Is the computed speciation of copper in a wide range of Chinese soils reliable?

Abstract

Free Cu species in soils is a key issue to its bioavailability. However, predictive models for Cu speciation across a wide range of soils were still unavailable. In this study, Cu speciation in 34 contaminated soil samples were investigated via analytical technique and predictive models. The results showed that most of free Cu²⁺ was underestimated when using default log K_CuFA and 65% active fulvic acid as inputs in models of WHAM VI and NICA-Donnan. The best prediction was found when using either adjusted active fulvic acid from 10% to 125% for WHAM VI or from 15% to 65% for NICA-Donnan model with the RMSE < 0.32 and r² > 0.96. In contrast, NICA-Donnan demonstrated a slightly stronger binding for Cu than WHAM VI due to extra 26% of samples was underestimated. This work presents a comprehensive database of Cu speciation and an effective attempt of free Cu²⁺ prediction in a wide range of Chinese soils.

Keywords:

• Copper activity
• soil pore water
• distribution
• model prediction

1. Introduction

It is well verified that the total metal concentration in soils is not a good indicator for its toxicity [1,2] which is mainly controlled by the metal in soil solution [3]. In solution systems, the free metal ion was hypothesized to control toxicity response of organisms [4]. It is relatively easy to determine the free metal speciation in fresh waters by analytical techniques or chemical models considering that properties of humic substances are comparatively stable and easily measureable [5]. In contrast, metal speciation in soils is more difficult to be confirmed due to the complexity, mainly in nature and sources of soil organic carbon (SOC) [6,7].

Copper (Cu) as an important trace element has been studied carefully in aquatic and terrestrial systems around the world. Also, it is one of the most affinity metals to humic substances compared with others [8–10]. It was reported that humic complexed form accounted for up to 99.9% of total Cu in solution [3,8,11] indicating that Cu speciation is extremely sensitive to humic substances heterogeneity in quantity and quality [12,13]. The dissolved organic matter (DOM) is a complex mixture of reactive molecules and its reactive part is dominated by humic substances, comprising humic acids (HA), fulvic acids (FA) and hydrophilic acid [8], and its effect on Cu speciation in soil systems are still unclear.

Free Cu²⁺ activity is considered as an important factor on evaluation of Cu toxicity [3,4,14] and could be determined via electrochemical analytical technique [15–17] or chemical model prediction. Given the intrinsic differences among models, the most reliable and well-tested chemical speciation models are recognized as WHAM (V-VII) [18,19] and NICA-Donnan [20] which were developed to describe the electrostatic and specific interactions between the charged humic substances and ions. Although both of models are considered as providing good fit to most data, but due to their different formulations, their predictions do not necessarily converge [21]. Also, a key question about evaluation of chemical speciation models is: how well do model predictions compare against speciation analytical technique? The uncertainties using these models are whether humic substances considered in the modelling are truly representative of the DOM in solution systems.

In the present study, Cu speciation was investigated based on a wide range of true soil pore water samples across China due to Chinese soils ranged widely in soil physicochemical properties [22] and Cu speciation as well as its partition in these soils are scarcely reported. The Visual MINTEQ incorporated with NICA-Donnan [23] and WHAM VI were used to estimate Cu speciation in soil pore waters and compare with results from a cupric ion-selective electrode (Cu-ISE) technique. The aim of this study is to find a good predictable model for free Cu²⁺ activity in Chinese soils and further applicability to the Cu risk assessment in terrestrial systems. It is very meaningful to undertake this exploration in Chinese soils not only because of the severely increasing pollution situation in China [24] but also presenting a comprehensive database of Cu speciation and modelling worldwide.

2. Materials and methods

2.1. Soil samples

Seventeen soil samples from multiple agricultural locations across China were collected. The bulk soil properties were measured and displayed in Table 1. Total concentrations of Cu in soils were determined by inductively coupled plasma optical emission spectroscopy (ICP-OES; Spectro Flame Modula, Spectro), following digestion with aqua regia [25]. Soil pH was determined in a mixture of 1:5 soil:water (w/v). Total concentrations of carbon were determined using a LECO combustion analyzer (CNS 2000). The cation exchange capacity (CEC) was measured using unbuffered silver-thiourea method. Two contaminated Cu concentrations for each soil were obtained through adding designed Cu chloride (CuCl₂) amounts. The concentrations selected were referenced to barley root elongation assay in 17 Chinese soils with toxicity thresholds ranging from EC₅ (5% inhibition concentration) to EC₉₀ (90% inhibition concentration) reported by Li et al. [26], and a lower and higher inhibition concentrations of Cu to barley root elongation were adopted for each soil, respectively. All Cu contaminated soil samples were air-dried, sieved to 2 mm plastic screen and kept six months in room temperature before use.

Table 1. Soil solid-phase properties.

