Organic Geochemistry 40 (2009) 922–930

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Organic Geochemistry

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Compound specific dD values of long chain n-alkanes derived from terrestrial higherplants are indicative of the dD of meteoric waters: Evidence from surface soilsin eastern China

Zhiguo Rao a,b, Zhaoyu Zhu b,*, Guodong Jia b, Andrew C.G. Henderson c, Qian Xue a, Suping Wang a

a Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou 730000, Chinab Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, Chinac Department of Geographical and Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK

a r t i c l e i n f o

Article history:Received 3 August 2008Received in revised form 20 April 2009Accepted 21 April 2009Available online 3 May 2009

0146-6380/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.orggeochem.2009.04.011

* Corresponding author. Fax: +86 20 85290130.E-mail address: zgrao@lzu.edu.cn (Z. Zhu).

a b s t r a c t

The stable carbon and hydrogen isotope composition of higher plant-derived long chain n-alkanes(d13Cn-alkanes and dDn-alkanes) from 45 surface soil samples (within well characterized vegetation zones)from eastern China (18�N–50�N) are reported. The weighted average dDn-alkanes value for n-C27, n-C29

and n-C31 in the samples and the annual average dD of meteoric water recorded at 12 weather stationsproximal to the sampling sites show similar spatial variations. The dD of n-alkanes shows a gradualdepletion in value with increasing latitude. The results demonstrate that, on a large spatial scale, thedD values of long chain n-alkanes derived from higher plants have the potential to record the dD of mete-oric water, although many other factors can also influence the isotope values. There appears to be noapparent relationship between the dD of the n-alkanes extracted from the surface soil and the overlyingvegetation type (i.e. forest/grassland or C3/C4 composition). Therefore, palaeoenvironmental studiesutilizing dDn-alkanes from higher plant-derived material in geological samples have the potential to provideadditional information with regard to the past hydrological cycle.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

The hydrogen isotope composition of long chain n-alkanes(dDn-alkanes) derived from higher plants has increasingly been usedto reconstruct past environments in a number of different settings,e.g. ombrotrophic mires (Xie et al., 2000, 2004), marine sediments(Pagani et al., 2006; Huang et al., 2007) and loess–paleosolsequences (Liu and Huang, 2005). For example, the hydrogen iso-tope composition of the n-C29 alkane, extracted from several mar-ine sediment cores close to Congo River mouth, has been used as aproxy for variations in precipitation (Schefuss et al., 2005). Therationale for this reconstruction is that higher plant-deriveddDn-alkanes preserve the dD signal of their source water (i.e. meteoricwater) and, in turn, the dD signal of source water is influenced by anumber of different environmental factors. These range from thetemperature effect on isotopic fractionation at the time of precip-itation condensation, the Raleigh distillation effects of transportof moisture to the ‘amount effect’ associated with precipitous re-gions (Darling et al., 2006). However, whether the sedimentarydDn-alkanes records a history of the dD of meteoric water or not re-mains to be fully tested. Thus, there is a need to investigate therelationship between the dD composition of lipids in modern

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plants, surface sediments and surface soils, and the dD compositionof precipitation.

Our understanding of the relationship between higher plantdDn-alkanes and their photosynthetic pathway and/or ecological lifeform is limited. A recent study that compared ‘cultured’ grassesgrown in a greenhouse and grasses taken from their ‘natural’ envi-ronment from the Great Plains in the USA shows that dDn-alkanes

values of C4 grasses are systematically more positive (ca. 20‰)than dDn-alkanes of C3 grasses (Smith and Freeman, 2006). However,recent data for modern plants in Thailand and Japan (Chikaraishiand Naraoka, 2003) show that dDn-alkanes (�171 ± 12‰) for C4

plants is more negative than for C3 angiosperms (�152 ± 26‰)and gymnosperms (�149 ± 16‰). The dDn-alkanes values of C4

grasses from central Queensland, Australia are also more negativerelative to those of Acacia leaves (C3 tree), but show no significantdifference relative to the leaves of the C3 Atalaya (Krull et al., 2006).dDn-alkanes values of 26 plant specimens from the South ChinaBotanical Garden demonstrate that there are not any well defineddiagnostic discriminators for plants utilizing different photosyn-thetic pathways (Bi et al., 2005). Similarly, the dDn-alkanes of 34modern plants from northwestern China shows no strong correla-tion with their mode of photosynthesis, but is more closely relatedto their ecological life forms (tree, shrub and grass), with dDn-alkanes

data of grasses being more negative than that of the co-occurringtrees and shrubs (Liu et al., 2006). If the photosynthetic pathway

Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930 923

or ecological life form of a plant controls dDn-alkanes, then it has thepotential to serve as a palaeoecological proxy. However, if the maincontrol on a higher plant’s dDn-alkanes composition is the dD ofmeteoric water, then there is the potential to establish a link be-tween regional climatic variations and the dDn-alkanes of the higherplants over time.

Results obtained from North America (Smith and Freeman,2006) and Europe (Sachse et al., 2006) have shown a relationshipbetween modern plant-derived dDn-alkanes and the dD of local mete-oric waters. However, on a large spatial scale, a statistically signif-icant positive correlation between terrestrial plant dD and that oflocal meteoric waters has only come from surface sediments fromlakes in Europe and North America (Sachse et al., 2004; Hou et al.,2008). Terrestrial lipids in lake surface sediments undergo trans-portation and can be a mixture of lipids from different plant typesthat are prevalent in the lake’s catchment and can represent differ-ent altitudes and latitudes. This can compromise any comparisonbetween lipid and meteoric water dD. Therefore we anticipate thedDn-alkanes of soils would be more suitable for investigating theautochthonous nature of soil lipids at a specific site. Moreover, soildDn-alkanes studies have been used to provide palaeoenvironmentalinformation derived from terrestrial deposits, such as the wellknown loess/paleosol sequences in China (Liu and Huang, 2005).

We present stable hydrogen (dDn-alkanes) and carbon(d13Cn-alkanes) isotope results, derived from long chain n-alkanes,for 45 surface soil samples collected from eastern China(18�N–50�N). The relationship between surface soil dDn-alkanes

and vegetation is investigated, with the photosynthetic pathway(C3 or C4) of the overlying vegetation constrained by d13Cn-alkanes.The data are combined with published carbon isotope data fromphytoliths in surface soils from eastern China (Lü et al., 2000). Totest the link between dDn-alkanes of surface soil and the isotope com-position of meteoric water, we compare the dD of precipitation re-corded at 12 meteorlogical stations within the study area (GNIPdata1, Fig. 1) to our dDn-alkanes results.

2. Methods

2.1. Study region

Twenty eight sampling sites, spanning 11 provinces in easternChina (ca. 109�E–130�E; 18�N–50�N; Fig. 1) were chosen. The re-gion is characterized by a monsoonal climate, with most precipita-tion falling during the summer months when air temperature ishigh, with both mean annual precipitation (MAP, ca. 500 mm–2500 mm) and mean annual temperature (MAT, ca. 0 �C–26 �C)increasing southward (Fig. 1). Based on this spatial variation in cli-mate, eastern China can be segregated into three main climaticzones:

(i) The cold region in northeastern China above 40�N, wherelower temperatures lead to limited evaporation and hence rela-tively high humidity, creating a cold and humid climatic zone dom-inated by coniferous and deciduous broad leaved forests; (ii) themid-latitude region (30�N–40�N) with a prevailing climate that ismoderate and is characterized by temperate deciduous broadleaved forests with scattered grasslands; and (iii) the low latitudes(<30�N) which are hot and humid and are dominated by broadleaved evergreen forests, seasonal rain forests and rain forests.

2.2. Sample collection

The 45 surface soil samples were collected from the 28 sam-pling sites having minimal anthropogenic impact between Sep-

1 http://isohis.iaea.org.

tember and November 2005. Samples were taken from weatheredbasaltic crusts or identifiable bedrock to avoid contamination byallochthonous organic matter. For each site, 1–3 surface soil sam-ples were collected from different physiographic locations and/orunder different vegetation regimes. The top 2 cm of soils were col-lected (Table 1). Except for sample 6A from the Changbai Moun-tains in northeastern China at an altitude of ca. 2000 m a.s.l.(Table 1), samples were taken from elevations <1000 m a.s.l.Instrumental weather data were provided from the closest meteo-rological station to each sampling site and data are available for1961–1990.2 Each site was further divided into two vegetationgroups – forest or grassland, based on modern observation(Table 1) for ease of discussion.

