Applicability of chemical weathering indices of eolian sands from the deserts in northern China

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Applicability of chemical weathering indices of eolian sands from the deserts in northern China Qiujie Chen, Zhuolun Li *, Shipei Dong, Qiangjing Yu, Cheng Zhang, Xinhui Yu College of Earth and Environmental Sciences, Center for Glacier and Desert Research, Lanzhou University, Lanzhou, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Chemical weathering Desert Eolian sand Geochemistry Climate Earth surface process

Chemical weathering is an important process that controls the evolution of the Earth’s surface. By accurately and reliably estimating the intensity of chemical weathering, we can improve our understanding of the processes occurring at the Earth’s surface as well as environmental changes. However, chemical weathering indices, which are influenced by source rocks and grain size, may not always accurately reflect the intensity of chemical weathering. Therefore, the use of chemical weathering indices to reliably reflect the changes in the intensity of chemical weathering in deserts remains uncertain. In this study, several chemical weathering indices, including the chemical index of alteration, Parker’s weathering index, chemical index of weathering, plagioclase index of alteration, chemical proxy of alteration, Rb/Sr, and αAlE indices, were calculated for 521 eolian sand samples collected from across twelve deserts in northern China. The results show that, for all the chemical weathering indices, eolian sands from the different deserts differed significantly. Rather than the climatic conditions in the studied deserts. factors such as source rocks and grain size had a larger impact on these indices. These indices cannot generally be used to reflect the intensity of chemical weathering. However, results showed that when the mean annual precipitation varies greatly between deserts, the changes in the WIP values of the fine fraction samples (<125 μm) could be used to reflect the intensity of chemical weathering. This study enables accurate and reliable estimations of the intensity of chemical weathering and its variations in arid and semi-arid areas.

1. Introduction

(CPA) (Buggle et al., 2011), Rb/Sr, and αAlE indices (Garzanti et al., 2013). These indices have been widely used in studies of fluvial sedi­ ments (Shao et al., 2012; Guo et al., 2018), bedrock (Udagedara et al., 2017; Okewale, 2020), loess-paleosol sequences (Yang et al., 2006; Liang et al., 2013), soil (Qiu et al., 2014; da Silva et al., 2020) and eolian sand (Zhu and Yang, 2009; Chen et al., 2018; Xie et al., 2018). The changes in these indices have revealed the variations in regional chemical weathering intensity, which have improved the understanding of the mobility and transfer of elements. As these chemical weathering indices can be used to reveal environmental changes, they have also been widely used in paleoenvironmental studies. However, as chemical weathering indices vary because of different source rocks and grain sizes, they may not always accurately reflect chemical weathering in­ tensity (Garzanti and Resentini, 2016; Guo et al., 2018). Thus, investi­ gating the applicability of weathering indices is important for accurate and reliable assessments of chemical weathering intensity. Deserts and semideserts occupy more than one-third of the global land surface (Laity, 2008). Chemical weathering is relatively weak in regions with arid and semi-arid climates. However, it does control

Chemical weathering is an important process that controls the evo­ lution of the Earth’s surface (Walker et al., 1981; Raymo and Ruddiman, 1992), shaping the landscape (Moquet et al., 2011), regulating global climate (Brady and Carroll, 1994; Dessert et al., 2005; Moosdorf et al., 2011), and affecting biogeochemical cycles and soil formation (Nesbitt and Young, 1982; Lupker et al., 2013; Babechuk et al., 2014; Berger and Frei, 2014). Thus, accurate and reliable estimates of chemical weath­ ering intensity and its variations, investigations into the patterns of mobility and transfer of elements, and the reconstruction of paleo­ environmental changes are all necessary prerequisites to better under­ stand the processes at the Earth’s surface (Nesbitt and Young, 1996; Chetelat et al., 2013; Baidya et al., 2019; Graly et al., 2020). Numerous chemical weathering indices have been proposed in pre­ vious studies. These include the chemical index of alteration (CIA) (Nesbitt and Young, 1982), Parker’s weathering index (WIP) (Parker, 1970), chemical index of weathering (CIW) (Harnois, 1988), plagioclase index of alteration (PIA) (Fedo et al., 1995), chemical proxy of alteration * Corresponding author. E-mail addresses: [email protected], [email protected] (Z. Li).

https://doi.org/10.1016/j.catena.2020.105032 Received 25 August 2020; Received in revised form 29 October 2020; Accepted 2 November 2020 0341-8162/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: Qiujie Chen, Catena, https://doi.org/10.1016/j.catena.2020.105032

