Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy

To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R² reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy.