Predicting As, Cd, Cu, Pb and Zn levels in grasses (Agrostis sp. and Poa sp.) and stinging nettle (Urtica dioica) applying soil-plant transfer models

Per- and polyfluoroalkyl substances (PFAS) in homegrown crops: Accumulation and human risk assessment

Homegrown crops can present a significant exposure source of per- and polyfluoroalkyl substances (PFAS) to humans. Field studies studying PFAS accumulation in multiple vegetable food categories and examining the potential influence of soil characteristics on vegetable bioavailability under realistic exposure conditions are very scarce. Crop PFAS accumulation depends on a complex combination of factors. The physicochemical differences among the numerous PFAS makes risk assessment very challenging. Thus, simplification of this complexity into key factors that govern crop PFAS accumulation is critical. This study analyzed 29 targeted legacy, precursor and emerging PFAS in the vertical soil profile (0-45 cm depth), rainwater and edible crop parts of 88 private gardens, at different distances from a major fluorochemical plant. Gardens closer to the plant site showed higher soil concentrations which could be linked with historical and recent industrial emissions. Most compounds showed little variation along the soil depth profile, regardless of the distance from the plant site, which could be due to gardening practices. Annual crops consistently accumulated higher sum PFAS concentrations than perennials. Highest concentrations were observed in vegetables, followed by fruits and walnuts. Single soil-crop relationships were weak, which indicated that other factors (e.g., porewater) may be better measures of bioavailability in homegrown crop accumulation. Regression models, which additionally considered soil characteristics showed limited predictive power (all R2 ≤ 35%), possibly due to low variability in crop concentrations. Human intake estimations revealed that the PFAS exposure risk via crop consumption was similar nearby and remotely from the plant site, although the contribution to the overall dietary exposure can be relatively large. The tolerable weekly intake was frequently exceeded with respect to fruit and vegetable consumption, thus potential health risks cannot be ruled out.

A novel prediction approach driven by graph representation learning for heavy metal concentrations

The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.

Ecological and health risk assessments of heavy metals and their accumulation in a peanut-soil system

Heavy metals pollution is a notable threat to environment and human health. This study evaluated the potential ecological and health risks of heavy metals (Cu, Cr, Cd, Pb, Zn, Ni, and As) and their accumulation in a peanut-soil system based on 34 soil and peanut kernel paired samples across China. Soil As and Cd posed the greatest pollution risk with 47.1% and 17.6% of soil samples exceeding the risk screen levels, respectively, with 26.5% and 20.6% of the soil sites at relatively strong potential ecological risk level, respectively, and with the geo-accumulation levels at several soil sites in the uncontaminated to moderately contaminated categories. About 35.29% and 2.94% of soil sites were moderately and severely polluted based on Nemerow comprehensive pollution index, respectively, and a total of 32.4% of samples were at moderate ecological hazard level based on comprehensive potential ecological risk index values. The Cd, Cr, Ni, and Cu contents exceeded the standard in 11.76, 8.82, 11.76 and 5.88% of the peanut kernel samples, respectively. Soil metals posed more health risks to children than adults in the order As > Ni > Cr > Cu > Pb > Zn > Cd for non-carcinogenic health risks and Ni > Cr ≫ Cd > As > Pb for carcinogenic health risks. The soil As non-cancer risk index for children was greater than the permitted limits at 14 sites, and soil Ni and Cr posed the greatest carcinogenic risk to adults and children at many soil sites. The metals in peanut did not pose a non-carcinogenic risk according to standard. Peanut kernels had strong enrichment ability for Cd with an average bio-concentration factor (BCF) of 1.62. Soil metals contents and significant soil properties accounted for 35-74% of the variation in the BCF values of metals based on empirical prediction models.