2.3. Extraction, separation and compound specific isotope analysis(CSIA)

After removal of the modern roots and gravel by sieving, fine-ground subsamples of ca. 10 g were extracted ultrasonically usingCH2Cl2 (3�) for 10 min. The extracts were concentrated using ro-tary evaporation, transferred to a 2 ml vials, and further evapo-rated under a gentle N2 stream. The extracts were then separatedvia column chromatography (20 � 0.7 cm) using 1.5 g 5% deacti-vated silica gel (70–230 mesh) with 15 ml pure n-hexane to obtainthe aliphatic hydrocarbons.

The fractions were analyzed using gas chromatography (GC)with an HP 6890 gas chromatograph equipped with an HP-5 MSfused silica column (30 m � 0.32 mm � 0.25 lm film thickness).The oven temperature was programmed from 80 �C (2 min) to220 �C (held 2 min) at 10 �C/min, and then to 290 �C (held15 min) at 3 �C/min. After GC analysis, compound specific carbonisotopic compositions of n-alkanes were determined using gaschromatography–isotope ratio-mass spectrometry (GC–IRMS). AnHP 6890 chromatograph was connected to a GV Instruments Iso-Prime mass spectrometry using the same conditions as describedabove for GC analysis. A set of n-alkanes (C12, C14, C16, C18, C20,C22, C25, C28, C30 and C32) with known d13C values, from the Univer-sity of Indiana (Indiana STD), was used as reference material andthe components were measured daily to ensure the accuracy ofthe instrument. Each sample was measured twice. The averagestandard deviation for duplicate analyses of samples and multi-analyses of reference materials was <±0.4‰. Carbon isotope valuesare expressed relative to the Vienna Pee Dee Belemnite (VPDB)standard. The carbon isotopic composition and relative abundanceof the n-C27, n-C29 and n-C31 n-alkanes in each sample were usedfor calculation of the weighted average d13C values (d13Cn-alkanes)using the following formula:

d13Cn�alkanes ¼d13C27 � C27 þ d13C29 � C29 þ d13C31 � C31

C27 þ C29 þ C31

An HP 6890 chromatograph connected to a Finnigan DELTAplus

XL isotope ratio mass spectrometry via a GCC III interface wereused for measurement of compound specific dD values of n-al-kanes. The fused silica column and temperature programme wereas described above. Most samples were measured two or threetimes, using the Indiana STD to monitor the stability and accuracyof the instrument. Most standard deviations for repeated analysesof the reference material and samples were <±4‰. Hydrogen isoto-pic values are expressed relative to the mean ocean water (SMOW)standard. Weighted average dD values (dDn-alkanes) were obtainedas with stable carbon isotope analyses.

The hydrogen and carbon isotopic compositions of the C27, C29

and C31 n-alkanes and their weighted average values are shown

2 http://cdc.cma.gov.cn.

Fig. 1. Sketch map showing locations of sampling sites and 12 weather stations, and the spatial variation in mean annual temperature and precipitation along latitude in thestudy region (meteorological data in Table 1).

924 Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930

in Table 1. All the samples were analyzed at the State Key Labora-tory of Organic Geochemistry, Guangzhou Institute of Geochemis-try, Chinese Academy of Sciences.

3. Results and discussion

3.1. Isotopic data

The n-alkanes range from C14 to C35 with a bimodal distribution(Fig. 2) and the short chain components, dominated by either C17

or C19, show no strong odd/even carbon number preference. Allsamples show a relatively high concentration of n-C23–n-C34 al-kanes with a strong odd/even preference, with n-C27, n-C29 andn-C31 dominant. The carbon preference index (CPI) for n-C23–n-C34 was calculated following Cranwell (1984) and Ratnayakeet al. (2006), the values ranging from 3 to 13 with an average valueof 6.4 (Table 1), consistent with those of modern higher plants (Egl-inton and Hamilton, 1967; Rieley et al., 1991).

Even though n-C25 or n-C33 are in high concentration in some ofthe samples (Fig. 2), their rather low abundance relative to n-C27,

n-C29 and n-C31 in the majority of other samples precluded mea-surement of accurate stable isotope values. Therefore, we reportonly the values for n-C27, n-C29 and n-C31 (cf. Liu and Huang,2005; Krull et al., 2006; Schefuss et al., 2005; Zhang et al., 2003;Huang et al., 2007; Hou et al., 2007). The dD and d13C compositionsof these three homologues show a positive co-variation (Fig. 3 andTable 2, n = 45, p < 0.001) and we use their weighted average iso-tope values to represent the mean isotope signal of higher plantvegetation.

