How to evaluate sediment fingerprinting source apportionments

Abstract

Abstract Purpose Evaluating sediment fingerprinting source apportionments with artificial mixtures is crucial for supporting decision-making and advancing modeling approaches. However, artificial mixtures are rarely incorporated into fingerprinting research and guidelines for model testing are currently lacking. Here, we demonstrate how to test source apportionments using laboratory and virtual mixtures by comparing the results from Bayesian and bootstrapped modeling approaches. Materials and methods Laboratory and virtual mixtures (n = 79) with known source proportions were created with soil samples from two catchments in Fukushima Prefecture, Japan. Soil samples were sieved at 63 µm and analyzed for colorimetric and geochemical parameters. The MixSIAR Bayesian framework and a bootstrapped mixing model (BMM) were used to estimate source contributions to the artificial mixtures. In addition, we proposed and demonstrated the use of multiple evaluation metrics to report on model uncertainty, residual errors, performance, and contingency criteria. Results and discussion Overall, there were negligible differences between source apportionments for the laboratory and virtual mixtures, for both models. The comparison between MixSIAR and BMM illustrated a trade-off between accuracy and precision in the model results. The more certain MixSIAR solutions encompassed a lesser proportion of known source values, whereas the BMM apportionments were markedly less precise. Although model performance declined for mixtures with a single source contributing greater than 0.75 of the material, both models represented the general trends in the mixtures and identified their major sources. Conclusions Virtual mixtures are as robust as laboratory mixtures for assessing fingerprinting mixing models if analytical errors are negligible. We therefore recommend to always include virtual mixtures as part of the model testing process. Additionally, we highlight the value of using evaluation metrics that consider the accuracy and precision of model results, and the importance of reporting uncertainty when modeling source apportionments.