Intelligent classification and pollution characteristics analysis of microplastics in urban surface waters using YNet.

ABSTRACT

Microplastics (MPs, ≤ 5 mm in size) are hazardous contaminants that pose threats to ecosystems and human health. YNet was developed to analyze MPs abundance and shape to gain insights into MPs pollution characteristics in urban surface waters. The study found that YNet achieved an accurate identification and intelligent classification performance, with a dice similarity coefficient (DSC) of 90.78%, precision of 94.17%, and recall of 89.14%. Analysis of initial MPs levels in wetlands and reservoirs revealed 127.3 items/L and 56.0 items/L. Additionally, the MPs in effluents were 27.0 items/L and 26.3 items/L, indicating the ability of wetlands and reservoirs to retain MPs. The concentration of MPs in the lower reaches of the river was higher (45.6 items/L) compared to the upper reaches (22.0 items/L). The majority of MPs detected in this study were fragments, accounting for 51.63%, 54.94%, and 74.74% in the river, wetland, and reservoir. Conversely, granules accounted for the smallest proportion of MPs in the river, wetland, and reservoir, representing only 11.43%, 10.38%, and 6.5%. The study proves that the trained YNet accurately identify and intelligently classify MPs. This tool is essential in comprehending the distribution of MPs in urban surface waters and researching their sources and fate.