Data for: Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Monitoring water quality in sewers is challenging, particularly because state-of-the-art technologies require contact with the raw wastewater. The presence of fat, oil, grease and solids makes automated grab sampling difficult and causes sensor fouling. To overcome these limitations, non-contact methods based on light reflectance, such as hyperspectral imaging (HSI), are gaining attention. However, HSI has never been tested for raw wastewater. To assess its accuracy for measuring pollution, we developed a laboratory setup and performed targeted experiments with a combination of raw and diluted wastewater, as well as synthetic turbidity stock solutions. We measured seven pollution variables: chemical oxygen demand, turbidity, dissolved organic compounds, ammonium, total nitrogen, phosphate, and sulphates. We used automated pixel selection and partial least squares regression to retrieve pollution information from the hyperspectral images. Our results, based on 144 samples, suggest that HSI can estimate pollution levels with a precision in the range of state-of-the-art absorbance spectroscopy methods. Additionally, we found that the combination of pixel and wavelength selection, enabled by the hyperspectral data structure, significantly influences the performance of partial least square modelling. Overall, our findings indicate that HSI is a promising technology for non-contact monitoring of water quality in raw wastewater.

对污水管网内的水质开展监测颇具挑战，尤其是当前主流先进技术均需直接接触原污水（raw wastewater）。污水中脂肪、油类、油脂与固体杂质的存在，不仅使得自动化瞬时采样（grab sampling）难以开展，还会造成传感器结垢。为克服上述局限，基于光反射原理的非接触式检测方法——例如高光谱成像（hyperspectral imaging, HSI）——正日益受到学界关注。然而，高光谱成像（HSI）尚未针对原污水开展过相关试验验证。为评估其在污染检测中的精度表现，本研究搭建了实验室测试平台，并针对原污水、稀释污水以及合成浊度储备液开展了针对性实验。本研究共测定7项污染指标：化学需氧量（chemical oxygen demand）、浊度、溶解性有机物（dissolved organic compounds）、铵盐（ammonium）、总氮、磷酸盐（phosphate）及硫酸盐（sulphates）。我们采用自动化像素选择与偏最小二乘回归（partial least squares regression）算法，从高光谱影像中提取污染相关信息。基于144份样本的实验结果表明，高光谱成像（HSI）对污染水平的估算精度，可媲美当前主流的吸光光谱技术。此外，本研究发现，依托高光谱数据结构实现的像素与波长联合选择策略，会对偏最小二乘建模的性能产生显著影响。总体而言，本研究结果证实，高光谱成像（HSI）是一项极具应用前景的原污水水质非接触式监测技术。