溶解有机碳
化学
环境化学
有机质
天然有机质
水生生态系统
渗滤液
总有机碳
浸出(土壤学)
分析化学(期刊)
环境科学
土壤科学
土壤水分
有机化学
作者
Yun-Kyung Lee,Seongjin Hong,Jin Hur
出处
期刊:Water Research
[Elsevier BV]
日期:2021-11-03
卷期号:207: 117833-117833
被引量:57
标识
DOI:10.1016/j.watres.2021.117833
摘要
Recently, studies have increasingly focused on the occurrence of plastic leachate and its effects on aquatic environments. However, few studies have aimed to identify microplastic-derived dissolved organic matter (MP-DOM) from environmental samples that are often enriched with natural organic matter (NOM). In this study, three MP-DOM (EPS-DOM, PVC-DOM, and PET-DOM) and eight aquatic NOM samples, and their mixtures, were used to identify a unique optical surrogate for MP-DOM within background NOM. Three major fluorescence peaks (peaks P, H, and L) were identified in the excitation emission matrix (EEM) spectra of both DOM sources (i.e., MP-DOM and NOM). The first two peaks were more pronounced for MP-DOM than for aquatic NOM, whereas peak L showed the opposite trend. The summed intensity ratio of the ranges of the first two peaks relative to peak L, namely, (H + P)/L, clearly distinguished between MP-DOM and NOM samples. The MP-DOM source discrimination capability was compared for several selected spectroscopic indices by tracking their changes in the mixtures of two source groups with increasing fraction of MP-DOM via end-member mixing analysis. This was further evaluated based on the three criteria built on the significance of the difference between the two groups, the correlation coefficients of the regressions, and the minimum fraction of MP-DOM in mixtures that can be distinguished from 100% NOM samples. Irrespective of the plastic type and leaching conditions (i.e., UV-irradiated or not), the new optical index, (H + P)/L, was superior at distinguishing MP-DOM from the mixtures when compared to other commonly used optical indices. The new index can serve as a sensitive, robust, and reliable fluorescence indicator with minimal interference from NOM for detecting plastic leachate in aquatic samples.
科研通智能强力驱动
Strongly Powered by AbleSci AI