Nontargeted identification of disinfection by-product precursors from soluble microbial products in municipal secondary effluent

JOURNAL OF CLEANER PRODUCTION(2023)

引用 0|浏览2
暂无评分
摘要
As soluble microbial products (SMPs) can serve as precursors for disinfection by-products (DBPs), the release of SMPs from municipal secondary effluent has the potential to increase the health hazards associated with reused water. Nontargeted identification of DBPs precursors from SMPs in municipal secondary effluent was explored in the present research. The hydrophobic (HPO) fraction that constituted the major portion of the SMPs was 48.0%, and the majority of organic SMPs were composed of low molecular weight (LMW) compounds. The results showed that the HPO fraction with MW < 1 kDa component has the highest disinfection by-product formation potentials (DBPFPs). X-ray photoelectron spectroscopy (XPS) results showed that the HPO fraction with MW < 1 kDa component was mainly composed of OC-OH (40.2%) and amide/peptide N (46.7%) functional groups. This finding was in line with the results showing the highest yields of DBPFPs in terms of HAAs (haloacetic acids) and trichloroacetamide (TCAcAm). Ultra-performance liquid chromatography coupled with tandem quadrupole-time-of-flight mass spectrometry (UPLC-Q-ToF-MS) data revealed that phospholipids, fatty acids, fatty acyls-fatty amides, phosphatidylcholine, glycosides, fatty acid esters, glycolipid, and steroids were the main chemical classes in SMPs. Among these chemical classes, phospholipids containing amino acids structures and fatty acids containing carboxyl functional groups with the maximum abundance could promote the formation of HAAs. This research offers a thorough characterization of SMPs derived from municipal secondary effluent. These results enable the implementation of targeted methods to decrease the formation of DBPs by identifying potential precursors of DBPs.
更多
查看译文
关键词
Nontargeted identification,Disinfection by-product precursors,Soluble microbial products,AAO-MBR,UPLC-Q-ToF-MS
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要