No.	Site	Location (longitude, latitude)	pH (1:5)	EC (μS cm^−1)	CEC (cmol^+ kg^−1)	SOC (%)	Clay (%, <2 μm)	Total Cu (mg kg^-1)
S1	Haikou	19°55′N,111°29′E	4.93	111	4.77	1.51	66	50.5
S2	Qiyang	26°45′N,111°52′E	5.31	74	5.52	0.87	46	22.2
S3	Hailun	47°28′N, 126°57′E	6.54	153	29.5	3.03	40	18.3
S4	Jiaxing	30º77′N,120°76′E	6.70	159	21.4	1.42	39	31.3
S5	Hangzhou	30°26′N,120º25′E	6.80	203	14.9	2.46	41	47.2
S6	Chongqing	30°26′N,106°26′E	7.12	71	26.7	0.99	27	10.7
S7	Guangzhou	23°10′N, 113°18′E	7.20	137	10.0	1.47	25	10.8
S8	Lingshan	39°55′N, 116°8′E	7.48	93	25.4	4.28	20	14.2
S9	Hulunber	46°03′N, 122°03′E	7.66	888	24.1	2.66	37	14.7
S10	Gongzhuling	42º40′N, 124º88′E	7.82	147	29.1	2.17	45	21.4
S11	Shijiazhuang	38°03′N, 114°26′E	8.19	302	16.8	1.00	10	16.5
S12	Urumchi	43º95′N, 87º46′E	8.72	227	14.8	0.87	25	26.5
S13	Yangling	34°19′N,108°0′E	8.83	83	14.4	0.62	16	21.5
S14	Langfang	39°31′N, 116°44′E	8.84	5.7	10.5	0.6	21	9.51
S15	Zhengzhou	34º47′N,112º40′E	8.86	109	13.3	1.57	28	13.4
S16	Zhangye	38°56′N,100°27′E	8.86	152	13.1	1.02	20	27.6
S17	Dezhou	37°20′N, 116°29′E	8.90	112	14.3	0.69	18	15.0

2.2. Extraction and analysis of soil pore water

Soil pore-water was extracted for contaminated soils using the method of Thibault and Sheppard [27]. The soils were rewetted to maximum water holding capacity (MWHC) with Milli-Q deionized water and incubated for 24 h at 25 °C in 25-ml filtration tubes. Soils were then centrifuged at 3500 RCF for 45 min with the filtration tube inside a 50-ml centrifuge tube that contained a small spacer in the bottom [1]. Extracted solutions were then centrifuged at 12,500 RCF for 45 min and filtered through a 0.45 μm filter. The total number of filtration tubes required generally varied from 10 to 15, giving a total volume of 60–100 ml, which was combined and then split into two duplicate samples for Cu-ISE and subsequent analysis. The extracted solutions were stored at 4 °C prior to analysis. The analysis of the solutions included dissolved organic carbon (DOC) (FormacsTOC/TNAnalyser), major cation concentration by ICP-OES [25], and major inorganic anion concentrations (i.e. CO₃²⁻, Cl⁻, NO₃⁻ and SO₄²⁻) by ion chromatography (IC; Dionex4000i, AS9-HC column).

2.3. Measurement of free Cu²⁺ activity in pore water

The free Cu²⁺ activity was determined with an Orion 94–29 Cu-ISE combination electrode with an AgS/CuS double junction reference electrode at 20 °C. The CuS crystal of the cupric electrode was polished for 2 min with 2 grades of alumina (Al₂O₃) (first polished with 0.3 μm followed by the 0.05 μm) before use. The electrode was conditioned in a 500 ppb Cu standard (approximate pH 4) overnight before measurements for improving electrode sensitivity. The Cu-ISE calibration solutions contained 10 of different Cu concentrations as follows: 5 ml of Cu(NO₃)₂ (1 mmol L⁻¹), 5 ml of iminodiacetic acid (0.01 mol L⁻¹), 5 ml of potassium acid phthalate (KHC₈H₄O₄) (0.025 mol L⁻¹), 5 ml of KNO₃ (0.1 mol L⁻¹) in each standard, 10 of various volume of NaOH (0.02 mol L⁻¹) (i.e. 1, 4, 6, 8, 9, 9.2, 9.3, 9.4, 9.7 and 10 mL) and corresponding Milli-Q water for a total volume of 50 mL to obtain 10 different concentrations of Cu standards with gradually decreased pH (between 9.3 and 4.3). The 10 standard solutions for the calibration were deemed sufficient to get a representative linear regression equation with r² ≥ 0.99, and prepared the previous day to allow chemical equilibration overnight.

The free Cu²⁺ activities in pore waters were measured immediately after extraction, and in order of increasing free Cu²⁺ ion for better reproducibility and faster analysis. The pH was measured using micro electrode pH (Thermo Fisher Scientific Inc., MA, USA) [28]. The electrode potential (EP, mV) determined by Cu-ISE was then plotted against free Cu²⁺ activity (expressed as logarithm-transformed base) calculated by Visual MINTEQ which was particularly added stability constants of iminodiacetic acid according to Rachou et al. [16]. This showed a liner relationship with the following regression equation.The slope of the electrode response obtained in the calibration (28.6 mV/free Cu²⁺) was close to the theoretical Nernstian slope of 29.08 at 20 °C.

