lable at ScienceDirect

Estuarine, Coastal and Shelf Science 151 (2014) 310e317

Contents lists avai

Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier .com/locate/ecss

Spatial variability of heavy metals in the coastal soils under long-termreclamation

Lin Wang a, Neil A. Coles b, Chunfa Wu a, Jiaping Wu c, *

a Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science & Technology, 210044 Nanjing, Chinab Institute of Agriculture, the University of Western Australia, 6009 WA, Australiac Institute of Islands and Coastal Ecosystems, Zhejiang University, 310058 Hangzhou, China

a r t i c l e i n f o

Article history:Received 2 February 2014Accepted 1 July 2014Available online 9 July 2014

Keywords:reclamationheavy metalsspatial distributiongeostatistics

* Corresponding author.E-mail address: jw67@zju.edu.cn (J. Wu).

http://dx.doi.org/10.1016/j.ecss.2014.07.0010272-7714/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

The coastal plain of Cixi City, China, has experienced over 1000 years of reclamation. With the rapiddevelopment of agriculture and industry after reclamation, successive inputs into agricultural soils havedrastically modified the soil environment. To determine the spatial distribution of heavy metals and toevaluate the influence of anthropogenic activities, a total of 329 top soil samples were taken along atransect on the coastal plain. The samples collected across 11 sea dikes, were selected by a nestedsampling methodology. Total Cu, Fe, Mn, Ni, Pb, and Zn concentrations, as well as their diethylenetri-amine penta-acetic acid (DTPA) extractable (available) concentrations were determined. Results indi-cated that except for Zn concentrations, there was neither heavy metals pollution nor mineral deficiencyin the soils. Heavy metals exhibited considerable spatial variability, obvious spatial dependence, andclose relationships on the reclaimed land. For most metals, the reclamation history was the maininfluencing factor. Metals concentrations generally showed discontinuities around the position of seadikes, and the longer reclamation histories tended to have higher metals concentrations than therecently reclaimed sectors. As for Cu and Zn total concentrations, stochastic factors, like industrial wastedischarge, fertilization and pesticide application, probably led to the high nugget effect and altered thisrelationship. The 6th and 10th zones generally had the highest total metals concentrations, due to theconcentration of household appliance manufacturers in these reclaimed areas. The first two zones werecharacterized by high available metals concentrations, probably due to the alternant flooding andemergence, low pH values and high organic matter contents in these paddy field soils. From the 3rd to7th zones with the same land use history and soil type, metals concentrations, especially availableconcentrations, showed homogeneity. The nested sampling method adopted demonstrated that the 500-m interval was enough to capture the spatial variation of the metals. These results were useful inevaluating the variation in the environment quality of the soils under long-term reclamation and toformulate plans for future reclamation projects.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Reclamation is an effective way to obtain new lands in order tosolve land shortage problems in coastal regions.Many countries, forexample, the Netherlands, Korea, Singapore, and Spain, use landreclamation in coastal areas as a means to create new lands (Glaseret al.,1991; deMulder et al., 1994; Kim et al., 2006). In China, coastalplains are usually well developed and densely populated. Acquiringland area from the sea has therefore become a necessity. In

Zhejiang province, China, a total of 230,000 ha of tidal lands havebeen reclaimed for agriculture, building residences, and industrydevelopment during the past 65 years. Nevertheless, an additional175,000 ha of tidal flats is targeted for reclamation in Zhejiangprovince by 2050.

In contrast to the natural environment, the coastal lands in thisregion have been substantially modified by the progressive landreclamation and dike construction. Man-made structures have anirreversible detrimental impact on these ecosystems. Bor�uvka andKoz�ak (2001) reported that spatial heterogeneity of coastal plainsoils differed greatly from that of natural soils due to theiranthropogenic origin. With the rapid development of agricultureand industry after reclamation, excessive agro-chemical inputs into

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317 311

these soils can impose a long-term burden on the biochemical cycle(Romic et al., 2012). Li et al. (2008) concluded that land use changesin reclaimed regions resulted in soil quality changes and that aredistribution of materials and energy occurred. Wang et al. (2014)found that reclamation history was the main factor in controllingspatial variability of soil properties.

Heavy metal concentrations in reclaimed regions have raisedsome concerns in recent years. Rahman and Ishiga (2011) reportedthat marine sediments became significantly polluted with heavymetals in coastal regions near urban areas and harbours. Chen andJiao (2008) demonstrated that heavy metals appeared to be easilytransferred from buried mud to ground water after reclamationrelative to different chemical forms of heavy metals in the mudcompared with reclamation time. Land use and reclamation historyhave been shown to be influencing factors on heavy metals in soils(Bai et al., 2011), while the soil physico-chemical changes inducedby reclamation has been shown to drive heavy metal mobility inthe sediment-ground water system (Chen and Jiao, 2008). Hall(1989) considered that the monitoring of key heavy metals dur-ing reclamation activities was desirable to determine if there wereadverse effects on marine organisms. Based on these studies, it canbe concluded that it is imperative to clarify detailed spatial distri-butions of heavy metals and understanding of processes resultingin redistribution in reclaimed regions, and to ascertain the influ-ence of anthropogenic factors.

