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Environmental Pollution 159 (2011) 84e91

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Environmental Pollution

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The influence of pH and organic matter content in paddy soil on heavy metalavailability and their uptake by rice plants

Fanrong Zeng a, Shafaqat Ali a, Haitao Zhang a, Younan Ouyang b, Boyin Qiu a, Feibo Wu a,Guoping Zhang b,*

aDepartment of Agronomy, College of Agriculture and Biotechnology, Huajiachi Campus, Zhejiang University, Hangzhou 310029, ChinabChina National Rice Research Institute, Fuyang 310041, China

Soil pH and organic matter content significantly affect heavy metal av

ailability and accumulation in rice plants.

a r t i c l e i n f o

Article history:Received 31 March 2010Received in revised form3 September 2010Accepted 16 September 2010

Keywords:AvailabilityHeavy metalOrganic matterRiceSoil pH

* Corresponding author.E-mail address: zhanggp@zju.edu.cn (G. Zhang).

0269-7491/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.envpol.2010.09.019

a b s t r a c t

The experiments were done to investigate the effect of soil pH and organic matter content on EDTA-extractable heavy metal contents in soils and heavy metal concentrations in rice straw and grains. EDTA-extractable Cr contents in soils and concentrations in rice tissues were negatively correlated with soil pH,but positively correlated with organic matter content. The combination of soil pH and organic mattercontent would produce the more precise regression models for estimation of EDTA-Cu, Pb and Zncontents in soils, demonstrating the distinct effect of the two factors on the availability of these heavymetals in soils. Soil pH greatly affected heavy metal concentrations in rice plants. Furthermore, inclusionof other soil properties in the stepwise regression analysis improved the regression models for predictingstraw Fe and grain Zn concentrations, indicating that other soil properties should be taken intoconsideration for precise predicting of heavy metal concentrations in rice plants.

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1. Introduction

Heavy metal contamination of soils became a severe issue inagricultural production around theworld in the past few decades asa result of anthropogenic activities, such as mining or industrialactivities and improper use of heavy metal-enriched materials inagriculture, including chemical fertilizer and pesticides, industrialeffluents, sewage sludge and wastewater irrigation (Ramadan andAl-Ashkar, 2007; Kuo et al., 2006). Although some of them, suchas Fe, Zn, Mn and Cu etc., are essential at low level, other metals,like Cd, Cr, Pb and As, are toxic and may pose a great threat toplants, animals and humans through the food chain (Costa, 2000).High contents of heavy metals in soils would increase the potentialuptake of these metals by plants. Therefore, a detailed riskassessment of heavy metal accumulation in agricultural lands isrequired for application of inorganic fertilizers, organic wastes andpesticides to soils in order to ensure the safe crop production(Papafilippaki et al., 2007).

Heavy metals in soils may be present in several forms withdifferent levels of solubility as follows: (i) dissolved (in soil solu-tion), (ii) exchangeable (in organic and inorganic components), (iii)

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structural components of the lattices in soils and (iv) insolublyprecipitated with other soil components (Zalidis et al., 1999;Aydinalp and Marinova, 2003). Usually, only the first two formsare able to be absorbed and utilized by plants. Therefore, plantuptake of a metal is mainly dependent on the metal mobility andavailability in soils.

In general, the mobility and availability of heavy metals arecontrolled by adsorption and desorption characteristics of soils(Krishnamurti et al., 1999). The adsorption and desorption of heavymetals have been demonstrated to be associated with soil proper-ties, including pH, organic matter content, cation exchange capacity(CEC), oxidationereduction status (Eh), the contents of clayminerals, calcium carbonate, Fe and Mn oxides (Kashem and Singh,2001; Antoniadis et al., 2008; Usman et al., 2008). Among these soilproperties, soil pH was found to play the most important role indetermining metal speciation, solubility from mineral surfaces,movement, and eventual bioavailability of metals, due to its strongeffects on solubility and speciation of metals both in the soil asa whole and particularly in the soil solution (Mühlbachová et al.,2005; Zhao et al., 2010). A negative correlation between soil pHand heavy metal mobility and availability to plants has been welldocumented in numerous studies. For example, with decreased soilpH, the dramatic increases in heavy metal desorption from soilconstituents and dissolution in soil solution were observed for Cd,Pb and Zn (Sukreeyapongse et al., 2002; Bang and Hesterberg,

F. Zeng et al. / Environmental Pollution 159 (2011) 84e91 85

2004). The mobility and bioavailability of heavy metals alsoincrease with decreased soil pH (Badawy et al., 2002; Wang et al.,2006; Du Laing et al., 2007), thus enhancing the uptake of heavymetals by plants and thereby posing a threat to human health(Braillier et al., 1996; Oliver et al., 1996). In addition, there wasa report that soil pH appeared to be the greatest determinant of Cr,Pb and Zn solubility and mobility in a light-textured sandy soil(Speir et al., 2003).