2.4. Calculation of Cu speciation by models

2.4.1. WHAM VI

The program was used to calculate free Cu²⁺ activities in soil pore water using measured parameters of pH, DOC concentration (g L⁻¹), total soluble cations (i.e. Cu, Ca, Mg, K and Na) and anions (i.e. Cl⁻, , , and ) as inputs. Default parameters for active proton and metal-binding by fulvic acid (FA, 50% C) were used and 100% DOM in soil solutions was defined as FA. In order to improve the prediction of free Cu²⁺ activity, reasonable assumptions on DOM active fraction (%AFA) in models and binding constants of FA to Cu (log K) were taken, which was actually a recalibration of the model localization [29]. The best agreement between measured and predicted free Cu²⁺ activity, expressed as log (free Cu²⁺ activity) was obtained according to the minimal root mean squared error (RMSE). The 65% AFA (35% FA as inert) as a starting value [30] for modelling, the FA input (g L⁻¹) for running the models was thus obtained by DOC × 2 × %AFA and dissolved cations and anions were expressed in total molar concentration (mol L⁻¹). As colloidal Fe(OH)₃ and Al(OH)₃ can easily pass 0.45 μm filters, which result in measured ‘dissolved’ Fe and Al higher than the true condition that can interact with DOM and other ligands [12,30], consequently, free Fe³⁺ and Al³⁺ activities were used as input and estimated from the solubility constants of colloidal Al(OH)₃ (log K_sol = 8.5) and Fe(OH)₃ (log K_sol = 3.0) at 20 °C [31]. The temperature of 293 K and the atmospheric partial pressure of CO₂ (P_CO2) of 10^−3.5 atm were used in models.

2.4.2. NICA-Donnan model

To ensure consistency, thermodynamic data (for inorganic metal complexes) from MINTEQA2 version 4 (http://www.epa.gov/ceampubl/mmedia/minteq/supple1.pdf) was used in databases of both humic substances. The temperature and P_CO2was used the same values in the two models. The default stability constants and 65% AFA were also used as the start values for Cu speciation computation. Similar to WHAM VI, the %AFA and stability constants were adjusted to get the best agreement between measured and predicted free Cu²⁺ activity.

3. Results

3.1. Soil solid-phase properties

Selected soil solid-phase properties were shown in Table 1. The analytical data showed that there was a wide variation in soil characteristics. The soil pHs ranged from 4.93 to 8.90 (70% of soil with pH > 7.0), soil organic carbon content (SOC) from 0.60 to 4.28%, CEC from 4.77 to 29.5 cmol⁺ kg⁻¹, and clay content from 10 to 66%. The Cu concentration varied from 31 to 1485 mg kg⁻¹ with 48-fold difference (Table 2).

Table 2. Soil pore water properties.

	Total Cu (mg kg^−1)	pH	EC (μS cm^−1)	DOC (mg L^−1)	Soluble cation (mmol L^−1)				Total Cu (μmol L^−1)	Log (free Cu^2+ activity) (mol L^-1)	%free Cu^2+ activity	Log (Kd) (L kg^−1)
					Ca^2+	Mg^2+	K^+	Na^+
Minimum	30.8	4.39	1148	102	2.10	0.63	0.07	0.48	1.10	−5.30	0.001	2.10
Quartile 1	151	6.89	2683	180	11.04	1.73	0.21	1.08	5.83	−7.80	0.006	2.50
Median	301	7.42	3367	246	14.46	4.83	0.38	2.13	10.2	−8.48	0.043	2.64
Mean	450	7.11	4816	299	17.32	6.77	0.68	11.4	14.1	−8.54	2.73	2.69
Quartile 3	674	7.80	5091	350	21.47	6.79	1.10	6.14	19.7	−9.69	0.435	2.89
Maximum	1485	8.07	17260	765	42.68	37.50	2.58	120	48.9	−11.5	39.8	3.31

3.2. Soil pore water properties and Cu distribution

Selected soil pore water properties were listed in Table 2. Soil pore water pH varied from 4.39 to 8.07. The concentrations of DOC ranged from 102 to 765 mg L⁻¹ with an average of 299 mg L⁻¹. Total concentrations of soluble Cu changed within two orders of magnitude, from 1.10 to 48.9 μmol L⁻¹ for 34 samples. The free Cu²⁺ activity (log (free Cu²⁺)) varied from −11.5 to −5.30 by up to six orders of magnitude. The percentage of free Cu²⁺ activity to total soluble Cu in solution was diverse from a value of <0.1 to 39.8% with an average of 2.73%. Most of fractions were less than 0.5% except for a number of acidic soils with soil pore water pH < 6.5.

The data in Figure 1 showed that total soluble Cu in soil pore water was correlated significantly to total Cu concentrations in soil with r² value of 0.55 and DOC in soil pore water to a lesser extent with r² of 0.45. The stepwise regression analysis showed that the combined the factors of total Cu concentrations in soils and DOC contents in soil pore water could explain 63% variances of soluble Cu in soil pore water (Equation 1 in Table 3).

Figure 1. Relationships between (a) soluble Cu in pore water and total Cu in soil. (b) soluble Cu and DOC in pore water.

