A dynamic model to calculate cadmium concentrations in bovine tissues from basic soil characteristics

Probability of cultivating Se-rich maize in Se-poor farmland based on intensive field sampling and artificial neural network modelling

Selenium (Se) is a necessary micronutrient for humans, and its supplementation from crop grains is important to address the ubiquitous Se deficiency in people worldwide. Se uptake by crops largely depend on soil bioavailable Se rather than soil total Se content, which provides possibilities to explore the Se-rich crops in Se-poor area. Here, the possibility of cultivating Se-rich maize grains in Se-poor farmland was tested based on intensive field sampling and mathematical modelling. Sampling was conducted at county scale, and a total of 7779 topsoil samples and 109 maize samples with paired rhizosphere soils samples were collected. Results showed that although the soil Se content in the study county from southwestern China was at a low level (0.01-2.75 mg kg^-1), 54.1% of the maize grain samples satisfied the standard for Se-rich products (0.02-0.30 mg kg^-1). Soil organic matter, iron oxide, and phosphorus levels were correlated negatively with Se bioconcentration factor (BCF) of maize grain. Compared with the multivariate linear regression model, the artificial neural network (ANN) model was more accurate and reliable in predicting maize Se BCF. Prediction using the ANN model showed that 22.7% of the county's farmland was suitable for cultivating naturally Se-rich maize, which increased 21.3% growing areas than that from cultivation based on simply soil total Se. This study provided a new methodological framework for natural Se-rich maize production and verified the probability of cultivating naturally Se-rich maize in Se-poor farmland.

Prediction models based on soil properties for evaluating the heavy metal uptake into Hordeum vulgare L. grown in agricultural soils amended with different rates of sewage sludge

The current study aims at forming new prediction models to be employed in the approximating the possible uptake of a range of 10 heavy metals (HMs) (Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb and Zn) by Hordeum vulgare tissues including roots, shoots and grains following its growth in soil amended with sewage sludge (SS) using conditions employed in greenhouses. The present study determined an insignificant difference between the actual and predicted quantities of the HMs in the three tissues using t values. The majority of the predicted quantities of the HMs were acceptable with the exception of Cd in the shoots, Cu in grains and Pb in roots. Consequently, it is possible to use these models in assessing the cultivation of barley plants in soil amended with SS in a safe way, while simultaneously monitoring any potential risks to the health of humans.

Regression Models to Estimate Accumulation Capability of Six Metals by Two Macrophytes, Typha domingensis and Typha elephantina, Grown in an Arid Climate in the Mountainous Region of Taif, Saudi Arabia

In this study, we explored the capacity for two promising macrophytes, Typha domingensis and Typha elephantina, to be used for the surveillance of contamination by six metals, i.e., Cu, Fe, Mn, Ni, Pb, and Zn, in the mountainous area of Taif City in Saudi Arabia. Regression models were generated in order to forecast the metal concentrations within the plants’ organs, i.e., the leaves, flowers, peduncles, rhizomes, and roots. The sediment mean values for pH and the six metals varied amongst the sampling locations for the respective macrophytes, indicating that similar life forms fail to indicate equivalent concentrations. For instance, dissimilar concentrations of the metals under investigation were observed within the organs of the two rooted macrophytes. The research demonstrated that the segregation of metals is a regular event in all the investigated species in which the metal concentrations vary amongst the different plant constituent types. In the current study, T. domingensis and T. elephantina varied in their capacity to absorb specific metals; the bioaccumulation of metals was greater within T. domingensis. The relationships between the observed and model-estimated metal levels, in combination with high R2 and modest mean averaged errors, offered an appraisal of the goodness of fit of most of the generated models. The t-tests revealed no variations between the observed and model-estimated concentrations of the six metals under investigation within the organs of the two macrophytes, which emphasised the precision of the models. These models offer the ability to perform hazard appraisals within ecosystems and to determine the reference criteria for sediment metal concentration. Lastly, T. domingensis and T. elephantina exhibit the potential for bioaccumulation for the alleviation of contamination from metals.