Among heavy metals in soils, Cu, Fe, Mn, and Zn are essential forplant growth, deficiencies of which can lead to health-relatedproblems, and imbalances of these metals are fatal to organisms.Nickel (Ni) is an essential element for plant growth, but has po-tential risks for human health and soil quality at concentrationsexceeding the permissible limits. Lead (Pb) is nonessential and mayexhibit extreme toxicity, for human health, even at low levels.

Accurate measurement of total metal concentrations in soils isrequired to assess the potential pollution risk. However, con-centrations of available or ‘transferable’ metals provide a bettersolution for evaluating toxicity of the soils (McLaughlin et al.,2000), as a measure of risk to human health through foodchain transfer. This is determined by mobility and availability ofheavy metals (Rojo et al., 2004). Therefore, total amount

Fig. 1. Location of the study area (a) and distribution of soil samples, sea dikes and soil se

measurements should be complemented with measures of theavailable fraction.

Geostatistics is one extensively applied method to evaluatespatial variability of heavy metals in soils (Wu et al., 2002; Lacarceet al., 2012). This methodology provides quantitative tools todescribe spatial patterns of soil variables, model them, and predicttheir spatial distribution within an uncertainty framework(Webster and Oliver, 2007). However, it is seldom used for theassessment of reclaimed soils.

Further, in previous studies only reclaimed regions with lessthan 100 years of history have been analyzed (Hall, 1989; Bai et al.,2011). Comparedwith soil formation period, this time span is rathershort. Estimates of heavy metal levels in long-term reclaimed re-gions may provide clearer insight into the consequences of inten-sive human activities on soil quality over time.

This study was conducted on a typical coastal plain which hasbeen continuously reclaimed since 1047. The main aim was todetermine the variability in the spatial distribution of heavy metals(i.e. Cu, Fe, Mn, Ni, Pb, and Zn), and reveal the effect of long-termreclamation history. The results will be used for assessingchanges in soil properties and environmental quality subject toreclamation activities and enable the development of appropriatemanagement strategies for reclaimed coastal regions.

2. Methods and materials

2.1. Study area

The study area is located on the coastal plain of southernHangzhou Bay, in Cixi City, Zhejiang province, China (Fig. 1). Morethan half of the Cixi area is reclaimed from the tidal flats.

In the 5th Century, locally constructed nongovernmental earthydikes began to appear to protect farmlands from seawater intru-sion. According to local records, 11 government funded dikes havebeen constructed since 1047 A.D., dividing the study area into 11reclamation zones (Fig. 1).

Seven soil series were distinguished (Fig. 1), namely the Sanhui,Sanjia, Andong, Kandun, Hushan, Fanshi, and Doumen Series, fromthe coast to inland respectively (Zhang et al., 2000). Soils in the 1st

ries (b) (Digital numbers, 1, 2, 3, and so on, denote the different reclamation zones).

Table 1Summary statistics of total and DTPA-extractable heavy metal concentrations (inmg kg�1).

Items Mean Minimum Maximum SD CV (%) Skewness Kurtosis

Cut 24.1 8.97 48.4 4.85 20.1 0.82 3.33Fet 17,642 9043 22,012 1949 11.0 �1.31 3.01Mnt 349 127 502 78.4 22.5 �0.86 �0.05Nit 28.6 12.6 46.7 4.41 15.4 �0.05 2.49Pbt 25.8 11.0 50.7 6.36 24.7 1.14 1.19Znt 108 43.5 362 54.1 50.1 2.25 6.14Cud 4.42 0.975 13.4 2.24 50.7 0.71 0.63Fed 93.4 17.3 484 94.5 101 1.52 1.31Mnd 61.0 15.9 159 35.0 57.4 1.11 0.22Nid 0.71 0.0543 2.99 0.59 83.1 1.94 3.77Pbd 2.87 0.954 8.03 1.38 48.1 0.63 �0.17Znd 3.18 0.0496 14.0 2.49 78.3 1.49 2.83

SD, standard deviation; CV, coefficient of variation. t, total concentrations of heavymetals; d, concentrations of heavy metals extracted by diethylenetriamine penta-acetic acid (DTPA).

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317312

zone developed from alternant deposition of marine and lacustrinematerials, while the other soils evolved from marine sediments.

2.2. Soil sampling and analyses

A total of 329 surface soil samples (0e20 cm) were collectedalong a transect from the coast to the inland, across the 11 recla-mation zones, inmid-October, 2005. In each reclaimed zone, nestedsampling was employed, with sampling distances at approximately100, 60, 36, 21, 12, 7, 4, 2, and 1 m.

