influences of soil properties and leaching on copper toxicity to barley root elongation
TRANSCRIPT
Environmental Toxicology and Chemistry, Vol. 29, No. 4, pp. 835–842, 2010# 2009 SETAC
Printed in the USADOI: 10.1002/etc.108
INFLUENCES OF SOIL PROPERTIES AND LEACHING ON COPPER TOXICITY TO
BARLEY ROOT ELONGATIONBO LI,y YIBING MA,*yz MIKE J. MCLAUGHLIN,z§ JASON K. KIRBY,z GILL COZENS,z and JIFANG LIUyyMinistry of Agriculture Key Laboratory of Plant Nutrition and Nutrient Cycling, Institute of Agricultural Resources and Regional Planning, Chinese Academy
of Agricultural Sciences, 12 Southern Street of Zhongguancun, Beijing, 100081 China
zSustainable Agriculture Flagship, Land and Water, Commonwealth Scientific and Industrial Research Organization, Private Mail Bag 2, Glen Osmond,South Australia, 5064 Australia
§Soil and Land Systems, University of Adelaide, Private Mail Bag 1, Glen Osmond, South Australia, 5064 Australia
(Submitted 5 May 2009; Returned for Revision 9 July 2009; Accepted 18 November 2009)
* T(ybma@
Pub(www.
Abstract—The relationships developed between soil properties and phytotoxicity threshold values for copper require validation in awide range of soils with different properties and climate characteristics before they can be applied for regulatory purposes in countriesthroughout the world. Seventeen soils, which are representative of the major soil types and properties in China, were spiked with Cuchloride. A subset of the Cu-spiked soils was leached with artificial rain water to compare toxicity with that in unleached soils. Barleyroot elongation tests were performed under controlled environmental conditions. The concentrations of added Cu causing a 50%inhibitory effect (EC50) ranged from 67 to 1,129 mg/kg in unleached soils and from 88 to 1,255 mg/kg in leached soil. Compared withthe unleached toxicity thresholds, the leached EC10 (10% inhibition) and EC50 were higher by an average of 1.43- and 1.15-fold,respectively. Soil leaching significantly (p � 0.05) decreased the toxicity of Cu in approximately 35% of the soils. In this study, no singlesoil property was found to explain over 35% of the variance in (log transformed) EC50. However, stepwise multiple regressions usingsoil pH, organic carbon (OC) content, and effective cation exchange capacity (eCEC) were found to explain over 80% of the variance inCu toxicity across soils. The model developed for Chinese soils based on these factors was found to predict significantly (r2, 0.90) thephytotoxicity of Cu in European soils. These quantitative relationships between Cu toxicity and soil properties are helpful for developingsoil-specific guidance on Cu toxicity thresholds. Environ. Toxicol. Chem. 2010;29:835–842. # 2009 SETAC
Keywords—Copper Leaching Phytotoxicity Risk assessment
INTRODUCTION
Soil physicochemical properties (soil pH, cation exchange
capacity [CEC], organic carbon [OC] content, etc.) are known
to be important parameters in predicting the phytotoxicity of
metals, such as copper, in soils [1–4]. The toxicity of Cu in soils
to barley measured using root elongation has been shown by
Daoust et al. [3] to be strongly correlated with soil pH, organic
matter, and clay content (r2¼ 0.92). The toxicity of Cu to barley
root elongation and tomato shoot growth in 18 European soils
varying widely in soil properties was found to vary approxi-
mately 15-fold and 39-fold, respectively, and soil eCEC (meas-
ured at actual soil pH) was the best single predictor for toxicity
values for both plant tests [4]. Warne et al. [5] also reported the
toxicity of Cu to wheat described by the median effective
concentration values (EC50) to range from 240 to 6,680 mg
Cu/kg in 14 Australian soils, with potential CEC (measured in
alcoholic 1 M ammonium chloride at pH 8.5 for alkaline soils
and pH 7.0 in 1 M ammonium chloride for all other soils)
and pH as the most important soil physicochemical properties
for the prediction of Cu phytotoxicity (r2adj, 0.91). Although it is
known that soil physicochemical properties play an important
role in modifying biotoxicity [1–7], few countries have con-
sidered their influence in soil quality guidelines [8–11] (see also
o whom correspondence may be addressedcaas.ac.cn).
lished online 31 December 2009 in Wiley InterScienceinterscience.wiley.com).
835
http://www.thenbs.com/PublicationIndex/DocumentSummary.
aspx?DocID¼ 266920; http://www.ccme.ca/publications/ceqg_rcqe.
html?category; and http://www.nthb.cn/standard/standard05/
20030411101228.html). With the large variation in soil types
experienced in most countries, a single value for soil quality
guideline is unlikely to provide the desired level of species
protection at any given site and is likely to result in unnecessary
or insufficient remediation or treatment depending on soil type.