Prediction of cadmium contents in rice grains from Quaternary sediment-distributed farmland using field investigations and machine learning

The Quaternary sediment-distributed regions of South China are suitable for rice cultivation, which is crucial for ensuring food security. Spatial correlations between soil cadmium (Cd) and rice Cd contents are generally poor, making the evaluation of rice quality and associated health risks challenging. In this study, we developed machine learning algorithms for predicting rice Cd contents using 654 data pairs of soil-rice samples collected in Guangxi province, China. After a comprehensive comparison, our results showed that the random forest (RF) had the better performance than artificial neural network (ANN) based on all the data (RMSEtesting 0.066 vs. 0.099 and R2testing 0.860 vs. 0.688). The feature importance analysis showed that soil CaO, Cd, elevation, and rainfall were the four most important features affecting the rice Cd contents in the study area. Using the established RF-predicated model, the rice Cd contents were predicted at the provincial level with an additional dataset of 1176 paddy soil samples. The prediction result revealed about 23 % of farmland cultivated rice with Cd content over 0.2 mg kg-1 in the study area. Therefore, it is recommended to implement strict measures by local agricultural departments to reduce rice Cd contents and ensure food safety in these areas. Our study provides valuable insights into the prediction of rice Cd contents, thus contributing to ensuring food safety and preventing Cd exposure-associated health risks.

Ensemble learning-based applied research on heavy metals prediction in a soil-rice system

Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil-rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each model, Multiple linear and Bayesian regressions were selected as benchmark models (BM), and mean absolute error (MAE), root mean square error (RMSE), and determination coefficient R2 were selected as evaluation indicators. In addition, sensitivity and spatial autocorrelation (SAC) analyses were used to examine the robustness of the model. The results showed that the R2 values of RF-HMC and GBM-HMC for modeling available cadmium (Cd) concentrations in soil were 0.654 and 0.690, respectively, with an average increase of 48.0 % compared to the BMs. The R2 values of RF-HMC and GBM-HMC for predicting Cd, lead (Pb), chromium (Cr), and mercury (Hg) concentrations in rice ranged from 0.618 to 0.824 and 0.645 to 0.850, respectively, with an average increase of 58.2 % compared with the BMs. The corresponding MAEs and RMSEs of RF-HMC and GBM-HMC had low error levels. Sensitivity analysis of the input features and the SAC of the prediction bias showed that the EL-HMC models have excellent robustness. Therefore, the EL technology-based prediction models for HMCs proposed herein are practical and feasible, demonstrating better accuracy and stability than the traditional model. This study verifies the application potential of EL technology in pollution ecology and provides a new perspective and solution for sustainable management and precise prevention of heavy metal pollution in farmland soil at the regional scale.

A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves.

Prediction Models Based on Soil Characteristics for Evaluation of the Accumulation Capacity of Nine Metals by Forage Sorghum Grown in Agricultural Soils Treated with Varying Amounts of Poultry Manure

Predictive models were generated to evaluate the degree to which nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) were absorbed by the leaves, stems and roots of forage sorghum in growing media comprising soil admixed with poultry manure concentrations of 0, 10, 20, 30 and 40 g/kg. The data revealed that the greatest contents of the majority of the metals were evident in the roots rather than in the stems and leaves. A bioaccumulation factor (BAF) < 1 was calculated for Cr, Fe, Ni, Pb and Zn; BAF values for Co, Cu, Mn and Cd were 3.99, 2.33, 1.44 and 1.40, respectively, i.e., > 1. Translocation factor values were < 1 for all metals with the exception of Co, Cr and Ni, which displayed values of 1.20, 1.67 and 1.35 for the leaves, and 1.12, 1.23 and 1.24, respectively, for the stems. The soil pH had a negative association with metal tissues in plant parts. A positive relationship was observed with respect to plant metal contents, electrical conductivity and organic matter quantity. The designed models exhibited a high standard of data precision; any variations between the predicted and experimentally observed contents for the nine metals in the three plant tissue components were nonsignificant. Thus, it was concluded that the presented predictive models constitute a pragmatic tool to establish the safety from risk to human well-being with respect to growing forage sorghum when cultivating media fortified with poultry manure.

