| 基于贝叶斯网络的给水管网消毒副产物生成因素分析 |
| 摘要点击 1591 全文点击 548 投稿时间：2021-06-17 修订日期：2021-08-05 |
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| 中文关键词 抗生素 贝叶斯网络 消毒副产物 给水管网 微生物群落 |
| 英文关键词 antibiotics Bayesian network disinfection byproducts drinking water distribution system microbial community |
| 作者 | 单位 | E-mail | | 江杉杉 | 华南师范大学环境学院, 广州 510006
华南师范大学广东省化学品污染与环境安全重点实验室&环境理论化学教育部重点实验室, 广州 510006 | shanshan.jiang@m.scnu.edu.cn | | 王臻宇 | 德累斯顿工业大学市政与环境工程学院, 德累斯顿 01069, 德国 | | | 高权 | 华南师范大学环境学院, 广州 510006
华南师范大学广东省化学品污染与环境安全重点实验室&环境理论化学教育部重点实验室, 广州 510006 | | | 杨愿愿 | 华南师范大学环境学院, 广州 510006
华南师范大学广东省化学品污染与环境安全重点实验室&环境理论化学教育部重点实验室, 广州 510006 | | | 高方舟 | 华南师范大学环境学院, 广州 510006
华南师范大学广东省化学品污染与环境安全重点实验室&环境理论化学教育部重点实验室, 广州 510006 | | | 华佩 | 华南师范大学环境学院, 广州 510006
华南师范大学广东省化学品污染与环境安全重点实验室&环境理论化学教育部重点实验室, 广州 510006 | pei.hua@m.scnu.edu.cn | | 应光国 | 华南师范大学环境学院, 广州 510006
华南师范大学广东省化学品污染与环境安全重点实验室&环境理论化学教育部重点实验室, 广州 510006 | |
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| 中文摘要 |
| 给水管网消毒副产物（disinfection byproducts，DBPs）的生成受管网环境因素、微生物群落特征和水厂未完全去除的有机物等多指标共同影响.各指标间相互关联形成复杂的网络结构，导致影响DBPs在管网内生成的主控因子较难确定.以广州某高校给水管网系统为研究对象，于2021年1~2月开展终端水质调查，利用吹扫捕集-气相色谱质谱法测定三卤甲烷赋存水平，利用超高效液相色谱-串联三重四级杆质谱法测定抗生素及亚硝胺类DBPs质量浓度，利用高通量测序确定微生物群落组成.基于实测数据，构建贝叶斯网络模型，定量分析各水质指标间的相互关联.结果表明，管网中三卤甲烷和亚硝胺类DBPs质量浓度分别为18.33~32.09 μg·L-1和13.08~53.50 ng·L-1.共检出23种抗生素，质量浓度范围为47.92~210.33 ng·L-1.高通量测序发现236种细菌物种，优势菌群为Rhizobiales和Caulobacterales.贝叶斯网络推理发现，四环素类、磺胺类和大环内酯类抗生素为三卤甲烷前体物，同时四环素也是亚硝胺类DBP前体物.Caulobacterales和Corynebacteriales丰度易受抗生素影响，其胞外聚合物是DBPs生成重要前体物.结果可为DBPs的前体物研究和前端控制提供理论支持. |
| 英文摘要 |
| 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. |
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