Soil samples were air-dried and sieved for the determination ofheavymetal concentrations and soil properties. Themethodused formeasuring total metal concentrations is described in Wang andWu(2008). Available concentrations were extracted using the dieth-ylenetriamine penta-acetic acid (DTPA) extraction technique(Lindsay and Norvell, 1978). Blanks and certified standard soilsamplesweredigested simultaneously tomonitor error levels.Metalconcentrationswere thendeterminedby inductive coupled plasma-mass spectrometry (ICP-MS) (Agilent 7500a, Agilent, Santa Clara,CA, USA). Other soil properties and their measurement, includingpH, organic matter contents (OM), electrical conductivity (EC), andparticle size distribution (PSD) is described in Wang et al. (2014).

2.3. Data processing

Data distribution was checked for normality using the Kolmo-goroveSmirnov (KeS) test. Most metal concentrations approxi-mated normal distribution. But total Cu, Fe, Zn concentrations andavailable Fe, Mn, Ni, and Zn data were log-transformed for geo-statistical analysis, to stabilize their variance as described byCambardella et al. (1994). Correlation between metal concentra-tions and soil properties was determined.

Analysis of variance (ANOVA) was performed to compare metalconcentrations in different reclamation zones. Mean values weregrouped using the Scheff�e test (P < 0.05), as the sample size perzone was unequal due to the variation in the distances between thedikes. Components of variance were estimated by the RestrictedMaximum Likelihood (REML) method (constrained to be non-negative) to calculate the contribution of different samplingstages to differences in heavy metal concentrations to define themain scale of soil variation after Webster et al. (2006). The statis-tical analyses were conducted with SPSS version 13.0 for windows(SPSS Inc., Chicago, IL, USA).

GS þ software (v3.1 for windows, Gamma Design Software,Plainville, MI, USA) was applied to perform the geostatisticalanalysis. Experimental semivariograms were calculated. The fittedmodels were chosen based on the regression coefficient (r2).Spherical and exponential models resulted in similar r2 values inour study. It suggests that the differences were very weak betweenthe spherical and alternative models, in such cases the former wasselected in order to allow direct comparison of nugget, sill, andrange values among different soil parameters as described byCambardella et al. (1994). Thus, spherical models were appliedhere, which are the most frequently used function.

In a spherical model, the semivariograms rise from the nuggetvariance (C0). This may be due to short-scale variability and/ormeasurement errors (Burgos et al., 2006). Themodel then stabilizesaround the sill (C0þ C). The distance atwhich the variogram reachesthe sill is called the range (A). The range can be interpreted as thedistance of spatial dependence, beyond which soil variables areconsidered as statistically independent. The nugget to sill ratio (C0/(C0þC)) was used to indicate the degree of spatial dependence. Ac-cording to Cambardella et al. (1994), if this ratio is <25%, it indicatesstrong spatial dependence, between 25 and 75% indicates moderatespatial dependence, and >75% indicates weak spatial dependence.

3. Results

3.1. Summary statistics

Descriptive statistics for total and available concentrations ofheavy metals are given in Table 1. ANOVA and multi-comparisonresults (Fig. 2) illustrate significant differences (P < 0.05) in themean of the elements concentrations between the reclaimed zones.

Total metal concentrations were in the following decreasingorder: Fe > Mn > Zn > Ni > Pb > Cu. Total Cu (Cut) concentrationsranged between 8.97 and 48.4 mg kg�1. The highest mean valueswere observed in the 6th and 10th zones (more than 30.0 mg kg�1),whilst the lowest values were from the 11th zone (19.5 mg kg�1).And it displayed homogeneity in the other zones.

The average total Fe (Fet) concentrationwas 17,642 mg kg�1. The6th and 10th zones had the highest values (more than19,000 mg kg�1), while the lowest mean was in the 1st zone(14,164 mg kg�1). There was little variation from the 2nd to the 9thzones.

Total Mn (Mnt) concentration ranged from 127 to 502 mg kg�1

and had the highest mean values in the 6th and 10th zones (about438 mg kg�1), and the lowest in the 1st zone (192 mg kg�1). The2nd zone also had a significantly lower Mnt concentration than theother zones.

Total Ni (Nit) concentrations varied from 12.6 to 46.7 mg kg�1.The highest mean zonal values were observed in the 10th and the2nd zones (about 33.0 mg kg�1), while the 11th and 1st zones hadthe lowest values (about 24.0 mg kg�1). There was no significantdifference from the 3rd to 9th zones.

Total Pb (Pbt) concentrations varied from 11.0 to 50.7 mg kg�1.The highest mean value was observed in the 1st zone(38.9 mg kg�1), followed by the 2nd zone (29.3 mg kg�1), and thenthe 10th zone (28.3 mg kg�1) as shown in Fig. 2. The 11th zone hadthe lowest level (17.8 mg kg�1). And it varied little between the 3rdto 9th zones.

Total Zn (Znt) concentrations varied between 43.5 and362 mg kg�1. There was no significant difference for all zones, withhigh standard deviation observed in each zone.

