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Partial Least Squares local calibration of a UV-visible spectrometer used for in situ measurements of COD and TSS concentrations in urban drainage systems

机译:紫外可见光谱仪的偏最小二乘局部校准,用于城市排水系统中COD和TSS浓度的现场测量

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摘要

Recent UV - visible spectrometers deliver on line and in situ absorbance spectra in wastewater or stormwater transported in urban drainage systems. After calibration with local data sets, spectra can be used to estimate pollutant concentrations. Calibration methods are usually based on PLS ( Partial Least Squares) regression. Their most important difficulty lies in the identification of the number of both i) the latent vectors and ii) the independent variables. A method is proposed to identify these variables, based on an exhaustive tests procedure ( Jackknife cross validation and matrix of prediction indicator). It was applied to estimate TSS ( total suspended solids) or COD ( chemical oxygen demand) concentrations at the inlet of a storage- settling tank in a stormwater separate sewer system, and compared to three other calibration methods used either for turbidity meters or UV - visible spectrometers. With the available calibration data set: i) the spectrometer gives results with better prediction quality than the turbidity meter, ii) for the spectrometer, local calibration gives better results than global calibration, iii) the proposed PLS method gives results with a similar order of magnitude in uncertainties as the manufacturer local calibration method, but is more open and transparent for the user. Similar results were obtained for a second data set.
机译:最近的紫外可见光谱仪在城市排水系统中输送的废水或雨水中提供在线和原位吸收光谱。在使用本地数据集进行校准后,光谱可用于估算污染物浓度。校准方法通常基于PLS(偏最小二乘)回归。它们最重要的困难在于识别i)潜矢量和ii)自变量的数量。提出了一种基于详尽的测试程序(折刀交叉验证和预测指标矩阵)来识别这些变量的方法。该方法可用于估算雨水分离下水道系统中存储沉淀池入口处的TSS(总悬浮固体)或COD(化学需氧量)浓度,并与用于浊度计或UV的其他三种校准方法进行比较-可见光谱仪。利用可用的校准数据集:i)光谱仪比浊度仪提供更好的预测质量结果; ii)光谱仪比本地校准提供更好的结果; iii)拟议的PLS方法给出的结果具有相似的阶次不确定度的大小与制造商本地校准方法相同,但对用户而言更加开放透明。对于第二个数据集获得了相似的结果。

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