Evaluation of uptake of eight metals by Sorghum bicolor grown in arable soil combined with sewage sludge based on prediction models

Prediction models were developed to estimate the extent to which aluminium, chromium, copper, iron, manganese, nickel, lead, and zinc were absorbed in the grains, leaves, stems, and roots of Sorghum bicolor cultivated in soil with various amendment rate of sewage sludge (0, 10, 20, 30, 40, and 50 g/kg) under greenhouse conditions. It was found that, aside from lead, all the examined metals occurred in significantly higher content in the roots compared to aerial tissues. Furthermore, the r-values were significantly negative between the bioconcentration factors of all metals, apart from aluminium and lead, and soil pH, whereas they were significantly positive between the bioconcentration factors, apart from lead, and soil organic matter content (OM). The r-values were typically significantly positive between the levels of all eight metals in the investigated tissues and in the soil. Moreover, the content of all the eight metals in the tissues exhibited a significant negative r-value with soil pH but a significant positive r-value with soil OM. The eight metal contents in the tissues given by the prediction models were quite similar to the real values, suggesting that the created models performed well, as shown by t-tests. It was thus concluded that prediction models were a viable option for evaluating how safe it was to grow S. bicolor in soils with sewage sludge content and at the same time for keeping track of possible human health hazards.

Trace element accumulation potential in lemongrass varieties (Cymbopogon species) and prediction through regression model equations followed by path analysis: a field study

A field investigation was conducted to monitor the trace element accumulation pattern and potential in lemongrass varieties when cultivated in varying concentrations of tannery sludge contaminated soils. Regression model equations were developed to predict the trace elements concentration in lemongrass plant parts. Model efficiency values ranged between 0.53 and 0.85 for roots and 0.50 to 0.77 for shoots. R² for all the equations was high and ranged between 0.52 and 0.95 for roots and 0.50 to 0.91 for shoots. Path analysis coefficients revealed the status of interdependence between soil properties and heavy metal concentrations in plant tissues and their translocation pattern in the same. TF > 1 for Ni and Pb was recorded in all test varieties at different tannery sludge concentrations in soil and for Cd in Suwarna variety grown in sole TS. BAF>1 and BAC>1 for Ni and Pb was observed for all test varieties. BCF>1 for Cr was found in Suwarna variety at sole tannery sludge. It can be concluded that lemongrass can accumulate lead and nickel in its harvestable plant parts. Hence, it can act as a suitable phytoextractor for the same metals. Range of essential oil content (percentage) varied in all test varieties i.e. Shikhar (1.04-1.29), Krishna (0.94-1.05), Suwarna (0.82-0.95) and Chirharit (0.87-1.06) at different tannery sludge concentrations. Moreover, heavy metal content in the essential oil of all test varieties was found to be within the permissible limits, hence cultivation of lemongrass can be recommended at metal contaminated sites with minimum risk of food chain contamination.

Biomonitoring potential of the native aquatic plant Typha domingensis by predicting trace metals accumulation in the Egyptian Lake Burullus

The ability of the native emergent macrophytes Typha domingensis for monitoring pollution with trace metals in Egyptian Lake Burullus was investigated through developing regression models for predicting their concentrations in the plant tissues. Plant samples (above-ground shoot and below-ground root and rhizome) as well as sediment samples were collected monthly during one growing season and analyzed. The association of trace metals concentration with several sediment characteristics (pH, organic matter, clay and silt) was also studied using the simple linear correlation coefficient (r). The concentration of some trace metals was significantly proportional to its values in the sediment such as Cd in the shoot, rhizome and root, Fe in the rhizome, and Ag in the root. There was positive relationship between the bioaccumulation factor (BAF) of Ag, Cd, Fe, Pb and Zn and sediment pH, organic matter and clay content. The developed regression models were significantly valid with high model efficiency and coefficient of determination, and low mean normalized average error. Trace metals were accumulated in the below-ground root and rhizome rather than in the shoot. Only Ag, Co and Ni provided bioaccumulation factor (BAF) < 1, while Ag was the only trace metal that could be transferred to some extend from the root to the rhizome and from there to the shoot [translocation factor (TF) 2.55 and 1.15, respectively]. Typha domingensis in Lake Burullus could be regarded as a bioindicator of trace metals pollution, and a good candidate as phytoremediator for Ag. The information on the phytoremediation capacity of T. domingensis certainly helps to solve contamination problems at Egyptian Lake Burullus region using this native plant.