3.2. Surface soil dDn-alkanes under grassland and forest vegetation

The weighted average dDn-alkanes for the 45 samples vary fromca. �164‰ to �217‰, with an average value of �186.3‰ (Table1). The 32 samples from forested soils have dDn-alkanes values rang-ing from ca. �164‰ to �217‰, with a mean value of �184.2‰.The dDn-alkanes values for the 13 samples from the grassland rangesfrom ca. �180‰ to �210‰ with an average value of �191.4‰. Theaverage dDn-alkanes value for the surface soils from grassland ismore negative than that from the forested regions, but is not

Table 1Details of surface soil samples collected from eastern China.

Sample Longitude(�E)

Latitude(�N)

Altitude(m)

Vegetationtype

RAa ofn-C27 (%)

RAa ofn-C29 (%)

RAa ofn-C31 (%)

CPI d13C n-C27

(‰, VPDB)d13C n-C29

(‰, VPDB)d13C n-C31

(‰, VPDB)dD n-C27

(‰, VSMOW)dD n-C29

(‰, VSMOW)dD n-C31

(‰, VSMOW)

1C 125.79 49.23 310 Forest 0.22 0.51 0.27 9.3 �31.5 �33.5 �34.5 �202.0 �202.8 �194.31A 125.79 49.22 371 Forest 0.28 0.57 0.15 13.0 �32.5 �33.8 �35.4 �204.7 �208.8 �208.21B 125.79 49.22 444 Forest 0.49 0.40 0.12 12.6 �31.4 �31.7 �33.2 �200.4 �205.6 �202.02A 126.00 48.61 275 Grassland 0.29 0.46 0.25 8.4 �32.9 �33.4 �34.0 �197.6 �204.8 �196.63A 127.41 45.74 255 Forest 0.33 0.52 0.16 7.8 �27.7 �29.6 �32.5 �195.0 �200.5 �201.73B 127.41 45.74 247 Forest 0.37 0.46 0.17 8.4 �30.9 �32.7 �33.5 �195.3 �199.6 �201.54A 129.58 44.48 357 Grassland 0.38 0.43 0.19 4.4 �30.1 �32.4 �33.0 �175.5 �189.6 �193.14B 129.58 44.48 336 Grassland 0.25 0.52 0.23 8.7 �31.5 �32.7 �33.4 �197.3 �211.0 �217.94C 128.53 44.19 868 Forest 0.38 0.50 0.12 7.7 �31.3 �31.5 �33.2 �201.5 �189.7 �171.85A 126.47 42.49 445 Forest 0.25 0.50 0.25 6.8 �30.7 �32.6 �33.7 �188.5 �197.0 �205.95B 126.47 42.46 464 Forest 0.43 0.43 0.14 8.7 �32.2 �33.3 �34.6 �192.8 �186.3 �201.75C 126.44 42.38 746 Forest 0.31 0.52 0.18 8.2 �32.3 �32.8 �34.5 �197.1 �193.1 �194.58A 117.58 42.16 1110 Grassland 0.48 0.32 0.20 7.8 �32.2 �33.4 �33.3 �193.5 �193.3 �183.06A 128.06 42.04 1960 Forest 0.26 0.53 0.22 9.5 �33.6 �33.3 �33.6 �213.5 �220.3 �213.79A 114.51 40.98 1760 Grassland 0.31 0.52 0.17 4.8 �31.9 �33.1 �33.9 �178.1 �198.2 –b