Concentration and mobility of trace elements (Li, Ba, Sr, Ag, Hg, B) and macronutrients (Ca, Mg, K) in soil-orchid system on different bedrock types

The mobility of chemical elements in the soil-orchid system has been poorly studied. The aim of this study is to evaluate the uptake and mobility of several trace (Li, Ba, Sr, Ag, Hg, and B) and macronutrients (Ca, Mg, and K) in the orchid Anacamptis morio (L.) R.M.Bateman, Pridgeon & M.W.Chase from soils in western Serbia. The sampling sites are characterized by three different bedrock types-cherts, limestones, and serpentines, which are the source of the significant chemical differences in the elemental status of the soil and plant tissues. The four-step Community Bureau of Reference sequential extraction procedure was used to determine the distribution of fractions and predict their potential phytoavailability. The orchid and soil samples were analyzed for total elemental content analysis using ICP-OES. The greatest potential for plant availability was determined for Ba and Sr, representing about 80% of the total soil content. More than 40% of Li in the soils was found to be potentially phytoavailable. Significant correlations were found between the total content of Li, B, and Sr in soils. Between 38 and 60% of Li content and more than 80% of Ba and Sr content were determined to be potentially phytoavailable by sequential analysis. The highest bioconcentration factor (> 1) was determined in the case of B and Sr for all orchid organs, while translocation factor for Li was highest in tubers and leaves. The studied elements were mainly stored in tubers and roots, indicating the exclusion strategy of A. morio as a metal tolerance mechanism. The data obtained showed significant differences in metal content in soils and plants originating from sites with different parent materials, suggesting that bedrock type and associated soil properties are important factors that determine chemical element mobility and uptake.

A field study to estimate heavy metal concentrations in a soil-rice system: Application of graph neural networks

Accurate prediction of the concentration of heavy metals is of great significance for assessing the quality of agricultural products and reducing health risks. However, the complexity and interconnectivity of the farmland ecosystem restricts the improvement of the prediction accuracy of traditional methods. This research explored the application potential of graph neural network (GNN) technology, which can extract and learn information in large-scale networks in detail, in the field of heavy metal prediction for the first time. In this study, a heavy metal prediction model for rice, CoNet-GNN, was proposed with 17 environmental factors as input variables using the co-occurrence network and GNN. Experimental results using a dataset from a field study showed that the R² of CoNet-GNN for predicting Cd, Pb, Cr, As, and Hg had outstanding values of 0.872, 0.711, 0.683, 0.489, and 0.824, respectively. Sensitivity analysis further indicated that CoNet-GNN had good stability and robustness. Compared with random forest, gradient boosting, and multilayer perceptron, CoNet-GNN made a remarkable improvement to the prediction accuracy of all studied heavy metals. Therefore, CoNet-GNN can effectively simulate the rich relationships and laws between various factors in the soil-rice system and effectively characterize the influence diffusion path. Furthermore, it provides new ideas for heavy metal prediction based on network research methods and expands the technical scope of heavy metal evaluation.

Convolutional neural network-based applied research on the enrichment of heavy metals in the soil-rice system in China

The enrichment of heavy metals in the soil-rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R² values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil-rice system and provided a new perspective and solution for heavy metal prediction.

Physiological Aspects of Absorption, Translocation, and Accumulation of Heavy Metals in Silphium perfoliatum L. Plants Grown in a Mining-Contaminated Soil

Soil pollution by heavy metals as a result of mining activities is increasingly taking place. Once accumulated in soil, the heavy metals can then be dispersed, with serious effects on the environment and human health. It is therefore necessary to minimize, or even remove, all heavy metals from polluted areas, and one of the environmentally friendly and sustainable methods to do so is phytoremediation. A greenhouse pots experiment was conducted to evaluate the phytoremediation capacity of Silphium perfoliatum L. plants, in the vegetative growth stages, on a soil polluted with Cu, Zn, Cr and Pb, taken from a former mining area compared to an unpolluted soil (Us). The initial heavy metal content of polluted soil (Ps) was 208.3 mg kg−1 Cu; 312.5 mg kg−1 Zn; 186.5 mg kg−1 Cr and 195.2 mg kg−1 Pb. This shows that for Cu and Pb, soil concentrations exceed the intervention threshold, and for Zn and Cr, they are above the alert threshold. The removal efficiency, bioaccumulation factor, translocation factor, metal uptake and contamination factor index of Cu, Zn, Cr and Pb by S. perfoliatum L. were quantified to determine the bioremediation success. The data show that plants grown in Ps accumulated a significantly higher amount of Cu by 189% and Zn by 37.95% compared to Us. The Cr and Pb content of the plants recorded a progressive and significant increase from one developmental stage to another, being more intense between three and five leaves.

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.

