Determining Suitable Fingerprinting Properties for Discrimination of Sediment Sources (Case study: Amrovan and Atary Catchments) مقالة

مؤلف: Kouhpeima، A ؛ Feiznia، S ؛ Ahmadi، H ؛ Asghar Hashemi، S.A ؛ Ghadimi، H ؛

DESERT Autumn 2012, Volume 17 - Number 3 التصنيف: العلمية - البحثية (‎10 صفحة - من 255 إلی 264 )

الکلمات المفتاحية: Iran Composite fingerprinting Sediment source discrimination Catchment

خلاصة:

This contribution determines suitable fingerprinting properties for sediment source discrimination within the Amrovan and Atary catchments in Semnan Province, Iran. These catchments are representative of a range of geology formations and should therefore provide a meaningful basis for a general assessment of the degree of sediment source discrimination afforded by a range of fingerprint properties. By field investigation, 10 representative samples were collected from each sediment sources per catchments. Geological formation map was selected as the base of grouping samples. For the case of Amrovan catchment Hezar Dareh, Upper Red and Quaterrnary formations as well as gully walls were selected as the origin of sediments whereas in Atary Catchment karaj, Qum, Upper Red, Hezar Dareh and Quaternary formations were selected as the origin of sediments. The 15 properties selected as a tracer, comprised five groups of fingerprinting properties, including Organic constituents (C, N, P), base cations (Na, K, Ca, Mg), acid extractable metals (Cr, Co), clay minerals (Smectite, Colorite, Illite, Kaolinite) and magnetic properties consisting of Low Frequency Magnetic Susceptibility (XLF) and Frequency Dependent Magnetic Susceptibility (XFD). Several statistical methods were applied to the data including the Kruskal-Wallis, discrimination function analysis (DFA) and multivariate stepwise selection algorithm. Results indicate that the most powerful individual fingerprint property is organic constituent C, which successfully classifies 70% and 66% of samples in Amrovan and Atary catchments respectively. Composite fingerprints incorporating constituents selected from several groups of properties using a stepwise statistical selection procedure consistently provide the most robust discrimination of potential sediment sources. Results show also that organic constituents group of properties is extremely useful for sediment source discrimination in this catchments.

ملخص الجهاز:

These have included mineralogy, and colour (Grimshaw and Lewin, 1980), mineral magnetism (Caitcheon, 1993), environmental radionuclides (Wallbrink and Murray, 1996), geogimical composition (Foster and Walling, 1994), Organic constiuence (Collins and walling, 2002), acid extractable metals (Collins and Walling, 2002) and particle size (Stone and Saunderson, 1992). Laboratory analysis of the source material samples involved the use of analytical procedures to assemble values for five groups of fingerprinting properties, including Organic constituents (C, N, P), base cations (Na, K, Ca, Mg), acid extractable metals (Cr, Co), clay minerals (Smectite, chlorite, Illite, Kaolinite) and mineral magnetism (XLf, XFD) because this property has proved useful in several fingerprinting studies. Secondly, DFA was employed to assess the discrimination of potential catchment sediment sources afforded by composite fingerprints comprising constituents passing the Kruskal-Wallis test drawn from the individual groups of fingerprint properties. Finally, a multivariate stepwise selection algorithm, based on the minimization wilks' lambda, was used to identify the smallest combination of properties (the optimum composite fingerprint), drawn from any group that provided the maximum discrimination of the source categories within each study catchment. The results from the stepwise DFA clearly indicate that the optimum composite fingerprint comprising constituents selected from a number of the different groups of properties generally affords the most robust discrimination of the sediment sources within the study catchment. For example, the final composite fingerprint identified for the Amrovan catchment, correctly classifies 100% of the source material samples, whereas the maximum discrimination afforded by an individual group of properties is 90%.

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