Uptake Prediction of Ten Heavy Metals by Eruca sativa Mill. Cultivated in Soils Amended with Sewage Sludge

This study was carried out to develop mathematical regression equations for predicting the uptake of ten heavy metals (HMs) (cadmium, Cd; cobalt, Co; chromium, Cr; copper, Cu; iron, Fe; manganese, Mn; molybdenum, Mo; nickel, Ni; lead, Pb; zinc, Zn) by a vegetable species (Eruca sativa Mill.) in the Abha region (Saudi Arabia) based on the concentration of these HMs in soils amended with sewage sludge, organic matter (OM) content and soil pH. The resultant regression equations indicated that the three soil factors were significant predictors for the uptake of the ten HMs in the plant tissues. By applying a t test, we found that there are no significant differences between the actual and predicted values of the ten HMs in the E. sativa roots and leaves (P > 0.05), which reflects the goodness of fit of these equations for predicting the uptake of these HMs. Such types of equations may be helpful for evaluating the risk of cultivation of E. sativa plants in soils amended with sewage sludge.

Prediction models for evaluating heavy metal uptake by Pisum sativum L. in soil amended with sewage sludge

The present study aims to develop prediction models for estimating the potential uptake of 10 heavy metals (HMs) (cadmium, Cd; cobalt, Co; chromium, Cr; copper, Cu; iron, Fe; manganese, Mn; molybdenum, Mo; nickel, Ni; lead, Pb; zinc, Zn) by the tissues of Pisum sativum (root, shoot and pod) grown in soil amended with sewage sludge (SS) under greenhouse conditions. Soil organic matter (OM) was estimated by loss-on-ignition at 550 °C for 2 h. The pH was determined by shaking the soil and pure water at a 1:5 ratio. For HM quantifications, 0.5-1.0 g of each soil or plant sample was digested using a tri-acid mixture digestion method. The quantities of selected HMs were estimated by means of inductively coupled plasma optical emission spectrometry. Bio-concentration (BCF) and translocation (TF) factors were <1 for most of the HMs. In addition, simple linear correlations were significantly negative between the BCF of all studied HMs and soil pH, except for Pb, Mn and Ni, whereas significant positive correlations were observed between BCFs and soil OM, except for Mn, Ni and Zn. The accumulation of the 10 HMs in P. sativum tissues was predicted using regression models based on the values of the same HM in the soil as well as its pH and OM. The calculated prediction models performed well for most HMs in P. sativum tissues (except Ni in the pod, Cd in the shoot and Mn in the root). All measured soil factors (HM, pH and OM) consistently contributed to HM concentrations in the three tissues of the studied plants. These models may help to evaluate the safe cultivation of this species in soil amended with SS.

Application of ecogeochemical prediction model to safely exploit seleniferous soil

