Rapid Determination of Nitrate Nitrogen Isotope in Water Using Fourier Transform Infrared Attenuated Total Reflectance Spectroscopy (FTIR-ATR) Coupled with Deconvolution Algorithm.

Nitrate is a prominent pollutant in water bodies around the world. The isotopes in nitrate provide an effective approach to trace the sources and transformations of nitrate in water bodies. However, determination of isotopic composition by conventional analytical techniques is time-consuming, laborious, and expensive, and alternative methods are urgently needed. In this study, the rapid determination of 15NO3- in water bodies using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with a deconvolution algorithm and a partial least squares regression (PLSR) model was explored. The results indicated that the characteristic peaks of 14NO3-/15NO3- mixtures with varied 14N/15N ratios were observed, and the proportion of 15NO3- was negatively correlated with the wavenumber of absorption peaks. The PLSR models for nitrate prediction of 14NO3-/15NO3- mixtures with different proportions were established based on deconvoluted spectra, which exhibited good performance with the ratio of prediction to deviation (RPD) values of more than 2.0 and the correlation coefficients (R2) of more than 0.84. Overall, the spectra pretreatment by the deconvolution algorithm dramatically improved the prediction models. Therefore, FTIR-ATR combined with deconvolution and PLSR provided a rapid, simple, and affordable method for determination of 15NO3- content in water bodies, which would facilitate and enhance the study of nitrate sources and water environment quality management.

In-situ rapid monitoring of nitrate in urban water bodies using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with deconvolution algorithm.

Urban river and lake systems show important ecological function, and eutrophication frequently occurs and results from human activities due to the limited self-regulating ability. Since nitrate (NO3-) is one of the key factors causing water eutrophication, its rapid qualification plays critical role in the eutrophication control and management. In this study, water samples were collected from typical water bodies from Nanjing in different seasons, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) was employed for the quantitative determination of NO3- coupled with algorithms of deconvolution and partial least squares regression (PLSR). Results indicated that the typical absorption band of NO3- at 1500-1200 cm-1 was observed and the intensity of the band around 1360 cm-1 was positively correlated with the concentration of NO3- through spectra deconvolution. PLSR models were established based on the deconvolution spectra, which were excellent with the correlation coefficients (R2) of more than 0.8886 and the ratio of prediction to deviation (RPD) of more than 2.76; it was found that the carbonate in water might impact the prediction due to its absorption around 1450 cm-1, but the prediction model performed well in condition that the carbonate content in a low level with less than 10 mg L-1. Significant temporal and spatial variations of NO3- were observed in the typical water bodies, and the Qinhuai River having the highest NO3- content, which mainly was influenced by human activities, and the impact of water pH and temperature were not significantly observed. Therefore, FTIR-ATR combined with deconvolution and PLSR, allowed a rapid determination of NO3- in urban water bodies, providing an alternative option for the monitoring of nitrate in natural water body, which will benefit the prevention and control of eutrophication.

In-situ monitoring of nitrate in industrial wastewater using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with chemometrics methods.

Quantitative prediction of nitrate contents in different industrial wastewater was carried out using Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy. The algorithm of Gaussian deconvolution was applied in the spectral range of 1500-1200 cm-1 to eliminate the background interferences on target information of nitrate, and partial least squares regression (PLSR) model and support vector machine (SVR) model were developed for the prediction of nitrate. The results showed that the PLSR model (Rv 2 = 0.921, RMSEv = 0.351 mg/L, RPDv = 3.56) and SVR model (Rv 2 = 0.856, RMSEv = 0.473 mg/L, RPDv = 3.15) reached excellent prediction accuracy and robustness for electroplating wastewater, and for metallurgical wastewater the SVR model (Rv 2 = 0.916, RMSEv = 1.38 mg/L, RPDv = 3.26) showed a better prediction performance. The PLSR and SVR models exhibited poor prediction accuracy of nitrate for pesticide wastewater and dyeing wastewater due to the strongly interference by carbonate. The spectra pretreatment by deconvolution dramatically improved the prediction models. Therefore, combined with deconvolution spectra pretreatment and chemometrics methods, FTIR-ATR could achieve a fast and effective in-situ monitoring of nitrate in industrial wastewater.