Apart from soil pH, organic matter content in soil is also one ofthe most important soil properties affecting heavy metal avail-ability. Organic matter is a major contributor to the ability of soilsfor retaining heavy metals in an exchangeable form. In addition,organic matter also supplies organic chemicals to the soil solutionthat can serve as chelates and increase metal availability to plants(McCauley et al., 2009). The role of organic matter on metal avail-ability has been extensively investigated. It was reported that heavymetal adsorption onto soil constituents declined with decreasedorganic matter content in soils (Hettiarachchi et al., 2003;Antoniadis et al., 2008). Moreover, the dissolved organic matterin soils could increase the mobility and uptake of heavy metals toplant roots (Impellitteri et al., 2002; Du Laing et al., 2009). Almåsand Singh (2001) reported that addition of organic matterincreased Cd mobility in soils and its uptake by rye grass. Dai et al.(2004) estimated DTPA-extractable Cd, Pb and Zn contents in heavymetal contaminated soils and also found that the contents of thesemetals were positively correlated with organic matter contents insoils.

Copper, Fe, Mn and Zn are essential minerals for plant growth,but Cr and Pb are non-essential but toxic to plants at low level andto humans through a food chain. Therefore, it is imperative toestimate the effect of soil properties on the availability and theuptake of heavy metals by plants to minimize the toxic effects andthe translocation to food chains. In viewing of the effect of pH andorganic matter on the solubility and availability of heavy metals insoils, the current experiments were conducted to (1) determineextractable contents of chromium (Cr), copper (Cu), iron (Fe),manganese (Mn), lead (Pb) and zinc (Zn) in soils and concentrationsof these metals in rice straw and grains; (2) make clear of thepotential effect of soil pH and organic matter on the availability ofthese heavy metals in soils and uptake by rice plants; (3) establishmultiple regression models between heavy metals concentrationsin rice plants and soil pH, and extractable heavy metal contents,thus determining the critical levels of these heavymetals in soils forsafe rice production and providing useful references for developingthe strategies of utilizing the contaminated soils in crop production.

2. Materials and methods

2.1. Sampling and preparation

Rice seeds were sown in late May and seedlings were transplanted in late Jun of2009. The total amounts of 250 kg N ha�1 as urea, 31.5 kg P ha�1 as calcium super-phosphate and 180 kg K ha�1 as potassium chloride were applied. The basal fertil-izers were broadcast 3e4 d prior to transplanting at the rates of 30% of total N and allof P and K. During the growth, 20% of total N was top-dressed at each of early-tillering (10 days after transplanting), 30% at mid-tillering and 20% at early spikedifferentiation, respectively. Field management was done as that locallyrecommended.

The soil and rice samples were taken from Nanhu, Tongxiang and Xiaoshancounties of Zhejiang province, China in order to cover a wide range of soil types,mainly in terms of pH, organic matter and heavy metal contents. A japonica ricecultivar Xiushui 63 was used and planted in the 9 paddy fields of each county(location). At maturity (about 120 d after transplanting), soils at the depth of0e25 cm and rice plants were randomly sampled for the determination of pH,organic matter and heavy metal contents.

The sampled soils were dried in an air-circulating room, and then were treatedto remove stones and plant residues, ground with wood grinder and passed througha 2 mm nylon sieve. The sieved samples were collected and half of themwere storedin plastic bags for measurement of pH and extractable heavy metal content. The

other half of them were ground again, passed through 0.2 mm nylon sieve andstored in plastic bags for measurement of organic matter content. The sampledplants were washed in tap water and deionized water in the order, then separatedinto straw and grains, over-dried in an oven at 80 �C to constant weight and milledinto powder for measurement of heavy metal content.

2.2. Chemical analysis

Soil pH was measured according to Chaturvedi and Sankar (2006). Organicmatter content was determined by the chromic acid titration method according toRyan et al. (2001). Three replications were conducted for each sample.

For analysis of available heavy metals in soil, air-dried soils were extracted bya mixture containing 0.05 mol L�1 ethylene-diamine-tetra-acetic acid disodium(EDTA-Na2), 0.01 mol L�1 CaCl2 and 0.1 mol L�1 Tri-ethanolamine (TEA) (soil-mixture ratio 1:2 at pH ¼ 7.0). Briefly, 20 ml of the EDTA solution (the EDTA-CaCl2eTEA mixture) was added to 10 g of soil sample placed in polypropylene tubes.The tubes were shaken on a rotating shaker for 3 h and then were centrifuged(Wang, 1991; Dong et al., 1995; Wu, 2002). Metal contents in the supernatant liquidwere measured with a flame atomic absorption spectrometry (FAAS). The sameprocedure without samples was used as control. Three replications were conductedfor each sample. Quality assurance and quality control (QA/QC) for metals in soilsamples were estimated by determining metal contents in the standard referencematerial GBW07443 (GSF-3) approved by General Administration of QualitySupervision, Inspection and Quarantine of the People’s Republic of China (AQSIQ).

For analysis of heavy metals in rice straw and grains, 1 g of milled sample wasdry-ashed at 500 �C for 12 h, extracted with 10 ml of 1:1 nitric acid (HNO3) for 3 hand then filtered. Contents of Cr, Cu, Fe, Mn, Pb and Zn were determined byinductively coupled plasma mass spectrometry (ICP-MS). The same procedurewithout samples was used as control. Three replications were conducted for eachsample. Quality assurance and quality control (QA/QC) for metals in rice plants wereestimated by determining metal contents in the standard reference materialNCSZC73008 approved by General Administration of Quality Supervision, Inspectionand Quarantine of the People’s Republic of China (AQSIQ).

