Chemosphere ( IF 5.778 ) Pub Date : 2021-02-22 , DOI: 10.1016/j.chemosphere.2021.130064 Ziyu Li; Nicolas M. Peleato
Fluorescence spectroscopy shows promise as a tool for monitoring water quality due to its real-time capabilities and sensitive detection of several compounds of interest. Previous work has shown the possible use of fluorescence to detect and quantify low levels of polycyclic aromatic hydrocarbons and fluorescing pesticides. However, the fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources to build and calibrate them for each source water of interest. In this study, the novel application of data processing techniques was investigated to enable the transfer of fluorescence detection models from one water source to another. A contaminant detection model from a relatively consistent and low organic background source (Lake Ontario, TOC: 2.07 to 2.26 mg L-1) was transferred to the Otonabee River, which has higher organic concentrations and distinct characteristics (TOC: 5.20 to 5.66 mg L-1). Only a few additional fluorescence spectra of the background water quality and contaminants of interest were required to successfully transfer the model, without the need for labelled samples in the new source. Notable differences in peak location and spectral shape of identical compounds were found in source-specific models between the two water sources, implying variability in fluorescence signals resulting from environmental conditions. Despite the impact of environmental conditions, features identified by principal component analysis (PCA) and an autoencoder produced sensitive transferred models capable of addressing the spatial and temporal source diversity with mean absolute error (MAE) < 0.5 μg L-1 for quantification of PAHs and pesticides at concentrations between 0.1 and 7 μg L-1. The results of this study show the potential of the cross-source transferred model to be implemented in a wide range of environmental conditions.
荧光光谱法具有实时功能和对几种目标化合物的灵敏检测，因此有望成为监测水质的工具。先前的工作表明可以使用荧光检测和定量检测低含量的多环芳烃和荧光农药。但是，基于荧光的污染物检测模型具有高度的源特定性，并且需要大量的精力和资源来构建和校准每种目标水。在这项研究中，研究了数据处理技术的新应用，以使荧光检测模型从一种水源转移到另一种水源。来自相对一致且有机背景较低的污染物检测模型（安大略湖，TOC：2.07至2.26 mg L -1）被转移到具有较高有机浓度和独特特征的Otonabee河（TOC：5.20至5.66 mg L -1）。仅需几个额外的背景水质和目标污染物的荧光光谱即可成功地转移模型，而无需在新来源中添加标记的样品。在两个水源之间的特定于源的模型中，发现了相同化合物的峰位置和光谱形状的显着差异，这表明环境条件导致的荧光信号存在差异。尽管受到环境条件的影响，但通过主成分分析（PCA）和自动编码器识别的功能仍可生成灵敏的转移模型，该模型能够解决空间和时间源多样性问题，其平均绝对误差（MAE）<0.5μgL -1，用于定量PAH和农药浓度在0.1至7μgL -1之间。这项研究的结果表明，在多种环境条件下实施跨源转移模型的潜力。