Cross-species extrapolation of prediction model for lead transfer from soil to corn grain under stress of exogenous lead

Prediction models for evaluating the heavy metal uptake by spinach (Spinacia oleracea L.) from soil amended with sewage sludge

The risk evaluation of polluted soil requires the application of precise models to predict the heavy metal uptake by plants so possible human risks can be identified. Therefore, the present work was conducted to develop regression models for predicting the concentrations of heavy metals in spinach plants from their concentration in the soil by using the organic matter content and soil pH as co-factors. The soil improved with sewage sludge was slightly alkaline and had a relatively high organic matter content. Similar to the soil analysis, Fe had the highest median concentration, while Cd had the lowest concentration in the roots and leaves. Heavy metals accumulated in the roots and leaves in the order Fe > Mn > Zn > Cu > Cr > Ni > Co > Pb > Cd. The bio-concentration factor of the investigated heavy metals, from soil to roots, did not exceed one. The spinach was recognized by a translocation factor <1.0 for all of the heavy metals except Zn. Plant heavy metal concentrations were positively correlated with the soil organic matter content and negatively correlated with soil pH. The leaf Cr, Fe and Zn and the root Cr, Fe, Pb and Zn concentrations were positively correlated with the respective soil heavy metals. In addition, a linear correlation was found between the bio-concentration factor of heavy metals and soil pH and organic matter content. Regression models with high model efficiency and coefficients of determination and low mean normalized average errors, which indicate the efficiency of the models, were produced for predicting the plant heavy metal contents by using the soil pH and organic matter content as co-factors.

Cross-Species Extrapolation of Models for Predicting Lead Transfer from Soil to Wheat Grain

The transfer of Pb from the soil to crops is a serious food hygiene security problem in China because of industrial, agricultural, and historical contamination. In this study, the characteristics of exogenous Pb transfer from 17 Chinese soils to a popular wheat variety (Xiaoyan 22) were investigated. In addition, bioaccumulation prediction models of Pb in grain were obtained based on soil properties. The results of the analysis showed that pH and OC were the most important factors contributing to Pb uptake by wheat grain. Using a cross-species extrapolation approach, the Pb uptake prediction models for cultivar Xiaoyan 22 in different soil Pb levels were satisfactorily applied to six additional non-modeled wheat varieties to develop a prediction model for each variety. Normalization of the bioaccumulation factor (BAF) to specific soil physico-chemistry is essential, because doing so could significantly reduce the intra-species variation of different wheat cultivars in predicted Pb transfer and eliminate the influence of soil properties on ecotoxicity parameters for organisms of interest. Finally, the prediction models were successfully verified against published data (including other wheat varieties and crops) and used to evaluate the ecological risk of Pb for wheat in contaminated agricultural soils.

Prediction models for transfer of arsenic from soil to corn grain (Zea mays L.)

In this study, the transfer of arsenic (As) from soil to corn grain was investigated in 18 soils collected from throughout China. The soils were treated with three concentrations of As and the transfer characteristics were investigated in the corn grain cultivar Zhengdan 958 in a greenhouse experiment. Through stepwise multiple-linear regression analysis, prediction models were developed combining the As bioconcentration factor (BCF) of Zhengdan 958 and soil pH, organic matter (OM) content, and cation exchange capacity (CEC). The possibility of applying the Zhengdan 958 model to other cultivars was tested through a cross-cultivar extrapolation approach. The results showed that the As concentration in corn grain was positively correlated with soil pH. When the prediction model was applied to non-model cultivars, the ratio ranges between the predicted and measured BCF values were within a twofold interval between predicted and measured values. The ratios were close to a 1:1 relationship between predicted and measured values. It was also found that the prediction model (Log [BCF]=0.064 pH-2.297) could effectively reduce the measured BCF variability for all non-model corn cultivars. The novel model is firstly developed for As concentration in crop grain from soil, which will be very useful for understanding the As risk in soil environment.

Derivation of soil thresholds for lead applying species sensitivity distribution: A case study for root vegetables

The combination of food quality standard and soil-plant transfer models can be used to derive critical limits of heavy metals for agricultural soils. In this paper, a robust methodology is presented, taking the variations of plant species and cultivars and soil properties into account to derive soil thresholds for lead (Pb) applying species sensitivity distribution (SSD). Three species of root vegetables (four cultivars each for radish, carrot, and potato) were selected to investigate their sensitivity differences for accumulating Pb through greenhouse experiment. Empirical soil-plant transfer model was developed from carrot New Kuroda grown in twenty-one soils covering a wide variation in physicochemical properties and was used to normalize the bioaccumulation data of non-model cultivars. The relationship was then validated to be reliable and would not cause over-protection using data from field experimental sites and published independent studies. The added hazardous concentration for protecting 95% of the cultivars not exceeding the food quality standard (HC5add) were then calculated from the Burr Type III function fitted SSD curves. The derived soil Pb thresholds based on the added risk approach (total soil concentration subtracting the natural background part) were presented as continuous or scenario criteria depending on the combination of soil pH and CEC.

Development and comparison of regression models for the uptake of metals into various field crops

Field crops represent one of the highest contributions to dietary metal exposure. The aim of this study was to develop specific regression models for the uptake of metals into various field crops and to compare the usability of other available models. We analysed samples of potato, hop, maize, barley, wheat, rape seed, and grass from 66 agricultural sites. The influence of measured soil concentrations and soil factors (pH, organic carbon, content of silt and clay) on the plant concentrations of Cd, Cr, Cu, Mo, Ni, Pb and Zn was evaluated. Bioconcentration factors (BCF) and plant-specific metal models (PSMM) developed from multivariate regressions were calculated. The explained variability of the models was from 19 to 64% and correlations between measured and predicted concentrations were between 0.43 and 0.90. The developed hop and rapeseed models are new in this field. Available models from literature showed inaccurate results, except for Cd; the modelling efficiency was mostly around zero. The use of interaction terms between parameters can significantly improve plant-specific models.

Transfer model of lead in soil-carrot (Daucus carota L.) system and food safety thresholds in soil

Reliable empirical models describing lead (Pb) transfer in soil-plant systems are needed to improve soil environmental quality standards. A greenhouse experiment was conducted to develop soil-plant transfer models to predict Pb concentrations in carrot (Daucus carota L.). Soil thresholds for food safety were then derived inversely using the prediction model in view of the maximum allowable limit for Pb in food. The 2 most important soil properties that influenced carrot Pb uptake factor (ratio of Pb concentration in carrot to that in soil) were soil pH and cation exchange capacity (CEC), as revealed by path analysis. Stepwise multiple linear regression models were based on soil properties and the pseudo total (aqua regia) or extractable (0.01 M CaCl2 and 0.005 M diethylenetriamine pentaacetic acid) soil Pb concentrations. Carrot Pb contents were best explained by the pseudo total soil Pb concentrations in combination with soil pH and CEC, with the percentage of variation explained being up to 93%. The derived soil thresholds based on added Pb (total soil Pb with the geogenic background part subtracted) have the advantage of better applicability to soils with high natural background Pb levels. Validation of the thresholds against data from field trials and literature studies indicated that the proposed thresholds are reasonable and reliable.