2.3. Statistical analysis

Data were statistically analyzed by ANOVA test using a statistical package, SPSSversion 13.0 (SPSS, Chicago, IL). Pearson correlation coefficients were calculated todetermine the relationship between soil pH value, organic matter and soil heavymetal contents. Single linear regression analysis was employed firstly to examinethe effect of soil pH or organic matter on the availability of heavy metals in soils andthe concentrations of heavy metals in rice straw and grains. Then, stepwise multiplelinear regression analysis was used to identify the significant soil variables, whichcould be used to fit a model of estimating the contents of EDTA-extractable heavymetals in soil and the concentrations of heavy metals in rice straw and grains. Incorrelation and regression analysis, soil organic matter content, EDTA-extractableheavy metal contents in soils and heavy metal concentrations in rice straw andgrains were Log10-transformed to ensure homogeneity of variances.

3. Results and discussions

3.1. Soil pH and organic matter content

The values of pH and organic matter content of 27 soil samplesare shown in Table 1. The soil pH ranged from 5.44 to 8.11, i.e. strongacid to mild alkaline, and it was location dependent. In detail, 9 soilsamples of Nanhu, Tongxiang and Xiaoshan ranged from 5.44 to5.85, from 5.68 to 7.19 and from 7.36 to 8.11, respectively. Organicmatter contents of all soil samples ranged from 4.26 to44.95 mg g�1. Among the three locations, Nanhu had the highestorganic matter content (ranging from 28.18 to 44.95 mg g�1), andXiaoshanwas the lowest (ranging from 4.26 to 31.43mg g�1). Theseresults indicated that the sampled soils covered a wide arrange ofpH and organic matter content, being suitable for studying theinfluence of soil pH and organic matter on availability and plantuptake of heavy metals.

3.2. Effects of soil pH and organic matter on the contentsof EDTA-extractable Cr, Cu, Fe, Mn, Pb and Zn in soils

The EDTA-extractable contents of Cr, Cu, Fe, Mn, Pb and Znin soils were determined in this study, and the results are shown inTable 1. There was a distinct difference among the three locations in

Table 3Fitted Simple linear regressions of soil EDTA-extractable heavymetal concentrationsto soil pH or organic matter content in the form of y ¼ a þ bx.

x y a b R2 Radj2 P value

pH Log10(EDTA-Cr)a 0.355 �0.081 0.307 0.298 <0.001Log10(EDTA-Cu) 1.963 �0.184 0.625 0.620 <0.001Log10(EDTA-Fe) 2.923 �0.132 0.400 0.392 <0.001Log10(EDTA-Mn) 3.324 �0.216 0.641 0.636 <0.001Log10(EDTA-Pb) 2.262 �0.200 0.531 0.525 <0.001Log10(EDTA-Zn) 1.393 �0.104 0.157 0.146 <0.001

Log10(OM) Log10(EDTA-Cr) �0.631 0.346 0.471 0.464 <0.001Log10(EDTA-Cu) �0.029 0.594 0.548 0.543 <0.001Log10(EDTA-Fe) 1.267 0.604 0.707 0.703 <0.001Log10(EDTA-Mn) 1.191 0.542 0.343 0.334 <0.001Log10(EDTA-Pb) �0.005 0.727 0.593 0.588 <0.001Log10(EDTA-Zn) �0.308 0.784 0.748 0.744 <0.001

a EDTA-Cr means the content of EDTA-extractable Cr in soil. Soil organic mattercontent and soil EDTA-extractable heavy metals contents were Log10-transformedto ensure homogeneity of variances.

Table 1Soil pH value, organic matter content and EDTA-extractable heavy metal concen-trations in soil from three locations.

Location Soil properties EDTA-extractable heavy metalconcentration (mg kg�1 dry soil)

OM (mg g�1

dry soil)pH Cr Cu Fe Mn Pb Zn

Nanhu Mean 34.4 5.64 0.82 7.32 136 106 11.4 7.74Maxa 45.0 5.85 1.12 8.01 137 119 12.0 11.4Minb 28.2 5.44 0.62 6.56 135 82.7 11.0 4.71CVc (%) 13.5 2.46 21.9 6.73 0.52 12.1 2.54 28.1

Tongxiang Mean 17.2 6.58 0.65 7.01 128 120 12.6 3.95Max 25.3 7.19 0.75 9.70 134 127 17.8 6.94Min 12.5 5.68 0.53 5.26 115 111 9.10 2.26CV (%) 30.4 6.67 12.6 19.5 4.65 4.97 23.8 38.0

Xiaoshan Mean 16.0 7.77 0.58 3.67 91.6 38.0 5.05 5.70Max 31.4 8.11 0.79 5.66 128 63.1 9.74 12.2Min 4.30 7.36 0.33 1.38 28. 8 22.7 2.04 1.40CV (%) 57.5 3.90 30.5 37.2 46. 8 38.2 51.6 58.3

a Max, maximum value.b Min, minimum value.c CV, coefficient of variance.

