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饮用水甲苯污染与5项在线监测指标的相关性分析 [中文引用][英文引用]

Correlation Analysis between Methylbenzene Pollution and 5 Indice of Drinking Water On-line Monitoring

作者:谈立峰  姚辉  褚苏春  惠高云 
作者(英文):TAN Lifeng  YAO Hui  CHU Suchun  HUI Gaoyun 
单位(英文): 
分类号:R123
出版年·卷·期(页码):2019·9·第5期(435-438)
DOI:
-----摘要:-------------------------------------------------------------------------------------------

目的 分析饮用水甲苯污染与在线监测指标pH、浑浊度、余氯、电导率和总有机碳(TOC)的关联性,进一步筛选饮用水在线监测预警的有效指标,探索构建甲苯污染浓度的预测模型。方法 采用实验加标法,分别从低浓度到高浓度5个不同剂量(0.35~5.6 mg/L进行加标;研究分析饮用水甲苯污染与在线监测指标pH、浑浊度、余氯、电导率和TOC的相关性,建立饮用水甲苯污染浓度预测的多元线性回归模型。结果 甲苯与TOC差值呈负相关(R=-0.526 7P=0.043 6),而与pH、浑浊度、余氯及电导率差值均无相关性(P>0.05);研究建立了饮用水甲苯污染浓度预测的多元线性回归模型:y=0.09-13.949x1-11.233 x2-5.642 x3y表示甲苯浓度估计值,x1表示pH差值,x2表示余氯差值,x3表示TOC差值)。结论 饮用水发生甲苯污染时,可通过研究建立的多元线性回归模型实现污染浓度预测。

-----英文摘要:---------------------------------------------------------------------------------------

Objectives The correlations between methylbenzene pollution and pH, turbidity, residual chlorine, electrical conductivity and total organic carbon (TOC) of on-line monitoring indices of drinking water were analyzed in order to screen effective indices of on-line monitoring and to establish the pollution concentration prediction model. Methods The method of adding standard substance in laboratory was used to analyze the correlations between methylbenzene pollution and the drinking water on-line monitoring indices. Five different doses of methylbenzene from low to high concentrations (0.35~5.6) mg/L were added respectively. A multivariate linear regression model was further established to predict the methylbenzene pollution concentration in drinking water. Results There was a negative correlation between methylbenzene and total organic carbon difference value(R=-0.526 7,P=0.043 6). There were no significant correlations between methylbenzene and pH, turbidity, residual chlorine as well as electrical conductivity difference values(P>0.05). The multivariate linear regression model for predicting the methylbenzene pollution concentration in drinking water was expressed as y=0.09-13.949x1-11.233x2-5.642x3(y=methylbenzene concentration estimated value; x1=pH difference value; x2=residual chlorine difference value; x3=total organic carbon difference value). Conclusions The pollution concentration prediction may be realized by using the multivariate linear regression model in case of the methylbenzene pollution in drinking water. It should be also further verified and improved in the actual drinking water pollution events.

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中文著录格式: 谈立峰,,姚辉,,褚苏春,,惠高云.饮用水甲苯污染与5项在线监测指标的相关性分析.环境卫生学杂志.2019;9(5):435-438.
英文著录格式: TAN,Lifeng,,YAO,Hui,,CHU,Suchun,,HUI,Gaoyun.Correlation Analysis between Methylbenzene Pollution and 5 Indice of Drinking Water On-line Monitoring..2019;9(5):435-438.

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