7A 124.75 40.73 518 Forest 0.34 0.45 0.21 6.4 �33.5 �34.5 �34.6 �173.3 �186.8 �183.210A 118.71 36.56 384 Grassland 0.16 0.37 0.48 8.3 �27.8 �29.2 �30.4 �187.4 �194.2 �188.311A 118.96 34.48 23 Grassland 0.22 0.47 0.30 7.5 �27.2 �30.6 �29.4 �179.9 �180.7 �196.912A 118.14 33.00 50 Grassland 0.16 0.64 0.20 9.9 �30.3 �32.4 �26.9 �174.3 �198.4 �188.713A 118.34 32.90 119 Grassland 0.17 0.43 0.40 7.9 �28.9 �33.0 �32.3 �165.0 �182.0 �185.813B 118.34 32.90 138 Grassland 0.19 0.52 0.29 5.3 �26.3 �31.6 �30.8 �161.0 �179.5 �191.914A 118.98 32.31 102 Forest 0.24 0.49 0.27 5.3 �29.6 �32.4 �32.1 �170.1 �176.3 �174.715B 120.75 29.58 139 Grassland 0.21 0.31 0.48 7.4 �27.6 �30.9 �26.4 �174.6 �189.5 �191.915A 120.79 29.29 373 Forest 0.28 0.47 0.25 8.4 �34.8 �35.3 �36.5 �191.7 �196.9 �200.116A 121.12 28.86 76 Grassland 0.14 0.42 0.43 3.2 �24.7 �27.9 �28.4 �160.0 �187.8 �190.317A 120.64 27.96 151 Forest 0.21 0.48 0.31 4.7 �33.2 �34.2 �35.9 �176.3 �181.8 �182.818A 118.77 27.53 –b Grassland 0.16 0.42 0.42 6.0 �28.7 �30.8 �31.1 �179.0 �190.4 �196.718B 118.77 27.53 –b Forest 0.10 0.36 0.54 4.2 �31.2 �34.1 �36.3 �181.8 �184.6 �180.419A 117.27 26.34 363 Forest 0.28 0.43 0.30 2.8 �32.7 �33.6 �34.7 �191.5 �191.4 �191.920A 117.41 25.30 232 Forest 0.23 0.39 0.37 3.5 �30.2 �31.1 �29.6 �165.8 �188.6 �193.921A 114.61 23.16 47 Forest 0.22 0.34 0.45 3.0 �26.1 �27.3 �26.0 �189.1 �189.2 �192.721B 114.61 23.16 23 Forest 0.29 0.32 0.39 3.6 �31.0 �30.5 �28.9 �172.5 �183.6 �200.622A 113.50 23.11 –b Forest 0.47 0.37 0.15 6.2 �32.8 �36.1 �35.7 �150.0 �181.5 �179.523A 110.21 19.93 211 Forest 0.16 0.41 0.43 5.0 �31.5 �32.4 �32.8 �170.5 �182.8 �176.723B 110.26 19.92 80 Forest 0.13 0.40 0.47 4.5 �25.1 �26.8 �30.0 �174.4 �174.2 �178.124B 110.64 19.58 61 Forest 0.15 0.48 0.37 5.2 �33.2 �33.4 �34.2 �168.2 �171.8 �156.724A 110.54 19.54 183 Forest 0.20 0.40 0.40 4.8 �32.2 �33.9 �33.5 �171.5 �173.9 �177.825A 109.88 19.18 317 Forest 0.18 0.41 0.40 4.4 �31.0 �33.3 �33.8 �168.6 �180.2 �183.726A 109.51 18.83 794 Forest 0.37 0.42 0.22 5.4 �33.2 �34.1 �34.9 �157.7 �166.7 �169.626B 109.51 18.83 794 Forest 0.16 0.47 0.37 5.4 �31.2 �33.7 �34.6 �158.2 �175.0 �176.127B 109.56 18.61 500 Forest 0.24 0.46 0.30 4.4 �32.0 �33.4 �34.0 �158.4 �168.3 �176.527A 109.57 18.61 439 Forest 0.22 0.46 0.32 4.9 �31.2 �34.0 �35.2 �159.6 �166.2 �175.427C 109.67 18.41 165 Forest 0.21 0.52 0.27 4.6 �32.2 �34.5 �35.7 �171.9 �172.2 �163.628A 109.54 18.32 36 Forest 0.20 0.51 0.28 5.0 �32.7 �34.5 �36.7 �164.4 �170.8 �170.328B 109.54 18.31 14 Forest 0.19 0.49 0.32 4.4 �32.2 �33.9 �35.8 �165.3 �171.0 �172.8

a Relative abundance.b No data.

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Fig. 3. Spatial variation in compound-specific carbon and hydrogen isotopes of the three dominant n-alkanes (C27, C29 and C31) with standard deviations (1r).

Fig. 2. Typical GC chromatogram of n-alkanes extracted from a surface soil sample collected in eastern China (sample 23b).

Table 2R2 values of correlation of carbon and hydrogen isotopes of the three dominantn-alkanes (C27, C29 and C31)a.

n-C27 alkane n-C29 alkane n-C31 alkane

n-C27 alkane 0.6786 0.4129n-C29 alkane 0.7584 0.6877n-C31 alkane 0.5955 0.6546

a Numbers in italics represent carbon isotopes; values in corresponding romanvalues represent hydrogen isotopes.

