2022
DOI: 10.1007/s11368-022-03157-4
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How to evaluate sediment fingerprinting source apportionments

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. … Show more

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Cited by 32 publications
(26 citation statements)
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“…This would allow some degree of independent validation of PSDs as a fingerprint. The independent validation of sediment source fingerprinting estimates has been rarely undertaken (e.g., Batista et al, 2022; Gaspar et al, 2019). To validate estimated source proportions using PSDs as a fingerprint herein, we used sediment budget estimates generated using conventional water sampling; this has, to date, been used in few sediment fingerprinting studies (e.g., Collins et al, 1998; Dabrin et al, 2021; Tiecher et al, 2022), mainly due to the extra costs associated with the installation of equipment and sampling (Collins et al, 2020, 2017; Collins & Walling, 2004).…”
Section: Discussionmentioning
confidence: 99%
“…This would allow some degree of independent validation of PSDs as a fingerprint. The independent validation of sediment source fingerprinting estimates has been rarely undertaken (e.g., Batista et al, 2022; Gaspar et al, 2019). To validate estimated source proportions using PSDs as a fingerprint herein, we used sediment budget estimates generated using conventional water sampling; this has, to date, been used in few sediment fingerprinting studies (e.g., Collins et al, 1998; Dabrin et al, 2021; Tiecher et al, 2022), mainly due to the extra costs associated with the installation of equipment and sampling (Collins et al, 2020, 2017; Collins & Walling, 2004).…”
Section: Discussionmentioning
confidence: 99%
“…The evaluation of MixSIAR outputs can be undertaken using artificial mixtures, which include those created in the laboratory (e.g., Haddadchi et al 2014 ; Gaspar et al 2019 ) and those created virtually (e.g., Batista et al 2022 ). In this study, virtual mixtures were used due to the ease with which the analysis is undertaken (i.e., mathematically) and due to the high cost of analysis that makes laboratory mixtures prohibitive.…”
Section: Methodsmentioning
confidence: 99%
“…In this study, virtual mixtures were used due to the ease with which the analysis is undertaken (i.e., mathematically) and due to the high cost of analysis that makes laboratory mixtures prohibitive. Additionally, Batista et al ( 2022 ) found the use of virtual mixtures to be nearly as useful as laboratory mixtures, though in the case of PAHs, an untested tracer, this may not be the case. A full evaluation of the use of virtual mixtures was not the main objective of this paper (see Batista et al 2022 ), but to evaluate both model output and subsequently, source discrimination, 15 virtual mixtures for both tributaries and the mainstem sources ( n = 60) were analyzed for both color and PAHs.…”
Section: Methodsmentioning
confidence: 99%
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“…Physical and biogeochemical properties of sediment were regarded as the reflection of its soil sources in the fingerprinting approach. Accordingly, it is feasible to use these differences in properties to identify the sources of the target sediment [8,[19][20][21].…”
Section: Introductionmentioning
confidence: 99%