引用本文:戴源,谢继征,袁静,赵小健,殷高方,沈薇,孙小平,王志刚.紫外光诱导荧光分析仪结合多元线性回归算法在城市河流常规污染指标监测中的应用[J].环境监控与预警,2021,13(2):29-34
DAI Yuan1, XIE Ji-zheng1, YUAN Jing1, ZHAO Xiao-jian1, YIN Gao-fang2, SHEN Wei1, SUN Xiao-ping1, WANG Zhi-gang3.Application of Ultraviolet Induced Fluorescence Spectrometer Combined with MLR in Monitoring of Non-specific Pollutants in Urban Rivers[J].Environmental Monitoring and Forewarning,2021,13(2):29-34
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紫外光诱导荧光分析仪结合多元线性回归算法在城市河流常规污染指标监测中的应用
戴源,谢继征,袁静,赵小健,殷高方,沈薇,孙小平,王志刚
作者单位
戴源1,谢继征1,袁静1,赵小健1,殷高方2,沈薇1,孙小平1,王志刚3 1. 江苏省扬州环境监测中心江苏 扬州 2251002. 中国科学院安徽光学精密机械研究所环境光学与技术重点实验室安徽 合肥 2300313. 扬州大学环境科学与工程学院江苏 扬州 225009 
摘要:
为建立一种针对城市河流水体常规污染指标的快速原位监测方法,首次运用紫外光诱导荧光分析仪对扬州市60条城市河流进行水体三维荧光光谱(EEM)测量,形成了具有多样性的水质样本集合。利用峰值拾取法、相关性分析和主成分分析3种方式从三维荧光光谱中提取溶解性有机物(DOM)污染信息,结合多元线性回归算法(MLR),建立与化学需氧量(CODCr)、高锰酸盐指数(IMn)、氨氮(NH3-N)和总磷(TP)4项常规水质污染指标相关的预测模型。研究结果表明,峰值拾取法结合相关性分析可以有效地反映水体EEM中的污染特征和状况,由此建立的4项水质指标预测模型训练集决定系数均>0.82,预测结果与国家及行业标准方法分析值之间具有较低的均方根误差,说明该预测方法具有较好的准确度和精密度,为城市广域水体的高效、原位监测提供了一种有效的解决方案。
关键词:  三维荧光光谱  多元线性回归  常规污染指标  原位监测
DOI:
分类号:X832;O657.3
文献标识码:B
基金项目:国家重点研发计划基金资助项目(2016YFC1400602);江苏省环境监测科研基金资助项目(1701);江苏省重点研发计划基金资助项目(BE2016709)
Application of Ultraviolet Induced Fluorescence Spectrometer Combined with MLR in Monitoring of Non-specific Pollutants in Urban Rivers
DAI Yuan1, XIE Ji-zheng1, YUAN Jing1, ZHAO Xiao-jian1, YIN Gao-fang2, SHEN Wei1, SUN Xiao-ping1, WANG Zhi-gang3
Abstract:
In order to establish a rapid in-situ monitoring method for non-specific pollutants in urban rivers, the ultraviolet induced fluorescence spectrometer was used for the first time to measure the excitation-emission matrix (EEM) of 60 urban rivers in Yangzhou, forming a diverse water quality sample set. Using peak-picking method, correlation analysis and principal component analysis (PCA) to extract the pollution information of dissolved organic matter (DOM) from the EEM, combined with the multiple linear regression algorithm (MLR), the prediction models were established, including chemical oxygen demand (CODCr), permanganate index (IMn), ammonia nitrogen (NH3-N) and total phosphorus (TP). The research results showed that the peak-picking method combined with correlation analysis could effectively inflect the characteristics and status of pollution from the EEM of the water bodies. The determination coefficients rc2 of the four prediction models were all greater than 0.82, and there was low RMSE between the prediction results and the analysis results by the national and industry standard methods, which indicated that this technology has good accuracy and precision, and it provides an effective solution for the efficient and in-situ monitoring of urban water bodies in large scale.
Key words:  EEM spectrum  Multiple linear regression  Non-specific pollution indexes  In situ monitoring