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Meta-analysis on microplastics monitoring in global water resource recovery facilities: An emphasis on overlooked factors
Summary
This meta-analysis pools data from water treatment facilities worldwide to assess how effectively they remove microplastics from wastewater. The findings reveal significant variation in removal rates depending on region and treatment methods, meaning the amount of microplastics that pass through into rivers and drinking water sources depends heavily on where you live.
As municipal water resource recovery facilities (WRRFs) provide an important conduit between microplastics (MPs) and the environment, it is critical to understand global trends. This meta-analysis integrates data from studies worldwide, providing a comprehensive overview of MP occurrence and removal from wastewater while emphasizing overlooked variables and regions. Principal component analysis (PCA) found that Europe and Asia form largely separate clusters in terms of MP removal performance, likely due to differences in study methodologies and the range of wealth within included countries. Asian studies tended to include countries of greater economic diversity, while European studies overall included smaller MPs and more often employed spectroscopy for polymer identification and quantification. Analysis of variance (ANOVA) identified study methodology, secondary treatment type, and wastewater type to have the most significant effects on MP removal (p-values <0.01) globally and continentally, with other variables both internal and external to WRRFs exerting varied effects depending on the socioeconomic lens (i.e., relative vs. absolute wealth in terms of gross domestic product, or GDP, per capita). Post hoc analysis identified China, South Korea, and Vietnam to display significantly different means in MP removal from other Asian countries. Lastly, component regression (PCR) and machine learning-based partial least squares regression (PLSR) were conducted to create prediction models for MP removal from WRRFs, which supported the regional patterns in behaviour identified with PCA and ANOVA while streamlining an efficient method for predicting WRRF performance. Future research should address global monitoring bias, mismanaged plastic waste, and standardized MP reporting and analysis.