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element mobility and transfer at the Earth’s surface (Gao et al., 2006; Joo et al., 2016; Shu et al., 2018). Thus, accurate understanding of chemical weathering intensity in deserts and semideserts can reveal the mechanism by which elements migrate within a landscape, as well as climate fluctuation and environmental evolution in desert areas (Liu et al., 2014; Li et al., 2018). The variations in source rocks and grain sizes complicate the assessment of chemical weathering intensity in deserts. As a result, the indices may not accurately reveal chemical weathering intensity (Chen et al., 2018). Therefore, investigation into the applicability of chemical weathering indices, as well as the gov­ erning factors behind chemical weathering, is necessary to estimate the level of chemical weathering in arid and semi-arid deserts. Deserts in northern China (Fig. 1) include sandy deserts (active dune fields) and sandy lands (fields of stabilized dunes) (Chen et al., 2007). These deserts have varying climate types (Rao et al., 2009), complex source rock types (Wang et al., 2018), and significant differences in the grain size of dune sands as well as other materials (Song et al., 2016). Therefore, these deserts are ideal for investigating the governing factors of chemical weathering indices and their applicability to deserts (Ren et al., 2014; Chen et al., 2018). The governing factors of chemical weathering indices of eolian sands in northern Chinese deserts are controversial. The Badain Jaran and Tengger deserts are both located in arid regions with similar climatic conditions. The variations in the CIA values of eolian sands were not largely affected by the climate in the Badain Jaran Desert (Chen et al., 2018), whereas they were affected mainly by the climate in the Tengger Desert (Li et al., 2018). In addition, in the Mu Us Sandy Land, Liu and Yang (2018) highlighted that prov­ enance rather than climatic conditions affected the changes in CIA for different regions of the desert. However, Shu et al. (2018) considered that the variations in chemical weathering indices are closely related to climatic conditions. Thus, the main factors influencing the changes in chemical weathering indices of eolian sands in northern Chinese deserts remains contentious. Whether chemical weathering indices can be used to reliably reflect the chemical weathering intensity of these deserts also remains uncertain. Furthermore, the relationship between chemical weathering indices and climate requires further study. To address the aforementioned gaps in knowledge, we calculated chemical weathering indices, namely the CIA, WIP, CIW, PIA, CPA, Rb/ Sr, and αAlE, for 521 eolian sand samples collected from twelve deserts

in northern China. The value of each index was determined for each of these deserts. We then determined to what extent the variations in these chemical weathering indices were caused by the source rock types and grain sizes of dune sands or other dune materials, and to what extent they were controlled by climatic conditions. Furthermore, we explored the relationship between chemical weathering indices and climate. 2. Study area Deserts in northern China (Fig. 1) have an area of 808,900 km2 (35–50◦ N, 75–125◦ E) (Fig. 1, Table 1). The mean annual temperature (MAT) did not differ significantly between deserts. Taklimakan Desert (Fig. 2a) had the maximum mean annual temperature (MAT; 10–14 ◦ C). The mean annual precipitation (MAP) ranges from 10 to 550 mm, with an increasing trend from west to east (Fig. 2b). The mean annual wind speed ranges from 1 to 5 m/s, with the Taklimakan and Gurbantunggut deserts having the lowest mean annual wind speeds (Zhu et al., 2013). According to the differences in aridity index (AI), defined as the ratio of annual precipitation to annual potential evapotranspiration, deserts in northern China were divided into hyper-arid (AI < 0.05), arid (0.05 < AI < 0.2), and semi-arid (0.2 < AI < 0.5) deserts (Table 1) (Li et al., 2015). The average grain size of the deserts is listed in Table 1, this is based on the study of Song et al. (2016) and references therein. The Gurbantunggut and Taklimakan deserts have the highest average grain size. These deserts are located in the western part of the study area (Song et al., 2016). Geologically, the Gurbantunggut Desert, Hulun Buir Sandy Land, and Horqin Sandy Land (Fig. 1) are located in or near the Central Asian Orogenic Belt (Chen et al., 2007). The Taklimakan and Kumtagh deserts (Fig. 1) in western China are located on the Tarim Block and are sur­ rounded by the Tian Shan Mountains, Kunlun Mountains, and Altyn Tagh Mountains. The Tian Shan Mountains and Kunlun Mountains are dominated by granitic rocks, from Proterozoic to Cretaceous ages, and sedimentary rocks (Jiang and Yang, 2019; Liang et al., 2020). The Qaidam Desert (Fig. 1) is located in the northeastern region of the Ti­ betan Plateau and is enclosed by the Kunlun Mountains, Altyn Tagh Mountains, and Qilian Mountains (Du et al., 2018). The Badain Jaran and Tengger deserts (Fig. 1) are located in a basin of the Alashan block, with Quaternary sediments up to 150 m (Chen et al., 2018). The Ulan

Fig. 1. Location of deserts in northern China. Deserts are indicated with numbers: 1. Gurbantunggut 2. Taklimakan 3. Qaidam 4. Kumtagh 5. Badain Jaran 6. Tengger 7. Ulan Buh 8. Hobq 9. Mu Us 10. Hunshandake 11. Horqin 12 Hulun Buir. 2

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Table 1 General description of deserts in northern China. Desert

Latitude (N)

Gurbantunggut Taklimakan Qaidam Kumtagh Badain Jaran Tengger Ulan Buh Hobq Mu Us Hunshandake Horqin Hulun Buir

44 11 –46 20 36◦ 52′ –42◦ 19′ 39◦ 04′ –41◦ 12′ 39◦ 07′ –41◦ 00′ 39◦ 04′ –42◦ 12′ 37◦ 26′ –39◦ 58′ 39◦ 16′ -40◦ 57′ 39◦ 30′ –40◦ 41′ 37◦ 28′ –39◦ 23′ 41◦ 56′ –44◦ 24′ 43◦ 28′ –45◦ 28′ 47◦ 20′ –49◦ 59′ ◦

