Liu Jiacheng, Liu Xiao, Wang Xueji, Lim Zi Heng, Liu Hong, Zhao Yubo, Yu Weixing, Yu Tao, Hu Bingliang
Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Environ Sci Technol. 2025 Apr 8;59(13):6649-6658. doi: 10.1021/acs.est.4c14209. Epub 2025 Mar 7.
Water, as a finite and vital resource, necessitates water quality monitoring to ensure its sustainable use. A key aspect of this process is the accurate measurement of critical parameters such as chemical oxygen demand (COD). However, current spectroscopic methods struggle with accurately and consistently measuring COD in large-scale, complex water environments due to an insufficient understanding of water spectra and limited generalizability. To address these limitations, we introduce the physicochemical-informed spectral Transformer (PIST) model, combined with ultraviolet-visible-shortwave-near-infrared (UV-vis-SWNIR) spectroscopy for water quality sensing. To the best of our knowledge, this is the first approach to combine Transformer with spectroscopy for water quality sensing. PIST integrates a physicochemical-informed block to incorporate existing physical and chemical information into the spectral encoding for domain adaptation, along with a feature embedding block for comprehensive spectral features extraction. We validated PIST using an actual surface water spectral data set with extensive geographic coverage including the Yangtze River and Poyang Lake. PIST demonstrated notable performance in COD sensing within complex water environments, achieving an impressive value of 0.9008 and reducing root mean squared error (RMSE) by 45.20% and 29.38% compared to benchmark models such as support vector regression (SVR) and convolutional neural network (CNN). These results emphasize PIST's accuracy and generalizability, marking a significant advancement in multidisciplinary approaches that combine spectroscopy with deep learning for rapid water quality sensing.
水作为一种有限且至关重要的资源，需要进行水质监测以确保其可持续利用。这一过程的一个关键方面是准确测量化学需氧量（COD）等关键参数。然而，由于对水光谱的理解不足以及通用性有限，当前的光谱方法在大规模、复杂水环境中难以准确且一致地测量COD。为了解决这些局限性，我们引入了理化信息光谱变换器（PIST）模型，并结合紫外 - 可见 - 短波 - 近红外（UV-vis-SWNIR）光谱进行水质传感。据我们所知，这是将变换器与光谱相结合用于水质传感的第一种方法。PIST集成了一个理化信息模块，将现有的物理和化学信息纳入光谱编码以进行域适应，同时还包括一个特征嵌入模块用于全面的光谱特征提取。我们使用了一个实际的地表水光谱数据集对PIST进行验证，该数据集具有广泛的地理覆盖范围，包括长江和鄱阳湖。PIST在复杂水环境中的COD传感方面表现出色，与支持向量回归（SVR）和卷积神经网络（CNN）等基准模型相比，取得了令人印象深刻的0.9008值，均方根误差（RMSE）降低了45.20%和29.38%。这些结果强调了PIST的准确性和通用性，标志着在将光谱学与深度学习相结合以实现快速水质传感的多学科方法方面取得了重大进展。