F. Zeng et al. / Environmental Pollution 159 (2011) 84e9186

the EDTA-extractable heavy metal contents. Nanhu had the highestEDTA-extractable contents of Cu, Cr, Fe and Zn and Xiaoshan hadthe lowest values of these heavy metals, except for Zn. On the otherhand, Tongxiang had the highest EDTA-extractable Mn and Pbcontents. Meanwhile there was a wide variation in any EDTA-extractable heavy metal content among the soil samples from eachlocation. For example, in Xiaoshan, EDTA-extractable heavy metalcontent ranged from 0.33 to 0.80 mg kg�1 for Cr, 1.38 to5.66 mg kg�1 for Cu, 28.78 to 128.41 mg kg�1 for Fe, 22.66 to63.11 mg kg�1 for Mn, 2.04 to 9.74 mg kg�1 for Pb and 1.40 to12.23 mg kg�1 for Zn, with the coefficients of variation being 30.5%,37.2%, 46.8%, 38.2%, 51.6% and 58.3% for Cr, Cu, Fe, Mn, Pb and Zn,respectively.

Correlation analysis showed that contents of EDTA-extractableCr, Cu, Fe, Mn, Pb and Zn were strongly affected by soil pH andorganic matter content (Table 2). There was a significantly negativecorrelation between the logarithmic value of EDTA-extractable Cr,Cu, Fe, Mn, Pb or Zn content and soil pH. The highest correlationcoefficient was found for Mn (r ¼ �0.800, P < 0.001), and thelowest was for Zn (r ¼ �0.396, P < 0.001). Obviously, the loga-rithmic values of EDTA-extractable heavymetal contents decreaseddramatically with the increasing soil pH (Tables 2 and 3). However,it is interesting to note that when the pH values of soils ranged from5.0 to 7.0, the mean values of extractable Cr, Cu, Fe, Mn and Pbcontents were approximately the same, while when soil pH valuewas above 7.0, contents of all the six heavy metals decreasedsignificantly with increasing soil pH values. Similar results wereobtained by other studies (Mitsios et al., 2005). Generally, in natural

Table 2Correlation coefficients between soil properties and extractable heavy metal concentrati

Soil properties Extractable heavy metal concentration

pH Log10(OM) Log10(EDTA-Cr)

Log10(OM) �0.689**Log10(EDTA-Cr) �0.554** 0.686**Log10(EDTA-Cu) �0.790** 0.741** 0.612**Log10(EDTA-Fe) �0.632** 0.841** 0.720**Log10(EDTA-Mn) �0.800** 0.585** 0.461**Log10(EDTA-Pb) �0.729** 0.770** 0.596**Log10(EDTA-Zn) �0.396** 0.865** 0.491**

**, Correlation is highly significant at the 0.01 probability level (SPSS Inc., 2004).Soil organic matter content and soil EDTA-extractable heavy metals contents were Log1

systems, mobility and availability of Cr usually increased with theincreasing soil pH, because of the reduction of soluble Cr (VI) todissoluble Cr (III) under the acid condition. Actually, the positivecorrelation of available Cr in soils and pH value was mentioned ina previous study (Sahibin et al., 2002). However, a significantlynegatively correlation of EDTA-extractable Cr content and soil pHwas observed in this study, indicating a hypothesis that apart fromsoil pH, other soil parameters, at least organic matter contentshould be included to investigate the influence of soil properties onthe availability of Cr in soil.

Organic matter content was positively correlated with the log-arithmic values of EDTA-extractable Cr, Cu, Fe, Mn, Pb and Zncontents (Table 2), and the relationships between them could bedescribed by linear regression models (Table 3). Organic matterplays a significant role in determining the availability and mobilityof heavy metals in soils in the two aspects. One is that organicmatter could reduce the bioavailability of heavy metals in soils byadsorption or forming stable complexes with humic substances(Liu et al., 2009). According to Halim et al. (2003), the addition ofhumic acid resulted in a reduction of the extractable heavy metalfraction in metal-enriched soils. On the other hand, organic matteris also involved in supplying organic chemicals to the soil solution,which may serve as chelates and increase metal availability toplants (Vega et al., 2004; McCauley et al., 2009). Increasingevidences also indicate that soluble Cr (III) organic complexes canform with natural organic matter in the environment (Shresthaet al., 2007; Luo et al., 2010) and become bio-available (Howeet al., 2003). This could partially explain the positive correlationof extractable Cr and organic matter contents observed in thecurrent and previous reports (Papafilippaki et al., 2007).

ons in soil.

in soil

Log10(EDTA-Cu) Log10(EDTA-Fe) Log10(EDTA-Mn) Log10(EDTA-Pb)

0.868**0.867** 0.741**0.896** 0.876** 0.894**0.507** 0.645** 0.287** 0.481**

0-transformed to ensure homogeneity of variances.

F. Zeng et al. / Environmental Pollution 159 (2011) 84e91 87

In order to reveal the multiple effects of pH and organic matteron EDTA-extractable heavy metal contents in soils, stepwisemultiple linear regression analysis was conducted (Fig. 1). Theresults showed that the combination of soil pH and Log10(OM) resultedin a more precise model for estimation of EDTA-extractable Cu, Pband Zn contents in soils (Fig. 1B, E and F). However, in the case of Cr,Fe and Mn, the combination of pH and organic matter had littleeffect on the improvement of the regression model. Therefore, itmay be concluded that soil organic matter was the major factoraffecting the EDTA-extractable Cr and Fe contents (Fig. 1A and C)and the variation of EDTA-extractable Mn contents in soils wasmainly affected by soil pH (Fig. 1D).

