Matches in SemOpenAlex for { <https://semopenalex.org/work/W2770628460> ?p ?o ?g. }
- W2770628460 endingPage "339" @default.
- W2770628460 startingPage "327" @default.
- W2770628460 abstract "Abstract The Sr (87Sr/86Sr) and Nd (eNd values) isotopic composition of sediments transported by the Ganga fluvial system and deposited within the Ganga Basin have been used by other studies to identify and characterize sediment provenance. Isotopic data have also been used for the purpose of quantitative source apportionment. Furthermore, isotopic signatures of sediments imply the Higher Himalaya (HHS) source to be the major contributor of sediments to the Ganga Basin and Bay of Bengal, contrary to the few studies from the adjacent Indus Basin to the west that inferred higher erosion and sediment contribution from the Lesser Himalaya (LHS) source in recent times. In the present study, we test the reliability of using 87Sr/86Sr and/or eNd of river sediments as proxies for source(s) characterization and their apportionment. First, we compiled all available Sr and Nd isotope ratios of the Ganga basin sediments as well as their probable sources reported in earlier studies. After subjecting the data set to statistical scrutiny and removing outliers, we carefully evaluated spatial variability in the isotopic composition vis-a-vis the composition of lithologies drained by a complex network of both the Himalayan- and the Peninsular-sourced tributaries in the Ganga fluvial system. Our analysis shows a large (isotope) compositional overlap among probable sources, which suggests that application of isotopic composition of the Ganga Basin sediments as a proxy for provenance is not straight forward and is fraught with complications. Following the general notion that the Ganga Basin sediments are primarily derived from the HHS and LHS sources, we performed Monte Carlo simulations aimed to model the isotopic composition of sediment mixture. These results show greater uncertainty associated with quantitative source apportionment estimates given the available set of isotopic constraints. Compared to the 87Sr/86Sr ratios that can be modified during fluvial transport, the eNd values serve as more reliable provenance indicator although the use of this parameter for source apportionment may still have a large amount of uncertainty. Nevertheless, a Kernel density estimate (KDE) plot of eNd of river sediments suggests the influence of various source lithologies on the eNd pattern of the Ganga main channel sediments and indicates presence of dominantly HHS- and LHS-sourced sediments. The average eNd of sediments along the Ganga main channel shows distinct change at places in the plains due to local influence of contributing tributaries, most likely Peninsular-sourced tributaries. At present, virtually no isotopic data is available on the sediments of Peninsular-sourced tributaries, which makes it difficult to assess the contribution of cratonic sources to the isotopic budget of the Ganga Basin sediments, particularly in the Ganga-Yamuna interfluve regions." @default.
- W2770628460 created "2017-12-04" @default.
- W2770628460 creator A5013956973 @default.
- W2770628460 creator A5015539782 @default.
- W2770628460 creator A5040638907 @default.
- W2770628460 date "2018-01-01" @default.
- W2770628460 modified "2023-10-17" @default.
- W2770628460 title "Sr and Nd isotope compositions of alluvial sediments from the Ganga Basin and their use as potential proxies for source identification and apportionment" @default.
- W2770628460 cites W1483272315 @default.
- W2770628460 cites W1615361574 @default.
- W2770628460 cites W1968776228 @default.
- W2770628460 cites W1971229472 @default.
- W2770628460 cites W1976467450 @default.
- W2770628460 cites W1977706175 @default.
- W2770628460 cites W1989182282 @default.
- W2770628460 cites W1989518329 @default.
- W2770628460 cites W1993280670 @default.
- W2770628460 cites W1996569500 @default.
- W2770628460 cites W2008604385 @default.
- W2770628460 cites W2009196217 @default.
- W2770628460 cites W2012416391 @default.
- W2770628460 cites W2012887763 @default.
- W2770628460 cites W2013158150 @default.
- W2770628460 cites W2013727374 @default.
- W2770628460 cites W2014130385 @default.