Available Cu (Cud) concentrations were relatively high in the 1st,2nd, 5th, 6th, and 10th zones (above 5.00 mg kg�1), and it was thelowest in the 3rd and 4th zones (about 2.10 mg kg�1) (Fig. 2).Available Fe (Fed) values were significantly higher in the first twozones (more than 228 mg kg�1), being approximately six times themean of the other zones. And there was no significant difference inits values from the 3rd to 11th zones. Average available Mn (Mnd)concentration was the highest in the 10th zone (101 mg kg�1), and

Fig. 2. Mean comparisons of heavy metals in soils for the different reclaimed zones (Histograms correspond to the mean values of metal concentrations. Error bars are ± onestandard deviation of the mean. Different letters indicate significant differences between reclaimed zones by the Scheff�e test (P < 0.05). Digital numbers, 1, 2, 3, and so on, denotethe different zones. Subscript t represents total concentrations of heavy metals; Subscript d represents concentrations of heavy metals extracted by diethylenetriamine penta-aceticacid (DTPA), and the same as below).

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317 313

the lowest in the 1st zone (28.6 mg kg�1). Available Ni (Nid) con-centration was significantly higher in the first two zones (about1.47 mg kg�1), as Fed did, and approximately tripled the meanvalues in the other zones. Average available Pb (Pbd) concentrationwas also observed to be significantly higher in the first two zones(about 4.49 mg kg�1), being approximately double the values foundin the other zones. Available Zn (Znd) concentration had a meanvalue of 3.18 mg kg�1, with the highest mean observed in the 9thzone (6.80mg kg�1), and the lowest in the 3rd and 4th zones (about0.890 mg kg�1).

According to CV classification as described by Wilding (1985),Fet displayed low variability, Znt displayed high variability, and

other total concentrations fell into the moderate variability cate-gory. All the available metal concentrations belonged to high vari-ability, showing higher CV values than the total concentrations. Soilproperties data, not listed, could be referred from Wang et al.(2014).

3.2. Correlation analysis

Total metal concentrations showed significant correlations witheach other, with the exception of Znt. Especially, Fet exhibited astrong correlation with Mnt (r ¼ 0.77, P < 0.01), and Nit (r ¼ 0.63,

Fig. 3. Semivariograms (dots) with fitted models (solid lines) for total and diethylenetriamine penta-acetic acid (DTPA) extractable metal concentrations in soils across all zones (C0,nugget variance; (C0 þ C), sill; A, range).

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317314

P < 0.01); and Pbt displayed a significantly positive correlationwithCut (r ¼ 0.36, P < 0.01) and Nit (r ¼ 0.17, P < 0.01).

Available concentrations also exhibited significant correlationswith each other (P < 0.01), with the exception of Mnd with Nid. Forpairs of total and available concentrations of the same metal, therewere significantly positive correlations, but Fe and Ni were theexception.

Soil pH showed significantly negative relationships with theavailable concentrations, except for Mnd. And it had significantlypositive relationwith Fet andMnt. Almost all the available elementshad significant positive relationships with OM, except Mnd. But OMhad complex relationships with total concentrations, positivelyrelated with Cut and Pbt, and negatively with Fet and Mnt. Soil ECand PSD showed loose correlation with metal concentrations.

3.3. Geostatistics

The semivariograms and model parameters of the heavy metalsdistribution for all the zonal soils are displayed in Fig. 3. All theelements in total and available forms had obvious spatial depen-dence. Strong spatial dependence were observed for Fet, Mnt, Pbt,Fed, Pbd, and Znd, while the other concentrations showed moderatespatial dependency, based on the nugget to sill ratio according tothe classification used by Cambardella et al. (1994).

Ranges of total metal concentrations varied from 4000 to7000 m, which corresponded to double or triple the average spanbetween the dikes of each reclaimed zone. Concentrations of Mntand Pbt displayed like spatial structures, similar in ranges and thenugget to sill ratio, although they had negative correlations and

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317 315

opposing distribution patterns in the ANOVA results. Meanwhile,Cut and Znt had smaller ranges and higher nugget to sill ratio,particularly Znt.

The ranges of available concentrations were smaller comparedwith the total concentrations, varying from 2500 to 4000 m, beingapproximating equal to or double the average span of the reclaimedinter-dike zones. Their spatial structures were alike, particularlyCud and Znd, with similar nugget, nugget to sill ratios, and ranges.

3.4. Components of variance

The variance components of heavy metals estimated using theREML method at each sampling stage was accumulated, as per-centages, and plotted against distance stages as shown in Fig. 4. Dueto the large span of each reclaimed zone, an extra nesting distancestage, 500 m, was added to determine the appropriate sampleevaluation strategy. Thus, there were 11 distance stages in thecalculation of variance. The 1st stage was the reclamation zonespan, i.e. the distance between two neighbouring dikes. The 2ndstage consisted of 500-m intervals among soil samples, with the3rd stage being the 100-m interval among samples, and so on.