′

◦

Longitude (E) ′

84 31 –90 00 75◦ 12′ –88◦ 10′ 90◦ 16′ –99◦ 16′ 89◦ 57′ –94◦ 54′ 99◦ 23′ –104◦ 34′ 102◦ 48′ –105◦ 38′ 106◦ 09′ -106◦ 57′ 107◦ 00′ –111◦ 30′ 107◦ 20′ –111◦ 30′ 112◦ 22′ –117◦ 57′ 119◦ 28′ –124◦ 28′ 117◦ 10′ –121◦ 12′ ◦

′

◦

′

Area (104 km2)

Average grain size (mm)

Classified

4.88 33.76 3.49 2.29 5.21 4.27 1.08 1.68 3.21 2.14 4.23 4.30

0.10 0.12 0.14 0.24 0.38 0.25 0.14 0.18 0.17 0.28 0.21 0.23

Arid Hyperarid Hyperarid Hyperarid Hyperarid Arid Arid Semi-arid Semi-arid Semi-arid Semi-arid Semi-arid

Fig. 2. Spatial distribution of mean annual temperature (MAT) (a) and mean annual precipitation (MAP) (b) in northern China during 1985–2015. Meteorological grid data from 1985–2015 were obtained from China Meteorological Data Sharing Service System.

Buh Desert (Fig. 1) is essentially part of the Jilantai-Hetao faulted basin that formed during the Cenozoic (Zhang et al., 2020). The surrounding Langshan Mountains are dominated by acidic magmatic rocks, low/ moderate-grade metamorphic rocks, and intermediate–basic magmatic

rocks (Table S1). The Hobq Desert and Mu Us Sandy Land (Fig. 1) are located in the Ordos Craton, one of the oldest and most stable conti­ nental crusts in the North China Plate (Liu and Yang, 2018), The un­ derlying bedrock is mainly composed of Quaternary and Cretaceous 3

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Fig. 3. Locations of sampling sites.

sandstones. The Hunshandake Sandy Land (Fig. 1) is located in the Haote Basin, bordered by the Yinshan Mountains in the south and by the Daxinganling Mountains in the east (Liu and Yang, 2013). Granitic rocks are widely distributed in the northern and southern regions of the Hunshandake Sandy Land (Wang et al., 2019). The types of rocks, minerals, and elemental characteristics of the underlying basement and surrounding mountains of the studied deserts are listed in Table S1 of the supplementary materials.

Table 2 Types and number of eolian sand samples collected from deserts of northern China. Desert

Sample type

Number

Parameters

References

Gurbantunggut

Bulk

15

Major elements

Taklimakan

Bulk

25

Major elements

Bulk

26

>63 μm

26

<63 μm

26

<150 μm

12

Major and trace elements Major and trace elements Major and trace elements Major elements

Bulk

6

Major elements

75–500

15

Trace elements

Zhao et al. (2019) Zhao et al. (2019) Jiang and Yang (2019) Jiang and Yang (2019) Jiang and Yang (2019) Zhu and Yang (2009) Zhao et al. (2019) Du et al. (2018)

<75 μm Bulk Bulk

15 33 37

<125 μm

11

Tengger

Bulk

69

Ulan Buh

Bulk

42

Hobq

Bulk

16

Bulk

14

<125 μm

9

Bulk

25

<125 μm

25

Bulk

17

Trace elements Major elements Major and trace elements Major and trace elements Major and trace elements Major and trace elements Major and trace elements Major and trace elements Major and trace elements Major and trace elements Major and trace elements Major elements

>250 μm

14

<125 μm

15

Horqin

Bulk

21

Major and trace elements Major and trace elements Major elements

Hulun Buir

Bulk

7

Major elements

Qaidam

μm Kumtagh Badain Jaran

Mu Us

Hunshandake

3. Material and methods 3.1. Sampling and data In this study, 164 surface samples of eolian sand from four deserts (Badain Jaran, Tengger, Ulan Buh, and Hobq) were collected to analyze the abundance of major and trace elements (Fig. 3, Table 2). Samples weighing 250 g or more from the top 3 cm of the surface were collected. These samples were mainly obtained from flat, interdune sandy areas, devoid of vegetation and away from lakes. In addition, geochemical data for 189 eolian sand samples and 168 different grain size fractions of eolian sand from another eight different deserts were obtained from recent studies (Fig. 3, Table 2). Thus, a total of 521 eolian sand samples were used in this study.

