show that nitrates and nitrites can be detected and quantified. The detection of nitrates poses some challenges due to the noise-to-signal ratio and background and related noise due to water or other materials. Selected pharmaceutical substances could be detected via Raman spectroscopy, but not at concentrations in the µg/l or ng/l range. Microplastic particles are non-soluble substances and can be detected and identified, but the measurements suffer from the heterogeneous distribution of the microparticles in flow experiments. [ABSTRACT FROM AUTHOR]

Microplastics pose numerous threats to aquatic environments, yet understanding their transport mechanisms remains limited. Drawing from natural sediment research provides valuable insights to address this knowledge gap. One key dimensionless number used to describe sediment transport is the transport stage, referring to the ratio between the flow shear velocity and the particle settling velocity. However, variations in physical properties, such as shape and density, raise concerns about the applicability of existing sediment transport theories to microplastics. To address this challenge, we employed a physical modeling approach, examining 24 different nonbuoyant microplastic particles in a turbulent open channel flow. Utilizing 3D particle tracking, a total of 720 trajectories were recorded and analyzed. Microplastic particles exhibited transport modes akin to natural sediments, including rolling/sliding, saltation, and suspension. The transport stage strongly correlated with these modes, as well as with the mean forward velocity and mean position in the water column. Notably, particle shape emerged as a critical factor influencing transport dynamics. Due to their lower settling velocity, fibers tended to stay closer to the water surface with lower forward velocities compared to spheres. Based on the laboratory results, a new phase diagram for microplastics is introduced analogous to an existing diagram for sediments.

Plastic accumulates in the environment because of insufficient waste handling and its high durability. Better understanding of plastic behavior in the aquatic environment is needed to estimate transport and accumulation, which can be used for monitoring, prevention, and reduction strategies. Plastic transport models benefit from accurate description of particle characteristics, such as rising and settling velocities. For macroplastics (>0.5 cm), these are however still scarce. In this study, the rising and settling behavior of three different polymer types (PET, PP, and PE) was investigated. The plastic particles were foils of different surface areas and shapes. The observational data were used to test the performance of four models, including one developed in this study, to estimate the rising/settling velocity on the basis of the plastic particle characteristics. These models are validated using the data generated in this research, and data from another study. From the models that were discussed, the best results are from the newly introduced foil velocity model ( R 2 = 0.96 and 0.29, for both data sets, respectively). The results of our paper can be used to further explore the vertical distribution of plastics in rivers, lakes, and oceans, which is crucial to optimize future plastic monitoring and reduction efforts., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

River flow is a major conveyance of microplastic (1-5000 μm) pollution from land to marine systems. However, the current approaches to monitoring and modeling fluvial transport of microplastic pollution have primarily relied on sampling the surface of flow and assumptions about microplastic concentration depth profiles to estimate the depth-averaged concentration. The Rouse profile was adapted to show that fluvial transport of microplastic pollution includes all traditional domains of transport (bed load, settling suspended load, and wash load), as well as additional domains specific to low-density materials with rising velocities in water (rising suspended load and surface load). The modified Rouse profile was applied to describe the positively buoyant particle concentration depth profiles and compared to field observations to showcase the utility of this approach. A procedure was developed for assessing the uncertainty and bias from using a surface sample to estimate the depth-averaged concentration while assuming either surface load or wash load concentration depth profiles. Both assumptions may introduce a large amount of uncertainty due to the range of suspended microplastic concentration depth profiles. Monitoring microplastic pollution and estimating the depth-averaged concentration of microplastics in fluvial systems would further benefit from broader adoption of depth-integrated sampling, characterization of particle concentration depth profiles, and estimation of uncertainties in depth-averaged concentration based on the sampling approach.

In this study, the infiltration behavior of 21 microplastic particles with different densities, diameters, and shapes was investigated using columns of glass spheres with different grain diameters. The glass spheres were considered as an analogy for natural sediment and the results were afterward transferred to natural sediment and compared to fine sediment infiltration. The infiltration depth of the microplastic particles increased with decreasing diameter of the microplastic particles ( d MP ) and with increasing diameter of the glass spheres ( d GS ). At ratios of d MP / d GS > 0.32, hardly any infiltration could be observed. In regard to the particle shape, the data shows that spherical particles infiltrate deeper than fragments and fibers. In case of fibers, the fiber diameter, in particular, influences the depth of infiltration, with thinner fiber diameters leading to deeper infiltration depths. Fragments, such as tire abrasion, infiltrated less deeply than spherical particles, probably due to the entanglement of the angular particles in the pores. Finally, based on the experiments, this study provides initial indications of reasonable sampling depths in dependence of the grain sizes of the bottom sediment and the microplastic particles. According to this, microplastics in detectable particle sizes (>100 μm) are only found on the surface of sediment consisting of coarse silt and fine sand, while the particles might infiltrate up to 13 cm into sediment consisting of coarse sand, fine gravel, and medium gravel. A statement of depth-variable microplastic concentrations is therefore mainly useful for these sediment types. Accordingly, in future sediment samples, the grain size distribution of the sediment should always be indicated to better evaluate the detected microplastic concentrations.