Arsenic contamination, impact and mitigation strategies in rice agro-environment: An inclusive insight

Arsenic (As) contamination and its adverse consequences on rice agroecosystem are well known. Rice has the credit to feed more than 50% of the world population but concurrently, rice accumulates a substantial amount of As, thereby compromising food security. The gravity of the situation lays in the fact that the population in theAs uncontaminated areas may be accidentally exposed to toxic levels of As from rice consumption. In this review, we are trying to summarize the documents on the impact of As contamination and phytotoxicity in past two decades. The unique feature of this attempt is wide spectrum coverages of topics, and that makes it truly an interdisciplinary review. Aprat from the behaviour of As in rice field soil, we have documented the cellular and molecular response of rice plant upon exposure to As. The potential of various mitigation strategies with particular emphasis on using biochar, seed priming technology, irrigation management, transgenic variety development and other agronomic methods have been critically explored. The review attempts to give a comprehensive and multidiciplinary insight into the behaviour of As in Paddy -Water - Soil - Plate prospective from molecular to post-harvest phase. From the comprehensive literature review, we may conclude that considerable emphasis on rice grain, nutritional and anti-nutritional components, and grain quality traits under arsenic stress condition is yet to be given. Besides these, some emerging mitigation options like seed priming technology, adoption of nanotechnological strategies, applications of biochar should be fortified in large scale without interfering with the proper use of biodiversity.

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.

Bioaccumulation of potentially toxic elements from the soils surrounding a legacy uranium mine in Brazil

It is important to understand the environmental fate and potential risks posed by metals and metalloids around mines and in legacy mining areas. In order to assess the bioavailable concentrations of several potentially toxic elements (PTEs: As, Pb, Cd, Ni, Cu, Cr, Mn, Zn, Ba, U) and rare earth elements (REEs: La to Lu), a multi-method evaluation of their concentrations/fractionation/speciation in soils was related to their biouptake in corn, for a region surrounding a legacy U mine in Brazil. Chemical fractions of the PTE and REE in soils were determined using the BCR (Community Bureau of Reference) sequential extraction procedure; a single extraction with Ca(NO₃)₂ and the diffusion gradient in thin films (DGT) technique. All techniques were better correlated to the metals accumulated by the crops as compared to total metal concentrations. Ba, Cu, Mn and Zn were shown to have high mobility and high bioaccumulation factors in the corn. Concentrations of U, As, Cd, and Pb were above threshold concentrations and strongly correlated, suggesting that they had a similar anthropogenic source. Geospatial modeling agreed with results from principal component analysis, indicating multiple sources for the contamination. Results highlighted the need for multi-method approaches when evaluating the long-term risks posed by PTEs and REEs in agricultural soils.

Predicting accumulation of Cd in rice (Oryza sativa L.) and soil threshold concentration of Cd for rice safe production

Rice contamination by cadmium (Cd) poses a serious threat to human health, which has attracted widespread concerns in China. It is imperative to determine major soil factors influencing the accumulation of Cd in rice and develop prediction models to derive the threshold concentration of Cd in soil for rice food safety. In this study, the bioavailability, accumulation, and transfer of Cd in the 18 typical paddy soil-rice systems with a wide range of soil properties was investigated using pot experiments. The regression-based models incorporated with total or extractable Cd and soil properties were constructed to predict Cd content of rice grain. Pot experimental results indicated that rice showed a high accumulation potential for Cd, while rice grains grown in acid soils displayed larger Cd contents than those in neutral and alkaline soils. The pH and MnO content were major soil factors influencing the Cd accumulation of rice. Multiple regression models based on the total Cd, extractable Cd, pH, and MnO content in soils could well describe the Cd content in rice grain. Measured Cd content of rice grains from field samples demonstrated that the empirical models could quantitatively predict the Cd content of rice grains. The threshold concentrations of Cd in soils for rice food safety could be back-calculated by both EDTA-extractable and total Cd contents in soils. The EDTA-extractable Cd in soils could use as an indication to derive the threshold concentrations of Cd for rice food safety. In conclusions, multiple regression models proved reliable and practical in predicting Cd accumulation in rice grain. These empirical models could well predict the content of Cd in rice grain and deduce soil Cd threshold criteria. These results could help to quantitatively evaluate the health risk of Cd accumulation in rice crop and provide a useful reference for safe production of rice.

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.

