UC Davis

Nitrate is a prominent pollutant in surface and groundwater bodies worldwide. Isotopes in nitrate provide a powerful approach for tracing nitrate sources and transformations in waters. Given that analytical techniques for determining isotopic compositions are generally time-consuming, laborious and expensive, alternative methods are warranted to supplement and enhance existing approaches. Hence, we developed a support vector regression (SVR) model and explored its feasibility to predict nitrogen isotopic composition of nitrate (δ¹⁵N-NO₃^-) in a rural-urban river system in Southeastern China. A total of 16 easily obtained hydro-chemical variables were measured in the wet season (September 2019) and dry season (January 2020) and used to develop the SVR prediction model. The grading method utilized ~75% (35) of the samples for model building while the remaining 11 samples assessed model performance. Principal component analysis (PCA) extracted 7 principal components for SVR model inputs as PCA reduces superfluous variables. We optimized tuning parameters in the SVR model using a grid search technique coupled with V-fold cross-validation. The optimized SVR model provided accurate δ¹⁵N-NO₃^- predictions with a determination coefficient (R²) of 0.88, Nash-Sutcliffe (NS) of 0.87, and mean square error (MSE) of 0.53‰ in the testing step, and performed much better than the corresponding multivariate linear regression model (R² = 0.60, NS = 0.58 and MSE = 1.76‰) and general regression neural network model (R² = 0.66, NS = 0.65 and MSE = 1.45‰). Overall, the SVR model provides a potential indirect method to predict environmental isotope values for water quality management that will complement and enhance the interpretation of direct measurements of δ¹⁵N-NO₃^-.