Also, one of the major problems with laboratory ecotoxicity
tests of metal salts is the effect of the counter-ion on metal
partitioning and on the organism used. For example, Smit and
Van Gestel [12] reported equilibration of the zinc contamina-
tion by percolating the soils with water before use in toxicity
experiments strongly reduced the difference in Zn toxicity
between laboratory-treated and aged soils. Stevens et al. [6]
indicated that, in two of the five soils tested, leaching increased
the EC50 values of lettuce (Lactuca sativa) significantly for Zn
by 1.4- to 3.7-fold, and, in three of the five soils, leaching
increased the EC50 values for lead by 1.6- to 3.0-fold. The shift
in EC50 values resulting from soil leaching was possibly due to
the direct concomitant toxicity of the counter-ion (NO�3 ) or an
indirect effect of the salinity on metal speciation in soil solution,
increasing its bioavailability. In addition, total Cu and Ni
toxicity thresholds for soil microbial processes (nitrification
potential, glucose-induced respiration, and maize residue
C-mineralization) derived in freshly spiked soils were shown to
be lower than threshold values measured in soils in which Cu and
Ni had equilibrated and leaching had removed excess salts [13,14].
836 Environ. Toxicol. Chem. 29, 2010 B. Li et al.
Recently, several models have been developed for European
and Australian soil to predict, based on soil properties, the
toxicity of Cu, Ni, and Zn [3–5,15–19]. However, it is unclear
whether these quantitative relationships derived from European
and Australian soils can be directly applied to soils in the
Southeast Asian region. Southeast Asian soils are known to
be dominated by variable charge surfaces and often are depleted
in organic matter [20], whereas European soils are dominated
by permanent charge minerals and have relatively high organic
matter contents. Australian soils used for toxicity model devel-
opment were also dominated by soils with permanent charge
minerals [5,19]. Also, it is unclear whether the models devel-
oped with a different range of soil characteristics can be
validated against each other. For laboratory ecotoxicity tests
of metal salts, one of the major problems is the effect of the
counter-ion on metal partitioning and on the organism used. For
example, Smit and Van Gestel [19] reported that equilibration
of the Zn contamination by percolating the soils with water
before use in the toxicity experiment strongly reduced the
difference in Zn toxicity between laboratory-treated and aged
soils. Stevens et al. [2] indicated that using soils spiked with
soluble metal salts was inappropriate for assessing risks from
gradual accumulation of metals in soils over the long term,
because toxicity was exaggerated by the salt effects on metal
partitioning, and metal salt anions also might contribute to the
toxic response. It was shown total Cu and Ni toxicity thresholds
for soil microbial processes (nitrification potential, glucose-
induced respiration, and maize residue C-mineralization)
derived in freshly spiked soils were lower than threshold values
measured in soils in which Cu and Ni had equilibrated and
leaching had removed excess salts, and this difference could be
attributed to decreasing Cu and Ni solubility with time and the
effect of leaching on pH and ionic strength of the soil solution
[20,21]. Hence, it was recommended that freshly spiked soils
should be leached and equilibrated before toxicity testing to
mimic more realistic exposure conditions.
Table 1. Selected physical and chemical prop
No. Site name SitepHa
(1:5)ECa
(mS/cm)e
(cm
S1 Haikou, Hainan 198550N, 1118290E 4.93 111S2 Qiyang, Hunan 268450N, 1118520E 5.31 74S3 Guangzhou, Guangdong 238100N, 1138180E 7.27 137S4 Jiaxing, Zhejiang, 308770N, 1208760E 6.70 159S5 Hangzhou, Zhejiang 308260N,1208250E 6.80 203S6 Chongqing, Sichuan 308260N, 1068260E 7.12 71S7 Yangling, Shanxi 348190N, 108800E 8.83 83S8 Zhengzhou, Henan 348470N, 1128400E 8.86 109S9 Zhangye, Gansu 388560N, 1008270E 8.86 152S10 Dezhou, Shandong, 378200N, 1168290E 8.90 112S11 Langfang, Hebei 398310N, 1168440E 8.84 5.7S12 Shijiazhuang, Hebei 388030N, 1148260E 8.19 302S13 Lingshan, Beijing 398550N, 116880E 7.48 93S14 Hulunber, Neimeng 468030N, 1228030E 7.66 888S15 Urumchi, Xinjiang 438950N, 878460E 8.72 227S16 Gongzhuling, Jilin 428400N, 1248880E 7.82 147S17 Hailun, Heilongjiang 478280N, 1268570E 6.56 153
a Measured in deionized water (soil:solution ratio 1:5).b Effective cation exchange capacity, determined using the unbuffered silver–thioc Determined by difference between total carbon and inorganic carbon content.d Oxalate extractable Fe.e Sedimentary method.