Over 80% of the variance of Mnt and Pbt concentrations occurredat the 1st nested stage, which meant reclamation captured the vastmajority of their variability. For Fet and Nit, 48% and 36% of theirvariation occurred at the 1st stage, respectively, meaning recla-mation was a major contributing factor for this variation, and thefirst two stages (distance � 500 m) accounted for more than 68% ofthe variance.

For Cut, the 1st distance stage only accounted for 21%, while the60-m stage accounted for about 63% of the total variance. For Znt,approximate 65% of the total variance occurred at the last stage(distance ¼ 1 m), and the first two stages only explained 10%. Thismeant reclamation was not their main influencing factor and therewas high variation in Cut and Znt concentrations at the field scale,which explained their high nugget to sill ratio in semivariogrammodels for the 500-m step.

For Fed, Nid, and Pbd concentrations, the 1st stage accounted forover 66% of the total variation, especially Fed with the first twostages accounting for 88% of its variability. The 1st stage explained43% of Cud variation, and 42% of Znd, and the first two stagesexplained more than half of their total variability. However, forMnd, only 20% variationwas accounted for in the 1st stage, with 51%from the 3rd stage (distance ¼ 100 m).

4. Discussion

Reclaimed regions are subjected to the most intensive modifi-cation by human activities. The natural ecosystems were converted

Fig. 4. Accumulated percentages of the total variance at different nested distance stages forin soils for all zones.

to anthropogenic ones, which often generates a variety of envi-ronmental problems.

In our study region, the total amounts of heavy metals weresimilar to those reported by other authors (e.g. Lu et al., 2005) fromstudies in the same coastal environments. The mean Fet and Mntconcentrations were below the Zhejiang and China backgroundlevels (SEPAC, 1990), while Cut, Nit, Pbt, and Znt concentrationswere a little higher, particularly for Znt. Elevated concentrations ofheavy metals in disturbed soils are commonly attributed toanthropogenic inputs. According to the standard GB 15,618-1995(National Environmental Protection Agency of China, 1995), about2% of samples had Znt concentrations exceeding the maximumallowable limit. However, in the light of the guideline given byKabata-Pendias (2000), no total soil metals content exceeded themaximum permissible level.

An average of 1.00 mg kg�1 for Znd is considered to be thecritical value for Zn deficiency (Ponnamperuma et al., 1981). For thereclaimed soils, 17 percent of the soil samples in our study had a Zndeficiency, and were mainly sampled in the 3rd, 4th, and 11thzones. Available concentrations of the other metals showed neitherdeficiency nor pollution according to the limits set by Lindsay andNorvell (1978), Lofts et al. (2004), and Pande et al. (2012).

The results of components of variance and geostatistics showedthat reclamation history was the main influencing factor in con-trolling spatial variability for most metals. For Cut and Znt, theelevated concentrations and short-distance spatial variabilityprobably resulted from the additions of fertilizers, pesticides fromagricultural activities, industrial emissions, tyre abrasion, and lu-bricants from manufacturing activities undertaken on thereclaimed areas (M€oller et al., 2005).

With larger CVs and smaller ranges, available metals soil con-centrations were shown to be more variable, less continuousspatially, and less stable over time, compared with the total con-centration data. The available concentration data appeared to bemore sensitive to human activities associated with agriculture. Forexample, Zn deficiency is often associated with high rates of Pfertilizer application (Teng and Timmer, 1990).

The total amounts of most elements were shown to be higher inthe 6th and 10th zones. One explanation for this phenomenon wasthat the soil samples in the 6th zone were taken in the town ofXinpu, which was famous for producing household appliances.Many individual workshops existed there, but therewas an absenceof effective waste disposal procedures. So heavy metal concentra-tions were greatly elevated. These workshops, once moved to the10th zone, similarly led to the high metal concentrations observedin the samples.

From the 3rd to 9th zones, most metals concentrations both intotal and available forms were relatively homogeneous. These

total and diethylenetriamine penta-acetic acid (DTPA) extractable metal concentrations

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317316

zones had been reclaimed for more than 35 years for uplandcultivation and cropping, like cotton, vegetable production, andnurseries. Soils in the zones 3e7 in particular belonged to the samesoil group, Cambosols, where soil properties displayed markedsimilarity (Wang et al., 2014). The 5th zonewas a densely populatedresidential area. Frequent application of organic fertilizersimproved availability of heavy metals, to some degree. Thus, thiszone had slightly higher available concentrations than the neigh-bouring zones.

The first two zones had higher available metal concentrationscompared with the other zones, although the total concentrationswere generally low. These zones were located in the low terrain andhad been cultivated as paddy fields for over 600 years, and coin-cided with high ground water levels. Soils were usually cultivatedunder a reducing biochemical environment, characterized by highorganic matter contents, low pH and salinity (Wang et al., 2014).Among them, pHwas considered to play the most important role indetermining metal availability and mobility (Anguelov andAnguelova, 2009). When pH is less than 7.0, availability would begreatly increased (Xian and Shokohifard, 1989). The negative cor-relation between pH and available metal concentrations furthersupported this view.