- W2770628460 cites W2015632520 @default.
- W2770628460 cites W2016503290 @default.
- W2770628460 cites W2017041819 @default.
- W2770628460 cites W2019554312 @default.
- W2770628460 cites W2022789387 @default.
- W2770628460 cites W2024415704 @default.
- W2770628460 cites W2033136921 @default.
- W2770628460 cites W2035407149 @default.
- W2770628460 cites W2039184285 @default.
- W2770628460 cites W2040301347 @default.
- W2770628460 cites W2048329679 @default.
- W2770628460 cites W2048857036 @default.
- W2770628460 cites W2050487135 @default.
- W2770628460 cites W2052887904 @default.
- W2770628460 cites W2059590151 @default.
- W2770628460 cites W2071385150 @default.
- W2770628460 cites W2075238897 @default.
- W2770628460 cites W2075733918 @default.
- W2770628460 cites W2077725001 @default.
- W2770628460 cites W2077890643 @default.
- W2770628460 cites W2080257037 @default.
- W2770628460 cites W2081368948 @default.
- W2770628460 cites W2083401529 @default.
- W2770628460 cites W2094575390 @default.
- W2770628460 cites W2095016078 @default.
- W2770628460 cites W2099637571 @default.
- W2770628460 cites W2103786909 @default.
- W2770628460 cites W2124962314 @default.
- W2770628460 cites W2136259891 @default.
- W2770628460 cites W2140110924 @default.
- W2770628460 cites W2147104100 @default.
- W2770628460 cites W2147741759 @default.
- W2770628460 cites W2159173511 @default.
- W2770628460 cites W2162297373 @default.
- W2770628460 cites W2170443119 @default.
- W2770628460 cites W2171377105 @default.
- W2770628460 cites W2520426474 @default.
- W2770628460 doi "https://doi.org/10.1016/j.chemgeo.2017.11.029" @default.
- W2770628460 hasPublicationYear "2018" @default.
- W2770628460 type Work @default.
- W2770628460 sameAs 2770628460 @default.
- W2770628460 citedByCount "31" @default.
- W2770628460 countsByYear W27706284602018 @default.
- W2770628460 countsByYear W27706284602019 @default.
- W2770628460 countsByYear W27706284602020 @default.
- W2770628460 countsByYear W27706284602021 @default.
- W2770628460 countsByYear W27706284602022 @default.
- W2770628460 countsByYear W27706284602023 @default.
- W2770628460 crossrefType "journal-article" @default.
- W2770628460 hasAuthorship W2770628460A5013956973 @default.
- W2770628460 hasAuthorship W2770628460A5015539782 @default.
- W2770628460 hasAuthorship W2770628460A5040638907 @default.
- W2770628460 hasConcept C109007969 @default.
- W2770628460 hasConcept C114793014 @default.
- W2770628460 hasConcept C116834253 @default.
- W2770628460 hasConcept C121332964 @default.
- W2770628460 hasConcept C127313418 @default.
- W2770628460 hasConcept C164304813 @default.
- W2770628460 hasConcept C17409809 @default.
- W2770628460 hasConcept C17744445 @default.
- W2770628460 hasConcept C187320778 @default.
- W2770628460 hasConcept C199539241 @default.
- W2770628460 hasConcept C2778337684 @default.
- W2770628460 hasConcept C59822182 @default.
- W2770628460 hasConcept C62520636 @default.
- W2770628460 hasConcept C69823785 @default.
- W2770628460 hasConcept C76886044 @default.
- W2770628460 hasConcept C86803240 @default.
- W2770628460 hasConceptScore W2770628460C109007969 @default.
- W2770628460 hasConceptScore W2770628460C114793014 @default.
- W2770628460 hasConceptScore W2770628460C116834253 @default.
- W2770628460 hasConceptScore W2770628460C121332964 @default.
- W2770628460 hasConceptScore W2770628460C127313418 @default.