Southeast Asia is a region of expanding urban populations
and rapid industrialization. Use of metals in the region is
growing rapidly, and government agencies, regulators, and
scientists are trying to implement policies to ensure environ-
mental protection. Effective policy development is hampered
by a lack of sound local data building on recent scientific
advances in the understanding of metal behavior and toxicity
in soils, waters, and sediments. Current soil regulations con-
cerning maximal permissible concentrations of metals in soils
in China were developed in the 1980s, based on experiments in
which soils were spiked with metal salts under laboratory
conditions [22]. These regulatory levels based on total metal
concentrations are known to lead to several artefacts that often
lead to overconservative toxicity threshold values.
The aims of the present study were to determine the influ-
ence of soil leaching on Cu toxicity (EC10 and EC50) on 17
Chinese soils using root elongation as the ecotoxicity endpoint
and to develop quantitative relationships between soil phys-
icochemical properties and ecotoxicity thresholds. Further-
more, we wished to examine whether the relationships with
soil properties developed to predict Cu toxicity thresholds on
Chinese soils could be applied to European temperate soils and
vice versa.
MATERIALS AND METHODS
Soil properties and treatments
Seventeen soils with varying physical and chemical proper-
ties were selected from multiple locations in China (Table 1).
The soils were selected to be representative of the major soil
types, soil pH, and organic matter content of agricultural soils in
China. Soils (0–20 cm) were air dried, sieved to <2 mm, and
spiked with Cu (as CuCl2 in deionized water, 50 ml/kg) at eight
dose rates (control plus seven Cu doses: 12.5–800 mg Cu/kg for
soils with pH < 5, 25–1,600 mg Cu/kg for soils with pH from
5 to 7, and 37.5–2,400 mg Cu/kg for soils with pH > 7). After
erties of Chinese soils used in the study
CECb
olþ/kg)OCc
(%)CaCO3
(%)Total N
(%)Fe oxided
(mg/kg)
Clay/silt/sande
(<2mm/2–20mm/20mm–2 mm)
8.75 1.51 0.0 0.12 1,337 66/18/167.47 0.87 0.0 0.09 1,146 46/35/198.30 1.47 0.15 0.13 1,810 25/13/62
19.3 1.42 0.0 0.15 6,211 41/42/1712.8 2.46 0.0 0.25 4,980 39/36/2522.3 0.99 0.0 0.09 989 27/25/48
8.46 0.62 8.92 0.08 707 16/14/708.50 1.57 0.15 0.07 581 28/41/318.08 1.02 7.75 0.10 1,980 20/24/568.33 0.69 6.17 0.08 644 18/18/646.36 0.60 2.42 0.06 537 10/4/86
11.7 1.01 3.84 0.11 826 21/22/5722.7 4.28 4.27 0.37 1,697 20/21/5922.7 2.66 0.27 0.25 2,477 37/16/4710.3 0.87 5.08 0.10 600 25/23/5228.8 2.17 0.27 0.20 1,447 45/26/2933.6 3.03 0.0 0.25 3,298 40/27/33
urea method.
Copper phytotoxicity in soils Environ. Toxicol. Chem. 29, 2010 837
spiking, each soil treatment was thoroughly mixed on a plastic
sheet by hand until the disappearance of wet lumps. The
unspiked control samples were treated in a similar manner
using deionized water only.
Soils were then split into unleached and leached treatments.
The unleached soils were incubated for 2 d at 100% water-
holding capacity, air dried at 258C, sieved to <2 mm using
plastic mesh, and stored for less than 2 months before the plant
assay commenced. The soils were allowed to equilibrate over-
night and leached with artificial rain water consisting of
5� 10�4 M calcium chloride, 5� 10�4 M calcium nitrate,
5� 10�4 M magnesium chloride, 10�4 M sodium sulfate,
and 10�4 M potassium chloride at pH 5.9 [13]. Soils were
saturated with artificial rain water by placing the individual
spiked soil treatments in a perforated pot (bottom covered with
filter cloth, mesh size 140–150mm) in a bucket containing the
leaching solution. When the water level was above the soil
surface, more solution was gently poured directly into the pots
to increase the leaching volume to approximately two pore
volumes and allowed to equilibrate overnight. Finally, the pots
were taken out of the buckets and left to drain overnight.
Similarly to the unleached soil, the leached soil treatments
were then air dried at 258C, sieved to <2 mm using a plastic
mesh, and stored for less than 2 months before the plant assay
commenced. Total Cu concentrations were measured in both
leached and unleached soils as outlined below. The unleached
and leached soils were incubated and equilibrated for 7 d at 70%
of pF 1.9 before starting the root elongation assay.