Correlation analysis was also carried out between heavy metals(Table 2). Extractable Fe content had a significant correlation withextractable Mn content (r ¼ 0.688, P < 0.001). Fe and Mn are themost abundant metals in the lithosphere, and they generally occuras FeeMn oxides and hydroxides, which play an important role inprecipitation or solubility of some heavy metals in soils (Jones andJacobsen, 2009). In this study, the EDTA-extractable contents of allheavy metals were significantly correlated with EDTA-extractableFe or Mn content. For example, FeeCr: r ¼ 0.599, P < 0.001; FeeCu:r ¼ 0.775, P < 0.001; FeePb: r ¼ 0.767, P < 0.001; FeeZn: r ¼ 0.516,P < 0.001; MneCr: r ¼ 0.300 P < 0.001; MneCu: r ¼ 0.835,P < 0.001; MnePb: r ¼ 0.829, P < 0.001. The results indicate the

Fig. 1. Multiple linear correlation between soil EDTA-extractable metal concentration and sCriteria: probability of F to enter P � 0.050, probability of F to remove P � 0.100). EDTA-CrEDTA-extractable heavy metals contents were Log10-transformed to ensure homogeneity o

retention of Cr, Cu, Pb and Zn on Fe or Mn-oxides (Aydinalp andMarinova, 2003).

3.3. Heavy metal concentrations in rice straw and grains

The concentrations of Cr, Cu, Fe, Mn, Pb and Zn in rice strawsand grains sampled from three locations are summarized in Table4. On the whole, concentrations of Cr, Fe and Mn in straws weremuch higher than those in grains. However, the concentrations ofCu, Pb and Zn showed little difference between straws and grains.It may be suggested that the remobilizing abilities of Cr, Fe and Mnfrom shoots to grains were much lower than those of Cu, Pband Zn.

There was a large difference among the three locations for allheavy metal concentrations in straw. Hence, Nanhu ranked thehighest for all 6 metal concentrations, while Tongxiang had thelowest Cr, Fe and Pb concentrations and Xiaoshan had the lowestCu, Mn and Zn concentrations. Similarly a large difference could befound among the locations in grain metal concentrations.Furthermore, grain Pb concentrations of over 80% samples excee-ded the maximum permitted concentration (MPC) of WHO(0.2 mg g�1) (Codex Alimentarius Commission, 2000), indicatingthat soil Pb contamination has become a health threat in riceproduction, as reported in a previous study (Zhao et al., 2009).

oil pH and organic matter content in the form of y ¼ a þ bx þ cz (Stepwise regression.means the content of EDTA-extractable Cr in soil. Soil organic matter content and soilf variances.

Table 4Heavy metal concentrations in straw and grain of rice grown in the differentlocations.

Location Heavy metal concentration (mg g�1 dry weight)

Cr Cu Fe Mn Pb Zn

StrawNanhu Mean 5.27 7.89 209 535 0.58 31.2

Maxa 9.55 13.3 346 687 0.79 43.6Minb 3.44 5.16 115 474 0.20 22.1CVc (%) 36.3 32.2 34.7 13.7 36.7 25.5

Tongxiang Mean 3.42 6.36 116 405 0.43 21.2Max 5.70 8.46 196 568 0.63 35.5Min 2.12 3.09 70.9 282 0.19 12.7CV (%) 41.6 25.6 32.6 21.0 35.1 39.7

Xiaoshan Mean 3.93 4.15 133 219 0.52 14.9Max 6.32 5.99 193 392 0.70 19.4Min 1.47 2.18 75.2 139 0.27 7.36CV (%) 34.8 34.4 26.6 39.6 26.9 26.7

GrainNanhu Mean 0.25 5.51 13.6 36.3 0.25 23.2

Max 0.35 8.86 17.1 48.5 0.49 27.1Min 0.18 2.87 11.5 26.9 0.06 21.1CV (%) 22.2 32.9 14.3 21.0 50.9 8.02

Tongxiang Mean 0.30 4.09 12.6 36.2 0.35 22.6Max 0.34 5.47 13.9 46.2 0.56 27.5Min 0.27 2.40 11.2 25.1 0.16 18.1CV (%) 8.06 23.9 8.39 20.5 41.6 18.5

Xiaoshan Mean 0.19 4.67 12.6 26.5 0.32 21.3Max 0.24 6.70 14.3 39.0 0.48 25.2Min 0.12 2.85 11.0 20.5 0.22 17.7CV (%) 21.0 25.0 8.85 22.8 26.9 12.5

a Max, maximum value.b Min, minimum value.c CV, coefficient of variance.