Note: The solid line represents regression line.

Table 3. The regression equations between Cu speciation and soil or soil pore-water properties.

No.	Regression equations	r^2	N
1	log (soluble Cu) = 0.515 log (total Cu in soil) + 1.223 Log (DOC) – 2.976	0.63	34
2	Log (Kd) = 0.475 Log (total Cu in soil) – 0.666 Log (DOC) + 3.124	0.28	34
3	Log (Kd) = −0.452 Log (soluble Cu) + 0.808 Log (CEC) + 2.179	0.42	34
4	Log (Kd) = 0.617 log (total Cu in soil) –1.391 log (DOC) + 1.545 log (CEC) + 2.549 for soils with pH >7.20	0.88	20
5	Log (free Cu^2+ activity) = −1.171 pH + 1.639 log (total Cu in soil) – 4.303	0.65	34
6	Log (free Cu^2+ activity) = −1.310 pH + 1.60 log (soluble Cu) – 0.783	0.65	34
7	Log (%free Cu^2+ activity) = −1.216 pH + 7.134	0.64	34

The distribution of Cu between the solid and aqueous phases was described by a partition coefficient, K_d (solid phase concentration/solution phase concentration) [32] were calculated and shown in Table 2. The range of K_d values varied from 126 to 2064 L kg⁻¹. The regression analysis between K_d and soil or soil pore water properties based on logarithmic transferred data showed that dissolved Cu content and CEC were two important factor affecting K_d with an r² value of 0.42 (Equation 3 in Table 3) and it was difficult to find a better correlation while took all soil pHs into consideration. However, there was an improved empirical prediction on K_d using total Cu concentrations in soil, CEC and DOC concentration when only analyzed the soils with pH > 7.2 (Equation 4 in Table 3). As soluble Cu was controlled by total Cu concentrations in soil and DOC in soil pore water, therefore, it was basically consistent for prediction on K_d for Equations 3 and 4.

3.3. Empirical prediction of free Cu²⁺activity using soil or soil pore water properties

The free Cu²⁺ activities were influenced mainly by two factors: soil pore water pH and total Cu concentration (Equations 5 and 6, Table 3). The regressions in Table 3 explained reasonable proportions (65%) of the variability in free Cu²⁺. Other soil parameters, including SOM, CEC and clay content were excluded in explaining the variability in free Cu²⁺. The percentage of free Cu²⁺ to total soluble Cu was negatively related to soil pore water pH with r² of 0.64 (Equation 7 in Table 3), which explained why the soils with low pH values had a higher percentage of free Cu²⁺ in pore water (3.64–39.8%). Also, Lofts et al. [33] showed a strong relationship between log (free Cu²⁺ activity) and pH, which is similar to the results in our study. These results from Sauvé et al. [34] (c.f. log (free Cu²⁺) = −1.40 pH + 1.70 log (total Cu) – 3.42, r² = 0.85) and Vulkan et al. [14] (c.f. log (free Cu²⁺) = −1.79 pH + 1.47 log (total Cu) + 0.53, r² = 0.89) also confirmed the effect of pH and total Cu content on free Cu²⁺, despite a slight difference on coefficients of equations.

3.4. Prediction of Cu speciation using models

3.4.1. WHAM VI

The agreement between calculated and measured free Cu²⁺ activity was evaluated using match degree of corresponding log (free Cu²⁺ activity) values due to free Cu²⁺ activity varied up to 6 orders of magnitude in soil pore water and almost 80% of free Cu²⁺ activity accounted for less than 0.5% of total soluble Cu. As the data shown in Figure 2(a), when 65% of AFA and default binding constants log K_CuFA as input values, 56% of free Cu²⁺ activity in soil pore water were severely underestimated by model especially for soils with lower pH values. In order to get the better agreement of free Cu²⁺ activity, adjusting AFA ratio between 10 and 30% for these 19 samples underrated and maintaining 65% AFA for the other 15 samples, the regression between measured and calculated free Cu²⁺ activity was significantly improved with decreased RMSE 0.42 and increased r² value of 0.94. Also, there were 4 in 34 samples (i.e. S7 and S11) overestimated by model for free Cu²⁺ activity (see Figure 2(a)), enhancing the AFA from 65 to 100% or 125% for the four samples could benefit the fit further (Table 4). The best agreement between measured and predicted free Cu²⁺ activity was achieved using adjusted AFA ratio ranging from 10 to 125% for all samples with the smallest RMSE 0.23 and the highest r² of 0.98 (see Figure 2(b) and Table 4). Generally, it was found that %AFA was positively correlated to soil pore water pH despite a few exceptions. It varied from 10 to 30% (15% on average) for samples with pore water pH < 6.5, from 10 to 65% (36% on average) for samples with pore water pH 6.5–7.5, from 30 to 125% (69% on average) for samples with pore water pH > 7.5. For the three samples using 125% AFA as inputs for modelling, free Cu²⁺ activity was all lower than 10⁻¹⁰ mol L⁻¹.

