微塑料
过滤(数学)
环境科学
超滤(肾)
自来水
流出物
污染
水质
环境化学
环境工程
化学
制浆造纸工业
色谱法
数学
生态学
生物
统计
工程类
作者
Chuqiao Yuan,Husein Almuhtaram,Michael J. McKie,Robert C. Andrews
出处
期刊:Chemosphere
[Elsevier BV]
日期:2021-08-12
卷期号:286: 131881-131881
被引量:42
标识
DOI:10.1016/j.chemosphere.2021.131881
摘要
To date, no standardized methods have been proposed for conducting microplastic analyses in treated drinking waters, resulting in challenges associated with direct comparisons among studies. This study compares known methods for collecting and extracting microplastics from drinking waters: an in-laboratory (in-lab) filtration method and an in-line filtration method (i.e., water filtered on-site without an intermediate storage and/or transportation step). In-lab methods have been the predominant method for sample collection in drinking water matrices, and in-line methods are emerging due to the potential to sample large volumes of water on site and minimize contamination from airborne particles, but the two methods have yet to be directly compared using real samples. In response, this study evaluates both methods in terms of recovering spiked reference microplastics, collecting microplastics from tap water samples using the same water volume, and quantifying the removal of microplastics through a full-scale ultrafiltration system. In-line filtration was shown to have higher recoveries for all the reference microplastics examined (+37 % for PVC fragments, +23 % for PET fragments, +22 % for nylon fibers and +7 % for PET fibers) and a greater potential to reduce microplastic contamination. It also resulted in lower standard deviations for total microplastic counts in the tap water and UF influent and effluent samples. The filtration capacity of the proposed in-line filtration method could exceed 350 L of treated water, but this is highly dependent on the water quality. This study therefore supports the use of in-line filtration methods towards the standardization of microplastic collection procedures in drinking water.
科研通智能强力驱动
Strongly Powered by AbleSci AI