Barley root elongation assay
The barley root elongation assay was based on International
Organization for Standardization (ISO) 11269-1 [23]. The assay
was performed in a growth chamber with 20-W tungsten lamps
to maintain a maximum light intensity of 24,000 lux/m2 for 5 d
under day (14 h at 228C)/night conditions (10 h at 188C). Soil
moisture content of treatments was maintained at 70% of pF 1.9
(60% water-holding capacity). Pregerminated barley seeds
(H. vulgare cv. Pinggu No.1) with radicles <2 mm in length
were planted in each of three replicate pots for each Cu treat-
ment. Parallel-sided pots described in ISO 11269-1 [23] were
replaced with a polyvinyl chloride cylinder with a inner diam-
eter of 35 mm and soil depth of 100 mm according to Rooney
et al. [4], and the number of seeds was reduced to four per pot.
Soils were packed into the cylinders to approximate the bulk
density of each soil (1.20–1.35 g/cm3). After 5 d, plant roots
were removed intact from soils, and the length of the longest
root on each plant recorded. Each replicate value represents the
mean root lengths of four plants per pot. The percentage of
barley root elongation with respect to controls (RE, %) in a test
medium was calculated using the equation
RE ¼ REt
REc� 100
where REt is the root length in the test medium and REc is the
root length in the control.
Soil analyses
Soil pH and electrical conductivity (EC) were determined in
a water suspension of soil using a 1:5 (soil: solution) ratio [24].
Total carbon and nitrogen were determined by high-temperature
combustion in an atmosphere of oxygen using a Leco CNS-
2000 (vario MAX CN elemental analyzer; Elementar Americas)
[25]. The inorganic carbon concentrations of soils were deter-
mined by measuring the liberated carbon dioxide contents
following the addition of hydrochloric acid [26]. Organic
carbon concentrations were determined as the difference
between total and inorganic carbon contents. The silver–
thiourea method [27] was used to measure eCEC and exchange-
able cations at the pH of the soil. Soil texture was analyzed
using the method of Bowman and Hutka [28]. Total Cu con-
centrations in soils were determined by digestion in aqua regia
(1:3 [v/v] nitric acid and hydrochloric acid) at 1408C, and
filtered digest solutions were analyzed by inductively coupled
plasma–optical emission spectroscopy (Spectroflame Modula;
Spectro) [29]. Reference samples were included as a quality
control (two replicates), and the recovery of certified reference
soil samples was 101% on average with a mean standard
deviation of 2.71%.
Data and statistical analysis
The dose–response data were fitted to a log–logistic curve
according to Haanstra et al. [30] in Microsoft1 Excel (Eqn. 1)
for each of the soils using a specialized curve-fitting macro
routine [5].
Y ¼ Y0
1 þ ebðX�MÞ (1)
where Y is relative barley root elongation (%) and X is common
logarithm of measured added Cu concentration (mg/kg), which
was the measured total Cu concentration in a soil minus
background Cu concentration. The zero metal dose to the control
soil was substituted to a very small value (0.1 mg/kg) to allow log
transformation before curve fitting. The M is the common
logarithm of ECx (effective concentration of added Cu that
decreases barley root elongation by a user-defined percentage,
i.e., EC10 or EC50), and Y0 and b are curve-fitting parameters.
The metal doses in soils causing 10% (EC10) and 50% (EC50)
inhibition in root elongation and their 95% confidence intervals
were derived from the fitted curve parameters and standard errors
according to Haanstra et al. [30].
Hormesis, a stimulation of response that can occur at low
doses followed by inhibition at higher doses, was modeled
according to Schabenberger et al. [31] using Tablecurve 2D
V5.01. The EC10 and EC50 values and their 95% confidence
limits were determined using Equation 2 as follows.
Y ¼ a þ bX
1 þ k100�k þ 100
100�k
� �bca
� �ed lnðX=cÞ (2)
where Y is relative barley root elongation (%); X is added Cu
concentration; a, b, c, and d are curve fitting parameters; and k is a
variable relating to effective restraining concentration. When kequals 10 or 50, the parameter c is defined as EC10 or EC50
values, respectively, and their confidence intervals can be
obtained through parameter fitting in Tablecurve 2D V5.01. The
parameter b is used to examine the significant level of hormesis
relative to the control; when 95% confidence intervals of
parameter b are above 0, the hormesis response is considered to
be significant.
838 Environ. Toxicol. Chem. 29, 2010 B. Li et al.
The adequacy of the predicted equations was checked by
examining the distribution of the residuals and ensuring the
minimum of calculated root mean squared error based on the
difference in observed values and predicted values. Stepwise
multiple linear regression analysis was employed in SPSS 12.0
for Windows1 to examine the relationships between toxicity
thresholds and soil properties [7]. Relationships were deemed
significant at p� 0.05.
