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Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0–20 and 20–40 cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The IgeoNi and IgeoPb values in both models at both depths indicated that strongly to extremely polluted (4–5) areas are quite high in the study area, while the IgeoCd values revealed that unpolluted areas are widespread. The mCdeg index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2–3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution. [ABSTRACT FROM AUTHOR]

There is an increasing concern that soils in e-waste recycling regions are severely contaminated by unregulated e-waste dismantling activities. Hence, it is urgent to reveal the spatial variation of hazardous elements in arable lands close to e-waste stacking and dismantling areas and their potential risks to human beings. We collected 349 topsoil samples based on an intensive grid of 100 m × 100 m in southeastern China. The average concentrations of heavy metals were 1.25 (Cd), 35.44 (Ni), 77.68 (Cr), 77.38 (Pb), 122.14 (Cu), 203.39 (Zn), 0.21 (Hg), and 4.74 (As) mg kg−1, respectively. Compared to the risk screening values of hazardous elements in Chinese agricultural land, Cd and Cu were severely accumulated in the soils. The results of ecological risk analysis revealed that Cd posed the crucial risk among the studied elements. However, the levels of non-carcinogenic and carcinogenic risk were still within the acceptable quantity for adults. Spatial distribution by kriging interpolation displayed that the heavy metals were mainly distributed close to e-waste dismantling sites. [ABSTRACT FROM AUTHOR]

Several studies have reported increased copper (Cu) and zinc (Zn) levels in agricultural soils worldwide, mainly due to organic waste and successive leaf fungicide applications in crops. However, the critical transfer thresholds in soils, which can indicate the real risk of environmental contamination and toxicity to plants, remain poorly understood. This study aimed to define the maximum Cu and Zn adsorption capacity (MAC) and threshold (T-Cu and T-Zn) in different soils in Southern Brazil, which present different clay and organic matter (OM) levels. Bw (Oxisol) and A horizon (Inceptisol) samples were used to obtain soils with clay and OM contents ranging from 4 to 70% and from 0.5 to 9.5%, respectively. Cu and Zn adsorption curves were plotted for MAC determination purposes. Based on Cu and Zn MAC values, different concentrations of these elements were applied to the soils for subsequent quantification of available Cu and Zn levels (Mehlich-1 and water). T-Cu in soils with different clay contents ranged from 81 to 595 mg Cu kg−1, whereas T-Zn, from 195 to 378 mg Zn kg−1. T-Cu in soils with different OM levels ranged from 97 to 667 mg Cu kg−1, whereas T-Zn, from 226 to 495 mg Zn kg−1. T-Cu can be calculated through the equation: T-Cu = 75 × (%CL0.34) × (%OM0.39), whereas T-Zn: T-Zn = 2.7 × (CL) + 126 (by taking into consideration the clay content) and T-Zn = − 9.3 × (%OM)2 + 92.4 × (%OM) + 66 (by taking into consideration OM content). T-Cu and T-Zn can be used by researchers, inspection bodies, technical assistance institutions, and farmers as safe indicators to monitor the potential for environmental contamination. [ABSTRACT FROM AUTHOR]

Heavy metal contamination in the soil and the subsequent accumulation in Brachystegia longifolia were investigated as a function of the wind direction and distance from a copper mine in Mufulira, Zambia. Soil and leaves of B. longifolia were collected along transects up to 12 km downwind and 19 km upwind. The total concentration of trace elements in the soil and leaves was determined through pXRF. Plant-available Cu, Fe, Mn, and Zn were extracted in a Mehlich III solution and analyzed using ICP-AES. The degree of soil contamination illustrates that Cu and Fe from the copper mine strongly pollute Mufulira and the surrounding forests. Bioavailable Cu, Fe, Mn, and Zn reduced with increasing distance from the mine. An average of 296 mg/kg Cu, 2337 mg/kg Fe, 1101 mg/kg Mn, and 109 mg/kg Zn were recorded in leaves at the most polluted site. Similarly, 55.21 mg/kg Cu, 516.4 mg/kg Fe, 3196 mg/kg Mn, and 154 mg/kg Zn were recorded at an unpolluted site 19 km upwind. The concentration of Cu and Fe reduced significantly with increasing distance, while Mn and Zn increased significantly. It was further established that B. longifolia leaves accumulated Mn (× 38) and Zn (× 15) more than their respective total concentration in the soil. The concentrations of Cu and Fe found in leaves near the mine, as well as the Mn concentration in leaves across the study sites, could be stressful for B. longifolia tree growth. [ABSTRACT FROM AUTHOR]

