Elinor S. Austin , Dana M. Austin , Linda Y. Tseng , Lin Liu , Zeth Kleinmeyer , Danielle Drake , Diego Rosso , Yian Sun
{"title":"全球水资源回收设施微塑料监测的荟萃分析:强调被忽视的因素","authors":"Elinor S. Austin , Dana M. Austin , Linda Y. Tseng , Lin Liu , Zeth Kleinmeyer , Danielle Drake , Diego Rosso , Yian Sun","doi":"10.1016/j.envpol.2025.127220","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":"386 ","pages":"Article 127220"},"PeriodicalIF":7.3000,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Meta-analysis on microplastics monitoring in global water resource recovery facilities: An emphasis on overlooked factors\",\"authors\":\"Elinor S. Austin , Dana M. Austin , Linda Y. Tseng , Lin Liu , Zeth Kleinmeyer , Danielle Drake , Diego Rosso , Yian Sun\",\"doi\":\"10.1016/j.envpol.2025.127220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":311,\"journal\":{\"name\":\"Environmental Pollution\",\"volume\":\"386 \",\"pages\":\"Article 127220\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Pollution\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0269749125015945\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Pollution","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0269749125015945","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Meta-analysis on microplastics monitoring in global water resource recovery facilities: An emphasis on overlooked factors
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.
期刊介绍:
Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health.
Subject areas include, but are not limited to:
• Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies;
• Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change;
• Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects;
• Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects;
• Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest;
• New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.