Changes of the trace element input/output balance in farmland across China (tons/year).

<p>Changes of the trace element input/output balance in farmland across China (tons/year).</p

Comparison of atmospheric deposition of trace elements to farmland and emission of trace elements to atmosphere (ratios in the picture were calculated by input/emission).

<p>Comparison of atmospheric deposition of trace elements to farmland and emission of trace elements to atmosphere (ratios in the picture were calculated by input/emission).</p

Changes of the trace element concentrations in livestock manures in China.

<p>Changes of the trace element concentrations in livestock manures in China.</p

Atmospheric deposition flux of trace elements in China and overseas countries or districts (mg/m²/year).

<p>Atmospheric deposition flux of trace elements in China and overseas countries or districts (mg/m²/year).</p

The change (%) of trace element inputs to farmland from 1999–2006 to 2006–2015.

<p>The change (%) of trace element inputs to farmland from 1999–2006 to 2006–2015.</p

The contribution of different sources for trace elements to total inputs in farmland across China (%).

<p>The contribution of different sources for trace elements to total inputs in farmland across China (%).</p

Current inventory and changes of the input/output balance of trace elements in farmland across China

<div><p>The inventory and input/output balance of trace elements in farmland play an important role in risk assessment and soil management, but there is little information about nationwide changes of the input/output balance of trace elements in farmland in China. In the present study, the inventory of trace element inputs to farmland was updated based on the dataset from the literature published during 2006–2015, and changes of the input/output balance were investigated. Compared with 1999–2006, net inputs of Cr, Ni, and Zn increased by 52.9%, 59.7%, and 20.6%, respectively. The increases in fossil fuel derived energy consumption, industrial manufacture, municipal solid waste incineration, and transportation were the predominant contributors to these increases. Net inputs of Cd, Cu, and Hg decreased dramatically by 46.7%, 25.2%, and 50.4%, respectively. The decreases are due to the strict management of feed additives, fertilizers, and emissions of atmospheric pollutants. Net inputs of As and Pb still remained relatively stable. These results demonstrated that better achievements have been gained by administration of air, water and soil in China. Regulation of atmospheric emission for Cr, Ni, and Zn was recommended as atmospheric deposition was the predominant source for increases of Cr, Ni, and Zn inputs to farmland across China.</p></div

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

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