RESULTS
Dose–response curves and toxicity thresholds in unleached andleached soils
The dose–response curves for effects of Cu on barley root
elongation in unleached and leached soils are shown in Figure 1,
Fig. 1. Dose–response curves of measured added Cu for barley root elongation in 1points, and lines are the fitted log–logistic curves. UL and L represent unleached
and predicted EC10 and EC50 values are presented in Table 2.
The EC10 values ranged from 31 to 444 mg Cu/kg in unleached
soils and from 38 to 715 mg Cu/kg in leached soils. EC50 values
ranged from 67 to 1,129 mg Cu/kg in unleached soils and from
88 to 1,255 mg Cu/kg in leached soils.
A significant (p � 0.05) increase in barley root elongation
(i.e., hormesis) with rate of Cu addition was observed in two of
the leached soils (i.e., S7 and S15; Table 2, Fig. 1). The
maximum hormesis response observed was a 10% and a
16% increase over the corresponding controls for the leached
S7 and S15 soils, respectively. Previous work on hormesis of Cu
for animal invertebrates or phytoplankton [32–36] showed that
the maximum stimulatory responses were generally approxi-
mately 30 to 70% greater than the controls, which is far larger
7 unleached and leached Chinese soils. Symbols represent all replicated dataand leached soils, respectively.
Table 2. Toxicity thresholds for Cu and leaching factors derived from barley root elongation tests in selected Chinese soils
Soils
Unleached soil Leached soil Significancea
EC10b
(mg/kg)EC50c
(mg/kg)EC10b
(mg/kg)EC50c
(mg/kg) EC10 EC50 LF10d LF50eD10f
(mg/kg)D50g
(mg/kg)
S1 64 (57–72)h 79 (75–83) 54 (43–66) 105 (94–115) NS 0.84 1.33 11 26S2 31 (22–45) 67 (60–76) 38 (29–51) 88 (78–100) NS 1.22 1.31 7 21S3 175 (141–218) 404 (339–482) 177 (134–234) 425 (363–497) NS NS 1.01 1.05 2 21S4 110 (76–161) 277 (237–323) 121 (85–172) 297 (258–342) NS NS 1.09 1.07 11 20S5 130 (96–178) 401 (349–461) 145 (100–210) 445 (378–524) NS NS 1.12 1.11 15 44S6 133 (116–152) 269 (254–284) 187 (162–216) 290 (277–304) NS 1.40 1.08 54 21S7i 82 (56–118) 524 (443–619) 154 (124–186) 467 (409–525) NS 1.88 0.89 72 57S8 76 (46–126) 410 (331–507) 96 (70–132) 338 (297–385) NS NS 1.27 0.83 20 72S9 151 (115–197) 578 (517–647) 215 (108–322) 627 (520–757) NS NS 1.43 1.08 64 49S10 86 (55–136) 421 (342–518) 187 (142–245) 513 (459–573) NS 2.17 1.22 101 92S11 80 (57–110) 229 (198–254) 112 (89–143) 282 (256–310) NS 1.41 1.23 32 53S12 84 (54–128) 307 (244–386) 106 (76–148) 327 (272–369) NS NS 1.26 1.06 22 10S13 444 (331–595) 1,073 (960–1,188) 715 (628–815) 1,255 (1,188–1,332) 1.61 1.17 271 182S14 221 (179–274) 589 (538–645) 647 (544–770) 1,192 (1,130–1,257) 2.92 2.02 426 603S15i 137 (101–187) 545 (477–622) 209 (166–254) 534 (466–603) NS NS 1.52 0.98 72 11S16 393 (293–526) 1,129 (1,007–1,267) 287 (173–477) 1,060 (892–1260) NS NS 0.73 0.94 106 69S17 325 (273–388) 644 (603–687) 446 (389–514) 780 (722–842) 1.37 1.21 121 136Mean — — — — — — 1.43 1.15 83 87
a Difference between unleached and leached EC10 (EC50) using t test: NS¼ not significant (p> 0.05); significant (p� 0.05).b Effective concentration (EC) of added Cu that caused a 10% reduction in the endpoint.c Effective concentration of added Cu that caused a 50% reduction in the endpoint.d LF10¼ leached EC10 values/unleached EC10 values.e LF50¼ leached EC50 values/unleached EC50 values.f Absolute difference between leached and unleached EC10 values.g Absolute difference between leached and unleached EC50 values.h Mean and ranges given in parentheses as �95% confidence interval.i Hormesis in leached soils.
