Advances and Prospects in Stable Isotope Mixing Model Researches

<p indent="0mm">Accurately identifying the sources of pollutants is a prerequisite for achieving high–quality ecological and environmental development. Stable isotope mixing models are a key technological means for precisely identifying pollutant sources, but their development has not yet been systematically reviewed. This paper analyzes the research trends and hotspots of these models from 2001 to 2024 and finds that the uncertainty of the results generated by the models themselves has received more attention in recent years. It also provides a detailed elaboration of the development history of stable isotope mixing models, their advantages and disadvantages, and offers a mathematical framework for evaluating the performance of MixSIAR (Bayesian Mixing Models in R) and SIMMR (Stable Isotope Mixing Models in R) models. The evaluation results show that in the application of both models, normally distributed data can reduce result uncertainty, while a decrease in the number of sources will lower the precision. Spectral feature indices should not be used alone as tracers. It is also suggested that in practical applications, prior information should be collected and organized according to the actual operational efficiency needs of the two models. Finally, the evaluation results indicate that MixSIAR is more suitable for analyzing complex ecological and environmental systems that require fine–tuning, while SIMMR is better suited for rapid assessment of pollutant sources in simpler situations.