926 Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930

statistically different and many values fall within the dDn-alkanes

range for forest sites (Fig. 4). There is, however, a common deple-tion in dDn-alkanes of surface soils in both grassland and forest sam-ples with increasing latitude. But there is no significant correlationbetween dDn-alkanes and latitude in samples from the grasslands(n = 32, R2 = 0.69, p < 0.001 for surface soil samples under forest;n = 13, R2 = 0.33 for surface soil samples under grassland). Simi-larly, there are no systematic differences between dDn-alkanes underthe grassland and the forest located at adjacent sampling sites(Fig. 4).

Fig. 4. Numerical range and spatial change in surface soil dDn-alkanes under forest and grassland in eastern China. In the upper graph, the surface soil samples were divided intotwo groups (under forest and grassland respectively, Table 1) and then re-numbered according to reverse latitudinal gradient.

Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930 927

Previous studies show that the dDn-alkanes of modern grasses aremore negative than those of co-existing trees and shrubs (Liu et al.,2006; Hou et al., 2007); however there is a large overlap indDn-alkanes values for the two plant types e.g. grasses vs. trees andshrubs. As the n-alkanes from surface soil can be derived from anumber of different plant types and species, the difference be-tween their dDn-alkanes values under different vegetation types(e.g. forests or grasslands) may be smaller than those betweendifferent plant types (e.g. grasses or trees and shrubs). Therefore,depending on the composition of the ecosystem, the dDn-alkanes

value between different ecosystem types can be very small (onaverage <10‰ in this study), which suggests surface soil dDn-alkanes

is not a discriminator between vegetation types.

3.3. Surface soil dDn-alkanes and d13Cn-alkanes

The d13C composition of long chain n-alkanes with an odd/evenpreference has been widely applied as an indicator of past C3/C4

changes in palaeoenvironmental reconstruction (Rieley et al.,1993; Street-Perrott et al., 1997; Huang et al., 2001; Zhang et al.,2003; Brincat et al., 2000). In a previous study, the comparison ofd13C values of total organic matter and long chain n-alkanes in sur-face soils from eastern China suggested that weighted mean d13Cvalues of �22‰ and �32‰ from long chain n-alkanes (C27, C29

and C31) are representative end members of soils dominated byC4 and C3 vegetation respectively (Rao et al., 2008). Both of thesedata sets demonstrate a higher contribution of C4 plants to the lo-cal biomass in the mid-latitudes. Furthermore, this is supported byd13C values of surface soil phytoliths (Lü et al., 2000; Fig. 5a).

The surface soil d13Cn-alkanes range from �26.4‰ to �35.4‰

with a mean value of �32.2‰ and fall within the range of thed13Cn-alkanes values of modern plants (Rieley et al., 1993; Collister

et al., 1994). The distribution of d13Cn-alkanes values show they aremore negative above 40�N and below 20�N (grey circles inFig. 5b). All the surface soil samples taken at sites below 20�N werecollected beneath forests (Table 1 and Fig. 4), and thus the verynegative surface soil d13Cn-alkanes values of this area support a pre-dominant C3 source. Similarly, the very negative surface soil d13Cn-

alkanes values from sites above 40�N would support a dominant C3

plant origin. Between 20�N and 40�N, the more positive d13Cn-alkanes

values suggest a higher contribution from C4

plants to the soils.However, surface soil dDn-alkanes values for these samples show amore complicated spatial pattern, with a decreasing trend fromlow to high latitude (n = 45, R2 = 0.65, p < 0.001, Fig. 5). This differ-ence between the d13Cn-alkanes and dDn-alkanes data suggests dDn-al-

kanes values are not reliable indicators of relative changes in C3

vs. C4 plants as has been suggested previously (Bi et al., 2005;Liu et al., 2006; Krull et al., 2006).

3.4. Surface soil dDn-alkanes and meteoric water dD

In eastern China, the modern annual weighted average dD val-ues of meteoric water from 12 meteorological stations range from�38.3‰ to �77.4‰, with a clear negative trend northwards(n = 12, R2 = 0.82, p < 0.001, Fig. 6d; Table 3; the original data areavailable at the website of Global Network of Isotopes in Precipita-tion1). More positive dD values in modern annual meteoric waterare associated with meteorological stations located at low latitude,such as, Haikou, Guangzhou, Hongkong and Fuzhou, and the morenegative ones with Haerbin and Qiqihaer located on the northernedge of the study region (Fig. 1).

