Purpose This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. Methods Web of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. Scope Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. Conclusions The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

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Identifying the sources of aeolian dust is a crucial step in mitigating the associated hazards. We apply a Generalized Likelihood Uncertainty Estimation (GLUE) model to constrain the uncertainties associated with sediment fingerprinting of atmospheric dust in the Sistan region on the Iran-Afghanistan border, one of the world's dustiest places. 57 dust samples were collected from the rooftop of the Zabol Department of Environmental Protection during a summer dusty period from June to October 2014, in addition to 31 surface soil samples collected from potential sources nearby, including cultivated land (n=8), uncultivated rangeland (n=7), and two dry lakes: Hamoun Puzak (n=10) and Hamoun Saberi (n=6). Dust and soil samples were analyzed for 24 tracers including 16 geochemical elements and 8 watersoluble ions. Five optimum composite fingerprints (Fe, Sr, Mn, Cr and Pb) were selected for discriminating sources by a two-stage statistical process involving a Kruskal-Wallis test and stepwise discriminant function analysis (DFA). Uncertainty ranges for source contributions of dust determined by the GLUE model showed that the dry lake Hamoun Puzak is the dominant source for all dust samples from Zabol and cultivated land is a secondary source. We found marked spatial variance in the importance of regional dry lake beds as dust sources, and temporal persistence in dust emissions from Hamoun Puzak, despite very large areas of adjacent lake beds drying during the study period. Aeolian sediment fingerprinting studies can benefit considerably from the constraints provided by modelling frameworks, such as GLUE, for quantifying the uncertainty in dust provenance data.

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