Fingerprinting the contribution of quarrying to fine‐grained bed sediment in a mountainous catchment, Iran

Abstract

The contribution of quarrying in the context of multiple catchment sources of fine‐grained sediment has rarely been investigated. This study assessed the relative importance of quarrying as a sediment source alongside rangeland surface soils and channel banks in a mountainous catchment in northern Tehran, Iran, using fingerprinting. Eight geochemical tracers were measured on 24 potential sediment source samples and four fine‐grained sediment samples. Statistical analysis to select three different composite fingerprints for discriminating the potential sediment sources comprised: (a) the Kruskal–Wallis H test (KW‐H), (b) a combination of KW‐H and discriminant function analysis (DFA), and (c) a combination of KW‐H and principal components and classification analysis (PCCA). A Bayesian unmixing model was used to apportion sediment source contributions using the three composite fingerprints. Using the KW‐H composite signature, the respective relative contributions (with uncertainty ranges) from channel banks, rangeland surface soils, and quarrying were estimated as 28.4% (10.9–46.8), 15.1% (6.6–22.7), and 56.6% (38.3–74.2), compared with 35.4% (11.9–60.1), 13.4% (4.1–22.2), and 51.3% (26.5–74.3) using a composite signature selected using a combination of KW‐H and DFA, or 20.7% (3.9–41.7), 17.2% (4.4–29.9), and 61.4% (44–78.8) using a fingerprint selected using KW‐H and PCCA. The different composite signatures therefore all consistently suggested that quarrying is the dominant source of the fine‐grained sediment samples. Potential mitigation measures targeting this land use include closure to permit revegetation to reduce exposure of bare surfaces to sediment mobilization. Limitations and uncertainties associated with this preliminary investigation are briefly discussed.

The efficiency of elemental geochemistry and weathering indices as tracers in aeolian sediment provenance fingerprinting

“…Except in the 125-250 µm fraction for both the weathering indices only and a combination of the geochemical elements and weathering indices, the errors were acceptable for all other fractions. Similar ndings have been reported by studies applying sediment source ngerprinting in both uvial and aeolian systems 23,51,52 .…”

“…The Modi ed MixSIR Bayesian model was used to apportion the aeolian sediment source contributions. This model has been mainly used in sediment source tracing studies in uvial systems [48][49][50][51] , but more recently in aeolian and storm dust source tracing 19 . The model was run for 10 6 iterations with the nine different nal composite ngerprints generated based on the different combinations of tracers.…”

“…To evaluate the MixSIAR predictions, known and modelled source contributions using each of the nine nal composite ngerprints were compared on the basis of the root mean squared error (RMSE), mean absolute error (MAE), and Willmott's index of agreement (d) 23,51,52 . Comparison of known and modelled source proportions using this virtual mixture approach is now seen as an informative step in source ngerprinting procedures 15 .…”

Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices

“…The RMSE results showed that 15 of 30 possible predicted values were < 10%, whilst in the case of MAE, 29 of the 30 values were < 10%. These error levels were judged to be acceptable in the context of those reported elsewhere in the international literature (e.g., Haddadchi et al, 2013; Nosrati and Collins, 2019; Palazón et al, 2015).…”

The Efficiency of Elemental Geochemistry and Weathering Indices as Tracers in Aeolian Sediment Provenance Fingerprinting

“…The Modi ed MixSIR Bayesian model was used to apportion the aeolian sediment source contributions. This model has been mainly used in sediment source tracing studies in uvial systems [48][49][50][51] , but more recently in aeolian and storm dust source tracing 19 . The model was run for 10 6 iterations with the nine different nal composite ngerprints generated based on the different combinations of tracers.…”