چكيده فارسي :
روش انگشت نگاري رسوب همراه با مدلهاي تركيبي يك رويكرد رايج در كمي نمودن سهم منابع رسوبات بهويژه رسوبات آبي مي باشد ولي در زمينه رسوبات بادي تحقيقات اندكي انجام شده است. در اين تحقيق، دو مدل تركيبي كالينز و هوگس مورد استفاده در انگشتنگاري رسوب به منظور كمي نمودن سهم منابع رسوبات تپه هاي ماسه اي در منطقه جازموريان، جنوب كرمان بكار برده شد. بدين منظور پس از تعيين جهات باد و تهيه نقشه زمينشناسي، اقدام به نمونه برداري از منابع بالقوه تپه هاي ماسه اي شامل پهنه هاي ماسه اي (Qs)، رسوبات آبرفتي ريزدانه و بستر خشك رودها (Qal)، رسوبات مخروط افكنه ها و پادگانهها (Qt) و تركيب رس و نمك (Qc)؛ و از مناطق رسوب يا تپه هاي ماسه اي (Qsd) گرديد. به طوري كه 58 نمونه سطحي از منابع بالقوه تپه هاي ماسه اي شامل 7 نمونه از Qs، 25 نمونه از Qal، 5 نمونه از Qt و 21 نمونه از Qc؛ و 14 نمونه از مناطق رسوب (Qsd) برداشت گرديد و پس از آماده سازي نمونه ها، غلظت ده عنصر (Ni, Cu, Co, Cr, Ga, Mn, P, Ba, Sr و Li) اندازه گيري شد. به منظور تفكيك منابع تپه هاي ماسه اي، از يك فرآيند آماري دو مرحله اي شامل تستهاي كروسكال واليس و آناليز تابع تشخيص گام به گام استفاده گرديد كه چهار ردياب شامل Cr, Li, Ni و Co به عنوان ردياب هاي بهينه انتخاب شدند و به عنوان پارامترهاي ورودي به مدلهاي تركيبي مورد استفاده قرار گرفتند. سهم هاي ارائه شده براي منابع تپههاي ماسه اي توسط هر دو مدل مشابه هم بدست آمد و منابع Qs و Qal به عنوان منابع غالب براي 14 نمونه تپه ماسه اي شناخته شدند. همچنين بر طبق نتايج، مقادير GOF محاسبه شده براي مدل كالينز (با بالاترين مقدار GOF برابر 99/95 درصد) بالاتر از مدل هوگس (با بالاترين GOF برابر 9/99 درصد) محاسبه شد كه نشان دهنده كارآيي بالاي اين مدل در منشايابي رسوبات تپه ماسه اي مي باشد. به طور كلي، استفاده از روش انگشت نگاري رسوب با مدل تركيبي كالينز براي كمي نمودن سهم منابع تپه هاي ماسه اي فعال در ساير مناطق پيشنهاد مي گردد.
چكيده لاتين :
Introduction
The entrainment, transport, and deposition of sediments by wind, or aeolian processes, affect all major components of the Earth system and provide important biogeochemical linkages between the atmosphere, hydrosphere, biosphere, and pedosphere (Field et al, 2010). Large part of Iran located in arid and semi-arid regions which wind erosion act as an important geomorphological process over an area of about 24 million ha and also, Iran’s dune fields as depositional Aeolian environments cover an area about 5.5 million ha (Gholami et al, 2017a). Aeolian deposits are widely distributed in the arid areas of Iran, forming ergs such as Yazd, Ashkzar, Kashan and Jazmurian. Jazmurian erg and its surrounding areas at the border of south of Kerman and Sistan-Baloochestan provinces, Iran experiences severe wind erosion, which causes both on-site and off-site effects.
Although there have been studies of Aeolian sand in both arid and semiarid zones, much of the focus has been on genesis of dune forms, sedimentary structures, and chronology of dune deposition. It is interesting that there has been much less work done on understanding dune sediment provenance (Muhs, 2017).
Therefore, Identifying and quantifying source contribution of Aeolian sediments is necessary to decreasing on-site and off-site effects of wind erosion in the arid and semi-arid regions of worldwide. Sediment fingerprinting was applied by many researchers to quantifying source contributions of fluvial sediments (e.g., Collins et al, 1997; Walling et al, 1999; Pulley et al, 2015; Haddadchi et al, 2013; Nosrati et al, 2014) and several studies applied this technique to quantify source contributions of Aeolian sediments (e.g., Gholami et al, 2017a,b; Liu et al, 2016). A remarkable range of properties has been employed in sediment fingerprinting studies including geochemical elements (Collins et al, 2012; Lamba et al, 2015; Liu et al, 2016a; Gholami et al, 2017b), geochemical indicators (Vale et al, 2016), isotopic reatios (Douglas et al, 1995), radionuclides (Walling et al, 1999; Olley et al, 2012), organic elements (Walling et al, 1999; Gellis et al, 2009), magnetic properties (Russell et al, 2001) and physical signatures (Kouhpeima et al, 2010).
Methodology
Sediment fingerprinting applied to quantifying source contributions of sand dune sediments in the Jazmurian region, south of Kerman. In order to, we collected 58 surficial samples from potential sources including 7 samples from Qs, 25 samples from Qal, 5 samples from Qt and 21 samples from Qc; and also, 14 samples collected from Qsd as sediment region. Then, 10 tracers including Ni, Cu, Co, Cr, Ga, Mn, P, Ba, Sr and Li measured at each samples. For discriminating sources of sand dune sediments applied a two-stage statistical procedure including Kruskal-Wallis and stepwise Discriminant Function Analysis. Finally, source contribution of sand dune sediments quantified using two mixing models including Collins et al, (1997) and Hughes et al, (2009). In all mixing models, the objective is to determine the source contribution for Aeolian sediment samples by minimizing the errors. Solutions to this models are obtained by finding values for proportional source contributions that minimize the calculated difference between the mean tracer concentrations in potential source and sediment samples (Smith et al., 2013). For calculating of optimum contributions, we used SOLVER tools in the Excel.
Results and Discussions
Results of two-stage statistical procedure showed that all 10 tracers are capable to discriminating sources of sand dune and results from second step showed that four tracers Cr, Ni, Co and Li were selected as optimum composite fingerprints as entrance parameters to mixing models. According to two mixing models results, Qs recognized as dominant source for seven sediment samples and also, Qal provided the most contributions for six sediment samples. GOF values ranged between 80 to 98% for both Collins and Hughes mixing models. Several researchers used a GOF for assessing results of Collins mixing model to sediment fingerprinting (e.g., Collins et al, 2010; Haddadchi et al, 2013; Collins et al, 2013; Stone et al, 2014; Lamba et al, 2015; Liu et al, 2016b).
Conclusion
Mixing models used in the sediment fingerprinting are effective tools for quantifying of source contribution of Aeolian sediments. Here, we applied a sediment fingerprinting technique to quantifying source contributions of sand dune sediments. Four optimum composite fingerprints generated highly acceptable GOF values for all four mixing models using the local optimization approach in the Solver. Qs and Qal recognized as major sources supplying sediments for sand dunes in the Jazmurian region. Based on GOF results, Collins mixing model was more efficient to Hughes mixing model to quantify source contributions. Finally, we suggests applying Collins mixing model to quantifying source contributions of sand dune sediments at other regions with active sand dunes.
