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Identification of heavy metals spatial variability in soil and plants may provide useful information on how to manage the polluted sites. The main objective of this study was to determine the spatial distribution of selected heavy metals in soils and natural plants of Zanjan city, northwest Iran. A total of 184 composite topsoil samples (0-10 cm) and 98 natural plant samples were systematically taken from an area of about 4000 ha located around an industrial complex, covering rangeland and agricultural and industrial land uses. All samples were analyzed for their total concentration of Zn, Pb and Cd. The results showed that the average concentrations of Zn, Pb and Cd in the soil samples were up to 294.2, 152.8 and 5.6 mg kg -1 , respectively, whereas in the plant samples, these values decreased to 131.4, 113.2 and 2.5 mg kg -1 , respectively. These contents are much higher than the normal range in soil and plant communities, leading to classify the studied area as a polluted site. Variography analyses revealed a similar spatial structure for the studied heavy metals in the soil and plant samples. Based on interpolated maps, the highest concentrations of the selected heavy metals in the soil and plant samples were found in the vicinity of industrial complex. These findings clearly highlight the role of industrial activities in simplifying the entrance of dangerous trace elements to the human food chain. Application of ordinary kriging technique to predict the heavy metals spatial variability in the plant community resulted in logical estimations with acceptable error values.

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This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

Accumulated anthropogenic heavy metals in the surface layer of agricultural soils may be transferred through the food chain via plant uptake processes. The objectives of this study were to assess the spatial distribution of lead (Pb) in the soils and wheat plants and to determine the soil properties which may affect the Pb transferring from soil to wheat plants in Zanjan Zinc Town area, northwestern Iran. A total of 110 topsoil samples (0-20 cm) were systematically collected from an agricultural area near a large metallurgical factory for the analyses of physico-chemical properties and total and bioavailable Pb concentrations. Furthermore, a total of 65 wheat samples collected at the same soil sampling locations were analyzed for Pb concentration in different plant parts. The results showed that elevated Pb concentrations were mostly found in soils located surrounding the industrial source of pollution. The bioavailable Pb concentration in the studied soils was up to 128.4 mg kg(-1), which was relatively high considering the observed soil alkalinity. 24.6% of the wheat grain samples exceeded the FAO/WHO maximum permitted concentration of Pb in wheat grain (0.2 mg kg(-1)). Correlation analyses revealed that soil organic matter, soil pH, and clay content showed insignificant correlation with Pb concentration in the soil and wheat grains, whereas calcium carbonate content showed significantly negative correlations with both total and bioavailable Pb in the soil, and Pb content in wheat grains, demonstrating the strong influences of calcium carbonate on Pb bioavailability in the polluted calcareous soils.

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