Figure 2. Comparison of WHAM VI calculated free Cu²⁺ ion and measured free Cu²⁺ ion in 34 samples of soil pore water. Free Cu²⁺ activity was predicted with WHAM VI using either (a) 65% AFA and default log K_CuFA, (b) adjusted AFA rate from 10% to 125% and default log K_CuFA, (c) 65% AFA and adjusted log K_CuFA from 1.0 to 2.3.

Note: The thin solid line represents 1:1 line and dot line represents 1 order of magnitude either side of the 1:1 line. The thick solid line represents regression line.

Table 4. Summary of the parameterization of WHAM 6 and NICA-Donnan parameters based on goodness-of-fit of measured free Cu²⁺ activity data.

WHAM 6 parameters		Model fit indices
log KCuFA	ΔLK2	%AFA	RMSE	Slope	r^2
Default values
2.1	2.34	65	1.23	0.44	0.54
2.1	2.34	30	1.06	0.43	0.53
2.1	2.34	20	1.18	0.43	0.55
2.1	2.34	10–30	0.84	0.63	0.81
2.1	2.34	10–65	0.42	0.83	0.94
2.1	2.34	10–125	0.23	0.92	0.98
Adjusting binding constant
1.8	2.34	65	0.84	0.59	0.72
1.0	2.34	65	0.77	0.71	0.74
1.0–2.3	2.34	65	0.49	0.89	0.91
Decreasing site heterogeneity
2.1	1.80	65	1.04	0.49	0.64
2.1	1.40	65	0.96	0.53	0.67
NICA-Donnan parameters			Model fit indices
log KCuFA1	log KCuFA2	%AFA	RMSE	Slope	r^2
Default values
0.26	8.24	65	1.34	0.72	0.78
0.26	8.24	30	0.78	0.67	0.77
0.26	8.24	20	0.79	0.64	0.76
0.26	8.24	15–30	0.61	0.78	0.84
0.26	8.24	15–65	0.32	0.94	0.96
Decreasing binding constant
0.26	8.00	65	1.14	0.64	0.55
0.26	7.50	65	1.09	0.56	0.50

Note:

The goodness-of-fit was evaluated by RMSE of free Cu²⁺ activity, and the slope of the linear regression line of measured v. predicted free Cu²⁺ activity. The best fit is shown in bold.

Meanwhile, in order to weaken the binding of Cu to DOM for samples undervalued, log K_CuFA was decreased from 2.1 to 1.0 and to intensify Cu affinity to DOM for samples overvalued, log K_CuFA was increased from 2.1 to 2.3, the correlation between estimated and measured free Cu²⁺ activity also improved significantly with RMSE 0.49 and r² 0.91 (Figure 2(c) and Table 4). Nevertheless, there was no consistent match between predicted and measured free Cu²⁺ activity on premise of adjusting site heterogeneity (ΔLK₂) from 1.4 to 2.34.

Due to contaminated concentrations of soils at between toxicity thresholds EC₅ and EC₉₀ according to Cu inhibition in barley root elongation, the fractions of Cu species to total soluble Cu were calculated by WHAM VI using the optimized AFA rate (10–125%) to analyze the impact of free Cu²⁺ concentration and its percentage on EC_x (see Figure 3). It was found that Cu bound to DOM was the predominant species (>98%) in the majority of soil pore waters obtained in the present study except for a number of low-pH soils (i.e. S1, S2, S3 and S6). The percentage of FA-Cu across 34 samples was insignificantly correlated to Cu inhibition to barley root elongation. In Figure 3, inhibition rate of barley root elongation at 5 and 90%, the log (free Cu²⁺) reached the maximum −5.3 (S3) and the minimum −11.5 (S7) mol L⁻¹, respectively. The correlative relationship between inhibition rate (%) of barley root elongation and log (free Cu²⁺ activity) or percentage of free Cu²⁺ concentration showed that these two factors could explain variances of inhibition rate (%) by 43% and 15%, respectively.

Figure 3. The proportions of Cu species calculated by WHAM 6 and measured free Cu²⁺ activity (log mol L⁻¹) in soil pore water for 34 samples at toxicity thresholds of EC_x (x ranged from 5 to 90). A lower and higher inhibition concentrations of Cu to barley root elongation were adopted for each soil, respectively.

3.4.2. NICA-Donnan model

Similarly to WHAM VI, free Cu²⁺ activity was underestimated for 82% of samples (Figure 4(a)) by NICA-Donnan model when using 65% AFA and default constants as inputs. Decreasing %AFA to 30% for samples underestimated, the prediction was improved significantly (see Table 4), but there were still some values far away from the 1:1 line. To continue to decrease %AFA to 15% for the 47% of samples with larger RMSE, the regression was extremely improved with the smallest RMSE 0.32 and the highest r² 0.96 (Figure 4(b)). Differently to WHAM VI, adjustment of binding constant of log K_CuFA2 could not benefit the improvement on prediction of free Cu²⁺ activity to a significant level. Across the adjusted %AFA of 34 samples for the best fit, it was 15% for samples with pH < 7.0, and 15–65% (35% on average) for samples with pH > 7.0.