Du et al. (2018) Xu et al. (2011) This study Hu and Yang (2016) This study

3.2. Laboratory analysis

This study

The concentrations of seven major elements (SiO2, Al2O3, Fe2O3, MgO, CaO, Na2O, and K2O) and five trace elements (Rb, Sr, Ba, Zr, and Hf) in the samples were measured. The concentrations of major elements were determined using X-ray fluorescence spectrometry (Panalytical Magix PW2403, The Netherlands) at Lanzhou University, China. The experimental steps followed those proposed by Chen et al. (2018). The repeatability of element analyses was measured by calculating the standard deviation of triplicate measurements, <0.5% for the major elements. The concentrations of trace elements were determined using inductively coupled plasma mass spectrometry (Thermo Fisher X Series 2) at Henan University, China. All samples used for trace element analysis were finely ground in an agate mortar until the bulk samples could be sieved through a 75 μm sieve (200 mesh according to Tyler standard screens). Approximately 100 mg of each sample powder was digested in HNO3, HClO4, and HF under high temperature following the procedure of Chen et al. (2015). Analytical uncertainties (relative standard deviations) associated with the trace element measurements were less than 5%.

This study Liu and Yang (2018) Liu and Yang (2018) Liu and Yang (2018) Liu and Yang (2018) Zhao et al. (2019) Liu and Yang (2013) Liu and Yang (2013) Zhao et al. (2019) Zhao et al. (2019)

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3.3. Proxy calculation and data analysis The chemical weathering intensity was estimated for each mobile element by comparing its concentration to that of non-mobile Al in our samples and in the upper continental crust (UCC), defined as αAlE = (Al/ E)Sed/(Al/E)UCC. Typically, αAlE > 1 indicates a depletion of element E in the sediment under investigation with respect to UCC, αAlE < 1 indicates enrichment, and αAlE = 1 indicates no net depletion (Garzanti et al., 2013). In addition to αAlE indices, many geochemical indices, such as the WIP, CIA, CIW, PIA, CPA, and Rb/Sr, can also be used to quantitatively indicate chemical weathering intensity. The WIP was used to evaluate the weathering intensity of silicate rocks based on the proportions of alkali and alkaline earth elements in weathered products. Other chem­ ical weathering indices rely on the principle that Na, Ca, Sr, and K, relative to Al and Rb, can easily be removed during plagioclase and Kfeldspar weathering. Generally, higher CIA, CIW, PIA, CPA, and Rb/Sr values, and lower WIP values, indicate stronger chemical weathering in sediments. These indices were calculated as follows: WIP = 100*[(2*Na2O/0.35) + (MgO/0.9) + (2*K2O/0.25) + (CaO/0.7)] (1) CIA = Al2O3/(Al2O3 + K2O + Na2O + CaO*) 100

(2)

PIA = [(Al2O3 − K2O) / (Al2O3 + CaO* + Na2O − K2O)] 100

(3)

CIW = [Al2O3/ (Al2O3 + Na2O + CaO*)] 100

(4)

CPA = [Al2O3/ (Al2O3 + Na2O)] 100

(5)

Fig. 4. αAlE indices in eolian sands from deserts of northern China.

spectrometry results (Table 3) revealed that the chemical composition of eolian sands in northern Chinese deserts showed regional variations. Eolian sand from the Taklimakan and Qaidam deserts in the western part of the study area had higher CaO and MgO concentrations and lower SiO2 concentrations (<70%) (Fig. 1, Table 3). Eolian sand from the Badain Jaran, Tengger, and Ulan Buh deserts, in the central part of the study area, were enriched in K2O and Ba in comparison with other de­ serts (Fig. 1, Table 3). Eolian sand from the Hulun Buir, Horqin, and Hunshandake sandy lands, located in the eastern part of the study area, were enriched in SiO2 compared with other deserts, but were signifi­ cantly depleted in terms of other major and trace elements (e.g., Al2O3, Fe2O3, MgO, CaO, and Na2O) (Fig. 1, Table 3). Furthermore, the chemical compositions of the eolian sands differed significantly between different grain size fractions. For example, major elements (Al2O3, Fe2O3, MgO, Cao, Na2O, and K2O) and trace elements (Rb, Sr, Ba, Zr, Hf, Cr, and Ni) were higher in the <125 μm fractions than in the >250 μm

*The amount of CaO incorporated into the silicate fraction of the samples is calculated as CaO* = 0.35 × 2 (Na2O in weight %)/62 (McLennan, 1993). Principal component analysis (PCA) was conducted using the whole multivariate dataset of eolian samples, with the axis being rotated to the maximum direction of variance. In addition, Spearman’s rank correla­ tion and linear regression analysis were used to investigate the rela­ tionship between chemical weathering indices and MAP. All statistical analyses were performed using SPSS version 22. 4. Results 4.1. Major and trace element composition in eolian sands X-ray

fluorescence

and

inductively

coupled

plasma

mass

Table 3 The average concentrations of major and trace elements in eolian sand samples (n = 521) from deserts of northern China. Desert

Samples

SiO2

Al2O3

Fe2O3

MgO

CaO

Na2O

K2O

TiO2

Rb

Sr

Ba

Zr

Hf

Gurbantunggut Taklimakan

Bulk Bulk <63 μm >63 μm <150 μm Bulk 75–500 μm <75 μm Bulk Bulk <125 μm Bulk Bulk Bulk <125 μm Bulk <125 μm Bulk >250 μm <125 μm Bulk Bulk

72.39 64.76 52.82 67.19 62.41 69.54 – – 71.68 79.79 69.63 79.88 78.06 76.46 71.91 79.71 68.92 84.92 88.53 76.44 87.03 84.36