F. Zeng et al. / Environmental Pollution 159 (2011) 84e9188

3.4. The influence of soil pH, organic matter and EDTA-extractableheavy metal contents on heavy metal concentrations in rice strawsand grains

Simple correlation and linear regression analysis wereperformed to identify the influence of pH, organic matter andEDTA-extractable heavy metal contents in soils on heavy metalconcentrations in rice straw and grains (Tables 5 and 6). Soil pHwassignificantly and negatively correlated with the logarithmic values

Table 5Correlation coefficients of total heavy metal concentration in rice straw and grain with s

Soil properties Extractable heavy metal concentration in soil (mg kg�1)

pH Log10(OM) Log10(EDTA-Cr) Log10(EDTA-

Total heavy metal concentration in rice straw (mg g�1)Log10(CrS)a �0.431** 0.193 0.103 0.216Log10(CuS) �0.593** 0.538** 0.527** 0.466**Log10(FeS) �0.501** 0.446** 0.232* 0.254*Log10(MnS) �0.680** 0.199 0.137 0.401**Log10(PbS) �0.110 0.156 0.117 �0.080Log10(ZnS) �0.652** 0.456** 0.243* 0.372**

Total heavy metal concentration in rice grain (mg g�1)Log10(CrG) e0.391** 0.060 �0.031 0.362**Log10(CuG) �0.152 0.272* 0.084 0.092Log10(FeG) �0.191 0.113 0.064 �0.013Log10(MnG) �0.522** �0.035 �0.022 0.406**Log10(PbG) 0.361** �0.358** �0.311* �0.284*Log10(ZnG) �0.127 �0.306** �0.190 �0.066

* and **, Correlation is significant at the 0.05 and 0.01 probability levels, respectively.a CrS means Cr concentration in rice straw; CrG means Cr concentration in rice grain; ED

soil EDTA-extractable heavy metals contents and heavy metals concentrations of rice str

of Cr (r ¼ �0.431, P < 0.001), Cu (r ¼ �0.593, P < 0.001), Fe(r ¼ �0.501, P < 0.001), Mn (r ¼ �680, P < 0.001) and Zn(r¼�0.652, P< 0.001) concentrations in straw, and it could explainfor 17.5%, 34.4%, 24.1%, 45.6% and 41.8% of variances in Cr, Cu, Fe, Mnand Zn concentrations in straw, respectively. These results sug-gested that the concentrations of Cr, Cu, Fe, Mn and Zn are lower inthe soils with relatively higher pH. These findings were in agree-ment with the previous reports. Wang et al. (2006) observeda similar effect of soil pH on Mn and Zn uptake in a pot experimentwith Thlaspi caerulescens. The reason for the relationship betweensoil pH and heavy metal concentrations in straw might be thathigher soil pH could reduce available contents of heavy metals inthe soil solution, and consequently reduce the uptake of heavymetals by crops. Similarly, soil pH was also significantly andnegatively correlated with the logarithmic values of Cr and Mnconcentrations in rice grains, while no significant correlation wasfound between Cu, Fe and Zn concentrations in grains and soil pH.On the other hand, grain Pb concentration was significantly andpositively correlated with soil pH (r ¼ 0.361, P < 0.001) (Tables 5and 6).

Metals exist mainly in four forms in soils: mineral, organic,sorbed (bound to soil), or dissolved. The majority of metals in soilsis bound inminerals and organicmatter (OM, such as passive OM orhumus), and is unavailable to plants (Jones and Jacobsen, 2009).However, soil organic matter (like active OM) may enhancenutrient availability to plants by increasing CEC in soils, providingmetal chelates and increasing the solubility of nutrients in soilsolution (McCauley et al., 2009). Hence, there were certainly somerelationships between organic matter and metal concentrations inrice straw and grains. In this study, Cu, Fe and Zn concentrations instraw were significantly and positively correlated with organicmatter content, and Pb and Zn concentrations in grains weresignificantly and negatively correlated with organic matter content(Tables 5 and 6). However, the correlations between soil organicmatter and Cr andMn concentrations in both straw and grains werenot significant. These results indicate that the influence of soilorganic matter on plant uptake of a heavy metal is not onlydependent on its content, but also its components.

In this study, the multiple effects of soil pH, organic matter andEDTA-extractable metal contents on heavy metal concentrations inrice straw and grains were investigated through stepwise regres-sion analysis (Table 7). For straw Pb and grain Fe concentrations, noobvious improvement of regression coefficients were found for any

oil pH, organic matter content and extractable heavy metal concentrations in soil.

Cu) Log10(EDTA-Fe) Log10(EDTA-Mn) Log10(EDTA-Pb) Log10(EDTA-Zn)

�0.015 0.108 0.030 0.0640.491** 0.494** 0.503** 0.313**0.105 0.149 0.196 0.349**0.105 0.564** 0.352** 0.002�0.056 �0.080 �0.069 0.0900.290** 0.499** 0.372** 0.218

0.184 0.540** 0.384** �0.1400.140 0.006 �0.001 0.471**0.073 0.103 0.057 0.1650.047 0.433** 0.221* �0.219*�0.226 �0.194 �0.253 �0.277*�0.308** 0.048 �0.244* �0.274*

TA-Cr means the content of EDTA-extractable Cr in soil. Soil organic matter content,aw and grain were Log10-transformed to ensure homogeneity of variances.