Arsenic Uptake by Two Tolerant Grass Species: Holcus lanatus and Agrostis capillaris Growing in Soils Contaminated by Historical Mining

The study focused on two grass species Holcus lanatus and Agrostis capillaris abundant in the sites of former As mining and processing in the Sudetes. Arsenic uptake from soils was examined to assess a risk associated with its accumulation in grass shoots and to check its dependence on soil fertilization. The research involved a field study and greenhouse experiment. In the field study, soil and plant samples were collected from 33 sites with 72-98,400 mg/kg total soil As. Arsenic uptake by grasses differed widely. Both species indicated a strategy typical for eliminators, although As concentrations in more than 50% of the shoot samples exceeded 4 mg/kg, a maximum permissible value for fodder. In the greenhouse experiment, commercial cultivars of both species were grown in five soils containing 394-19,600 mg/kg, untreated and fertilized. All seedlings died in the soil with highest total As, and considerable phytotoxicity was observed in other soils, particularly in nonfertilized ones. Fertilization resulted in the improvement of plant growth and reduction of As uptake except for Agrostis capillaris fertilized with manure. Further research should focus on identifying tolerant genotypes growing in extremely enriched sites and analysis of factors that will efficiently reduce As phytoaccumulation.

Predicting the modifying effect of soils on arsenic phytotoxicity and phytoaccumulation using soil properties or soil extraction methods

Soils have the ability to modify contaminant bioavailability and toxicity. Prediction the modifying effect of soil on arsenic phytoaccumulation and phytoavailability using either soil property data or soil chemical extraction data in risk assessment of contaminated soil is highly desirable. In this study, plant bioassays important to ecological receptors, were conducted with 20 soils with a wide range in chemical and physical soil properties to determine the relationships between As measured by soil chemical extraction (soil pore water, Bray-1, sodium phosphate solution, hydroxylamine hydrochloride, and acid ammonium oxalate) or soil physico/chemical properties on arsenic phytotoxicity and phytoaccumulation. Soil pore water As and Bray-1 extracted As were significantly (P < 0.01) correlated with lettuce tissue As and those extractants and sodium phosphate were correlated with ryegrass tissue As. Hydroxylamine and acid ammonium oxalate extractions did not correlate with plant bioassay endpoints. Simple regression results showed that lettuce tissue relative dry matter growth (RDMG) was inversely related to tissue As concentration (r² = 0.85, P < 0.01), with no significant relationship for ryegrass. Soil clay exhibited strong adsorption for As and significantly reduce tissue As for lettuce and ryegrass. In addition to clay content, reactive aluminum oxide (AlOx), reactive Fe oxide (FeOx) and eCEC was inversely related to ryegrass tissue As. Multiple regression equation was strongly predictive (r² = 0.83) for ryegrass tissue As (log transformed) using soil AlOx, organic matter, pH, and eCEC as variables. Soil properties can greatly reduce contaminant phytoavailability, plant exposure and risk, which should be considered when assessing contaminant exposure and site-specific risk in As-contaminated soils.

Bioavailability of trace metals and rare earth elements (REE) from the tropical soils of a coal mining area

In order to assess the environmental risks related to mining activities in Southern Brazil, the transfer of trace metals and rare earth elements (REE) from soils to soybeans was evaluated in a U-rich area associated with coal mining. In some samples, As, Ba, Co, Cu and Ni were higher than the guidelines proposed by the Brazilian environmental agency. Soil, coal, ash, tailings and soybean were systematically sampled so that the chemical fractionation/speciation of the elements could be related to their bioavailability. In addition to total concentrations quantified by ICP-MS after microwave digestion, elemental measurements were made following different evaluations of the bioavailable metal, including chemical extractions (10 mM Ca(NO₃)₂ and 3-step sequential extraction), diffusive gradient in thin films technique (DGT) and chemical modeling (WHAM-free ion). Lower pH and higher clay and organic matter content were reflected by higher metal assimilation by the plants, especially by the roots and leaves. The bioaccumulation factor (BF) was generally higher for the leaves (e.g. Cu, Mn, Sr, Zn, Ba, REE with exception of Tm and Yb) and roots (e.g. Cd, Th and U). The results revealed that for Ba, Cd, Sr, Pb, U and most of the REE, the free ion concentration was strongly correlated with the metal content in the plants, especially for the grains. Values obtained by DGT were also correlated with the bioavailable portion of Ba, Mn, Sr, Zn, Pb, U and REE. Measurements obtained from Ca extractions correlated well with the bioavailable metals for Ba, Cd, Sr, Rb, Pb and Th. The free or extractable metal fractions gave much better correlations of the bioavailable fractions than did the total metal concentrations from the soils, especially for the REE. The paper validates some simplified means of estimating the risks associated with metals and REE in tropical soils affected by mining activities.