Copper phytotoxicity in soils Environ. Toxicol. Chem. 29, 2010 839
than observed in S7 and S15 soils. A hormetic effect for an
essential element (i.e., Cu) is not surprising [37]; hence, the
toxicity thresholds for S7 and S15 soils were fitted by including
hormesis in the dose–response curves.
The influence of leaching on Cu toxicity was found to be
variable among the soils examined in the present study
(Table 2). A significant difference was found between
unleached and leached toxicity values (EC10 and EC50) for
approximately 35% of the soils examined in the present study.
Overall, leaching increased toxicity thresholds by an average
factor of 1.43 for EC10 and 1.15 for EC50 values, respectively.
However, the average absolute differences in toxicity thresh-
olds between leached and unleached soils were 83 mg/kg for
EC10 and 87 mg/kg for EC50 (Table 2).
Multiple linear regression models to predict Cu toxicity in soils
The significant (p � 0.05) linear regression models to
predict Cu toxicity thresholds (EC10 and EC50 values) in
relation to soil properties are presented in Table 3. In the
present study, logarithm of eCEC was found to be the best
single factor in predicting Cu toxicity in unleached EC10
(r2¼ 0.58), leached EC10 (r2¼ 0.46), and leached EC50
(r2¼ 0.32) values; and soil pH was found to be the best single
factor in predicting unleached EC50 (r2¼ 0.35) values. From
the results of single regression analysis, no single factor was
found to explain >35% of variance in logarithm of EC50 values
in leached and unleached soils. However, when two factors (soil
pH and log OC or log eCEC) were introduced into the regres-
sion models, the predictability of regression models was
improved significantly, with r2> 0.65 for log EC10 and with
r2� 0.75 for log EC50; and, when incorporating three factors
(soil pH, log OC, along with log eCEC), the multiple linear
regression models were further improved, with r2� 0.81 for log
EC10 and r2� 0.88 for log EC50 across soils. These results
suggest that soil pH, OC, and eCEC are simultaneously respon-
sible for predicting Cu toxicity in Chinese soils. Other soil
factors, such as clay content, calcium carbonate (CaCO3) con-
tent, and iron oxide concentrations did not significantly improve
the models, so they were excluded from regression equations.
DISCUSSION
Influence of soil leaching on Cu toxicity
In the present study, soil leaching was found to decrease
significantly (p � 0.05) the toxicity (EC10 and EC50 values) of
Cu in approximately 35% of the soils examined. Usually,
effects of concentrations of counter-ion (chloride) in soil sol-
ution on root growth or on metal partitioning would be expected
to be greater at higher Cu doses. From the average differences
(83 and 87 mg/kg) of leached EC10 (or EC50) and unleached
EC10 (or EC50) in Table 2, it was found that, in general, the
effect of leaching on EC50 values was slightly greater than that
on EC10. The analysis of soil solutions (data not shown)
showed that, depending on soil type, leaching removed
water-soluble Cu, chloride, calcium, and sodium and increased
soil solution pH by up to 0.75 units, likely because most of the
soils had net negative charge. Also, in the present study, more
Cu (2.23–4.80% of total Cu) was found to be leached from soils
with pH �7.5 and OC contents �1.5% than from soils with pH
�7.5 and OC content >2.0% (0.02–0.39% of total Cu). These
Table 3. Simple and multiple linear regressions for Cu phytotoxicity values (EC10 and EC50) in unleached and leached soils from barley root elongation testsbased on total metal concentrations (mg/kg) and soil properties
Regression equation r2 p
Unleached soil (n¼ 17) — — — —1 log EC10¼ 1.993 þ 0.849 log OC 0.52 0.001 — —2 log EC10¼ 1.028 þ 0.977 log eCEC 0.58 <0.001 — —3 log EC10¼ 1.134 þ 0.110 soil pH þ 1.044 log OC 0.70 0.011 <0.001 —4 log EC10¼ 0.540 þ 0.106 soil pH þ 0.629 log OC þ 0.617 log eCEC 0.81 0.005 0.011 0.0155 log EC50¼ 1.785 þ 0.717 log eCEC 0.26 0.037 — —6 log EC50¼ 1.388 þ 0.157 soil pH 0.35 0.012 — —7 log EC50¼ 0.025 þ 0.197 soil pH þ 0.956 log eCEC 0.79 <0.001 <0.001 —8 log EC50¼ 0.725 þ 0.227 soil pH þ 0.964 log OC 0.83 <0.001 <0.001 —9 log EC50¼ 0.241þ 0.224 soil pH þ 0.627 log OC þ 0.502 log eCEC 0.89 <0.001 0.004 0.017— Leached soil (n¼ 17) — — — —10 log EC10¼ 2.133 þ 0.777 log OC 0.34 0.014 — —11 log EC10¼ 1.165 þ 0.972 log eCEC 0.46 0.003 — —12 log EC10¼ 0.853 þ 0.164 soil pH þ 1.068 log OC 0.66 0.003 <0.001 —13 log EC10¼�0.029 þ 0.134 soil pH þ 1.134 log eCEC 0.69 0.006 <0.001 —14 log EC10¼ 1.177 þ 0.159 soil pH þ 0.597 log OC þ 0.702 log eCEC 0.83 0.001 0.038 0.02215 log EC50¼ 1.632 þ 0.131 soil pH 0.26 0.037 — —16 log EC50¼ 2.541 þ 0.649 log OC 0.26 0.037 — —17 log EC50¼ 1.763 þ 0.783 log eCEC 0.32 0.017 — —18 log EC50¼ 0.214 þ 0.174 soil pH þ 0.994 log eCEC 0.75 <0.001 <0.001 —19 log EC50¼ 0.933 þ 0.206 soil pH þ 1.014 log OC 0.81 <0.001 <0.001 —20 log EC50¼ 0.447 þ 0.202 soil pH þ 0.675 log OC þ 0.505 log eCEC 0.88 <0.001 0.003 0.022
c r2¼ coefficient of determination (percentage of variance accounted for by the regression model); p¼ significant level; eCEC¼ effective cation exchangecapacity; OC¼ organic carbon content; EC50¼median effective concentration value; EC10¼ 10% of effective concentration value.