Table 6Fitted Simple linear regressions of rice straw and grain heavy metals concentrations to soil pH, organic matter content and soil EDTA-extractable heavy metals concentrationsin the form of y ¼ a þ bx.

xFitted Simple linear regressions

y Straw Grain

a b R2 Radj2 P value a b R2 Radj

2 P value

Soil pH Log10(CrS)a 1.106 �0.078 0.186 0.175 <0.001 �0.269 �0.053 0.153 0.142 <0.001Log10(CuS) 1.493 �0.112 0.352 0.344 <0.001 0.804 �0.022 0.023 0.011 0.176Log10(FeS) 2.736 �0.089 0.251 0.241 <0.001 1.182 �0.011 0.037 0.024 0.089Log10(MnS) 3.528 �0.148 0.463 0.456 <0.001 1.911 �0.061 0.273 0.264 <0.001Log10(PbS) �0.184 �0.022 0.012 �0.002 0.358 �1.155 0.088 0.130 0.113 0.008Log10(ZnS) 2.171 �0.128 0.425 0.418 <0.001 1.405 �0.009 0.016 0.004 0.259

Log10(OM) Log10(CrS) 0.428 0.121 0.037 0.025 0.085 �0.656 0.028 0.004 �0.009 0.595Log10(CuS) 0.289 0.357 0.289 0.280 <0.001 0.479 0.137 0.074 0.062 0.014Log10(FeS) 1.790 0.271 0.199 0.189 <0.001 1.078 0.023 0.013 0.000 0.319Log10(MnS) 2.366 0.147 0.040 0.027 0.079 1.523 �0.014 0.001 �0.011 0.756Log10(PbS) �0.463 0.105 0.024 0.011 0.189 �0.178 �0.303 0.128 0.111 0.008Log10(ZnS) 0.920 0.308 0.208 0.198 <0.001 1.439 �0.071 0.094 0.082 0.005

Log10(EDTA-Cr) Log10(CrS) 0.607 0.128 0.011 �0.002 0.364 �0.625 �0.028 0.001 �0.012 0.783Log10(EDTA-Cu) Log10(CuS) 0.465 0.387 0.217 0.207 <0.001 0.613 0.058 0.008 �0.004 0.415Log10(EDTA-Fe) Log10(FeS) 1.957 0.088 0.011 �0.002 0.357 1.064 0.021 0.005 �0.007 0.517Log10(EDTA-Mn) Log10(MnS) 1.691 0.457 0.318 0.309 <0.001 1.151 0.188 0.188 0.177 <0.001Log10(EDTA-Pb) Log10(PbS) �0.285 �0.049 0.005 �0.010 0.566 �0.354 �0.229 0.064 0.046 0.068Log10(EDTA-Zn) Log10(ZnS) 1.202 0.163 0.048 0.036 0.050 1.397 �0.070 0.075 0.064 0.013

Soil organic matter content, soil EDTA-extractable heavy metals contents and heavy metals concentrations of rice straw and grain were Log10-transformed to ensurehomogeneity of variances.

a CrS means Cr concentration in rice straw; CrG means Cr concentration in rice grain; EDTA-Cr means the content of EDTA-extractable Cr in soil.

F. Zeng et al. / Environmental Pollution 159 (2011) 84e91 89

combination of three soil parameters in comparison with thecorrelation and simple linear regression analysis (Tables 5 and 6). Itmay be suggested that under field conditions the uptake of Pb andthe transportation of Fe in plants are mainly determined by cropphysiological traits. However, apart from physiological functions ofplants, soil physico-chemical properties also have a great contri-bution to the metal uptake and transportation of plants (Adriano,1986). The results of multiple linear regression analysis showedthat only soil pH was retained for explaining the variance of Crconcentration in rice straw, coinciding with the result from simplecorrelation and linear regression analysis. Although significant

Table 7Summary of multiple linear correlation between metal concentrations in rice strawand grain and soil pH, organic matter content and soil EDTA-extractable metalconcentrations in the form of y ¼ a þ b x1 þ c x2 þ d x3 (Stepwise regression.Criteria: probability of F to enter P � 0.050, probability of F to remove P � 0.100).

Element Equation R2 Radj2 P value

StrawCr Log10(CrS)a ¼ 1.106e0.078 pH 0.186 0.175 <0.001Cu Log10(CuS) ¼ 1.493e0.112 pH 0.352 0.344 <0.001Fe Log10(FeS) ¼ 3.653e0.080 pH þ 0.613

Log10(OM) � 0.867 Log10(EDTA-Fe)0.565 0.547 <0.001

Mn Log10(MnS) ¼ 4.571e0.228 pH � 0.391 Log10(OM) 0.608 0.597 <0.001Pb No stepwise regression equation d d d

Zn Log10(ZnS) ¼ 2.171e0.128 pH 0.425 0.418 <0.001

GrainCr Log10(CrG) ¼ �0.153e0.079 pH � 0.328

Log10(EDTA-Cr)0.241 0.221 <0.001

Cu Log10(CuG) ¼ 0.479 þ 0.137 Log10(OM) 0.074 0.062 0.014Fe No stepwise regression equation d d d

Mn Log10(MnG) ¼ 2.699e0.121 pH � 0.301 Log10(OM) 0.569 0.558 <0.001Pb Log10(PbG) ¼ �1.155 þ 0.088 pH 0.130 0.113 0.008Zn Log10(ZnG) ¼ 2.078e0.060 pH � 0.348

