A direct, reagent-free, ultraviolet spectroscopic method for the simultaneous determination of nitrate (NO3−), nitrite (NO2−), and salinity in seawater is presented. The method is based on measuring the absorption spectra of the raw seawater range of 200–300 nm, combined with partial least squares (PLS) regression for resolving the spectral overlapping of NO3−, NO2−, and sea salt (or salinity). The interference from chromophoric dissolved organic matter (CDOM) UV absorbance was reduced according to its exponential relationship between 275 and 295 nm. The results of the cross-validation of calibration and the prediction sets were used to select the number of factors (4 for NO3−, NO2−, and salinity) and to optimize the wavelength range (215–240 nm) with a 1 nm wavelength interval. The linear relationship between the predicted and the actual values of NO3−, NO2−, salinity, and the recovery of spiked water samples suggest that the proposed PLS model can be a valuable alternative method to the wet chemical methods. Due to its simplicity and fast response, the proposed PLS model can be used as an algorithm for building nitrate and nitrite sensors. The comparison study of PLS and a classic least squares (CLS) model shows both PLS and CLS can give satisfactory results for predicting NO3− and salinity. However, for NO2− in some samples, PLS is superior to CLS, which may be due to the interference from unknown substances not included in the CLS algorithm. The proposed method was applied to the analysis of NO3−, NO2−, and salinity in the Changjiang (Yangtze River) estuary water samples and the results are comparable with that determined by the colorimetric Griess assay.

The ultraviolet absorption spectra of chromophoric dissolved organic matter (CDOM) can be used to trace its sources and to explore the dynamic of the CDOM pool. In previous studies, only the spectra above 240 nm can be used directly to characterize the CDOM in seawater, due to the overlapping of CDOM absorption spectra below 240 nm with inorganic chemicals such as NO3−, NO2−, Cl- and Br-. In this study, three different machine learning models, back propagation neural network (BPNN), random forest (RF) and extreme gradient boosting (XGBoost), were built to predict the CDOM ultraviolet absorption spectra between 215 and 350 nm after being trained with the raw absorption spectra of seawater. The optimal input wavelength range of the raw seawater spectra is 250-350 nm, and the optimal model parameters of machine learning algorithms were determined by using five-fold cross validation. The results show that the three models can well predict the CDOM absorption spectra. Comparatively, the XGBoost model gave the best prediction results. The reasons might be related to the fact that the XGBoost algorithm focuses on the residuals generated by the last iteration, which can reduce both variance and bias, especially for datasets with small sample sizes. Based on the predicted spectra by XGBoost algorithm, we calculated the spectra slopes of short wavelengths between 215 and 240 nm (S215-240) and between 215 and 275 nm (S215-275). The results show that the S215-240 and S215-275 are ~2 times the widely used spectra slopes between 275 and 295 nm (S275-295) obtained by traditional method based on the raw spectra. Moreover, the S215-240 and S215-275 are more relavant with salinity for marine CDOM than S275-295, suggesting spectra slopes of shorter wavelengths might be the better proxies for marine CDOM than that of longer wavelengths.