Policy impact on microplastic reduction in China: Observation and prediction using statistical model in an intensive mariculture bay

Plastic pollution in the environment has spurred debate among scientists, policymakers, and the general public over how industrialization and consumerism are wreaking havoc on our ecosystem, but some policies might assist to ameliorate the problem in the near future. In this study, the decision tree classifier and Bayesian Structural Time Series (BSTS) model was used to anticipate the possible sources of microplastics and their near future state in 26 surface sediment and a sediment core, respectively in Sansha Bay, which has been criticized for its intensive mariculture applications. An inventory of microplastics in the sediment core was estimated, and it was discovered that during the previous six decades, an average of 181.95 tons of microplastics were deposited, with an average deposition (by a layer of sediment) of 179.44 tons/cm. According to the DT classifier, mariculture was the primary source of microplastics, whereas urban and industrial areas were the primary sources of POPs. The Bayesian Structural Time Series (BSTS) model revealed a microplastic downward slope, indicating that regional and national strategies implemented might successfully reduce microplastic pollution regionally.