As described above, our surface soil dDn-alkanes values in easternChina decrease with increasing latitude (n = 45, R2 = 0.65, p < 0.001,Fig. 6e), showing a similar spatial pattern to the distribution of dD

Fig. 5. Comparison of spatial variation in d13Cn-alkanes (b) and dDn-alkanes (c) for the 45 surface soil samples and carbon isotopic data of phytoliths (a) in surface soils collected ineastern China by Lü et al. (2000).

928 Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930

in meteoric water. However, the range of values in dDn-alkanes in ourstudy is larger than that of meteoric water, for both the wholestudy region (53‰ and 39.1‰, respectively) and within the sameclimate zone (approximate 30‰ and 20‰, respectively). This dif-ference in the dDn-alkanes range might result from different environ-mental conditions at each sampling site compared to themeteorological station, for example, a change in the altitudinal dis-tribution of sampling sites (ca. 14–1960 m a.s.l; Table 1) and themeteorological stations (ca. 3–237 m a.s.l; Table 3).

Our results demonstrate similar spatial patterns in soildDn-alkanes and meteoric water dD, consistent with various sitesand sediment archives (Sachse et al., 2004; Hou et al., 2008; Jiaet al., 2008). For example, the dD of higher plant n-alkanes from13 European surface lacustrine sediments tracked the variationin meteoric water dD (Sachse et al., 2004; Fig. 6a and 6b), suggest-ing that long chain n-alkane dD records the local meteoric waterdD. In addition, the effect of altitude on meteoric water dD was ob-served in soil n-alkane dD variations along a Chinese mountaintransect in a recent study (Jia et al., 2008). Our results, togetherwith those from different environmental settings in Europe, EastAsia, and North America corroborate the use of dD of higher plant

n-alkanes as a proxy for the dD of meteoric waters. Together withreported correlations between the dD value of modern higher plantn-alkanes and those of local meteoric water (Smith and Freeman,2006; Sachse et al., 2006), the sedimentary results demonstratethat the dD of meteoric waters can not only be recorded bydDn-alkanes of standing terrestrial higher plants, but can also be pre-served in dDn-alkanes from both terrestrial soils and lacustrine sedi-ments that integrate various plant signals at different vegetationallevels.

The discrimination (e) between the n-C29 dDn-alkanes of the 13European surface lacustrine sediments and local meteoric waterscalculated by their respective regression lines with latitude, showmore positive values from ca. �130‰ at 40�N to ca. �120‰ at60�N (Sachse et al., 2004; Fig. 6a,b). The calculated e value for sur-face soil dDn-alkanes in eastern China relative to the dD of local mete-oric water varies from ca. �130‰ to �140‰, and increases fromlow latitude sites (Fig. 6d,e). This increasing e value with latitudemay result from variations in relative humidity. In our study area,the 30 year annual mean relative humidity data from 1961 to 1990for 32 meteorological stations ranges from 86% to 58% and de-creases significantly northwards (n = 32, R2 = 0.74, p < 0.001;

Fig. 6. Spatial characters of the 45 surface soil dDn-alkanes (e) and dD of meteoric waters of 12 weather stations (d) and relative humidity from 31 weather stations (c) in easternChina (both the latter meteorological data in Table 3), and the spatial characters of dD of n-C29 n-alkanes extracted from surface lacustrine sediments of 13 European lakes (b)and their corresponding meteoric water dD (a) (Sachse et al., 2004).

Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930 929

Fig. 6c). This decrease in relative humidity may enhance evapora-tion and transpiration, and hence results in a more positive waterdD in soil and plant leaf, leading to increased e values (Smith andFreeman, 2006; Jia et al., 2008). However, the effect of relativehumidity changes (�30%) on e values appears not to be significantgiven the relatively small changes in our calculated e values (ca.10‰). Therefore, the differences between meteoric and soil dDmay potentially result from unknown controls such as decreasesin temperature or changes in vegetational input.

The calculated e values for this study of �140‰ and �130‰ areconsistent with a mean value of �133 ± 16‰ for terrestrial plantssynthesized by Mügler et al. (2008), in addition to surface soildDn-alkanes values falling within a range documented for modernplants (Grice et al., 2003; Bi et al., 2005; Liu et al., 2006; Chikaraishiand Naraoka, 2003; Krull et al., 2006; Sachse et al., 2006; Smith andFreeman, 2006). Therefore, diagenetic alteration appears not toalter the signal of soil dDn-alkanes values, even though diageneticeffects have been thought significantly to alter the dD of higherplant-derived long chain n-alkanes (Chikaraishi and Naraoka,2006).