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.

Prediction models for monitoring heavy-metal accumulation by wheat (Triticum aestivum L.) plants grown in sewage sludge amended soil

Prediction of heavy-metal concentration in the edible parts of economic crops, based on their concentration in soil and other environmental factors, is urgently required for human risk assessment. The present investigation aimed to develop regression models for predicting heavy-metal concentration in wheat plants via their contents in sewage sludge amended soil, organic matter (OM) content and soil pH. The concentration of heavy metals in the plant tissues reflected its concentration in the soil with high Fe followed by Al, Mn, Cr, Zn, Ni, Co, Cu, and Pb. Soil OM content had a significant positive correlation with all investigated heavy-metal concentrations in the different tissues of wheat plants, while soil pH was negatively significant with most heavy metals except spike Pb and grain Cr. The bio-concentration factor of Al, Cu, and Zn from soil to wheat root was >1, while that of shoot, spikes, and grains was <1 for all heavy metals. Significantly valid regression models were developed with fluctuated coefficient of determination (R²), high model efficiency (ME) values and low mean normalized average error (MNAE). The significant positive correlations between the concentration of some heavy metals in the soil and the same in wheat tissues indicate the potential of this plant as a biomonitor for these metals in contaminated soils. The significant correlations between heavy-metal concentrations in soil and its properties (pH and OM) with metal concentrations in wheat plants support the prediction model as an appropriate option. This study recommends the use of models with R² greater than 50% and recommend other researchers to use our models according to their own specific conditions.

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.

Cd accumulation and transfer in pepper (Capsicum annuum L.) grown in typical soils of China: pot experiments

Food chain contamination by soil cadmium (Cd) through vegetable consumption poses a threat to human health. It is imperative to understand the Cd uptake and transfer in different soil-vegetable systems. The aims of this study were to understand the effect of soil characteristics on Cd accumulation and transfer in pepper and to derive regression models to predict Cd concentrations in the vegetable grown on a wide range of soils with different properties. The accumulation and transfer of Cd in the root, stem, and fruit of pepper (Capsicum annuum L.) grown in 18 typical soils of China were investigated through pot experiments. The bioavailability of Cd in soil was evaluated by using EDTA and HNO₃ extraction methods. The pot experiments included a control and two concentration levels of Cd salt added to soils according to Soil Environmental Quality Standards of China. The results showed that the Cd content in pepper fruits ranged from 0.007 to 0.049 for the control, 0.045 to 0.260 for the low Cd treatment, and 0.076 to 0.345 mg/kg for the high Cd treatment, respectively. The concentrations of Cd in the different parts of pepper decreased in the order of root > stem > fruit, and there were significant correlations among the Cd concentrations in pepper root, stem, and fruit tissues. Bioaccumulation factor (BCF) and transfer factor (TF) of Cd in pepper fruits exhibited a low accumulation of Cd in the fruit of pepper. The Cd accumulation in pepper fruit could be quantitatively predicted by EDTA-extractable Cd content in soils. Multiple linear regression models proved functional in predicting Cd accumulation in different parts of pepper. The Cd content in pepper tissues was well predicted using EDTA-extractable Cd and soil variables, such as pH, EC, CEC, total phosphorus, and CaCO₃ content. Soil pH and EC were major soil factors influencing Cd transfer from soil to pepper fruits, whereas total phosphorus content presented a negative effect on Cd accumulation in stem and root parts of pepper.

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.

