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- W4221036155 abstract "Disinfection byproducts (DBPs) in drinking water distribution systems are affected by multi-factors, such as basic water quality parameters, microbial community structures, and residual organic pollutants that cannot be removed by the water treatment process. The relationship between the above-mentioned factors that forms a complicated network structure, which causes the dominating factor that affects DBPs formation unclear. This study investigated the water quality in regional tap water in January-February 2021. Trihalomethanes were determined using P&T-GC-MS, and antibiotics and nitrosamines were determined using UPLC-MS/MS. Microbial communities were determined using Illumina 16S rRNA gene sequencing. A Bayesian network was constructed to evaluate the intercorrelation between the factors. Three species of trihalomethanes, six species of nitrosamines, 23 types of antibiotics, and 236 OTUs were detected in the tap water. The mass concentrations of trihalomethanes, nitrosamines, and antibiotics were 18.33-32.09 μg·L-1, 13.08-53.50 ng·L-1, and 47.92-210.33 ng·L-1, respectively. The dominant microbial orders were Rhizobiales and Caulobacterales. Based on the Bayesian-network inference, tetracycline, sulfonamides, and macrocyclic antibiotics were precursors of trihalomethanes, whereas tetracyclines were the nitrosamine precursor. The abundances of Caulobacterales and Corynebacteriales were both affected by antibiotics and associated with DBPs formation. The extracellular polymeric substances of these bacteria were highly suspected to be important DBPs precursors. The results of the proposed project revealed the internal relationship between multi-water-quality parameters and DBPs formation, which could provide a theoretical support to guarantee the safety of drinking water." @default.
- W4221036155 created "2022-04-03" @default.
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- W4221036155 date "2022-03-08" @default.
- W4221036155 modified "2023-10-18" @default.
- W4221036155 title "[Factor Analysis of Disinfection Byproduct Formation in Drinking Water Distribution Systems Through the Bayesian Network]." @default.
- W4221036155 doi "https://doi.org/10.13227/j.hjkx.202106138" @default.
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