840 Environ. Toxicol. Chem. 29, 2010 B. Li et al.
results are similar to those previously published by Oorts et al.
[13], who observed more Cu (up to 50% of the added Cu) loss
from an acidic sandy soil after leaching than alkaline and high-
OC soils (less than 10% at the highest Cu doses). Almost all
leached EC50 values were less that 1.5-fold greater than
unleached EC50 values, except for the S14 soil that had a high
initial electrical conductivity (888mS/cm).
Leaching increased EC50 values for barley root elongation
in the present study by a median factor of 1.08, which was
lower than that (1.3) for the soil microbial assays (potential
nitrification rate, glucose-induced respiration, and maize
residue C-mineralization) performed by Oorts et al. [13].
Generally speaking, the effect of leaching on increasing
EC50 values was not significant for all Chinese soils except
for S14 soil with LF50 2.02. However, the influence of leaching
on Cu toxicity thresholds could not be neglected for the
high-saline soils.
Predicting Cu toxicity in soils
Multiple linear regression models developed using soil pH,
OC, and eCEC to predict unleached and leached EC50 values
(r2� 0.88) for Cu were in good agreement with measured
toxicity thresholds (Fig. 2). The predicted EC50 values were
found fully to lie within a twofold range of the measured values.
The finding in the present study of the importance of soil pH,
OC, and eCEC in predicting Cu toxicity is consistent with the
findings previously published by Daoust et al. [3], Broos et al.
[19], and Oorts et al. [13]. In the study by Daoust et al. [3], it was
reported that increases in soil pH and organic matter content
contributed to a decrease in Cu toxicity for barley root elonga-
tion (r2adj ¼ 0.91) using 10 artificial soils. Broos et al. [19] found
that Cu toxicity to substrate-induced nitrification in 12 Aus-
tralian soils was affected first by soil pH (r2adj ¼ 0.73) and
equally second by potential CEC (r2adj ¼ 0.63) and clay content
(r2adj ¼ 0.63). Oorts et al. [13] showed that OC (r2¼ 0.57) and
pH (r2¼ 0.52) were the two most important soil properties in
predicting Cu toxicity on glucose-induced respiration and maize
residue mineralization in 19 European soils. It is widely rec-
ognized that soil pH is one of the most important soil properties
that determines the partitioning of trace metals in soils [38,39]
and that partitioning of Cu is associated closely with organic
matter contents in soils [40].
Comparison of Cu phytotoxicity models
The models developed for Chinese soils were compared with
those published previously by Rooney et al. [4] for European
soils (Fig. 2). Because Rooney et al. [4] assessed Cu phytotox-
icity in unleached soils, we compared the relevant models for
unleached soils from the present study (Eqn. 9 in Table 3).