Surface mining activities, despite their benefits, lead to the deterioration of local and regional environmental quality and play a role in global ecosystem pollution. This research aimed to estimate the culturable microbial population structure at five locations near the opencast coal mine "Kakanj" (Bosnia and Herzegovina) via agar plate and phospholipid fatty acids (PLFA) method and to establish its relationship to the physical and chemical properties of soil. Using the ICP-OES method, the heavy metal pollution of all examined locations (overburden, former grass yard, forest, arable soil, and greenhouse) was observed. Substantial variations among the sites regarding the most expressed indicators of heavy metal pollution were noted; Cr, Pb, Ni, and Cu content ranged from 63.17 to 524.47, 20.57 to 349.47, 139.13 to 2785.67, and 25.97 to 458.73 mg/kg, respectively. In the overburden sample, considerable low microbial activity was detected; the bacterial count was approximately 6- to 18-fold lower in comparison with the other samples. PLFA analysis showed the reduction of microbial diversity, reflected through the prevalence of normal and branched saturated fatty acids, their ratio (ranged from 0.92 to 7.13), and the absence of fungal marker 18:2ω6 fatty acid. The principal component analysis showed a strong negative impact of heavy metals Na and B on main microbial and PLFA profiles. In contrast, stock of main chemical parameters, including Ca, K, Fe, and pH, was positively correlated with the microbial community structure. [ABSTRACT FROM AUTHOR]

The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP. [ABSTRACT FROM AUTHOR]

The Indus Basin Irrigation Network (IBIN) plays a vital role in the agricultural system of Pakistan, irrigating seventeen million hectares of cultivated areas. Rapid urbanization, industrialization, and agricultural activities along the Indus basin have influenced the soil quality and human health; it is, therefore, critical to know its pollution characteristics. Soil samples from Indus basin, i.e., Abbottabad (ABT), Haripur (HRP), Attock (ATC), and Islamabad (ISB) have been analyzed for the total contents of potentially toxic metal(loid)s (PTMs) in the top layer. The topsoil samples from 0 to 10 cm depth have been further investigated using different pollution indices and human health risk assessment models. The contamination degree of soil pollution was highest in ISB (33.75), followed by ABT (25.30) and ATC (23.57). The assessment of the daily intake of PTMs by children and adults through different pathways revealed ingestion as the significant exposure pathway. Cr was found to be the major element posing non-carcinogenic health risks to children at ATC whereas the non-carcinogenic risks posed by all other PTMs were within the safe limit. Furthermore, life-time carcinogenic risks for Ni followed by Cr and Cd were greatly exceeded at all locations and As at ATC and ISB for both age groups, but comparatively children were found to be at a higher risk of carcinogenicity. Hence, efficient remediation strategies are needed to reduce the increasing content and health risks of PTMs in the Indus basin. [ABSTRACT FROM AUTHOR]

An imbalance in the environment's composition leads to significant effect on human activities such as farming. Of importance are heavy metals which are introduced anthropogenically or naturally. This calls for environmental monitoring and subsequent remediation if needed. An environmental monitoring exercise was conducted on Ikwo soils of Ebonyi State, eastern Nigeria with the aim of determining concentration levels for possible remediation. A total of 18 soil composite samples taken at 0-50 cm below soil surface from fallowed and cultivated soils not fertilized were subjected to heavy metal analyses and fertility indices like: organic matter (OM), cation exchange capacity (CEC), % total nitrogen (%TN), organic carbon (OC), and salinity. A correlation at 95% confidence level between geo-accumulations (Igeo) of the various heavy metals with salinity, OM, and CEC of the sampled soils reveals that Igeo could be a contributing factor to the fertility status of the soils. With the aid of inductively coupled plasma atomic emission spectrophotometer (ICP-AES), the distribution pattern was determined as Mn> Fe> Zn>Cu> Mo> Cd> V>Hg>Ti> Ni>Bi> Pb> Co>Ag>Au> Cr>Pd>Pt. The Igeo of the heavy metals in the study area varied from heavily to extremely contaminated levels. A remediation exercise was recommended on Ikwo soils due to their high salinity level and low CEC. [ABSTRACT FROM AUTHOR]