12.37 10.02 8.87 10.19 9.93 9.43 – – 10.16 7.30 9.90 8.37 8.15 8.58 9.75 9.97 10.84 7.47 6.16 10.71 6.55 7.04

2.97 2.57 4.47 2.41 3.08 2.13 – – 2.75 1.87 4.42 2.22 1.86 2.11 3.49 1.83 6.74 0.92 0.74 3.81 0.80 0.98

1.55 1.93 3.08 1.68 2.23 1.37 – – 1.71 0.66 2.21 0.80 0.91 0.72 1.24 0.58 1.43 0.33 0.25 0.67 0.23 0.46

2.71 8.49 14.13 7.30 8.71 6.83 – – 4.31 2.12 4.05 2.21 2.38 2.72 4.27 1.43 2.73 0.62 0.40 1.49 0.38 1.29

2.56 2.33 1.86 2.45 2.34 2.36 – – 2.59 1.97 2.18 1.79 1.80 1.91 2.05 2.52 2.42 1.55 1.52 2.67 1.22 1.35

2.43 2.07 1.58 2.13 1.91 2.01 – – 2.27 2.12 2.00 2.73 2.64 2.23 1.88 2.23 1.93 2.72 2.25 2.93 2.57 2.50

0.46 0.36 0.93 0.35 0.49 0.36 – – 0.38 0.15 7.56 0.18 0.17 0.25 0.62 0.33 1.62 0.18 0.10 1.14 0.18 0.15

– 76.61 53.49 76.59 – – 66.80 82.66 – 60.80 59.40 72.54 65.35 70.61 69.43 66.38 63.38 – 69.94 92.15 – –

– 281.62 275.08 275.19 – – 245.43 235.87 – 176.41 177.55 166.42 197.82 197.34 203.44 243.52 223.60 – 138.09 256.73 – –

– 588.42 523.77 577.58 – – 473.21 516.13 – 523.91 485.36 563.76 601.31 525.58 436.22 646.40 494.28 – 524.86 701.87 – –

– 103.62 808.04 75.23 – – 82.94 48.18 – 91.11 298.27 109.84 116.89 168.82 176.67 195.66 1486.28 – 60.23 1451.60 – –

– 3.64 24.98 2.80 – – 2.73 1.59 – 2.77 7.63 3.53 3.59 4.85 4.68 4.94 34.60 – 1.66 35.43 – –

Qaidam Kumtagh Badain Jaran Tengger Ulan Buh Hobq Mu Us Hunshandake Hulun Buir Horqin

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Table 4 The average values of chemical weathering indices in samples of eolian sands from deserts of northern China. Desert

WIP

CIA

CPA

PIA

CIW

Rb/Sr

Gurbantunggut Taklimakan Qaidam Kumtagh Badain Jaran Tengger Ulan Buh Hobq Mu Us Hunshandake Hulun Buir Horqin

52.03 66.02 59.97 58.83 43.39 46.33 47.61 45.46 47.48 39.92 34.63 38.26

54.41 53.36 51.73 51.18 48.43 52.12 50.66 52.49 51.56 50.60 51.34 51.81

74.09 72.36 70.85 70.42 69.31 75.20 73.28 73.15 71.06 74.67 76.85 76.64

56.36 54.42 52.25 51.62 47.73 53.37 51.00 53.46 52.12 50.92 52.26 52.97

63.05 60.64 58.84 58.44 57.08 64.15 61.77 61.58 59.12 63.44 66.17 65.90

– 0.27 – – 0.39 0.48 0.35 0.38 0.31 – – –

Table 5 Factor score coefficients of the rotated component matrix in eolian sediment samples of the studies deserts. Highly positive values (>0.8) are shown in bold. αAlE indices

PC1

PC2

PC3

αAlMg αAlCa αAlNa αAlK αAlRb αAlSr αAlBa

0.88 0.91 − 0.25 − 0.35 0.18 0.33 − 0.55 31.58

− 0.09 0.05 − 0.40 0.83 0.85 − 0.09 0.55 26.95

− 0.07 0.21 0.58 − 0.15 − 0.16 0.81 0.49 19.13

Variance (%)

1.09 ± 0.10), and Qaidam (αAlK = 1.05 ± 0.15) deserts than in other deserts. αAlRb and αAlBa show relatively small regional variation in the deserts of northern China. The αAlRb values were 1.09 in Mu Us Sandy Land, which is higher than in other deserts. High αAlBa values were observed in the Taklimakan Desert (αAlBa = 0.63 ± 0.04). Table 4 shows the variations in chemical weathering indices of bulk eolian sands from the deserts of northern China. The WIP values of these eolian sands varied between 34.63 and 66.02 (Table 4). Moreover, the WIP values were higher in deserts located in the western part of the study area (the Taklimakan and Qaidam deserts) than those in the eastern part of the study area (the Hunshandake, Hulun Buir, and Horqin sandy lands). Other chemical weathering indices showed less variation (CIA, 48.43–54.41; PIA, 47.73–56.36; CIW, 57.08–66.17; CPA, 69.31–76.85; Rb/Sr, 0.27–0.48) between the studied deserts (Table 4). The CIA and PIA had the highest values in the eolian sand of the Gur­ bantunggut Desert, located in the western part of the study area, with average values of 54.41 and 56.36, respectively (Table 4). The CIW and CPA had the highest values in the eolian sands of the Hulun Buir Sandy Land, located in the eastern part of the study area, with average values of 66.17 and 76.85, respectively (Table 4). The Rb/Sr values were highest in the Tengger Desert, located in the central part of the study area (Table 4). Fig. 5 shows that the CIA, PIA, CPA, CIW, and WIP were higher in the