4. Conclusions

Compound-specific carbon and hydrogen isotope analysis hasbeen conducted on long chain n-alkanes extracted from 45 surface

soil samples collected in eastern China and spanning 18�N–50�Nlatitude. The samples contain high amounts of long chain n-alkanesindicative of a predominant terrestrial plant input. Soil dDn-alkanes

are found not to be a viable indicator of prevailing plant type (grassvs. forest) or mode of photosynthesis (C3 vs. C4 as indicated by soild13Cn-alkanes). Even so, a decreasing trend in dDn-alkanes values fromlow to high latitude is clearly observed, and has a similar spatialdistribution to the dD of meteoric water. This similarity indistribution suggests a close relationship between the dD of higherplant-derived n-alkanes and the dD of meteoric water. Thecalculated discrimination between soil dDn-alkanes relative to mete-oric water dD ranges between �140‰ and �130‰, which is con-sistent with previous studies. Our data confirm the validity ofsoil dDn-alkanes as a potential proxy for meteoric water dD.

Acknowledgements

The work was supported by the Knowledge Innovation Pro-grams of the Chinese Academy of Sciences (Grant No. KZCX3-SW-152), the National Natural Science Foundation of China (GrantNos. 40672121, 40872111), the National Basic Research Program ofChina (Grant No. 2004CB720204) and NSFC National InnovativeResearch Team Project (Grant No. 40721061). We are grateful toJiangwei Han for assistance in the field and Wanglu Jia for assis-

Table 3Location, relative humidity and dD of meteoric water recorded at weather stations ineastern China.

Station Latitude (�N) Latitude (�N) Altitude(m a.s.l.)

Relativehumidity (%)

Nenjiang 125.23 49.13 242.2 67Qiqihaer 123.92 47.06 145.9 61Haerbin 126.77 45.74 142.3 66Shijiazhuang 114.42 37.8 80.5 63Mudanjiang 129.6 44.55 241.4 66Changchun 125.22 44.31 236.8 64Jinzhou 121.12 41.14 65.9 58Shenyang 123.45 41.9 42.8 63Linjiang 126.92 41.73 332.7 70Dandong 124.33 39.88 15.1 70Tianjin 117.17 39.06 3.3 61Cangzhou 116.83 38.03 9.6 62Huimin 117.53 37.48 11.3 66Weifang 119.08 37.07 44.1 65Heze 115.43 35.14 49.7 69Yanzhou 116.85 35.4 51.7 68Xuzhou 117.15 34.36 41 69Ganyu 119.12 35.26 2.1 74Bengbu 117.38 33.29 18.7 72Nanjing 118.8 31.52 8.9 77Dongtai 120.32 33.2 4.3 79Hangzhou 120.17 30.27 41.7 79Dinghai 122.1 29.99 35.7 79Wenzhou 120.67 27.6 6 81Nanping 118.17 26.94 125.6 78Fuzhou 119.28 25.96 83.8 77Xiamen 118.07 24.73 139.4 77Guangzhou 113.32 23.02 6.6 78Shantou 116.68 23.23 1.1 82Shanwei 115.37 23.03 4.6 79Haikou 110.35 19.84 14.1 85Qionghai 110.47 19.09 24 86

dD (‰, VPDB)

Changchun 125.22 43.9 237 �63.3Fuzhou 119.17 26.05 16 �38.3Guangzhou 113.32 23.13 7 �40.7Haerbin 126.62 45.68 172 �73.4Haikou 110.21 20.02 15 �39.5Hongkong 114.17 22.32 65 �42.3Jinzhou 121.1 41.13 66 �59.3Nanjiang 118.18 32.18 26 �53.8Qiqihaer 123.55 47.23 147 �77.4Shijiazhuang 114.25 38.02 80 �57.1Tianjin 117.1 39.06 3 �49.8Yantai 121.4 37.53 47 �49.2

930 Z. Rao et al. / Organic Geochemistry 40 (2009) 922–930

tance during instrumental analysis. The manuscript greatly bene-fitted from constructive comments by two anonymous reviewers.

Associate Editor—K. Grice

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