Regression models for monitoring trace metal accumulations by Faba sativa Bernh. plants grown in soils amended with different rates of sewage sludge

The present study was conducted using a pot experiment to develop regression models for the prediction of trace metal concentrations in faba bean (Faba sativa Bernh.) plants cultivated in soils amended with different rates of sewage sludge to monitor possible human health risks. The trace metal concentrations in the different tissues of faba bean showed that most of the investigated trace metals were accumulated in the plant roots rather than in the other tissues. Meanwhile, the fruits accumulated the lowest concentration of most trace metals. The trace metal concentrations of the faba bean plants had a significant positive correlation with the organic matter content and a significant negative correlation with the soil pH. Transfer of trace metals from the soil to faba bean roots indicated that Al, Cu, Pb and Zn had a transfer factor that exceeded one, whereas the TF of the investigated trace metals from the roots to the fruits did not exceed one. The daily intake rate of the investigated trace metals did not exceed one in both adults and children. On the other side, the hazard quotient of trace metals from consuming faba bean fruits had values <1 for most investigated trace metals except Al and Mn in adults and in children. It is worth mentioning that the predicted trace metal concentrations via the established regression models and measured values from the validation data set were not significantly different (P > 0.05). Therefore, these developed models will be useful for prediction of trace metals uptake by faba bean grown in soil amended with sewage sludge so possible human risks can be identified.

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.

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.

Phytoavailability, bioaccumulation, and human health risks of metal(loid) elements in an agroecosystem near a lead-zinc mine

Soil near a Pb-Zn-Mn mine was polluted by mining, which may have an impact on human health via the food chain. To evaluate the pollution effects, arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), lead (Pb), and zinc (Zn) in vegetables were determined by inductively coupled plasma atomic emission and mass spectrometry. Lead species were analyzed by X-ray absorption near-edge structure (XANES). Phytoavailability of the elements was evaluated by bioaccumulation of the elements, the sequential extraction procedure, Pb species, and plant uptakes. The target health quotient (THQ) was calculated to evaluate the human health risks. It was found that (1) high concentrations of As, Cd, Cr, and Pb were detectable in vegetables, and bioaccumulation was in the order of Mn > Zn > Cr > Pb > Cu > As > Cd; (2) phytoavailability of the elements was controlled mainly by the soluble fraction, and a linear relationship observed between the soluble fraction and bioaccumulation; (3) a new Pb-fulvic acid complex (Pb-FA) was identified by XANES in rhizosphere soil, and high content of Pb organic matter (60%) and soluble Pb (18%) were found; (4) both Cd and Zn accumulated in both of the Amaranthaceae and the Apiaceae families, indicating that the plants in the same family have the same bioaccumulation trend for the elements in the same group; (5) agricultural activities and plant growing increased phytoavailability of As, Cd, Cu, Pb, and Zn by decreasing the residual and raising the soluble and extractable fractions; (6) arsenic is top of the high health risks, followed by Pb, Cd, and Mn. Coriander, celery, and spinach were the top three highest health risks in the area.

Native Phytoremediation Potential of Urtica dioica for Removal of PCBs and Heavy Metals Can Be Improved by Genetic Manipulations Using Constitutive CaMV 35S Promoter

Although stinging nettle (Urtica dioica) has been shown to reduce HM (heavy metal) content in soil, its wider phytoremediation potential has been neglected. Urtica dioica was cultivated in soils contaminated with HMs or polychlorinated biphenyls (PCBs). After four months, up to 33% of the less chlorinated biphenyls and 8% of HMs (Zn, Pb, Cd) had been removed. Bacteria were isolated from the plant tissue, with the endophytic bacteria Bacillus shackletonii and Streptomyces badius shown to have the most significant effect. These bacteria demonstrated not only benefits for plant growth, but also extreme tolerance to As, Zn and Pb. Despite these results, the native phytoremediation potential of nettles could be improved by biotechnologies. Transient expression was used to investigate the functionality of the most common constitutive promoter, CaMV 35S in Urtica dioica. This showed the expression of the CUP and bphC transgenes. Collectively, our findings suggest that remediation by stinging nettle could have a much wider range of applications than previously thought.

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.

Modeling the transfer of arsenic from soil to carrot (Daucus carota L.)--a greenhouse and field-based study

Reliable empirical models describing arsenic (As) transfer in soil-plant systems are needed to estimate the human As burden from dietary intake. A greenhouse experiment was conducted in parallel with a field trial located at three sites through China to develop and validate soil-plant transfer models to predict As concentrations in carrot (Daucus carota L.). Stepwise multiple linear regression relationships were based on soil properties and the pseudo total (aqua regia) or available (0.5 M NaHCO3) soil As fractions. Carrot As contents were best predicted by the pseudo total soil As concentrations in combination with soil pH and Fe oxide, with the percentage of variation explained being up to 70 %. The constructed prediction model was further validated and improved to avoid overprotection using data from the field trial. The final obtained model is of great practical relevance to the prediction of As uptake under field conditions.