Figure 4. Comparison of NICA-Donnan calculated free Cu²⁺ ion and measured free Cu²⁺ ion in the 34 samples of soil pore water. Free Cu²⁺ activity was predicted with NICA-Donnan using either (a) 65% AFA and default log K_CuFA2, (b) adjusted AFA rate from 15% to 65% and default log K_CuFA2.

Note: The thin solid line represents 1:1 line and dot line represents 1 order of magnitude either side of the 1:1 line. The thick solid line represents regression line.

4. Discussion

4.1. Distribution of Cu in soil pore water and soil solid

It is showed in Table 3 that soluble Cu was only correlative to total Cu in soil and DOC in soil pore water, which is partly similar to the result from Lamb et al. [35] (c.f. log (soluble Cu) = −0.0261 log (total Cu in soil) + 0.254 pH + 0.635 log (SOC) − 2.813, r² = 0.72) because pH was excluded from the regression equation in our study. Howbeit, Luo et al. [36] also concluded that only SOC in soil could control soluble Cu content in soil and pH played a negligible effect on it. Similarly, all of these results confirmed the influence of SOC (or expressed as DOC) on soluble Cu concentrations in soils.

For solid-solution distribution of Cu, it was very difficult to obtain a linear relationship between K_d and soil pore water pH across 34 samples (Table 3), which is dissimilar to studies from Sauvé et al. [32] (c.f. log (K_d) = 0.21 pH – 0.51 log (SOM) + 1.75) and Vulkan et al. [14] (c.f. log (K_d) = 0.34 pH – 0.58 log (DOC) + 1.74) due to pH as the top important factor to K_d in their studies. However, Luo et al. [36] (c.f. log (K_d) = 1.04 log (total Cu) – 0.91 log (SOC) + 0.70, r² = 0.57) and Lamb et al. [35] (K_d of Cu was only correlated with DOC, r² = 0.72) also determined pH was not an indispensible factor to Cu distribution, which is generally consistent to the results in our study. Also, Antoniadis and Golia [37] expressed that K_d of Cu was significantly related to soil CEC instead of soil pH and SOC due to soil samples were strongly wreathed, given that CEC was also an effective predictor on K_d of Cu in our study (see Equations 3 and 4 in Table 3), therefore, Cu distribution is supposed to be strongly soil-dependent.

4.2. Prediction of free Cu²⁺ activity using models

For both of models, the best prediction on free Cu²⁺ was obtained only when adjusting AFA rate to the bigger values with increase of soil pore water pH, which is possibly due to competition between H⁺ and Cu²⁺ ion for DOM binding sites resulting in most of DOM in acidic soils inefficient [38,39]. The free Cu²⁺ activity at EC_x varied by more than four orders of magnitude among the soils tested and was related to Cu concentration in tomato shoots with r² of 0.41 [3], which is quite similar to these results in our study. Although there was a significant correlation between free Cu²⁺ concentration and its toxicity to barley root elongation, however, free Cu²⁺ activity alone was not a good predictor of plant toxicity due to it only explained approximately 50% of variance of toxicity. Given most of soluble Cu as DOM-complexed form in soil pore water, presumably, a small proportion of Cu-organic complexes could be toxic along with free Cu²⁺. Guo et al. [40] found that weakly bound Cu-organic complexes, such as malate-complexed Cu, was nearly 0.5-fold toxic to plant in contrast to free Cu²⁺ ion; and strongly bound Cu-organic complexes, such as EDTA-complexed Cu was completely nontoxic. Due to affinity of Cu to EDTA, malate and FA was 20.49 (log K), 4.53 (in Visual MINTEQ), and 2.16 (in WHAM VII), therefore, a small amount of FA-bound Cu could be possibly toxic to barley root elongation to explain another half variances of toxicity response. As for how much proportion of DOM-complexed Cu could contribute to its toxicity to plant should be further considered.

Dissolved Al and Fe concentrations in soil pore water were lower than the limit of detection of 0.25 mg L⁻¹ and 0.5 mg L⁻¹, respectively, except S1 and S2 with total soluble Al ranging from 0.58 to 1.06 mg L⁻¹. Due to dissolved Al³⁺ could be considered as a critical parameter in free metal speciation [41] and most of Al in Ferrosol with pH < 4.5 existed as free Al³⁺ ion [42], the Cu²⁺ speciation was recalculated for both S1 and S2 using measured Al concentrations instead of calculated Al³⁺ activity from Al(OH)₃ colloid as inputs in WHAM VI. The results showed that free Cu²⁺ activity derived from measured Al was approximately 0.36 order of magnitude closer to measured values when using adjusted %AFA (10–30%) as active DOM. Thus, the effect of Al³⁺ ion on free Cu²⁺ species in acidic S1 and S2 should be considered carefully. It was reported that for samples with >99% of Cu as DOM complex, the fraction of Cu species associated with AlO(OH) was negligible [11]. In our study, most of samples with high percentage of FA-bound Cu (>98%), hence, impacts of Al and Fe on prediction of Cu speciation are extremely limited.