fractions from the Hunshandake Sandy Land (Table 3). The <75 μm fractions of eolian sands in the Qaidam Desert have higher concentra­ tions of Rb and Ba than the 75–500 μm fractions (Table 3). 4.2. Variations in chemical weathering indices in eolian sands Fig. 4 shows that the level of element mobility for eolian sands in the deserts of northern China can be expressed as follows:

αAlMg ≈ αAlCa > αAlNa > αAlSr > αAlK ≈ αAlRb > αAlBa αAlMg (0.77–4.72) and αAlCa (0.36–5.32) varied between the deserts of northern China: αAlMg and αAlCa values were the lowest in the Taklimakan (αAlMg = 0.77 ± 0.12; αAlCa = 0.36 ± 0.04) and Qaidam deserts (αAlMg = 1.06 ± 0.28; αAlCa = 0.46 ± 0.08); however, high

values were obtained in the sandy lands in northeastern China (Fig. 4). The αAlNa, αAlK, and αAlSr values showed regional variation. The average αAlNa value was 0.96 in the Badain Jaran Desert and was lower than that in other deserts. Relatively low αAlSr values were obtained for the Taklimakan Desert (αAlSr = 0.84 ± 0.06) (Fig. 4). The αAlK value was higher in the Gurbantunggut (αAlK = 1.08 ± 0.30), Taklimakan (αAlK =

Fig. 5. Chemical weathering indices of fine and coarse fractions of eolian sands in deserts of northern China. 6

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<63 μm fraction than in the >63 μm fraction of eolian sand from the Taklimakan Desert, located in the western part of the study area. However, the Rb/Sr values were lower in the <63 μm fraction than in the >63 μm fraction of eolian sand. In Hunshandake Sandy Land situ­ ated in the eastern part of the study area, CIA, PIA, and WIP are much higher in the <125 μm fraction than in the >125 μm fraction of eolian sand (Fig. 5).

northern China. Thus, the diverse and heterogeneous source lithology has a significant impact on the αAlE indices, especially αAlMg, αAlCa, αAlK, αAlRb, and αAlBa, of eolian sands in the deserts of northern China. 5.1.2. Source-rock lithology and CIA, PIA, CPA, CIW, and WIP The source rocks in the deserts of northern Chinese also affect the values of CIA, PIA, CPA, CIW, and WIP of eolian sands to a certain extent. The Mu Us and Hulun Buir sandy lands, provide a good example. They are located in the central and eastern parts of the study area, respectively (Fig. 1), and are both semi-arid deserts with similar climatic conditions and grain sizes (Table 1 and Fig. 2), but different source rocks. Xie et al. (2018) suggested that the provenance of eolian sand in the Hulun Buir Sandy Land are primarily from the Daxinganling Mountains (Fig. 1). The Daxinganling Mountains is lithologically dominated by Paleozoic granitoid sedimentary strata, together with Mesozoic granites and related volcanic and sedimentary rocks (Table S1). The main rock-forming minerals are quartz, orthoclase, plagioclase, and biotite (Table S1). Some studies have pointed out that the eolian sands in the Mu Us Sandy Land are mainly derived from un­ derlying sandstone in the desert itself, as well as detrital sediments of the Qilian Mountains via fluvial processes (Stevens et al., 2013; Liu and Yang, 2018). The underlying sandstone is mainly composed of albite, quartz, and muscovite. Our results revealed that the values of CIA, PIA, CPA, and CIW in the Hulun Buir Sandy Land were greater than those in Mu Us Sandy Land. However, the values of WIP suggest the opposite trend (Table 4). This indicates that the different values of CIA, PIA, CPA, CIW, and WIP between the Mu Us and Hulun Buir sandy lands were mostly caused by varying source rocks. Spatial differences in source rocks may result in significant differ­ ences in the chemical weathering indices of eolian sands in the deserts of northern China.