Вплив екскрецій Capreolus capreolus і Sus scrofa на активність аланінамінотрансферази в листках Glechoma hederacea в умовах забруднення кадмієм

Досліджено активність (нМ піровиноградної кислоти/мл∙с) ферменту азотного метаболізму аланінамінотрансфераза (АLТ, EC 2.6.1.2) та вміст альбумінів (мг/мл) у листках Glechoma hederacea L., яка домінувала на дослідній території (липово-ясенева діброва зі Stellaria holostea L.) на фоні солей Cd, екскретів ссавців Capreolus capreolus L. та Sus scrofa L. та їх спільної дії. Cd вносили у вигляді солі Cd(NO3)2 у концентраціях 0,25, 1,25, та 2,50 г/м2, що еквівалентно 1, 5 та 10 ГДК Cd. При внесенні враховували кількість ГДК для Cd (5 мг/кг ґрунту). Виявлено достовірне інгібіювання активності АЛТ у 3–4 рази (на фоні 1 та 5 ГДК Cd). Подібний відгук на стрес відбувався й у білковому метаболізмі. Визначено достовірне зниження вмісту альбумінів до 72% та 80% (на фоні 1 та 5 ГДК Cd) відносно контролю (ділянка без забруднення Cd та екскретів ссавців), що доводить неспецифічність реакції на стрес. Використання екскрементів C. capreolus та S. scrofa показало нівелювання впливу Cd за рахунок підвищення активності метаболізму АЛТ на 41% та 105% відповідно (на фоні 1 ГДК Cd) відносно контролю (контроль 1 ГДК Cd). Дія Cd у дозі 5 ГДК достовірно відносно контролю (контроль 5 ГДК Cd) нівельована тільки на фоні екскрементів C. capreolus. Виявлено нормалізацію вмісту альбумінів при залученні екскрементів С. capreolus при 1 та 5 ГДК Cd відносно контролів. За умов 10 ГДК Cd інгібіювався нітратний обмін на 50% на фоні екскретів як C. capreolus, так і S. scrofa відносно контролю (контроль 10 ГДК Cd). Eкскреції S. scrofa порівняно із C. capreolus сприяли відновленню на 10% вмісту альбумінів відносно контролю. З’ясовано доцільність використання різних представників зооценозу для комплексного регулювання змін навколишнього середовища в умовах Степу України. 

Sources of variation in innate immunity in great tit nestlings living along a metal pollution gradient: an individual-based approach

Excessive deposition of metals in the environment is a well-known example of pollution worldwide. Chronic exposure of organisms to metals can have a detrimental effect on reproduction, behavior, health and survival, due to the negative effects on components of the immune system. However, little is known about the effects of chronic sublethal metal exposure on immunity, especially for wildlife. In our study, we examined the constitutive innate immunity of great tit (Parus major) nestlings (N=234) living in four populations along a metal pollution gradient. For each nestling, we determined the individual metal concentrations (lead, cadmium, arsenic) present in the red blood cells and measured four different innate immune parameters (agglutination, lysis, haptoglobin concentrations and nitric oxide concentrations) to investigate the relationship between metal exposure and immunological condition. While we found significant differences in endogenous metal concentrations among populations with the highest concentrations closest to the pollution source, we did not observe corresponding patterns in our immune measures. However, when evaluating relationships between metal concentrations and immune parameters at the individual level, we found negative effects of lead and, to a lesser extent, arsenic and cadmium on lysis. In addition, high arsenic concentrations appear to elicit inflammation, as reflected by elevated haptoglobin concentrations. Thus despite the lack of a geographic association between pollution and immunity, this type of association was present at the individual level at a very early life stage. The high variation in metal concentrations and immune measures observed within populations indicates a high level of heterogeneity along an existing pollution gradient. Interestingly, we also found substantial within nest variation, for which the sources remain unclear, and which highlights the need of an individual-based approach.