Application of Isotopic Method to Identify the Sources of Nitrate in Freshwater: Principles, Biases, and...

Accurate identification of nitrate sources in aquatic environments is vital for implementing effective measures to prevent and control nitrate pollution. The combination of isotopes of nitrate with Bayesian models has proven effective in tracing nitrate pollution sources in aquatic environments. Nevertheless, broad systematic errors in both qualitative and quantitative assessments lead to significant uncertainties in quantifying the contributions of nitrate sources. This review initiates by outlining the fundamental principles and procedures of employing nitrate isotopes in conjunction with Bayesian models. It further consolidates the empirically determined values for two pivotal parameters, isotope abundances and effects, within these models and provides a detailed analysis of their spatial and temporal variability sources. Then it meticulously dissects the origins of systematic errors encountered in applying this technique, including overlooking isotope effects, disregarding the spatiotemporal variability of isotopes, and failing to validate whether data conform to the normal distribution assumption inherent in Bayesian models. Subsequently, the review compiles and discusses emerging strategies from both qualitative, including emerging isotopes that increase the precision of source identification, fecal-specific indicators, statistical tools, and fluorescence spectrum methods, and quantitative, including mathematical methods and/or their combination with mass balance models perspectives to mitigate these systematic errors. It also forecasts the trajectory of development within this technical domain. By examining the application of isotope technology for nitrate source identification from multiple angles, this review offers theoretical references for the prevention, control, and mitigation of nitrate in water, as well as for the formulation and implementation of pertinent policies by regulatory authorities.