4.3. Principal component analysis The PCA results revealed that three principal components accounted for 77.6% of the total variance, with eigenvalues greater than 1 (Table 5). Factor 1 (variance proportion: 31.58%) was positively correlated with αAlMg and αAlCa (Table 5). Factor 2 (26.95%) had high positive loadings of αAlK, αAlRb, and αAlBa (Table 5). Factor 3 (19.13%) exhibited high loadings of αAlNa and αAlSr (Table 5). 5. Discussion 5.1. Source-rock lithology and chemical weathering indices Chemical weathering indices are a function of multiple environ­ mental factors, including source rocks, grain size, and climatic condi­ tions (Campodonico et al., 2016; Pang et al., 2018; Hatano et al., 2019). Many previous studies have suggested that the effect of source rocks on chemical weathering indices cannot be ignored (Chetelat et al., 2013; Garzanti and Resentini, 2016). Therefore, investigating whether diverse and heterogeneous source lithology does exert a significant influence on the weathering indices of these eolian sediments in the deserts of northern China is important for the accurate and reliable assessment of chemical weathering intensity. 5.1.1. Source-rock lithology and αAlE indices The PCA results suggest that there are at least three dominant factors influencing the variations in αAlE indices (Table 5). PC1 had high loadings of αAlMg and αAlCa (Table 5). Generally, with increasing weathering conditions, small cations (Na+) are more easily removed than Mg. However, in our study, the values of αAlMg and αAlCa in sed­ iments were notably higher than other αAlE indices, including αAlNa. This indicates that the variations in αAlMg and αAlCa are not controlled by chemical weathering. In addition to chemical weathering, the enrichment of carbonates in bedrock usually results in lower values of αAlMg and αAlCa (Dinis et al., 2017). Meng et al. (2019) suggests that the total carbonate content is much higher in the eolian sands of the Taklimakan Desert, located in the western part of the study area, than in the northeastern sandy lands owing to the presence of carbonate rocks of the Paleozoic age in the Tian Shan Mountains and Carboniferous car­ bonate rocks in the Kunlun Mountains (Fig. 1). Meanwhile, carbonate minerals as well as Ca and Mg, are relatively low in the southern Gobi Altay Mountains, middle southern Qilian Mountains, the Ordos Plateau, and the northeastern China Block (Fig. 1) (Xie et al., 2012; Meng et al., 2019). In this study, both αAlMg and αAlCa distributions (Fig. 4) are consistent with the distributions of dolomite and other carbonate min­ erals (Meng et al., 2019), further indicating that in the deserts of northern China, αAlMg and αAlCa are mainly determined by carbonate levels of the source rock. PC2 exhibited high loadings of αAlK, αAlRb, and αAlBa (Table 5). Garzanti and Resentini (2016) confirmed that the low values of αAlK, αAlRb, and αAlBa in modern sands of western Taiwan rivers are closely related to the enrichment of K-rich minerals. Chen et al. (2018) sug­ gested that K2O, Rb, and Ba may have derived from potassium feldspars in the Badain Jaran Desert. In this study, the high values of these indices in the western deserts (the Gurbantunggut, Taklimakan, and Qaidam deserts), roughly corresponds to the low values of the K-feldspar content in the these deserts (Zhao et al., 2019). Thus, αAlK, αAlRb, and αAlBa may be affected by K-rich minerals present in the source rock of the deserts of

5.2. Effects of grain size on chemical weathering indices Grain size also strongly affects chemical weathering indices when chemical weathering indices are used to infer weathering conditions in the source area (Guo et al., 2018; Hatano et al., 2019). Products (0.45–5 μm in diameter) generated by chemical weathering are concentrated in the clay fraction (Chen et al., 2018). The least durable minerals (e.g., sheet silicates) are enriched in fine fractions due to physical weathering, which also leads to high chemical weathering indices in the very fine silt to clay fractions (von Eynatten et al., 2012). Thus, most of the studied chemical weathering indices had higher values in the clay fractions than in the sand fractions of the sediments. Our results revealed that different grain size fractions in eolian sand from the same desert were distinct in terms of chemical weathering indices (Fig. 5). This indicates that grain size strongly affects chemical weathering indices in eolian sands in northern Chinese deserts. Furthermore, grain sizes in these deserts differ (Table 1) (Song et al., 2016). The geochemical compositions of siliciclastic sediments correlate strongly with grain size distribution. Generally, coarse-grained sedi­ ments are SiO2-rich owing to the presence of quartz. In contrast, finegrained sediments have higher Al2O3 content because of the enrich­ ment of phyllosilicates and clay minerals; thus, SiO2/Al2O3 is a good proxy that is widely used as a grain size parameter (Hatano et al., 2019). Fig. 6 shows that the WIP and αAlK values are significantly correlated with SiO2/Al2O3, suggesting that the WIP and αAlK values are highly influenced by grain size. Therefore, significant differences in grain size parameters may result in spatial differences in the chemical weathering indices of eolian sands between deserts. Thus, the effects of grain size differentiation on chemical weathering indices contributes to the fact that chemical weathering indices of eolian sand cannot accurately reveal chemical weathering intensity in the de­ serts of northern China.

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Fig.6. Correlation coefficients of chemical weathering indices and SiO2/Al2O3 in eolian sands from deserts of northern China. Plots for the best correlations are shown below.

Fig. 7. Relationship between chemical weathering indices of samples and mean annual precipitation in deserts of northern China. Several plots with the best correlation are shown below.

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Fig. 8. Relationship between chemical weathering indices for fine fraction (<125 μm) of sands and mean annual precipitation in deserts of northern China. Several plots with the best correlation are shown below.

5.3. Relationship between chemical weathering indices and climate

difference in chemical weathering intensity when the MAP varies greatly between deserts. Other indices for the fine fraction samples had no correlations with MAP (Table S3 and Fig. 8), indicating that MAP was not the main factor affecting those indices. Thus, in northern Chinese deserts, climatic conditions are not the primary factor determining the values of chemical weathering indices in the bulk sample. However, WIP values in the fine fraction of eolian sands are affected by climatic conditions in northern Chinese deserts when the MAP varies greatly.