Concentrations of Pbt were high in the first two zones, whichmight originate from anthropogenic sources, such as automobileexhausts, industrial activities, or the application of manure andpesticides. These two zones had the longest reclamation historyand the largest populations. Furthermore, paddy fields have beenreported to have higher concentrations of Pb than the vegetablelands, probably due to the Pb uptake function of vegetables or thehigher OM levels in paddy fields (Liu et al., 2011).

Concentrations Fet and Mnt were low in the 1st zone, probablydue to the fact that under the reducing environment, these metalscould be leached downwards from the surface into the soil profiles.The longer period over which the paddy soils developed, the moreFe and Mn would be leached (Cheng et al., 2008). Hence, theirconcentrations in the 1st zone were lower than those in the 2ndone. Aerobic conditions, upon emergence of the paddy soils, couldresult in the formation of fine grained hydrous oxides (Fe(OH)3).Upon the submergence of the paddy soils, Fe(OH)3 would dissolveto give ferrous iron (Alloway, 1995). Heavy metals that adsorbedand co-precipitated with Fe(OH)3, like Ni and Pb, would becomeavailable and release into the environment (Adriano, 2001), whichcould partly explain the close relationship and similar spatialcharacteristics of these metals.

The newly reclaimed zone (the 11th) was free of industry andagriculture. Both total and available metal concentrations werelower than those in the other zones. Therefore, it was probably asuitable area inwhich to establish the normal background levels forheavy metals in soils.

The positive correlations between available metal concentra-tions and OM may be due to the fact that organic-matter fractions,like fulvic acids, could form chelate structures with some metals,which could bind metals and improve their availability to plants(Fageria, 2012).

Romic et al. (2012) reported that it was quite common forgeochemical phenomena to be correlated. Further, according to theGoldschmidt classification (Goldschmidt, 1937), Fe, Mn, and Nibelong to the siderophile group, while Cu, Pb, and Zn fall into thechalcophile group. So each grouping displayed a certain similarityin their spatial distributions. However, stochastic factors, such ascultivation, traffic, and agricultural management, may have influ-enced the relationships between Cu, Pb, and Zn.

The universally close relationship between total and DTPA-extractable metal contents probably meant that total contentscould indicate plant-available levels, to some extent. However, a

negative relationship between Fet and Fed, and a non-significantrelationship between Nit and Nid, were observed, which weresupported by similar observation presented by Katyal and Sharma(1991) and Caridad-Cancela et al. (2005). This observation maypossibly be explained by soil physical attributes and hydraulicproperties obscuring the availability and accumulation of thesemetals (Cook and Coles, 1997; Sharma et al., 2008).

The geostatistics results displayed some indications of periodiccharacteristics in the semivariograms of Cut and Pbd. But theirperiodicity was weak compared with the typical values for thisphenomenon. If a periodic function is applied, the wave-like fluc-tuation tends to exaggerate the estimates. Thus, it was determinedthat these features were treated as noise within the study datasetsimilar to the approach adopted by Chirrico et al. (2007), as thesimplest model reproducing the important features of the experi-mental variogram may be the best one. Hence, spherical modelswere still applied for these two soil variables.

The nearly pure nugget effect of Znt reflected the fact that Zntseemed to be spatially independent at the applied scale. Theanalysis of components of variance supported the assumption thatZnt might be only spatial dependent within 1m. As a result, this soilvariable would not be interpolated in future estimations, due to thelow level accuracy and applicability.

The nested sampling methodology adopted demonstrated thattherewas little benefit in sampling intervals less than 500m formostheavy metals in these reclaimed coastal environments. The 500-mintervalwasshortenoughtocapture themajorityof spatialviability insimilar soil types, and thesefindingsmay beuseful in designing smallscale soil investigation in similar regions in the future.

5. Conclusions

Land reclamation on the coastal plain surrounding Cixi City, hasbeen in progress for over 1000 years, and has been subjected tointensive human modification. This investigation has shown thatheavy metals exhibited considerable spatial variability and spatialdependence. Reclamation and land use history were the mostinfluential factors in accounting for their variability. Widespreadapplication of fertilizer and pesticides, vehicular traffic, anddischarge of industrial waste may account for the elevation of somemetal concentrations and their stochastic spatial characteristics.

Land use types and parent materials could also partly explaintheir distinct spatial patterns. Heavy metal concentrations tendedto be spatially homogeneous in association with similar land usetypes. Paddy fields had higher metal availability than the uplands.The available metal concentrations tended to be influenced byhuman activities, and displayed more temporal and spatial vari-ability compared to the total metal data. Soil pH, as well as OM,displayed a close relationship with metal concentrations, especiallythe available concentrations.

In future, soil contamination risk should be integrated indecision-making process to assist in decision-making in the plan-ning and monitoring processes before reclamation projects arecommenced. A continuous monitoring program is recommendedincluding establishing a baseline concentration before activities areundertaken on reclaimed lands to account for environmentalchanges during and after the completion of reclamation projects.