Log10(OM) þ 0.171 Log10(EDTA-Zn)0.390 0.366 <0.001

Soil organic matter content, soil EDTA-extractable heavy metals contents and heavymetals concentrations of rice straw and grain were Log10-transformed to ensurehomogeneity of variances.

a CrS means Cr concentration in rice straw; CrG means Cr concentration in ricegrain; EDTA-Cr means the content of EDTA-extractable Cr in soil.

simple linear regression models have been established for Log10(CuS)

(CuS ¼ Cu content in straw) with soil pH, Log10(OM) and Log10(EDTA-Cu)

(EDTA-Cu ¼ EDTA-extractable Cu concentration) respectively, onlysoil pH was retained in the multiple regression analysis forexplaining the variance of Cu content in straw. The similar eventswere also observed for straw Zn and grain Pb concentrations. Theseresults indicate that soil pH has a larger effect on straw Cr, Cu andZn and grain Pb concentrations than organic matter or soil EDTA-extractable metal contents. The combination of soil pH, Log10(OM) andthe paired EDTA-extractable metal content produced significant(P < 0.001) multiple linear regression models, which could explain54.7% and 36.6% of the variance in straw Fe and grain Zn concen-trations, respectively. Furthermore, it was found that the variancesof straw Fe and grain Zn concentrations were mainly dominated bysoil pH and organic matter content, respectively. In case of Mnconcentrations in both straw and grains, soil EDTA-extractable Mncontent was not retained in the stepwise multiple linear regressionanalysis. The combination of soil pH, and Log10(OM) produced highlysignificant (P < 0.001) multiple linear regression models, whichcould explain 59.7% and 55.8% of variance in the straw and grainMnconcentrations, respectively.

The correlation between straw metal concentrations andextractable contents of soil metals were highly significant atP < 0.001 for Cu and Mn (Tables 5 and 6). The same result was alsoobserved for the correlation of grain Mn and Zn concentrations andtheir paired extractable metal contents in soil. These findingssuggest that the soil extractable metal contents could serve asa factor affecting heavy uptake by plants. In fact, numerousresearches reported that the uptake of metals was dominated bythe mobility and availability of metals (Papadopoulos et al., 2007;Adams et al., 2004). However, in case of other heavy metals suchas Cr, Fe and Pb, no significant correlation was observed betweenstraw and grain metal concentrations and their paired EDTA-extractable metal contents. In addition, it was found that the EDTA-extractable Pb content in soils was significantly correlated with theconcentrations of Cr, Cu and Zn in rice straw and grains (Table 5).The similar results were also observed for Cu, Fe, Mn and Zn. Thecurrent results indicate that the movement of metals from soil

F. Zeng et al. / Environmental Pollution 159 (2011) 84e9190

solution to rice roots and the translocation of metals in rice tissuesare complex, and dependent on many soil properties and physio-logical functions of plants. Therefore, apart from soil pH, organicmatter and extractable metal content, as well as other soil prop-erties, such as redox potential, hydrological conditions, salinity,calcium carbonate, cation exchange capacity and gypsum etc. (Ryanet al., 2001; Du Laing et al., 2007, 2008, 2009; Papafilippaki et al.,2007) and plant physiological functions should be taken intoconsideration for precisely predicting heavy metal concentrationsin rice plants under field conditions.

4. Conclusions

The present results showed that the EDTA-extractable heavymetal contents were much higher in Nanhu soils than in Tongxiangand Xiaoshan soils, being coincident with soil organic mattercontent but contrary to soil pH value. Simple correlation and linearregression analysis suggested that the EDTA-extractable contents ofCr, Cu, Fe, Mn, Pb and Zn were negatively correlated with soil pHvalue, but positively correlated with organic matter content. Thestepwise multiple linear regression analysis indicated that thecombination of soil pH and Log10(OM) could produce a more precisemodel for estimation of EDTA-extractable Cu, Pb and Zn contents insoils. However, the regression models for Cr, Fe and Mn were littleaffected.

Rice plants grown in Nanhu soil had much higher straw heavymetal concentrations than those grown in other two soils, coin-ciding with the results of soil EDTA-extractable heavy metalcontents. Simple correlation and linear regression analysis sug-gested that heavy metal concentrations in rice straw and grainswere negatively correlated with soil pH value, but positivelycorrelated with soil organic matter content, except grain Pb and Znconcentrations. The significant correlation between metalconcentrations in rice plants and extractable metal contents in soilwas only observed for Cu and Mn in rice straw and Mn and Zn inrice grains, suggesting that heavy metal concentrations in ricestraw and grains were not satisfactorily predicted by extractableheavy contents in soils. Stepwise multiple linear regression analysisindicated that soil pH played an important role in predicting heavymetal concentrations in rice plants. Furthermore, inclusion of othersoil physico-chemical properties in the stepwise regression analysiswould improve the regression models as did for straw Fe and grainZn concentrations in this study, indicating that other soil propertiesand plant physiological functions should be considered forprecisely predicting heavy metal concentrations in rice plantsunder field conditions.

Acknowledgements

The project was supported by Zhejiang Bureau of Science andTechnology (2009C12050) and Graduate Students’ InnovationProjects of Zhejiang Province (YK2008015).

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