A large number of studies adopted between 50% and 100% AFA as assumption of active DOM to compute free Cu²⁺ concentration in solution [14,30,43–45], it was changeable depending on DOM nature and quantity in systems. Amery et al. [44] found that 65% AFA in WHAM VI could fit measured FA by UV-absorbance at 254 nm to a great extent, but Stockdale et al. [17] concluded that 65% AFA in WHAM VII could cause free Cu²⁺ more than one order of magnitude higher than predicted values for 38% of samples. Vulkan et al. [14] found that 69% AFA used in WHAM VI could ensure predicted free Cu²⁺ in soil solution closer to measured values, but this ratio was only consistent to that for alkaline soils, not acidic soils in our study. Some studies also used certain %HA as active DOM [10,46] to predict Cu speciation. For example, Zhu and Guéguen [11] adjusted FA:HA ratio from 1:0 to 1:1 to compute free Cu²⁺ concentration via WHAM VII, and found a very small effect on it when Cu bound to DOM was > 99%. However, Ponthieu et al. [10] concluded that FA:HA ratio ranging from 1:0 to 0:1 influenced up to an order of magnitude of free Cu²⁺ concentration due to HA with log _CuHA (2.38) complexed more Cu than FA with log _CuFA (2.16). In our study, assuming HA in WHAM VI as active DOM instead of FA at the best optimized %AFA (10–125%), the predicted free Cu²⁺ activity was approximately 1.6 orders of magnitude lower than measured values even for these samples with higher proportion of organic complexes. Given that HA proportion in soil solutions was quite low [8], hence, assuming FA as active DOM for modelling rather than HA is more realistic in this study.

In our study, the frequently-used ratio of 65% AFA generally caused the underprediction of free Cu²⁺ for both of models, and 10–125% AFA for WHAM VI and 15–65% for NICA-Donnan were the optimized ratio for best agreement of free Cu²⁺ activity. Groenenberg et al. [47] found the humic substances fractions of DOM in the soil solution varied between 14% and 63% depending on the samples. Ren et al. [8] also reported only 16–42% of DOM was humic substances and consisted mainly of FA as the most crucial affecting factor for Cu speciation. These results are very similar to those from NICA-Donnan predictions in this study. As to 125% AFA as input in WHAM VI for the three samples with measured free Cu²⁺ activity < 10^−10.4 mol L⁻¹, it was possibly related to either high pHs (7.77 and 7.79) for 2 samples and lower total soluble Cu (2.84 μmol L⁻¹) for 1 sample. The model did not predict any concentrations to be below 10⁻¹³ mol L⁻¹, but measured values could be as low as 10⁻¹⁶ mol L⁻¹ [19], adjusting %AFA to a range (>100%) could be a recalibration of the model intrinsically for the sample with quite low free Cu²⁺ activity. Besides, Amery et al. [44] estimated that active DOM ranged from 28% to 152% in soil solutions in a soil profile collected over time and Djae et al. [45] found that free Cu²⁺ speciation in 55 soil solutions was mainly overestimated when using default constant and 65% AFA in WHAM VII, and using an optimized AFA percentage (35–215%) and log K_CuFA (1.84–2.46) could make the fit better. In this study, the adjusted AFA (10–125%) and binding constant log K_CuFA (1.0–2.3) in WHAM VI also ensured the prediction maximally optimized. Differently, the parameters were mainly adjusted downward in our study but upward in study of Djae et al. [45] from the start value of 65% AFA and default constants for the better estimation.

However, the difference in optimization of parameters in these models was connected to nature/heterogeneity of samples along with various analytical issues but more likely attributed to the DOM isolation procedure. The DOM affinity for metals could be affected by extraction procedures and soil pre-treatment methods [48]. In this study, DOC concentrations (300 mg L⁻¹ on average) were approximately 3-times higher than the values (100 mg L⁻¹ on average) in study of Nolan et al. [13] using different soil pre-treatment methods despite similar SOC variations. Meanwhile, Nolan et al. [1] also found that predicted free Cu²⁺ by model using 70% AFA for Australian soils was underestimated about three orders of magnitude compared with measured results when adopted similar DOC extraction procedure (217 mg L⁻¹ on average) to the present study. One-month soil incubation could decrease DOC concentration by 2–3 times and elevated its affinity for metals by 1–1.5-fold in contrast to 4 days’ incubation [44]. All of soils were incubated only 24 h before extraction in our study, which probably accounted for the high DOC concentrations but low affinity capacity for Cu in soil pore water.