Seleniferous soil and crops have recently attracted attention worldwide. Cultivating seleniferous crops in the absence of heavy metals is greatly challenging. This study aimed to develop approaches for the safe exploitation of seleniferous soil. We collected 246 pairs of rice grain samples and their corresponding rhizosphere soil samples and 8542 topsoil samples from Binyang and Xingbin in Guangxi. The Cd, Cu, Hg, Pb, Zn, and Se contents of soil and rice grain samples were tested. Several soil properties, including CaO, Mn, Mo, and S contents; total organic carbon content; and pH were also measured. Soil and rice grain samples that were classified as seleniferous accounted for 85.77% and 88% of all samples, respectively. More than 30% of soil and rice grain samples were potentially polluted by Cd. The percentage of seleniferous rice grain samples increased as soil Se concentration increased. Notably, however, the percentage of Cd-polluted rice grain samples decreased with the increase in soil Cd concentration. Models for the prediction of BAFs of heavy metal and Se were accurately established on the basis of significant partial correlations between log₁₀ (BAFs) and log₁₀ (soil properties). Farmlands with seleniferous soil under preferential protection and with safely exploited seleniferous soil accounted for 82.61% of the total study area. Sites that require remediation or land-use changes accounted for only 14.7% of the total study area and were mainly distributed in the center of the study area.

Prediction models for evaluating the uptake of heavy metals by cucumbers (Cucumis sativus L.) grown in agricultural soils amended with sewage sludge

Heavy metal (HM) concentrations in edible plants can develop many serious health risks to humans. The precise prediction of plant uptake of HMs is highly important. Thus, the present investigation was carried out to develop regression models for predicting the concentrations of HMs in cucumbers (Cucumis sativus L.) from their concentration in the soil and using the organic matter (OM) content and soil pH as co-factors. The results showed that cucumber roots had the highest significant concentrations of all HMs at P < 0.001, except Cd, Cu, and Zn were in fruits. The lowest concentrations of Cd, Co, Cr, Mn, Ni, and Pb were recorded in stems. HM concentrations in cucumbers were strongly correlated with soil HM, pH, and OM content. Soil pH and OM content had negative and positive correlations with all HMs in cucumber tissues, respectively. Regression analysis indicated that soil HM, pH, and OM contents were good predictors for HM concentrations in cucumbers. The regression models for root Co, Cr, Fe, and Zn were described by high model efficiency values that explain 48-58% variability. The best regression models for cucumber stem were for Cu, Mn, Ni, and Zn that are characterized by high R² and model efficiency values. For cucumber fruits, R² values were ranged from 54 to 82%, with best models for Cr, Pb, Cd, Cu, Ni, and Co in the fruit. We expect that these models will be beneficial for risk assessment studies on sewage sludge utilization in agriculture.

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.

Modeling the cadmium balance in Australian agricultural systems in view of potential impacts on food and water quality

The historical build up and future cadmium (Cd) concentrations in top soils and in crops of four Australian agricultural systems are predicted with a mass balance model, focusing on the period 1900-2100. The systems include a rotation of dryland cereals, a rotation of sugarcane and peanuts/soybean, intensive dairy production and intensive horticulture. The input of Cd to soil is calculated from fertilizer application and atmospheric deposition and also examines options including biosolid and animal manure application in the sugarcane rotation and dryland cereal production systems. Cadmium output from the soil is calculated from leaching to deeper horizons and removal with the harvested crop or with livestock products. Parameter values for all Cd fluxes were based on a number of measurements on Australian soil-plant systems. In the period 1900-2000, soil Cd concentrations were predicted to increase on average between 0.21 mg kg(-1) in dryland cereals, 0.42 mg kg(-1) in intensive agriculture and 0.68 mg kg(-1) in dairy production, which are within the range of measured increases in soils in these systems. Predicted soil concentrations exceed critical soil Cd concentrations, based on food quality criteria for Cd in crops during the simulation period in clay-rich soils under dairy production and intensive horticulture. Predicted dissolved Cd concentrations in soil pore water exceed a ground water quality criterion of 2 μg l(-1) in light textured soils, except for the sugarcane rotation due to large water leaching fluxes. Results suggest that the present fertilizer Cd inputs in Australia are in excess of the long-term critical loads in heavy-textured soils for dryland cereals and that all other systems are at low risk. Calculated critical Cd/P ratios in P fertilizers vary from <50 to >1000 mg Cd kg P(-1) for the different soil, crop and environmental conditions applied.