Acknowledgements

This work was supported by the Natural Science Foundation ofChina (41240013), and the Jiangsu Government Scholarship forOverseas Studies, China. We thank Prof. LI Ren-an of the College ofEnvironment and Natural Resources, Zhejiang University for hisassistance.

L. Wang et al. / Estuarine, Coastal and Shelf Science 151 (2014) 310e317 317

References

Alloway, B.J., 1995. Soil processes and the behavior of metals. In: Alloway, B.J. (Ed.),Heavy Metals in Soils. Blackie Academic & Professional, London, pp. 11e37.

Anguelov, G., Anguelova, I., 2009. Assessment of land-use effect on trace elementsconcentrations in soil solution from Ultisols in North Florida. Agric. Ecosyst.Environ. 130, 59e66.

Adriano, D.C., 2001. Trace Elements in Terrestrial Environments: Biogeochemistry,Bioavailability, and Risks of Metals. Springer-Verlag, New York, 867 pp.

Bai, J., Xiao, R., Cui, B., Zhang, K., Wang, Q., Liu, X., Gao, H., Huang, L., 2011.Assessment of heavy metal pollution in wetland soils from the young and oldreclaimed regions in the Pearl River Estuary. South China. Environ. Pollut. 159,817e824.

Bor�uvka, L., Koz�ak, J., 2001. Geostatistical investigation of a reclaimed dumpsite soilwith emphasis on aluminum. Soil Tillage Res. 59, 115e126.

Burgos, P., Madej�on, E., P�erez-de-Mora, A., Cabrera, F., 2006. Spatial variability of thechemical characteristics of a trace-element-contaminated soil before and afterremediation. Geoderma 130, 157e175.

Cambardella, C.A., Moorman, T.B., Parkin, T.B., Karlen, D.L., Novak, J.M., Turco, R.F.,Konopka, A.E., 1994. Field-scale variability of soil properties in central Iowasoils. Soil Sci. Soc. Am. J. 58, 1501e1511.

Caridad-Cancela, R., Paz-Gonz�alez, A., de Abreu, C.A., 2005. Total and extractablenickel and cadmium contents in natural soils. Commun. Soil Sci. Plant Anal. 36,241e252.

Chen, K., Jiao, J.J., 2008. Metal concentrations and mobility in marine sediment andgroundwater in coastal reclamation areas: a case study in Shenzhen, China.Environ. Pollut. 151, 576e584.

Cheng, Y., Yang, L., Kong, L., Ci, E., 2008. Influence of rice-planting duration on ironand manganese oxides in paddy soils. Soils 40, 784e791 (In Chinese).

Chirico, G.B., Medina, H., Romano, N., 2007. Uncertainty in predicting soil hydraulicproperties at the hillslope scale with indirect methods. J. Hydrol. 334, 405e422.

Cook, S.E., Coles, N.A., 1997. A comparison of soil survey methods in relation tocatchment hydrology. Aust. J. Soils Res. 35, 1379e1396.

de Mulder, E.F.J., van Bruchem, A.J., Claessen, F.A.M., Hannink, G., Hulsbergen, J.G.,Satijn, H.M.C., 1994. Environmental impact assessment on land reclamationprojects in the Netherlands: a case history. Eng. Geol. 37, 15e23.

Fageria, N.K., 2012. Role of soil organic matter in maintaining sustainability ofcropping systems. Commun. Soil Sci. Plant Anal. 43, 2063e2113.

Glaser, R., Haberzettl, P., Walsh, R.P.D., 1991. Land reclamation in Singapore, HongKong and Macau. GeoJournal 24, 365e373.

Goldschmidt, V., 1937. The principles of distribution of chemical elements in min-erals and rocks. J. Chem. Soc., 655e673.

Hall, L.A., 1989. The effects of dredging and reclamation on metal levels in water andsediments from an estuarine environment off Trinidad, West Indies. Environ.Pollut. 56, 189e207.

Kabata-Pendias, A., 2000. Trace Elements in Soils and Plants. CRC Press, Boca Raton,Florida, 331 pp.

Katyal, J.C., Sharma, B.D., 1991. DTPA-extractable and total Zn, Cu, Mn, and Fe in Indiansoils and their associationwith some soil properties. Geoderma 49, 165e179.

Kim, R., Kim, J., Ryu, J., Chang, H., 2006. Salinization properties of a shallowgroundwater in a coastal reclaimed area, Yeonggwang, Korea. Environ. Geol. 49,1180e1194.

Lacarce, E., Saby, N.P.A., Martin, M.P., Marchant, B.P., Boulonne, L., Meersmans, J.,Jolivet, C., Bispo, A., Arrouays, D., 2012. Mapping soil Pb stocks and availabilityin mainland France combining regression trees with robust geostatistics. Geo-derma 170, 359e368.

Li, J., Li, W., Yang, X., Zhang, D., Tong, Y., 2008. The impact of land use change onquality evolution of soil genetic layers on the coastal plain of south HangzhouBay. J. Geogr. Sci. 18, 469e482.

