A new model integrating short- and long-term aging of copper added to soils

<div><p>Aging refers to the processes by which the bioavailability/toxicity, isotopic exchangeability, and extractability of metals added to soils decline overtime. We studied the characteristics of the aging process in copper (Cu) added to soils and the factors that affect this process. Then we developed a semi-mechanistic model to predict the lability of Cu during the aging process with descriptions of the diffusion process using complementary error function. In the previous studies, two semi-mechanistic models to separately predict short-term and long-term aging of Cu added to soils were developed with individual descriptions of the diffusion process. In the short-term model, the diffusion process was linearly related to the square root of incubation time (t^1/2), and in the long-term model, the diffusion process was linearly related to the natural logarithm of incubation time (lnt). Both models could predict short-term or long-term aging processes separately, but could not predict the short- and long-term aging processes by one model. By analyzing and combining the two models, we found that the short- and long-term behaviors of the diffusion process could be described adequately using the complementary error function. The effect of temperature on the diffusion process was obtained in this model as well. The model can predict the aging process continuously based on four factors—soil pH, incubation time, soil organic matter content and temperature.</p></div

The measured E values (E_m) versus the E values predicted by erfc model (E_p) for short-term data.

<p>The measured E values (E_m) versus the E values predicted by erfc model (E_p) for short-term data.</p

Average Cu labile pool (E value as fraction of total added Cu) in 17 short-term soil samples as a function of incubation time and temperature (vertical lines represent the standard errors).

<p>Average Cu labile pool (E value as fraction of total added Cu) in 17 short-term soil samples as a function of incubation time and temperature (vertical lines represent the standard errors).</p

The measured E values (E_m) versus the predicted E values of the erfc model (E_p).

<p>The measured E values (E_m) versus the predicted E values of the erfc model (E_p).</p

The measured E values and predicted E values of field-contamination soil samples.

<p>Vertical lines represent standard errors where they exceed the height of columns.</p

Soil pH, temperature (K), time (year, since soil field-contamination with Cu salts), soil organic carbon content (w/w%), total Cu (mg kg^-1), measured E values (E_m, fraction).

<p>Soil pH, temperature (K), time (year, since soil field-contamination with Cu salts), soil organic carbon content (w/w%), total Cu (mg kg^-1), measured E values (E_m, fraction).</p

Multiple regressions between log Ni_dis (soluble Ni concentration in soil pore water) and log Ni_tot (total Ni in soil) together with soil properties.

<p>Ni_dis: soluble Ni concentration in soil pore water; Ni_tot: total Ni concentration in soil; R²: coefficient of determination; R_adj²: adjusted coefficient of determination; p: significant level of factors in regression equations; *: 5% significant level</p><p>**: 1% significant level</p><p>***: 1‰ significant level.</p><p>Multiple regressions between log Ni_dis (soluble Ni concentration in soil pore water) and log Ni_tot (total Ni in soil) together with soil properties.</p

The estimated values of parameters, R² and RMSE in erfc model.

<p>The estimated values of parameters, R² and RMSE in erfc model.</p

Predicting Soluble Nickel in Soils Using Soil Properties and Total Nickel

<div><p>Soil soluble nickel (Ni) concentration is very important for determining soil Ni toxicity. In the present study, the relationships between soil properties, total and soluble Ni concentrations in soils were developed in a wide range of soils with different properties and climate characteristics. The multiple regressions showed that soil pH and total soil Ni concentrations were the most significant parameters in predicting soluble Ni concentrations with the adjusted determination coefficients (R_adj²) values of 0.75 and 0.68 for soils spiked with soluble Ni salt and the spiked soils leached with artificial rainwater to mimic field conditions, respectively. However, when the soils were divided into three categories (pH < 7, 7–8 and > 8), they obtained better predictions with R_adj² values of 0.78–0.90 and 0.79–0.94 for leached and unleached soils, respectively. Meanwhile, the other soil properties, such as amorphous Fe and Al oxides and clay, were also found to be important for determining soluble Ni concentrations, indicating that they were also presented as active adsorbent surfaces. Additionally, the whole soil speciation including bulk soil properties and total soils Ni concentrations were analyzed by mechanistic speciation models WHAM VI and Visual MINTEQ3.0. It was found that WHAM VI provided the best predictions for the soils with pH < 7, was relatively reasonable for pH 7 to 8, and gave an overestimation for pH > 8. The Visual MINTEQ3.0 could provide better estimation for pH < 8 and meanwhile quite reasonable results for pH > 8. These results indicated the possibility and applicability of these models to predict soil soluble Ni concentration by soil properties.</p></div

Measured soluble Ni concentration versus predicted Ni concentration using WHAM VI in unleached soils (Ni_dis represented the soluble Ni concentration in soil pore water).

<p>Measured soluble Ni concentration versus predicted Ni concentration using WHAM VI in unleached soils (Ni_dis represented the soluble Ni concentration in soil pore water).</p