Climatic conditions, especially precipitation, have a dominant in­ fluence on chemical weathering (White and Blum, 1995; Dupr´e et al., 2003; Hartmann et al., 2014; Dinis et al., 2020). Most chemical weathering indices are generally greater in regions with higher precip­ itation (Buggle et al., 2011). We investigated the relationship between chemical weathering indices and MAP in the deserts of northern China. In our study, WIP, CPA, αAlMg, and αAlCa of the bulk samples have weak correlations with MAP across the deserts (Fig. 7), although they have higher correlation with MAP than with other indices (Table S2). When MAP increased significantly in deserts, the values of chemical weathering indices of the bulk samples had no corresponding trends (Fig. 7). This indicates that source rocks and grain size, rather than climatic conditions, were the main factors determining the values of chemical weathering indices of the bulk samples. To reduce the interference of the differences in grain size on chem­ ical weathering indices, we investigated the relationships between chemical weathering indices in the fine fraction of samples and MAP. The fine fraction samples refer to <125 μm fraction samples, and the fine fraction samples in the Taklimakan Desert refer to <150 μm owing to the absence of geochemical data in the <125 μm fraction samples (Table S3 and Fig. 8). Fig. 8 shows that WIP (R2 = 0.68) has significant correlations with MAP in the fine fraction samples. However, the correlation is weaker or even lost in hyper-arid, arid, and semi-arid deserts. This revealed that the changes in WIP values for the fine fraction samples could indicate a

5.4. Environmental significance This study revealed that chemical weathering indices of eolian sands may not accurately reveal the chemical weathering intensity in the de­ serts of northern China. Previous studies in other arid and semi-arid deserts have revealed similar results. Dinis et al. (2017) observed poor positive correlation with rainfall for all chemical weathering indices in sands, especially in Angola of southwestern Africa, with low rainfall (<600 mm). This indicates that chemical weathering indices were not largely influenced by local climatic conditions. In the Altar Desert (MAP < 100 mm), the heterogeneity of the Na plagioclase and K-feldspar content was the main cause for spatial variation in the CIA of bulk sand samples of the Mexico coastal dune sands (Kasper-Zubillaga et al., 2007). The results of both the aforementioned studies and this study suggests that the spatial variation in chemical weathering indices is not mainly influenced by climate. In addition, rainfall in the deserts of northern China is generally low and it fluctuates within a narrow range. 9

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As chemical weathering intensity is also low, its responses to small changes in rainfall are less evident. Chemical weathering indices may not accurately reveal the chemical weathering intensity in desert regions. The variations in chemical weathering indices have previously been used as proxies for the degree of chemical weathering and effective moisture in paleoenvironmental studies (Nesbitt and Young, 1982; Chen et al., 1999), which include the profiles of eolian sands in desert areas (Liu et al., 2014; Li et al., 2018). However, these indices are affected by factors such as the changes in the provenance of eolian sand and grain size. Therefore, the changes in the values of these indices cannot accu­ rately indicate the degree of chemical weathering. Under these cir­ cumstances, these proxies from the profiles of eolian sands in desert areas cannot be directly used for the reconstruction of paleoenviron­ ments. However, the WIP values can more accurately reflect the chem­ ical weathering intensity in fine fraction samples, when the MAP varies greatly. This suggests that these indices could be used as proxies for effective moisture and therefore can be used for paleoenvironmental reconstruction in desert regions.

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6. Conclusions In the deserts of northern China, the chemical weathering indices of bulk eolian sands were greatly influenced by source rocks and grain size rather than climate. Precipitation itself is generally low and it fluctuates within a narrow range. As chemical weathering intensity is also low, its responses to small changes in precipitation are less evident. Therefore, most of the chemical weathering indices do not accurately reveal the chemical weathering intensity. The changes in the WIP values of the fine fraction samples (<125 μm) could indicate chemical weathering intensity when the MAP varies greatly. This suggests that these indices could be used as proxies for effective moisture and therefore can be used for paleoenvironmental reconstruction in desert regions. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors thank Dr. Dylan Ward and three anonymous reviewers for their constructive comments, which led to the significant improve­ ment of this manuscript. The authors thank Dr. Zhiwei Xu for providing geochemical data of the Kumutag Desert, Dr. Yongqiu Wu for providing geochemical data of the Qaidam Desert, and Dr. Wanchang Zhao for providing geochemical data of the Gurbantunggut, Taklimakan, and Qaidam deserts and the Horqin, Hunshandake, and Hulun Buir sandy lands. This work was supported by National Natural Science Foundation of China (No. 41530745, 41771211, and 41971195). Author contributions Z. Li conceived the project; Z. Li, Q. Chen, and C. Zhang collected eolian samples in the field; Q. Chen, S. Dong, and Q. Yu performed the experiment; Q. Chen, Z. Li., S. Dong, and X. Yu performed data analysis. Q. Chen and Z. Li wrote the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.catena.2020.105032. 10

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