Lindsay, W.L., Norvell, W.A., 1978. Development of a DTPA soil test for zinc, iron,manganese, and copper. Soil Sci. Soc. Am. J. 42, 421e428.

Liu, H., Guo, P., Wu, W., Wang, Z., 2011. Assessment of soil arsenic, chromium,mercury, and lead at an agricultural landscape scale. Soil Sediment Contam. 20,995e1007.

Lofts, S., Spurgeon, D.J., Svendsen, C., Tipping, E., 2004. Deriving soil critical limitsfor Cu, Zn, Cd, and Pb: a method based on free ion concentrations. Environ. Sci.Technol. 38, 3623e3631.

Lu, H., Xu, D., Li, R., Zhu, Y., 2005. Soil Environment Quality and AgriculturalProducts Safety Measure in the Cixi City. Zhejiang University Press, Hangzhou,116 pp. (In Chinese).

McLaughlin, M.J., Zarcinas, B.A., Stevens, D.P., Cook, N., 2000. Soil testing for heavymetals. Commun. Soil Sci. Plant Anal. 31, 1661e1700.

M€oller, A., Müller, H.W., Abdullah, A., Abdelgawad, G., Utermann, J., 2005. Urban soilpollution in Damascus: concentrations and patterns of heavy metals in the soilsof the Damascus Ghouta. Geoderma 124, 63e71.

National Environmental Protection Agency of China, 1995. Environmental QualityStandard for Soils (GB 15618-1995).

Pande, J., Srivastava, P.C., Singh, S.K., 2012. Plant availability of nickel as influencedby farmyard manure and its critical toxic limits in French bean. J. Plant Nutr. 35,384e395.

Ponnamperuma, F.N., Cayton, M.T., Lantin, R.S., 1981. Dilute hydrochloric acid as anextractant for available zinc, copper and boron in rice soils. Plant Soil 61,297e310.

Rahman, M.A., Ishiga, H., 2011. Trace metal concentrations in tidal flat coastalsediments, Yamaguchi Prefecture, southwest Japan. Environ. Monit. Assess. 184,5755e5771.

Rojo, P.A., Frances, F.S., Garcia-sanchez, A., Alvarez-ayuso, E., 2004. Baseline con-centrations of heavy metals in native soils of the Salamanca and Valladolidprovinces, Spain. Arid Land Res. Manag. 18, 241e250.

Romic, D., Romic, M., Zovko, M., Bakic, H., Ondrasek, G., 2012. Trace metals in thecoastal soils developed from estuarine floodplain sediments in the CroatianMediterranean region. Environ. Geochem. Health 34, 399e416.

SEPAC (State Environment Protection Administration of China), 1990. The Back-ground Levels of Element in Soil in China. Chinese Environmental Science Press,Beijing (in Chinese).

Sharma, B.D., Chahal, D.S., Singh, P.K., Raj-Kumar, 2008. Forms of iron and theirassociation with soil properties in four soil taxonomic orders of arid and semi-arid soils of Punjab, India. Commun. Soil Sci. Plant Anal. 39, 2550e2567.

Teng, Y.X., Timmer, V.R., 1990. Phosphorus-induced micronutrient disorders inhybrid poplar I. Preliminary diagnosis. Plant Soil 126, 19e29.

Wang, L., Wu, J., 2008. Spatial variability of heavy metals in soils across a valleyplain in Southeastern China. Environ. Geol. 55, 1207e1217.

Wang, L., Coles, N., Wu, C., Wu, J., 2014. Effect of long-term reclamation on soilproperties on a coastal plain, southeast China. J. Coast. Res. 30 (4) http://dx.doi.org/10.2112/JCOASTRES-D-13e00129.1.

Webster, R., Oliver, M.A., 2007. Geostatistics for Environmental Scientists. JohnWiley & Sons Ltd, the Atrium, Southern Gate, Chichester, England, 330 pp.

Webster, R., Welham, S.J., Potts, J.M., Oliver, M.A., 2006. Estimating the spatial scalesof regionalized variables by nested sampling, hierarchical analysis of varianceand residual maximum likelihood. Comput. Geosci. 32, 1320e1333.

Wilding, L.P., 1985. Spatial variability: its documentation, accommodation andimplication to soil surveys. In: Nielsen, D.R., Bouma, J. (Eds.), Soil Spatial Vari-ability. Pudoc, Wageningen, the Netherlands, pp. 166e193.

Wu, J., Norvell, W.A., Hopkins, D.G., Welch, R.M., 2002. Spatial variability of graincadmium and soil characteristics in a durum wheat field. Soil Sci. Soc. Am. J. 66,268e275.

Xian, X., Shokohifard, G.I., 1989. Effect of pH on chemical forms and plant avail-ability of cadmium, zinc, and lead in polluted soils. Water, Air Soil Pollut. 45,265e273.

Zhang, M., Wei, X., Li, R., 2000. Introduction to Zhejiang Soil Series. Chinese Agri-cultural Science and Technology Press, Beijing, 256 pp. (In Chinese).