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The accuracy of particulate organic matter (POM) detection in sewage was established as the fundamental basis for the systematic characterization of water pollution features. A multi-dimensional synergistic pretreatment system was developed, through which efficient fragmentation and solubilization of POM were achieved by coupled energy field interactions—alkaline hydrolysis (chemical energy), thermal treatment (thermal energy), and ultrasonic cavitation (mechanical energy). This integrated approach was found to enhance sample homogeneity, thereby optimizing spectroscopic detection outcomes. Based on single-factor experimental results, Box-Behnken response surface methodology (RSM) was employed to conduct multi-parameter collaborative optimization of critical control parameters (pH, temperature, and ultrasonic energy) in the composite pretreatment process. The results indicated that: significant differential impacts of the treatments on POM disruption efficiency were observed. Ultrasonic treatment contributed more to TCOD detection accuracy (A_COD was improved from 60.8%±14.9% to 93.3%±3.7%) and total carbohydrate (TCHO) detection accuracy (A_CHO was increased from 59.1%±9.6% to 97.9%±2.8%), while alkaline treatment showed superior optimization effects on total protein (TPN) detection accuracy (A_PN was enhanced from 32.7%±9.7% to 82.7%±3.9%); Optimal process parameters were obtained through RSM model optimization: pH=13.3, T=66.5℃, t=5min, under which enhanced detection accuracies of 96.1% for TCOD, 98.7% for TPN, and 97.5% for TCHO were achieved. The synergistic pretreatment method was demonstrated to have broad applicability across various wastewater scenarios, including primary pipeline sewage, influent of wastewater treatment plants, and effluent from biochemical reactors, effectively reducing detection duration while maintaining robust universality.

, authors=Yu-jie REN¹, Zhi-qiang ZHANG^{1, 2}, Lin-jun LI¹, Zi-geng ZHANG¹, Qing XU¹, Jin-suo LU^{1, 2, *}, authorsList=Yu-jie REN, Zhi-qiang ZHANG, Lin-jun LI, Zi-geng ZHANG, Qing XU, Jin-suo LU, authorCompany=null, correspAuthors=Jin-suo LU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314922079632319044, articleId=1314922078969619010, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于破碎增溶的污水颗粒有机物检测优化方法, columnId=1234106386565624579, journalTitle=中国环境科学, columnName=水污染与控制, runingTitle=null, highlight=null, articleAbstract=

污水中颗粒有机物（POM）检测的准确性是系统评价水质污染特征的基础，构建了多维协同预处理体系，通过碱解（化学能）、热处理（热能）与超声空化（机械能）的能量场耦合作用实现POM基质的高效破解与增溶，提高样品均质性以优化光谱法检测结果．基于单因素实验结果，采用Box-Behnken响应面法（RSM）对复合预处理中关键控制参数（pH值、温度、超声能量）进行多参数协同优化．结果表明：各处理对POM破解效率的影响具有显著差异性．超声处理对TCOD检测准确度（A_COD由60.8%±14.9%升至93.3%±3.7%）及总碳水化合物（TCHO）检测准确度（A_CHO由59.1%±9.6%升至97.9%±2.8%）贡献度更高，而碱处理对总蛋白（TPN）检测准确度（A_PN由32.7%±9.7%升至82.7%±3.9%）优化效果更佳；通过RSM模型优化获得最佳工艺条件为pH=13.3、T= 66.5℃、t=5min，该条件下TCOD、TPN、TCHO的检测准确度提升至96.1%、98.7%及97.5%；协同预处理方法适用于管网首端污水、污水厂进水、生化池出水等多场景下的污水颗粒有机物检测，可有效缩短检测时间，具有较好普适性．

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任昱杰（1997-），男，河南郑州人，西安建筑科技大学博士研究生，主要研究方向为污水管道颗粒物迁移转化机制及调控措施．发表论文6篇．renyujie@xauat.edu.cn.

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任昱杰（1997-），男，河南郑州人，西安建筑科技大学博士研究生，主要研究方向为污水管道颗粒物迁移转化机制及调控措施．发表论文6篇．renyujie@xauat.edu.cn.

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