It was found that the models developed in this study using
soil pH, log OC content, and log eCEC for Chinese soils could
be applied to predict accurately the phytotoxicity of Cu in the
European soils (Fig. 2a). However, the Cu toxicity models
developed using eCEC and Fe oxide (e.g., log EC50¼ 0.803
þ 0.542 log eCEC þ 0.279 log Fe oxide) by Rooney et al. [4] for
European soils were found to poorly predict the phytotoxicity of
Cu in Chinese soils (Fig. 2b), although the method of eCEC
measurement in the paper of Rooney et al. [4] was the same as
that in the present study; both were based on the silver–thiourea
method [27]. Hence, the data from Rooney et al. [4] were
reanalyzed, and a better regression equation between toxicity
thresholds (EC50) and soil pH and OC content was obtained for
European soils (log EC50¼ 0.803 þ 0.216 soil pH þ 0.633 log
OC, r2¼ 0.93). When this equation was used to predict the
toxicity thresholds for Chinese soils in this present study, a
comparatively good prediction for log EC50 values was also
produced, with an r2 of 0.78. These results indicated that soil pH
and OC content are two important factors in predicting toxicity
thresholds in both Chinese and European soils. The importance
of eCEC in predicting EC50 values for the European soils was
Fig. 2. Measured toxicity thresholds versus predicted toxicity thresholds from barley root elongation tests derived from regression Equation 9 (log EC50¼ 0.241þ 0.224 soil pHþ 0.627 log OCþ 0.502 log eCEC) in the present study (a) and the regression Eqn. (log EC50¼ 0.803þ 0.542 log eCECþ 0.279 log Fe oxide) ofRooney et al. (b). The YE represents Rooney’s predicted thresholds, and YA represents unleached predicted thresholds in this paper. EC50¼median effectiveconcentration; OC¼ organic carbon; eCEC¼ effective cation exchange capacity;
Copper phytotoxicity in soils Environ. Toxicol. Chem. 29, 2010 841
probably because the variation of eCEC in the European soils in
the study of Rooney et al. [4] came mainly from the variances of
soil clay, OC content, and soil pH (r2¼ 0.90). However, for
Chinese soils in the present study, the eCEC could not be
predicted well by soil pH, clay, and OC content, although it
was correlated only with OC content (r2¼ 0.46) because of the
differences in clay mineralogy in the range of Chinese soils.
Hence, the regression model based on soil eCEC and iron oxide
developed using the European soils mostly was not applicable
to Chinese soils. Furthermore, there were two European soils
with high OC content (Rhydtalog and Zegveld, 12.9 and
23.3%), which were far beyond those in Chinese soils used
in the present study. To ensure the accuracy in validating the
mutual regression models derived from European and Chinese
soils, the two high-OC soils were excluded from regression
models, so the ranges of soil properties for European [4] and
Chinese soils reported in the present study are approximately
consistent. It was revealed that soil pH, OC content, and eCEC
were the three most significant parameters explaining EC50
values based on the 16 European soils (log EC50¼ 0.881 þ0.174 soil pH þ 0.674 log OC þ 0.172 log eCEC, r2¼ 0.96).
When the equation with three parameters (pH, log OC, and log
eCEC) derived from European soils [4] was used to predict the
log EC50 values for Chinese soils, the predictability for toxicity
thresholds was improved from an r2 of 0.78 to an r2 of 0.87,
compared with results from the equation with two parameters
(pH and log OC). Broos et al. [19] also reported regression
models based on the soil microbial toxicity assays for the
European and Australian soils were quite similar after omitting
three high-OC soils. Therefore, it was concluded that soil pH
and OC content are two very important factors in controlling Cu
toxicity to barley root elongation in European and Chinese soils,
and eCEC should also be considered because of the significant
improvement for regression models for Chinese soils (Table 3)
and European soils. Finally, an equation based on soil pH, OC
content, and eCEC combining Chinese and European soils
together was established to predict toxicity thresholds (log
EC50¼ 0.708 þ 0.205 soil pH þ 0.748 log OC þ 0.169 log
eCEC), with a high r2 of 0.92. Thus, the regression model could
be applied to predict the phytotoxicity of Cu in both Chinese and
European soils accurately by considering soil pH, OC content,
and eCEC.
CONCLUSIONS
Toxicity of Cu to barley root elongation across a wide range
of Chinese soils varied 17- to 14-fold in unleached and leached
soils, respectively, indicating that soil physicochemical proper-
ties strongly influenced phytotoxicity. Soil leaching decreased
Cu toxicity significantly in approximately 35% of the soils
examined and was found to be greatest in saline S14 soil.
Freshly spiked soils should be leached and equilibrated before
toxicity testing to mimic more realistic field exposure condi-
tions. Multiple linear regression analysis showed that soil pH,
OC, and eCEC were the three most useful predictors of Cu
toxicity to barley root elongation and could explain >80% of
the variation in phytotoxicity across soils. These empirically
based models using simple, easily measured (and already
mapped) soil properties have the potential to improve risk
assessments significantly for Cu in soils using soil-specific
ecotoxicity thresholds.
Acknowledgement—The authors thank the Natural Science Foundation ofChina (projects 20677077 and 40620120436), the International CopperAssociation, Rio Tinto, and the Nickel Producers Environmental ResearchAssociation for financial support. The authors also thank the nationallong-term soil experimental stations in China for soil collection and CathyFiebiger for technical assistance.
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