Generally, free Cu²⁺ prediction is more complicated than other trace element, e.g. Zn, Ni, Cd, and Co [7,17,19] due to a large proportion (up to 99.9%) of soluble Cu was DOM-complexed form [11]. The DOM nature is extremely crucial to Cu speciation and how to select a reliable technique to measure its nature is another issue. Amery et al. [44] studied that specific UV-absorbance of DOM at 254 nm could represent aromaticity of DOM, and was positive proportionally to %AFA. Ahmed et al. [21] used the three-dimensional fluorescence excitation emission matrix spectroscopy to evaluate the fractions of FA and HA in DOM and found using these fractions improved model predictability in contrast to using conventional 65% AFA assumption. Ren et al. [8] adopted a rapid DAX-8 resin technique to fractionate DOM mainly into FA and HA, and found that measured FA concentration incorporated with NICA-Donnan to calculate free Cu²⁺ could fit the measured values much better than an assumed FA ratio. Further, the selection of analytical techniques for free Cu²⁺ activity could play a role on its modelling correction especially for samples with low [Cu]/[DOC] ratios [5,19,21]. Tipping and Lofts [19] described that Donnan membrane technique was more consistent to the model prediction than voltammetric technique (overrated) and ion exchange column method (underrated). However, Stockdale et al. [17] assessed that voltammetry yielded the greatest fraction of agreement for Cu²⁺ activity than ISE technique and the competitive ligand method. There are substantial variations among different analysis techniques but no systematic bias from the model was observed across techniques.

4.3. Comparison between methods

Although both of models obtained excellent fit after adjusting %AFA or binding constant, there was a slight difference on free Cu²⁺ predictability for them. The NICA-Donnan model caused slightly higher bias for free Cu²⁺ prediction when using 65% AFA and default constants as input values for all samples in this study. Also, NICA-Donnan appeared to predict much stronger binding of DOC to Cu²⁺ than WHAM VI, because more than three-quarter samples were underestimated for NICA-Donnan and only half samples for WHAM VI when using 65% AFA as input. The AFA ratio was 125% as the maximum in WHAM VI but 65% in NICA-Donnan model to fulfill the prediction. Weng et al. [30] compared free Cu²⁺ activity obtained with WHAM VI and NICA-Donnan with a variation of DOM composition, and concluded that WHAM VI was slightly better than NICA-Donnan with lower RMSE. Ahmed et al. [21] found that NICA-Donnan showed 2-times higher RMSE values for free Cu²⁺ prediction than WHAM VII especially for samples with low [Cu]/[DOM] ratio. Sierra et al. [49] confirmed that WHAM VII was especially practical for Cu speciation calculation where chemical speciation was driven basically for organic matter.

Besides, the difference in free Cu²⁺ prediction was further analyzed when using 65% AFA and default constant as inputs, it was less than 0.7 orders of magnitude between two models for the samples with pH <7.7, and close to one order of magnitude for samples with pH >7.7. Similarly, Ponthieu et al. [10] evaluated the impact of DOM composition in Cu speciation in 36 calcareous soil solutions (pH 7.0–8.4) using WHAM VI and NICA-Donnan, and difference of free Cu²⁺ predicted by two models was enlarged with increase of pH. The difference of free Cu²⁺ prediction might be related to the distinction of intrinsic property in these two humic substances models. The NICA-Donnan model uses a bimodal, continuous distribution of affinities for protons and metal ions, whereas WHAM VI is based on a discrete set of sites, and phenolic contents for the two models are also negatively correlated [18,20]. As a whole, predictability of models for Cu speciation is strongly dependent on humic substances nature and quantity in soil solution.

5. Conclusion

The Cu concentrations and free Cu²⁺ activities in pore waters for 34 Chinese soil samples were measured in order to determine its controlling factors and predictive models. Total Cu in soils and DOC in soil pore water were two key properties explaining soluble Cu content in soil pore water and its distribution. The free Cu²⁺ activity in soil pore water was controlled mainly by pore water pH and total Cu concentrations.

The predictability of WHAM VI and NICA-Donnan models for free Cu²⁺ was evaluated, which indicated that both models overestimated combination of DOM and Cu resulting in the lower free Cu²⁺ activities. While using varied AFA ratio from 10% to 125% for WHAM VI and from 15% to 65% for NICA-Donnan these two models provided preferable estimates of free Cu²⁺ activity with r² > 0.96. Also, NICA-Donnan model showed a slightly stronger affinity for Cu²⁺ in pore water than WHAM VI considering that extra 26% of samples were undervalued on free Cu²⁺ activity. The impact of the composition of the DOM on the free Cu²⁺ prediction by models could vary up to two orders of magnitude depending on what ratio of AFA was used to represent DOM. Despite DOM nature was not measured by analytical techniques in this study, this work made a good attempt on Cu speciation prediction in a wide range of Chinese soils.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Natural Science Foundation of China [grant number 41401361]; the project of Research on Migration/Transformation and Safety Threshold of Heavy Metals in Farmland Systems [grant number 2016YFD0800406]; the Natural Science Foundation of Liaoning Province [grant number 2015020584]; and The High-level Leading Talent Introduction Program of GDAS.

Acknowledgements

The authors thank the financial support by the Natural Science Foundation of China (41401361), the project of Research on Migration/Transformation and Safety Threshold of Heavy Metals in Farmland Systems (2016YFD0800406), the Natural Science Foundation of Liaoning Province (2015020584) and The High-level Leading Talent Introduction Program of GDAS. Also the authors thank national long-term soil experimental stations in China for soil collection and Mike McLaughlin for his support and Cathy Fiebiger in CSIRO for technical assistance.